03.12.2018 Views

Hidden Unemployment

Hidden Unemployment

Hidden Unemployment

SHOW MORE
SHOW LESS

Create successful ePaper yourself

Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.

The e-Advocate<br />

Monthly<br />

…a Compendium of Works on:<br />

<strong>Hidden</strong><br />

<strong>Unemployment</strong><br />

Leviticus 19:35-36 | Ezekiel 45:10<br />

Proverbs 11:1 ; 16:11; 20:10,23<br />

Jeremiah 5:1 | Hosea 12:6-7<br />

Amos 8:4-8 | Micah 6:10-13<br />

The Effective (True)<br />

<strong>Unemployment</strong> Rate<br />

The U6 Indicator<br />

“Helping Individuals, Organizations & Communities<br />

Achieve Their Full Potential”<br />

Special Edition | AF – December 2021


Walk by Faith; Serve with Abandon<br />

Expect to Win!<br />

Page 2 of 149


The Advocacy Foundation, Inc.<br />

Helping Individuals, Organizations & Communities<br />

Achieve Their Full Potential<br />

Since its founding in 2003, The Advocacy Foundation has become recognized as an effective<br />

provider of support to those who receive our services, having real impact within the communities<br />

we serve. We are currently engaged in community and faith-based collaborative initiatives,<br />

having the overall objective of eradicating all forms of youth violence and correcting injustices<br />

everywhere. In carrying-out these initiatives, we have adopted the evidence-based strategic<br />

framework developed and implemented by the Office of Juvenile Justice & Delinquency<br />

Prevention (OJJDP).<br />

The stated objectives are:<br />

1. Community Mobilization;<br />

2. Social Intervention;<br />

3. Provision of Opportunities;<br />

4. Organizational Change and Development;<br />

5. Suppression [of illegal activities].<br />

Moreover, it is our most fundamental belief that in order to be effective, prevention and<br />

intervention strategies must be Community Specific, Culturally Relevant, Evidence-Based, and<br />

Collaborative. The Violence Prevention and Intervention programming we employ in<br />

implementing this community-enhancing framework include the programs further described<br />

throughout our publications, programs and special projects both domestically and<br />

internationally.<br />

www.TheAdvocacy.Foundation<br />

ISBN: ......... ../2017<br />

......... Printed in the USA<br />

Advocacy Foundation Publishers<br />

Philadelphia, PA<br />

(878) 222-0450 | Voice | Data | SMS<br />

Page 3 of 149


Dedication<br />

______<br />

Every publication in our many series’ is dedicated to everyone, absolutely everyone, who by<br />

virtue of their calling and by Divine inspiration, direction and guidance, is on the battlefield dayafter-day<br />

striving to follow God’s will and purpose for their lives. And this is with particular affinity<br />

for those Spiritual warriors who are being transformed into excellence through daily academic,<br />

professional, familial, and other challenges.<br />

We pray that you will bear in mind:<br />

Matthew 19:26 (NLT)<br />

Jesus looked at them intently and said, “Humanly speaking, it is impossible.<br />

But with God everything is possible.” (Emphasis added)<br />

To all of us who daily look past our circumstances, and naysayers, to what the Lord says we will<br />

accomplish:<br />

Blessings!!<br />

- The Advocacy Foundation, Inc.<br />

Page 4 of 149


The Transformative Justice Project<br />

Eradicating Juvenile Delinquency Requires a Multi-Disciplinary Approach<br />

The Juvenile Justice system is incredibly<br />

overloaded, and Solutions-Based programs are<br />

woefully underfunded. Our precious children,<br />

therefore, particularly young people of color, often<br />

get the “swift” version of justice whenever they<br />

come into contact with the law.<br />

Decisions to build prison facilities are often based<br />

on elementary school test results, and our country<br />

incarcerates more of its young than any other<br />

nation on earth. So we at The Foundation labor to<br />

pull our young people out of the “school to prison”<br />

pipeline, and we then coordinate the efforts of the<br />

legal, psychological, governmental and<br />

educational professionals needed to bring an end<br />

to delinquency.<br />

We also educate families, police, local businesses,<br />

elected officials, clergy, and schools and other<br />

stakeholders about transforming whole communities, and we labor to change their<br />

thinking about the causes of delinquency with the goal of helping them embrace the<br />

idea of restoration for the young people in our care who demonstrate repentance for<br />

their<br />

mistakes.<br />

The way we accomplish all this is a follows:<br />

1. We vigorously advocate for charges reductions, wherever possible, in the<br />

adjudicatory (court) process, with the ultimate goal of expungement or pardon, in order<br />

to maximize the chances for our clients to graduate high school and progress into<br />

college, military service or the workforce without the stigma of a criminal record;<br />

2. We then enroll each young person into an Evidence-Based, Data-Driven<br />

Restorative Justice program designed to facilitate their rehabilitation and subsequent<br />

reintegration back into the community;<br />

3. While those projects are operating, we conduct a wide variety of ComeUnity-<br />

ReEngineering seminars and workshops on topics ranging from Juvenile Justice to<br />

Parental Rights, to Domestic issues to Police friendly contacts, to mental health<br />

intervention, to CBO and FBO accountability and compliance;<br />

Page 5 of 149


4. Throughout the process, we encourage and maintain frequent personal contact<br />

between all parties;<br />

5 Throughout the process we conduct a continuum of events and fundraisers<br />

designed to facilitate collaboration among professionals and community stakeholders;<br />

and finally<br />

6. 1 We disseminate Quarterly publications, like our e-Advocate series Newsletter<br />

and our e-Advocate Quarterly electronic Magazine to all regular donors in order to<br />

facilitate a lifelong learning process on the ever-evolving developments in the Justice<br />

system.<br />

And in addition to the help we provide for our young clients and their families, we also<br />

facilitate Community Engagement through the Restorative Justice process,<br />

thereby balancing the interests of local businesses, schools, clergy, social assistance<br />

organizations, elected officials, law enforcement entities, and all interested<br />

stakeholders. Through these efforts, relationships are rebuilt & strengthened, local<br />

businesses and communities are enhanced & protected from victimization, young<br />

careers are developed, and our precious young people are kept out of the prison<br />

pipeline.<br />

Additionally, we develop Transformative “Void Resistance” (TVR) initiatives to elevate<br />

concerns of our successes resulting in economic hardship for those employed by the<br />

penal system.<br />

TVR is an innovative-comprehensive process that works in conjunction with our<br />

Transformative Justice initiatives to transition the original use and purpose of current<br />

systems into positive social impact operations, which systematically retrains current<br />

staff, renovates facilities, creates new employment opportunities, increases salaries and<br />

is data proven to enhance employee’s mental wellbeing and overall quality of life – an<br />

exponential Transformative Social Impact benefit for ALL community stakeholders.<br />

This is a massive undertaking, and we need all the help and financial support you can<br />

give! We plan to help 75 young persons per quarter-year (aggregating to a total of 250<br />

per year) in each jurisdiction we serve) at an average cost of under $2,500 per client,<br />

per year. *<br />

Thank you in advance for your support!<br />

* FYI:<br />

1<br />

In addition to supporting our world-class programming and support services, all regular donors receive our Quarterly e-Newsletter<br />

(The e-Advocate), as well as The e-Advocate Quarterly Magazine.<br />

Page 6 of 149


1. The national average cost to taxpayers for minimum-security youth incarceration,<br />

is around $43,000.00 per child, per year.<br />

2. The average annual cost to taxpayers for maximum-security youth incarceration<br />

is well over $148,000.00 per child, per year.<br />

- (US News and World Report, December 9, 2014);<br />

3. In every jurisdiction in the nation, the Plea Bargain rate is above 99%.<br />

The Judicial system engages in a tri-partite balancing task in every single one of these<br />

matters, seeking to balance Rehabilitative Justice with Community Protection and<br />

Judicial Economy, and, although the practitioners work very hard to achieve positive<br />

outcomes, the scales are nowhere near balanced where people of color are involved.<br />

We must reverse this trend, which is right now working very much against the best<br />

interests of our young.<br />

Our young people do not belong behind bars.<br />

- Jack Johnson<br />

Page 7 of 149


Page 8 of 149


The Advocacy Foundation, Inc.<br />

Helping Individuals, Organizations & Communities<br />

Achieve Their Full Potential<br />

…a compendium of works on<br />

<strong>Hidden</strong> <strong>Unemployment</strong><br />

“Turning the Improbable Into the Exceptional”<br />

Atlanta<br />

Philadelphia<br />

______<br />

John C Johnson III<br />

Founder & CEO<br />

(878) 222-0450<br />

Voice | Data | SMS<br />

www.TheAdvocacy.Foundation<br />

Page 9 of 149


Page 10 of 149


Biblical Authority<br />

______<br />

Leviticus 19:35-36 'You shall do no wrong in judgment, in measurement of weight, or capacity.<br />

'You shall have just balances, just weights, a just ephah, and a just hin; I am the LORD your<br />

God, who brought you out from the land of Egypt.<br />

Ezekiel 45:10 "You shall have just balances, a just ephah and a just bath.<br />

Proverbs 20:10 Differing weights and differing measures, Both of them are abominable to the<br />

LORD.<br />

Proverbs 11:1 A false balance is an abomination to the LORD, But a just weight is His delight.<br />

Proverbs 16:11 A just balance and scales belong to the LORD; All the weights of the bag are<br />

His concern.<br />

Proverbs 20:23 Differing weights are an abomination to the LORD, And a false scale is not<br />

good.<br />

Jeremiah 5:1 "Roam to and fro through the streets of Jerusalem, And look now and take note<br />

And seek in her open squares, If you can find a man, If there is one who does justice, who seeks<br />

truth, Then I will pardon her.<br />

Hosea 12:6-7 Therefore, return to your God, Observe kindness and justice, And wait for your<br />

God continually. A merchant, in whose hands are false balances, He loves to oppress.<br />

Amos 8:4-8 Hear this, you who trample the needy, to do away with the humble of the land,<br />

saying, "When will the new moon be over, So that we may sell grain, And the sabbath, that we<br />

may open the wheat market, To make the bushel smaller and the shekel bigger, And to cheat<br />

with dishonest scales, So as to buy the helpless for money And the needy for a pair of sandals,<br />

And that we may sell the refuse of the wheat?"<br />

Micah 6:10-13 "Is there yet a man in the wicked house, Along with treasures of wickedness And<br />

a short measure that is cursed? "Can I justify wicked scales And a bag of deceptive weights?<br />

"For the rich men of the city are full of violence, Her residents speak lies, And their tongue is<br />

deceitful in their mouth.<br />

Page 11 of 149


Page 12 of 149


Table of Contents<br />

…a compilation of works on<br />

<strong>Hidden</strong> <strong>Unemployment</strong><br />

Biblical Authority<br />

I. Introduction: The <strong>Unemployment</strong> Rate in The U.S..………………..... 15<br />

II. The Effective (True) <strong>Unemployment</strong> Rate – The U6 Indicator.……… 49<br />

III. Involuntary <strong>Unemployment</strong>……….…………………………………….. 61<br />

IV. Underemployment…………………..…………………………………… 65<br />

V. Discouraged Workers………………………..………………………….. 71<br />

VI. The Working Poor…………………….………………………………….. 75<br />

VII. Wage Slavery……………………….………………………………........ 89<br />

VIII. The <strong>Unemployment</strong>-to-Population Ratio…..………………………….. 105<br />

IX. List of Countries by Employment Rate..………………………………. 111<br />

X. References……………………………………………………………..... 113<br />

______<br />

Attachments<br />

A. The <strong>Unemployment</strong> Situation in The U.S.<br />

B. Employment and <strong>Unemployment</strong> Among Youth in The U.S.<br />

C. The Consequences of Long-Term <strong>Unemployment</strong><br />

Copyright © 2003 – 2018 The Advocacy Foundation, Inc. All Rights Reserved.<br />

Page 13 of 149


This work is not meant to be a piece of original academic<br />

analysis, but rather draws very heavily on the work of<br />

scholars in a diverse range of fields. All material drawn upon<br />

is referenced appropriately.<br />

Page 14 of 149


I. Introduction<br />

The <strong>Unemployment</strong> Rate in The U.S.<br />

<strong>Unemployment</strong> or Joblessness is the situation of actively looking for employment, but<br />

not being currently employed..<br />

The unemployment rate is a measure of the prevalence of unemployment and it is<br />

calculated as a percentage by dividing the number of unemployed individuals by all<br />

individuals currently in the labor force. During periods of recession, an economy usually<br />

experiences a relatively high unemployment rate. Six-percent (6%) of the world's<br />

workforce were without a job in 2012.<br />

The causes of unemployment are heavily debated. Classical economics, new classical<br />

economics, and the Austrian School of economics argued that market mechanisms are<br />

reliable means of resolving unemployment. These theories argue against interventions<br />

imposed on the labor market from the outside, such as unionization, bureaucratic work<br />

rules, minimum wage laws, taxes, and other regulations that they claim discourage the<br />

hiring of workers. Keynesian economics emphasizes the cyclical nature of<br />

unemployment and recommends government interventions in the economy that it claims<br />

will reduce unemployment during recessions. This theory focuses on<br />

recurrent shocks that suddenly reduce aggregate demand for goods and services and<br />

thus reduce demand for workers. Keynesian models recommend government<br />

interventions designed to increase demand for workers; these can include financial<br />

Page 15 of 149


stimuli, publicly funded job creation, and expansionist monetary policies. Its namesake<br />

economist John Maynard Keynes, believed that the root cause of unemployment is the<br />

desire of investors to receive more money rather than produce more products, which is<br />

not possible without public bodies producing new money. A third group of theories<br />

emphasize the need for a stable supply of capital and investment to maintain full<br />

employment. On this view, government should guarantee full employment through fiscal<br />

policy, monetary policy and trade policy as stated, for example, in the US Employment<br />

Act of 1946, by counteracting private sector or trade investment volatility, and<br />

reducing inequality.<br />

In addition to these comprehensive theories of unemployment, there are a few<br />

categorizations of unemployment that are used to more precisely model the effects of<br />

unemployment within the economic system. Some of the main types of unemployment<br />

include structural unemployment and frictional unemployment, as well as cyclical<br />

unemployment, involuntary unemployment, and classical unemployment. Structural<br />

unemployment focuses on foundational problems in the economy and inefficiencies<br />

inherent in labor markets, including a mismatch between the supply and demand of<br />

laborers with necessary skill sets. Structural arguments emphasize causes and<br />

solutions related to disruptive technologies and globalization.<br />

Discussions of frictional unemployment focus on voluntary decisions to work based on<br />

each individuals' valuation of their own work and how that compares to current wage<br />

rates plus the time and effort required to find a job. Causes and solutions for frictional<br />

unemployment often address job entry threshold and wage rates.<br />

Definitions, Types, and Theories<br />

The state of being without any work for an educated person, for earning one's livelihood<br />

is meant by unemployment. Economists distinguish between various overlapping types<br />

of and theories of unemployment, including cyclical or Keynesian<br />

unemployment, frictional unemployment, structural unemployment and classical<br />

unemployment. Some additional types of unemployment that are occasionally<br />

mentioned are seasonal unemployment, hardcore unemployment, and hidden<br />

unemployment.<br />

Though there have been several definitions of "voluntary" and "involuntary<br />

unemployment" in the economics literature, a simple distinction is often applied.<br />

Voluntary unemployment is attributed to the individual's decisions, whereas involuntary<br />

unemployment exists because of the socio-economic environment (including the market<br />

structure, government intervention, and the level of aggregate demand) in which<br />

individuals operate. In these terms, much or most of frictional unemployment is<br />

voluntary, since it reflects individual search behavior. Voluntary unemployment includes<br />

workers who reject low wage jobs whereas involuntary unemployment includes workers<br />

fired due to an economic crisis, industrial decline, company bankruptcy, or<br />

organizational restructuring.<br />

Page 16 of 149


On the other hand, cyclical unemployment, structural unemployment, and classical<br />

unemployment are largely involuntary in nature. However, the existence of structural<br />

unemployment may reflect choices made by the unemployed in the past, while classical<br />

(natural) unemployment may result from the legislative and economic choices made by<br />

labour unions or political parties.<br />

The clearest cases of involuntary unemployment are those where there are fewer job<br />

vacancies than unemployed workers even when wages are allowed to adjust, so that<br />

even if all vacancies were to be filled, some unemployed workers would still remain.<br />

This happens with cyclical unemployment, as macroeconomic forces cause<br />

microeconomic unemployment which can boomerang back and exacerbate these<br />

macroeconomic forces.<br />

Classical <strong>Unemployment</strong><br />

Classical, or real-wage unemployment, occurs when real wages for a job are set above<br />

the market-clearing level causing the number of job-seekers to exceed the number of<br />

vacancies. On the other hand, most economists argue that as wages fall below a livable<br />

wage many choose to fall out of the labor market and no longer seek employment. This<br />

is especially true in countries where low-income families are supported through public<br />

welfare systems. In such cases, wages would have to be high enough to motivate<br />

people to choose employment over what they receive through public welfare. Wages<br />

below a livable wage are likely to result in lower labor market participation in above<br />

stated scenario. In addition, it must be noted that consumption of goods and services is<br />

the primary driver of increased need for labor. Higher wages lead to workers having<br />

more income available to consume goods and services. Therefore, higher wages<br />

Page 17 of 149


increase general consumption and as a result need for labor increases and<br />

unemployment decreases in the economy.<br />

Many economists have argued that unemployment increases with increased<br />

governmental regulation. For example, minimum wage laws raise the cost of some lowskill<br />

laborers above market equilibrium, resulting in increased unemployment as people<br />

who wish to work at the going rate cannot (as the new and higher enforced wage is now<br />

greater than the value of their labor). Laws restricting layoffs may make businesses less<br />

likely to hire in the first place, as hiring becomes more risky.<br />

However, this argument overly simplifies the relationship between wage rates and<br />

unemployment, ignoring numerous factors, which contribute to unemployment. Some,<br />

such as Murray Rothbard, suggest that even social taboos can prevent wages from<br />

falling to the market-clearing level.<br />

In Out of Work: <strong>Unemployment</strong> and Government in the Twentieth-Century America,<br />

economists Richard Vedder and Lowell Gallaway argue that the empirical record of<br />

wages rates, productivity, and unemployment in American validates classical<br />

unemployment theory. Their data shows a strong correlation between adjusted real<br />

wage and unemployment in the United States from 1900 to 1990. However, they<br />

maintain that their data does not take into account exogenous events.<br />

Cyclical <strong>Unemployment</strong><br />

Cyclical, deficient-demand, or Keynesian unemployment, occurs when there is not<br />

enough aggregate demand in the economy to provide jobs for everyone who wants to<br />

work. Demand for most goods and services falls, less production is needed and<br />

consequently fewer workers are needed, wages are sticky and do not fall to meet the<br />

equilibrium level, and mass unemployment results. Its name is derived from the frequent<br />

shifts in the business cycle although unemployment can also be persistent as occurred<br />

during the Great Depression of the 1930s.<br />

With cyclical unemployment, the number of unemployed workers exceeds the number<br />

of job vacancies, so that even if full employment were attained and all open jobs were<br />

filled, some workers would still remain unemployed. Some associate cyclical<br />

unemployment with frictional unemployment because the factors that cause the friction<br />

are partially caused by cyclical variables. For example, a surprise decrease in the<br />

money supply may shock rational economic factors and suddenly inhibit aggregate<br />

demand.<br />

Keynesian economists on the other hand see the lack of supply for jobs as potentially<br />

resolvable by government intervention. One suggested interventions involves deficit<br />

spendingto boost employment and demand. Another intervention involves an<br />

expansionary monetary policy that increases the supply of money which should<br />

reduce interest rates which should lead to an increase in non-governmental spending.<br />

Page 18 of 149


Marxian Theory of <strong>Unemployment</strong><br />

It is in the very nature of the capitalist mode of production to overwork some<br />

workers while keeping the rest as a reserve army of unemployed paupers.<br />

— Marx, Theory of Surplus Value [18]<br />

Marxists share the Keynesian viewpoint of the relationship between economic demand<br />

and employment, but with the caveat that the market system's propensity to slash<br />

wages and reduce labor participation on an enterprise level causes a requisite decrease<br />

in aggregate demand in the economy as a whole, causing crises of unemployment and<br />

periods of low economic activity before the capital accumulation (investment) phase of<br />

economic growth can continue.<br />

According to Karl Marx, unemployment is inherent within the unstable capitalist system<br />

and periodic crises of mass unemployment are to be expected. He theorized that<br />

unemployment was inevitable and even a necessary part of the capitalist system, with<br />

recovery and regrowth also part of the process. The function of the proletariat within the<br />

capitalist system is to provide a "reserve army of labour" that creates downward<br />

pressure on wages. This is accomplished by dividing the proletariat into surplus labour<br />

(employees) and under-employment (unemployed). This reserve army of labour fight<br />

among themselves for scarce jobs at lower and lower wages.<br />

At first glance, unemployment seems inefficient since unemployed workers do not<br />

increase profits, but unemployment is profitable within the global capitalist system<br />

because unemployment lowers wages which are costs from the perspective of the<br />

owners. From this perspective low wages benefit the system by reducing economic<br />

rents. Yet, it does not benefit workers; according to Karl Marx, the workers (proletariat)<br />

work to benefit the bourgeoisie through their production of capital. Capitalist systems<br />

Page 19 of 149


unfairly manipulate the market for labour by perpetuating unemployment which lowers<br />

laborers' demands for fair wages. Workers are pitted against one another at the service<br />

of increasing profits for owners. As a result of the capitalist mode of production, Marx<br />

argued that workers experienced alienation and estrangement through their economic<br />

identity.<br />

According to Marx, the only way to permanently eliminate unemployment would be to<br />

abolish capitalism and the system of forced competition for wages and then shift to a<br />

socialist or communist economic system. For contemporary Marxists, the existence of<br />

persistent unemployment is proof of the inability of capitalism to ensure full employment.<br />

Full Employment<br />

In demand-based theory, it is possible to abolish cyclical unemployment by increasing<br />

the aggregate demand for products and workers. However, eventually the economy hits<br />

an "inflation barrier" imposed by the four other kinds of unemployment to the extent that<br />

they exist. Historical experience suggests that low unemployment affects inflation in the<br />

short term but not the long term. In the long term, the velocity of money supply<br />

measures such as the MZM ("money zero maturity", representing cash and<br />

equivalent demand deposits) velocity is far more predictive of inflation than low<br />

unemployment.<br />

Some demand theory economists see the inflation barrier as corresponding to<br />

the natural rate of unemployment. The "natural" rate of unemployment is defined as the<br />

rate of unemployment that exists when the labour market is in equilibrium and there is<br />

pressure for neither rising inflation rates nor falling inflation rates. An alternative<br />

technical term for this rate is the NAIRU, or the Non-Accelerating Inflation Rate of<br />

<strong>Unemployment</strong>. No matter what its name, demand theory holds that this means that if<br />

the unemployment rate gets "too low," inflation will accelerate in the absence of wage<br />

and price controls (incomes policies).<br />

One of the major problems with the NAIRU theory is that no one knows exactly what the<br />

NAIRU is (while it clearly changes over time). The margin of error can be quite high<br />

relative to the actual unemployment rate, making it hard to use the NAIRU in policymaking.<br />

Another, normative, definition of full employment might be called<br />

the ideal unemployment rate. It would exclude all types of unemployment that represent<br />

forms of inefficiency. This type of "full employment" unemployment would correspond to<br />

only frictional unemployment (excluding that part encouraging the McJobs management<br />

strategy) and would thus be very low. However, it would be impossible to attain this fullemployment<br />

target using only demand-side Keynesian stimulus without getting below<br />

the NAIRU and causing accelerating inflation (absent incomes policies). Training<br />

programs aimed at fighting structural unemployment would help here.<br />

Page 20 of 149


To the extent that hidden unemployment exists, it implies that official unemployment<br />

statistics provide a poor guide to what unemployment rate coincides with "full<br />

employment".<br />

Structural <strong>Unemployment</strong><br />

Structural unemployment occurs when a labour market is unable to provide jobs for<br />

everyone who wants one because there is a mismatch between the skills of the<br />

unemployed workers and the skills needed for the available jobs. Structural<br />

unemployment is hard to separate empirically from frictional unemployment, except to<br />

say that it lasts longer. As with frictional unemployment, simple demand-side stimulus<br />

will not work to easily abolish this type of unemployment.<br />

Structural unemployment may also be encouraged to rise by persistent cyclical<br />

unemployment: if an economy suffers from long-lasting low aggregate demand, it<br />

means that many of the unemployed become disheartened, while their skills<br />

(including job-searching skills) become "rusty" and obsolete. Problems with debt may<br />

lead to homelessness and a fall into the vicious circle of poverty.<br />

This means that they may not fit the job vacancies that are created when the economy<br />

recovers. The implication is that sustained highdemand may lower structural<br />

unemployment. This theory of persistence in structural unemployment has been<br />

referred to as an example of path dependence or "hysteresis".<br />

Much technological unemployment, due to the replacement of workers by machines,<br />

might be counted as structural unemployment. Alternatively, technological<br />

Page 21 of 149


unemployment might refer to the way in which steady increases in labour productivity<br />

mean that fewer workers are needed to produce the same level of output every year.<br />

The fact that aggregate demand can be raised to deal with this problem suggests that<br />

this problem is instead one of cyclical unemployment. As indicated by Okun's Law, the<br />

demand side must grow sufficiently quickly to absorb not only the growing labour force<br />

but also the workers made redundant by increased labour productivity.<br />

Seasonal unemployment may be seen as a kind of structural unemployment, since it is<br />

a type of unemployment that is linked to certain kinds of jobs (construction work,<br />

migratory farm work). The most-cited official unemployment measures erase this kind of<br />

unemployment from the statistics using "seasonal adjustment" techniques. This results<br />

in substantial, permanent structural unemployment.<br />

Frictional <strong>Unemployment</strong><br />

Frictional unemployment is the time period between jobs when a worker<br />

is searching for, or transitioning from one job to another. It is sometimes called search<br />

unemployment and can be voluntary based on the circumstances of the unemployed<br />

individual.<br />

Frictional unemployment exists because both jobs and workers are heterogeneous, and<br />

a mismatch can result between the characteristics of supply and demand. Such a<br />

mismatch can be related to skills, payment, work-time, location, seasonal industries,<br />

attitude, taste, and a multitude of other factors. New entrants (such as graduating<br />

students) and re-entrants (such as former homemakers) can also suffer a spell of<br />

frictional unemployment.<br />

Workers as well as employers accept a certain level of imperfection, risk or<br />

compromise, but usually not right away; they will invest some time and effort to find a<br />

better match. This is in fact beneficial to the economy since it results in a better<br />

allocation of resources. However, if the search takes too long and mismatches are too<br />

frequent, the economy suffers, since some work will not get done. Therefore,<br />

governments will seek ways to reduce unnecessary frictional unemployment through<br />

multiple means including providing education, advice, training, and assistance such<br />

as daycare centers.<br />

The frictions in the labour market are sometimes illustrated graphically with a Beveridge<br />

curve, a downward-sloping, convex curve that shows a correlation between the<br />

unemployment rate on one axis and the vacancy rate on the other. Changes in the<br />

supply of or demand for labour cause movements along this curve. An increase<br />

(decrease) in labour market frictions will shift the curve outwards (inwards).<br />

<strong>Hidden</strong> <strong>Unemployment</strong><br />

<strong>Hidden</strong>, or covered, unemployment is the unemployment of potential<br />

workers that are not reflected in official unemployment statistics, due<br />

Page 22 of 149


to the way the statistics are collected. In many countries, only those who have<br />

no work but are actively looking for work (and/or qualifying for social security benefits)<br />

are counted as unemployed. Those who have given up looking for work<br />

(and sometimes those who are on Government "retraining" programs)<br />

are not officially counted among the unemployed, even though they<br />

are not employed.<br />

The statistic also does not count the "underemployed"—those<br />

working fewer hours than they would prefer or in a job that doesn't<br />

make good use of their capabilities. In addition, those who are of<br />

working age but are currently in full-time education are usually not<br />

considered unemployed in government statistics. Traditional unemployed<br />

native societies who survive by gathering, hunting, herding, and farming in wilderness<br />

areas, may or may not be counted in unemployment statistics. Official statistics<br />

Page 23 of 149


often underestimate unemployment rates because of hidden<br />

unemployment. [emphasis added].<br />

Long-Term <strong>Unemployment</strong><br />

Long-term unemployment is defined in European Union statistics, as unemployment<br />

lasting for longer than one year. The United States Bureau of Labor Statistics (BLS),<br />

which reports current long-term unemployment rate at 1.9 percent, defines this as<br />

unemployment lasting 27 weeks or longer. Long-term unemployment is a component<br />

of structural unemployment, which results in long-term unemployment existing in every<br />

social group, industry, occupation, and all levels of education.<br />

Measurement<br />

There are also different ways national statistical agencies measure unemployment.<br />

These differences may limit the validity of international comparisons of unemployment<br />

data. To some degree these differences remain despite national statistical agencies<br />

increasingly adopting the definition of unemployment by the International Labour<br />

Organization. To facilitate international comparisons, some organizations, such as<br />

the OECD, Eurostat, and International Labor Comparisons Program, adjust data on<br />

unemployment for comparability across countries.<br />

Though many people care about the number of unemployed individuals, economists<br />

typically focus on the unemployment rate. This corrects for the normal increase in the<br />

number of people employed due to increases in population and increases in the labour<br />

force relative to the population. The unemployment rate is expressed as a percentage,<br />

and is calculated as follows:<br />

As defined by the International Labour Organization, "unemployed workers" are those<br />

who are currently not working but are willing and able to work for pay, currently<br />

available to work, and have actively searched for work. Individuals who are actively<br />

seeking job placement must make the effort to: be in contact with an employer, have job<br />

interviews, contact job placement agencies, send out resumes, submit applications,<br />

respond to advertisements, or some other means of active job searching within the prior<br />

four weeks. Simply looking at advertisements and not responding will not count as<br />

actively seeking job placement. Since not all unemployment may be "open" and counted<br />

by government agencies, official statistics on unemployment may not be accurate. In<br />

the United States, for example, the unemployment rate does not take into consideration<br />

those individuals who are not actively looking for employment, such as those still<br />

attending college.<br />

The ILO describes 4 different methods to calculate the unemployment rate:<br />

<br />

Labor Force Sample Surveys are the most preferred method of unemployment<br />

rate calculation since they give the most comprehensive results and enables<br />

Page 24 of 149


calculation of unemployment by different group categories such as race and<br />

gender. This method is the most internationally comparable.<br />

<br />

<br />

Official Estimates are determined by a combination of information from one or<br />

more of the other three methods. The use of this method has been declining in<br />

favor of Labor Surveys.<br />

Social Insurance Statistics such as unemployment benefits, are computed base<br />

on the number of persons insured representing the total labour force and the<br />

number of persons who are insured that are collecting benefits. This method has<br />

been heavily criticized due to the expiration of benefits before the person finds<br />

work.<br />

<br />

Employment Office Statistics are the least effective being that they only include a<br />

monthly tally of unemployed persons who enter employment offices. This method<br />

also includes unemployed who are not unemployed per the ILO definition.<br />

Page 25 of 149


The primary measure of unemployment, U3, allows for comparisons between countries.<br />

<strong>Unemployment</strong> differs from country to country and across different time periods. For<br />

example, during the 1990s and 2000s, the United States had lower unemployment<br />

levels than many countries in the European Union, which had significant internal<br />

variation, with countries like the UK and Denmark outperforming Italy and France.<br />

However, large economic events such as the Great Depression can lead to similar<br />

unemployment rates across the globe.<br />

European Union (Eurostat)<br />

Eurostat, the statistical office of the European Union, defines unemployed as those<br />

persons age 15 to 74 who are not working, have looked for work in the last four weeks,<br />

and ready to start work within two weeks, which conform to ILO standards. Both the<br />

actual count and rate of unemployment are reported. Statistical data are available by<br />

member state, for the European Union as a whole (EU28) as well as for the euro area<br />

(EA19). Eurostat also includes a long-term unemployment rate. This is defined as part<br />

of the unemployed who have been unemployed for an excess of 1 year.<br />

The main source used is the European Union Labour Force Survey (EU-LFS). The EU-<br />

LFS collects data on all member states each quarter. For monthly calculations, national<br />

surveys or national registers from employment offices are used in conjunction with<br />

quarterly EU-LFS data. The exact calculation for individual countries, resulting in<br />

harmonized monthly data, depends on the availability of the data.<br />

United States Bureau of Labor Statistics<br />

The Bureau of Labor Statistics measures employment and unemployment (of those<br />

over 17 years of age) using two different labor force surveys conducted by the United<br />

States Census Bureau (within the United States Department of Commerce) and/or the<br />

Bureau of Labor Statistics (within the United States Department of Labor) that gather<br />

employment statistics monthly. The Current Population Survey (CPS), or "Household<br />

Survey", conducts a survey based on a sample of 60,000 households. This Survey<br />

measures the unemployment rate based on the ILO definition.<br />

The Current Employment Statistics survey (CES), or "Payroll Survey", conducts a<br />

survey based on a sample of 160,000 businesses and government agencies that<br />

represent 400,000 individual employers. This survey measures only civilian<br />

nonagricultural employment; thus, it does not calculate an unemployment rate, and it<br />

differs from the ILO unemployment rate definition. These two sources have different<br />

classification criteria, and usually produce differing results. Additional data are also<br />

available from the government, such as the unemployment insurance weekly claims<br />

report available from the Office of Workforce Security, within the U.S. Department of<br />

Labor Employment & Training Administration. The Bureau of Labor Statistics provides<br />

up-to-date numbers via a PDF linked here. The BLS also provides a readable concise<br />

current Employment Situation Summary, updated monthly.<br />

Page 26 of 149


The Bureau of Labor Statistics also calculates six alternate measures of unemployment,<br />

U1 through U6, that measure different aspects of unemployment:<br />

<br />

<br />

<br />

U1: Percentage of labor force unemployed 15 weeks or longer.<br />

U2: Percentage of labor force who lost jobs or completed temporary work.<br />

U3: Official unemployment rate per the ILO definition occurs when people are<br />

without jobs and they have actively looked for work within the past four weeks.<br />

<br />

<br />

<br />

U4: U3 + "discouraged workers", or those who have stopped looking for work<br />

because current economic conditions make them believe that no work is<br />

available for them.<br />

U5: U4 + other "marginally attached workers", or "loosely attached workers", or<br />

those who "would like" and are able to work, but have not looked for work<br />

recently.<br />

U6: U5 + Part-time workers who want to work full-time, but cannot due to<br />

economic reasons (underemployment).<br />

Note: "Marginally attached workers" are added to the total labor force for unemployment<br />

rate calculation for U4, U5, and U6. The BLS revised the CPS in 1994 and among the<br />

changes the measure representing the official unemployment rate was renamed U3<br />

instead of U5. In 2013, Representative Hunter proposed that the Bureau of Labor<br />

Statistics use the U5 rate instead of the current U3 rate.<br />

Page 27 of 149


Statistics for the U.S. economy as a whole hide variations among groups. For example,<br />

in January 2008 U.S. unemployment rates were 4.4% for adult men, 4.2% for adult<br />

women, 4.4% for Caucasians, 6.3% for Hispanics or Latinos (all races), 9.2% for African<br />

Americans, 3.2% for Asian Americans, and 18.0% for teenagers. Also, the U.S.<br />

unemployment rate would be at least 2% higher if prisoners and jail inmates were<br />

counted.<br />

The unemployment rate is included in a number of major economic indexes including<br />

the United States' Conference Board's Index of Leading indicators a macroeconomic<br />

measure of the state of the economy.<br />

Alternatives<br />

Limitations of The <strong>Unemployment</strong> Definition<br />

Some critics believe that current methods of measuring unemployment are inaccurate in<br />

terms of the impact of unemployment on people as these methods do not take into<br />

account the 1.5% of the available working population incarcerated in U.S. prisons (who<br />

may or may not be working while incarcerated); those who have lost their jobs and have<br />

become discouraged over time from actively looking for work; those who are selfemployed<br />

or wish to become self-employed, such as tradesmen or building contractors<br />

or IT consultants; those who have retired before the official retirement age but would still<br />

like to work (involuntary early retirees); those on disability pensions who, while not<br />

possessing full health, still wish to work in occupations suitable for their medical<br />

conditions; or those who work for payment for as little as one hour per week but would<br />

like to work full-time.<br />

These last people are "involuntary part-time" workers, those who are underemployed,<br />

e.g., a computer programmer who is working in a retail store until he can find a<br />

permanent job, involuntary stay-at-home mothers who would prefer to work, and<br />

graduate and Professional school students who were unable to find worthwhile jobs<br />

after they graduated with their bachelor's degrees.<br />

Internationally, some nations' unemployment rates are sometimes muted or appear less<br />

severe due to the number of self-employed individuals working in agriculture. Small<br />

independent farmers are often considered self-employed; so, they cannot be<br />

unemployed. The impact of this is that in non-industrialized economies, such as the<br />

United States and Europe during the early 19th century, overall unemployment was<br />

approximately 3% because so many individuals were self-employed, independent<br />

farmers; yet, unemployment outside of agriculture was as high as 80%.<br />

Many economies industrialize and experience increasing numbers of non-agricultural<br />

workers. For example, the United States' non-agricultural labour force increased from<br />

20% in 1800, to 50% in 1850, to 97% in 2000. The shift away from self-employment<br />

increases the percentage of the population who are included in unemployment rates.<br />

Page 28 of 149


When comparing unemployment rates between countries or time periods, it is best to<br />

consider differences in their levels of industrialization and self-employment.<br />

Additionally, the measures of employment and unemployment may be "too high". In<br />

some countries, the availability of unemployment benefitscan inflate statistics since they<br />

give an incentive to register as unemployed. People who do not seek work may choose<br />

to declare themselves unemployed so as to get benefits; people with undeclared paid<br />

occupations may try to get unemployment benefits in addition to the money they earn<br />

from their work.<br />

However, in countries such as the United States, Canada, Mexico, Australia, Japan and<br />

the European Union, unemployment is measured using a sample survey (akin to<br />

a Galluppoll). According to the BLS, a number of Eastern European nations have<br />

instituted labour force surveys as well. The sample survey has its own problems<br />

because the total number of workers in the economy is calculated based on a sample<br />

rather than a census.<br />

It is possible to be neither employed nor unemployed by ILO definitions, i.e., to be<br />

outside of the "labour force". These are people who have no job and are not looking for<br />

one. Many of these people are going to school or are retired. Family responsibilities<br />

keep others out of the labour force. Still others have a physical or mental disability<br />

which prevents them from participating in labour force activities. Some people simply<br />

elect not to work preferring to be dependent on others for sustenance.<br />

Page 29 of 149


Typically, employment and the labour force include only work done for monetary gain.<br />

Hence, a homemaker is neither part of the labour force nor unemployed. Nor are fulltime<br />

students nor prisoners considered to be part of the labour force or<br />

unemployment. The latter can be important. In 1999, economists Lawrence F. Katz and<br />

Alan B. Krueger estimated that increased incarceration lowered measured<br />

unemployment in the United States by 0.17% between 1985 and the late 1990s.<br />

In particular, as of 2005, roughly 0.7% of the U.S. population is incarcerated (1.5% of<br />

the available working population). Additionally, children, the elderly, and some<br />

individuals with disabilities are typically not counted as part of the labour force in and<br />

are correspondingly not included in the unemployment statistics. However, some elderly<br />

and many disabled individuals are active in the labour market<br />

In the early stages of an economic boom, unemployment often rises. This is because<br />

people join the labour market (give up studying, start a job hunt, etc.) as a result of the<br />

improving job market, but until they have actually found a position they are counted as<br />

unemployed. Similarly, during a recession, the increase in the unemployment rate is<br />

moderated by people leaving the labour force or being otherwise discounted from the<br />

labour force, such as with the self-employed.<br />

For the fourth quarter of 2004, according to OECD, (source Employment Outlook<br />

2005 ISBN 92-64-01045-9), normalized unemployment for men aged 25 to 54 was 4.6%<br />

in the U.S. and 7.4% in France. At the same time and for the same population the<br />

employment rate (number of workers divided by population) was 86.3% in the U.S. and<br />

86.7% in France. This example shows that the unemployment rate is 60% higher in<br />

France than in the U.S., yet more people in this demographic are working in France<br />

than in the U.S., which is counterintuitive if it is expected that the unemployment rate<br />

reflects the health of the labour market.<br />

Due to these deficiencies, many labour market economists prefer to look at a range of<br />

economic statistics such as labour market participation rate, the percentage of people<br />

aged between 15 and 64 who are currently employed or searching for employment, the<br />

total number of full-time jobs in an economy, the number of people seeking work as a<br />

raw number and not a percentage, and the total number of person-hours worked in a<br />

month compared to the total number of person-hours people would like to work. In<br />

particular the NBER does not use the unemployment rate but prefer various<br />

employment rates to date recessions.<br />

Labor Force Participation Rate<br />

The labor force participation rate is the ratio between the labor force and the overall size<br />

of their cohort (national population of the same age range). In the West, during the later<br />

half of the 20th century, the labor force participation rate increased significantly, due to<br />

an increase in the number of women who entered the workplace.<br />

Page 30 of 149


In the United States, there have been four significant stages of women's participation in<br />

the labor force—increases in the 20th century and decreases in the 21st century. Male<br />

labor force participation decreased from 1953 until 2013. Since October 2013 men have<br />

been increasingly joining the labor force.<br />

During the late 19th century through the 1920s, very few women worked outside the<br />

home. They were young single women who typically withdrew from the labor force at<br />

marriage unless family needed two incomes. These women worked primarily in<br />

the textile manufacturingindustry or as domestic workers. This profession empowered<br />

women and allowed them to earn a living wage. At times, they were a financial help to<br />

their families.<br />

Between 1930 and 1950, female labor force participation increased primarily due to the<br />

increased demand for office workers, women's participation in the high school<br />

movement, and due to electrification which reduced the time spent on household<br />

chores. Between the 1950s to the early 1970s, most women were secondary earners<br />

working mainly as secretaries, teachers, nurses, and librarians (pink-collar jobs).<br />

Between the mid-1970s to the late 1990s, there was a period of revolution of women in<br />

the labor force brought on by a source of different factors, many of which arose from<br />

the second wave feminism movement. Women more accurately planned for their future<br />

in the work force, investing in more applicable majors in college that prepared them to<br />

enter and compete in the labor market. In the United States, the female labor force<br />

participation rate rose from approximately 33% in 1948 to a peak of 60.3% in 2000. As<br />

of April 2015, the female labor force participation is at 56.6%, the male labor force<br />

participation rate is at 69.4% and the total is 62.8%.<br />

Page 31 of 149


A common theory in modern economics claims that the rise of women participating in<br />

the U.S. labor force in the 1950s to the 1990s was due to the introduction of a new<br />

contraceptive technology, birth control pills, and the adjustment of age of majority laws.<br />

The use of birth control gave women the flexibility of opting to invest and advance their<br />

career while maintaining a relationship.<br />

By having control over the timing of their fertility, they were not running a risk of<br />

thwarting their career choices. However, only 40% of the population actually used the<br />

birth control pill.<br />

This implies that other factors may have contributed to women choosing to invest in<br />

advancing their careers. One factor may be that more and more men delayed the age of<br />

marriage, allowing women to marry later in life without worrying about the quality of<br />

older men. Other factors include the changing nature of work, with machines replacing<br />

physical labor, eliminating many traditional male occupations, and the rise of the service<br />

sector, where many jobs are gender neutral.<br />

Another factor that may have contributed to the trend was The Equal Pay Act of 1963,<br />

which aimed at abolishing wage disparity based on sex. Such legislation diminished<br />

sexual discrimination and encouraged more women to enter the labor market by<br />

receiving fair remuneration to help raising families and children.<br />

At the turn of the 21st century the labor force participation began to reverse its long<br />

period of increase. Reasons for this change include a rising share of older workers, an<br />

increase in school enrollment rates among young workers and a decrease in female<br />

labor force participation.<br />

The labor force participation rate can decrease when the rate of growth of the<br />

population outweighs that of the employed and unemployed together. The labor force<br />

participation rate is a key component in long-term economic growth, almost as important<br />

as productivity.<br />

A historic shift began around the end of the great recession as women began leaving<br />

the labor force in the United States and other developed countries. The female labor<br />

force participation rate in the United States has steadily decreased since 2009 and as of<br />

April 2015 the female labor force participation rate has gone back down to 1988 levels<br />

of 56.6%.<br />

Participation rates are defined as follows:<br />

The labor force participation rate explains how an increase in the unemployment rate<br />

can occur simultaneously with an increase in employment. If a large amount of new<br />

workers enter the labor force but only a small fraction become employed, then the<br />

increase in the number of unemployed workers can outpace the growth in employment.<br />

Page 32 of 149


<strong>Unemployment</strong> Ratio<br />

The unemployment ratio calculates the share of unemployed for the whole population.<br />

Particularly many young people between 15 and 24 are studying full-time and are<br />

therefore neither working nor looking for a job. This means they are not part of the<br />

labour force which is used as the denominator for calculating the unemployment<br />

rate. The youth unemployment ratios in the European Union range from 5.2 (Austria) to<br />

20.6 percent (Spain). These are considerably lower than the standard youth<br />

unemployment rates, ranging from 7.9 (Germany) to 57.9 percent (Greece).<br />

Effects<br />

High and persistent unemployment, in which economic inequality increases, has a<br />

negative effect on subsequent long-run economic growth. <strong>Unemployment</strong> can harm<br />

growth not only because it is a waste of resources, but also because it generates<br />

redistributive pressures and subsequent distortions, drives people to poverty, constrains<br />

liquidity limiting labor mobility, and erodes self-esteem promoting social dislocation,<br />

unrest and conflict.<br />

2013 Economics Nobel prize winner Robert J. Shiller said that rising inequality in the<br />

United States and elsewhere is the most important problem.<br />

Page 33 of 149


Costs<br />

Individual<br />

Unemployed individuals are unable to earn money to meet financial obligations. Failure<br />

to pay mortgage payments or to pay rent may lead<br />

to homelessness through foreclosure or eviction. Across the United States the growing<br />

ranks of people made homeless in the foreclosure crisis are generating tent cities.<br />

<strong>Unemployment</strong> increases susceptibility to cardiovascular disease, somatization, anxiety<br />

disorders, depression, and suicide. In addition, unemployed people have higher rates of<br />

medication use, poor diet, physician visits, tobacco smoking, alcoholic<br />

beverage consumption, drug use, and lower rates of exercise. According to a study<br />

published in Social Indicator Research, even those who tend to be optimistic find it<br />

difficult to look on the bright side of things when unemployed. Using interviews and data<br />

from German participants aged 16 to 94—including individuals coping with the stresses<br />

of real life and not just a volunteering student population—the researchers determined<br />

that even optimists struggled with being unemployed.<br />

In 1979, Brenner found that for every 10% increase in the number of unemployed there<br />

is an increase of 1.2% in total mortality, a 1.7% increase in cardiovascular disease,<br />

1.3% more cirrhosis cases, 1.7% more suicides, 4.0% more arrests, and 0.8% more<br />

assaults reported to the police.<br />

A study by Ruhm, in 2000, on the effect of recessions on health found that several<br />

measures of health actually improve during recessions. As for the impact of an<br />

economic downturn on crime, during the Great Depression the crime rate did not<br />

decrease. The unemployed in the U.S. often use welfare programs such as Food<br />

Stamps or accumulating debt because unemployment insurance in the U.S. generally<br />

does not replace a majority of the income one received on the job (and one cannot<br />

receive such aid indefinitely).<br />

Not everyone suffers equally from unemployment. In a prospective study of 9570<br />

individuals over four years, highly conscientious people suffered more than twice as<br />

much if they became unemployed. The authors suggested this may be due to<br />

conscientious people making different attributions about why they became unemployed,<br />

or through experiencing stronger reactions following failure. There is also possibility of<br />

reverse causality from poor health to unemployment.<br />

Some researchers hold that many of the low-income jobs are not really a better option<br />

than unemployment with a welfare state (with its unemployment insurance benefits). But<br />

since it is difficult or impossible to get unemployment insurance benefits without having<br />

worked in the past, these jobs and unemployment are more complementary than they<br />

are substitutes. (These jobs are often held short-term, either by students or by those<br />

trying to gain experience; turnover in most low-paying jobs is high.)<br />

Page 34 of 149


Another cost for the unemployed is that the combination of unemployment, lack of<br />

financial resources, and social responsibilities may push unemployed workers to take<br />

jobs that do not fit their skills or allow them to use their talents. <strong>Unemployment</strong> can<br />

cause underemployment, and fear of job loss can spur psychological anxiety. As well as<br />

anxiety, it can cause depression, lack of confidence, and huge amounts of stress. This<br />

stress is increased when the unemployed are faced with health issues, poverty, and<br />

lack of relational support.<br />

Another personal cost of unemployment is its impact on relationships. A 2008 study<br />

from Covizzi, which examines the relationship between unemployment and divorce,<br />

found that the rate of divorce is greater for couples when one partner is<br />

unemployed. However, a more recent study has found that some couples often stick<br />

together in "unhappy" or "unhealthy" marriages when unemployed to buffer financial<br />

costs. A 2014 study by Van der Meer found that the stigma that comes from being<br />

unemployed affects personal well-being, especially for men, who often feel as though<br />

their masculine identities are threatened by unemployment.<br />

<strong>Unemployment</strong> can also bring personal costs in relation to gender. One study found that<br />

women are more likely to experience unemployment than men and that they are less<br />

likely to move from temporary positions to permanent positions. Another study on<br />

gender and unemployment found that men, however, are more likely to experience<br />

greater stress, depression, and adverse effects from unemployment, largely stemming<br />

Page 35 of 149


from the perceived threat to their role as breadwinner. This study found that men expect<br />

themselves to be viewed as "less manly" after a job loss than they actually are, and as a<br />

result they engage in compensating behaviors, such as financial risk-taking and<br />

increased assertiveness, because of it.<br />

Costs of unemployment also vary depending on age. The young and the old are the two<br />

largest age groups currently experiencing unemployment. A 2007 study from Jacob and<br />

Kleinert found that young people (ages 18 to 24) who have fewer resources and limited<br />

work experiences are more likely to be unemployed. Other researchers have found that<br />

today’s high school seniors place a lower value on work than those in the past, and this<br />

is likely because they recognize the limited availability of jobs. At the other end of the<br />

age spectrum, studies have found that older individuals have more barriers than<br />

younger workers to employment, require stronger social networks to acquire work, and<br />

are also less likely to move from temporary to permanent positions. Additionally, some<br />

older people see age discrimination as the reason they are not getting hired.<br />

Social<br />

An economy with high unemployment is not using all of the resources, specifically<br />

labour, available to it. Since it is operating below its production possibility frontier, it<br />

could have higher output if all the workforce were usefully employed. However, there is<br />

a trade-off between economic efficiency and unemployment: if the frictionally<br />

unemployed accepted the first job they were offered, they would be likely to be<br />

operating at below their skill level, reducing the economy's efficiency.<br />

During a long period of unemployment, workers can lose their skills, causing a loss<br />

of human capital. Being unemployed can also reduce the life expectancy of workers by<br />

about seven years.<br />

High unemployment can encourage xenophobia and protectionism as workers fear that<br />

foreigners are stealing their jobs. Efforts to preserve existing jobs of domestic and<br />

native workers include legal barriers against "outsiders" who want jobs, obstacles to<br />

immigration, and/or tariffs and similar trade barriers against foreign competitors.<br />

High unemployment can also cause social problems such as crime; if people have less<br />

disposable income than before, it is very likely that crime levels within the economy will<br />

increase.<br />

A 2015 study published in The Lancet, estimates that unemployment causes 45,000<br />

suicides a year globally.<br />

Socio-Political<br />

High levels of unemployment can be causes of civil unrest, in some cases leading to<br />

revolution, and particularly totalitarianism. The fall of the Weimar Republic in 1933<br />

and Adolf Hitler's rise to power, which culminated in World War II and the deaths of tens<br />

Page 36 of 149


of millions and the destruction of much of the physical capital of Europe, is attributed to<br />

the poor economic conditions in Germany at the time, notably a high unemployment<br />

rate of above 20%; see Great Depression in Central Europe for details.<br />

Note that the hyperinflation in the Weimar Republic is not directly blamed for the Nazi<br />

rise—the Hyperinflation in the Weimar Republicoccurred primarily in the period 1921–<br />

23, which was contemporary with Hitler's Beer Hall Putsch of 1923, and is blamed for<br />

damaging the credibility of democratic institutions, but the Nazis did not assume<br />

government until 1933, ten years after the hyperinflation but in the midst of high<br />

unemployment.<br />

Rising unemployment has traditionally been regarded by the public and media in any<br />

country as a key guarantor of electoral defeat for any government which oversees it.<br />

This was very much the consensus in the United Kingdom until 1983, when Margaret<br />

Thatcher's Conservative government won a landslide in the general election, despite<br />

overseeing a rise in unemployment from 1,500,000 to 3,200,000 since its election four<br />

years earlier.<br />

Benefits<br />

The primary benefit of unemployment is that people are available for hire, without<br />

being headhunted away from their existing employers. This permits new and old<br />

businesses to take on staff.<br />

<strong>Unemployment</strong> is argued to be "beneficial" to the people who are not unemployed in the<br />

sense that it averts inflation, which itself has damaging effects, by providing<br />

(in Marxianterms) a reserve army of labour, that keeps wages in check. However, the<br />

direct connection between full local employment and local inflation has been disputed<br />

Page 37 of 149


y some due to the recent increase in international trade that supplies low-priced goods<br />

even while local employment rates rise to full employment.<br />

Full employment cannot be achieved because workers would shirk, if they were not<br />

threatened with the possibility of unemployment. The curve for the no-shirking condition<br />

(labeled NSC) goes to infinity at full employment as a result. The inflation-fighting<br />

benefits to the entire economy arising from a presumed optimum level of unemployment<br />

have been studied extensively. The Shapiro–Stiglitz model suggests that wages are not<br />

bid down sufficiently to ever reach 0% unemployment. This occurs because employers<br />

know that when wages decrease, workers will shirk and expend less effort. Employers<br />

avoid shirking by preventing wages from decreasing so low that workers give up and<br />

become unproductive. These higher wages perpetuate unemployment while the threat<br />

of unemployment reduces shirking.<br />

Before current levels of world trade were developed, unemployment was demonstrated<br />

to reduce inflation, following the Phillips curve, or to decelerate inflation, following the<br />

NAIRU/natural rate of unemployment theory, since it is relatively easy to seek a new job<br />

without losing one's current one. And when more jobs are available for fewer workers<br />

(lower unemployment), it may allow workers to find the jobs that better fit their tastes,<br />

talents, and needs.<br />

As in the Marxian theory of unemployment, special interests may also benefit: some<br />

employers may expect that employees with no fear of losing their jobs will not work as<br />

hard, or will demand increased wages and benefit. According to this theory,<br />

unemployment may promote general labour productivity and profitability by increasing<br />

employers' rationale for their monopsony-like power (and profits).<br />

Optimal unemployment has also been defended as an environmental tool to brake the<br />

constantly accelerated growth of the GDP to maintain levels sustainable in the context<br />

of resource constraints and environmental impacts. However the tool of denying jobs to<br />

willing workers seems a blunt instrument for conserving resources and the<br />

environment—it reduces the consumption of the unemployed across the board, and<br />

only in the short term. Full employment of the unemployed workforce, all focused toward<br />

the goal of developing more environmentally efficient methods for production and<br />

consumption might provide a more significant and lasting cumulative environmental<br />

benefit and reduced resource consumption. If so the future economy and workforce<br />

would benefit from the resultant structural increases in the sustainable level of GDP<br />

growth.<br />

Some critics of the "culture of work" such as anarchist Bob Black see employment as<br />

overemphasized culturally in modern countries. Such critics often propose quitting jobs<br />

when possible, working less, reassessing the cost of living to this end, creation of jobs<br />

which are "fun" as opposed to "work," and creating cultural norms where work is seen<br />

as unhealthy. These people advocate an "anti-work" ethic for life.<br />

Page 38 of 149


Decline In Work Hours<br />

As a result of productivity, the work week declined considerably during the 19th<br />

century. By the 1920s in the U.S. the average work week was 49 hours, but the work<br />

week was reduced to 40 hours (after which overtime premium was applied) as part of<br />

the National Industrial Recovery Act of 1933. At the time of the Great Depression of the<br />

1930s, it was believed that due to the enormous productivity gains due<br />

to electrification, mass production and agricultural mechanization, there was no need for<br />

a large number of previously employed workers.<br />

Controlling or Reducing <strong>Unemployment</strong><br />

United States Families on Relief (in 1,000's<br />

Workers employed<br />

1936 1937 1938 1939 1940 1941<br />

WPA 1,995 2,227 1,932 2,911 1,971 1,638<br />

CCC and NYA 712 801 643 793 877 919<br />

Other federal work projects 554 663 452 488 468 681<br />

Cases on public assistance<br />

Social security programs 602 1,306 1,852 2,132 2,308 2,517<br />

General relief 2,946 1,484 1,611 1,647 1,570 1,206<br />

Totals<br />

Total families helped 5,886 5,660 5,474 6,751 5,860 5,167<br />

Unemployed workers (BLS) 9,030 7,700 10,390 9,480 8,120 5,560<br />

Coverage (cases/unemployed) 65% 74% 53% 71% 72% 93%<br />

Societies try a number of different measures to get as many people as possible into<br />

work, and various societies have experienced close to full employment for extended<br />

periods, particularly during the Post-World War II economic expansion.<br />

The United Kingdom in the 1950s and 1960s averaged 1.6% unemployment, while in<br />

Australia the 1945 White Paper on Full Employment in Australia established a<br />

government policy of full employment, which policy lasted until the 1970s when the<br />

government ran out of money.<br />

However, mainstream economic discussions of full employment since the 1970s<br />

suggest that attempts to reduce the level of unemployment below the natural rate of<br />

unemployment will fail, resulting only in less output and more inflation.<br />

Page 39 of 149


Demand-Side Solutions<br />

Increases in the demand for labor will move the economy along the demand curve,<br />

increasing wages and employment. The demand for labor in an economy is derived<br />

from the demand for goods and services. As such, if the demand for goods and services<br />

in the economy increases, the demand for labor will increase, increasing employment<br />

and wages.<br />

There are many ways to stimulate demand for goods and services. Increasing wages to<br />

the working class (those more likely to spend the increased funds on goods and<br />

services, rather than various types of savings, or commodity purchases) is one theory<br />

proposed. Increased wages are believed to be more effective in boosting demand for<br />

goods and services than central banking strategies that put the increased money supply<br />

mostly into the hands of wealthy persons and institutions. Monetarists suggest that<br />

increasing money supply in general will increase short-term demand. Long-term the<br />

increased demand will be negated by inflation. A rise in fiscal expenditures is another<br />

strategy for boosting aggregate demand.<br />

Providing aid to the unemployed is a strategy used to prevent cutbacks in consumption<br />

of goods and services which can lead to a vicious cycle of further job losses and further<br />

decreases in consumption/demand. Many countries aid the unemployed through social<br />

welfare programs. These unemployment benefits include unemployment<br />

insurance, unemployment compensation, welfare and subsidies to aid in retraining. The<br />

main goal of these programs is to alleviate short-term hardships and, more importantly,<br />

to allow workers more time to search for a job.<br />

A direct demand-side solution to unemployment is government-funded employment of<br />

the able-bodied poor. This was notably implemented in Britain from the 17th century<br />

until 1948 in the institution of the workhouse, which provided jobs for the unemployed<br />

with harsh conditions and poor wages to dissuade their use. A modern alternative is<br />

a job guarantee, where the government guarantees work at a living wage.<br />

Temporary measures can include public works programs such as the Works Progress<br />

Administration. Government-funded employment is not widely advocated as a solution<br />

to unemployment, except in times of crisis; this is attributed to the public sector jobs'<br />

existence depending directly on the tax receipts from private sector employment.<br />

In the U.S., the unemployment insurance allowance one receives is based solely on<br />

previous income (not time worked, family size, etc.) and usually compensates for onethird<br />

of one's previous income. To qualify, one must reside in their respective state for at<br />

least a year and work. The system was established by the Social Security Act of 1935.<br />

Although 90% of citizens are covered by unemployment insurance, less than 40% apply<br />

for and receive benefits. However, the number applying for and receiving benefits<br />

increases during recessions. In cases of highly seasonal industries, the system provides<br />

income to workers during the off seasons, thus encouraging them to stay attached to<br />

the industry.<br />

Page 40 of 149


According to classical economic theory, markets reach equilibrium where supply equals<br />

demand; everyone who wants to sell at the market price can. Those who do not want to<br />

sell at this price do not; in the labour market this is classical unemployment. Monetary<br />

policy and fiscal policy can both be used to increase short-term growth in the economy,<br />

increasing the demand for labour and decreasing unemployment.<br />

Supply-Side Solutions<br />

However, the labor market is not 100% efficient, although it may be more efficient than<br />

the bureaucracy. Some argue that minimum wages and union activity keep wages from<br />

falling, which means too many people want to sell their labour at the going price but<br />

cannot. This assumes perfect competition exists in the labour market, specifically that<br />

no single entity is large enough to affect wage levels and that employees are similar in<br />

ability.<br />

Advocates of supply-side policies believe those policies can solve this by making the<br />

labor market more flexible. These include removing the minimum wage and reducing<br />

the power of unions. Supply-siders argue the reforms increase long-term growth by<br />

reducing labour costs. This increased supply of goods and services requires more<br />

workers, increasing employment.<br />

It is argued that supply-side policies, which include cutting taxes on businesses and<br />

reducing regulation, create jobs, reduce unemployment and decrease labour's share of<br />

national income. Other supply-side policies include education to make workers more<br />

attractive to employers.<br />

Page 41 of 149


History<br />

There are relatively limited historical records on unemployment because it has not<br />

always been acknowledged or measured systematically. Industrialization involves<br />

economies of scale that often prevent individuals from having the capital to create their<br />

own jobs to be self-employed. An individual who cannot either join an enterprise or<br />

create a job is unemployed. As individual farmers, ranchers, spinners, doctors and<br />

merchants are organized into large enterprises, those who cannot join or compete<br />

become unemployed.<br />

Recognition of unemployment occurred slowly as economies across the world<br />

industrialized and bureaucratized. Before this, traditional self sufficient native societies<br />

have no concept of unemployment. The recognition of the concept of "unemployment" is<br />

best exemplified through the well documented historical records in England. For<br />

example, in 16th century England no distinction was made between vagrants and the<br />

jobless; both were simply categorized as "sturdy beggars", to be punished and moved<br />

on.<br />

The closing of the monasteries in the 1530s increased poverty, as the church had<br />

helped the poor. In addition, there was a significant rise in enclosure during the Tudor<br />

period. Also the population was rising. Those unable to find work had a stark choice:<br />

starve or break the law. In 1535, a bill was drawn up calling for the creation of a system<br />

of public works to deal with the problem of unemployment, to be funded by a tax on<br />

income and capital. A law passed a year later allowed vagabonds to be whipped and<br />

hanged.<br />

In 1547, a bill was passed that subjected vagrants to some of the more extreme<br />

provisions of the criminal law, namely two years servitude and branding with a "V" as<br />

the penalty for the first offense and death for the second. During the reign of Henry VIII,<br />

as many as 72,000 people are estimated to have been executed. In the 1576 Act each<br />

town was required to provide work for the unemployed.<br />

The Elizabethan Poor Law of 1601, one of the world's first government-sponsored<br />

welfare programs, made a clear distinction between those who were unable to work and<br />

those able-bodied people who refused employment. Under the Poor Law systems<br />

of England and Wales, Scotland and Ireland a workhouse was a place where people<br />

who were unable to support themselves, could go to live and work.<br />

Industrial Revolution to Late 19th Century<br />

Poverty was a highly visible problem in the eighteenth century, both in cities and in the<br />

countryside. In France and Britain by the end of the century, an estimated 10 percent of<br />

the people depended on charity or begging for their food.<br />

— Jackson J. Spielvogel (2008), Cengage Learning. p.566.<br />

By 1776 some 1,912 parish and corporation workhouses had been established in<br />

England and Wales, housing almost 100,000 paupers.<br />

Page 42 of 149


A description of the miserable living standards of the mill workers in England in 1844<br />

was given by Fredrick Engels in The Condition of the Working-Class in England in<br />

1844. In the preface to the 1892 edition Engels notes that the extreme poverty he wrote<br />

about in 1844 had largely disappeared. David Ames Wells also noted that living<br />

conditions in England had improved near the end of the 19th century and that<br />

unemployment was low.<br />

The scarcity and high price of labor in the U.S. during the 19th century was well<br />

documented by contemporary accounts, as in the following:<br />

"The laboring classes are comparatively few in number, but this is counterbalanced by,<br />

and indeed, may be one of the causes of the eagerness by which they call in the use of<br />

machinery in almost every department of industry. Wherever it can be applied as a<br />

substitute for manual labor, it is universally and willingly resorted to ....It is this condition<br />

of the labor market, and this eager resort to machinery wherever it can be applied, to<br />

which, under the guidance of superior education and intelligence, the remarkable<br />

prosperity of the United States is due." Joseph Whitworth, 1854<br />

Page 43 of 149


Scarcity of labor was a factor in the economics of slavery in the United States.<br />

As new territories were opened and Federal land sales conducted, land had to be<br />

cleared and new homesteads established. Hundreds of thousands of immigrants<br />

annually came to the U.S. and found jobs digging canals and building railroads. Almost<br />

all work during most of the 19th century was done by hand or with horses, mules, or<br />

oxen, because there was very little mechanization. The workweek during most of the<br />

19th century was 60 hours. <strong>Unemployment</strong> at times was between one and two percent.<br />

The tight labor market was a factor in productivity gains allowing workers to maintain or<br />

increase their nominal wages during the secular deflation that caused real wages to rise<br />

at various times in the 19th century, especially in the final decades.<br />

20th Century<br />

There were labor shortages during WW I. Ford Motor Co. doubled wages to reduce<br />

turnover. After 1925 unemployment began to gradually rise.<br />

The decade of the 1930s saw the Great Depression impact unemployment across the<br />

globe. One Soviet trading corporation in New York averaged 350 applications a day<br />

from Americans seeking jobs in the Soviet Union. [121] In Germany the unemployment<br />

rate reached nearly 25% in 1932.<br />

In some towns and cities in the north east of England, unemployment reached as high<br />

as 70%; the national unemployment level peaked at more than 22% in<br />

1932. <strong>Unemployment</strong> in Canada reached 27% at the depth of the Depression in<br />

1933. In 1929, the U.S. unemployment rate averaged 3%.<br />

In the U.S., the Works Progress Administration (1935–43) was the largest make-work<br />

program. It hired men (and some women) off the relief roles ("dole") typically for<br />

unskilled labor.<br />

In Cleveland, Ohio, the unemployment rate was 60%; in Toledo, Ohio, 80%.There were<br />

two million homeless people migrating across the United States. Over 3 million<br />

unemployed young men were taken out of the cities and placed into 2600+ work camps<br />

managed by the Civilian Conservation Corps.<br />

<strong>Unemployment</strong> in the United Kingdom fell later in the 1930s as the depression eased,<br />

and remained low (in six figures) after World War II.<br />

Fredrick Mills found that in the U.S., 51% of the decline in work hours was due to the fall<br />

in production and 49% was from increased productivity. By 1972 unemployment in the<br />

UK had crept back up above 1,000,000, and was even higher by the end of the decade,<br />

with inflation also being high. Although the monetarist economic policies of Margaret<br />

Thatcher's Conservative government saw inflation reduced after 1979, unemployment<br />

soared in the early 1980s, exceeding 3,000,000—a level not seen for some 50 years—<br />

by 1982. This represented one in eight of the workforce, with unemployment exceeding<br />

Page 44 of 149


20% in some parts of the United Kingdom which had relied on the now-declining<br />

industries such as coal mining.<br />

However, this was a time of high unemployment in all major industrialised nations. By<br />

the spring of 1983, unemployment in the United Kingdom had risen by 6% in the<br />

previous 12 months; compared to 10% in Japan, 23% in the United States of<br />

America and 34% in West Germany (seven years before reunification).<br />

<strong>Unemployment</strong> in the United Kingdom remained above 3,000,000 until the spring of<br />

1987, by which time the economy was enjoying a boom. By the end of 1989,<br />

unemployment had fallen to 1,600,000. However, inflation had reached 7.8% and the<br />

following year it reached a nine-year high of 9.5%; leading to increased interest rates.<br />

Another recession began during 1990 and lasted until 1992. <strong>Unemployment</strong> began to<br />

increase and by the end of 1992 nearly 3,000,000 in the United Kingdom were<br />

unemployed. Then came a strong economic recovery. With inflation down to 1.6% by<br />

1993, unemployment then began to fall rapidly, standing at 1,800,000 by early 1997.<br />

21st Century<br />

The official unemployment rate in the 16 EU countries that use the Euro rose to 10% in<br />

December 2009 as a result of another recession. Latvia had the highest unemployment<br />

Page 45 of 149


ate in the EU at 22.3% for November 2009. Europe's young workers have been<br />

especially hard hit. In November 2009, the unemployment rate in the EU27 for those<br />

aged 15–24 was 18.3%. For those under-25, the unemployment rate in Spain was<br />

43.8%. <strong>Unemployment</strong> has risen in two-thirds of European countries since 2010.<br />

Into the 21st century, unemployment in the United Kingdom remained low and the<br />

economy remaining strong, while at this time several other European economies—<br />

namely, France and Germany (reunified a decade earlier)—experienced a minor<br />

recession and a substantial rise in unemployment.<br />

In 2008, when the recession brought on another increase in the United Kingdom, after<br />

15 years of economic growth and no major rises in unemployment. Early in 2009,<br />

unemployment passed the 2,000,000 mark, by which time economists were predicting it<br />

would soon reach 3,000,000. However, the end of the recession was declared in<br />

January 2010 and unemployment peaked at nearly 2,700,000 in 2011, appearing to<br />

ease fears of unemployment reaching 3,000,000. The unemployment rate of Britain's<br />

young black people was 47.4% in 2011. 2013/2014 has seen the employment rate<br />

increase from 1,935,836 to 2,173,012 as supported by showing the UK is creating more<br />

job opportunities and forecasts the rate of increase in 2014/2015 will be another 7.2%.<br />

A 26 April 2005 Asia Times article notes that, "In regional giant South Africa, some<br />

300,000 textile workers have lost their jobs in the past two years due to the influx of<br />

Chinese goods". The increasing U.S. trade deficit with China cost 2.4 million American<br />

jobs between 2001-2008, according to a study by the Economic Policy<br />

Institute (EPI). From 2000-2007, the United States lost a total of 3.2 million<br />

manufacturing jobs. 12.1% of US military veterans who had served after the September<br />

11 attacks in 2001 were unemployed as of 2011; 29.1% of male veterans aged 18–24<br />

were unemployed.<br />

As of September 2016, the total veteran unemployment rate was 4.3 percent. By<br />

September 2017, that figure had dropped to 3 percent.<br />

About 25,000,000 people in the world's thirty richest countries will have lost their jobs<br />

between the end of 2007 and the end of 2010 as the economic downturn pushes most<br />

countries into recession. In April 2010, the U.S. unemployment rate was 9.9%, but the<br />

government's broader U-6 unemployment rate was 17.1%.<br />

In April 2012, the unemployment rate was 4.6% in Japan. In a 2012 news story,<br />

the Financial Post reported, "Nearly 75 million youth are unemployed around the world,<br />

an increase of more than 4 million since 2007. In the European Union, where a debt<br />

crisis followed the financial crisis, the youth unemployment rate rose to 18% last year<br />

from 12.5% in 2007, the ILO report shows." In March 2018, according to U.S.<br />

<strong>Unemployment</strong> Rate Statistics, the unemployment rate was 4.1%, below the 4.5 to 5.0%<br />

norm.<br />

________<br />

Page 46 of 149


Male <strong>Unemployment</strong><br />

Male <strong>Unemployment</strong> is unemployment, being out of work and actively seeking to<br />

work, among men.<br />

The 2008–2012 global recession has been called a "mancession" because of the<br />

disproportionate number of men who lost their jobs as compared to women. This gender<br />

gap became wide in the United States in 2009, when 10.5% of men in the labor<br />

force were unemployed, compared with 8% of women. Three quarters of the jobs lost in<br />

the recession in the U.S. were held by men.<br />

Page 47 of 149


Page 48 of 149


II. The Effective (True)<br />

<strong>Unemployment</strong> Rate<br />

The U6 Indicator<br />

The unemployment rate announced by United States Department of Labor does not<br />

include those too discouraged to look for work any longer or those parttime<br />

workers who are working fewer hours than they would like. By adding these two<br />

groups to the unemployment rate, the rate becomes the effective unemployment rate.<br />

The Bureau of Labor Statistics in the United States keeps an alternative unemployment<br />

rate indicator similar to the effective unemployment rate called U6.<br />

________<br />

The Two Different Labor<br />

Force Surveys<br />

The Bureau of Labor Statistics measures<br />

employment and unemployment (of those over<br />

17 years of age) using two different labor force<br />

surveys conducted by the United States Census<br />

Bureau (within the United States Department of<br />

Commerce) and/or the Bureau of Labor<br />

Statistics (within the United States Department<br />

of Labor) that gather employment statistics<br />

monthly. The Current Population Survey (CPS),<br />

or "Household Survey", conducts a survey<br />

based on a sample of 60,000 households. This<br />

Survey measures the unemployment rate based<br />

on the ILO definition.<br />

The Current Employment Statistics survey (CES), or "Payroll Survey", conducts a<br />

survey based on a sample of 160,000 businesses and government agencies that<br />

represent 400,000 individual employers. This survey measures only civilian<br />

nonagricultural employment; thus, it does not calculate an unemployment rate, and it<br />

differs from the ILO unemployment rate definition. These two sources have different<br />

classification criteria, and usually produce differing results. Additional data are also<br />

available from the government, such as the unemployment insurance weekly claims<br />

report available from the Office of Workforce Security, within the U.S. Department of<br />

Labor Employment & Training Administration. The Bureau of Labor Statistics provides<br />

Page 49 of 149


up-to-date numbers via a PDF linked here. The BLS also provides a readable concise<br />

current Employment Situation Summary, updated monthly.<br />

The Bureau of Labor Statistics also calculates six alternate measures of unemployment,<br />

U1 through U6, that measure different aspects of unemployment:<br />

<br />

<br />

<br />

<br />

<br />

<br />

U1: Percentage of labor force unemployed 15 weeks or longer.<br />

U2: Percentage of labor force who lost jobs or completed temporary work.<br />

U3: Official unemployment rate per the ILO definition occurs when people are<br />

without jobs and they have actively looked for work within the past four weeks.<br />

U4: U3 + "discouraged workers", or those who have stopped looking for work<br />

because current economic conditions make them believe that no work is<br />

available for them.<br />

U5: U4 + other "marginally attached workers", or "loosely attached workers", or<br />

those who "would like" and are able to work, but have not looked for work<br />

recently.<br />

U6: U5 + Part-time workers who want to work full-time, but cannot due to<br />

economic reasons (underemployment).<br />

Note: "Marginally attached workers" are added to the total labour force for<br />

unemployment rate calculation for U4, U5, and U6. The BLS revised the CPS in<br />

1994 and among the changes the measure representing the official unemployment rate<br />

was renamed U3 instead of U5. In 2013,<br />

Representative Hunter proposed that the Bureau of Labor Statistics use the U5 rate<br />

instead of the current U3 rate.<br />

Statistics for the U.S. economy as a whole hide variations among groups. For example,<br />

in January 2008 U.S. unemployment rates were 4.4% for adult men, 4.2% for adult<br />

women, 4.4% for Caucasians, 6.3% for Hispanics or Latinos (all races), 9.2% for African<br />

Americans, 3.2% for Asian Americans, and 18.0% for teenagers. Also, the U.S.<br />

unemployment rate would be at least 2% higher if prisoners and jail inmates were<br />

counted.<br />

The unemployment rate is included in a number of major economic indexes including<br />

the United States' Conference Board's Index of Leading indicators a macroeconomic<br />

measure of the state of the economy.<br />

________<br />

Page 50 of 149


U.S. <strong>Unemployment</strong> Forecast 2017 Suggests<br />

“Real” <strong>Unemployment</strong> Could Hit 30%<br />

U.S. Economy - By Alessandro Bruno BA, MA | February 27th, 2017<br />

U.S. <strong>Unemployment</strong> Rate Forecast for 2017 Is 4.6% But It’s Too Optimistic<br />

The U.S. unemployment rate forecast for 2017 is 4.7%. That means no major change<br />

from 2016. The official numbers show that unemployment was falling just before the<br />

presidential election. The unemployment rate fell 0.3% in November 2016 compared to<br />

October.<br />

At first, the sheer magnitude of the improvement should impress. But, on closer<br />

examination, it should raise concerns.<br />

The unemployment rate in the U.S. and its purported improvements have a less<br />

impressive explanation. The improvement over the past year has resulted, at least in<br />

part, by a reduction in the numbers of people actively looking for jobs. Hiring has slowed<br />

in 2016. (Source: “U.S. job creation weak, even as unemployment rate falls to 4.7%,”<br />

CNN, June 3, 2016.)<br />

In May of 2016, the economy added just 38,000 jobs. That was the lowest monthly job<br />

number statistic since 2010. (Source: Ibid.)<br />

Real <strong>Unemployment</strong> Rate Could Hit 30% by 2020. Trump Thinks It’s Already There<br />

Trump is right not to trust the official statistics. He already believes the real U.S.<br />

unemployment rate to be considerably higher. During the presidential campaign of<br />

2016, Trump convinced many supporters that the real jobless figures were closer to<br />

28%–35% than to official figure.<br />

Five percent unemployment was the official target during the last administration. That<br />

target has been reached and improved, but Steven Mnuchin, the Secretary of the<br />

Treasury, has backed President Trump’s view.<br />

“The unemployment rate is not real … I’ve traveled for the last year. I’ve seen this.” said<br />

Mnuchin to the Senate Finance Committee. (Source: “Ahead Of Trump’s First Jobs<br />

Report, A Look At His Remarks On The Numbers,” NPR, January 29, 2017.)<br />

The real unemployment rate rarely has anything to do with the actual unemployment<br />

rate. Confusing the two may have been one of the fatal mistakes made by Democratic<br />

presidential candidate Hillary Clinton. In the months preceding the Democratic Party<br />

convention, unemployment appeared to be going down.<br />

For the record, in 2010, the economy was still in full post-2008 crisis mode. Thus, there<br />

can be but one conclusion: U.S. jobless numbers dropped because too many<br />

Page 51 of 149


Americans grew frustrated and stopped looking for work. When an economy pushes so<br />

many of its productive members of society to such frustration, it’s no occasion to<br />

celebrate.<br />

Then there’s the regional distribution of unemployment that also contributes to social<br />

schisms. The unemployment rate by state varies significantly.<br />

The states with the highest unemployment rates are as surprising as they are indicative<br />

of labor market turmoil. As of September 2016, apart from Puerto Rico (a territory, not a<br />

state)—which has an unemployment rate of 11.6%—the highest official unemployment<br />

rates by state were New Mexico (6.7%), Louisiana (6.4%), Mississippi (6.0%), Nevada<br />

(5.8%), and Pennsylvania (5.7%).<br />

It’s interesting to note that Pennsylvania has been a traditional Democratic Party<br />

stronghold in presidential elections, yet Pennsylvanians voted for Trump. Correlation is<br />

Page 52 of 149


not causation but, if traditionally Democratic voters switch to a Republican with a clear<br />

message of addressing the problem of declining job numbers and the weakening U.S.<br />

labor force, it suggests that the real unemployment rate is much worse than the story<br />

that the numbers tell.<br />

Official Narrative Is<br />

That Donald Trump<br />

Has Inherited a<br />

Healthy Economy<br />

The stock<br />

market has<br />

shot up like<br />

New Year’s<br />

Eve fireworks,<br />

but how<br />

healthy is the<br />

economy<br />

when real<br />

jobless<br />

numbers are<br />

hidden behind<br />

deceptive<br />

statistics?<br />

The official<br />

numbers measure unemployment by taking into consideration only people formally<br />

looking for work.<br />

These are the people receiving unemployment benefits and, as such, they are<br />

registered into the system as part of the U.S. labor force looking for work. It would be<br />

safe to suggest that these numbers especially account for people who have been<br />

unemployed for a period ranging from zero to one year. What happens when the<br />

benefits run out before they find a new job?<br />

Donald Trump was elected in a country where the official unemployment rate in<br />

November 2016 fell to 4.6%, which was its lowest level in nine years. Some 178,000<br />

jobs were supposedly created in November alone. Why then did so many vote for<br />

Trump, who ran a campaign that was doubtless trying to capture the attention of the<br />

jobless?<br />

In other words, if the U.S. economy was so great that it had generated such high job<br />

numbers, why didn’t Hillary Clinton win? After all, she promised to uphold Barack<br />

Obama’s policies. Well, the alleged job creation achievements of the Obama<br />

administration can only be addressed by the discouragement factor that skews the real<br />

unemployment rate.<br />

The simple reason, in large part, is that, many unemployed Americans give up finding a<br />

job. Anyone who has ever submitted a resume knows how frustrating and emotionally<br />

Page 53 of 149


draining a job search can be. Donald Trump tapped into that feeling. It was one of the<br />

main themes of his campaign.<br />

The problem illustrates what, in the eyes of many Americans, is the paradox of the<br />

allegedly healthy U.S. economy. The stock market is buoyant and equities are at record<br />

highs yet, to so many, the economy is in a terrible state. And these unemployed people<br />

don’t fit any stereotype. They include skilled professionals, engineers, managers,<br />

factory workers, and retail sales assistants.<br />

Middle Class Faces More <strong>Unemployment</strong><br />

The new phenomenon might be just how much unemployment has hit the middle class.<br />

In fact, Trump secured many votes among the angry and frustrated middle class. The<br />

protest vote from the American middle class was the very driver of the U.S. election<br />

results. The polls—and one must wonder where pollsters went and whom they<br />

interviewed—failed to get a sense of the anger.<br />

The polls and the optimistic Obama-loving media missed a fundamental fact. They<br />

overlooked it because they were too busy gushing over the performance of stock<br />

market indices. What they ignored is a phenomenon that economists have described as<br />

labor polarization (or the polarization of labor).<br />

Yet, U.S. Federal Reserve Chair Janet Yellen has spoken about it often. (Source:<br />

“Yellen’s ‘Polarization’ A New, More Ominous Jobs Factor,” EFXNews, August 25,<br />

2014.)<br />

Yellen has made no secret of the fact that she believes polarization is a major problem.<br />

She suggests that it’s much more troubling as a technical objective than keeping the<br />

U.S. unemployment rate at below five percent.<br />

So, what is this polarization of labor?<br />

Work in America, in recent years, has been characterized by what can be described as<br />

a dualism. On the one hand, there has been an explosion of the so-called Uberization of<br />

work. That is, on-demand work in which the worker is only partially an employee. Often,<br />

as in the case of Uber, workers must supply their own equipment or property.<br />

This Uberization of work, however, has one important effect: intermittent incomes. On<br />

the polar opposite side (hence the term “polarization”), we find the highly paid and<br />

highly educated engineers who work for the Silicon Valley-type companies. Often, they<br />

create the work that leads to Uberization. In between, there’s little else.<br />

The direct consequence of this phenomenon has been a massive rise in inequality. The<br />

polarization of the labor market has strangled the middle class. When the much-praised<br />

Obama started his first term as president in January 2009, he said in his inaugural<br />

address that the Wall Street crisis represented a great opportunity for change.<br />

Page 54 of 149


So, what has really changed? Yes, the official unemployment rate is just below five<br />

percent. At the height of the financial crisis in 2008, it rose to 10% but steadily declined.<br />

Still, something is missing, because Trump won by tapping into a pervasive sense<br />

of economic depression. The only conclusion must be that Obama has not met his<br />

target.<br />

Middle America Feels Left Out<br />

Many Americans still feel left out. In fact, they feel abandoned by the traditional political<br />

leadership. If you are looking for proof that the real unemployment rate is much higher<br />

than they’re telling us, look to the White House. Who is sitting in the Oval Office?<br />

Trump’s win is the proof.<br />

But it’s not Obama’s fault. He was merely the latest president to confront an<br />

unemployment or underemployment problem that started in the 1990s. It was at the<br />

height of the Bill Clinton bull market that the American middle class stopped being able<br />

to save. Consumption has grown, but it has been fueled by credit cards and mortgages,<br />

often of the sub-prime variety.<br />

Then there was the 2008 financial crisis. It was prompted, nobody should forget, by a<br />

gradual rise in interest rates to curb the real estate bubble. That made it harder for<br />

people to pay back their debts. Savings were wiped out.<br />

Since the so-called recovery, American families have had to rely only on salaries and<br />

payroll benefits. But even these were greatly scaled down.<br />

Page 55 of 149


The welfare and social security benefits they enjoyed in the past have had to face reality<br />

checks as well. Even the formerly powerful unions, where they still had any clout, had to<br />

accept significant cuts in exchange for keeping jobs and new hires. Now that Yellen is<br />

urging at least two more interest rate hikes, the financial and economic picture is<br />

approaching an eerie similarity to 2007.<br />

Then, as now, many banks are overleveraged in derivatives. It will take just one event, a<br />

black swan that nobody has foreseen, to send the whole market crashing, and the<br />

economy with it. In 2008, the unemployment rate spiked from about 4.5% to 10%<br />

overnight. The very same could happen next month or next year.<br />

The crucial point is that nothing has changed to avert a catastrophe, and the next crisis<br />

could hit harder than previous ones. In 2017, compared to 2008, salaries, wages, and<br />

benefits have been resized (shrunk). There’s also more precarious—or Uberized—<br />

employment than before. Discontent is already high. The market gains are benefiting an<br />

ever-smaller group of people, who always seem to get richer.<br />

Add to this the increase in personal taxes. Meanwhile, so-called blue-collar jobs have<br />

moved to countries where wages and other production costs are lower. Trump has been<br />

trying to bring some of the jobs back, but the numbers are too small to make a<br />

significant dent. Meanwhile, labor costs have become so competitive internationally that<br />

even Chinese companies are outsourcing!<br />

But Trump hasn’t got all the answers either. His protectionist prescription for the<br />

economy could end up causing a global recession. To really change the employment<br />

Page 56 of 149


numbers, more dramatic solutions are needed. In the wake of the 1929 crash, the<br />

economy gradually recovered after a series of policies was adopted.<br />

These include the reduction of working hours to employ more people at the expense of<br />

efficiency. Meanwhile, social protections were gained through generous welfare<br />

programs while banks and the financial system were slapped with restrictions to better<br />

manage risk. Overly free-market solutions in post-World War I Germany led to the<br />

socioeconomic disaster of Nazism.<br />

Then there’s the issue of robotization. Technology is simply going to wipe out many jobs<br />

in the next decade. Whole sectors will be made redundant. At that point, even the<br />

official U.S. unemployment rate could match the real unemployment rate of 28–30%<br />

that Trump and Mnuchin mentioned.<br />

At these current levels, unemployment can hardly go lower, because everything is<br />

based on the index of investor confidence. And to invest, you must trust the future.<br />

Sooner, rather than later, the markets will face reality.<br />

________<br />

August 28, 2015<br />

The Real <strong>Unemployment</strong> Numbers and Why I’m Not Counted<br />

As a Daily Work job seeker, I was pleased to hear that unemployment rate in the state<br />

of Minnesota is one of the lowest in the nation. I wondered if this means that everyone<br />

who lost their jobs is now returning to work and there is a happy ending to this story.<br />

Sadly, I know so many people who are still really struggling. So, I did my research and I<br />

wasn’t surprised to learn that there are a number of factors that do not reflect the real<br />

number of unemployed people in Minnesota and elsewhere.<br />

The Truth about the <strong>Unemployment</strong> Rate<br />

According to Jim Clifton, chairman and CEO at Gallup, the Bureau of Labor Statistics<br />

unemployment rate is a “big lie.” Here’s why:<br />

<br />

Anyone who has given up their job search or who hasn’t looked for work for at<br />

least four weeks is not included in the Department of Labor’s unemployment<br />

statistics<br />

Page 57 of 149


People performing odd jobs for just one hour per week and paid at least $20 –<br />

again, not included.<br />

<br />

People working part-time, but who want full-time or who are underemployed<br />

(senior people working more entry-level jobs….you guessed it: Not Included!<br />

A blog post on Money Crashers.com brought up some other important factors that<br />

contribute to creating a misleading unemployment rate such as:<br />

<br />

<br />

The household survey size used (60,000 households) is too small, which often<br />

does not accurately represent the big picture.<br />

Millions of people are not represented in the data collected including:<br />

Recent graduates who are not yet looking for work<br />

People who just lost their jobs, but who were employed the week of the<br />

survey<br />

Workers on temporary leaves or disabled workers still in transition from a<br />

former job they are no longer able to work<br />

People working jobs that do not pay them enough to survive (but they are<br />

technically “working”).<br />

<br />

People with multiple jobs are often counted more than once, throwing off the<br />

correct number of employed workers.<br />

My Story<br />

I am a perfect example of people not included in the unemployment statistics. After<br />

months of seeking work, I decided to go back to school because I couldn’t find a job.<br />

After a couple of years of going to school, I developed several chronic illnesses and I<br />

was unable to work or go to school. With no way to support myself, I also became<br />

homeless. While living at the homeless shelter, I met others in the same situation,<br />

including people with felonies who were unable to find work due to their criminal history<br />

and people like me, who just couldn’t find jobs and eventually became homeless. From<br />

personal experience, I can tell you that not having a home makes finding and keeping a<br />

job very difficult.<br />

About this time, I thought I would never find a way out of homelessness and<br />

joblessness, but then I found Daily Work and I started to get the help I needed. Change<br />

didn’t happen right away, because by now, I had a lot of struggles facing me – I was in<br />

poor health, homeless and, I was losing hope for the future.<br />

But Daily Work did not give up on me. They helped me feel hope again and the services<br />

they told me about gave me the boost I needed to stabilize my health and living<br />

situation so I could start building a new foundation for a successful life.<br />

Page 58 of 149


As I look back and reflect on my experience, I realize that Daily Work differs from other<br />

social services organizations in several ways. For example, when I first entered their<br />

office I expected more of the same; to be shuffled through the system and treated like a<br />

number, not a person.<br />

I was so wrong. Although Daily Work offers a lot of the same services as other help<br />

agencies, they work with me and ALL their clients as people first. Because I was treated<br />

as a human being with potential and value, it made a world of difference to my selfesteem.<br />

Daily Work sees my eagerness to return to being a productive member of the workforce<br />

and continues to support me in achieving my career goals. Right now, I am volunteering<br />

as an “intern” at Daily Work by working on the website, posting on social media, and<br />

even writing this blog post. This internship is giving me the skills and experience I need<br />

to get back in the workforce in my chosen field, graphic design and communications.<br />

Page 59 of 149


Daily Work is a gift to me and so many others. It demonstrates how nonprofits can think<br />

outside the box and provide a person like me with support and help that isn’t possible in<br />

other organizations.<br />

Page 60 of 149


III. Involuntary <strong>Unemployment</strong><br />

Involuntary <strong>Unemployment</strong> occurs when a person is willing to work at the<br />

prevailing wage yet is unemployed. Involuntary unemployment is distinguished from<br />

voluntary unemployment, where workers choose not to work because their reservation<br />

wage is higher than the prevailing wage. In an economy with involuntary unemployment<br />

there is a surplus of labor at the current real wage. Involuntary unemployment cannot be<br />

represented with a basic supply and demand model at a competitive equilibrium: All<br />

workers on the labor supply curve above the market wage would voluntarily choose not<br />

to work, and all those below the market wage would be employed. Given the basic<br />

supply and demand model, involuntarily unemployed workers lie somewhere off of the<br />

labor supply curve. Economists have several theories explaining the possibility of<br />

involuntary unemployment including implicit contract theory, disequilibrium theory,<br />

staggered wage setting, and efficiency wages.<br />

Explanations<br />

In the Shapiro-Stiglitz model workers are paid at a level where they do not shirk. This<br />

prevents wages from dropping to market clearing levels. Full employment cannot be<br />

achieved because workers would slack off if they were not threatened with the<br />

possibility of unemployment. The curve for the no-shirking condition (labeled NSC) goes<br />

to infinity at full employment.<br />

Page 61 of 149


Models based on implicit contract theory, like that of Azariadis (1975), are based on the<br />

hypothesis that labor contracts make it difficult for employers to cut wages. Employers<br />

often resort to layoffs rather than implement wage reductions. Azariadis showed that<br />

given risk-averse workers and risk-neutral employers, contracts with the possibility of<br />

layoff would be the optimal outcome.<br />

Efficiency wage models suggest that employers pay their workers above market<br />

clearing wages in order to enhance their productivity. In efficiency wage models based<br />

on shirking, employers are worried that workers may shirk knowing that they can simply<br />

move to another job if they are caught. Employers make shirking costly by paying<br />

workers more than the wages they would receive elsewhere. This gives workers an<br />

incentive not to shirk. [1] When all firms behave this way, an equilibrium is reached where<br />

there are unemployed workers willing to work at prevailing wages.<br />

Following earlier disequilibrium research including that of Robert Barro and Herschel<br />

Grossman, work by Edmond Malinvaud clarified the distinction between classical<br />

unemployment, where real wages are too high for markets to clear, and Keynesian<br />

unemployment, involuntary unemployment due to inadequate aggregate demand. In<br />

Malinvaud's model, classical unemployment is remedied by cutting the real wage while<br />

Keynesian unemployment requires an exogenous stimulus in demand. Unlike implicit<br />

contrary theory and efficiency wages, this line of research does not rely on a higher than<br />

market-clearing wage level. This type of involuntary unemployment is consistent with<br />

Keynes's definition while efficiency wages and implicit contract theory do not fit well with<br />

Keynes's focus on demand deficiency.<br />

Perspectives<br />

For many economists, involuntary unemployment is a real-world phenomenon of central<br />

importance to economics. Many economic theories have been motivated by the desire<br />

to understand and control involuntary unemployment. However, acceptance of the<br />

concept of involuntary unemployment isn't universal among economists. Some do not<br />

accept it as a real or coherent aspect of economic theory.<br />

Shapiro and Stiglitz, developers of an influential shirking model, stated "To us,<br />

involuntary unemployment is a real and important phenomenon with grave social<br />

consequences that needs to be explained and understood."<br />

Mancur Olson argued that real world events like the Great Depression could not be<br />

understood without the concept of involuntary unemployment. He argued against<br />

economists who denied involuntary unemployment and put their theories ahead of<br />

"common sense and the observations and experiences of literally hundreds of millions<br />

of people... that there is also involuntary unemployment and that it is by no means an<br />

isolated or rare phenomenon".<br />

Page 62 of 149


Other economists do not believe that true involuntary unemployment exists or question<br />

its relevance to economic theory. Robert Lucasclaims "...there is an involuntary element<br />

in all unemployment in the sense that no one chooses bad luck over good; there is also<br />

a voluntary element in all unemployment, in the sense that, however miserable one's<br />

current work options, one can always choose to accept them"and "the unemployed<br />

worker at any time can always find some job at once". Lucas dismissed the need for<br />

theorists to explain involuntary unemployment since it is "not a fact or a phenomenon<br />

which it is the task of theorists to explain. It is, on the contrary, a theoretical construct<br />

which Keynes introduced in the hope it would be helpful in discovering a correct<br />

explanation for a genuine phenomenon: large-scale fluctuations in measured, total<br />

unemployment." Along those lines real business cycle and other models from<br />

Lucas's new classical school explain fluctuations in employment by shifts in labor supply<br />

driven by changes in workers' productivity and preferences for leisure.<br />

Involuntary unemployment is also conceptually problematic with search and matching<br />

theories of unemployment. In these models, unemployment is voluntary in the sense<br />

that a worker might choose to endure unemployment during a long search for a higher<br />

paying job than those immediately available; however, there is an involuntary element in<br />

the sense that a worker does not have control of the economic circumstances that force<br />

them to look for new work in the first place.<br />

Page 63 of 149


Page 64 of 149


IV. Underemployment<br />

Underemployment is the under-use of a worker due to a job that does not use the<br />

worker's skills, or is part time, or leaves the worker idle. Examples include holding a<br />

part-time job despite desiring full-time work, and overqualification, where the employee<br />

has education, experience, or skills beyond the requirements of the job.<br />

Underemployment has been studied from a variety of perspectives,<br />

including economics, management, psychology, and sociology. In economics, for<br />

example, the term underemployment has three different distinct meanings and<br />

applications. All meanings involve a situation in which a person is working,<br />

unlike unemployment, where a person who is searching for work cannot find a job. All<br />

meanings involve under-utilization of labor which is missed by most official<br />

(governmental agency) definitions and measurements of unemployment.<br />

In economics, underemployment can refer to:<br />

1. "Overqualification", or "overeducation", or the employment of workers with high<br />

education, skill levels, or experience in jobs that do not require such abilities. For<br />

example, a trained medical doctor with a foreign credential who works as<br />

a taxi driver would experience this type of underemployment.<br />

2. "Involuntary part-time" work, where workers who could (and would like to) be<br />

working for a full work-week can only find part-time work. By extension, the term<br />

is also used in regional planning to describe regions where economic<br />

activity rates are unusually low, due to a lack of job<br />

Page 65 of 149


opportunities, training opportunities, or due to a lack of services such<br />

as childcare and public transportation.<br />

3. "Overstaffing" or "hidden unemployment" or "disguised unemployment" (also<br />

called "labor hoarding"), the practice in which businesses or<br />

entire economies employ workers who are not fully occupied—for example,<br />

workers currently not being used to produce goods or services due to legal or<br />

social restrictions or because the work is highly seasonal.<br />

Underemployment is a significant cause of poverty: although the worker may be able to<br />

find part-time work, the part-time pay may not be sufficient for basic needs.<br />

Underemployment is a problem particularly in developing countries, where the<br />

unemployment rate is often quite low, as most workers are doing subsistence work or<br />

occasional part-time jobs. The global average of full-time workers per adult population is<br />

only 26%, compared to 30–52% in developed countries and 5–20% in most of Africa.<br />

Underutilization of Skills<br />

In one usage, underemployment describes the employment of workers with<br />

high skill levels and postsecondary education who are working in relatively lowskilled,<br />

low-wage jobs. For example, someone with a college degree may be tending<br />

bar, or working as a factory assembly line worker. This may result from the existence<br />

of unemployment, which makes workers with bills to pay (and responsibilities) take<br />

almost any jobs available, even if they do not use their full talents. This can also occur<br />

with individuals who are being discriminatedagainst, lack appropriate trade<br />

certification or academic degrees (such as a high school or college diploma), have<br />

disabilities or mental illnesses, or have served time in prison.<br />

Two common situations which can lead to underemployment are immigrants and new<br />

graduates. When highly trained immigrants arrive in a country, their foreign<br />

credentials may not be recognized or accepted in their new country, or they may have<br />

to do a lengthy or costly re-credentialing process. As a result, when doctors or<br />

engineers from other countries immigrate, they may be unable to work in their<br />

profession, and they may have to seek menial work. New graduates may also face<br />

underemployment, because even though they have completed the technical training for<br />

a given field for which there is a good job market, they lack experience. So a recent<br />

graduate with a master's degree in accounting or business administration may have to<br />

work in a low-paid job as a barista or store clerk–jobs which do not require a university<br />

degree–until they are able to find work in their professional field.<br />

Another example of underemployment is someone who holds high skills for which there<br />

is low market-place demand. While it is costly in terms of money and time to<br />

acquire academic credentials, many types of degrees, particularly those in the liberal<br />

arts, produce significantly more graduates than can be properly employed. Employers<br />

have responded to the oversupply of graduates by raising the academic requirements of<br />

many occupations higher than is really necessary to perform the work. A number of<br />

Page 66 of 149


surveys show that skill-based underemployment in North America and Europe can be a<br />

long-lasting phenomenon. If university graduates spend too long in situations of<br />

underemployment, the skills they gained from their degrees can atrophy from disuse or<br />

become out of date. For example, a person who graduates with a PhD in English<br />

literature has advanced research and writing skills when they graduate, but if she or he<br />

works as a store clerk for a number of years, these research and writing skills will<br />

atrophy from disuse. Similarly, technically specialized workers may find themselves<br />

unable to acquire positions commensurate with their skills for extended lengths of time<br />

following layoffs. A skilled machinist who is laid off may find that she cannot find another<br />

machinist job, so she may work as a server in a restaurant, a position which does not<br />

use her professional skills.<br />

Given that most university study in Western countries is subsidized (either because it<br />

takes place at a state university or public university, or because the student receives<br />

government loans or grants), this type of underemployment may also be an ineffective<br />

use of public resources. Several solutions have been proposed to reduce skill-based<br />

underemployment: for example, government-imposed restrictions on enrollment in<br />

public universities in fields with a very low labor market demand (e.g. fine arts), or<br />

changes in degree cost structure that reflected potential labour market demand.<br />

A related kind of underemployment refers to "involuntary part-time" workers. These are<br />

workers who could (and would like to) be working for the standard work-week<br />

(typically full-time employment means 40 hours per week in the United States) who can<br />

only find part-time work. Underemployment is more prevalent during times of economic<br />

stagnation (during recessions or depressions). Obviously, during the Great<br />

Depression of the 1930s, many of those who were not unemployed were<br />

underemployed. These kinds of underemployment arise because labor markets typically<br />

do not "clear" using wage adjustment. Instead, there is non-wage rationing of jobs.<br />

Underuse of Economic Capacity<br />

Underemployment can also be used in regional planning to describe localities<br />

where economic activity rates are unusually low. This can be induced by a lack of job<br />

opportunities, training opportunities, or services such as childcare and public<br />

transportation. Such difficulties may lead residents to accept economic inactivity rather<br />

than register as unemployed or actively seek jobs because their prospects for regular<br />

Page 67 of 149


employment appear so bleak. (These people are often called discouraged workers and<br />

are not counted officially as being "unemployed.") The tendency to get by without work<br />

(to exit the labor force, living off relatives, friends, personal savings, or non-recorded<br />

economic activities) can be aggravated if it is made difficult to obtain unemployment<br />

benefits.<br />

Relatedly, in macroeconomics, "underemployment" simply refers to excess<br />

unemployment, i.e., high unemployment relative to full employment or the natural rate of<br />

unemployment, also called the NAIRU. Thus, in Keynesian economics, reference is<br />

made to underemployment equilibrium. Economists calculate the cyclically-adjusted full<br />

employmentunemployment rate, e.g. 4% or 6% unemployment, which in a given context<br />

is regarded as "normal" and acceptable. Sometimes, this rate is equated with<br />

the NAIRU. The difference between the observed unemployment rate and cyclically<br />

adjusted full employment unemployment rate is one measure of the societal level of<br />

underemployment. By Okun's Law, it is correlated with the gap between potential<br />

output and the actual real GDP. This "GDP gap" and the degree of underemployment of<br />

labor would be larger if they incorporated the roles of underemployed labor, involuntary<br />

part-time labor, and discouraged workers.<br />

Underuse of Employed Workers<br />

The third definition of "underemployment" describes a polar opposite phenomenon: to<br />

some economists, the term refers to "overstaffing" or "hidden unemployment," the<br />

practice of businesses or entire economies employing workers who are not fully<br />

occupied (in other words, employees who are not economically productive, or<br />

underproductive, or economically inefficient). This may be because of legal or social<br />

restrictions on firing and lay-offs (e.g. union rules requiring managers to make a case to<br />

fire a worker or spend time and money fighting the union) or because they<br />

are overhead workers, or because the work is highly seasonal (which is the case<br />

in accounting firms focusing on tax preparation, as well as agriculture). The presence of<br />

this issue in white collar office jobs is described in the boreout phenomenon, which<br />

posits that the major issue facing office workers is lack of work and boredom.<br />

This kind of underemployment does not refer to the kind of non-work time done by, for<br />

instance, firefighters or lifeguards, who spend a lot of their time waiting and watching for<br />

emergency or rescue work to do; this kind of activity is necessary to ensure that if (e.g.)<br />

three fires occur at once, there are sufficient firefighters available.<br />

This kind of underemployment may exist for structural or cyclical reasons. In many<br />

economies, some firms become insulated from fierce competitive pressures and<br />

grow inefficient, because they are awarded a government monopoly (e.g., telephone or<br />

electrical utilities) or due to a situation of abuse of market power (e.g., holding<br />

a monopoly position in a certain industry). As such, if they may employ more workers<br />

than necessary, they might not be getting the market signals that would pressure them<br />

to reduce their labour force, and they may end up carrying the resultant<br />

excess costs and depressed profits.<br />

Page 68 of 149


In some countries, labour laws or practices (e.g. powerful unions) may force employers<br />

to retain excess employees. Other countries (e.g. Japan) often have significant cultural<br />

influences (the relatively great importance attached to worker solidarity as opposed<br />

to shareholder rights) that result in a reluctance to shed labour in times of difficulty. In<br />

Japan, there is a long-held tradition that if a worker commits to serve a company with<br />

long and loyal service, the company will, in return, keep the worker on the payroll even<br />

during economic downturns. In centrally-planned economies, lay-offs were often not<br />

allowed, so that some state-run companies would have periods when they had more<br />

workers than they needed to complete the organization's tasks.<br />

Cyclical underemployment refers to the tendency for the capacity utilization of firms<br />

(and therefore of their demand for labor) to be lower at times of recession or economic<br />

depression. At such times, underemployment of workers may be tolerated—and indeed<br />

may be wise business policy—given the financial cost and the degradation<br />

of morale from shedding and then re-hiring staff. Alternatively, paying underused<br />

overhead workers is seen as an investment in their future contributions to production.<br />

This kind of underemployment has been given as a possible reason<br />

why Airbus gained market share from Boeing. Unlike Airbus, which had more flexibility,<br />

Boeing was unable to ramp up production fast enough when prosperous times returned<br />

because the company had dismissed a great part of its personnel in lean times.<br />

Another example is the tourism sector, which faces cyclical demand in areas where<br />

attractions are weather-related. In some tourism sectors, such as the sun and sand<br />

tours operated by Club Med, the company can shed bartenders, lifeguards, and sports<br />

instructors, and other staff in the off-season, because there is such a strong demand<br />

amongst young people to work for the company, because its glamorous beachfront<br />

properties are desirable places to work. However, not all tourism sectors find it so easy<br />

Page 69 of 149


to recruit staff. Some tourism sectors require workers with unusual or hard-to-find skills.<br />

Northern Ontario hunting and fishing camps that require skilled guides may have an<br />

incentive to retain their staff in the off-season. Another example is companies which run<br />

tours for foreign tourists using staff speaking the travelers' native tongue. In Canada,<br />

guided tours are available for Japanese and German tourists in their native languages;<br />

in some locations, it may be hard for companies to find Japanese- or German-speaking<br />

staff, so the companies may retain their staff in the off-season.<br />

Page 70 of 149


V. Discouraged Workers<br />

In economics, a discouraged worker is a person of legal employment age who is not<br />

actively seeking employment or who does not find employment after longterm<br />

unemployment. This is usually because an individual has given up looking or has<br />

had no success in finding a job, hence the term "discouraged".<br />

In other words, even if a person is still looking actively for a job, that person may have<br />

fallen out of the core statistics of unemployment rate after long-term unemployment and<br />

is therefore by default classified as "discouraged" (since the person does not appear in<br />

the core statistics of unemployment rate). In some cases, their belief may derive from a<br />

variety of factors including a shortage of jobs in their locality or line of<br />

work; discrimination for reasons such as age, race, sex, religion, sexual orientation,<br />

and disability; a lack of necessary skills, training, or experience; or, a chronic<br />

illness or disability.<br />

As a general practice, discouraged workers, who are often classified as marginally<br />

attached to the labor force, on the margins of the labor force, or as part of hidden<br />

unemployment, are not considered part of the labor force, and are thus not counted in<br />

most official unemployment rates—which influences the appearance and interpretation<br />

of unemployment statistics. Although some countries offer alternative measures of<br />

unemployment rate, the existence of discouraged workers can be inferred from a<br />

low employment-to-population ratio.<br />

United States<br />

Discouraged Workers (US, 2004-09)<br />

In the United States, a discouraged worker is defined as a person not<br />

in the labor force who wants and is available for a job and who has<br />

looked for work sometime in the past 12 months (or since the end of<br />

his or her last job if a job was held within the past 12 months), but who<br />

is not currently looking because of real or perceived poor employment<br />

prospects.<br />

The Bureau of Labor Statistics does not count discouraged workers as unemployed but<br />

rather refers to them as only "marginally attached to the labor force". This means that<br />

the officially measured unemployment captures so-called "frictional unemployment" and<br />

not much else. This has led some economists to believe that the actual unemployment<br />

rate in the United States is higher than what is officially reported while others suggest<br />

that discouraged workers voluntarily choose not to work. Nonetheless, the U.S. Bureau<br />

of Labor Statistics has published the discouraged worker rate in alternative measures of<br />

labor underutilization under U-4 since 1994 when the most recent redesign of<br />

the CPS was implemented.<br />

Page 71 of 149


The United States Department of Labor first began tracking discouraged workers in<br />

1967 and found 500,000 at the time. Today, In the United States, according to the U.S.<br />

Bureau of Labor Statistics as of April 2009, there are 740,000 discouraged workers.<br />

There is an ongoing debate as to whether discouraged workers should be included in<br />

the official unemployment rate. Over time, it has been shown that a disproportionate<br />

number of young people, blacks, Hispanics, and men make up discouraged<br />

workers. Nonetheless, it is generally believed that the discouraged worker is<br />

underestimated because it does not include homeless people or those who have not<br />

looked for or held a job during the past twelve months and is often poorly tracked.<br />

According to the U.S. Bureau of Labor Statistics, the top five reasons for<br />

discouragement are the following:<br />

1. The worker thinks no work is available.<br />

2. The worker could not find work.<br />

3. The worker lacks schooling or training.<br />

4. The worker is viewed as too young or too old by the prospective employer.<br />

5. The worker is the target of various types of discrimination.<br />

Canada<br />

In Canada, discouraged workers are often referred to as hidden unemployed because<br />

of their behavioral pattern, and are often described as on the margins of the labour<br />

force. Since the numbers of discouraged workers and of unemployed generally move in<br />

the same direction during the business cycle and the seasons (both tend to rise in<br />

periods of low economic activity and vice versa), some economists have suggested that<br />

discouraged workers should be included in the unemployment numbers because of the<br />

close association.<br />

The information on the number and composition of the discouraged worker group<br />

in Canada originates from two main sources. One source is the monthly Labour Force<br />

Survey (LFS), which identifies persons who looked for work in the past six months but<br />

who have since stopped searching. The other source is the Survey of Job Opportunities<br />

(SJO), which is much closer in design to the approach used in many other countries. In<br />

this survey, all those expressing a desire for work and who are available for work are<br />

counted, irrespective of their past job search activity.<br />

In Canada, while discouraged workers were once less educated than "average<br />

workers", they now have better training and education but still tend to be concentrated<br />

in areas of high unemployment. Discouraged workers are not seeking a job for one of<br />

two reasons: labour market-related reasons (worker discouragement, waiting for recall<br />

to a former job or waiting for replies to earlier job search efforts) and personal and other<br />

reasons (illness or disability, personal or family responsibilities, going to school, and so<br />

on).<br />

Page 72 of 149


European Union<br />

<strong>Unemployment</strong> statistics published according to the ILO methodology may understate<br />

actual unemployment in the economy. The EU statistical bureau EUROSTAT started<br />

publishing figures on discouraged workers in 2010. According to the method used by<br />

EUROSTAT there are 3 categories that make up discouraged workers;<br />

<br />

<br />

<br />

underemployed part-time workers<br />

jobless persons seeking a job but not immediately available for work,<br />

persons available for work but not seeking it<br />

The first group are contained in the employed statistics of the European Labour Force<br />

Survey while the second two are contained in the inactive persons statistics of that<br />

survey. In 2012 there were 9.2 million underemployed part-time workers, 2.3 million<br />

jobless persons seeking a job but not immediately available for work, and 8.9 million<br />

persons available for work but not seeking it, an increase of 0.6 million for<br />

underemployed and 0.3 million for the two groups making up discouraged workers.<br />

If the discouraged workers and underemployed are added to official unemployed<br />

statistics Spain has the highest number real unemployed (8.4 Million), followed by Italy<br />

(6.4 Million), United Kingdom (5.5 Million), France (4.8 Million) and Germany (3.6<br />

Million).<br />

List Of EU Countries <strong>Hidden</strong> <strong>Unemployment</strong> In 2012<br />

Country<br />

Underemployed<br />

Part-time workers<br />

Thousands<br />

Jobless persons<br />

seeking a job but<br />

not immediately<br />

available for work<br />

Thousands<br />

Persons<br />

available for<br />

work but not<br />

seeking it<br />

Thousands<br />

Unemployed<br />

Thousands<br />

Belgium 158 100 60 369<br />

Bulgaria 29 270 26 410<br />

Czech<br />

Republic<br />

27 62 17 367<br />

Denmark 88 69 24 219<br />

Germany 1,810 582 508 2,316<br />

Estonia 10 41 3 71<br />

Ireland 147 44 13 316<br />

Greece 190 91 36 1,204<br />

Spain 1,385 1,071 236 5,769<br />

France 1,144 285 444 3,002<br />

Italy 605 2,975 111 2,744<br />

Cyprus 20 15 3 52<br />

Page 73 of 149


List Of EU Countries <strong>Hidden</strong> <strong>Unemployment</strong> In 2012<br />

Country<br />

Underemployed<br />

Part-time workers<br />

Thousands<br />

Jobless persons<br />

seeking a job but<br />

not immediately<br />

available for work<br />

Thousands<br />

Persons<br />

available for<br />

work but not<br />

seeking it<br />

Thousands<br />

Unemployed<br />

Thousands<br />

Latvia 44 67 6 155<br />

Lithuania 37 16 197<br />

Luxembourg 5 13 2 13<br />

Hungary 88 215 11 476<br />

Malta 5 5 12<br />

Netherlands 138 308 85 469<br />

Austria 148 144 39 189<br />

Poland 344 632 102 1,749<br />

Portugal 256 232 29 860<br />

Romania 239 458 701<br />

Slovenia 18 13 90<br />

Slovakia 37 41 13 378<br />

Finland 75 111 63 207<br />

Sweden 237 134 101 403<br />

United<br />

Kingdom<br />

1,907 774 334 2,511<br />

Norway 81 67 22 85<br />

Page 74 of 149


VI. The Working Poor<br />

The Working Poor are working people whose incomes fall below a given poverty<br />

line due to lack of work hours and/or low wages. Largely because they are earning such<br />

low wages, the working poor face numerous obstacles that make it difficult for many of<br />

them to find and keep a job, save up money, and maintain a sense of self-worth.<br />

The official working poverty rate in the US has remained relatively static over the past<br />

four decades, but many social scientists argue that the official rate is set too low, and<br />

that the proportion of workers facing significant financial hardship has instead increased<br />

over the years. Changes in the economy, especially the shift from a manufacturingbased<br />

to a service-based economy, have resulted in the polarization of the labor<br />

market. This means that there are more jobs at the top and the bottom of the income<br />

spectrum, but fewer jobs in the middle.<br />

There are a wide range of anti-poverty policies that have been shown to improve the<br />

situation of the working poor. Research suggests that increasing welfare<br />

state generosity is the most effective way to reduce poverty and working poverty.<br />

Conceptualizing Working Poverty<br />

In the United States, the issue of working poverty was initially brought to the public's<br />

attention during the Progressive Era (1890s–1920s). Progressive Era thinkers<br />

like Robert Hunter, Jane Addams, and W.E.B. Du Bois saw society's unequal<br />

opportunity structure as the root cause of poverty and working poverty, but they also<br />

Page 75 of 149


saw a link between moral factors and poverty. In his study of Philadelphia's African<br />

American neighborhoods, W.E.B. Du Bois draws a distinction between "hardworking"<br />

poor people who fail to escape poverty due to racial discrimination and those who are<br />

poor due to moral deficiencies such as laziness or lack of perseverance.<br />

After the Great Depression, the New Deal, and World War II, the United States<br />

experienced an era of prosperity during which most workers experienced significant<br />

gains in wages and working conditions. During this period (1930s–1950s), scholars<br />

shifted their attention away from poverty and working poverty. However, in the late<br />

1950s and early 1960s American scholars and policymakers began to revisit the<br />

problem. Influential books like John Kenneth Galbraith's The Affluent<br />

Society (1958) and Michael Harrington's The Other America (1962) reinvigorated the<br />

discussions on poverty and working poverty in the United States.<br />

Since the start of the War on Poverty in the 1960s, scholars and policymakers on both<br />

ends of the political spectrum have paid an increasing amount of attention to working<br />

poverty. One of the key ongoing debates concerns the distinction between the working<br />

and the nonworking (unemployed) poor. Conservative scholars tend to see nonworking<br />

poverty as a more urgent problem than working poverty because they believe that nonwork<br />

is a moral hazard that leads to welfare dependency and laziness, whereas work,<br />

even poorly paid work, is morally beneficial. In order to solve the problem of nonworking<br />

poverty, some conservative scholars argue that the government must stop "coddling"<br />

the poor with welfare benefits like AFDC/TANF.<br />

On the other hand, liberal scholars and policymakers often argue that most working and<br />

nonworking poor people are quite similar. Studies comparing single mothers on and off<br />

welfare show that receiving welfare payments does not degrade a person's desire to<br />

find a job and get off of welfare. The main difference between the working and the<br />

nonworking poor, they argue, is that the nonworking poor have a more difficult time<br />

overcoming basic barriers to entry into the labor market, such as arranging for<br />

affordable childcare, finding housing near potential jobs, or arranging for transportation<br />

to and from work. In order to help the nonworking poor gain entry into the labor market,<br />

liberal scholars and policymakers argue that the government should provide more<br />

housing assistance, childcare, and other kinds of aid to poor families.<br />

Discussions about the alleviation of working poverty are also politically charged.<br />

Conservative scholars and policymakers often attribute the prevalence of inequality and<br />

working poverty to overregulation and overtaxation, which they claim constricts job<br />

growth. In order to lower the rate of working poverty, conservatives advocate reducing<br />

welfare benefits and enacting less stringent labor laws. On the other hand, many<br />

liberals argue that working poverty can only be solved through increased, not<br />

decreased, government intervention. This government intervention could include<br />

workplace reforms (such as higher minimum wages, living wage laws, job training<br />

programs, etc.) and an increase in government transfers (such as housing, food,<br />

childcare, and healthcare subsidies).<br />

Page 76 of 149


Measuring Working Poverty<br />

Absolute<br />

According to the US Department of Labor, the working poor "are persons who spent at<br />

least 27 weeks [in the past year] in the labor force (that is, working or looking for work),<br />

but whose incomes fell below the official poverty level." In other words, if someone<br />

spent more than half of the past year in the labor force without earning more than the<br />

official poverty threshold, the US Department of Labor would classify them as "working<br />

poor." (Note: The official poverty threshold, which is set by the US Census Bureau,<br />

varies depending on the size of a family and the age of the family members.) The US<br />

Bureau of Labor Statistics calculates working poverty rates for all working individuals, all<br />

families with at least one worker, and all "unrelated individuals." The individual-level<br />

working poverty rate calculates the percentage of all workers whose incomes fall below<br />

the poverty threshold. In 2009, the individual-level working poverty rate in the US was<br />

7%, compared to 4.7% in 2000. The family-level working poverty rate only includes<br />

families of two or more people who are related by birth, marriage, or adoption.<br />

According to the Bureau of Labor Statistics' definition of family-level working poverty, a<br />

family is working poor if the combined cash income of the family falls below the poverty<br />

threshold for a family of their size. In 2009, the family-level working poverty rate in the<br />

Page 77 of 149


US was 7.9%, compared to 5.6% in 2000. Finally, the unrelated individual working<br />

poverty rate measures working poverty among those who do not currently live with any<br />

family members. In 2009, 11.7% of employed unrelated individuals were poor,<br />

compared to 7.6% in 2000.<br />

Relative<br />

In Europe and other non-US, high-income countries, poverty and working poverty are<br />

defined in relative terms. A relative measure of poverty is based on a country's income<br />

distribution rather than an absolute amount of money. Eurostat, the statistical office of<br />

the European Union, classifies a household as poor if its income is less than 60 percent<br />

of the country's median household income. According to Eurostat, a relative measure of<br />

poverty is appropriate because "minimal acceptable standards usually differ between<br />

societies according to their general level of prosperity: someone regarded as poor in a<br />

rich developed country might be regarded as rich in a poor developing country."<br />

When conducting cross-national research on working poverty, scholars tend to use a<br />

relative measure of poverty. In these studies, to be classified as "working poor," a<br />

household must satisfy the following two conditions: 1) at least one member of the<br />

household must be "working" (which can be defined in various ways), and 2) the total<br />

household income must be less than 60% (or 50%, or 40%) of the median income for<br />

that country. Brady, Fullerton, and Cross's 2010 cross-national study on working poverty<br />

in high-income countries defines a household as working poor if 1) it has at least one<br />

employed person and 2) the total household income falls below 50% of the median<br />

income for that country. According to this relative definition, the US's working poverty<br />

rate was 11% in the year 2000, nearly double the rate produced by the US<br />

government's absolute definition. For the same year, Canada's working poverty rate<br />

was 7.8%, the UK's was 4%, and Germany's was 3.8%.<br />

Prevalence and Trends<br />

Poverty rates by gender<br />

and work for Americans aged 65 and over<br />

Poverty is often associated with joblessness, but a<br />

large proportion of poor people are actually working or<br />

looking for work. In 2009, according to the US Census<br />

Bureau's official definition of poverty, 8.8 million US<br />

families were below the poverty line (11.1% of all<br />

families). Of these families, 5.19 million, or 58.9%, had<br />

at least one person who was classified as working. In the same year, there were 11.7<br />

million unrelated individuals (people who do not live with family members) whose<br />

incomes fell below the official poverty line (22% of all unrelated individuals). 3.9 million<br />

of these poor individuals, or 33%, were part of the working poor. The cost of raising a<br />

child from birth to age 18 for a middle-income, two-parent family averaged $226,920 last<br />

year (not including college), according to the U.S. Department of Agriculture. That's up<br />

Page 78 of 149


nearly 40% -- or more than $60,000—from 10 years ago. Just one year of spending on<br />

a child can cost up to $13,830 in 2010, compared to $9,860 a decade ago.<br />

Using the US Census Bureau's definition of poverty, the working poverty rate seems to<br />

have remained relatively stable since 1978. However, many scholars have argued that<br />

the official poverty threshold is too low, and that real wages and working conditions<br />

have actually declined for many workers over the past three or four decades. Social<br />

scientists like Arne Kalleberg have found that the decline in US manufacturing and the<br />

subsequent polarization of the labor market has led to an overall worsening of wages,<br />

job stability, and working conditions for people with lower skill levels and less formal<br />

education. From the mid-1940s to the mid-1970s, manufacturing jobs offered many lowskilled<br />

and medium-skilled workers stable, well-paying jobs. Due to global competition,<br />

technological advances, and other factors, US manufacturing jobs have been<br />

disappearing for decades. (From 1970 to 2008, the percentage of the labor force<br />

employed in the manufacturing sector shrank from 23.4% to 9.1%.) During this period of<br />

decline, job growth became polarized on either end of the labor market. That is, the jobs<br />

that replaced medium-pay, low- to medium-skill manufacturing jobs were high-paying,<br />

high-skill jobs and low-paying, low-skill jobs. Therefore, many low- to medium-skilled<br />

workers who would have been able to work in the manufacturing sector in 1970 must<br />

now take low-paying, precarious jobs in the service sector.<br />

US Compared to Europe<br />

Other high-income countries have also experienced declining manufacturing sectors<br />

over the past four decades, but most of them have not experienced as much labor<br />

market polarization as the United States. Labor market polarization has been the most<br />

Page 79 of 149


severe in liberal market economies like the US, Britain, and Australia. Countries like<br />

Denmark and France have been subject to the same economic pressures, but due to<br />

their more "inclusive" (or "egalitarian") labor market institutions, such as centralized and<br />

solidaristic collective bargaining and strong minimum wage laws, they have experienced<br />

less polarization.<br />

Cross-national studies have found that European countries' working poverty rates are<br />

much lower than the US's. Most of this difference can be explained by the fact that<br />

European countries' welfare states are more generous than the US's. The relationship<br />

between generous welfare states and low rates of working poverty is elaborated upon in<br />

the "Risk Factors" and "Anti-Poverty Policies" sections.<br />

The following graph uses data from Brady, Fullerton, and Cross (2010) to show the<br />

working poverty rates for a small sample of countries. Brady, Fullerton, and Cross<br />

(2010) accessed this data through the Luxembourg Income Study. This graph measures<br />

household, rather than person-level, poverty rates. A household is coded as "poor" if its<br />

income is less than 50% of its country's median income. This is a relative, rather than<br />

absolute, measure of poverty. A household is classified as "working" if at least one<br />

member of the household was employed at the time of the survey. The most important<br />

insight contained in this graph is that the US has strikingly higher working poverty rates<br />

than European countries.<br />

Risk Factors<br />

There are five major categories of risk factors that increase a person's likelihood of<br />

experiencing working poverty: sectoral factors, demographic factors, economic factors,<br />

labor market institutions, and welfare generosity. Working poverty is a phenomenon that<br />

Page 80 of 149


affects a very wide range of people, but there are some employment sectors,<br />

demographic groups, political factors, and economic factors that are correlated with<br />

higher rates of working poverty than others. Sectoral and demographic factors help<br />

explain why certain people within a given country are more likely than others to be<br />

working poor. Political and economic factors can explain why different countries have<br />

different working poverty rates.<br />

Sectoral Tendencies<br />

Working poverty is not distributed equally among employment sectors. The service<br />

sector has the highest rate of working poverty. In fact, 13.3% of US service sector<br />

workers found themselves below the poverty line in 2009. Examples of low-wage<br />

service sector workers include fast-food workers, home health aids, waiters/waitresses,<br />

and retail workers.<br />

Although the service sector has the highest rate of working poverty, a significant portion<br />

of the working poor are blue-collar workers in the manufacturing, agriculture, and<br />

construction industries. Most manufacturing jobs used to offer generous wages and<br />

benefits, but manufacturing job quality has declined over the years. Nowadays, many<br />

US manufacturing jobs are located in right-to-work states, where it is nearly impossible<br />

for workers to form a union. This means that manufacturing employers are able to pay<br />

lower wages and offer fewer benefits than they used to.<br />

Page 81 of 149


Demographic Factors<br />

In her book, No Shame in My Game, Katherine Newman finds that "[t]he nation's young,<br />

its single parents, the poorly educated, and minorities are more likely than other workers<br />

to be poor" (p. 42). These factors, in addition to being part of a large household, being<br />

part of a single-earner household, being female, and having a part-time (instead of a<br />

full-time) job have been found to be important working poverty "risk factors." Immigrant<br />

workers and self-employed workers are also more likely to be working poor than other<br />

kinds of workers.<br />

Economic Factors<br />

There is a widespread assumption that economic growth leads to tighter labor markets<br />

and higher wages. However, the evidence suggests that economic growth does not<br />

always benefit each part of the population equally. For example, the 1980s was a period<br />

of economic growth and prosperity in the United States, but most of the economic gains<br />

were concentrated at the top of the income spectrum. This means that people near the<br />

bottom of the labor market did not benefit from the economic gains of the 1980s. In fact,<br />

many have argued that low-skilled workers experienced declining prosperity in the<br />

1980s. Therefore, changing economic conditions do not have as large of an impact on<br />

working poverty rates as one might expect.<br />

Labor Market Institutions<br />

Labor markets can be egalitarian, efficient, or somewhere in the middle. According to<br />

Brady, Fullerton, and Cross (2010), "[e]fficient labor markets typically feature flexibility,<br />

low unemployment, and higher economic growth, and facilitate the rapid hiring and firing<br />

of workers. Egalitarian labor markets are bolstered by strong labor market institutions,<br />

higher wages, and greater security" (p562). The United States has an efficient labor<br />

market, whereas most European countries have egalitarian labor markets. Each system<br />

has its drawbacks, but the egalitarian labor market model is typically associated with<br />

lower rates of working poverty. One tradeoff to this is that the "lowest skilled and least<br />

employable" people are sometimes excluded from an egalitarian labor market, and must<br />

instead rely on government aid in order to survive (p. 563). If the United States switched<br />

from an efficient to an egalitarian labor market, it might have to increase its welfare state<br />

generosity in order to cope with a higher unemployment rate.<br />

Centralized wage bargaining is a key component of egalitarian labor markets. In a<br />

country with centralized wage bargaining institutions, wages for entire industries are<br />

negotiated at the regional or national level. This means that similar workers earn similar<br />

wages, which reduces income inequality. Lohmann (2009) finds that countries with<br />

centralized wage bargaining institutions have lower rates of "pre-transfer" working<br />

poverty. The "pre-transfer" working poverty rate is the percentage of workers who fall<br />

below the poverty threshold based on their earned wages (not counting government<br />

transfers).<br />

Page 82 of 149


Welfare State Generosity<br />

Cross-national studies are in agreement that the most important factor affecting working<br />

poverty rates is welfare state generosity. A generous welfare state spends a higher<br />

proportion of its GDP on things like unemployment insurance, social security, family<br />

assistance, childcare subsidies, healthcare subsidies, housing subsidies, transportation<br />

subsidies, and food subsidies. Studies on working poverty have found that these kinds<br />

of government spending can pull a significant number of people out of poverty, even if<br />

they earn low wages. Lohmann's 2009 study shows that welfare state generosity has a<br />

significant impact on the "post-transfer" rate of working poverty. The "post-transfer" rate<br />

of working poverty is the percentage of working households that fall below the poverty<br />

threshold after government aid has been taken into account.<br />

Different types of transfers benefit different kinds of low-wage families. Family benefits<br />

will benefit households with children and unemployment benefits will benefit households<br />

that include workers with significant work experience. Transfers such as old-age<br />

benefits are unlikely to benefit low-wage households unless the elderly are living in the<br />

same household. Sometimes, even when benefits are available, those who qualify do<br />

not take advantage of them. Migrants in particular are less likely to take advantage of<br />

the available benefits.<br />

Page 83 of 149


Obstacles To Uplift<br />

The working poor face many of the same daily life struggles as the nonworking poor, but<br />

they also face some unique obstacles. Some studies, many of them qualitative, provide<br />

detailed insights into the obstacles that hinder workers' ability to find jobs, keep jobs,<br />

and make ends meet. Some of the most common struggles faced by the working poor<br />

are finding affordable housing, arranging transportation to and from work, buying basic<br />

necessities, arranging childcare, having unpredictable work schedules, juggling two or<br />

more jobs, and coping with low-status work.<br />

Housing<br />

Working poor people who do not have friends or relatives with whom they can live often<br />

find themselves unable to rent an apartment of their own. Although the working poor are<br />

employed at least some of the time, they often find it difficult to save enough money for<br />

a deposit on a rental property. As a result, many working poor people end up in living<br />

situations that are actually more costly than a month-to-month rental. For instance,<br />

many working poor people, especially those who are in some kind of transitional phase,<br />

rent rooms in week-to-week motels. These motel rooms tend to cost much more than a<br />

traditional rental, but they are accessible to the working poor because they do not<br />

require a large deposit. If someone is unable or unwilling to pay for a room in a motel,<br />

they might live in his/her car, in a homeless shelter, or on the street. This is not a<br />

marginal phenomenon; in fact, according to the 2008 US Conference of Mayors, one in<br />

five homeless people are currently employed.<br />

Of course, some working poor people are able to access housing subsidies (such as<br />

a Section 8 Housing Choice Voucher) to help cover their housing expenses. However,<br />

these housing subsidies are not available to everyone who meets the Section 8 income<br />

specifications. In fact, less than 25% of people who qualify for a housing subsidy<br />

receive one.<br />

Education<br />

The issue with education starts many times with the working poor from childhood and<br />

follows them into their struggle for a substantial income. Children growing up in families<br />

of the working poor are not provided the same educational opportunities as their middleclass<br />

counterpart. In many cases the low income community is filled with schools that<br />

are lacking necessities and support needed to form a solid education. This follows<br />

students as they continue in education. In many cases this hinders the possibility for<br />

America's youth to continue on to higher education. The grades and credits just are not<br />

attained in many cases, and the lack of guidance in the schools leaves the children of<br />

the working poor with no degree. Also, the lack of funds for continuing education causes<br />

these children to fall behind. In many cases, their parents did not continue on into higher<br />

education and because of this have a difficult time finding jobs with salaries that can<br />

support a family. Today a college degree is a requirement for many jobs, and it is the<br />

low skill jobs that usually only require a high school degree or GED. The inequality in<br />

Page 84 of 149


available education continues the vicious cycle of families entering into the working<br />

poor.<br />

Transportation<br />

Given the fact that many working poor people do not own a car or cannot afford to drive<br />

their car, where they live can significantly limit where they are able to work, and vice<br />

versa. Given the fact that public transportation in many US cities is sparse, expensive,<br />

or non-existent, this is a particularly salient obstacle. Some working poor people are<br />

able to use their social networks—if they have them—to meet their transportation<br />

needs. In a study on low-income single mothers, Edin and Lein found that single<br />

mothers who had someone to drive them to and from work were much more likely to be<br />

able to support themselves without relying on government aid.<br />

Basic Necessities<br />

Like the unemployed poor, the working poor struggle to pay for basic necessities like<br />

food, clothing, housing, and transportation. In some cases, however, the working poor's<br />

basic expenses can be higher than the unemployed poor's. For instance, the working<br />

Page 85 of 149


poor's clothing expenses may be higher than the unemployed poor's because they must<br />

purchase specific clothes or uniforms for their jobs. Also, because the working poor are<br />

spending much of their time at work, they may not have the time to prepare their own<br />

food. In this case, they may frequently resort to eating fast food, which is less healthful<br />

and more expensive than home-prepared food.<br />

Childcare<br />

Working poor parents with young children, especially single parents, face significantly<br />

more childcare-related obstacles than other people. Often, childcare costs can exceed a<br />

low-wage earners' income, making work, especially in a job with no potential for<br />

advancement, an economically illogical activity. However, some single parents are able<br />

to rely on their social networks to provide free or below-market-cost childcare. There are<br />

also some free childcare options provided by the government, such as the Head Start<br />

Program. However, these free options are only available during certain hours, which<br />

may limit parents' ability to take jobs that require late-night shifts.The U.S. "average"<br />

seems to suggest that for one toddler, in full-time day care, on weekdays, the cost is<br />

approximately $600.00 per month. But, that figure can rise to well over $1000.00 per<br />

month in major metro areas, and fall to less than $350 in rural areas.The average cost<br />

of center-based daycare in the United States is $11,666 per year ($972 a month), but<br />

prices range from $3,582 to $18,773 a year ($300 to $1,564 monthly), according to the<br />

National Association of Child Care Resource & Referral Agencies (NACCRRA).<br />

Work Schedules<br />

Many low-wage jobs force workers to accept irregular schedules. In fact, some<br />

employers will not hire someone unless they have "open availability," which means<br />

being available to work any time, any day. This makes it difficult for workers to arrange<br />

for childcare and to take on a second job. In addition, working poor people's working<br />

hours can fluctuate wildly from one week to the next, making it difficult for them to<br />

budget effectively and save up money.<br />

Multiple Jobs<br />

Many low-wage workers have to work multiple jobs in order to make ends meet. In<br />

1996, 6.2 percent of the workforce held two or more full- or part-time jobs. Most of these<br />

people held two part-time jobs or one part-time job and one full-time job, but 4% of men<br />

and 2% of women held two full-time jobs at the same time. This can be physically<br />

exhausting and can often lead to short and long-term health problems.<br />

Low-Status Work<br />

Many low-wage service sector jobs require a great deal of customer service work.<br />

Although not all customer service jobs are low-wage or low-status, many of them are.<br />

Some argue that the low status nature of some jobs can have negative psychological<br />

effects on workers, but others argue that low status workers come up with coping<br />

Page 86 of 149


mechanisms that allow them to maintain a strong sense of self-worth. One of these<br />

coping mechanisms is called boundary work. Boundary work happens when one group<br />

of people valorize their own social position by comparing themselves to another group,<br />

who they perceive to be inferior in some way. For example, Newman (1999) found that<br />

fast food workers in New York City cope with the low-status nature of their job by<br />

comparing themselves to the unemployed, who they perceive to be even lower-status<br />

than themselves. Thus, although the low-status nature of working poor people's jobs<br />

may have some negative psychological effects, some, but probably not all, of these<br />

negative effects can be counteracted through coping mechanisms such as boundary<br />

work.<br />

Anti-Poverty Policies<br />

Scholars, policymakers, and others have come up with a variety of proposals for how to<br />

reduce or eliminate working poverty. Most of these proposals are directed toward the<br />

United States, but they might also be relevant to other countries. The remainder of the<br />

section outlines the pros and cons of some of the most commonly proposed solutions.<br />

Welfare State Generosity<br />

Cross-national studies like Lohmann (2009) and Brady, Fullerton, and Cross (2010)<br />

clearly show that countries with generous welfare states have lower levels of working<br />

Page 87 of 149


poverty than countries with less-generous welfare states, even when factors like<br />

demography, economic performance, and labor market institutions are taken into<br />

account. Having a generous welfare state does two key things to reduce working<br />

poverty: it raises the minimum level of wages that people are willing to accept, and it<br />

pulls a large portion of low-wage workers out of poverty by providing them with an array<br />

of cash and non-cash government benefits. Many think that increasing the United<br />

States' welfare state generosity would lower the working poverty rate. A common<br />

critique of this proposal is that a generous welfare state would not work because it<br />

would stagnate the economy, raise unemployment, and degrade people's work<br />

ethic. However, as of 2011, most European countries have a lower unemployment<br />

rate than the US. Furthermore, although Western European economies' growth rates<br />

can be lower than the US's from time to time, their growth rates tend to be more stable,<br />

whereas the US's tends to fluctuate relatively severely. Individual states offer financial<br />

assistance for child care, but the aid varies widely.<br />

Most assistance is administered through the Child Care and Development Block Grants.<br />

Check here to find the contact information for your state. Many subsidies have strict<br />

income guidelines and are generally for families with children under 13 (the age limit is<br />

often extended if the child has a disability). Many subsidies permit home-based care,<br />

but some only accept a day care center, so check the requirements. If you need to use<br />

an authorized provider, ask if they will put you in touch with an agency that can help you<br />

find one.<br />

Some states distribute funds through social or health departments or agencies (like this<br />

one in Washington State). For example, the Children's Cabinet in Nevada can refer<br />

families to providers, help them apply for subsidies and can even help families who<br />

want to pay a relative for care. North Carolina's Smart Start is a public/private<br />

partnership that offers funding for child care. Check the National Women's Law Center<br />

for each state's child care assistance policy.<br />

Wages And Benefits<br />

In the conclusion of her book, Nickel and Dimed (2001), Barbara Ehrenreich argues that<br />

Americans need to pressure employers to improve worker compensation. Generally<br />

speaking, this implies a need to strengthen the labor movement. Cross-national<br />

statistical studies on working poverty suggest that generous welfare states have a larger<br />

impact on working poverty than strong labor movements. The labor movements in<br />

various countries have accomplished this through political parties of their own (labor<br />

parties) or strategic alliances with non-labor parties, for instance, when striving to put a<br />

meaningful minimum wage in place. The federal government offers a Flexible Spending<br />

Account (FSA) that's administered through workplaces.<br />

If your job offers an FSA (also known as a Dependent Care Account), you can put aside<br />

up to $5,000 in per-tax dollars to pay for child care expenses. If both you and your<br />

spouse have an FSA, the family limit is $5,000—but you could get as much as $2,000 in<br />

tax savings if your combined contributions reach the maximum.<br />

Page 88 of 149


Education And Training<br />

Some argue that more vocational training and active labour market policies, especially<br />

in growth industries like healthcare and renewable energy, is the solution to working<br />

poverty. To be sure, wider availability of vocational training could pull some people out<br />

of working poverty, but the fact remains that the low-wage service sector is a rapidly<br />

growing part of the US economy. Even if more nursing and clean energy jobs were<br />

added to the economy, there would still be a huge portion of the workforce in low-wage<br />

service sector jobs like retail, food service, and cleaning. Therefore, it seems clear that<br />

any significant reduction in the working poverty rate will have to come from offering<br />

higher wages and more benefits to the current, and future, population of service sector<br />

workers.<br />

Child Support Assurance<br />

Given the fact that such a large proportion of working poor households are headed by a<br />

single mother, one clear way to reduce working poverty would be to make sure that<br />

children's fathers share the cost of child rearing. In cases where the father cannot<br />

provide child support, scholars like Irwin Garfinkel advocate for the implementation of a<br />

child support guarantee, whereby the government pays childcare costs if the father<br />

cannot. Child support is not always a guarantee if the father or mother does not work.<br />

For example, if the parent without custody is not working then the parent with custody<br />

does not receive any child support unless the non working parent is employed at their<br />

job longer than 90 days, excluding if the non begins to work for its a city or government.<br />

Also, the government does not pay for childcare cost if you make more than the cut off<br />

range (your gross, per county or state.)<br />

Page 89 of 149


Marriage<br />

Households with two wage-earners have a significantly lower rate of working poverty<br />

than households with only one wage-earner. Also, households with two adults, but only<br />

one wage-earner, have lower working poverty rates than households with only one<br />

adult. Therefore, it seems clear that having two adults in a household, especially if there<br />

are children present, is more likely to keep a household out of poverty than having just<br />

one adult in a household. Many scholars and policymakers have used this fact to argue<br />

that encouraging people to get married and stay married is an effective way to reduce<br />

working poverty (and poverty in general). However, this is easier said than done.<br />

Research has shown that low-income people marry less often than higher-income<br />

people because they have a more difficult time finding a partner who is employed, which<br />

is often seen as a prerequisite for marriage. Therefore, unless the employment<br />

opportunity structure is improved, simply increasing the number of marriages among<br />

low-income people would be unlikely to lower working poverty rates.<br />

Ultimately, effective solutions to working poverty are multifaceted. Each of the<br />

aforementioned proposals could help reduce working poverty in the United States, but<br />

they might have a greater impact if at least a few of them were pursued simultaneously.<br />

Page 90 of 149


VII. Wage Slavery<br />

Wage Slavery is a term used to draw an analogy between slavery and wage labor by<br />

focusing on similarities between owning and renting a person. It is usually used to refer<br />

to a situation where a person's livelihood depends on wages or a salary, especially<br />

when the dependence is total and immediate.<br />

The term "wage slavery" has been used to criticize exploitation of labor and social<br />

stratification, with the former seen primarily as unequal bargaining power between labor<br />

and capital (particularly when workers are paid comparatively low wages, e.g.<br />

in sweatshops) and the latter as a lack of workers' self-management, fulfilling job<br />

choices and leisure in an economy. The criticism of social stratification covers a wider<br />

range of employment choices bound by the pressures of a hierarchical society to<br />

perform otherwise unfulfilling work that deprives humans of their "species character" not<br />

only under threat of starvation or poverty, but also of social<br />

stigma and status diminution.<br />

Similarities between wage labor and slavery were noted as early as Cicero in Ancient<br />

Rome, such as in De Officiis. With the advent of the Industrial Revolution, thinkers such<br />

as Pierre-Joseph Proudhon and Karl Marx elaborated the comparison between wage<br />

labor and slavery, while Luddites emphasized the dehumanization brought about by<br />

machines. Before the American Civil War, Southern defenders of African<br />

American slavery invoked the concept of wage slavery to favorably compare the<br />

condition of their slaves to workers in the North. The United States abolished slavery<br />

after the Civil War, but labor union activists found the metaphor useful and appropriate.<br />

According to Lawrence Glickman, in the Gilded Age "[r]eferences abounded in the labor<br />

press, and it is hard to find a speech by a labor leader without the phrase".<br />

The introduction of wage labor in 18th-century Britain was met with resistance, giving<br />

rise to the principles of syndicalism. Historically, some labor organizations and individual<br />

social activists have espoused workers' self-management or worker cooperatives as<br />

possible alternatives to wage labor.<br />

History<br />

Emma Goldman famously denounced wage slavery by saying:<br />

"The only difference is that you are hired slaves<br />

instead of block slaves"<br />

The view that working for wages is akin to slavery dates back to the<br />

ancient world. In ancient Rome, Cicero wrote that "whoever gives his<br />

labor for money sells himself and puts himself in the rank of slaves". [11]<br />

In 1763, the French journalist Simon Linguet published an influential<br />

description of wage slavery:<br />

Page 91 of 149


The slave was precious to his master because of the money he had cost him ... They<br />

were worth at least as much as they could be sold for in the market ... It is the<br />

impossibility of living by any other means that compels our farm laborers to till the soil<br />

whose fruits they will not eat and our masons to construct buildings in which they will<br />

not live ... It is want that compels them to go down on their knees to the rich man in<br />

order to get from him permission to enrich him ... what effective gain [has] the<br />

suppression of slavery brought [him ?] He is free, you say. Ah! That is his misfortune ...<br />

These men ... [have] the most terrible, the most imperious of masters, that is, need. ...<br />

They must therefore find someone to hire them, or die of hunger. Is that to be free?<br />

The view that wage work has substantial similarities with chattel slavery was actively put<br />

forward in the late 18th and 19th centuries by defenders of chattel slavery (most notably<br />

in the Southern states of the United States) and by opponents of capitalism (who were<br />

also critics of chattel slavery). Some defenders of slavery, mainly from the Southern<br />

slave states, argued that Northern workers were "free but in name – the slaves of<br />

endless toil" and that their slaves were better off. This contention has been partly<br />

corroborated by some modern studies that indicate slaves' material conditions in the<br />

19th century were "better than what was typically available to free urban laborers at the<br />

time". In this period, Henry David Thoreau wrote that "[i]t is hard to have a Southern<br />

overseer; it is worse to have a Northern one; but worst of all when you are the slavedriver<br />

of yourself".<br />

Some abolitionists in the United States regarded the analogy as spurious. They<br />

believed that wage workers were "neither wronged nor oppressed". Abraham<br />

Lincoln and the Republicans argued that the condition of wage workers was different<br />

from slavery as laborers were likely to have the opportunity to work for themselves in<br />

the future, achieving self-employment. The abolitionist and former slave Frederick<br />

Douglass initially declared "now I am my own master", upon taking a paying<br />

job. [32] However, later in life he concluded to the contrary, saying "experience<br />

demonstrates that there may be a slavery of wages only a little less galling and crushing<br />

in its effects than chattel slavery, and that this slavery of wages must go down with the<br />

other". Douglass went on to speak about these conditions as arising from the unequal<br />

bargaining power between the ownership/capitalist class and the non-ownership/laborer<br />

class within a compulsory monetary market: "No more crafty and effective devise for<br />

defrauding the southern laborers could be adopted than the one that substitutes orders<br />

upon shopkeepers for currency in payment of wages. It has the merit of a show of<br />

honesty, while it puts the laborer completely at the mercy of the land-owner and the<br />

shopkeeper".<br />

Self-employment became less common as the artisan tradition slowly disappeared in<br />

the later part of the 19th century. In 1869, The New York Times described the system of<br />

wage labor as "a system of slavery as absolute if not as degrading as that which lately<br />

prevailed at the South". E. P. Thompson notes that for British workers at the end of the<br />

18th and beginning of the 19th centuries, the "gap in status between a 'servant,' a hired<br />

wage-laborer subject to the orders and discipline of the master, and an artisan, who<br />

Page 92 of 149


might 'come and go' as he pleased, was wide enough for men to shed blood rather than<br />

allow themselves to be pushed from one side to the other. And, in the value system of<br />

the community, those who resisted degradation were in the right". A "Member of the<br />

Builders' Union" in the 1830s argued that the trade unions "will not only strike for less<br />

work, and more wages, but will ultimately abolish wages, become their own masters<br />

and work for each other; labor and capital will no longer be separate but will be<br />

indissolubly joined together in the hands of workmen and work-women".This<br />

perspective inspired the Grand National Consolidated Trades Union of 1834 which had<br />

the "two-fold purpose of syndicalist unions – the protection of the workers under the<br />

existing system and the formation of the nuclei of the future society" when the unions<br />

"take over the whole industry of the country". "Research has shown",<br />

summarises William Lazonick, "that the 'free-born Englishman' of the eighteenth century<br />

– even those who, by force of circumstance, had to submit to agricultural wage labor –<br />

tenaciously resisted entry into the capitalist workshop".<br />

The use of the term "wage slave" by labor organizations may originate from the labor<br />

protests of the Lowell Mill Girls in 1836. The imagery of wage slavery was widely used<br />

by labor organizations during the mid-19th century to object to the lack of workers' selfmanagement.<br />

However, it was gradually replaced by the more neutral term "wage work"<br />

towards the end of the 19th century as labor organizations shifted their focus to raising<br />

wages.<br />

Karl Marx described capitalist society as infringing on individual autonomy because it is<br />

based on a materialistic and commodified concept of the body and its liberty (i.e. as<br />

something that is sold, rented, or alienated in a class society). According to Friedrich<br />

Engels:<br />

The slave is sold once and for all; the proletarian must sell himself daily and hourly. The<br />

individual slave, property of one master, is assured an existence, however miserable it<br />

Page 93 of 149


may be, because of the master's interest. The individual proletarian, property as it were<br />

of the entire bourgeois class which buys his labor only when someone has need of it,<br />

has no secure existence.<br />

Similarities of Wage Work with Slavery<br />

Critics of wage work have drawn several similarities between wage work and slavery:<br />

1. Since the chattel slave is property, his value to an owner is in some ways higher<br />

than that of a worker who may quit, be fired or replaced. The chattel slave's<br />

owner has made a greater investment in terms of the money he paid for the<br />

slave. For this reason, in times of recession chattel slaves could not be fired like<br />

wage laborers. A "wage slave" could also be harmed at no (or less) cost.<br />

American chattel slaves in the 19th century had improved their standard of living<br />

from the 18th century and – according to historians Fogel and Engerman –<br />

plantation records show that slaves worked less, were better fed and whipped<br />

only occasionally – their material conditions in the 19th century being "better than<br />

what was typically available to free urban laborers at the time". This was partially<br />

due to slave psychological strategies under an economic system different from<br />

capitalist wage slavery. According to Mark Michael Smith of the Economic<br />

History Society, "although intrusive and oppressive, paternalism, the way<br />

masters employed it, and the methods slaves used to manipulate it, rendered<br />

slaveholders' attempts to institute capitalistic work regimens on their plantation<br />

ineffective and so allowed slaves to carve out a degree of autonomy".<br />

2. Unlike a chattel slave, a wage laborer can (barring unemployment or lack of job<br />

offers) choose between employers, but they usually constitute a minority of<br />

owners in the population for which the wage laborer must work while attempts to<br />

implement workers' control on employers' businesses may be considered an act<br />

of theft or insubordination and thus be met with violence, imprisonment or other<br />

legal and social measures. The wage laborer's starkest choice is to work for an<br />

employer or face poverty or starvation. If a chattel slave refuses to work, a<br />

number of punishments are also available; from beatings to food deprivation –<br />

although economically rational slave owners practiced positive reinforcement to<br />

achieve best results and before losing their investment by killing an expensive<br />

slave.<br />

3. Historically, the range of occupations and status positions held by chattel slaves<br />

has been nearly as broad as that held by free persons, indicating some<br />

similarities between chattel slavery and wage slavery as well.<br />

4. Like chattel slavery, wage slavery does not stem from some immutable "human<br />

nature", but represents a "specific response to material and historical conditions"<br />

that "reproduce[s] the inhabitants, the social relations… the ideas… [and] the<br />

social form of daily life".<br />

Page 94 of 149


5. Similarities were blurred by the fact that proponents of wage labor won<br />

the American Civil War, in which they competed for legitimacy with defenders of<br />

chattel slavery. Both presented an over-positive assessment of their system<br />

while denigrating the opponent.<br />

According to American anarcho-syndicalist philosopher Noam Chomsky, the similarities<br />

between chattel and wage slavery were noticed by the workers themselves. He noted<br />

that the 19th-century Lowell Mill Girls, who without any reported knowledge of<br />

European Marxism or anarchism condemned the "degradation and subordination" of the<br />

newly emerging industrial system and the "new spirit of the age: gain wealth, forgetting<br />

all but self", maintaining that "those who work in the mills should own them". They<br />

expressed their concerns in a protest song during their 1836 strike:<br />

Oh! isn't it a pity, such a pretty girl as I<br />

Should be sent to the factory to pine away and die?<br />

Oh! I cannot be a slave, I will not be a slave,<br />

For I'm so fond of liberty,<br />

That I cannot be a slave.<br />

Defenses of wage labor<br />

and chattel slavery in the<br />

literature have linked the<br />

subjection of man to man<br />

with the subjection of man<br />

to nature – arguing that<br />

hierarchy and a social<br />

system's particular<br />

relations of production<br />

represent human nature<br />

and are no more coercive<br />

than the reality of life<br />

itself. According to this<br />

narrative, any wellintentioned<br />

attempt to<br />

fundamentally change<br />

the status quo is naively<br />

utopian and will result in<br />

more oppressive<br />

conditions. Bosses in both<br />

of these long-lasting<br />

systems argued that their<br />

system created a lot of<br />

wealth and prosperity. In<br />

some sense, both did create jobs and their investment entailed risk. For example, slave<br />

owners risked losing money by buying chattel slaves who later became ill or died; while<br />

bosses risked losing money by hiring workers (wage slaves) to make products that did<br />

Page 95 of 149


not sell well on the market. Marginally, both chattel and wage slaves may become<br />

bosses; sometimes by working hard. It may be the "rags to riches" story which<br />

occasionally occurs in capitalism, or the "slave to master" story that occurred in places<br />

like colonial Brazil, where slaves could buy their own freedom and become business<br />

owners, self-employed, or slave owners themselves. [48] Social mobility, or the hard work<br />

and risk that it may entail, are thus not considered to be a redeeming factor by critics of<br />

the concept of wage slavery.<br />

Anthropologist David Graeber has noted that historically the first wage labor contracts<br />

we know about – whether in ancient Greece or Rome, or in the Malay or Swahili city<br />

states in the Indian Ocean – were in fact contracts for the rental of chattel slaves<br />

(usually the owner would receive a share of the money and the slave another, with<br />

which to maintain his or her living expenses). According to Graeber, such arrangements<br />

were quite common in New World slavery as well, whether in the United States or<br />

Brazil. C. L. R. James argued that most of the techniques of human organization<br />

employed on factory workers during the Industrial Revolution were first developed on<br />

slave plantations.<br />

Decline in Use Of Term<br />

The usage of the term "wage slavery" shifted to "wage work" at the end of the 19th<br />

century as groups like the Knights of Labor and American Federation of Labor shifted to<br />

a more reformist, trade union ideology instead of worker's self-management. Much of<br />

the decline was caused by the rapid increase in manufacturing after the Industrial<br />

Revolution and the subsequent dominance of wage labor as a result. Another factor<br />

was immigration and demographic changes that led to ethnic tension between the<br />

workers.<br />

As Hallgrimsdottir and Benoit point out:<br />

[I]ncreased centralization of production ... declining wages ... [an] expanding ... labor<br />

pool ... intensifying competition, and ... [t]he loss of competence and independence<br />

experienced by skilled labor" meant that "a critique that referred to all [wage] work as<br />

slavery and avoided demands for wage concessions in favor of supporting the creation<br />

of the producerist republic (by diverting strike funds towards funding ... co-operatives,<br />

for example) was far less compelling than one that identified the specific conditions of<br />

slavery as low wages.<br />

Treatment in Various Economic Systems<br />

Some anti-capitalist thinkers claim that the elite maintain wage slavery and a divided<br />

working class through their influence over the media and entertainment<br />

industry, [51][52] educational institutions, unjust laws, nationalist and<br />

corporate propaganda, pressures and incentives to internalize values serviceable to the<br />

power structure, state violence, fear of unemployment, and a historical legacy of<br />

exploitation and profit accumulation/transfer under prior systems, which shaped the<br />

Page 96 of 149


development of economic theory. Adam Smith noted that employers often conspire<br />

together to keep wages low and have the upper hand in conflicts between workers and<br />

employers:<br />

The interest of the dealers ... in any particular branch of trade or manufactures, is<br />

always in some respects different from, and even opposite to, that of the public... [They]<br />

have generally an interest to deceive and even to oppress the public ... We rarely hear,<br />

it has been said, of the combinations of masters, though frequently of those of<br />

workmen. But whoever imagines, upon this account, that masters rarely combine, is as<br />

ignorant of the world as of the subject. Masters are always and everywhere in a sort of<br />

tacit, but constant and uniform combination, not to raise the wages of labor above their<br />

actual rate ... It is not, however, difficult to foresee which of the two parties must, upon<br />

all ordinary occasions, have the advantage in the dispute, and force the other into a<br />

compliance with their terms.<br />

Capitalism<br />

The concept of wage slavery could conceivably be traced back to pre-capitalist figures<br />

like Gerrard Winstanley from the radical Christian Diggers movement in England, who<br />

wrote in his 1649 pamphlet, The New Law of Righteousness, that there "shall be no<br />

Page 97 of 149


uying or selling, no fairs nor markets, but the whole earth shall be a common treasury<br />

for every man" and "there shall be none Lord over others, but every one shall be a Lord<br />

of himself".<br />

Aristotle stated that "the citizens must not live a mechanic or a mercantile life (for such a<br />

life is ignoble and inimical to virtue), nor yet must those who are to be citizens in the<br />

best state be tillers of the soil (for leisure is needed both for the development of virtue<br />

and for active participation in politics)", often paraphrased as "all paid jobs absorb and<br />

degrade the mind". Cicero wrote in 44 BC that "vulgar are the means of livelihood of all<br />

hired workmen whom we pay for mere manual labour, not for artistic skill; for in their<br />

case the very wage they receive is a pledge of their slavery".<br />

Somewhat similar criticisms have also been expressed by some proponents<br />

of liberalism, like Silvio Gesell and Thomas Paine; Henry George, who inspired the<br />

economic philosophy known as Georgism; and the Distributist school of thought within<br />

the Catholic Church.<br />

To Karl Marx and anarchist thinkers like Mikhail Bakunin and Peter Kropotkin, wage<br />

slavery was a class condition in place due to the existence of private property and<br />

the state. This class situation rested primarily on:<br />

1. The existence of property not intended for active use;<br />

2. The concentration of ownership in few hands;<br />

3. The lack of direct access by workers to the means of production and<br />

consumption goods; and<br />

4. The perpetuation of a reserve army of unemployed workers.<br />

And secondarily on:<br />

1. The waste of workers' efforts and resources on producing useless luxuries;<br />

2. The waste of goods so that their price may remain high; and<br />

3. The waste of all those who sit between the producer and consumer, taking their<br />

own shares at each stage without actually contributing to the production of<br />

goods, i.e. the middle man.<br />

Fascism<br />

Fascist economic policies were more hostile to independent trade unions than modern<br />

economies in Europe or the United States. Fascism was more widely accepted in the<br />

1920s and 1930s, and foreign corporate investment (notably from the United States) in<br />

Italy and Germany increased after the fascists took power.<br />

Fascism has been perceived by some notable critics, like Buenaventura Durruti, to be a<br />

last resort weapon of the privileged to ensure the maintenance of wage slavery:<br />

No government fights fascism to destroy it. When the bourgeoisie sees that power is<br />

slipping out of its hands, it brings up fascism to hold onto their privileges.<br />

Page 98 of 149


Psychological Effects<br />

According to Noam Chomsky, analysis of the psychological implications of wage slavery<br />

goes back to the Enlightenment era. In his 1791 book The Limits of State Action,<br />

classical liberal thinker Wilhelm von Humboldt explained how "whatever does not spring<br />

from a man's free choice, or is only the result of instruction and guidance, does not<br />

enter into his very nature; he does not perform it with truly human energies, but merely<br />

with mechanical exactness" and so when the laborer works under external control, "we<br />

may admire what he does, but we despise what he is". Because they explore human<br />

authority and obedience, both the Milgram and Stanford experiments have been found<br />

useful in the psychological study of wage-based workplace relations.<br />

Self-Identity Problems and Stress<br />

According to research [citation needed] , modern work provides people with a sense of<br />

personal and social identity that is tied to:<br />

1. The particular work role, even if unfulfilling; and<br />

2. The social role it entails e.g. family bread-winning, friendship forming and so on.<br />

Page 99 of 149


Thus job loss entails the loss of this identity.<br />

Erich Fromm argued that if a person perceives himself as being what he owns, then<br />

when that person loses (or even thinks of losing) what he "owns" (e.g. the good looks or<br />

sharp mind that allow him to sell his labor for high wages) a fear of loss may create<br />

anxiety and authoritarian tendencies because that person's sense of identity is<br />

threatened. In contrast, when a person's sense of self is based on what he experiences<br />

in a state of being (creativity, love, sadness, taste, sight and the like) with a less<br />

materialistic regard for what he once had and lost, or may lose, then less authoritarian<br />

tendencies prevail. In his view, the state of being flourishes under a worker-managed<br />

workplace and economy, whereas self-ownership entails a materialistic notion of self,<br />

created to rationalize the lack of worker control that would allow for a state of being.<br />

Investigative journalist Robert Kuttner analyzed the work of public-health scholars<br />

Jeffrey Johnson and Ellen Hall about modern conditions of work and concludes that "to<br />

be in a life situation where one experiences relentless demands by others, over which<br />

one has relatively little control, is to be at risk of poor health, physically as well as<br />

mentally". Under wage labor, "a relatively small elite demands and gets empowerment,<br />

self-actualization, autonomy, and other work satisfaction that partially compensate for<br />

long hours" while "epidemiological data confirm that lower-paid, lower-status workers<br />

are more likely to experience the most clinically damaging forms of stress, in part<br />

because they have less control over their work".<br />

Wage slavery and the educational system that precedes it "implies power held by the<br />

leader. Without power the leader is inept. The possession of power inevitably leads to<br />

corruption ... in spite of ... good intentions ... [Leadership means] power of initiative, this<br />

sense of responsibility, the self-respect which comes from expressed manhood, is taken<br />

from the men, and consolidated in the leader. The sum of their initiative, their<br />

responsibility, their self-respect becomes his ... [and the] order and system he maintains<br />

is based upon the suppression of the men, from being independent thinkers into being<br />

'the men' ... In a word, he is compelled to become an autocrat and a foe to democracy".<br />

For the "leader", such marginalization can be beneficial, for a leader "sees no need for<br />

any high level of intelligence in the rank and file, except to applaud his actions. Indeed<br />

such intelligence from his point of view, by breeding criticism and opposition, is an<br />

obstacle and causes confusion". Wage slavery "implies erosion of the human<br />

personality ... [because] some men submit to the will of others, arousing in these<br />

instincts which predispose them to cruelty and indifference in the face of the suffering of<br />

their fellows".<br />

Higher Wages<br />

Psychological Control<br />

In 19th-century discussions of labor relations, it was normally assumed that the threat of<br />

starvation forced those without property to work for wages. Proponents of the view that<br />

modern forms of employment constitute wage slavery, even when workers appear to<br />

Page 100 of 149


have a range of available alternatives, have attributed its perpetuation to a variety of<br />

social factors that maintain the hegemony of the employer class.<br />

In an account of the Lowell Mill Girls, Harriet Hanson Robinson wrote that generously<br />

high wages were offered to overcome the degrading nature of the work:<br />

At the time the Lowell cotton mills were started the caste of the factory girl was the<br />

lowest among the employments of women. ... She was represented as subjected to<br />

influences that must destroy her purity and self-respect. In the eyes of her overseer she<br />

was but a brute, a slave, to be beaten, pinched and pushed about. It was to overcome<br />

this prejudice that such high wages had been offered to women that they might be<br />

induced to become millgirls, in spite of the opprobrium that still clung to this degrading<br />

occupation.<br />

In his book Disciplined Minds, Jeff Schmidt points out that professionals are trusted to<br />

run organizations in the interests of their employers. Because employers cannot be on<br />

hand to manage every decision, professionals are trained to "ensure that each and<br />

every detail of their work favors the right interests–or skewers the disfavored ones" in<br />

the absence of overt control:<br />

The resulting professional is an obedient thinker, an intellectual property whom<br />

employers can trust to experiment, theorize, innovate and create safely within the<br />

confines of an assigned ideology.<br />

Parecon (participatory economics) theory posits a social class "between labor and<br />

capital" of higher paid professionals such as "doctors, lawyers, engineers, managers<br />

and others" who monopolize empowering labor and constitute a class above wage<br />

laborers who do mostly "obedient, rote work".<br />

Page 101 of 149


Lower Wages<br />

The terms "employee" or "worker" have often been replaced by "associate". This plays<br />

up the allegedly voluntary nature of the interaction while playing down the subordinate<br />

status of the wage laborer as well as the worker-boss class distinction emphasized by<br />

labor movements. Billboards as well as television, Internet and newspaper<br />

advertisements consistently show low-wage workers with smiles on their faces,<br />

appearing happy.<br />

Job interviews and other data on requirements for lower skilled workers in developed<br />

countries – particularly in the growing service sector – indicate that the more workers<br />

depend on low wages and the less skilled or desirable their job is, the more employers<br />

screen for workers without better employment options and expect them to feign<br />

unremunerative motivation. Such screening and feigning may not only contribute to the<br />

positive self-image of the employer as someone granting desirable employment, but<br />

also signal wage-dependence by indicating the employee's willingness to feign, which in<br />

turn may discourage the dissatisfaction normally associated with job-switching or union<br />

activity.<br />

At the same time, employers in the service industry have justified unstable, part-time<br />

employment and low wages by playing down the importance of service jobs for the lives<br />

of the wage laborers (e.g. just temporary before finding something better, student<br />

summer jobs and the like).<br />

In the early 20th century, "scientific methods of strikebreaking" [79] were devised –<br />

employing a variety of tactics that emphasized how strikes undermined "harmony" and<br />

"Americanism".<br />

Workers' Self-Management<br />

Some social activists objecting to the market system or price system of wage working<br />

historically have considered syndicalism, worker cooperatives, workers' selfmanagement<br />

and workers' control as possible alternatives to the current wage<br />

system. [4][5][6][19]<br />

Labor and Government<br />

The American philosopher John Dewey believed that until "industrial feudalism" is<br />

replaced by "industrial democracy", politics will be "the shadow cast on society by big<br />

business". Thomas Ferguson has postulated in his investment theory of party<br />

competition that the undemocratic nature of economic institutions under capitalism<br />

causes elections to become occasions when blocs of investors coalesce and compete<br />

to control the state.<br />

Noam Chomsky has argued that political theory tends to blur the 'elite' function of<br />

government:<br />

Page 102 of 149


Modern political theory stresses Madison's belief that "in a just and a free government<br />

the rights both of property and of persons ought to be effectually guarded." But in this<br />

case too it is useful to look at the doctrine more carefully. There are no rights of<br />

property, only rights to property that is, rights of persons with property,...<br />

[In] representative democracy, as in, say, the United States or Great Britain […] there is<br />

a monopoly of power centralized in the state, and secondly – and critically – […] the<br />

representative democracy is limited to the political sphere and in no serious way<br />

encroaches on the economic sphere […] That is, as long as individuals are compelled to<br />

rent themselves on the market to those who are willing to hire them, as long as their role<br />

in production is simply that of ancillary tools, then there are striking elements of coercion<br />

and oppression that make talk of democracy very limited, if even meaningful.<br />

In this regard, Chomsky has used Bakunin's theories about an "instinct for freedom", the<br />

militant history of labor movements, Kropotkin's mutual aid evolutionary principle of<br />

survival and Marc Hauser's theories supporting an innate and universal moral faculty, to<br />

explain the incompatibility of oppression with certain aspects of human nature.<br />

Page 103 of 149


Influence on Environmental Degradation<br />

Loyola University philosophy professor John Clark and libertarian socialist<br />

philosopher Murray Bookchin have criticized the system of wage labor for encouraging<br />

environmental destruction, arguing that a self-managed industrial society would better<br />

manage the environment. Like other anarchists, they attribute much of the Industrial<br />

Revolution's pollution to the "hierarchical" and "competitive" economic relations<br />

accompanying it.<br />

Employment Contracts<br />

Some criticize wage slavery on strictly contractual grounds, e.g. David<br />

Ellerman and Carole Pateman, arguing that the employment contract is a legal fiction in<br />

that it treats human beings juridically as mere tools or inputs by abdicating responsibility<br />

and self-determination, which the critics argue are inalienable. As Ellerman points out,<br />

"[t]he employee is legally transformed from being a co-responsible partner to being only<br />

an input supplier sharing no legal responsibility for either the input liabilities [costs] or<br />

the produced outputs [revenue, profits] of the employer's business". Such contracts are<br />

inherently invalid "since the person remain[s] a de factofully capacitated adult person<br />

with only the contractual role of a non-person" as it is impossible to physically transfer<br />

self-determination. As Pateman argues:<br />

The contractarian argument is unassailable all the time it is accepted that abilities can<br />

'acquire' an external relation to an individual, and can be treated as if they were<br />

property. To treat abilities in this manner is also implicitly to accept that the 'exchange'<br />

between employer and worker is like any other exchange of material property . . . The<br />

answer to the question of how property in the person can be contracted out is that no<br />

such procedure is possible. Labour power, capacities or services, cannot be separated<br />

from the person of the worker like pieces of property.<br />

In a modern liberal capitalist society, the employment contract is enforced while the<br />

enslavement contract is not; the former being considered valid because of its<br />

consensual/non-coercive nature and the latter being considered inherently invalid,<br />

consensual or not. The noted economist Paul Samuelson described this discrepancy:<br />

Since slavery was abolished, human earning power is forbidden by law to be<br />

capitalized. A man is not even free to sell himself; he must rent himself at a wage.<br />

Some advocates of right-libertarianism, among them philosopher Robert Nozick,<br />

address this inconsistency in modern societies arguing that a consistently libertarian<br />

society would allow and regard as valid consensual/non-coercive enslavement<br />

contracts, rejecting the notion of inalienable rights:<br />

The comparable question about an individual is whether a free system will allow him to<br />

sell himself into slavery. I believe that it would.<br />

Page 104 of 149


Others like Murray Rothbard allow for the possibility of debt slavery, asserting that a<br />

lifetime labor contract can be broken so long as the slave pays appropriate damages:<br />

[I]f A has agreed to work for life for B in exchange for 10,000 grams of gold, he will have<br />

to return the proportionate amount of property if he terminates the arrangement and<br />

ceases to work.<br />

Schools of Economics<br />

In the philosophy of mainstream, neoclassical economics, wage labor is seen as<br />

the voluntary sale of one's own time and efforts, just like a carpenter would sell a chair,<br />

or a farmer would sell wheat. It is considered neither an antagonistic nor abusive<br />

relationship and carries no particular moral implications.<br />

Austrian economics argues that a person is not "free" unless they can sell their labor<br />

because otherwise that person has no self-ownership and will be owned by a "third<br />

party" of individuals.<br />

Post-Keynesian economics perceives wage slavery as resulting from inequality of<br />

bargaining power between labor and capital, which exists when the economy does not<br />

"allow labor to organize and form a strong countervailing force".<br />

The two main forms of socialist economics perceive wage slavery differently:<br />

1. Libertarian socialism sees it as a lack of workers' self-management in the context<br />

of substituting state and capitalist control with political and economic<br />

decentralization and confederation.<br />

2. State socialists view it as an injustice perpetrated by capitalists and solved<br />

through nationalization and social ownership of the means of production.<br />

Page 105 of 149


Page 106 of 149


VIII. The Employment-to-Population<br />

Ratio<br />

A Much More Accurate Indicator<br />

of The <strong>Unemployment</strong> Rate of A Nation<br />

The Organization for Economic Co-operation and Development defines<br />

the employment rate as the employment-to-population ratio.<br />

This is a statistical ratio that measures the proportion of the country's working<br />

age population (statistics are often given for ages 15 to 64) that is employed. This<br />

includes people that have stopped looking for work. The International Labour<br />

Organization states that a person is considered employed if they have worked at least 1<br />

hour in "gainful" employment in the most recent week.<br />

Background<br />

The employment-population ratio has not always been looked at for labor statistics and<br />

where specific areas are economically, but after the recent recession it has been given<br />

more attention worldwide, especially by economists. The National Bureau Of Economic<br />

Research (NBER) states that the Great Recession ended in June 2009. During 2009<br />

Page 107 of 149


and 2010, however, many areas were still struggling economically, which is the reason<br />

the employment-population ratio is still used by both Americans and people around the<br />

world.<br />

Key Definitions<br />

Key terms that explain the use of the ratio follow:<br />

Employed Persons All those who, (1) do any work at all as paid employees, work in<br />

their own business or profession or on their own farm, or work 15 hours or more as<br />

unpaid workers in a family-operated enterprise; and (2) all those who do not work but<br />

had jobs or businesses from which they were temporarily absent due to illness, bad<br />

weather, vacation, childcare problems, labor dispute, maternity or paternity leave, or<br />

other family or personal obligations—whether or not they were paid by their employers<br />

for the time off and whether or not they were seeking other jobs.<br />

Unemployed Persons All those who, (1) have no employment during the reference<br />

week; (2) are available for work, except for temporary illness; and (3) have made<br />

specific efforts, such as contacting employers, to find employment sometime during the<br />

past 4-week period.<br />

Participant Rate This represents the proportion of the population that is in the labor<br />

force.<br />

Not in the labor force. Included in this group are all persons in the civilian<br />

noninstitutional population who are neither employed nor unemployed. Information is<br />

collected on their desire for and availability to take a job at the time of the CPS<br />

interview, jobsearch activity in the prior year, and reason for not looking for work in past<br />

4-week period.<br />

Multiple jobholders. These are employed persons who, have two or more jobs as a<br />

wage and salary worker, are self-employed and also held a wage and salary job, or<br />

work as an unpaid family worker and also hold a wage and salary job.<br />

Use<br />

The ratio is used to evaluate the ability of the economy to create jobs and therefore is<br />

used in conjunction with the unemployment rate for a general evaluation of the labour<br />

market stance. Having a high ratio means that an important proportion of the population<br />

in working age is employed, which in general will have positive effects on the GDP per<br />

capita. Nevertheless, the ratio does not give an indication of working conditions, number<br />

of hours worked per person, earnings or the size of the black market. Therefore, the<br />

analysis of the labour market must be done in conjunction with other statistics.<br />

This measure comes from dividing the civilian noninstitutionalized population who are<br />

employed by the total noninstitutionalized population and multiplying by 100.<br />

Page 108 of 149


Employment-To-Population Ratio in The World<br />

In general, a high ratio is considered to be above 70 percent of the working-age<br />

population whereas a ratio below 50 percent is considered to be low. The economies<br />

with low ratios are generally situated in the Middle East and North Africa.<br />

Employment-to-population ratios are typically higher for men than for women.<br />

Nevertheless, in the past decades, the ratios tended to fall for men and increase in the<br />

case of women, which made the differences between both to be reduced.<br />

Co<br />

un<br />

try<br />

Aust<br />

ralia<br />

Aust<br />

ria<br />

Belgi<br />

um<br />

Cana<br />

da<br />

1<br />

9<br />

7<br />

0<br />

1<br />

9<br />

8<br />

0<br />

1<br />

9<br />

9<br />

0<br />

Employment-To-Population Ratio In OECD Countries<br />

Persons Aged 15–64 Years (Percentages)<br />

2<br />

0<br />

0<br />

0<br />

2<br />

0<br />

0<br />

1<br />

2<br />

0<br />

0<br />

2<br />

2<br />

0<br />

0<br />

3<br />

2<br />

0<br />

0<br />

4<br />

2<br />

0<br />

0<br />

5<br />

66.7 65.2 68.4 69.3 69.0 69.4 70.0 70.3 71.5 72.2 72.9 73.2 72.0 72.4 72.7 72.4 72.0<br />

2<br />

0<br />

0<br />

6<br />

68.3 68.2 68.8 68.9 67.8 68.6 70.2 71.4 72.1 71.6 71.7 72.1 72.5 72.3<br />

54.4 60.9 59.7 59.9 59.6 60.3 61.1 61.0 62.0 62.4 61.6 62.0 61.9 61.8 61.8<br />

66.1 70.3 70.9 70.8 71.4 72.2 72.5 72.4 72.8 73.5 73.6 71.5 71.5 72.0 72.2 72.5<br />

Chile 53.3 52.7 52.6 53.5 53.6 54.4 55.5 56.3 57.3 56.1 59.3 61.3 61.8 62.3<br />

Czec<br />

h<br />

Rep<br />

ublic<br />

Den<br />

mark<br />

Esto<br />

nia<br />

Finla<br />

nd<br />

Fran<br />

ce<br />

Ger<br />

man<br />

y<br />

Gree<br />

ce<br />

Hun<br />

gary<br />

Icela<br />

65.2 65.3 65.7 64.9 64.2 64.8 65.3 66.1 66.6 65.4 65.0 65.7 66.5 67.7<br />

75.4 76.4 75.9 75.9 75.1 75.7 75.9 77.4 77.0 77.9 75.3 73.3 73.1 72.6 72.5<br />

78.2 61.0 61.4 62.0 62.8 62.9 64.2 67.9 69.2 69.7 63.5 61.0 65.2 67.2 68.5<br />

70.4 70.7 74.7 67.5 68.3 68.3 67.9 67.8 68.5 69.6 70.5 71.3 68.4 68.3 69.2 69.5 68.5<br />

60.8 61.7 62.7 62.9 64.0 63.7 63.7 63.6 64.3 64.8 64.0 63.9 63.9 63.9 64.1<br />

66.9 65.2 64.1 65.6 65.8 65.3 64.6 65.0 65.5 67.2 69.0 70.2 70.4 71.2 72.6 72.8 73.3<br />

54.8 55.9 55.6 57.5 58.7 59.4 60.1 61.0 61.4 61.9 61.2 59.6 55.6 51.3 49.3<br />

56.0 56.2 56.2 57.0 56.8 56.9 57.3 57.3 56.7 55.4 55.4 55.8 57.2 58.4<br />

84.6 84.6 82.8 84.1 82.8 84.4 85.3 85.7 84.2 78.9 78.9 79.0 80.2 81.8<br />

2<br />

0<br />

0<br />

7<br />

2<br />

0<br />

0<br />

8<br />

2<br />

0<br />

0<br />

9<br />

2<br />

0<br />

1<br />

0<br />

2<br />

0<br />

1<br />

1<br />

2<br />

0<br />

1<br />

2<br />

2<br />

0<br />

1<br />

3<br />

Page 109 of 149


Co<br />

un<br />

try<br />

nd<br />

Irela<br />

nd<br />

Israe<br />

l<br />

1<br />

9<br />

7<br />

0<br />

1<br />

9<br />

8<br />

0<br />

1<br />

9<br />

9<br />

0<br />

Employment-To-Population Ratio In OECD Countries<br />

Persons Aged 15–64 Years (Percentages)<br />

2<br />

0<br />

0<br />

0<br />

2<br />

0<br />

0<br />

1<br />

2<br />

0<br />

0<br />

2<br />

2<br />

0<br />

0<br />

3<br />

2<br />

0<br />

0<br />

4<br />

2<br />

0<br />

0<br />

5<br />

2<br />

0<br />

0<br />

6<br />

52.1 65.1 65.7 65.2 65.2 65.9 67.5 68.5 69.2 67.9 62.2 60.0 59.2 58.8 60.2<br />

51.8 56.1 55.7 54.8 55.0 55.7 56.7 57.6 58.9 59.8 59.2 60.2 60.9 66.5 67.1<br />

Italy 52.0 53.9 52.6 53.9 54.9 55.6 56.2 57.4 57.5 58.4 58.7 58.7 58.3 57.7 57.8 57.6 56.4<br />

Japa<br />

n<br />

Kore<br />

a<br />

Luxe<br />

mbo<br />

urg<br />

Mexi<br />

co<br />

Neth<br />

erlan<br />

ds<br />

New<br />

Zeal<br />

and<br />

Nor<br />

way<br />

Pola<br />

nd<br />

Port<br />

ugal<br />

Slov<br />

ak<br />

Rep<br />

ublic<br />

Slov<br />

enia<br />

Spai<br />

n<br />

Swe<br />

den<br />

Swit<br />

zerla<br />

nd<br />

67.9 66.8 68.6 68.9 68.8 68.2 68.4 68.7 69.3 70.0 70.7 70.7 70.0 70.1 70.3 70.6 71.7<br />

59.2 61.2 61.5 62.1 63.3 63.0 63.6 63.7 63.8 63.9 63.8 62.9 63.3 63.9 64.2 64.4<br />

59.2 62.7 63.0 63.6 62.2 62.5 63.6 63.6 64.2 63.4 65.2 65.2 64.6 65.8 65.7<br />

60.1 59.4 59.3 58.8 59.9 59.6 61.0 61.1 61.3 59.4 60.3 59.8 61.3 61.0<br />

54.5 61.8 72.1 72.6 73.0 71.6 71.1 71.5 72.5 74.4 75.9 75.6 74.7 74.9 75.1 74.3<br />

67.5 70.4 71.4 72.2 72.2 73.2 74.3 74.9 75.2 74.7 72.9 72.3 72.6 72.1 73.1<br />

72.2 73.0 77.9 77.5 77.1 75.8 75.6 75.2 75.5 76.9 78.1 76.5 75.4 75.3 75.8 75.5<br />

55.0 53.5 51.7 51.4 51.9 53.0 54.5 57.0 59.2 59.3 58.9 59.3 59.7 60.0<br />

64.3 65.6 68.3 68.9 68.7 68.0 67.8 67.5 67.9 67.8 68.2 66.3 65.6 64.2 61.8 60.6<br />

56.8 56.9 56.9 57.7 57.0 57.7 59.4 60.7 62.3 60.2 58.8 59.5 59.7 59.9<br />

2<br />

0<br />

0<br />

7<br />

63.4 62.6 65.3 66.0 66.6 67.8 68.6 67.5 66.2 64.4 64.1 63.3<br />

52.7 51.8 57.4 58.8 59.5 60.7 62.0 64.3 65.7 66.6 65.3 60.6 59.4 58.5 56.2 55.6<br />

72.3 79.8 83.1 74.3 75.4 75.2 74.4 73.7 74.0 74.6 74.2 74.3 72.2 72.1 73.6 73.8 74.4<br />

78.4 79.2 78.9 77.9 77.4 77.2 77.9 78.6 79.5 79.0 78.6 79.3 79.4 79.6<br />

2<br />

0<br />

0<br />

8<br />

2<br />

0<br />

0<br />

9<br />

2<br />

0<br />

1<br />

0<br />

2<br />

0<br />

1<br />

1<br />

2<br />

0<br />

1<br />

2<br />

2<br />

0<br />

1<br />

3<br />

Page 110 of 149


Co<br />

un<br />

try<br />

Turk<br />

ey<br />

Unit<br />

ed<br />

King<br />

dom<br />

Unit<br />

ed<br />

State<br />

s<br />

Brazi<br />

l<br />

Chin<br />

a<br />

Colo<br />

mbia<br />

1<br />

9<br />

7<br />

0<br />

1<br />

9<br />

8<br />

0<br />

1<br />

9<br />

9<br />

0<br />

Employment-To-Population Ratio In OECD Countries<br />

Persons Aged 15–64 Years (Percentages)<br />

2<br />

0<br />

0<br />

0<br />

2<br />

0<br />

0<br />

1<br />

2<br />

0<br />

0<br />

2<br />

2<br />

0<br />

0<br />

3<br />

2<br />

0<br />

0<br />

4<br />

2<br />

0<br />

0<br />

5<br />

2<br />

0<br />

0<br />

6<br />

54.5 48.9 47.8 46.7 45.5 44.1 44.4 44.6 44.6 44.9 44.3 46.3 48.4 48.9 49.5<br />

72.5 72.2 72.5 72.3 72.6 72.7 72.7 72.6 72.4 72.7 70.6 70.3 70.4 70.9 71.3<br />

57.4 59.2 62.8 74.1 73.1 71.9 71.2 71.2 71.5 72.0 71.8 70.9 67.6 66.7 66.6 67.1 67.4<br />

2<br />

0<br />

0<br />

7<br />

64.3 65.4 65.0 66.5 67.0 67.4 67.4 68.3 67.7 66.9 67.2 66.7<br />

79.3 75.1<br />

57.5 57.0 58.5 57.5 58.1 56.7 56.9 58.9 60.2 61.4 62.5 62.7<br />

India 54.8 53.3<br />

Russ<br />

ian<br />

Fede<br />

ratio<br />

n<br />

Sout<br />

h<br />

Afric<br />

a<br />

Latvi<br />

a<br />

EU-<br />

21<br />

EU-<br />

15<br />

Euro<br />

pe<br />

G7<br />

Cou<br />

ntrie<br />

s<br />

OEC<br />

D<br />

Cou<br />

ntrie<br />

63.3 63.2 64.5 64.1 65.5 66.3 66.8 68.5 68.6 66.9 67.3 68.0 69.0 68.8<br />

44.1 42.8 41.5 41.6 43.4 44.9 44.4 44.8 42.7 40.8 40.8 41.0 42.7<br />

2<br />

0<br />

0<br />

8<br />

2<br />

0<br />

0<br />

9<br />

63.3 66.3 68.1 68.2 60.3 58.5 60.8 63.0 65.0<br />

61.0 59.5 61.7 62.6 63.0 63.0 63.2 63.5 64.1 64.9 65.9 66.4 65.2 64.8 65.0 64.8 64.8<br />

61.0 59.5 61.6 63.6 64.3 64.4 64.6 65.0 65.5 66.3 67.0 67.4 66.1 65.8 65.9 65.6 65.4<br />

61.0 59.7 60.9 61.3 61.5 61.3 61.2 61.4 61.9 62.7 63.4 63.9 62.8 62.7 63.1 63.0 63.0<br />

63.9 65.2 67.7 69.0 68.9 68.3 68.2 68.4 68.7 69.3 69.7 69.5 67.7 67.3 67.5 67.9 68.2<br />

64.2 64.2 65.7 65.4 65.2 64.9 64.7 65.0 65.3 66.0 66.5 66.5 64.8 64.6 64.8 65.1 65.3<br />

2<br />

0<br />

1<br />

0<br />

2<br />

0<br />

1<br />

1<br />

2<br />

0<br />

1<br />

2<br />

2<br />

0<br />

1<br />

3<br />

Page 111 of 149


Co<br />

un<br />

try<br />

s<br />

Cou<br />

ntry<br />

1<br />

9<br />

7<br />

0<br />

1<br />

9<br />

8<br />

0<br />

1<br />

9<br />

9<br />

0<br />

Employment-To-Population Ratio In OECD Countries<br />

Persons Aged 15–64 Years (Percentages)<br />

2<br />

0<br />

0<br />

0<br />

2<br />

0<br />

0<br />

1<br />

2<br />

0<br />

0<br />

2<br />

2<br />

0<br />

0<br />

3<br />

2<br />

0<br />

0<br />

4<br />

2<br />

0<br />

0<br />

5<br />

1970 1980 1990 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br />

2<br />

0<br />

0<br />

6<br />

2<br />

0<br />

0<br />

7<br />

2<br />

0<br />

0<br />

8<br />

2<br />

0<br />

0<br />

9<br />

2<br />

0<br />

1<br />

0<br />

2<br />

0<br />

1<br />

1<br />

2<br />

0<br />

1<br />

2<br />

2<br />

0<br />

1<br />

3<br />

Source: OECD.StatExtracts, except as noted.<br />

Page 112 of 149


IX. List of Countries by<br />

Employment Rate<br />

This is a list of countries by employment rate, this being the proportion of employed<br />

adults in the working age. The definition of "working age" varies: Many sources,<br />

including the OECD, use 15–64 years old, [1] but the Office for National Statistics of the<br />

United Kingdom uses 16–64 years old [2] and EUROSTAT uses 20–64 years old. [3]<br />

List<br />

Rank Country Employment<br />

rate<br />

(%)<br />

Age<br />

range<br />

Date of<br />

information<br />

Source<br />

1 Iceland 86.3 15–64 2016 OECD [1]<br />

2 Switzerland 79.6 15–64 2016 OECD [1]<br />

3 Sweden 76.2 15–64 2016 OECD [1]<br />

4<br />

United<br />

Kingdom<br />

75.7 16–64 2018<br />

Office for National<br />

Statistics [2]<br />

5 New Zealand 75.6 15–64 2016 OECD [1]<br />

6 China 75.1 15–64 2010 OECD [1]<br />

7 Denmark 74.9 15–64 2016 OECD [1]<br />

8 Netherlands 74.8 15–64 2016 OECD [1]<br />

9 Germany 74.7 15–64 2016 OECD [1]<br />

10 Norway 74.4 15–64 2016 OECD [1]<br />

11 Japan 74.3 15–64 2016 OECD [1]<br />

12 Canada 72.6 15–64 2016 OECD [1]<br />

13 Australia 72.4 15–64 2016 OECD [1]<br />

14 Estonia 72.0 15–64 2016 OECD [1]<br />

14 Czech Republic 72.0 15–64 2016 OECD [1]<br />

16 Austria 71.5 15–64 2016 OECD [1]<br />

17 Russia 70.0 15–64 2016 OECD [1]<br />

18 Lithuania 69.4 15–64 2016 OECD [1]<br />

18 United States 69.4 13–64 2016 OECD [1]<br />

20 Finland 69.2 15–64 2016 OECD [1]<br />

21 Latvia 68.7 15–64 2016 OECD [1]<br />

22 Israel 68.6 15–64 2016 OECD [1]<br />

Page 113 of 149


Rank Country Employment<br />

rate<br />

(%)<br />

Age<br />

range<br />

Date of<br />

information<br />

Source<br />

23 Colombia 67.2 15–64 2016 OECD [1]<br />

24 OECD Average 67.0 15–64 2016 OECD [1]<br />

25 Hungary 66.5 15–64 2016 OECD [1]<br />

26 South Korea 66.1 15–64 2016 OECD [1]<br />

27 Slovenia 65.8 15–64 2016 OECD [1]<br />

28 Luxembourg 65.6 15–64 2016 OECD [1]<br />

29 Portugal 65.2 15–64 2016 OECD [1]<br />

30 Slovakia 64.9 15–64 2016 OECD [1]<br />

31 Ireland 64.7 15–64 2016 OECD [1]<br />

32 France 64.6 15–64 2016 OECD [1]<br />

33 Poland 64.5 15–64 2016 OECD [1]<br />

34 Brazil 64.4 15–64 2015 OECD [1]<br />

35 Belgium 62.3 15–64 2016 OECD [1]<br />

36 Chile 62.2 15–64 2016 OECD [1]<br />

37 Mexico 61.0 15–64 2016 OECD [1]<br />

38 Spain 60.5 15–64 2016 OECD [1]<br />

39 Costa Rica 58.7 15–64 2016 OECD [1]<br />

40 Italy 57.2 15–64 2016 OECD [1]<br />

41 Albania 56.2 15–64 2016 (Q4) INSTAT [4]<br />

42 India 53.3 15–64 2012 OECD [1]<br />

43 Greece 52.0 15–64 2016 OECD [1]<br />

44 Turkey 50.6 15–64 2016 OECD [1]<br />

45<br />

Bosnia<br />

Herzegovina<br />

and<br />

43.0 15–64 2017<br />

Agency of Statistics of<br />

Bosnia and<br />

Herzegovina [5]<br />

45 South Africa 43.0 15–64 2010 OECD [1]<br />

47 Kosovo 40.2 15–64 2016 (Q3)<br />

Kosovo Agency of<br />

Statistics<br />

Page 114 of 149


X. References<br />

1. https://en.wikipedia.org/wiki/<strong>Unemployment</strong><br />

2.. https://en.wikipedia.org/wiki/Effective_unemployment_rate<br />

3. https://www.lombardiletter.com/u-s-unemployment-forecast-2017/7810/<br />

4. https://en.wikipedia.org/wiki/<strong>Unemployment</strong>#United_States_Bureau_of_Labor_statistics<br />

5. http://daily-work.org/the-real-unemployment-numbers-and-why-im-not-counted/<br />

6. https://en.wikipedia.org/wiki/Involuntary_unemployment<br />

7. https://en.wikipedia.org/wiki/Underemployment<br />

8. https://en.wikipedia.org/wiki/Discouraged_worker<br />

9. https://en.wikipedia.org/wiki/Working_poor<br />

10. https://en.wikipedia.org/wiki/Wage_slavery<br />

11. https://en.wikipedia.org/wiki/Employment-to-population_ratio<br />

12. https://en.wikipedia.org/wiki/List_of_countries_by_employment_rate<br />

13. https://www.bls.gov/news.release/pdf/empsit.pdf<br />

14. https://www.bls.gov/news.release/pdf/youth.pdf<br />

15. https://www.urban.org/sites/default/files/publication/23921/412887-Consequences-of-<br />

Long-Term-<strong>Unemployment</strong>.PDF<br />

Page 115 of 149


Notes<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

Page 116 of 149


Notes<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

_____________________________________________________________________________________<br />

Page 117 of 149


Page 118 of 149


Attachment A<br />

The <strong>Unemployment</strong> Situation<br />

in The U.S.<br />

Page 119 of 149


Transmission of material in this news release is embargoed until<br />

8:30 a.m. (EDT) Friday, November 2, 2018<br />

USDL-18-1739<br />

Technical information:<br />

Household data:<br />

Establishment data:<br />

Media contact:<br />

(202) 691-6378 • cpsinfo@bls.gov • www.bls.gov/cps<br />

(202) 691-6555 • cesinfo@bls.gov • www.bls.gov/ces<br />

(202) 691-5902 • PressOffice@bls.gov<br />

THE EMPLOYMENT SITUATION — OCTOBER 2018<br />

Total nonfarm payroll employment rose by 250,000 in October, and the unemployment rate was<br />

unchanged at 3.7 percent, the U.S. Bureau of Labor Statistics reported today. Job gains occurred in<br />

health care, in manufacturing, in construction, and in transportation and warehousing.<br />

Chart 1. <strong>Unemployment</strong> rate, seasonally adjusted,<br />

October 2016 – October 2018<br />

Chart 2. Nonfarm payroll employment over-the-month change,<br />

seasonally adjusted, October 2016 – October 2018<br />

Percent<br />

Thousands<br />

6.0<br />

400<br />

5.5<br />

350<br />

300<br />

5.0<br />

250<br />

4.5<br />

200<br />

150<br />

4.0<br />

100<br />

3.5<br />

50<br />

0<br />

3.0<br />

Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18<br />

-50<br />

Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18<br />

Hurricane Michael<br />

Hurricane Michael made landfall in the Florida Panhandle on October 10, 2018, during the reference<br />

periods for both the establishment and household surveys. Hurricane Michael had no discernible effect<br />

on the national employment and unemployment estimates for October, and response rates for the two<br />

surveys were within normal ranges. For information on how severe weather can affect employment and<br />

hours data, see Question 8 in the Frequently Asked Questions section of this news release.<br />

BLS will release the state estimates of employment and unemployment on November 16, 2018, at 10:00<br />

a.m. (EST).


Household Survey Data<br />

The unemployment rate remained at 3.7 percent in October, and the number of unemployed persons<br />

was little changed at 6.1 million. Over the year, the unemployment rate and the number of unemployed<br />

persons declined by 0.4 percentage point and 449,000, respectively. (See table A-1.)<br />

Among the major worker groups, the unemployment rates for adult men (3.5 percent), adult women<br />

(3.4 percent), teenagers (11.9 percent), Whites (3.3 percent), Blacks (6.2 percent), Asians (3.2 percent),<br />

and Hispanics (4.4 percent) showed little or no change in October. (See tables A-1, A-2, and A-3.)<br />

The number of long-term unemployed (those jobless for 27 weeks or more) was essentially unchanged<br />

at 1.4 million in October and accounted for 22.5 percent of the unemployed. (See table A-12.)<br />

The labor force participation rate increased by 0.2 percentage point to 62.9 percent in October but has<br />

shown little change over the year. The employment-population ratio edged up by 0.2 percentage point<br />

to 60.6 percent in October and has increased by 0.4 percentage point over the year. (See table A-1.)<br />

The number of persons employed part time for economic reasons (sometimes referred to as<br />

involuntary part-time workers) was essentially unchanged at 4.6 million in October. These individuals,<br />

who would have preferred full-time employment, were working part time because their hours had been<br />

reduced or they were unable to find full-time jobs. (See table A-8.)<br />

In October, 1.5 million persons were marginally attached to the labor force, little changed from a year<br />

earlier. (Data are not seasonally adjusted.) These individuals were not in the labor force, wanted and<br />

were available for work, and had looked for a job sometime in the prior 12 months. They were not<br />

counted as unemployed because they had not searched for work in the 4 weeks preceding the survey.<br />

(See table A-16.)<br />

Among the marginally attached, there were 506,000 discouraged workers in October, about unchanged<br />

from a year earlier. (Data are not seasonally adjusted.) Discouraged workers are persons not currently<br />

looking for work because they believe no jobs are available for them. The remaining 984,000 persons<br />

marginally attached to the labor force in October had not searched for work for reasons such as school<br />

attendance or family responsibilities. (See table A-16.)<br />

Establishment Survey Data<br />

Total nonfarm payroll employment increased by 250,000 in October, following an average monthly<br />

gain of 211,000 over the prior 12 months. In October, job growth occurred in health care, in<br />

manufacturing, in construction, and in transportation and warehousing. (See table B-1.)<br />

Health care added 36,000 jobs in October. Within the industry, employment growth occurred in<br />

hospitals (+13,000) and in nursing and residential care facilities (+8,000). Employment in ambulatory<br />

health care services continued to trend up (+14,000). Over the past 12 months, health care employment<br />

grew by 323,000.<br />

In October, employment in manufacturing increased by 32,000. Most of the increase occurred in<br />

durable goods manufacturing, with a gain in transportation equipment (+10,000). Manufacturing has<br />

added 296,000 jobs over the year, largely in durable goods industries.<br />

-2-


Construction employment rose by 30,000 in October, with nearly half of the gain occurring among<br />

residential specialty trade contractors (+14,000). Over the year, construction has added 330,000 jobs.<br />

Transportation and warehousing added 25,000 jobs in October. Within the industry, employment<br />

growth occurred in couriers and messengers (+8,000) and in warehousing and storage (+8,000). Over the<br />

year, employment in transportation and warehousing has increased by 184,000.<br />

Employment in leisure and hospitality edged up in October (+42,000). Employment was unchanged in<br />

September, likely reflecting the impact of Hurricane Florence. The average gain for the 2 months<br />

combined (+21,000) was the same as the average monthly gain in the industry for the 12-month period<br />

prior to September.<br />

In October, employment in professional and business services continued to trend up (+35,000). Over<br />

the year, the industry has added 516,000 jobs.<br />

Employment in mining also continued to trend up over the month (+5,000). The industry has added<br />

65,000 jobs over the year, with most of the gain in support activities for mining.<br />

Employment in other major industries—including wholesale trade, retail trade, information,<br />

financial activities, and government—showed little change over the month.<br />

The average workweek for all employees on private nonfarm payrolls increased by 0.1 hour to 34.5<br />

hours in October. In manufacturing, the workweek edged down by 0.1 hour to 40.8 hours, and overtime<br />

was unchanged at 3.5 hours. The average workweek for production and nonsupervisory employees on<br />

private nonfarm payrolls, at 33.7 hours, was unchanged over the month. (See tables B-2 and B-7.)<br />

In October, average hourly earnings for all employees on private nonfarm payrolls rose by 5 cents to<br />

$27.30. Over the year, average hourly earnings have increased by 83 cents, or 3.1 percent. Average<br />

hourly earnings of private-sector production and nonsupervisory employees increased by 7 cents to<br />

$22.89 in October. (See tables B-3 and B-8.)<br />

The change in total nonfarm payroll employment for September was revised down from +134,000 to<br />

+118,000, and the change for August was revised up from +270,000 to +286,000. The downward<br />

revision in September offset the upward revision in August. (Monthly revisions result from additional<br />

reports received from businesses and government agencies since the last published estimates and from<br />

the recalculation of seasonal factors.) After revisions, job gains have averaged 218,000 over the past 3<br />

months.<br />

_____________<br />

The Employment Situation for November is scheduled to be released on Friday, December 7,<br />

2018, at 8:30 a.m. (EST).<br />

-3-


HOUSEHOLD DATA<br />

Summary table A. Household data, seasonally adjusted<br />

[Numbers in thousands]<br />

Category<br />

Oct.<br />

2017<br />

Aug.<br />

2018<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

Change from:<br />

Sept. 2018-<br />

Oct. 2018<br />

Employment status<br />

Civilian noninstitutional population........................................... 255,766 258,066 258,290 258,514 224<br />

Civilian labor force........................................................... 160,371 161,776 161,926 162,637 711<br />

Participation rate.......................................................... 62.7 62.7 62.7 62.9 0.2<br />

Employed................................................................... 153,846 155,542 155,962 156,562 600<br />

Employment-population ratio.......................................... 60.2 60.3 60.4 60.6 0.2<br />

Unemployed............................................................... 6,524 6,234 5,964 6,075 111<br />

<strong>Unemployment</strong> rate.................................................... 4.1 3.9 3.7 3.7 0.0<br />

Not in labor force............................................................ 95,395 96,290 96,364 95,877 -487<br />

<strong>Unemployment</strong> rates<br />

Total, 16 years and over...................................................... 4.1 3.9 3.7 3.7 0.0<br />

Adult men (20 years and over)............................................ 3.8 3.5 3.4 3.5 0.1<br />

Adult women (20 years and over)......................................... 3.6 3.6 3.3 3.4 0.1<br />

Teenagers (16 to 19 years)................................................ 13.7 12.8 12.8 11.9 -0.9<br />

White.......................................................................... 3.5 3.4 3.3 3.3 0.0<br />

Black or African American.................................................. 7.3 6.3 6.0 6.2 0.2<br />

Asian.......................................................................... 3.0 3.0 3.5 3.2 -0.3<br />

Hispanic or Latino ethnicity................................................. 4.8 4.7 4.5 4.4 -0.1<br />

Total, 25 years and over...................................................... 3.3 3.2 3.0 3.1 0.1<br />

Less than a high school diploma.......................................... 6.1 5.7 5.5 6.0 0.5<br />

High school graduates, no college........................................ 4.3 3.9 3.7 4.0 0.3<br />

Some college or associate degree........................................ 3.6 3.5 3.2 3.0 -0.2<br />

Bachelor’s degree and higher.............................................. 2.0 2.1 2.0 2.0 0.0<br />

Reason for unemployment<br />

Job losers and persons who completed temporary jobs.................. 3,214 2,875 2,796 2,850 54<br />

Job leavers...................................................................... 731 862 730 726 -4<br />

Reentrants....................................................................... 2,001 1,846 1,877 1,906 29<br />

New entrants.................................................................... 626 584 586 606 20<br />

Duration of unemployment<br />

Less than 5 weeks............................................................. 2,128 2,208 2,065 2,057 -8<br />

5 to 14 weeks................................................................... 1,943 1,720 1,720 1,821 101<br />

15 to 26 weeks................................................................. 856 923 861 856 -5<br />

27 weeks and over............................................................. 1,645 1,332 1,384 1,373 -11<br />

Employed persons at work part time<br />

Part time for economic reasons.............................................. 4,880 4,379 4,642 4,621 -21<br />

Slack work or business conditions........................................ 2,960 2,551 2,782 2,816 34<br />

Could only find part-time work............................................. 1,615 1,365 1,447 1,436 -11<br />

Part time for noneconomic reasons.......................................... 20,897 21,781 21,464 21,512 48<br />

Persons not in the labor force (not seasonally adjusted)<br />

Marginally attached to the labor force....................................... 1,535 1,443 1,577 1,491 –<br />

Discouraged workers........................................................ 524 434 383 506 –<br />

- Over-the-month changes are not displayed for not seasonally adjusted data.<br />

NOTE: Persons whose ethnicity is identified as Hispanic or Latino may be of any race. Detail for the seasonally adjusted data shown in this table will<br />

not necessarily add to totals because of the independent seasonal adjustment of the various series. Updated population controls are introduced<br />

annually with the release of January data.


ESTABLISHMENT DATA<br />

Summary table B. Establishment data, seasonally adjusted<br />

Category<br />

Oct.<br />

2017<br />

Aug.<br />

2018<br />

Sept.<br />

2018 p Oct.<br />

2018 p<br />

EMPLOYMENT BY SELECTED INDUSTRY<br />

(Over-the-month change, in thousands)<br />

Total nonfarm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 286 118 250<br />

Total private. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 267 121 246<br />

Goods-producing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 49 42 67<br />

Mining and logging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 7 4 5<br />

Construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 31 20 30<br />

Manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 11 18 32<br />

Durable goods 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 11 14 21<br />

Motor vehicles and parts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -1.6 2.7 1.0 6.8<br />

Nondurable goods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 0 4 11<br />

Private service-providing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 218 79 179<br />

Wholesale trade. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 20.6 3.3 9.1<br />

Retail trade. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 9.1 -32.4 2.4<br />

Transportation and warehousing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.7 23.1 20.8 24.8<br />

Utilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.0 0.9 0.1 1.2<br />

Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 -4 -4 7<br />

Financial activities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 9 15 7<br />

Professional and business services 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 54 46 35<br />

Temporary help services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.8 10.8 7.6 3.3<br />

Education and health services 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 67 26 44<br />

Health care and social assistance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.7 52.5 34.9 46.7<br />

Leisure and hospitality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 30 0 42<br />

Other services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 8 4 7<br />

Government. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -6 19 -3 4<br />

(3-month average change, in thousands)<br />

Total nonfarm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 220 190 218<br />

Total private. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 199 175 211<br />

WOMEN AND PRODUCTION AND NONSUPERVISORY EMPLOYEES<br />

AS A PERCENT OF ALL EMPLOYEES 2<br />

Total nonfarm women employees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.5 49.7 49.7 49.7<br />

Total private women employees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.1 48.3 48.3 48.3<br />

Total private production and nonsupervisory employees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.4 82.4 82.4 82.4<br />

HOURS AND EARNINGS<br />

ALL EMPLOYEES<br />

Total private<br />

Average weekly hours. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.4 34.5 34.4 34.5<br />

Average hourly earnings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . $26.47 $27.17 $27.25 $27.30<br />

Average weekly earnings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . $910.57 $937.37 $937.40 $941.85<br />

Index of aggregate weekly hours (2007=100) 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107.8 110.0 109.7 110.3<br />

Over-the-month percent change. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.5 0.3 -0.3 0.5<br />

Index of aggregate weekly payrolls (2007=100) 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136.5 142.8 143.0 143.9<br />

Over-the-month percent change. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.4 0.6 0.1 0.6<br />

DIFFUSION INDEX<br />

(Over 1-month span) 5<br />

Total private (258 industries). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.2 64.5 60.7 65.7<br />

Manufacturing (76 industries). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.8 56.6 65.1 62.5<br />

1 Includes other industries, not shown separately.<br />

2 Data relate to production employees in mining and logging and manufacturing, construction employees in construction, and nonsupervisory employees in the<br />

service-providing industries.<br />

3 The indexes of aggregate weekly hours are calculated by dividing the current month’s estimates of aggregate hours by the corresponding annual average aggregate<br />

hours.<br />

4 The indexes of aggregate weekly payrolls are calculated by dividing the current month’s estimates of aggregate weekly payrolls by the corresponding annual average<br />

aggregate weekly payrolls.<br />

5 Figures are the percent of industries with employment increasing plus one-half of the industries with unchanged employment, where 50 percent indicates an equal<br />

balance between industries with increasing and decreasing employment.<br />

p Preliminary<br />

NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.


Frequently Asked Questions about Employment and <strong>Unemployment</strong> Estimates<br />

1. Why are there two monthly measures of employment?<br />

The household survey and establishment survey both produce sample-based estimates of<br />

employment, and both have strengths and limitations. The establishment survey employment series<br />

has a smaller margin of error on the measurement of month-to-month change than the household<br />

survey because of its much larger sample size. An over-the-month employment change of about<br />

100,000 is statistically significant in the establishment survey, while the threshold for a statistically<br />

significant change in the household survey is about 500,000. However, the household survey has a<br />

more expansive scope than the establishment survey because it includes self-employed workers<br />

whose businesses are unincorporated, unpaid family workers, agricultural workers, and private<br />

household workers, who are excluded by the establishment survey. The household survey also<br />

provides estimates of employment for demographic groups. For more information on the differences<br />

between the two surveys, please visit https://www.bls.gov/web/empsit/ces_cps_trends.htm.<br />

2. Are undocumented immigrants counted in the surveys?<br />

It is likely that both surveys include at least some undocumented immigrants. However, neither the<br />

establishment nor the household survey is designed to identify the legal status of workers. Therefore,<br />

it is not possible to determine how many are counted in either survey. The establishment survey does<br />

not collect data on the legal status of workers. The household survey does include questions which<br />

identify the foreign and native born, but it does not include questions about the legal status of the<br />

foreign born. Data on the foreign and native born are published each month in table A-7 of The<br />

Employment Situation news release.<br />

3. Why does the establishment survey have revisions?<br />

The establishment survey revises published estimates to improve its data series by incorporating<br />

additional information that was not available at the time of the initial publication of the estimates.<br />

The establishment survey revises its initial monthly estimates twice, in the immediately succeeding<br />

2 months, to incorporate additional sample receipts from respondents in the survey and recalculated<br />

seasonal adjustment factors. For more information on the monthly revisions, please visit<br />

https://www.bls.gov/ces/cesrevinfo.htm.<br />

On an annual basis, the establishment survey incorporates a benchmark revision that re-anchors<br />

estimates to nearly complete employment counts available from unemployment insurance tax<br />

records. The benchmark helps to control for sampling and modeling errors in the estimates. For more<br />

information on the annual benchmark revision, please visit<br />

https://www.bls.gov/web/empsit/cesbmart.htm.<br />

4. Does the establishment survey sample include small firms?<br />

Yes; about 40 percent of the establishment survey sample is comprised of business establishments<br />

with fewer than 20 employees. The establishment survey sample is designed to maximize the<br />

reliability of the statewide total nonfarm employment estimate; firms from all states, size classes, and<br />

industries are appropriately sampled to achieve that goal.


5. Does the establishment survey account for employment from new businesses?<br />

Yes; monthly establishment survey estimates include an adjustment to account for the net<br />

employment change generated by business births and deaths. The adjustment comes from an<br />

econometric model that forecasts the monthly net jobs impact of business births and deaths based<br />

on the actual past values of the net impact that can be observed with a lag from the Quarterly Census<br />

of Employment and Wages. The establishment survey uses modeling rather than sampling for this<br />

purpose because the survey is not immediately able to bring new businesses into the sample. There<br />

is an unavoidable lag between the birth of a new firm and its appearance on the sampling frame and<br />

availability for selection. BLS adds new businesses to the survey twice a year.<br />

6. Is the count of unemployed persons limited to just those people receiving unemployment<br />

insurance benefits?<br />

No; the estimate of unemployment is based on a monthly sample survey of households. All persons<br />

who are without jobs and are actively seeking and available to work are included among the<br />

unemployed. (People on temporary layoff are included even if they do not actively seek work.) There<br />

is no requirement or question relating to unemployment insurance benefits in the monthly survey.<br />

7. Does the official unemployment rate exclude people who want a job but are not currently<br />

looking for work?<br />

Yes; however, there are separate estimates of persons outside the labor force who want a job,<br />

including those who are not currently looking because they believe no jobs are available (discouraged<br />

workers). In addition, alternative measures of labor underutilization (some of which include<br />

discouraged workers and other groups not officially counted as unemployed) are published each<br />

month in table A-15 of The Employment Situation news release. For more information about these<br />

alternative measures, please visit https://www.bls.gov/cps/lfcharacteristics.htm#altmeasures.<br />

8. How can unusually severe weather affect employment and hours estimates?<br />

In the establishment survey, the reference period is the pay period that includes the 12th of the<br />

month. Unusually severe weather is more likely to have an impact on average weekly hours than<br />

on employment. Average weekly hours are estimated for paid time during the pay period, including<br />

pay for holidays, sick leave, or other time off. The impact of severe weather on hours estimates<br />

typically, but not always, results in a reduction in average weekly hours. For example, some<br />

employees may be off work for part of the pay period and not receive pay for the time missed,<br />

while some workers, such as those dealing with cleanup or repair, may work extra hours.<br />

Typically, it is not possible to precisely quantify the effect of extreme weather on payroll<br />

employment estimates. In order for severe weather conditions to reduce employment estimates,<br />

employees have to be off work without pay for the entire pay period. Employees<br />

who receive pay for any part of the pay period, even 1 hour, are counted in the payroll<br />

employment figures. For more information on how often employees are paid, please visit<br />

https://www.bls.gov/opub/btn/volume-3/how-frequently-do-private-businesses-pay-workers.htm.<br />

In the household survey, the reference period is generally the calendar week that includes the 12th<br />

of the month. Persons who miss the entire week's work for weather-related events are counted as<br />

employed whether or not they are paid for the time off. The household survey collects data on the<br />

number of persons who had a job but were not at work due to bad weather. It also provides a measure<br />

of the number of persons who usually work full time but had reduced hours due to bad weather.<br />

Current and historical data are available on the household survey's most requested statistics page,<br />

please visit https://data.bls.gov/cgi-bin/surveymost?ln.


Technical Note<br />

This news release presents statistics from two major<br />

surveys, the Current Population Survey (CPS; household<br />

survey) and the Current Employment Statistics survey (CES;<br />

establishment survey). The household survey provides<br />

information on the labor force, employment, and<br />

unemployment that appears in the "A" tables, marked<br />

HOUSEHOLD DATA. It is a sample survey of about 60,000<br />

eligible households conducted by the U.S. Census Bureau for<br />

the U.S. Bureau of Labor Statistics (BLS).<br />

The establishment survey provides information on<br />

employment, hours, and earnings of employees on nonfarm<br />

payrolls; the data appear in the "B" tables, marked<br />

ESTABLISHMENT DATA. BLS collects these data each<br />

month from the payroll records of a sample of<br />

nonagricultural business establishments. Each month the<br />

CES program surveys about 149,000 businesses and<br />

government agencies, representing approximately 651,000<br />

individual worksites, in order to provide detailed industry<br />

data on employment, hours, and earnings of workers on<br />

nonfarm payrolls. The active sample includes approximately<br />

one-third of all nonfarm payroll employees.<br />

For both surveys, the data for a given month relate to a<br />

particular week or pay period. In the household survey, the<br />

reference period is generally the calendar week that contains<br />

the 12th day of the month. In the establishment survey, the<br />

reference period is the pay period including the 12th, which<br />

may or may not correspond directly to the calendar week.<br />

Coverage, definitions, and differences between surveys<br />

Household survey. The sample is selected to reflect<br />

the entire civilian noninstitutional population. Based on<br />

responses to a series of questions on work and job search<br />

activities, each person 16 years and over in a sample<br />

household is classified as employed, unemployed, or not in<br />

the labor force.<br />

People are classified as employed if they did any work<br />

at all as paid employees during the reference week; worked<br />

in their own business, profession, or on their own farm; or<br />

worked without pay at least 15 hours in a family business or<br />

farm. People are also counted as employed if they were<br />

temporarily absent from their jobs because of illness, bad<br />

weather, vacation, labor-management disputes, or personal<br />

reasons.<br />

People are classified as unemployed if they meet all of<br />

the following criteria: they had no employment during the<br />

reference week; they were available for work at that time;<br />

and they made specific efforts to find employment sometime<br />

during the 4-week period ending with the reference week.<br />

Persons laid off from a job and expecting recall need not be<br />

looking for work to be counted as unemployed. The<br />

unemployment data derived from the household survey in no<br />

way depend upon the eligibility for or receipt of<br />

unemployment insurance benefits.<br />

The civilian labor force is the sum of employed and<br />

unemployed persons. Those persons not classified as<br />

employed or unemployed are not in the labor force. The<br />

unemployment rate is the number unemployed as a percent<br />

of the labor force. The labor force participation rate is the<br />

labor force as a percent of the population, and<br />

the employment-population ratio is the employed as a<br />

percent of the population. Additional information<br />

about the household survey can be found at<br />

https://www.bls.gov/cps/documentation.htm.<br />

Establishment survey. The sample establishments are<br />

drawn from private nonfarm businesses such as factories,<br />

offices, and stores, as well as from federal, state, and local<br />

government entities. Employees on nonfarm payrolls are<br />

those who received pay for any part of the reference pay<br />

period, including persons on paid leave. Persons are counted<br />

in each job they hold. Hours and earnings data are produced<br />

for the private sector for all employees and for production<br />

and nonsupervisory employees. Production and<br />

nonsupervisory employees are defined as production and<br />

related employees in manufacturing and mining and logging,<br />

construction workers in construction, and non-supervisory<br />

employees in private service-providing industries.<br />

Industries are classified on the basis of an<br />

establishment’s principal activity in accordance with the<br />

2017 version of the North American Industry Classification<br />

System. Additional information about the establishment<br />

survey can be found at https://www.bls.gov/ces/.<br />

Differences in employment estimates. The numerous<br />

conceptual and methodological differences between the<br />

household and establishment surveys result in important<br />

distinctions in the employment estimates derived from the<br />

surveys. Among these are:<br />

• The household survey includes agricultural<br />

workers, self-employed workers whose businesses<br />

are unincorporated, unpaid family workers, and<br />

private household workers among the employed.<br />

These groups are excluded from the establishment<br />

survey.<br />

• The household survey includes people on unpaid<br />

leave among the employed. The establishment<br />

survey does not.<br />

• The household survey is limited to workers 16 years<br />

of age and older. The establishment survey is not<br />

limited by age.<br />

• The household survey has no duplication of<br />

individuals, because individuals are counted only<br />

once, even if they hold more than one job. In the<br />

establishment survey, employees working at more<br />

than one job and thus appearing on more than one<br />

payroll are counted separately for each appearance.


Seasonal adjustment<br />

Over the course of a year, the size of the nation's labor<br />

force and the levels of employment and unemployment<br />

undergo regularly occurring fluctuations. These events may<br />

result from seasonal changes in weather, major holidays, and<br />

the opening and closing of schools. The effect of such<br />

seasonal variation can be very large.<br />

Because these seasonal events follow a more or less<br />

regular pattern each year, their influence on the level of a<br />

series can be tempered by adjusting for regular seasonal<br />

variation. These adjustments make nonseasonal<br />

developments, such as declines in employment or increases<br />

in the participation of women in the labor force, easier to<br />

spot. For example, in the household survey, the large number<br />

of youth entering the labor force each June is likely to<br />

obscure any other changes that have taken place relative to<br />

May, making it difficult to determine if the level of economic<br />

activity has risen or declined. Similarly, in the establishment<br />

survey, payroll employment in education declines by about<br />

20 percent at the end of the spring term and later rises with<br />

the start of the fall term, obscuring the underlying<br />

employment trends in the industry. Because seasonal<br />

employment changes at the end and beginning of the school<br />

year can be estimated, the statistics can be adjusted to make<br />

underlying employment patterns more discernable. The<br />

seasonally adjusted figures provide a more useful tool with<br />

which to analyze changes in month-to-month economic<br />

activity.<br />

Many seasonally adjusted series are independently<br />

adjusted in both the household and establishment surveys.<br />

However, the adjusted series for many major estimates, such<br />

as total payroll employment, employment in most major<br />

sectors, total employment, and unemployment are computed<br />

by aggregating independently adjusted component series.<br />

For example, total unemployment is derived by summing the<br />

adjusted series for four major age-sex components; this<br />

differs from the unemployment estimate that would be<br />

obtained by directly adjusting the total or by combining the<br />

duration, reasons, or more detailed age categories.<br />

For both the household and establishment surveys, a<br />

concurrent seasonal adjustment methodology is used in<br />

which new seasonal factors are calculated each month using<br />

all relevant data, up to and including the data for the current<br />

month. In the household survey, new seasonal factors are<br />

used to adjust only the current month's data. In the<br />

establishment survey, however, new seasonal factors are<br />

used each month to adjust the three most recent monthly<br />

estimates. The prior 2 months are routinely revised to<br />

incorporate additional sample reports and recalculated<br />

seasonal adjustment factors. In both surveys, 5-year<br />

revisions to historical data are made once a year.<br />

Reliability of the estimates<br />

Statistics based on the household and establishment<br />

surveys are subject to both sampling and nonsampling error.<br />

When a sample, rather than the entire population, is<br />

surveyed, there is a chance that the sample estimates may<br />

differ from the true population values they represent. The<br />

component of this difference that occurs because samples<br />

differ by chance is known as sampling error, and its<br />

variability is measured by the standard error of the estimate.<br />

There is about a 90-percent chance, or level of confidence,<br />

that an estimate based on a sample will differ by no more<br />

than 1.6 standard errors from the true population value<br />

because of sampling error. BLS analyses are generally<br />

conducted at the 90-percent level of confidence.<br />

For example, the confidence interval for the monthly<br />

change in total nonfarm employment from the establishment<br />

survey is on the order of plus or minus 115,000. Suppose the<br />

estimate of nonfarm employment increases by 50,000 from<br />

one month to the next. The 90-percent confidence interval on<br />

the monthly change would range from -65,000 to +165,000<br />

(50,000 +/- 115,000). These figures do not mean that the<br />

sample results are off by these magnitudes, but rather that<br />

there is about a 90-percent chance that the true over-themonth<br />

change lies within this interval. Since this range<br />

includes values of less than zero, we could not say with<br />

confidence that nonfarm employment had, in fact, increased<br />

that month. If, however, the reported nonfarm employment<br />

rise was 250,000, then all of the values within the 90-percent<br />

confidence interval would be greater than zero. In this case,<br />

it is likely (at least a 90-percent chance) that nonfarm<br />

employment had, in fact, risen that month. At an<br />

unemployment rate of around 6.0 percent, the 90-percent<br />

confidence interval for the monthly change in unemployment<br />

as measured by the household survey is about +/- 300,000,<br />

and for the monthly change in the unemployment rate it is<br />

about +/- 0.2 percentage point.<br />

In general, estimates involving many individuals or<br />

establishments have lower standard errors (relative to the<br />

size of the estimate) than estimates which are based on a<br />

small number of observations. The precision of estimates<br />

also is improved when the data are cumulated over time, such<br />

as for quarterly and annual averages.<br />

The household and establishment surveys are also<br />

affected by nonsampling error, which can occur for many<br />

reasons, including the failure to sample a segment of the<br />

population, inability to obtain information for all respondents<br />

in the sample, inability or unwillingness of respondents to<br />

provide correct information on a timely basis, mistakes made<br />

by respondents, and errors made in the collection or<br />

processing of the data.<br />

For example, in the establishment survey, estimates for<br />

the most recent 2 months are based on incomplete returns;<br />

for this reason, these estimates are labeled preliminary in the<br />

tables. It is only after two successive revisions to a monthly<br />

estimate, when nearly all sample reports have been received,<br />

that the estimate is considered final.<br />

Another major source of nonsampling error in the<br />

establishment survey is the inability to capture, on a timely<br />

basis, employment generated by new firms. To correct for<br />

this systematic underestimation of employment growth, an<br />

estimation procedure with two components is used to<br />

account for business births. The first component excludes<br />

employment losses from business deaths from sample-based


estimation in order to offset the missing employment gains<br />

from business births. This is incorporated into the samplebased<br />

estimation procedure by simply not reflecting sample<br />

units going out of business, but imputing to them the same<br />

employment trend as the other firms in the sample. This<br />

procedure accounts for most of the net birth/death<br />

employment.<br />

The second component is an ARIMA time series model<br />

designed to estimate the residual net birth/death employment<br />

not accounted for by the imputation. The historical time<br />

series used to create and test the ARIMA model was derived<br />

from the unemployment insurance universe micro-level<br />

database, and reflects the actual residual net of births and<br />

deaths over the past 5 years.<br />

The sample-based estimates from the establishment<br />

survey are adjusted once a year (on a lagged basis) to<br />

universe counts of payroll employment obtained from<br />

administrative records of the unemployment insurance<br />

program. The difference between the March sample-based<br />

employment estimates and the March universe counts is<br />

known as a benchmark revision, and serves as a rough proxy<br />

for total survey error. The new benchmarks also incorporate<br />

changes in the classification of industries. Over the past<br />

decade, absolute benchmark revisions for total nonfarm<br />

employment have averaged 0.3 percent, with a range from<br />

-0.7 percent to 0.6 percent.<br />

Other information<br />

Information in this release will be made available to<br />

sensory impaired individuals upon request. Voice phone:<br />

(202) 691-5200; Federal Relay Service: (800) 877-8339.


HOUSEHOLD DATA<br />

Table A-1. Employment status of the civilian population by sex and age<br />

[Numbers in thousands]<br />

Employment status, sex, and age<br />

Oct.<br />

2017<br />

Not seasonally adjusted Seasonally adjusted 1<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

June<br />

2018<br />

July<br />

2018<br />

Aug.<br />

2018<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

TOTAL<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 255,766 258,290 258,514 255,766 257,642 257,843 258,066 258,290 258,514<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160,465 161,958 162,723 160,371 162,140 162,245 161,776 161,926 162,637<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.7 62.7 62.9 62.7 62.9 62.9 62.7 62.7 62.9<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154,223 156,191 156,952 153,846 155,576 155,965 155,542 155,962 156,562<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 60.3 60.5 60.7 60.2 60.4 60.5 60.3 60.4 60.6<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,242 5,766 5,771 6,524 6,564 6,280 6,234 5,964 6,075<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9 3.6 3.5 4.1 4.0 3.9 3.9 3.7 3.7<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95,301 96,332 95,792 95,395 95,502 95,598 96,290 96,364 95,877<br />

Persons who currently want a job. . . . . . . . . . . . . . . . . . 4,938 5,070 5,048 5,232 5,258 5,163 5,389 5,237 5,309<br />

Men, 16 years and over<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 123,617 124,928 125,041 123,617 124,604 124,704 124,816 124,928 125,041<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85,236 85,815 86,081 85,247 86,056 85,950 85,854 85,856 86,146<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69.0 68.7 68.8 69.0 69.1 68.9 68.8 68.7 68.9<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81,875 82,814 83,052 81,667 82,522 82,684 82,545 82,645 82,903<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 66.2 66.3 66.4 66.1 66.2 66.3 66.1 66.2 66.3<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,362 3,002 3,029 3,580 3,534 3,266 3,309 3,211 3,243<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9 3.5 3.5 4.2 4.1 3.8 3.9 3.7 3.8<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38,380 39,113 38,960 38,370 38,548 38,754 38,962 39,072 38,895<br />

Men, 20 years and over<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 115,120 116,437 116,546 115,120 116,115 116,220 116,328 116,437 116,546<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82,455 83,115 83,332 82,366 83,115 83,058 83,030 83,063 83,286<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.6 71.4 71.5 71.5 71.6 71.5 71.4 71.3 71.5<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79,530 80,458 80,637 79,248 80,013 80,240 80,134 80,225 80,405<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 69.1 69.1 69.2 68.8 68.9 69.0 68.9 68.9 69.0<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,925 2,658 2,694 3,118 3,102 2,818 2,895 2,837 2,881<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 3.2 3.2 3.8 3.7 3.4 3.5 3.4 3.5<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32,665 33,322 33,215 32,755 33,001 33,162 33,298 33,374 33,260<br />

Women, 16 years and over<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 132,149 133,362 133,474 132,149 133,038 133,139 133,250 133,362 133,474<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75,228 76,142 76,642 75,124 76,084 76,295 75,922 76,070 76,491<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56.9 57.1 57.4 56.8 57.2 57.3 57.0 57.0 57.3<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72,348 73,378 73,900 72,179 73,054 73,281 72,997 73,317 73,659<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 54.7 55.0 55.4 54.6 54.9 55.0 54.8 55.0 55.2<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,880 2,765 2,742 2,945 3,030 3,013 2,925 2,753 2,832<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 3.6 3.6 3.9 4.0 3.9 3.9 3.6 3.7<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56,921 57,220 56,832 57,026 56,954 56,844 57,328 57,292 56,983<br />

Women, 20 years and over<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 123,882 125,091 125,200 123,882 124,771 124,875 124,983 125,091 125,200<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72,443 73,280 73,683 72,187 73,139 73,285 73,154 73,039 73,391<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.5 58.6 58.9 58.3 58.6 58.7 58.5 58.4 58.6<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69,872 70,858 71,270 69,576 70,419 70,598 70,529 70,656 70,909<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 56.4 56.6 56.9 56.2 56.4 56.5 56.4 56.5 56.6<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,571 2,422 2,412 2,611 2,720 2,687 2,625 2,383 2,482<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 3.3 3.3 3.6 3.7 3.7 3.6 3.3 3.4<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51,440 51,811 51,517 51,696 51,633 51,590 51,829 52,052 51,809<br />

Both sexes, 16 to 19 years<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 16,763 16,762 16,768 16,763 16,755 16,748 16,755 16,762 16,768<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,567 5,562 5,708 5,818 5,886 5,902 5,592 5,824 5,960<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.2 33.2 34.0 34.7 35.1 35.2 33.4 34.7 35.5<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,821 4,876 5,045 5,022 5,144 5,127 4,879 5,081 5,248<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 28.8 29.1 30.1 30.0 30.7 30.6 29.1 30.3 31.3<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 746 686 664 796 743 775 714 743 712<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4 12.3 11.6 13.7 12.6 13.1 12.8 12.8 11.9<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11,196 11,199 11,060 10,945 10,869 10,846 11,163 10,938 10,808<br />

1 The population figures are not adjusted for seasonal variation; therefore, identical numbers appear in the unadjusted and seasonally adjusted columns.<br />

NOTE: Updated population controls are introduced annually with the release of January data.


HOUSEHOLD DATA<br />

Table A-2. Employment status of the civilian population by race, sex, and age<br />

[Numbers in thousands]<br />

Employment status, race, sex, and age<br />

Oct.<br />

2017<br />

Not seasonally adjusted Seasonally adjusted 1<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

June<br />

2018<br />

July<br />

2018<br />

Aug.<br />

2018<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

WHITE<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 199,298 200,476 200,596 199,298 200,132 200,236 200,356 200,476 200,596<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124,777 125,413 126,018 124,757 125,784 125,720 125,306 125,483 126,027<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.6 62.6 62.8 62.6 62.9 62.8 62.5 62.6 62.8<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120,692 121,500 122,170 120,400 121,347 121,506 121,027 121,398 121,904<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 60.6 60.6 60.9 60.4 60.6 60.7 60.4 60.6 60.8<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,085 3,913 3,848 4,356 4,437 4,214 4,279 4,085 4,123<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 3.1 3.1 3.5 3.5 3.4 3.4 3.3 3.3<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74,520 75,063 74,578 74,541 74,348 74,517 75,049 74,993 74,569<br />

Men, 20 years and over<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65,318 65,625 65,737 65,244 65,855 65,614 65,505 65,611 65,724<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.7 71.5 71.6 71.6 71.9 71.6 71.4 71.5 71.5<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63,380 63,796 63,933 63,155 63,695 63,690 63,486 63,629 63,760<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 69.6 69.5 69.6 69.3 69.5 69.5 69.2 69.3 69.4<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,937 1,829 1,803 2,090 2,159 1,924 2,019 1,982 1,964<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.0 2.8 2.7 3.2 3.3 2.9 3.1 3.0 3.0<br />

Women, 20 years and over<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55,149 55,404 55,941 55,014 55,420 55,534 55,351 55,297 55,752<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57.5 57.5 58.0 57.4 57.6 57.7 57.5 57.4 57.8<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53,515 53,811 54,325 53,298 53,608 53,746 53,592 53,722 54,067<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 55.8 55.8 56.3 55.6 55.7 55.8 55.6 55.7 56.1<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,635 1,594 1,615 1,715 1,812 1,787 1,759 1,575 1,685<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.0 2.9 2.9 3.1 3.3 3.2 3.2 2.8 3.0<br />

Both sexes, 16 to 19 years<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,310 4,383 4,341 4,499 4,510 4,572 4,450 4,574 4,552<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.9 35.6 35.3 36.5 36.6 37.2 36.2 37.2 37.0<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,798 3,893 3,911 3,947 4,044 4,069 3,949 4,046 4,078<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 30.8 31.6 31.8 32.0 32.8 33.1 32.1 32.9 33.1<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 490 429 552 466 502 501 528 474<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.9 11.2 9.9 12.3 10.3 11.0 11.3 11.5 10.4<br />

BLACK OR AFRICAN AMERICAN<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 32,370 32,848 32,887 32,370 32,737 32,771 32,810 32,848 32,887<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20,264 20,484 20,706 20,134 20,364 20,495 20,404 20,513 20,590<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.6 62.4 63.0 62.2 62.2 62.5 62.2 62.4 62.6<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18,744 19,295 19,397 18,654 19,045 19,144 19,114 19,272 19,310<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 57.9 58.7 59.0 57.6 58.2 58.4 58.3 58.7 58.7<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,520 1,189 1,309 1,479 1,319 1,351 1,289 1,240 1,280<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 5.8 6.3 7.3 6.5 6.6 6.3 6.0 6.2<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12,106 12,365 12,181 12,236 12,373 12,276 12,406 12,336 12,297<br />

Men, 20 years and over<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,276 9,369 9,477 9,218 9,162 9,320 9,407 9,359 9,426<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68.6 68.0 68.7 68.2 66.8 67.9 68.4 68.0 68.4<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8,580 8,842 8,862 8,539 8,573 8,751 8,841 8,813 8,836<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 63.4 64.2 64.3 63.1 62.5 63.7 64.3 64.0 64.1<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696 526 615 679 589 569 566 546 590<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 5.6 6.5 7.4 6.4 6.1 6.0 5.8 6.3<br />

Women, 20 years and over<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,291 10,421 10,415 10,204 10,406 10,426 10,361 10,427 10,337<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.0 62.8 62.7 62.5 63.0 63.0 62.5 62.9 62.3<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,609 9,892 9,892 9,560 9,838 9,793 9,766 9,874 9,834<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 58.8 59.6 59.6 58.5 59.5 59.2 59.0 59.5 59.2<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 682 529 523 644 568 633 595 554 503<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 5.1 5.0 6.3 5.5 6.1 5.7 5.3 4.9<br />

Both sexes, 16 to 19 years<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697 694 815 712 796 750 635 726 827<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.8 27.8 32.7 28.4 31.9 30.0 25.5 29.1 33.2<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 560 643 555 635 600 508 585 640<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 22.1 22.5 25.8 22.1 25.4 24.1 20.3 23.5 25.7<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 134 172 157 162 149 128 140 187<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.4 19.3 21.1 22.0 20.3 19.9 20.1 19.3 22.6<br />

See footnotes at end of table.


HOUSEHOLD DATA<br />

Table A-2. Employment status of the civilian population by race, sex, and age — Continued<br />

[Numbers in thousands]<br />

Employment status, race, sex, and age<br />

Oct.<br />

2017<br />

Not seasonally adjusted Seasonally adjusted 1<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

ASIAN<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 15,466 16,011 16,030 15,466 15,934 15,922 16,093 16,011 16,030<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,794 10,289 10,213 9,864 10,140 10,153 10,259 10,300 10,284<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3 64.3 63.7 63.8 63.6 63.8 63.7 64.3 64.2<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,507 9,933 9,899 9,565 9,817 9,838 9,950 9,938 9,959<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 61.5 62.0 61.8 61.8 61.6 61.8 61.8 62.1 62.1<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 356 314 299 322 314 309 362 324<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 3.5 3.1 3.0 3.2 3.1 3.0 3.5 3.2<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,671 5,722 5,817 5,602 5,794 5,769 5,834 5,712 5,746<br />

Oct.<br />

2017<br />

June<br />

2018<br />

July<br />

2018<br />

Aug.<br />

2018<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

1 The population figures are not adjusted for seasonal variation; therefore, identical numbers appear in the unadjusted and seasonally adjusted columns.<br />

NOTE: Estimates for the above race groups will not sum to totals shown in table A-1 because data are not presented for all races. Updated population controls are<br />

introduced annually with the release of January data.


HOUSEHOLD DATA<br />

Table A-3. Employment status of the Hispanic or Latino population by sex and age<br />

[Numbers in thousands]<br />

Employment status, sex, and age<br />

Not seasonally adjusted Seasonally adjusted 1<br />

Oct.<br />

2017<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

June<br />

2018<br />

July<br />

2018<br />

Aug.<br />

2018<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

HISPANIC OR LATINO ETHNICITY<br />

Civilian noninstitutional population................ 41,665 42,959 43,054 41,665 42,679 42,767 42,863 42,959 43,054<br />

Civilian labor force................................ 27,328 28,316 28,512 27,319 28,369 28,495 28,242 28,346 28,500<br />

Participation rate............................... 65.6 65.9 66.2 65.6 66.5 66.6 65.9 66.0 66.2<br />

Employed....................................... 26,077 27,105 27,343 25,999 27,077 27,223 26,927 27,059 27,252<br />

Employment-population ratio............... 62.6 63.1 63.5 62.4 63.4 63.7 62.8 63.0 63.3<br />

Unemployed.................................... 1,250 1,211 1,169 1,321 1,292 1,273 1,315 1,287 1,248<br />

<strong>Unemployment</strong> rate......................... 4.6 4.3 4.1 4.8 4.6 4.5 4.7 4.5 4.4<br />

Not in labor force................................. 14,337 14,642 14,542 14,346 14,310 14,272 14,621 14,613 14,554<br />

Men, 20 years and over<br />

Civilian labor force................................ 14,987 15,414 15,442 14,959 15,557 15,519 15,421 15,416 15,440<br />

Participation rate............................... 79.9 79.5 79.4 79.7 80.8 80.4 79.7 79.5 79.4<br />

Employed....................................... 14,459 14,888 14,898 14,380 14,961 15,017 14,849 14,822 14,844<br />

Employment-population ratio............... 77.1 76.8 76.6 76.7 77.7 77.8 76.7 76.4 76.4<br />

Unemployed.................................... 528 526 544 579 596 502 572 594 596<br />

<strong>Unemployment</strong> rate......................... 3.5 3.4 3.5 3.9 3.8 3.2 3.7 3.9 3.9<br />

Women, 20 years and over<br />

Civilian labor force................................ 11,158 11,682 11,778 11,146 11,626 11,684 11,593 11,673 11,756<br />

Participation rate............................... 58.6 59.6 59.9 58.5 59.7 59.8 59.2 59.5 59.8<br />

Employed....................................... 10,639 11,175 11,305 10,601 11,065 11,131 11,009 11,172 11,255<br />

Employment-population ratio............... 55.9 57.0 57.5 55.7 56.8 57.0 56.3 57.0 57.3<br />

Unemployed.................................... 519 507 472 546 561 553 584 501 501<br />

<strong>Unemployment</strong> rate......................... 4.6 4.3 4.0 4.9 4.8 4.7 5.0 4.3 4.3<br />

Both sexes, 16 to 19 years<br />

Civilian labor force................................ 1,184 1,220 1,293 1,214 1,186 1,292 1,228 1,257 1,304<br />

Participation rate............................... 30.6 30.9 32.6 31.4 30.2 32.8 31.1 31.8 32.9<br />

Employed....................................... 980 1,042 1,140 1,018 1,051 1,075 1,068 1,065 1,153<br />

Employment-population ratio............... 25.3 26.3 28.8 26.3 26.7 27.3 27.1 26.9 29.1<br />

Unemployed.................................... 204 179 152 196 135 218 160 192 151<br />

<strong>Unemployment</strong> rate......................... 17.2 14.6 11.8 16.1 11.4 16.8 13.0 15.3 11.6<br />

1 The population figures are not adjusted for seasonal variation; therefore, identical numbers appear in the unadjusted and seasonally adjusted<br />

columns.<br />

NOTE: Persons whose ethnicity is identified as Hispanic or Latino may be of any race. Updated population controls are introduced annually with the<br />

release of January data.


HOUSEHOLD DATA<br />

Table A-4. Employment status of the civilian population 25 years and over by educational attainment<br />

[Numbers in thousands]<br />

Educational attainment<br />

Not seasonally adjusted<br />

Oct.<br />

2017<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

June<br />

2018<br />

Seasonally adjusted<br />

July<br />

2018<br />

Aug.<br />

2018<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

Less than a high school diploma<br />

Civilian labor force.................................. 10,141 10,273 10,078 10,328 10,508 10,212 10,311 10,189 10,262<br />

Participation rate.................................. 45.3 46.4 46.9 46.2 45.9 46.9 46.3 46.0 47.7<br />

Employed.......................................... 9,624 9,773 9,567 9,699 9,926 9,695 9,728 9,626 9,651<br />

Employment-population ratio................. 43.0 44.2 44.5 43.4 43.4 44.5 43.7 43.5 44.9<br />

Unemployed....................................... 516 499 510 629 582 517 583 563 611<br />

<strong>Unemployment</strong> rate............................ 5.1 4.9 5.1 6.1 5.5 5.1 5.7 5.5 6.0<br />

High school graduates, no college 1<br />

Civilian labor force.................................. 35,788 36,540 36,254 35,572 36,050 36,534 36,121 36,224 36,092<br />

Participation rate.................................. 57.2 58.0 57.8 56.9 57.9 57.9 57.3 57.5 57.6<br />

Employed.......................................... 34,358 35,268 34,879 34,050 34,549 35,056 34,699 34,873 34,638<br />

Employment-population ratio................. 54.9 56.0 55.6 54.4 55.5 55.5 55.0 55.3 55.3<br />

Unemployed....................................... 1,431 1,272 1,375 1,522 1,501 1,478 1,422 1,351 1,454<br />

<strong>Unemployment</strong> rate............................ 4.0 3.5 3.8 4.3 4.2 4.0 3.9 3.7 4.0<br />

Some college or associate degree<br />

Civilian labor force.................................. 37,987 37,364 37,808 37,761 37,863 37,531 37,300 37,423 37,598<br />

Participation rate.................................. 66.2 65.2 65.7 65.8 65.3 65.4 65.6 65.3 65.3<br />

Employed.......................................... 36,636 36,204 36,706 36,385 36,602 36,340 35,987 36,239 36,462<br />

Employment-population ratio................. 63.8 63.1 63.8 63.4 63.2 63.3 63.3 63.2 63.4<br />

Unemployed....................................... 1,351 1,160 1,102 1,376 1,261 1,191 1,313 1,184 1,136<br />

<strong>Unemployment</strong> rate............................ 3.6 3.1 2.9 3.6 3.3 3.2 3.5 3.2 3.0<br />

Bachelor’s degree and higher 2<br />

Civilian labor force.................................. 55,728 57,279 58,022 55,612 56,613 56,940 57,638 57,258 57,856<br />

Participation rate.................................. 73.9 73.6 73.6 73.8 74.0 73.4 74.0 73.6 73.4<br />

Employed.......................................... 54,604 56,160 56,890 54,477 55,296 55,672 56,452 56,124 56,700<br />

Employment-population ratio................. 72.4 72.2 72.2 72.3 72.2 71.8 72.5 72.1 71.9<br />

Unemployed....................................... 1,124 1,120 1,132 1,135 1,317 1,268 1,186 1,134 1,156<br />

<strong>Unemployment</strong> rate............................ 2.0 2.0 2.0 2.0 2.3 2.2 2.1 2.0 2.0<br />

1 Includes persons with a high school diploma or equivalent.<br />

2 Includes persons with bachelor’s, master’s, professional, and doctoral degrees.<br />

NOTE: Updated population controls are introduced annually with the release of January data.


HOUSEHOLD DATA<br />

Table A-5. Employment status of the civilian population 18 years and over by veteran status, period of service,<br />

and sex, not seasonally adjusted<br />

[Numbers in thousands]<br />

Employment status, veteran status, and period of service<br />

Oct.<br />

2017<br />

Total Men Women<br />

VETERANS, 18 years and over<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20,493 19,090 18,457 17,218 2,036 1,872<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,184 9,369 8,958 8,255 1,227 1,114<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.7 49.1 48.5 47.9 60.2 59.5<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,906 9,100 8,705 8,019 1,201 1,081<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.3 47.7 47.2 46.6 59.0 57.7<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 269 253 236 26 33<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 2.9 2.8 2.9 2.1 3.0<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,309 9,721 9,499 8,963 809 758<br />

Gulf War-era II veterans<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,161 4,141 3,421 3,405 740 736<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,316 3,384 2,816 2,861 500 523<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79.7 81.7 82.3 84.0 67.5 71.1<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,196 3,279 2,713 2,770 483 509<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76.8 79.2 79.3 81.3 65.3 69.2<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 105 104 91 16 14<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 3.1 3.7 3.2 3.3 2.7<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 845 757 605 545 241 213<br />

Gulf War-era I veterans<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,365 3,147 2,863 2,677 502 470<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,618 2,450 2,253 2,109 365 341<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77.8 77.9 78.7 78.8 72.7 72.6<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,570 2,383 2,214 2,047 356 336<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76.4 75.7 77.3 76.5 70.9 71.4<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 67 40 61 9 5<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9 2.7 1.8 2.9 2.5 1.6<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 746 697 610 568 137 129<br />

World War II, Korean War, and Vietnam-era veterans<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,964 7,502 7,676 7,246 288 256<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,779 1,545 1,708 1,491 71 54<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.3 20.6 22.3 20.6 24.6 21.2<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,730 1,498 1,659 1,447 71 52<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.7 20.0 21.6 20.0 24.6 20.2<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 47 50 44 0 3<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 3.0 2.9 3.0 – –<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,185 5,957 5,968 5,755 217 202<br />

Veterans of other service periods<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,003 4,300 4,497 3,890 506 410<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,471 1,990 2,179 1,795 291 195<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.4 46.3 48.5 46.1 57.5 47.6<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,411 1,940 2,120 1,755 291 184<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.2 45.1 47.2 45.1 57.5 45.0<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 50 59 39 0 11<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 2.5 2.7 2.2 0.1 5.6<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,532 2,310 2,318 2,095 215 215<br />

NONVETERANS, 18 years and over<br />

Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225,807 230,542 100,389 103,266 125,418 127,276<br />

Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148,074 151,214 75,253 76,864 72,821 74,351<br />

Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65.6 65.6 75.0 74.4 58.1 58.4<br />

Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142,414 145,912 72,312 74,170 70,101 71,743<br />

Employment-population ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.1 63.3 72.0 71.8 55.9 56.4<br />

Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,660 5,302 2,941 2,694 2,719 2,608<br />

<strong>Unemployment</strong> rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 3.5 3.9 3.5 3.7 3.5<br />

Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77,734 79,328 25,136 26,402 52,598 52,925<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

Oct.<br />

2018<br />

NOTE: Veterans served on active duty in the U.S. Armed Forces and were not on active duty at the time of the survey. Nonveterans never served on active duty in the<br />

U.S. Armed Forces. Veterans could have served anywhere in the world during these periods of service: Gulf War era II (September 2001-present), Gulf War era I (August<br />

1990-August 2001), Vietnam era (August 1964-April 1975), Korean War (July 1950-January 1955), World War II (December 1941-December 1946), and other service<br />

periods (all other time periods). Veterans who served in more than one wartime period are classified only in the most recent one. Veterans who served during one of the<br />

selected wartime periods and another period are classified only in the wartime period. Dash indicates no data or data that do not meet publication criteria (values not<br />

shown where base is less than 75,000).


HOUSEHOLD DATA<br />

Table A-6. Employment status of the civilian population by sex, age, and disability status, not seasonally<br />

adjusted<br />

[Numbers in thousands]<br />

Employment status, sex, and age<br />

Persons with a disability<br />

Oct.<br />

2017<br />

Oct.<br />

2018<br />

Persons with no disability<br />

TOTAL, 16 years and over<br />

Civilian noninstitutional population...................................................... 30,255 30,145 225,511 228,370<br />

Civilian labor force..................................................................... 6,360 6,468 154,105 156,255<br />

Participation rate..................................................................... 21.0 21.5 68.3 68.4<br />

Employed............................................................................. 5,877 5,987 148,346 150,965<br />

Employment-population ratio.................................................... 19.4 19.9 65.8 66.1<br />

Unemployed.......................................................................... 482 480 5,760 5,290<br />

<strong>Unemployment</strong> rate............................................................... 7.6 7.4 3.7 3.4<br />

Not in labor force....................................................................... 23,895 23,677 71,406 72,114<br />

Men, 16 to 64 years<br />

Civilian labor force..................................................................... 2,805 2,740 77,085 77,736<br />

Participation rate..................................................................... 36.5 36.4 82.4 82.4<br />

Employed............................................................................. 2,588 2,520 74,130 75,067<br />

Employment-population ratio.................................................... 33.7 33.5 79.2 79.6<br />

Unemployed.......................................................................... 217 221 2,955 2,669<br />

<strong>Unemployment</strong> rate............................................................... 7.8 8.1 3.8 3.4<br />

Not in labor force....................................................................... 4,872 4,790 16,478 16,560<br />

Women, 16 to 64 years<br />

Civilian labor force..................................................................... 2,347 2,511 68,608 69,508<br />

Participation rate..................................................................... 30.2 32.0 71.0 71.6<br />

Employed............................................................................. 2,133 2,304 66,080 67,136<br />

Employment-population ratio.................................................... 27.4 29.4 68.3 69.2<br />

Unemployed.......................................................................... 214 207 2,528 2,372<br />

<strong>Unemployment</strong> rate............................................................... 9.1 8.2 3.7 3.4<br />

Not in labor force....................................................................... 5,433 5,329 28,086 27,560<br />

Both sexes, 65 years and over<br />

Civilian labor force..................................................................... 1,208 1,216 8,412 9,011<br />

Participation rate..................................................................... 8.2 8.2 23.9 24.4<br />

Employed............................................................................. 1,156 1,163 8,136 8,762<br />

Employment-population ratio.................................................... 7.8 7.9 23.1 23.7<br />

Unemployed.......................................................................... 51 53 276 249<br />

<strong>Unemployment</strong> rate............................................................... 4.2 4.3 3.3 2.8<br />

Not in labor force....................................................................... 13,590 13,559 26,841 27,994<br />

Oct.<br />

2017<br />

Oct.<br />

2018<br />

NOTE: A person with a disability has at least one of the following conditions: is deaf or has serious difficulty hearing; is blind or has serious difficulty<br />

seeing even when wearing glasses; has serious difficulty concentrating, remembering, or making decisions because of a physical, mental, or<br />

emotional condition; has serious difficulty walking or climbing stairs; has difficulty dressing or bathing; or has difficulty doing errands alone such as<br />

visiting a doctor’s office or shopping because of a physical, mental, or emotional condition. Updated population controls are introduced annually with<br />

the release of January data.


HOUSEHOLD DATA<br />

Table A-7. Employment status of the civilian population by nativity and sex, not seasonally adjusted<br />

[Numbers in thousands]<br />

Employment status and nativity<br />

Oct.<br />

2017<br />

Total Men Women<br />

Foreign born, 16 years and over<br />

Civilian noninstitutional population................................... 41,668 43,051 20,191 20,795 21,476 22,256<br />

Civilian labor force.................................................. 27,374 28,410 15,774 16,198 11,600 12,212<br />

Participation rate.................................................. 65.7 66.0 78.1 77.9 54.0 54.9<br />

Employed.......................................................... 26,343 27,533 15,296 15,763 11,046 11,770<br />

Employment-population ratio................................. 63.2 64.0 75.8 75.8 51.4 52.9<br />

Unemployed....................................................... 1,031 877 477 436 554 441<br />

<strong>Unemployment</strong> rate............................................ 3.8 3.1 3.0 2.7 4.8 3.6<br />

Not in labor force.................................................... 14,294 14,641 4,417 4,597 9,877 10,044<br />

Native born, 16 years and over<br />

Civilian noninstitutional population................................... 214,099 215,463 103,426 104,245 110,673 111,218<br />

Civilian labor force.................................................. 133,091 134,313 69,463 69,882 63,629 64,431<br />

Participation rate.................................................. 62.2 62.3 67.2 67.0 57.5 57.9<br />

Employed.......................................................... 127,880 129,419 66,578 67,290 61,302 62,130<br />

Employment-population ratio................................. 59.7 60.1 64.4 64.5 55.4 55.9<br />

Unemployed....................................................... 5,211 4,894 2,884 2,593 2,327 2,301<br />

<strong>Unemployment</strong> rate............................................ 3.9 3.6 4.2 3.7 3.7 3.6<br />

Not in labor force.................................................... 81,007 81,150 33,963 34,363 47,044 46,787<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

Oct.<br />

2018<br />

NOTE: The foreign born are those residing in the United States who were not U.S. citizens at birth. That is, they were born outside the United States<br />

or one of its outlying areas such as Puerto Rico or Guam, to parents neither of whom was a U.S. citizen. The native born are persons who were born<br />

in the United States or one of its outlying areas such as Puerto Rico or Guam or who were born abroad of at least one parent who was a U.S. citizen.<br />

Updated population controls are introduced annually with the release of January data.


HOUSEHOLD DATA<br />

Table A-8. Employed persons by class of worker and part-time status<br />

[In thousands]<br />

Category<br />

Not seasonally adjusted<br />

Oct.<br />

2017<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

June<br />

2018<br />

Seasonally adjusted<br />

July<br />

2018<br />

Aug.<br />

2018<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

CLASS OF WORKER<br />

Agriculture and related industries................. 2,559 2,555 2,484 2,471 2,350 2,498 2,345 2,474 2,406<br />

Wage and salary workers 1 ...................... 1,814 1,726 1,771 1,707 1,567 1,658 1,528 1,640 1,658<br />

Self-employed workers, unincorporated. . . . . .. 721 806 692 749 736 783 772 812 731<br />

Unpaid family workers........................... 24 23 21 – – – – – –<br />

Nonagricultural industries.......................... 151,664 153,636 154,468 151,334 153,309 153,473 153,262 153,474 154,152<br />

Wage and salary workers 1 ...................... 142,564 144,570 145,325 142,294 144,524 144,447 144,276 144,389 145,109<br />

Government..................................... 20,753 20,674 21,187 20,755 20,986 20,900 20,791 20,743 21,212<br />

Private industries............................... 121,811 123,896 124,139 121,578 123,478 123,541 123,513 123,634 123,968<br />

Private households.......................... 571 741 769 – – – – – –<br />

Other industries.............................. 121,240 123,154 123,370 121,012 122,757 122,772 122,749 122,842 123,167<br />

Self-employed workers, unincorporated. . . . . .. 9,067 8,970 9,055 8,956 8,728 8,880 8,861 8,959 8,943<br />

Unpaid family workers........................... 33 96 88 – – – – – –<br />

PERSONS AT WORK PART TIME 2<br />

All industries<br />

Part time for economic reasons 3 .................. 4,553 4,306 4,246 4,880 4,743 4,567 4,379 4,642 4,621<br />

Slack work or business conditions............. 2,762 2,606 2,600 2,960 3,042 2,877 2,551 2,782 2,816<br />

Could only find part-time work.................. 1,609 1,464 1,433 1,615 1,447 1,431 1,365 1,447 1,436<br />

Part time for noneconomic reasons 4 .............. 21,395 21,475 21,979 20,897 21,304 21,532 21,781 21,464 21,512<br />

Nonagricultural industries<br />

Part time for economic reasons 3 .................. 4,496 4,238 4,169 4,799 4,662 4,482 4,311 4,547 4,523<br />

Slack work or business conditions............. 2,734 2,572 2,541 2,944 3,004 2,836 2,522 2,752 2,763<br />

Could only find part-time work.................. 1,594 1,459 1,428 1,600 1,431 1,415 1,355 1,441 1,431<br />

Part time for noneconomic reasons 4 .............. 21,034 21,077 21,616 20,552 20,941 21,177 21,448 21,057 21,143<br />

1 Includes self-employed workers whose businesses are incorporated.<br />

2 Refers to those who worked 1 to 34 hours during the survey reference week and excludes employed persons who were absent from their jobs for<br />

the entire week.<br />

3 Refers to those who worked 1 to 34 hours during the reference week for an economic reason such as slack work or unfavorable business<br />

conditions, inability to find full-time work, or seasonal declines in demand.<br />

4 Refers to persons who usually work part time for noneconomic reasons such as childcare problems, family or personal obligations, school or<br />

training, retirement or Social Security limits on earnings, and other reasons. This excludes persons who usually work full time but worked only 1 to<br />

34 hours during the reference week for reasons such as vacations, holidays, illness, and bad weather.<br />

- Data not available.<br />

NOTE: Detail for the seasonally adjusted data shown in this table will not necessarily add to totals because of the independent seasonal adjustment<br />

of the various series. Updated population controls are introduced annually with the release of January data.


HOUSEHOLD DATA<br />

Table A-9. Selected employment indicators<br />

[Numbers in thousands]<br />

Characteristic<br />

Oct.<br />

2017<br />

Not seasonally adjusted<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

June<br />

2018<br />

Seasonally adjusted<br />

July<br />

2018<br />

Aug.<br />

2018<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

AGE AND SEX<br />

Total, 16 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154,223 156,191 156,952 153,846 155,576 155,965 155,542 155,962 156,562<br />

16 to 19 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,821 4,876 5,045 5,022 5,144 5,127 4,879 5,081 5,248<br />

16 to 17 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,903 1,826 1,940 1,932 1,797 1,815 1,770 1,766 1,956<br />

18 to 19 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,918 3,050 3,105 3,070 3,344 3,315 3,110 3,293 3,293<br />

20 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149,402 151,315 151,908 148,824 150,432 150,838 150,663 150,881 151,314<br />

20 to 24 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14,180 13,910 13,865 14,183 14,046 14,128 13,841 14,026 13,902<br />

25 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135,222 137,405 138,042 134,716 136,422 136,762 136,749 136,856 137,506<br />

25 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99,584 100,763 101,289 99,227 100,204 100,417 100,276 100,316 100,903<br />

25 to 34 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34,755 35,658 35,802 34,666 35,288 35,444 35,316 35,500 35,699<br />

35 to 44 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32,254 32,661 33,017 32,094 32,566 32,690 32,636 32,489 32,853<br />

45 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32,574 32,444 32,469 32,468 32,350 32,283 32,324 32,327 32,352<br />

55 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35,638 36,642 36,753 35,489 36,218 36,346 36,473 36,540 36,602<br />

Men, 16 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81,875 82,814 83,052 81,667 82,522 82,684 82,545 82,645 82,903<br />

16 to 19 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,345 2,356 2,415 2,419 2,509 2,444 2,410 2,420 2,498<br />

16 to 17 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 857 799 863 884 855 814 812 782 893<br />

18 to 19 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,488 1,557 1,552 1,528 1,644 1,629 1,598 1,637 1,614<br />

20 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79,530 80,458 80,637 79,248 80,013 80,240 80,134 80,225 80,405<br />

20 to 24 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,248 7,107 7,031 7,280 7,191 7,149 6,990 7,162 7,087<br />

25 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72,282 73,351 73,606 72,016 72,869 73,126 73,106 73,020 73,341<br />

25 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53,242 53,808 53,957 53,082 53,588 53,673 53,599 53,569 53,796<br />

25 to 34 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18,655 19,199 19,196 18,617 19,006 19,054 19,007 19,121 19,171<br />

35 to 44 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17,414 17,594 17,704 17,320 17,598 17,691 17,642 17,509 17,619<br />

45 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17,173 17,015 17,056 17,145 16,984 16,928 16,950 16,939 17,005<br />

55 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19,040 19,542 19,650 18,934 19,281 19,453 19,507 19,451 19,546<br />

Women, 16 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72,348 73,378 73,900 72,179 73,054 73,281 72,997 73,317 73,659<br />

16 to 19 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,477 2,520 2,630 2,603 2,635 2,683 2,468 2,661 2,751<br />

16 to 17 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,046 1,027 1,077 1,048 942 1,001 958 983 1,063<br />

18 to 19 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,430 1,493 1,553 1,541 1,700 1,687 1,512 1,656 1,679<br />

20 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69,872 70,858 71,270 69,576 70,419 70,598 70,529 70,656 70,909<br />

20 to 24 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,932 6,803 6,835 6,903 6,855 6,979 6,851 6,864 6,815<br />

25 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62,940 64,055 64,436 62,700 63,553 63,636 63,643 63,836 64,165<br />

25 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46,342 46,954 47,332 46,146 46,616 46,744 46,677 46,747 47,108<br />

25 to 34 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16,100 16,459 16,606 16,049 16,282 16,390 16,309 16,379 16,527<br />

35 to 44 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14,840 15,067 15,313 14,774 14,969 14,998 14,994 14,979 15,234<br />

45 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15,401 15,428 15,413 15,323 15,366 15,355 15,374 15,388 15,347<br />

55 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16,598 17,100 17,104 16,555 16,937 16,892 16,966 17,089 17,057<br />

MARITAL STATUS<br />

Married men, spouse present 1 . . . . . . . . . . . . . . . . . . . . . . . . . . 45,886 46,205 46,354 45,776 45,689 45,751 45,858 45,966 46,228<br />

Married women, spouse present 1 . . . . . . . . . . . . . . . . . . . . . . . 36,072 36,090 36,278 35,853 35,976 35,986 36,070 35,984 36,040<br />

Women who maintain families 2 . . . . . . . . . . . . . . . . . . . . . . . . . 9,829 9,887 10,060 – – – – – –<br />

FULL- OR PART-TIME STATUS<br />

Full-time workers 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127,055 129,466 129,627 126,636 128,568 129,021 128,577 128,894 129,212<br />

Part-time workers 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27,168 26,726 27,325 27,142 27,028 26,992 26,913 27,055 27,297<br />

MULTIPLE JOBHOLDERS<br />

Total multiple jobholders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,409 7,670 8,093 7,209 7,619 8,072 7,944 7,707 7,883<br />

Percent of total employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 4.9 5.2 4.7 4.9 5.2 5.1 4.9 5.0<br />

SELF-EMPLOYMENT<br />

Self-employed workers, incorporated. . . . . . . . . . . . . . . . . . . 6,171 5,840 6,057 – – – – – –<br />

Self-employed workers, unincorporated. . . . . . . . . . . . . . . . . 9,789 9,776 9,747 9,705 9,464 9,663 9,633 9,771 9,674<br />

1 Refers to persons in opposite-sex married couples only.<br />

2 Refers to female householders residing with one or more family members, but not an opposite-sex spouse.<br />

3 Employed full-time workers are persons who usually work 35 hours or more per week.<br />

4 Employed part-time workers are persons who usually work less than 35 hours per week.<br />

- Data not available.<br />

NOTE: Detail for the seasonally adjusted data shown in this table will not necessarily add to totals because of the independent seasonal adjustment of the various series.<br />

Updated population controls are introduced annually with the release of January data.


HOUSEHOLD DATA<br />

Table A-10. Selected unemployment indicators, seasonally adjusted<br />

Characteristic<br />

Oct.<br />

2017<br />

Number of<br />

unemployed persons<br />

(in thousands)<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

June<br />

2018<br />

<strong>Unemployment</strong> rates<br />

July<br />

2018<br />

Aug.<br />

2018<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

AGE AND SEX<br />

Total, 16 years and over........................... 6,524 5,964 6,075 4.1 4.0 3.9 3.9 3.7 3.7<br />

16 to 19 years..................................... 796 743 712 13.7 12.6 13.1 12.8 12.8 11.9<br />

16 to 17 years.................................. 340 282 235 15.0 14.4 13.6 13.4 13.8 10.7<br />

18 to 19 years.................................. 467 446 492 13.2 11.4 12.5 12.2 11.9 13.0<br />

20 years and over................................ 5,728 5,221 5,363 3.7 3.7 3.5 3.5 3.3 3.4<br />

20 to 24 years.................................. 1,095 1,036 1,010 7.2 7.4 6.9 6.8 6.9 6.8<br />

25 years and over.............................. 4,620 4,210 4,335 3.3 3.3 3.2 3.2 3.0 3.1<br />

25 to 54 years............................... 3,501 3,149 3,269 3.4 3.3 3.2 3.2 3.0 3.1<br />

25 to 34 years............................. 1,593 1,299 1,350 4.4 3.9 3.5 4.1 3.5 3.6<br />

35 to 44 years............................. 1,003 969 1,010 3.0 3.1 3.1 2.9 2.9 3.0<br />

45 to 54 years............................. 905 882 908 2.7 3.0 2.9 2.6 2.7 2.7<br />

55 years and over........................... 1,134 1,056 1,073 3.1 3.1 3.1 3.1 2.8 2.8<br />

Men, 16 years and over............................ 3,580 3,211 3,243 4.2 4.1 3.8 3.9 3.7 3.8<br />

16 to 19 years..................................... 462 373 362 16.0 14.7 15.5 14.7 13.4 12.7<br />

16 to 17 years.................................. 186 134 114 17.4 16.5 16.1 15.6 14.6 11.3<br />

18 to 19 years.................................. 284 241 251 15.7 13.2 14.5 14.1 12.8 13.5<br />

20 years and over................................ 3,118 2,837 2,881 3.8 3.7 3.4 3.5 3.4 3.5<br />

20 to 24 years.................................. 635 575 565 8.0 8.2 7.4 7.5 7.4 7.4<br />

25 years and over.............................. 2,462 2,281 2,294 3.3 3.2 3.0 3.1 3.0 3.0<br />

25 to 54 years............................... 1,825 1,707 1,761 3.3 3.2 3.0 3.1 3.1 3.2<br />

25 to 34 years............................. 844 704 747 4.3 3.8 3.5 4.1 3.6 3.7<br />

35 to 44 years............................. 499 539 554 2.8 2.9 2.6 2.6 3.0 3.0<br />

45 to 54 years............................. 482 464 460 2.7 3.0 2.8 2.4 2.7 2.6<br />

55 years and over........................... 638 574 533 3.3 3.2 3.1 3.0 2.9 2.7<br />

Women, 16 years and over........................ 2,945 2,753 2,832 3.9 4.0 3.9 3.9 3.6 3.7<br />

16 to 19 years..................................... 334 370 350 11.4 10.5 10.9 10.8 12.2 11.3<br />

16 to 17 years.................................. 154 148 121 12.8 12.4 11.4 11.5 13.1 10.2<br />

18 to 19 years.................................. 182 205 241 10.6 9.5 10.5 10.0 11.0 12.6<br />

20 years and over................................ 2,611 2,383 2,482 3.6 3.7 3.7 3.6 3.3 3.4<br />

20 to 24 years.................................. 460 461 445 6.3 6.6 6.3 6.1 6.3 6.1<br />

25 years and over.............................. 2,157 1,930 2,041 3.3 3.4 3.4 3.3 2.9 3.1<br />

25 to 54 years............................... 1,676 1,442 1,508 3.5 3.4 3.4 3.4 3.0 3.1<br />

25 to 34 years............................. 749 595 604 4.5 4.0 3.6 4.1 3.5 3.5<br />

35 to 44 years............................. 504 430 456 3.3 3.3 3.6 3.2 2.8 2.9<br />

45 to 54 years............................. 423 418 448 2.7 2.9 3.0 2.7 2.6 2.8<br />

55 years and over........................... 494 500 532 2.9 3.0 3.0 3.1 2.8 3.0<br />

MARITAL STATUS<br />

Married men, spouse present 1 .................... 954 898 897 2.0 2.1 2.0 2.0 1.9 1.9<br />

Married women, spouse present 1 ................. 889 780 836 2.4 2.5 2.5 2.5 2.1 2.3<br />

Women who maintain families 2 .................... 578 529 565 5.6 5.5 5.6 5.4 5.1 5.3<br />

FULL- OR PART-TIME STATUS<br />

Full-time workers 3 ................................... 5,246 4,662 4,838 4.0 4.0 3.8 3.7 3.5 3.6<br />

Part-time workers 4 .................................. 1,275 1,267 1,241 4.5 4.3 4.4 4.3 4.5 4.3<br />

1 Refers to persons in opposite-sex couples only.<br />

2 Data are not seasonally adjusted. Refers to female householders residing with one or more family members, but not an opposite-sex spouse.<br />

3 Full-time workers are unemployed persons who have expressed a desire to work full time (35 hours or more per week) or are on layoff from full-time<br />

jobs.<br />

4 Part-time workers are unemployed persons who have expressed a desire to work part time (less than 35 hours per week) or are on layoff from<br />

part-time jobs.<br />

NOTE: Detail for the seasonally adjusted data shown in this table will not necessarily add to totals because of the independent seasonal adjustment<br />

of the various series. Updated population controls are introduced annually with the release of January data.


HOUSEHOLD DATA<br />

Table A-11. Unemployed persons by reason for unemployment<br />

[Numbers in thousands]<br />

Reason<br />

Not seasonally adjusted<br />

Oct.<br />

2017<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

June<br />

2018<br />

Seasonally adjusted<br />

July<br />

2018<br />

Aug.<br />

2018<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

NUMBER OF UNEMPLOYED<br />

Job losers and persons who completed<br />

temporary jobs.................................... 2,859 2,474 2,510 3,214 3,065 3,017 2,875 2,796 2,850<br />

On temporary layoff.............................. 561 507 507 862 906 890 872 820 793<br />

Not on temporary layoff.......................... 2,298 1,967 2,003 2,352 2,159 2,127 2,003 1,975 2,057<br />

Permanent job losers.......................... 1,648 1,238 1,317 1,688 1,486 1,455 1,345 1,248 1,355<br />

Persons who completed temporary jobs. . .. 650 729 686 664 673 672 658 727 701<br />

Job leavers........................................... 763 794 746 731 811 844 862 730 726<br />

Reentrants............................................ 2,040 1,939 1,951 2,001 2,086 1,799 1,846 1,877 1,906<br />

New entrants......................................... 580 559 564 626 578 591 584 586 606<br />

PERCENT DISTRIBUTION<br />

Job losers and persons who completed<br />

temporary jobs.................................... 45.8 42.9 43.5 48.9 46.9 48.3 46.6 46.7 46.8<br />

On temporary layoff.............................. 9.0 8.8 8.8 13.1 13.9 14.2 14.1 13.7 13.0<br />

Not on temporary layoff.......................... 36.8 34.1 34.7 35.8 33.0 34.0 32.5 33.0 33.8<br />

Job leavers........................................... 12.2 13.8 12.9 11.1 12.4 13.5 14.0 12.2 11.9<br />

Reentrants............................................ 32.7 33.6 33.8 30.5 31.9 28.8 29.9 31.3 31.3<br />

New entrants......................................... 9.3 9.7 9.8 9.5 8.8 9.5 9.5 9.8 10.0<br />

UNEMPLOYED AS A PERCENT OF THE<br />

CIVILIAN LABOR FORCE<br />

Job losers and persons who completed<br />

temporary jobs.................................... 1.8 1.5 1.5 2.0 1.9 1.9 1.8 1.7 1.8<br />

Job leavers........................................... 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4<br />

Reentrants............................................ 1.3 1.2 1.2 1.2 1.3 1.1 1.1 1.2 1.2<br />

New entrants......................................... 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4<br />

NOTE: Updated population controls are introduced annually with the release of January data.


HOUSEHOLD DATA<br />

Table A-12. Unemployed persons by duration of unemployment<br />

[Numbers in thousands]<br />

Duration<br />

Not seasonally adjusted<br />

Oct.<br />

2017<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

June<br />

2018<br />

Seasonally adjusted<br />

July<br />

2018<br />

Aug.<br />

2018<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

NUMBER OF UNEMPLOYED<br />

Less than 5 weeks.................................. 1,958 2,043 1,866 2,128 2,227 2,091 2,208 2,065 2,057<br />

5 to 14 weeks........................................ 1,839 1,566 1,734 1,943 1,882 1,820 1,720 1,720 1,821<br />

15 weeks and over.................................. 2,445 2,157 2,171 2,500 2,314 2,406 2,255 2,245 2,229<br />

15 to 26 weeks.................................... 847 790 824 856 836 971 923 861 856<br />

27 weeks and over............................... 1,598 1,366 1,347 1,645 1,478 1,435 1,332 1,384 1,373<br />

Average (mean) duration, in weeks............... 27.1 24.7 23.8 25.8 21.2 23.2 22.6 24.0 22.5<br />

Median duration, in weeks......................... 10.1 9.4 9.9 9.8 8.9 9.5 9.1 9.2 9.4<br />

PERCENT DISTRIBUTION<br />

Less than 5 weeks.................................. 31.4 35.4 32.3 32.4 34.7 33.1 35.7 34.3 33.7<br />

5 to 14 weeks........................................ 29.5 27.2 30.0 29.6 29.3 28.8 27.8 28.5 29.8<br />

15 weeks and over.................................. 39.2 37.4 37.6 38.0 36.0 38.1 36.5 37.2 36.5<br />

15 to 26 weeks.................................... 13.6 13.7 14.3 13.0 13.0 15.4 14.9 14.3 14.0<br />

27 weeks and over............................... 25.6 23.7 23.3 25.0 23.0 22.7 21.5 22.9 22.5<br />

NOTE: Updated population controls are introduced annually with the release of January data.


HOUSEHOLD DATA<br />

Table A-13. Employed and unemployed persons by occupation, not seasonally adjusted<br />

[Numbers in thousands]<br />

Occupation<br />

Oct.<br />

2017<br />

Employed<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

Unemployed<br />

Oct.<br />

2018<br />

<strong>Unemployment</strong><br />

rates<br />

Oct.<br />

2017<br />

Oct.<br />

2018<br />

Total, 16 years and over 1 ............................................. 154,223 156,952 6,242 5,771 3.9 3.5<br />

Management, professional, and related occupations........... 61,062 62,929 1,285 1,246 2.1 1.9<br />

Management, business, and financial operations<br />

occupations..................................................... 25,620 26,165 498 535 1.9 2.0<br />

Professional and related occupations.......................... 35,442 36,764 787 711 2.2 1.9<br />

Service occupations................................................ 27,075 26,689 1,444 1,247 5.1 4.5<br />

Sales and office occupations...................................... 33,633 33,730 1,363 1,233 3.9 3.5<br />

Sales and related occupations.................................. 15,795 15,927 656 599 4.0 3.6<br />

Office and administrative support occupations............... 17,838 17,803 707 634 3.8 3.4<br />

Natural resources, construction, and maintenance<br />

occupations........................................................ 14,324 14,588 724 546 4.8 3.6<br />

Farming, fishing, and forestry occupations.................... 1,222 1,181 67 63 5.2 5.0<br />

Construction and extraction occupations...................... 8,183 8,558 495 400 5.7 4.5<br />

Installation, maintenance, and repair occupations........... 4,920 4,849 163 83 3.2 1.7<br />

Production, transportation, and material moving<br />

occupations........................................................ 18,129 19,015 833 920 4.4 4.6<br />

Production occupations.......................................... 8,575 8,904 311 414 3.5 4.4<br />

Transportation and material moving occupations............ 9,554 10,112 522 506 5.2 4.8<br />

1 Persons with no previous work experience and persons whose last job was in the U.S. Armed Forces are included in the unemployed total.<br />

NOTE: Updated population controls are introduced annually with the release of January data.


HOUSEHOLD DATA<br />

Table A-14. Unemployed persons by industry and class of worker, not seasonally adjusted<br />

Industry and class of worker<br />

Oct.<br />

2017<br />

Number of<br />

unemployed<br />

persons<br />

(in thousands)<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

<strong>Unemployment</strong><br />

rates<br />

Total, 16 years and over 1 ................................................................ 6,242 5,771 3.9 3.5<br />

Nonagricultural private wage and salary workers.................................. 4,810 4,458 3.8 3.5<br />

Mining, quarrying, and oil and gas extraction.................................... 39 14 4.8 1.7<br />

Construction.......................................................................... 418 352 4.5 3.6<br />

Manufacturing........................................................................ 501 504 3.2 3.2<br />

Durable goods..................................................................... 314 260 3.2 2.5<br />

Nondurable goods................................................................ 187 245 3.2 4.3<br />

Wholesale and retail trade......................................................... 849 794 4.2 3.9<br />

Transportation and utilities......................................................... 284 217 4.1 3.1<br />

Information............................................................................ 105 84 3.9 3.2<br />

Financial activities................................................................... 214 205 2.2 2.0<br />

Professional and business services............................................... 715 691 4.1 4.0<br />

Education and health services..................................................... 677 649 2.9 2.7<br />

Leisure and hospitality.............................................................. 796 747 5.8 5.4<br />

Other services........................................................................ 210 200 3.1 3.0<br />

Agriculture and related private wage and salary workers......................... 79 74 4.3 4.1<br />

Government workers................................................................... 462 411 2.2 1.9<br />

Self-employed workers, unincorporated, and unpaid family workers............ 311 265 3.1 2.6<br />

Oct.<br />

2018<br />

1 Persons with no previous work experience and persons whose last job was in the U.S. Armed Forces are included in the unemployed total.<br />

NOTE: Updated population controls are introduced annually with the release of January data.


HOUSEHOLD DATA<br />

Table A-15. Alternative measures of labor underutilization<br />

[Percent]<br />

Measure<br />

Not seasonally adjusted<br />

Oct.<br />

2017<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

June<br />

2018<br />

Seasonally adjusted<br />

July<br />

2018<br />

Aug.<br />

2018<br />

Sept.<br />

2018<br />

Oct.<br />

2018<br />

U-1 Persons unemployed 15 weeks or longer,<br />

as a percent of the civilian labor force......... 1.5 1.3 1.3 1.6 1.4 1.5 1.4 1.4 1.4<br />

U-2 Job losers and persons who completed<br />

temporary jobs, as a percent of the civilian<br />

labor force......................................... 1.8 1.5 1.5 2.0 1.9 1.9 1.8 1.7 1.8<br />

U-3 Total unemployed, as a percent of the<br />

civilian labor force (official unemployment<br />

rate)................................................ 3.9 3.6 3.5 4.1 4.0 3.9 3.9 3.7 3.7<br />

U-4 Total unemployed plus discouraged<br />

workers, as a percent of the civilian labor<br />

force plus discouraged workers................. 4.2 3.8 3.8 4.4 4.3 4.2 4.1 3.9 4.0<br />

U-5 Total unemployed, plus discouraged<br />

workers, plus all other persons marginally<br />

attached to the labor force, as a percent of<br />

the civilian labor force plus all persons<br />

marginally attached to the labor force. . . . . . . .. 4.8 4.5 4.4 5.0 4.9 4.8 4.7 4.6 4.6<br />

U-6 Total unemployed, plus all persons<br />

marginally attached to the labor force, plus<br />

total employed part time for economic<br />

reasons, as a percent of the civilian labor<br />

force plus all persons marginally attached to<br />

the labor force..................................... 7.6 7.1 7.0 8.0 7.8 7.5 7.4 7.5 7.4<br />

NOTE: Persons marginally attached to the labor force are those who currently are neither working nor looking for work but indicate that they want and<br />

are available for a job and have looked for work sometime in the past 12 months. Discouraged workers, a subset of the marginally attached, have<br />

given a job-market related reason for not currently looking for work. Persons employed part time for economic reasons are those who want and are<br />

available for full-time work but have had to settle for a part-time schedule. Updated population controls are introduced annually with the release of<br />

January data.


HOUSEHOLD DATA<br />

Table A-16. Persons not in the labor force and multiple jobholders by sex, not seasonally adjusted<br />

[Numbers in thousands]<br />

Category<br />

Oct.<br />

2017<br />

Total Men Women<br />

NOT IN THE LABOR FORCE<br />

Total not in the labor force........................................... 95,301 95,792 38,380 38,960 56,921 56,832<br />

Persons who currently want a job................................ 4,938 5,048 2,287 2,298 2,650 2,750<br />

Marginally attached to the labor force 1 ........................ 1,535 1,491 837 823 698 668<br />

Discouraged workers 2 ......................................... 524 506 335 314 190 192<br />

Other persons marginally attached to the labor force 3 . .. 1,010 984 503 509 508 476<br />

MULTIPLE JOBHOLDERS<br />

Total multiple jobholders 4 ............................................. 7,409 8,093 3,681 3,958 3,727 4,135<br />

Percent of total employed.......................................... 4.8 5.2 4.5 4.8 5.2 5.6<br />

Primary job full time, secondary job part time................... 4,167 4,484 2,256 2,490 1,912 1,994<br />

Primary and secondary jobs both part time..................... 1,770 2,180 587 723 1,183 1,457<br />

Primary and secondary jobs both full time....................... 295 284 226 171 70 114<br />

Hours vary on primary or secondary job......................... 1,124 1,073 598 539 526 534<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

Oct.<br />

2018<br />

Oct.<br />

2017<br />

Oct.<br />

2018<br />

1 Data refer to persons who want a job, have searched for work during the prior 12 months, and were available to take a job during the reference<br />

week, but had not looked for work in the past 4 weeks.<br />

2 Includes those who did not actively look for work in the prior 4 weeks for reasons such as thinks no work available, could not find work, lacks<br />

schooling or training, employer thinks too young or old, and other types of discrimination.<br />

3 Includes those who did not actively look for work in the prior 4 weeks for such reasons as school or family responsibilities, ill health, and<br />

transportation problems, as well as a number for whom reason for nonparticipation was not determined.<br />

4 Includes a small number of persons who work part time on their primary job and full time on their secondary job(s), not shown separately.<br />

NOTE: Updated population controls are introduced annually with the release of January data.


ESTABLISHMENT DATA<br />

Table B-1. Employees on nonfarm payrolls by industry sector and selected industry detail<br />

[In thousands]<br />

Industry<br />

Oct.<br />

2017<br />

Not seasonally adjusted<br />

Aug.<br />

2018<br />

Sept. Oct. Oct.<br />

2018 p 2018 p 2017<br />

Aug.<br />

2018<br />

Seasonally adjusted<br />

Change<br />

Sept. Oct.<br />

from:<br />

2018 p 2018 p Sept.2018<br />

-<br />

Oct.2018 p<br />

Total nonfarm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148,203 149,406 149,738 150,753 147,234 149,382 149,500 149,750 250<br />

Total private. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125,516 127,986 127,368 127,982 124,903 126,986 127,107 127,353 246<br />

Goods-producing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20,391 21,115 21,024 21,064 20,168 20,750 20,792 20,859 67<br />

Mining and logging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 700 763 759 764 691 747 751 756 5<br />

Logging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.6 49.5 48.7 47.9 48.2 47.9 47.3 47.0 -0.3<br />

Mining. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 650.5 713.3 710.0 716.5 643.2 698.8 703.5 708.6 5.1<br />

Oil and gas extraction. . . . . . . . . . . . . . . . . . . . . . . 146.3 155.5 152.6 153.4 145.8 152.8 152.4 153.2 0.8<br />

Mining, except oil and gas. . . . . . . . . . . . . . . . . . . 187.9 195.3 193.2 194.3 185.9 190.5 190.3 191.3 1.0<br />

Coal mining. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.0 53.2 52.9 53.1 52.7 53.0 52.8 52.6 -0.2<br />

Metal ore mining. . . . . . . . . . . . . . . . . . . . . . . . . . 38.5 39.6 39.0 39.0 38.7 39.1 38.9 39.1 0.2<br />

Nonmetallic mineral mining and<br />

quarrying. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96.4 102.5 101.3 102.2 94.5 98.4 98.6 99.6 1.0<br />

Support activities for mining. . . . . . . . . . . . . . . . . 316.3 362.5 364.2 368.8 311.5 355.5 360.8 364.1 3.3<br />

Construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,182 7,529 7,474 7,500 6,988 7,268 7,288 7,318 30<br />

Construction of buildings. . . . . . . . . . . . . . . . . . . . . . . 1,576.4 1,656.5 1,639.5 1,644.2 1,543.5 1,615.8 1,615.6 1,617.3 1.7<br />

Residential building. . . . . . . . . . . . . . . . . . . . . . . . . . 769.2 821.3 811.9 815.9 751.2 801.3 799.8 802.6 2.8<br />

Nonresidential building. . . . . . . . . . . . . . . . . . . . . . 807.2 835.2 827.6 828.3 792.3 814.5 815.8 814.7 -1.1<br />

Heavy and civil engineering construction. . . . . . 1,046.1 1,092.6 1,093.3 1,099.6 984.1 1,023.2 1,029.0 1,036.1 7.1<br />

Specialty trade contractors. . . . . . . . . . . . . . . . . . . . 4,559.4 4,779.4 4,741.4 4,756.6 4,460.0 4,628.8 4,643.6 4,664.9 21.3<br />

Residential specialty trade contractors. . . . . . 1,997.7 2,103.5 2,077.0 2,085.7 1,961.2 2,033.0 2,039.5 2,053.3 13.8<br />

Nonresidential specialty trade contractors. . . 2,561.7 2,675.9 2,664.4 2,670.9 2,498.8 2,595.8 2,604.1 2,611.6 7.5<br />

Manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12,509 12,823 12,791 12,800 12,489 12,735 12,753 12,785 32<br />

Durable goods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,770 8,001 7,982 8,005 7,765 7,970 7,984 8,005 21<br />

Wood products. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398.4 409.1 407.6 409.0 398.1 406.3 407.3 407.8 0.5<br />

Nonmetallic mineral products. . . . . . . . . . . . . . . . 416.9 426.2 425.6 428.8 410.9 419.1 422.5 424.3 1.8<br />

Primary metals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.3 382.2 381.7 381.7 374.4 382.1 382.2 381.9 -0.3<br />

Fabricated metal products. . . . . . . . . . . . . . . . . . . 1,443.7 1,500.3 1,495.2 1,494.8 1,443.0 1,494.4 1,495.5 1,495.4 -0.1<br />

Machinery. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,082.5 1,132.7 1,127.6 1,131.7 1,083.8 1,129.1 1,130.7 1,135.5 4.8<br />

Computer and electronic products. . . . . . . . . . . 1,047.9 1,073.2 1,068.3 1,071.4 1,047.9 1,068.5 1,069.7 1,071.9 2.2<br />

Computer and peripheral equipment. . . . . . 163.2 170.7 170.3 171.1 162.5 169.3 170.4 171.0 0.6<br />

Communications equipment. . . . . . . . . . . . . . . 86.8 85.1 85.1 85.5 87.0 85.1 85.2 85.6 0.4<br />

Semiconductors and electronic<br />

components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.4 371.7 369.1 368.7 363.7 370.0 369.7 369.1 -0.6<br />

Electronic instruments. . . . . . . . . . . . . . . . . . . . . 400.7 413.1 410.9 412.4 401.0 411.5 411.7 412.9 1.2<br />

Miscellaneous computer and electronic<br />

products. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.8 32.6 32.9 33.7 33.6 32.5 32.8 33.3 0.5<br />

Electrical equipment and appliances. . . . . . . . 392.3 408.3 408.9 407.8 392.8 406.7 408.3 408.1 -0.2<br />

Transportation equipment 1 . . . . . . . . . . . . . . . . . . 1,627.9 1,674.6 1,678.5 1,684.6 1,630.0 1,673.0 1,676.3 1,686.5 10.2<br />

Motor vehicles and parts 2 . . . . . . . . . . . . . . . . . 950.1 961.3 963.4 968.5 950.5 961.7 962.7 969.5 6.8<br />

Furniture and related products. . . . . . . . . . . . . . 393.4 393.2 390.4 391.0 393.3 389.9 391.0 390.7 -0.3<br />

Miscellaneous durable goods<br />

manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592.2 601.0 598.2 604.4 590.9 600.5 600.5 603.3 2.8<br />

Nondurable goods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,739 4,822 4,809 4,795 4,724 4,765 4,769 4,780 11<br />

Food manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . 1,623.7 1,675.1 1,669.2 1,658.7 1,613.4 1,637.4 1,642.1 1,648.9 6.8<br />

Textile mills. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.5 112.6 111.9 111.0 112.5 112.3 111.6 111.0 -0.6<br />

Textile product mills. . . . . . . . . . . . . . . . . . . . . . . . . 113.0 112.0 111.3 110.9 112.4 110.9 110.8 110.4 -0.4<br />

Apparel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117.3 112.9 111.3 110.9 116.3 113.2 110.9 110.8 -0.1<br />

Paper and paper products. . . . . . . . . . . . . . . . . . . 368.2 377.9 378.2 377.4 369.2 377.1 378.9 378.2 -0.7<br />

Printing and related support activities. . . . . . . 440.1 434.2 432.8 431.7 439.5 432.6 432.2 430.9 -1.3<br />

Petroleum and coal products. . . . . . . . . . . . . . . . 118.5 120.8 119.9 118.9 116.0 116.9 116.6 116.2 -0.4<br />

Chemicals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825.8 837.8 836.0 837.2 828.0 834.9 836.9 838.6 1.7<br />

Plastics and rubber products. . . . . . . . . . . . . . . . 718.0 727.7 727.1 727.0 721.7 726.9 728.0 730.6 2.6<br />

Miscellaneous nondurable goods<br />

manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.3 311.2 310.8 311.6 294.5 303.1 301.4 304.0 2.6<br />

Private service-providing. . . . . . . . . . . . . . . . . . . . . . . . . . . 105,125 106,871 106,344 106,918 104,735 106,236 106,315 106,494 179<br />

Trade, transportation, and utilities. . . . . . . . . . . . . . . . 27,604 27,797 27,715 27,910 27,553 27,836 27,828 27,865 37<br />

Wholesale trade. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,933.2 6,028.7 6,011.2 6,028.6 5,923.3 6,004.8 6,008.1 6,017.2 9.1<br />

Durable goods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,977.4 3,045.9 3,035.8 3,041.5 2,978.5 3,033.5 3,035.9 3,042.6 6.7<br />

Nondurable goods. . . . . . . . . . . . . . . . . . . . . . . . . . . 2,059.7 2,062.3 2,054.8 2,065.0 2,049.3 2,055.3 2,052.5 2,053.9 1.4<br />

See footnotes at end of table.


ESTABLISHMENT DATA<br />

Table B-1. Employees on nonfarm payrolls by industry sector and selected industry detail<br />

— Continued<br />

[In thousands]<br />

Industry<br />

Wholesale trade - Continued<br />

Oct.<br />

2017<br />

Not seasonally adjusted<br />

Aug.<br />

2018<br />

Sept. Oct. Oct.<br />

2018 p 2018 p 2017<br />

Aug.<br />

2018<br />

Seasonally adjusted<br />

Change<br />

Sept. Oct.<br />

from:<br />

2018 p 2018 p Sept.2018<br />

-<br />

Oct.2018 p<br />

Electronic markets and agents and<br />

brokers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 896.1 920.5 920.6 922.1 895.5 916.0 919.7 920.7 1.0<br />

Retail trade. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15,869.5 15,927.6 15,770.8 15,893.7 15,859.8 15,926.4 15,894.0 15,896.4 2.4<br />

Motor vehicle and parts dealers. . . . . . . . . . . . . 2,019.3 2,047.9 2,043.3 2,046.4 2,017.3 2,036.6 2,037.0 2,042.5 5.5<br />

Automobile dealers. . . . . . . . . . . . . . . . . . . . . . . . 1,304.2 1,315.0 1,315.8 1,316.4 1,301.7 1,311.1 1,312.2 1,313.1 0.9<br />

Other motor vehicle dealers. . . . . . . . . . . . . . . 154.3 163.1 158.6 157.1 155.6 156.3 157.2 158.3 1.1<br />

Auto parts, accessories, and tire stores. . . 560.8 569.8 568.9 572.9 559.9 569.2 567.6 571.1 3.5<br />

Furniture and home furnishings stores. . . . . . 479.9 477.7 477.7 488.0 476.6 482.4 484.3 485.9 1.6<br />

Electronics and appliance stores. . . . . . . . . . . . 499.4 490.7 486.8 490.0 500.4 494.8 491.8 490.9 -0.9<br />

Building material and garden supply<br />

stores. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,268.2 1,305.3 1,278.8 1,277.2 1,288.9 1,306.3 1,303.8 1,304.1 0.3<br />

Food and beverage stores. . . . . . . . . . . . . . . . . . 3,084.9 3,118.5 3,088.4 3,103.3 3,079.6 3,100.3 3,097.4 3,100.7 3.3<br />

Health and personal care stores. . . . . . . . . . . . 1,057.3 1,048.7 1,047.0 1,058.6 1,058.1 1,057.3 1,057.9 1,060.0 2.1<br />

Gasoline stations. . . . . . . . . . . . . . . . . . . . . . . . . . . . 934.0 950.5 944.1 939.9 932.0 938.5 940.6 938.4 -2.2<br />

Clothing and clothing accessories stores. . . . 1,348.8 1,367.5 1,325.0 1,337.2 1,361.3 1,358.7 1,352.9 1,352.6 -0.3<br />

Sporting goods, hobby, book, and music<br />

stores. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605.2 557.5 554.9 557.4 606.0 571.0 566.5 560.4 -6.1<br />

General merchandise stores. . . . . . . . . . . . . . . . 3,152.0 3,131.0 3,102.3 3,155.8 3,143.7 3,151.4 3,138.8 3,140.5 1.7<br />

Department stores. . . . . . . . . . . . . . . . . . . . . . . . 1,183.2 1,161.9 1,144.1 1,169.8 1,185.2 1,178.8 1,170.0 1,168.5 -1.5<br />

General merchandise stores, including<br />

warehouse clubs and supercenters. . . . . 1,968.8 1,969.1 1,958.2 1,986.0 1,958.5 1,972.6 1,968.8 1,972.0 3.2<br />

Miscellaneous store retailers. . . . . . . . . . . . . . . . 834.9 837.7 826.1 829.5 820.2 829.1 821.7 818.1 -3.6<br />

Nonstore retailers. . . . . . . . . . . . . . . . . . . . . . . . . . . 585.6 594.6 596.4 610.4 575.7 600.0 601.3 602.3 1.0<br />

Transportation and warehousing. . . . . . . . . . . . . . . 5,247.4 5,287.5 5,382.8 5,435.6 5,215.4 5,353.5 5,374.3 5,399.1 24.8<br />

Air transportation. . . . . . . . . . . . . . . . . . . . . . . . . . . . 497.8 508.4 506.2 505.2 498.5 504.8 505.5 505.6 0.1<br />

Rail transportation. . . . . . . . . . . . . . . . . . . . . . . . . . . 213.2 215.6 215.6 215.6 212.7 214.4 215.3 215.0 -0.3<br />

Water transportation. . . . . . . . . . . . . . . . . . . . . . . . . 65.7 68.3 66.2 65.8 65.4 65.6 65.1 65.4 0.3<br />

Truck transportation. . . . . . . . . . . . . . . . . . . . . . . . . 1,472.8 1,505.9 1,508.1 1,509.0 1,455.6 1,484.0 1,489.8 1,492.2 2.4<br />

Transit and ground passenger<br />

transportation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515.5 431.8 508.1 517.1 497.9 495.1 496.7 498.2 1.5<br />

Pipeline transportation. . . . . . . . . . . . . . . . . . . . . . . 47.6 46.9 46.9 47.0 47.7 46.9 46.8 47.0 0.2<br />

Scenic and sightseeing transportation. . . . . . . 37.5 45.6 40.6 36.5 36.5 35.3 34.7 35.3 0.6<br />

Support activities for transportation. . . . . . . . . . 698.2 716.0 713.5 721.7 693.1 713.9 712.8 717.6 4.8<br />

Couriers and messengers. . . . . . . . . . . . . . . . . . . 684.5 715.2 729.3 746.8 704.2 751.8 757.0 764.6 7.6<br />

Warehousing and storage. . . . . . . . . . . . . . . . . . . 1,014.6 1,033.8 1,048.3 1,070.9 1,003.8 1,041.7 1,050.6 1,058.2 7.6<br />

Utilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553.4 553.2 549.7 551.6 554.2 551.2 551.3 552.5 1.2<br />

Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,784 2,787 2,753 2,776 2,784 2,766 2,762 2,769 7<br />

Publishing industries, except Internet. . . . . . . . . . 720.5 718.3 717.2 715.2 719.1 714.4 714.6 713.7 -0.9<br />

Motion picture and sound recording<br />

industries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422.7 426.6 403.1 422.8 424.5 413.6 412.5 416.8 4.3<br />

Broadcasting, except Internet. . . . . . . . . . . . . . . . . . 266.5 262.5 264.7 265.2 264.7 263.2 263.2 263.1 -0.1<br />

Telecommunications. . . . . . . . . . . . . . . . . . . . . . . . . . . 771.3 754.5 747.0 746.2 772.7 754.0 748.5 748.6 0.1<br />

Data processing, hosting and related<br />

services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320.7 326.5 325.8 328.4 319.7 325.8 326.9 328.0 1.1<br />

Other information services. . . . . . . . . . . . . . . . . . . . . 282.6 298.6 294.8 298.5 283.3 295.4 296.0 298.6 2.6<br />

Financial activities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8,499 8,654 8,606 8,616 8,494 8,587 8,602 8,609 7<br />

Finance and insurance. . . . . . . . . . . . . . . . . . . . . . . . . 6,287.0 6,353.7 6,326.1 6,331.9 6,287.6 6,331.0 6,334.0 6,332.4 -1.6<br />

Monetary authorities - central bank. . . . . . . . . . 18.9 19.4 19.1 19.2 19.0 19.3 19.2 19.2 0.0<br />

Credit intermediation and related<br />

activities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,651.5 2,675.3 2,661.8 2,659.2 2,656.7 2,664.4 2,665.1 2,664.0 -1.1<br />

Depository credit intermediation 1 . . . . . . . . . . 1,710.3 1,725.1 1,712.3 1,711.9 1,717.4 1,718.3 1,718.6 1,718.3 -0.3<br />

Commercial banking. . . . . . . . . . . . . . . . . . . . 1,318.6 1,323.9 1,313.3 1,311.2 1,323.8 1,319.0 1,318.7 1,316.3 -2.4<br />

Nondepository credit intermediation. . . . . . . 628.4 636.9 634.4 631.0 627.2 633.1 631.2 629.5 -1.7<br />

Activities related to credit intermediation.. . 312.8 313.3 315.1 316.3 312.1 312.9 315.3 316.1 0.8<br />

Securities, commodity contracts,<br />

investments, and funds and trusts. . . . . . . . 948.8 978.0 969.5 968.9 945.6 968.9 970.0 967.3 -2.7<br />

Insurance carriers and related activities. . . . . 2,667.8 2,681.0 2,675.7 2,684.6 2,666.3 2,678.4 2,679.7 2,681.9 2.2<br />

Real estate and rental and leasing. . . . . . . . . . . . 2,212.2 2,299.9 2,280.0 2,283.9 2,206.2 2,256.3 2,268.0 2,276.5 8.5<br />

Real estate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,610.5 1,658.0 1,644.1 1,651.4 1,605.2 1,634.4 1,641.2 1,645.5 4.3<br />

See footnotes at end of table.


ESTABLISHMENT DATA<br />

Table B-1. Employees on nonfarm payrolls by industry sector and selected industry detail<br />

— Continued<br />

[In thousands]<br />

Industry<br />

Real estate and rental and leasing -<br />

Continued<br />

Oct.<br />

2017<br />

Not seasonally adjusted<br />

Aug.<br />

2018<br />

Sept. Oct. Oct.<br />

2018 p 2018 p 2017<br />

Aug.<br />

2018<br />

Seasonally adjusted<br />

Change<br />

Sept. Oct.<br />

from:<br />

2018 p 2018 p Sept.2018<br />

-<br />

Oct.2018 p<br />

Rental and leasing services. . . . . . . . . . . . . . . . . 577.6 616.2 610.9 607.2 576.9 596.7 601.7 605.7 4.0<br />

Lessors of nonfinancial intangible assets. . . . 24.1 25.7 25.0 25.3 24.1 25.2 25.1 25.3 0.2<br />

Professional and business services. . . . . . . . . . . . . . 20,815 21,217 21,185 21,352 20,630 21,065 21,111 21,146 35<br />

Professional and technical services. . . . . . . . . . . . 9,045.0 9,272.1 9,206.2 9,301.3 9,061.1 9,273.3 9,287.9 9,307.6 19.7<br />

Legal services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,139.1 1,137.5 1,131.3 1,140.7 1,137.0 1,136.5 1,137.5 1,138.1 0.6<br />

Accounting and bookkeeping services. . . . . . 938.9 960.0 950.3 963.3 998.0 1,013.1 1,011.9 1,014.1 2.2<br />

Architectural and engineering services. . . . . . 1,455.1 1,507.2 1,488.6 1,500.5 1,447.7 1,486.7 1,485.4 1,492.3 6.9<br />

Specialized design services. . . . . . . . . . . . . . . . . 139.0 139.2 140.0 139.7 137.4 138.8 140.0 138.4 -1.6<br />

Computer systems design and related<br />

services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,068.3 2,126.1 2,109.5 2,138.1 2,058.4 2,116.2 2,120.9 2,126.6 5.7<br />

Management and technical consulting<br />

services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,427.9 1,467.4 1,463.6 1,484.0 1,413.5 1,460.7 1,465.9 1,469.8 3.9<br />

Scientific research and development<br />

services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661.3 689.0 681.6 685.4 663.8 682.9 684.6 687.1 2.5<br />

Advertising and related services. . . . . . . . . . . . . 492.6 500.2 495.8 497.6 489.8 497.3 496.8 495.6 -1.2<br />

Other professional and technical services. . . 722.8 745.5 745.5 752.0 715.4 741.0 744.9 745.5 0.6<br />

Management of companies and enterprises. . . 2,299.4 2,344.0 2,331.9 2,336.4 2,302.6 2,333.8 2,336.9 2,339.1 2.2<br />

Administrative and waste services. . . . . . . . . . . . . 9,470.8 9,600.7 9,646.4 9,714.6 9,266.3 9,457.4 9,486.5 9,498.9 12.4<br />

Administrative and support services. . . . . . . . . 9,054.3 9,162.6 9,209.4 9,276.8 8,850.8 9,025.2 9,051.9 9,062.5 10.6<br />

Office administrative services. . . . . . . . . . . . . 517.7 528.0 526.8 527.4 517.5 527.3 527.1 527.0 -0.1<br />

Facilities support services. . . . . . . . . . . . . . . . . 156.0 158.4 159.0 159.4 154.9 157.5 157.6 158.1 0.5<br />

Employment services 1 . . . . . . . . . . . . . . . . . . . . 3,788.6 3,764.6 3,825.4 3,882.0 3,663.2 3,735.7 3,749.9 3,749.4 -0.5<br />

Temporary help services. . . . . . . . . . . . . . . . 3,106.3 3,066.1 3,123.6 3,178.0 2,992.0 3,047.3 3,054.9 3,058.2 3.3<br />

Business support services. . . . . . . . . . . . . . . . 921.4 899.8 910.6 924.9 906.4 911.4 911.7 910.5 -1.2<br />

Travel arrangement and reservation<br />

services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216.8 219.1 218.3 216.9 216.9 216.5 217.1 216.8 -0.3<br />

Investigation and security services. . . . . . . . 930.5 948.2 955.6 957.7 922.6 945.2 947.5 950.2 2.7<br />

Services to buildings and dwellings. . . . . . . 2,190.6 2,304.6 2,270.4 2,261.6 2,141.4 2,193.9 2,199.5 2,208.6 9.1<br />

Other support services. . . . . . . . . . . . . . . . . . . . 332.7 339.9 343.3 346.9 327.9 337.7 341.6 341.9 0.3<br />

Waste management and remediation<br />

services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416.5 438.1 437.0 437.8 415.5 432.2 434.6 436.4 1.8<br />

Education and health services. . . . . . . . . . . . . . . . . . . 23,515 23,430 23,691 24,018 23,312 23,741 23,767 23,811 44<br />

Educational services. . . . . . . . . . . . . . . . . . . . . . . . . . . 3,852.9 3,453.8 3,711.5 3,917.7 3,686.6 3,764.9 3,755.8 3,753.3 -2.5<br />

Health care and social assistance. . . . . . . . . . . . . 19,662.3 19,976.3 19,979.9 20,099.9 19,625.7 19,975.9 20,010.8 20,057.5 46.7<br />

Health care 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15,845.5 16,100.5 16,089.4 16,171.0 15,814.7 16,074.3 16,101.6 16,137.2 35.6<br />

Ambulatory health care services. . . . . . . . . . 7,383.8 7,529.6 7,522.3 7,571.2 7,357.5 7,518.9 7,531.2 7,545.4 14.2<br />

Offices of physicians. . . . . . . . . . . . . . . . . . . . 2,613.1 2,643.8 2,643.5 2,661.2 2,603.1 2,643.4 2,648.1 2,651.8 3.7<br />

Offices of dentists. . . . . . . . . . . . . . . . . . . . . . . 937.4 954.3 949.4 955.3 935.0 949.8 949.0 952.5 3.5<br />

Offices of other health practitioners. . . . . 897.5 936.8 931.3 945.1 895.7 933.9 936.8 942.9 6.1<br />

Outpatient care centers. . . . . . . . . . . . . . . . . 910.3 938.1 935.8 942.5 909.9 937.1 938.4 942.0 3.6<br />

Medical and diagnostic laboratories. . . . 272.9 282.4 284.8 284.0 271.9 282.4 283.9 283.4 -0.5<br />

Home health care services. . . . . . . . . . . . . 1,438.9 1,468.2 1,469.8 1,478.7 1,432.1 1,465.9 1,468.6 1,470.5 1.9<br />

Other ambulatory health care<br />

services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.7 306.0 307.7 304.4 309.8 306.3 306.5 302.3 -4.2<br />

Hospitals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,114.2 5,194.7 5,203.3 5,221.9 5,109.2 5,191.7 5,203.1 5,216.1 13.0<br />

Nursing and residential care facilities. . . . . 3,347.5 3,376.2 3,363.8 3,377.9 3,348.0 3,363.7 3,367.3 3,375.7 8.4<br />

Nursing care facilities. . . . . . . . . . . . . . . . . . . 1,622.9 1,617.2 1,611.3 1,617.4 1,621.7 1,612.1 1,611.7 1,615.2 3.5<br />

Residential mental health facilities. . . . . . 628.9 639.2 636.8 636.7 629.8 637.1 637.9 637.7 -0.2<br />

Community care facilities for the<br />

elderly. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 926.8 946.1 942.5 949.3 927.8 942.2 944.2 948.5 4.3<br />

Other residential care facilities. . . . . . . . . . 168.9 173.7 173.2 174.5 168.7 172.4 173.5 174.3 0.8<br />

Social assistance. . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,816.8 3,875.8 3,890.5 3,928.9 3,811.0 3,901.6 3,909.2 3,920.3 11.1<br />

Individual and family services. . . . . . . . . . . . . 2,360.4 2,441.7 2,433.6 2,456.5 2,363.1 2,441.2 2,449.1 2,457.9 8.8<br />

Emergency and other relief services. . . . . . 168.2 175.5 174.9 175.1 169.0 174.9 175.3 175.9 0.6<br />

Vocational rehabilitation services. . . . . . . . . 343.1 351.8 346.0 349.0 343.6 347.3 347.3 349.5 2.2<br />

Child day care services. . . . . . . . . . . . . . . . . . . 945.1 906.8 936.0 948.3 935.3 938.2 937.5 937.0 -0.5<br />

Leisure and hospitality. . . . . . . . . . . . . . . . . . . . . . . . . . . 16,100 17,076 16,530 16,360 16,156 16,368 16,368 16,410 42<br />

Arts, entertainment, and recreation. . . . . . . . . . . . 2,299.4 2,650.9 2,412.1 2,334.1 2,347.7 2,349.1 2,363.3 2,372.3 9.0<br />

Performing arts and spectator sports. . . . . . . . 504.8 514.1 511.1 511.1 499.0 485.2 498.6 501.9 3.3<br />

See footnotes at end of table.


ESTABLISHMENT DATA<br />

Table B-1. Employees on nonfarm payrolls by industry sector and selected industry detail<br />

— Continued<br />

[In thousands]<br />

Industry<br />

Arts, entertainment, and recreation -<br />

Continued<br />

Oct.<br />

2017<br />

Not seasonally adjusted<br />

Aug.<br />

2018<br />

Sept. Oct. Oct.<br />

2018 p 2018 p 2017<br />

Aug.<br />

2018<br />

Seasonally adjusted<br />

Change<br />

Sept. Oct.<br />

from:<br />

2018 p 2018 p Sept.2018<br />

-<br />

Oct.2018 p<br />

Museums, historical sites, and similar<br />

institutions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168.7 186.6 176.8 176.2 168.0 173.8 174.5 175.5 1.0<br />

Amusements, gambling, and recreation. . . . . 1,625.9 1,950.2 1,724.2 1,646.8 1,680.7 1,690.1 1,690.2 1,694.9 4.7<br />

Accommodation and food services. . . . . . . . . . . . 13,800.5 14,425.3 14,117.4 14,026.1 13,808.4 14,018.7 14,005.1 14,038.1 33.0<br />

Accommodation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,997.9 2,182.8 2,079.1 2,023.0 2,010.4 2,040.3 2,036.7 2,036.2 -0.5<br />

Food services and drinking places. . . . . . . . . . 11,802.6 12,242.5 12,038.3 12,003.1 11,798.0 11,978.4 11,968.4 12,001.9 33.5<br />

Other services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,808 5,910 5,864 5,886 5,806 5,873 5,877 5,884 7<br />

Repair and maintenance. . . . . . . . . . . . . . . . . . . . . . . 1,312.2 1,323.3 1,324.5 1,328.7 1,311.3 1,323.3 1,324.6 1,327.5 2.9<br />

Personal and laundry services. . . . . . . . . . . . . . . . . 1,502.2 1,536.3 1,533.6 1,543.1 1,499.7 1,530.1 1,534.7 1,539.5 4.8<br />

Membership associations and organizations. . . 2,993.8 3,050.0 3,005.7 3,014.5 2,995.3 3,019.8 3,017.4 3,017.1 -0.3<br />

Government. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22,687 21,420 22,370 22,771 22,331 22,396 22,393 22,397 4<br />

Federal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,802.0 2,806.0 2,801.0 2,799.0 2,807.0 2,797.0 2,798.0 2,799.0 1.0<br />

Federal, except U.S. Postal Service. . . . . . . . . . . . . 2,187.7 2,202.3 2,196.4 2,194.8 2,190.3 2,191.0 2,191.9 2,194.5 2.6<br />

U.S. Postal Service. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614.5 603.6 604.3 603.7 616.9 606.3 605.7 604.9 -0.8<br />

State government. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,279.0 4,863.0 5,194.0 5,297.0 5,135.0 5,137.0 5,138.0 5,139.0 1.0<br />

State government education. . . . . . . . . . . . . . . . . . . . . 2,616.1 2,180.8 2,527.6 2,641.1 2,462.7 2,472.3 2,473.6 2,474.9 1.3<br />

State government, excluding education. . . . . . . . . . 2,662.7 2,682.5 2,666.5 2,656.0 2,671.8 2,664.4 2,664.8 2,663.7 -1.1<br />

Local government. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14,606.0 13,751.0 14,375.0 14,675.0 14,389.0 14,462.0 14,457.0 14,459.0 2.0<br />

Local government education. . . . . . . . . . . . . . . . . . . . . 8,190.6 7,079.3 7,895.2 8,226.5 7,936.7 7,974.0 7,968.9 7,968.7 -0.2<br />

Local government, excluding education. . . . . . . . . . 6,415.6 6,671.3 6,479.6 6,448.7 6,452.0 6,488.3 6,488.3 6,490.1 1.8<br />

1 Includes other industries, not shown separately.<br />

2 Includes motor vehicles, motor vehicle bodies and trailers, and motor vehicle parts.<br />

3 Includes ambulatory health care services, hospitals, and nursing and residential care facilities.<br />

p Preliminary<br />

NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.


ESTABLISHMENT DATA<br />

Table B-2. Average weekly hours and overtime of all employees on private nonfarm payrolls by industry<br />

sector, seasonally adjusted<br />

Industry<br />

Oct.<br />

2017<br />

Aug.<br />

2018<br />

Sept.<br />

2018 p Oct.<br />

2018 p<br />

AVERAGE WEEKLY HOURS<br />

Total private............................................................................. 34.4 34.5 34.4 34.5<br />

Goods-producing.................................................................... 40.4 40.5 40.3 40.3<br />

Mining and logging................................................................ 45.3 46.0 45.8 45.9<br />

Construction....................................................................... 39.0 39.2 38.9 38.9<br />

Manufacturing..................................................................... 40.9 40.9 40.9 40.8<br />

Durable goods.................................................................. 41.4 41.3 41.2 41.2<br />

Nondurable goods.............................................................. 40.0 40.3 40.2 40.1<br />

Private service-providing............................................................ 33.2 33.3 33.3 33.3<br />

Trade, transportation, and utilities.............................................. 34.3 34.4 34.4 34.3<br />

Wholesale trade................................................................ 39.0 39.0 38.9 38.8<br />

Retail trade...................................................................... 30.9 30.9 30.9 30.8<br />

Transportation and warehousing............................................. 38.7 39.0 38.9 39.0<br />

Utilities........................................................................... 42.1 42.0 42.2 42.1<br />

Information......................................................................... 36.3 36.1 36.3 36.1<br />

Financial activities................................................................ 37.5 37.6 37.4 37.8<br />

Professional and business services............................................ 36.0 36.1 36.1 36.2<br />

Education and health services.................................................. 32.9 33.0 33.0 33.0<br />

Leisure and hospitality........................................................... 26.1 26.1 26.0 26.1<br />

Other services..................................................................... 31.7 31.8 31.8 31.9<br />

AVERAGE OVERTIME HOURS<br />

Manufacturing........................................................................... 3.5 3.5 3.5 3.5<br />

Durable goods....................................................................... 3.5 3.5 3.5 3.5<br />

Nondurable goods................................................................... 3.5 3.6 3.4 3.5<br />

p Preliminary<br />

NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.


ESTABLISHMENT DATA<br />

Table B-3. Average hourly and weekly earnings of all employees on private nonfarm payrolls by industry<br />

sector, seasonally adjusted<br />

Industry<br />

Oct.<br />

2017<br />

Average hourly earnings<br />

Aug.<br />

2018<br />

Sept. Oct. Oct.<br />

2018 p 2018 p 2017<br />

Average weekly earnings<br />

Aug.<br />

2018<br />

Sept. Oct.<br />

2018 p 2018 p<br />

Total private............................................... $26.47 $27.17 $27.25 $27.30 $910.57 $937.37 $937.40 $941.85<br />

Goods-producing....................................... 27.72 28.29 28.39 28.40 1,119.89 1,145.75 1,144.12 1,144.52<br />

Mining and logging.................................. 32.13 32.75 33.06 32.85 1,455.49 1,506.50 1,514.15 1,507.82<br />

Construction.......................................... 29.09 30.00 30.17 30.21 1,134.51 1,176.00 1,173.61 1,175.17<br />

Manufacturing........................................ 26.72 27.07 27.11 27.11 1,092.85 1,107.16 1,108.80 1,106.09<br />

Durable goods.................................... 27.99 28.46 28.51 28.49 1,158.79 1,175.40 1,174.61 1,173.79<br />

Nondurable goods................................ 24.58 24.68 24.71 24.74 983.20 994.60 993.34 992.07<br />

Private service-providing.............................. 26.18 26.91 26.98 27.04 869.18 896.10 898.43 900.43<br />

Trade, transportation, and utilities................. 22.86 23.48 23.56 23.61 784.10 807.71 810.46 809.82<br />

Wholesale trade.................................. 29.98 30.66 30.82 30.83 1,169.22 1,195.74 1,198.90 1,196.20<br />

Retail trade........................................ 18.24 18.84 18.89 18.96 563.62 582.16 583.70 583.97<br />

Transportation and warehousing............... 24.01 24.45 24.45 24.50 929.19 953.55 951.11 955.50<br />

Utilities............................................. 39.62 40.78 41.08 41.00 1,668.00 1,712.76 1,733.58 1,726.10<br />

Information........................................... 38.39 39.86 40.31 40.39 1,393.56 1,438.95 1,463.25 1,458.08<br />

Financial activities................................... 33.66 34.93 35.03 35.03 1,262.25 1,313.37 1,310.12 1,324.13<br />

Professional and business services.............. 31.77 32.65 32.70 32.76 1,143.72 1,178.67 1,180.47 1,185.91<br />

Education and health services..................... 26.47 27.09 27.10 27.19 870.86 893.97 894.30 897.27<br />

Leisure and hospitality.............................. 15.58 16.03 16.07 16.11 406.64 418.38 417.82 420.47<br />

Other services....................................... 24.10 24.42 24.50 24.55 763.97 776.56 779.10 783.15<br />

p Preliminary<br />

NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.


ESTABLISHMENT DATA<br />

Table B-4. Indexes of aggregate weekly hours and payrolls for all employees on private nonfarm payrolls by<br />

industry sector, seasonally adjusted<br />

[2007=100]<br />

Industry<br />

Oct.<br />

2017<br />

Index of aggregate weekly hours 1 Index of aggregate weekly payrolls 2<br />

Aug.<br />

2018<br />

Percent<br />

change<br />

Sept.<br />

from:<br />

Oct.<br />

2018 p 2018 p 2018 Sept. Oct.<br />

-<br />

2017<br />

Oct.<br />

2018 p<br />

Aug.<br />

2018<br />

Sept.<br />

2018 p Oct.<br />

2018 p Percent<br />

change<br />

from:<br />

2018 -<br />

Oct.<br />

2018 p<br />

Total private..................................... 107.8 110.0 109.7 110.3 0.5 136.5 142.8 143.0 143.9 0.6<br />

Goods-producing............................. 92.8 95.7 95.5 95.8 0.3 116.3 122.4 122.5 122.9 0.3<br />

Mining and logging........................ 98.4 108.0 108.1 109.0 0.8 126.9 142.0 143.5 143.8 0.2<br />

Construction................................ 93.9 98.2 97.7 98.1 0.4 118.7 128.0 128.1 128.8 0.5<br />

Manufacturing.............................. 91.9 93.7 93.8 93.8 0.0 114.2 118.0 118.3 118.3 0.0<br />

Durable goods........................... 90.5 92.7 92.6 92.9 0.3 112.5 117.1 117.3 117.5 0.2<br />

Nondurable goods...................... 94.3 95.8 95.7 95.7 0.0 117.6 120.0 120.0 120.1 0.1<br />

Private service-providing.................... 111.9 113.8 113.9 114.1 0.2 142.3 148.8 149.3 149.9 0.4<br />

Trade, transportation, and utilities....... 102.8 104.1 104.1 103.9 -0.2 126.5 131.6 132.0 132.1 0.1<br />

Wholesale trade......................... 100.8 102.2 102.0 101.9 -0.1 126.1 130.7 131.2 131.1 -0.1<br />

Retail trade.............................. 99.5 100.0 99.8 99.5 -0.3 120.0 124.5 124.6 124.7 0.1<br />

Transportation and warehousing...... 115.5 119.5 119.7 120.5 0.7 140.8 148.3 148.5 149.8 0.9<br />

Utilities.................................... 100.9 100.2 100.7 100.6 -0.1 132.1 135.0 136.6 136.3 -0.2<br />

Information................................. 92.3 91.2 91.6 91.3 -0.3 126.2 129.4 131.4 131.3 -0.1<br />

Financial activities......................... 104.3 105.7 105.4 106.6 1.1 137.0 144.1 144.0 145.6 1.1<br />

Professional and business services..... 116.8 119.6 119.9 120.4 0.4 150.3 158.2 158.8 159.8 0.6<br />

Education and health services........... 125.2 127.9 128.1 128.3 0.2 159.5 166.7 167.0 167.8 0.5<br />

Leisure and hospitality.................... 120.3 121.9 121.4 122.2 0.7 151.2 157.6 157.4 158.8 0.9<br />

Other services............................. 106.1 107.7 107.8 108.2 0.4 140.2 144.1 144.7 145.6 0.6<br />

1 The indexes of aggregate weekly hours are calculated by dividing the current month’s estimates of aggregate hours by the corresponding 2007<br />

annual average aggregate hours. Aggregate hours estimates are the product of estimates of average weekly hours and employment.<br />

2 The indexes of aggregate weekly payrolls are calculated by dividing the current month’s estimates of aggregate weekly payrolls by the<br />

corresponding 2007 annual average aggregate weekly payrolls. Aggregate payrolls estimates are the product of estimates of average hourly<br />

earnings, average weekly hours, and employment.<br />

p Preliminary<br />

NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.


ESTABLISHMENT DATA<br />

Table B-5. Employment of women on nonfarm payrolls by industry sector, seasonally adjusted<br />

Industry<br />

Oct.<br />

2017<br />

Women employees (in thousands)<br />

Aug.<br />

2018<br />

Sept. Oct. Oct.<br />

2018 p 2018 p 2017<br />

Percent of all employees<br />

Aug.<br />

2018<br />

Sept. Oct.<br />

2018 p 2018 p<br />

Total nonfarm............................................. 72,880 74,213 74,285 74,441 49.5 49.7 49.7 49.7<br />

Total private............................................ 60,063 61,290 61,363 61,512 48.1 48.3 48.3 48.3<br />

Goods-producing.................................... 4,430 4,604 4,621 4,638 22.0 22.2 22.2 22.2<br />

Mining and logging............................... 91 95 94 95 13.2 12.7 12.5 12.6<br />

Construction....................................... 889 933 940 943 12.7 12.8 12.9 12.9<br />

Manufacturing..................................... 3,450 3,576 3,587 3,600 27.6 28.1 28.1 28.2<br />

Durable goods.................................. 1,818 1,891 1,897 1,904 23.4 23.7 23.8 23.8<br />

Nondurable goods............................. 1,632 1,685 1,690 1,696 34.5 35.4 35.4 35.5<br />

Private service-providing........................... 55,633 56,686 56,742 56,874 53.1 53.4 53.4 53.4<br />

Trade, transportation, and utilities.............. 11,032 11,169 11,160 11,164 40.0 40.1 40.1 40.1<br />

Wholesale trade................................ 1,749.4 1,797.6 1,795.4 1,800.8 29.5 29.9 29.9 29.9<br />

Retail trade..................................... 7,879.0 7,918.8 7,905.1 7,893.6 49.7 49.7 49.7 49.7<br />

Transportation and warehousing............. 1,276.6 1,323.6 1,330.9 1,341.3 24.5 24.7 24.8 24.8<br />

Utilities........................................... 127.1 128.7 128.5 128.5 22.9 23.3 23.3 23.3<br />

Information......................................... 1,101 1,090 1,090 1,093 39.5 39.4 39.5 39.5<br />

Financial activities................................ 4,795 4,846 4,849 4,856 56.5 56.4 56.4 56.4<br />

Professional and business services............ 9,284 9,538 9,558 9,599 45.0 45.3 45.3 45.4<br />

Education and health services.................. 17,938 18,307 18,335 18,376 76.9 77.1 77.1 77.2<br />

Leisure and hospitality........................... 8,408 8,610 8,621 8,651 52.0 52.6 52.7 52.7<br />

Other services..................................... 3,075 3,126 3,129 3,135 53.0 53.2 53.2 53.3<br />

Government............................................. 12,817 12,923 12,922 12,929 57.4 57.7 57.7 57.7<br />

p Preliminary<br />

NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.


ESTABLISHMENT DATA<br />

Table B-6. Employment of production and nonsupervisory employees on private nonfarm payrolls by industry<br />

sector, seasonally adjusted 1<br />

[In thousands]<br />

Industry<br />

Oct.<br />

2017<br />

Aug.<br />

2018<br />

Sept.<br />

2018 p Oct.<br />

2018 p<br />

Total private............................................................................... 102,980 104,624 104,720 104,942<br />

Goods-producing....................................................................... 14,497 14,899 14,919 14,986<br />

Mining and logging.................................................................. 505 556 556 566<br />

Construction.......................................................................... 5,220 5,409 5,419 5,437<br />

Manufacturing........................................................................ 8,772 8,934 8,944 8,983<br />

Durable goods..................................................................... 5,332 5,482 5,494 5,524<br />

Nondurable goods................................................................ 3,440 3,452 3,450 3,459<br />

Private service-providing.............................................................. 88,483 89,725 89,801 89,956<br />

Trade, transportation, and utilities................................................. 23,262 23,522 23,532 23,548<br />

Wholesale trade................................................................... 4,749.7 4,813.6 4,819.8 4,824.5<br />

Retail trade........................................................................ 13,526.8 13,602.3 13,587.6 13,576.3<br />

Transportation and warehousing................................................ 4,539.6 4,664.7 4,682.2 4,703.6<br />

Utilities.............................................................................. 445.5 441.5 442.0 443.5<br />

Information............................................................................ 2,243 2,230 2,222 2,231<br />

Financial activities................................................................... 6,608 6,654 6,671 6,682<br />

Professional and business services............................................... 16,846 17,208 17,238 17,280<br />

Education and health services..................................................... 20,474 20,855 20,877 20,914<br />

Leisure and hospitality.............................................................. 14,245 14,394 14,393 14,425<br />

Other services........................................................................ 4,805 4,862 4,868 4,876<br />

1 Data relate to production employees in mining and logging and manufacturing, construction employees in construction, and nonsupervisory<br />

employees in the service-providing industries. These groups account for approximately four-fifths of the total employment on private nonfarm<br />

payrolls.<br />

p Preliminary<br />

NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.


ESTABLISHMENT DATA<br />

Table B-7. Average weekly hours and overtime of production and nonsupervisory employees on private<br />

nonfarm payrolls by industry sector, seasonally adjusted 1<br />

Industry<br />

Oct.<br />

2017<br />

Aug.<br />

2018<br />

Sept.<br />

2018 p Oct.<br />

2018 p<br />

AVERAGE WEEKLY HOURS<br />

Total private............................................................................. 33.7 33.8 33.7 33.7<br />

Goods-producing.................................................................... 41.2 41.5 41.3 41.3<br />

Mining and logging................................................................ 46.0 47.0 46.5 46.7<br />

Construction....................................................................... 39.5 39.9 39.6 39.6<br />

Manufacturing..................................................................... 42.0 42.2 42.1 42.1<br />

Durable goods.................................................................. 42.4 42.5 42.3 42.4<br />

Nondurable goods.............................................................. 41.3 41.6 41.7 41.5<br />

Private service-providing............................................................ 32.4 32.5 32.4 32.4<br />

Trade, transportation, and utilities.............................................. 33.9 34.0 33.9 33.8<br />

Wholesale trade................................................................ 39.0 39.0 38.8 38.7<br />

Retail trade...................................................................... 30.3 30.4 30.4 30.2<br />

Transportation and warehousing............................................. 38.3 38.3 38.2 38.2<br />

Utilities........................................................................... 42.5 42.8 42.6 42.6<br />

Information......................................................................... 35.8 35.6 35.6 35.5<br />

Financial activities................................................................ 36.9 37.1 37.0 37.0<br />

Professional and business services............................................ 35.4 35.4 35.2 35.3<br />

Education and health services.................................................. 32.2 32.2 32.2 32.2<br />

Leisure and hospitality........................................................... 24.8 24.9 24.8 24.8<br />

Other services..................................................................... 30.6 30.8 30.8 31.0<br />

AVERAGE OVERTIME HOURS<br />

Manufacturing........................................................................... 4.5 4.5 4.5 4.5<br />

Durable goods....................................................................... 4.6 4.6 4.6 4.7<br />

Nondurable goods................................................................... 4.3 4.4 4.3 4.3<br />

1 Data relate to production employees in mining and logging and manufacturing, construction employees in construction, and nonsupervisory<br />

employees in the service-providing industries. These groups account for approximately four-fifths of the total employment on private nonfarm<br />

payrolls.<br />

p Preliminary<br />

NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.


ESTABLISHMENT DATA<br />

Table B-8. Average hourly and weekly earnings of production and nonsupervisory employees on private<br />

nonfarm payrolls by industry sector, seasonally adjusted 1<br />

Industry<br />

Oct.<br />

2017<br />

Average hourly earnings<br />

Aug.<br />

2018<br />

Sept. Oct. Oct.<br />

2018 p 2018 p 2017<br />

Average weekly earnings<br />

Aug.<br />

2018<br />

Sept. Oct.<br />

2018 p 2018 p<br />

Total private............................................... $22.18 $22.76 $22.82 $22.89 $747.47 $769.29 $769.03 $771.39<br />

Goods-producing....................................... 23.31 24.02 24.10 24.19 960.37 996.83 995.33 999.05<br />

Mining and logging.................................. 27.45 28.55 28.93 28.89 1,262.70 1,341.85 1,345.25 1,349.16<br />

Construction.......................................... 26.88 27.80 27.90 28.01 1,061.76 1,109.22 1,104.84 1,109.20<br />

Manufacturing........................................ 21.05 21.54 21.61 21.68 884.10 908.99 909.78 912.73<br />

Durable goods.................................... 22.03 22.53 22.60 22.64 934.07 957.53 955.98 959.94<br />

Nondurable goods................................ 19.48 19.92 20.00 20.10 804.52 828.67 834.00 834.15<br />

Private service-providing.............................. 21.94 22.50 22.55 22.61 710.86 731.25 730.62 732.56<br />

Trade, transportation, and utilities................. 19.42 20.02 20.08 20.16 658.34 680.68 680.71 681.41<br />

Wholesale trade.................................. 24.72 25.34 25.55 25.58 964.08 988.26 991.34 989.95<br />

Retail trade........................................ 15.36 15.99 16.00 16.07 465.41 486.10 486.40 485.31<br />

Transportation and warehousing............... 21.52 21.96 22.01 22.09 824.22 841.07 840.78 843.84<br />

Utilities............................................. 36.26 36.95 37.05 36.87 1,541.05 1,581.46 1,578.33 1,570.66<br />

Information........................................... 30.86 31.97 32.22 32.43 1,104.79 1,138.13 1,147.03 1,151.27<br />

Financial activities................................... 26.77 26.96 27.00 27.02 987.81 1,000.22 999.00 999.74<br />

Professional and business services.............. 26.16 26.85 26.90 26.94 926.06 950.49 946.88 950.98<br />

Education and health services..................... 23.15 23.72 23.72 23.77 745.43 763.78 763.78 765.39<br />

Leisure and hospitality.............................. 13.49 13.94 14.00 14.05 334.55 347.11 347.20 348.44<br />

Other services....................................... 20.33 20.66 20.73 20.78 622.10 636.33 638.48 644.18<br />

1 Data relate to production employees in mining and logging and manufacturing, construction employees in construction, and nonsupervisory<br />

employees in the service-providing industries. These groups account for approximately four-fifths of the total employment on private nonfarm<br />

payrolls.<br />

p Preliminary<br />

NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.


ESTABLISHMENT DATA<br />

Table B-9. Indexes of aggregate weekly hours and payrolls for production and nonsupervisory employees on<br />

private nonfarm payrolls by industry sector, seasonally adjusted 1<br />

[2002=100]<br />

Industry<br />

Oct.<br />

2017<br />

Index of aggregate weekly hours 2 Index of aggregate weekly payrolls 3<br />

Aug.<br />

2018<br />

Percent<br />

change<br />

Sept.<br />

from:<br />

Oct.<br />

2018 p 2018 p 2018 Sept. Oct.<br />

-<br />

2017<br />

Oct.<br />

2018 p<br />

Aug.<br />

2018<br />

Sept.<br />

2018 p Oct.<br />

2018 p Percent<br />

change<br />

from:<br />

2018 -<br />

Oct.<br />

2018 p<br />

Total private..................................... 115.6 117.8 117.6 117.8 0.2 171.4 179.2 179.3 180.2 0.5<br />

Goods-producing............................. 91.3 94.5 94.2 94.6 0.4 130.3 139.0 138.9 140.1 0.9<br />

Mining and logging........................ 123.4 138.9 137.4 140.5 2.3 197.1 230.6 231.2 236.0 2.1<br />

Construction................................ 103.2 108.1 107.4 107.8 0.4 149.8 162.2 161.9 163.0 0.7<br />

Manufacturing.............................. 84.6 86.5 86.4 86.8 0.5 116.4 121.9 122.2 123.1 0.7<br />

Durable goods........................... 85.0 87.6 87.3 88.0 0.8 116.9 123.2 123.2 124.4 1.0<br />

Nondurable goods...................... 83.7 84.6 84.8 84.6 -0.2 115.2 119.1 119.8 120.1 0.3<br />

Private service-providing.................... 122.1 124.2 123.9 124.1 0.2 183.7 191.6 191.6 192.4 0.4<br />

Trade, transportation, and utilities....... 109.9 111.5 111.2 111.0 -0.2 152.3 159.2 159.3 159.6 0.2<br />

Wholesale trade......................... 109.1 110.6 110.1 110.0 -0.1 158.9 165.0 165.8 165.7 -0.1<br />

Retail trade.............................. 103.7 104.7 104.6 103.8 -0.8 136.6 143.4 143.4 142.9 -0.3<br />

Transportation and warehousing...... 130.9 134.5 134.6 135.3 0.5 178.7 187.3 188.0 189.5 0.8<br />

Utilities.................................... 96.8 96.6 96.3 96.6 0.3 146.6 149.0 148.9 148.7 -0.1<br />

Information................................. 91.6 90.6 90.3 90.4 0.1 140.0 143.4 144.0 145.1 0.8<br />

Financial activities......................... 114.8 116.2 116.2 116.4 0.2 189.1 192.8 193.0 193.5 0.3<br />

Professional and business services..... 133.7 136.5 136.0 136.7 0.5 208.1 218.1 217.7 219.2 0.7<br />

Education and health services........... 140.6 143.2 143.3 143.6 0.2 214.8 224.2 224.4 225.3 0.4<br />

Leisure and hospitality.................... 129.4 131.3 130.7 131.0 0.2 198.2 207.8 207.9 209.1 0.6<br />

Other services............................. 103.1 105.0 105.2 106.0 0.8 152.7 158.1 158.8 160.5 1.1<br />

1 Data relate to production employees in mining and logging and manufacturing, construction employees in construction, and nonsupervisory<br />

employees in the service-providing industries. These groups account for approximately four-fifths of the total employment on private nonfarm<br />

payrolls.<br />

2 The indexes of aggregate weekly hours are calculated by dividing the current month’s estimates of aggregate hours by the corresponding 2002<br />

annual average aggregate hours. Aggregate hours estimates are the product of estimates of average weekly hours and employment.<br />

3 The indexes of aggregate weekly payrolls are calculated by dividing the current month’s estimates of aggregate weekly payrolls by the<br />

corresponding 2002 annual average aggregate weekly payrolls. Aggregate payrolls estimates are the product of estimates of average hourly<br />

earnings, average weekly hours, and employment.<br />

p Preliminary<br />

NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.


Page 120 of 149


Attachment B<br />

Employment and <strong>Unemployment</strong><br />

Among Youth in The U.S.<br />

Page 121 of 149


For release 10:00 a.m. (EDT) Thursday, August 16, 2018<br />

USDL-18-1316<br />

Technical information: (202) 691-6378 • cpsinfo@bls.gov • www.bls.gov/cps<br />

Media contact: (202) 691-5902 • PressOffice@bls.gov<br />

EMPLOYMENT AND UNEMPLOYMENT AMONG YOUTH — SUMMER 2018<br />

From April to July 2018, the number of employed youth 16 to 24 years old increased by 2.0 million to<br />

20.9 million, the U.S. Bureau of Labor Statistics reported today. This year, 55.0 percent of young people<br />

were employed in July, little changed from a year earlier. (The month of July typically is the<br />

summertime peak in youth employment.) The unemployment rate for youth was 9.2 percent in July, also<br />

little changed from July 2017. (Because this analysis focuses on the seasonal changes in youth<br />

employment and unemployment that occur each spring and summer, the data are not seasonally<br />

adjusted.)<br />

Labor Force<br />

The youth labor force—16- to 24-year-olds working or actively looking for work—grows sharply<br />

between April and July each year. During these months, large numbers of high school and college<br />

students search for or take summer jobs, and many graduates enter the labor market to look for or begin<br />

permanent employment. This summer, the youth labor force grew by 2.6 million, or 12.7 percent, to a<br />

total of 23.0 million in July. (See table 1.)<br />

The labor force participation rate for all youth was 60.6 percent in July, unchanged from a year earlier.<br />

(The labor force participation rate is the proportion of the civilian noninstitutional population that is<br />

working or looking and available for work.) (See table 2.) The summer labor force participation rate of<br />

youth has held fairly steady since July 2010, after trending downward for the prior two decades.<br />

The summer youth labor force participation rate peaked at 77.5 percent in July 1989.<br />

The July 2018 labor force participation rate for 16- to 24-year-old men, at 61.1 percent, was down 1.2<br />

percentage points over the year. The rate for young women, at 60.0 percent, rose 1.2 percentage points<br />

during the same period, reducing the gap in labor force participation between young men and women.<br />

Whites had the highest youth labor force participation rate in July 2018, at 62.8 percent. The rate was<br />

56.5 percent for Blacks, 43.3 percent for Asians, and 58.0 percent for Hispanics. Over the year, the labor<br />

force participation rate rose for Hispanics (+1.4 percentage points) and declined for Asians (-4.1 points).<br />

The decline among Asians offset a similar increase (+4.3 percentage points) between July 2016 and<br />

2017. Labor force participation rates in July 2018 for Whites and Blacks were essentially unchanged<br />

from a year earlier.


Employment<br />

In July 2018, there were 20.9 million employed 16- to 24-year-olds, about the same number as the<br />

summer before. Between April and July 2018, the number of employed youth rose by 2.0 million, in line<br />

with the change between April and July 2017. The employment-population ratio for youth—the<br />

proportion of the 16- to 24-year-old civilian noninstitutional population with a job—was 55.0 percent in<br />

July 2018, little changed from the prior year. (See tables 1 and 2.)<br />

Employment-population ratios in July 2018 were higher than a year earlier for young women (54.8<br />

percent), Whites (58.0 percent), and Hispanics (51.7 percent). The ratios declined for young men (55.2<br />

percent) and Asians (39.7 percent). The ratio for Blacks, at 47.2 percent in July, was about unchanged<br />

from the summer before.<br />

In July 2018, the largest percentage of employed youth worked in the leisure and hospitality industry<br />

(26 percent), which includes food services. An additional 18 percent of employed youth worked in the<br />

retail trade industry, and 11 percent worked in education and health services. (See table 3.)<br />

<strong>Unemployment</strong><br />

<strong>Unemployment</strong> among youth rose by 567,000 from April to July 2018, compared with an increase of<br />

458,000 for the same period in 2017.<br />

The youth unemployment rate, at 9.2 percent in July 2018, was little changed from July 2017. This<br />

represents the lowest summer youth unemployment rate since July 1966. The number of unemployed<br />

youth was 2.1 million in July 2018, little different from a year earlier. Of the 2.1 million unemployed<br />

16- to 24-year-olds, 1.5 million were looking for full-time work in July 2018, also little changed from<br />

July 2017. (See tables 1 and 2.)<br />

In July 2018, the unemployment rates for both young men (9.8 percent) and women (8.6 percent) were<br />

little changed from the summer before. The July 2018 rate for young Asians (8.4 percent) declined over<br />

the year, while the rates for young Whites (7.6 percent), Blacks (16.5 percent), and Hispanics (10.8<br />

percent) showed little change over the year. (See table 2.)<br />

- 2 -


Technical Note<br />

The estimates in this release were obtained from the<br />

Current Population Survey (CPS), a national sample survey<br />

of about 60,000 eligible households conducted monthly for<br />

the Bureau of Labor Statistics (BLS) by the U.S. Census<br />

Bureau. The data in this release relate to the employment<br />

status of youth (16- to 24-year-olds) during the months of<br />

April-July. This period was selected as being the most<br />

representative time frame in which to measure the full<br />

summertime transition from school to work. July is the peak<br />

summer month of youth employment.<br />

Beginning in January of each year, data reflect revised<br />

population controls used in the CPS. Additional information<br />

about population controls is available on the BLS website at<br />

www.bls.gov/cps/documentation.htm#pop.<br />

Information in this release will be made available to<br />

sensory impaired individuals upon request. Voice phone:<br />

(202) 691-5200; Federal Relay Service: (800) 877-8339.<br />

Reliability of the estimates<br />

Statistics based on the CPS are subject to both<br />

sampling and nonsampling error. When a sample, rather than<br />

the entire population, is surveyed, there is a chance that the<br />

sample estimates may differ from the true population values<br />

they represent. The component of this difference that occurs<br />

because samples differ by chance is known as sampling<br />

error, and its variability is measured by the standard error of<br />

the estimate. There is about a 90-percent chance, or level of<br />

confidence, that an estimate based on a sample will differ by<br />

no more than 1.6 standard errors from the true population<br />

value because of sampling error. BLS analyses are generally<br />

conducted at the 90-percent level of confidence.<br />

The CPS data also are affected by nonsampling error.<br />

Nonsampling error can occur for many reasons, including<br />

the failure to sample a segment of the population, inability to<br />

obtain information for all respondents in the sample,<br />

inability or unwillingness of respondents to provide correct<br />

information, and errors made in the collection or processing<br />

of the data.<br />

More information on the reliability of data from the<br />

CPS and estimating standard errors is available online at<br />

www.bls.gov/cps/documentation.htm#reliability.<br />

Definitions<br />

The principal definitions used in this release are<br />

described briefly below.<br />

Employed. Employed persons are all those who, during<br />

the survey reference week (which is generally the week<br />

including the 12th day of the month), (a) did any work at all<br />

as paid employees; (b) worked in their own business,<br />

profession, or on their own farm; (c) worked 15 hours or<br />

more as unpaid workers in a family member’s business.<br />

Persons who were temporarily absent from their jobs<br />

because of illness, bad weather, vacation, labor dispute, or<br />

another reason also are counted as employed.<br />

Unemployed. The unemployed are those who had no<br />

employment during the reference week, were available for<br />

work at that time, and had made specific efforts to find<br />

employment sometime during the 4-week period ending with<br />

the reference week. Persons who were waiting to be recalled<br />

to a job from which they had been laid off need not have<br />

been looking for work to be classified as unemployed.<br />

Looking for full-time work refers to 35 hours or more per<br />

week; part-time work refers to fewer than 35 hours per<br />

week.<br />

Civilian labor force. This group comprises all persons<br />

classified as employed or unemployed.<br />

<strong>Unemployment</strong> rate. The unemployment rate is the<br />

number of unemployed persons as a percent of the civilian<br />

labor force.<br />

Labor force participation rate. The labor force<br />

participation rate is the labor force as a percent of the<br />

population.<br />

Employment-population ratio. The employmentpopulation<br />

ratio is the employed as a percent of the<br />

population.<br />

Not in the labor force. Included in this group are all<br />

persons in the civilian noninstitutional population who are<br />

neither employed nor unemployed.<br />

Industry and class of worker. This information applies<br />

to the job held during the reference week. Persons with two<br />

or more jobs are classified in the job at which they worked<br />

the greatest number of hours. Persons are classified using the<br />

2012 Census industry classification system. The class-ofworker<br />

breakdown assigns workers to the following<br />

categories: Private and government wage and salary workers,<br />

unincorporated self-employed workers, and unpaid family<br />

workers.<br />

Wage and salary workers. Included in this group are<br />

persons who receive wages, salary, commissions, tips, or<br />

pay in kind from a private employer or from a government<br />

entity.<br />

Self-employed workers. Included in this group are those<br />

who work for profit or fees in their own unincorporated<br />

business, profession, trade, or farm. Only unincorporated<br />

self-employed are included in the self-employed category.<br />

Self-employed persons whose businesses are incorporated<br />

are included with private wage and salary workers.<br />

Unpaid family workers. Included in this group are<br />

persons working without pay for 15 hours a week or more on<br />

a farm or in a business operated by a family member in their<br />

household.


Table 1. Employment status of the civilian noninstitutional population 16 to 24 years of age by sex, race, and<br />

Hispanic or Latino ethnicity, April-July 2018<br />

[Numbers in thousands. Data are not seasonally adjusted.]<br />

Employment status, sex, race, and<br />

Hispanic or Latino ethnicity<br />

April May June July<br />

April-July changes<br />

Number<br />

Percent<br />

TOTAL<br />

Civilian noninstitutional population................................... 38,039 38,023 38,009 37,997 -42 -0.1<br />

Civilian labor force.................................................. 20,425 20,779 22,640 23,016 2,591 12.7<br />

Participation rate.................................................. 53.7 54.6 59.6 60.6 6.9 12.8<br />

Employed.......................................................... 18,873 18,984 20,332 20,897 2,024 10.7<br />

Employment-population ratio................................. 49.6 49.9 53.5 55.0 5.4 10.9<br />

Unemployed....................................................... 1,552 1,795 2,309 2,119 567 36.5<br />

Looking for full-time work..................................... 1,028 1,285 1,695 1,458 430 41.8<br />

Looking for part-time work.................................... 523 510 614 660 137 26.2<br />

<strong>Unemployment</strong> rate.............................................. 7.6 8.6 10.2 9.2 1.6 21.1<br />

Not in labor force.................................................... 17,614 17,245 15,369 14,981 -2,633 -14.9<br />

Men<br />

Civilian noninstitutional population................................... 19,153 19,144 19,135 19,128 -25 -0.1<br />

Civilian labor force.................................................. 10,489 10,659 11,639 11,695 1,206 11.5<br />

Participation rate.................................................. 54.8 55.7 60.8 61.1 6.3 11.5<br />

Employed.......................................................... 9,572 9,613 10,328 10,550 978 10.2<br />

Employment-population ratio................................. 50.0 50.2 54.0 55.2 5.2 10.4<br />

Unemployed....................................................... 916 1,045 1,312 1,145 229 25.0<br />

Looking for full-time work..................................... 631 764 1,008 828 197 31.2<br />

Looking for part-time work.................................... 285 281 303 317 32 11.2<br />

<strong>Unemployment</strong> rate.............................................. 8.7 9.8 11.3 9.8 1.1 12.6<br />

Not in labor force.................................................... 8,664 8,485 7,496 7,433 -1,231 -14.2<br />

Women<br />

Civilian noninstitutional population................................... 18,886 18,880 18,874 18,869 -17 -0.1<br />

Civilian labor force.................................................. 9,936 10,120 11,001 11,321 1,385 13.9<br />

Participation rate.................................................. 52.6 53.6 58.3 60.0 7.4 14.1<br />

Employed.......................................................... 9,301 9,371 10,004 10,347 1,046 11.2<br />

Employment-population ratio................................. 49.2 49.6 53.0 54.8 5.6 11.4<br />

Unemployed....................................................... 635 749 997 974 339 53.4<br />

Looking for full-time work..................................... 397 520 686 630 233 58.7<br />

Looking for part-time work.................................... 238 229 311 344 106 44.5<br />

<strong>Unemployment</strong> rate.............................................. 6.4 7.4 9.1 8.6 2.2 34.4<br />

Not in labor force.................................................... 8,950 8,759 7,873 7,548 -1,402 -15.7<br />

White<br />

Civilian noninstitutional population................................... 27,910 27,894 27,879 27,866 -44 -0.2<br />

Civilian labor force.................................................. 15,574 15,876 17,227 17,509 1,935 12.4<br />

Participation rate.................................................. 55.8 56.9 61.8 62.8 7.0 12.5<br />

Employed.......................................................... 14,535 14,680 15,692 16,174 1,639 11.3<br />

Employment-population ratio................................. 52.1 52.6 56.3 58.0 5.9 11.3<br />

Unemployed....................................................... 1,039 1,195 1,535 1,335 296 28.5<br />

Looking for full-time work..................................... 656 818 1,106 863 207 31.6<br />

Looking for part-time work.................................... 383 378 429 471 88 23.0<br />

<strong>Unemployment</strong> rate.............................................. 6.7 7.5 8.9 7.6 0.9 13.4<br />

Not in labor force.................................................... 12,336 12,019 10,652 10,357 -1,979 -16.0<br />

Black or African American<br />

Civilian noninstitutional population................................... 5,688 5,681 5,674 5,668 -20 -0.4<br />

Civilian labor force.................................................. 2,795 2,866 3,152 3,203 408 14.6<br />

Participation rate.................................................. 49.1 50.5 55.5 56.5 7.4 15.1<br />

Employed.......................................................... 2,441 2,494 2,651 2,675 234 9.6<br />

Employment-population ratio................................. 42.9 43.9 46.7 47.2 4.3 10.0<br />

Unemployed....................................................... 354 372 501 528 174 49.2<br />

Looking for full-time work..................................... 267 301 406 438 171 64.0<br />

Looking for part-time work.................................... 86 71 95 90 4 4.7<br />

<strong>Unemployment</strong> rate.............................................. 12.7 13.0 15.9 16.5 3.8 29.9<br />

Not in labor force.................................................... 2,893 2,815 2,523 2,465 -428 -14.8


Table 1. Employment status of the civilian noninstitutional population 16 to 24 years of age by sex, race, and<br />

Hispanic or Latino ethnicity, April-July 2018 — Continued<br />

[Numbers in thousands. Data are not seasonally adjusted.]<br />

Employment status, sex, race, and<br />

Hispanic or Latino ethnicity<br />

April May June July<br />

April-July changes<br />

Number<br />

Percent<br />

Asian<br />

Civilian noninstitutional population................................... 2,294 2,264 2,308 2,302 8 0.3<br />

Civilian labor force.................................................. 895 811 950 997 102 11.4<br />

Participation rate.................................................. 39.0 35.8 41.1 43.3 4.3 11.0<br />

Employed.......................................................... 845 755 847 913 68 8.0<br />

Employment-population ratio................................. 36.9 33.3 36.7 39.7 2.8 7.6<br />

Unemployed....................................................... 49 56 102 84 35 71.4<br />

Looking for full-time work..................................... 32 42 73 59 27 84.4<br />

Looking for part-time work.................................... 17 14 29 25 8 47.1<br />

<strong>Unemployment</strong> rate.............................................. 5.5 6.9 10.7 8.4 2.9 52.7<br />

Not in labor force.................................................... 1,399 1,453 1,358 1,305 -94 -6.7<br />

Hispanic or Latino ethnicity<br />

Civilian noninstitutional population................................... 8,659 8,668 8,677 8,687 28 0.3<br />

Civilian labor force.................................................. 4,592 4,676 4,784 5,035 443 9.6<br />

Participation rate.................................................. 53.0 53.9 55.1 58.0 5.0 9.4<br />

Employed.......................................................... 4,227 4,259 4,302 4,492 265 6.3<br />

Employment-population ratio................................. 48.8 49.1 49.6 51.7 2.9 5.9<br />

Unemployed....................................................... 365 417 482 543 178 48.8<br />

Looking for full-time work..................................... 242 279 359 362 120 49.6<br />

Looking for part-time work.................................... 123 137 123 181 58 47.2<br />

<strong>Unemployment</strong> rate.............................................. 7.9 8.9 10.1 10.8 2.9 36.7<br />

Not in labor force.................................................... 4,067 3,992 3,894 3,652 -415 -10.2<br />

NOTE: Estimates for the above race groups (White, Black or African American, and Asian) do not sum to totals because data are not presented for all<br />

races. Persons whose ethnicity is identified as Hispanic or Latino may be of any race. Updated population controls are introduced annually with the<br />

release of January data.


Table 2. Employment status of the civilian noninstitutional population 16 to 24 years of age by sex, race, and<br />

Hispanic or Latino ethnicity, July 2015-2018<br />

[Numbers in thousands. Data are not seasonally adjusted.]<br />

Employment status, sex, race, and<br />

Hispanic or Latino ethnicity<br />

TOTAL<br />

Civilian noninstitutional population...................................................... 38,589 38,450 38,152 37,997<br />

Civilian labor force..................................................................... 23,162 23,104 23,107 23,016<br />

Participation rate..................................................................... 60.0 60.1 60.6 60.6<br />

Employed............................................................................. 20,333 20,456 20,890 20,897<br />

Employment-population ratio.................................................... 52.7 53.2 54.8 55.0<br />

Unemployed.......................................................................... 2,829 2,648 2,217 2,119<br />

Looking for full-time work........................................................ 2,134 1,912 1,607 1,458<br />

Looking for part-time work....................................................... 695 736 610 660<br />

<strong>Unemployment</strong> rate................................................................. 12.2 11.5 9.6 9.2<br />

Not in labor force....................................................................... 15,426 15,346 15,045 14,981<br />

Men<br />

Civilian noninstitutional population...................................................... 19,442 19,380 19,219 19,128<br />

Civilian labor force..................................................................... 12,011 12,094 11,983 11,695<br />

Participation rate..................................................................... 61.8 62.4 62.3 61.1<br />

Employed............................................................................. 10,488 10,638 10,773 10,550<br />

Employment-population ratio.................................................... 53.9 54.9 56.1 55.2<br />

Unemployed.......................................................................... 1,523 1,455 1,210 1,145<br />

Looking for full-time work........................................................ 1,195 1,169 944 828<br />

Looking for part-time work....................................................... 328 286 266 317<br />

<strong>Unemployment</strong> rate................................................................. 12.7 12.0 10.1 9.8<br />

Not in labor force....................................................................... 7,431 7,287 7,236 7,433<br />

Women<br />

Civilian noninstitutional population...................................................... 19,147 19,069 18,932 18,869<br />

Civilian labor force..................................................................... 11,151 11,010 11,124 11,321<br />

Participation rate..................................................................... 58.2 57.7 58.8 60.0<br />

Employed............................................................................. 9,846 9,818 10,117 10,347<br />

Employment-population ratio.................................................... 51.4 51.5 53.4 54.8<br />

Unemployed.......................................................................... 1,306 1,193 1,007 974<br />

Looking for full-time work........................................................ 939 743 663 630<br />

Looking for part-time work....................................................... 367 450 344 344<br />

<strong>Unemployment</strong> rate................................................................. 11.7 10.8 9.1 8.6<br />

Not in labor force....................................................................... 7,996 8,059 7,808 7,548<br />

White<br />

Civilian noninstitutional population...................................................... 28,488 28,297 28,038 27,866<br />

Civilian labor force..................................................................... 17,735 17,734 17,423 17,509<br />

Participation rate..................................................................... 62.3 62.7 62.1 62.8<br />

Employed............................................................................. 15,903 15,981 16,031 16,174<br />

Employment-population ratio.................................................... 55.8 56.5 57.2 58.0<br />

Unemployed.......................................................................... 1,832 1,754 1,392 1,335<br />

Looking for full-time work........................................................ 1,308 1,222 974 863<br />

Looking for part-time work....................................................... 524 532 418 471<br />

<strong>Unemployment</strong> rate................................................................. 10.3 9.9 8.0 7.6<br />

Not in labor force....................................................................... 10,754 10,562 10,615 10,357<br />

Black or African American<br />

Civilian noninstitutional population...................................................... 5,916 5,850 5,749 5,668<br />

Civilian labor force..................................................................... 3,337 3,149 3,214 3,203<br />

Participation rate..................................................................... 56.4 53.8 55.9 56.5<br />

Employed............................................................................. 2,645 2,499 2,694 2,675<br />

Employment-population ratio.................................................... 44.7 42.7 46.9 47.2<br />

Unemployed.......................................................................... 691 650 520 528<br />

Looking for full-time work........................................................ 604 533 412 438<br />

Looking for part-time work....................................................... 87 117 108 90<br />

<strong>Unemployment</strong> rate................................................................. 20.7 20.6 16.2 16.5<br />

Not in labor force....................................................................... 2,580 2,701 2,535 2,465<br />

July<br />

2015<br />

July<br />

2016<br />

July<br />

2017<br />

July<br />

2018


Table 2. Employment status of the civilian noninstitutional population 16 to 24 years of age by sex, race, and<br />

Hispanic or Latino ethnicity, July 2015-2018 — Continued<br />

[Numbers in thousands. Data are not seasonally adjusted.]<br />

Employment status, sex, race, and<br />

Hispanic or Latino ethnicity<br />

Asian<br />

Civilian noninstitutional population...................................................... 2,148 2,212 2,208 2,302<br />

Civilian labor force..................................................................... 957 954 1,047 997<br />

Participation rate..................................................................... 44.6 43.1 47.4 43.3<br />

Employed............................................................................. 855 859 944 913<br />

Employment-population ratio.................................................... 39.8 38.8 42.7 39.7<br />

Unemployed.......................................................................... 102 95 103 84<br />

Looking for full-time work........................................................ 68 65 70 59<br />

Looking for part-time work....................................................... 34 30 33 25<br />

<strong>Unemployment</strong> rate................................................................. 10.7 10.0 9.9 8.4<br />

Not in labor force....................................................................... 1,191 1,258 1,162 1,305<br />

Hispanic or Latino ethnicity<br />

Civilian noninstitutional population...................................................... 8,406 8,497 8,535 8,687<br />

Civilian labor force..................................................................... 4,728 4,776 4,835 5,035<br />

Participation rate..................................................................... 56.2 56.2 56.6 58.0<br />

Employed............................................................................. 4,127 4,235 4,347 4,492<br />

Employment-population ratio.................................................... 49.1 49.8 50.9 51.7<br />

Unemployed.......................................................................... 601 540 488 543<br />

Looking for full-time work........................................................ 458 385 353 362<br />

Looking for part-time work....................................................... 143 155 135 181<br />

<strong>Unemployment</strong> rate................................................................. 12.7 11.3 10.1 10.8<br />

Not in labor force....................................................................... 3,679 3,721 3,700 3,652<br />

NOTE: Estimates for the above race groups (White, Black or African American, and Asian) do not sum to totals because data are not presented for all<br />

races. Persons whose ethnicity is identified as Hispanic or Latino may be of any race. Updated population controls are introduced annually with the<br />

release of January data.<br />

July<br />

2015<br />

July<br />

2016<br />

July<br />

2017<br />

July<br />

2018


Table 3. Employed persons 16 to 24 years of age by industry, class of worker, race, and Hispanic or Latino<br />

ethnicity, July 2017-2018<br />

[Numbers in thousands. Data are not seasonally adjusted.]<br />

Industry and class of worker<br />

July<br />

2017<br />

Total<br />

July<br />

2018<br />

July<br />

2017<br />

White<br />

July<br />

2018<br />

Black or African<br />

American<br />

July<br />

2017<br />

July<br />

2018<br />

July<br />

2017<br />

Asian<br />

July<br />

2018<br />

Hispanic or Latino<br />

ethnicity<br />

Total employed.................................. 20,890 20,897 16,031 16,174 2,694 2,675 944 913 4,347 4,492<br />

Agriculture and related industries.......... 336 391 306 360 14 8 3 0 105 103<br />

Nonagricultural industries................... 20,555 20,506 15,724 15,814 2,680 2,667 940 913 4,242 4,389<br />

Private wage and salary workers 1 ....... 18,794 18,720 14,395 14,468 2,449 2,414 862 833 3,992 4,125<br />

Mining, quarrying, and oil and gas<br />

extraction.............................. 32 77 26 75 0 0 0 0 3 23<br />

Construction............................. 1,061 1,000 965 888 44 44 9 13 368 283<br />

Manufacturing........................... 1,361 1,409 1,062 1,124 161 141 72 72 273 306<br />

Durable goods........................ 820 815 683 684 83 66 37 35 140 147<br />

Nondurable goods.................... 540 595 380 439 79 74 35 37 134 159<br />

Wholesale trade......................... 290 304 236 234 28 52 15 13 70 77<br />

Retail trade.............................. 3,978 3,682 2,996 2,800 570 574 190 164 907 840<br />

Transportation and utilities............. 595 577 380 447 157 73 17 16 144 184<br />

Information............................... 305 276 199 212 46 25 31 23 57 49<br />

Financial activities...................... 721 797 556 621 89 99 36 30 115 176<br />

Professional and business services... 1,724 1,738 1,328 1,322 217 232 96 108 317 389<br />

Education and health services........ 2,466 2,383 1,848 1,778 379 404 108 83 442 469<br />

Leisure and hospitality................. 5,403 5,463 4,107 4,158 680 666 242 270 1,141 1,125<br />

Other services........................... 858 1,014 691 812 78 104 46 41 155 204<br />

Government wage and salary<br />

workers................................... 1,491 1,398 1,096 1,064 210 190 72 66 213 176<br />

Federal................................... 194 177 107 100 45 48 20 9 24 18<br />

State...................................... 541 465 392 375 67 47 31 30 71 44<br />

Local...................................... 756 757 597 589 98 96 20 28 118 114<br />

Self-employed, unincorporated, and<br />

unpaid family workers.................. 270 388 233 282 21 62 6 14 37 88<br />

July<br />

2017<br />

July<br />

2018<br />

1 Includes self-employed workers whose businesses are incorporated.<br />

NOTE: Estimates for the above race groups (White, Black or African American, and Asian) do not sum to totals because data are not presented for all<br />

races. Persons whose ethnicity is identified as Hispanic or Latino may be of any race. Updated population controls are introduced annually with the<br />

release of January data.


Page 122 of 149


Attachment C<br />

The Consequences<br />

of Long-Term <strong>Unemployment</strong><br />

Page 123 of 149


URBAN<br />

INSTITUTE<br />

2100 M Street, NW<br />

Washington, D.C. 20037-1264<br />

Consequences of Long-Term <strong>Unemployment</strong><br />

Austin Nichols, Josh Mitchell, and Stephan Lindner


Copyright © July 2013. The Urban Institute. All rights reserved. Permission is granted for<br />

reproduction of this file, with attribution to the Urban Institute.<br />

This report was prepared for the Rockefeller Foundation under grant 2013 SRC 105.<br />

The Urban Institute is a nonprofit, nonpartisan policy research and educational organization that<br />

examines the social, economic, and governance problems facing the nation. The views expressed<br />

are those of the author and should not be attributed to the Urban Institute, its trustees, or its<br />

funders.<br />

1


Contents<br />

Challenges to Measuring Effects of Long-Term <strong>Unemployment</strong> 2<br />

Declining Income and Consumption 3<br />

Declining Reemployment Wages 4<br />

Declining Human and Social Capital 7<br />

Impacts on Future Labor Market Attachment 8<br />

Impacts on Physical and Mental Health 9<br />

Impacts on Children and Families 10<br />

Impact on Communities 11<br />

Conclusions 12<br />

References 14


URBAN<br />

INSTITUTE<br />

Consequences of Long-Term <strong>Unemployment</strong><br />

The unemployment rate has been over 7 percent since December 2008, and peaked at 10 percent in<br />

late 2009. Despite the gradual improvement in the labor market, long-term unemployment—the<br />

share of the unemployed who have been out of work for more than six months—remains at<br />

unprecedented levels. The fraction of unemployed workers who are long-term unemployed has<br />

hovered around 40 percent from late 2009 into 2013, although it had never previously risen above<br />

30 percent since the Great Depression.<br />

Being out of work for six months or more is associated with lower well-being among the longterm<br />

unemployed, their families, and their communities. Each week out of work means more lost<br />

income. The long-term unemployed also tend to earn less once they find new jobs. They tend to be<br />

in poorer health and have children with worse academic performance than similar workers who<br />

avoided unemployment. Communities with a higher share of long-term unemployed workers also<br />

tend to have higher rates of crime and violence.<br />

Although there is considerable research documenting the association between long-term<br />

unemployment and poor socioeconomic outcomes, it is not clear what drives those associations.<br />

Those who become long-term unemployed may have issues that contribute to their unemployment<br />

status and also to their poor future outcomes. In this case, long-term unemployment can be<br />

associated with, but is not the underlying cause of, poor future outcomes, a phenomenon referred to<br />

as a “selection” effect. Another complicating factor is the extent to which the association between<br />

poor outcomes and long-term unemployment is rooted in the fact of an involuntary job loss itself<br />

and not the time spent looking for work. Last, to a certain extent, health, family, and child outcomes<br />

are influenced by the loss of income associated with long-term unemployment, and isolating the<br />

income effects from the direct effects on long-term unemployment can be difficult.<br />

In this paper, we discuss various channels through which longer unemployment duration might<br />

influence outcomes for the unemployed. The discussion of prior research that follows shows that<br />

direct evidence for many of these channels is very underdeveloped, somewhat surprisingly. Still,<br />

there are plausible channels through which longer unemployment duration might result in worse<br />

outcomes, most notably loss of human or social capital. We also discuss evidence for how job loss<br />

itself affects various outcomes. Our review shows that most of the literature finds significant


negative effects in many areas, starting with lower reemployment wages of those directly affected by<br />

job loss, and continuing on to health, family structure, children’s well-being, and whole<br />

communities.<br />

The measured impacts of unemployment can increase with the duration of unemployment.<br />

Cumulative loss of income increases as unemployment continues, but expected wages at<br />

reemployment also fall, leading to a permanent loss of future income. Many authors have<br />

documented long-run losses of wages following an unemployment event in addition to many other<br />

long-run impacts on measured well-being.<br />

Challenges to Measuring Effects of Long-Term <strong>Unemployment</strong><br />

Long-term unemployment can plausibly affect individuals, families, and communities in direct ways.<br />

When individuals are out of work, their skills may erode through lack of use. That erosion or<br />

“depreciation of human capital” increases as time passes, meaning that the potential wages the<br />

unemployed can earn on finding a new job and even the chances of finding a new job decrease the<br />

longer they are out of work. Similarly, being out of work may reduce a worker’s “social capital”—the<br />

network of business contacts that make finding new and good jobs easier. Social capital may<br />

decrease with longer unemployment duration because social circles defined by work contact can<br />

decay when work contact ceases, or because being out of work is increasingly stigmatizing the longer<br />

a person cannot find new employment. That erosion of social capital means that the longer a worker<br />

is unemployed, the less likely he or she is to find a new job. In addition, the stress of being out of<br />

work can influence an individual’s physical and mental health, family dynamics, and the well-being of<br />

his or her children. Involuntary job loss is a stressful event, creating a variety of problems<br />

immediately, and long periods of unemployment can compound those problems.<br />

Long-term unemployment can also influence outcomes indirectly. While a worker is<br />

unemployed, that worker’s family income falls due to the lack of earnings, and that loss of income<br />

(which becomes larger as unemployment is longer) can affect the worker and the worker’s<br />

household. The loss of income can reduce the quantity and quality of goods and services the<br />

worker’s family can purchase. Further, dealing with the loss of income can exacerbate stress. To the<br />

extent that the negative consequences of long-term unemployment have an effect through the loss<br />

of income, tax and transfer programs can help mitigate those consequences. Finally, if many workers<br />

in the same geographic area are experiencing long-term unemployment, their communities could<br />

2 The Urban Institute


suffer because of an increase in demand for public services and a decrease in the tax base used to<br />

fund those services. Declines in community services, such as increased class sizes in public schools<br />

or fewer public safety workers, can also feed back on individuals and families.<br />

Identifying the mechanisms by which long-term unemployment affects individuals, families, and<br />

communities is further complicated by the fact that even during severe recessions, job loss and long<br />

periods of unemployment are not random events. Workers with less advantageous characteristics<br />

tend to remain out of work longer and tend to experience worse outcomes regardless of when they<br />

are reemployed. To the extent that the selection effect is important, differences observed across<br />

workers with different durations of unemployment are not caused by longer duration. Selection is<br />

often discussed as a problem in the context of wages or earnings, but the pattern of selection due to<br />

workers with less advantageous characteristics left unemployed longer may also operate in other<br />

domains, such as health or other measures of well-being.<br />

Understanding the underlying mechanisms through which time out of work relates to outcomes<br />

both during and after unemployment is essential in devising effective policies to ameliorate the<br />

consequences of job loss. Policy responses to long-term unemployment may have different effects<br />

depending on which of the possible explanations is dominant in accounting for any particular<br />

outcome.<br />

In short, there are plausible theoretical links connecting duration of unemployment to worse<br />

outcomes. Much research on long-term consequences of job loss in previous recessions may reflect<br />

both job loss and longer unemployment in a recession. Correlations between various outcomes and<br />

job loss and long-term unemployment probably reflect at least some causal relationship. However,<br />

as we examine the literature on various consequences of job loss and long-term unemployment, we<br />

must bear in mind that many apparent consequences of job loss or longer duration may be due to<br />

differences among workers out of work for different durations, and may also be caused by the loss<br />

of income directly, not by unemployment itself.<br />

Declining Income and Consumption<br />

Longer unemployment has its most direct impact on family resources through lost earnings, which<br />

add up quickly with each additional week of unemployment. In the Great Recession, family incomes<br />

fell 40 percent or more for most long-term unemployed workers (Johnson and Feng 2013). In 2011,<br />

Consequences Of Long-Term <strong>Unemployment</strong> 3


long-term unemployed workers were almost twice as likely to be poor as those unemployed less than<br />

six months, and almost four times as likely to be poor as those never unemployed (Nichols 2012);<br />

three of every four single parents who were unemployed more than 26 weeks were poor in 2011.<br />

Browning and Crossley (2001) find that families with an unemployed worker have consumption<br />

16 percent lower after six months of unemployment, but 24 percent lower if the sole worker in the<br />

family became unemployed, relative to those who do not lose employment. Consumption drops less<br />

than income following unemployment in part because of borrowing or spending down savings,<br />

which is far from costless. Borie-Holtz, Van Horn, and Zukin (2010) show that the long-term<br />

unemployed borrowed money from friends, spent down savings, and missed mortgage or rent<br />

payments. About half of unemployed workers reported a poor financial situation in 2010, and about<br />

a tenth had filed for bankruptcy (Godofsky, Van Horn, and Zukin 2010).<br />

Consumption drops can have longer-term costs in addition to lowering well-being during<br />

unemployment if family members defer needed expenditures. Among long-term unemployed or<br />

underemployed workers in late 2011, 63 percent skipped dental visits, 56 percent put off needed<br />

health care, and 40 percent reported not filling a prescription, with each proportion roughly twice<br />

that for full-time workers. 1<br />

Declining Reemployment Wages<br />

The negative effect of unemployment on later wages is well documented (Jacobson, LaLonde, and<br />

Sullivan 1993; Ruhm 1991). Hamermesh (1989) summarizes prior empirical work on dislocated and<br />

displaced workers 2 showing that reemployed workers earned about 5 to 15 percent less than similar<br />

workers who did not lose their jobs and that half of displaced workers finding jobs in some samples<br />

had been unemployed as long as nine months. Across many studies, there were not large differences<br />

1 Marilyn Geewax, “The Impacts of Long-Term <strong>Unemployment</strong>,” part of an NPR special series, Still No Job: Over a Year<br />

without Enough Work, December 12, 2011. http://www.npr.org/2011/12/09/143438731/the-impacts-of-long-termunemployment.<br />

2 A dislocated worker, as defined by the Workforce Investment Act, is a person who is terminated or laid off due to<br />

plant or company closure or downsizing. Self-employed workers who become unemployed due to economic conditions<br />

and homemakers who are no longer supported by another family member are also considered dislocated workers.<br />

Displaced workers are similarly defined as persons 20 years of age and older who lost or left jobs because their plant or<br />

company closed or moved, there was insufficient work for them to do, or their position or shift was abolished. Often<br />

attention is restricted to long-tenure displaced workers, who had at least three years on the job they lost, following early<br />

Displaced Worker Supplement survey design in the Current Population Survey. Hamermesh (1989) discusses studies<br />

examining different types of job loss, but limits attention to displaced workers whose employer’s establishments (plants)<br />

closed.<br />

4 The Urban Institute


in wage losses by race or ethnicity (although minority workers are displaced with greater probability)<br />

or sex. Predisplacement wage and occupation play some role, with displaced blue-collar workers<br />

tending to lose a greater fraction of lifetime earnings.<br />

Workers displaced in the early 1980s suffered declines of 30 percent or more in wages at<br />

reemployment (Jacobson et al. 1993) and had earnings 20 percent lower than otherwise comparable<br />

workers even 15 to 20 years after displacement (von Wachter, Song, and Manchester 2009). This is<br />

partly because of steadily declining mean reemployment wages as unemployment duration increases,<br />

and the persistence of reemployment wage discounts, meaning that the effects increase with<br />

duration of unemployment. Barnette and Michaud (2012) find that laid-off workers, once<br />

reemployed, have wages about 15 percent lower than continuously employed workers, 1 to 20 years<br />

after layoff, whereas workers displaced by company closings, once reemployed, have wages about 5<br />

to 10 percent lower than continuously employed workers. The wage disadvantage for workers<br />

displaced by company closings dissipates 4 to 10 years after separation, suggesting that closings<br />

convey less clear information to future employers about worker quality than other kinds of layoffs.<br />

Because displacement leads to an increase of employment instability, some of the impact is also<br />

due to multiple subsequent unemployment spells repeatedly lowering wages and reducing<br />

accumulated job tenure and experience. Stevens (1997) showed that displaced workers are more<br />

likely to leave their subsequent jobs than nondisplaced workers. Brand (2004) and Rosenfeld (1992)<br />

document long-term reductions in career prospects for job losers, resulting in lower wage growth<br />

over time.<br />

Reservation wages (the lowest wage at which a job would be accepted) also decline over time, as<br />

workers’ expectations degrade and their needs increase. 3 The reductions in wages over the course of<br />

unemployment spells are also related to type of job loss 4 and to changing macroeconomic<br />

conditions. 5<br />

3 See, for example, Devine and Kiefer (1991).<br />

4 Gibbons and Katz (1991) found that displaced workers received higher reemployment wages than workers fired for<br />

cause. Lower wages at reemployment lead to persistent earnings disadvantages because workers’ entire future wage paths<br />

depend on starting wages (Rosenfeld 1992).<br />

5 Higher levels of long-term unemployment, such as in the early 1980s and the Great Recession, can create a cycle of<br />

lower employment because job-finding rates are lowest among workers unemployed the longest and numbers of workers<br />

per vacancy can be artificially inflated. As the labor market improves during a recovery, laid-off workers continue to be<br />

reemployed at reduced wages, and the long-term unemployed tend to be the last to find a job, as documented in, for<br />

example, von Wachter, Song, and Manchester (2009).<br />

Consequences Of Long-Term <strong>Unemployment</strong> 5


Selection and screening both play a role in steadily declining mean wages as unemployment<br />

continues. Selection refers to preexisting differences among workers who lose their jobs, producing<br />

a pattern of more productive workers finding employment more quickly at less of a wage discount,<br />

leaving less productive workers still unemployed facing lower wages. Screening is a similar<br />

phenomenon on the employers’ side, where employers observing a worker unemployed longer will<br />

infer that he or she must be a lower-productivity worker. 6 An important difference is that selection<br />

does not reflect a causal impact (if we could intervene to shorten unemployment, it might not affect<br />

outcomes), whereas screening can have impacts on individual workers negatively affected by<br />

employer expectations.<br />

Empirically, screening is an important mechanism. Kroft, Lange, and Notowidigdo (2012) show<br />

in an experiment that employer screening resulted in interview rates cut nearly in half among the<br />

long-term unemployed relative to the newly unemployed. Schmieder, von Wachter, and Bender<br />

(2012a, 2012b, 2012c) show that increases in duration of unemployment caused by benefit changes<br />

drive down the wage offers of employers but do not substantially affect reservation wages. This line<br />

of research is one of the very few to plausibly establish a direct causal impact of increases in duration<br />

of unemployment. 7<br />

Much of the research on wage losses at reemployment cannot distinguish between losses due to<br />

longer duration of unemployment for an individual and losses due to less favorable labor market<br />

conditions (higher unemployment, longer average durations) that are correlated with own duration.<br />

Davis and von Wachter (2011) find that men lose twice as much in lifetime earnings from being<br />

displaced via a mass layoff when the unemployment rate exceeds 8 percent as they do if displaced<br />

when the national unemployment rate is below 6 percent. However, it is not clear whether this is<br />

due to downward pressure on reemployment wages across the economy, or due to causal impacts of<br />

longer unemployment spells that accompany higher national unemployment.<br />

6 One can see selection and screening as two sides of the same coin, if workers anticipate firm screening and firms<br />

correctly anticipate reductions in average worker productivity. But they need not be, if firms’ expectations about worker<br />

quality declining with unemployment duration are incorrect but workers accept lower wages over time because the path<br />

of future wages looks bleak owing to unrealistic firm expectations. That is, screening can drive a vicious cycle of<br />

declining wages. Selection can operate without screening as well, if workers with higher ability are paid the same wage<br />

regardless of unemployment duration, but also exit faster, so that unemployment duration is associated with lower wages<br />

only due to the heterogeneity of workers.<br />

7 Whether wage offers are driven down by information about declining average worker productivity, due to either<br />

selection or skill erosion, or information about workers’ increasing desperation to find work, is not established. This line<br />

of work does cast doubt on explanations that rely on social networks among job losers, however.<br />

6 The Urban Institute


Declining Human and Social Capital<br />

Declines in wages need not be due to only selection on (or screening of) heterogeneous workers and<br />

declining expectations, but also due to real declines in productivity as human or social capital<br />

depreciates, possibly as the labor market moves farther away from the type of work lost, or as the<br />

displaced worker looks farther afield for new work. A real decline in productivity due to<br />

deteriorating human capital would be a true causal impact of longer unemployment, unlike selection<br />

due to the changing composition of the unemployed at different durations. 8<br />

One of the stories explaining declining reemployment wages via a causal mechanism instead of<br />

pure selection or screening is that human or social capital decays as workers are out of work longer.<br />

Yet most research purporting to measure human capital depreciation (e.g., Hollenbeck 1990) uses<br />

wages as a measure of human capital, so one cannot distinguish between the effect of human capital<br />

and other factors that might affect wages.<br />

The few studies that attempt to measure human capital directly do not deal with selection, so<br />

that all observed correlation between unemployment duration and human capital might be because<br />

people with lower human capital are likely to remain out of work for a longer time. For example,<br />

Edin and Gustavsson (2008) find that a year out of work is associated with general skills 5 percentile<br />

points lower relative to continuously employed workers, but there is no attempt to isolate the causal<br />

impact of time out of work from selection driving a larger fraction of unemployed workers being<br />

lower-ability workers over time, as higher-ability workers return to work. Edin and Gustavsson also<br />

cannot rule out reverse causation where “negative trends in skills lead to non-employment” (2008,<br />

174); that is, workers who experience skill depreciation relative to the rest of the workforce are more<br />

likely to be laid off.<br />

The research on the deterioration of skills is far from persuasive and plagued by data problems. 9<br />

Losses of job tenure or experience are losses of human capital compared with otherwise identical<br />

individuals who were never unemployed, not losses of human capital relative to pre-unemployment<br />

8 Declining reservation wages could reflect a decline in human capital, or endogenous changes in expectations related to<br />

human capital. For example, if laid-off workers believe their own industry or occupation, where their specific human<br />

capital is valued, will not recover fast enough, they may be induced to switch industry or occupation further afield over<br />

time, trading off losses in future wages for higher exit rates from unemployment in the near term.<br />

9 Measurement error in past test scores leads the coefficient to be biased toward zero, which many authors have taken as<br />

evidence of “depreciation” of skills over time in the absence of intervention.<br />

Consequences Of Long-Term <strong>Unemployment</strong> 7


human capital of the individual. Losses of firm-specific and industry-specific human capital are<br />

incurred at separation, and are unlikely to increase much in magnitude over time spent unemployed.<br />

A far more plausible loss increasing in magnitude with prolonged unemployment is of social<br />

capital, but little direct evidence is available. As Machin and Manning (1999) discuss:<br />

Many studies have documented the importance of the use of current workers to<br />

recruit friends and relatives. Something like a third of jobs in the UK are filled in this<br />

way (Gregg and Wadsworth, 1996). The reasons given are generally that it is costeffective<br />

and workers are unlikely to recommend others who they know are going to<br />

prove to be unsuitable workers. There is other evidence that suggests that the<br />

unemployed lose social contacts as their spells lengthen and that what social contacts<br />

they do maintain come to be increasingly made up of other unemployed.<br />

If loss of human or social capital is the driving mechanism behind reemployment wages<br />

declining over time, then policies designed to keep unemployed workers using skills or in contact<br />

with other workers would be an effective means of heading off long-term wage losses. Evidence on<br />

job training programs has produced mixed evidence about training as an efficient means to raise<br />

reemployment wages, although Holzer (2011) and Jacobson, LaLonde, and Sullivan (2011) provide<br />

guidance on improving practice. Subsidized employment programs may hold out even better<br />

prospects, by addressing both human and social capital depreciation. 10<br />

Impacts on Future Labor Market Attachment<br />

Unemployed workers become more likely to leave the labor force and retire, enroll in disability<br />

programs, or simply become “discouraged workers” as unemployment continues. The exit to<br />

disability is most worrisome because it tends to be permanent. Once someone identifies as being<br />

disabled, the individual is very unlikely to return to work; in fact, retired people are far more likely to<br />

reenter the labor market than the disabled. Rupp and Stapleton (1995) find that higher<br />

unemployment tends to increase the number of applicants to the Social Security Disability Insurance<br />

(DI) program, and eventually the number of successful applicants. Lindner and Nichols (2012) find<br />

that expansions of unemployment insurance staved off some applications for DI benefits.<br />

10 Evaluations of subsidized employment and transitional jobs (Bloom 2010) indicate relatively small gains in<br />

employment and earnings, but larger gains for some subgroups, especially single mothers (Michalopoulos 2005).<br />

Programs that integrate subsidized employment into unemployment insurance systems, such as the German<br />

Arbeitsbeschaffungsmaßnahmen, also seem to produce small increases in future employment and income on average, but<br />

larger effects for some subgroups (Caliendo, Hujer, and Thomsen 2008; Hujer, Caliendo, and Thomsen 2004).<br />

8 The Urban Institute


Impacts on Physical and Mental Health<br />

Burgard, Brand, and House (2007) report large declines in self-reported health status following job<br />

loss, even after taking differences in characteristics of job losers into account. Losses are largest<br />

among those who lose jobs for reasons related to health, implying the causal impact of job loss is<br />

much smaller than the impact observed by comparing job losers to other workers. Furthermore, job<br />

losses for other reasons increase depressive symptoms but have little impact on other measures of<br />

health. There is little evidence of health deteriorating over the course of an unemployment spell<br />

(Salm 2009). In fact, Ruhm (2000, 2001, 2005, 2007) documents improvements in health as<br />

unemployed workers get more exercise, smoke and drink less, lose weight, and suffer less from jobrelated<br />

or commute-related health risks.<br />

Sullivan and von Wachter (2009) find that the mortality consequences of displacement are<br />

severe, with a 50 to 100 percent increase in death rates the year following displacement and 10 to 15<br />

percent increases in death rates for the next 20 years. For a 40-year-old worker, that implies a decline<br />

in life expectancy of a year to a year and a half. Long-term joblessness results in higher mortality, but<br />

voluntary and involuntary separations seem to have similar impacts on mortality (Couch et al. 2013).<br />

The mechanism for these mortality increases is unclear but could be related to income loss, increases<br />

in risky health behavior (Browning and Heinesen 2012), and losses of health insurance coverage<br />

(Olson 1992).<br />

Although job loss increases the subsequent risk of death, the impacts of longer duration of<br />

unemployment on health or mortality are not clearly identified. Given that longer-duration<br />

unemployment is associated with higher mortality, but health does not seem to deteriorate during a<br />

spell, the observed correlations may be related to lower lifetime income (via both more forgone<br />

earnings and lower future wages earned by the long-term unemployed), and not through any direct<br />

impact on health. The link between income and health is also not clearly causal, however, and there<br />

is some evidence that it is increased labor force attachment that lowers mortality, not higher income<br />

(Snyder and Evans 2006).<br />

There is a long history of research showing that becoming unemployed has large negative effects<br />

on mental health, but that mental health does not deteriorate substantially with longer duration of<br />

unemployment. Whooley and colleagues (2002) found that depression strongly predicts future job<br />

and income losses, suggesting reverse causation is an important threat to such comparisons. Clark<br />

Consequences Of Long-Term <strong>Unemployment</strong> 9


and Oswald (1994) found duration of unemployment is actually positively correlated with well-being,<br />

conditional on being unemployed. Winkelmann and Winkelmann (1998) found no evidence of<br />

satisfaction changing over the course of a spell of unemployment. 11 On the other hand, Classen and<br />

Dunn (2012) estimated that higher rates of long-term unemployment increase suicide rates, although<br />

this may in part reflect general economic conditions. Browning and Heinesen (2012) used microlevel<br />

data from Denmark and found that job loss increases alcohol-related disease, mental illness,<br />

and suicide and suicide attempts, but these effects could be due to job loss itself, and unrelated to<br />

unemployment duration.<br />

Theoretically, links between declining employment prospects and declining mental health seem<br />

clear. As economic stress increases, the incidence of anxiety disorders should increase, and as<br />

individuals fall in the social hierarchy, serotonin-pathway disorders, including depression, should<br />

become more prevalent. 12 Alternatively, as expectations fall, people may adjust to a new normal and<br />

take fuller advantage of leisure time, leading to improvements in measured mental health. On<br />

balance, the empirical evidence for the link between longer durations of unemployment and worse<br />

mental health is far from clear.<br />

Effects on Children and Families<br />

There are a large variety of negative effects of job loss observed in the families of workers, although<br />

the causal mechanism is not always well known. Kalil and De Leire (2002) found that the negative<br />

effects of job loss for children were limited to those associated with the loss of a father’s job.<br />

Similarly, Lindo (2011) documented a negative impact of paternal job loss on infant birth weight.<br />

Rege, Telle, and Votruba (2011) also showed that paternal job loss lowers children’s school<br />

performance, and the negative effect of paternal job loss is not mediated by income, a shift in<br />

maternal time toward employment, marital dissolution, or residential relocation. Stevens and Schaller<br />

(2011) showed that layoffs affect children’s grade retention, and Wightman (2012) documented a<br />

11 Diette and colleagues (2012) suggest that psychological distress is higher among long-term unemployed than shortterm<br />

unemployed or continuously employed workers who have not experienced prior psychological distress, but they do<br />

not distinguish statistically between effects of long-term unemployment and short-term unemployment.<br />

12 Serotonin regulates self-esteem and may also regulate risky behavior and suicidal ideation. McGuire and Raleigh (1986)<br />

showed that position in a hierarchy affects serotonin levels, and that manipulation of serotonin can affect position in a<br />

hierarchy. Many studies show that relative income is an important predictor of health and mortality (Eibner and Evans<br />

2005), more so than absolute income in some cases, meaning that position in a social hierarchy can have large effects on<br />

long-term outcomes. Hierarchical position can also explain a lack of increasing stress or depression with longer duration<br />

unemployment, however, if people increasingly identify with a more disadvantaged peer group in which they rank<br />

higher.<br />

10 The Urban Institute


eduction in the probability that children finish high school after paternal job loss. Oreopoulos,<br />

Page, and Stevens (2008) traced the impact of job loss on children’s later earnings as adults. Katz<br />

(2010) pointed out that financial aid based on prior year income does not address the immediate<br />

needs of students whose parents are laid off, perhaps leading to losses of educational opportunity in<br />

the second generation. Loss of continuous health insurance coverage could also play a role in worse<br />

child outcomes, as Johnson and Schoeni (2011) show that health insurance can play a large role in<br />

intergenerational transmission of disadvantage.<br />

Job losses and long-term unemployment can affect children’s outcomes through increased<br />

family stress and reduced incomes. McLoyd and colleagues (1994) documented how financial stress<br />

from job loss affects the emotional well-being of mothers, producing increased cognitive distress<br />

and depressive symptoms in adolescent children and more negative assessments of maternal<br />

interaction. Children whose parents suffer longer unemployment and larger lifetime income losses<br />

can be expected to suffer greater detriment to their emotional well-being, and this may result in<br />

worse education and labor market outcomes in the children’s generation.<br />

Changes in family structure may be another mechanism by which the negative consequences of<br />

job losses are transmitted to the next generation. Del Bono, Weber, and Winter-Ebmer (2008) and<br />

Lindo (2010) showed that layoff affects fertility rates, and Lindner and Peters (2013) found negative<br />

effects of job loss of mothers and fathers on family stability, especially for married parents, which is<br />

one factor through which parental job loss may affect the well-being of children. Charles and<br />

Stephens (2004) documented an increase in divorce following layoffs but not plant closings or<br />

disability, suggesting that the higher divorce rate is more strongly related to the job loser’s<br />

productivity and other attributes rather than diminished financial prospects.<br />

Impact on Communities<br />

High rates of long-term unemployment can devastate local communities, as reduced lifetime income<br />

prospects induce a variety of behavioral changes, and alter social networks. Wilson (1987) building<br />

on Kain (1968), argued that a lack of available jobs close to where the disadvantaged unemployed<br />

workers live, or “spatial mismatch,” contributes to long durations of joblessness, in part because<br />

social networks become largely populated by other jobless workers. Persistent joblessness for men is<br />

then linked to breakdowns in traditional family arrangements, increased use of public assistance, and<br />

Consequences Of Long-Term <strong>Unemployment</strong> 11


high crime. As long-term unemployment becomes more concentrated, the neighborhood becomes a<br />

source of persistent poverty.<br />

Even if long-term unemployment does not have important effects on job finding via social<br />

networks, it can induce behavioral changes that have important spillover effects on the community<br />

as a whole. In addition to engaging in riskier health behavior and reducing investments in housing<br />

and other capital stocks that benefit the community as a whole, the long-term unemployed may be<br />

induced to seek out work in the illegal sector. Although crime rates fell in many areas during the<br />

Great Recession, they seem to have fallen due to a long-term trend, and to have fallen less in places<br />

hit harder by job loss. 13<br />

Rege, Telle, and Votruba (2012) document a 14 percentage point higher probability of arrest<br />

among workers affected by plant closings, but there are important dynamic and spillover effects as<br />

well. As Machin and Manning (1999) discuss:<br />

a cycle develops whereby involvement in crime reduces subsequent employment<br />

prospects which then raises the likelihood of participating in crime (see Thornberry<br />

and Christensen, 1984). In this vein Freeman (1992) and Grogger (1992) show some<br />

association between the persistence of joblessness and crime. Fagan and Freeman<br />

(1997) also review evidence that show important links between unemployment and<br />

crime…. It should be emphasized again that it is difficult to distinguish between<br />

heterogeneity and true duration dependence as the explanation for these correlations.<br />

Impacts on communities may be driven in part by increases in concentrated poverty due to longterm<br />

unemployment. The evidence of higher impacts due to greater spatial concentration of poverty<br />

is mixed, however, as discussed by Turner, Nichols, and Comey (2012) in the context of the Moving<br />

to Opportunity experiment.<br />

Conclusions<br />

The extensive evidence on far-reaching negative consequences of job loss is clear: Loss of a job can<br />

lead to losses of income in the short run, permanently lower wages, and result in worse mental and<br />

physical health and higher mortality rates. Further, parental job loss hampers children’s educational<br />

progress and lowers their future earnings. The link between longer duration of unemployment and<br />

worse consequences is more tenuous. Lower wages and lifetime incomes are associated with longer<br />

13 John Roman, “Learning About the Crime Decline from Big City Experiences with Homicide,” MetroTrends (blog),<br />

December 16, 2012, http://blog.metrotrends.org/2012/12/learning-crime-decline-big-city-experiences-homicide/.<br />

12 The Urban Institute


periods of unemployment, but the reason for the decreasing earnings prospects is not clear. In<br />

domains where we might expect to see strong evidence, such as mental health outcomes, the<br />

evidence is murky at best. When there are patterns of declining well-being as unemployment extends<br />

longer, the extent to which declining well-being is due to increasing loss of lifetime income alone or<br />

to time out of work is not clear.<br />

The need to distinguish among competing explanations for the observed patterns is pressing,<br />

because different policy responses would be called for depending on which of the potential<br />

explanations is the dominant one. Further research should identify more clearly whether selection,<br />

declining reservation wages, human capital depreciation, or some form of employer discrimination<br />

seems to be the dominant explanation for reemployment wage declining with unemployment<br />

duration. We also need to explore whether other long-run negative impacts of job loss and<br />

unemployment duration are due to those same factors, or to loss of income or social position.<br />

Consequences Of Long-Term <strong>Unemployment</strong> 13


References<br />

Barnette, Justin, and Amanda Michaud. 2012. “Wage Scars from Job Loss.” Working paper. Akron, OH:<br />

University of Akron. http://www.uakron.edu/dotAsset/2264615.pdf.<br />

Bloom, Dan. 2010. Transitional Jobs: Background, Program Models, and Evaluation Evidence. Washington, DC: US<br />

Department of Health and Human Services.<br />

http://www.acf.hhs.gov/sites/default/files/opre/tj_09_paper_embed.pdf.<br />

Borie-Holtz, Debbie, Carl Van Horn, and Cliff Zukin. 2010. No End in Sight: The Agony of Prolonged <strong>Unemployment</strong>.<br />

New Brunswick, NJ: John J. Heldrich Center for Workforce Development, Rutgers University.<br />

Brand, Jennie E. 2004. “Enduring Effects of Job Displacement on Career Outcomes.” Ph.D. dissertation,<br />

University of Wisconsin–Madison,<br />

http://www.ssc.wisc.edu/wlsresearch/publications/files/_private/Brand_Enduring.Effects.of.Job.Displacem<br />

ent.on.Career.Outcomes.pdf.<br />

Browning, Martin, and Thomas F. Crossley. 2001. “<strong>Unemployment</strong> Insurance Levels and Consumption Changes.”<br />

Journal of Public Economics 80(1): 1–23.<br />

Browning, Martin, and Eskil Heinesen. 2012. “Effect of Job Loss due to Plant Closure on Mortality and<br />

Hospitalization.” Journal of Health Economics 31(4): 599–616.<br />

Burgard, Sarah A., Jennie E. Brand, and James S. House. 2007. “Toward a Better Estimation of the Effect of Job<br />

Loss on Health.” Journal of Health and Social Behavior 48(4): 369–84.<br />

http://www.jstor.org/stable/10.2307/27638722 (accessed 06/20/2013).<br />

Caliendo, Marco, Reinhard Hujer, and Thomsen Stephan L. 2008. “Identifying Effect Heterogeneity to Improve<br />

the Efficiency of Job-Creation Schemes in Germany.” Applied Economics 40(9): 1101–22.<br />

Charles, Kerwin Kofi, and Melvin Stephens. 2004. “Disability, Job Displacement and Divorce.” Journal of Labor<br />

Economics 22(2): 489–522.<br />

Clark, Andrew E., and Andrew J. Oswald. 1994. “Unhappiness and <strong>Unemployment</strong>.” The Economic Journal 104(424):<br />

648–59.<br />

Classen, Timothy J., and Richard A. Dunn. 2012. “The Effect of Job Loss and <strong>Unemployment</strong> Duration on<br />

Suicide Risk in the United States: A New Look Using Mass-Layoffs and <strong>Unemployment</strong> Duration.” Health<br />

Economics 21:338–50.<br />

Couch, Kenneth, Howard Iams, Gayle Reznik, and Chris Tamborini. 2013. “Economic and Health Implications of<br />

Long-Term <strong>Unemployment</strong>: Earnings, Disability Benefits, and Mortality.” Paper presented at 2013 ASSA<br />

meetings, San Diego, January 6.<br />

Davis, Steven J., and Till von Wachter. 2011. “Recessions and the Costs of Job Loss.” Brookings Papers on<br />

Economic Activity Conference Proceedings.<br />

http://www.brookings.edu/~/media/Files/Programs/ES/BPEA/2011_fall_bpea_papers/2011_fall_bpea_c<br />

onference_davis.pdf.<br />

Del Bono, Emilia, Andrea Weber, and Rudolf Winter-Ebmer. 2012. “Clash of Career and Family: Fertility<br />

Decisions after Job Displacement.” Journal of the European Economic Association 10(4): 659–83.<br />

Devine, Theresa J., and Nicholas M. Kiefer. 1991. Empirical Labor Economics–The Search Approach. New York:<br />

Oxford University Press.<br />

14 The Urban Institute


Diette, Timothy M., Arthur H. Goldsmith, Darrick Hamilton, and William Darity Jr. 2012. “Causality in the<br />

Relationship between Mental Health and <strong>Unemployment</strong>.” In Reconnecting to Work: Policies to Mitigate Long-Term<br />

<strong>Unemployment</strong> and Its Consequences, edited by Lauren D. Appelbaum (63–94). Kalamazoo, MI: W.E. Upjohn<br />

Institute for Employment Research.<br />

Edin, Per-Anders, and Magnus Gustavsson. 2008. “Time Out of Work and Skill Depreciation.” Industrial and Labor<br />

Relations Review 61(2): 163–80.<br />

http://digitalcommons.ilr.cornell.edu/cgi/viewcontent.cgi?article=1318&context=ilrreview.<br />

Eibner, Christine, and William N. Evans. 2005. “Relative Deprivation, Poor Health Habits, and Mortality.” Journal<br />

of Human Resources 60(3): 591–620.<br />

Gibbons, Robert, and Lawrence F. Katz. 1991. “Layoffs and Lemons.” Journal of Labor Economics 9(4): 351–80.<br />

Godofsky, Jessica, Carl van Horn, and Cliff Zukin. 2010. “American Workers Assess an Economic Disaster.” New<br />

Brunswick, NJ: John J. Heldrich Center for Workforce Development, Rutgers University.<br />

Hamermesh, Daniel S. 1989. “What Do We Know About Worker Displacement in the U.S.?” Industrial Relations<br />

28:51–59.<br />

Hollenbeck, Kevin. 1990. “Dislocated Worker Human Capital Depreciation and Recovery.” Working Paper 90-04.<br />

Kalamazoo, MI: W.E. Upjohn Institute for Employment Research.<br />

http://research.upjohn.org/up_workingpapers/4.<br />

Holzer, Harry J. 2011. “Raising Job Quality and Skills for American Workers: Creating More-Effective Education<br />

and Workforce Development Systems in the States.” Hamilton Project Discussion Paper 2011-10.<br />

Washington, DC: The Brookings Institution.<br />

http://www.hamiltonproject.org/files/downloads_and_links/11_workforce_holzer_paper.pdf.<br />

Hujer, Reinhard, Marco Caliendo, and Stephan L. Thomsen. 2004. “New Evidence on the Effects of Job-Creation<br />

Schemes in Germany: A Matching Approach with Threefold Heterogeneity.” Research in Economics 58(4): 257–<br />

302.<br />

Jacobson, Louis, Robert LaLonde, and Daniel Sullivan. 1993. “Earnings Losses of Displaced Workers.” American<br />

Economic Review 83(4): 685–709.<br />

———. 2011. “Policies to Reduce High-Tenured Displaced Workers’ Earnings Losses through Retraining.”<br />

Hamilton Project Discussion Paper 2011-11. Washington, DC: The Brookings Institution.<br />

http://www.hamiltonproject.org/files/downloads_and_links/11_displaced_JLS_paper.pdf.<br />

Johnson, Rucker C., and Robert F. Schoeni. 2011. “The Influence of Early-Life Events on Human Capital, Health<br />

Status, and Labor Market Outcomes over the Life Course.” B.E. Journal of Economic Analysis & Policy: Advances<br />

11(3).<br />

Johnson, Richard W., and Alice Feng. 2013. “Financial Consequences of Long-Term <strong>Unemployment</strong> during the<br />

Great Recession and Recovery.” Washington, DC: The Urban Institute.<br />

http://www.urban.org/publications/412800.html.<br />

Kain, John F. 1968. “Housing Segregation, Negro Employment, and Metropolitan Decentralization.” Quarterly<br />

Journal of Economics 82:175–97.<br />

Kalil, Ariel, and Thomas DeLeire. 2002. “Parental Job Loss and Early Adolescent Development in Black and<br />

White Families.” Working Paper 282. Chicago and Evanston, IL: Joint Center for Poverty Research.<br />

Katz, Larry. 2010. “Long-Term <strong>Unemployment</strong> in the Great Recession.” Testimony before the Joint Economic<br />

Committee, April 29.<br />

http://scholar.harvard.edu/files/lkatz/files/long_term_unemployment_in_the_great_recession.pdf.<br />

Consequences Of Long-Term <strong>Unemployment</strong> 15


Kroft, Kory, Fabian Lange, and Matthew J. Notowidigdo. 2012. “Duration Dependence and Labor Market<br />

Conditions: Theory and Evidence from a Field Experiment.” Working Paper 18387. Cambridge, MA:<br />

National Bureau of Economic Research. http://papers.nber.org/papers/w18387.<br />

Lindner, Stephan, and Austin Nichols. 2012. “The Impact of Temporary Assistance Programs on Disability Rolls<br />

and Reemployment.” Working Paper 2012-2. Boston, MA: Center for Retirement Research at Boston College.<br />

http://crr.bc.edu/wp-content/uploads/2012/01/wp_2012-2-508.pdf.<br />

Lindner, Stephan, and Elizabeth Peters. 2013. “How Does <strong>Unemployment</strong> Affect Family Arrangements for<br />

Children?” Unpublished manuscript. Microsoft Word file.<br />

Lindo, Jason M. 2010. “Are Children Really Inferior Goods? Evidence from Displacement-Driven Income<br />

Shocks.” Journal of Human Resources 45(2): 301–27.<br />

———. 2011. “Parental Job Loss and Infant Health.” Journal of Health Economics 30(5): 869–79.<br />

Machin, Stephen, and Alan Manning. 1999. “The Causes and Consequences of Long-term <strong>Unemployment</strong> in<br />

Europe.” In Handbook of Labor Economics, Vol. III, edited by O. Ashenfelter and D. Card (3085–3139).<br />

Amsterdam: North-Holland.<br />

McGuire, Michael T., and Michael J. Raleigh. 1986. “Behavioral and Physiological Correlates of Ostracism.”<br />

Ethology and Sociobiology 7:187–200.<br />

McLoyd, Vonnie C., Toby Epstein Jayaratne, Rosario Ceballo, and Julio Borquez. 1994. “<strong>Unemployment</strong> and<br />

Work Interruption among African American Single Mothers: Effects on Parenting and Adolescent Socio-<br />

Emotional Functioning.” Child Development 65:562–89. http://www.jstor.org/stable/1131402.<br />

Michalopoulos, Charles. 2005. Does Making Work Still Pay? An Update on the Effects of Four Earnings Supplement<br />

Programs on Employment, Earnings, and Income. New York: MDRC.<br />

Nichols, Austin. 2012. “Poverty and <strong>Unemployment</strong>.” Washington, DC: The Urban Institute.<br />

http://www.urban.org/publications/412652.html.<br />

Olson, Craig A. 1992. “The Impact of Permanent Job Loss on Health Insurance Benefits.” Industrial Relations<br />

Section Working Paper 305. Princeton, NJ: Princeton University.<br />

http://dataspace.princeton.edu/jspui/bitstream/88435/dsp01q811kj632/1/305.pdf.<br />

Oreopoulos, P., M. Page, and A. Stevens. 2008. “The Intergenerational Effect of Worker Displacement.” Journal of<br />

Labor Economics 26(3): 455–50.<br />

Rege, Mari, Kjetil Telle, and Mark Votruba. 2011. “Parental Job Loss and Children’s School Performance.” Review<br />

of Economic Studies 78(4): 1462–89.<br />

———. 2012. “Social Interaction Effects in Disability Pension Participation: Evidence from Plant Downsizing.”<br />

Scandinavian Journal of Economics 114(4): 1208–39.<br />

Rosenfeld, Rachel A. 1992. “Job Mobility and Career Processes.” Annual Review of Sociology 18:39–61.<br />

Ruhm, Christopher J. 1991. “Are Workers Permanently Scarred by Job Displacement?” American Economic Review<br />

81(1): 319–24.<br />

———. 2000. “Are Recessions Good for Your Health?” Quarterly Journal of Economics 115(2): 617–50.<br />

———. 2001. “Economic Expansions Are Unhealthy: Evidence from Microdata.” Working Paper 8447.<br />

Cambridge, MA: National Bureau of Economic Research. http://www.nber.org/papers/w8447.<br />

———. 2005. “Healthy Living in Hard Times.” Journal of Health Economics 24(2): 341–63.<br />

16 The Urban Institute


———. 2007. “A Healthy Economy Can Break Your Heart.” Demography 44(4): 829–48.<br />

Rupp, Kalman, and David Stapleton. 1995. “Determinants of the Growth in the Social Security Administration’s<br />

Disability Programs–An Overview.” Social Security Bulletin 58(4): 43–70.<br />

Salm, Martin. 2009. “Does Job Loss Cause Ill Health?” Health Economics 18:1075–89.<br />

Schmieder, Johannes F., Till von Wachter, and Stefan Bender. 2012a. “The Effect of <strong>Unemployment</strong> Insurance<br />

Extensions on Reemployment Wages.” Working paper. Boston, MA: Boston University.<br />

http://www.irs.princeton.edu/sites/irs/files/event/uploads/matchqual_wp_March2012.pdf.<br />

———. 2012b. “The Effects of Extended <strong>Unemployment</strong> Insurance Over the Business Cycle: Evidence from<br />

Regression Discontinuity Estimates Over 20 Years.” Quarterly Journal of Economics 127(2): 701–52.<br />

———. 2012c. “The Long-Term Effects of UI Extensions on Employment.” American Economic Review 102(3):<br />

514–19.<br />

Snyder, Stephen E., and William N. Evans. 2006. “The Effect of Income on Mortality: Evidence from the Social<br />

Security Notch.” Review of Economics and Statistics 88(3): 482–95.<br />

Stevens, Ann Huff. 1997. “Persistent Effects of Job Displacement: The Importance of Multiple Job Losses.”<br />

Journal of Labor Economics 15(1): 165–88.<br />

Stevens, Ann, and Jesamyn Schaller. 2011. “Short-Run Effects of Parental Job Loss on Children’s Academic<br />

Achievement.” Economics of Education Review 30(2): 289–99.<br />

Sullivan, Daniel, and Till von Wachter. 2009. “Job Displacement and Mortality: An Analysis Using Administrative<br />

Data.” Quarterly Journal of Economics 124(3): 1265–1306.<br />

Turner, Margery Austin, Austin Nichols, and Jennifer Comey. 2012. “Benefits of Living in High-Opportunity<br />

Neighborhoods: Insights from the Moving to Opportunity Demonstration.” Washington DC: The Urban<br />

Institute. http://www.urban.org/publications/412648.html.<br />

Von Wachter, Till, Jae Song, and Joyce Manchester. 2009. “Long-Term Earnings Losses due to Mass Layoffs<br />

during the 1982 Recession: An Analysis Using US Administrative Data from 1974 to 2004.” Unpublished<br />

paper, Columbia University. Word document.<br />

http://www.columbia.edu/~vw2112/papers/mass_layoffs_1982.pdf.<br />

Whooley, Mary A., Catarina I. Kiefe, Margaret A. Chesney, Jerome H. Markovitz, Karen Matthews, and Stephen B.<br />

Hulley. 2002. “Depressive Symptoms, <strong>Unemployment</strong>, and Loss of Income: The CARDIA Study.” Archives of<br />

Internal Medicine 162(22): 2614–20.<br />

Wightman, Patrick. 2012. “Parental Job Loss, Parental Ability and Children’s Educational Attainment.” Research<br />

Report 12-761. Ann Arbor: Population Studies Center, University of Michigan.<br />

http://www.psc.isr.umich.edu/pubs/pdf/rr12-761.pdf.<br />

Wilson, William Julius. 1987. The Truly Disadvantaged: The Inner City, the Underclass and Public Policy. Chicago, IL:<br />

University of Chicago Press.<br />

Winkelmann, Liliana, and Rainer Winkelmann. 1998. “Why Are the Unemployed So Unhappy? Evidence from<br />

Panel Data.” Economica 65(257): 1–15.<br />

Consequences Of Long-Term <strong>Unemployment</strong> 17


Page 124 of 149


Advocacy Foundation Publishers<br />

Page 125 of 149


Advocacy Foundation Publishers<br />

The e-Advocate Quarterly<br />

Page 126 of 149


Issue Title Quarterly<br />

Vol. I 2015 The Fundamentals<br />

I<br />

The ComeUnity ReEngineering<br />

Project Initiative<br />

Q-1 2015<br />

II The Adolescent Law Group Q-2 2015<br />

III<br />

Landmark Cases in US<br />

Juvenile Justice (PA)<br />

Q-3 2015<br />

IV The First Amendment Project Q-4 2015<br />

Vol. II 2016 Strategic Development<br />

V The Fourth Amendment Project Q-1 2016<br />

VI<br />

Landmark Cases in US<br />

Juvenile Justice (NJ)<br />

Q-2 2016<br />

VII Youth Court Q-3 2016<br />

VIII<br />

The Economic Consequences of Legal<br />

Decision-Making<br />

Q-4 2016<br />

Vol. III 2017 Sustainability<br />

IX The Sixth Amendment Project Q-1 2017<br />

X<br />

The Theological Foundations of<br />

US Law & Government<br />

Q-2 2017<br />

XI The Eighth Amendment Project Q-3 2017<br />

XII<br />

The EB-5 Investor<br />

Immigration Project*<br />

Q-4 2017<br />

Vol. IV 2018 Collaboration<br />

XIII Strategic Planning Q-1 2018<br />

XIV<br />

The Juvenile Justice<br />

Legislative Reform Initiative<br />

Q-2 2018<br />

XV The Advocacy Foundation Coalition Q-3 2018<br />

Page 127 of 149


XVI<br />

for Drug-Free Communities<br />

Landmark Cases in US<br />

Juvenile Justice (GA)<br />

Q-4 2018<br />

Page 128 of 149


Issue Title Quarterly<br />

Vol. V 2019 Organizational Development<br />

XVII The Board of Directors Q-1 2019<br />

XVIII The Inner Circle Q-2 2019<br />

XIX Staff & Management Q-3 2019<br />

XX Succession Planning Q-4 2019<br />

XXI The Budget* Bonus #1<br />

XXII Data-Driven Resource Allocation* Bonus #2<br />

Vol. VI 2020 Missions<br />

XXIII Critical Thinking Q-1 2020<br />

XXIV<br />

The Advocacy Foundation<br />

Endowments Initiative Project<br />

Q-2 2020<br />

XXV International Labor Relations Q-3 2020<br />

XXVI Immigration Q-4 2020<br />

Vol. VII 2021 Community Engagement<br />

XXVII<br />

The 21 st Century Charter Schools<br />

Initiative<br />

Q-1 2021<br />

XXVIII The All-Sports Ministry @ ... Q-2 2021<br />

XXIX Lobbying for Nonprofits Q-3 2021<br />

XXX<br />

XXXI<br />

Advocacy Foundation Missions -<br />

Domestic<br />

Advocacy Foundation Missions -<br />

International<br />

Q-4 2021<br />

Bonus<br />

Page 129 of 149


Vol. VIII<br />

2022 ComeUnity ReEngineering<br />

XXXII<br />

The Creative & Fine Arts Ministry<br />

@ The Foundation<br />

Q-1 2022<br />

XXXIII The Advisory Council & Committees Q-2 2022<br />

XXXIV<br />

The Theological Origins<br />

of Contemporary Judicial Process<br />

Q-3 2022<br />

XXXV The Second Chance Ministry @ ... Q-4 2022<br />

Vol. IX 2023 Legal Reformation<br />

XXXVI The Fifth Amendment Project Q-1 2023<br />

XXXVII The Judicial Re-Engineering Initiative Q-2 2023<br />

XXXVIII<br />

The Inner-Cities Strategic<br />

Revitalization Initiative<br />

Q-3 2023<br />

XXXVIX Habeas Corpus Q-4 2023<br />

Vol. X 2024 ComeUnity Development<br />

XXXVX<br />

The Inner-City Strategic<br />

Revitalization Plan<br />

Q-1 2024<br />

XXXVXI The Mentoring Initiative Q-2 2024<br />

XXXVXII The Violence Prevention Framework Q-3 2024<br />

XXXVXIII The Fatherhood Initiative Q-4 2024<br />

Vol. XI 2025 Public Interest<br />

XXXVXIV Public Interest Law Q-1 2025<br />

L (50) Spiritual Resource Development Q-2 2025<br />

Page 130 of 149


LI<br />

Nonprofit Confidentiality<br />

In The Age of Big Data<br />

Q-3 2025<br />

LII Interpreting The Facts Q-4 2025<br />

Vol. XII 2026 Poverty In America<br />

LIII<br />

American Poverty<br />

In The New Millennium<br />

Q-1 2026<br />

LIV Outcome-Based Thinking Q-2 2026<br />

LV Transformational Social Leadership Q-3 2026<br />

LVI The Cycle of Poverty Q-4 2026<br />

Vol. XIII 2027 Raising Awareness<br />

LVII ReEngineering Juvenile Justice Q-1 2027<br />

LVIII Corporations Q-2 2027<br />

LVIX The Prison Industrial Complex Q-3 2027<br />

LX Restoration of Rights Q-4 2027<br />

Vol. XIV 2028 Culturally Relevant Programming<br />

LXI Community Culture Q-1 2028<br />

LXII Corporate Culture Q-2 2028<br />

LXIII Strategic Cultural Planning Q-3 2028<br />

LXIV<br />

The Cross-Sector/ Coordinated<br />

Service Approach to Delinquency<br />

Prevention<br />

Q-4 2028<br />

Page 131 of 149


Vol. XV 2029 Inner-Cities Revitalization<br />

LXIV<br />

LXV<br />

LXVI<br />

Part I – Strategic Housing<br />

Revitalization<br />

(The Twenty Percent Profit Margin)<br />

Part II – Jobs Training, Educational<br />

Redevelopment<br />

and Economic Empowerment<br />

Part III - Financial Literacy<br />

and Sustainability<br />

Q-1 2029<br />

Q-2 2029<br />

Q-3 2029<br />

LXVII Part IV – Solutions for Homelessness Q-4 2029<br />

LXVIII<br />

The Strategic Home Mortgage<br />

Initiative<br />

Bonus<br />

Vol. XVI 2030 Sustainability<br />

LXVIII Social Program Sustainability Q-1 2030<br />

LXIX<br />

The Advocacy Foundation<br />

Endowments Initiative<br />

Q-2 2030<br />

LXX Capital Gains Q-3 2030<br />

LXXI Sustainability Investments Q-4 2030<br />

Vol. XVII 2031 The Justice Series<br />

LXXII Distributive Justice Q-1 2031<br />

LXXIII Retributive Justice Q-2 2031<br />

LXXIV Procedural Justice Q-3 2031<br />

LXXV (75) Restorative Justice Q-4 2031<br />

LXXVI Unjust Legal Reasoning Bonus<br />

Page 132 of 149


Vol. XVIII 2032 Public Policy<br />

LXXVII Public Interest Law Q-1 2032<br />

LXXVIII Reforming Public Policy Q-2 2032<br />

LXXVIX ... Q-3 2032<br />

LXXVX ... Q-4 2032<br />

Page 133 of 149


The e-Advocate Monthly Review<br />

2018<br />

Transformational Problem Solving January 2018<br />

The Advocacy Foundation February 2018<br />

Opioid Initiative<br />

Native-American Youth March 2018<br />

In the Juvenile Justice System<br />

Barriers to Reducing Confinement April 2018<br />

Latino and Hispanic Youth May 2018<br />

In the Juvenile Justice System<br />

Social Entrepreneurship June 2018<br />

The Economic Consequences of<br />

Homelessness in America S.Ed – June 2018<br />

African-American Youth July 2018<br />

In the Juvenile Justice System<br />

Gang Deconstruction August 2018<br />

Social Impact Investing September 2018<br />

Opportunity Youth: October 2018<br />

Disenfranchised Young People<br />

The Economic Impact of Social November 2018<br />

of Social Programs Development<br />

Gun Control December 2018<br />

2019<br />

The U.S. Stock Market January 2019<br />

Prison-Based Gerrymandering February 2019<br />

Literacy-Based Prison Construction March 2019<br />

Children of Incarcerated Parents April 2019<br />

Page 134 of 149


African-American Youth in The May 2019<br />

Juvenile Justice System<br />

Racial Profiling June 2019<br />

Mass Collaboration July 2019<br />

Concentrated Poverty August 2019<br />

De-Industrialization September 2019<br />

Overcoming Dyslexia October 2019<br />

Overcoming Attention Deficit November 2019<br />

The Gift of Adversity December 2019<br />

2020<br />

The Gift of Hypersensitivity January 2020<br />

The Gift of Introspection February 2020<br />

The Gift of Introversion March 2020<br />

The Gift of Spirituality April 2020<br />

The Gift of Transformation May 2020<br />

Property Acquisition for<br />

Organizational Sustainability June 2020<br />

Investing for Organizational<br />

Sustainability July 2020<br />

Biblical Law & Justice TLFA August 2020<br />

Gentrification AF September 2020<br />

Environmental Racism NpA October 2020<br />

Law for The Poor AF November 2020<br />

…<br />

Page 135 of 149


2021<br />

Biblically Responsible Investing TLFA – January 2021<br />

International Criminal Procedure LMI – February 2021<br />

Spiritual Rights TLFA – March 2021<br />

The Theology of Missions TLFA – April 2021<br />

Legal Evangelism, Intelligence,<br />

Reconnaissance & Missions LMI – May 2021<br />

The Law of War LMI – June 2021<br />

Generational Progression AF – July 2021<br />

Predatory Lending AF – August 2021<br />

The Community Assessment Process NpA – September 2021<br />

Accountability NpA – October 2021<br />

Nonprofit Transparency NpA – November 2021<br />

Redefining <strong>Unemployment</strong> AF – December 2021<br />

2022<br />

…<br />

Page 136 of 149


The e-Advocate Quarterly<br />

Special Editions<br />

Crowdfunding Winter-Spring 2017<br />

Social Media for Nonprofits October 2017<br />

Mass Media for Nonprofits November 2017<br />

The Opioid Crisis in America: January 2018<br />

Issues in Pain Management<br />

The Opioid Crisis in America: February 2018<br />

The Drug Culture in the U.S.<br />

The Opioid Crisis in America: March 2018<br />

Drug Abuse Among Veterans<br />

The Opioid Crisis in America: April 2018<br />

Drug Abuse Among America’s<br />

Teens<br />

The Opioid Crisis in America: May 2018<br />

Alcoholism<br />

The Economic Consequences of June 2018<br />

Homelessness in The US<br />

The Economic Consequences of July 2018<br />

Opioid Addiction in America<br />

Page 137 of 149


The e-Advocate Journal<br />

of Theological Jurisprudence<br />

Vol. I - 2017<br />

The Theological Origins of Contemporary Judicial Process<br />

Scriptural Application to The Model Criminal Code<br />

Scriptural Application for Tort Reform<br />

Scriptural Application to Juvenile Justice Reformation<br />

Vol. II - 2018<br />

Scriptural Application for The Canons of Ethics<br />

Scriptural Application to Contracts Reform<br />

& The Uniform Commercial Code<br />

Scriptural Application to The Law of Property<br />

Scriptural Application to The Law of Evidence<br />

Page 138 of 149


Legal Missions International<br />

Page 139 of 149


Issue Title Quarterly<br />

Vol. I 2015<br />

I<br />

II<br />

God’s Will and The 21 st Century<br />

Democratic Process<br />

The Community<br />

Engagement Strategy<br />

Q-1 2015<br />

Q-2 2015<br />

III Foreign Policy Q-3 2015<br />

IV<br />

Public Interest Law<br />

in The New Millennium<br />

Q-4 2015<br />

Vol. II 2016<br />

V Ethiopia Q-1 2016<br />

VI Zimbabwe Q-2 2016<br />

VII Jamaica Q-3 2016<br />

VIII Brazil Q-4 2016<br />

Vol. III 2017<br />

IX India Q-1 2017<br />

X Suriname Q-2 2017<br />

XI The Caribbean Q-3 2017<br />

XII United States/ Estados Unidos Q-4 2017<br />

Vol. IV 2018<br />

XIII Cuba Q-1 2018<br />

XIV Guinea Q-2 2018<br />

XV Indonesia Q-3 2018<br />

XVI Sri Lanka Q-4 2018<br />

Page 140 of 149


Vol. V 2019<br />

XVII Russia Q-1 2019<br />

XVIII Australia Q-2 2019<br />

XIV South Korea Q-3 2019<br />

XV Puerto Rico Q-4 2019<br />

Issue Title Quarterly<br />

Vol. VI 2020<br />

XVI Trinidad & Tobago Q-1 2020<br />

XVII Egypt Q-2 2020<br />

XVIII Sierra Leone Q-3 2020<br />

XIX South Africa Q-4 2020<br />

XX Israel Bonus<br />

Vol. VII 2021<br />

XXI Haiti Q-1 2021<br />

XXII Peru Q-2 2021<br />

XXIII Costa Rica Q-3 2021<br />

XXIV China Q-4 2021<br />

XXV Japan Bonus<br />

Vol VIII 2022<br />

XXVI Chile Q-1 2022<br />

Page 141 of 149


The e-Advocate Juvenile Justice Report<br />

______<br />

Vol. I – Juvenile Delinquency in The US<br />

Vol. II. – The Prison Industrial Complex<br />

Vol. III – Restorative/ Transformative Justice<br />

Vol. IV – The Sixth Amendment Right to The Effective Assistance of Counsel<br />

Vol. V – The Theological Foundations of Juvenile Justice<br />

Vol. VI – Collaborating to Eradicate Juvenile Delinquency<br />

Page 142 of 149


The e-Advocate Newsletter<br />

Genesis of The Problem<br />

Family Structure<br />

Societal Influences<br />

Evidence-Based Programming<br />

Strengthening Assets v. Eliminating Deficits<br />

2012 - Juvenile Delinquency in The US<br />

Introduction/Ideology/Key Values<br />

Philosophy/Application & Practice<br />

Expungement & Pardons<br />

Pardons & Clemency<br />

Examples/Best Practices<br />

2013 - Restorative Justice in The US<br />

2014 - The Prison Industrial Complex<br />

25% of the World's Inmates Are In the US<br />

The Economics of Prison Enterprise<br />

The Federal Bureau of Prisons<br />

The After-Effects of Incarceration/Individual/Societal<br />

The Fourth Amendment Project<br />

The Sixth Amendment Project<br />

The Eighth Amendment Project<br />

The Adolescent Law Group<br />

2015 - US Constitutional Issues In The New Millennium<br />

Page 143 of 149


2018 - The Theological Law Firm Academy<br />

The Theological Foundations of US Law & Government<br />

The Economic Consequences of Legal Decision-Making<br />

The Juvenile Justice Legislative Reform Initiative<br />

The EB-5 International Investors Initiative<br />

2017 - Organizational Development<br />

The Board of Directors<br />

The Inner Circle<br />

Staff & Management<br />

Succession Planning<br />

Bonus #1 The Budget<br />

Bonus #2 Data-Driven Resource Allocation<br />

2018 - Sustainability<br />

The Data-Driven Resource Allocation Process<br />

The Quality Assurance Initiative<br />

The Advocacy Foundation Endowments Initiative<br />

The Community Engagement Strategy<br />

2019 - Collaboration<br />

Critical Thinking for Transformative Justice<br />

International Labor Relations<br />

Immigration<br />

God's Will & The 21st Century Democratic Process<br />

The Community Engagement Strategy<br />

The 21st Century Charter Schools Initiative<br />

2020 - Community Engagement<br />

Page 144 of 149


Extras<br />

The Nonprofit Advisors Group Newsletters<br />

The 501(c)(3) Acquisition Process<br />

The Board of Directors<br />

The Gladiator Mentality<br />

Strategic Planning<br />

Fundraising<br />

501(c)(3) Reinstatements<br />

The Collaborative US/ International Newsletters<br />

How You Think Is Everything<br />

The Reciprocal Nature of Business Relationships<br />

Accelerate Your Professional Development<br />

The Competitive Nature of Grant Writing<br />

Assessing The Risks<br />

Page 145 of 149


Page 146 of 149


About The Author<br />

John C (Jack) Johnson III<br />

Founder & CEO<br />

Jack was educated at Temple University, in Philadelphia, Pennsylvania and Rutgers<br />

Law School, in Camden, New Jersey. In 1999, he moved to Atlanta, Georgia to pursue<br />

greater opportunities to provide Advocacy and Preventive Programmatic services for atrisk/<br />

at-promise young persons, their families, and Justice Professionals embedded in the<br />

Juvenile Justice process in order to help facilitate its transcendence into the 21 st Century.<br />

There, along with a small group of community and faith-based professionals, “The Advocacy Foundation, Inc." was conceived<br />

and developed over roughly a thirteen year period, originally chartered as a Juvenile Delinquency Prevention and Educational<br />

Support Services organization consisting of Mentoring, Tutoring, Counseling, Character Development, Community Change<br />

Management, Practitioner Re-Education & Training, and a host of related components.<br />

The Foundation’s Overarching Mission is “To help Individuals, Organizations, & Communities Achieve Their Full Potential”, by<br />

implementing a wide array of evidence-based proactive multi-disciplinary "Restorative & Transformative Justice" programs &<br />

projects currently throughout the northeast, southeast, and western international-waters regions, providing prevention and support<br />

services to at-risk/ at-promise youth, to young adults, to their families, and to Social Service, Justice and Mental<br />

Health professionals” everywhere. The Foundation has since relocated its headquarters to Philadelphia, Pennsylvania, and been<br />

expanded to include a three-tier mission.<br />

In addition to his work with the Foundation, Jack also served as an Adjunct Professor of Law & Business at National-Louis<br />

University of Atlanta (where he taught Political Science, Business & Legal Ethics, Labor & Employment Relations, and Critical<br />

Thinking courses to undergraduate and graduate level students). Jack has also served as Board President for a host of wellestablished<br />

and up & coming nonprofit organizations throughout the region, including “Visions Unlimited Community<br />

Development Systems, Inc.”, a multi-million dollar, award-winning, Violence Prevention and Gang Intervention Social Service<br />

organization in Atlanta, as well as Vice-Chair of the Georgia/ Metropolitan Atlanta Violence Prevention Partnership, a state-wide<br />

300 organizational member, violence prevention group led by the Morehouse School of Medicine, Emory University and The<br />

Original, Atlanta-Based, Martin Luther King Center.<br />

Attorney Johnson’s prior accomplishments include a wide-array of Professional Legal practice areas, including Private Firm,<br />

Corporate and Government postings, just about all of which yielded significant professional awards & accolades, the history and<br />

chronology of which are available for review online. Throughout his career, Jack has served a wide variety of for-profit<br />

corporations, law firms, and nonprofit organizations as Board Chairman, Secretary, Associate, and General Counsel since 1990.<br />

www.TheAdvocacy.Foundation<br />

Clayton County Youth Services Partnership, Inc. – Chair; Georgia Violence Prevention Partnership, Inc – Vice Chair; Fayette<br />

County NAACP - Legal Redress Committee Chairman; Clayton County Fatherhood Initiative Partnership – Principal<br />

Investigator; Morehouse School of Medicine School of Community Health Feasibility Study - Steering Committee; Atlanta<br />

Violence Prevention Capacity Building Project – Project Partner; Clayton County Minister’s Conference, President 2006-2007;<br />

Liberty In Life Ministries, Inc. – Board Secretary; Young Adults Talk, Inc. – Board of Directors; ROYAL, Inc - Board of<br />

Directors; Temple University Alumni Association; Rutgers Law School Alumni Association; Sertoma International; Our<br />

Common Welfare Board of Directors – President)2003-2005; River’s Edge Elementary School PTA (Co-President); Summerhill<br />

Community Ministries; Outstanding Young Men of America; Employee of the Year; Academic All-American - Basketball;<br />

Church Trustee.<br />

Page 147 of 149


www.TheAdvocacy.Foundation<br />

Page 148 of 149


Page 149 of 149

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!