Hidden Unemployment

The e-Advocate
Monthly
…a Compendium of Works on:
Hidden
Unemployment
Leviticus 19:35-36 | Ezekiel 45:10
Proverbs 11:1 ; 16:11; 20:10,23
Jeremiah 5:1 | Hosea 12:6-7
Amos 8:4-8 | Micah 6:10-13
The Effective (True)
Unemployment Rate
The U6 Indicator
“Helping Individuals, Organizations & Communities
Achieve Their Full Potential”
Special Edition | AF – December 2021

Walk by Faith; Serve with Abandon
Expect to Win!
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The Advocacy Foundation, Inc.
Helping Individuals, Organizations & Communities
Achieve Their Full Potential
Since its founding in 2003, The Advocacy Foundation has become recognized as an effective
provider of support to those who receive our services, having real impact within the communities
we serve. We are currently engaged in community and faith-based collaborative initiatives,
having the overall objective of eradicating all forms of youth violence and correcting injustices
everywhere. In carrying-out these initiatives, we have adopted the evidence-based strategic
framework developed and implemented by the Office of Juvenile Justice & Delinquency
Prevention (OJJDP).
The stated objectives are:
1. Community Mobilization;
2. Social Intervention;
3. Provision of Opportunities;
4. Organizational Change and Development;
5. Suppression [of illegal activities].
Moreover, it is our most fundamental belief that in order to be effective, prevention and
intervention strategies must be Community Specific, Culturally Relevant, Evidence-Based, and
Collaborative. The Violence Prevention and Intervention programming we employ in
implementing this community-enhancing framework include the programs further described
throughout our publications, programs and special projects both domestically and
internationally.
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Dedication
______
Every publication in our many series’ is dedicated to everyone, absolutely everyone, who by
virtue of their calling and by Divine inspiration, direction and guidance, is on the battlefield dayafter-day
striving to follow God’s will and purpose for their lives. And this is with particular affinity
for those Spiritual warriors who are being transformed into excellence through daily academic,
professional, familial, and other challenges.
We pray that you will bear in mind:
Matthew 19:26 (NLT)
Jesus looked at them intently and said, “Humanly speaking, it is impossible.
But with God everything is possible.” (Emphasis added)
To all of us who daily look past our circumstances, and naysayers, to what the Lord says we will
accomplish:
Blessings!!
- The Advocacy Foundation, Inc.
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The Transformative Justice Project
Eradicating Juvenile Delinquency Requires a Multi-Disciplinary Approach
The Juvenile Justice system is incredibly
overloaded, and Solutions-Based programs are
woefully underfunded. Our precious children,
therefore, particularly young people of color, often
get the “swift” version of justice whenever they
come into contact with the law.
Decisions to build prison facilities are often based
on elementary school test results, and our country
incarcerates more of its young than any other
nation on earth. So we at The Foundation labor to
pull our young people out of the “school to prison”
pipeline, and we then coordinate the efforts of the
legal, psychological, governmental and
educational professionals needed to bring an end
to delinquency.
We also educate families, police, local businesses,
elected officials, clergy, and schools and other
stakeholders about transforming whole communities, and we labor to change their
thinking about the causes of delinquency with the goal of helping them embrace the
idea of restoration for the young people in our care who demonstrate repentance for
their
mistakes.
The way we accomplish all this is a follows:
1. We vigorously advocate for charges reductions, wherever possible, in the
adjudicatory (court) process, with the ultimate goal of expungement or pardon, in order
to maximize the chances for our clients to graduate high school and progress into
college, military service or the workforce without the stigma of a criminal record;
2. We then enroll each young person into an Evidence-Based, Data-Driven
Restorative Justice program designed to facilitate their rehabilitation and subsequent
reintegration back into the community;
3. While those projects are operating, we conduct a wide variety of ComeUnity-
ReEngineering seminars and workshops on topics ranging from Juvenile Justice to
Parental Rights, to Domestic issues to Police friendly contacts, to mental health
intervention, to CBO and FBO accountability and compliance;
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4. Throughout the process, we encourage and maintain frequent personal contact
between all parties;
5 Throughout the process we conduct a continuum of events and fundraisers
designed to facilitate collaboration among professionals and community stakeholders;
and finally
6. 1 We disseminate Quarterly publications, like our e-Advocate series Newsletter
and our e-Advocate Quarterly electronic Magazine to all regular donors in order to
facilitate a lifelong learning process on the ever-evolving developments in the Justice
system.
And in addition to the help we provide for our young clients and their families, we also
facilitate Community Engagement through the Restorative Justice process,
thereby balancing the interests of local businesses, schools, clergy, social assistance
organizations, elected officials, law enforcement entities, and all interested
stakeholders. Through these efforts, relationships are rebuilt & strengthened, local
businesses and communities are enhanced & protected from victimization, young
careers are developed, and our precious young people are kept out of the prison
pipeline.
Additionally, we develop Transformative “Void Resistance” (TVR) initiatives to elevate
concerns of our successes resulting in economic hardship for those employed by the
penal system.
TVR is an innovative-comprehensive process that works in conjunction with our
Transformative Justice initiatives to transition the original use and purpose of current
systems into positive social impact operations, which systematically retrains current
staff, renovates facilities, creates new employment opportunities, increases salaries and
is data proven to enhance employee’s mental wellbeing and overall quality of life – an
exponential Transformative Social Impact benefit for ALL community stakeholders.
This is a massive undertaking, and we need all the help and financial support you can
give! We plan to help 75 young persons per quarter-year (aggregating to a total of 250
per year) in each jurisdiction we serve) at an average cost of under $2,500 per client,
per year. *
Thank you in advance for your support!
* FYI:
1
In addition to supporting our world-class programming and support services, all regular donors receive our Quarterly e-Newsletter
(The e-Advocate), as well as The e-Advocate Quarterly Magazine.
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1. The national average cost to taxpayers for minimum-security youth incarceration,
is around $43,000.00 per child, per year.
2. The average annual cost to taxpayers for maximum-security youth incarceration
is well over $148,000.00 per child, per year.
- (US News and World Report, December 9, 2014);
3. In every jurisdiction in the nation, the Plea Bargain rate is above 99%.
The Judicial system engages in a tri-partite balancing task in every single one of these
matters, seeking to balance Rehabilitative Justice with Community Protection and
Judicial Economy, and, although the practitioners work very hard to achieve positive
outcomes, the scales are nowhere near balanced where people of color are involved.
We must reverse this trend, which is right now working very much against the best
interests of our young.
Our young people do not belong behind bars.
- Jack Johnson
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The Advocacy Foundation, Inc.
Helping Individuals, Organizations & Communities
Achieve Their Full Potential
…a compendium of works on
Hidden Unemployment
“Turning the Improbable Into the Exceptional”
Atlanta
Philadelphia
______
John C Johnson III
Founder & CEO
(878) 222-0450
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www.TheAdvocacy.Foundation
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Biblical Authority
______
Leviticus 19:35-36 'You shall do no wrong in judgment, in measurement of weight, or capacity.
'You shall have just balances, just weights, a just ephah, and a just hin; I am the LORD your
God, who brought you out from the land of Egypt.
Ezekiel 45:10 "You shall have just balances, a just ephah and a just bath.
Proverbs 20:10 Differing weights and differing measures, Both of them are abominable to the
LORD.
Proverbs 11:1 A false balance is an abomination to the LORD, But a just weight is His delight.
Proverbs 16:11 A just balance and scales belong to the LORD; All the weights of the bag are
His concern.
Proverbs 20:23 Differing weights are an abomination to the LORD, And a false scale is not
good.
Jeremiah 5:1 "Roam to and fro through the streets of Jerusalem, And look now and take note
And seek in her open squares, If you can find a man, If there is one who does justice, who seeks
truth, Then I will pardon her.
Hosea 12:6-7 Therefore, return to your God, Observe kindness and justice, And wait for your
God continually. A merchant, in whose hands are false balances, He loves to oppress.
Amos 8:4-8 Hear this, you who trample the needy, to do away with the humble of the land,
saying, "When will the new moon be over, So that we may sell grain, And the sabbath, that we
may open the wheat market, To make the bushel smaller and the shekel bigger, And to cheat
with dishonest scales, So as to buy the helpless for money And the needy for a pair of sandals,
And that we may sell the refuse of the wheat?"
Micah 6:10-13 "Is there yet a man in the wicked house, Along with treasures of wickedness And
a short measure that is cursed? "Can I justify wicked scales And a bag of deceptive weights?
"For the rich men of the city are full of violence, Her residents speak lies, And their tongue is
deceitful in their mouth.
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Table of Contents
…a compilation of works on
Hidden Unemployment
Biblical Authority
I. Introduction: The Unemployment Rate in The U.S..………………..... 15
II. The Effective (True) Unemployment Rate – The U6 Indicator.……… 49
III. Involuntary Unemployment……….…………………………………….. 61
IV. Underemployment…………………..…………………………………… 65
V. Discouraged Workers………………………..………………………….. 71
VI. The Working Poor…………………….………………………………….. 75
VII. Wage Slavery……………………….………………………………........ 89
VIII. The Unemployment-to-Population Ratio…..………………………….. 105
IX. List of Countries by Employment Rate..………………………………. 111
X. References……………………………………………………………..... 113
______
Attachments
A. The Unemployment Situation in The U.S.
B. Employment and Unemployment Among Youth in The U.S.
C. The Consequences of Long-Term Unemployment
Copyright © 2003 – 2018 The Advocacy Foundation, Inc. All Rights Reserved.
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This work is not meant to be a piece of original academic
analysis, but rather draws very heavily on the work of
scholars in a diverse range of fields. All material drawn upon
is referenced appropriately.
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I. Introduction
The Unemployment Rate in The U.S.
Unemployment or Joblessness is the situation of actively looking for employment, but
not being currently employed..
The unemployment rate is a measure of the prevalence of unemployment and it is
calculated as a percentage by dividing the number of unemployed individuals by all
individuals currently in the labor force. During periods of recession, an economy usually
experiences a relatively high unemployment rate. Six-percent (6%) of the world's
workforce were without a job in 2012.
The causes of unemployment are heavily debated. Classical economics, new classical
economics, and the Austrian School of economics argued that market mechanisms are
reliable means of resolving unemployment. These theories argue against interventions
imposed on the labor market from the outside, such as unionization, bureaucratic work
rules, minimum wage laws, taxes, and other regulations that they claim discourage the
hiring of workers. Keynesian economics emphasizes the cyclical nature of
unemployment and recommends government interventions in the economy that it claims
will reduce unemployment during recessions. This theory focuses on
recurrent shocks that suddenly reduce aggregate demand for goods and services and
thus reduce demand for workers. Keynesian models recommend government
interventions designed to increase demand for workers; these can include financial
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stimuli, publicly funded job creation, and expansionist monetary policies. Its namesake
economist John Maynard Keynes, believed that the root cause of unemployment is the
desire of investors to receive more money rather than produce more products, which is
not possible without public bodies producing new money. A third group of theories
emphasize the need for a stable supply of capital and investment to maintain full
employment. On this view, government should guarantee full employment through fiscal
policy, monetary policy and trade policy as stated, for example, in the US Employment
Act of 1946, by counteracting private sector or trade investment volatility, and
reducing inequality.
In addition to these comprehensive theories of unemployment, there are a few
categorizations of unemployment that are used to more precisely model the effects of
unemployment within the economic system. Some of the main types of unemployment
include structural unemployment and frictional unemployment, as well as cyclical
unemployment, involuntary unemployment, and classical unemployment. Structural
unemployment focuses on foundational problems in the economy and inefficiencies
inherent in labor markets, including a mismatch between the supply and demand of
laborers with necessary skill sets. Structural arguments emphasize causes and
solutions related to disruptive technologies and globalization.
Discussions of frictional unemployment focus on voluntary decisions to work based on
each individuals' valuation of their own work and how that compares to current wage
rates plus the time and effort required to find a job. Causes and solutions for frictional
unemployment often address job entry threshold and wage rates.
Definitions, Types, and Theories
The state of being without any work for an educated person, for earning one's livelihood
is meant by unemployment. Economists distinguish between various overlapping types
of and theories of unemployment, including cyclical or Keynesian
unemployment, frictional unemployment, structural unemployment and classical
unemployment. Some additional types of unemployment that are occasionally
mentioned are seasonal unemployment, hardcore unemployment, and hidden
unemployment.
Though there have been several definitions of "voluntary" and "involuntary
unemployment" in the economics literature, a simple distinction is often applied.
Voluntary unemployment is attributed to the individual's decisions, whereas involuntary
unemployment exists because of the socio-economic environment (including the market
structure, government intervention, and the level of aggregate demand) in which
individuals operate. In these terms, much or most of frictional unemployment is
voluntary, since it reflects individual search behavior. Voluntary unemployment includes
workers who reject low wage jobs whereas involuntary unemployment includes workers
fired due to an economic crisis, industrial decline, company bankruptcy, or
organizational restructuring.
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On the other hand, cyclical unemployment, structural unemployment, and classical
unemployment are largely involuntary in nature. However, the existence of structural
unemployment may reflect choices made by the unemployed in the past, while classical
(natural) unemployment may result from the legislative and economic choices made by
labour unions or political parties.
The clearest cases of involuntary unemployment are those where there are fewer job
vacancies than unemployed workers even when wages are allowed to adjust, so that
even if all vacancies were to be filled, some unemployed workers would still remain.
This happens with cyclical unemployment, as macroeconomic forces cause
microeconomic unemployment which can boomerang back and exacerbate these
macroeconomic forces.
Classical Unemployment
Classical, or real-wage unemployment, occurs when real wages for a job are set above
the market-clearing level causing the number of job-seekers to exceed the number of
vacancies. On the other hand, most economists argue that as wages fall below a livable
wage many choose to fall out of the labor market and no longer seek employment. This
is especially true in countries where low-income families are supported through public
welfare systems. In such cases, wages would have to be high enough to motivate
people to choose employment over what they receive through public welfare. Wages
below a livable wage are likely to result in lower labor market participation in above
stated scenario. In addition, it must be noted that consumption of goods and services is
the primary driver of increased need for labor. Higher wages lead to workers having
more income available to consume goods and services. Therefore, higher wages
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increase general consumption and as a result need for labor increases and
unemployment decreases in the economy.
Many economists have argued that unemployment increases with increased
governmental regulation. For example, minimum wage laws raise the cost of some lowskill
laborers above market equilibrium, resulting in increased unemployment as people
who wish to work at the going rate cannot (as the new and higher enforced wage is now
greater than the value of their labor). Laws restricting layoffs may make businesses less
likely to hire in the first place, as hiring becomes more risky.
However, this argument overly simplifies the relationship between wage rates and
unemployment, ignoring numerous factors, which contribute to unemployment. Some,
such as Murray Rothbard, suggest that even social taboos can prevent wages from
falling to the market-clearing level.
In Out of Work: Unemployment and Government in the Twentieth-Century America,
economists Richard Vedder and Lowell Gallaway argue that the empirical record of
wages rates, productivity, and unemployment in American validates classical
unemployment theory. Their data shows a strong correlation between adjusted real
wage and unemployment in the United States from 1900 to 1990. However, they
maintain that their data does not take into account exogenous events.
Cyclical Unemployment
Cyclical, deficient-demand, or Keynesian unemployment, occurs when there is not
enough aggregate demand in the economy to provide jobs for everyone who wants to
work. Demand for most goods and services falls, less production is needed and
consequently fewer workers are needed, wages are sticky and do not fall to meet the
equilibrium level, and mass unemployment results. Its name is derived from the frequent
shifts in the business cycle although unemployment can also be persistent as occurred
during the Great Depression of the 1930s.
With cyclical unemployment, the number of unemployed workers exceeds the number
of job vacancies, so that even if full employment were attained and all open jobs were
filled, some workers would still remain unemployed. Some associate cyclical
unemployment with frictional unemployment because the factors that cause the friction
are partially caused by cyclical variables. For example, a surprise decrease in the
money supply may shock rational economic factors and suddenly inhibit aggregate
demand.
Keynesian economists on the other hand see the lack of supply for jobs as potentially
resolvable by government intervention. One suggested interventions involves deficit
spendingto boost employment and demand. Another intervention involves an
expansionary monetary policy that increases the supply of money which should
reduce interest rates which should lead to an increase in non-governmental spending.
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Marxian Theory of Unemployment
It is in the very nature of the capitalist mode of production to overwork some
workers while keeping the rest as a reserve army of unemployed paupers.
— Marx, Theory of Surplus Value [18]
Marxists share the Keynesian viewpoint of the relationship between economic demand
and employment, but with the caveat that the market system's propensity to slash
wages and reduce labor participation on an enterprise level causes a requisite decrease
in aggregate demand in the economy as a whole, causing crises of unemployment and
periods of low economic activity before the capital accumulation (investment) phase of
economic growth can continue.
According to Karl Marx, unemployment is inherent within the unstable capitalist system
and periodic crises of mass unemployment are to be expected. He theorized that
unemployment was inevitable and even a necessary part of the capitalist system, with
recovery and regrowth also part of the process. The function of the proletariat within the
capitalist system is to provide a "reserve army of labour" that creates downward
pressure on wages. This is accomplished by dividing the proletariat into surplus labour
(employees) and under-employment (unemployed). This reserve army of labour fight
among themselves for scarce jobs at lower and lower wages.
At first glance, unemployment seems inefficient since unemployed workers do not
increase profits, but unemployment is profitable within the global capitalist system
because unemployment lowers wages which are costs from the perspective of the
owners. From this perspective low wages benefit the system by reducing economic
rents. Yet, it does not benefit workers; according to Karl Marx, the workers (proletariat)
work to benefit the bourgeoisie through their production of capital. Capitalist systems
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unfairly manipulate the market for labour by perpetuating unemployment which lowers
laborers' demands for fair wages. Workers are pitted against one another at the service
of increasing profits for owners. As a result of the capitalist mode of production, Marx
argued that workers experienced alienation and estrangement through their economic
identity.
According to Marx, the only way to permanently eliminate unemployment would be to
abolish capitalism and the system of forced competition for wages and then shift to a
socialist or communist economic system. For contemporary Marxists, the existence of
persistent unemployment is proof of the inability of capitalism to ensure full employment.
Full Employment
In demand-based theory, it is possible to abolish cyclical unemployment by increasing
the aggregate demand for products and workers. However, eventually the economy hits
an "inflation barrier" imposed by the four other kinds of unemployment to the extent that
they exist. Historical experience suggests that low unemployment affects inflation in the
short term but not the long term. In the long term, the velocity of money supply
measures such as the MZM ("money zero maturity", representing cash and
equivalent demand deposits) velocity is far more predictive of inflation than low
unemployment.
Some demand theory economists see the inflation barrier as corresponding to
the natural rate of unemployment. The "natural" rate of unemployment is defined as the
rate of unemployment that exists when the labour market is in equilibrium and there is
pressure for neither rising inflation rates nor falling inflation rates. An alternative
technical term for this rate is the NAIRU, or the Non-Accelerating Inflation Rate of
Unemployment. No matter what its name, demand theory holds that this means that if
the unemployment rate gets "too low," inflation will accelerate in the absence of wage
and price controls (incomes policies).
One of the major problems with the NAIRU theory is that no one knows exactly what the
NAIRU is (while it clearly changes over time). The margin of error can be quite high
relative to the actual unemployment rate, making it hard to use the NAIRU in policymaking.
Another, normative, definition of full employment might be called
the ideal unemployment rate. It would exclude all types of unemployment that represent
forms of inefficiency. This type of "full employment" unemployment would correspond to
only frictional unemployment (excluding that part encouraging the McJobs management
strategy) and would thus be very low. However, it would be impossible to attain this fullemployment
target using only demand-side Keynesian stimulus without getting below
the NAIRU and causing accelerating inflation (absent incomes policies). Training
programs aimed at fighting structural unemployment would help here.
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To the extent that hidden unemployment exists, it implies that official unemployment
statistics provide a poor guide to what unemployment rate coincides with "full
employment".
Structural Unemployment
Structural unemployment occurs when a labour market is unable to provide jobs for
everyone who wants one because there is a mismatch between the skills of the
unemployed workers and the skills needed for the available jobs. Structural
unemployment is hard to separate empirically from frictional unemployment, except to
say that it lasts longer. As with frictional unemployment, simple demand-side stimulus
will not work to easily abolish this type of unemployment.
Structural unemployment may also be encouraged to rise by persistent cyclical
unemployment: if an economy suffers from long-lasting low aggregate demand, it
means that many of the unemployed become disheartened, while their skills
(including job-searching skills) become "rusty" and obsolete. Problems with debt may
lead to homelessness and a fall into the vicious circle of poverty.
This means that they may not fit the job vacancies that are created when the economy
recovers. The implication is that sustained highdemand may lower structural
unemployment. This theory of persistence in structural unemployment has been
referred to as an example of path dependence or "hysteresis".
Much technological unemployment, due to the replacement of workers by machines,
might be counted as structural unemployment. Alternatively, technological
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unemployment might refer to the way in which steady increases in labour productivity
mean that fewer workers are needed to produce the same level of output every year.
The fact that aggregate demand can be raised to deal with this problem suggests that
this problem is instead one of cyclical unemployment. As indicated by Okun's Law, the
demand side must grow sufficiently quickly to absorb not only the growing labour force
but also the workers made redundant by increased labour productivity.
Seasonal unemployment may be seen as a kind of structural unemployment, since it is
a type of unemployment that is linked to certain kinds of jobs (construction work,
migratory farm work). The most-cited official unemployment measures erase this kind of
unemployment from the statistics using "seasonal adjustment" techniques. This results
in substantial, permanent structural unemployment.
Frictional Unemployment
Frictional unemployment is the time period between jobs when a worker
is searching for, or transitioning from one job to another. It is sometimes called search
unemployment and can be voluntary based on the circumstances of the unemployed
individual.
Frictional unemployment exists because both jobs and workers are heterogeneous, and
a mismatch can result between the characteristics of supply and demand. Such a
mismatch can be related to skills, payment, work-time, location, seasonal industries,
attitude, taste, and a multitude of other factors. New entrants (such as graduating
students) and re-entrants (such as former homemakers) can also suffer a spell of
frictional unemployment.
Workers as well as employers accept a certain level of imperfection, risk or
compromise, but usually not right away; they will invest some time and effort to find a
better match. This is in fact beneficial to the economy since it results in a better
allocation of resources. However, if the search takes too long and mismatches are too
frequent, the economy suffers, since some work will not get done. Therefore,
governments will seek ways to reduce unnecessary frictional unemployment through
multiple means including providing education, advice, training, and assistance such
as daycare centers.
The frictions in the labour market are sometimes illustrated graphically with a Beveridge
curve, a downward-sloping, convex curve that shows a correlation between the
unemployment rate on one axis and the vacancy rate on the other. Changes in the
supply of or demand for labour cause movements along this curve. An increase
(decrease) in labour market frictions will shift the curve outwards (inwards).
Hidden Unemployment
Hidden, or covered, unemployment is the unemployment of potential
workers that are not reflected in official unemployment statistics, due
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to the way the statistics are collected. In many countries, only those who have
no work but are actively looking for work (and/or qualifying for social security benefits)
are counted as unemployed. Those who have given up looking for work
(and sometimes those who are on Government "retraining" programs)
are not officially counted among the unemployed, even though they
are not employed.
The statistic also does not count the "underemployed"—those
working fewer hours than they would prefer or in a job that doesn't
make good use of their capabilities. In addition, those who are of
working age but are currently in full-time education are usually not
considered unemployed in government statistics. Traditional unemployed
native societies who survive by gathering, hunting, herding, and farming in wilderness
areas, may or may not be counted in unemployment statistics. Official statistics
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often underestimate unemployment rates because of hidden
unemployment. [emphasis added].
Long-Term Unemployment
Long-term unemployment is defined in European Union statistics, as unemployment
lasting for longer than one year. The United States Bureau of Labor Statistics (BLS),
which reports current long-term unemployment rate at 1.9 percent, defines this as
unemployment lasting 27 weeks or longer. Long-term unemployment is a component
of structural unemployment, which results in long-term unemployment existing in every
social group, industry, occupation, and all levels of education.
Measurement
There are also different ways national statistical agencies measure unemployment.
These differences may limit the validity of international comparisons of unemployment
data. To some degree these differences remain despite national statistical agencies
increasingly adopting the definition of unemployment by the International Labour
Organization. To facilitate international comparisons, some organizations, such as
the OECD, Eurostat, and International Labor Comparisons Program, adjust data on
unemployment for comparability across countries.
Though many people care about the number of unemployed individuals, economists
typically focus on the unemployment rate. This corrects for the normal increase in the
number of people employed due to increases in population and increases in the labour
force relative to the population. The unemployment rate is expressed as a percentage,
and is calculated as follows:
As defined by the International Labour Organization, "unemployed workers" are those
who are currently not working but are willing and able to work for pay, currently
available to work, and have actively searched for work. Individuals who are actively
seeking job placement must make the effort to: be in contact with an employer, have job
interviews, contact job placement agencies, send out resumes, submit applications,
respond to advertisements, or some other means of active job searching within the prior
four weeks. Simply looking at advertisements and not responding will not count as
actively seeking job placement. Since not all unemployment may be "open" and counted
by government agencies, official statistics on unemployment may not be accurate. In
the United States, for example, the unemployment rate does not take into consideration
those individuals who are not actively looking for employment, such as those still
attending college.
The ILO describes 4 different methods to calculate the unemployment rate:
Labor Force Sample Surveys are the most preferred method of unemployment
rate calculation since they give the most comprehensive results and enables
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calculation of unemployment by different group categories such as race and
gender. This method is the most internationally comparable.
Official Estimates are determined by a combination of information from one or
more of the other three methods. The use of this method has been declining in
favor of Labor Surveys.
Social Insurance Statistics such as unemployment benefits, are computed base
on the number of persons insured representing the total labour force and the
number of persons who are insured that are collecting benefits. This method has
been heavily criticized due to the expiration of benefits before the person finds
work.
Employment Office Statistics are the least effective being that they only include a
monthly tally of unemployed persons who enter employment offices. This method
also includes unemployed who are not unemployed per the ILO definition.
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The primary measure of unemployment, U3, allows for comparisons between countries.
Unemployment differs from country to country and across different time periods. For
example, during the 1990s and 2000s, the United States had lower unemployment
levels than many countries in the European Union, which had significant internal
variation, with countries like the UK and Denmark outperforming Italy and France.
However, large economic events such as the Great Depression can lead to similar
unemployment rates across the globe.
European Union (Eurostat)
Eurostat, the statistical office of the European Union, defines unemployed as those
persons age 15 to 74 who are not working, have looked for work in the last four weeks,
and ready to start work within two weeks, which conform to ILO standards. Both the
actual count and rate of unemployment are reported. Statistical data are available by
member state, for the European Union as a whole (EU28) as well as for the euro area
(EA19). Eurostat also includes a long-term unemployment rate. This is defined as part
of the unemployed who have been unemployed for an excess of 1 year.
The main source used is the European Union Labour Force Survey (EU-LFS). The EU-
LFS collects data on all member states each quarter. For monthly calculations, national
surveys or national registers from employment offices are used in conjunction with
quarterly EU-LFS data. The exact calculation for individual countries, resulting in
harmonized monthly data, depends on the availability of the data.
United States Bureau of Labor Statistics
The Bureau of Labor Statistics measures employment and unemployment (of those
over 17 years of age) using two different labor force surveys conducted by the United
States Census Bureau (within the United States Department of Commerce) and/or the
Bureau of Labor Statistics (within the United States Department of Labor) that gather
employment statistics monthly. The Current Population Survey (CPS), or "Household
Survey", conducts a survey based on a sample of 60,000 households. This Survey
measures the unemployment rate based on the ILO definition.
The Current Employment Statistics survey (CES), or "Payroll Survey", conducts a
survey based on a sample of 160,000 businesses and government agencies that
represent 400,000 individual employers. This survey measures only civilian
nonagricultural employment; thus, it does not calculate an unemployment rate, and it
differs from the ILO unemployment rate definition. These two sources have different
classification criteria, and usually produce differing results. Additional data are also
available from the government, such as the unemployment insurance weekly claims
report available from the Office of Workforce Security, within the U.S. Department of
Labor Employment & Training Administration. The Bureau of Labor Statistics provides
up-to-date numbers via a PDF linked here. The BLS also provides a readable concise
current Employment Situation Summary, updated monthly.
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The Bureau of Labor Statistics also calculates six alternate measures of unemployment,
U1 through U6, that measure different aspects of unemployment:
U1: Percentage of labor force unemployed 15 weeks or longer.
U2: Percentage of labor force who lost jobs or completed temporary work.
U3: Official unemployment rate per the ILO definition occurs when people are
without jobs and they have actively looked for work within the past four weeks.
U4: U3 + "discouraged workers", or those who have stopped looking for work
because current economic conditions make them believe that no work is
available for them.
U5: U4 + other "marginally attached workers", or "loosely attached workers", or
those who "would like" and are able to work, but have not looked for work
recently.
U6: U5 + Part-time workers who want to work full-time, but cannot due to
economic reasons (underemployment).
Note: "Marginally attached workers" are added to the total labor force for unemployment
rate calculation for U4, U5, and U6. The BLS revised the CPS in 1994 and among the
changes the measure representing the official unemployment rate was renamed U3
instead of U5. In 2013, Representative Hunter proposed that the Bureau of Labor
Statistics use the U5 rate instead of the current U3 rate.
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Statistics for the U.S. economy as a whole hide variations among groups. For example,
in January 2008 U.S. unemployment rates were 4.4% for adult men, 4.2% for adult
women, 4.4% for Caucasians, 6.3% for Hispanics or Latinos (all races), 9.2% for African
Americans, 3.2% for Asian Americans, and 18.0% for teenagers. Also, the U.S.
unemployment rate would be at least 2% higher if prisoners and jail inmates were
counted.
The unemployment rate is included in a number of major economic indexes including
the United States' Conference Board's Index of Leading indicators a macroeconomic
measure of the state of the economy.
Alternatives
Limitations of The Unemployment Definition
Some critics believe that current methods of measuring unemployment are inaccurate in
terms of the impact of unemployment on people as these methods do not take into
account the 1.5% of the available working population incarcerated in U.S. prisons (who
may or may not be working while incarcerated); those who have lost their jobs and have
become discouraged over time from actively looking for work; those who are selfemployed
or wish to become self-employed, such as tradesmen or building contractors
or IT consultants; those who have retired before the official retirement age but would still
like to work (involuntary early retirees); those on disability pensions who, while not
possessing full health, still wish to work in occupations suitable for their medical
conditions; or those who work for payment for as little as one hour per week but would
like to work full-time.
These last people are "involuntary part-time" workers, those who are underemployed,
e.g., a computer programmer who is working in a retail store until he can find a
permanent job, involuntary stay-at-home mothers who would prefer to work, and
graduate and Professional school students who were unable to find worthwhile jobs
after they graduated with their bachelor's degrees.
Internationally, some nations' unemployment rates are sometimes muted or appear less
severe due to the number of self-employed individuals working in agriculture. Small
independent farmers are often considered self-employed; so, they cannot be
unemployed. The impact of this is that in non-industrialized economies, such as the
United States and Europe during the early 19th century, overall unemployment was
approximately 3% because so many individuals were self-employed, independent
farmers; yet, unemployment outside of agriculture was as high as 80%.
Many economies industrialize and experience increasing numbers of non-agricultural
workers. For example, the United States' non-agricultural labour force increased from
20% in 1800, to 50% in 1850, to 97% in 2000. The shift away from self-employment
increases the percentage of the population who are included in unemployment rates.
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When comparing unemployment rates between countries or time periods, it is best to
consider differences in their levels of industrialization and self-employment.
Additionally, the measures of employment and unemployment may be "too high". In
some countries, the availability of unemployment benefitscan inflate statistics since they
give an incentive to register as unemployed. People who do not seek work may choose
to declare themselves unemployed so as to get benefits; people with undeclared paid
occupations may try to get unemployment benefits in addition to the money they earn
from their work.
However, in countries such as the United States, Canada, Mexico, Australia, Japan and
the European Union, unemployment is measured using a sample survey (akin to
a Galluppoll). According to the BLS, a number of Eastern European nations have
instituted labour force surveys as well. The sample survey has its own problems
because the total number of workers in the economy is calculated based on a sample
rather than a census.
It is possible to be neither employed nor unemployed by ILO definitions, i.e., to be
outside of the "labour force". These are people who have no job and are not looking for
one. Many of these people are going to school or are retired. Family responsibilities
keep others out of the labour force. Still others have a physical or mental disability
which prevents them from participating in labour force activities. Some people simply
elect not to work preferring to be dependent on others for sustenance.
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Typically, employment and the labour force include only work done for monetary gain.
Hence, a homemaker is neither part of the labour force nor unemployed. Nor are fulltime
students nor prisoners considered to be part of the labour force or
unemployment. The latter can be important. In 1999, economists Lawrence F. Katz and
Alan B. Krueger estimated that increased incarceration lowered measured
unemployment in the United States by 0.17% between 1985 and the late 1990s.
In particular, as of 2005, roughly 0.7% of the U.S. population is incarcerated (1.5% of
the available working population). Additionally, children, the elderly, and some
individuals with disabilities are typically not counted as part of the labour force in and
are correspondingly not included in the unemployment statistics. However, some elderly
and many disabled individuals are active in the labour market
In the early stages of an economic boom, unemployment often rises. This is because
people join the labour market (give up studying, start a job hunt, etc.) as a result of the
improving job market, but until they have actually found a position they are counted as
unemployed. Similarly, during a recession, the increase in the unemployment rate is
moderated by people leaving the labour force or being otherwise discounted from the
labour force, such as with the self-employed.
For the fourth quarter of 2004, according to OECD, (source Employment Outlook
2005 ISBN 92-64-01045-9), normalized unemployment for men aged 25 to 54 was 4.6%
in the U.S. and 7.4% in France. At the same time and for the same population the
employment rate (number of workers divided by population) was 86.3% in the U.S. and
86.7% in France. This example shows that the unemployment rate is 60% higher in
France than in the U.S., yet more people in this demographic are working in France
than in the U.S., which is counterintuitive if it is expected that the unemployment rate
reflects the health of the labour market.
Due to these deficiencies, many labour market economists prefer to look at a range of
economic statistics such as labour market participation rate, the percentage of people
aged between 15 and 64 who are currently employed or searching for employment, the
total number of full-time jobs in an economy, the number of people seeking work as a
raw number and not a percentage, and the total number of person-hours worked in a
month compared to the total number of person-hours people would like to work. In
particular the NBER does not use the unemployment rate but prefer various
employment rates to date recessions.
Labor Force Participation Rate
The labor force participation rate is the ratio between the labor force and the overall size
of their cohort (national population of the same age range). In the West, during the later
half of the 20th century, the labor force participation rate increased significantly, due to
an increase in the number of women who entered the workplace.
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In the United States, there have been four significant stages of women's participation in
the labor force—increases in the 20th century and decreases in the 21st century. Male
labor force participation decreased from 1953 until 2013. Since October 2013 men have
been increasingly joining the labor force.
During the late 19th century through the 1920s, very few women worked outside the
home. They were young single women who typically withdrew from the labor force at
marriage unless family needed two incomes. These women worked primarily in
the textile manufacturingindustry or as domestic workers. This profession empowered
women and allowed them to earn a living wage. At times, they were a financial help to
their families.
Between 1930 and 1950, female labor force participation increased primarily due to the
increased demand for office workers, women's participation in the high school
movement, and due to electrification which reduced the time spent on household
chores. Between the 1950s to the early 1970s, most women were secondary earners
working mainly as secretaries, teachers, nurses, and librarians (pink-collar jobs).
Between the mid-1970s to the late 1990s, there was a period of revolution of women in
the labor force brought on by a source of different factors, many of which arose from
the second wave feminism movement. Women more accurately planned for their future
in the work force, investing in more applicable majors in college that prepared them to
enter and compete in the labor market. In the United States, the female labor force
participation rate rose from approximately 33% in 1948 to a peak of 60.3% in 2000. As
of April 2015, the female labor force participation is at 56.6%, the male labor force
participation rate is at 69.4% and the total is 62.8%.
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A common theory in modern economics claims that the rise of women participating in
the U.S. labor force in the 1950s to the 1990s was due to the introduction of a new
contraceptive technology, birth control pills, and the adjustment of age of majority laws.
The use of birth control gave women the flexibility of opting to invest and advance their
career while maintaining a relationship.
By having control over the timing of their fertility, they were not running a risk of
thwarting their career choices. However, only 40% of the population actually used the
birth control pill.
This implies that other factors may have contributed to women choosing to invest in
advancing their careers. One factor may be that more and more men delayed the age of
marriage, allowing women to marry later in life without worrying about the quality of
older men. Other factors include the changing nature of work, with machines replacing
physical labor, eliminating many traditional male occupations, and the rise of the service
sector, where many jobs are gender neutral.
Another factor that may have contributed to the trend was The Equal Pay Act of 1963,
which aimed at abolishing wage disparity based on sex. Such legislation diminished
sexual discrimination and encouraged more women to enter the labor market by
receiving fair remuneration to help raising families and children.
At the turn of the 21st century the labor force participation began to reverse its long
period of increase. Reasons for this change include a rising share of older workers, an
increase in school enrollment rates among young workers and a decrease in female
labor force participation.
The labor force participation rate can decrease when the rate of growth of the
population outweighs that of the employed and unemployed together. The labor force
participation rate is a key component in long-term economic growth, almost as important
as productivity.
A historic shift began around the end of the great recession as women began leaving
the labor force in the United States and other developed countries. The female labor
force participation rate in the United States has steadily decreased since 2009 and as of
April 2015 the female labor force participation rate has gone back down to 1988 levels
of 56.6%.
Participation rates are defined as follows:
The labor force participation rate explains how an increase in the unemployment rate
can occur simultaneously with an increase in employment. If a large amount of new
workers enter the labor force but only a small fraction become employed, then the
increase in the number of unemployed workers can outpace the growth in employment.
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Unemployment Ratio
The unemployment ratio calculates the share of unemployed for the whole population.
Particularly many young people between 15 and 24 are studying full-time and are
therefore neither working nor looking for a job. This means they are not part of the
labour force which is used as the denominator for calculating the unemployment
rate. The youth unemployment ratios in the European Union range from 5.2 (Austria) to
20.6 percent (Spain). These are considerably lower than the standard youth
unemployment rates, ranging from 7.9 (Germany) to 57.9 percent (Greece).
Effects
High and persistent unemployment, in which economic inequality increases, has a
negative effect on subsequent long-run economic growth. Unemployment can harm
growth not only because it is a waste of resources, but also because it generates
redistributive pressures and subsequent distortions, drives people to poverty, constrains
liquidity limiting labor mobility, and erodes self-esteem promoting social dislocation,
unrest and conflict.
2013 Economics Nobel prize winner Robert J. Shiller said that rising inequality in the
United States and elsewhere is the most important problem.
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Costs
Individual
Unemployed individuals are unable to earn money to meet financial obligations. Failure
to pay mortgage payments or to pay rent may lead
to homelessness through foreclosure or eviction. Across the United States the growing
ranks of people made homeless in the foreclosure crisis are generating tent cities.
Unemployment increases susceptibility to cardiovascular disease, somatization, anxiety
disorders, depression, and suicide. In addition, unemployed people have higher rates of
medication use, poor diet, physician visits, tobacco smoking, alcoholic
beverage consumption, drug use, and lower rates of exercise. According to a study
published in Social Indicator Research, even those who tend to be optimistic find it
difficult to look on the bright side of things when unemployed. Using interviews and data
from German participants aged 16 to 94—including individuals coping with the stresses
of real life and not just a volunteering student population—the researchers determined
that even optimists struggled with being unemployed.
In 1979, Brenner found that for every 10% increase in the number of unemployed there
is an increase of 1.2% in total mortality, a 1.7% increase in cardiovascular disease,
1.3% more cirrhosis cases, 1.7% more suicides, 4.0% more arrests, and 0.8% more
assaults reported to the police.
A study by Ruhm, in 2000, on the effect of recessions on health found that several
measures of health actually improve during recessions. As for the impact of an
economic downturn on crime, during the Great Depression the crime rate did not
decrease. The unemployed in the U.S. often use welfare programs such as Food
Stamps or accumulating debt because unemployment insurance in the U.S. generally
does not replace a majority of the income one received on the job (and one cannot
receive such aid indefinitely).
Not everyone suffers equally from unemployment. In a prospective study of 9570
individuals over four years, highly conscientious people suffered more than twice as
much if they became unemployed. The authors suggested this may be due to
conscientious people making different attributions about why they became unemployed,
or through experiencing stronger reactions following failure. There is also possibility of
reverse causality from poor health to unemployment.
Some researchers hold that many of the low-income jobs are not really a better option
than unemployment with a welfare state (with its unemployment insurance benefits). But
since it is difficult or impossible to get unemployment insurance benefits without having
worked in the past, these jobs and unemployment are more complementary than they
are substitutes. (These jobs are often held short-term, either by students or by those
trying to gain experience; turnover in most low-paying jobs is high.)
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Another cost for the unemployed is that the combination of unemployment, lack of
financial resources, and social responsibilities may push unemployed workers to take
jobs that do not fit their skills or allow them to use their talents. Unemployment can
cause underemployment, and fear of job loss can spur psychological anxiety. As well as
anxiety, it can cause depression, lack of confidence, and huge amounts of stress. This
stress is increased when the unemployed are faced with health issues, poverty, and
lack of relational support.
Another personal cost of unemployment is its impact on relationships. A 2008 study
from Covizzi, which examines the relationship between unemployment and divorce,
found that the rate of divorce is greater for couples when one partner is
unemployed. However, a more recent study has found that some couples often stick
together in "unhappy" or "unhealthy" marriages when unemployed to buffer financial
costs. A 2014 study by Van der Meer found that the stigma that comes from being
unemployed affects personal well-being, especially for men, who often feel as though
their masculine identities are threatened by unemployment.
Unemployment can also bring personal costs in relation to gender. One study found that
women are more likely to experience unemployment than men and that they are less
likely to move from temporary positions to permanent positions. Another study on
gender and unemployment found that men, however, are more likely to experience
greater stress, depression, and adverse effects from unemployment, largely stemming
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from the perceived threat to their role as breadwinner. This study found that men expect
themselves to be viewed as "less manly" after a job loss than they actually are, and as a
result they engage in compensating behaviors, such as financial risk-taking and
increased assertiveness, because of it.
Costs of unemployment also vary depending on age. The young and the old are the two
largest age groups currently experiencing unemployment. A 2007 study from Jacob and
Kleinert found that young people (ages 18 to 24) who have fewer resources and limited
work experiences are more likely to be unemployed. Other researchers have found that
today’s high school seniors place a lower value on work than those in the past, and this
is likely because they recognize the limited availability of jobs. At the other end of the
age spectrum, studies have found that older individuals have more barriers than
younger workers to employment, require stronger social networks to acquire work, and
are also less likely to move from temporary to permanent positions. Additionally, some
older people see age discrimination as the reason they are not getting hired.
Social
An economy with high unemployment is not using all of the resources, specifically
labour, available to it. Since it is operating below its production possibility frontier, it
could have higher output if all the workforce were usefully employed. However, there is
a trade-off between economic efficiency and unemployment: if the frictionally
unemployed accepted the first job they were offered, they would be likely to be
operating at below their skill level, reducing the economy's efficiency.
During a long period of unemployment, workers can lose their skills, causing a loss
of human capital. Being unemployed can also reduce the life expectancy of workers by
about seven years.
High unemployment can encourage xenophobia and protectionism as workers fear that
foreigners are stealing their jobs. Efforts to preserve existing jobs of domestic and
native workers include legal barriers against "outsiders" who want jobs, obstacles to
immigration, and/or tariffs and similar trade barriers against foreign competitors.
High unemployment can also cause social problems such as crime; if people have less
disposable income than before, it is very likely that crime levels within the economy will
increase.
A 2015 study published in The Lancet, estimates that unemployment causes 45,000
suicides a year globally.
Socio-Political
High levels of unemployment can be causes of civil unrest, in some cases leading to
revolution, and particularly totalitarianism. The fall of the Weimar Republic in 1933
and Adolf Hitler's rise to power, which culminated in World War II and the deaths of tens
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of millions and the destruction of much of the physical capital of Europe, is attributed to
the poor economic conditions in Germany at the time, notably a high unemployment
rate of above 20%; see Great Depression in Central Europe for details.
Note that the hyperinflation in the Weimar Republic is not directly blamed for the Nazi
rise—the Hyperinflation in the Weimar Republicoccurred primarily in the period 1921–
23, which was contemporary with Hitler's Beer Hall Putsch of 1923, and is blamed for
damaging the credibility of democratic institutions, but the Nazis did not assume
government until 1933, ten years after the hyperinflation but in the midst of high
unemployment.
Rising unemployment has traditionally been regarded by the public and media in any
country as a key guarantor of electoral defeat for any government which oversees it.
This was very much the consensus in the United Kingdom until 1983, when Margaret
Thatcher's Conservative government won a landslide in the general election, despite
overseeing a rise in unemployment from 1,500,000 to 3,200,000 since its election four
years earlier.
Benefits
The primary benefit of unemployment is that people are available for hire, without
being headhunted away from their existing employers. This permits new and old
businesses to take on staff.
Unemployment is argued to be "beneficial" to the people who are not unemployed in the
sense that it averts inflation, which itself has damaging effects, by providing
(in Marxianterms) a reserve army of labour, that keeps wages in check. However, the
direct connection between full local employment and local inflation has been disputed
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y some due to the recent increase in international trade that supplies low-priced goods
even while local employment rates rise to full employment.
Full employment cannot be achieved because workers would shirk, if they were not
threatened with the possibility of unemployment. The curve for the no-shirking condition
(labeled NSC) goes to infinity at full employment as a result. The inflation-fighting
benefits to the entire economy arising from a presumed optimum level of unemployment
have been studied extensively. The Shapiro–Stiglitz model suggests that wages are not
bid down sufficiently to ever reach 0% unemployment. This occurs because employers
know that when wages decrease, workers will shirk and expend less effort. Employers
avoid shirking by preventing wages from decreasing so low that workers give up and
become unproductive. These higher wages perpetuate unemployment while the threat
of unemployment reduces shirking.
Before current levels of world trade were developed, unemployment was demonstrated
to reduce inflation, following the Phillips curve, or to decelerate inflation, following the
NAIRU/natural rate of unemployment theory, since it is relatively easy to seek a new job
without losing one's current one. And when more jobs are available for fewer workers
(lower unemployment), it may allow workers to find the jobs that better fit their tastes,
talents, and needs.
As in the Marxian theory of unemployment, special interests may also benefit: some
employers may expect that employees with no fear of losing their jobs will not work as
hard, or will demand increased wages and benefit. According to this theory,
unemployment may promote general labour productivity and profitability by increasing
employers' rationale for their monopsony-like power (and profits).
Optimal unemployment has also been defended as an environmental tool to brake the
constantly accelerated growth of the GDP to maintain levels sustainable in the context
of resource constraints and environmental impacts. However the tool of denying jobs to
willing workers seems a blunt instrument for conserving resources and the
environment—it reduces the consumption of the unemployed across the board, and
only in the short term. Full employment of the unemployed workforce, all focused toward
the goal of developing more environmentally efficient methods for production and
consumption might provide a more significant and lasting cumulative environmental
benefit and reduced resource consumption. If so the future economy and workforce
would benefit from the resultant structural increases in the sustainable level of GDP
growth.
Some critics of the "culture of work" such as anarchist Bob Black see employment as
overemphasized culturally in modern countries. Such critics often propose quitting jobs
when possible, working less, reassessing the cost of living to this end, creation of jobs
which are "fun" as opposed to "work," and creating cultural norms where work is seen
as unhealthy. These people advocate an "anti-work" ethic for life.
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Decline In Work Hours
As a result of productivity, the work week declined considerably during the 19th
century. By the 1920s in the U.S. the average work week was 49 hours, but the work
week was reduced to 40 hours (after which overtime premium was applied) as part of
the National Industrial Recovery Act of 1933. At the time of the Great Depression of the
1930s, it was believed that due to the enormous productivity gains due
to electrification, mass production and agricultural mechanization, there was no need for
a large number of previously employed workers.
Controlling or Reducing Unemployment
United States Families on Relief (in 1,000's
Workers employed
1936 1937 1938 1939 1940 1941
WPA 1,995 2,227 1,932 2,911 1,971 1,638
CCC and NYA 712 801 643 793 877 919
Other federal work projects 554 663 452 488 468 681
Cases on public assistance
Social security programs 602 1,306 1,852 2,132 2,308 2,517
General relief 2,946 1,484 1,611 1,647 1,570 1,206
Totals
Total families helped 5,886 5,660 5,474 6,751 5,860 5,167
Unemployed workers (BLS) 9,030 7,700 10,390 9,480 8,120 5,560
Coverage (cases/unemployed) 65% 74% 53% 71% 72% 93%
Societies try a number of different measures to get as many people as possible into
work, and various societies have experienced close to full employment for extended
periods, particularly during the Post-World War II economic expansion.
The United Kingdom in the 1950s and 1960s averaged 1.6% unemployment, while in
Australia the 1945 White Paper on Full Employment in Australia established a
government policy of full employment, which policy lasted until the 1970s when the
government ran out of money.
However, mainstream economic discussions of full employment since the 1970s
suggest that attempts to reduce the level of unemployment below the natural rate of
unemployment will fail, resulting only in less output and more inflation.
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Demand-Side Solutions
Increases in the demand for labor will move the economy along the demand curve,
increasing wages and employment. The demand for labor in an economy is derived
from the demand for goods and services. As such, if the demand for goods and services
in the economy increases, the demand for labor will increase, increasing employment
and wages.
There are many ways to stimulate demand for goods and services. Increasing wages to
the working class (those more likely to spend the increased funds on goods and
services, rather than various types of savings, or commodity purchases) is one theory
proposed. Increased wages are believed to be more effective in boosting demand for
goods and services than central banking strategies that put the increased money supply
mostly into the hands of wealthy persons and institutions. Monetarists suggest that
increasing money supply in general will increase short-term demand. Long-term the
increased demand will be negated by inflation. A rise in fiscal expenditures is another
strategy for boosting aggregate demand.
Providing aid to the unemployed is a strategy used to prevent cutbacks in consumption
of goods and services which can lead to a vicious cycle of further job losses and further
decreases in consumption/demand. Many countries aid the unemployed through social
welfare programs. These unemployment benefits include unemployment
insurance, unemployment compensation, welfare and subsidies to aid in retraining. The
main goal of these programs is to alleviate short-term hardships and, more importantly,
to allow workers more time to search for a job.
A direct demand-side solution to unemployment is government-funded employment of
the able-bodied poor. This was notably implemented in Britain from the 17th century
until 1948 in the institution of the workhouse, which provided jobs for the unemployed
with harsh conditions and poor wages to dissuade their use. A modern alternative is
a job guarantee, where the government guarantees work at a living wage.
Temporary measures can include public works programs such as the Works Progress
Administration. Government-funded employment is not widely advocated as a solution
to unemployment, except in times of crisis; this is attributed to the public sector jobs'
existence depending directly on the tax receipts from private sector employment.
In the U.S., the unemployment insurance allowance one receives is based solely on
previous income (not time worked, family size, etc.) and usually compensates for onethird
of one's previous income. To qualify, one must reside in their respective state for at
least a year and work. The system was established by the Social Security Act of 1935.
Although 90% of citizens are covered by unemployment insurance, less than 40% apply
for and receive benefits. However, the number applying for and receiving benefits
increases during recessions. In cases of highly seasonal industries, the system provides
income to workers during the off seasons, thus encouraging them to stay attached to
the industry.
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According to classical economic theory, markets reach equilibrium where supply equals
demand; everyone who wants to sell at the market price can. Those who do not want to
sell at this price do not; in the labour market this is classical unemployment. Monetary
policy and fiscal policy can both be used to increase short-term growth in the economy,
increasing the demand for labour and decreasing unemployment.
Supply-Side Solutions
However, the labor market is not 100% efficient, although it may be more efficient than
the bureaucracy. Some argue that minimum wages and union activity keep wages from
falling, which means too many people want to sell their labour at the going price but
cannot. This assumes perfect competition exists in the labour market, specifically that
no single entity is large enough to affect wage levels and that employees are similar in
ability.
Advocates of supply-side policies believe those policies can solve this by making the
labor market more flexible. These include removing the minimum wage and reducing
the power of unions. Supply-siders argue the reforms increase long-term growth by
reducing labour costs. This increased supply of goods and services requires more
workers, increasing employment.
It is argued that supply-side policies, which include cutting taxes on businesses and
reducing regulation, create jobs, reduce unemployment and decrease labour's share of
national income. Other supply-side policies include education to make workers more
attractive to employers.
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History
There are relatively limited historical records on unemployment because it has not
always been acknowledged or measured systematically. Industrialization involves
economies of scale that often prevent individuals from having the capital to create their
own jobs to be self-employed. An individual who cannot either join an enterprise or
create a job is unemployed. As individual farmers, ranchers, spinners, doctors and
merchants are organized into large enterprises, those who cannot join or compete
become unemployed.
Recognition of unemployment occurred slowly as economies across the world
industrialized and bureaucratized. Before this, traditional self sufficient native societies
have no concept of unemployment. The recognition of the concept of "unemployment" is
best exemplified through the well documented historical records in England. For
example, in 16th century England no distinction was made between vagrants and the
jobless; both were simply categorized as "sturdy beggars", to be punished and moved
on.
The closing of the monasteries in the 1530s increased poverty, as the church had
helped the poor. In addition, there was a significant rise in enclosure during the Tudor
period. Also the population was rising. Those unable to find work had a stark choice:
starve or break the law. In 1535, a bill was drawn up calling for the creation of a system
of public works to deal with the problem of unemployment, to be funded by a tax on
income and capital. A law passed a year later allowed vagabonds to be whipped and
hanged.
In 1547, a bill was passed that subjected vagrants to some of the more extreme
provisions of the criminal law, namely two years servitude and branding with a "V" as
the penalty for the first offense and death for the second. During the reign of Henry VIII,
as many as 72,000 people are estimated to have been executed. In the 1576 Act each
town was required to provide work for the unemployed.
The Elizabethan Poor Law of 1601, one of the world's first government-sponsored
welfare programs, made a clear distinction between those who were unable to work and
those able-bodied people who refused employment. Under the Poor Law systems
of England and Wales, Scotland and Ireland a workhouse was a place where people
who were unable to support themselves, could go to live and work.
Industrial Revolution to Late 19th Century
Poverty was a highly visible problem in the eighteenth century, both in cities and in the
countryside. In France and Britain by the end of the century, an estimated 10 percent of
the people depended on charity or begging for their food.
— Jackson J. Spielvogel (2008), Cengage Learning. p.566.
By 1776 some 1,912 parish and corporation workhouses had been established in
England and Wales, housing almost 100,000 paupers.
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A description of the miserable living standards of the mill workers in England in 1844
was given by Fredrick Engels in The Condition of the Working-Class in England in
1844. In the preface to the 1892 edition Engels notes that the extreme poverty he wrote
about in 1844 had largely disappeared. David Ames Wells also noted that living
conditions in England had improved near the end of the 19th century and that
unemployment was low.
The scarcity and high price of labor in the U.S. during the 19th century was well
documented by contemporary accounts, as in the following:
"The laboring classes are comparatively few in number, but this is counterbalanced by,
and indeed, may be one of the causes of the eagerness by which they call in the use of
machinery in almost every department of industry. Wherever it can be applied as a
substitute for manual labor, it is universally and willingly resorted to ....It is this condition
of the labor market, and this eager resort to machinery wherever it can be applied, to
which, under the guidance of superior education and intelligence, the remarkable
prosperity of the United States is due." Joseph Whitworth, 1854
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Scarcity of labor was a factor in the economics of slavery in the United States.
As new territories were opened and Federal land sales conducted, land had to be
cleared and new homesteads established. Hundreds of thousands of immigrants
annually came to the U.S. and found jobs digging canals and building railroads. Almost
all work during most of the 19th century was done by hand or with horses, mules, or
oxen, because there was very little mechanization. The workweek during most of the
19th century was 60 hours. Unemployment at times was between one and two percent.
The tight labor market was a factor in productivity gains allowing workers to maintain or
increase their nominal wages during the secular deflation that caused real wages to rise
at various times in the 19th century, especially in the final decades.
20th Century
There were labor shortages during WW I. Ford Motor Co. doubled wages to reduce
turnover. After 1925 unemployment began to gradually rise.
The decade of the 1930s saw the Great Depression impact unemployment across the
globe. One Soviet trading corporation in New York averaged 350 applications a day
from Americans seeking jobs in the Soviet Union. [121] In Germany the unemployment
rate reached nearly 25% in 1932.
In some towns and cities in the north east of England, unemployment reached as high
as 70%; the national unemployment level peaked at more than 22% in
1932. Unemployment in Canada reached 27% at the depth of the Depression in
1933. In 1929, the U.S. unemployment rate averaged 3%.
In the U.S., the Works Progress Administration (1935–43) was the largest make-work
program. It hired men (and some women) off the relief roles ("dole") typically for
unskilled labor.
In Cleveland, Ohio, the unemployment rate was 60%; in Toledo, Ohio, 80%.There were
two million homeless people migrating across the United States. Over 3 million
unemployed young men were taken out of the cities and placed into 2600+ work camps
managed by the Civilian Conservation Corps.
Unemployment in the United Kingdom fell later in the 1930s as the depression eased,
and remained low (in six figures) after World War II.
Fredrick Mills found that in the U.S., 51% of the decline in work hours was due to the fall
in production and 49% was from increased productivity. By 1972 unemployment in the
UK had crept back up above 1,000,000, and was even higher by the end of the decade,
with inflation also being high. Although the monetarist economic policies of Margaret
Thatcher's Conservative government saw inflation reduced after 1979, unemployment
soared in the early 1980s, exceeding 3,000,000—a level not seen for some 50 years—
by 1982. This represented one in eight of the workforce, with unemployment exceeding
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20% in some parts of the United Kingdom which had relied on the now-declining
industries such as coal mining.
However, this was a time of high unemployment in all major industrialised nations. By
the spring of 1983, unemployment in the United Kingdom had risen by 6% in the
previous 12 months; compared to 10% in Japan, 23% in the United States of
America and 34% in West Germany (seven years before reunification).
Unemployment in the United Kingdom remained above 3,000,000 until the spring of
1987, by which time the economy was enjoying a boom. By the end of 1989,
unemployment had fallen to 1,600,000. However, inflation had reached 7.8% and the
following year it reached a nine-year high of 9.5%; leading to increased interest rates.
Another recession began during 1990 and lasted until 1992. Unemployment began to
increase and by the end of 1992 nearly 3,000,000 in the United Kingdom were
unemployed. Then came a strong economic recovery. With inflation down to 1.6% by
1993, unemployment then began to fall rapidly, standing at 1,800,000 by early 1997.
21st Century
The official unemployment rate in the 16 EU countries that use the Euro rose to 10% in
December 2009 as a result of another recession. Latvia had the highest unemployment
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ate in the EU at 22.3% for November 2009. Europe's young workers have been
especially hard hit. In November 2009, the unemployment rate in the EU27 for those
aged 15–24 was 18.3%. For those under-25, the unemployment rate in Spain was
43.8%. Unemployment has risen in two-thirds of European countries since 2010.
Into the 21st century, unemployment in the United Kingdom remained low and the
economy remaining strong, while at this time several other European economies—
namely, France and Germany (reunified a decade earlier)—experienced a minor
recession and a substantial rise in unemployment.
In 2008, when the recession brought on another increase in the United Kingdom, after
15 years of economic growth and no major rises in unemployment. Early in 2009,
unemployment passed the 2,000,000 mark, by which time economists were predicting it
would soon reach 3,000,000. However, the end of the recession was declared in
January 2010 and unemployment peaked at nearly 2,700,000 in 2011, appearing to
ease fears of unemployment reaching 3,000,000. The unemployment rate of Britain's
young black people was 47.4% in 2011. 2013/2014 has seen the employment rate
increase from 1,935,836 to 2,173,012 as supported by showing the UK is creating more
job opportunities and forecasts the rate of increase in 2014/2015 will be another 7.2%.
A 26 April 2005 Asia Times article notes that, "In regional giant South Africa, some
300,000 textile workers have lost their jobs in the past two years due to the influx of
Chinese goods". The increasing U.S. trade deficit with China cost 2.4 million American
jobs between 2001-2008, according to a study by the Economic Policy
Institute (EPI). From 2000-2007, the United States lost a total of 3.2 million
manufacturing jobs. 12.1% of US military veterans who had served after the September
11 attacks in 2001 were unemployed as of 2011; 29.1% of male veterans aged 18–24
were unemployed.
As of September 2016, the total veteran unemployment rate was 4.3 percent. By
September 2017, that figure had dropped to 3 percent.
About 25,000,000 people in the world's thirty richest countries will have lost their jobs
between the end of 2007 and the end of 2010 as the economic downturn pushes most
countries into recession. In April 2010, the U.S. unemployment rate was 9.9%, but the
government's broader U-6 unemployment rate was 17.1%.
In April 2012, the unemployment rate was 4.6% in Japan. In a 2012 news story,
the Financial Post reported, "Nearly 75 million youth are unemployed around the world,
an increase of more than 4 million since 2007. In the European Union, where a debt
crisis followed the financial crisis, the youth unemployment rate rose to 18% last year
from 12.5% in 2007, the ILO report shows." In March 2018, according to U.S.
Unemployment Rate Statistics, the unemployment rate was 4.1%, below the 4.5 to 5.0%
norm.
________
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Male Unemployment
Male Unemployment is unemployment, being out of work and actively seeking to
work, among men.
The 2008–2012 global recession has been called a "mancession" because of the
disproportionate number of men who lost their jobs as compared to women. This gender
gap became wide in the United States in 2009, when 10.5% of men in the labor
force were unemployed, compared with 8% of women. Three quarters of the jobs lost in
the recession in the U.S. were held by men.
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II. The Effective (True)
Unemployment Rate
The U6 Indicator
The unemployment rate announced by United States Department of Labor does not
include those too discouraged to look for work any longer or those parttime
workers who are working fewer hours than they would like. By adding these two
groups to the unemployment rate, the rate becomes the effective unemployment rate.
The Bureau of Labor Statistics in the United States keeps an alternative unemployment
rate indicator similar to the effective unemployment rate called U6.
________
The Two Different Labor
Force Surveys
The Bureau of Labor Statistics measures
employment and unemployment (of those over
17 years of age) using two different labor force
surveys conducted by the United States Census
Bureau (within the United States Department of
Commerce) and/or the Bureau of Labor
Statistics (within the United States Department
of Labor) that gather employment statistics
monthly. The Current Population Survey (CPS),
or "Household Survey", conducts a survey
based on a sample of 60,000 households. This
Survey measures the unemployment rate based
on the ILO definition.
The Current Employment Statistics survey (CES), or "Payroll Survey", conducts a
survey based on a sample of 160,000 businesses and government agencies that
represent 400,000 individual employers. This survey measures only civilian
nonagricultural employment; thus, it does not calculate an unemployment rate, and it
differs from the ILO unemployment rate definition. These two sources have different
classification criteria, and usually produce differing results. Additional data are also
available from the government, such as the unemployment insurance weekly claims
report available from the Office of Workforce Security, within the U.S. Department of
Labor Employment & Training Administration. The Bureau of Labor Statistics provides
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up-to-date numbers via a PDF linked here. The BLS also provides a readable concise
current Employment Situation Summary, updated monthly.
The Bureau of Labor Statistics also calculates six alternate measures of unemployment,
U1 through U6, that measure different aspects of unemployment:
U1: Percentage of labor force unemployed 15 weeks or longer.
U2: Percentage of labor force who lost jobs or completed temporary work.
U3: Official unemployment rate per the ILO definition occurs when people are
without jobs and they have actively looked for work within the past four weeks.
U4: U3 + "discouraged workers", or those who have stopped looking for work
because current economic conditions make them believe that no work is
available for them.
U5: U4 + other "marginally attached workers", or "loosely attached workers", or
those who "would like" and are able to work, but have not looked for work
recently.
U6: U5 + Part-time workers who want to work full-time, but cannot due to
economic reasons (underemployment).
Note: "Marginally attached workers" are added to the total labour force for
unemployment rate calculation for U4, U5, and U6. The BLS revised the CPS in
1994 and among the changes the measure representing the official unemployment rate
was renamed U3 instead of U5. In 2013,
Representative Hunter proposed that the Bureau of Labor Statistics use the U5 rate
instead of the current U3 rate.
Statistics for the U.S. economy as a whole hide variations among groups. For example,
in January 2008 U.S. unemployment rates were 4.4% for adult men, 4.2% for adult
women, 4.4% for Caucasians, 6.3% for Hispanics or Latinos (all races), 9.2% for African
Americans, 3.2% for Asian Americans, and 18.0% for teenagers. Also, the U.S.
unemployment rate would be at least 2% higher if prisoners and jail inmates were
counted.
The unemployment rate is included in a number of major economic indexes including
the United States' Conference Board's Index of Leading indicators a macroeconomic
measure of the state of the economy.
________
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U.S. Unemployment Forecast 2017 Suggests
“Real” Unemployment Could Hit 30%
U.S. Economy - By Alessandro Bruno BA, MA | February 27th, 2017
U.S. Unemployment Rate Forecast for 2017 Is 4.6% But It’s Too Optimistic
The U.S. unemployment rate forecast for 2017 is 4.7%. That means no major change
from 2016. The official numbers show that unemployment was falling just before the
presidential election. The unemployment rate fell 0.3% in November 2016 compared to
October.
At first, the sheer magnitude of the improvement should impress. But, on closer
examination, it should raise concerns.
The unemployment rate in the U.S. and its purported improvements have a less
impressive explanation. The improvement over the past year has resulted, at least in
part, by a reduction in the numbers of people actively looking for jobs. Hiring has slowed
in 2016. (Source: “U.S. job creation weak, even as unemployment rate falls to 4.7%,”
CNN, June 3, 2016.)
In May of 2016, the economy added just 38,000 jobs. That was the lowest monthly job
number statistic since 2010. (Source: Ibid.)
Real Unemployment Rate Could Hit 30% by 2020. Trump Thinks It’s Already There
Trump is right not to trust the official statistics. He already believes the real U.S.
unemployment rate to be considerably higher. During the presidential campaign of
2016, Trump convinced many supporters that the real jobless figures were closer to
28%–35% than to official figure.
Five percent unemployment was the official target during the last administration. That
target has been reached and improved, but Steven Mnuchin, the Secretary of the
Treasury, has backed President Trump’s view.
“The unemployment rate is not real … I’ve traveled for the last year. I’ve seen this.” said
Mnuchin to the Senate Finance Committee. (Source: “Ahead Of Trump’s First Jobs
Report, A Look At His Remarks On The Numbers,” NPR, January 29, 2017.)
The real unemployment rate rarely has anything to do with the actual unemployment
rate. Confusing the two may have been one of the fatal mistakes made by Democratic
presidential candidate Hillary Clinton. In the months preceding the Democratic Party
convention, unemployment appeared to be going down.
For the record, in 2010, the economy was still in full post-2008 crisis mode. Thus, there
can be but one conclusion: U.S. jobless numbers dropped because too many
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Americans grew frustrated and stopped looking for work. When an economy pushes so
many of its productive members of society to such frustration, it’s no occasion to
celebrate.
Then there’s the regional distribution of unemployment that also contributes to social
schisms. The unemployment rate by state varies significantly.
The states with the highest unemployment rates are as surprising as they are indicative
of labor market turmoil. As of September 2016, apart from Puerto Rico (a territory, not a
state)—which has an unemployment rate of 11.6%—the highest official unemployment
rates by state were New Mexico (6.7%), Louisiana (6.4%), Mississippi (6.0%), Nevada
(5.8%), and Pennsylvania (5.7%).
It’s interesting to note that Pennsylvania has been a traditional Democratic Party
stronghold in presidential elections, yet Pennsylvanians voted for Trump. Correlation is
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not causation but, if traditionally Democratic voters switch to a Republican with a clear
message of addressing the problem of declining job numbers and the weakening U.S.
labor force, it suggests that the real unemployment rate is much worse than the story
that the numbers tell.
Official Narrative Is
That Donald Trump
Has Inherited a
Healthy Economy
The stock
market has
shot up like
New Year’s
Eve fireworks,
but how
healthy is the
economy
when real
jobless
numbers are
hidden behind
deceptive
statistics?
The official
numbers measure unemployment by taking into consideration only people formally
looking for work.
These are the people receiving unemployment benefits and, as such, they are
registered into the system as part of the U.S. labor force looking for work. It would be
safe to suggest that these numbers especially account for people who have been
unemployed for a period ranging from zero to one year. What happens when the
benefits run out before they find a new job?
Donald Trump was elected in a country where the official unemployment rate in
November 2016 fell to 4.6%, which was its lowest level in nine years. Some 178,000
jobs were supposedly created in November alone. Why then did so many vote for
Trump, who ran a campaign that was doubtless trying to capture the attention of the
jobless?
In other words, if the U.S. economy was so great that it had generated such high job
numbers, why didn’t Hillary Clinton win? After all, she promised to uphold Barack
Obama’s policies. Well, the alleged job creation achievements of the Obama
administration can only be addressed by the discouragement factor that skews the real
unemployment rate.
The simple reason, in large part, is that, many unemployed Americans give up finding a
job. Anyone who has ever submitted a resume knows how frustrating and emotionally
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draining a job search can be. Donald Trump tapped into that feeling. It was one of the
main themes of his campaign.
The problem illustrates what, in the eyes of many Americans, is the paradox of the
allegedly healthy U.S. economy. The stock market is buoyant and equities are at record
highs yet, to so many, the economy is in a terrible state. And these unemployed people
don’t fit any stereotype. They include skilled professionals, engineers, managers,
factory workers, and retail sales assistants.
Middle Class Faces More Unemployment
The new phenomenon might be just how much unemployment has hit the middle class.
In fact, Trump secured many votes among the angry and frustrated middle class. The
protest vote from the American middle class was the very driver of the U.S. election
results. The polls—and one must wonder where pollsters went and whom they
interviewed—failed to get a sense of the anger.
The polls and the optimistic Obama-loving media missed a fundamental fact. They
overlooked it because they were too busy gushing over the performance of stock
market indices. What they ignored is a phenomenon that economists have described as
labor polarization (or the polarization of labor).
Yet, U.S. Federal Reserve Chair Janet Yellen has spoken about it often. (Source:
“Yellen’s ‘Polarization’ A New, More Ominous Jobs Factor,” EFXNews, August 25,
2014.)
Yellen has made no secret of the fact that she believes polarization is a major problem.
She suggests that it’s much more troubling as a technical objective than keeping the
U.S. unemployment rate at below five percent.
So, what is this polarization of labor?
Work in America, in recent years, has been characterized by what can be described as
a dualism. On the one hand, there has been an explosion of the so-called Uberization of
work. That is, on-demand work in which the worker is only partially an employee. Often,
as in the case of Uber, workers must supply their own equipment or property.
This Uberization of work, however, has one important effect: intermittent incomes. On
the polar opposite side (hence the term “polarization”), we find the highly paid and
highly educated engineers who work for the Silicon Valley-type companies. Often, they
create the work that leads to Uberization. In between, there’s little else.
The direct consequence of this phenomenon has been a massive rise in inequality. The
polarization of the labor market has strangled the middle class. When the much-praised
Obama started his first term as president in January 2009, he said in his inaugural
address that the Wall Street crisis represented a great opportunity for change.
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So, what has really changed? Yes, the official unemployment rate is just below five
percent. At the height of the financial crisis in 2008, it rose to 10% but steadily declined.
Still, something is missing, because Trump won by tapping into a pervasive sense
of economic depression. The only conclusion must be that Obama has not met his
target.
Middle America Feels Left Out
Many Americans still feel left out. In fact, they feel abandoned by the traditional political
leadership. If you are looking for proof that the real unemployment rate is much higher
than they’re telling us, look to the White House. Who is sitting in the Oval Office?
Trump’s win is the proof.
But it’s not Obama’s fault. He was merely the latest president to confront an
unemployment or underemployment problem that started in the 1990s. It was at the
height of the Bill Clinton bull market that the American middle class stopped being able
to save. Consumption has grown, but it has been fueled by credit cards and mortgages,
often of the sub-prime variety.
Then there was the 2008 financial crisis. It was prompted, nobody should forget, by a
gradual rise in interest rates to curb the real estate bubble. That made it harder for
people to pay back their debts. Savings were wiped out.
Since the so-called recovery, American families have had to rely only on salaries and
payroll benefits. But even these were greatly scaled down.
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The welfare and social security benefits they enjoyed in the past have had to face reality
checks as well. Even the formerly powerful unions, where they still had any clout, had to
accept significant cuts in exchange for keeping jobs and new hires. Now that Yellen is
urging at least two more interest rate hikes, the financial and economic picture is
approaching an eerie similarity to 2007.
Then, as now, many banks are overleveraged in derivatives. It will take just one event, a
black swan that nobody has foreseen, to send the whole market crashing, and the
economy with it. In 2008, the unemployment rate spiked from about 4.5% to 10%
overnight. The very same could happen next month or next year.
The crucial point is that nothing has changed to avert a catastrophe, and the next crisis
could hit harder than previous ones. In 2017, compared to 2008, salaries, wages, and
benefits have been resized (shrunk). There’s also more precarious—or Uberized—
employment than before. Discontent is already high. The market gains are benefiting an
ever-smaller group of people, who always seem to get richer.
Add to this the increase in personal taxes. Meanwhile, so-called blue-collar jobs have
moved to countries where wages and other production costs are lower. Trump has been
trying to bring some of the jobs back, but the numbers are too small to make a
significant dent. Meanwhile, labor costs have become so competitive internationally that
even Chinese companies are outsourcing!
But Trump hasn’t got all the answers either. His protectionist prescription for the
economy could end up causing a global recession. To really change the employment
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numbers, more dramatic solutions are needed. In the wake of the 1929 crash, the
economy gradually recovered after a series of policies was adopted.
These include the reduction of working hours to employ more people at the expense of
efficiency. Meanwhile, social protections were gained through generous welfare
programs while banks and the financial system were slapped with restrictions to better
manage risk. Overly free-market solutions in post-World War I Germany led to the
socioeconomic disaster of Nazism.
Then there’s the issue of robotization. Technology is simply going to wipe out many jobs
in the next decade. Whole sectors will be made redundant. At that point, even the
official U.S. unemployment rate could match the real unemployment rate of 28–30%
that Trump and Mnuchin mentioned.
At these current levels, unemployment can hardly go lower, because everything is
based on the index of investor confidence. And to invest, you must trust the future.
Sooner, rather than later, the markets will face reality.
________
August 28, 2015
The Real Unemployment Numbers and Why I’m Not Counted
As a Daily Work job seeker, I was pleased to hear that unemployment rate in the state
of Minnesota is one of the lowest in the nation. I wondered if this means that everyone
who lost their jobs is now returning to work and there is a happy ending to this story.
Sadly, I know so many people who are still really struggling. So, I did my research and I
wasn’t surprised to learn that there are a number of factors that do not reflect the real
number of unemployed people in Minnesota and elsewhere.
The Truth about the Unemployment Rate
According to Jim Clifton, chairman and CEO at Gallup, the Bureau of Labor Statistics
unemployment rate is a “big lie.” Here’s why:
Anyone who has given up their job search or who hasn’t looked for work for at
least four weeks is not included in the Department of Labor’s unemployment
statistics
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People performing odd jobs for just one hour per week and paid at least $20 –
again, not included.
People working part-time, but who want full-time or who are underemployed
(senior people working more entry-level jobs….you guessed it: Not Included!
A blog post on Money Crashers.com brought up some other important factors that
contribute to creating a misleading unemployment rate such as:
The household survey size used (60,000 households) is too small, which often
does not accurately represent the big picture.
Millions of people are not represented in the data collected including:
Recent graduates who are not yet looking for work
People who just lost their jobs, but who were employed the week of the
survey
Workers on temporary leaves or disabled workers still in transition from a
former job they are no longer able to work
People working jobs that do not pay them enough to survive (but they are
technically “working”).
People with multiple jobs are often counted more than once, throwing off the
correct number of employed workers.
My Story
I am a perfect example of people not included in the unemployment statistics. After
months of seeking work, I decided to go back to school because I couldn’t find a job.
After a couple of years of going to school, I developed several chronic illnesses and I
was unable to work or go to school. With no way to support myself, I also became
homeless. While living at the homeless shelter, I met others in the same situation,
including people with felonies who were unable to find work due to their criminal history
and people like me, who just couldn’t find jobs and eventually became homeless. From
personal experience, I can tell you that not having a home makes finding and keeping a
job very difficult.
About this time, I thought I would never find a way out of homelessness and
joblessness, but then I found Daily Work and I started to get the help I needed. Change
didn’t happen right away, because by now, I had a lot of struggles facing me – I was in
poor health, homeless and, I was losing hope for the future.
But Daily Work did not give up on me. They helped me feel hope again and the services
they told me about gave me the boost I needed to stabilize my health and living
situation so I could start building a new foundation for a successful life.
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As I look back and reflect on my experience, I realize that Daily Work differs from other
social services organizations in several ways. For example, when I first entered their
office I expected more of the same; to be shuffled through the system and treated like a
number, not a person.
I was so wrong. Although Daily Work offers a lot of the same services as other help
agencies, they work with me and ALL their clients as people first. Because I was treated
as a human being with potential and value, it made a world of difference to my selfesteem.
Daily Work sees my eagerness to return to being a productive member of the workforce
and continues to support me in achieving my career goals. Right now, I am volunteering
as an “intern” at Daily Work by working on the website, posting on social media, and
even writing this blog post. This internship is giving me the skills and experience I need
to get back in the workforce in my chosen field, graphic design and communications.
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Daily Work is a gift to me and so many others. It demonstrates how nonprofits can think
outside the box and provide a person like me with support and help that isn’t possible in
other organizations.
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III. Involuntary Unemployment
Involuntary Unemployment occurs when a person is willing to work at the
prevailing wage yet is unemployed. Involuntary unemployment is distinguished from
voluntary unemployment, where workers choose not to work because their reservation
wage is higher than the prevailing wage. In an economy with involuntary unemployment
there is a surplus of labor at the current real wage. Involuntary unemployment cannot be
represented with a basic supply and demand model at a competitive equilibrium: All
workers on the labor supply curve above the market wage would voluntarily choose not
to work, and all those below the market wage would be employed. Given the basic
supply and demand model, involuntarily unemployed workers lie somewhere off of the
labor supply curve. Economists have several theories explaining the possibility of
involuntary unemployment including implicit contract theory, disequilibrium theory,
staggered wage setting, and efficiency wages.
Explanations
In the Shapiro-Stiglitz model workers are paid at a level where they do not shirk. This
prevents wages from dropping to market clearing levels. Full employment cannot be
achieved because workers would slack off if they were not threatened with the
possibility of unemployment. The curve for the no-shirking condition (labeled NSC) goes
to infinity at full employment.
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Models based on implicit contract theory, like that of Azariadis (1975), are based on the
hypothesis that labor contracts make it difficult for employers to cut wages. Employers
often resort to layoffs rather than implement wage reductions. Azariadis showed that
given risk-averse workers and risk-neutral employers, contracts with the possibility of
layoff would be the optimal outcome.
Efficiency wage models suggest that employers pay their workers above market
clearing wages in order to enhance their productivity. In efficiency wage models based
on shirking, employers are worried that workers may shirk knowing that they can simply
move to another job if they are caught. Employers make shirking costly by paying
workers more than the wages they would receive elsewhere. This gives workers an
incentive not to shirk. [1] When all firms behave this way, an equilibrium is reached where
there are unemployed workers willing to work at prevailing wages.
Following earlier disequilibrium research including that of Robert Barro and Herschel
Grossman, work by Edmond Malinvaud clarified the distinction between classical
unemployment, where real wages are too high for markets to clear, and Keynesian
unemployment, involuntary unemployment due to inadequate aggregate demand. In
Malinvaud's model, classical unemployment is remedied by cutting the real wage while
Keynesian unemployment requires an exogenous stimulus in demand. Unlike implicit
contrary theory and efficiency wages, this line of research does not rely on a higher than
market-clearing wage level. This type of involuntary unemployment is consistent with
Keynes's definition while efficiency wages and implicit contract theory do not fit well with
Keynes's focus on demand deficiency.
Perspectives
For many economists, involuntary unemployment is a real-world phenomenon of central
importance to economics. Many economic theories have been motivated by the desire
to understand and control involuntary unemployment. However, acceptance of the
concept of involuntary unemployment isn't universal among economists. Some do not
accept it as a real or coherent aspect of economic theory.
Shapiro and Stiglitz, developers of an influential shirking model, stated "To us,
involuntary unemployment is a real and important phenomenon with grave social
consequences that needs to be explained and understood."
Mancur Olson argued that real world events like the Great Depression could not be
understood without the concept of involuntary unemployment. He argued against
economists who denied involuntary unemployment and put their theories ahead of
"common sense and the observations and experiences of literally hundreds of millions
of people... that there is also involuntary unemployment and that it is by no means an
isolated or rare phenomenon".
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Other economists do not believe that true involuntary unemployment exists or question
its relevance to economic theory. Robert Lucasclaims "...there is an involuntary element
in all unemployment in the sense that no one chooses bad luck over good; there is also
a voluntary element in all unemployment, in the sense that, however miserable one's
current work options, one can always choose to accept them"and "the unemployed
worker at any time can always find some job at once". Lucas dismissed the need for
theorists to explain involuntary unemployment since it is "not a fact or a phenomenon
which it is the task of theorists to explain. It is, on the contrary, a theoretical construct
which Keynes introduced in the hope it would be helpful in discovering a correct
explanation for a genuine phenomenon: large-scale fluctuations in measured, total
unemployment." Along those lines real business cycle and other models from
Lucas's new classical school explain fluctuations in employment by shifts in labor supply
driven by changes in workers' productivity and preferences for leisure.
Involuntary unemployment is also conceptually problematic with search and matching
theories of unemployment. In these models, unemployment is voluntary in the sense
that a worker might choose to endure unemployment during a long search for a higher
paying job than those immediately available; however, there is an involuntary element in
the sense that a worker does not have control of the economic circumstances that force
them to look for new work in the first place.
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IV. Underemployment
Underemployment is the under-use of a worker due to a job that does not use the
worker's skills, or is part time, or leaves the worker idle. Examples include holding a
part-time job despite desiring full-time work, and overqualification, where the employee
has education, experience, or skills beyond the requirements of the job.
Underemployment has been studied from a variety of perspectives,
including economics, management, psychology, and sociology. In economics, for
example, the term underemployment has three different distinct meanings and
applications. All meanings involve a situation in which a person is working,
unlike unemployment, where a person who is searching for work cannot find a job. All
meanings involve under-utilization of labor which is missed by most official
(governmental agency) definitions and measurements of unemployment.
In economics, underemployment can refer to:
1. "Overqualification", or "overeducation", or the employment of workers with high
education, skill levels, or experience in jobs that do not require such abilities. For
example, a trained medical doctor with a foreign credential who works as
a taxi driver would experience this type of underemployment.
2. "Involuntary part-time" work, where workers who could (and would like to) be
working for a full work-week can only find part-time work. By extension, the term
is also used in regional planning to describe regions where economic
activity rates are unusually low, due to a lack of job
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opportunities, training opportunities, or due to a lack of services such
as childcare and public transportation.
3. "Overstaffing" or "hidden unemployment" or "disguised unemployment" (also
called "labor hoarding"), the practice in which businesses or
entire economies employ workers who are not fully occupied—for example,
workers currently not being used to produce goods or services due to legal or
social restrictions or because the work is highly seasonal.
Underemployment is a significant cause of poverty: although the worker may be able to
find part-time work, the part-time pay may not be sufficient for basic needs.
Underemployment is a problem particularly in developing countries, where the
unemployment rate is often quite low, as most workers are doing subsistence work or
occasional part-time jobs. The global average of full-time workers per adult population is
only 26%, compared to 30–52% in developed countries and 5–20% in most of Africa.
Underutilization of Skills
In one usage, underemployment describes the employment of workers with
high skill levels and postsecondary education who are working in relatively lowskilled,
low-wage jobs. For example, someone with a college degree may be tending
bar, or working as a factory assembly line worker. This may result from the existence
of unemployment, which makes workers with bills to pay (and responsibilities) take
almost any jobs available, even if they do not use their full talents. This can also occur
with individuals who are being discriminatedagainst, lack appropriate trade
certification or academic degrees (such as a high school or college diploma), have
disabilities or mental illnesses, or have served time in prison.
Two common situations which can lead to underemployment are immigrants and new
graduates. When highly trained immigrants arrive in a country, their foreign
credentials may not be recognized or accepted in their new country, or they may have
to do a lengthy or costly re-credentialing process. As a result, when doctors or
engineers from other countries immigrate, they may be unable to work in their
profession, and they may have to seek menial work. New graduates may also face
underemployment, because even though they have completed the technical training for
a given field for which there is a good job market, they lack experience. So a recent
graduate with a master's degree in accounting or business administration may have to
work in a low-paid job as a barista or store clerk–jobs which do not require a university
degree–until they are able to find work in their professional field.
Another example of underemployment is someone who holds high skills for which there
is low market-place demand. While it is costly in terms of money and time to
acquire academic credentials, many types of degrees, particularly those in the liberal
arts, produce significantly more graduates than can be properly employed. Employers
have responded to the oversupply of graduates by raising the academic requirements of
many occupations higher than is really necessary to perform the work. A number of
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surveys show that skill-based underemployment in North America and Europe can be a
long-lasting phenomenon. If university graduates spend too long in situations of
underemployment, the skills they gained from their degrees can atrophy from disuse or
become out of date. For example, a person who graduates with a PhD in English
literature has advanced research and writing skills when they graduate, but if she or he
works as a store clerk for a number of years, these research and writing skills will
atrophy from disuse. Similarly, technically specialized workers may find themselves
unable to acquire positions commensurate with their skills for extended lengths of time
following layoffs. A skilled machinist who is laid off may find that she cannot find another
machinist job, so she may work as a server in a restaurant, a position which does not
use her professional skills.
Given that most university study in Western countries is subsidized (either because it
takes place at a state university or public university, or because the student receives
government loans or grants), this type of underemployment may also be an ineffective
use of public resources. Several solutions have been proposed to reduce skill-based
underemployment: for example, government-imposed restrictions on enrollment in
public universities in fields with a very low labor market demand (e.g. fine arts), or
changes in degree cost structure that reflected potential labour market demand.
A related kind of underemployment refers to "involuntary part-time" workers. These are
workers who could (and would like to) be working for the standard work-week
(typically full-time employment means 40 hours per week in the United States) who can
only find part-time work. Underemployment is more prevalent during times of economic
stagnation (during recessions or depressions). Obviously, during the Great
Depression of the 1930s, many of those who were not unemployed were
underemployed. These kinds of underemployment arise because labor markets typically
do not "clear" using wage adjustment. Instead, there is non-wage rationing of jobs.
Underuse of Economic Capacity
Underemployment can also be used in regional planning to describe localities
where economic activity rates are unusually low. This can be induced by a lack of job
opportunities, training opportunities, or services such as childcare and public
transportation. Such difficulties may lead residents to accept economic inactivity rather
than register as unemployed or actively seek jobs because their prospects for regular
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employment appear so bleak. (These people are often called discouraged workers and
are not counted officially as being "unemployed.") The tendency to get by without work
(to exit the labor force, living off relatives, friends, personal savings, or non-recorded
economic activities) can be aggravated if it is made difficult to obtain unemployment
benefits.
Relatedly, in macroeconomics, "underemployment" simply refers to excess
unemployment, i.e., high unemployment relative to full employment or the natural rate of
unemployment, also called the NAIRU. Thus, in Keynesian economics, reference is
made to underemployment equilibrium. Economists calculate the cyclically-adjusted full
employmentunemployment rate, e.g. 4% or 6% unemployment, which in a given context
is regarded as "normal" and acceptable. Sometimes, this rate is equated with
the NAIRU. The difference between the observed unemployment rate and cyclically
adjusted full employment unemployment rate is one measure of the societal level of
underemployment. By Okun's Law, it is correlated with the gap between potential
output and the actual real GDP. This "GDP gap" and the degree of underemployment of
labor would be larger if they incorporated the roles of underemployed labor, involuntary
part-time labor, and discouraged workers.
Underuse of Employed Workers
The third definition of "underemployment" describes a polar opposite phenomenon: to
some economists, the term refers to "overstaffing" or "hidden unemployment," the
practice of businesses or entire economies employing workers who are not fully
occupied (in other words, employees who are not economically productive, or
underproductive, or economically inefficient). This may be because of legal or social
restrictions on firing and lay-offs (e.g. union rules requiring managers to make a case to
fire a worker or spend time and money fighting the union) or because they
are overhead workers, or because the work is highly seasonal (which is the case
in accounting firms focusing on tax preparation, as well as agriculture). The presence of
this issue in white collar office jobs is described in the boreout phenomenon, which
posits that the major issue facing office workers is lack of work and boredom.
This kind of underemployment does not refer to the kind of non-work time done by, for
instance, firefighters or lifeguards, who spend a lot of their time waiting and watching for
emergency or rescue work to do; this kind of activity is necessary to ensure that if (e.g.)
three fires occur at once, there are sufficient firefighters available.
This kind of underemployment may exist for structural or cyclical reasons. In many
economies, some firms become insulated from fierce competitive pressures and
grow inefficient, because they are awarded a government monopoly (e.g., telephone or
electrical utilities) or due to a situation of abuse of market power (e.g., holding
a monopoly position in a certain industry). As such, if they may employ more workers
than necessary, they might not be getting the market signals that would pressure them
to reduce their labour force, and they may end up carrying the resultant
excess costs and depressed profits.
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In some countries, labour laws or practices (e.g. powerful unions) may force employers
to retain excess employees. Other countries (e.g. Japan) often have significant cultural
influences (the relatively great importance attached to worker solidarity as opposed
to shareholder rights) that result in a reluctance to shed labour in times of difficulty. In
Japan, there is a long-held tradition that if a worker commits to serve a company with
long and loyal service, the company will, in return, keep the worker on the payroll even
during economic downturns. In centrally-planned economies, lay-offs were often not
allowed, so that some state-run companies would have periods when they had more
workers than they needed to complete the organization's tasks.
Cyclical underemployment refers to the tendency for the capacity utilization of firms
(and therefore of their demand for labor) to be lower at times of recession or economic
depression. At such times, underemployment of workers may be tolerated—and indeed
may be wise business policy—given the financial cost and the degradation
of morale from shedding and then re-hiring staff. Alternatively, paying underused
overhead workers is seen as an investment in their future contributions to production.
This kind of underemployment has been given as a possible reason
why Airbus gained market share from Boeing. Unlike Airbus, which had more flexibility,
Boeing was unable to ramp up production fast enough when prosperous times returned
because the company had dismissed a great part of its personnel in lean times.
Another example is the tourism sector, which faces cyclical demand in areas where
attractions are weather-related. In some tourism sectors, such as the sun and sand
tours operated by Club Med, the company can shed bartenders, lifeguards, and sports
instructors, and other staff in the off-season, because there is such a strong demand
amongst young people to work for the company, because its glamorous beachfront
properties are desirable places to work. However, not all tourism sectors find it so easy
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to recruit staff. Some tourism sectors require workers with unusual or hard-to-find skills.
Northern Ontario hunting and fishing camps that require skilled guides may have an
incentive to retain their staff in the off-season. Another example is companies which run
tours for foreign tourists using staff speaking the travelers' native tongue. In Canada,
guided tours are available for Japanese and German tourists in their native languages;
in some locations, it may be hard for companies to find Japanese- or German-speaking
staff, so the companies may retain their staff in the off-season.
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V. Discouraged Workers
In economics, a discouraged worker is a person of legal employment age who is not
actively seeking employment or who does not find employment after longterm
unemployment. This is usually because an individual has given up looking or has
had no success in finding a job, hence the term "discouraged".
In other words, even if a person is still looking actively for a job, that person may have
fallen out of the core statistics of unemployment rate after long-term unemployment and
is therefore by default classified as "discouraged" (since the person does not appear in
the core statistics of unemployment rate). In some cases, their belief may derive from a
variety of factors including a shortage of jobs in their locality or line of
work; discrimination for reasons such as age, race, sex, religion, sexual orientation,
and disability; a lack of necessary skills, training, or experience; or, a chronic
illness or disability.
As a general practice, discouraged workers, who are often classified as marginally
attached to the labor force, on the margins of the labor force, or as part of hidden
unemployment, are not considered part of the labor force, and are thus not counted in
most official unemployment rates—which influences the appearance and interpretation
of unemployment statistics. Although some countries offer alternative measures of
unemployment rate, the existence of discouraged workers can be inferred from a
low employment-to-population ratio.
United States
Discouraged Workers (US, 2004-09)
In the United States, a discouraged worker is defined as a person not
in the labor force who wants and is available for a job and who has
looked for work sometime in the past 12 months (or since the end of
his or her last job if a job was held within the past 12 months), but who
is not currently looking because of real or perceived poor employment
prospects.
The Bureau of Labor Statistics does not count discouraged workers as unemployed but
rather refers to them as only "marginally attached to the labor force". This means that
the officially measured unemployment captures so-called "frictional unemployment" and
not much else. This has led some economists to believe that the actual unemployment
rate in the United States is higher than what is officially reported while others suggest
that discouraged workers voluntarily choose not to work. Nonetheless, the U.S. Bureau
of Labor Statistics has published the discouraged worker rate in alternative measures of
labor underutilization under U-4 since 1994 when the most recent redesign of
the CPS was implemented.
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The United States Department of Labor first began tracking discouraged workers in
1967 and found 500,000 at the time. Today, In the United States, according to the U.S.
Bureau of Labor Statistics as of April 2009, there are 740,000 discouraged workers.
There is an ongoing debate as to whether discouraged workers should be included in
the official unemployment rate. Over time, it has been shown that a disproportionate
number of young people, blacks, Hispanics, and men make up discouraged
workers. Nonetheless, it is generally believed that the discouraged worker is
underestimated because it does not include homeless people or those who have not
looked for or held a job during the past twelve months and is often poorly tracked.
According to the U.S. Bureau of Labor Statistics, the top five reasons for
discouragement are the following:
1. The worker thinks no work is available.
2. The worker could not find work.
3. The worker lacks schooling or training.
4. The worker is viewed as too young or too old by the prospective employer.
5. The worker is the target of various types of discrimination.
Canada
In Canada, discouraged workers are often referred to as hidden unemployed because
of their behavioral pattern, and are often described as on the margins of the labour
force. Since the numbers of discouraged workers and of unemployed generally move in
the same direction during the business cycle and the seasons (both tend to rise in
periods of low economic activity and vice versa), some economists have suggested that
discouraged workers should be included in the unemployment numbers because of the
close association.
The information on the number and composition of the discouraged worker group
in Canada originates from two main sources. One source is the monthly Labour Force
Survey (LFS), which identifies persons who looked for work in the past six months but
who have since stopped searching. The other source is the Survey of Job Opportunities
(SJO), which is much closer in design to the approach used in many other countries. In
this survey, all those expressing a desire for work and who are available for work are
counted, irrespective of their past job search activity.
In Canada, while discouraged workers were once less educated than "average
workers", they now have better training and education but still tend to be concentrated
in areas of high unemployment. Discouraged workers are not seeking a job for one of
two reasons: labour market-related reasons (worker discouragement, waiting for recall
to a former job or waiting for replies to earlier job search efforts) and personal and other
reasons (illness or disability, personal or family responsibilities, going to school, and so
on).
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European Union
Unemployment statistics published according to the ILO methodology may understate
actual unemployment in the economy. The EU statistical bureau EUROSTAT started
publishing figures on discouraged workers in 2010. According to the method used by
EUROSTAT there are 3 categories that make up discouraged workers;
underemployed part-time workers
jobless persons seeking a job but not immediately available for work,
persons available for work but not seeking it
The first group are contained in the employed statistics of the European Labour Force
Survey while the second two are contained in the inactive persons statistics of that
survey. In 2012 there were 9.2 million underemployed part-time workers, 2.3 million
jobless persons seeking a job but not immediately available for work, and 8.9 million
persons available for work but not seeking it, an increase of 0.6 million for
underemployed and 0.3 million for the two groups making up discouraged workers.
If the discouraged workers and underemployed are added to official unemployed
statistics Spain has the highest number real unemployed (8.4 Million), followed by Italy
(6.4 Million), United Kingdom (5.5 Million), France (4.8 Million) and Germany (3.6
Million).
List Of EU Countries Hidden Unemployment In 2012
Country
Underemployed
Part-time workers
Thousands
Jobless persons
seeking a job but
not immediately
available for work
Thousands
Persons
available for
work but not
seeking it
Thousands
Unemployed
Thousands
Belgium 158 100 60 369
Bulgaria 29 270 26 410
Czech
Republic
27 62 17 367
Denmark 88 69 24 219
Germany 1,810 582 508 2,316
Estonia 10 41 3 71
Ireland 147 44 13 316
Greece 190 91 36 1,204
Spain 1,385 1,071 236 5,769
France 1,144 285 444 3,002
Italy 605 2,975 111 2,744
Cyprus 20 15 3 52
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List Of EU Countries Hidden Unemployment In 2012
Country
Underemployed
Part-time workers
Thousands
Jobless persons
seeking a job but
not immediately
available for work
Thousands
Persons
available for
work but not
seeking it
Thousands
Unemployed
Thousands
Latvia 44 67 6 155
Lithuania 37 16 197
Luxembourg 5 13 2 13
Hungary 88 215 11 476
Malta 5 5 12
Netherlands 138 308 85 469
Austria 148 144 39 189
Poland 344 632 102 1,749
Portugal 256 232 29 860
Romania 239 458 701
Slovenia 18 13 90
Slovakia 37 41 13 378
Finland 75 111 63 207
Sweden 237 134 101 403
United
Kingdom
1,907 774 334 2,511
Norway 81 67 22 85
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VI. The Working Poor
The Working Poor are working people whose incomes fall below a given poverty
line due to lack of work hours and/or low wages. Largely because they are earning such
low wages, the working poor face numerous obstacles that make it difficult for many of
them to find and keep a job, save up money, and maintain a sense of self-worth.
The official working poverty rate in the US has remained relatively static over the past
four decades, but many social scientists argue that the official rate is set too low, and
that the proportion of workers facing significant financial hardship has instead increased
over the years. Changes in the economy, especially the shift from a manufacturingbased
to a service-based economy, have resulted in the polarization of the labor
market. This means that there are more jobs at the top and the bottom of the income
spectrum, but fewer jobs in the middle.
There are a wide range of anti-poverty policies that have been shown to improve the
situation of the working poor. Research suggests that increasing welfare
state generosity is the most effective way to reduce poverty and working poverty.
Conceptualizing Working Poverty
In the United States, the issue of working poverty was initially brought to the public's
attention during the Progressive Era (1890s–1920s). Progressive Era thinkers
like Robert Hunter, Jane Addams, and W.E.B. Du Bois saw society's unequal
opportunity structure as the root cause of poverty and working poverty, but they also
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saw a link between moral factors and poverty. In his study of Philadelphia's African
American neighborhoods, W.E.B. Du Bois draws a distinction between "hardworking"
poor people who fail to escape poverty due to racial discrimination and those who are
poor due to moral deficiencies such as laziness or lack of perseverance.
After the Great Depression, the New Deal, and World War II, the United States
experienced an era of prosperity during which most workers experienced significant
gains in wages and working conditions. During this period (1930s–1950s), scholars
shifted their attention away from poverty and working poverty. However, in the late
1950s and early 1960s American scholars and policymakers began to revisit the
problem. Influential books like John Kenneth Galbraith's The Affluent
Society (1958) and Michael Harrington's The Other America (1962) reinvigorated the
discussions on poverty and working poverty in the United States.
Since the start of the War on Poverty in the 1960s, scholars and policymakers on both
ends of the political spectrum have paid an increasing amount of attention to working
poverty. One of the key ongoing debates concerns the distinction between the working
and the nonworking (unemployed) poor. Conservative scholars tend to see nonworking
poverty as a more urgent problem than working poverty because they believe that nonwork
is a moral hazard that leads to welfare dependency and laziness, whereas work,
even poorly paid work, is morally beneficial. In order to solve the problem of nonworking
poverty, some conservative scholars argue that the government must stop "coddling"
the poor with welfare benefits like AFDC/TANF.
On the other hand, liberal scholars and policymakers often argue that most working and
nonworking poor people are quite similar. Studies comparing single mothers on and off
welfare show that receiving welfare payments does not degrade a person's desire to
find a job and get off of welfare. The main difference between the working and the
nonworking poor, they argue, is that the nonworking poor have a more difficult time
overcoming basic barriers to entry into the labor market, such as arranging for
affordable childcare, finding housing near potential jobs, or arranging for transportation
to and from work. In order to help the nonworking poor gain entry into the labor market,
liberal scholars and policymakers argue that the government should provide more
housing assistance, childcare, and other kinds of aid to poor families.
Discussions about the alleviation of working poverty are also politically charged.
Conservative scholars and policymakers often attribute the prevalence of inequality and
working poverty to overregulation and overtaxation, which they claim constricts job
growth. In order to lower the rate of working poverty, conservatives advocate reducing
welfare benefits and enacting less stringent labor laws. On the other hand, many
liberals argue that working poverty can only be solved through increased, not
decreased, government intervention. This government intervention could include
workplace reforms (such as higher minimum wages, living wage laws, job training
programs, etc.) and an increase in government transfers (such as housing, food,
childcare, and healthcare subsidies).
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Measuring Working Poverty
Absolute
According to the US Department of Labor, the working poor "are persons who spent at
least 27 weeks [in the past year] in the labor force (that is, working or looking for work),
but whose incomes fell below the official poverty level." In other words, if someone
spent more than half of the past year in the labor force without earning more than the
official poverty threshold, the US Department of Labor would classify them as "working
poor." (Note: The official poverty threshold, which is set by the US Census Bureau,
varies depending on the size of a family and the age of the family members.) The US
Bureau of Labor Statistics calculates working poverty rates for all working individuals, all
families with at least one worker, and all "unrelated individuals." The individual-level
working poverty rate calculates the percentage of all workers whose incomes fall below
the poverty threshold. In 2009, the individual-level working poverty rate in the US was
7%, compared to 4.7% in 2000. The family-level working poverty rate only includes
families of two or more people who are related by birth, marriage, or adoption.
According to the Bureau of Labor Statistics' definition of family-level working poverty, a
family is working poor if the combined cash income of the family falls below the poverty
threshold for a family of their size. In 2009, the family-level working poverty rate in the
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US was 7.9%, compared to 5.6% in 2000. Finally, the unrelated individual working
poverty rate measures working poverty among those who do not currently live with any
family members. In 2009, 11.7% of employed unrelated individuals were poor,
compared to 7.6% in 2000.
Relative
In Europe and other non-US, high-income countries, poverty and working poverty are
defined in relative terms. A relative measure of poverty is based on a country's income
distribution rather than an absolute amount of money. Eurostat, the statistical office of
the European Union, classifies a household as poor if its income is less than 60 percent
of the country's median household income. According to Eurostat, a relative measure of
poverty is appropriate because "minimal acceptable standards usually differ between
societies according to their general level of prosperity: someone regarded as poor in a
rich developed country might be regarded as rich in a poor developing country."
When conducting cross-national research on working poverty, scholars tend to use a
relative measure of poverty. In these studies, to be classified as "working poor," a
household must satisfy the following two conditions: 1) at least one member of the
household must be "working" (which can be defined in various ways), and 2) the total
household income must be less than 60% (or 50%, or 40%) of the median income for
that country. Brady, Fullerton, and Cross's 2010 cross-national study on working poverty
in high-income countries defines a household as working poor if 1) it has at least one
employed person and 2) the total household income falls below 50% of the median
income for that country. According to this relative definition, the US's working poverty
rate was 11% in the year 2000, nearly double the rate produced by the US
government's absolute definition. For the same year, Canada's working poverty rate
was 7.8%, the UK's was 4%, and Germany's was 3.8%.
Prevalence and Trends
Poverty rates by gender
and work for Americans aged 65 and over
Poverty is often associated with joblessness, but a
large proportion of poor people are actually working or
looking for work. In 2009, according to the US Census
Bureau's official definition of poverty, 8.8 million US
families were below the poverty line (11.1% of all
families). Of these families, 5.19 million, or 58.9%, had
at least one person who was classified as working. In the same year, there were 11.7
million unrelated individuals (people who do not live with family members) whose
incomes fell below the official poverty line (22% of all unrelated individuals). 3.9 million
of these poor individuals, or 33%, were part of the working poor. The cost of raising a
child from birth to age 18 for a middle-income, two-parent family averaged $226,920 last
year (not including college), according to the U.S. Department of Agriculture. That's up
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nearly 40% -- or more than $60,000—from 10 years ago. Just one year of spending on
a child can cost up to $13,830 in 2010, compared to $9,860 a decade ago.
Using the US Census Bureau's definition of poverty, the working poverty rate seems to
have remained relatively stable since 1978. However, many scholars have argued that
the official poverty threshold is too low, and that real wages and working conditions
have actually declined for many workers over the past three or four decades. Social
scientists like Arne Kalleberg have found that the decline in US manufacturing and the
subsequent polarization of the labor market has led to an overall worsening of wages,
job stability, and working conditions for people with lower skill levels and less formal
education. From the mid-1940s to the mid-1970s, manufacturing jobs offered many lowskilled
and medium-skilled workers stable, well-paying jobs. Due to global competition,
technological advances, and other factors, US manufacturing jobs have been
disappearing for decades. (From 1970 to 2008, the percentage of the labor force
employed in the manufacturing sector shrank from 23.4% to 9.1%.) During this period of
decline, job growth became polarized on either end of the labor market. That is, the jobs
that replaced medium-pay, low- to medium-skill manufacturing jobs were high-paying,
high-skill jobs and low-paying, low-skill jobs. Therefore, many low- to medium-skilled
workers who would have been able to work in the manufacturing sector in 1970 must
now take low-paying, precarious jobs in the service sector.
US Compared to Europe
Other high-income countries have also experienced declining manufacturing sectors
over the past four decades, but most of them have not experienced as much labor
market polarization as the United States. Labor market polarization has been the most
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severe in liberal market economies like the US, Britain, and Australia. Countries like
Denmark and France have been subject to the same economic pressures, but due to
their more "inclusive" (or "egalitarian") labor market institutions, such as centralized and
solidaristic collective bargaining and strong minimum wage laws, they have experienced
less polarization.
Cross-national studies have found that European countries' working poverty rates are
much lower than the US's. Most of this difference can be explained by the fact that
European countries' welfare states are more generous than the US's. The relationship
between generous welfare states and low rates of working poverty is elaborated upon in
the "Risk Factors" and "Anti-Poverty Policies" sections.
The following graph uses data from Brady, Fullerton, and Cross (2010) to show the
working poverty rates for a small sample of countries. Brady, Fullerton, and Cross
(2010) accessed this data through the Luxembourg Income Study. This graph measures
household, rather than person-level, poverty rates. A household is coded as "poor" if its
income is less than 50% of its country's median income. This is a relative, rather than
absolute, measure of poverty. A household is classified as "working" if at least one
member of the household was employed at the time of the survey. The most important
insight contained in this graph is that the US has strikingly higher working poverty rates
than European countries.
Risk Factors
There are five major categories of risk factors that increase a person's likelihood of
experiencing working poverty: sectoral factors, demographic factors, economic factors,
labor market institutions, and welfare generosity. Working poverty is a phenomenon that
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affects a very wide range of people, but there are some employment sectors,
demographic groups, political factors, and economic factors that are correlated with
higher rates of working poverty than others. Sectoral and demographic factors help
explain why certain people within a given country are more likely than others to be
working poor. Political and economic factors can explain why different countries have
different working poverty rates.
Sectoral Tendencies
Working poverty is not distributed equally among employment sectors. The service
sector has the highest rate of working poverty. In fact, 13.3% of US service sector
workers found themselves below the poverty line in 2009. Examples of low-wage
service sector workers include fast-food workers, home health aids, waiters/waitresses,
and retail workers.
Although the service sector has the highest rate of working poverty, a significant portion
of the working poor are blue-collar workers in the manufacturing, agriculture, and
construction industries. Most manufacturing jobs used to offer generous wages and
benefits, but manufacturing job quality has declined over the years. Nowadays, many
US manufacturing jobs are located in right-to-work states, where it is nearly impossible
for workers to form a union. This means that manufacturing employers are able to pay
lower wages and offer fewer benefits than they used to.
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Demographic Factors
In her book, No Shame in My Game, Katherine Newman finds that "[t]he nation's young,
its single parents, the poorly educated, and minorities are more likely than other workers
to be poor" (p. 42). These factors, in addition to being part of a large household, being
part of a single-earner household, being female, and having a part-time (instead of a
full-time) job have been found to be important working poverty "risk factors." Immigrant
workers and self-employed workers are also more likely to be working poor than other
kinds of workers.
Economic Factors
There is a widespread assumption that economic growth leads to tighter labor markets
and higher wages. However, the evidence suggests that economic growth does not
always benefit each part of the population equally. For example, the 1980s was a period
of economic growth and prosperity in the United States, but most of the economic gains
were concentrated at the top of the income spectrum. This means that people near the
bottom of the labor market did not benefit from the economic gains of the 1980s. In fact,
many have argued that low-skilled workers experienced declining prosperity in the
1980s. Therefore, changing economic conditions do not have as large of an impact on
working poverty rates as one might expect.
Labor Market Institutions
Labor markets can be egalitarian, efficient, or somewhere in the middle. According to
Brady, Fullerton, and Cross (2010), "[e]fficient labor markets typically feature flexibility,
low unemployment, and higher economic growth, and facilitate the rapid hiring and firing
of workers. Egalitarian labor markets are bolstered by strong labor market institutions,
higher wages, and greater security" (p562). The United States has an efficient labor
market, whereas most European countries have egalitarian labor markets. Each system
has its drawbacks, but the egalitarian labor market model is typically associated with
lower rates of working poverty. One tradeoff to this is that the "lowest skilled and least
employable" people are sometimes excluded from an egalitarian labor market, and must
instead rely on government aid in order to survive (p. 563). If the United States switched
from an efficient to an egalitarian labor market, it might have to increase its welfare state
generosity in order to cope with a higher unemployment rate.
Centralized wage bargaining is a key component of egalitarian labor markets. In a
country with centralized wage bargaining institutions, wages for entire industries are
negotiated at the regional or national level. This means that similar workers earn similar
wages, which reduces income inequality. Lohmann (2009) finds that countries with
centralized wage bargaining institutions have lower rates of "pre-transfer" working
poverty. The "pre-transfer" working poverty rate is the percentage of workers who fall
below the poverty threshold based on their earned wages (not counting government
transfers).
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Welfare State Generosity
Cross-national studies are in agreement that the most important factor affecting working
poverty rates is welfare state generosity. A generous welfare state spends a higher
proportion of its GDP on things like unemployment insurance, social security, family
assistance, childcare subsidies, healthcare subsidies, housing subsidies, transportation
subsidies, and food subsidies. Studies on working poverty have found that these kinds
of government spending can pull a significant number of people out of poverty, even if
they earn low wages. Lohmann's 2009 study shows that welfare state generosity has a
significant impact on the "post-transfer" rate of working poverty. The "post-transfer" rate
of working poverty is the percentage of working households that fall below the poverty
threshold after government aid has been taken into account.
Different types of transfers benefit different kinds of low-wage families. Family benefits
will benefit households with children and unemployment benefits will benefit households
that include workers with significant work experience. Transfers such as old-age
benefits are unlikely to benefit low-wage households unless the elderly are living in the
same household. Sometimes, even when benefits are available, those who qualify do
not take advantage of them. Migrants in particular are less likely to take advantage of
the available benefits.
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Obstacles To Uplift
The working poor face many of the same daily life struggles as the nonworking poor, but
they also face some unique obstacles. Some studies, many of them qualitative, provide
detailed insights into the obstacles that hinder workers' ability to find jobs, keep jobs,
and make ends meet. Some of the most common struggles faced by the working poor
are finding affordable housing, arranging transportation to and from work, buying basic
necessities, arranging childcare, having unpredictable work schedules, juggling two or
more jobs, and coping with low-status work.
Housing
Working poor people who do not have friends or relatives with whom they can live often
find themselves unable to rent an apartment of their own. Although the working poor are
employed at least some of the time, they often find it difficult to save enough money for
a deposit on a rental property. As a result, many working poor people end up in living
situations that are actually more costly than a month-to-month rental. For instance,
many working poor people, especially those who are in some kind of transitional phase,
rent rooms in week-to-week motels. These motel rooms tend to cost much more than a
traditional rental, but they are accessible to the working poor because they do not
require a large deposit. If someone is unable or unwilling to pay for a room in a motel,
they might live in his/her car, in a homeless shelter, or on the street. This is not a
marginal phenomenon; in fact, according to the 2008 US Conference of Mayors, one in
five homeless people are currently employed.
Of course, some working poor people are able to access housing subsidies (such as
a Section 8 Housing Choice Voucher) to help cover their housing expenses. However,
these housing subsidies are not available to everyone who meets the Section 8 income
specifications. In fact, less than 25% of people who qualify for a housing subsidy
receive one.
Education
The issue with education starts many times with the working poor from childhood and
follows them into their struggle for a substantial income. Children growing up in families
of the working poor are not provided the same educational opportunities as their middleclass
counterpart. In many cases the low income community is filled with schools that
are lacking necessities and support needed to form a solid education. This follows
students as they continue in education. In many cases this hinders the possibility for
America's youth to continue on to higher education. The grades and credits just are not
attained in many cases, and the lack of guidance in the schools leaves the children of
the working poor with no degree. Also, the lack of funds for continuing education causes
these children to fall behind. In many cases, their parents did not continue on into higher
education and because of this have a difficult time finding jobs with salaries that can
support a family. Today a college degree is a requirement for many jobs, and it is the
low skill jobs that usually only require a high school degree or GED. The inequality in
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available education continues the vicious cycle of families entering into the working
poor.
Transportation
Given the fact that many working poor people do not own a car or cannot afford to drive
their car, where they live can significantly limit where they are able to work, and vice
versa. Given the fact that public transportation in many US cities is sparse, expensive,
or non-existent, this is a particularly salient obstacle. Some working poor people are
able to use their social networks—if they have them—to meet their transportation
needs. In a study on low-income single mothers, Edin and Lein found that single
mothers who had someone to drive them to and from work were much more likely to be
able to support themselves without relying on government aid.
Basic Necessities
Like the unemployed poor, the working poor struggle to pay for basic necessities like
food, clothing, housing, and transportation. In some cases, however, the working poor's
basic expenses can be higher than the unemployed poor's. For instance, the working
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poor's clothing expenses may be higher than the unemployed poor's because they must
purchase specific clothes or uniforms for their jobs. Also, because the working poor are
spending much of their time at work, they may not have the time to prepare their own
food. In this case, they may frequently resort to eating fast food, which is less healthful
and more expensive than home-prepared food.
Childcare
Working poor parents with young children, especially single parents, face significantly
more childcare-related obstacles than other people. Often, childcare costs can exceed a
low-wage earners' income, making work, especially in a job with no potential for
advancement, an economically illogical activity. However, some single parents are able
to rely on their social networks to provide free or below-market-cost childcare. There are
also some free childcare options provided by the government, such as the Head Start
Program. However, these free options are only available during certain hours, which
may limit parents' ability to take jobs that require late-night shifts.The U.S. "average"
seems to suggest that for one toddler, in full-time day care, on weekdays, the cost is
approximately $600.00 per month. But, that figure can rise to well over $1000.00 per
month in major metro areas, and fall to less than $350 in rural areas.The average cost
of center-based daycare in the United States is $11,666 per year ($972 a month), but
prices range from $3,582 to $18,773 a year ($300 to $1,564 monthly), according to the
National Association of Child Care Resource & Referral Agencies (NACCRRA).
Work Schedules
Many low-wage jobs force workers to accept irregular schedules. In fact, some
employers will not hire someone unless they have "open availability," which means
being available to work any time, any day. This makes it difficult for workers to arrange
for childcare and to take on a second job. In addition, working poor people's working
hours can fluctuate wildly from one week to the next, making it difficult for them to
budget effectively and save up money.
Multiple Jobs
Many low-wage workers have to work multiple jobs in order to make ends meet. In
1996, 6.2 percent of the workforce held two or more full- or part-time jobs. Most of these
people held two part-time jobs or one part-time job and one full-time job, but 4% of men
and 2% of women held two full-time jobs at the same time. This can be physically
exhausting and can often lead to short and long-term health problems.
Low-Status Work
Many low-wage service sector jobs require a great deal of customer service work.
Although not all customer service jobs are low-wage or low-status, many of them are.
Some argue that the low status nature of some jobs can have negative psychological
effects on workers, but others argue that low status workers come up with coping
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mechanisms that allow them to maintain a strong sense of self-worth. One of these
coping mechanisms is called boundary work. Boundary work happens when one group
of people valorize their own social position by comparing themselves to another group,
who they perceive to be inferior in some way. For example, Newman (1999) found that
fast food workers in New York City cope with the low-status nature of their job by
comparing themselves to the unemployed, who they perceive to be even lower-status
than themselves. Thus, although the low-status nature of working poor people's jobs
may have some negative psychological effects, some, but probably not all, of these
negative effects can be counteracted through coping mechanisms such as boundary
work.
Anti-Poverty Policies
Scholars, policymakers, and others have come up with a variety of proposals for how to
reduce or eliminate working poverty. Most of these proposals are directed toward the
United States, but they might also be relevant to other countries. The remainder of the
section outlines the pros and cons of some of the most commonly proposed solutions.
Welfare State Generosity
Cross-national studies like Lohmann (2009) and Brady, Fullerton, and Cross (2010)
clearly show that countries with generous welfare states have lower levels of working
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poverty than countries with less-generous welfare states, even when factors like
demography, economic performance, and labor market institutions are taken into
account. Having a generous welfare state does two key things to reduce working
poverty: it raises the minimum level of wages that people are willing to accept, and it
pulls a large portion of low-wage workers out of poverty by providing them with an array
of cash and non-cash government benefits. Many think that increasing the United
States' welfare state generosity would lower the working poverty rate. A common
critique of this proposal is that a generous welfare state would not work because it
would stagnate the economy, raise unemployment, and degrade people's work
ethic. However, as of 2011, most European countries have a lower unemployment
rate than the US. Furthermore, although Western European economies' growth rates
can be lower than the US's from time to time, their growth rates tend to be more stable,
whereas the US's tends to fluctuate relatively severely. Individual states offer financial
assistance for child care, but the aid varies widely.
Most assistance is administered through the Child Care and Development Block Grants.
Check here to find the contact information for your state. Many subsidies have strict
income guidelines and are generally for families with children under 13 (the age limit is
often extended if the child has a disability). Many subsidies permit home-based care,
but some only accept a day care center, so check the requirements. If you need to use
an authorized provider, ask if they will put you in touch with an agency that can help you
find one.
Some states distribute funds through social or health departments or agencies (like this
one in Washington State). For example, the Children's Cabinet in Nevada can refer
families to providers, help them apply for subsidies and can even help families who
want to pay a relative for care. North Carolina's Smart Start is a public/private
partnership that offers funding for child care. Check the National Women's Law Center
for each state's child care assistance policy.
Wages And Benefits
In the conclusion of her book, Nickel and Dimed (2001), Barbara Ehrenreich argues that
Americans need to pressure employers to improve worker compensation. Generally
speaking, this implies a need to strengthen the labor movement. Cross-national
statistical studies on working poverty suggest that generous welfare states have a larger
impact on working poverty than strong labor movements. The labor movements in
various countries have accomplished this through political parties of their own (labor
parties) or strategic alliances with non-labor parties, for instance, when striving to put a
meaningful minimum wage in place. The federal government offers a Flexible Spending
Account (FSA) that's administered through workplaces.
If your job offers an FSA (also known as a Dependent Care Account), you can put aside
up to $5,000 in per-tax dollars to pay for child care expenses. If both you and your
spouse have an FSA, the family limit is $5,000—but you could get as much as $2,000 in
tax savings if your combined contributions reach the maximum.
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Education And Training
Some argue that more vocational training and active labour market policies, especially
in growth industries like healthcare and renewable energy, is the solution to working
poverty. To be sure, wider availability of vocational training could pull some people out
of working poverty, but the fact remains that the low-wage service sector is a rapidly
growing part of the US economy. Even if more nursing and clean energy jobs were
added to the economy, there would still be a huge portion of the workforce in low-wage
service sector jobs like retail, food service, and cleaning. Therefore, it seems clear that
any significant reduction in the working poverty rate will have to come from offering
higher wages and more benefits to the current, and future, population of service sector
workers.
Child Support Assurance
Given the fact that such a large proportion of working poor households are headed by a
single mother, one clear way to reduce working poverty would be to make sure that
children's fathers share the cost of child rearing. In cases where the father cannot
provide child support, scholars like Irwin Garfinkel advocate for the implementation of a
child support guarantee, whereby the government pays childcare costs if the father
cannot. Child support is not always a guarantee if the father or mother does not work.
For example, if the parent without custody is not working then the parent with custody
does not receive any child support unless the non working parent is employed at their
job longer than 90 days, excluding if the non begins to work for its a city or government.
Also, the government does not pay for childcare cost if you make more than the cut off
range (your gross, per county or state.)
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Marriage
Households with two wage-earners have a significantly lower rate of working poverty
than households with only one wage-earner. Also, households with two adults, but only
one wage-earner, have lower working poverty rates than households with only one
adult. Therefore, it seems clear that having two adults in a household, especially if there
are children present, is more likely to keep a household out of poverty than having just
one adult in a household. Many scholars and policymakers have used this fact to argue
that encouraging people to get married and stay married is an effective way to reduce
working poverty (and poverty in general). However, this is easier said than done.
Research has shown that low-income people marry less often than higher-income
people because they have a more difficult time finding a partner who is employed, which
is often seen as a prerequisite for marriage. Therefore, unless the employment
opportunity structure is improved, simply increasing the number of marriages among
low-income people would be unlikely to lower working poverty rates.
Ultimately, effective solutions to working poverty are multifaceted. Each of the
aforementioned proposals could help reduce working poverty in the United States, but
they might have a greater impact if at least a few of them were pursued simultaneously.
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VII. Wage Slavery
Wage Slavery is a term used to draw an analogy between slavery and wage labor by
focusing on similarities between owning and renting a person. It is usually used to refer
to a situation where a person's livelihood depends on wages or a salary, especially
when the dependence is total and immediate.
The term "wage slavery" has been used to criticize exploitation of labor and social
stratification, with the former seen primarily as unequal bargaining power between labor
and capital (particularly when workers are paid comparatively low wages, e.g.
in sweatshops) and the latter as a lack of workers' self-management, fulfilling job
choices and leisure in an economy. The criticism of social stratification covers a wider
range of employment choices bound by the pressures of a hierarchical society to
perform otherwise unfulfilling work that deprives humans of their "species character" not
only under threat of starvation or poverty, but also of social
stigma and status diminution.
Similarities between wage labor and slavery were noted as early as Cicero in Ancient
Rome, such as in De Officiis. With the advent of the Industrial Revolution, thinkers such
as Pierre-Joseph Proudhon and Karl Marx elaborated the comparison between wage
labor and slavery, while Luddites emphasized the dehumanization brought about by
machines. Before the American Civil War, Southern defenders of African
American slavery invoked the concept of wage slavery to favorably compare the
condition of their slaves to workers in the North. The United States abolished slavery
after the Civil War, but labor union activists found the metaphor useful and appropriate.
According to Lawrence Glickman, in the Gilded Age "[r]eferences abounded in the labor
press, and it is hard to find a speech by a labor leader without the phrase".
The introduction of wage labor in 18th-century Britain was met with resistance, giving
rise to the principles of syndicalism. Historically, some labor organizations and individual
social activists have espoused workers' self-management or worker cooperatives as
possible alternatives to wage labor.
History
Emma Goldman famously denounced wage slavery by saying:
"The only difference is that you are hired slaves
instead of block slaves"
The view that working for wages is akin to slavery dates back to the
ancient world. In ancient Rome, Cicero wrote that "whoever gives his
labor for money sells himself and puts himself in the rank of slaves". [11]
In 1763, the French journalist Simon Linguet published an influential
description of wage slavery:
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The slave was precious to his master because of the money he had cost him ... They
were worth at least as much as they could be sold for in the market ... It is the
impossibility of living by any other means that compels our farm laborers to till the soil
whose fruits they will not eat and our masons to construct buildings in which they will
not live ... It is want that compels them to go down on their knees to the rich man in
order to get from him permission to enrich him ... what effective gain [has] the
suppression of slavery brought [him ?] He is free, you say. Ah! That is his misfortune ...
These men ... [have] the most terrible, the most imperious of masters, that is, need. ...
They must therefore find someone to hire them, or die of hunger. Is that to be free?
The view that wage work has substantial similarities with chattel slavery was actively put
forward in the late 18th and 19th centuries by defenders of chattel slavery (most notably
in the Southern states of the United States) and by opponents of capitalism (who were
also critics of chattel slavery). Some defenders of slavery, mainly from the Southern
slave states, argued that Northern workers were "free but in name – the slaves of
endless toil" and that their slaves were better off. This contention has been partly
corroborated by some modern studies that indicate slaves' material conditions in the
19th century were "better than what was typically available to free urban laborers at the
time". In this period, Henry David Thoreau wrote that "[i]t is hard to have a Southern
overseer; it is worse to have a Northern one; but worst of all when you are the slavedriver
of yourself".
Some abolitionists in the United States regarded the analogy as spurious. They
believed that wage workers were "neither wronged nor oppressed". Abraham
Lincoln and the Republicans argued that the condition of wage workers was different
from slavery as laborers were likely to have the opportunity to work for themselves in
the future, achieving self-employment. The abolitionist and former slave Frederick
Douglass initially declared "now I am my own master", upon taking a paying
job. [32] However, later in life he concluded to the contrary, saying "experience
demonstrates that there may be a slavery of wages only a little less galling and crushing
in its effects than chattel slavery, and that this slavery of wages must go down with the
other". Douglass went on to speak about these conditions as arising from the unequal
bargaining power between the ownership/capitalist class and the non-ownership/laborer
class within a compulsory monetary market: "No more crafty and effective devise for
defrauding the southern laborers could be adopted than the one that substitutes orders
upon shopkeepers for currency in payment of wages. It has the merit of a show of
honesty, while it puts the laborer completely at the mercy of the land-owner and the
shopkeeper".
Self-employment became less common as the artisan tradition slowly disappeared in
the later part of the 19th century. In 1869, The New York Times described the system of
wage labor as "a system of slavery as absolute if not as degrading as that which lately
prevailed at the South". E. P. Thompson notes that for British workers at the end of the
18th and beginning of the 19th centuries, the "gap in status between a 'servant,' a hired
wage-laborer subject to the orders and discipline of the master, and an artisan, who
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might 'come and go' as he pleased, was wide enough for men to shed blood rather than
allow themselves to be pushed from one side to the other. And, in the value system of
the community, those who resisted degradation were in the right". A "Member of the
Builders' Union" in the 1830s argued that the trade unions "will not only strike for less
work, and more wages, but will ultimately abolish wages, become their own masters
and work for each other; labor and capital will no longer be separate but will be
indissolubly joined together in the hands of workmen and work-women".This
perspective inspired the Grand National Consolidated Trades Union of 1834 which had
the "two-fold purpose of syndicalist unions – the protection of the workers under the
existing system and the formation of the nuclei of the future society" when the unions
"take over the whole industry of the country". "Research has shown",
summarises William Lazonick, "that the 'free-born Englishman' of the eighteenth century
– even those who, by force of circumstance, had to submit to agricultural wage labor –
tenaciously resisted entry into the capitalist workshop".
The use of the term "wage slave" by labor organizations may originate from the labor
protests of the Lowell Mill Girls in 1836. The imagery of wage slavery was widely used
by labor organizations during the mid-19th century to object to the lack of workers' selfmanagement.
However, it was gradually replaced by the more neutral term "wage work"
towards the end of the 19th century as labor organizations shifted their focus to raising
wages.
Karl Marx described capitalist society as infringing on individual autonomy because it is
based on a materialistic and commodified concept of the body and its liberty (i.e. as
something that is sold, rented, or alienated in a class society). According to Friedrich
Engels:
The slave is sold once and for all; the proletarian must sell himself daily and hourly. The
individual slave, property of one master, is assured an existence, however miserable it
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may be, because of the master's interest. The individual proletarian, property as it were
of the entire bourgeois class which buys his labor only when someone has need of it,
has no secure existence.
Similarities of Wage Work with Slavery
Critics of wage work have drawn several similarities between wage work and slavery:
1. Since the chattel slave is property, his value to an owner is in some ways higher
than that of a worker who may quit, be fired or replaced. The chattel slave's
owner has made a greater investment in terms of the money he paid for the
slave. For this reason, in times of recession chattel slaves could not be fired like
wage laborers. A "wage slave" could also be harmed at no (or less) cost.
American chattel slaves in the 19th century had improved their standard of living
from the 18th century and – according to historians Fogel and Engerman –
plantation records show that slaves worked less, were better fed and whipped
only occasionally – their material conditions in the 19th century being "better than
what was typically available to free urban laborers at the time". This was partially
due to slave psychological strategies under an economic system different from
capitalist wage slavery. According to Mark Michael Smith of the Economic
History Society, "although intrusive and oppressive, paternalism, the way
masters employed it, and the methods slaves used to manipulate it, rendered
slaveholders' attempts to institute capitalistic work regimens on their plantation
ineffective and so allowed slaves to carve out a degree of autonomy".
2. Unlike a chattel slave, a wage laborer can (barring unemployment or lack of job
offers) choose between employers, but they usually constitute a minority of
owners in the population for which the wage laborer must work while attempts to
implement workers' control on employers' businesses may be considered an act
of theft or insubordination and thus be met with violence, imprisonment or other
legal and social measures. The wage laborer's starkest choice is to work for an
employer or face poverty or starvation. If a chattel slave refuses to work, a
number of punishments are also available; from beatings to food deprivation –
although economically rational slave owners practiced positive reinforcement to
achieve best results and before losing their investment by killing an expensive
slave.
3. Historically, the range of occupations and status positions held by chattel slaves
has been nearly as broad as that held by free persons, indicating some
similarities between chattel slavery and wage slavery as well.
4. Like chattel slavery, wage slavery does not stem from some immutable "human
nature", but represents a "specific response to material and historical conditions"
that "reproduce[s] the inhabitants, the social relations… the ideas… [and] the
social form of daily life".
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5. Similarities were blurred by the fact that proponents of wage labor won
the American Civil War, in which they competed for legitimacy with defenders of
chattel slavery. Both presented an over-positive assessment of their system
while denigrating the opponent.
According to American anarcho-syndicalist philosopher Noam Chomsky, the similarities
between chattel and wage slavery were noticed by the workers themselves. He noted
that the 19th-century Lowell Mill Girls, who without any reported knowledge of
European Marxism or anarchism condemned the "degradation and subordination" of the
newly emerging industrial system and the "new spirit of the age: gain wealth, forgetting
all but self", maintaining that "those who work in the mills should own them". They
expressed their concerns in a protest song during their 1836 strike:
Oh! isn't it a pity, such a pretty girl as I
Should be sent to the factory to pine away and die?
Oh! I cannot be a slave, I will not be a slave,
For I'm so fond of liberty,
That I cannot be a slave.
Defenses of wage labor
and chattel slavery in the
literature have linked the
subjection of man to man
with the subjection of man
to nature – arguing that
hierarchy and a social
system's particular
relations of production
represent human nature
and are no more coercive
than the reality of life
itself. According to this
narrative, any wellintentioned
attempt to
fundamentally change
the status quo is naively
utopian and will result in
more oppressive
conditions. Bosses in both
of these long-lasting
systems argued that their
system created a lot of
wealth and prosperity. In
some sense, both did create jobs and their investment entailed risk. For example, slave
owners risked losing money by buying chattel slaves who later became ill or died; while
bosses risked losing money by hiring workers (wage slaves) to make products that did
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not sell well on the market. Marginally, both chattel and wage slaves may become
bosses; sometimes by working hard. It may be the "rags to riches" story which
occasionally occurs in capitalism, or the "slave to master" story that occurred in places
like colonial Brazil, where slaves could buy their own freedom and become business
owners, self-employed, or slave owners themselves. [48] Social mobility, or the hard work
and risk that it may entail, are thus not considered to be a redeeming factor by critics of
the concept of wage slavery.
Anthropologist David Graeber has noted that historically the first wage labor contracts
we know about – whether in ancient Greece or Rome, or in the Malay or Swahili city
states in the Indian Ocean – were in fact contracts for the rental of chattel slaves
(usually the owner would receive a share of the money and the slave another, with
which to maintain his or her living expenses). According to Graeber, such arrangements
were quite common in New World slavery as well, whether in the United States or
Brazil. C. L. R. James argued that most of the techniques of human organization
employed on factory workers during the Industrial Revolution were first developed on
slave plantations.
Decline in Use Of Term
The usage of the term "wage slavery" shifted to "wage work" at the end of the 19th
century as groups like the Knights of Labor and American Federation of Labor shifted to
a more reformist, trade union ideology instead of worker's self-management. Much of
the decline was caused by the rapid increase in manufacturing after the Industrial
Revolution and the subsequent dominance of wage labor as a result. Another factor
was immigration and demographic changes that led to ethnic tension between the
workers.
As Hallgrimsdottir and Benoit point out:
[I]ncreased centralization of production ... declining wages ... [an] expanding ... labor
pool ... intensifying competition, and ... [t]he loss of competence and independence
experienced by skilled labor" meant that "a critique that referred to all [wage] work as
slavery and avoided demands for wage concessions in favor of supporting the creation
of the producerist republic (by diverting strike funds towards funding ... co-operatives,
for example) was far less compelling than one that identified the specific conditions of
slavery as low wages.
Treatment in Various Economic Systems
Some anti-capitalist thinkers claim that the elite maintain wage slavery and a divided
working class through their influence over the media and entertainment
industry, [51][52] educational institutions, unjust laws, nationalist and
corporate propaganda, pressures and incentives to internalize values serviceable to the
power structure, state violence, fear of unemployment, and a historical legacy of
exploitation and profit accumulation/transfer under prior systems, which shaped the
Page 96 of 149

development of economic theory. Adam Smith noted that employers often conspire
together to keep wages low and have the upper hand in conflicts between workers and
employers:
The interest of the dealers ... in any particular branch of trade or manufactures, is
always in some respects different from, and even opposite to, that of the public... [They]
have generally an interest to deceive and even to oppress the public ... We rarely hear,
it has been said, of the combinations of masters, though frequently of those of
workmen. But whoever imagines, upon this account, that masters rarely combine, is as
ignorant of the world as of the subject. Masters are always and everywhere in a sort of
tacit, but constant and uniform combination, not to raise the wages of labor above their
actual rate ... It is not, however, difficult to foresee which of the two parties must, upon
all ordinary occasions, have the advantage in the dispute, and force the other into a
compliance with their terms.
Capitalism
The concept of wage slavery could conceivably be traced back to pre-capitalist figures
like Gerrard Winstanley from the radical Christian Diggers movement in England, who
wrote in his 1649 pamphlet, The New Law of Righteousness, that there "shall be no
Page 97 of 149

uying or selling, no fairs nor markets, but the whole earth shall be a common treasury
for every man" and "there shall be none Lord over others, but every one shall be a Lord
of himself".
Aristotle stated that "the citizens must not live a mechanic or a mercantile life (for such a
life is ignoble and inimical to virtue), nor yet must those who are to be citizens in the
best state be tillers of the soil (for leisure is needed both for the development of virtue
and for active participation in politics)", often paraphrased as "all paid jobs absorb and
degrade the mind". Cicero wrote in 44 BC that "vulgar are the means of livelihood of all
hired workmen whom we pay for mere manual labour, not for artistic skill; for in their
case the very wage they receive is a pledge of their slavery".
Somewhat similar criticisms have also been expressed by some proponents
of liberalism, like Silvio Gesell and Thomas Paine; Henry George, who inspired the
economic philosophy known as Georgism; and the Distributist school of thought within
the Catholic Church.
To Karl Marx and anarchist thinkers like Mikhail Bakunin and Peter Kropotkin, wage
slavery was a class condition in place due to the existence of private property and
the state. This class situation rested primarily on:
1. The existence of property not intended for active use;
2. The concentration of ownership in few hands;
3. The lack of direct access by workers to the means of production and
consumption goods; and
4. The perpetuation of a reserve army of unemployed workers.
And secondarily on:
1. The waste of workers' efforts and resources on producing useless luxuries;
2. The waste of goods so that their price may remain high; and
3. The waste of all those who sit between the producer and consumer, taking their
own shares at each stage without actually contributing to the production of
goods, i.e. the middle man.
Fascism
Fascist economic policies were more hostile to independent trade unions than modern
economies in Europe or the United States. Fascism was more widely accepted in the
1920s and 1930s, and foreign corporate investment (notably from the United States) in
Italy and Germany increased after the fascists took power.
Fascism has been perceived by some notable critics, like Buenaventura Durruti, to be a
last resort weapon of the privileged to ensure the maintenance of wage slavery:
No government fights fascism to destroy it. When the bourgeoisie sees that power is
slipping out of its hands, it brings up fascism to hold onto their privileges.
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Psychological Effects
According to Noam Chomsky, analysis of the psychological implications of wage slavery
goes back to the Enlightenment era. In his 1791 book The Limits of State Action,
classical liberal thinker Wilhelm von Humboldt explained how "whatever does not spring
from a man's free choice, or is only the result of instruction and guidance, does not
enter into his very nature; he does not perform it with truly human energies, but merely
with mechanical exactness" and so when the laborer works under external control, "we
may admire what he does, but we despise what he is". Because they explore human
authority and obedience, both the Milgram and Stanford experiments have been found
useful in the psychological study of wage-based workplace relations.
Self-Identity Problems and Stress
According to research [citation needed] , modern work provides people with a sense of
personal and social identity that is tied to:
1. The particular work role, even if unfulfilling; and
2. The social role it entails e.g. family bread-winning, friendship forming and so on.
Page 99 of 149

Thus job loss entails the loss of this identity.
Erich Fromm argued that if a person perceives himself as being what he owns, then
when that person loses (or even thinks of losing) what he "owns" (e.g. the good looks or
sharp mind that allow him to sell his labor for high wages) a fear of loss may create
anxiety and authoritarian tendencies because that person's sense of identity is
threatened. In contrast, when a person's sense of self is based on what he experiences
in a state of being (creativity, love, sadness, taste, sight and the like) with a less
materialistic regard for what he once had and lost, or may lose, then less authoritarian
tendencies prevail. In his view, the state of being flourishes under a worker-managed
workplace and economy, whereas self-ownership entails a materialistic notion of self,
created to rationalize the lack of worker control that would allow for a state of being.
Investigative journalist Robert Kuttner analyzed the work of public-health scholars
Jeffrey Johnson and Ellen Hall about modern conditions of work and concludes that "to
be in a life situation where one experiences relentless demands by others, over which
one has relatively little control, is to be at risk of poor health, physically as well as
mentally". Under wage labor, "a relatively small elite demands and gets empowerment,
self-actualization, autonomy, and other work satisfaction that partially compensate for
long hours" while "epidemiological data confirm that lower-paid, lower-status workers
are more likely to experience the most clinically damaging forms of stress, in part
because they have less control over their work".
Wage slavery and the educational system that precedes it "implies power held by the
leader. Without power the leader is inept. The possession of power inevitably leads to
corruption ... in spite of ... good intentions ... [Leadership means] power of initiative, this
sense of responsibility, the self-respect which comes from expressed manhood, is taken
from the men, and consolidated in the leader. The sum of their initiative, their
responsibility, their self-respect becomes his ... [and the] order and system he maintains
is based upon the suppression of the men, from being independent thinkers into being
'the men' ... In a word, he is compelled to become an autocrat and a foe to democracy".
For the "leader", such marginalization can be beneficial, for a leader "sees no need for
any high level of intelligence in the rank and file, except to applaud his actions. Indeed
such intelligence from his point of view, by breeding criticism and opposition, is an
obstacle and causes confusion". Wage slavery "implies erosion of the human
personality ... [because] some men submit to the will of others, arousing in these
instincts which predispose them to cruelty and indifference in the face of the suffering of
their fellows".
Higher Wages
Psychological Control
In 19th-century discussions of labor relations, it was normally assumed that the threat of
starvation forced those without property to work for wages. Proponents of the view that
modern forms of employment constitute wage slavery, even when workers appear to
Page 100 of 149

have a range of available alternatives, have attributed its perpetuation to a variety of
social factors that maintain the hegemony of the employer class.
In an account of the Lowell Mill Girls, Harriet Hanson Robinson wrote that generously
high wages were offered to overcome the degrading nature of the work:
At the time the Lowell cotton mills were started the caste of the factory girl was the
lowest among the employments of women. ... She was represented as subjected to
influences that must destroy her purity and self-respect. In the eyes of her overseer she
was but a brute, a slave, to be beaten, pinched and pushed about. It was to overcome
this prejudice that such high wages had been offered to women that they might be
induced to become millgirls, in spite of the opprobrium that still clung to this degrading
occupation.
In his book Disciplined Minds, Jeff Schmidt points out that professionals are trusted to
run organizations in the interests of their employers. Because employers cannot be on
hand to manage every decision, professionals are trained to "ensure that each and
every detail of their work favors the right interests–or skewers the disfavored ones" in
the absence of overt control:
The resulting professional is an obedient thinker, an intellectual property whom
employers can trust to experiment, theorize, innovate and create safely within the
confines of an assigned ideology.
Parecon (participatory economics) theory posits a social class "between labor and
capital" of higher paid professionals such as "doctors, lawyers, engineers, managers
and others" who monopolize empowering labor and constitute a class above wage
laborers who do mostly "obedient, rote work".
Page 101 of 149

Lower Wages
The terms "employee" or "worker" have often been replaced by "associate". This plays
up the allegedly voluntary nature of the interaction while playing down the subordinate
status of the wage laborer as well as the worker-boss class distinction emphasized by
labor movements. Billboards as well as television, Internet and newspaper
advertisements consistently show low-wage workers with smiles on their faces,
appearing happy.
Job interviews and other data on requirements for lower skilled workers in developed
countries – particularly in the growing service sector – indicate that the more workers
depend on low wages and the less skilled or desirable their job is, the more employers
screen for workers without better employment options and expect them to feign
unremunerative motivation. Such screening and feigning may not only contribute to the
positive self-image of the employer as someone granting desirable employment, but
also signal wage-dependence by indicating the employee's willingness to feign, which in
turn may discourage the dissatisfaction normally associated with job-switching or union
activity.
At the same time, employers in the service industry have justified unstable, part-time
employment and low wages by playing down the importance of service jobs for the lives
of the wage laborers (e.g. just temporary before finding something better, student
summer jobs and the like).
In the early 20th century, "scientific methods of strikebreaking" [79] were devised –
employing a variety of tactics that emphasized how strikes undermined "harmony" and
"Americanism".
Workers' Self-Management
Some social activists objecting to the market system or price system of wage working
historically have considered syndicalism, worker cooperatives, workers' selfmanagement
and workers' control as possible alternatives to the current wage
system. [4][5][6][19]
Labor and Government
The American philosopher John Dewey believed that until "industrial feudalism" is
replaced by "industrial democracy", politics will be "the shadow cast on society by big
business". Thomas Ferguson has postulated in his investment theory of party
competition that the undemocratic nature of economic institutions under capitalism
causes elections to become occasions when blocs of investors coalesce and compete
to control the state.
Noam Chomsky has argued that political theory tends to blur the 'elite' function of
government:
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Modern political theory stresses Madison's belief that "in a just and a free government
the rights both of property and of persons ought to be effectually guarded." But in this
case too it is useful to look at the doctrine more carefully. There are no rights of
property, only rights to property that is, rights of persons with property,...
[In] representative democracy, as in, say, the United States or Great Britain […] there is
a monopoly of power centralized in the state, and secondly – and critically – […] the
representative democracy is limited to the political sphere and in no serious way
encroaches on the economic sphere […] That is, as long as individuals are compelled to
rent themselves on the market to those who are willing to hire them, as long as their role
in production is simply that of ancillary tools, then there are striking elements of coercion
and oppression that make talk of democracy very limited, if even meaningful.
In this regard, Chomsky has used Bakunin's theories about an "instinct for freedom", the
militant history of labor movements, Kropotkin's mutual aid evolutionary principle of
survival and Marc Hauser's theories supporting an innate and universal moral faculty, to
explain the incompatibility of oppression with certain aspects of human nature.
Page 103 of 149

Influence on Environmental Degradation
Loyola University philosophy professor John Clark and libertarian socialist
philosopher Murray Bookchin have criticized the system of wage labor for encouraging
environmental destruction, arguing that a self-managed industrial society would better
manage the environment. Like other anarchists, they attribute much of the Industrial
Revolution's pollution to the "hierarchical" and "competitive" economic relations
accompanying it.
Employment Contracts
Some criticize wage slavery on strictly contractual grounds, e.g. David
Ellerman and Carole Pateman, arguing that the employment contract is a legal fiction in
that it treats human beings juridically as mere tools or inputs by abdicating responsibility
and self-determination, which the critics argue are inalienable. As Ellerman points out,
"[t]he employee is legally transformed from being a co-responsible partner to being only
an input supplier sharing no legal responsibility for either the input liabilities [costs] or
the produced outputs [revenue, profits] of the employer's business". Such contracts are
inherently invalid "since the person remain[s] a de factofully capacitated adult person
with only the contractual role of a non-person" as it is impossible to physically transfer
self-determination. As Pateman argues:
The contractarian argument is unassailable all the time it is accepted that abilities can
'acquire' an external relation to an individual, and can be treated as if they were
property. To treat abilities in this manner is also implicitly to accept that the 'exchange'
between employer and worker is like any other exchange of material property . . . The
answer to the question of how property in the person can be contracted out is that no
such procedure is possible. Labour power, capacities or services, cannot be separated
from the person of the worker like pieces of property.
In a modern liberal capitalist society, the employment contract is enforced while the
enslavement contract is not; the former being considered valid because of its
consensual/non-coercive nature and the latter being considered inherently invalid,
consensual or not. The noted economist Paul Samuelson described this discrepancy:
Since slavery was abolished, human earning power is forbidden by law to be
capitalized. A man is not even free to sell himself; he must rent himself at a wage.
Some advocates of right-libertarianism, among them philosopher Robert Nozick,
address this inconsistency in modern societies arguing that a consistently libertarian
society would allow and regard as valid consensual/non-coercive enslavement
contracts, rejecting the notion of inalienable rights:
The comparable question about an individual is whether a free system will allow him to
sell himself into slavery. I believe that it would.
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Others like Murray Rothbard allow for the possibility of debt slavery, asserting that a
lifetime labor contract can be broken so long as the slave pays appropriate damages:
[I]f A has agreed to work for life for B in exchange for 10,000 grams of gold, he will have
to return the proportionate amount of property if he terminates the arrangement and
ceases to work.
Schools of Economics
In the philosophy of mainstream, neoclassical economics, wage labor is seen as
the voluntary sale of one's own time and efforts, just like a carpenter would sell a chair,
or a farmer would sell wheat. It is considered neither an antagonistic nor abusive
relationship and carries no particular moral implications.
Austrian economics argues that a person is not "free" unless they can sell their labor
because otherwise that person has no self-ownership and will be owned by a "third
party" of individuals.
Post-Keynesian economics perceives wage slavery as resulting from inequality of
bargaining power between labor and capital, which exists when the economy does not
"allow labor to organize and form a strong countervailing force".
The two main forms of socialist economics perceive wage slavery differently:
1. Libertarian socialism sees it as a lack of workers' self-management in the context
of substituting state and capitalist control with political and economic
decentralization and confederation.
2. State socialists view it as an injustice perpetrated by capitalists and solved
through nationalization and social ownership of the means of production.
Page 105 of 149

Page 106 of 149

VIII. The Employment-to-Population
Ratio
A Much More Accurate Indicator
of The Unemployment Rate of A Nation
The Organization for Economic Co-operation and Development defines
the employment rate as the employment-to-population ratio.
This is a statistical ratio that measures the proportion of the country's working
age population (statistics are often given for ages 15 to 64) that is employed. This
includes people that have stopped looking for work. The International Labour
Organization states that a person is considered employed if they have worked at least 1
hour in "gainful" employment in the most recent week.
Background
The employment-population ratio has not always been looked at for labor statistics and
where specific areas are economically, but after the recent recession it has been given
more attention worldwide, especially by economists. The National Bureau Of Economic
Research (NBER) states that the Great Recession ended in June 2009. During 2009
Page 107 of 149

and 2010, however, many areas were still struggling economically, which is the reason
the employment-population ratio is still used by both Americans and people around the
world.
Key Definitions
Key terms that explain the use of the ratio follow:
Employed Persons All those who, (1) do any work at all as paid employees, work in
their own business or profession or on their own farm, or work 15 hours or more as
unpaid workers in a family-operated enterprise; and (2) all those who do not work but
had jobs or businesses from which they were temporarily absent due to illness, bad
weather, vacation, childcare problems, labor dispute, maternity or paternity leave, or
other family or personal obligations—whether or not they were paid by their employers
for the time off and whether or not they were seeking other jobs.
Unemployed Persons All those who, (1) have no employment during the reference
week; (2) are available for work, except for temporary illness; and (3) have made
specific efforts, such as contacting employers, to find employment sometime during the
past 4-week period.
Participant Rate This represents the proportion of the population that is in the labor
force.
Not in the labor force. Included in this group are all persons in the civilian
noninstitutional population who are neither employed nor unemployed. Information is
collected on their desire for and availability to take a job at the time of the CPS
interview, jobsearch activity in the prior year, and reason for not looking for work in past
4-week period.
Multiple jobholders. These are employed persons who, have two or more jobs as a
wage and salary worker, are self-employed and also held a wage and salary job, or
work as an unpaid family worker and also hold a wage and salary job.
Use
The ratio is used to evaluate the ability of the economy to create jobs and therefore is
used in conjunction with the unemployment rate for a general evaluation of the labour
market stance. Having a high ratio means that an important proportion of the population
in working age is employed, which in general will have positive effects on the GDP per
capita. Nevertheless, the ratio does not give an indication of working conditions, number
of hours worked per person, earnings or the size of the black market. Therefore, the
analysis of the labour market must be done in conjunction with other statistics.
This measure comes from dividing the civilian noninstitutionalized population who are
employed by the total noninstitutionalized population and multiplying by 100.
Page 108 of 149

Employment-To-Population Ratio in The World
In general, a high ratio is considered to be above 70 percent of the working-age
population whereas a ratio below 50 percent is considered to be low. The economies
with low ratios are generally situated in the Middle East and North Africa.
Employment-to-population ratios are typically higher for men than for women.
Nevertheless, in the past decades, the ratios tended to fall for men and increase in the
case of women, which made the differences between both to be reduced.
Co
un
try
Aust
ralia
Aust
ria
Belgi
um
Cana
da
1
9
7
0
1
9
8
0
1
9
9
0
Employment-To-Population Ratio In OECD Countries
Persons Aged 15–64 Years (Percentages)
2
0
0
0
2
0
0
1
2
0
0
2
2
0
0
3
2
0
0
4
2
0
0
5
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
2
0
0
6
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
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
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
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
Czec
h
Rep
ublic
Den
mark
Esto
nia
Finla
nd
Fran
ce
Ger
man
y
Gree
ce
Hun
gary
Icela
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
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
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
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
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
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
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
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
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
2
0
0
7
2
0
0
8
2
0
0
9
2
0
1
0
2
0
1
1
2
0
1
2
2
0
1
3
Page 109 of 149

Co
un
try
nd
Irela
nd
Israe
l
1
9
7
0
1
9
8
0
1
9
9
0
Employment-To-Population Ratio In OECD Countries
Persons Aged 15–64 Years (Percentages)
2
0
0
0
2
0
0
1
2
0
0
2
2
0
0
3
2
0
0
4
2
0
0
5
2
0
0
6
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
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
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
Japa
n
Kore
a
Luxe
mbo
urg
Mexi
co
Neth
erlan
ds
New
Zeal
and
Nor
way
Pola
nd
Port
ugal
Slov
ak
Rep
ublic
Slov
enia
Spai
n
Swe
den
Swit
zerla
nd
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
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
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
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
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
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
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
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
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
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
2
0
0
7
63.4 62.6 65.3 66.0 66.6 67.8 68.6 67.5 66.2 64.4 64.1 63.3
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
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
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
2
0
0
8
2
0
0
9
2
0
1
0
2
0
1
1
2
0
1
2
2
0
1
3
Page 110 of 149

Co
un
try
Turk
ey
Unit
ed
King
dom
Unit
ed
State
s
Brazi
l
Chin
a
Colo
mbia
1
9
7
0
1
9
8
0
1
9
9
0
Employment-To-Population Ratio In OECD Countries
Persons Aged 15–64 Years (Percentages)
2
0
0
0
2
0
0
1
2
0
0
2
2
0
0
3
2
0
0
4
2
0
0
5
2
0
0
6
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
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
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
2
0
0
7
64.3 65.4 65.0 66.5 67.0 67.4 67.4 68.3 67.7 66.9 67.2 66.7
79.3 75.1
57.5 57.0 58.5 57.5 58.1 56.7 56.9 58.9 60.2 61.4 62.5 62.7
India 54.8 53.3
Russ
ian
Fede
ratio
n
Sout
h
Afric
a
Latvi
a
EU-
21
EU-
15
Euro
pe
G7
Cou
ntrie
s
OEC
D
Cou
ntrie
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
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
2
0
0
8
2
0
0
9
63.3 66.3 68.1 68.2 60.3 58.5 60.8 63.0 65.0
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
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
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
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
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
2
0
1
0
2
0
1
1
2
0
1
2
2
0
1
3
Page 111 of 149

Co
un
try
s
Cou
ntry
1
9
7
0
1
9
8
0
1
9
9
0
Employment-To-Population Ratio In OECD Countries
Persons Aged 15–64 Years (Percentages)
2
0
0
0
2
0
0
1
2
0
0
2
2
0
0
3
2
0
0
4
2
0
0
5
1970 1980 1990 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
2
0
0
6
2
0
0
7
2
0
0
8
2
0
0
9
2
0
1
0
2
0
1
1
2
0
1
2
2
0
1
3
Source: OECD.StatExtracts, except as noted.
Page 112 of 149

IX. List of Countries by
Employment Rate
This is a list of countries by employment rate, this being the proportion of employed
adults in the working age. The definition of "working age" varies: Many sources,
including the OECD, use 15–64 years old, [1] but the Office for National Statistics of the
United Kingdom uses 16–64 years old [2] and EUROSTAT uses 20–64 years old. [3]
List
Rank Country Employment
rate
(%)
Age
range
Date of
information
Source
1 Iceland 86.3 15–64 2016 OECD [1]
2 Switzerland 79.6 15–64 2016 OECD [1]
3 Sweden 76.2 15–64 2016 OECD [1]
4
United
Kingdom
75.7 16–64 2018
Office for National
Statistics [2]
5 New Zealand 75.6 15–64 2016 OECD [1]
6 China 75.1 15–64 2010 OECD [1]
7 Denmark 74.9 15–64 2016 OECD [1]
8 Netherlands 74.8 15–64 2016 OECD [1]
9 Germany 74.7 15–64 2016 OECD [1]
10 Norway 74.4 15–64 2016 OECD [1]
11 Japan 74.3 15–64 2016 OECD [1]
12 Canada 72.6 15–64 2016 OECD [1]
13 Australia 72.4 15–64 2016 OECD [1]
14 Estonia 72.0 15–64 2016 OECD [1]
14 Czech Republic 72.0 15–64 2016 OECD [1]
16 Austria 71.5 15–64 2016 OECD [1]
17 Russia 70.0 15–64 2016 OECD [1]
18 Lithuania 69.4 15–64 2016 OECD [1]
18 United States 69.4 13–64 2016 OECD [1]
20 Finland 69.2 15–64 2016 OECD [1]
21 Latvia 68.7 15–64 2016 OECD [1]
22 Israel 68.6 15–64 2016 OECD [1]
Page 113 of 149

Rank Country Employment
rate
(%)
Age
range
Date of
information
Source
23 Colombia 67.2 15–64 2016 OECD [1]
24 OECD Average 67.0 15–64 2016 OECD [1]
25 Hungary 66.5 15–64 2016 OECD [1]
26 South Korea 66.1 15–64 2016 OECD [1]
27 Slovenia 65.8 15–64 2016 OECD [1]
28 Luxembourg 65.6 15–64 2016 OECD [1]
29 Portugal 65.2 15–64 2016 OECD [1]
30 Slovakia 64.9 15–64 2016 OECD [1]
31 Ireland 64.7 15–64 2016 OECD [1]
32 France 64.6 15–64 2016 OECD [1]
33 Poland 64.5 15–64 2016 OECD [1]
34 Brazil 64.4 15–64 2015 OECD [1]
35 Belgium 62.3 15–64 2016 OECD [1]
36 Chile 62.2 15–64 2016 OECD [1]
37 Mexico 61.0 15–64 2016 OECD [1]
38 Spain 60.5 15–64 2016 OECD [1]
39 Costa Rica 58.7 15–64 2016 OECD [1]
40 Italy 57.2 15–64 2016 OECD [1]
41 Albania 56.2 15–64 2016 (Q4) INSTAT [4]
42 India 53.3 15–64 2012 OECD [1]
43 Greece 52.0 15–64 2016 OECD [1]
44 Turkey 50.6 15–64 2016 OECD [1]
45
Bosnia
Herzegovina
and
43.0 15–64 2017
Agency of Statistics of
Bosnia and
Herzegovina [5]
45 South Africa 43.0 15–64 2010 OECD [1]
47 Kosovo 40.2 15–64 2016 (Q3)
Kosovo Agency of
Statistics
Page 114 of 149

X. References
1. https://en.wikipedia.org/wiki/Unemployment
2.. https://en.wikipedia.org/wiki/Effective_unemployment_rate
3. https://www.lombardiletter.com/u-s-unemployment-forecast-2017/7810/
4. https://en.wikipedia.org/wiki/Unemployment#United_States_Bureau_of_Labor_statistics
5. http://daily-work.org/the-real-unemployment-numbers-and-why-im-not-counted/
6. https://en.wikipedia.org/wiki/Involuntary_unemployment
7. https://en.wikipedia.org/wiki/Underemployment
8. https://en.wikipedia.org/wiki/Discouraged_worker
9. https://en.wikipedia.org/wiki/Working_poor
10. https://en.wikipedia.org/wiki/Wage_slavery
11. https://en.wikipedia.org/wiki/Employment-to-population_ratio
12. https://en.wikipedia.org/wiki/List_of_countries_by_employment_rate
13. https://www.bls.gov/news.release/pdf/empsit.pdf
14. https://www.bls.gov/news.release/pdf/youth.pdf
15. https://www.urban.org/sites/default/files/publication/23921/412887-Consequences-of-
Long-Term-Unemployment.PDF
Page 115 of 149

Notes
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Page 117 of 149

Page 118 of 149

Attachment A
The Unemployment Situation
in The U.S.
Page 119 of 149

Transmission of material in this news release is embargoed until
8:30 a.m. (EDT) Friday, November 2, 2018
USDL-18-1739
Technical information:
Household data:
Establishment data:
Media contact:
(202) 691-6378 • cpsinfo@bls.gov • www.bls.gov/cps
(202) 691-6555 • cesinfo@bls.gov • www.bls.gov/ces
(202) 691-5902 • PressOffice@bls.gov
THE EMPLOYMENT SITUATION — OCTOBER 2018
Total nonfarm payroll employment rose by 250,000 in October, and the unemployment rate was
unchanged at 3.7 percent, the U.S. Bureau of Labor Statistics reported today. Job gains occurred in
health care, in manufacturing, in construction, and in transportation and warehousing.
Chart 1. Unemployment rate, seasonally adjusted,
October 2016 – October 2018
Chart 2. Nonfarm payroll employment over-the-month change,
seasonally adjusted, October 2016 – October 2018
Percent
Thousands
6.0
400
5.5
350
300
5.0
250
4.5
200
150
4.0
100
3.5
50
0
3.0
Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18
-50
Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18
Hurricane Michael
Hurricane Michael made landfall in the Florida Panhandle on October 10, 2018, during the reference
periods for both the establishment and household surveys. Hurricane Michael had no discernible effect
on the national employment and unemployment estimates for October, and response rates for the two
surveys were within normal ranges. For information on how severe weather can affect employment and
hours data, see Question 8 in the Frequently Asked Questions section of this news release.
BLS will release the state estimates of employment and unemployment on November 16, 2018, at 10:00
a.m. (EST).

Household Survey Data
The unemployment rate remained at 3.7 percent in October, and the number of unemployed persons
was little changed at 6.1 million. Over the year, the unemployment rate and the number of unemployed
persons declined by 0.4 percentage point and 449,000, respectively. (See table A-1.)
Among the major worker groups, the unemployment rates for adult men (3.5 percent), adult women
(3.4 percent), teenagers (11.9 percent), Whites (3.3 percent), Blacks (6.2 percent), Asians (3.2 percent),
and Hispanics (4.4 percent) showed little or no change in October. (See tables A-1, A-2, and A-3.)
The number of long-term unemployed (those jobless for 27 weeks or more) was essentially unchanged
at 1.4 million in October and accounted for 22.5 percent of the unemployed. (See table A-12.)
The labor force participation rate increased by 0.2 percentage point to 62.9 percent in October but has
shown little change over the year. The employment-population ratio edged up by 0.2 percentage point
to 60.6 percent in October and has increased by 0.4 percentage point over the year. (See table A-1.)
The number of persons employed part time for economic reasons (sometimes referred to as
involuntary part-time workers) was essentially unchanged at 4.6 million in October. These individuals,
who would have preferred full-time employment, were working part time because their hours had been
reduced or they were unable to find full-time jobs. (See table A-8.)
In October, 1.5 million persons were marginally attached to the labor force, little changed from a year
earlier. (Data are not seasonally adjusted.) These individuals were not in the labor force, wanted and
were available for work, and had looked for a job sometime in the prior 12 months. They were not
counted as unemployed because they had not searched for work in the 4 weeks preceding the survey.
(See table A-16.)
Among the marginally attached, there were 506,000 discouraged workers in October, about unchanged
from a year earlier. (Data are not seasonally adjusted.) Discouraged workers are persons not currently
looking for work because they believe no jobs are available for them. The remaining 984,000 persons
marginally attached to the labor force in October had not searched for work for reasons such as school
attendance or family responsibilities. (See table A-16.)
Establishment Survey Data
Total nonfarm payroll employment increased by 250,000 in October, following an average monthly
gain of 211,000 over the prior 12 months. In October, job growth occurred in health care, in
manufacturing, in construction, and in transportation and warehousing. (See table B-1.)
Health care added 36,000 jobs in October. Within the industry, employment growth occurred in
hospitals (+13,000) and in nursing and residential care facilities (+8,000). Employment in ambulatory
health care services continued to trend up (+14,000). Over the past 12 months, health care employment
grew by 323,000.
In October, employment in manufacturing increased by 32,000. Most of the increase occurred in
durable goods manufacturing, with a gain in transportation equipment (+10,000). Manufacturing has
added 296,000 jobs over the year, largely in durable goods industries.
-2-

Construction employment rose by 30,000 in October, with nearly half of the gain occurring among
residential specialty trade contractors (+14,000). Over the year, construction has added 330,000 jobs.
Transportation and warehousing added 25,000 jobs in October. Within the industry, employment
growth occurred in couriers and messengers (+8,000) and in warehousing and storage (+8,000). Over the
year, employment in transportation and warehousing has increased by 184,000.
Employment in leisure and hospitality edged up in October (+42,000). Employment was unchanged in
September, likely reflecting the impact of Hurricane Florence. The average gain for the 2 months
combined (+21,000) was the same as the average monthly gain in the industry for the 12-month period
prior to September.
In October, employment in professional and business services continued to trend up (+35,000). Over
the year, the industry has added 516,000 jobs.
Employment in mining also continued to trend up over the month (+5,000). The industry has added
65,000 jobs over the year, with most of the gain in support activities for mining.
Employment in other major industries—including wholesale trade, retail trade, information,
financial activities, and government—showed little change over the month.
The average workweek for all employees on private nonfarm payrolls increased by 0.1 hour to 34.5
hours in October. In manufacturing, the workweek edged down by 0.1 hour to 40.8 hours, and overtime
was unchanged at 3.5 hours. The average workweek for production and nonsupervisory employees on
private nonfarm payrolls, at 33.7 hours, was unchanged over the month. (See tables B-2 and B-7.)
In October, average hourly earnings for all employees on private nonfarm payrolls rose by 5 cents to
$27.30. Over the year, average hourly earnings have increased by 83 cents, or 3.1 percent. Average
hourly earnings of private-sector production and nonsupervisory employees increased by 7 cents to
$22.89 in October. (See tables B-3 and B-8.)
The change in total nonfarm payroll employment for September was revised down from +134,000 to
+118,000, and the change for August was revised up from +270,000 to +286,000. The downward
revision in September offset the upward revision in August. (Monthly revisions result from additional
reports received from businesses and government agencies since the last published estimates and from
the recalculation of seasonal factors.) After revisions, job gains have averaged 218,000 over the past 3
months.
_____________
The Employment Situation for November is scheduled to be released on Friday, December 7,
2018, at 8:30 a.m. (EST).
-3-

HOUSEHOLD DATA
Summary table A. Household data, seasonally adjusted
[Numbers in thousands]
Category
Oct.
2017
Aug.
2018
Sept.
2018
Oct.
2018
Change from:
Sept. 2018-
Oct. 2018
Employment status
Civilian noninstitutional population........................................... 255,766 258,066 258,290 258,514 224
Civilian labor force........................................................... 160,371 161,776 161,926 162,637 711
Participation rate.......................................................... 62.7 62.7 62.7 62.9 0.2
Employed................................................................... 153,846 155,542 155,962 156,562 600
Employment-population ratio.......................................... 60.2 60.3 60.4 60.6 0.2
Unemployed............................................................... 6,524 6,234 5,964 6,075 111
Unemployment rate.................................................... 4.1 3.9 3.7 3.7 0.0
Not in labor force............................................................ 95,395 96,290 96,364 95,877 -487
Unemployment rates
Total, 16 years and over...................................................... 4.1 3.9 3.7 3.7 0.0
Adult men (20 years and over)............................................ 3.8 3.5 3.4 3.5 0.1
Adult women (20 years and over)......................................... 3.6 3.6 3.3 3.4 0.1
Teenagers (16 to 19 years)................................................ 13.7 12.8 12.8 11.9 -0.9
White.......................................................................... 3.5 3.4 3.3 3.3 0.0
Black or African American.................................................. 7.3 6.3 6.0 6.2 0.2
Asian.......................................................................... 3.0 3.0 3.5 3.2 -0.3
Hispanic or Latino ethnicity................................................. 4.8 4.7 4.5 4.4 -0.1
Total, 25 years and over...................................................... 3.3 3.2 3.0 3.1 0.1
Less than a high school diploma.......................................... 6.1 5.7 5.5 6.0 0.5
High school graduates, no college........................................ 4.3 3.9 3.7 4.0 0.3
Some college or associate degree........................................ 3.6 3.5 3.2 3.0 -0.2
Bachelor’s degree and higher.............................................. 2.0 2.1 2.0 2.0 0.0
Reason for unemployment
Job losers and persons who completed temporary jobs.................. 3,214 2,875 2,796 2,850 54
Job leavers...................................................................... 731 862 730 726 -4
Reentrants....................................................................... 2,001 1,846 1,877 1,906 29
New entrants.................................................................... 626 584 586 606 20
Duration of unemployment
Less than 5 weeks............................................................. 2,128 2,208 2,065 2,057 -8
5 to 14 weeks................................................................... 1,943 1,720 1,720 1,821 101
15 to 26 weeks................................................................. 856 923 861 856 -5
27 weeks and over............................................................. 1,645 1,332 1,384 1,373 -11
Employed persons at work part time
Part time for economic reasons.............................................. 4,880 4,379 4,642 4,621 -21
Slack work or business conditions........................................ 2,960 2,551 2,782 2,816 34
Could only find part-time work............................................. 1,615 1,365 1,447 1,436 -11
Part time for noneconomic reasons.......................................... 20,897 21,781 21,464 21,512 48
Persons not in the labor force (not seasonally adjusted)
Marginally attached to the labor force....................................... 1,535 1,443 1,577 1,491 –
Discouraged workers........................................................ 524 434 383 506 –
- Over-the-month changes are not displayed for not seasonally adjusted data.
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
not necessarily add to totals because of the independent seasonal adjustment of the various series. Updated population controls are introduced
annually with the release of January data.

ESTABLISHMENT DATA
Summary table B. Establishment data, seasonally adjusted
Category
Oct.
2017
Aug.
2018
Sept.
2018 p Oct.
2018 p
EMPLOYMENT BY SELECTED INDUSTRY
(Over-the-month change, in thousands)
Total nonfarm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 286 118 250
Total private. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 267 121 246
Goods-producing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 49 42 67
Mining and logging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 7 4 5
Construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 31 20 30
Manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 11 18 32
Durable goods 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 11 14 21
Motor vehicles and parts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -1.6 2.7 1.0 6.8
Nondurable goods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 0 4 11
Private service-providing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 218 79 179
Wholesale trade. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 20.6 3.3 9.1
Retail trade. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 9.1 -32.4 2.4
Transportation and warehousing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.7 23.1 20.8 24.8
Utilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.0 0.9 0.1 1.2
Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 -4 -4 7
Financial activities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 9 15 7
Professional and business services 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 54 46 35
Temporary help services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.8 10.8 7.6 3.3
Education and health services 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 67 26 44
Health care and social assistance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.7 52.5 34.9 46.7
Leisure and hospitality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 30 0 42
Other services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 8 4 7
Government. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -6 19 -3 4
(3-month average change, in thousands)
Total nonfarm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 220 190 218
Total private. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 199 175 211
WOMEN AND PRODUCTION AND NONSUPERVISORY EMPLOYEES
AS A PERCENT OF ALL EMPLOYEES 2
Total nonfarm women employees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.5 49.7 49.7 49.7
Total private women employees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.1 48.3 48.3 48.3
Total private production and nonsupervisory employees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.4 82.4 82.4 82.4
HOURS AND EARNINGS
ALL EMPLOYEES
Total private
Average weekly hours. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.4 34.5 34.4 34.5
Average hourly earnings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . $26.47 $27.17 $27.25 $27.30
Average weekly earnings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . $910.57 $937.37 $937.40 $941.85
Index of aggregate weekly hours (2007=100) 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107.8 110.0 109.7 110.3
Over-the-month percent change. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.5 0.3 -0.3 0.5
Index of aggregate weekly payrolls (2007=100) 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136.5 142.8 143.0 143.9
Over-the-month percent change. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.4 0.6 0.1 0.6
DIFFUSION INDEX
(Over 1-month span) 5
Total private (258 industries). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.2 64.5 60.7 65.7
Manufacturing (76 industries). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.8 56.6 65.1 62.5
1 Includes other industries, not shown separately.
2 Data relate to production employees in mining and logging and manufacturing, construction employees in construction, and nonsupervisory employees in the
service-providing industries.
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
hours.
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
aggregate weekly payrolls.
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
balance between industries with increasing and decreasing employment.
p Preliminary
NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.

Frequently Asked Questions about Employment and Unemployment Estimates
1. Why are there two monthly measures of employment?
The household survey and establishment survey both produce sample-based estimates of
employment, and both have strengths and limitations. The establishment survey employment series
has a smaller margin of error on the measurement of month-to-month change than the household
survey because of its much larger sample size. An over-the-month employment change of about
100,000 is statistically significant in the establishment survey, while the threshold for a statistically
significant change in the household survey is about 500,000. However, the household survey has a
more expansive scope than the establishment survey because it includes self-employed workers
whose businesses are unincorporated, unpaid family workers, agricultural workers, and private
household workers, who are excluded by the establishment survey. The household survey also
provides estimates of employment for demographic groups. For more information on the differences
between the two surveys, please visit https://www.bls.gov/web/empsit/ces_cps_trends.htm.
2. Are undocumented immigrants counted in the surveys?
It is likely that both surveys include at least some undocumented immigrants. However, neither the
establishment nor the household survey is designed to identify the legal status of workers. Therefore,
it is not possible to determine how many are counted in either survey. The establishment survey does
not collect data on the legal status of workers. The household survey does include questions which
identify the foreign and native born, but it does not include questions about the legal status of the
foreign born. Data on the foreign and native born are published each month in table A-7 of The
Employment Situation news release.
3. Why does the establishment survey have revisions?
The establishment survey revises published estimates to improve its data series by incorporating
additional information that was not available at the time of the initial publication of the estimates.
The establishment survey revises its initial monthly estimates twice, in the immediately succeeding
2 months, to incorporate additional sample receipts from respondents in the survey and recalculated
seasonal adjustment factors. For more information on the monthly revisions, please visit
https://www.bls.gov/ces/cesrevinfo.htm.
On an annual basis, the establishment survey incorporates a benchmark revision that re-anchors
estimates to nearly complete employment counts available from unemployment insurance tax
records. The benchmark helps to control for sampling and modeling errors in the estimates. For more
information on the annual benchmark revision, please visit
https://www.bls.gov/web/empsit/cesbmart.htm.
4. Does the establishment survey sample include small firms?
Yes; about 40 percent of the establishment survey sample is comprised of business establishments
with fewer than 20 employees. The establishment survey sample is designed to maximize the
reliability of the statewide total nonfarm employment estimate; firms from all states, size classes, and
industries are appropriately sampled to achieve that goal.

5. Does the establishment survey account for employment from new businesses?
Yes; monthly establishment survey estimates include an adjustment to account for the net
employment change generated by business births and deaths. The adjustment comes from an
econometric model that forecasts the monthly net jobs impact of business births and deaths based
on the actual past values of the net impact that can be observed with a lag from the Quarterly Census
of Employment and Wages. The establishment survey uses modeling rather than sampling for this
purpose because the survey is not immediately able to bring new businesses into the sample. There
is an unavoidable lag between the birth of a new firm and its appearance on the sampling frame and
availability for selection. BLS adds new businesses to the survey twice a year.
6. Is the count of unemployed persons limited to just those people receiving unemployment
insurance benefits?
No; the estimate of unemployment is based on a monthly sample survey of households. All persons
who are without jobs and are actively seeking and available to work are included among the
unemployed. (People on temporary layoff are included even if they do not actively seek work.) There
is no requirement or question relating to unemployment insurance benefits in the monthly survey.
7. Does the official unemployment rate exclude people who want a job but are not currently
looking for work?
Yes; however, there are separate estimates of persons outside the labor force who want a job,
including those who are not currently looking because they believe no jobs are available (discouraged
workers). In addition, alternative measures of labor underutilization (some of which include
discouraged workers and other groups not officially counted as unemployed) are published each
month in table A-15 of The Employment Situation news release. For more information about these
alternative measures, please visit https://www.bls.gov/cps/lfcharacteristics.htm#altmeasures.
8. How can unusually severe weather affect employment and hours estimates?
In the establishment survey, the reference period is the pay period that includes the 12th of the
month. Unusually severe weather is more likely to have an impact on average weekly hours than
on employment. Average weekly hours are estimated for paid time during the pay period, including
pay for holidays, sick leave, or other time off. The impact of severe weather on hours estimates
typically, but not always, results in a reduction in average weekly hours. For example, some
employees may be off work for part of the pay period and not receive pay for the time missed,
while some workers, such as those dealing with cleanup or repair, may work extra hours.
Typically, it is not possible to precisely quantify the effect of extreme weather on payroll
employment estimates. In order for severe weather conditions to reduce employment estimates,
employees have to be off work without pay for the entire pay period. Employees
who receive pay for any part of the pay period, even 1 hour, are counted in the payroll
employment figures. For more information on how often employees are paid, please visit
https://www.bls.gov/opub/btn/volume-3/how-frequently-do-private-businesses-pay-workers.htm.
In the household survey, the reference period is generally the calendar week that includes the 12th
of the month. Persons who miss the entire week's work for weather-related events are counted as
employed whether or not they are paid for the time off. The household survey collects data on the
number of persons who had a job but were not at work due to bad weather. It also provides a measure
of the number of persons who usually work full time but had reduced hours due to bad weather.
Current and historical data are available on the household survey's most requested statistics page,
please visit https://data.bls.gov/cgi-bin/surveymost?ln.

Technical Note
This news release presents statistics from two major
surveys, the Current Population Survey (CPS; household
survey) and the Current Employment Statistics survey (CES;
establishment survey). The household survey provides
information on the labor force, employment, and
unemployment that appears in the "A" tables, marked
HOUSEHOLD DATA. It is a sample survey of about 60,000
eligible households conducted by the U.S. Census Bureau for
the U.S. Bureau of Labor Statistics (BLS).
The establishment survey provides information on
employment, hours, and earnings of employees on nonfarm
payrolls; the data appear in the "B" tables, marked
ESTABLISHMENT DATA. BLS collects these data each
month from the payroll records of a sample of
nonagricultural business establishments. Each month the
CES program surveys about 149,000 businesses and
government agencies, representing approximately 651,000
individual worksites, in order to provide detailed industry
data on employment, hours, and earnings of workers on
nonfarm payrolls. The active sample includes approximately
one-third of all nonfarm payroll employees.
For both surveys, the data for a given month relate to a
particular week or pay period. In the household survey, the
reference period is generally the calendar week that contains
the 12th day of the month. In the establishment survey, the
reference period is the pay period including the 12th, which
may or may not correspond directly to the calendar week.
Coverage, definitions, and differences between surveys
Household survey. The sample is selected to reflect
the entire civilian noninstitutional population. Based on
responses to a series of questions on work and job search
activities, each person 16 years and over in a sample
household is classified as employed, unemployed, or not in
the labor force.
People are classified as employed if they did any work
at all as paid employees during the reference week; worked
in their own business, profession, or on their own farm; or
worked without pay at least 15 hours in a family business or
farm. People are also counted as employed if they were
temporarily absent from their jobs because of illness, bad
weather, vacation, labor-management disputes, or personal
reasons.
People are classified as unemployed if they meet all of
the following criteria: they had no employment during the
reference week; they were available for work at that time;
and they made specific efforts to find employment sometime
during the 4-week period ending with the reference week.
Persons laid off from a job and expecting recall need not be
looking for work to be counted as unemployed. The
unemployment data derived from the household survey in no
way depend upon the eligibility for or receipt of
unemployment insurance benefits.
The civilian labor force is the sum of employed and
unemployed persons. Those persons not classified as
employed or unemployed are not in the labor force. The
unemployment rate is the number unemployed as a percent
of the labor force. The labor force participation rate is the
labor force as a percent of the population, and
the employment-population ratio is the employed as a
percent of the population. Additional information
about the household survey can be found at
https://www.bls.gov/cps/documentation.htm.
Establishment survey. The sample establishments are
drawn from private nonfarm businesses such as factories,
offices, and stores, as well as from federal, state, and local
government entities. Employees on nonfarm payrolls are
those who received pay for any part of the reference pay
period, including persons on paid leave. Persons are counted
in each job they hold. Hours and earnings data are produced
for the private sector for all employees and for production
and nonsupervisory employees. Production and
nonsupervisory employees are defined as production and
related employees in manufacturing and mining and logging,
construction workers in construction, and non-supervisory
employees in private service-providing industries.
Industries are classified on the basis of an
establishment’s principal activity in accordance with the
2017 version of the North American Industry Classification
System. Additional information about the establishment
survey can be found at https://www.bls.gov/ces/.
Differences in employment estimates. The numerous
conceptual and methodological differences between the
household and establishment surveys result in important
distinctions in the employment estimates derived from the
surveys. Among these are:
• The household survey includes agricultural
workers, self-employed workers whose businesses
are unincorporated, unpaid family workers, and
private household workers among the employed.
These groups are excluded from the establishment
survey.
• The household survey includes people on unpaid
leave among the employed. The establishment
survey does not.
• The household survey is limited to workers 16 years
of age and older. The establishment survey is not
limited by age.
• The household survey has no duplication of
individuals, because individuals are counted only
once, even if they hold more than one job. In the
establishment survey, employees working at more
than one job and thus appearing on more than one
payroll are counted separately for each appearance.

Seasonal adjustment
Over the course of a year, the size of the nation's labor
force and the levels of employment and unemployment
undergo regularly occurring fluctuations. These events may
result from seasonal changes in weather, major holidays, and
the opening and closing of schools. The effect of such
seasonal variation can be very large.
Because these seasonal events follow a more or less
regular pattern each year, their influence on the level of a
series can be tempered by adjusting for regular seasonal
variation. These adjustments make nonseasonal
developments, such as declines in employment or increases
in the participation of women in the labor force, easier to
spot. For example, in the household survey, the large number
of youth entering the labor force each June is likely to
obscure any other changes that have taken place relative to
May, making it difficult to determine if the level of economic
activity has risen or declined. Similarly, in the establishment
survey, payroll employment in education declines by about
20 percent at the end of the spring term and later rises with
the start of the fall term, obscuring the underlying
employment trends in the industry. Because seasonal
employment changes at the end and beginning of the school
year can be estimated, the statistics can be adjusted to make
underlying employment patterns more discernable. The
seasonally adjusted figures provide a more useful tool with
which to analyze changes in month-to-month economic
activity.
Many seasonally adjusted series are independently
adjusted in both the household and establishment surveys.
However, the adjusted series for many major estimates, such
as total payroll employment, employment in most major
sectors, total employment, and unemployment are computed
by aggregating independently adjusted component series.
For example, total unemployment is derived by summing the
adjusted series for four major age-sex components; this
differs from the unemployment estimate that would be
obtained by directly adjusting the total or by combining the
duration, reasons, or more detailed age categories.
For both the household and establishment surveys, a
concurrent seasonal adjustment methodology is used in
which new seasonal factors are calculated each month using
all relevant data, up to and including the data for the current
month. In the household survey, new seasonal factors are
used to adjust only the current month's data. In the
establishment survey, however, new seasonal factors are
used each month to adjust the three most recent monthly
estimates. The prior 2 months are routinely revised to
incorporate additional sample reports and recalculated
seasonal adjustment factors. In both surveys, 5-year
revisions to historical data are made once a year.
Reliability of the estimates
Statistics based on the household and establishment
surveys are subject to both sampling and nonsampling error.
When a sample, rather than the entire population, is
surveyed, there is a chance that the sample estimates may
differ from the true population values they represent. The
component of this difference that occurs because samples
differ by chance is known as sampling error, and its
variability is measured by the standard error of the estimate.
There is about a 90-percent chance, or level of confidence,
that an estimate based on a sample will differ by no more
than 1.6 standard errors from the true population value
because of sampling error. BLS analyses are generally
conducted at the 90-percent level of confidence.
For example, the confidence interval for the monthly
change in total nonfarm employment from the establishment
survey is on the order of plus or minus 115,000. Suppose the
estimate of nonfarm employment increases by 50,000 from
one month to the next. The 90-percent confidence interval on
the monthly change would range from -65,000 to +165,000
(50,000 +/- 115,000). These figures do not mean that the
sample results are off by these magnitudes, but rather that
there is about a 90-percent chance that the true over-themonth
change lies within this interval. Since this range
includes values of less than zero, we could not say with
confidence that nonfarm employment had, in fact, increased
that month. If, however, the reported nonfarm employment
rise was 250,000, then all of the values within the 90-percent
confidence interval would be greater than zero. In this case,
it is likely (at least a 90-percent chance) that nonfarm
employment had, in fact, risen that month. At an
unemployment rate of around 6.0 percent, the 90-percent
confidence interval for the monthly change in unemployment
as measured by the household survey is about +/- 300,000,
and for the monthly change in the unemployment rate it is
about +/- 0.2 percentage point.
In general, estimates involving many individuals or
establishments have lower standard errors (relative to the
size of the estimate) than estimates which are based on a
small number of observations. The precision of estimates
also is improved when the data are cumulated over time, such
as for quarterly and annual averages.
The household and establishment surveys are also
affected by nonsampling error, which can occur for many
reasons, including the failure to sample a segment of the
population, inability to obtain information for all respondents
in the sample, inability or unwillingness of respondents to
provide correct information on a timely basis, mistakes made
by respondents, and errors made in the collection or
processing of the data.
For example, in the establishment survey, estimates for
the most recent 2 months are based on incomplete returns;
for this reason, these estimates are labeled preliminary in the
tables. It is only after two successive revisions to a monthly
estimate, when nearly all sample reports have been received,
that the estimate is considered final.
Another major source of nonsampling error in the
establishment survey is the inability to capture, on a timely
basis, employment generated by new firms. To correct for
this systematic underestimation of employment growth, an
estimation procedure with two components is used to
account for business births. The first component excludes
employment losses from business deaths from sample-based

estimation in order to offset the missing employment gains
from business births. This is incorporated into the samplebased
estimation procedure by simply not reflecting sample
units going out of business, but imputing to them the same
employment trend as the other firms in the sample. This
procedure accounts for most of the net birth/death
employment.
The second component is an ARIMA time series model
designed to estimate the residual net birth/death employment
not accounted for by the imputation. The historical time
series used to create and test the ARIMA model was derived
from the unemployment insurance universe micro-level
database, and reflects the actual residual net of births and
deaths over the past 5 years.
The sample-based estimates from the establishment
survey are adjusted once a year (on a lagged basis) to
universe counts of payroll employment obtained from
administrative records of the unemployment insurance
program. The difference between the March sample-based
employment estimates and the March universe counts is
known as a benchmark revision, and serves as a rough proxy
for total survey error. The new benchmarks also incorporate
changes in the classification of industries. Over the past
decade, absolute benchmark revisions for total nonfarm
employment have averaged 0.3 percent, with a range from
-0.7 percent to 0.6 percent.
Other information
Information in this release will be made available to
sensory impaired individuals upon request. Voice phone:
(202) 691-5200; Federal Relay Service: (800) 877-8339.

HOUSEHOLD DATA
Table A-1. Employment status of the civilian population by sex and age
[Numbers in thousands]
Employment status, sex, and age
Oct.
2017
Not seasonally adjusted Seasonally adjusted 1
Sept.
2018
Oct.
2018
Oct.
2017
June
2018
July
2018
Aug.
2018
Sept.
2018
Oct.
2018
TOTAL
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 255,766 258,290 258,514 255,766 257,642 257,843 258,066 258,290 258,514
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160,465 161,958 162,723 160,371 162,140 162,245 161,776 161,926 162,637
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.7 62.7 62.9 62.7 62.9 62.9 62.7 62.7 62.9
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154,223 156,191 156,952 153,846 155,576 155,965 155,542 155,962 156,562
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 60.3 60.5 60.7 60.2 60.4 60.5 60.3 60.4 60.6
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,242 5,766 5,771 6,524 6,564 6,280 6,234 5,964 6,075
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9 3.6 3.5 4.1 4.0 3.9 3.9 3.7 3.7
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95,301 96,332 95,792 95,395 95,502 95,598 96,290 96,364 95,877
Persons who currently want a job. . . . . . . . . . . . . . . . . . 4,938 5,070 5,048 5,232 5,258 5,163 5,389 5,237 5,309
Men, 16 years and over
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 123,617 124,928 125,041 123,617 124,604 124,704 124,816 124,928 125,041
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85,236 85,815 86,081 85,247 86,056 85,950 85,854 85,856 86,146
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69.0 68.7 68.8 69.0 69.1 68.9 68.8 68.7 68.9
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81,875 82,814 83,052 81,667 82,522 82,684 82,545 82,645 82,903
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 66.2 66.3 66.4 66.1 66.2 66.3 66.1 66.2 66.3
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,362 3,002 3,029 3,580 3,534 3,266 3,309 3,211 3,243
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9 3.5 3.5 4.2 4.1 3.8 3.9 3.7 3.8
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38,380 39,113 38,960 38,370 38,548 38,754 38,962 39,072 38,895
Men, 20 years and over
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 115,120 116,437 116,546 115,120 116,115 116,220 116,328 116,437 116,546
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82,455 83,115 83,332 82,366 83,115 83,058 83,030 83,063 83,286
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.6 71.4 71.5 71.5 71.6 71.5 71.4 71.3 71.5
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79,530 80,458 80,637 79,248 80,013 80,240 80,134 80,225 80,405
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 69.1 69.1 69.2 68.8 68.9 69.0 68.9 68.9 69.0
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,925 2,658 2,694 3,118 3,102 2,818 2,895 2,837 2,881
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 3.2 3.2 3.8 3.7 3.4 3.5 3.4 3.5
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32,665 33,322 33,215 32,755 33,001 33,162 33,298 33,374 33,260
Women, 16 years and over
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 132,149 133,362 133,474 132,149 133,038 133,139 133,250 133,362 133,474
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75,228 76,142 76,642 75,124 76,084 76,295 75,922 76,070 76,491
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56.9 57.1 57.4 56.8 57.2 57.3 57.0 57.0 57.3
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72,348 73,378 73,900 72,179 73,054 73,281 72,997 73,317 73,659
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 54.7 55.0 55.4 54.6 54.9 55.0 54.8 55.0 55.2
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,880 2,765 2,742 2,945 3,030 3,013 2,925 2,753 2,832
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 3.6 3.6 3.9 4.0 3.9 3.9 3.6 3.7
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56,921 57,220 56,832 57,026 56,954 56,844 57,328 57,292 56,983
Women, 20 years and over
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 123,882 125,091 125,200 123,882 124,771 124,875 124,983 125,091 125,200
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72,443 73,280 73,683 72,187 73,139 73,285 73,154 73,039 73,391
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.5 58.6 58.9 58.3 58.6 58.7 58.5 58.4 58.6
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69,872 70,858 71,270 69,576 70,419 70,598 70,529 70,656 70,909
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 56.4 56.6 56.9 56.2 56.4 56.5 56.4 56.5 56.6
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,571 2,422 2,412 2,611 2,720 2,687 2,625 2,383 2,482
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 3.3 3.3 3.6 3.7 3.7 3.6 3.3 3.4
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51,440 51,811 51,517 51,696 51,633 51,590 51,829 52,052 51,809
Both sexes, 16 to 19 years
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 16,763 16,762 16,768 16,763 16,755 16,748 16,755 16,762 16,768
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,567 5,562 5,708 5,818 5,886 5,902 5,592 5,824 5,960
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.2 33.2 34.0 34.7 35.1 35.2 33.4 34.7 35.5
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,821 4,876 5,045 5,022 5,144 5,127 4,879 5,081 5,248
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 28.8 29.1 30.1 30.0 30.7 30.6 29.1 30.3 31.3
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 746 686 664 796 743 775 714 743 712
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4 12.3 11.6 13.7 12.6 13.1 12.8 12.8 11.9
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11,196 11,199 11,060 10,945 10,869 10,846 11,163 10,938 10,808
1 The population figures are not adjusted for seasonal variation; therefore, identical numbers appear in the unadjusted and seasonally adjusted columns.
NOTE: Updated population controls are introduced annually with the release of January data.

HOUSEHOLD DATA
Table A-2. Employment status of the civilian population by race, sex, and age
[Numbers in thousands]
Employment status, race, sex, and age
Oct.
2017
Not seasonally adjusted Seasonally adjusted 1
Sept.
2018
Oct.
2018
Oct.
2017
June
2018
July
2018
Aug.
2018
Sept.
2018
Oct.
2018
WHITE
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 199,298 200,476 200,596 199,298 200,132 200,236 200,356 200,476 200,596
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124,777 125,413 126,018 124,757 125,784 125,720 125,306 125,483 126,027
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.6 62.6 62.8 62.6 62.9 62.8 62.5 62.6 62.8
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120,692 121,500 122,170 120,400 121,347 121,506 121,027 121,398 121,904
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 60.6 60.6 60.9 60.4 60.6 60.7 60.4 60.6 60.8
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,085 3,913 3,848 4,356 4,437 4,214 4,279 4,085 4,123
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 3.1 3.1 3.5 3.5 3.4 3.4 3.3 3.3
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74,520 75,063 74,578 74,541 74,348 74,517 75,049 74,993 74,569
Men, 20 years and over
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65,318 65,625 65,737 65,244 65,855 65,614 65,505 65,611 65,724
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.7 71.5 71.6 71.6 71.9 71.6 71.4 71.5 71.5
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63,380 63,796 63,933 63,155 63,695 63,690 63,486 63,629 63,760
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 69.6 69.5 69.6 69.3 69.5 69.5 69.2 69.3 69.4
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,937 1,829 1,803 2,090 2,159 1,924 2,019 1,982 1,964
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.0 2.8 2.7 3.2 3.3 2.9 3.1 3.0 3.0
Women, 20 years and over
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55,149 55,404 55,941 55,014 55,420 55,534 55,351 55,297 55,752
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57.5 57.5 58.0 57.4 57.6 57.7 57.5 57.4 57.8
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53,515 53,811 54,325 53,298 53,608 53,746 53,592 53,722 54,067
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 55.8 55.8 56.3 55.6 55.7 55.8 55.6 55.7 56.1
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,635 1,594 1,615 1,715 1,812 1,787 1,759 1,575 1,685
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.0 2.9 2.9 3.1 3.3 3.2 3.2 2.8 3.0
Both sexes, 16 to 19 years
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,310 4,383 4,341 4,499 4,510 4,572 4,450 4,574 4,552
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.9 35.6 35.3 36.5 36.6 37.2 36.2 37.2 37.0
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,798 3,893 3,911 3,947 4,044 4,069 3,949 4,046 4,078
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 30.8 31.6 31.8 32.0 32.8 33.1 32.1 32.9 33.1
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 490 429 552 466 502 501 528 474
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.9 11.2 9.9 12.3 10.3 11.0 11.3 11.5 10.4
BLACK OR AFRICAN AMERICAN
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 32,370 32,848 32,887 32,370 32,737 32,771 32,810 32,848 32,887
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20,264 20,484 20,706 20,134 20,364 20,495 20,404 20,513 20,590
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.6 62.4 63.0 62.2 62.2 62.5 62.2 62.4 62.6
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18,744 19,295 19,397 18,654 19,045 19,144 19,114 19,272 19,310
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 57.9 58.7 59.0 57.6 58.2 58.4 58.3 58.7 58.7
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,520 1,189 1,309 1,479 1,319 1,351 1,289 1,240 1,280
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 5.8 6.3 7.3 6.5 6.6 6.3 6.0 6.2
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12,106 12,365 12,181 12,236 12,373 12,276 12,406 12,336 12,297
Men, 20 years and over
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,276 9,369 9,477 9,218 9,162 9,320 9,407 9,359 9,426
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68.6 68.0 68.7 68.2 66.8 67.9 68.4 68.0 68.4
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8,580 8,842 8,862 8,539 8,573 8,751 8,841 8,813 8,836
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 63.4 64.2 64.3 63.1 62.5 63.7 64.3 64.0 64.1
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696 526 615 679 589 569 566 546 590
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 5.6 6.5 7.4 6.4 6.1 6.0 5.8 6.3
Women, 20 years and over
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,291 10,421 10,415 10,204 10,406 10,426 10,361 10,427 10,337
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.0 62.8 62.7 62.5 63.0 63.0 62.5 62.9 62.3
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,609 9,892 9,892 9,560 9,838 9,793 9,766 9,874 9,834
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 58.8 59.6 59.6 58.5 59.5 59.2 59.0 59.5 59.2
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 682 529 523 644 568 633 595 554 503
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 5.1 5.0 6.3 5.5 6.1 5.7 5.3 4.9
Both sexes, 16 to 19 years
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697 694 815 712 796 750 635 726 827
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.8 27.8 32.7 28.4 31.9 30.0 25.5 29.1 33.2
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 560 643 555 635 600 508 585 640
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 22.1 22.5 25.8 22.1 25.4 24.1 20.3 23.5 25.7
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 134 172 157 162 149 128 140 187
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.4 19.3 21.1 22.0 20.3 19.9 20.1 19.3 22.6
See footnotes at end of table.

HOUSEHOLD DATA
Table A-2. Employment status of the civilian population by race, sex, and age — Continued
[Numbers in thousands]
Employment status, race, sex, and age
Oct.
2017
Not seasonally adjusted Seasonally adjusted 1
Sept.
2018
Oct.
2018
ASIAN
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . 15,466 16,011 16,030 15,466 15,934 15,922 16,093 16,011 16,030
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,794 10,289 10,213 9,864 10,140 10,153 10,259 10,300 10,284
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3 64.3 63.7 63.8 63.6 63.8 63.7 64.3 64.2
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,507 9,933 9,899 9,565 9,817 9,838 9,950 9,938 9,959
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . 61.5 62.0 61.8 61.8 61.6 61.8 61.8 62.1 62.1
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 356 314 299 322 314 309 362 324
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 3.5 3.1 3.0 3.2 3.1 3.0 3.5 3.2
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,671 5,722 5,817 5,602 5,794 5,769 5,834 5,712 5,746
Oct.
2017
June
2018
July
2018
Aug.
2018
Sept.
2018
Oct.
2018
1 The population figures are not adjusted for seasonal variation; therefore, identical numbers appear in the unadjusted and seasonally adjusted columns.
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
introduced annually with the release of January data.

HOUSEHOLD DATA
Table A-3. Employment status of the Hispanic or Latino population by sex and age
[Numbers in thousands]
Employment status, sex, and age
Not seasonally adjusted Seasonally adjusted 1
Oct.
2017
Sept.
2018
Oct.
2018
Oct.
2017
June
2018
July
2018
Aug.
2018
Sept.
2018
Oct.
2018
HISPANIC OR LATINO ETHNICITY
Civilian noninstitutional population................ 41,665 42,959 43,054 41,665 42,679 42,767 42,863 42,959 43,054
Civilian labor force................................ 27,328 28,316 28,512 27,319 28,369 28,495 28,242 28,346 28,500
Participation rate............................... 65.6 65.9 66.2 65.6 66.5 66.6 65.9 66.0 66.2
Employed....................................... 26,077 27,105 27,343 25,999 27,077 27,223 26,927 27,059 27,252
Employment-population ratio............... 62.6 63.1 63.5 62.4 63.4 63.7 62.8 63.0 63.3
Unemployed.................................... 1,250 1,211 1,169 1,321 1,292 1,273 1,315 1,287 1,248
Unemployment rate......................... 4.6 4.3 4.1 4.8 4.6 4.5 4.7 4.5 4.4
Not in labor force................................. 14,337 14,642 14,542 14,346 14,310 14,272 14,621 14,613 14,554
Men, 20 years and over
Civilian labor force................................ 14,987 15,414 15,442 14,959 15,557 15,519 15,421 15,416 15,440
Participation rate............................... 79.9 79.5 79.4 79.7 80.8 80.4 79.7 79.5 79.4
Employed....................................... 14,459 14,888 14,898 14,380 14,961 15,017 14,849 14,822 14,844
Employment-population ratio............... 77.1 76.8 76.6 76.7 77.7 77.8 76.7 76.4 76.4
Unemployed.................................... 528 526 544 579 596 502 572 594 596
Unemployment rate......................... 3.5 3.4 3.5 3.9 3.8 3.2 3.7 3.9 3.9
Women, 20 years and over
Civilian labor force................................ 11,158 11,682 11,778 11,146 11,626 11,684 11,593 11,673 11,756
Participation rate............................... 58.6 59.6 59.9 58.5 59.7 59.8 59.2 59.5 59.8
Employed....................................... 10,639 11,175 11,305 10,601 11,065 11,131 11,009 11,172 11,255
Employment-population ratio............... 55.9 57.0 57.5 55.7 56.8 57.0 56.3 57.0 57.3
Unemployed.................................... 519 507 472 546 561 553 584 501 501
Unemployment rate......................... 4.6 4.3 4.0 4.9 4.8 4.7 5.0 4.3 4.3
Both sexes, 16 to 19 years
Civilian labor force................................ 1,184 1,220 1,293 1,214 1,186 1,292 1,228 1,257 1,304
Participation rate............................... 30.6 30.9 32.6 31.4 30.2 32.8 31.1 31.8 32.9
Employed....................................... 980 1,042 1,140 1,018 1,051 1,075 1,068 1,065 1,153
Employment-population ratio............... 25.3 26.3 28.8 26.3 26.7 27.3 27.1 26.9 29.1
Unemployed.................................... 204 179 152 196 135 218 160 192 151
Unemployment rate......................... 17.2 14.6 11.8 16.1 11.4 16.8 13.0 15.3 11.6
1 The population figures are not adjusted for seasonal variation; therefore, identical numbers appear in the unadjusted and seasonally adjusted
columns.
NOTE: Persons whose ethnicity is identified as Hispanic or Latino may be of any race. Updated population controls are introduced annually with the
release of January data.

HOUSEHOLD DATA
Table A-4. Employment status of the civilian population 25 years and over by educational attainment
[Numbers in thousands]
Educational attainment
Not seasonally adjusted
Oct.
2017
Sept.
2018
Oct.
2018
Oct.
2017
June
2018
Seasonally adjusted
July
2018
Aug.
2018
Sept.
2018
Oct.
2018
Less than a high school diploma
Civilian labor force.................................. 10,141 10,273 10,078 10,328 10,508 10,212 10,311 10,189 10,262
Participation rate.................................. 45.3 46.4 46.9 46.2 45.9 46.9 46.3 46.0 47.7
Employed.......................................... 9,624 9,773 9,567 9,699 9,926 9,695 9,728 9,626 9,651
Employment-population ratio................. 43.0 44.2 44.5 43.4 43.4 44.5 43.7 43.5 44.9
Unemployed....................................... 516 499 510 629 582 517 583 563 611
Unemployment rate............................ 5.1 4.9 5.1 6.1 5.5 5.1 5.7 5.5 6.0
High school graduates, no college 1
Civilian labor force.................................. 35,788 36,540 36,254 35,572 36,050 36,534 36,121 36,224 36,092
Participation rate.................................. 57.2 58.0 57.8 56.9 57.9 57.9 57.3 57.5 57.6
Employed.......................................... 34,358 35,268 34,879 34,050 34,549 35,056 34,699 34,873 34,638
Employment-population ratio................. 54.9 56.0 55.6 54.4 55.5 55.5 55.0 55.3 55.3
Unemployed....................................... 1,431 1,272 1,375 1,522 1,501 1,478 1,422 1,351 1,454
Unemployment rate............................ 4.0 3.5 3.8 4.3 4.2 4.0 3.9 3.7 4.0
Some college or associate degree
Civilian labor force.................................. 37,987 37,364 37,808 37,761 37,863 37,531 37,300 37,423 37,598
Participation rate.................................. 66.2 65.2 65.7 65.8 65.3 65.4 65.6 65.3 65.3
Employed.......................................... 36,636 36,204 36,706 36,385 36,602 36,340 35,987 36,239 36,462
Employment-population ratio................. 63.8 63.1 63.8 63.4 63.2 63.3 63.3 63.2 63.4
Unemployed....................................... 1,351 1,160 1,102 1,376 1,261 1,191 1,313 1,184 1,136
Unemployment rate............................ 3.6 3.1 2.9 3.6 3.3 3.2 3.5 3.2 3.0
Bachelor’s degree and higher 2
Civilian labor force.................................. 55,728 57,279 58,022 55,612 56,613 56,940 57,638 57,258 57,856
Participation rate.................................. 73.9 73.6 73.6 73.8 74.0 73.4 74.0 73.6 73.4
Employed.......................................... 54,604 56,160 56,890 54,477 55,296 55,672 56,452 56,124 56,700
Employment-population ratio................. 72.4 72.2 72.2 72.3 72.2 71.8 72.5 72.1 71.9
Unemployed....................................... 1,124 1,120 1,132 1,135 1,317 1,268 1,186 1,134 1,156
Unemployment rate............................ 2.0 2.0 2.0 2.0 2.3 2.2 2.1 2.0 2.0
1 Includes persons with a high school diploma or equivalent.
2 Includes persons with bachelor’s, master’s, professional, and doctoral degrees.
NOTE: Updated population controls are introduced annually with the release of January data.

HOUSEHOLD DATA
Table A-5. Employment status of the civilian population 18 years and over by veteran status, period of service,
and sex, not seasonally adjusted
[Numbers in thousands]
Employment status, veteran status, and period of service
Oct.
2017
Total Men Women
VETERANS, 18 years and over
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20,493 19,090 18,457 17,218 2,036 1,872
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,184 9,369 8,958 8,255 1,227 1,114
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.7 49.1 48.5 47.9 60.2 59.5
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,906 9,100 8,705 8,019 1,201 1,081
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.3 47.7 47.2 46.6 59.0 57.7
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 269 253 236 26 33
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 2.9 2.8 2.9 2.1 3.0
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,309 9,721 9,499 8,963 809 758
Gulf War-era II veterans
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,161 4,141 3,421 3,405 740 736
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,316 3,384 2,816 2,861 500 523
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79.7 81.7 82.3 84.0 67.5 71.1
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,196 3,279 2,713 2,770 483 509
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76.8 79.2 79.3 81.3 65.3 69.2
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 105 104 91 16 14
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 3.1 3.7 3.2 3.3 2.7
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 845 757 605 545 241 213
Gulf War-era I veterans
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,365 3,147 2,863 2,677 502 470
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,618 2,450 2,253 2,109 365 341
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77.8 77.9 78.7 78.8 72.7 72.6
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,570 2,383 2,214 2,047 356 336
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76.4 75.7 77.3 76.5 70.9 71.4
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 67 40 61 9 5
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9 2.7 1.8 2.9 2.5 1.6
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 746 697 610 568 137 129
World War II, Korean War, and Vietnam-era veterans
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,964 7,502 7,676 7,246 288 256
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,779 1,545 1,708 1,491 71 54
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.3 20.6 22.3 20.6 24.6 21.2
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,730 1,498 1,659 1,447 71 52
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.7 20.0 21.6 20.0 24.6 20.2
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 47 50 44 0 3
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 3.0 2.9 3.0 – –
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,185 5,957 5,968 5,755 217 202
Veterans of other service periods
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,003 4,300 4,497 3,890 506 410
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,471 1,990 2,179 1,795 291 195
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.4 46.3 48.5 46.1 57.5 47.6
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,411 1,940 2,120 1,755 291 184
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.2 45.1 47.2 45.1 57.5 45.0
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 50 59 39 0 11
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 2.5 2.7 2.2 0.1 5.6
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,532 2,310 2,318 2,095 215 215
NONVETERANS, 18 years and over
Civilian noninstitutional population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225,807 230,542 100,389 103,266 125,418 127,276
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148,074 151,214 75,253 76,864 72,821 74,351
Participation rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65.6 65.6 75.0 74.4 58.1 58.4
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142,414 145,912 72,312 74,170 70,101 71,743
Employment-population ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.1 63.3 72.0 71.8 55.9 56.4
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,660 5,302 2,941 2,694 2,719 2,608
Unemployment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 3.5 3.9 3.5 3.7 3.5
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77,734 79,328 25,136 26,402 52,598 52,925
Oct.
2018
Oct.
2017
Oct.
2018
Oct.
2017
Oct.
2018
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
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
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
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
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
shown where base is less than 75,000).

HOUSEHOLD DATA
Table A-6. Employment status of the civilian population by sex, age, and disability status, not seasonally
adjusted
[Numbers in thousands]
Employment status, sex, and age
Persons with a disability
Oct.
2017
Oct.
2018
Persons with no disability
TOTAL, 16 years and over
Civilian noninstitutional population...................................................... 30,255 30,145 225,511 228,370
Civilian labor force..................................................................... 6,360 6,468 154,105 156,255
Participation rate..................................................................... 21.0 21.5 68.3 68.4
Employed............................................................................. 5,877 5,987 148,346 150,965
Employment-population ratio.................................................... 19.4 19.9 65.8 66.1
Unemployed.......................................................................... 482 480 5,760 5,290
Unemployment rate............................................................... 7.6 7.4 3.7 3.4
Not in labor force....................................................................... 23,895 23,677 71,406 72,114
Men, 16 to 64 years
Civilian labor force..................................................................... 2,805 2,740 77,085 77,736
Participation rate..................................................................... 36.5 36.4 82.4 82.4
Employed............................................................................. 2,588 2,520 74,130 75,067
Employment-population ratio.................................................... 33.7 33.5 79.2 79.6
Unemployed.......................................................................... 217 221 2,955 2,669
Unemployment rate............................................................... 7.8 8.1 3.8 3.4
Not in labor force....................................................................... 4,872 4,790 16,478 16,560
Women, 16 to 64 years
Civilian labor force..................................................................... 2,347 2,511 68,608 69,508
Participation rate..................................................................... 30.2 32.0 71.0 71.6
Employed............................................................................. 2,133 2,304 66,080 67,136
Employment-population ratio.................................................... 27.4 29.4 68.3 69.2
Unemployed.......................................................................... 214 207 2,528 2,372
Unemployment rate............................................................... 9.1 8.2 3.7 3.4
Not in labor force....................................................................... 5,433 5,329 28,086 27,560
Both sexes, 65 years and over
Civilian labor force..................................................................... 1,208 1,216 8,412 9,011
Participation rate..................................................................... 8.2 8.2 23.9 24.4
Employed............................................................................. 1,156 1,163 8,136 8,762
Employment-population ratio.................................................... 7.8 7.9 23.1 23.7
Unemployed.......................................................................... 51 53 276 249
Unemployment rate............................................................... 4.2 4.3 3.3 2.8
Not in labor force....................................................................... 13,590 13,559 26,841 27,994
Oct.
2017
Oct.
2018
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
seeing even when wearing glasses; has serious difficulty concentrating, remembering, or making decisions because of a physical, mental, or
emotional condition; has serious difficulty walking or climbing stairs; has difficulty dressing or bathing; or has difficulty doing errands alone such as
visiting a doctor’s office or shopping because of a physical, mental, or emotional condition. Updated population controls are introduced annually with
the release of January data.

HOUSEHOLD DATA
Table A-7. Employment status of the civilian population by nativity and sex, not seasonally adjusted
[Numbers in thousands]
Employment status and nativity
Oct.
2017
Total Men Women
Foreign born, 16 years and over
Civilian noninstitutional population................................... 41,668 43,051 20,191 20,795 21,476 22,256
Civilian labor force.................................................. 27,374 28,410 15,774 16,198 11,600 12,212
Participation rate.................................................. 65.7 66.0 78.1 77.9 54.0 54.9
Employed.......................................................... 26,343 27,533 15,296 15,763 11,046 11,770
Employment-population ratio................................. 63.2 64.0 75.8 75.8 51.4 52.9
Unemployed....................................................... 1,031 877 477 436 554 441
Unemployment rate............................................ 3.8 3.1 3.0 2.7 4.8 3.6
Not in labor force.................................................... 14,294 14,641 4,417 4,597 9,877 10,044
Native born, 16 years and over
Civilian noninstitutional population................................... 214,099 215,463 103,426 104,245 110,673 111,218
Civilian labor force.................................................. 133,091 134,313 69,463 69,882 63,629 64,431
Participation rate.................................................. 62.2 62.3 67.2 67.0 57.5 57.9
Employed.......................................................... 127,880 129,419 66,578 67,290 61,302 62,130
Employment-population ratio................................. 59.7 60.1 64.4 64.5 55.4 55.9
Unemployed....................................................... 5,211 4,894 2,884 2,593 2,327 2,301
Unemployment rate............................................ 3.9 3.6 4.2 3.7 3.7 3.6
Not in labor force.................................................... 81,007 81,150 33,963 34,363 47,044 46,787
Oct.
2018
Oct.
2017
Oct.
2018
Oct.
2017
Oct.
2018
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
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
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.
Updated population controls are introduced annually with the release of January data.

HOUSEHOLD DATA
Table A-8. Employed persons by class of worker and part-time status
[In thousands]
Category
Not seasonally adjusted
Oct.
2017
Sept.
2018
Oct.
2018
Oct.
2017
June
2018
Seasonally adjusted
July
2018
Aug.
2018
Sept.
2018
Oct.
2018
CLASS OF WORKER
Agriculture and related industries................. 2,559 2,555 2,484 2,471 2,350 2,498 2,345 2,474 2,406
Wage and salary workers 1 ...................... 1,814 1,726 1,771 1,707 1,567 1,658 1,528 1,640 1,658
Self-employed workers, unincorporated. . . . . .. 721 806 692 749 736 783 772 812 731
Unpaid family workers........................... 24 23 21 – – – – – –
Nonagricultural industries.......................... 151,664 153,636 154,468 151,334 153,309 153,473 153,262 153,474 154,152
Wage and salary workers 1 ...................... 142,564 144,570 145,325 142,294 144,524 144,447 144,276 144,389 145,109
Government..................................... 20,753 20,674 21,187 20,755 20,986 20,900 20,791 20,743 21,212
Private industries............................... 121,811 123,896 124,139 121,578 123,478 123,541 123,513 123,634 123,968
Private households.......................... 571 741 769 – – – – – –
Other industries.............................. 121,240 123,154 123,370 121,012 122,757 122,772 122,749 122,842 123,167
Self-employed workers, unincorporated. . . . . .. 9,067 8,970 9,055 8,956 8,728 8,880 8,861 8,959 8,943
Unpaid family workers........................... 33 96 88 – – – – – –
PERSONS AT WORK PART TIME 2
All industries
Part time for economic reasons 3 .................. 4,553 4,306 4,246 4,880 4,743 4,567 4,379 4,642 4,621
Slack work or business conditions............. 2,762 2,606 2,600 2,960 3,042 2,877 2,551 2,782 2,816
Could only find part-time work.................. 1,609 1,464 1,433 1,615 1,447 1,431 1,365 1,447 1,436
Part time for noneconomic reasons 4 .............. 21,395 21,475 21,979 20,897 21,304 21,532 21,781 21,464 21,512
Nonagricultural industries
Part time for economic reasons 3 .................. 4,496 4,238 4,169 4,799 4,662 4,482 4,311 4,547 4,523
Slack work or business conditions............. 2,734 2,572 2,541 2,944 3,004 2,836 2,522 2,752 2,763
Could only find part-time work.................. 1,594 1,459 1,428 1,600 1,431 1,415 1,355 1,441 1,431
Part time for noneconomic reasons 4 .............. 21,034 21,077 21,616 20,552 20,941 21,177 21,448 21,057 21,143
1 Includes self-employed workers whose businesses are incorporated.
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
the entire week.
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
conditions, inability to find full-time work, or seasonal declines in demand.
4 Refers to persons who usually work part time for noneconomic reasons such as childcare problems, family or personal obligations, school or
training, retirement or Social Security limits on earnings, and other reasons. This excludes persons who usually work full time but worked only 1 to
34 hours during the reference week for reasons such as vacations, holidays, illness, and bad weather.
- Data not available.
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. Updated population controls are introduced annually with the release of January data.

HOUSEHOLD DATA
Table A-9. Selected employment indicators
[Numbers in thousands]
Characteristic
Oct.
2017
Not seasonally adjusted
Sept.
2018
Oct.
2018
Oct.
2017
June
2018
Seasonally adjusted
July
2018
Aug.
2018
Sept.
2018
Oct.
2018
AGE AND SEX
Total, 16 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154,223 156,191 156,952 153,846 155,576 155,965 155,542 155,962 156,562
16 to 19 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,821 4,876 5,045 5,022 5,144 5,127 4,879 5,081 5,248
16 to 17 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,903 1,826 1,940 1,932 1,797 1,815 1,770 1,766 1,956
18 to 19 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,918 3,050 3,105 3,070 3,344 3,315 3,110 3,293 3,293
20 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149,402 151,315 151,908 148,824 150,432 150,838 150,663 150,881 151,314
20 to 24 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14,180 13,910 13,865 14,183 14,046 14,128 13,841 14,026 13,902
25 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135,222 137,405 138,042 134,716 136,422 136,762 136,749 136,856 137,506
25 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99,584 100,763 101,289 99,227 100,204 100,417 100,276 100,316 100,903
25 to 34 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34,755 35,658 35,802 34,666 35,288 35,444 35,316 35,500 35,699
35 to 44 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32,254 32,661 33,017 32,094 32,566 32,690 32,636 32,489 32,853
45 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32,574 32,444 32,469 32,468 32,350 32,283 32,324 32,327 32,352
55 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35,638 36,642 36,753 35,489 36,218 36,346 36,473 36,540 36,602
Men, 16 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81,875 82,814 83,052 81,667 82,522 82,684 82,545 82,645 82,903
16 to 19 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,345 2,356 2,415 2,419 2,509 2,444 2,410 2,420 2,498
16 to 17 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 857 799 863 884 855 814 812 782 893
18 to 19 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,488 1,557 1,552 1,528 1,644 1,629 1,598 1,637 1,614
20 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79,530 80,458 80,637 79,248 80,013 80,240 80,134 80,225 80,405
20 to 24 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,248 7,107 7,031 7,280 7,191 7,149 6,990 7,162 7,087
25 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72,282 73,351 73,606 72,016 72,869 73,126 73,106 73,020 73,341
25 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53,242 53,808 53,957 53,082 53,588 53,673 53,599 53,569 53,796
25 to 34 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18,655 19,199 19,196 18,617 19,006 19,054 19,007 19,121 19,171
35 to 44 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17,414 17,594 17,704 17,320 17,598 17,691 17,642 17,509 17,619
45 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17,173 17,015 17,056 17,145 16,984 16,928 16,950 16,939 17,005
55 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19,040 19,542 19,650 18,934 19,281 19,453 19,507 19,451 19,546
Women, 16 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72,348 73,378 73,900 72,179 73,054 73,281 72,997 73,317 73,659
16 to 19 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,477 2,520 2,630 2,603 2,635 2,683 2,468 2,661 2,751
16 to 17 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,046 1,027 1,077 1,048 942 1,001 958 983 1,063
18 to 19 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,430 1,493 1,553 1,541 1,700 1,687 1,512 1,656 1,679
20 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69,872 70,858 71,270 69,576 70,419 70,598 70,529 70,656 70,909
20 to 24 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,932 6,803 6,835 6,903 6,855 6,979 6,851 6,864 6,815
25 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62,940 64,055 64,436 62,700 63,553 63,636 63,643 63,836 64,165
25 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46,342 46,954 47,332 46,146 46,616 46,744 46,677 46,747 47,108
25 to 34 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16,100 16,459 16,606 16,049 16,282 16,390 16,309 16,379 16,527
35 to 44 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14,840 15,067 15,313 14,774 14,969 14,998 14,994 14,979 15,234
45 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15,401 15,428 15,413 15,323 15,366 15,355 15,374 15,388 15,347
55 years and over. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16,598 17,100 17,104 16,555 16,937 16,892 16,966 17,089 17,057
MARITAL STATUS
Married men, spouse present 1 . . . . . . . . . . . . . . . . . . . . . . . . . . 45,886 46,205 46,354 45,776 45,689 45,751 45,858 45,966 46,228
Married women, spouse present 1 . . . . . . . . . . . . . . . . . . . . . . . 36,072 36,090 36,278 35,853 35,976 35,986 36,070 35,984 36,040
Women who maintain families 2 . . . . . . . . . . . . . . . . . . . . . . . . . 9,829 9,887 10,060 – – – – – –
FULL- OR PART-TIME STATUS
Full-time workers 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127,055 129,466 129,627 126,636 128,568 129,021 128,577 128,894 129,212
Part-time workers 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27,168 26,726 27,325 27,142 27,028 26,992 26,913 27,055 27,297
MULTIPLE JOBHOLDERS
Total multiple jobholders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,409 7,670 8,093 7,209 7,619 8,072 7,944 7,707 7,883
Percent of total employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 4.9 5.2 4.7 4.9 5.2 5.1 4.9 5.0
SELF-EMPLOYMENT
Self-employed workers, incorporated. . . . . . . . . . . . . . . . . . . 6,171 5,840 6,057 – – – – – –
Self-employed workers, unincorporated. . . . . . . . . . . . . . . . . 9,789 9,776 9,747 9,705 9,464 9,663 9,633 9,771 9,674
1 Refers to persons in opposite-sex married couples only.
2 Refers to female householders residing with one or more family members, but not an opposite-sex spouse.
3 Employed full-time workers are persons who usually work 35 hours or more per week.
4 Employed part-time workers are persons who usually work less than 35 hours per week.
- Data not available.
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.
Updated population controls are introduced annually with the release of January data.

HOUSEHOLD DATA
Table A-10. Selected unemployment indicators, seasonally adjusted
Characteristic
Oct.
2017
Number of
unemployed persons
(in thousands)
Sept.
2018
Oct.
2018
Oct.
2017
June
2018
Unemployment rates
July
2018
Aug.
2018
Sept.
2018
Oct.
2018
AGE AND SEX
Total, 16 years and over........................... 6,524 5,964 6,075 4.1 4.0 3.9 3.9 3.7 3.7
16 to 19 years..................................... 796 743 712 13.7 12.6 13.1 12.8 12.8 11.9
16 to 17 years.................................. 340 282 235 15.0 14.4 13.6 13.4 13.8 10.7
18 to 19 years.................................. 467 446 492 13.2 11.4 12.5 12.2 11.9 13.0
20 years and over................................ 5,728 5,221 5,363 3.7 3.7 3.5 3.5 3.3 3.4
20 to 24 years.................................. 1,095 1,036 1,010 7.2 7.4 6.9 6.8 6.9 6.8
25 years and over.............................. 4,620 4,210 4,335 3.3 3.3 3.2 3.2 3.0 3.1
25 to 54 years............................... 3,501 3,149 3,269 3.4 3.3 3.2 3.2 3.0 3.1
25 to 34 years............................. 1,593 1,299 1,350 4.4 3.9 3.5 4.1 3.5 3.6
35 to 44 years............................. 1,003 969 1,010 3.0 3.1 3.1 2.9 2.9 3.0
45 to 54 years............................. 905 882 908 2.7 3.0 2.9 2.6 2.7 2.7
55 years and over........................... 1,134 1,056 1,073 3.1 3.1 3.1 3.1 2.8 2.8
Men, 16 years and over............................ 3,580 3,211 3,243 4.2 4.1 3.8 3.9 3.7 3.8
16 to 19 years..................................... 462 373 362 16.0 14.7 15.5 14.7 13.4 12.7
16 to 17 years.................................. 186 134 114 17.4 16.5 16.1 15.6 14.6 11.3
18 to 19 years.................................. 284 241 251 15.7 13.2 14.5 14.1 12.8 13.5
20 years and over................................ 3,118 2,837 2,881 3.8 3.7 3.4 3.5 3.4 3.5
20 to 24 years.................................. 635 575 565 8.0 8.2 7.4 7.5 7.4 7.4
25 years and over.............................. 2,462 2,281 2,294 3.3 3.2 3.0 3.1 3.0 3.0
25 to 54 years............................... 1,825 1,707 1,761 3.3 3.2 3.0 3.1 3.1 3.2
25 to 34 years............................. 844 704 747 4.3 3.8 3.5 4.1 3.6 3.7
35 to 44 years............................. 499 539 554 2.8 2.9 2.6 2.6 3.0 3.0
45 to 54 years............................. 482 464 460 2.7 3.0 2.8 2.4 2.7 2.6
55 years and over........................... 638 574 533 3.3 3.2 3.1 3.0 2.9 2.7
Women, 16 years and over........................ 2,945 2,753 2,832 3.9 4.0 3.9 3.9 3.6 3.7
16 to 19 years..................................... 334 370 350 11.4 10.5 10.9 10.8 12.2 11.3
16 to 17 years.................................. 154 148 121 12.8 12.4 11.4 11.5 13.1 10.2
18 to 19 years.................................. 182 205 241 10.6 9.5 10.5 10.0 11.0 12.6
20 years and over................................ 2,611 2,383 2,482 3.6 3.7 3.7 3.6 3.3 3.4
20 to 24 years.................................. 460 461 445 6.3 6.6 6.3 6.1 6.3 6.1
25 years and over.............................. 2,157 1,930 2,041 3.3 3.4 3.4 3.3 2.9 3.1
25 to 54 years............................... 1,676 1,442 1,508 3.5 3.4 3.4 3.4 3.0 3.1
25 to 34 years............................. 749 595 604 4.5 4.0 3.6 4.1 3.5 3.5
35 to 44 years............................. 504 430 456 3.3 3.3 3.6 3.2 2.8 2.9
45 to 54 years............................. 423 418 448 2.7 2.9 3.0 2.7 2.6 2.8
55 years and over........................... 494 500 532 2.9 3.0 3.0 3.1 2.8 3.0
MARITAL STATUS
Married men, spouse present 1 .................... 954 898 897 2.0 2.1 2.0 2.0 1.9 1.9
Married women, spouse present 1 ................. 889 780 836 2.4 2.5 2.5 2.5 2.1 2.3
Women who maintain families 2 .................... 578 529 565 5.6 5.5 5.6 5.4 5.1 5.3
FULL- OR PART-TIME STATUS
Full-time workers 3 ................................... 5,246 4,662 4,838 4.0 4.0 3.8 3.7 3.5 3.6
Part-time workers 4 .................................. 1,275 1,267 1,241 4.5 4.3 4.4 4.3 4.5 4.3
1 Refers to persons in opposite-sex couples only.
2 Data are not seasonally adjusted. Refers to female householders residing with one or more family members, but not an opposite-sex spouse.
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
jobs.
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
part-time jobs.
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. Updated population controls are introduced annually with the release of January data.

HOUSEHOLD DATA
Table A-11. Unemployed persons by reason for unemployment
[Numbers in thousands]
Reason
Not seasonally adjusted
Oct.
2017
Sept.
2018
Oct.
2018
Oct.
2017
June
2018
Seasonally adjusted
July
2018
Aug.
2018
Sept.
2018
Oct.
2018
NUMBER OF UNEMPLOYED
Job losers and persons who completed
temporary jobs.................................... 2,859 2,474 2,510 3,214 3,065 3,017 2,875 2,796 2,850
On temporary layoff.............................. 561 507 507 862 906 890 872 820 793
Not on temporary layoff.......................... 2,298 1,967 2,003 2,352 2,159 2,127 2,003 1,975 2,057
Permanent job losers.......................... 1,648 1,238 1,317 1,688 1,486 1,455 1,345 1,248 1,355
Persons who completed temporary jobs. . .. 650 729 686 664 673 672 658 727 701
Job leavers........................................... 763 794 746 731 811 844 862 730 726
Reentrants............................................ 2,040 1,939 1,951 2,001 2,086 1,799 1,846 1,877 1,906
New entrants......................................... 580 559 564 626 578 591 584 586 606
PERCENT DISTRIBUTION
Job losers and persons who completed
temporary jobs.................................... 45.8 42.9 43.5 48.9 46.9 48.3 46.6 46.7 46.8
On temporary layoff.............................. 9.0 8.8 8.8 13.1 13.9 14.2 14.1 13.7 13.0
Not on temporary layoff.......................... 36.8 34.1 34.7 35.8 33.0 34.0 32.5 33.0 33.8
Job leavers........................................... 12.2 13.8 12.9 11.1 12.4 13.5 14.0 12.2 11.9
Reentrants............................................ 32.7 33.6 33.8 30.5 31.9 28.8 29.9 31.3 31.3
New entrants......................................... 9.3 9.7 9.8 9.5 8.8 9.5 9.5 9.8 10.0
UNEMPLOYED AS A PERCENT OF THE
CIVILIAN LABOR FORCE
Job losers and persons who completed
temporary jobs.................................... 1.8 1.5 1.5 2.0 1.9 1.9 1.8 1.7 1.8
Job leavers........................................... 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4
Reentrants............................................ 1.3 1.2 1.2 1.2 1.3 1.1 1.1 1.2 1.2
New entrants......................................... 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4
NOTE: Updated population controls are introduced annually with the release of January data.

HOUSEHOLD DATA
Table A-12. Unemployed persons by duration of unemployment
[Numbers in thousands]
Duration
Not seasonally adjusted
Oct.
2017
Sept.
2018
Oct.
2018
Oct.
2017
June
2018
Seasonally adjusted
July
2018
Aug.
2018
Sept.
2018
Oct.
2018
NUMBER OF UNEMPLOYED
Less than 5 weeks.................................. 1,958 2,043 1,866 2,128 2,227 2,091 2,208 2,065 2,057
5 to 14 weeks........................................ 1,839 1,566 1,734 1,943 1,882 1,820 1,720 1,720 1,821
15 weeks and over.................................. 2,445 2,157 2,171 2,500 2,314 2,406 2,255 2,245 2,229
15 to 26 weeks.................................... 847 790 824 856 836 971 923 861 856
27 weeks and over............................... 1,598 1,366 1,347 1,645 1,478 1,435 1,332 1,384 1,373
Average (mean) duration, in weeks............... 27.1 24.7 23.8 25.8 21.2 23.2 22.6 24.0 22.5
Median duration, in weeks......................... 10.1 9.4 9.9 9.8 8.9 9.5 9.1 9.2 9.4
PERCENT DISTRIBUTION
Less than 5 weeks.................................. 31.4 35.4 32.3 32.4 34.7 33.1 35.7 34.3 33.7
5 to 14 weeks........................................ 29.5 27.2 30.0 29.6 29.3 28.8 27.8 28.5 29.8
15 weeks and over.................................. 39.2 37.4 37.6 38.0 36.0 38.1 36.5 37.2 36.5
15 to 26 weeks.................................... 13.6 13.7 14.3 13.0 13.0 15.4 14.9 14.3 14.0
27 weeks and over............................... 25.6 23.7 23.3 25.0 23.0 22.7 21.5 22.9 22.5
NOTE: Updated population controls are introduced annually with the release of January data.

HOUSEHOLD DATA
Table A-13. Employed and unemployed persons by occupation, not seasonally adjusted
[Numbers in thousands]
Occupation
Oct.
2017
Employed
Oct.
2018
Oct.
2017
Unemployed
Oct.
2018
Unemployment
rates
Oct.
2017
Oct.
2018
Total, 16 years and over 1 ............................................. 154,223 156,952 6,242 5,771 3.9 3.5
Management, professional, and related occupations........... 61,062 62,929 1,285 1,246 2.1 1.9
Management, business, and financial operations
occupations..................................................... 25,620 26,165 498 535 1.9 2.0
Professional and related occupations.......................... 35,442 36,764 787 711 2.2 1.9
Service occupations................................................ 27,075 26,689 1,444 1,247 5.1 4.5
Sales and office occupations...................................... 33,633 33,730 1,363 1,233 3.9 3.5
Sales and related occupations.................................. 15,795 15,927 656 599 4.0 3.6
Office and administrative support occupations............... 17,838 17,803 707 634 3.8 3.4
Natural resources, construction, and maintenance
occupations........................................................ 14,324 14,588 724 546 4.8 3.6
Farming, fishing, and forestry occupations.................... 1,222 1,181 67 63 5.2 5.0
Construction and extraction occupations...................... 8,183 8,558 495 400 5.7 4.5
Installation, maintenance, and repair occupations........... 4,920 4,849 163 83 3.2 1.7
Production, transportation, and material moving
occupations........................................................ 18,129 19,015 833 920 4.4 4.6
Production occupations.......................................... 8,575 8,904 311 414 3.5 4.4
Transportation and material moving occupations............ 9,554 10,112 522 506 5.2 4.8
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.
NOTE: Updated population controls are introduced annually with the release of January data.

HOUSEHOLD DATA
Table A-14. Unemployed persons by industry and class of worker, not seasonally adjusted
Industry and class of worker
Oct.
2017
Number of
unemployed
persons
(in thousands)
Oct.
2018
Oct.
2017
Unemployment
rates
Total, 16 years and over 1 ................................................................ 6,242 5,771 3.9 3.5
Nonagricultural private wage and salary workers.................................. 4,810 4,458 3.8 3.5
Mining, quarrying, and oil and gas extraction.................................... 39 14 4.8 1.7
Construction.......................................................................... 418 352 4.5 3.6
Manufacturing........................................................................ 501 504 3.2 3.2
Durable goods..................................................................... 314 260 3.2 2.5
Nondurable goods................................................................ 187 245 3.2 4.3
Wholesale and retail trade......................................................... 849 794 4.2 3.9
Transportation and utilities......................................................... 284 217 4.1 3.1
Information............................................................................ 105 84 3.9 3.2
Financial activities................................................................... 214 205 2.2 2.0
Professional and business services............................................... 715 691 4.1 4.0
Education and health services..................................................... 677 649 2.9 2.7
Leisure and hospitality.............................................................. 796 747 5.8 5.4
Other services........................................................................ 210 200 3.1 3.0
Agriculture and related private wage and salary workers......................... 79 74 4.3 4.1
Government workers................................................................... 462 411 2.2 1.9
Self-employed workers, unincorporated, and unpaid family workers............ 311 265 3.1 2.6
Oct.
2018
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.
NOTE: Updated population controls are introduced annually with the release of January data.

HOUSEHOLD DATA
Table A-15. Alternative measures of labor underutilization
[Percent]
Measure
Not seasonally adjusted
Oct.
2017
Sept.
2018
Oct.
2018
Oct.
2017
June
2018
Seasonally adjusted
July
2018
Aug.
2018
Sept.
2018
Oct.
2018
U-1 Persons unemployed 15 weeks or longer,
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
U-2 Job losers and persons who completed
temporary jobs, as a percent of the civilian
labor force......................................... 1.8 1.5 1.5 2.0 1.9 1.9 1.8 1.7 1.8
U-3 Total unemployed, as a percent of the
civilian labor force (official unemployment
rate)................................................ 3.9 3.6 3.5 4.1 4.0 3.9 3.9 3.7 3.7
U-4 Total unemployed plus discouraged
workers, as a percent of the civilian labor
force plus discouraged workers................. 4.2 3.8 3.8 4.4 4.3 4.2 4.1 3.9 4.0
U-5 Total unemployed, plus discouraged
workers, plus all other persons marginally
attached to the labor force, as a percent of
the civilian labor force plus all persons
marginally attached to the labor force. . . . . . . .. 4.8 4.5 4.4 5.0 4.9 4.8 4.7 4.6 4.6
U-6 Total unemployed, plus all persons
marginally attached to the labor force, plus
total employed part time for economic
reasons, as a percent of the civilian labor
force plus all persons marginally attached to
the labor force..................................... 7.6 7.1 7.0 8.0 7.8 7.5 7.4 7.5 7.4
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
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
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
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
January data.

HOUSEHOLD DATA
Table A-16. Persons not in the labor force and multiple jobholders by sex, not seasonally adjusted
[Numbers in thousands]
Category
Oct.
2017
Total Men Women
NOT IN THE LABOR FORCE
Total not in the labor force........................................... 95,301 95,792 38,380 38,960 56,921 56,832
Persons who currently want a job................................ 4,938 5,048 2,287 2,298 2,650 2,750
Marginally attached to the labor force 1 ........................ 1,535 1,491 837 823 698 668
Discouraged workers 2 ......................................... 524 506 335 314 190 192
Other persons marginally attached to the labor force 3 . .. 1,010 984 503 509 508 476
MULTIPLE JOBHOLDERS
Total multiple jobholders 4 ............................................. 7,409 8,093 3,681 3,958 3,727 4,135
Percent of total employed.......................................... 4.8 5.2 4.5 4.8 5.2 5.6
Primary job full time, secondary job part time................... 4,167 4,484 2,256 2,490 1,912 1,994
Primary and secondary jobs both part time..................... 1,770 2,180 587 723 1,183 1,457
Primary and secondary jobs both full time....................... 295 284 226 171 70 114
Hours vary on primary or secondary job......................... 1,124 1,073 598 539 526 534
Oct.
2018
Oct.
2017
Oct.
2018
Oct.
2017
Oct.
2018
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
week, but had not looked for work in the past 4 weeks.
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
schooling or training, employer thinks too young or old, and other types of discrimination.
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
transportation problems, as well as a number for whom reason for nonparticipation was not determined.
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.
NOTE: Updated population controls are introduced annually with the release of January data.

ESTABLISHMENT DATA
Table B-1. Employees on nonfarm payrolls by industry sector and selected industry detail
[In thousands]
Industry
Oct.
2017
Not seasonally adjusted
Aug.
2018
Sept. Oct. Oct.
2018 p 2018 p 2017
Aug.
2018
Seasonally adjusted
Change
Sept. Oct.
from:
2018 p 2018 p Sept.2018
-
Oct.2018 p
Total nonfarm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148,203 149,406 149,738 150,753 147,234 149,382 149,500 149,750 250
Total private. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125,516 127,986 127,368 127,982 124,903 126,986 127,107 127,353 246
Goods-producing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20,391 21,115 21,024 21,064 20,168 20,750 20,792 20,859 67
Mining and logging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 700 763 759 764 691 747 751 756 5
Logging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.6 49.5 48.7 47.9 48.2 47.9 47.3 47.0 -0.3
Mining. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 650.5 713.3 710.0 716.5 643.2 698.8 703.5 708.6 5.1
Oil and gas extraction. . . . . . . . . . . . . . . . . . . . . . . 146.3 155.5 152.6 153.4 145.8 152.8 152.4 153.2 0.8
Mining, except oil and gas. . . . . . . . . . . . . . . . . . . 187.9 195.3 193.2 194.3 185.9 190.5 190.3 191.3 1.0
Coal mining. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.0 53.2 52.9 53.1 52.7 53.0 52.8 52.6 -0.2
Metal ore mining. . . . . . . . . . . . . . . . . . . . . . . . . . 38.5 39.6 39.0 39.0 38.7 39.1 38.9 39.1 0.2
Nonmetallic mineral mining and
quarrying. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96.4 102.5 101.3 102.2 94.5 98.4 98.6 99.6 1.0
Support activities for mining. . . . . . . . . . . . . . . . . 316.3 362.5 364.2 368.8 311.5 355.5 360.8 364.1 3.3
Construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,182 7,529 7,474 7,500 6,988 7,268 7,288 7,318 30
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
Residential building. . . . . . . . . . . . . . . . . . . . . . . . . . 769.2 821.3 811.9 815.9 751.2 801.3 799.8 802.6 2.8
Nonresidential building. . . . . . . . . . . . . . . . . . . . . . 807.2 835.2 827.6 828.3 792.3 814.5 815.8 814.7 -1.1
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
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
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
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
Manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12,509 12,823 12,791 12,800 12,489 12,735 12,753 12,785 32
Durable goods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,770 8,001 7,982 8,005 7,765 7,970 7,984 8,005 21
Wood products. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398.4 409.1 407.6 409.0 398.1 406.3 407.3 407.8 0.5
Nonmetallic mineral products. . . . . . . . . . . . . . . . 416.9 426.2 425.6 428.8 410.9 419.1 422.5 424.3 1.8
Primary metals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.3 382.2 381.7 381.7 374.4 382.1 382.2 381.9 -0.3
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
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
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
Computer and peripheral equipment. . . . . . 163.2 170.7 170.3 171.1 162.5 169.3 170.4 171.0 0.6
Communications equipment. . . . . . . . . . . . . . . 86.8 85.1 85.1 85.5 87.0 85.1 85.2 85.6 0.4
Semiconductors and electronic
components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.4 371.7 369.1 368.7 363.7 370.0 369.7 369.1 -0.6
Electronic instruments. . . . . . . . . . . . . . . . . . . . . 400.7 413.1 410.9 412.4 401.0 411.5 411.7 412.9 1.2
Miscellaneous computer and electronic
products. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.8 32.6 32.9 33.7 33.6 32.5 32.8 33.3 0.5
Electrical equipment and appliances. . . . . . . . 392.3 408.3 408.9 407.8 392.8 406.7 408.3 408.1 -0.2
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
Motor vehicles and parts 2 . . . . . . . . . . . . . . . . . 950.1 961.3 963.4 968.5 950.5 961.7 962.7 969.5 6.8
Furniture and related products. . . . . . . . . . . . . . 393.4 393.2 390.4 391.0 393.3 389.9 391.0 390.7 -0.3
Miscellaneous durable goods
manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592.2 601.0 598.2 604.4 590.9 600.5 600.5 603.3 2.8
Nondurable goods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,739 4,822 4,809 4,795 4,724 4,765 4,769 4,780 11
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
Textile mills. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.5 112.6 111.9 111.0 112.5 112.3 111.6 111.0 -0.6
Textile product mills. . . . . . . . . . . . . . . . . . . . . . . . . 113.0 112.0 111.3 110.9 112.4 110.9 110.8 110.4 -0.4
Apparel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117.3 112.9 111.3 110.9 116.3 113.2 110.9 110.8 -0.1
Paper and paper products. . . . . . . . . . . . . . . . . . . 368.2 377.9 378.2 377.4 369.2 377.1 378.9 378.2 -0.7
Printing and related support activities. . . . . . . 440.1 434.2 432.8 431.7 439.5 432.6 432.2 430.9 -1.3
Petroleum and coal products. . . . . . . . . . . . . . . . 118.5 120.8 119.9 118.9 116.0 116.9 116.6 116.2 -0.4
Chemicals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825.8 837.8 836.0 837.2 828.0 834.9 836.9 838.6 1.7
Plastics and rubber products. . . . . . . . . . . . . . . . 718.0 727.7 727.1 727.0 721.7 726.9 728.0 730.6 2.6
Miscellaneous nondurable goods
manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.3 311.2 310.8 311.6 294.5 303.1 301.4 304.0 2.6
Private service-providing. . . . . . . . . . . . . . . . . . . . . . . . . . . 105,125 106,871 106,344 106,918 104,735 106,236 106,315 106,494 179
Trade, transportation, and utilities. . . . . . . . . . . . . . . . 27,604 27,797 27,715 27,910 27,553 27,836 27,828 27,865 37
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
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
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
See footnotes at end of table.

ESTABLISHMENT DATA
Table B-1. Employees on nonfarm payrolls by industry sector and selected industry detail
— Continued
[In thousands]
Industry
Wholesale trade - Continued
Oct.
2017
Not seasonally adjusted
Aug.
2018
Sept. Oct. Oct.
2018 p 2018 p 2017
Aug.
2018
Seasonally adjusted
Change
Sept. Oct.
from:
2018 p 2018 p Sept.2018
-
Oct.2018 p
Electronic markets and agents and
brokers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 896.1 920.5 920.6 922.1 895.5 916.0 919.7 920.7 1.0
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
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
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
Other motor vehicle dealers. . . . . . . . . . . . . . . 154.3 163.1 158.6 157.1 155.6 156.3 157.2 158.3 1.1
Auto parts, accessories, and tire stores. . . 560.8 569.8 568.9 572.9 559.9 569.2 567.6 571.1 3.5
Furniture and home furnishings stores. . . . . . 479.9 477.7 477.7 488.0 476.6 482.4 484.3 485.9 1.6
Electronics and appliance stores. . . . . . . . . . . . 499.4 490.7 486.8 490.0 500.4 494.8 491.8 490.9 -0.9
Building material and garden supply
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
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
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
Gasoline stations. . . . . . . . . . . . . . . . . . . . . . . . . . . . 934.0 950.5 944.1 939.9 932.0 938.5 940.6 938.4 -2.2
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
Sporting goods, hobby, book, and music
stores. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605.2 557.5 554.9 557.4 606.0 571.0 566.5 560.4 -6.1
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
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
General merchandise stores, including
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
Miscellaneous store retailers. . . . . . . . . . . . . . . . 834.9 837.7 826.1 829.5 820.2 829.1 821.7 818.1 -3.6
Nonstore retailers. . . . . . . . . . . . . . . . . . . . . . . . . . . 585.6 594.6 596.4 610.4 575.7 600.0 601.3 602.3 1.0
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
Air transportation. . . . . . . . . . . . . . . . . . . . . . . . . . . . 497.8 508.4 506.2 505.2 498.5 504.8 505.5 505.6 0.1
Rail transportation. . . . . . . . . . . . . . . . . . . . . . . . . . . 213.2 215.6 215.6 215.6 212.7 214.4 215.3 215.0 -0.3
Water transportation. . . . . . . . . . . . . . . . . . . . . . . . . 65.7 68.3 66.2 65.8 65.4 65.6 65.1 65.4 0.3
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
Transit and ground passenger
transportation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515.5 431.8 508.1 517.1 497.9 495.1 496.7 498.2 1.5
Pipeline transportation. . . . . . . . . . . . . . . . . . . . . . . 47.6 46.9 46.9 47.0 47.7 46.9 46.8 47.0 0.2
Scenic and sightseeing transportation. . . . . . . 37.5 45.6 40.6 36.5 36.5 35.3 34.7 35.3 0.6
Support activities for transportation. . . . . . . . . . 698.2 716.0 713.5 721.7 693.1 713.9 712.8 717.6 4.8
Couriers and messengers. . . . . . . . . . . . . . . . . . . 684.5 715.2 729.3 746.8 704.2 751.8 757.0 764.6 7.6
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
Utilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553.4 553.2 549.7 551.6 554.2 551.2 551.3 552.5 1.2
Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,784 2,787 2,753 2,776 2,784 2,766 2,762 2,769 7
Publishing industries, except Internet. . . . . . . . . . 720.5 718.3 717.2 715.2 719.1 714.4 714.6 713.7 -0.9
Motion picture and sound recording
industries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422.7 426.6 403.1 422.8 424.5 413.6 412.5 416.8 4.3
Broadcasting, except Internet. . . . . . . . . . . . . . . . . . 266.5 262.5 264.7 265.2 264.7 263.2 263.2 263.1 -0.1
Telecommunications. . . . . . . . . . . . . . . . . . . . . . . . . . . 771.3 754.5 747.0 746.2 772.7 754.0 748.5 748.6 0.1
Data processing, hosting and related
services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320.7 326.5 325.8 328.4 319.7 325.8 326.9 328.0 1.1
Other information services. . . . . . . . . . . . . . . . . . . . . 282.6 298.6 294.8 298.5 283.3 295.4 296.0 298.6 2.6
Financial activities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8,499 8,654 8,606 8,616 8,494 8,587 8,602 8,609 7
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
Monetary authorities - central bank. . . . . . . . . . 18.9 19.4 19.1 19.2 19.0 19.3 19.2 19.2 0.0
Credit intermediation and related
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
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
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
Nondepository credit intermediation. . . . . . . 628.4 636.9 634.4 631.0 627.2 633.1 631.2 629.5 -1.7
Activities related to credit intermediation.. . 312.8 313.3 315.1 316.3 312.1 312.9 315.3 316.1 0.8
Securities, commodity contracts,
investments, and funds and trusts. . . . . . . . 948.8 978.0 969.5 968.9 945.6 968.9 970.0 967.3 -2.7
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
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
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
See footnotes at end of table.

ESTABLISHMENT DATA
Table B-1. Employees on nonfarm payrolls by industry sector and selected industry detail
— Continued
[In thousands]
Industry
Real estate and rental and leasing -
Continued
Oct.
2017
Not seasonally adjusted
Aug.
2018
Sept. Oct. Oct.
2018 p 2018 p 2017
Aug.
2018
Seasonally adjusted
Change
Sept. Oct.
from:
2018 p 2018 p Sept.2018
-
Oct.2018 p
Rental and leasing services. . . . . . . . . . . . . . . . . 577.6 616.2 610.9 607.2 576.9 596.7 601.7 605.7 4.0
Lessors of nonfinancial intangible assets. . . . 24.1 25.7 25.0 25.3 24.1 25.2 25.1 25.3 0.2
Professional and business services. . . . . . . . . . . . . . 20,815 21,217 21,185 21,352 20,630 21,065 21,111 21,146 35
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
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
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
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
Specialized design services. . . . . . . . . . . . . . . . . 139.0 139.2 140.0 139.7 137.4 138.8 140.0 138.4 -1.6
Computer systems design and related
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
Management and technical consulting
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
Scientific research and development
services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661.3 689.0 681.6 685.4 663.8 682.9 684.6 687.1 2.5
Advertising and related services. . . . . . . . . . . . . 492.6 500.2 495.8 497.6 489.8 497.3 496.8 495.6 -1.2
Other professional and technical services. . . 722.8 745.5 745.5 752.0 715.4 741.0 744.9 745.5 0.6
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
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
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
Office administrative services. . . . . . . . . . . . . 517.7 528.0 526.8 527.4 517.5 527.3 527.1 527.0 -0.1
Facilities support services. . . . . . . . . . . . . . . . . 156.0 158.4 159.0 159.4 154.9 157.5 157.6 158.1 0.5
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
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
Business support services. . . . . . . . . . . . . . . . 921.4 899.8 910.6 924.9 906.4 911.4 911.7 910.5 -1.2
Travel arrangement and reservation
services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216.8 219.1 218.3 216.9 216.9 216.5 217.1 216.8 -0.3
Investigation and security services. . . . . . . . 930.5 948.2 955.6 957.7 922.6 945.2 947.5 950.2 2.7
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
Other support services. . . . . . . . . . . . . . . . . . . . 332.7 339.9 343.3 346.9 327.9 337.7 341.6 341.9 0.3
Waste management and remediation
services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416.5 438.1 437.0 437.8 415.5 432.2 434.6 436.4 1.8
Education and health services. . . . . . . . . . . . . . . . . . . 23,515 23,430 23,691 24,018 23,312 23,741 23,767 23,811 44
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
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
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
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
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
Offices of dentists. . . . . . . . . . . . . . . . . . . . . . . 937.4 954.3 949.4 955.3 935.0 949.8 949.0 952.5 3.5
Offices of other health practitioners. . . . . 897.5 936.8 931.3 945.1 895.7 933.9 936.8 942.9 6.1
Outpatient care centers. . . . . . . . . . . . . . . . . 910.3 938.1 935.8 942.5 909.9 937.1 938.4 942.0 3.6
Medical and diagnostic laboratories. . . . 272.9 282.4 284.8 284.0 271.9 282.4 283.9 283.4 -0.5
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
Other ambulatory health care
services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.7 306.0 307.7 304.4 309.8 306.3 306.5 302.3 -4.2
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
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
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
Residential mental health facilities. . . . . . 628.9 639.2 636.8 636.7 629.8 637.1 637.9 637.7 -0.2
Community care facilities for the
elderly. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 926.8 946.1 942.5 949.3 927.8 942.2 944.2 948.5 4.3
Other residential care facilities. . . . . . . . . . 168.9 173.7 173.2 174.5 168.7 172.4 173.5 174.3 0.8
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
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
Emergency and other relief services. . . . . . 168.2 175.5 174.9 175.1 169.0 174.9 175.3 175.9 0.6
Vocational rehabilitation services. . . . . . . . . 343.1 351.8 346.0 349.0 343.6 347.3 347.3 349.5 2.2
Child day care services. . . . . . . . . . . . . . . . . . . 945.1 906.8 936.0 948.3 935.3 938.2 937.5 937.0 -0.5
Leisure and hospitality. . . . . . . . . . . . . . . . . . . . . . . . . . . 16,100 17,076 16,530 16,360 16,156 16,368 16,368 16,410 42
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
Performing arts and spectator sports. . . . . . . . 504.8 514.1 511.1 511.1 499.0 485.2 498.6 501.9 3.3
See footnotes at end of table.

ESTABLISHMENT DATA
Table B-1. Employees on nonfarm payrolls by industry sector and selected industry detail
— Continued
[In thousands]
Industry
Arts, entertainment, and recreation -
Continued
Oct.
2017
Not seasonally adjusted
Aug.
2018
Sept. Oct. Oct.
2018 p 2018 p 2017
Aug.
2018
Seasonally adjusted
Change
Sept. Oct.
from:
2018 p 2018 p Sept.2018
-
Oct.2018 p
Museums, historical sites, and similar
institutions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168.7 186.6 176.8 176.2 168.0 173.8 174.5 175.5 1.0
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
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
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
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
Other services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,808 5,910 5,864 5,886 5,806 5,873 5,877 5,884 7
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
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
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
Government. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22,687 21,420 22,370 22,771 22,331 22,396 22,393 22,397 4
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
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
U.S. Postal Service. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614.5 603.6 604.3 603.7 616.9 606.3 605.7 604.9 -0.8
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
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
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
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
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
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
1 Includes other industries, not shown separately.
2 Includes motor vehicles, motor vehicle bodies and trailers, and motor vehicle parts.
3 Includes ambulatory health care services, hospitals, and nursing and residential care facilities.
p Preliminary
NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.

ESTABLISHMENT DATA
Table B-2. Average weekly hours and overtime of all employees on private nonfarm payrolls by industry
sector, seasonally adjusted
Industry
Oct.
2017
Aug.
2018
Sept.
2018 p Oct.
2018 p
AVERAGE WEEKLY HOURS
Total private............................................................................. 34.4 34.5 34.4 34.5
Goods-producing.................................................................... 40.4 40.5 40.3 40.3
Mining and logging................................................................ 45.3 46.0 45.8 45.9
Construction....................................................................... 39.0 39.2 38.9 38.9
Manufacturing..................................................................... 40.9 40.9 40.9 40.8
Durable goods.................................................................. 41.4 41.3 41.2 41.2
Nondurable goods.............................................................. 40.0 40.3 40.2 40.1
Private service-providing............................................................ 33.2 33.3 33.3 33.3
Trade, transportation, and utilities.............................................. 34.3 34.4 34.4 34.3
Wholesale trade................................................................ 39.0 39.0 38.9 38.8
Retail trade...................................................................... 30.9 30.9 30.9 30.8
Transportation and warehousing............................................. 38.7 39.0 38.9 39.0
Utilities........................................................................... 42.1 42.0 42.2 42.1
Information......................................................................... 36.3 36.1 36.3 36.1
Financial activities................................................................ 37.5 37.6 37.4 37.8
Professional and business services............................................ 36.0 36.1 36.1 36.2
Education and health services.................................................. 32.9 33.0 33.0 33.0
Leisure and hospitality........................................................... 26.1 26.1 26.0 26.1
Other services..................................................................... 31.7 31.8 31.8 31.9
AVERAGE OVERTIME HOURS
Manufacturing........................................................................... 3.5 3.5 3.5 3.5
Durable goods....................................................................... 3.5 3.5 3.5 3.5
Nondurable goods................................................................... 3.5 3.6 3.4 3.5
p Preliminary
NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.

ESTABLISHMENT DATA
Table B-3. Average hourly and weekly earnings of all employees on private nonfarm payrolls by industry
sector, seasonally adjusted
Industry
Oct.
2017
Average hourly earnings
Aug.
2018
Sept. Oct. Oct.
2018 p 2018 p 2017
Average weekly earnings
Aug.
2018
Sept. Oct.
2018 p 2018 p
Total private............................................... $26.47 $27.17 $27.25 $27.30 $910.57 $937.37 $937.40 $941.85
Goods-producing....................................... 27.72 28.29 28.39 28.40 1,119.89 1,145.75 1,144.12 1,144.52
Mining and logging.................................. 32.13 32.75 33.06 32.85 1,455.49 1,506.50 1,514.15 1,507.82
Construction.......................................... 29.09 30.00 30.17 30.21 1,134.51 1,176.00 1,173.61 1,175.17
Manufacturing........................................ 26.72 27.07 27.11 27.11 1,092.85 1,107.16 1,108.80 1,106.09
Durable goods.................................... 27.99 28.46 28.51 28.49 1,158.79 1,175.40 1,174.61 1,173.79
Nondurable goods................................ 24.58 24.68 24.71 24.74 983.20 994.60 993.34 992.07
Private service-providing.............................. 26.18 26.91 26.98 27.04 869.18 896.10 898.43 900.43
Trade, transportation, and utilities................. 22.86 23.48 23.56 23.61 784.10 807.71 810.46 809.82
Wholesale trade.................................. 29.98 30.66 30.82 30.83 1,169.22 1,195.74 1,198.90 1,196.20
Retail trade........................................ 18.24 18.84 18.89 18.96 563.62 582.16 583.70 583.97
Transportation and warehousing............... 24.01 24.45 24.45 24.50 929.19 953.55 951.11 955.50
Utilities............................................. 39.62 40.78 41.08 41.00 1,668.00 1,712.76 1,733.58 1,726.10
Information........................................... 38.39 39.86 40.31 40.39 1,393.56 1,438.95 1,463.25 1,458.08
Financial activities................................... 33.66 34.93 35.03 35.03 1,262.25 1,313.37 1,310.12 1,324.13
Professional and business services.............. 31.77 32.65 32.70 32.76 1,143.72 1,178.67 1,180.47 1,185.91
Education and health services..................... 26.47 27.09 27.10 27.19 870.86 893.97 894.30 897.27
Leisure and hospitality.............................. 15.58 16.03 16.07 16.11 406.64 418.38 417.82 420.47
Other services....................................... 24.10 24.42 24.50 24.55 763.97 776.56 779.10 783.15
p Preliminary
NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.

ESTABLISHMENT DATA
Table B-4. Indexes of aggregate weekly hours and payrolls for all employees on private nonfarm payrolls by
industry sector, seasonally adjusted
[2007=100]
Industry
Oct.
2017
Index of aggregate weekly hours 1 Index of aggregate weekly payrolls 2
Aug.
2018
Percent
change
Sept.
from:
Oct.
2018 p 2018 p 2018 Sept. Oct.
-
2017
Oct.
2018 p
Aug.
2018
Sept.
2018 p Oct.
2018 p Percent
change
from:
2018 -
Oct.
2018 p
Total private..................................... 107.8 110.0 109.7 110.3 0.5 136.5 142.8 143.0 143.9 0.6
Goods-producing............................. 92.8 95.7 95.5 95.8 0.3 116.3 122.4 122.5 122.9 0.3
Mining and logging........................ 98.4 108.0 108.1 109.0 0.8 126.9 142.0 143.5 143.8 0.2
Construction................................ 93.9 98.2 97.7 98.1 0.4 118.7 128.0 128.1 128.8 0.5
Manufacturing.............................. 91.9 93.7 93.8 93.8 0.0 114.2 118.0 118.3 118.3 0.0
Durable goods........................... 90.5 92.7 92.6 92.9 0.3 112.5 117.1 117.3 117.5 0.2
Nondurable goods...................... 94.3 95.8 95.7 95.7 0.0 117.6 120.0 120.0 120.1 0.1
Private service-providing.................... 111.9 113.8 113.9 114.1 0.2 142.3 148.8 149.3 149.9 0.4
Trade, transportation, and utilities....... 102.8 104.1 104.1 103.9 -0.2 126.5 131.6 132.0 132.1 0.1
Wholesale trade......................... 100.8 102.2 102.0 101.9 -0.1 126.1 130.7 131.2 131.1 -0.1
Retail trade.............................. 99.5 100.0 99.8 99.5 -0.3 120.0 124.5 124.6 124.7 0.1
Transportation and warehousing...... 115.5 119.5 119.7 120.5 0.7 140.8 148.3 148.5 149.8 0.9
Utilities.................................... 100.9 100.2 100.7 100.6 -0.1 132.1 135.0 136.6 136.3 -0.2
Information................................. 92.3 91.2 91.6 91.3 -0.3 126.2 129.4 131.4 131.3 -0.1
Financial activities......................... 104.3 105.7 105.4 106.6 1.1 137.0 144.1 144.0 145.6 1.1
Professional and business services..... 116.8 119.6 119.9 120.4 0.4 150.3 158.2 158.8 159.8 0.6
Education and health services........... 125.2 127.9 128.1 128.3 0.2 159.5 166.7 167.0 167.8 0.5
Leisure and hospitality.................... 120.3 121.9 121.4 122.2 0.7 151.2 157.6 157.4 158.8 0.9
Other services............................. 106.1 107.7 107.8 108.2 0.4 140.2 144.1 144.7 145.6 0.6
1 The indexes of aggregate weekly hours are calculated by dividing the current month’s estimates of aggregate hours by the corresponding 2007
annual average aggregate hours. Aggregate hours estimates are the product of estimates of average weekly hours and employment.
2 The indexes of aggregate weekly payrolls are calculated by dividing the current month’s estimates of aggregate weekly payrolls by the
corresponding 2007 annual average aggregate weekly payrolls. Aggregate payrolls estimates are the product of estimates of average hourly
earnings, average weekly hours, and employment.
p Preliminary
NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.

ESTABLISHMENT DATA
Table B-5. Employment of women on nonfarm payrolls by industry sector, seasonally adjusted
Industry
Oct.
2017
Women employees (in thousands)
Aug.
2018
Sept. Oct. Oct.
2018 p 2018 p 2017
Percent of all employees
Aug.
2018
Sept. Oct.
2018 p 2018 p
Total nonfarm............................................. 72,880 74,213 74,285 74,441 49.5 49.7 49.7 49.7
Total private............................................ 60,063 61,290 61,363 61,512 48.1 48.3 48.3 48.3
Goods-producing.................................... 4,430 4,604 4,621 4,638 22.0 22.2 22.2 22.2
Mining and logging............................... 91 95 94 95 13.2 12.7 12.5 12.6
Construction....................................... 889 933 940 943 12.7 12.8 12.9 12.9
Manufacturing..................................... 3,450 3,576 3,587 3,600 27.6 28.1 28.1 28.2
Durable goods.................................. 1,818 1,891 1,897 1,904 23.4 23.7 23.8 23.8
Nondurable goods............................. 1,632 1,685 1,690 1,696 34.5 35.4 35.4 35.5
Private service-providing........................... 55,633 56,686 56,742 56,874 53.1 53.4 53.4 53.4
Trade, transportation, and utilities.............. 11,032 11,169 11,160 11,164 40.0 40.1 40.1 40.1
Wholesale trade................................ 1,749.4 1,797.6 1,795.4 1,800.8 29.5 29.9 29.9 29.9
Retail trade..................................... 7,879.0 7,918.8 7,905.1 7,893.6 49.7 49.7 49.7 49.7
Transportation and warehousing............. 1,276.6 1,323.6 1,330.9 1,341.3 24.5 24.7 24.8 24.8
Utilities........................................... 127.1 128.7 128.5 128.5 22.9 23.3 23.3 23.3
Information......................................... 1,101 1,090 1,090 1,093 39.5 39.4 39.5 39.5
Financial activities................................ 4,795 4,846 4,849 4,856 56.5 56.4 56.4 56.4
Professional and business services............ 9,284 9,538 9,558 9,599 45.0 45.3 45.3 45.4
Education and health services.................. 17,938 18,307 18,335 18,376 76.9 77.1 77.1 77.2
Leisure and hospitality........................... 8,408 8,610 8,621 8,651 52.0 52.6 52.7 52.7
Other services..................................... 3,075 3,126 3,129 3,135 53.0 53.2 53.2 53.3
Government............................................. 12,817 12,923 12,922 12,929 57.4 57.7 57.7 57.7
p Preliminary
NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.

ESTABLISHMENT DATA
Table B-6. Employment of production and nonsupervisory employees on private nonfarm payrolls by industry
sector, seasonally adjusted 1
[In thousands]
Industry
Oct.
2017
Aug.
2018
Sept.
2018 p Oct.
2018 p
Total private............................................................................... 102,980 104,624 104,720 104,942
Goods-producing....................................................................... 14,497 14,899 14,919 14,986
Mining and logging.................................................................. 505 556 556 566
Construction.......................................................................... 5,220 5,409 5,419 5,437
Manufacturing........................................................................ 8,772 8,934 8,944 8,983
Durable goods..................................................................... 5,332 5,482 5,494 5,524
Nondurable goods................................................................ 3,440 3,452 3,450 3,459
Private service-providing.............................................................. 88,483 89,725 89,801 89,956
Trade, transportation, and utilities................................................. 23,262 23,522 23,532 23,548
Wholesale trade................................................................... 4,749.7 4,813.6 4,819.8 4,824.5
Retail trade........................................................................ 13,526.8 13,602.3 13,587.6 13,576.3
Transportation and warehousing................................................ 4,539.6 4,664.7 4,682.2 4,703.6
Utilities.............................................................................. 445.5 441.5 442.0 443.5
Information............................................................................ 2,243 2,230 2,222 2,231
Financial activities................................................................... 6,608 6,654 6,671 6,682
Professional and business services............................................... 16,846 17,208 17,238 17,280
Education and health services..................................................... 20,474 20,855 20,877 20,914
Leisure and hospitality.............................................................. 14,245 14,394 14,393 14,425
Other services........................................................................ 4,805 4,862 4,868 4,876
1 Data relate to production employees in mining and logging and manufacturing, construction employees in construction, and nonsupervisory
employees in the service-providing industries. These groups account for approximately four-fifths of the total employment on private nonfarm
payrolls.
p Preliminary
NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.

ESTABLISHMENT DATA
Table B-7. Average weekly hours and overtime of production and nonsupervisory employees on private
nonfarm payrolls by industry sector, seasonally adjusted 1
Industry
Oct.
2017
Aug.
2018
Sept.
2018 p Oct.
2018 p
AVERAGE WEEKLY HOURS
Total private............................................................................. 33.7 33.8 33.7 33.7
Goods-producing.................................................................... 41.2 41.5 41.3 41.3
Mining and logging................................................................ 46.0 47.0 46.5 46.7
Construction....................................................................... 39.5 39.9 39.6 39.6
Manufacturing..................................................................... 42.0 42.2 42.1 42.1
Durable goods.................................................................. 42.4 42.5 42.3 42.4
Nondurable goods.............................................................. 41.3 41.6 41.7 41.5
Private service-providing............................................................ 32.4 32.5 32.4 32.4
Trade, transportation, and utilities.............................................. 33.9 34.0 33.9 33.8
Wholesale trade................................................................ 39.0 39.0 38.8 38.7
Retail trade...................................................................... 30.3 30.4 30.4 30.2
Transportation and warehousing............................................. 38.3 38.3 38.2 38.2
Utilities........................................................................... 42.5 42.8 42.6 42.6
Information......................................................................... 35.8 35.6 35.6 35.5
Financial activities................................................................ 36.9 37.1 37.0 37.0
Professional and business services............................................ 35.4 35.4 35.2 35.3
Education and health services.................................................. 32.2 32.2 32.2 32.2
Leisure and hospitality........................................................... 24.8 24.9 24.8 24.8
Other services..................................................................... 30.6 30.8 30.8 31.0
AVERAGE OVERTIME HOURS
Manufacturing........................................................................... 4.5 4.5 4.5 4.5
Durable goods....................................................................... 4.6 4.6 4.6 4.7
Nondurable goods................................................................... 4.3 4.4 4.3 4.3
1 Data relate to production employees in mining and logging and manufacturing, construction employees in construction, and nonsupervisory
employees in the service-providing industries. These groups account for approximately four-fifths of the total employment on private nonfarm
payrolls.
p Preliminary
NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.

ESTABLISHMENT DATA
Table B-8. Average hourly and weekly earnings of production and nonsupervisory employees on private
nonfarm payrolls by industry sector, seasonally adjusted 1
Industry
Oct.
2017
Average hourly earnings
Aug.
2018
Sept. Oct. Oct.
2018 p 2018 p 2017
Average weekly earnings
Aug.
2018
Sept. Oct.
2018 p 2018 p
Total private............................................... $22.18 $22.76 $22.82 $22.89 $747.47 $769.29 $769.03 $771.39
Goods-producing....................................... 23.31 24.02 24.10 24.19 960.37 996.83 995.33 999.05
Mining and logging.................................. 27.45 28.55 28.93 28.89 1,262.70 1,341.85 1,345.25 1,349.16
Construction.......................................... 26.88 27.80 27.90 28.01 1,061.76 1,109.22 1,104.84 1,109.20
Manufacturing........................................ 21.05 21.54 21.61 21.68 884.10 908.99 909.78 912.73
Durable goods.................................... 22.03 22.53 22.60 22.64 934.07 957.53 955.98 959.94
Nondurable goods................................ 19.48 19.92 20.00 20.10 804.52 828.67 834.00 834.15
Private service-providing.............................. 21.94 22.50 22.55 22.61 710.86 731.25 730.62 732.56
Trade, transportation, and utilities................. 19.42 20.02 20.08 20.16 658.34 680.68 680.71 681.41
Wholesale trade.................................. 24.72 25.34 25.55 25.58 964.08 988.26 991.34 989.95
Retail trade........................................ 15.36 15.99 16.00 16.07 465.41 486.10 486.40 485.31
Transportation and warehousing............... 21.52 21.96 22.01 22.09 824.22 841.07 840.78 843.84
Utilities............................................. 36.26 36.95 37.05 36.87 1,541.05 1,581.46 1,578.33 1,570.66
Information........................................... 30.86 31.97 32.22 32.43 1,104.79 1,138.13 1,147.03 1,151.27
Financial activities................................... 26.77 26.96 27.00 27.02 987.81 1,000.22 999.00 999.74
Professional and business services.............. 26.16 26.85 26.90 26.94 926.06 950.49 946.88 950.98
Education and health services..................... 23.15 23.72 23.72 23.77 745.43 763.78 763.78 765.39
Leisure and hospitality.............................. 13.49 13.94 14.00 14.05 334.55 347.11 347.20 348.44
Other services....................................... 20.33 20.66 20.73 20.78 622.10 636.33 638.48 644.18
1 Data relate to production employees in mining and logging and manufacturing, construction employees in construction, and nonsupervisory
employees in the service-providing industries. These groups account for approximately four-fifths of the total employment on private nonfarm
payrolls.
p Preliminary
NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.

ESTABLISHMENT DATA
Table B-9. Indexes of aggregate weekly hours and payrolls for production and nonsupervisory employees on
private nonfarm payrolls by industry sector, seasonally adjusted 1
[2002=100]
Industry
Oct.
2017
Index of aggregate weekly hours 2 Index of aggregate weekly payrolls 3
Aug.
2018
Percent
change
Sept.
from:
Oct.
2018 p 2018 p 2018 Sept. Oct.
-
2017
Oct.
2018 p
Aug.
2018
Sept.
2018 p Oct.
2018 p Percent
change
from:
2018 -
Oct.
2018 p
Total private..................................... 115.6 117.8 117.6 117.8 0.2 171.4 179.2 179.3 180.2 0.5
Goods-producing............................. 91.3 94.5 94.2 94.6 0.4 130.3 139.0 138.9 140.1 0.9
Mining and logging........................ 123.4 138.9 137.4 140.5 2.3 197.1 230.6 231.2 236.0 2.1
Construction................................ 103.2 108.1 107.4 107.8 0.4 149.8 162.2 161.9 163.0 0.7
Manufacturing.............................. 84.6 86.5 86.4 86.8 0.5 116.4 121.9 122.2 123.1 0.7
Durable goods........................... 85.0 87.6 87.3 88.0 0.8 116.9 123.2 123.2 124.4 1.0
Nondurable goods...................... 83.7 84.6 84.8 84.6 -0.2 115.2 119.1 119.8 120.1 0.3
Private service-providing.................... 122.1 124.2 123.9 124.1 0.2 183.7 191.6 191.6 192.4 0.4
Trade, transportation, and utilities....... 109.9 111.5 111.2 111.0 -0.2 152.3 159.2 159.3 159.6 0.2
Wholesale trade......................... 109.1 110.6 110.1 110.0 -0.1 158.9 165.0 165.8 165.7 -0.1
Retail trade.............................. 103.7 104.7 104.6 103.8 -0.8 136.6 143.4 143.4 142.9 -0.3
Transportation and warehousing...... 130.9 134.5 134.6 135.3 0.5 178.7 187.3 188.0 189.5 0.8
Utilities.................................... 96.8 96.6 96.3 96.6 0.3 146.6 149.0 148.9 148.7 -0.1
Information................................. 91.6 90.6 90.3 90.4 0.1 140.0 143.4 144.0 145.1 0.8
Financial activities......................... 114.8 116.2 116.2 116.4 0.2 189.1 192.8 193.0 193.5 0.3
Professional and business services..... 133.7 136.5 136.0 136.7 0.5 208.1 218.1 217.7 219.2 0.7
Education and health services........... 140.6 143.2 143.3 143.6 0.2 214.8 224.2 224.4 225.3 0.4
Leisure and hospitality.................... 129.4 131.3 130.7 131.0 0.2 198.2 207.8 207.9 209.1 0.6
Other services............................. 103.1 105.0 105.2 106.0 0.8 152.7 158.1 158.8 160.5 1.1
1 Data relate to production employees in mining and logging and manufacturing, construction employees in construction, and nonsupervisory
employees in the service-providing industries. These groups account for approximately four-fifths of the total employment on private nonfarm
payrolls.
2 The indexes of aggregate weekly hours are calculated by dividing the current month’s estimates of aggregate hours by the corresponding 2002
annual average aggregate hours. Aggregate hours estimates are the product of estimates of average weekly hours and employment.
3 The indexes of aggregate weekly payrolls are calculated by dividing the current month’s estimates of aggregate weekly payrolls by the
corresponding 2002 annual average aggregate weekly payrolls. Aggregate payrolls estimates are the product of estimates of average hourly
earnings, average weekly hours, and employment.
p Preliminary
NOTE: Data have been revised to reflect March 2017 benchmark levels and updated seasonal adjustment factors.

Page 120 of 149

Attachment B
Employment and Unemployment
Among Youth in The U.S.
Page 121 of 149

For release 10:00 a.m. (EDT) Thursday, August 16, 2018
USDL-18-1316
Technical information: (202) 691-6378 • cpsinfo@bls.gov • www.bls.gov/cps
Media contact: (202) 691-5902 • PressOffice@bls.gov
EMPLOYMENT AND UNEMPLOYMENT AMONG YOUTH — SUMMER 2018
From April to July 2018, the number of employed youth 16 to 24 years old increased by 2.0 million to
20.9 million, the U.S. Bureau of Labor Statistics reported today. This year, 55.0 percent of young people
were employed in July, little changed from a year earlier. (The month of July typically is the
summertime peak in youth employment.) The unemployment rate for youth was 9.2 percent in July, also
little changed from July 2017. (Because this analysis focuses on the seasonal changes in youth
employment and unemployment that occur each spring and summer, the data are not seasonally
adjusted.)
Labor Force
The youth labor force—16- to 24-year-olds working or actively looking for work—grows sharply
between April and July each year. During these months, large numbers of high school and college
students search for or take summer jobs, and many graduates enter the labor market to look for or begin
permanent employment. This summer, the youth labor force grew by 2.6 million, or 12.7 percent, to a
total of 23.0 million in July. (See table 1.)
The labor force participation rate for all youth was 60.6 percent in July, unchanged from a year earlier.
(The labor force participation rate is the proportion of the civilian noninstitutional population that is
working or looking and available for work.) (See table 2.) The summer labor force participation rate of
youth has held fairly steady since July 2010, after trending downward for the prior two decades.
The summer youth labor force participation rate peaked at 77.5 percent in July 1989.
The July 2018 labor force participation rate for 16- to 24-year-old men, at 61.1 percent, was down 1.2
percentage points over the year. The rate for young women, at 60.0 percent, rose 1.2 percentage points
during the same period, reducing the gap in labor force participation between young men and women.
Whites had the highest youth labor force participation rate in July 2018, at 62.8 percent. The rate was
56.5 percent for Blacks, 43.3 percent for Asians, and 58.0 percent for Hispanics. Over the year, the labor
force participation rate rose for Hispanics (+1.4 percentage points) and declined for Asians (-4.1 points).
The decline among Asians offset a similar increase (+4.3 percentage points) between July 2016 and
2017. Labor force participation rates in July 2018 for Whites and Blacks were essentially unchanged
from a year earlier.

Employment
In July 2018, there were 20.9 million employed 16- to 24-year-olds, about the same number as the
summer before. Between April and July 2018, the number of employed youth rose by 2.0 million, in line
with the change between April and July 2017. The employment-population ratio for youth—the
proportion of the 16- to 24-year-old civilian noninstitutional population with a job—was 55.0 percent in
July 2018, little changed from the prior year. (See tables 1 and 2.)
Employment-population ratios in July 2018 were higher than a year earlier for young women (54.8
percent), Whites (58.0 percent), and Hispanics (51.7 percent). The ratios declined for young men (55.2
percent) and Asians (39.7 percent). The ratio for Blacks, at 47.2 percent in July, was about unchanged
from the summer before.
In July 2018, the largest percentage of employed youth worked in the leisure and hospitality industry
(26 percent), which includes food services. An additional 18 percent of employed youth worked in the
retail trade industry, and 11 percent worked in education and health services. (See table 3.)
Unemployment
Unemployment among youth rose by 567,000 from April to July 2018, compared with an increase of
458,000 for the same period in 2017.
The youth unemployment rate, at 9.2 percent in July 2018, was little changed from July 2017. This
represents the lowest summer youth unemployment rate since July 1966. The number of unemployed
youth was 2.1 million in July 2018, little different from a year earlier. Of the 2.1 million unemployed
16- to 24-year-olds, 1.5 million were looking for full-time work in July 2018, also little changed from
July 2017. (See tables 1 and 2.)
In July 2018, the unemployment rates for both young men (9.8 percent) and women (8.6 percent) were
little changed from the summer before. The July 2018 rate for young Asians (8.4 percent) declined over
the year, while the rates for young Whites (7.6 percent), Blacks (16.5 percent), and Hispanics (10.8
percent) showed little change over the year. (See table 2.)
- 2 -

Technical Note
The estimates in this release were obtained from the
Current Population Survey (CPS), a national sample survey
of about 60,000 eligible households conducted monthly for
the Bureau of Labor Statistics (BLS) by the U.S. Census
Bureau. The data in this release relate to the employment
status of youth (16- to 24-year-olds) during the months of
April-July. This period was selected as being the most
representative time frame in which to measure the full
summertime transition from school to work. July is the peak
summer month of youth employment.
Beginning in January of each year, data reflect revised
population controls used in the CPS. Additional information
about population controls is available on the BLS website at
www.bls.gov/cps/documentation.htm#pop.
Information in this release will be made available to
sensory impaired individuals upon request. Voice phone:
(202) 691-5200; Federal Relay Service: (800) 877-8339.
Reliability of the estimates
Statistics based on the CPS are subject to both
sampling and nonsampling error. When a sample, rather than
the entire population, is surveyed, there is a chance that the
sample estimates may differ from the true population values
they represent. The component of this difference that occurs
because samples differ by chance is known as sampling
error, and its variability is measured by the standard error of
the estimate. There is about a 90-percent chance, or level of
confidence, that an estimate based on a sample will differ by
no more than 1.6 standard errors from the true population
value because of sampling error. BLS analyses are generally
conducted at the 90-percent level of confidence.
The CPS data also are affected by nonsampling error.
Nonsampling error can occur for many reasons, including
the failure to sample a segment of the population, inability to
obtain information for all respondents in the sample,
inability or unwillingness of respondents to provide correct
information, and errors made in the collection or processing
of the data.
More information on the reliability of data from the
CPS and estimating standard errors is available online at
www.bls.gov/cps/documentation.htm#reliability.
Definitions
The principal definitions used in this release are
described briefly below.
Employed. Employed persons are all those who, during
the survey reference week (which is generally the week
including the 12th day of the month), (a) did any work at all
as paid employees; (b) worked in their own business,
profession, or on their own farm; (c) worked 15 hours or
more as unpaid workers in a family member’s business.
Persons who were temporarily absent from their jobs
because of illness, bad weather, vacation, labor dispute, or
another reason also are counted as employed.
Unemployed. The unemployed are those who had no
employment during the reference week, were available for
work at that time, and had made specific efforts to find
employment sometime during the 4-week period ending with
the reference week. Persons who were waiting to be recalled
to a job from which they had been laid off need not have
been looking for work to be classified as unemployed.
Looking for full-time work refers to 35 hours or more per
week; part-time work refers to fewer than 35 hours per
week.
Civilian labor force. This group comprises all persons
classified as employed or unemployed.
Unemployment rate. The unemployment rate is the
number of unemployed persons as a percent of the civilian
labor force.
Labor force participation rate. The labor force
participation rate is the labor force as a percent of the
population.
Employment-population ratio. The employmentpopulation
ratio is the employed as a percent of the
population.
Not in the labor force. Included in this group are all
persons in the civilian noninstitutional population who are
neither employed nor unemployed.
Industry and class of worker. This information applies
to the job held during the reference week. Persons with two
or more jobs are classified in the job at which they worked
the greatest number of hours. Persons are classified using the
2012 Census industry classification system. The class-ofworker
breakdown assigns workers to the following
categories: Private and government wage and salary workers,
unincorporated self-employed workers, and unpaid family
workers.
Wage and salary workers. Included in this group are
persons who receive wages, salary, commissions, tips, or
pay in kind from a private employer or from a government
entity.
Self-employed workers. Included in this group are those
who work for profit or fees in their own unincorporated
business, profession, trade, or farm. Only unincorporated
self-employed are included in the self-employed category.
Self-employed persons whose businesses are incorporated
are included with private wage and salary workers.
Unpaid family workers. Included in this group are
persons working without pay for 15 hours a week or more on
a farm or in a business operated by a family member in their
household.

Table 1. Employment status of the civilian noninstitutional population 16 to 24 years of age by sex, race, and
Hispanic or Latino ethnicity, April-July 2018
[Numbers in thousands. Data are not seasonally adjusted.]
Employment status, sex, race, and
Hispanic or Latino ethnicity
April May June July
April-July changes
Number
Percent
TOTAL
Civilian noninstitutional population................................... 38,039 38,023 38,009 37,997 -42 -0.1
Civilian labor force.................................................. 20,425 20,779 22,640 23,016 2,591 12.7
Participation rate.................................................. 53.7 54.6 59.6 60.6 6.9 12.8
Employed.......................................................... 18,873 18,984 20,332 20,897 2,024 10.7
Employment-population ratio................................. 49.6 49.9 53.5 55.0 5.4 10.9
Unemployed....................................................... 1,552 1,795 2,309 2,119 567 36.5
Looking for full-time work..................................... 1,028 1,285 1,695 1,458 430 41.8
Looking for part-time work.................................... 523 510 614 660 137 26.2
Unemployment rate.............................................. 7.6 8.6 10.2 9.2 1.6 21.1
Not in labor force.................................................... 17,614 17,245 15,369 14,981 -2,633 -14.9
Men
Civilian noninstitutional population................................... 19,153 19,144 19,135 19,128 -25 -0.1
Civilian labor force.................................................. 10,489 10,659 11,639 11,695 1,206 11.5
Participation rate.................................................. 54.8 55.7 60.8 61.1 6.3 11.5
Employed.......................................................... 9,572 9,613 10,328 10,550 978 10.2
Employment-population ratio................................. 50.0 50.2 54.0 55.2 5.2 10.4
Unemployed....................................................... 916 1,045 1,312 1,145 229 25.0
Looking for full-time work..................................... 631 764 1,008 828 197 31.2
Looking for part-time work.................................... 285 281 303 317 32 11.2
Unemployment rate.............................................. 8.7 9.8 11.3 9.8 1.1 12.6
Not in labor force.................................................... 8,664 8,485 7,496 7,433 -1,231 -14.2
Women
Civilian noninstitutional population................................... 18,886 18,880 18,874 18,869 -17 -0.1
Civilian labor force.................................................. 9,936 10,120 11,001 11,321 1,385 13.9
Participation rate.................................................. 52.6 53.6 58.3 60.0 7.4 14.1
Employed.......................................................... 9,301 9,371 10,004 10,347 1,046 11.2
Employment-population ratio................................. 49.2 49.6 53.0 54.8 5.6 11.4
Unemployed....................................................... 635 749 997 974 339 53.4
Looking for full-time work..................................... 397 520 686 630 233 58.7
Looking for part-time work.................................... 238 229 311 344 106 44.5
Unemployment rate.............................................. 6.4 7.4 9.1 8.6 2.2 34.4
Not in labor force.................................................... 8,950 8,759 7,873 7,548 -1,402 -15.7
White
Civilian noninstitutional population................................... 27,910 27,894 27,879 27,866 -44 -0.2
Civilian labor force.................................................. 15,574 15,876 17,227 17,509 1,935 12.4
Participation rate.................................................. 55.8 56.9 61.8 62.8 7.0 12.5
Employed.......................................................... 14,535 14,680 15,692 16,174 1,639 11.3
Employment-population ratio................................. 52.1 52.6 56.3 58.0 5.9 11.3
Unemployed....................................................... 1,039 1,195 1,535 1,335 296 28.5
Looking for full-time work..................................... 656 818 1,106 863 207 31.6
Looking for part-time work.................................... 383 378 429 471 88 23.0
Unemployment rate.............................................. 6.7 7.5 8.9 7.6 0.9 13.4
Not in labor force.................................................... 12,336 12,019 10,652 10,357 -1,979 -16.0
Black or African American
Civilian noninstitutional population................................... 5,688 5,681 5,674 5,668 -20 -0.4
Civilian labor force.................................................. 2,795 2,866 3,152 3,203 408 14.6
Participation rate.................................................. 49.1 50.5 55.5 56.5 7.4 15.1
Employed.......................................................... 2,441 2,494 2,651 2,675 234 9.6
Employment-population ratio................................. 42.9 43.9 46.7 47.2 4.3 10.0
Unemployed....................................................... 354 372 501 528 174 49.2
Looking for full-time work..................................... 267 301 406 438 171 64.0
Looking for part-time work.................................... 86 71 95 90 4 4.7
Unemployment rate.............................................. 12.7 13.0 15.9 16.5 3.8 29.9
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
Hispanic or Latino ethnicity, April-July 2018 — Continued
[Numbers in thousands. Data are not seasonally adjusted.]
Employment status, sex, race, and
Hispanic or Latino ethnicity
April May June July
April-July changes
Number
Percent
Asian
Civilian noninstitutional population................................... 2,294 2,264 2,308 2,302 8 0.3
Civilian labor force.................................................. 895 811 950 997 102 11.4
Participation rate.................................................. 39.0 35.8 41.1 43.3 4.3 11.0
Employed.......................................................... 845 755 847 913 68 8.0
Employment-population ratio................................. 36.9 33.3 36.7 39.7 2.8 7.6
Unemployed....................................................... 49 56 102 84 35 71.4
Looking for full-time work..................................... 32 42 73 59 27 84.4
Looking for part-time work.................................... 17 14 29 25 8 47.1
Unemployment rate.............................................. 5.5 6.9 10.7 8.4 2.9 52.7
Not in labor force.................................................... 1,399 1,453 1,358 1,305 -94 -6.7
Hispanic or Latino ethnicity
Civilian noninstitutional population................................... 8,659 8,668 8,677 8,687 28 0.3
Civilian labor force.................................................. 4,592 4,676 4,784 5,035 443 9.6
Participation rate.................................................. 53.0 53.9 55.1 58.0 5.0 9.4
Employed.......................................................... 4,227 4,259 4,302 4,492 265 6.3
Employment-population ratio................................. 48.8 49.1 49.6 51.7 2.9 5.9
Unemployed....................................................... 365 417 482 543 178 48.8
Looking for full-time work..................................... 242 279 359 362 120 49.6
Looking for part-time work.................................... 123 137 123 181 58 47.2
Unemployment rate.............................................. 7.9 8.9 10.1 10.8 2.9 36.7
Not in labor force.................................................... 4,067 3,992 3,894 3,652 -415 -10.2
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
races. Persons whose ethnicity is identified as Hispanic or Latino may be of any race. Updated population controls are introduced annually with the
release of January data.

Table 2. Employment status of the civilian noninstitutional population 16 to 24 years of age by sex, race, and
Hispanic or Latino ethnicity, July 2015-2018
[Numbers in thousands. Data are not seasonally adjusted.]
Employment status, sex, race, and
Hispanic or Latino ethnicity
TOTAL
Civilian noninstitutional population...................................................... 38,589 38,450 38,152 37,997
Civilian labor force..................................................................... 23,162 23,104 23,107 23,016
Participation rate..................................................................... 60.0 60.1 60.6 60.6
Employed............................................................................. 20,333 20,456 20,890 20,897
Employment-population ratio.................................................... 52.7 53.2 54.8 55.0
Unemployed.......................................................................... 2,829 2,648 2,217 2,119
Looking for full-time work........................................................ 2,134 1,912 1,607 1,458
Looking for part-time work....................................................... 695 736 610 660
Unemployment rate................................................................. 12.2 11.5 9.6 9.2
Not in labor force....................................................................... 15,426 15,346 15,045 14,981
Men
Civilian noninstitutional population...................................................... 19,442 19,380 19,219 19,128
Civilian labor force..................................................................... 12,011 12,094 11,983 11,695
Participation rate..................................................................... 61.8 62.4 62.3 61.1
Employed............................................................................. 10,488 10,638 10,773 10,550
Employment-population ratio.................................................... 53.9 54.9 56.1 55.2
Unemployed.......................................................................... 1,523 1,455 1,210 1,145
Looking for full-time work........................................................ 1,195 1,169 944 828
Looking for part-time work....................................................... 328 286 266 317
Unemployment rate................................................................. 12.7 12.0 10.1 9.8
Not in labor force....................................................................... 7,431 7,287 7,236 7,433
Women
Civilian noninstitutional population...................................................... 19,147 19,069 18,932 18,869
Civilian labor force..................................................................... 11,151 11,010 11,124 11,321
Participation rate..................................................................... 58.2 57.7 58.8 60.0
Employed............................................................................. 9,846 9,818 10,117 10,347
Employment-population ratio.................................................... 51.4 51.5 53.4 54.8
Unemployed.......................................................................... 1,306 1,193 1,007 974
Looking for full-time work........................................................ 939 743 663 630
Looking for part-time work....................................................... 367 450 344 344
Unemployment rate................................................................. 11.7 10.8 9.1 8.6
Not in labor force....................................................................... 7,996 8,059 7,808 7,548
White
Civilian noninstitutional population...................................................... 28,488 28,297 28,038 27,866
Civilian labor force..................................................................... 17,735 17,734 17,423 17,509
Participation rate..................................................................... 62.3 62.7 62.1 62.8
Employed............................................................................. 15,903 15,981 16,031 16,174
Employment-population ratio.................................................... 55.8 56.5 57.2 58.0
Unemployed.......................................................................... 1,832 1,754 1,392 1,335
Looking for full-time work........................................................ 1,308 1,222 974 863
Looking for part-time work....................................................... 524 532 418 471
Unemployment rate................................................................. 10.3 9.9 8.0 7.6
Not in labor force....................................................................... 10,754 10,562 10,615 10,357
Black or African American
Civilian noninstitutional population...................................................... 5,916 5,850 5,749 5,668
Civilian labor force..................................................................... 3,337 3,149 3,214 3,203
Participation rate..................................................................... 56.4 53.8 55.9 56.5
Employed............................................................................. 2,645 2,499 2,694 2,675
Employment-population ratio.................................................... 44.7 42.7 46.9 47.2
Unemployed.......................................................................... 691 650 520 528
Looking for full-time work........................................................ 604 533 412 438
Looking for part-time work....................................................... 87 117 108 90
Unemployment rate................................................................. 20.7 20.6 16.2 16.5
Not in labor force....................................................................... 2,580 2,701 2,535 2,465
July
2015
July
2016
July
2017
July
2018

Table 2. Employment status of the civilian noninstitutional population 16 to 24 years of age by sex, race, and
Hispanic or Latino ethnicity, July 2015-2018 — Continued
[Numbers in thousands. Data are not seasonally adjusted.]
Employment status, sex, race, and
Hispanic or Latino ethnicity
Asian
Civilian noninstitutional population...................................................... 2,148 2,212 2,208 2,302
Civilian labor force..................................................................... 957 954 1,047 997
Participation rate..................................................................... 44.6 43.1 47.4 43.3
Employed............................................................................. 855 859 944 913
Employment-population ratio.................................................... 39.8 38.8 42.7 39.7
Unemployed.......................................................................... 102 95 103 84
Looking for full-time work........................................................ 68 65 70 59
Looking for part-time work....................................................... 34 30 33 25
Unemployment rate................................................................. 10.7 10.0 9.9 8.4
Not in labor force....................................................................... 1,191 1,258 1,162 1,305
Hispanic or Latino ethnicity
Civilian noninstitutional population...................................................... 8,406 8,497 8,535 8,687
Civilian labor force..................................................................... 4,728 4,776 4,835 5,035
Participation rate..................................................................... 56.2 56.2 56.6 58.0
Employed............................................................................. 4,127 4,235 4,347 4,492
Employment-population ratio.................................................... 49.1 49.8 50.9 51.7
Unemployed.......................................................................... 601 540 488 543
Looking for full-time work........................................................ 458 385 353 362
Looking for part-time work....................................................... 143 155 135 181
Unemployment rate................................................................. 12.7 11.3 10.1 10.8
Not in labor force....................................................................... 3,679 3,721 3,700 3,652
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
races. Persons whose ethnicity is identified as Hispanic or Latino may be of any race. Updated population controls are introduced annually with the
release of January data.
July
2015
July
2016
July
2017
July
2018

Table 3. Employed persons 16 to 24 years of age by industry, class of worker, race, and Hispanic or Latino
ethnicity, July 2017-2018
[Numbers in thousands. Data are not seasonally adjusted.]
Industry and class of worker
July
2017
Total
July
2018
July
2017
White
July
2018
Black or African
American
July
2017
July
2018
July
2017
Asian
July
2018
Hispanic or Latino
ethnicity
Total employed.................................. 20,890 20,897 16,031 16,174 2,694 2,675 944 913 4,347 4,492
Agriculture and related industries.......... 336 391 306 360 14 8 3 0 105 103
Nonagricultural industries................... 20,555 20,506 15,724 15,814 2,680 2,667 940 913 4,242 4,389
Private wage and salary workers 1 ....... 18,794 18,720 14,395 14,468 2,449 2,414 862 833 3,992 4,125
Mining, quarrying, and oil and gas
extraction.............................. 32 77 26 75 0 0 0 0 3 23
Construction............................. 1,061 1,000 965 888 44 44 9 13 368 283
Manufacturing........................... 1,361 1,409 1,062 1,124 161 141 72 72 273 306
Durable goods........................ 820 815 683 684 83 66 37 35 140 147
Nondurable goods.................... 540 595 380 439 79 74 35 37 134 159
Wholesale trade......................... 290 304 236 234 28 52 15 13 70 77
Retail trade.............................. 3,978 3,682 2,996 2,800 570 574 190 164 907 840
Transportation and utilities............. 595 577 380 447 157 73 17 16 144 184
Information............................... 305 276 199 212 46 25 31 23 57 49
Financial activities...................... 721 797 556 621 89 99 36 30 115 176
Professional and business services... 1,724 1,738 1,328 1,322 217 232 96 108 317 389
Education and health services........ 2,466 2,383 1,848 1,778 379 404 108 83 442 469
Leisure and hospitality................. 5,403 5,463 4,107 4,158 680 666 242 270 1,141 1,125
Other services........................... 858 1,014 691 812 78 104 46 41 155 204
Government wage and salary
workers................................... 1,491 1,398 1,096 1,064 210 190 72 66 213 176
Federal................................... 194 177 107 100 45 48 20 9 24 18
State...................................... 541 465 392 375 67 47 31 30 71 44
Local...................................... 756 757 597 589 98 96 20 28 118 114
Self-employed, unincorporated, and
unpaid family workers.................. 270 388 233 282 21 62 6 14 37 88
July
2017
July
2018
1 Includes self-employed workers whose businesses are incorporated.
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
races. Persons whose ethnicity is identified as Hispanic or Latino may be of any race. Updated population controls are introduced annually with the
release of January data.

Page 122 of 149

Attachment C
The Consequences
of Long-Term Unemployment
Page 123 of 149

URBAN
INSTITUTE
2100 M Street, NW
Washington, D.C. 20037-1264
Consequences of Long-Term Unemployment
Austin Nichols, Josh Mitchell, and Stephan Lindner

Copyright © July 2013. The Urban Institute. All rights reserved. Permission is granted for
reproduction of this file, with attribution to the Urban Institute.
This report was prepared for the Rockefeller Foundation under grant 2013 SRC 105.
The Urban Institute is a nonprofit, nonpartisan policy research and educational organization that
examines the social, economic, and governance problems facing the nation. The views expressed
are those of the author and should not be attributed to the Urban Institute, its trustees, or its
funders.
1

Contents
Challenges to Measuring Effects of Long-Term Unemployment 2
Declining Income and Consumption 3
Declining Reemployment Wages 4
Declining Human and Social Capital 7
Impacts on Future Labor Market Attachment 8
Impacts on Physical and Mental Health 9
Impacts on Children and Families 10
Impact on Communities 11
Conclusions 12
References 14

URBAN
INSTITUTE
Consequences of Long-Term Unemployment
The unemployment rate has been over 7 percent since December 2008, and peaked at 10 percent in
late 2009. Despite the gradual improvement in the labor market, long-term unemployment—the
share of the unemployed who have been out of work for more than six months—remains at
unprecedented levels. The fraction of unemployed workers who are long-term unemployed has
hovered around 40 percent from late 2009 into 2013, although it had never previously risen above
30 percent since the Great Depression.
Being out of work for six months or more is associated with lower well-being among the longterm
unemployed, their families, and their communities. Each week out of work means more lost
income. The long-term unemployed also tend to earn less once they find new jobs. They tend to be
in poorer health and have children with worse academic performance than similar workers who
avoided unemployment. Communities with a higher share of long-term unemployed workers also
tend to have higher rates of crime and violence.
Although there is considerable research documenting the association between long-term
unemployment and poor socioeconomic outcomes, it is not clear what drives those associations.
Those who become long-term unemployed may have issues that contribute to their unemployment
status and also to their poor future outcomes. In this case, long-term unemployment can be
associated with, but is not the underlying cause of, poor future outcomes, a phenomenon referred to
as a “selection” effect. Another complicating factor is the extent to which the association between
poor outcomes and long-term unemployment is rooted in the fact of an involuntary job loss itself
and not the time spent looking for work. Last, to a certain extent, health, family, and child outcomes
are influenced by the loss of income associated with long-term unemployment, and isolating the
income effects from the direct effects on long-term unemployment can be difficult.
In this paper, we discuss various channels through which longer unemployment duration might
influence outcomes for the unemployed. The discussion of prior research that follows shows that
direct evidence for many of these channels is very underdeveloped, somewhat surprisingly. Still,
there are plausible channels through which longer unemployment duration might result in worse
outcomes, most notably loss of human or social capital. We also discuss evidence for how job loss
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
job loss, and continuing on to health, family structure, children’s well-being, and whole
communities.
The measured impacts of unemployment can increase with the duration of unemployment.
Cumulative loss of income increases as unemployment continues, but expected wages at
reemployment also fall, leading to a permanent loss of future income. Many authors have
documented long-run losses of wages following an unemployment event in addition to many other
long-run impacts on measured well-being.
Challenges to Measuring Effects of Long-Term Unemployment
Long-term unemployment can plausibly affect individuals, families, and communities in direct ways.
When individuals are out of work, their skills may erode through lack of use. That erosion or
“depreciation of human capital” increases as time passes, meaning that the potential wages the
unemployed can earn on finding a new job and even the chances of finding a new job decrease the
longer they are out of work. Similarly, being out of work may reduce a worker’s “social capital”—the
network of business contacts that make finding new and good jobs easier. Social capital may
decrease with longer unemployment duration because social circles defined by work contact can
decay when work contact ceases, or because being out of work is increasingly stigmatizing the longer
a person cannot find new employment. That erosion of social capital means that the longer a worker
is unemployed, the less likely he or she is to find a new job. In addition, the stress of being out of
work can influence an individual’s physical and mental health, family dynamics, and the well-being of
his or her children. Involuntary job loss is a stressful event, creating a variety of problems
immediately, and long periods of unemployment can compound those problems.
Long-term unemployment can also influence outcomes indirectly. While a worker is
unemployed, that worker’s family income falls due to the lack of earnings, and that loss of income
(which becomes larger as unemployment is longer) can affect the worker and the worker’s
household. The loss of income can reduce the quantity and quality of goods and services the
worker’s family can purchase. Further, dealing with the loss of income can exacerbate stress. To the
extent that the negative consequences of long-term unemployment have an effect through the loss
of income, tax and transfer programs can help mitigate those consequences. Finally, if many workers
in the same geographic area are experiencing long-term unemployment, their communities could
2 The Urban Institute

suffer because of an increase in demand for public services and a decrease in the tax base used to
fund those services. Declines in community services, such as increased class sizes in public schools
or fewer public safety workers, can also feed back on individuals and families.
Identifying the mechanisms by which long-term unemployment affects individuals, families, and
communities is further complicated by the fact that even during severe recessions, job loss and long
periods of unemployment are not random events. Workers with less advantageous characteristics
tend to remain out of work longer and tend to experience worse outcomes regardless of when they
are reemployed. To the extent that the selection effect is important, differences observed across
workers with different durations of unemployment are not caused by longer duration. Selection is
often discussed as a problem in the context of wages or earnings, but the pattern of selection due to
workers with less advantageous characteristics left unemployed longer may also operate in other
domains, such as health or other measures of well-being.
Understanding the underlying mechanisms through which time out of work relates to outcomes
both during and after unemployment is essential in devising effective policies to ameliorate the
consequences of job loss. Policy responses to long-term unemployment may have different effects
depending on which of the possible explanations is dominant in accounting for any particular
outcome.
In short, there are plausible theoretical links connecting duration of unemployment to worse
outcomes. Much research on long-term consequences of job loss in previous recessions may reflect
both job loss and longer unemployment in a recession. Correlations between various outcomes and
job loss and long-term unemployment probably reflect at least some causal relationship. However,
as we examine the literature on various consequences of job loss and long-term unemployment, we
must bear in mind that many apparent consequences of job loss or longer duration may be due to
differences among workers out of work for different durations, and may also be caused by the loss
of income directly, not by unemployment itself.
Declining Income and Consumption
Longer unemployment has its most direct impact on family resources through lost earnings, which
add up quickly with each additional week of unemployment. In the Great Recession, family incomes
fell 40 percent or more for most long-term unemployed workers (Johnson and Feng 2013). In 2011,
Consequences Of Long-Term Unemployment 3

long-term unemployed workers were almost twice as likely to be poor as those unemployed less than
six months, and almost four times as likely to be poor as those never unemployed (Nichols 2012);
three of every four single parents who were unemployed more than 26 weeks were poor in 2011.
Browning and Crossley (2001) find that families with an unemployed worker have consumption
16 percent lower after six months of unemployment, but 24 percent lower if the sole worker in the
family became unemployed, relative to those who do not lose employment. Consumption drops less
than income following unemployment in part because of borrowing or spending down savings,
which is far from costless. Borie-Holtz, Van Horn, and Zukin (2010) show that the long-term
unemployed borrowed money from friends, spent down savings, and missed mortgage or rent
payments. About half of unemployed workers reported a poor financial situation in 2010, and about
a tenth had filed for bankruptcy (Godofsky, Van Horn, and Zukin 2010).
Consumption drops can have longer-term costs in addition to lowering well-being during
unemployment if family members defer needed expenditures. Among long-term unemployed or
underemployed workers in late 2011, 63 percent skipped dental visits, 56 percent put off needed
health care, and 40 percent reported not filling a prescription, with each proportion roughly twice
that for full-time workers. 1
Declining Reemployment Wages
The negative effect of unemployment on later wages is well documented (Jacobson, LaLonde, and
Sullivan 1993; Ruhm 1991). Hamermesh (1989) summarizes prior empirical work on dislocated and
displaced workers 2 showing that reemployed workers earned about 5 to 15 percent less than similar
workers who did not lose their jobs and that half of displaced workers finding jobs in some samples
had been unemployed as long as nine months. Across many studies, there were not large differences
1 Marilyn Geewax, “The Impacts of Long-Term Unemployment,” part of an NPR special series, Still No Job: Over a Year
without Enough Work, December 12, 2011. http://www.npr.org/2011/12/09/143438731/the-impacts-of-long-termunemployment.
2 A dislocated worker, as defined by the Workforce Investment Act, is a person who is terminated or laid off due to
plant or company closure or downsizing. Self-employed workers who become unemployed due to economic conditions
and homemakers who are no longer supported by another family member are also considered dislocated workers.
Displaced workers are similarly defined as persons 20 years of age and older who lost or left jobs because their plant or
company closed or moved, there was insufficient work for them to do, or their position or shift was abolished. Often
attention is restricted to long-tenure displaced workers, who had at least three years on the job they lost, following early
Displaced Worker Supplement survey design in the Current Population Survey. Hamermesh (1989) discusses studies
examining different types of job loss, but limits attention to displaced workers whose employer’s establishments (plants)
closed.
4 The Urban Institute

in wage losses by race or ethnicity (although minority workers are displaced with greater probability)
or sex. Predisplacement wage and occupation play some role, with displaced blue-collar workers
tending to lose a greater fraction of lifetime earnings.
Workers displaced in the early 1980s suffered declines of 30 percent or more in wages at
reemployment (Jacobson et al. 1993) and had earnings 20 percent lower than otherwise comparable
workers even 15 to 20 years after displacement (von Wachter, Song, and Manchester 2009). This is
partly because of steadily declining mean reemployment wages as unemployment duration increases,
and the persistence of reemployment wage discounts, meaning that the effects increase with
duration of unemployment. Barnette and Michaud (2012) find that laid-off workers, once
reemployed, have wages about 15 percent lower than continuously employed workers, 1 to 20 years
after layoff, whereas workers displaced by company closings, once reemployed, have wages about 5
to 10 percent lower than continuously employed workers. The wage disadvantage for workers
displaced by company closings dissipates 4 to 10 years after separation, suggesting that closings
convey less clear information to future employers about worker quality than other kinds of layoffs.
Because displacement leads to an increase of employment instability, some of the impact is also
due to multiple subsequent unemployment spells repeatedly lowering wages and reducing
accumulated job tenure and experience. Stevens (1997) showed that displaced workers are more
likely to leave their subsequent jobs than nondisplaced workers. Brand (2004) and Rosenfeld (1992)
document long-term reductions in career prospects for job losers, resulting in lower wage growth
over time.
Reservation wages (the lowest wage at which a job would be accepted) also decline over time, as
workers’ expectations degrade and their needs increase. 3 The reductions in wages over the course of
unemployment spells are also related to type of job loss 4 and to changing macroeconomic
conditions. 5
3 See, for example, Devine and Kiefer (1991).
4 Gibbons and Katz (1991) found that displaced workers received higher reemployment wages than workers fired for
cause. Lower wages at reemployment lead to persistent earnings disadvantages because workers’ entire future wage paths
depend on starting wages (Rosenfeld 1992).
5 Higher levels of long-term unemployment, such as in the early 1980s and the Great Recession, can create a cycle of
lower employment because job-finding rates are lowest among workers unemployed the longest and numbers of workers
per vacancy can be artificially inflated. As the labor market improves during a recovery, laid-off workers continue to be
reemployed at reduced wages, and the long-term unemployed tend to be the last to find a job, as documented in, for
example, von Wachter, Song, and Manchester (2009).
Consequences Of Long-Term Unemployment 5

Selection and screening both play a role in steadily declining mean wages as unemployment
continues. Selection refers to preexisting differences among workers who lose their jobs, producing
a pattern of more productive workers finding employment more quickly at less of a wage discount,
leaving less productive workers still unemployed facing lower wages. Screening is a similar
phenomenon on the employers’ side, where employers observing a worker unemployed longer will
infer that he or she must be a lower-productivity worker. 6 An important difference is that selection
does not reflect a causal impact (if we could intervene to shorten unemployment, it might not affect
outcomes), whereas screening can have impacts on individual workers negatively affected by
employer expectations.
Empirically, screening is an important mechanism. Kroft, Lange, and Notowidigdo (2012) show
in an experiment that employer screening resulted in interview rates cut nearly in half among the
long-term unemployed relative to the newly unemployed. Schmieder, von Wachter, and Bender
(2012a, 2012b, 2012c) show that increases in duration of unemployment caused by benefit changes
drive down the wage offers of employers but do not substantially affect reservation wages. This line
of research is one of the very few to plausibly establish a direct causal impact of increases in duration
of unemployment. 7
Much of the research on wage losses at reemployment cannot distinguish between losses due to
longer duration of unemployment for an individual and losses due to less favorable labor market
conditions (higher unemployment, longer average durations) that are correlated with own duration.
Davis and von Wachter (2011) find that men lose twice as much in lifetime earnings from being
displaced via a mass layoff when the unemployment rate exceeds 8 percent as they do if displaced
when the national unemployment rate is below 6 percent. However, it is not clear whether this is
due to downward pressure on reemployment wages across the economy, or due to causal impacts of
longer unemployment spells that accompany higher national unemployment.
6 One can see selection and screening as two sides of the same coin, if workers anticipate firm screening and firms
correctly anticipate reductions in average worker productivity. But they need not be, if firms’ expectations about worker
quality declining with unemployment duration are incorrect but workers accept lower wages over time because the path
of future wages looks bleak owing to unrealistic firm expectations. That is, screening can drive a vicious cycle of
declining wages. Selection can operate without screening as well, if workers with higher ability are paid the same wage
regardless of unemployment duration, but also exit faster, so that unemployment duration is associated with lower wages
only due to the heterogeneity of workers.
7 Whether wage offers are driven down by information about declining average worker productivity, due to either
selection or skill erosion, or information about workers’ increasing desperation to find work, is not established. This line
of work does cast doubt on explanations that rely on social networks among job losers, however.
6 The Urban Institute

Declining Human and Social Capital
Declines in wages need not be due to only selection on (or screening of) heterogeneous workers and
declining expectations, but also due to real declines in productivity as human or social capital
depreciates, possibly as the labor market moves farther away from the type of work lost, or as the
displaced worker looks farther afield for new work. A real decline in productivity due to
deteriorating human capital would be a true causal impact of longer unemployment, unlike selection
due to the changing composition of the unemployed at different durations. 8
One of the stories explaining declining reemployment wages via a causal mechanism instead of
pure selection or screening is that human or social capital decays as workers are out of work longer.
Yet most research purporting to measure human capital depreciation (e.g., Hollenbeck 1990) uses
wages as a measure of human capital, so one cannot distinguish between the effect of human capital
and other factors that might affect wages.
The few studies that attempt to measure human capital directly do not deal with selection, so
that all observed correlation between unemployment duration and human capital might be because
people with lower human capital are likely to remain out of work for a longer time. For example,
Edin and Gustavsson (2008) find that a year out of work is associated with general skills 5 percentile
points lower relative to continuously employed workers, but there is no attempt to isolate the causal
impact of time out of work from selection driving a larger fraction of unemployed workers being
lower-ability workers over time, as higher-ability workers return to work. Edin and Gustavsson also
cannot rule out reverse causation where “negative trends in skills lead to non-employment” (2008,
174); that is, workers who experience skill depreciation relative to the rest of the workforce are more
likely to be laid off.
The research on the deterioration of skills is far from persuasive and plagued by data problems. 9
Losses of job tenure or experience are losses of human capital compared with otherwise identical
individuals who were never unemployed, not losses of human capital relative to pre-unemployment
8 Declining reservation wages could reflect a decline in human capital, or endogenous changes in expectations related to
human capital. For example, if laid-off workers believe their own industry or occupation, where their specific human
capital is valued, will not recover fast enough, they may be induced to switch industry or occupation further afield over
time, trading off losses in future wages for higher exit rates from unemployment in the near term.
9 Measurement error in past test scores leads the coefficient to be biased toward zero, which many authors have taken as
evidence of “depreciation” of skills over time in the absence of intervention.
Consequences Of Long-Term Unemployment 7

human capital of the individual. Losses of firm-specific and industry-specific human capital are
incurred at separation, and are unlikely to increase much in magnitude over time spent unemployed.
A far more plausible loss increasing in magnitude with prolonged unemployment is of social
capital, but little direct evidence is available. As Machin and Manning (1999) discuss:
Many studies have documented the importance of the use of current workers to
recruit friends and relatives. Something like a third of jobs in the UK are filled in this
way (Gregg and Wadsworth, 1996). The reasons given are generally that it is costeffective
and workers are unlikely to recommend others who they know are going to
prove to be unsuitable workers. There is other evidence that suggests that the
unemployed lose social contacts as their spells lengthen and that what social contacts
they do maintain come to be increasingly made up of other unemployed.
If loss of human or social capital is the driving mechanism behind reemployment wages
declining over time, then policies designed to keep unemployed workers using skills or in contact
with other workers would be an effective means of heading off long-term wage losses. Evidence on
job training programs has produced mixed evidence about training as an efficient means to raise
reemployment wages, although Holzer (2011) and Jacobson, LaLonde, and Sullivan (2011) provide
guidance on improving practice. Subsidized employment programs may hold out even better
prospects, by addressing both human and social capital depreciation. 10
Impacts on Future Labor Market Attachment
Unemployed workers become more likely to leave the labor force and retire, enroll in disability
programs, or simply become “discouraged workers” as unemployment continues. The exit to
disability is most worrisome because it tends to be permanent. Once someone identifies as being
disabled, the individual is very unlikely to return to work; in fact, retired people are far more likely to
reenter the labor market than the disabled. Rupp and Stapleton (1995) find that higher
unemployment tends to increase the number of applicants to the Social Security Disability Insurance
(DI) program, and eventually the number of successful applicants. Lindner and Nichols (2012) find
that expansions of unemployment insurance staved off some applications for DI benefits.
10 Evaluations of subsidized employment and transitional jobs (Bloom 2010) indicate relatively small gains in
employment and earnings, but larger gains for some subgroups, especially single mothers (Michalopoulos 2005).
Programs that integrate subsidized employment into unemployment insurance systems, such as the German
Arbeitsbeschaffungsmaßnahmen, also seem to produce small increases in future employment and income on average, but
larger effects for some subgroups (Caliendo, Hujer, and Thomsen 2008; Hujer, Caliendo, and Thomsen 2004).
8 The Urban Institute

Impacts on Physical and Mental Health
Burgard, Brand, and House (2007) report large declines in self-reported health status following job
loss, even after taking differences in characteristics of job losers into account. Losses are largest
among those who lose jobs for reasons related to health, implying the causal impact of job loss is
much smaller than the impact observed by comparing job losers to other workers. Furthermore, job
losses for other reasons increase depressive symptoms but have little impact on other measures of
health. There is little evidence of health deteriorating over the course of an unemployment spell
(Salm 2009). In fact, Ruhm (2000, 2001, 2005, 2007) documents improvements in health as
unemployed workers get more exercise, smoke and drink less, lose weight, and suffer less from jobrelated
or commute-related health risks.
Sullivan and von Wachter (2009) find that the mortality consequences of displacement are
severe, with a 50 to 100 percent increase in death rates the year following displacement and 10 to 15
percent increases in death rates for the next 20 years. For a 40-year-old worker, that implies a decline
in life expectancy of a year to a year and a half. Long-term joblessness results in higher mortality, but
voluntary and involuntary separations seem to have similar impacts on mortality (Couch et al. 2013).
The mechanism for these mortality increases is unclear but could be related to income loss, increases
in risky health behavior (Browning and Heinesen 2012), and losses of health insurance coverage
(Olson 1992).
Although job loss increases the subsequent risk of death, the impacts of longer duration of
unemployment on health or mortality are not clearly identified. Given that longer-duration
unemployment is associated with higher mortality, but health does not seem to deteriorate during a
spell, the observed correlations may be related to lower lifetime income (via both more forgone
earnings and lower future wages earned by the long-term unemployed), and not through any direct
impact on health. The link between income and health is also not clearly causal, however, and there
is some evidence that it is increased labor force attachment that lowers mortality, not higher income
(Snyder and Evans 2006).
There is a long history of research showing that becoming unemployed has large negative effects
on mental health, but that mental health does not deteriorate substantially with longer duration of
unemployment. Whooley and colleagues (2002) found that depression strongly predicts future job
and income losses, suggesting reverse causation is an important threat to such comparisons. Clark
Consequences Of Long-Term Unemployment 9

and Oswald (1994) found duration of unemployment is actually positively correlated with well-being,
conditional on being unemployed. Winkelmann and Winkelmann (1998) found no evidence of
satisfaction changing over the course of a spell of unemployment. 11 On the other hand, Classen and
Dunn (2012) estimated that higher rates of long-term unemployment increase suicide rates, although
this may in part reflect general economic conditions. Browning and Heinesen (2012) used microlevel
data from Denmark and found that job loss increases alcohol-related disease, mental illness,
and suicide and suicide attempts, but these effects could be due to job loss itself, and unrelated to
unemployment duration.
Theoretically, links between declining employment prospects and declining mental health seem
clear. As economic stress increases, the incidence of anxiety disorders should increase, and as
individuals fall in the social hierarchy, serotonin-pathway disorders, including depression, should
become more prevalent. 12 Alternatively, as expectations fall, people may adjust to a new normal and
take fuller advantage of leisure time, leading to improvements in measured mental health. On
balance, the empirical evidence for the link between longer durations of unemployment and worse
mental health is far from clear.
Effects on Children and Families
There are a large variety of negative effects of job loss observed in the families of workers, although
the causal mechanism is not always well known. Kalil and De Leire (2002) found that the negative
effects of job loss for children were limited to those associated with the loss of a father’s job.
Similarly, Lindo (2011) documented a negative impact of paternal job loss on infant birth weight.
Rege, Telle, and Votruba (2011) also showed that paternal job loss lowers children’s school
performance, and the negative effect of paternal job loss is not mediated by income, a shift in
maternal time toward employment, marital dissolution, or residential relocation. Stevens and Schaller
(2011) showed that layoffs affect children’s grade retention, and Wightman (2012) documented a
11 Diette and colleagues (2012) suggest that psychological distress is higher among long-term unemployed than shortterm
unemployed or continuously employed workers who have not experienced prior psychological distress, but they do
not distinguish statistically between effects of long-term unemployment and short-term unemployment.
12 Serotonin regulates self-esteem and may also regulate risky behavior and suicidal ideation. McGuire and Raleigh (1986)
showed that position in a hierarchy affects serotonin levels, and that manipulation of serotonin can affect position in a
hierarchy. Many studies show that relative income is an important predictor of health and mortality (Eibner and Evans
2005), more so than absolute income in some cases, meaning that position in a social hierarchy can have large effects on
long-term outcomes. Hierarchical position can also explain a lack of increasing stress or depression with longer duration
unemployment, however, if people increasingly identify with a more disadvantaged peer group in which they rank
higher.
10 The Urban Institute

eduction in the probability that children finish high school after paternal job loss. Oreopoulos,
Page, and Stevens (2008) traced the impact of job loss on children’s later earnings as adults. Katz
(2010) pointed out that financial aid based on prior year income does not address the immediate
needs of students whose parents are laid off, perhaps leading to losses of educational opportunity in
the second generation. Loss of continuous health insurance coverage could also play a role in worse
child outcomes, as Johnson and Schoeni (2011) show that health insurance can play a large role in
intergenerational transmission of disadvantage.
Job losses and long-term unemployment can affect children’s outcomes through increased
family stress and reduced incomes. McLoyd and colleagues (1994) documented how financial stress
from job loss affects the emotional well-being of mothers, producing increased cognitive distress
and depressive symptoms in adolescent children and more negative assessments of maternal
interaction. Children whose parents suffer longer unemployment and larger lifetime income losses
can be expected to suffer greater detriment to their emotional well-being, and this may result in
worse education and labor market outcomes in the children’s generation.
Changes in family structure may be another mechanism by which the negative consequences of
job losses are transmitted to the next generation. Del Bono, Weber, and Winter-Ebmer (2008) and
Lindo (2010) showed that layoff affects fertility rates, and Lindner and Peters (2013) found negative
effects of job loss of mothers and fathers on family stability, especially for married parents, which is
one factor through which parental job loss may affect the well-being of children. Charles and
Stephens (2004) documented an increase in divorce following layoffs but not plant closings or
disability, suggesting that the higher divorce rate is more strongly related to the job loser’s
productivity and other attributes rather than diminished financial prospects.
Impact on Communities
High rates of long-term unemployment can devastate local communities, as reduced lifetime income
prospects induce a variety of behavioral changes, and alter social networks. Wilson (1987) building
on Kain (1968), argued that a lack of available jobs close to where the disadvantaged unemployed
workers live, or “spatial mismatch,” contributes to long durations of joblessness, in part because
social networks become largely populated by other jobless workers. Persistent joblessness for men is
then linked to breakdowns in traditional family arrangements, increased use of public assistance, and
Consequences Of Long-Term Unemployment 11

high crime. As long-term unemployment becomes more concentrated, the neighborhood becomes a
source of persistent poverty.
Even if long-term unemployment does not have important effects on job finding via social
networks, it can induce behavioral changes that have important spillover effects on the community
as a whole. In addition to engaging in riskier health behavior and reducing investments in housing
and other capital stocks that benefit the community as a whole, the long-term unemployed may be
induced to seek out work in the illegal sector. Although crime rates fell in many areas during the
Great Recession, they seem to have fallen due to a long-term trend, and to have fallen less in places
hit harder by job loss. 13
Rege, Telle, and Votruba (2012) document a 14 percentage point higher probability of arrest
among workers affected by plant closings, but there are important dynamic and spillover effects as
well. As Machin and Manning (1999) discuss:
a cycle develops whereby involvement in crime reduces subsequent employment
prospects which then raises the likelihood of participating in crime (see Thornberry
and Christensen, 1984). In this vein Freeman (1992) and Grogger (1992) show some
association between the persistence of joblessness and crime. Fagan and Freeman
(1997) also review evidence that show important links between unemployment and
crime…. It should be emphasized again that it is difficult to distinguish between
heterogeneity and true duration dependence as the explanation for these correlations.
Impacts on communities may be driven in part by increases in concentrated poverty due to longterm
unemployment. The evidence of higher impacts due to greater spatial concentration of poverty
is mixed, however, as discussed by Turner, Nichols, and Comey (2012) in the context of the Moving
to Opportunity experiment.
Conclusions
The extensive evidence on far-reaching negative consequences of job loss is clear: Loss of a job can
lead to losses of income in the short run, permanently lower wages, and result in worse mental and
physical health and higher mortality rates. Further, parental job loss hampers children’s educational
progress and lowers their future earnings. The link between longer duration of unemployment and
worse consequences is more tenuous. Lower wages and lifetime incomes are associated with longer
13 John Roman, “Learning About the Crime Decline from Big City Experiences with Homicide,” MetroTrends (blog),
December 16, 2012, http://blog.metrotrends.org/2012/12/learning-crime-decline-big-city-experiences-homicide/.
12 The Urban Institute

periods of unemployment, but the reason for the decreasing earnings prospects is not clear. In
domains where we might expect to see strong evidence, such as mental health outcomes, the
evidence is murky at best. When there are patterns of declining well-being as unemployment extends
longer, the extent to which declining well-being is due to increasing loss of lifetime income alone or
to time out of work is not clear.
The need to distinguish among competing explanations for the observed patterns is pressing,
because different policy responses would be called for depending on which of the potential
explanations is the dominant one. Further research should identify more clearly whether selection,
declining reservation wages, human capital depreciation, or some form of employer discrimination
seems to be the dominant explanation for reemployment wage declining with unemployment
duration. We also need to explore whether other long-run negative impacts of job loss and
unemployment duration are due to those same factors, or to loss of income or social position.
Consequences Of Long-Term Unemployment 13

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Consequences Of Long-Term Unemployment 17

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Advocacy Foundation Publishers
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Advocacy Foundation Publishers
The e-Advocate Quarterly
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Issue Title Quarterly
Vol. I 2015 The Fundamentals
I
The ComeUnity ReEngineering
Project Initiative
Q-1 2015
II The Adolescent Law Group Q-2 2015
III
Landmark Cases in US
Juvenile Justice (PA)
Q-3 2015
IV The First Amendment Project Q-4 2015
Vol. II 2016 Strategic Development
V The Fourth Amendment Project Q-1 2016
VI
Landmark Cases in US
Juvenile Justice (NJ)
Q-2 2016
VII Youth Court Q-3 2016
VIII
The Economic Consequences of Legal
Decision-Making
Q-4 2016
Vol. III 2017 Sustainability
IX The Sixth Amendment Project Q-1 2017
X
The Theological Foundations of
US Law & Government
Q-2 2017
XI The Eighth Amendment Project Q-3 2017
XII
The EB-5 Investor
Immigration Project*
Q-4 2017
Vol. IV 2018 Collaboration
XIII Strategic Planning Q-1 2018
XIV
The Juvenile Justice
Legislative Reform Initiative
Q-2 2018
XV The Advocacy Foundation Coalition Q-3 2018
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XVI
for Drug-Free Communities
Landmark Cases in US
Juvenile Justice (GA)
Q-4 2018
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Issue Title Quarterly
Vol. V 2019 Organizational Development
XVII The Board of Directors Q-1 2019
XVIII The Inner Circle Q-2 2019
XIX Staff & Management Q-3 2019
XX Succession Planning Q-4 2019
XXI The Budget* Bonus #1
XXII Data-Driven Resource Allocation* Bonus #2
Vol. VI 2020 Missions
XXIII Critical Thinking Q-1 2020
XXIV
The Advocacy Foundation
Endowments Initiative Project
Q-2 2020
XXV International Labor Relations Q-3 2020
XXVI Immigration Q-4 2020
Vol. VII 2021 Community Engagement
XXVII
The 21 st Century Charter Schools
Initiative
Q-1 2021
XXVIII The All-Sports Ministry @ ... Q-2 2021
XXIX Lobbying for Nonprofits Q-3 2021
XXX
XXXI
Advocacy Foundation Missions -
Domestic
Advocacy Foundation Missions -
International
Q-4 2021
Bonus
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Vol. VIII
2022 ComeUnity ReEngineering
XXXII
The Creative & Fine Arts Ministry
@ The Foundation
Q-1 2022
XXXIII The Advisory Council & Committees Q-2 2022
XXXIV
The Theological Origins
of Contemporary Judicial Process
Q-3 2022
XXXV The Second Chance Ministry @ ... Q-4 2022
Vol. IX 2023 Legal Reformation
XXXVI The Fifth Amendment Project Q-1 2023
XXXVII The Judicial Re-Engineering Initiative Q-2 2023
XXXVIII
The Inner-Cities Strategic
Revitalization Initiative
Q-3 2023
XXXVIX Habeas Corpus Q-4 2023
Vol. X 2024 ComeUnity Development
XXXVX
The Inner-City Strategic
Revitalization Plan
Q-1 2024
XXXVXI The Mentoring Initiative Q-2 2024
XXXVXII The Violence Prevention Framework Q-3 2024
XXXVXIII The Fatherhood Initiative Q-4 2024
Vol. XI 2025 Public Interest
XXXVXIV Public Interest Law Q-1 2025
L (50) Spiritual Resource Development Q-2 2025
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LI
Nonprofit Confidentiality
In The Age of Big Data
Q-3 2025
LII Interpreting The Facts Q-4 2025
Vol. XII 2026 Poverty In America
LIII
American Poverty
In The New Millennium
Q-1 2026
LIV Outcome-Based Thinking Q-2 2026
LV Transformational Social Leadership Q-3 2026
LVI The Cycle of Poverty Q-4 2026
Vol. XIII 2027 Raising Awareness
LVII ReEngineering Juvenile Justice Q-1 2027
LVIII Corporations Q-2 2027
LVIX The Prison Industrial Complex Q-3 2027
LX Restoration of Rights Q-4 2027
Vol. XIV 2028 Culturally Relevant Programming
LXI Community Culture Q-1 2028
LXII Corporate Culture Q-2 2028
LXIII Strategic Cultural Planning Q-3 2028
LXIV
The Cross-Sector/ Coordinated
Service Approach to Delinquency
Prevention
Q-4 2028
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Vol. XV 2029 Inner-Cities Revitalization
LXIV
LXV
LXVI
Part I – Strategic Housing
Revitalization
(The Twenty Percent Profit Margin)
Part II – Jobs Training, Educational
Redevelopment
and Economic Empowerment
Part III - Financial Literacy
and Sustainability
Q-1 2029
Q-2 2029
Q-3 2029
LXVII Part IV – Solutions for Homelessness Q-4 2029
LXVIII
The Strategic Home Mortgage
Initiative
Bonus
Vol. XVI 2030 Sustainability
LXVIII Social Program Sustainability Q-1 2030
LXIX
The Advocacy Foundation
Endowments Initiative
Q-2 2030
LXX Capital Gains Q-3 2030
LXXI Sustainability Investments Q-4 2030
Vol. XVII 2031 The Justice Series
LXXII Distributive Justice Q-1 2031
LXXIII Retributive Justice Q-2 2031
LXXIV Procedural Justice Q-3 2031
LXXV (75) Restorative Justice Q-4 2031
LXXVI Unjust Legal Reasoning Bonus
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Vol. XVIII 2032 Public Policy
LXXVII Public Interest Law Q-1 2032
LXXVIII Reforming Public Policy Q-2 2032
LXXVIX ... Q-3 2032
LXXVX ... Q-4 2032
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The e-Advocate Monthly Review
2018
Transformational Problem Solving January 2018
The Advocacy Foundation February 2018
Opioid Initiative
Native-American Youth March 2018
In the Juvenile Justice System
Barriers to Reducing Confinement April 2018
Latino and Hispanic Youth May 2018
In the Juvenile Justice System
Social Entrepreneurship June 2018
The Economic Consequences of
Homelessness in America S.Ed – June 2018
African-American Youth July 2018
In the Juvenile Justice System
Gang Deconstruction August 2018
Social Impact Investing September 2018
Opportunity Youth: October 2018
Disenfranchised Young People
The Economic Impact of Social November 2018
of Social Programs Development
Gun Control December 2018
2019
The U.S. Stock Market January 2019
Prison-Based Gerrymandering February 2019
Literacy-Based Prison Construction March 2019
Children of Incarcerated Parents April 2019
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African-American Youth in The May 2019
Juvenile Justice System
Racial Profiling June 2019
Mass Collaboration July 2019
Concentrated Poverty August 2019
De-Industrialization September 2019
Overcoming Dyslexia October 2019
Overcoming Attention Deficit November 2019
The Gift of Adversity December 2019
2020
The Gift of Hypersensitivity January 2020
The Gift of Introspection February 2020
The Gift of Introversion March 2020
The Gift of Spirituality April 2020
The Gift of Transformation May 2020
Property Acquisition for
Organizational Sustainability June 2020
Investing for Organizational
Sustainability July 2020
Biblical Law & Justice TLFA August 2020
Gentrification AF September 2020
Environmental Racism NpA October 2020
Law for The Poor AF November 2020
…
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2021
Biblically Responsible Investing TLFA – January 2021
International Criminal Procedure LMI – February 2021
Spiritual Rights TLFA – March 2021
The Theology of Missions TLFA – April 2021
Legal Evangelism, Intelligence,
Reconnaissance & Missions LMI – May 2021
The Law of War LMI – June 2021
Generational Progression AF – July 2021
Predatory Lending AF – August 2021
The Community Assessment Process NpA – September 2021
Accountability NpA – October 2021
Nonprofit Transparency NpA – November 2021
Redefining Unemployment AF – December 2021
2022
…
Page 136 of 149

The e-Advocate Quarterly
Special Editions
Crowdfunding Winter-Spring 2017
Social Media for Nonprofits October 2017
Mass Media for Nonprofits November 2017
The Opioid Crisis in America: January 2018
Issues in Pain Management
The Opioid Crisis in America: February 2018
The Drug Culture in the U.S.
The Opioid Crisis in America: March 2018
Drug Abuse Among Veterans
The Opioid Crisis in America: April 2018
Drug Abuse Among America’s
Teens
The Opioid Crisis in America: May 2018
Alcoholism
The Economic Consequences of June 2018
Homelessness in The US
The Economic Consequences of July 2018
Opioid Addiction in America
Page 137 of 149

The e-Advocate Journal
of Theological Jurisprudence
Vol. I - 2017
The Theological Origins of Contemporary Judicial Process
Scriptural Application to The Model Criminal Code
Scriptural Application for Tort Reform
Scriptural Application to Juvenile Justice Reformation
Vol. II - 2018
Scriptural Application for The Canons of Ethics
Scriptural Application to Contracts Reform
& The Uniform Commercial Code
Scriptural Application to The Law of Property
Scriptural Application to The Law of Evidence
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Legal Missions International
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Issue Title Quarterly
Vol. I 2015
I
II
God’s Will and The 21 st Century
Democratic Process
The Community
Engagement Strategy
Q-1 2015
Q-2 2015
III Foreign Policy Q-3 2015
IV
Public Interest Law
in The New Millennium
Q-4 2015
Vol. II 2016
V Ethiopia Q-1 2016
VI Zimbabwe Q-2 2016
VII Jamaica Q-3 2016
VIII Brazil Q-4 2016
Vol. III 2017
IX India Q-1 2017
X Suriname Q-2 2017
XI The Caribbean Q-3 2017
XII United States/ Estados Unidos Q-4 2017
Vol. IV 2018
XIII Cuba Q-1 2018
XIV Guinea Q-2 2018
XV Indonesia Q-3 2018
XVI Sri Lanka Q-4 2018
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Vol. V 2019
XVII Russia Q-1 2019
XVIII Australia Q-2 2019
XIV South Korea Q-3 2019
XV Puerto Rico Q-4 2019
Issue Title Quarterly
Vol. VI 2020
XVI Trinidad & Tobago Q-1 2020
XVII Egypt Q-2 2020
XVIII Sierra Leone Q-3 2020
XIX South Africa Q-4 2020
XX Israel Bonus
Vol. VII 2021
XXI Haiti Q-1 2021
XXII Peru Q-2 2021
XXIII Costa Rica Q-3 2021
XXIV China Q-4 2021
XXV Japan Bonus
Vol VIII 2022
XXVI Chile Q-1 2022
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The e-Advocate Juvenile Justice Report
______
Vol. I – Juvenile Delinquency in The US
Vol. II. – The Prison Industrial Complex
Vol. III – Restorative/ Transformative Justice
Vol. IV – The Sixth Amendment Right to The Effective Assistance of Counsel
Vol. V – The Theological Foundations of Juvenile Justice
Vol. VI – Collaborating to Eradicate Juvenile Delinquency
Page 142 of 149

The e-Advocate Newsletter
Genesis of The Problem
Family Structure
Societal Influences
Evidence-Based Programming
Strengthening Assets v. Eliminating Deficits
2012 - Juvenile Delinquency in The US
Introduction/Ideology/Key Values
Philosophy/Application & Practice
Expungement & Pardons
Pardons & Clemency
Examples/Best Practices
2013 - Restorative Justice in The US
2014 - The Prison Industrial Complex
25% of the World's Inmates Are In the US
The Economics of Prison Enterprise
The Federal Bureau of Prisons
The After-Effects of Incarceration/Individual/Societal
The Fourth Amendment Project
The Sixth Amendment Project
The Eighth Amendment Project
The Adolescent Law Group
2015 - US Constitutional Issues In The New Millennium
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2018 - The Theological Law Firm Academy
The Theological Foundations of US Law & Government
The Economic Consequences of Legal Decision-Making
The Juvenile Justice Legislative Reform Initiative
The EB-5 International Investors Initiative
2017 - Organizational Development
The Board of Directors
The Inner Circle
Staff & Management
Succession Planning
Bonus #1 The Budget
Bonus #2 Data-Driven Resource Allocation
2018 - Sustainability
The Data-Driven Resource Allocation Process
The Quality Assurance Initiative
The Advocacy Foundation Endowments Initiative
The Community Engagement Strategy
2019 - Collaboration
Critical Thinking for Transformative Justice
International Labor Relations
Immigration
God's Will & The 21st Century Democratic Process
The Community Engagement Strategy
The 21st Century Charter Schools Initiative
2020 - Community Engagement
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Extras
The Nonprofit Advisors Group Newsletters
The 501(c)(3) Acquisition Process
The Board of Directors
The Gladiator Mentality
Strategic Planning
Fundraising
501(c)(3) Reinstatements
The Collaborative US/ International Newsletters
How You Think Is Everything
The Reciprocal Nature of Business Relationships
Accelerate Your Professional Development
The Competitive Nature of Grant Writing
Assessing The Risks
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Page 146 of 149

About The Author
John C (Jack) Johnson III
Founder & CEO
Jack was educated at Temple University, in Philadelphia, Pennsylvania and Rutgers
Law School, in Camden, New Jersey. In 1999, he moved to Atlanta, Georgia to pursue
greater opportunities to provide Advocacy and Preventive Programmatic services for atrisk/
at-promise young persons, their families, and Justice Professionals embedded in the
Juvenile Justice process in order to help facilitate its transcendence into the 21 st Century.
There, along with a small group of community and faith-based professionals, “The Advocacy Foundation, Inc." was conceived
and developed over roughly a thirteen year period, originally chartered as a Juvenile Delinquency Prevention and Educational
Support Services organization consisting of Mentoring, Tutoring, Counseling, Character Development, Community Change
Management, Practitioner Re-Education & Training, and a host of related components.
The Foundation’s Overarching Mission is “To help Individuals, Organizations, & Communities Achieve Their Full Potential”, by
implementing a wide array of evidence-based proactive multi-disciplinary "Restorative & Transformative Justice" programs &
projects currently throughout the northeast, southeast, and western international-waters regions, providing prevention and support
services to at-risk/ at-promise youth, to young adults, to their families, and to Social Service, Justice and Mental
Health professionals” everywhere. The Foundation has since relocated its headquarters to Philadelphia, Pennsylvania, and been
expanded to include a three-tier mission.
In addition to his work with the Foundation, Jack also served as an Adjunct Professor of Law & Business at National-Louis
University of Atlanta (where he taught Political Science, Business & Legal Ethics, Labor & Employment Relations, and Critical
Thinking courses to undergraduate and graduate level students). Jack has also served as Board President for a host of wellestablished
and up & coming nonprofit organizations throughout the region, including “Visions Unlimited Community
Development Systems, Inc.”, a multi-million dollar, award-winning, Violence Prevention and Gang Intervention Social Service
organization in Atlanta, as well as Vice-Chair of the Georgia/ Metropolitan Atlanta Violence Prevention Partnership, a state-wide
300 organizational member, violence prevention group led by the Morehouse School of Medicine, Emory University and The
Original, Atlanta-Based, Martin Luther King Center.
Attorney Johnson’s prior accomplishments include a wide-array of Professional Legal practice areas, including Private Firm,
Corporate and Government postings, just about all of which yielded significant professional awards & accolades, the history and
chronology of which are available for review online. Throughout his career, Jack has served a wide variety of for-profit
corporations, law firms, and nonprofit organizations as Board Chairman, Secretary, Associate, and General Counsel since 1990.
www.TheAdvocacy.Foundation
Clayton County Youth Services Partnership, Inc. – Chair; Georgia Violence Prevention Partnership, Inc – Vice Chair; Fayette
County NAACP - Legal Redress Committee Chairman; Clayton County Fatherhood Initiative Partnership – Principal
Investigator; Morehouse School of Medicine School of Community Health Feasibility Study - Steering Committee; Atlanta
Violence Prevention Capacity Building Project – Project Partner; Clayton County Minister’s Conference, President 2006-2007;
Liberty In Life Ministries, Inc. – Board Secretary; Young Adults Talk, Inc. – Board of Directors; ROYAL, Inc - Board of
Directors; Temple University Alumni Association; Rutgers Law School Alumni Association; Sertoma International; Our
Common Welfare Board of Directors – President)2003-2005; River’s Edge Elementary School PTA (Co-President); Summerhill
Community Ministries; Outstanding Young Men of America; Employee of the Year; Academic All-American - Basketball;
Church Trustee.
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www.TheAdvocacy.Foundation
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