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120 WORLD DEVELOPMENT REPORT 2016 Figure 2.14 Falling labor shares in national income are associated with rising inequality Growth in Gini coefficient vs. growth in labor share in national income, 1995–2010 Change in the Gini coefficient in consumption or income (%) 30 25 20 15 10 5 0 –5 –10 –15 –20 CHN LVA DNK Change in the share of national output going to labor (%) Source: Eden and Gaggl 2015, for the WDR 2016. Data at http://bit.do/WDR2016-Fig2_14. FIN BGR EST CRI GBR ITA NOR ESP HND GRC ARG PAN TUN –30 –25 –20 –15 –10 –5 0 5 10 15 20 25 IRL PRY BLR Employment polarization Declining labor shares coincide with the polarization of labor markets, most notably in high-income countries. 109 Employment is growing in high-skilled, high-paying occupations (managers, professionals, technicians) and low-skilled, low-paying occupations (elementary, service, and sales workers). Middleskilled, middle-paying occupations (clerks, plant and machine operators) are being squeezed (figure 2.15). In high-income countries, on average, the share of routine labor in employment has fallen by about 0.59 percentage points a year since 1995, or almost 12 percentage points for the period. In the United States, local labor markets susceptible to automation due to specialization in routine task-intensive occupations have no net decline in employment, but they experience polarization in manufacturing and services. 110 There are signs that employment is also polarizing in a number of low- and middle-income countries. The average decline in the share of routine employment has been 0.39 percentage points a year, or 7.8 percentage points for the period. China is an exception, since the mechanization of agriculture increased the share of routine employment. 111 Labor markets in lowincome countries such as Ethiopia, with a large share of employment in manual occupations, are also not polarizing; neither is employment in Mongolia or Latin American countries where other factors—such as a commodity-driven boom benefiting low-skilled workers—could play a larger role in shaping labor markets. 112 Yet declining labor shares in income and job polarization are only symptoms. At their heart is the fact that digital technologies complement and augment some skills (and thus some workers) while replacing others. Since not everybody has the skills that go well with digital technologies, many can end up falling behind. Education and skills thus determine whether the promise of digital dividends is achieved, or whether technological advances translate into more inequality in a race between skills and technology. 113 Understanding this dynamic is critical: inequality will increase if more workers do not acquire the modern skills most in demand. But if education and training systems increase the supply of workers who meet the changes in skill demands, more workers would benefit from technological change and inequality could decrease. The race between skills and technology With rising computing power, combined with the connectivity and informational value of the internet, digital technologies are taking on more tasks. They are particularly good at performing tasks that follow explicit, codifiable procedures—that is, routine tasks. Some of these tasks are cognitive, such as processing payrolls, keeping books, or doing arithmetic. Others are manual or physical, requiring simple motions and muscle power, such as driving a train or assembling goods. These tasks can be easily automated. Nonroutine tasks, by contrast, are less amenable to automation. Doing research, maintaining personal relationships, and designing new products have proven hard to automate; so have manual tasks that involve significant dexterity, such as cleaning and providing security services or personal care. Technology is even moving into areas once thought to be the exclusive domain of humans, such as driving or writing news articles. So, the impact of digital technologies on jobs depends on the type of tasks and how technology either complements or substitutes workers in those tasks. A job comprises many tasks, each characterized by the skills most used to perform it (cognitive, socioemotional, or manual) and by how amenable it is to automation or codification (table 2.3). In some cases, technology augments labor by complementing workers. Both the researcher and the hairdresser do tasks that are nonroutine and not easily programmed into a computer, but technology makes the researcher (who uses more advanced skills at work) much more productive, while barely affecting the hairdresser. This means that technology is skill-biased.
EXPANDING OPPORTUNITIES 121 Figure 2.15 The labor market is becoming polarized in both developed and developing countries Annual average change in employment share, circa 1995–circa 2012 1.5 a. High-income countries 1.0 Percentage points Percentage points 0.5 0 –0.5 –1.0 –1.5 2.0 1.5 1.0 0.5 Luxembourg Greece 0 –0.5 –1.0 –1.5 –2.0 –2.5 Macedonia, FYR Panama Slovenia France Spain Iceland Lithuania Austria Poland United Kingdom Sweden Israel Korea, Rep. Switzerland Estonia Ireland Guatemala Turkey Philippines South Africa Malaysia Honduras Dominican Republic Mauritius Tanzania Norway Finland Denmark Germany Croatia New Zealand Canada Italy Australia Netherlands b. Low- and middle-income countries Sources: WDR 2016 team, based on ILO Laborsta (various years); I2D2 (World Bank, various years); National Bureau of Statistics of China (various years). Data at http://bit.do /WDR2016-Fig2_15. Note: The figures display changes in employment shares between circa 1995 and circa 2012 for countries with at least seven years of data. The classification follows Autor 2014. High-skilled occupations include legislators, senior officials and managers, professionals, and technicians and associate professionals. Middle-skilled occupations comprise clerks, craft and related trades workers, plant and machine operators and assemblers. Low-skilled occupations refer to service and sales workers and elementary occupations. For the United States, comparable data could be accessed only for a short period (2003–08); consistent with Autor (2014), the observed polarization is limited in this period, with most of it having taken place in earlier years. Portugal Argentina United States Hungary Chile Russian Federation Czech Republic Uruguay Slovak Republic Barbados Ukraine Uganda Serbia Bolivia El Salvador Thailand India Jamaica Sri Lanka Egypt, Arab Rep. Bhutan Costa Rica Kazakhstan Namibia Mongolia Ghana Pakistan Peru Barbados Nicaragua Botswana Ethiopia China High-skilled occupations (intensive in nonroutine cognitive and interpersonal skills) Middle-skilled occupations (intensive in routine cognitive and manual skills) Low-skilled occupations (intensive in nonroutine manual skills) In other cases, workers are in jobs that are routine, whether mostly manual or mostly cognitive, and are susceptible to automation and to seeing their jobs profoundly transformed or vanishing. In these cases, technology is labor-saving. The fundamental questions then become: To what extent are different occupations and countries’ labor markets affected by skill-biased and labor-saving digital technologies?
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Public Disclosure Authorized 102725
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A World Bank Group Flagship Report
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Contents xiii xv xix Foreword Ackno
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CONTENTS vii Boxes O.1 5 Frequently
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CONTENTS ix O.24 35 Digital safegua
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CONTENTS xi B3.8.1 178 Autocratic g
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Foreword We find ourselves in the m
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Acknowledgments This Report was pre
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ACKNOWLEDGMENTS xvii Karl Chua, Mas
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xx ABBREVIATIONS GDP GIS GNI GPS GS
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xxii ABBREVIATIONS BFA Burkina Faso
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Inclusion Efficiency Innovation
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OVERVIEW 3 Figure O.1 Digital techn
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OVERVIEW 5 Box O.1 Frequently asked
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OVERVIEW 7 Map O.1 The internet is
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OVERVIEW 9 Figure O.6 The digital d
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OVERVIEW 11 This mechanism operates
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OVERVIEW 13 Figure O.10 The size of
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OVERVIEW 15 Box O.3 Bridging the di
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OVERVIEW 17 sector is thus challeng
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OVERVIEW 19 skills that help them b
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OVERVIEW 21 Figure O.15 Labor share
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OVERVIEW 23 Figure O.18 From a tech
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OVERVIEW 25 governments and citizen
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OVERVIEW 27 Box O.6 Nailing Jell-O
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OVERVIEW 29 as well as higher secur
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OVERVIEW 31 Figure O.22 Policy prio
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OVERVIEW 35 Box O.12 Can continuous
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OVERVIEW 37 Box O.13 European Union
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OVERVIEW 39 29. Data for 2014 of ne
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OVERVIEW 41 OECD (Organisation for
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HOW THE INTERNET PROMOTES DEVELOPME
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ACCELERATING GROWTH 51 varies with
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ACCELERATING GROWTH 53 Map 1.1 Many
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ACCELERATING GROWTH 61 Map 1.2 Chin
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ACCELERATING GROWTH 67 goods tradin
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e-HEALTH 191 Figure F3.1 Sequencing
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4. Sectoral policies 5. National pr
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SECTORAL POLICIES 223 the internet
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SECTORAL POLICIES 225 Protecting pe
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SECTORAL POLICIES 227 grown since t
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SECTORAL POLICIES 229 Box 4.9 Tech
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SECTORAL POLICIES 231 Box 4.10 Isra
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SECTORAL POLICIES 233 US$74 million
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SECTORAL POLICIES 235 has been clas
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SECTORAL POLICIES 237 Kende, Michae
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THE DATA REVOLUTION 245 Figure S5.1
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THE DATA REVOLUTION 247 A journalis
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NATIONAL PRIORITIES 249 • Rising
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NATIONAL PRIORITIES 251 schools, ho
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NATIONAL PRIORITIES 283 dence.” N
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ENVIRONMENTAL MANAGEMENT 323 Map F6
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