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Earnings Inequality: Stylized Facts, Underlying Causes, and Policy
Barry Hirsch
Department of Economics
Andrew Young School of Policy Sciences
Georgia State University
Prepared for Atlanta Economics Club luncheon address, Federal Reserve Bank of Atlanta, December 17, 2014
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Earnings Inequality: Stylized Facts, Underlying Causes, and Policy
Overview:
1. Types of inequality – how and why they matter
2. Descriptive evidence on US earnings inequality
3. Underlying causes of rising earnings inequality – demand, supply, and institutional forces
[Demand] Skill biased technological change (SBTC) [Supply] Slow growth in educated workers (skills) relative to demand [D & S] Globalization: Flows of Goods (trade), Capital (investment, off-
shoring), People (immigration), and Knowledge [Institutional] Low minimum wages (MW) [Institutional] Decline in private sector unionism
4. Policy implications
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Focus on wage and earnings differences in the labor market. Economic theory of wage differentials (Adam Smith 1776)
In competitive (and non-competitive) labor markets, wage differences (i.e., inequality) arise due to worker and job differences and the interaction of labor supply and demand.
Institutions and laws (unions, minimum wages, etc.) also matter.
Inequality inevitable and desirable, but large inequalities undermine societies to the extent that they: result from unequal opportunities, are perceived as not fully deserved, and distort political outcomes. Fairness matters. Distinction between (in)equality of opportunity vs. (in)equality of results.
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What types of inequality are relevant?
We will focus on wage or earnings inequality – these are tied to labor market outcomes
Depending on the question/issue being addressed, we also care about:
Household income inequality
Wealth inequality (asset wealth vs. human capital wealth)
Consumption inequality
Other issues:
Cross-sectional vs Lifetime inequality (inequality age-related) (total vs. residual inequality)
Earnings and income Mobility within and across generations
Measurement
Multiple measures: Gini coefficient, log variance, percentile ratios 90/10, 90/50, 50/10
No single measure can summarize an entire earnings (or income) distribution
Inequality can increase due to more persons at bottom, more at top, fewer in middle
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Descriptive data on US earnings inequality
Primarily from Daron Acemoglu and David Autor, “What Does Human Capital Do? A Review of Goldin and Katz’s The Race Between Education and Technology,” Journal of Economic Literature, 2012.
Figure 5a: Trends in Full-‐Time, Full-‐Year Weekly Wages
Figure 5b: Trends in Hourly Wages
0.2
.4.6
Cum
ulat
ive
Log
Cha
nge
in W
eekl
y W
ages
1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009
10th Percentile 50th Percentile90th Percentile
Cumulative Log Change in Real Weekly Earnings at the 90th, 50th and 10thWage Percentiles
1963-2008: Full-Time Full-Year Males
-.20
.2.4
Cum
ulat
ive
Log
Cha
nge
in H
ourly
Wag
es
1974 1978 1982 1986 1990 1994 1998 2002 20062008
10th Percentile 50th Percentile90th Percentile
Cumulative Log Change in Real Hourly Earnings at the 90th, 50th and 10thWage Percentiles1974-2008: Males
Figure 6
Source: March CPS data for earnings years 1963-‐2008. See note to Figure 1. The real log weekly wage for each education group is the weighted average of the relevant composition adjusted cells using a fixed set of weights equal to the average employment share of each group.
0.2
.4.6
Comp
ositio
n-Ad
juste
d Re
al Lo
g W
eekly
Wag
es
1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008Year
HSD HSGSMC CLGGTC
Real, Composition-Adjusted Log Weekly Wages for Full-Time Full-Year Workers1963-2008 Males
Figure 8
Source: Census IPUMS 5 percent samples for years 1980, 1990, and 2000, and Census American Community Survey for 2008. All occupation and earnings measures in these samples refer to prior year’s employment. The figure plots log changes in employment shares by 1980 occupational skill percentile rank using a locally weighted smoothing regression (bandwidth 0.8 with 100 observations), where skill percentiles are measured as the employment-‐weighted percentile rank of an occupation’s mean log wage in the Census IPUMS 1980 5 percent extract. Mean education in each occupation is calculated using workers’ hours of annual labor supply times the Census sampling weights. Consistent occupation codes for Census years 1980, 1990, and 2000, and 2008 are from Autor and Dorn (2009a).
-.05
0.05
.1.15
.2
0 20 40 60 80 100Skill Percentile (Ranked by Occupational Mean Wage)
1979-1989 1990-2007
100 x
Cha
nge i
n Emp
loyme
nt Sh
are
Smoothed Changes in Employment by Occupational Skill Percentile 1979-2007
Figure 9
Source: May/ORG CPS files for earnings years 1979-‐2010. The data include all persons ages 16-‐64 who reported having worked last year, excluding those employed by the military and in agricultural occupations. Occupations are first converted from their respective scheme into 326 occupation groups consistent over the given time period. All non-‐military, non-‐agriculture occupations are assigned to one of ten broad occupations presented in the figure.
-.20
.2.4
.6
Personal Care
Food/Cleaning Service
Protective Service
Operators/Laborers
Production
Office/Admin
SalesTechnicians
Professionals
Managers
Percent Change in Employment by Occupation, 1979-2010
1979-1989 1989-19991999-2007 2007-2010
Figure 10
Source: Data on EU employment are from from Goos, Manning and Salomons, 2009a. US data are from the May/ORG CPS files for earnings years 1993-‐2006. The data include all persons ages 16-‐64 who reported having worked last year, excluding those employed by the military and in agricultural occupations. Occupations are first converted from their respective scheme into 326 occupation groups consistent over the given time period. These occupations are then grouped into three broad categories by wage level.
-.15
-.1-.0
50
.05
.1.1
5.2
Per
cent
Cha
nge
Portugal
Ireland
Finland
Norw
ay
Netherlands
Greece
UK
Sw
eden
Germ
any
Spain
Belgium
Denm
ark
Luxembourg
France
Austria
Italy
EU
Average
US
AChange in Employment Shares by Occupation 1993-2006 in 16 European Countries
Occupations Grouped by Wage Tercile: Low, Middle, High
Lowest Paying 3rd Middle Paying 3rdHighest Paying 3rd
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Note: Analysis using CPS data and 90/10 percentiles misses wage growth at the very top (public files have top-coded earnings to protect confidentiality). Recent growth in inequality has come disproportionately from top 1% (Piketty and Saez).
Thomas Piketty and Emmanual Saez, "Income Inequality in the United States, 1913-1998," Quarterly Journal of Economics, 2003
Wojciech Kopczuk, Emmanual Saez, and Jae Song, "Earnings Inequality and Mobility in the United States: Evidence from Social Security Data since 1937," Quarterly Journal of Economics, 2009
Much of the earnings increase at the very top has been associated with “incentive” compensation (bonuses, stock options, etc.).
Thomas Lemieux, W. Bentley MacLeod, and Daniel Parent, “Performance Pay and Wage Inequality,” Quarterly Journal of Economics, 2009
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Why has earnings inequality increased? Demand, supply, and institutional forces
[Demand] Skill biased technological change (SBTC), increased demand for skills
[Supply] Slow growth in educated workers relative to demand (losing the “race”
between technology and education)
[Demand and Supply]: Globalization Flows of goods (trade), capital (investment/
plants), and people (immigration)
[Institutional] Low minimum wages (MW)
[Institutional] Decline in private sector unionism
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“Simple” skill biased technological change (Simple SBTC) and “The Race” between education (supply) and technology (demand)
Think of information technology/computers and other technologies
Technology substitutes (decreases demand) for lower skill workers
Technology complements (increases demand and productivity) for higher skill workers
Inequality is a “race” between SBTC demand changes and supply of educated workers (Lawrence Katz and Claudia Goldin, The Race between Education and Technology, 2008)
This is an over-simplification, but provides a rough approximation of why earnings inequality has increased. Does a good job explaining decreasing and increasing returns to college, decreasing in the 1970s and increasing in the 1980s. Does not explain well what has occurred since the 1990s (“hollowing” of the middle).
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“Job task” or “nuanced” SBTC (David Autor, others) from Information Technology (IT)
IT is labor saving (decreases labor demand) for “routinizable” or “programmable” tasks Production workers in plants (robotics) Information based workers: bank tellers, reservation agents, bookkeepers, etc.
IT complements (increases productivity) for non-routinizable abstract or analytical tasks Examples: lawyers, accountants, administrative assistants, architects, economists
Note: IT and the Internet may allow analytic tasks to be provided from a distance through outsourcing or telecommuting; e.g., call centers, business accounting
IT has little effect on manual, non-programmable tasks delivered in person Examples: hair stylists, child-care workers, landscaping & groundskeepers, physical
therapists
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Autor, Levy, Murnane (2003)
Evidence: consistent with “Job task SBTC approach
Industries in which jobs had high levels of routine tasks in 1960s had substantially higher levels of IT (computer adoption) through 1997
Employment growth through 1998 high in nonroutine jobs and falling in routinizable jobs since 1980
Computers → task shifts within jobs → college going and patterns of employment
Computers explain much of task changes which in turn affect educational attainment through changes in occupational employment and tasks performed within occupations. Principal results
Relative decrease in employment & earnings for jobs with routinizable tasks
Relative increase in employment & earnings for jobs with non-routinizable tasks
Little change in relative earnings for jobs with manual, non-programmable tasks
Loss of middle-class jobs. A “hollowing” out of the middle of the earnings distribution.
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So let’s return to other possible explanations (suspects) for rising inequality Globalization: Movement of goods (trade), capital (investment/plants), people (immigration), knowledge
Wage differences have narrowed across countries International trade increasingly important, particularly Chinese trade since 2001 and its effects on manufacturing industries. Also important are increased mobility of capital and “off-shoring” of production. Note: Inequality within almost all countries has increased over time, but worldwide inequality in incomes across all persons/households has decreased quite a bit. Explanation: Relative earnings and incomes in many developing countries (China and India) have sharply increased lowering worldwide poverty and inequality. Yet within countries more skilled workers have fared well relatively to less skilled.
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Immigration
Immigration in U.S. has increased steadily until Great Recession. 15% of US wage and salary employment is foreign-born.
Concentrated in the tails of the skill distribution Many college and graduate degree immigrants who are educated in U.S. and stay Concentration of young, low-skill immigrants, many from Mexico/Central America
Theory suggests low-skill immigration should lower wages for low skill workers and raise or have little effect on real wage of higher skill workers.
Evidence
Negative effect of immigration on low-skill wages, but surprisingly small
Increase in overall employment, but little effect on native employment
New immigration lower wages of earlier immigrants who most directly compete
Immigration the principal cause of rising inequality – timing not right
Immigration cannot explain sharp deterioration in left tail during the 1980s
Large immigration increases in 1990s and 2000s, but left tail held up well. Middle-class jobs deteriorated by were little affected by immigration
Immigration flows highly sensitive to job opportunities in US & in source countries.
Bottom line: Immigration plays small or modest role in increasing inequality
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Minimum Wages
MW fell during 1980s and has remained low by historical standards
MW affects inequality through changes in the lower tail of the distribution
Helps explain some of the sharply rising inequality in 1980s, but little since then
MW much more important for wage inequality for women than for men
Higher minimum wages does little to prevent middle class job and wage erosion
Roughly 4% of workers at or below current $7.25 Federal MW
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Federal Minimum Wage in Nominal (blue) and Real (red) $2014 dollars. Many cities & states depart from $7.25 federal MW. In 2014, San Francisco has highest MW at $10.55. Highest state MW is Washington's at $9.32; Oregon's second at $9.10.
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Decline in Private Sector Unionism
Private sector union density currently below 7% (see figure). Public union density much higher. Roughly half of all union members now work for federal, state, local government
In 1940s-1970s we had union governance workplace norm in the industrial sector (manufacturing, construction, and transportation, communications, and utilities). Union governance highly formalized (contractual) with reduced management discretion
Unionization decreases wage dispersion/inequality through:
Compressing wages top to bottom
Standardizing wages (less individualized wage dispersion) through contractual terms tying wages to designated job positions and seniority
Limits executive compensation
Bottom line:
Decline in union density appears to account for roughly 15-20% of increase in male wage inequality in US. Less important for women.
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0
5
10
15
20
25
1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013
Un
ion
Mem
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(M
illi
on
s)
Union Membership among U.S.
Wage and Salary Workers, 1973-2013
Private Public Overall
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30
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1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013
Per
cen
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Union Membership Density among U.S.
Wage and Salary Workers, 1973-2013
Private Public Overall
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Policy implications – difficult to decrease inequality through desirable policies
Discourage technological change? No. Changes in technology provide the principal engine for economic and income growth.
Increase supply of educated workers? Yes, but difficult to do. We heavily subsidize college and graduate education, yet schooling growth is slow. Large numbers of high school grads start college but complete less than a year. Greater gains might come from investing in pre-school children and improving the quality of primary and secondary schools.
Enact trade and investment (capital flow) barriers. Might decrease inequality to some small degree but would weaken growth and real incomes (partly through higher prices) for the U.S. and world.
Slow immigration. Would have small effect on inequality and would retard economic growth given the low birth rate in the U.S. (and other developed countries).
Raise minimum wages. Usual criticism by economists is MW lowers employment of least skilled. Evidence shows these effects small. MW may be attractive policy to decrease left-tail inequality; does little to expand middle class.
Encourage unionism in the private sector. Unionization will decrease inequality and raise wages for covered workers. But wage increases not fully offset by productivity increases. U.S. unionism associated with poorer firm performance – lower profits, investment, growth. Traditional unionism may not be the best way to enhance worker voice and cooperative employment participation in the workplace.
