The Decline in Labor Efficiency in the United States

Key Points

  • Workers’ performance on the job is related to all of their demographic and economic attributes, including education, age, family structure, gender, race, labor force status (full- or part-time work), peer group (birth-year), and others.
  • The annual market-wide “effective labor input” depends upon the quantity (number of work hours contributed) and the efficiency (related to worker attributes) of individuals engaged in market production.
  • Labor efficiency is projected to decline in the future and offset growth in labor quantity to slow growth of aggregate “effective labor input.” Official government analysts typically do not project changes in labor efficiency, thereby imparting a more optimistic outlook to budget projections.

The Decline in Labor Efficiency in the United States

Introduction

A factory that makes 100 cell phones per hour is said to increase their productivity if they begin producing 200 cell phones per hour without the need for increasing the number of workers or worker hours. The decades-old process-and-technological revolution has enhanced labor productivity: Getting more done with less has been the norm for the past century. Firms are striving continuously to enhance worker efficiency by improving technology, adding more and better equipment, and improving the work environment. However, the application and use of those techniques, equipment, and its conditions depends on worker characteristics that are determined outside the labor market. The sum-total of worker attributes has a momentum of its own. An appropriate focus for policymakers is to provide appropriate incentives for all workers — from pre-school students to highly qualified Google employees — to generate better outcomes.

Of Ants and Grasshoppers

In economic terms, labor productivity is measured as the average hourly output per worker produced each year. A large part of this likely depends on the efficiency of workers. For example, suppose there are three types of workers in the economy: “Tiny ants,” “giant ants,” and “grasshoppers.” If there are 10 tiny ants, zero giant ants, and two grasshoppers then the total number of work years is equal to 12. But that doesn’t tell the full story. Suppose that each tiny ant makes one unit of output during a given year, a giant ant makes five units, and a grasshopper makes 10. The total output, therefore, is 10(1) + 0(5) + 2(10) = 30. But suppose that five of the tiny ants turn into giant ants by acquiring education or experience. Then the total number of work years is still 12. But output increases to 5(1) + 5(5) + 2(10) = 50. Therefore, labor efficiency has increased by two thirds of the original level.

In general, the rate at which output increases over time depends on three factors: The total number of workers, the productivity of each type of worker, and the mix of workers by type. Output would double to 60 if we simply doubled the total number of each type of worker to 24, although output per worker remains unchanged. Output would also double if each of the original 12 workers was twice as productive as before: tiny ants now produce two units, a giant ant produces 10, and a grasshopper produces 20. Output per worker, therefore, also doubles since the number of workers has not changed. But notice that we can also increase the output of the original 12 workers to 62 by educating eight tiny ants to become giant ants: 2(1) + 8(5) + 2(10) = 62.

How does this example relate to government projections? Government projections routinely focus only on, by analogy, the total number of workers, and their average productivity (output per worker). For entitlement programs like Medicare and Social Security, a considerable amount of effort, therefore, is expended on estimating future fertility and mortality rates in order to determine the number of workers and retirees. Adjustments are also made for overall productivity. But government projections routinely ignore labor efficiency, that is, the productivity of specific types of workers and the mix of workers by type. As the example shows, the distribution (mix) of workers by type and its evolution can play a non-trivial role in determining overall worker productivity (output per worker).

Unfortunately, the demographic changes described below are causing labor efficiency to fall even as the total number of workers continues to increase. This has implications for how rapidly overall worker productivity will increase over time. In the extreme, total output per worker could fall if labor efficiency declines faster than the increase in the number of workers. While we don’t expect total output per worker to decline, future growth in output per worker is likely being overestimated, leading to optimistic projections for important budget and policy relevant metrics such as the exhaustion date of the Social Security trust fund. The analysis below provides pointers on how to slow or even reverse the decline in labor efficiency.

Worker Attributes and Productivity: How Are They Related?

Gender

The wage gap between men and women is hardly news: Women earn less than men, on average, even after controlling for education, experience, labor force status such as full- and part-time workers, and other differences. Part of the overall pay gap between genders arises from the type of work each gender pursues, for example, women are more likely to choose service-oriented jobs that tend to pay lower wages.1

The first panel of Figure 1 shows age-earnings profiles for employed men and women.2 The expected present value of lifetime earnings is larger for males than for females as men tend to work in higher productivity jobs than women.

As shown in the second panel of Figure 1, the employment ratio of males to females declined after World War II until the 2000s. This change was driven by a rapid increase in women in the labor market, especially in service-oriented jobs that required less physical labor. But the female employment rate peaked at the turn of the century. Since then, male and female employment as a share of their populations declined in tandem to maintain the relative male-employment ratio stable at just above 1.0.

If (a) the gender composition of employment remains steady, and (b) the gender differential in job allocations by productivity persists in the future, the gender mix will not affect future U.S. labor productivity. But if female employment plateaus and male employment continues to decline in the future, as the data suggests, gender composition will reduce future labor productivity growth. However, if more women move into higher productivity jobs, then this trend could be mitigated or even reversed. For example, changes in maternal leave policies could cause women to seek higher productivity jobs with less risk of losing them after childbirth.


Figure 1: Age-Earnings Profiles and Relative Employment by Gender

Source: PWBM calculations based on the Panel Study of Income Dynamics (PSID) and Federal Reserve Economic Data – St. Louis Fed.


Boomer Retirements

America’s workforce is graying, similar to experiences in many parts of Europe and Japan. Currently, 76 million strong baby boomers are still in the workforce. By 2031, however, the youngest baby boomer born in 1964 will be aged 67, and most boomers will be retired.

As shown in Figure 2, the U.S. population has grown progressively older as the baby boomers advanced in age and mortality among older ages declined. Figure 2 also shows average earnings by age, conditional on working, estimated from the Panel Study of Income Dynamics (PSID) surveys. Notice that worker productivity and earnings increase with age after entering the workforce. However, earnings eventually level out and decline slightly as workers approach retirement, consistent with smaller increases in productivity later in life.


Figure 2: Workforce Aging in the United States Compared to Age-Earnings Profiles

Source: PWBM calculations based on the Panel Study of Income Dynamics (PSID) and Current Population Surveys.


Baby boomers increased the population share of high productivity workers during the 1970s and 1980s. This entry of boomers into the workforce is part of the explanation for rising labor productivity observed during past decades.

However, the situation is rapidly changing: Boomers currently in their fifties and sixties are leaving the workforce. Furthermore, those boomers remaining in the workforce are reaching the flat part of their productivity profiles. As a result, the population weight of workers is shifting toward younger and less productive workers.

Education

The acquisition of education and skills is one way to increase productivity, especially for younger workers. Apart from direct effects on worker productivity from acquiring work-relevant skills, additional education and training also has indirect salutary effects by inducing productivity enhancing behaviors. For example, they improve workers’ health and reinforce non-cognitive abilities such as persistence, focus, and attention to detail.


Figure 3: Population Shares by Race and Gender of Those with No High School Diploma, with High School Diploma but No college Degree, and with College Degree or More Education

Source: PWBM calculations from Current Population Surveys.


Americans have become more educated over time. As Figure 3 shows, the share of college dropouts (with completed education through grade 11 or less) has declined over time, and the share of those with a college degree or more (16 years or more of completed education) has increased across gender and race. Figure 4 shows that higher education is indeed associated with greater earnings. The consistent advance in the share of higher-educated individuals, therefore, bodes well for future productivity growth. Still, about 70 percent of white workers and 80 percent of non-white workers do not have a college degree. If current trends continue, those values will likely decline. Policies that remove some of the barriers to college attainment could potentially work to increase the overall efficiency of the labor force.


Figure 4: Age-Earnings Profiles by Completed Education Level

Source: PWBM calculations based on the Panel Study of Income Dynamics (PSID).


Labor Force Status
Whether individuals work full-time or part-time depends upon many personal and workplace factors: including the opportunity cost of time associated with child-rearing, availability of jobs in one’s occupation, other labor market opportunities, and costs and benefits associated with full- and part-time work. Figure 5 shows that while the share of female part-time workers did not change by much during the 2000s, male workers shifted from full-time to part-time work, on net.


Figure 5: Shares of Full- and Part-Time Workers, 1990 and 2013

Source: PWBM calculations based on the Current Population Surveys.


Figure 6 shows that the average hourly wages of male part-timers are lower than those of male full-timers, especially prior to age 70. Moreover, the wage-differential between full-time hourly wages and part-time hourly wages has grown larger since 2000. The shift among males toward part-time work, implies a higher proportion of workers holding low-value-added jobs, implying a downward drag on overall worker productivity.


Figure 6: Male Full-Time and Part-Time Wages by Age, 2000 and 2010

Source: PWBM calculations based on the Current Population Surveys.


The shift toward part-time work is likely to continue as higher federal income tax rates and health care policies reduce the incentives to remain attached to full-time salaried employment. To be sure, the Affordable Care Act has increased the number of Americans with health insurance. However, the Congressional Budget Office estimates that the Affordable Care Act is likely to reduce the growth of full-time jobs. The Act provides subsidies on a sliding scale based on income for those earning between 100-400 percent of the federal poverty line, thereby discouraging full-time work. In addition, the Act is likely to increase the demand for part-time workers by firms. Under the Act, employers with 50 or more full-time workers must provide health insurance for their workers. This requirement will likely persuade employers to hire more part-time workers.3

Family Structure and Labor Productivity


Figure 7: Female Marriage Formation by Age and Birth Years

Source: PWBM calculations based on the Panel Study of Income Dynamics (PSID).


Traditional family structures are shifting, with many millennials choosing to remain single, cohabitate, and rear children outside of marriage. Gary Becker, the father of family economics, demonstrated that marriage permits better specialization in work at home versus work in the marketplace.4 But several factors are diluting the returns to specialization. Technology has reduced the labor require to accomplish home production tasks. It has also increased the ability to postpone childbirth. Women are seeking greater educational attainment and entering the workforce to become economically independent.5 Single parenthood has increased substantially during the past three decades among poorer households, and children of single parents are less likely to obtain a college education.6

Two-parent families, therefore, have given way to more singles and single-parent families. A lower and declining frequency of two-parent families is likely to impose a drag on future labor productivity growth. Figure 8 shows age-earnings profiles for married and unmarried workers calculated from the Panel Study of Income Dynamics. Married couples tend to do better. The evidence suggests that a continued decline in marriage will increase the fraction of workers with lower earnings and productivity in the future.


Figure 8: Age-Earnings Profiles by Marital Status

Source: PWBM calculations based on the Panel Study of Income Dynamics (PSID).


Conclusion

Labor efficiency is paramount for economic growth. The labor efficiency depends on the demographics of the labor force such as gender, boomer retirement, level of education, full- and part-time status, and family structure. The increasing fraction of highly educated workers is likely to enhance future labor productivity growth. But most other changes are working in the opposite direction. These factors include a continuing decline in labor force engagement by males, a shift toward part-time work by men, the ongoing retirement of the experienced baby boomers, and a decline in marriage and two-parent families which promotes efficiency through better division of labor between home and market production. Understanding how these processes will affect future labor productivity on net requires an integrated approach that includes all of these economic and demographic factors when making economic and budget projections.

Standard government budget projections, however, routinely ignore these factors, likely producing overly optimistic forecasts. The PWBM federal budget model, however, was built from the “ground up” to include all of these factors. For example, our model suggests that the Social Security Administration is over-estimating the exhaustion date of the Social Security trust fund by four to five years. These factors will also be fundamental to PWBM's future analysis of Medicare projections and potential reforms.


  1. Occupations and establishments where women are more numerous pay lower wages, on average, relative to occupations and establishments with comparable functional and skill requirements where males are more numerous. See: Trond Petersen and Laurie A. Morgan, “Separate and Unequal: Occupation-establishment Sex Segregation and the Gender Wage Gap,” American Journal of Sociology 101, no. 2 (1995): 329-365, available at: http://www.jstor.org/stable/2782431.  ↩

  2. The age-earnings profiles are estimated using Panel Survey of Income Dynamics micro-data between 1995 and 2013. The estimation procedure adjusts wage earnings for changes over time in economy-wide total factor productivity, capital intensity, and inflation in order to place all wages on par with each other before estimation. The estimation also includes controls for birth-year effects, earnings from the previous year, and many demographic attributes such as marital status, family size, disability, race, and labor force status (full- or part-time worker).  ↩

  3. The Congressional Budget Office, “The Budget and Economic Outlook: 2014-2024,” Appendix C, February 2014, available at: http://www.cbo.gov/sites/default/files/cbofiles/attachments/45010-breakout-AppendixC.pd  ↩

  4. Gary S. Becker, A Treatise on the Family (Cambridge: Harvard University Press, 1991).  ↩

  5. Shelly Lundberg and Robert A. Pollak, “The American Family and Family Economics,” The Journal of Economic Perspectives 21, no. 2 (2007): 3–26, available at: http://www.jstor.org/stable/30033715. See also: Claudia Goldin and Lawrence F. Katz, “The Power of the Pill: Oral Contraceptives and Women’s Career and Marriage Decisions,” Journal of Political Economy 110, no.4 (2002): 730–770, available at: http://www.jstor.org/stable/10.1086/340778.  ↩

  6. See PPI Background Brief on Education for more detailed information: https://framethedebate.squarespace.com/issues/2016/2/22/education-and-income-growth  ↩

,Female,Male
18,12242.39,13348.70
19,15281.24,17108.26
20,17548.49,19710.75
21,19443.98,21723.73
22,21183.24,23474.15
23,22862.90,25114.13
24,24520.33,26706.92
25,26165.77,28275.30
26,27797.47,29824.43
27,29408.61,31352.00
28,30990.34,32852.75
29,32533.27,34320.40
30,34028.13,35748.51
31,35466.12,37130.93
32,36839.17,38461.99
33,38140.07,39736.60
34,39362.55,40950.35
35,40501.37,42099.53
36,41552.33,43181.17
37,42512.33,44193.02
38,43379.33,45133.60
39,44152.32,46002.13
40,44831.34,46798.53
41,45417.37,47523.41
42,45912.26,48178.00
43,46318.70,48764.14
44,46640.13,49284.18
45,46880.61,49741.00
46,47044.79,50137.91
47,47137.76,50478.60
48,47165.04,50767.12
49,47132.42,51007.80
50,47045.94,51205.23
51,46911.79,51364.17
52,46736.24,51489.57
53,46525.61,51586.49
54,46286.18,51660.10
55,46024.20,51715.60
56,45745.80,51758.28
57,45457.02,51793.44
58,45163.73,51826.40
59,44871.69,51862.49
60,44586.49,51907.06
61,44313.60,51965.49
62,44058.35,52043.19
63,43825.94,52145.60
64,43621.51,52278.26
65,43450.15,52446.79
66,43316.89,52656.97
67,43226.83,52914.73
68,43185.13,53226.26
69,43197.06,53597.99
70,43268.10,54036.73
, Men/Male population,Ratio: men/women,Women/Female population
1948,83.5,2.510800891,31.3
1949,81.3,2.447055306,31.2
1950,82,2.397785595,31.9
1951,84,2.29798702,33.1
1952,83.9,2.244695746,33.4
1953,83.6,2.263228078,33.3
1954,81,2.250892374,32.5
1955,81.8,2.180213822,33.9
1956,82.3,2.124283825,35.1
1957,81.3,2.092980593,35.2
1958,78.5,2.05807015,34.5
1959,79.3,2.053723304,35
1960,78.9,2.007177799,35.5
1961,77.6,1.976368328,35.4
1962,77.7,1.961155998,35.6
1963,77.1,1.932828392,35.8
1964,77.3,1.908064787,36.3
1965,77.5,1.872474543,37.1
1966,77.9,1.806074137,38.3
1967,78,1.765478005,39
1968,77.8,1.730283742,39.6
1969,77.6,1.678517398,40.7
1970,76.2,1.650161681,40.8
1971,74.9,1.647684815,40.4
1972,75,1.628307259,41
1973,75.5,1.600152835,42
1974,74.9,1.570180634,42.6
1975,71.7,1.525728912,42
1976,72.1,1.492011793,43.2
1977,72.8,1.467698249,44.5
1978,73.8,1.42738002,46.4
1979,73.8,1.397641807,47.5
1980,72,1.357789016,47.7
1981,71.3,1.334813953,48
1982,69,1.300883114,47.7
1983,68.8,1.289236497,48
1984,70.7,1.286965044,49.5
1985,70.9,1.267293002,50.4
1986,71,1.250195048,51.4
1987,71.5,1.233897564,52.5
1988,72,1.223943825,53.4
1989,72.5,1.212863071,54.3
1990,72,1.212613384,54.3
1991,70.4,1.200519665,53.7
1992,69.8,1.192185303,53.8
1993,70,1.190111091,54.1
1994,70.4,1.173678819,55.3
1995,70.8,1.171305391,55.6
1996,70.9,1.165911694,56.1
1997,71.3,1.163880213,56.8
1998,71.6,1.163287806,57.1
1999,71.7,1.15157474,57.4
2000,71.9,1.152848111,57.5
2001,70.9,1.148406734,57
2002,69.7,1.146598094,56.3
2003,68.9,1.13862493,56.1
2004,69.2,1.151340996,56
2005,69.6,1.155359886,56.2
2006,70.1,1.158025282,56.6
2007,69.8,1.154324994,56.6
2008,68.5,1.141581708,56.3
2009,64.5,1.112705413,54.4
2010,63.7,1.116490374,53.6
2011,63.9,1.132832157,53.2
2012,64.4,1.129135906,53.1
2013,64.4,1.129866671,53.2
2014,64.9,1.132321863,53.5
,1980 workers,1980 population,Age-earnings profile (right axis)
18,1210574,2104079,12007.8
19,1450612,2078248,14870.4
20,1551515,2138417,17019.1
21,1483444,2034894,18827.5
22,1534912,2062323,20493.4
23,1508490,2083281,22104.9
24,1411845,1922950,23695.6
25,1410413,1933797,25274.2
26,1425033,1924292,26838.6
27,1335524,1832457,28381.9
28,1230989,1821630,29895.6
29,1237019,1840908,31370.7
30,1209716,1784955,32798.1
31,1150539,1734622,34169.5
32,1152378,1750343,35477.3
33,1203648,1836300,36714.4
34,940004,1451295,37875.1
35,851942,1362811,38954.3
36,960691,1540915,39948.1
37,974149,1533379,40853.6
38,887512,1340843,41669.0
39,886178,1304225,42393.4
40,864041,1270319,43027.1
41,803111,1185439,43570.9
42,799347,1170758,44026.9
43,732714,1100826,44397.8
44,804648,1227247,44686.8
45,821312,1205781,44898.1
46,665967,1005339,45036.1
47,733066,1153583,45105.9
48,699208,1145087,45112.8
49,698748,1148942,45062.4
50,727941,1183053,44960.5
51,706243,1168888,44813.2
52,675919,1220764,44626.5
53,685149,1244358,44406.5
54,704572,1189519,44159.1
55,695857,1274735,43890.5
56,635081,1231672,43606.4
57,551003,1164052,43312.7
58,608227,1185265,43014.9
59,515780,1046909,42718.5
60,461862,1137281,42428.9
61,440290,1029922,42151.3
62,380757,1051687,41890.6
63,317501,937636,41651.9
64,277851,970372,41439.9
65,255311,1024193,41259.6
66,191669,961138,41115.5
67,152846,988078,41012.5
68,96497,867818,40955.5
69,111900,915898,40949.4
70,95742,864872,40999.3
,1980 workers,1980 population,Age-earnings profile (right axis)
18,1438601,2110378,12520.1
19,1554321,1929986,15703.9
20,1690584,2040626,18001.4
21,1660405,1951275,19862.9
22,1749387,2008255,21535.3
23,1782960,1986620,23130.5
24,1627959,1810714,24693.5
25,1825109,1998482,26238.5
26,1701367,1860572,27766.5
27,1641398,1776597,29272.6
28,1647788,1775958,30750.1
29,1563131,1661055,32191.3
30,1690089,1817372,33588.7
31,1667647,1773902,34934.9
32,1596249,1701434,36223.6
33,1558822,1645242,37448.7
34,1266921,1328314,38605.3
35,1220530,1312009,39689.1
36,1319892,1406859,40696.8
37,1392095,1477437,41625.7
38,1193651,1293176,42474.4
39,1149479,1225362,43242.0
40,1124689,1180923,43928.7
41,1102332,1160837,44535.2
42,1016340,1085444,45063.1
43,982526,1027365,45514.7
44,1059544,1122234,45892.8
45,1031831,1095087,46200.8
46,983957,1059131,46442.5
47,996786,1078678,46622.4
48,997274,1058963,46745.0
49,999406,1069987,46815.1
50,1052510,1135353,46838.1
51,1005873,1119841,46819.0
52,1053295,1132115,46763.5
53,932455,1063581,46676.9
54,1015300,1144664,46564.8
55,971945,1119356,46432.6
56,895501,1067212,46285.8
57,880952,1057743,46130.0
58,922177,1116175,45970.4
59,812632,1006690,45812.3
60,765713,987734,45661.0
61,690777,926230,45521.6
62,623932,885142,45399.3
63,535237,870088,45299.0
64,472948,829993,45225.8
65,382919,891904,45184.8
66,287363,809056,45181.0
67,225935,802689,45219.7
68,204958,672685,45306.1
69,164905,636179,45445.8
70,178603,637173,45644.4
,1990 workers,1990 population,Age-earnings profile (right axis)
18,982219,1767029,12007.8
19,1352276,1915264,14870.4
20,1280849,1796105,17019.1
21,1305831,1745923,18827.5
22,1439424,1904352,20493.4
23,1471722,1873149,22104.9
24,1444373,1854725,23695.6
25,1598206,2063483,25274.2
26,1596794,2064171,26838.6
27,1631829,2123594,28381.9
28,1641398,2191147,29895.6
29,1703191,2238863,31370.7
30,1566513,2178663,32798.1
31,1673106,2256895,34169.5
32,1698133,2256742,35477.3
33,1679961,2224932,36714.4
34,1593513,2172886,37875.1
35,1563518,2182456,38954.3
36,1584529,2065476,39948.1
37,1499973,1997383,40853.6
38,1446921,1892559,41669.0
39,1470090,1906364,42393.4
40,1529361,1957562,43027.1
41,1402386,1836786,43570.9
42,1385817,1838734,44026.9
43,1380517,1740802,44397.8
44,1123642,1440072,44686.8
45,1090378,1527758,44898.1
46,1117224,1445296,45036.1
47,1179441,1508551,45105.9
48,961755,1309169,45112.8
49,979061,1306650,45062.4
50,862593,1277425,44960.5
51,869377,1207882,44813.2
52,797221,1159023,44626.5
53,743234,1121244,44406.5
54,728560,1144519,44159.1
55,697018,1110704,43890.5
56,628010,1060037,43606.4
57,639620,1137220,43312.7
58,580230,1111386,43014.9
59,608241,1116322,42718.5
60,540988,1156205,42428.9
61,492935,1111112,42151.3
62,479173,1168257,41890.6
63,390631,1107207,41651.9
64,309186,1147497,41439.9
65,316403,1199673,41259.6
66,273969,1126116,41115.5
67,194994,1053077,41012.5
68,174830,1100240,40955.5
69,161265,1047882,40949.4
70,134545,1031510,40999.3
,1990 workers,1990 population,Age-earnings profile (right axis)
18,1085516,1788602,12520.1
19,1425017,1849309,15703.9
20,1419480,1735303,18001.4
21,1483266,1770190,19862.9
22,1463090,1681257,21535.3
23,1565729,1734077,23130.5
24,1718381,1887892,24693.5
25,1879176,2041453,26238.5
26,1935802,2073998,27766.5
27,1958906,2084962,29272.6
28,2021303,2126400,30750.1
29,2068260,2185308,32191.3
30,2180610,2327959,33588.7
31,1998734,2143658,34934.9
32,2070280,2208766,36223.6
33,2053698,2166252,37448.7
34,1979168,2096959,38605.3
35,2041111,2160551,39689.1
36,1941200,2038066,40696.8
37,1871992,1987439,41625.7
38,1745513,1853140,42474.4
39,1712071,1801682,43242.0
40,1666896,1782242,43928.7
41,1623986,1731924,44535.2
42,1724986,1858545,45063.1
43,1605915,1700026,45514.7
44,1299276,1411539,45892.8
45,1378848,1473890,46200.8
46,1302975,1414796,46442.5
47,1285538,1375846,46622.4
48,1188816,1287530,46745.0
49,1092207,1205699,46815.1
50,1065927,1188104,46838.1
51,1082020,1165070,46819.0
52,953723,1053181,46763.5
53,889168,1013341,46676.9
54,965311,1111003,46564.8
55,882384,1047968,46432.6
56,813269,992173,46285.8
57,845864,1062229,46130.0
58,705148,898381,45970.4
59,797149,1009200,45812.3
60,779153,1052929,45661.0
61,680620,1020111,45521.6
62,645355,1024756,45399.3
63,521845,972459,45299.0
64,423418,918942,45225.8
65,445600,1048003,45184.8
66,328494,940904,45181.0
67,283832,857509,45219.7
68,209144,886861,45306.1
69,193205,862953,45445.8
70,185694,832488,45644.4
,2000 workers,2000 population,Age earnings profile (right axis)
18,1081295,2015481,12007.8
19,1364197,1992476,14870.4
20,1390871,1937159,17019.1
21,1358661,1841142,18827.5
22,1410171,1812832,20493.4
23,1385932,1807014,22104.9
24,1384713,1832608,23695.6
25,1412254,1813335,25274.2
26,1359371,1702590,26838.6
27,1348935,1726281,28381.9
28,1503884,1859066,29895.6
29,1777201,2222624,31370.7
30,1532628,1919289,32798.1
31,1566687,2025936,34169.5
32,1411227,1881933,35477.3
33,1498573,1919387,36714.4
34,1621954,2147461,37875.1
35,1708294,2202845,38954.3
36,1748885,2283936,39948.1
37,1717386,2310231,40853.6
38,1706712,2164936,41669.0
39,1756582,2323878,42393.4
40,1843662,2340158,43027.1
41,1853317,2296453,43570.9
42,1856745,2322174,44026.9
43,1695208,2155255,44397.8
44,1779370,2264857,44686.8
45,1731560,2202016,44898.1
46,1652557,2062581,45036.1
47,1626371,2051765,45105.9
48,1518330,1878101,45112.8
49,1511450,1897364,45062.4
50,1531695,1914529,44960.5
51,1482243,1846856,44813.2
52,1368799,1831100,44626.5
53,1354157,1741991,44406.5
54,921258,1310285,44159.1
55,957504,1412836,43890.5
56,1018480,1410228,43606.4
57,865100,1367874,43312.7
58,749501,1281081,43014.9
59,760861,1289489,42718.5
60,613831,1184479,42428.9
61,575533,1092892,42151.3
62,456804,1088793,41890.6
63,403388,1021422,41651.9
64,367300,1098133,41439.9
65,299862,1096804,41259.6
66,204972,921082,41115.5
67,213818,1002771,41012.5
68,176849,982074,40955.5
69,184300,972405,40949.4
70,115843,1052003,40999.3
,2000 workers,2000 population,Age-earnings profile (right axis)
18,1137767,2000282,12520.1
19,1492863,2081232,15703.9
20,1424767,1958092,18001.4
21,1569820,1947582,19862.9
22,1541664,1825733,21535.3
23,1469042,1750754,23130.5
24,1527082,1723924,24693.5
25,1521857,1697518,26238.5
26,1563227,1694344,27766.5
27,1665671,1763168,29272.6
28,1735114,1863397,30750.1
29,1767554,1921585,32191.3
30,1853122,1971095,33588.7
31,1854803,1981711,34934.9
32,1758174,1850721,36223.6
33,1786346,1896858,37448.7
34,1793651,1918325,38605.3
35,2095384,2268370,39689.1
36,2032418,2172443,40696.8
37,2062249,2181985,41625.7
38,1982217,2087839,42474.4
39,2166815,2318751,43242.0
40,2081632,2287848,43928.7
41,1925898,2118718,44535.2
42,2019451,2176444,45063.1
43,2107640,2316609,45514.7
44,1989088,2201130,45892.8
45,1981352,2227213,46200.8
46,1713125,1849626,46442.5
47,1728226,1957022,46622.4
48,1691380,1864350,46745.0
49,1582968,1753605,46815.1
50,1671372,1837928,46838.1
51,1561195,1761866,46819.0
52,1522434,1747150,46763.5
53,1410699,1628634,46676.9
54,1107838,1257868,46564.8
55,1091088,1359612,46432.6
56,1025797,1274344,46285.8
57,1044619,1328692,46130.0
58,817978,1056819,45970.4
59,800771,1083592,45812.3
60,758517,1089725,45661.0
61,748839,1014978,45521.6
62,692087,1086489,45399.3
63,534159,962960,45299.0
64,423828,876654,45225.8
65,401611,945489,45184.8
66,331534,887762,45181.0
67,281278,873063,45219.7
68,240715,841777,45306.1
69,245045,826501,45445.8
70,185243,796228,45644.4
,2010 workers,2010 population,Age-earnings profile (right axis)
18,715028,2077732,12007.8
19,894616,1926678,14870.4
20,1287446,2170461,17019.1
21,1196770,2023965,18827.5
22,1314342,2042776,20493.4
23,1413699,2052868,22104.9
24,1544076,2176675,23695.6
25,1568791,2110623,25274.2
26,1447948,1924152,26838.6
27,1561813,2094260,28381.9
28,1604309,2151152,29895.6
29,1671875,2241830,31370.7
30,1430949,2025743,32798.1
31,1478269,2081796,34169.5
32,1323938,1858635,35477.3
33,1450228,1957368,36714.4
34,1383131,1943353,37875.1
35,1447938,2009838,38954.3
36,1380174,1909030,39948.1
37,1423767,1973880,40853.6
38,1465075,2018901,41669.0
39,1533139,2070615,42393.4
40,1642886,2212129,43027.1
41,1551927,2140774,43570.9
42,1439009,1954737,44026.9
43,1464388,2016478,44397.8
44,1515715,2058610,44686.8
45,1753455,2285872,44898.1
46,1692743,2229203,45036.1
47,1715519,2309472,45105.9
48,1736247,2262090,45112.8
49,1732829,2344449,45062.4
50,1703232,2304428,44960.5
51,1638696,2193271,44813.2
52,1573474,2166151,44626.5
53,1602248,2176903,44406.5
54,1712009,2324142,44159.1
55,1609587,2177738,43890.5
56,1376129,1923991,43606.4
57,1316978,1993865,43312.7
58,1262308,1940588,43014.9
59,1120796,1815164,42718.5
60,1081478,1808872,42428.9
61,1006815,1839261,42151.3
62,959956,1802846,41890.6
63,812290,1719444,41651.9
64,581425,1383002,41439.9
65,489219,1381353,41259.6
66,396077,1213639,41115.5
67,376029,1267010,41012.5
68,307511,1243898,40955.5
69,215719,1172407,40949.4
70,191409,1138861,40999.3
,2010 workers,2010 population,Age-earnings ratio (right axis)
18,656041,2197908,12520.1
19,991997,1953213,15703.9
20,1299914,2211207,18001.4
21,1479538,2232737,19862.9
22,1516012,2173238,21535.3
23,1518744,2029031,23130.5
24,1555917,2035160,24693.5
25,1833219,2241150,26238.5
26,1828160,2193689,27766.5
27,1807103,2149810,29272.6
28,1830887,2133261,30750.1
29,1921120,2206874,32191.3
30,1867999,2163107,33588.7
31,1706951,1923482,34934.9
32,1659480,1932792,36223.6
33,1694584,1909985,37448.7
34,1649890,1828317,38605.3
35,1717115,1987989,39689.1
36,1646437,1855066,40696.8
37,1682933,1859805,41625.7
38,1837773,2041524,42474.4
39,1927212,2154014,43242.0
40,1960363,2219383,43928.7
41,1798525,2038519,44535.2
42,1677648,1928198,45063.1
43,1675762,1939527,45514.7
44,1750896,2043096,45892.8
45,1977982,2256742,46200.8
46,1949895,2305282,46442.5
47,1804572,2149852,46622.4
48,1902601,2221328,46745.0
49,1786321,2155024,46815.1
50,1821840,2198891,46838.1
51,1759376,2174499,46819.0
52,1766553,2119957,46763.5
53,1720469,2104441,46676.9
54,1757795,2090436,46564.8
55,1543111,1956744,46432.6
56,1595287,1957609,46285.8
57,1400893,1850509,46130.0
58,1320762,1772972,45970.4
59,1285689,1777229,45812.3
60,1146151,1685506,45661.0
61,1142098,1687254,45521.6
62,1010456,1584281,45399.3
63,916504,1544622,45299.0
64,609989,1162971,45225.8
65,609160,1417093,45184.8
66,470945,1280498,45181.0
67,400950,1084297,45219.7
68,335894,937724,45306.1
69,311361,1024659,45445.8
70,255200,887023,45644.4
,e<=HS,HS<e<Coll,e>=Coll
1976,0.36,0.48,0.16
1977,0.35,0.48,0.17
1978,0.34,0.49,0.17
1979,0.33,0.50,0.18
1980,0.32,0.50,0.18
1981,0.31,0.51,0.18
1982,0.30,0.51,0.19
1983,0.28,0.51,0.20
1984,0.28,0.52,0.20
1985,0.27,0.52,0.21
1986,0.27,0.53,0.21
1987,0.26,0.53,0.21
1988,0.25,0.53,0.22
1989,0.24,0.53,0.22
1990,0.24,0.54,0.22
1991,0.23,0.55,0.22
1992,0.22,0.56,0.22
1993,0.21,0.56,0.23
1994,0.21,0.56,0.23
1995,0.20,0.55,0.24
1996,0.21,0.55,0.24
1997,0.21,0.55,0.24
1998,0.20,0.56,0.24
1999,0.20,0.55,0.25
2000,0.19,0.56,0.25
2001,0.19,0.55,0.25
2002,0.20,0.54,0.26
2003,0.20,0.54,0.26
2004,0.18,0.55,0.27
2005,0.19,0.55,0.26
2006,0.19,0.55,0.26
2007,0.18,0.56,0.27
2008,0.16,0.56,0.28
2009,0.16,0.57,0.27
2010,0.15,0.57,0.28
2011,0.15,0.57,0.28
2012,0.15,0.56,0.29
2013,0.14,0.56,0.30
,e<=HS,HS<e<Coll,e>=Coll
1976,0.36,0.54,0.10
1977,0.35,0.54,0.11
1978,0.34,0.55,0.11
1979,0.32,0.56,0.12
1980,0.31,0.57,0.12
1981,0.30,0.58,0.12
1982,0.29,0.58,0.13
1983,0.28,0.59,0.14
1984,0.26,0.59,0.14
1985,0.26,0.59,0.15
1986,0.25,0.60,0.15
1987,0.24,0.60,0.15
1988,0.24,0.60,0.16
1989,0.23,0.60,0.17
1990,0.22,0.60,0.18
1991,0.21,0.61,0.18
1992,0.21,0.62,0.18
1993,0.20,0.62,0.18
1994,0.20,0.62,0.19
1995,0.19,0.61,0.20
1996,0.19,0.61,0.20
1997,0.19,0.60,0.21
1998,0.18,0.61,0.21
1999,0.18,0.60,0.22
2000,0.17,0.60,0.22
2001,0.17,0.60,0.23
2002,0.18,0.59,0.23
2003,0.18,0.59,0.23
2004,0.16,0.59,0.24
2005,0.17,0.59,0.24
2006,0.17,0.59,0.25
2007,0.15,0.59,0.26
2008,0.14,0.59,0.27
2009,0.14,0.59,0.27
2010,0.13,0.59,0.28
2011,0.13,0.58,0.28
2012,0.13,0.58,0.29
2013,0.12,0.58,0.29
,e<=HS,HS<e<Coll,e>=Coll
1976,0.55,0.37,0.08
1977,0.52,0.39,0.08
1978,0.51,0.40,0.09
1979,0.50,0.41,0.09
1980,0.49,0.42,0.08
1981,0.46,0.44,0.10
1982,0.44,0.46,0.11
1983,0.43,0.45,0.12
1984,0.42,0.45,0.12
1985,0.41,0.45,0.13
1986,0.39,0.47,0.13
1987,0.38,0.48,0.14
1988,0.37,0.49,0.14
1989,0.37,0.48,0.15
1990,0.36,0.49,0.15
1991,0.35,0.51,0.14
1992,0.34,0.50,0.15
1993,0.32,0.52,0.16
1994,0.31,0.53,0.16
1995,0.31,0.53,0.15
1996,0.30,0.54,0.16
1997,0.29,0.53,0.17
1998,0.28,0.54,0.18
1999,0.28,0.54,0.18
2000,0.27,0.53,0.19
2001,0.25,0.54,0.21
2002,0.26,0.53,0.21
2003,0.25,0.54,0.21
2004,0.24,0.54,0.22
2005,0.25,0.55,0.21
2006,0.24,0.54,0.21
2007,0.23,0.55,0.22
2008,0.23,0.54,0.22
2009,0.23,0.55,0.22
2010,0.22,0.56,0.22
2011,0.22,0.56,0.22
2012,0.21,0.55,0.24
2013,0.21,0.55,0.24
,e<=HS,HS<e<Coll,e>=Coll
1976,0.51,0.41,0.07
1977,0.52,0.41,0.08
1978,0.50,0.43,0.08
1979,0.48,0.44,0.08
1980,0.47,0.45,0.08
1981,0.45,0.46,0.09
1982,0.44,0.47,0.09
1983,0.41,0.49,0.10
1984,0.39,0.50,0.11
1985,0.38,0.50,0.12
1986,0.36,0.52,0.11
1987,0.36,0.52,0.12
1988,0.36,0.51,0.13
1989,0.36,0.51,0.13
1990,0.34,0.53,0.13
1991,0.33,0.53,0.13
1992,0.33,0.54,0.13
1993,0.30,0.56,0.14
1994,0.29,0.56,0.15
1995,0.29,0.57,0.14
1996,0.29,0.55,0.16
1997,0.27,0.57,0.16
1998,0.26,0.57,0.17
1999,0.26,0.56,0.18
2000,0.25,0.56,0.19
2001,0.24,0.56,0.20
2002,0.25,0.55,0.20
2003,0.24,0.55,0.21
2004,0.24,0.55,0.21
2005,0.23,0.55,0.21
2006,0.23,0.55,0.22
2007,0.22,0.56,0.22
2008,0.22,0.55,0.23
2009,0.21,0.55,0.24
2010,0.20,0.56,0.24
2011,0.20,0.56,0.24
2012,0.19,0.55,0.25
2013,0.19,0.56,0.26
,Coll<=e,HS<=e<Coll,e<HS
18,12686.1,12866.3,13524.3
19,16264.6,16266.3,17037.7
20,19101.0,18657.1,19083.0
21,21588.7,20546.0,20375.7
22,23948.1,22216.7,21320.3
23,26278.6,23799.7,22102.7
24,28616.1,25348.7,22801.5
25,30966.4,26882.5,23448.8
26,33321.6,28404.6,24056.9
27,35667.3,29912.2,24630.4
28,37986.8,31399.6,25170.8
29,40262.5,32860.2,25678.3
30,42477.2,34287.3,26153.1
31,44614.3,35674.3,26595.3
32,46658.6,37015.2,27005.1
33,48596.6,38304.0,27383.1
34,50416.3,39535.8,27730.0
35,52107.8,40706.0,28046.7
36,53663.3,41810.9,28334.3
37,55076.8,42847.1,28594.2
38,56344.8,43812.4,28827.9
39,57465.6,44704.9,29037.0
40,58439.4,45523.5,29223.2
41,59268.5,46267.9,29388.6
42,59956.5,46938.3,29534.9
43,60508.9,47535.6,29664.4
44,60932.5,48061.2,29779.2
45,61235.0,48517.0,29881.5
46,61425.3,48905.5,29973.5
47,61513.1,49229.5,30057.6
48,61508.5,49492.4,30136.1
49,61422.4,49697.8,30211.4
50,61265.6,49849.5,30285.8
51,61049.4,49951.6,30361.8
52,60784.8,50008.5,30441.8
53,60483.2,50024.7,30528.3
54,60155.4,50004.8,30623.8
55,59812.3,49953.3,30730.9
56,59464.6,49875.1,30852.0
57,59122.5,49774.9,30989.9
58,58796.3,49657.4,31147.2
59,58495.9,49527.3,31326.8
60,58231.0,49389.3,31531.5
61,58011.2,49248.1,31764.4
62,57846.1,49108.1,32028.6
63,57745.2,48974.0,32327.4
64,57718.2,48850.1,32664.4
65,57775.2,48741.0,33043.4
66,57926.2,48651.1,33468.4
67,58182.4,48584.7,33943.8
68,58555.1,48546.3,34474.2
69,59056.8,48540.2,35065.0
70,59701.3,48571.0,35721.6
,2000 Part-timers' share,2013 Part-timers' share
18,0.88,0.90
19,0.75,0.85
20,0.66,0.69
21,0.64,0.71
22,0.57,0.62
23,0.47,0.54
24,0.38,0.46
25,0.35,0.35
26,0.31,0.32
27,0.28,0.31
28,0.30,0.26
29,0.29,0.30
30,0.28,0.27
31,0.32,0.27
32,0.28,0.29
33,0.30,0.29
34,0.31,0.30
35,0.30,0.28
36,0.30,0.26
37,0.28,0.30
38,0.31,0.32
39,0.34,0.29
40,0.26,0.30
41,0.30,0.23
42,0.28,0.29
43,0.25,0.26
44,0.26,0.23
45,0.26,0.26
46,0.24,0.27
47,0.26,0.27
48,0.27,0.28
49,0.27,0.25
50,0.25,0.22
51,0.24,0.26
52,0.25,0.26
53,0.28,0.25
54,0.24,0.26
55,0.26,0.24
56,0.24,0.26
57,0.34,0.25
58,0.29,0.23
59,0.31,0.27
60,0.34,0.30
61,0.31,0.34
62,0.41,0.31
63,0.45,0.38
64,0.45,0.39
65,0.57,0.44
66,0.63,0.55
67,0.63,0.49
68,0.56,0.54
69,0.64,0.60
70,0.63,0.59
,2000 part-timers' share,2013 part-timers' share
18,0.85,0.84
19,0.69,0.76
20,0.61,0.68
21,0.52,0.59
22,0.45,0.51
23,0.33,0.45
24,0.32,0.34
25,0.22,0.29
26,0.18,0.23
27,0.17,0.24
28,0.11,0.17
29,0.14,0.19
30,0.12,0.20
31,0.11,0.14
32,0.10,0.15
33,0.10,0.17
34,0.09,0.18
35,0.10,0.14
36,0.09,0.15
37,0.10,0.14
38,0.11,0.16
39,0.09,0.15
40,0.09,0.15
41,0.10,0.14
42,0.10,0.14
43,0.08,0.13
44,0.09,0.14
45,0.07,0.15
46,0.08,0.14
47,0.09,0.15
48,0.09,0.12
49,0.10,0.14
50,0.10,0.11
51,0.12,0.13
52,0.09,0.15
53,0.11,0.12
54,0.12,0.16
55,0.13,0.14
56,0.14,0.16
57,0.10,0.15
58,0.18,0.15
59,0.15,0.15
60,0.17,0.22
61,0.17,0.20
62,0.25,0.27
63,0.36,0.26
64,0.28,0.28
65,0.42,0.38
66,0.50,0.35
67,0.47,0.38
68,0.51,0.45
69,0.58,0.45
70,0.49,0.43
,Part-time 2000,Full-time 2000
18,5.0,0.8
19,5.0,2.3
20,4.8,3.1
21,4.4,3.8
22,4.6,4.8
23,3.3,6.6
24,4.1,7.1
25,2.6,10.0
26,2.3,11.5
27,2.1,11.6
28,1.4,13.0
29,2.1,13.4
30,1.5,13.9
31,1.2,14.4
32,1.2,15.8
33,1.1,16.0
34,1.9,15.7
35,1.6,16.3
36,1.0,16.5
37,1.1,16.4
38,1.3,17.4
39,1.2,17.6
40,1.6,16.8
41,2.0,17.0
42,1.9,18.1
43,1.1,17.9
44,1.3,17.4
45,1.0,18.5
46,1.4,18.3
47,1.5,18.4
48,2.0,20.3
49,1.6,19.2
50,2.0,19.2
51,1.9,19.2
52,1.8,19.1
53,1.8,19.9
54,2.2,18.1
55,2.7,19.0
56,3.9,17.9
57,3.0,20.3
58,3.2,16.7
59,2.5,17.8
60,2.2,15.5
61,3.0,14.9
62,4.2,14.1
63,6.5,12.7
64,5.6,13.3
65,7.0,11.8
66,8.5,9.1
67,4.4,8.0
68,8.8,9.3
69,6.7,5.9
70,5.3,7.7
,Part-time 2010,Full-time 2010
18,5.7,0.6
19,6.3,2.2
20,7.4,3.2
21,5.5,4.5
22,6.4,5.9
23,6.4,7.3
24,5.8,9.2
25,4.7,11.5
26,5.8,13.6
27,5.1,13.4
28,3.8,14.0
29,3.7,16.9
30,3.6,16.2
31,3.3,16.3
32,4.0,18.5
33,3.9,18.5
34,2.8,19.1
35,3.1,20.3
36,3.4,21.6
37,3.5,20.3
38,2.3,21.4
39,2.6,20.5
40,3.3,21.5
41,3.9,22.4
42,8.6,21.4
43,3.7,21.6
44,3.4,23.5
45,3.4,23.4
46,2.5,22.4
47,3.3,22.0
48,2.7,26.2
49,2.9,22.0
50,5.2,22.5
51,2.8,22.2
52,3.4,22.5
53,4.1,25.1
54,4.1,23.1
55,3.7,22.2
56,4.5,22.2
57,3.2,23.1
58,4.3,23.9
59,3.8,21.2
60,3.8,21.0
61,4.5,24.8
62,6.4,21.5
63,6.2,18.5
64,8.7,21.4
65,8.4,16.9
66,7.4,16.3
67,12.2,17.5
68,8.8,11.9
69,9.9,11.1
70,8.5,9.5
,18-25,26-35,36-50,51-80
1925-1945,19.8,11.2,4.7,0.9
1946-1964,12.5,8.7,4.3,1.4
1965-1975,8.0,9.4,3.3,(no observations)
,Married,Not married
18,12896.9,12496.1
19,16418.0,15574.3
20,18985.1,17716.2
21,21077.1,19397.0
22,22963.8,20875.3
23,24768.9,22269.0
24,26542.1,23626.1
25,28298.8,24963.6
26,30039.3,26284.8
27,31757.5,27587.4
28,33444.8,28866.8
29,35092.0,30117.7
30,36689.7,31334.4
31,38229.1,32511.6
32,39702.3,33644.5
33,41101.9,34728.5
34,42422.0,35759.6
35,43657.3,36734.4
36,44803.7,37650.1
37,45858.5,38504.3
38,46819.6,39295.3
39,47686.3,40022.1
40,48458.9,40684.2
41,49138.4,41281.5
42,49727.1,41814.7
43,50227.9,42284.7
44,50644.3,42693.0
45,50980.7,43041.7
46,51242.1,43333.0
47,51433.7,43569.6
48,51561.3,43754.5
49,51631.1,43890.8
50,51649.4,43982.0
51,51622.6,44031.8
52,51557.5,44043.8
53,51460.7,44022.0
54,51338.9,43970.3
55,51198.9,43892.7
56,51047.2,43793.3
57,50890.5,43675.9
58,50735.3,43544.7
59,50588.0,43403.6
60,50454.9,43256.6
61,50342.3,43107.5
62,50256.6,42960.1
63,50203.9,42818.3
64,50190.6,42685.7
65,50223.0,42566.1
66,50307.6,42463.1
67,50451.1,42380.4
68,50660.4,42321.5
69,50942.9,42290.1
70,51306.2,42290.0