Wage Inequality and the Rise in Returns to Skill: An Analysis of Changing Wages for Males, Notas de estudo de Economia

Baixe Wage Inequality and the Rise in Returns to Skill: An Analysis of Changing Wages for Males e outras Notas de estudo em PDF para Economia, somente na Docsity! Wage Inequality and the Rise in Returns to Skill Chinhui Juhn; Kevin M. Murphy; Brooks Pierce The Journal of Political Economy, Vol. 101, No. 3. (Jun., 1993), pp. 410-442. Stable URL: http://links.jstor.org/sici?sici=0022-3808%28199306%29101%3A3%3C410%3AWIATRI%3E2.0.CO%3B2-I The Journal of Political Economy is currently published by The University of Chicago Press. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/ucpress.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. The JSTOR Archive is a trusted digital repository providing for long-term preservation and access to leading academic journals and scholarly literature from around the world. The Archive is supported by libraries, scholarly societies, publishers, and foundations. It is an initiative of JSTOR, a not-for-profit organization with a mission to help the scholarly community take advantage of advances in technology. For more information regarding JSTOR, please contact support@jstor.org. http://www.jstor.org Tue Nov 13 01:58:26 2007 Wage Inequality and the Rise in Returns to Skill Chinhui Juhn University of Houston Kevin M. Murphy University of Chicago Brooks Pierce Texas AUM University Using data from the March Current Population Survey, we docu- ment an increase over the past 30 years in wage inequality for males. Between 1963 and 1989, real average weekly wages for the least skilled workers (as measured by the tenth percentile of the wage distribution) declined by about 5 percent, whereas wages for the most skilled workers (as measured by the ninetieth percentile of the wage distribution) rose by about 40 percent. We find that the trend toward increased wage inequality is apparent within narrowly de- fined education and labor market experience groups. Our interpre- tation is that much of the increase in wage inequality for males over the last 20 years is due to increased returns to the components of skill other than years of schooling and years of labor market experi- ence. Our primary explanation for the general rise in returns to skill is that the demand for skill rose in the United States over this period. I. Introduction Between 1963 and 1989, the average weekly wage of working men increased by about 20 percent. However, as we show in this paper, This research was supported by grants from the National Science Foundation and the Sloan Foundation. [Journal of Pobttcal Econom), 1993, vol. 101, no. 31 O 1993 by The University of Chicago. .411 rights reserved. 0022-3808193/0103-0001$0l,50 WAGE IPI'EQUALITY 4 l 3 composition of the work force (i.e., changes in age and education composition) and changes in the patterns of employment across occu- pations and industries have affected the level of wage inequality. On both margins we find that these composition effects are relatively unimportant. In particular, the shift of the economy to the service sector and changes in the occupation distribution have had only very minor influences on wage inequality (accounting for less than 15 per- cent of the increase). It is important to stress the relevance of this conclusion to current research topics in labor economics. In particular, studies that make wage comparisons over time for groups that are relatively concen- trated in the upper or lower tail of the wage distribution must con- sider the importance of rising skill prices. For example, the well- documented convergence of black and white wage rates over the 1940-80 period (Smith and Welch 1986) has slowed in the 1980s (Juhn, Murphy, and Pierce 1991). A substantial part of this slowdown in convergence is explainable by rising skill prices in combination with the fact that blacks are relatively concentrated in the lower half of the skill distribution (because, say, premarket discrimination has been manifested in lower quantity and quality of schooling for blacks). Similarly, some small part of the findings that entering co- horts of immigrants have become less skilled may be due to the fact that skill prices have risen (as opposed to skill quantities falling) over time (see, e.g., Borjas 1985; Chiswick 1986; LaLonde and Tope1 1991). The paper is organized as follows. Section I1 describes the data we use in our analysis. Section I11 presents our results on wage inequality and the rise in returns to skill. Section IV decomposes the rise in inequality into components associated with observed differences (ed- ucation and experience) and the residual or unobserved component and identifies the time patterns of returns to alternative measures of skill. Section V attempts to explain the overall rise in the returns to skill in terms of demand changes and composition effects. Section VI relates our findings on wage inequality to changes in earnings in- equality and points out several key difference~ between the series. Section VII presents concluding remarks. 11. The Data The results in this paper are based on wage data for men drawn from 27 years of the March Current Population Survey (CPS), survey years 1964-90, and the 1960 decennial census. The data from the CPS come from the March annual demographic supplement and refer to earnings and weeks worked in the calendar year preceding the March 4l4 JOURNAL OF POLITICAL ECONOMY survey. The data from the 1960 census refer to earnings and weeks worked in 1959. As a result, our sample measures wages for 1959 and 1963-89. Throughout the paper we focus on log weekly wages or log hourly wages for full-time workers (defined as those that usu- ally work 35 hours or more per week). We deflate annual earnings by the personal consumption expenditure deflator from the National Income and Product Accounts and define the log average weekly wage as the natural logarithm of deflated annual wage and salary earnings divided by weeks worked and the log hourly wage as the natural logarithm of deflated annual wage and salary earnings di- vided by the product of weeks worked and usual weekly hours. We present initial analyses for both weekly and hourly wages and focus on weekly wages for the remainder of the results. For purposes of analysis we selected a sample that we felt would be representative of workers with a reasonably strong labor force attachment. Our sample inclusion criteria were that the workers be aged 18-65, work full time, not be self-employed or working without pay, not live in group quarters, work at least 14 weeks, have a positive number of years of potential labor market experience, not work part of a year because of retirement or school, and earn a minimum of $67 per week in 1982 dollars (equal to one-half of the 1982 real minimum based on a 40-hour week). We imputed weekly earnings for workers top-coded at the census maximum as 1.33 times the top- coded value. For the most part, the qualitative results in the paper are not sensitive to the exclusion and imputation procedures we used. The one major exception is the exclusion of workers earning less than half of the real 1982 minimum wage. When these workers are included, wage inequality actually declines somewhat more over the 1963-69 period. For example, when these workers are included, the log weekly wage differential between the ninetieth and tenth percen- tiles falls by three points from 1963 to 1969, whereas in our reported results it actually rises slightly. This distinction, however, does arise for other time periods. The question of whether one believes that wage differentials fell or remained stable over the late 1960s turns on the credibility one lends to these relatively extreme observations. In previous versions of this paper we noted that because of changes in the survey structure between the 1975 and 1976 surveys (wage data for 1974 and 1975), measured inequality dropped significantly. We have subsequently been able to overcome most of this problem by improving our predictions of weeks and hours worked last year, deleting all individuals with imputed wage and salary information, and excluding self-employed workers on the basis of the existence of any significant self-employment income (i.e., those with nega- tive self-employment income or more than $100 [I9821 of self- WAGE INEQUALITY 4 l5 employment income). While it appears that some spurious fall in inequality still occurs between 1974 and 1975, particularly in the low- est and highest deciles, we do not use any correction factors in this version. In fact, the results reported here are very close to our previ- ous corrected numbers and not the previous uncorrected numbers, which gives us considerable confidence that our prior corrections were in fact moving our answers in the right direction. 111. The Rise in Inequality Figure 1 graphs the median, tenth percentile, and ninetieth percen- tile of the real weekly wage distribution of men for 1963-89. For ease of comparison, wages for the three groups are indexed to an average of 100 in 1963 and 1964 for all three series. The median wage series tells a well-known story. Real wages increase relatively steadily from 1963 through about 1973, decline sharply from 1973 through 1975, rise slightly from 1975 through 1977, decline from 1977 through 1982, and then recover somewhat from 1983 through 1985, only to fall back again from 1985 to 1989. The basic story is that real wages were about 25 percent higher in 1973 than in 1963 and 1964 and about 5 percent lower in 1989 than in 1973. As is clear from the figure, the story is significantly different for the tenth and ninetieth percentiles. For the least skilled (proxied here by the tenth 10th Percentile to) Median ( 1 90th Percentile (t) I I I I I I I I I I I I I I I I I I I I I I I I I I 65 70 75 00 05 YEAR FIG. 1.-Indexed real weekly wages by percentile, 1963-89 68-96] “amusorod 4q saem daneçoa ui safuryo—p “14 gruta ar org q wu qo qo a q q q o eg qa q q dt we mg ovo | oro Esvo- É | soo- - 5 E | ova E AP É ova - 7 - Eooo É | svo Foro Foro D8-2865 mM E8-FOBF Nou sbuauo “O CR-TOGE 9% LE“GLBF UOU) SÔUNIO "O SE LNTItrad AUINTRSS em q ge a q qe o q qm q eq q q q ap qm q q p oro Fora Foro É A? tada Lou ver” i [ paro x É soa Foro foro 44-46 Da VE-BRBL MOU) SQUAUS “A FL-SD6E 03 CO-ERB NOM) OVAS “y DEDA BArISTEM DOM 47 BSUaO DOBM RAtASTaM BON Ur aluno WAGE INEQUALITY 4 l 9 The next three panels show much clearer moves toward greater wage inequality. In the six years from 1969-71 to 1975-77, workers at or below the tenth percentile of the wage distribution lost about 7 percent relative to the average (9 percent relative to the median), and workers in the upper quartile gained about 3-4 percent on the aver- age worker. The changes from 1975-77 to 1981-83 are slightly larger, particularly at the extreme upper percentiles, but the overall nature of change remains the same. Workers at the upper percentiles gained significantly (about 7 percent) relative to the average, and workers at the lowest percentiles lost about 6-7 percent. The change over the most recent period (from 1981-83 to 1987-89) is about the same. Workers at the lowest percentiles lose about 7 percent relative to the mean, and workers at the highest percentiles gain about 7 percent. Again, as in the change for 1963-89 as a whole, the expan- sion in wage differentials by percentiles is pervasive. The percentage increase in wages is roughly a linear function of the percentile, with wage increases being 1.4 percent higher for each 10 percentile points up in the wage distribution. Table 1 quantifies these changes by giving inequality measures for 1959 (i.e., from the 1960 census) and the same 3-year intervals used to summarize changes in figure 4. Panel A presents results for weekly wages, and panel B presents results for hourly wages. From 1959 to 1988 (the mean year of the final interval), the standard deviation of weekly wages increases from .44 to .59, an increase of about 33 per- cent. Similarly, the log wage differential between the ninetieth and tenth percentiles increases from 1.05 to 1.46, an increase of 42 per- cent. Over the full period the increase in inequality has been slightly larger below the mean than above the mean. The differential between the ninetieth percentile and the median increased by .17, and the difference between the median and the tenth percentile increased by .23. Similarly, the seventy-fifth percentile-median differential in- creased by . lo, and the median-twenty-fifth percentile differential increased by .15. In spite of the differences, the basic message is that inequality has increased substantially in all parts of the wage distribution. The general pattern of accelerating change shown in figure 4 comes through in the comparisons in table 1 as well. The results presented so far refer only to changes in the overall wage distribution and do not tell us how these changes break down into changes within groups (defined by education and experience) and changes between groups. They also do not tell us whether the changes have been greater for some subgroups than for others. Fig- ure 5 addresses both of these issues by looking at log wage changes by percentile separately for workers with 1-10 years of experience and workers with 2 1-30 years of experience. The percentile numbers 420 JOURNAL OF POLITICAL ECONOMY TABLE 1 A. Weekly Wages Standard deviation Percentile differential: 90-10 1.12 1.21 (.0050) (.0054) .54 .61 (.0029) (.0031) .54 .El5 (.0035) (.0036) .58 .66 (.0037) (.0042) .26 .29 (.0021) (.0022) .28 .32 (.0021) (.0025) Observations 54,369 54,760 -~ B. Hourly Wages Standard deviation Percentile differential: 90-10 75-25 90-50 50- 10 75-50 50-25 Observations NOTE -Standard errors are in parentheses. Data for 1964-88 are 3-)ear a\.erages of surrounding years from 1964-90 l l a r ~ h Current Populat~on Surveys. Data for 1959 are taken from the 1960 Puhl~c Use Micro Census Tapes on the bottom refer to percentiles of the individual groups' wage distribution. As the figure illustrates, wage differentials across experi- ence groups have increased at all percentiles (with a slightly larger divergence at the mean than at the extremes). Overall, the wage dif- ferential between the groups increased by about .20. Given the ex- isting positive wage differential between those with 21-30 years of experience and those with 1-10 years of experience, this change has served to increase overall wage inequality. Inequality has also gone up enormously within each group. For the youngest group we find WAGE INEQUALITY 4*3 best high school graduates (say the ninetieth percentile) gained sig- nificantly on the low-end college graduates (say the tenth percentile). The basic message of figures 5 and 6 is that wage inequality has increased significantly within groups defined by experience and edu- cation. Table 2 takes this analysis one step further by looking at the distribution of regression residuals from a regression of log weekly wages on a very flexible specification of education and experience effects.' Looking at regression residuals allows us to look within very narrowly defined education and experience categories. A striking fea- ture of the table is the similarity of the inequality measures for 1959 and 1970. Apparently there was very little change in within-group inequality over the 11 years from 1959 to 1970. In contrast, the pe- riod from 1970 to 1988 is characterized by an enormous increase in inequality, with workers at the ninetieth percentile of the residual distribution gaining about 26 percent relative to workers at the tenth percentile. T o understand the magnitude of the changes we describe, consider the following frame of reference. In 1964 the standard deviation of log weekly wages was .45, from table 1. In 1988 the standard devia- tion based on regression residuals was approximately .49, from table 2. This means that wage inequality as measured by standard devia- tions rose by an amount greater than the predictive power of the observables in our wage equation. The magnitude of the inequality increase is greater than the wage variation explainable by experience and education ~ o m b i n e d . ~ As stated in the Introduction, we view this increase in within-group wage inequality as a trend toward higher skill prices. However, the argument could be made that it is the result of increased dispersion in unobserved ability within recent entry cohorts due to, say, increas- ingly unequal educational opportunities. T o evaluate this potential objection, table 3 documents wage inequality growth over time within synthetic cohort groups. Panel A gives the ninetieth-tenth percentile differential for log weekly wages of various 6-year entry cohorts. One follows a cohort over time by moving horizontally across columns within the same row. One follows the same experience group over time by moving upward along a diagonal (those who enter in 1959-64 have the same experience level in 1964 as entrants in 1965-70 do in 1970). Within cohorts, inequality changes over time are attributable ' We estimated a wage equation with education dummies for less than high school, high school, some college, and college graduates and with linear terms in education within these groups. The regressions also include a quartic in experience fully inter- acted with the education variables and regional dummies. Typically, education and experience observables can explain about a quarter to a third of the observed log weekly wage variation in a cross-sectional regression. 4*4 JOURNAL OF POLITICAL ECONOMY TABLE 2 INEQUALITY BASED REGRESSION FOR MEN,1959-88MEASURES ON RESIDUALS Standard deviation .38 .39 .39 .41 .45 .49 Percentile differential: 90- 10 .89 .94 .92 .99 1.10 1.18 (.0022) (.0053) (.0042) (.0044) (.0046) (.0048) 75-25 .43 .46 .46 .50 .57 .60 (.0012) (.0030) (.0024) (.0026) (.0028) (.0028) 90-50 .42 .44 .44 .46 .51 .54 (.0014) (.0035) (.0029) (.0029) (.0031) (.0033) 50-10 .47 .50 .48 .53 .59 .64 (.0017) (.0042) (.0033) (.0036) (.0037) (.0038) 75-50 .22 .22 .22 .24 .28 .28 (.0008) (.0021) (.0017) (.OOl8) (.0020) (.0020) 50-25 .22 .24 .23 .26 .30 .32 (.0009) (.0023) (.0019) (.0020) (.0022) (.0023) Observations 212,127 42,780 54,369 54,760 59,922 66,669 NOTE-Data for 1964-88 are 3-year averages of surrounding years from 1964-90 hldrch Current Population Surveys. Ddta for 1939 are taken from the 1960 Publ~c Use hlicro Census Tapes. to age or time effects. In contrast, changes in inequality within an experience group are due to cohort or time effects. As in tables 1 and 2, data are 3-year averages for surrounding years. Obviously changes within recent cohorts cannot adequately explain the trends toward greater inequality because past cohorts have also experienced this trend in recent years. But the striking feature of table 3 is that changes over time within cohorts (along rows) and within experience groups (along diagonals) show remarkably similar patterns. For example, the ninetieth-tenth percentile wage differen- tial within the 1959-64 entry cohort increased from 1.13 in 1964 to 1.40 in 1988, and the differential for the 1-6 years of experience group increased from 1.13 to 1.38. Further, the timing of the in- creases is quite similar across the two series. We summarize the growth of inequality within experience and co- hort groups by averaging 6-year changes across the six cohort groups for which comparisons can be made. The average change within a cohort has the same magnitude and time pattern as that within an experience group. This basically means that the older cohorts leaving the data and the new cohorts entering the data across a 6-year span have similar levels of inequality. We obtain the same results using wage residuals in panel B of table 3. Given the fact that inequality increases are age-neutral in the cross section (fig. 5), we surmise that the similarity of entering and exiting cohorts implies that trends to- ward inequality are due mainly to increasing skill prices over time, and not to increasing dispersion of quality in more recent cohorts. WAGE INEQUALITY TABLE 3 Year of Market Entry 1964 1970 1976 1982 1988 Average Changes within Cohorts and Experience Levels Average Change 1964-70 1970-76 1976-82 1982-88 Within cohorts ,018 ,073 ,153 ,115 Within experience levels ,015 ,069 ,145 ,110 Average Changes within Cohorts and Experience Levels -. Average Change 1964-70 1970-76 1976-82 1982-88 Within cohorts ,007 ,096 .I45 ,101 Within experience levels -.016 ,076 ,123 ,076 Of course, it is possible that cohort and age effects are equal and have such a magnitude as to appear to be time effects. This follows from the usual identification problem arising when one tries to sepa- rate cohort, age, and time effects. While we cannot calculate growth in inequality over time separately from inequality growth across co- horts and ages, we can identify the change in inequality growth over time. We can difference inequality measures within a cohort (elimi- nating the cohort effect) and compare this difference across adjacent 428 JOURNAL OF POLITICAL ECONOMY in the distribution of the X's), changes in the prices of observable skills (i.e., changes in the P's), and changes in the distribution of the residuals. If we define p to be the average prices for observables over the whole period and F(. I X,,) to be the average cumulative distribu- tion, we can decompose the level of inequality into corresponding components as The first term captures the effect of a changing education and experi- ence distribution at fixed prices. The second term captures the effects of changing skill prices for observables at fixed X's, and the final term captures the effects of changes in the distribution of wage residuals. Armed with this simple framework, we can reconstruct what the wage distribution would look like with any subset of components held fixed. For example, with fixed observable prices and a fixed residual distri- bution, wages would be determined as In practice, we can estimate how this distribution would have changed through time by predicting wages for all workers in the sample in year t using the average coefficients, p,and computing a residual for each worker based on his actual percentile in that year's residual distribution and the average cumulative distribution over the full sample. The major advantage of this over the more standard variance accounting framework is that it allows us to look at how composition changes have affected the entire wage distribution and not just the variance. We can determine how changes in the distribution of ob- servable~ have affected other inequality measures such as the inter- quartile range or the ninetieth-tenth percentile differential or how the effects have been different for inequality above and below the mean. If we want to allow both observable prices and observable quantities to vary through time, then we can generate wages by In this case we predict wages for each worker in year t given his observable characteristics and the wage equation estimated for year t and again assign him a residual based on the cumulative distribution for all years. Finally, if we allow observable prices and quantities and the distribution of residuals to change through time, we obtain 429 WAGE INEQUALITY which replicates the actual wage distribution since u,, = F ; ' ( o , , ( x , , ) by definition of the cumulative wage distribution. Our basic technique will be to calculate the distribution of Y:, Y z , and Y : for each year and attribute the change through time in in- equality in the Y: distribution to changes in observable quantities. We then attribute any additional change in inequality in Y z to changes in observable prices, and finally we attribute any additional changes in inequality for Y ; beyond those found for Y: to changes in the distri- bution of unobservables (i.e., changes in unmeasured prices and quantities). The same analysis can be done in other orders and would simply rearrange the assignment of interaction terms. The remaining three panels in figure 7 give the part of the nine- tieth-tenth percentile log wage differential accounted for by each of these three components (each is measured as a deviation from its overall mean). Panel B gives the effects of changes in the distribution of observables. As is clear from the figure, changes in observable characteristics have had only a very modest impact on overall inequal- ity. This implies that the changes in the age and education composi- tion of the work force have not had a direct effect on the level of inequality (with skill prices held fixed). Panel C looks at the compo- nent of changes in inequality due to changes in observable prices (i.e., changes in the returns to education and experience). As the figure illustrates, changes in observable prices had a very modest effect on inequality until about 1980. Since 1980, however, the rapid increases in education differentials and returns to experience (for the less edu- cated groups) have increased the ninetieth-tenth percentile log wage differential by about 12 percentage points (more than half of the total increase over the 1980s). Panel D examines the component due to changes in unmeasured prices and quantities (i.e., the residual). As the figure shows, this component is by far the most important for the overall increase in inequality (accounting for about two-thirds of the increase). More- over, unlike the increase in observable skill prices, the increase in inequality based on unobservables has been operating since the late 1960s. While it is fair to say that skill premia based on both observed skill differences and unobserved skill differences have increased since 1970, it is important to note that the timing of these changes is very different. The rise in within-group inequality (measured by the resid- ual component) preceded the increase in returns to observables by over a decade. On the basis of this difference in timing, it seems clear to us that there are at least two unique dimensions of skill (education and skill differences within an education group) that receive unique prices in the labor market. Table 4 quantifies the contribution of observed quantities and 43O JOURNAL OF POLITICAL ECONOMY TABLE 4 Unobserved Total Observed Observed Prices and Change Quantities Prices Quantities Differential (1) (2) (3) (4) N ~ T E . - T ~ ~years refer to the middle point of the 3-year interval. Col. 1 gives the change in the indicated statistics over the years shown. Components in cols. 2-4 are calculated on the basis of the full distribution accounting scheme outlined in the text. prices and the unobservables to the increase in the standard deviation as well as the increase in the ninetieth-tenth percentile differential. The major information contained in the table that was not apparent in figure 7 is the difference in explanatory power for inequality above and below the mean. This ability to estimate how different parts of the wage distribution have been affected by the various components is the major advantage of the full distribution accounting scheme proposed here over the more conventional variance accounting framework. Panel A in table 4 refers to the change over the period 1964-88 (the years refer to the middle year of the 3-year interval). As the table shows, changes in observed quantities account for only about 7 percent of the increase in wage inequality between the fiftieth and tenth percentiles (primarily because of the decline in the number of men with very low education levels) but have accounted for about 14 percent (i.e., .020/.146) of the increase in inequality above the mean. Similarly, the rise in observable prices (primarily the increase in col- lege returns) has accounted for about 47 percent of the increase in the ninetieth-fiftieth percentile differential but only about 26 percent of the growth in the fiftieth-tenth percentile differential. As a result, the unobserved component accounts for 65 percent of the increase 433 WAGE INEQUALITY demand shift toward the most skilled. Such a shift in labor demand can be due to either a shift across industries toward industries that demand more skilled workers or a technological shift within indus- tries toward production methods that favor the most skilled. To mea- sure these shifts empirically, we divided the economy into the 12 industries and 11 occupation categories given in table 5. The columns of the table give the fractions of workers in the bottom 10 percent, the middle 10 percent, and the top 10 percent of the wage distribution employed in each occupation and industry. As the table shows, skill composition varies significantly across industry and occupation cate- gories. As a result, shifts in industrial and occupational composition should generate significant changes in relative demand. T o construct a demand index, we write the output of an occupation by industry cell (this can be thought of as an intermediate good) as TABLE 5 INDUSTRY DISTRIBUTIOX 1959-89A N D OCCUPATION BY PERCEXTILES, - PERCENTILES Industry: Agriculturelmining Construction Manufacturing: Low-tech Basic High-tech Commercial transportation and utilities Wholesale Retail Professional services, finance, insurance, and real estate Education and welfare Public administration Other service Occupation: Professionalltechnical Managers Sales Clerical Craft Operatives Transportation operatives Laborer Farm private household Service 434 JOURNAL OF POLITICAL ECONOMY where YY is the output produced by industry/occupation cell i j , and Fq is the corresponding production function giving output as a func- tion of the number of workers from each percentile (XI j , . . . ,XlOoZi ) . With constant returns to scale the dual of this problem is then where XI,is the 100 x 1 vector of employment by percentile in indus- tryloccupation cell ij, Dq is the vector of unit demand functions for each percentile, and W,, . . . , Wlooare the wages at the different percentiles. The change in labor demand associated with a given change in the industrial and occupational structure is then Empirically, we measure the change in output of an industry by occupation cell by the change in factor inputs at fixed reference prices (note that this also allows for factor-neutral technological change within industries and occupations) and measure X,, by the employ- ment distribution across industries and occupations by percentile over the entire sample. Demand growth for any group of workers is mea- sured as a weighted average of the growth in factor inputs in indus- tryloccupation cells; the weights are industry by occupation shares for that group. Therefore, groups employed largely in expanding sectors will experience rising demand, and groups employed largely in contracting sectors will have reduced demand. Changes in the cod- ing of industries and occupations limit our ability to make these calcu- lations for years prior to 1967, so we limit this analysis to the 1967-89 period and 1959. Figure 9 graphs the percentage change in relative demand at each percentile of the overall wage distribution accounted for by shifts in employment across our industry and occupation categories for the whole period and three ~ u b ~ e r i o d s . ~ Panel A gives the change in demand over the full period, 1959-89. As the figure shows, demand fell by roughly 10 percent for workers below the median and in- creased by between 5 and 40 percent for workers in the top quartile. Since the demand shifts that can be proxied by our crude demand measures are likely to be a small part of the true change in demand, such large relative movements suggest a significant shift in favor of the most skilled. The remaining three panels compute demand 'Given the rapid rise in skill premia over the period, these "measured" demand shifts must understate the "true" changes in demand that would have occurred with fixed skill prices. sajuuasuad a8em Aq puviop 10qr| u safuzg—6 “Dt3 etrusca ScrAusSutA Mm qm up q q q q q Eru EE gp É oro Foro soe so penas Laos E tao a Logo Loo É | sos boo É | oro ê É sra | evo É ozo É oz mesi-sess 9 sesi-ames “a ersaosea arrusgata oq a q lg q qa a p E E A ic E E Voa É oro | aoo- L Ê | ooo hoo o L ê É oro - Roo 2 L koro É [era Esso É + oco É oro É oro essi-sesr a sosi-sear puESoo vi sbumuo Dor DUB ur s8uaug 097 438 JOURNAL OF POLITICAL ECONOMY roughly 100 percent). Given that wage premia have increased in spite of this enormous growth in supply, it seems clear that there must have been significant growth in the demand for education. One view for the fall in education returns over the 1970s and the subsequent rapid rise in the 1980s (suggested by Murphy and Welch [1989]) is that supply grew faster than demand over the 1970s and then slower than demand during the 1980s. Similarly, perhaps much of the in- crease in the returns to experience can be attributed to the arrival of the baby boom cohorts and the associated youthening of the labor force. However, as Katz and Murphy (1992) point out, this does not seem to be a sufficient explanation for the rapid rise in experience returns in the 1980s as the baby boom cohorts moved up in the age distribution. A complete explanation of the economic phenomena behind the data we have described would appear to require at least two addi- tional components. First, it seems clear from the data that the demand for skill has risen. Further research must identify the sources of this demand shift; likely but untested candidates are biased rates of tech- nological progress and changes in the world economy. Also, an accu- rate description of the determinants of the timing in observable skill prices requires explicit consideration of supply and demand forces for the skill in question. Continued success in understanding the forces bringing about greater wage inequality hinges on the progress of future work in these areas. VI. Wage Inequality versus Income Inequality Our discussion to this point has focused on inequality in weekly and hourly wages. While this focus is probably correct for purposes of measuring the returns to skill, income rather than wage inequality has historically been more widely described and analyzed. Our purpose in this section is not to choose between these two concepts as measures of welfare or anything else but simply to show that empirically the two concepts are quite distinct. For our purpose here we focus on the contrast between inequality in annual earnings and inequality in weekly wages. Since annual earnings and weekly wages for an individ- ual are much more closely linked than total family income (a common income measure) and weekly wages, any contrast we find here for weekly wages and annual earnings is likely to greatly understate the true incomelwage distinction. Panel A of figure 10 graphs the variance of log annual earnings for 1963-89 using the same CPS data used for our wage inequality calculations, except that we no longer exclude those working 1-13 weeks. As is clear from the figure, earnings inequality declined some- 68-8961 “Susuodaos puz comerrea sSuuavo jenuue BoT—1 “sig ra Ba BR ra Rs sa BP Es paro | oro Loo Lico z t eso o + eso í k pao ê - raro É svo | evo Loro Loco FALETUNA WBUTUVEZ TENUVY DO) 40 JUSUNÓNO) ESUAfURADO “O PENNOM EHEOA BOT ja AuTUA “O vei ve 11 E P Poa Ma FP P, e PE, Fa, | seo aro r F . é | mo [à F x | oe» bos E Los Leo teea E | oro Loro foro ao0am ATI DOT 30 eSMTUSA “A SOUTAMS TORAIY BOT 40 GOMA “Y PUMIOK Ux9M BOT 40 BoUsLIA SÓUtUIOZ TETRA HOT 40 SMRTUSA 44O JOURNAL OF POLITICAL ECONOMY what from the mid-1960s through about 1968 and has increased sig- nificantly since. Business cycle swings are also clearly important, as evidenced by the large increases in inequality during the recessions of 197 1, 1975, and 1982. Overall from 1968 through 1989 the vari- ance of log earnings increased by about 80 percent (from .25 to .45). The remaining panels of the figure decompose this increase into the weeks worked variance, the weekly wage variance, and the covariance of weekly wages and weeks worked. Since annual earnings are simply the product of weeks worked and the weekly wage, we can write the log of annual earnings as where y is the log of annual earnings, 1 is the log of weeks worked, and w is the log weekly wage. Using this notation we can write the variance of log annual earnings, a:, as where u:, is the variance of log weekly wages, u: is the variance of log weeks worked, and u,,, is the covariance of log earnings and log weeks. The variance of weekly wages is shown in panel B of figure 10 and follows a pattern quite similar to the ninetieth-tenth percentile log wage differential shown in figure 7. The variance of weekly wages is very steady from 1963 through 1968 and then increases relatively smoothly from 1968 until 1986. In contrast, the variance of log weeks (panel C) shows a distinct cyclic pattern, with sharp rises in 1970-7 1, 1975, and 1982. This cyclic pattern results from the fact that reduc- tions in annual hours are very unevenly distributed across workers (i.e., relatively few workers work many fewer weeks). The covariance term in panel D reflects the growing positive associ- ation between wages and time worked. As two of us have found in our other work (Juhn, Murphy, and Tope1 1991; Juhn 1992), this strengthening of the cross-sectional labor supply relationship occurs mostly prior to 1975, after which the relationship is relatively stable. Over the period as a whole, the increase in the weekly wage variance accounts for about .14 of the overall .18 increase in the annual earn- ings variance. The remainder is attributable to the increased variance of weeks worked and a small rise in the covariance of weekly wages and weeks worked. We stress the need to distinguish between the earnings and wage inequality concepts. For example, because of the highly cyclic pattern of weeks worked, the variance of log annual earnings is actually lower in 1989 than in 1982, whereas the variance of weekly wages is actually about 20 percent higher in 1989 than in 1982. As measured by the variance of log annual earnings, inequality is lower now than in 1982,