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Do sheepskin effects help explain racial earnings differences?
Economics of Education Review 28 (2009) 759–766
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Do sheepskin effects help explain racial earnings differences? John D. Bitzan ∗ College of Business, North Dakota State University, Putnam Hall, P.O. Box 6050, Fargo, ND 58108-6050, United States
a r t i c l e
i n f o
Article history: Received 16 October 2007 Accepted 8 October 2008 JEL classification: J31 Keywords: Educational economics Human capital Salary wage differentials
a b s t r a c t This study examines the role of sheepskin effects in explaining white–black earnings differences. The study finds significant differences in sheepskin effects between white men and black men, with white men receiving higher rewards for lower level signals (degrees of a college education or less) and black men receiving higher rewards for higher level signals (graduate degrees). In performing an Oaxaca decomposition of earnings differences, it is apparent that signaling plays an important role in explaining white–black earnings differences and that a portion of the gap may be explained by statistical discrimination. © 2009 Elsevier Ltd. All rights reserved.
1. Introduction Recent studies of black–white earnings differentials show a narrowing gap, particularly during the 1990s. Couch and Daly (2003–2004), find that the black–white weekly earnings differential for males decreased from an average of .44 in the 1968–1972 period to .27 in the 1998–2001 period – a 38% decline. They attribute the reduction in this gap to increases in human capital among black males relative to white males, to increased occupational diversity among black males relative to white males, and to possible reductions in discrimination. However, despite the progress in reducing the black–white earnings differential, a significant gap still exists (average of 31% by the estimates of Couch and Daly).1 Moreover, while the overall black–white earnings differential has decreased a lot over time, the unexplained portion of the differential has not shown the same type of reduction. Couch and Daly (2003–2004) estimate that 23% of the 1968–1979 wage gap was due to unmeasured factors, while 62% of the 1989–2001 wage gap was due to unmeasured factors.
While several studies have attempted to explain black–white wage differentials using a variety of human capital measures, occupational choice, and general wage inequality, none have examined the ability of degree attainment to explain a portion of the gap. This study revisits the sheepskin hypothesis in the context of black–white earnings differentials, by examining the ability of sheepskin effects to explain a larger portion of black–white earnings differentials than educational attainment alone. A finding of an increasing share of the black–white earnings differential explained by sheepskin effects would provide information in two distinct lines of research. First, it would provide a further confirmation of the sheepskin hypothesis – that is, degrees provide an additional return beyond educational attainment alone. Second, it may suggest that sheepskin effects should be considered in subsequent evaluations of racial earnings differentials. The following section examines previous studies of sheepskin effects. Next, wage equations are estimated for black and white males using a sheepskin model. An Oaxaca decomposition of earnings differences follows. Finally, a summary and implications are presented. 2. Sheepskin effects
∗ Tel.: +1 701 231 8949; fax: +1 701 231 7508. E-mail address: [email protected]. 1 e.2714 − 1 = .31. 0272-7757/$ – see front matter © 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.econedurev.2008.10.003
According to the signaling hypothesis, education does not (only) make workers more productive, but it serves
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as a signal to employers that workers are more productive. Although it is not possible to directly observe whether signaling takes place in the labor market, the finding of sheepskin (or diploma) effects on worker earnings provides general support for the idea that signaling takes place. Several recent studies have examined the role of sheepskin effects in determining the earnings of U.S. workers. For the most part, these studies have provided support for the notion of sheepskin effects. A popular approach to examining sheepskin effects is to use a punctuated spline function that allows different returns to different years of education and different returns to degree years. Three of these studies (Belman & Heywood, 1991; Hungerford & Solon, 1987; Jaeger & Page, 1996) are particularly relevant to the current study. Hungerford and Solon (1987) were the first to use the punctuated spline approach in testing for sheepskin effects. In examining returns to education for white males from the 1978 Current Population Survey, they found significantly higher returns to diploma years than to other years. Although they did not have data on actual degrees received, the punctuated spline function approach provided more convincing evidence on the importance of a signaling effect than previous approaches. Belman and Heywood (1991), and Jaeger and Page (1996) not only provided additional confirmation of the sheepskin hypothesis, but also provided insight into the role of signaling in statistical discrimination. In extending the work of Hungerford and Solon (1987), Belman and Heywood (1991) examined sheepskin effects for minorities. They developed a theoretical model that suggests that if it is more costly to obtain an inaccurately high signal for minorities or if minorities have fewer resources to obtain an inaccurately high signal, then signals will be more accurate for minorities than for whites. Thus, steeper earnings profiles will exist for minorities (lower rewards to low-level signals, and higher rewards to high-level signals). Belman and Heywood found evidence that sheepskin effects for minorities were consistent with such a signaling model – that is, the return to a high school education was lower for minorities, while the return to a college education or graduate school education were higher. Jaeger and Page (1996) extended the work of both of these studies by using data with years of education and degrees. While Hungerford and Solon (1987) and Belman and Heywood (1991) found significant sheepskin effects, Jaeger and Page (1996) noted that sheepskin effects could be biased because of a lack of data on actual degrees received. Using a unique dataset, Jaeger and Page (1996) matched the 1991 and 1992 Current Population Survey files to obtain information on degrees obtained and on years of education obtained. They found evidence that sheepskin effects were strong for white men, minorities, and women. However, while the pattern of sheepskin effects they found was consistent with that found by Belman and Heywood (lower returns to lower level signals for black workers than white workers, and higher returns to higher level signals for black workers), they found that such differences were generally not statistically significant (an exception is statistically higher returns to graduate degrees for black men than white men).
Although other studies have not examined the role of sheepskins in statistical discrimination, many have attempted to measure whether sheepskin effects occur in other countries. For example, Denny and Harmon (2001) found evidence of sheepskin effects in Canada, Sweden, Great Britain, Ireland, and the U.S., while Ferrer and Riddell (2002) found evidence of sheepskin effects in Canada. It should be noted that while sheepskin effects are consistent with the notion of signaling, they could also be consistent with human capital explanations. Chiswick (1973) and Lang and Kropp (1986) provide two such explanations. Chiswick (1973) suggests that people who receive degrees are more efficient learners, and thus those who receive higher returns from education. Lang and Kropp (1986) suggest that returns to degree years may be higher because these years enhance productivity more than other years due to the extra effect of having all the coursework in a particular area above that from the additional coursework taken that year. Because of these types of problems, other authors have performed additional tests of the sheepskin hypothesis. Two of these have examined changes in sheepskin effects over time on the job. Belman and Heywood (1997) provided evidence suggesting that sheepskin effects diminish with time on the job. This suggests that diplomas are initially used as a signal, and then these signals are replaced with stronger signals when more information about individual worker productivity is known. In an extension of this work, Habermalz (2006) found that returns to high-level degrees initially increased after employment, but then declined over time. These results are also consistent with improved job matching between employers and workers once more information on true worker productivity becomes available. Other studies using unique approaches to examine the potential role of signaling include Heywood (1994), Lang and Kropp (1986), Bedard (2001), and Gullason (1999). Heywood (1994) found that sheepskin effects were not as prevalent in the public sector as in the private sector. As Belman and Heywood (1997) pointed out, this may strengthen support for the signaling hypothesis under the belief that cost minimization is more important to the private sector than the public sector. Lang and Kropp (1986) found that compulsory attendance laws increased educational attainment for groups not constrained by the laws, consistent with the signaling hypothesis. Bedard (2001) found that increased university access increased high school dropout rates, which is also consistent with the signaling hypothesis. Finally, Gullason (1999) found that workers whose jobs did not utilize what they learned in school had significant sheepskin effects. The present study does not attempt to resolve the signaling vs. human capital debate. Rather, this study examines whether the consideration of the role played by sheepskins can explain more of the white–black earnings gap, and whether statistical discrimination may play a role in white–black earnings differences. The next section of the study estimates a sheepskin model of earnings for black and white males to examine the importance of sheepskin effects and differences between the two groups.
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3. Sheepskin model of earnings This study uses pooled 1999–2003 Current Population Survey Merged Outgoing Rotation Groups to estimate wage equations for nonunion, private sector white and black males using a sheepskin model.2,3 In addition to testing for the existence of sheepskin effects in each racial group, a wage equation of the combined sample is estimated that allows for the testing of differences in returns to sheepskins and human capital characteristics between white and black men. This is an important extension of the work of Belman and Heywood (1991) and of Jaeger and Page (1996). Both of those studies examined sheepskin effects of different racial groups, and both found steeper earnings profiles for minorities (consistent with the idea that signals may be more accurate for minorities). However, Belman and Heywood (1991) were not able to test for statistical significance of such differences. Moreover, while Jaeger and Page were able to test for statistical significance (finding a lack of statistical significance in such differences), their small sample size suggests less efficient estimates (711 black males, with only 10 having professional degrees and 10 having doctoral degrees). The current study uses a sample size with nearly 12,000 black males and 167,000 white males (including 58 and 63 black males with professional degrees and doctoral degrees, respectively). This study also goes beyond the previous studies by applying an Oaxaca decomposition to white–black earnings differences using the sheepskin model. The sheepskin model estimated in this study is similar to that estimated by Jaeger and Page (1996), in that it uses information on specific degrees obtained as well as years of education.4 Like previously estimated sheepskin models by Hungerford and Solon (1987), Belman and Heywood (1991), Heywood (1994), and Jaeger and Page (1996), the model is a punctuated spline function, allowing different returns to education after obtaining a high school degree and after obtaining a college degree. However, unlike previous estimations (including Jaeger and Page), the return to a high school diploma is differentiated from the return to a GED.5 Specifically, the following sheepskin model is estimated: ln w = ˇ0 + ˇ1 Exper + ˇ2 Exper2 + ˇ3 Educ + ˇ4 D12(Educ − 12) + ˇ5 D16(Educ − 16) + ˇ6 GED + ˇ7 HS + ˇ8 VOC + ˇ9 ASSO + ˇ10 BA + ˇ11 MA + ˇ12 PROF + ˇ13 DOC + ˇ14 Married + ˇ15 SMSA+Regional Dummies+Year Dummies+ε
2 Although 2004 data are available, the black male sample is much smaller with only 4 individuals having a doctoral degree. 3 Observations with top coded earnings or with allocated earnings, those with less than the minimum wage of $5.15, and those with usual hours of work less than 20 or more than 99 are deleted. Means of all variables are shown in Table 3 of Appendix A. 4 Years of education are proxied by NBER’s imputed highest grade of school completed (CPS Labor Extracts, NBER, February 2005). 5 Studies by Heckman and others suggest that receiving a GED is much different than achieving a high school diploma. See for example, Cameron and Heckman (1993), and Heckman and Rubinstein (2001).
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where Exper = Age-Educ-6, Educ = imputed years of education from NBER, D12 = Educ > 12, D16 = Educ > 16, HS = high school degree or higher, GED = GED degree, VOC = vocational school degree, ASSO = associate’s degree, BA = bachelor’s degree or higher, MA = master’s degree, PROF = professional degree, DOC = doctoral degree, Married = (1 = married; 0 = not married), and SMSA = (1 = resides in metropolitan area; 0 = resides in non-metro area). Table 1 presents the parameter estimates, along with t-tests of significant differences in parameter estimates between black males and white males. As the table shows, all parameter estimates are of the expected sign. Returns to experience and education are positive and significant, as the human capital model would predict. Moreover, most sheepskin effects are positive and significant at conventional levels. Tests of joint significance of sheepskin effects provide F-statistics that are significant at the 1% level in both black male and white male models. Further examination of Table 1 shows that there are differences in sheepskin effects between white and black men. This is an important finding, providing increased support for the notion that statistical discrimination may play a role in racial earnings differences. In examining individual sheepskin effects, the returns to a GED degree, an associate’s degree, and a bachelor’s degree are significantly higher for white men in comparison to black men, while returns to a doctorate degree are significantly higher for black men in comparison to white men. In addition, although not statistically significant, returns for most degrees less than a bachelor’s degree are lower for black men (the exception is a vocational degree), while those for all degrees above a bachelor’s degree are higher for black men.6 These findings are consistent with the findings of Belman and Heywood (1991) that returns to lower level sheepskins are higher for white men, while those for higher level signals are higher for black men. Moreover, they are also consistent with those of Jaeger and Page (1996), although they did not find that such differences were significant. While there are several possible explanations for these significant sheepskins and significant differences in sheepskins between black and white men, they are consistent with a signaling model. Belman and Heywood (1991) show that if the cost of obtaining inaccurately high signals is higher for minorities (than for whites) or if minorities have fewer resources to spend on inaccurately high signals, the signaling model predicts higher rewards to high-level signals and lower rewards to low-level signals for minority groups (in comparison to whites). Most other explanations for the differences in sheepskins should show up as differences in returns to education as well. For example, one of the most obvious explanations might be that there are
6 A sheepskin model estimated with a step function (dummies for each year of education from 9 through 18) and degrees provides similar results. This is shown in Table 4 of Appendix A. In the step function model, the return to a high school diploma is significantly smaller for black males at the 5% level, the return to a GED is significantly smaller for black males at the 15% level, the return to a bachelor’s degree is significantly smaller for black males at the 1% level, and the return to a doctorate degree is significantly higher for black males at the 1% level.
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Table 1 Parameter estimates for sheepskin model of earnings (non-union, private sector males). Variable
White
Black
t-Test of significant differences (black vs. white)
Intercept Experience Experience squared Years of education Spline for ed > 12 Spline for ed > 16
0.90288* (0.01016) 0.03177* (0.00027) −0.00055* (0.000006) 0.03744* (0.00096) 0.02570* (0.00168) −0.03702* (0.00341)
1.18539* (0.04837) 0.02096* (0.00094) −0.00034* (0.00002) 0.01262 (0.00434) 0.06241* (0.00625) −0.01346 (0.01404)
5.38* −10.43* 9.35* −5.25* 5.34* 1.54****
Sheepskin effects GED High school diploma or higher Vocational degree Associate degree Bachelor’s degree or higher Master’s degree Professional school degree Doctorate degree
0.12207* (0.00618) 0.14917* (0.00442) 0.04696* (0.00512) 0.06940* (0.00601) 0.20126* (0.00548) 0.07524* (0.00685) 0.16476* (0.01117) 0.15171* (0.01097)
0.08377* (0.02109) 0.13578* (0.01422) 0.06815* (0.02011) 0.02929 (0.02081) 0.13714* (0.01959) 0.09745* (0.03047) 0.20436* (0.05405) 0.29733* (0.05589)
−1.64*** −0.85 0.96 −1.75*** −2.97* 0.67 0.67 2.40**
Other controls Married SMSA NE region Mid Atlantic region East North Central region West North Central region South Atlantic region East South Central region West South Central region Mountain region 2000 2001 2002 2003
0.15232* (0.00231) 0.12545* (0.00249) 0.02584* (0.00442) 0.00532 (0.00438) 0.00512 (0.00400) −0.07617* (0.00414) −0.04417* (0.00393) −0.06342* (0.00561) −0.07528* (0.00446) −0.04769* (0.00397) 0.00243 (0.00326) 0.01372* (0.00322) 0.01710* (0.00316) 0.0158* (0.00320)
0.12074* (0.00759) 0.12884* (0.01098) −0.04190*** (0.02310) −0.02129 (0.01777) −0.06381* (0.01717) −0.12150* (0.02262) −0.04855* (0.01517) −0.10374* (0.01834) −0.12451* (0.01736) −0.09619* (0.02289) 0.00563 (0.01124) 0.02066*** (0.01135) 0.02980* (0.01118) 0.00003 (0.01143)
−3.75* 0.28 −2.71* −1.37 −3.68* −1.85*** −0.26 −1.98** −2.59* −1.96** 0.26 0.55 1.03 −0.84
N Adjusted R2
167,306 0.4173
11,791 0.3351
Standard errors in parentheses. * Significant difference at the 1% level. ** Significant difference at the 5% level. *** Significant difference at the 10% level. **** Significant difference at the 15% level.
separate labor markets for black and white workers with different demand and supply conditions. If black workers receive larger rewards to high quality sheepskins due to a more limited supply of workers with such sheepskins, we should expect to see higher returns to education for black workers in comparison to white workers. As Table 4 of Appendix A shows, the return to each educational dummy relative to less than 9 years of education for black males is smaller than for white males. The following section of the paper performs an Oaxaca decomposition on the white–black earnings differential, in order to examine the ability of sheepskin effects to explain a greater portion earnings differences. 4. Oaxaca decomposition of white–black earnings differences In order to further examine the importance of sheepskin effects in the U.S. labor market and to examine the ability of sheepskin effects to explain white–black earnings differences, this study performs Oaxaca decompositions of earnings differences using the sheepskin and human capital models. Unlike most applications of the Oaxaca decomposition, this study quantifies earnings differences due to differences in parameter estimates in addition
to quantifying differences due to differences in characteristics. This allows the sheepskin model to provide an increased understanding of earnings differences on two fronts: (1) do differences in sheepskin attainment among white and black men explain more of the earnings gap than differences in educational attainment alone?, and (2) does statistical discrimination explain part of the earnings gap? The standard Oaxaca decomposition is a method for separating the total earnings gap between two groups into two portions; one portion is explained by differences in personal characteristics, and the other is due to differences in estimated coefficients between the two groups. The second portion is the one that is troublesome, and may represent statistical or deliberate discrimination or other unmeasured factors.7 As noted by Neumark (1988), the original decomposition introduced by Oaxaca (1973) uses either the black or the white wage structure as the nondiscriminatory wage structure – when the black wage structure is used, it is assumed that white workers receive a premium
7 Extreme caution must be used in interpreting the magnitude of the “unexplained” portion in this study, however, as the models are not intended to explain as much of the gap as possible. The models are only designed to examine the role of sheepskin effects in explaining a portion of the gap.
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Table 2 Percent of black–white earnings differences explained by differences in characteristics and by differences in rewards for characteristics. Log earnings differential = .2120 Differences in characteristics
Differences in rewards
Intercept Experience Experience squared Exp. total
0.0068* (0.0001) −0.0061* (0.0001) 0.0008* (0.00002)
−0.2825* (0.0373) 0.2007* (0.0224) −0.1009* (0.0094) 0.0998* (0.0102)
Years of education Spline for ed > 12 Spline for ed > 16 Educ. total
0.0122* (0.0003) 0.0135* (0.0008) −0.0033* (0.0003) 0.0224* (0.0006)
0.3219* (0.0814) −0.0498* (0.0109) −0.0031 (0.0031) 0.2689* (0.0679)
Sheepskin GED High school diploma or higher Vocational degree Associate degree Bachelor’s degree or higher Master’s degree Professional school degree Doctorate degree Sheepskin total
−0.0002* (0.000008) 0.0012* (0.00003) 0.0007* (0.00006) −0.00003* (0.000003) 0.0214* (0.0006) 0.0019* (0.0002) 0.0009* (0.0001) 0.0010* (0.0001) 0.0269* (0.0007)
0.0014*** (0.0008) 0.0109 (0.0124) −0.0008 (0.0010) 0.0014*** (0.0008) 0.0106*** (0.0055) −0.0007 (0.0016) −0.0002 (0.0006) −0.0008 (0.0006) 0.0218**** (0.0149)
Married SMSA Region Year
0.0220* (0.0003) −0.0115* (0.0002) 0.0100* (0.0008) 0.0002* (0.00005)
0.0145* (0.0049) −0.0034 (0.0087) 0.0247*** (0.0137) −0.0027 (0.0075)
0.0708* (0.00094)
TOTAL
0.1413* (0.0059)
Standard errors are in parentheses. * Significant at the 1% level. ** Significant at the 5% level. *** Significant at the 10% level. **** Significant at the 15% level.
over the nondiscriminatory wage structure, while when the white wage structure is used, it is assumed that black workers receive a discount from the nondiscriminatory wage structure. The more general model introduced by Neumark (1988) suggests that the log wage differential between two groups can be separated into an explained portion and an unexplained portion as follows:
ˆ W − ˇ) ˆ − XB (ˇ ˆ B − ˇ) ˆ ˆ + XW (ˇ ln WW −ln WB = XW −XB ˇ
where WW is the mean white wage, WB the mean black wage, XW the vector of mean white characteristics, XB the ˆ the vector of coeffivector of mean black characteristics, ˇ ˆ W the vector cients for nondiscriminatory wage structure, ˇ ˆ B is the vector of estiof estimated white coefficients, and ˇ mated black coefficients. The first term on the right hand side of this equation represents the portion of the white–black earnings differential that is explained by differences in characteristics. It is the part that is often referred to as the “explained” portion of the gap. The second term on the right hand side of the equation is the “unexplained” portion. It can be broken down into two separate components – a portion accounted for by a premium paid to white workers, and a portion accounted for by a discount paid to black workers. Neumark (1988) shows that the nondiscriminatory wage structure can be estimated by applying OLS to the full sample.8
8 Using a model of discrimination due to employer preferences, he shows that if employer utility functions with respect to each type of labor
This study uses the Oaxaca decomposition to examine whether sheepskin effects can explain more of the white–black earnings gap. Unlike previous studies, individual components of this “unexplained” portion are highlighted due to the insight this might provide into the role of statistical discrimination in explaining white–black earnings differences. Table 2 shows the Oaxaca decomposition of the white–black log earnings gap.9 As the table shows, the overall gap in white–black earnings in the sample is 23.6% (e.2120 − 1). The sheepskin model is not able to explain large portions of the gap (about 33% of the gap is the explained portion). However, it is important to note that this study does not aim to explain the largest possible portion of the gap. Rather, it aims to show the additional power of including sheepskin effects in explaining differences. In examining the explained portion of the gap, several things are apparent. First, differences in experience explain less than 1% of the gap in either the human capital model or the sheepskin model. Second, the explained portion of the gap by differences in educational attainment alone is about 11%. However, when sheepskin effects are considered, the total portion of the gap explained by education or sheep-
is homogenous of degree zero, then the common wage structure can be estimated in this way. This study estimates the nondiscriminatory wage structure using OLS on the combined sample. The estimation results from the combined sample are available from the author upon request. 9 Standard errors are estimated using the approach introduced by Oaxaca and Ransom (1998).
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skin variables is more than 23%. By comparison, a human capital model that allows for nonlinearity in returns to education (includes spline variables for education beyond 12 and 16 years) shows that education explains 20% of the gap, and a human capital model that does not allow for nonlinearity in returns to education shows education to explain 14% of the gap.10 These results suggest three things. First, examining wage differences due to differences in educational attainment alone is unable to capture the full impact of educational differences on the wage gap – that is, it is unable to capture the larger impact of the role of education as a signal on wages, in addition to its impact on human capital attainment. Second, it is important to allow for nonlinearity in returns to education to capture the full impact of differences in human capital attainment on the wage gap. Third, even though the improvement in explaining the earnings gap from educational and sheepskin difference may seem small in comparison to a human capital model that allows for nonlinearity in education, the fact that sheepskins are statistically important suggests that the human capital model mistakenly attributes a portion of the gap to differences in years of education that is really attributable to degree attainment. This is especially important in formulating policy – policies aimed at encouraging degree achievement may be different than those encouraging increased education. It is also interesting to look at wage differences explained by differences in parameter estimates. Differences in rewards to experience are shown to have a large impact on the wage differential. However, this is very misleading, as a likely reason for the large differences in returns to experience among white and black workers are due to vintage effects. As pointed out by Hoffman (1979), vintage effects occur because of the fact that different age cohorts are captured in a single cross-section. Because of a decline in discrimination for younger cohorts, and because of a relative improvement in the quality of education for minorities, white–black earnings differences are larger for older cohorts. Thus, experience that is measured by ageeducation-6 will reflect this larger gap for older cohorts. Table 2 also shows that a large portion of the white–black earnings differential is explained by differences in returns to education and degrees. In focusing on the portion of the differential explained by differences in returns to degrees, it is apparent that positive portions of the wage gap are explained by higher rewards to white men for lower level signals and negative portions of the gap are explained by higher rewards to black men for higher level signals. However, because few white or black men obtain higher level signals (master’s, professional school, or doctorate), the differences in rewards to lower level signals explain much larger portions of the gap. Statistically significant differences in the wage gap explained by differences in rewards to degrees include 5% explained by a higher reward to a bachelors degree for white men and 1.3% explained by higher rewards to a GED and an associate’s degree for white men. Other portions
of the wage gap explained by differences in rewards to degrees that are not statistically significant include that explained by higher rewards for a high school degree for white men, and negative portions explained by higher rewards for graduate degrees for black men. In total, differences in returns to sheepskins are estimated to account for about 10% of the wage gap. While this is not a large number, it suggests that statistical discrimination plays a role in explaining white–black earnings differences. Moreover, the overall findings of the Oaxaca decomposition suggest that the role of signaling should be taken into account in future racial earnings difference explorations. The following section of the paper presents a summary of the findings. 5. Summary and implications This study examines the role of sheepskin effects in explaining white–black earnings differences. In order to examine these differences, punctuated spline models are estimated for samples of nonunion, private sector white and black men. After estimating these models, an Oaxaca decomposition of earnings differences is performed. In estimating the sheepskin model, several things are apparent. First, there are significant sheepskin effects for both white and black men. Second, there are significant differences in these effects between white men and black men, with white men receiving higher rewards for lower level signals and black men receiving higher rewards for higher level signals. These findings are suggestive of an important role for signaling in U.S. labor markets. Moreover, the steeper earnings profile for black men in education is consistent with the model presented by Belman and Heywood (1991) which predicts more accurate signals for minorities under an assumption that it is more costly for minorities (or minorities have fewer resources) to obtain inaccurately high signals. In performing the Oaxaca decomposition, it is apparent that signaling plays an important role in explaining white–black earnings differences and that a portion of the gap may be explained by statistical discrimination. In the sheepskin model, differences in education alone accounts for 11% of the white–black earnings gap, while education and sheepskins account for 23% of the gap. Moreover, it is estimated that 10% of the white–black earnings gap is explained by statistically significant differences in rewards for signals between white and black men. These findings provide increased support for the importance of signaling in the U.S. labor market, and suggest that signaling should be considered in future explorations of racial earnings differences. Acknowledgements I am grateful to Bahman Bahrami, Steffen Habermalz, John Heywood, and two anonymous referees for comments and suggestions on earlier versions of this paper. Any remaining errors are, of course, my responsibility. Appendix A.
10
These results can be obtained from the author upon request.
See Tables 3 and 4.
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Table 3 Mean values (standard deviations in parentheses). Full sample (N = 179,097)
White (N = 167,306)
Black (N = 11,791)
ln wage (wage is in 1982–84 $, deflated by CPI-U) Experience Experience squared Years of education Spline for ed > 12 Spline for ed > 16
2.1095 (0.5395) 18.7553 (12.2174) 501.0265 (561.7379) 13.2613 (2.9194) 1.7624 (2.1018) 0.2154 (0.6004)
2.1235 (0.5411) 18.7700 (12.2258) 501.7832 (561.0974) 13.2839 (2.9506) 1.7936 (2.1147) 0.2213 (0.6075)
1.9115 (0.4734) 18.5473 (12.0955) 490.2903 (570.6664) 12.9402 (2.4111) 1.3202 (1.8538) 0.1313 (0.4797)
Sheepskin effects GED High school diploma or higher Vocational degree Associate degree Bachelor’s degree or higher Master’s degree Professional school degree Doctorate degree
0.0354 (0.1847) 0.8225 (0.3821) 0.0483 (0.2143) 0.0360 (0.1853) 0.2678 (0.4428) 0.0528 (0.2237) 0.0105 (0.1017) 0.0109 (0.1039)
0.0353 (0.1845) 0.8231 (0.3816) 0.0491 (0.2161) 0.0356 (0.1852) 0.2747 (0.4464) 0.0544 (0.2268) 0.0108 (0.1034) 0.0113 (0.1059)
0.0366 (0.1879) 0.8150 (0.3883) 0.0365 (0.1875) 0.0360 (0.1864) 0.1695 (0.3752) 0.0304 (0.1716) 0.0053 (0.0729) 0.0049 (0.0700)
Other controls Married SMSA NE region Mid Atlantic region East North Central region West North Central region South Atlantic region East South Central region West South Central region Mountain region Pacific region 1999 2000 2001 2002 2003
0.6085 (0.4881) 0.7726 (0.4192) 0.0910 (0.2876) 0.0963 (0.2950) 0.1363 (0.3431) 0.1209 (0.3260) 0.1604 (0.3700) 0.0487 (0.2153) 0.0913 (0.2880) 0.1386 (0.3455) 0.1166 (0.3209) 0.1926 (0.3944) 0.1902 (0.3925) 0.1984 (0.3988) 0.2148 (0.4107) 0.2040 (0.4030)
0.6178 (0.4859) 0.7663 (0.4232) 0.0948 (0.2929) 0.9578 (0.2943) 0.1371 (0.3439) 0.1266 (0.3325) 0.1458 (0.3529) 0.0450 (0.2073) 0.0892 (0.2850) 0.1457 (0.3528) 0.1201 (0.3251) 0.1923 (0.3941) 0.1893 (0.3917) 0.1985 (0.3988) 0.2151 (0.4109) 0.2049 (0.4036)
0.4768 (0.4995) 0.8616 (0.3454) 0.0373 (0.1896) 0.1035 (0.3046) 0.1247 (0.3304) 0.0399 (0.1956) 0.3683 (0.4824) 0.1014 (0.3019) 0.1208 (0.3259) 0.0383 (0.1918) 0.0660 (0.2483) 0.1970 (0.3978) 0.2038 (0.4028) 0.1970 (0.3978) 0.2099 (0.4073) 0.1923 (0.3941)
Table 4 Parameter estimates for step function model of earnings (non-union, private sector males). Variable
White
Black *
t-Test of significant differences (black vs. white) *
5.68* −10.33* 9.23* −3.16* −5.39* −7.09* −4.51* −3.72* −4.11* −2.85* −1.55**** −0.09 −0.24
Intercept Experience Experience squared Ed = 9 Ed = 10 Ed = 11 Ed = 12 Ed = 13 Ed = 14 Ed = 15 Ed = 16 Ed = 17 Ed = 18
1.10607 (0.00664) 0.03182* (0.00027) −0.00055* (0.000006) 0.10444* (0.00806) 0.18046* (0.00714) 0.22315* (0.00675) 0.23187* (0.00903) 0.31575* (0.01002) 0.38838* (0.00987) 0.41136* (0.01073) 0.42881* (0.01271) 0.48447* (0.01454) 0.47617* (0.01420)
1.29071 (0.02987) 0.02111* (0.00094) −0.00035* (0.00002) −0.00879 (0.03277) 0.01489 (0.02802) 0.01907 (0.02628) 0.07738** (0.03100) 0.17548* (0.03411) 0.23597* (0.03358) 0.29652* (0.03644) 0.35098* (0.04555) 0.47899* (0.05522) 0.46224* (0.05295)
Sheepskin effects GED High school diploma or higher Vocational degree Associate degree Bachelor’s degree or higher Master’s degree Professional school degree Doctorate degree
0.12094* (0.00623) 0.15728* (0.00800) 0.03519* (0.00527) 0.06251* (0.00612) 0.26006* (0.00911) 0.07941* (0.00694) 0.17196* (0.01132) 0.15906* (0.01113)
0.08654* (0.02114) 0.10170* (0.02268) 0.06560* (0.02034) 0.03010 (0.02095) 0.16218* (0.03457) 0.10925* (0.03088) 0.22145* (0.05453) 0.31470* (0.05636)
−1.47**** −2.18** 1.36 −1.40 −2.58* 0.89 0.84 2.55*
Other controls Married SMSA NE region Mid Atlantic region East North Central region West North Central region
0.15240* (0.00232) 0.12446* (0.00250) 0.02756* (0.00444) 0.00734*** (0.00440) 0.00700*** (0.00402) −0.07464* (0.00416)
0.12006* (0.00760) 0.12764* (0.01099) −0.04327*** (0.02311) −0.02130 (0.01778) −0.06342* (0.01717) −0.12056* (0.02263)
−3.84* 0.27 −2.83* −1.47**** −3.76* −1.88***
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Table 4 (Continued ) Variable
White
Black
South Atlantic region East South Central region West South Central region Mountain region 2000 2001 2002 2003
−0.04244* (0.00395) −0.06139* (0.00564) −0.07382* (0.00448) −0.04651* (0.00398) 0.00248 (0.00327) 0.01352* (0.00323) 0.01711* (0.00317) 0.01062* (0.00321)
−0.04814* (0.01517) −0.10331* (0.01835) −0.12341* (0.01736) −0.09704* (0.02290) 0.00511 (0.01124) 0.02018*** (0.01135) 0.02951* (0.01118) −0.000003 (0.01144)
t-Test of significant differences (black vs. white)
N Adjusted R2
167,306 0.4174
11,791 0.3353
−0.34 −2.06** −2.61* −2.04** 0.21 0.53 1.01 −0.84
Standard errors in parentheses. * Significant difference at the 1% level. ** Significant difference at the 5% level. *** Significant difference at the 10% level. **** Significant difference at the 15% level.
References Bedard, K. (2001). Human capital versus signaling models: University access and high school dropouts. Journal of Political Economy, 109(4), 749–775. Belman, D., & Heywood, J. S. (1991). Sheepskin effects in the returns to education: An examination of women and minorities. The Review of Economics and Statistics, 73(4), 720–724. Belman, D., & Heywood, J. S. (1997). Sheepskin effects by cohort: Implications of job matching in a signaling model. Oxford Economic Papers, 49, 623–637. Cameron, S. V., & Heckman, J. J. (1993). The nonequivalence of high school equivalents. Journal of Labor Economics, 11(1), 1–47. Chiswick, B. R. (1973). Schooling, screening, and income. In L. Solomon, & P. Taubman (Eds.), Does college matter? New York: Academic Press. Couch, K. A., & Daly, M. C. (2003–2004). The improving relative status of black men. Journal of Income Distribution, 12(3–4), 56–78. Denny, K., & Harmon, C. (2001). Testing for sheepskin effects in earnings equations: Evidence for five countries. Applied Economics Letters, 8(9), 635–637. Ferrer, A., & Riddell, W. C. (2002). The role of credentials in the Canadian labour market. Canadian Journal of Economics, 35(November (4)), 879–905. Gullason, E. T. (1999). The stability pattern of sheepskin effects and its implications for the human capital theory—Screening hypothesis debate. Eastern Economic Journal, 25(2), 141–149.
Habermalz, S. (2006). More detail on the pattern of returns to educational signals. Southern Economic Journal, 73(1), 125–135. Heckman, J. J., & Rubinstein, Y. (2001). The importance of noncognitive skills: Lessons from the GED testing program. American Economic Review, 91(2), 145–149. Heywood, J. S. (1994). How widespread are sheepskin returns to education in the U.S.? Economics of Education Review, 13(3), 227–234. Hoffman, S. (1979). Black–white life cycle earnings differences and the vintage hypothesis: A longitudinal analysis. American Economic Review, 69(5), 855–867. Hungerford, T., & Solon, G. (1987). Sheepskin effects in returns to education. The Review of Economics and Statistics, 69(1), 175–178. Jaeger, D. A., & Page, M. E. (1996). Degrees matter: New evidence on sheepskin effects in returns to education. The Review of Economics and Statistics, 78(4), 733–740. Lang, K., & Kropp, D. (1986). Human capital versus sorting: The effects of compulsory attendance laws. The Quarterly Journal of Economics, 101(3), 609–624. Neumark, D. (1988). Employers’ discriminatory behavior and the estimation of wage discrimination. The Journal of Human Resources, 23(3), 279–295. Oaxaca, R. L. (1973). Male–female wage differentials in urban labor markets. International Economic Review, 14(3), 693–709. Oaxaca, R. L., & Ransom, M. (1998). Calculation of approximate variances for wage decomposition differentials. Journal of Economic and Social Measurement, 24, 55–61.
Contents lists available at ScienceDirect
Economics of Education Review journal homepage: www.elsevier.com/locate/econedurev
Do sheepskin effects help explain racial earnings differences? John D. Bitzan ∗ College of Business, North Dakota State University, Putnam Hall, P.O. Box 6050, Fargo, ND 58108-6050, United States
a r t i c l e
i n f o
Article history: Received 16 October 2007 Accepted 8 October 2008 JEL classification: J31 Keywords: Educational economics Human capital Salary wage differentials
a b s t r a c t This study examines the role of sheepskin effects in explaining white–black earnings differences. The study finds significant differences in sheepskin effects between white men and black men, with white men receiving higher rewards for lower level signals (degrees of a college education or less) and black men receiving higher rewards for higher level signals (graduate degrees). In performing an Oaxaca decomposition of earnings differences, it is apparent that signaling plays an important role in explaining white–black earnings differences and that a portion of the gap may be explained by statistical discrimination. © 2009 Elsevier Ltd. All rights reserved.
1. Introduction Recent studies of black–white earnings differentials show a narrowing gap, particularly during the 1990s. Couch and Daly (2003–2004), find that the black–white weekly earnings differential for males decreased from an average of .44 in the 1968–1972 period to .27 in the 1998–2001 period – a 38% decline. They attribute the reduction in this gap to increases in human capital among black males relative to white males, to increased occupational diversity among black males relative to white males, and to possible reductions in discrimination. However, despite the progress in reducing the black–white earnings differential, a significant gap still exists (average of 31% by the estimates of Couch and Daly).1 Moreover, while the overall black–white earnings differential has decreased a lot over time, the unexplained portion of the differential has not shown the same type of reduction. Couch and Daly (2003–2004) estimate that 23% of the 1968–1979 wage gap was due to unmeasured factors, while 62% of the 1989–2001 wage gap was due to unmeasured factors.
While several studies have attempted to explain black–white wage differentials using a variety of human capital measures, occupational choice, and general wage inequality, none have examined the ability of degree attainment to explain a portion of the gap. This study revisits the sheepskin hypothesis in the context of black–white earnings differentials, by examining the ability of sheepskin effects to explain a larger portion of black–white earnings differentials than educational attainment alone. A finding of an increasing share of the black–white earnings differential explained by sheepskin effects would provide information in two distinct lines of research. First, it would provide a further confirmation of the sheepskin hypothesis – that is, degrees provide an additional return beyond educational attainment alone. Second, it may suggest that sheepskin effects should be considered in subsequent evaluations of racial earnings differentials. The following section examines previous studies of sheepskin effects. Next, wage equations are estimated for black and white males using a sheepskin model. An Oaxaca decomposition of earnings differences follows. Finally, a summary and implications are presented. 2. Sheepskin effects
∗ Tel.: +1 701 231 8949; fax: +1 701 231 7508. E-mail address: [email protected]. 1 e.2714 − 1 = .31. 0272-7757/$ – see front matter © 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.econedurev.2008.10.003
According to the signaling hypothesis, education does not (only) make workers more productive, but it serves
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as a signal to employers that workers are more productive. Although it is not possible to directly observe whether signaling takes place in the labor market, the finding of sheepskin (or diploma) effects on worker earnings provides general support for the idea that signaling takes place. Several recent studies have examined the role of sheepskin effects in determining the earnings of U.S. workers. For the most part, these studies have provided support for the notion of sheepskin effects. A popular approach to examining sheepskin effects is to use a punctuated spline function that allows different returns to different years of education and different returns to degree years. Three of these studies (Belman & Heywood, 1991; Hungerford & Solon, 1987; Jaeger & Page, 1996) are particularly relevant to the current study. Hungerford and Solon (1987) were the first to use the punctuated spline approach in testing for sheepskin effects. In examining returns to education for white males from the 1978 Current Population Survey, they found significantly higher returns to diploma years than to other years. Although they did not have data on actual degrees received, the punctuated spline function approach provided more convincing evidence on the importance of a signaling effect than previous approaches. Belman and Heywood (1991), and Jaeger and Page (1996) not only provided additional confirmation of the sheepskin hypothesis, but also provided insight into the role of signaling in statistical discrimination. In extending the work of Hungerford and Solon (1987), Belman and Heywood (1991) examined sheepskin effects for minorities. They developed a theoretical model that suggests that if it is more costly to obtain an inaccurately high signal for minorities or if minorities have fewer resources to obtain an inaccurately high signal, then signals will be more accurate for minorities than for whites. Thus, steeper earnings profiles will exist for minorities (lower rewards to low-level signals, and higher rewards to high-level signals). Belman and Heywood found evidence that sheepskin effects for minorities were consistent with such a signaling model – that is, the return to a high school education was lower for minorities, while the return to a college education or graduate school education were higher. Jaeger and Page (1996) extended the work of both of these studies by using data with years of education and degrees. While Hungerford and Solon (1987) and Belman and Heywood (1991) found significant sheepskin effects, Jaeger and Page (1996) noted that sheepskin effects could be biased because of a lack of data on actual degrees received. Using a unique dataset, Jaeger and Page (1996) matched the 1991 and 1992 Current Population Survey files to obtain information on degrees obtained and on years of education obtained. They found evidence that sheepskin effects were strong for white men, minorities, and women. However, while the pattern of sheepskin effects they found was consistent with that found by Belman and Heywood (lower returns to lower level signals for black workers than white workers, and higher returns to higher level signals for black workers), they found that such differences were generally not statistically significant (an exception is statistically higher returns to graduate degrees for black men than white men).
Although other studies have not examined the role of sheepskins in statistical discrimination, many have attempted to measure whether sheepskin effects occur in other countries. For example, Denny and Harmon (2001) found evidence of sheepskin effects in Canada, Sweden, Great Britain, Ireland, and the U.S., while Ferrer and Riddell (2002) found evidence of sheepskin effects in Canada. It should be noted that while sheepskin effects are consistent with the notion of signaling, they could also be consistent with human capital explanations. Chiswick (1973) and Lang and Kropp (1986) provide two such explanations. Chiswick (1973) suggests that people who receive degrees are more efficient learners, and thus those who receive higher returns from education. Lang and Kropp (1986) suggest that returns to degree years may be higher because these years enhance productivity more than other years due to the extra effect of having all the coursework in a particular area above that from the additional coursework taken that year. Because of these types of problems, other authors have performed additional tests of the sheepskin hypothesis. Two of these have examined changes in sheepskin effects over time on the job. Belman and Heywood (1997) provided evidence suggesting that sheepskin effects diminish with time on the job. This suggests that diplomas are initially used as a signal, and then these signals are replaced with stronger signals when more information about individual worker productivity is known. In an extension of this work, Habermalz (2006) found that returns to high-level degrees initially increased after employment, but then declined over time. These results are also consistent with improved job matching between employers and workers once more information on true worker productivity becomes available. Other studies using unique approaches to examine the potential role of signaling include Heywood (1994), Lang and Kropp (1986), Bedard (2001), and Gullason (1999). Heywood (1994) found that sheepskin effects were not as prevalent in the public sector as in the private sector. As Belman and Heywood (1997) pointed out, this may strengthen support for the signaling hypothesis under the belief that cost minimization is more important to the private sector than the public sector. Lang and Kropp (1986) found that compulsory attendance laws increased educational attainment for groups not constrained by the laws, consistent with the signaling hypothesis. Bedard (2001) found that increased university access increased high school dropout rates, which is also consistent with the signaling hypothesis. Finally, Gullason (1999) found that workers whose jobs did not utilize what they learned in school had significant sheepskin effects. The present study does not attempt to resolve the signaling vs. human capital debate. Rather, this study examines whether the consideration of the role played by sheepskins can explain more of the white–black earnings gap, and whether statistical discrimination may play a role in white–black earnings differences. The next section of the study estimates a sheepskin model of earnings for black and white males to examine the importance of sheepskin effects and differences between the two groups.
J.D. Bitzan / Economics of Education Review 28 (2009) 759–766
3. Sheepskin model of earnings This study uses pooled 1999–2003 Current Population Survey Merged Outgoing Rotation Groups to estimate wage equations for nonunion, private sector white and black males using a sheepskin model.2,3 In addition to testing for the existence of sheepskin effects in each racial group, a wage equation of the combined sample is estimated that allows for the testing of differences in returns to sheepskins and human capital characteristics between white and black men. This is an important extension of the work of Belman and Heywood (1991) and of Jaeger and Page (1996). Both of those studies examined sheepskin effects of different racial groups, and both found steeper earnings profiles for minorities (consistent with the idea that signals may be more accurate for minorities). However, Belman and Heywood (1991) were not able to test for statistical significance of such differences. Moreover, while Jaeger and Page were able to test for statistical significance (finding a lack of statistical significance in such differences), their small sample size suggests less efficient estimates (711 black males, with only 10 having professional degrees and 10 having doctoral degrees). The current study uses a sample size with nearly 12,000 black males and 167,000 white males (including 58 and 63 black males with professional degrees and doctoral degrees, respectively). This study also goes beyond the previous studies by applying an Oaxaca decomposition to white–black earnings differences using the sheepskin model. The sheepskin model estimated in this study is similar to that estimated by Jaeger and Page (1996), in that it uses information on specific degrees obtained as well as years of education.4 Like previously estimated sheepskin models by Hungerford and Solon (1987), Belman and Heywood (1991), Heywood (1994), and Jaeger and Page (1996), the model is a punctuated spline function, allowing different returns to education after obtaining a high school degree and after obtaining a college degree. However, unlike previous estimations (including Jaeger and Page), the return to a high school diploma is differentiated from the return to a GED.5 Specifically, the following sheepskin model is estimated: ln w = ˇ0 + ˇ1 Exper + ˇ2 Exper2 + ˇ3 Educ + ˇ4 D12(Educ − 12) + ˇ5 D16(Educ − 16) + ˇ6 GED + ˇ7 HS + ˇ8 VOC + ˇ9 ASSO + ˇ10 BA + ˇ11 MA + ˇ12 PROF + ˇ13 DOC + ˇ14 Married + ˇ15 SMSA+Regional Dummies+Year Dummies+ε
2 Although 2004 data are available, the black male sample is much smaller with only 4 individuals having a doctoral degree. 3 Observations with top coded earnings or with allocated earnings, those with less than the minimum wage of $5.15, and those with usual hours of work less than 20 or more than 99 are deleted. Means of all variables are shown in Table 3 of Appendix A. 4 Years of education are proxied by NBER’s imputed highest grade of school completed (CPS Labor Extracts, NBER, February 2005). 5 Studies by Heckman and others suggest that receiving a GED is much different than achieving a high school diploma. See for example, Cameron and Heckman (1993), and Heckman and Rubinstein (2001).
761
where Exper = Age-Educ-6, Educ = imputed years of education from NBER, D12 = Educ > 12, D16 = Educ > 16, HS = high school degree or higher, GED = GED degree, VOC = vocational school degree, ASSO = associate’s degree, BA = bachelor’s degree or higher, MA = master’s degree, PROF = professional degree, DOC = doctoral degree, Married = (1 = married; 0 = not married), and SMSA = (1 = resides in metropolitan area; 0 = resides in non-metro area). Table 1 presents the parameter estimates, along with t-tests of significant differences in parameter estimates between black males and white males. As the table shows, all parameter estimates are of the expected sign. Returns to experience and education are positive and significant, as the human capital model would predict. Moreover, most sheepskin effects are positive and significant at conventional levels. Tests of joint significance of sheepskin effects provide F-statistics that are significant at the 1% level in both black male and white male models. Further examination of Table 1 shows that there are differences in sheepskin effects between white and black men. This is an important finding, providing increased support for the notion that statistical discrimination may play a role in racial earnings differences. In examining individual sheepskin effects, the returns to a GED degree, an associate’s degree, and a bachelor’s degree are significantly higher for white men in comparison to black men, while returns to a doctorate degree are significantly higher for black men in comparison to white men. In addition, although not statistically significant, returns for most degrees less than a bachelor’s degree are lower for black men (the exception is a vocational degree), while those for all degrees above a bachelor’s degree are higher for black men.6 These findings are consistent with the findings of Belman and Heywood (1991) that returns to lower level sheepskins are higher for white men, while those for higher level signals are higher for black men. Moreover, they are also consistent with those of Jaeger and Page (1996), although they did not find that such differences were significant. While there are several possible explanations for these significant sheepskins and significant differences in sheepskins between black and white men, they are consistent with a signaling model. Belman and Heywood (1991) show that if the cost of obtaining inaccurately high signals is higher for minorities (than for whites) or if minorities have fewer resources to spend on inaccurately high signals, the signaling model predicts higher rewards to high-level signals and lower rewards to low-level signals for minority groups (in comparison to whites). Most other explanations for the differences in sheepskins should show up as differences in returns to education as well. For example, one of the most obvious explanations might be that there are
6 A sheepskin model estimated with a step function (dummies for each year of education from 9 through 18) and degrees provides similar results. This is shown in Table 4 of Appendix A. In the step function model, the return to a high school diploma is significantly smaller for black males at the 5% level, the return to a GED is significantly smaller for black males at the 15% level, the return to a bachelor’s degree is significantly smaller for black males at the 1% level, and the return to a doctorate degree is significantly higher for black males at the 1% level.
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Table 1 Parameter estimates for sheepskin model of earnings (non-union, private sector males). Variable
White
Black
t-Test of significant differences (black vs. white)
Intercept Experience Experience squared Years of education Spline for ed > 12 Spline for ed > 16
0.90288* (0.01016) 0.03177* (0.00027) −0.00055* (0.000006) 0.03744* (0.00096) 0.02570* (0.00168) −0.03702* (0.00341)
1.18539* (0.04837) 0.02096* (0.00094) −0.00034* (0.00002) 0.01262 (0.00434) 0.06241* (0.00625) −0.01346 (0.01404)
5.38* −10.43* 9.35* −5.25* 5.34* 1.54****
Sheepskin effects GED High school diploma or higher Vocational degree Associate degree Bachelor’s degree or higher Master’s degree Professional school degree Doctorate degree
0.12207* (0.00618) 0.14917* (0.00442) 0.04696* (0.00512) 0.06940* (0.00601) 0.20126* (0.00548) 0.07524* (0.00685) 0.16476* (0.01117) 0.15171* (0.01097)
0.08377* (0.02109) 0.13578* (0.01422) 0.06815* (0.02011) 0.02929 (0.02081) 0.13714* (0.01959) 0.09745* (0.03047) 0.20436* (0.05405) 0.29733* (0.05589)
−1.64*** −0.85 0.96 −1.75*** −2.97* 0.67 0.67 2.40**
Other controls Married SMSA NE region Mid Atlantic region East North Central region West North Central region South Atlantic region East South Central region West South Central region Mountain region 2000 2001 2002 2003
0.15232* (0.00231) 0.12545* (0.00249) 0.02584* (0.00442) 0.00532 (0.00438) 0.00512 (0.00400) −0.07617* (0.00414) −0.04417* (0.00393) −0.06342* (0.00561) −0.07528* (0.00446) −0.04769* (0.00397) 0.00243 (0.00326) 0.01372* (0.00322) 0.01710* (0.00316) 0.0158* (0.00320)
0.12074* (0.00759) 0.12884* (0.01098) −0.04190*** (0.02310) −0.02129 (0.01777) −0.06381* (0.01717) −0.12150* (0.02262) −0.04855* (0.01517) −0.10374* (0.01834) −0.12451* (0.01736) −0.09619* (0.02289) 0.00563 (0.01124) 0.02066*** (0.01135) 0.02980* (0.01118) 0.00003 (0.01143)
−3.75* 0.28 −2.71* −1.37 −3.68* −1.85*** −0.26 −1.98** −2.59* −1.96** 0.26 0.55 1.03 −0.84
N Adjusted R2
167,306 0.4173
11,791 0.3351
Standard errors in parentheses. * Significant difference at the 1% level. ** Significant difference at the 5% level. *** Significant difference at the 10% level. **** Significant difference at the 15% level.
separate labor markets for black and white workers with different demand and supply conditions. If black workers receive larger rewards to high quality sheepskins due to a more limited supply of workers with such sheepskins, we should expect to see higher returns to education for black workers in comparison to white workers. As Table 4 of Appendix A shows, the return to each educational dummy relative to less than 9 years of education for black males is smaller than for white males. The following section of the paper performs an Oaxaca decomposition on the white–black earnings differential, in order to examine the ability of sheepskin effects to explain a greater portion earnings differences. 4. Oaxaca decomposition of white–black earnings differences In order to further examine the importance of sheepskin effects in the U.S. labor market and to examine the ability of sheepskin effects to explain white–black earnings differences, this study performs Oaxaca decompositions of earnings differences using the sheepskin and human capital models. Unlike most applications of the Oaxaca decomposition, this study quantifies earnings differences due to differences in parameter estimates in addition
to quantifying differences due to differences in characteristics. This allows the sheepskin model to provide an increased understanding of earnings differences on two fronts: (1) do differences in sheepskin attainment among white and black men explain more of the earnings gap than differences in educational attainment alone?, and (2) does statistical discrimination explain part of the earnings gap? The standard Oaxaca decomposition is a method for separating the total earnings gap between two groups into two portions; one portion is explained by differences in personal characteristics, and the other is due to differences in estimated coefficients between the two groups. The second portion is the one that is troublesome, and may represent statistical or deliberate discrimination or other unmeasured factors.7 As noted by Neumark (1988), the original decomposition introduced by Oaxaca (1973) uses either the black or the white wage structure as the nondiscriminatory wage structure – when the black wage structure is used, it is assumed that white workers receive a premium
7 Extreme caution must be used in interpreting the magnitude of the “unexplained” portion in this study, however, as the models are not intended to explain as much of the gap as possible. The models are only designed to examine the role of sheepskin effects in explaining a portion of the gap.
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Table 2 Percent of black–white earnings differences explained by differences in characteristics and by differences in rewards for characteristics. Log earnings differential = .2120 Differences in characteristics
Differences in rewards
Intercept Experience Experience squared Exp. total
0.0068* (0.0001) −0.0061* (0.0001) 0.0008* (0.00002)
−0.2825* (0.0373) 0.2007* (0.0224) −0.1009* (0.0094) 0.0998* (0.0102)
Years of education Spline for ed > 12 Spline for ed > 16 Educ. total
0.0122* (0.0003) 0.0135* (0.0008) −0.0033* (0.0003) 0.0224* (0.0006)
0.3219* (0.0814) −0.0498* (0.0109) −0.0031 (0.0031) 0.2689* (0.0679)
Sheepskin GED High school diploma or higher Vocational degree Associate degree Bachelor’s degree or higher Master’s degree Professional school degree Doctorate degree Sheepskin total
−0.0002* (0.000008) 0.0012* (0.00003) 0.0007* (0.00006) −0.00003* (0.000003) 0.0214* (0.0006) 0.0019* (0.0002) 0.0009* (0.0001) 0.0010* (0.0001) 0.0269* (0.0007)
0.0014*** (0.0008) 0.0109 (0.0124) −0.0008 (0.0010) 0.0014*** (0.0008) 0.0106*** (0.0055) −0.0007 (0.0016) −0.0002 (0.0006) −0.0008 (0.0006) 0.0218**** (0.0149)
Married SMSA Region Year
0.0220* (0.0003) −0.0115* (0.0002) 0.0100* (0.0008) 0.0002* (0.00005)
0.0145* (0.0049) −0.0034 (0.0087) 0.0247*** (0.0137) −0.0027 (0.0075)
0.0708* (0.00094)
TOTAL
0.1413* (0.0059)
Standard errors are in parentheses. * Significant at the 1% level. ** Significant at the 5% level. *** Significant at the 10% level. **** Significant at the 15% level.
over the nondiscriminatory wage structure, while when the white wage structure is used, it is assumed that black workers receive a discount from the nondiscriminatory wage structure. The more general model introduced by Neumark (1988) suggests that the log wage differential between two groups can be separated into an explained portion and an unexplained portion as follows:
ˆ W − ˇ) ˆ − XB (ˇ ˆ B − ˇ) ˆ ˆ + XW (ˇ ln WW −ln WB = XW −XB ˇ
where WW is the mean white wage, WB the mean black wage, XW the vector of mean white characteristics, XB the ˆ the vector of coeffivector of mean black characteristics, ˇ ˆ W the vector cients for nondiscriminatory wage structure, ˇ ˆ B is the vector of estiof estimated white coefficients, and ˇ mated black coefficients. The first term on the right hand side of this equation represents the portion of the white–black earnings differential that is explained by differences in characteristics. It is the part that is often referred to as the “explained” portion of the gap. The second term on the right hand side of the equation is the “unexplained” portion. It can be broken down into two separate components – a portion accounted for by a premium paid to white workers, and a portion accounted for by a discount paid to black workers. Neumark (1988) shows that the nondiscriminatory wage structure can be estimated by applying OLS to the full sample.8
8 Using a model of discrimination due to employer preferences, he shows that if employer utility functions with respect to each type of labor
This study uses the Oaxaca decomposition to examine whether sheepskin effects can explain more of the white–black earnings gap. Unlike previous studies, individual components of this “unexplained” portion are highlighted due to the insight this might provide into the role of statistical discrimination in explaining white–black earnings differences. Table 2 shows the Oaxaca decomposition of the white–black log earnings gap.9 As the table shows, the overall gap in white–black earnings in the sample is 23.6% (e.2120 − 1). The sheepskin model is not able to explain large portions of the gap (about 33% of the gap is the explained portion). However, it is important to note that this study does not aim to explain the largest possible portion of the gap. Rather, it aims to show the additional power of including sheepskin effects in explaining differences. In examining the explained portion of the gap, several things are apparent. First, differences in experience explain less than 1% of the gap in either the human capital model or the sheepskin model. Second, the explained portion of the gap by differences in educational attainment alone is about 11%. However, when sheepskin effects are considered, the total portion of the gap explained by education or sheep-
is homogenous of degree zero, then the common wage structure can be estimated in this way. This study estimates the nondiscriminatory wage structure using OLS on the combined sample. The estimation results from the combined sample are available from the author upon request. 9 Standard errors are estimated using the approach introduced by Oaxaca and Ransom (1998).
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skin variables is more than 23%. By comparison, a human capital model that allows for nonlinearity in returns to education (includes spline variables for education beyond 12 and 16 years) shows that education explains 20% of the gap, and a human capital model that does not allow for nonlinearity in returns to education shows education to explain 14% of the gap.10 These results suggest three things. First, examining wage differences due to differences in educational attainment alone is unable to capture the full impact of educational differences on the wage gap – that is, it is unable to capture the larger impact of the role of education as a signal on wages, in addition to its impact on human capital attainment. Second, it is important to allow for nonlinearity in returns to education to capture the full impact of differences in human capital attainment on the wage gap. Third, even though the improvement in explaining the earnings gap from educational and sheepskin difference may seem small in comparison to a human capital model that allows for nonlinearity in education, the fact that sheepskins are statistically important suggests that the human capital model mistakenly attributes a portion of the gap to differences in years of education that is really attributable to degree attainment. This is especially important in formulating policy – policies aimed at encouraging degree achievement may be different than those encouraging increased education. It is also interesting to look at wage differences explained by differences in parameter estimates. Differences in rewards to experience are shown to have a large impact on the wage differential. However, this is very misleading, as a likely reason for the large differences in returns to experience among white and black workers are due to vintage effects. As pointed out by Hoffman (1979), vintage effects occur because of the fact that different age cohorts are captured in a single cross-section. Because of a decline in discrimination for younger cohorts, and because of a relative improvement in the quality of education for minorities, white–black earnings differences are larger for older cohorts. Thus, experience that is measured by ageeducation-6 will reflect this larger gap for older cohorts. Table 2 also shows that a large portion of the white–black earnings differential is explained by differences in returns to education and degrees. In focusing on the portion of the differential explained by differences in returns to degrees, it is apparent that positive portions of the wage gap are explained by higher rewards to white men for lower level signals and negative portions of the gap are explained by higher rewards to black men for higher level signals. However, because few white or black men obtain higher level signals (master’s, professional school, or doctorate), the differences in rewards to lower level signals explain much larger portions of the gap. Statistically significant differences in the wage gap explained by differences in rewards to degrees include 5% explained by a higher reward to a bachelors degree for white men and 1.3% explained by higher rewards to a GED and an associate’s degree for white men. Other portions
of the wage gap explained by differences in rewards to degrees that are not statistically significant include that explained by higher rewards for a high school degree for white men, and negative portions explained by higher rewards for graduate degrees for black men. In total, differences in returns to sheepskins are estimated to account for about 10% of the wage gap. While this is not a large number, it suggests that statistical discrimination plays a role in explaining white–black earnings differences. Moreover, the overall findings of the Oaxaca decomposition suggest that the role of signaling should be taken into account in future racial earnings difference explorations. The following section of the paper presents a summary of the findings. 5. Summary and implications This study examines the role of sheepskin effects in explaining white–black earnings differences. In order to examine these differences, punctuated spline models are estimated for samples of nonunion, private sector white and black men. After estimating these models, an Oaxaca decomposition of earnings differences is performed. In estimating the sheepskin model, several things are apparent. First, there are significant sheepskin effects for both white and black men. Second, there are significant differences in these effects between white men and black men, with white men receiving higher rewards for lower level signals and black men receiving higher rewards for higher level signals. These findings are suggestive of an important role for signaling in U.S. labor markets. Moreover, the steeper earnings profile for black men in education is consistent with the model presented by Belman and Heywood (1991) which predicts more accurate signals for minorities under an assumption that it is more costly for minorities (or minorities have fewer resources) to obtain inaccurately high signals. In performing the Oaxaca decomposition, it is apparent that signaling plays an important role in explaining white–black earnings differences and that a portion of the gap may be explained by statistical discrimination. In the sheepskin model, differences in education alone accounts for 11% of the white–black earnings gap, while education and sheepskins account for 23% of the gap. Moreover, it is estimated that 10% of the white–black earnings gap is explained by statistically significant differences in rewards for signals between white and black men. These findings provide increased support for the importance of signaling in the U.S. labor market, and suggest that signaling should be considered in future explorations of racial earnings differences. Acknowledgements I am grateful to Bahman Bahrami, Steffen Habermalz, John Heywood, and two anonymous referees for comments and suggestions on earlier versions of this paper. Any remaining errors are, of course, my responsibility. Appendix A.
10
These results can be obtained from the author upon request.
See Tables 3 and 4.
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Table 3 Mean values (standard deviations in parentheses). Full sample (N = 179,097)
White (N = 167,306)
Black (N = 11,791)
ln wage (wage is in 1982–84 $, deflated by CPI-U) Experience Experience squared Years of education Spline for ed > 12 Spline for ed > 16
2.1095 (0.5395) 18.7553 (12.2174) 501.0265 (561.7379) 13.2613 (2.9194) 1.7624 (2.1018) 0.2154 (0.6004)
2.1235 (0.5411) 18.7700 (12.2258) 501.7832 (561.0974) 13.2839 (2.9506) 1.7936 (2.1147) 0.2213 (0.6075)
1.9115 (0.4734) 18.5473 (12.0955) 490.2903 (570.6664) 12.9402 (2.4111) 1.3202 (1.8538) 0.1313 (0.4797)
Sheepskin effects GED High school diploma or higher Vocational degree Associate degree Bachelor’s degree or higher Master’s degree Professional school degree Doctorate degree
0.0354 (0.1847) 0.8225 (0.3821) 0.0483 (0.2143) 0.0360 (0.1853) 0.2678 (0.4428) 0.0528 (0.2237) 0.0105 (0.1017) 0.0109 (0.1039)
0.0353 (0.1845) 0.8231 (0.3816) 0.0491 (0.2161) 0.0356 (0.1852) 0.2747 (0.4464) 0.0544 (0.2268) 0.0108 (0.1034) 0.0113 (0.1059)
0.0366 (0.1879) 0.8150 (0.3883) 0.0365 (0.1875) 0.0360 (0.1864) 0.1695 (0.3752) 0.0304 (0.1716) 0.0053 (0.0729) 0.0049 (0.0700)
Other controls Married SMSA NE region Mid Atlantic region East North Central region West North Central region South Atlantic region East South Central region West South Central region Mountain region Pacific region 1999 2000 2001 2002 2003
0.6085 (0.4881) 0.7726 (0.4192) 0.0910 (0.2876) 0.0963 (0.2950) 0.1363 (0.3431) 0.1209 (0.3260) 0.1604 (0.3700) 0.0487 (0.2153) 0.0913 (0.2880) 0.1386 (0.3455) 0.1166 (0.3209) 0.1926 (0.3944) 0.1902 (0.3925) 0.1984 (0.3988) 0.2148 (0.4107) 0.2040 (0.4030)
0.6178 (0.4859) 0.7663 (0.4232) 0.0948 (0.2929) 0.9578 (0.2943) 0.1371 (0.3439) 0.1266 (0.3325) 0.1458 (0.3529) 0.0450 (0.2073) 0.0892 (0.2850) 0.1457 (0.3528) 0.1201 (0.3251) 0.1923 (0.3941) 0.1893 (0.3917) 0.1985 (0.3988) 0.2151 (0.4109) 0.2049 (0.4036)
0.4768 (0.4995) 0.8616 (0.3454) 0.0373 (0.1896) 0.1035 (0.3046) 0.1247 (0.3304) 0.0399 (0.1956) 0.3683 (0.4824) 0.1014 (0.3019) 0.1208 (0.3259) 0.0383 (0.1918) 0.0660 (0.2483) 0.1970 (0.3978) 0.2038 (0.4028) 0.1970 (0.3978) 0.2099 (0.4073) 0.1923 (0.3941)
Table 4 Parameter estimates for step function model of earnings (non-union, private sector males). Variable
White
Black *
t-Test of significant differences (black vs. white) *
5.68* −10.33* 9.23* −3.16* −5.39* −7.09* −4.51* −3.72* −4.11* −2.85* −1.55**** −0.09 −0.24
Intercept Experience Experience squared Ed = 9 Ed = 10 Ed = 11 Ed = 12 Ed = 13 Ed = 14 Ed = 15 Ed = 16 Ed = 17 Ed = 18
1.10607 (0.00664) 0.03182* (0.00027) −0.00055* (0.000006) 0.10444* (0.00806) 0.18046* (0.00714) 0.22315* (0.00675) 0.23187* (0.00903) 0.31575* (0.01002) 0.38838* (0.00987) 0.41136* (0.01073) 0.42881* (0.01271) 0.48447* (0.01454) 0.47617* (0.01420)
1.29071 (0.02987) 0.02111* (0.00094) −0.00035* (0.00002) −0.00879 (0.03277) 0.01489 (0.02802) 0.01907 (0.02628) 0.07738** (0.03100) 0.17548* (0.03411) 0.23597* (0.03358) 0.29652* (0.03644) 0.35098* (0.04555) 0.47899* (0.05522) 0.46224* (0.05295)
Sheepskin effects GED High school diploma or higher Vocational degree Associate degree Bachelor’s degree or higher Master’s degree Professional school degree Doctorate degree
0.12094* (0.00623) 0.15728* (0.00800) 0.03519* (0.00527) 0.06251* (0.00612) 0.26006* (0.00911) 0.07941* (0.00694) 0.17196* (0.01132) 0.15906* (0.01113)
0.08654* (0.02114) 0.10170* (0.02268) 0.06560* (0.02034) 0.03010 (0.02095) 0.16218* (0.03457) 0.10925* (0.03088) 0.22145* (0.05453) 0.31470* (0.05636)
−1.47**** −2.18** 1.36 −1.40 −2.58* 0.89 0.84 2.55*
Other controls Married SMSA NE region Mid Atlantic region East North Central region West North Central region
0.15240* (0.00232) 0.12446* (0.00250) 0.02756* (0.00444) 0.00734*** (0.00440) 0.00700*** (0.00402) −0.07464* (0.00416)
0.12006* (0.00760) 0.12764* (0.01099) −0.04327*** (0.02311) −0.02130 (0.01778) −0.06342* (0.01717) −0.12056* (0.02263)
−3.84* 0.27 −2.83* −1.47**** −3.76* −1.88***
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Table 4 (Continued ) Variable
White
Black
South Atlantic region East South Central region West South Central region Mountain region 2000 2001 2002 2003
−0.04244* (0.00395) −0.06139* (0.00564) −0.07382* (0.00448) −0.04651* (0.00398) 0.00248 (0.00327) 0.01352* (0.00323) 0.01711* (0.00317) 0.01062* (0.00321)
−0.04814* (0.01517) −0.10331* (0.01835) −0.12341* (0.01736) −0.09704* (0.02290) 0.00511 (0.01124) 0.02018*** (0.01135) 0.02951* (0.01118) −0.000003 (0.01144)
t-Test of significant differences (black vs. white)
N Adjusted R2
167,306 0.4174
11,791 0.3353
−0.34 −2.06** −2.61* −2.04** 0.21 0.53 1.01 −0.84
Standard errors in parentheses. * Significant difference at the 1% level. ** Significant difference at the 5% level. *** Significant difference at the 10% level. **** Significant difference at the 15% level.
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