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Differences in returns to education: an analysis by race
American Journal of Economics and Sociology, The, July, 1994 by Javed Ashraf
I
Introduction
INVESTMENT IN HUMAN CAPITAL has been a subject of considerable interest to economists. The returns to education have a bearing on the quantity of investment undertaken in this area. Productivity and social justice are involved. This article identifies three levels of educational attainment, high-school, some college, and college or higher, and estimates the percentage gain in earnings at each level (relative to a fourth group consisting of those who did not complete high school) for black and white workers separately. Unlike previous research, this study also tracks the trend in these returns over a twenty year period, 1967-86. The racial gap in these returns highlights the differential incentive members of each race have to invest in schooling, and from a policy point of view sheds light on where governmental efforts in education should be most concentrated.
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II
Previous Studies
STUDIES computing the rate of return to education in general are plentiful. A number of studies have estimated the returns to education for a number of different countries. In one of the more comprehensive studies, Psacharopoulos (1985) calculated the returns for 61 countries. Grouping countries by their level of economic development, he confirmed the earlier well-documented declining rate of return pattern by level of education. Psacharopoulos found that the most profitable educational investment opportunity was primary education, and secondary education was second. He attributed this to the low cost of primary education, and the high productivity differential between primary school graduates and those who are illiterate. He found that the returns to any level of education were highest in the least-developed countries, and lowest in the advanced countries of the West. For the United States, Psacharopoulos reported a return of 11% for secondary school graduates, and 5.3% for those with a higher level of education in 1976.
More recently, Pencavel (1991) showed that college graduates earned 65% more than those with high school diplomas. However, this differential was sensitive to age, with the college high-school earnings gap ranging from 46% for younger workers, to 69% for workers aged 45 to 54. Although Pencavel examined earnings differentials by gender, he did not compute them by race.
Lydon (1989) examined earnings premiums arising from the completion of a college education. His data included the 1940, 1950, 1960, 1970 and 1980 censuses as well as the 1988 Current Population Survey. Lydon limited his focus to whites, and found that a college education led to earnings premiums ranging from 30% in 1950 to 64% in 1988 (relative to a high school education). But this recent work has neglected the effects of education across racial lines. Little has been done since Freeman's (1976) excellent work.
III
Data and Methodology
ALL RESULTS COMPUTED for this article are based on data from the Panel Study of Income Dynamics, Waves (I-XX) covering the period 1967 to 1986. The data were drawn from households that had at least one member of the non-institutionalized population of the 48 contiguous states and the District of Columbia. A subsample of the PSID data was drawn from low-income non-elderly households sampled by the Census Bureau. These households, drawn with unequal probabilities of selection that depended on geographic location, age, race, and income, were added to the sample to insure that there would be a sufficient number of low income and, especially, black low-income households to permit separate analyses of these populations. The sample is a combination of a representative cross-section of nearly 3,000 families selected from the Survey Research Center's master sampling frame, and a subsample of about 1,900 low-income families previously interviewed by the Census Bureau for the Office of Economic Opportunity. The combined sample is appropriately weighted to be representative of all people in the United States.(1)
Only white and black workers were considered herein since the number of individuals representing other ethnic groups were small. In addition, only individuals employed full-time, and between the ages of 18 and 65 were included. The wage equation used to compute the estimates (with separate regressions for whites and blacks) was:
lnhrlyW = [a.sub.o] + [a.sub.1]MALE + [a.sub.2]SOUTH + [a.sub.3]AGE + [a.sub.4]AGE-SQ
+ [a.sub.5]EXPERIENCE + [a.sub.6]EXPERIENCE-SQUARED + [a.sub.7]EDUC2
+ [a.sub.8]EDUC3 + [a.sub.9]EDUC4 + [a.sub.10]WT-COLLAR + [a.sub.11]CHILDREN
+ [a.sub.12]MARITAL-STATUS + [a.sub.13]UNION-MEMBER
+ [a.sub.14]SELECTIVITY-VARIABLE [1]
The log of hourly wages was represented by lnhrlyW. WHITE was a dichotomous variable with a value of 1 if the respondent was white, zero otherwise. Similarly, SOUTH took a value of 1 if respondent was from the south, zero otherwise. Four levels of educational attainment were defined: EDUC2 for high-school graduates, EDUC3 representing respondents with 13-15 years of schooling, and EDUC4 denoted respondents with 16 or more years of schooling. EDUC1, representing respondents who did not complete high school, was the missing base variable in the wage equation. WTCOLLAR portrayed white-collar workers, and CHILDREN was a dummy variable with a value of 1 for respondents with children, zero for those without offspring. Since there was no explicit question about experience, the EXPERIENCE variable was defined, following Jacob Mincer (1974) as [Age-Schooling-6]. MARRIED had a value of one for those who were married, zero otherwise. AGE, UNION-MEMBER, and the squared variables are self-explanatory.
