AN ECONOMIC ANALYSIS OF ANTI-HISPANIC DISCRIMINATION IN THE AMERICAN LABOR MARKET: 1970s-1990s – Statistical Data Included
Rebecca K. Woods
Introduction
Measurement of labor market discrimination against women and minorities has become an increasingly intense focus for research in the past several years, although uncertainty remains as to exactly what discrimination consists of and how it may be measured in a way that people can understand. Economists have devised several methods for measuring the discrimination-based portion of earnings differences among white males and females and/or minority groups. This paper will provide as analysis of labor market discrimination using the methodology developed in economics by Oaxaca (1973)(1) and extended by Cotton (1988).(2) The paper will focus on earnings differentials between white and Hispanic sub-groups over the period from 1970/1976 through 1995, and will seek to determine how much relative earnings have changed, and whether discrimination continues to be a factor.
In section II, we will look at just what discrimination is to an economist and how it can be measured by incorporating a human capital approach, or by observing the controllable traits that an individual possesses. Section III discusses the model used in conjunction with the variables, and the results from regression equations are presented in section IV. In section V, we look at our results for data from the 1970s as well as the 1990s, followed by a summary and conclusions in section VI.
Discrimination and the Human Capital Model
It is commonplace in our society to hear that blacks, women, Hispanics, and increasingly, white males are victims of labor market discrimination. There are two main costs associated with discrimination: costs to the national economy in the form of reduced Gross National Product and costs to the group discriminated against. Labor market discrimination occurs when two equally qualified individuals are treated differently solely on the basis of their gender, race, age, disability, etc. In this section, we will discuss exactly how discrimination is measured and what it consists of using women’s experience as an example.(3)
The human capital model seeks to separate earnings differentials, which may be due to differences in qualifications from differentials which might result from discrimination. In general, human capital investment consists of resources that are invested today in an individual in order to increase future productivity and earnings, including formal education, on-the-job training, job search, and geographic migration. Labor markets are dissimilar from other markets in that labor services cannot be separated from the individuals who provide them. People tend to take more into account than simply the monetary results of their personal decisions, which makes motives behind pursuing certain jobs or careers more difficult to determine.(4)
Several factors contribute to differences :in earnings among different groups of people. Earnings are assumed to increase with education due to a resulting increase in worker productivity. Another means of increasing worker productivity is through on-the-job training. However, even when these factors are taken into account, at each worker category, women continue to earn less than their male counterparts. Women are being paid less than their marginal products.
In the male-female discrimination situation, from 60 to 66% of the gender pay inequity can be explained as resulting from qualification differences, such as differences in fields of study and education levels. Earnings differentials, which cannot be explained by differences in labor market characteristics, are what economists consider to be evidence of actual discrimination. The main problem lies in the fact that we do not have information on all characteristics affecting productivity. Thus, the wage differential due to discrimination is almost surely an overstatement. How much of an overstatement is what makes discrimination still an evolving field of study.
The discrimination coefficient measures the strength of an individual’s discriminatory taste or the costs of hiring the discriminated group in money terms. In this case, a firm would continue to hire the preferred group until the wages that a preferred individual would require, less the discrimination coefficient, equal the wages that a member of the discriminated group would require. In perfect competition, we should find that a firm could not survive in the business world while hiring the relatively more expensive worker. The reason firms can afford to pay this higher price stems from a lack of competitive pressures in the economy. If some coworkers dislike working with this group, they may exact even higher wages than otherwise. Customer discrimination results from personal feelings felt by the customer against working with salespeople from the discriminated group. In order to make the same sale as another, a discriminated individual would have to charge the price charged by the non-discriminated less the value of the perceived inconvenience of having to do business with a member of this group.
In general, discrimination is the dollar value of wage differentials that cannot be explained through a difference in qualifications. As discrimination rises, wages for the discriminated group fall. Due to imperfections in the system for measuring such differences a great deal of literature continues to be published concerning how to better measure discrimination and some of the more recent trends in its appearance.
The Model
The model used to measure white-Hispanic earnings differentials was developed by Oaxaca (1973)(5) and extended by Cotton (1988)(6) and Neumark (1988).(7) The specific form of the model employed here, follows closely that used by Gaynor and Durden (1995).(8) The model incorporates four main types of variables: human capital-related, job-related, personal, and regional.
The following equation establishes the theoretical linkages between earnings and the determinants of earnings, according to the human capital model:
Equation 1: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].
In this equation, [Y.sub.i] represents yearly earnings for each individual, [X.sub.ji] represents the independent variables, which determine earnings, and [[Beta].sub.ji] the corresponding coefficients. The [[Beta].sub.ji] coefficients are measures of the change in the dependent variable for changes in each independent variable. For example if an individual gains a year of education, ceteris paribus, the human capital model says that individual’s earnings will rise. The [Beta] coefficient on the education variable measures how much earnings will increase for a unit increase in education. The symbol e represents the stochastic error term for each individual i.
In the empirical analysis below, a version of this equation is estimated for white males, white females, Hispanic males and Hispanic females in order to obtain the data required for measuring the total earnings difference between white males and the reference groups. This total earnings difference is then separated into the portion attributable to differences in market characteristics (the endowment effect) and a residual portion, which may be due to labor market discrimination.
The following equation shows the earnings differential broken down into the endowment and residual proportions:
Equation 2: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],
where [y.sup.wm] is the estimated earnings level for white males and [y.sup.r] is the estimated earnings level for each reference group (white females, Hispanic males, and Hispanic females). The Use of B* incorporates the methodological extension developed separately by Cotton (1988)(9) and Neumark (1988).(10) The B* coefficient estimates are those which would occur in a discrimination-free world, while the B coefficient estimates are from the equations estimated for white males (wm) and reference groups (r). If there were no discrimination, then white male coefficients would fall while reference group coefficients would rise due to changes in labor market supply and demand. B* represents a weighted average of the coefficients for the reference group and white males. It is obtained from a regression that includes all groups of the population (white males, white females, Hispanic males, and Hispanic females). The equation thus compares the current estimated earnings of all groups with what they would be in a discrimination-free world. The various means ([x.sub.j]) are the means of different characteristics for white males (wm) and reference group males and females (r).
Using such a model, Durden and Gaynor found that the 1990 discrimination-based earnings difference (in logarithmic values) between white and Hispanic males was about 1.1% and for Hispanic females about 22.2%.(11) Reimers found a 15% differential between white and Hispanic males,(12) and Pagan and Cardenas found a similar difference in logarithmic values of .1919 for 1992 Hispanic males and .2916 for 1992 Hispanic females.(13) In dollar values, Torres found a residual of $3,808 for 1980 Puerto Rican-born Hispanic males and -$310 for females; for 1980 U.S.-born Hispanics the discrimination residuals were $1,590 for males and -$641 for females.(14) Verdugo found a larger difference in 1987 for Mexican-American workers of $2,098.(15) In this paper, we will also be examining earnings in dollar, rather than logarithmic, values.
The Variables and Equation
Using 1970, 1976, and 1995 Census data from the Inter-University Consortium for Political and Social Research, the following human-capital based regression models (based on equation l) were estimated for each year:
Equation 3: [Y.sub.i] = [b.sub.0] + [b.sub.1] ([X.sub.1]) + [b.sub.2]([X.sub.2]) + [b.sub.3]([X.sub.3]) + [b.sub.4]([X.sub.4]) + [b.sub.5]([X.sub.5]) + [e.sub.i],
where, for white and Hispanic males and females, [Y.sub.i] is estimated earnings, [X.sub.1] – [X.sub.5] are vectors of independent variables proxies used to estimate the effects of differences in worker productivity and other respondent characteristics, and [b.sub.i] are the corresponding coefficient estimates for human-capital variables, spatial-related variables, industry and occupation variables, family-related variables, and other influences. The variables included in each of the five categories are summarized in Table 1.
Table 1: Variables included in the regression equations
HGHGRADE The highest grade of schooling completed
EXPERIENCE Number of years of on-the-job experience
SMSA Value = 1 if the individual resides in the SMSA,
otherwise 0
EXPSQ Number of years of on-the-job experience squared
WEST Value = 1 if the individual resides in the West,
otherwise 0
SOUTH Value = 1 if the individual resides in the South,
otherwise 0
MIDWEST Value = 1 if the individual resides in the Midwest,
otherwise 0
GOVT Value = 1 if the individual is a government employee,
otherwise 0
WHTRADE Value = 1 if the individual works in wholesale trade,
otherwise 0
RETRADE Value = 1 if the individual works in retail trade,
otherwise 0
CONST Value = 1 if the individual works in construction,
otherwise 0
FIRE Value = 1 if the individual works in finance,
insurance, and real estate, otherwise 0
DUMANUF Value = l if the individual works in durable goods
manufacturing, otherwise 0
NDMANUF Value = 1 if the individual works in non-durable goods
manufacturing, otherwise 0
TRANSCOM Value = 1 if the individual works in transportation
and communications, otherwise 0
PROFSER Value = 1 if the individual works in the professional
services, otherwise 0
PROF_TEC Value = 1 if the individual works in professional or
technical fields, otherwise 0
MANAGER Value = 1 if the individual works as a manager,
otherwise 0
CLERICAL Value = 1 if the individual works in the clerical
fields, otherwise 0
OTH_SER Value = 1 if the individual works in other services,
otherwise 0
LAB_OPER Value = 1 if the individual works as a laboratory
operator, otherwise 0
SALES Value = 1 if the individual works in sales,
otherwise 0
CRAFTS Value = 1 if the individual works in crafts,
otherwise 0
MARRIED Value = 1 if the individual is married, otherwise 0
HHEAD Value = 1 if the individual is the head of the
household, otherwise 0
FULLTIME Value = 1 if the individual is employed fulltime,
otherwise 0
PURICAN Value = 1 if the individual is an Hispanic of Puerto
Rican descent, otherwise 0
CUBAN Value = 1 if the individual is an Hispanic Of Cuban
descent, otherwise 0
BLACK Value = 1 if the individual is an Hispanic of Black
descent, otherwise 0
Empirical Evidence
Using 1970, 1976, and 1995 U.S. Census data, we regressed the previously discussed variables on the earnings of individual Hispanic males (1976 and 1995), Hispanic females (1976 and 1995), white males (1970 and 1995), and white females (1970 and 1995). All earnings values are in 1982 dollars so that use of the specially collected 1976 Hispanic data should provide estimates, which can reasonably be compared with estimations for white males and females using 1970-CPS data. Sufficient data on Hispanics are not available in the 1970-CPS files.
The results of the regressions are shown in Tables 2 and 3. All means and significant coefficients to the 0.01 level together with corresponding [R.sup.2] values are presented. The equations are robust with respect to explanatory power, and most of the important coefficients are significant and signed as expected.
[TABULAR DATA 2-3 NOT REPRODUCIBLE IN ASCII]
In following with our model, we then calculated the B* “discrimination-free” coefficient estimates using a weighted average of the corresponding estimates of each subgroup of the population. Equation 2 (above) was then used to compute the components shown in Table 4, which shows earnings comparisons. Column 1 shows total estimated earnings, and column 2 displays the total earnings differential between white males and each reference group. In column 3, we look more specifically at the difference attributable to skill represented in equation 2 by the expression [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. Recall that this is a measure of the earnings effects of differences in such characteristics as education, experience, industry and job category, etc. Computed this way, the skill differential is that which exists in a discrimination-free world. Column 4 shows the dollar value of the advantage associated solely with being a white male. In our equation, this factor is in equation 2, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] which shows the difference, for example, between what white males earn for an additional year of education as compared to what they would earn in a discrimination free world. If [B.sup.wm] exceeds B*, then white males enjoy an earnings premium, given current labor market conditions. Column 5 represents a similar variable for the disadvantage associated solely with being a member of the target reference group. This factor is shown in our equation by the sum of two components, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. Column 6 simply subtracts the differential attributable to skill (column 3) from the total difference between estimated earnings for white males and the reference group (column 2). This residual difference (it is also the sum of white male advantage and reference group disadvantage) is the estimated earnings difference due to discrimination. As discussed earlier, this estimate is almost surely an overstatement due to the certain omission of some quantified variables and the use of proxies for non-quantified ones.
Table 4:
(1) (2) (3) (4) (5) (6)
Estimated Total Skill White Reference Residual
Earnings Diffe- Diffe- Male Group Diffe-
rence rential Advan- Disad- rence
tage vantage
White Males
1970: $18962
1995: $20597
White Females
1970: $8125 $10837 $4911 $3901 $2026 $5927
1995: $12823 $7775 $2701 $2851 $2222 $5073
$ Change: $4698 $-3062 $-2210 $-1050 $196 $-854
% Change: 57.8 -28.3 -45.0 -26.9 9.7 -14.4
Hispanic Males
1976: $8782 $10180 $528 $3901 $5751 $9652
1995: $13946 $6651 $4953 $2851 $-1153 $1698
$ Change: $5164 $-3529 $4425 $-1050 $-6904 $-7954
% Change: 58.8 -34.7 838.1 -26.9 -120 -82.4
Hispanic
Females
1976: $4510 $14452 $2791 $3901 $7760 $11661
1995: $10416 $10181 $5589 $2851 $1740 $4591
$ Change: $5906 $-4271 $2798 $-1050 $-6020 $-7070
% Change: 131.0 -29.6 100.3 -26.9 -77.6 -60.6
Over the time period of our analysis, we find a general decrease in the total differential between white males and all reference groups of around 30% (column 2 of Table 4). The change in skill differential shown in column 3 of the table is considerably less consistent. The dollar denominated skill differential decreased from $4911 to $2701 (-45%) for white females, but actually increased for Hispanic males ($528 to $4953, +838%) and females ($2791 to $5589, 100%). We would have expected the skill differential to narrow for Hispanic males and females, as compared to white males, but this is not the case. The primary reason is that white females and males have significantly increased their education levels between 1970 and 1995, but for Hispanics, education level has been relatively constant (see Table 2). This probably means that more recent Hispanic immigrants are less well-educated than the those already in the United States, a condition which should change as newcomers become assimilated and gain more education and training.
The white male relative advantage has fallen from $3901 to $2851, -27%, for the time period (column 4 of Table 4). The reference group disadvantage effects shown in column 5 show an interesting result. White females are at about the same dollar disadvantage now ($2222) as they were in the early seventies ($2026) a decrease of 9.7%. For Hispanic males and females, however, the disadvantage effect has decreased dramatically, and for the latter has actually become negative, decreasing from $5751 to -$1153 (-120%). The reduction for Hispanic females is from $7760 to $1740 (-77%). What this means is that the coefficients on the Hispanic variables, in total, are not much different in 1995 from the coefficients, which would exist in a discrimination-free world. Their specific labor market disadvantage has diminished to less than zero for males and almost nil for females.
The residual difference (column 6) is the sum of advantage and disadvantage effects, and constitutes the earnings differential, which may be attributable to discrimination (since market-based differences have been controlled for in the regressions). The differential has changed little for white females, from $5927 to $5073, -14.4%, but has dramatically fallen for Hispanic males and females. For Hispanic males, the differential has decreased over 80%, from $9652 to $1698. For females, the decreases are over 60% and from $11661 to $4591 in dollars. The main reason for these changes, of course, is that the Hispanic disadvantage effect has decreased so substantially. These figures suggest reason for optimism in the future for Hispanics, as discrimination seems to have fallen substantially for the Hispanic population. Although white females earn about as much as Hispanic males and substantially more than females, however, this is primarily because white females generally have higher skill levels (for example, more education). White females seem to incur greater costs from pure discrimination than do Hispanics.
When the discrimination residual is viewed in relative percentage terms, white females fare somewhat better, since the discrimination residual ($5073) is about 40% of total earnings as compared to 44% ($4591) for Hispanic females. Hispanic males in 1995 clearly suffer lower pure discrimination costs, $1698, 12% of their total earnings.
The findings of various authors discussed in section III cannot really be compared with our results because of different data used and different time periods of evaluation. However, the earlier work found that discrimination-based earnings costs exist for Hispanic males and females and showed Cotton (1988) and Durden and Gaynor (1998))(16,17), that the costs of being female exceed those of either race or ethnicity. These results support the earlier findings and have the advantage of measuring how earnings differences have changed over time. Our results indicate that, while discrimination costs may be receding for Hispanic males and females, they are still substantial for the latter and for white females.
Conclusions
In this paper, we have defined earnings discrimination as viewed by economists, discussed the literature on discrimination against Hispanics in the United States, and explained the human capital approach to the measurement of discrimination. Taking account of many human capital factors as proxies for personal endowments, we have discovered that from the early 1970s to the mid-1990s, discrimination appears to have declined substantially for Hispanics, especially males, less so for females, but has not changed much for white females. Labor market discriminations appears much more costly for Hispanic females and white females as compared to Hispanic males, a finding which supports those of Cotton (1988)(18) and Durden and Gaynor (1998).(19)
It will certainly prove an interesting endeavor to learn more about what this trend will do in the future, especially whether the relatively higher costs to females will change. Other areas of future research include whether there are differences in accessibility to various means of increasing human capital such as education and training and whether women and minorities have equal access and opportunity with respect to the development of small business and advancement in existing firms.
ENDPOINTS
(1.) R. Oaxaca, “Male-Female Wage Differentials in Urban Labor Markets.” International Economic Review, 14 (1973): 693-709.
(2.) J. Cotton, “On the Decomposition of Wage Differentials.” The Review of Economics and Statistics, (1988): 236-243.
(3.) J. Kain, Race and Poverty: The Economics of Discrimination (Englewood Cliffs: Prentice Hall, 1969).
(4.) The Discussions in this paragraph and the following three paragraphs are based on: F. Blau and M. Ferber, The Economics of Men, Women, and Work (Englewood Cliffs: Prentice Hall, 1992).
(5.) Oaxaca.
(6.) Cotton.
(7.) D. Neumark, “Employer’s Discriminatory Behavior and the Estimation of Wage Discrimination.” Journal of Human Resources, 23 (1988): 693-709.
(8.) G. Durden and P. Gaynor, “Measuring the Extent of Earnings Discrimination: An Update.” Applied Economics, 27 (1995): 669-676.
(9.) Cotton.
(10.) Neumark.
(11.) G. Durden and P. Gaynor, “More on the Cost of Being Other than White and Male: Measurement of Race, Ehtnic and Gender Effects on Yearly Earnings.” The American Journal of Economics and Sociology, 57 (1998): 95-103.
(12.) C. Reimers, “Labor Market Discrimination against Hispanic and Black Men.” Review of Economics and Statistics, 63 (1983): 570-579.
(13.) J. Pagan and G. Cardenas, “The Role of Occupational Attainment, Labor Market Structure and Earnings Inequality on the Relative Earnings of Mexican Americans: 19861992.” Hispanic Journal of Behavioral Sciences, 19 (1997): 243-267
(14.) A. Torres, “Nativity, Gender and Earnings Discrimination.” Hispanic Journal of Behavioral Sciences, 14 (1992): 134-143.
(15.) R. Verdugo, “Earnings Differentials between Black, Mexican American and Non-Hispanic White Male Workers: On the Cost of Being a Minority Worker, 1972-1987.” Social Science Quarterly, 73 (1992): 663-673.
(16.) Cotton.
(17.) Durden and Gaynor, 1998.
(18.) Cotton.
(19.) Durden and Gaynor, 1998.
REBECCA WOODS is a former student at Appalachian State University, and is currently in the MA in Economics Program at the University of North Carolina at Greensboro. She will begin Ph.D. work in economics in the fall of 2000.
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