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Research in Social Stratification and Mobility 25 (2007) 323–335

Available online at www.sciencedirect.com

Racial disparities in training, pay-raise attainment, and income

Song YangDepartment of Sociology and Criminal Justice, 211 Old Main, University of Arkansas, Fayetteville, AR 72701, United States

Received 15 July 2006; received in revised form 17 July 2007; accepted 8 August 2007

bstract

Data from the 2001 to 2002 California Workforce Survey are used to assess statistical discrimination and social-closure theoriesn explaining racial disparities in crucial labor outcomes. The results are mixed. Racial differences between minority groups andhites appear in three crucial labor outcomes: training attainment, pay raises, and income. In particular, compared with white
orkers, Hispanics are less likely to obtain employer-paid job training, blacks are less likely to receive pay raises, and receive lower
ncome. In-depth statistical analyses are conducted to identify the roots of those inequalities. The conclusion summarizes majorndings and discusses their implications for future studies in ascriptive inequalities.2007 International Sociological Association Research Committee 28 on Social Stratification and Mobility. Published by Elsevier

td. All rights reserved.

y raise;
eywords: Race and ethnicity; Workplace inequality; Job training; Pa
. Introduction

The past decades have witnessed thriving studiesn the consequences of ascriptive status on the work-lace. Researchers have made significant contributionso our understanding of differential of job-training attain-

ent (Caputo, 2002; Knoke & Ishio, 1998), pay raisettainment (Browne, Hewitt, Tigges, & Green, 2001;aufman, 1983), job-authority (Smith, 1997), and workissolution (Elvira & Zatzick, 2002). However, Ameri-an racial landscaping has been undergoing significanthanges: Hispanics have replaced blacks as the largestinority group (U.S. Census Bureau, 2000). Hispanics

nd Asian populations will double in the next 50 years, inontrast to a slight increase of the black population anddecline in the white population (U.S. Census Bureau,
002). This new racial and ethnicity makeup of con-emporary American society has rendered studies thatee race as a strictly white–black issue anachronistic
E-mail address: [email protected].

276-5624/$ – see front matter © 2007 International Sociological Association Research Committee

doi:10.1016/j.rssm.2007.08.004

Income

and outdated. This study fills in the gap by investigat-ing racial disparities in training, pay-raise attainment,and income between major minority groups such as His-panics, Africans, Asians, and whites.

Although evidence of various workplace inequalitiesmounts, theoretical explanations of the sources of thoseinequalities are incongruous. Two major competingtheories have emerged to account for the observed work-place inequalities based on ascriptive status. Derivingfrom early economics studies of employer discrimi-nation, the statistical-discrimination theory asserts thatbecause of incomplete information, employers usegroup-level attributes to assess job candidates’ suitabilityfor certain positions (Aigner & Cain, 1977). Employersprefer white male workers to women and minority work-ers because white male workers allegedly have bettergroup-level attributes in job commitment, higher workmorale, and greater productivity than women and minori-
ties. The social closure theory, in contrast, attributesworkplace inequalities to discrimination. It states thatwhite male workers already in advantageous positionsstrive to exclude women and minority workers from
28 on Social Stratification and Mobility. Published by Elsevier Ltd. All rights reserved.

mailto:[email protected]

dx.doi.org/10.1016/j.rssm.2007.08.004

fication
324 S. Yang / Research in Social Strati
good jobs to protect their own privileged status (Reskin,1988), to ease communication, and to promote socialcertainty (Kanter, 1977). Thus, women and minoritiesare channeled into less-desirable jobs and workplaces,which explains their disadvantages in income, training,authority, and promotion (Tomaskovic-Devey, 1993).

Nonetheless, empirical attestation to the two theo-ries is scarce, except for two recent studies that buildtheir empirical appraisals on an assumption: the sta-tistical discrimination theory and social closure theorydiffer in who is responsible for workplace inequalities(Tomaskovic-Devey & Skaggs, 1999; Tomaskovic-Devey & Skaggs, 2002). They reasoned that while thestatistical discrimination theory blames the employersfor workplace inequalities, the social closure theorycharges white male co-workers as the culprits. Thus,by identifying those responsible for workplace inequal-ities, the validity of the two theories is evaluated. Here,I argue that to the extent that tracing those account-able for workplace racial disparities helps to assess thevalidity between the statistical discrimination theory andthe social closure theory, other distinctions between thetwo theories await further scrutiny. One highly criti-cal distinction pertains to the scope of the theoreticalapplication in that the statistical discrimination theoryrestricts its focus on job-training differential, whereas thesocial-closure theory explains a wide range of workforceinequalities, such as pay (Cancio, Evans, & Maume,1996), promotion (Maume, 1999), and job authority(Smith, 1997).

This research has two objectives in mind: (1) by com-paring whites with all nonwhite groups in receiving workbenefits, it goes beyond the white–black dichotomy toinvestigate to what extent the causal mechanisms forthe white–black difference can explain the differencebetween white and other minority groups; (2) by exam-ining training, pay raise, and income differentials, itassesses the respective validity of the statistical discrim-ination theory and the social closure theory. In the theorysection, I provide scrupulous discussions about the dis-tinction between the two theories, drawn by reviewingtheir respective academic traditions and development.

2. Theories

2.1. Statistical discrimination theory

The statistical discrimination theory grows from an
early economics explanation of racial and gender dispar-ities in hiring and training acquisition (Aigner & Cain,1977; Thurow, 1975). Because assessing workers’ suit-ability and job qualifications is extremely costly, rational
and Mobility 25 (2007) 323–335

cost-minimizing employers tend to hire workers with themost preferred characteristics, namely white males, overother groups. Statistical discrimination occurs wheneverindividuals are judged on the basis of average character-istics of the group to which that person belongs ratherthan the person’s individual characteristics. For exam-ple, millions of women and minority workers are highlycommitted to their work, enduring little interruption oftheir job careers. Yet they receive appraisal based on theirgroup’s average characteristics of suitability and quali-fication, which are lower than the average white male’sgroup characteristics.

Sociologists contribute to much of the later devel-opment in this line of work (England, 1992; Moss &Tilly, 2001). The debate centers on whether statisticaldiscrimination is based on employers’ judgments thatare correct, factual, and objective: that the group actu-ally has the characteristics that are ascribed to it, whichis assumed in the early economics statistical discrim-ination theory (Aigner & Cain, 1977; Thurow, 1975:172). Empirical evidence lends little support to this alle-gation. Little gender difference shows in job turnoverrates (England, 1992) or in organizational commitment(Marsden, Kalleberg, & Cook, 1996). Contradictingstatistical-discrimination prediction, African Americanshave a lower job-quitting rate (Haber, Lamas, & Green,1983), higher organizational commitment, and higherproductivity (Tomaskovic-Devey, 1994) than do theirwhite coworkers. Establishment-level analyses reportthat female and minority workforce composition arenot related to aggregate productivity and labor costs(Tomaskovic-Devey & Skaggs, 1999).

As evidence belying statistical discrimination theo-ries mounted, researchers developed an alternative weakversion of statistical discrimination in which employ-ers discriminate against women and minorities using anincorrect stereotype: that they are less productive than arewhite males (England, 1992; Tomaskovic-Devey, 1993).Operating on a false stereotype that women and minorityworkers have lower work commitment and productiv-ity and higher job turnover, employers tend to excludewomen and minorities from desirable positions. In thisvein, the statistical discrimination theory is tied into thehuman capital theory to predict that women and minor-ity workers will be placed in those jobs that receive lessfirm-specific skill training (Becker, 1957; Tomaskovic-Devey, 1993: 68). The seminal human capital theorydistinguished two types of job training: general versus
specific training (Becker, 1993). Because a firm’s gen-eral training increases workers’ productivity inside thefirm to the same extent it increases workers’ productivityoutside the firm, rational employers will not pay for this

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S. Yang / Research in Social Strati

ype of training because they realize the possibility of los-ng the trainees to competing firms. In contrast, specificraining increases workers’ productivity only inside therm that provides the training; employers will thus payor this type of training and recoup the returns from theirraining investments. Following this logic, as employersrroneously perceive women and minority workers toave more career trajectories and less stable professionalaths than do white male workers, they tend to placehese workers in those dead-end jobs that necessitate lessmployer-paid, firm-specific training.

.2. Social closure theory

As subsequent empirical evidence in support of thetatistical discrimination theory is largely discordant,lternative explanations for gender and racial dispari-ies in the workplace burgeon (Bielby & Baron, 1986;omaskovic-Devey & Skaggs, 1999, 2002). One of theain competing premises is the social closure the-

ry, which argues that white male employees placeomen and minorities into less desirable jobs with loweray, no promotion, and little skill upgrading to protectheir own advantageous positions, to reduce uncertain-ies, and to ease communication (Tomaskovic-Devey &kaggs, 1999). In addition, theories such as the employerxploitation argument and the gendered-labor processlso emerge to account for racial and gender workplacenequalities. The former stipulates that employers placeomen and minorities into positions with less pay and

ess employer-sponsored training in order to take morerofits. The latter maintains that white male employeesnd employers all participate in deciding which individ-als and jobs need specialized training, which in turnffects earnings directly or indirectly through task com-lexity and supervisory authority (Reskin & Roos, 1990;omaskovic-Devey & Skaggs, 2002). Inevitably, womennd minority workers, with their lack of participationn this crucial decision-making process, will find them-elves mostly in those dead-end positions.

Empirical evidence leans persuasively towards theocial closure theory. For example, researchers reportedhat women and African Americans receive lower wagesecause of job allocations within firms rather than fromroductivity and profit variation between establishmentsTomaskovic-Devey & Skaggs, 1999). Women receiveess on-the-job training time because white male cowork-rs exclude them from jobs with long training periods
Tomaskovic-Devey & Skaggs, 2002). More cogently,esearchers report that social closure benefits white maleorkers at the expense of women, minority workers, and
ven employers (Tomaskovic-Devey & Skaggs, 1999:

and Mobility 25 (2007) 323–335 325

437–440). This finding strongly indicates that white maleworkers advocate for their self-interest to preserve theirjobs by expelling women and minority workers fromthose advantageous positions with much promotion andjob training.

2.3. Hypotheses: statistical discrimination versussocial closure

Here I assert that the statistical discrimination the-ory and the social closure theory are distinctive in thescope of their theoretical applications, which remainlargely understudied. The statistical discrimination the-ory, since its inception, has been associated with thehuman capital theory to explain bias in hiring and jobplacement. Employers operating on incomplete informa-tion have to use statistical averages, actual or perceived,to discriminate between candidates. Because women andminorities allegedly have many career interruptions, eco-nomically rational employers hire them for jobs with lessfirm-specific skills and on-the-job training (Arrow, 1973;Bielby & Baron, 1986; Thurow, 1975). To statistical dis-crimination theorists, job training is the pivotal criteriondistinguishing good jobs from bad ones. Thus, trainingdifferential among workers is the paramount concern ofthis theory. Hence, the research hypothesis confirmingthe statistical discrimination theory follows:

H1. A racial gap appears only in employer-paid jobtraining: minority workers are less likely to receive suchjob training than are white workers.

In contrast, the social closure theory alleges thatthe division between advantageous and disadvantageousjobs can be drawn along many dimensions, such as jobtraining, pay raises, income, and promotion perspectives(Kalleberg, Reskin, & Hudson, 2000; Tomaskovic-Devey, 1993). It is concerned with how white maleworkers and women/minority workers hold differentjobs, defined by a wide range of internal labor-marketprospects, such as income, pay raises, and promotions.In other words, rather than restricting attention to jobtraining, the social closure theory emphasizes that racialand gender workplace inequalities take place in a varietyof domains. Compared with white male workers, womenand minorities are not only deprived of training, but alsodenied pay increases, promotion prospects, and posi-tions with much discretion and power. The following
hypothesis substantiates social closure prediction.
H2. A racial gap appears in both employer-paid trainingand salary. Minority workers are less likely to receive

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training and pay raises, and receive less income than arewhite workers.

3. Data and measures

The dataset used in my analyses is the 2001–2002California Workforce Survey, which was designed toassess working conditions in California and to mea-sure the extent to which various groups of workersdiffer in wages, hours, benefits, and work control intheir working environment (Data Archive & TechnicalAssistance, University of California at Berkeley). Thesurvey research center at the University of California,Berkeley, conducted telephone interviews with Califor-nia residential households during 2001 and 2002. Atechnique called list-assisted random-digit sampling isused to take advantage of large computer databases oftelephone-directory information. Three steps are appliedto eliminate business and nonworking phone numbers.Telephone interviews of the eligible residential house-holds produced a sample with 1404 respondents. Amongthe total respondents, 1045 were working full-time orpart-time during the survey period. Because this studyfocuses on work-related issues such as employer-paidtraining and wages, I used the subsample of 1045 work-ing respondents.

Job training is one of the dependent variables mea-sured with the question “In the past 3 years, have youparticipated in any job-training program that was paidby your employer, whether at your workplace or anotherlocation? (yes = 1; no = 0)” Pay raise is another depen-dent variable measured with the question “Since you firststarted working for your present employer, have youever received a pay raise? (yes = 1; no = 0)” Income isthe third dependent variable, measured with the ques-tions “How much do you earn per hour/month/year atthis job?” Because respondents provided informationon the number of hours they work per week, I firstcomputed weekly wages for those who reported theirhourly wage by multiplying their hourly rate with thenumber of hours they worked per week. I then com-puted their annual salary by multiplying their weeklyrate by 52. For those who reported their monthly wage,I computed their annual salary by multiplying theirmonthly wage by 12. Thus, the dependent variableis the respondent’s annual income. I transformed thepersonal income into the nature log form to stabilizesample variance and reduce heteroscedasticity (Allison,
1999: 128).
Race is the key independent variable, mea-sured with “Which of the following best describesyour race or ethnic group?” The coding of race

and Mobility 25 (2007) 323–335

follows the original classification in the question-naire, except that I group “Native Americans” and“Middle Easterners” as “other minorities.” Spacelimitations prohibit elaboration of variable construc-tions. For details of variable constructions, seeAppendix A.

4. Findings

4.1. Prevalence of racial disparities in training andpay raises

Despite of years of a federal push for Equal Employ-ment Opportunity and Affirmative Action (EEO/AA),this study shows that the racial gap in training, payraises, and income is prevalent in contemporary Ameri-can workplaces. Table 1 shows that racial disparities arepronounced between whites and Hispanics in terms oftraining, pay-raise attainment, and income, and betweenwhite and black workers with respect to pay-raise attain-ment and income. Compared with white workers, blackand Hispanic workers have significant lower proportionsof receiving pay raises by 19.5 and 9.3%, respectively.Hispanic workers have a 28.4% lower proportion thando white workers in receiving training. The averageincome of black workers is 11.06% lower than thatof white workers, while Hispanic workers’ averageincome is lower than that of white workers by a siz-able gap of 41.25%. This bivariate statistical analysisreveals that racial differentials appear in all three cru-cial work benefits: training, pay-raise attainment, andincome. To identify the roots that may account for thosedifferences, the next analyses attempt to profile white,black, and Hispanic workers by comparing them interms of human capital indicators, jobs, and types ofworkplaces.

4.2. Profiling white, black, and hispanic workers:human capital, jobs, and workplace differentials

Table 2 shows the differences between white andblack worker and between white and Hispanic work-ers in human capital indicators, job characteristics, andworkplace variables, using white workers as a referencegroup. Surprisingly little differences emerge betweenblack and white workers, except that black workers areless likely to have bachelor degrees but more likely tohave some college education. Black workers – by merely
4% – are also more likely to work for wholesale industrythan are white.
In contrast, differences between Hispanic and whiteworkers are enormous, appearing in all dimensions that

S. Yang / Research in Social Stratification and Mobility 25 (2007) 323–335 327

Table 1Percentages of receiving pay raise, employer-sponsored training, and average income of different races

Race Percentages of receiving pay raiseand training, and average income

Percentage differences(compared with whites; whitesincome = 100%)

Z-Test or t-test

Alla (Npay-raise = 859; Ntraining = 858;Nincome = 774)

Pay-raise: 78.3; training: 56.9;income: $42,145

Pay-raise: −4.5; training: −7.5;income: −12.8

–

Black or African Americans(Npay-raise = 60; Ntraining = 60;Nincome = 48)



Zpay-raise: 3.03**; ZTraining:.17; tincome: 2.12*

Hispanicsa (Npay-raise = 215;Ntraining = 214; Nincome = 206)



Zpay-raise: 2.70**; ZTraining:7.26***; tincome: 7.00***

Asians (Npay-raise = 57; Ntraining = 57;Nincome = 50)


Pay-raise: −2.1; training: .5;income: −9.28

Zpay-raise: .38; Ztraining: .075;tincome: .889

Other minorities (Npay-raise = 21;Ntraining = 21; Nincome = 17)


Pay-raise: −15.7; training:−16.8; income: −16.94

Zpay-raise: 1.51; Ztraining:1.53; tincome: 1.23

Caucasians or white (NPay-raise = 506;Ntraining = 506; Nincome = 445)


– –

Pay-raise: χ2 = 22.647, d.f. = 4, p < .001; training: χ2 = 53.109, d.f. = 4, p < .001; income: F = 12.287, d.f. = 4, and ∞, p < .001. *p < .05; **p < .01;***p < .001 (two-tail test).

a Depending on the variable, the valid number of cases are different. For example, for the entire sample, 859 respondents provide valid responseregarding their pay raise, 858 respond training question, and 774 reports their income. For black or African Americans, valid number of responsesfor training and pay-raise is 60, whereas for income is 48.

Table 2Mean value differences in key dimensions among whites, blacks, and hispanics

Variables Black/N (S.D.) Hispanic/N (S.D.) White/N (S.D.) Black–white (t- orz-test)

Hispanic–white(t- or z-test)

Education: graduate .19/68 (.39) .05/240 (.22) .22/613 (.41) −.03 (z = .59) −.17*** (z = 7.77)Education: BA .13/68 (.35) .11/240 (.31) .25/613 (.43) −.12** (z = 2.70) −.14*** (z = 5.24)Education: some college .52/68 (.50) .34/240 (.47) .35/613 (.48) .17** (z = 2.67) −.01 (z = .28)Education: high school .15/68 (.36) .26/240 (.44) .18/613 (.38) −.03 (z = .65) .08* (z = 2.48)Education: lower than high school .02/68 (.14) .24/240 (.43) .02/613 (.14) 0 (z = 0) .22*** (z = 7.82)Job training (employed-sponsored) .63/60 (.23) .36/214 (.23) .64/506 (.23) −1.1 (z = .17) −28.4*** (z = 7.26)Tenure 7.27/60 (9.39) 5.72/218 (7.37) 8.55/510 (8.77) −1.28 (−1.06) −2.83*** (−4.18)Full-time status .84/68 (.37) .80/246 (.39) .82/620 (.38) .02 (.41) −.02 (−.69)In secretary occupation .32/57 (.47) .28/216 (.44) .25/509 (.43) .07 (1.15) .03 (.85)In machine operating occupations .05/57 (.23) .22/216 (.41) .05/509 (.21) .00 (.00) .17*** (7.36)In farming occupations .00/57 (.00) .05/216 (.22) .01/509 (.11) −.01 (−.69) .05 (1.89)In service occupations .16/57 (.37) .16/216 (.36) .11/509 (.31) .05 (1.13) .04* (3.26)In professional/technical occupations .28/57 (.45) .14/216 (.35) .36/509 (.48) −.08 (−1.20) −.22*** (−6.08)In managerial occupations .14/57 (57) .07/216 (.25) .14/509 (.34) .00 (.00) −.07** (−2.73)Workplace size 2.93/56 (1.41) 2.80/202 (1.25) 2.98/501 (1.29) −.05 (−.27) −.18 (1.69)Workplace independence .75/59 (.43) .65/207 (.48) .73/207 (.44) .02 (z = .33) −.07* (z = 2.07)Government .32/59 (.47) .17/215 (.38) .33/505 (.47) −.01 (z = .16) −.16*** (z = 4.84)Nonprofit .14/59 (.35) .07/215 (.26) .11/505 (.31) .03 (z = .63) −.04 (z = 1.80)For-profit .54/59 (.50) .75/215 (.43) .57/505 (.50) −.03 (z = .44) .18*** (z = 4.88)In agriculture industry .07/57 (.26) .08/509 (.27) .19/216 (.39) −.01 (−.26) .11*** (4.36)In manufacturing industry .04/57 (.19) .09/509 (.28) .12/216 (.32) −.05 (−1.31) .03 (1.26)In finance industry .18/57 (.38) .14/509 (.35) .09/216 (.29) .04 (.82) −.05 (−1.84)In personal service industry .04/57 (.19) .01/509 (.10) .01/216 (.12) .03 (1.91) .00 (.00)In professional service industry .39/57 (.49) .40/509 (.24) .25/216 (.43) −.01 (−.26) .01 (.99)In retail industry .09/57 (.29) .11/509 (.31) .20/216 (.40) −.02 (−.46) .09** (3.27)In wholesale industry .05/57 (.23) .01/509 (.11) .02/216 (.15) .04* (2.25) .01 (.99)In transportation industry .11/57 (.31) .08/509 (.27) .08/216 (.27) .03 (.78) .00 (.00)In public administration industry .05/57 (23) .09/509 (.28) .03/216 (.19) −.04 (−1.03) −.06** (−2.87)

*p < .05; **p < .01; ***p < .001 (two-tail test).

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may account for their differentials in training, pay-raiseattainment, and income. Compared with white workers,Hispanic workers are significantly less likely to receivegraduate and bachelor degrees but more likely to achieveonly high school or even lower than high school edu-cational levels. Hispanic workers also have a shorterwork tenure, are over-represented in service and machineoperating occupations, but are underrepresented in man-agerial/professional/technical occupations. In addition,Hispanic workers are more likely to work for for-profitfirms, and in retail and agricultural industries but lesslikely to work in government agencies, and in pub-lic administration. A recent study reported that badjobs with low pay and less health coverage and pen-sions are mostly those that require less education, andlocate in such industries as service, machine operat-ing, retail, and agricultures. In contrast, those good jobscommonly appear in managerial/professional/technicalpositions and in public administration (Kalleberg et al.,2000).

This round of analysis shows little difference in cru-cial labor profiles between white and black workers butwide-range discrepancies between Hispanics and whiteworkers. Therefore, to the extent that we can explicatethe observed racial gaps in pay-raise, training attain-ment, and income with differentials in human capitals,jobs, and workplace characteristics, we can explain thosegaps between white and Hispanic workers more easilythan between white and black workers. In other words,the relatively low human capital investment and thepoor job and workplace attributes of Hispanic work-ers may explain why they lag behind white workers inpay raises, job training attainment, and income. How-ever, the absence of those differences between whiteand black workers provides us with no clue in findingthe responsible factor to account for the differentialsbetween the two groups. To provide more affirmativeevidence for those allegations, the next round of analy-sis regresses training, pay-raise attainment, and incomeon race, while controlling for different sets of inde-pendent control variables. Controlling for mediatingindependent factors reveals the roots of the observedracial gaps in training, pay-raise attainment, and incomeby comparing workers at the same level within thedimensions defined by those mediating variables. Forexample, if that minority worker concentrates in dis-advantageous positions that necessitate less frequentpay-raise explains why they receive less pay-raise than
do white, controlling for those job variations, thus com-paring workers between minorities and whites within thesame job category, should eliminate the observed gaps inpay-raise.
and Mobility 25 (2007) 323–335

4.3. Roots of inequalities: human capitaldifferentials or job separations

In this section, I ran several different multiple regres-sion models, each of which involves race, and a differentset of predictors, such as human capital indicators, jobcharacteristics, and workplace variations. Each modelreports the changes in the impact of race on the depen-dent labor outcome with additions of other predictors,thus divulging the causal mechanism for racial dis-parities on those outcomes. To clarify, Fig. 1 showsthat the first regression analysis strives to determinewhether the white-Hispanic workers’ difference in train-ing attainment (see Table 1) can be explained by humancapital indicators, job/occupational separations, or by thecombined impacts of human capital, job/occupational,and workplace variations. The second regression modelattempts to examine whether the racial disparities inpay-raise attainment between white–black workers andbetween white–Hispanic workers can be explained bythe three groups of mediating factors, with the job train-ing being added to the human capital group as oneof the mediating factors. The third regression modelanalyzes racial disparity in income, adding human cap-ital variables (job training, education, and tenure), jobcharacteristics (including pay-raise attainment), and thecombined effects of human capital, jobs, and work-place variations to determine which mediating groupcan account for the racial disparity in income betweenwhite–black and white–Hispanic workers.

Table 3 shows the unstandardized coefficients oflogistic regression of job-training acquisition. The gap intraining acquisition between Hispanics and whites can-not be explained by human capital variables, job-levelcharacteristics, or the combined effects of human capitalindicators, job separation, and workplace-level charac-teristics. Model 3 controls for all variables and showsthat the odds for Hispanics to receive employer-paidjob training is more than 50% (1 − exp(−.702) = .504)lower than whites. However, also note that Table 1shows that the odds for receiving job training for His-panic workers is .56 (.36/.64), which is significantlylower than that of white workers (.644/.356 = 1.809)by 69% (1 − .56/1.809 = .69). Thus, although the dis-advantageous features of Hispanic workers in humancapital, job separation, and workplace variations do notcompletely eliminate their gaps with white workers inreceiving training, those differences do taper the training
gap between the two groups.
Table 4 shows the results reflective of whether jobtraining, along with other human capital indicators, jobseparation, or the combined effects of human capital,


ial disp

jtabihiHsitriblr

bawtbtwn

Fig. 1. Elaboration model of rac

ob separation, and workplace variations account forhe racial gap in pay-raise attainment between whitend black, and white and Hispanic workers. The gapetween white and Hispanic workers is not significantn all models. Controlling for the mediating factors ofuman capital, of job separations, or of the combinedmpacts eliminates the observed gap between white andispanics in pay-raise attainment. In contrast, the results

how that black workers have significantly lower oddsn receiving pay raises than do white workers, despitehe independent controls of human capital or job sepa-ations. More strikingly, this gap persists in Model 3,n which all the mediating factors are under control:lack workers still have 65% (1 − exp(−1.055) = 65%)ower odds than do white workers in receiving payaises.

To assess what may account for the income gapetween white and Hispanic workers and between whitend black workers, Table 5 regresses income on race,ith each group of independent controls being added in

hree different models. To summarize, the income gap
etween white and black workers persists, despite sta-istical controls of human capital, job separations, andorkplace variations. Model 3 in Table 5 shows a sig-ificant income gap between white and black workers,
arity in crucial work outcomes.

in which the average income of black workers accountsfor only 78% (exp(−.246) = 78%) of that of white work-ers, controlling for all mediating variables. In contrast,although the income gap between Hispanic and whiteworkers materializes in Models 1 and 2, which controlsfor human capital variables and job/occupational sepa-rations respectively, such a gap disappears in Model 3,which controls for all mediating factors of human capital,job, and workplace variations.

Tables 4 and 5 produce much confirmative evidencesof those factors responsible for racial disparities inpay-raise attainment and income. The pay-raise gapdisappears between white and Hispanic workers oncethe model controls for human capital or job vari-ables, suggesting that Hispanic workers can eliminatetheir disadvantage in receiving pay raises by escalat-ing their human capital investment or by moving upthe occupational ladder into advantageous positions.Likewise, the income gap between Hispanic and whiteworkers disappears when all mediating variables arecontrolled—Hispanic workers can make up their income
deficiency with white workers by increasing their humancapital, moving up the occupational ladders, and work-ing for those “good workplaces” (Kalleberg et al., 2000).In contrast, the continual pay-raise gap and income dif-

330 S. Yang / Research in Social Stratification and Mobility 25 (2007) 323–335

Table 3Unstandardized coefficients of logistic regression of job training

Predictors Model 1 Model 2 Model 3

Constant −2.567*** (.551) .480 (.345) −1.488 (.848)

Individual characteristicsBlack or African American −.010 (.298) −.007 (.306) −.037 (.337)Hispanic −.680*** (.197) −.875*** (.190) −.702*** (.217)Asian and Pacific Islander −.192 (.311) .053 (.309) .123 (.341)Other Minorities −.553 (.467) −.731 (.462) −.661 (.514)White (Ref.) – – –Male .136 (.153) −.039 (.158) .256 (.186)Age .010 (.007) .001 (.006) −.021** (.008)

Human capitalsEducation: graduate 2.053*** (.423) 1.052* (.509)Education: BA 2.192*** (.412) 1.543** (.493)Education: some college 1.400*** (.388) .915 (.460)Education: high school 1.193*** (.399) .887 (.462)Education: below high school (Ref.) – – –Tenure .258*** (.055) .239 (.062)

Job characteristicsFull-time work .619** (.209) .391* (.245)Occupation: managerial −.138 (.249) −.150 (.274)Occupation: secretary −.803*** (.190) −.451* (.226)Occupation: machine operating −.988** (.281) −.499 (.347)Occupation: farming −3.195** (1.052) −3.480** (1.169)Occupation: service −.748** (.244) −.647* (.316)Occupation: professional/technical (Ref.) – –

Workplace characteristicsWorkplace size .193** (.067)Workplace independence .652*** (.195)Nonprofit public .137 (.272)Nonprofit private .896** (.349)Profit (Ref.) –Industry: agriculture −.950* (.555)Industry: manufacturing −1.884*** (.552)Industry: finance −1.062* (.518)Industry: personal service −1.492 (.826)Industry: professional service −.962* (.458)Industry: retail −1.516** (.527)Industry: wholesale −1.582* (.746)Industry: transportation −1.259* (.527)Industry: public administration (Ref.) –

Model χ2 (d.f.) 120*** (11) 104*** (12) 193*** (29)

< .001.

Number of cases 843

Numbers in parentheses are standard errors. *P < .05; **P < .01; ***P

ferentials between white and black workers, despite allindependent controls, suggest that black workers can-not eliminate their discrepancies with white workers inpay-raise attainment or income by raising their humancapital, moving up to upper-echelon jobs, or changing
workplaces. Indeed, Table 2 reports that white and blackworkers have almost identical profiles in their humancapital levels, job occupations, and workplace charac-teristics. In spite of those similarities, black workers still
802 798

are less likely to receive pay raises and still receivelower income than their white counterparts, suggest-ing a sheer employer discrimination that favors whiteworkers over black workers for nothing else but racialdifference.

To summarize, while some of the racial disparities intraining, pay raises, and income reported in the bivariatestatistics can be explained by human capital variables,job separations, or the combined effect from human


Table 4Unstandardized coefficients of logistic regression of pay raise


Constant −6.335*** (1.359) −2.915** (1.042) −5.489** (1.838)Individual characteristics

Black or African American −.991* (.395) −1.012*** (.318) −1.055* (.437)Hispanic .048 (.290) −.235 (.221) −.086 (.325)Asian and Pacific Islander −.725 (.438) −.015 (.374) −.631 (.495)Other minorities −1.076 (.602) −1.309 (1.473) −1.170 (.653)White (reference group) – – –Male .604** (.227) .208 (.196) .425 (.283)Age −.427 (.361) 1.102*** (.279) −.888 (.445)

Human capitalsEducation: graduate .661 (.513) .215 (.692)Education: BA 1.483** (.512) 1.378* (.688)Education: some college .667 (.442) .494 (.605)Education: high school .883 (.467) .465 (.614)Education: below high school (Ref.) – –Training (employer-sponsored) .430 (.235) .138 (.276)Tenure 1.231*** (.101) 1.314*** (.118)

Job characteristicsFull-time work .827*** (.222) .312 (.322)Occupation: managerial −.142 (.331) −.191 (.450)Occupation: secretary −.543* (.247) −.252 (.373)Occupation: machine operators −.213 (.364) .666 (.626)Occupation: craft −.312 (.369) −.409 (.578)Occupation: farming −.304 (.638) 1.134 (1.527)Occupation: service −.591* (.299) −.385 (.509)Occupation: professional/technical (reference group) – – –

Workplace characteristicsWorkplace size .128 (.101)Workplace independence .081 (.281)Government 1.217* (.513)Nonprofit private .746 (.443)Profit (reference group) —Industry: agriculture .742 (.937)Industry: manufacturing .452 (.923)Industry: finance −.030 (.843)Industry: personal service −.640 (1.110)Industry: professional service −.212 (.774)Industry: retail −.393 (.863)Industry: wholesale .686 (1.139)Industry: transportation .969 (.963)Industry: public administration (reference group) –

Model χ2 (d.f.) 336 (12)*** 74 (12)*** 341 (30)***N

N < .001 (

ctrddcdm

umber of cases 843

umbers in parentheses are standard errors. *p < .05; **p < .01; ***p

apital, jobs, and workplaces, three of those gaps –he white–Hispanic training gap, the white–black pay-aise gap, and the white–black income gap – persist,espite controls of all mediating factors. Such results
o not resonate consistently with either the statisti-al discrimination or the social closure theory. Rather,ifferent underpinning dynamics apply to differentinority groups—Hispanic workers suffer from statis-
840 798

two-tail test).

tical discrimination, whereas black workers from socialclosure.

5. Discussion

I analyzed survey data from the California Work-force Survey to extend our understanding of workplaceinequalities along racial dimensions. Racial disparities

332 S. Yang / Research in Social Stratification and Mobility 25 (2007) 323–335

Table 5Unstandardized coefficients of OLS regression of annual income


Constant 8.792*** (.162) 9.331*** (.108) 9.150*** (.200)

Individual characteristicsBlack or African American −.255* (.103) −.263** (.094) −.246** (.090)Hispanic −.171** (.065) −.296*** (.056) −.125 (.096)Asian and Pacific Islander −.204 (.203) −.090 (.092) −.202 (.109)Other minorities −.048 (.166) −.054 (.150) −.080 (.144)White (reference group) – – –Male .392*** (.050) .249*** (.047) .202*** (.047)Age .007** (.002) .007*** (.002) .005* (.002)

Human capitalsEducation: graduate .909*** (.121) .734*** (.121)Education: BA .746*** (.117) .466*** (.117)Education: some college .395*** (.107) .248* (.106)Education: high school .225* (.111) .074 (.106)Education: below high school (Ref.) – – –Training (employer-sponsored) .255*** (.053) .172*** (.048)Tenure .079*** (.018) .030 (.019)

Job characteristicsReceiving pay raise .222*** (.057) .156* (.068)Full-time work .899*** (.064) .781*** (.063)Occupation: managerial .095 (.074) .025 (.071)Occupation: secretary −.293*** (.057) −.165** (.059)Occupation: machine operators −.605*** (.086) −.339*** (.091)Occupation: farming −.711*** (.169) −.518** (.186)Occupation: service −.525*** (.075) −.275** (.082)Occupation: professional/technical (reference group) – – –

Workplace characteristicsWorkplace size .049** (.017)Workplace independence −.086 (.052)Government −.220** (.071)Nonprofit private −.226** (.087)Profit (reference group) – – –Industry: agriculture −.271* (.130)Industry: manufacturing −.403** (.126)Industry: finance −.181 (.116)Industry: personal service −.835*** (.207)Industry: professional service −.325** (.094)Industry: retail −.404** (.122)Industry: wholesale −.348 (.184)Industry: transportation −.164 (.118)Industry: public administration (reference group) – – –

R2 34.9% 47.2% 55.4%

< .001 (

Number of cases 748

Numbers in parentheses are standard errors. *p < .05; **p < .01; ***p

between minority groups such as Hispanic and blackworkers and whites reveal themselves in employer-paidjob training, pay raises, and income. Profit-maximizing
employers may prefer white males to other groups tofill positions needing great firm-specific skills becauseof the mistaken stereotype that white males havefewer career trajectories than do other groups. How-
749 714

two-tail test).

ever, it is inconceivable that employers raise salariesmore often and offer higher wages to whites thanto blacks to make more profits, when workers from
both groups have the same human capital level,the same job and workplace characteristics that mayexplain their income differentials. Instead, the evi-dence suggests that the economic profit-maximizing

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octrine cannot fully account for such conduct bymerican employers—discrimination plays a part,

oo.However, evidence from this study does not align con-

istently with either statistical discrimination or sociallosure. Regarding Hispanics, the jobs and workplaceshat hired the most Hispanics – such as machine opera-ion, farm, and sales – offered fewer pay raises, whereashe jobs and workplaces that hired fewer Hispanics –

ostly managerial, professional, and technical positionsoffered more pay increases. And the combined effectsf low human capital level, poor job and workplacerofiles explain why Hispanic workers receive lowerncomes than do white workers. On the other hand, theersistent inequality between Hispanics and whites ineceiving employer-paid training defies easy explana-ion. One plausible explanation is that while Hispanicsust surpassed African Americans nationally as a domi-ant minority, they are much more numerically dominantn the California labor market than they are nationally.hus, statistical discrimination is used to against Hispan-

cs and social closure against African Americans becausehere are far more Hispanics—it is much harder to engageocial closure dynamics to against Hispanics than toeprive them of training opportunities. This should alsolert us to a limitation of this research that uses the Cal-fornia Labor Survey, which may not be representativef the U.S. labor force nationally—future studies withore representative data should ascertain to what extentndings from this study can hold at the broader nationalontext.

No discernible patterns emerge between whites andlacks, except that blacks are slightly more oftenmployed in the wholesale industry than are whites. Todentify the social and economic conditions that explainlacks’ disadvantage in receiving work benefits, vari-bles at the job level and the organization level needo be sharply defined. In this vein, detailed job-levelharacteristics show that women and minority work-rs are confined in less-desirable jobs that offer lessraining (Bielby & Baron, 1986; Tomaskovic-Devey &kaggs, 1999, 2002). Highly institutionalized organiza-

ions or formalized establishments provide good benefitackages, including training (Knoke & Kalleberg,994; Knoke & Yang, 2003; Scott & Meyer, 1991),edical care, and flexible work schedules (Knoke,

996).To the extent that more fine-grained job and

orkplace characteristics are needed to account for dif-erentials in pay raises and income between whites andlacks, the roots that are responsible for those gaps areeep. The results are alarming. Despite statistical con-

and Mobility 25 (2007) 323–335 333

trols of all mediating factors, which ensure that workersof different races are identical in human capital, job, andworkplace characteristics, pay-raise and income gapsmaterialize between white and black workers. Statisticalanalyses employed in this study may be short in dis-closing the intricate work processes that systematicallydisadvantage women or minority workers. An ethno-graphic study unravels complex workplace dynamicsthat deprive women employees of their entitled train-ing opportunity (Tomaskovic-Devey, 1993: 161). To givea new woman pilot a “fair” chance to prove herself,the male pilots did not show her the location of equip-ment and did not introduce her to the control-towerstaff and maintenance crews. The women eventuallyresigned in protest and frustration. With a little stretch,this finding helps us understand how minority work-ers can lose their training and pay-raise opportunities,or receive lower wages than their white coworkers.The few fortunate Hispanics and blacks who achievedthe same work profiles as their white male coworkersconfronted mounting workplace resistance. The moti-vations for such resistance from white male workerscan be many, such as discrimination, competition forscarce corporate resources, and anxiety reduction byshunning non-Native English speakers. Yet, the conse-quences are definite: minority workers are systemicallypassed by for receiving training, pay-raises, and highersalaries.

Analyzing data from the 2001 to 2002 CaliforniaWorkforce Survey, this study should make significantcontributions to the extant literature on workplace racialinequalities. By investigating variations in job training,pay raises, and income among different racial groups, itproduces empirical results indicative of the two majortheories of racial disparity at work: statistical discrim-ination is used to against Hispanic workers and socialclosure theory against black workers. By scrutinizingminority groups such as Hispanics and Asians, it goesbeyond the traditional white–black dichotomy to ascer-tain to what extent those causes of workplace inequalitiesbetween white and black are applicable to analyz-ing the inequalities between white and other minoritygroups.

Acknowledgments

I am grateful to Professor Kevin T. Leicht and an
anonymous reviewer for their constructive comments,which facilitate the revision and improve the quality ofthe analyses. I thank Professor Steven Worden for hiseditorial assistant.

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Appendix A

Variable names Measuring items

Job training In the past 3 years, have you partin any job-training program thatby your employer, whether at yoworkplace or another location?

Pay raise Since you first started working fopresent employer, have you evera pay raise?

Race Which of the following best descyour race or ethnic group—BlacAfrican American, Native AmeriHispanic or Latino, Filipino, AsiPacific Islander, White or Caucassome other group?

Gender Are you male or female?Age How old were you on your last bEducation What’s the highest grade of scho

year of college you have compleTenure How long have you worked for y

present employer?Work status Are you currently working full ti

(35+ h/week) or part time?Workplace size About how many people are emp

where you work?Independent workplace Is the place where you work part

larger company?Workplace types Do you work for a business, a

government, or a nonprofit organRespondent’s occupation What is your job title?

Workplace industries What kind of business or industrwork for at this job?

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dYes = 1; No = 0

dYes = 1; No = 0

A multiple dummy variable including AfricanAmerican, Hispanic, Asian, other minorities andCaucasians

Male = 1; female = 0? Respondent’s actual age in years

Respondent’s actual years of schooling

Responses are coded in years.

Full time = 1; part time = 0

1: fewer than 10; 2: From 10 to 50; 3: From 51 to 100;4: From 101 to 1000; 5: Over 10001: no, independent; 2: yes, dependent

A multiple dummy variable where business is referencegroupA multiple dummy variable including the followinggroups: managerial, professional and technical, service,secretary, machine operator, craft, and farming.Professional/technical is the reference group inregression

u A multiple dummy variable including the followinggroups: agriculture, manufacturing, finance, personalservices, professional services, retail, wholesale,transportation, and public administration. Publicadministration is the reference group

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racial disparities in training, pay-raise attainment, and income

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