English non-fluency and income penalty for Hispanic workers.

Using the 2001-2002 California Workforce Survey, this paper examines the income gap between Hispanic and Caucasian workers. I attribute the income gap between Hispanic and Caucasian workers to differentials in their human capital. However, data analyses indicate that classical human capital indicators such as education, job training, and work experiences are not sufficient to account for the observed income gap between Hispanics and Caucasians. Instead, English fluency is a highly valuable aspect of human capital for Hispanic workers. English non-fluency, along with less education, job training, and work experiences explain why Hispanic workers earn less than Caucasian workers. However, variations in English fluency do not affect the incomes of Asian workers. Those findings suggest that English non-fluency is a unique source of income penalty for Hispanic workers. It may be attributed to stereotyping by employers.

Keywords: Hispanic, income, workers, non-fluency, stereotyping

Introduction

This research focuses on workplace inequality by investigating sources of the income gap between Hispanic and Caucasian workers. For several decades, researchers on ascriptive workplace inequalities has made significant contributions to our understanding of differentials in job training attainment (Knoke and Ishio 1998; Caputo 2002), pay raises (Kaufman 1983; Browne et al. 2001), job authority attainment (Smith 1997), and work dissolution (Elvira and Zatzick 2002). However, the majority of those studies have focused on two groups: Caucasians and African Americans. Indeed, in much of the literature on workplace inequality, minority is synonymous with African American. However, the turn to the new millennium has witnessed drastic changes in the American demographic landscape. The 2000 U.S. Census Bureau reported that Hispanics (12.5 percent) replaced African Americans (12.3 percent) to become the largest minority group in the nation (http://www.census.gov/census2000/states /us.html). The newly released statistical yearbook of the Citizenship and Immigration Services (CIS) reported that Mexico is among the top five countries sending immigrants to the U.S. in recent years, along with India, People's Republic of China, the Philippines, and Vietnam (http://www.bcis.gov/graphics/ shared/aboutus/statistics/index.htm). The U.S. Census Bureau projected that the Hispanic and Asian populations will double in the next 50 years, in contrast to a slight increase of the African American population and a decline in the Caucasian population (http://www.census.gov/ population/www/projections/natpr oj.html). Indeed, a mosaic is emerging in the American racial landscape, yet research addressing racial discrepancies between Hispanic and Caucasian workers in crucial labor outcomes is scarce.

Decades of studies on workplace ascriptive inequalities have accumulated a large body of knowledge on the causal factors of those inequalities. Early economic studies focused on both sides of labor demand and supply. On the demand side, the observed wage gap between Caucasians and African Americans was due to employer's "discriminatory taste" (Becker 1957) or employer's "statistical discrimination" (Thurow 1975). On the supply side, classical human capital theory states that the low level or low quality of education received by African Americans explains why African Americans make less money than Caucasians (Becker 1993). Later sociological studies report that job and workplace segregations and the devaluation of female and minority jobs are to be blamed for the resulting wage gaps between men and women, Caucasians and non-Caucasians (England 1992; Tomaskovic-Devey and Skaggs 1999; Tomaskovic-Devey and Skaggs 2002).

However, because most studies on racial inequalities have focused on Caucasian-African comparisons, results and models from those studies are not readily applicable to explain differentials between Caucasians and Hispanics. For example, English fluency has been found as one of the most significant factors that explains the income gap (McManus, Gould, and Welch 1983) and occupational differences between Hispanic and Caucasian workers (Stolzenberg 1990). However conventional studies comparing Caucasian and African American workers have paid scant attention to the issue of language proficiency, as a vast majority of Caucasians and African Americans are native-English speakers. This study uses data from the 2001-2002 California Workforce Survey to re-investigate the roots of the income gap between Hispanic and Caucasian workers. I attempt to understand this income gap with insights from human capital theory.

Human Capital Theory and Income Inequalities

Human capital theorists stipulate that human capital, like other forms of capital, results from long term deliberate investments in areas such as education, job training and health and produces considerable returns to its carriers (Schultz 1961). In several treatises, Mincer (1962; 1991; 1994) estimates income returns to job training. Although the exact figures vary depending on data, methodology, and time frame, job training recipients are guaranteed returns that commonly are expressed as higher income in the post-training session. Noble Prize laureate Gary Becker (1993) also analyzed how firms react differently depending on the consequences of job training.

Several empirical studies have fruitfully applied human capital theory to explain gender and racial differences in salaries and training attainment (Duncan and Hoffman 1979; Olsen and Sexton 1996; Barron and Black 1993; Altonji and Spletzer 1991). Two aspects of human capital are used to explain income differentials between men and women, and between Caucasians and minorities. The first aspect concerns the quantity of human capital; women and minorities receive lower pay than their Caucasian male coworkers because women and minorities have a lower level of human capital stocks than do Caucasian male workers. Women and minorities complete less training, which explains why their income is lower than that of Caucasian men (Duncan and Hoffman 1979). Another study reports that differences in training duration explained 45 percent of the income difference between men and women in the post-training session (Barron, Black and Loewenstein 1993).

The second aspect concerns the quality of human capital; women and minorities receive lower returns from their human capital investment than do Caucasian male workers because the quality of their human capital is relatively lower (Becker 1993: 195-204). However, empirical evidence diverges on the quality argument. Some scholars reported that the quality of schooling African Americans receive is lower than that received by Caucasians (Card and Krueger 1998; Farkas 1996). But others found that industrial productivity is higher in industries with a high proportion of African American employment (Galle et al. 1985), indicating a disjuncture between quality of schooling and productivity. In fact, the very observation that minorities receive a lower rate of return from their human capital investment, despite the lack of conclusive evidence indicative of their low quality of education or productivity consequences of educational quality, has become striking evidence of employment discrimination against minorities (Finkelstein and Levin 1990).

English Proficiency: A Crucial Component of Human Capital

Earlier human capital scholars stressed the pivotal roles of education, job training, and work experience in affecting income. Because a vast majority of Caucasian and African American workers are native English speakers, classic studies on Caucasian-African American employment inequalities do not include English language proficiency as one of the explanatory factors (Siegel 1965; Duncan 1969). However, English language proficiency has taken on increasing importance due to demographic changes over the past three decades. Since the 1970s, American workplaces have hired increasing numbers of Hispanic and Asian workers, most of whom are not native English speakers (Veltman 1990). Studies on those new immigrants identified a unique source of labor market penalty: English language deficiency (Chiswick 1978; 1979). One study documented that low incomes for minority groups are a consequence of their low English fluency, along with other human capital factors such as low educational attainment and job training (McManus, Gould, and Welch 1983).

To the extent that language proficiency facilitates communication with others in the workplaces, speaking the majority language can be considered as an integral component of workers' human capital. Accurate language communication is essential to customer satisfaction, coordination with coworkers, and learning what to do and how to do a job (Stolzenberg and Tienda 1997). In contrast, language non-fluency handicaps communications, limiting the range of people with whom workers can provide services or coordinate work. Language non-fluency has been found to reduce not only job opportunities in general, but also chances of obtaining high-paying jobs for which workers are otherwise qualified (Devine and Kiefer 1991). Studies of an economic ethnic enclave found that workers' lack of English skills often led them to obtain jobs in economic sectors that use languages other than English (Portes and Manning 1987; Robinson 1988). But those non-English sectors often have harsh work environments, hire small numbers of workers, and offer low wages (Sanders and Nee 1987). Therefore, gaining language proficiency in the majority language is an indispensable component of immigrant workers' human capital because fluency in the majority language can increase their potential incomes and outputs (Mora, 1998).

Workplace Discrimination and Segregations: Other Sources of Income Penalty for Minority Workers

Besides the human capital model, workplace discrimination is another major source of income gaps between predominant Caucasian and minority groups. In this vein, comparable worth models were developed to ascertain how organizations create discriminatory job structures that sort women and minorities into minority-dominated and women-dominated jobs (England 1992). As a result, those female- and minority-jobs normally require less training and schooling, are easily replaced, less likely to lead to promotion and have lower pay than those jobs whose incumbents are dominantly Caucasian males. A recent study reported that the source of the gender gap in income is that women are disproportionally placed, largely by Caucasian male employees allied with employers, in jobs that require less skill, involve lower task complexity, and entail low job authority (Tomaskovic-Devey and Skaggs 1999). Scholars also have argued for an eclectic approach that simultaneously accounts for individual, job, and organizational variations in assessing training differentials between men and women (Knoke and Ishio 1998), and employment benefits (Kalleberg et al 2000).

Although employment discrimination and job segregation are not the focus of this study, the above discussion indicates that job-level and workplace-level variations may mediate racial income differences. For example, if women and minorities are disproportionally congregated in less desirable positions or workplaces, which in turn provide low incomes to their incumbents or workers, controlling for job and workplace variations would erase the significant income gap between minority and Caucasian workers. Therefore, this study also regresses income on races along with mediating independent variables at the job and workplace levels. The main purpose of including job and workplace independent variables is to identify the original source that explains the income gap between Hispanic and Caucasian workers. By comparing and contrasting job and workplace models with human capital models, I attempt to spot the roots of the income gap between Hispanics and Caucasian workers.

What Produces Incomes Disparities: An Empirical Assessment

The orthodox human capital model has been used to explain a large portion of income differentials between Caucasian and minority groups. Particular to Hispanic workers, English fluency was identified as one of the main human capital factors that explain why Hispanics have lower occupational status and income than do Caucasian workers (Mora 1998; Davila and Mora 2000; Carliner 1981; McManus et al. 1983; Grenier 1984; Stolzenberg 1990; Stolzenberg and Tienda 1997). But the need to overcome the language barrier for career development is not unique to Hispanic workers; other groups, particularly Asian immigrants, face a similar obstacle (Schmid 2003). Therefore, in light of human capital theory, English fluency, much like other conventional human capital factors such as education, training, and work experience, should significantly increase incomes for Asian and Hispanic workers, and thus mitigate income gaps between them and Caucasian workers. This discussion leads to the following two testable hypotheses:

[H.sub.1]: The average incomes for Hispanic and Asian workers are significantly lower than that for Caucasian workers.

[H.sub.2]: The income-gap between Caucasian and Hispanics/Asian workers can be explained by the difference in their stock of Human capital factors including English proficiency, education, training, and work experiences.

Data and Measures

The dataset used in my analyses is the 2001-2002 California Workforce Survey (Data Archive & Technical Assistance, University of California at Berkeley). The survey was designed to assess working conditions in California and to measure the extent to which various groups of workers differ in regard to wages, hours, benefits, and work control in their working environment. The survey research center at the University of California, Berkeley conducted telephone interviews to California residential households during 2001-2002. A technique called list-assisted, random-digit sampling was used to take advantage of large computer databases of telephone directory information. Three steps were applied to eliminate business and non-working phone numbers. Telephone interviews of the eligible residential households produced a sample with 1404 respondents (For details on the survey design, see 2001-2002 California Workforce Survey Codebook). Among the total respondents, 1045 were working full-time or part-time during the survey period. Because this study investigates work wage differentials among multiple racial groups, these 1045 workers comprise the final sample for my statistical analyses.

Income is the dependent variable, measured with the question "how much do you earn per hour/month/year at this job?" Because respondents provided information on the number of hours they work per week, I first computed weekly wage for those who reported their hourly wage by multiplying their hourly rate with number of hours they work per week. I then computed their annual salary by multiplying their weekly rate by 52. Thus the dependent variable is respondent's annual income. I transformed the personal income into the nature log form to stabilize sample variance and reduce heteroscedasticity (Allison 1999: 128)

Race is the crucial independent variable, whose relationship with income is the main focus of this paper. The survey asked respondents "Which of the following best describes your race or ethnic group?" The original coding has six racial groups: Caucasians (620), African-Americans (68), Hispanics (246), Asians (69), Native Americans (17) and Middle Eastern (9). Because the numbers for Native Americans and Middle Easterners are too small to warranty a significant statistical inference, I created a new classification that contains Caucasians (620), African-Americans (68), Hispanics (246), Asians (69) and others (26) including Native Americans and Middle Easterners.

I also acknowledge that Hispanic cannot be a valid racial group as Hispanics have many phenotypes and racial identifications, ranging from "white" to "black" (Bailey 2001; Fergus 2003). But Hispanic is a socially identifiable group that is constructed by their national origins, length of residency, and language knowledge and use (Portes and Macleod 1996).

Human capital is an important independent variable that may mediates the relation between race and income. It includes education, work experiences, employer-paid job training, worker-paid job training, and language ability. To capture the nonlinearity in the monetary returns to education, I reclassified education into five dummy variables: less than high school, high school, some college, BA, and postgraduate level. Work experience is measured with the number of years respondents have worked for the current employer. Employer-paid and worker-paid training is measured respectively with "did you participate in any employer-paid training in the last 3 years? (yes = 1, no = 0)" and "did you ever participate in a training not paid by your employer in the last 3 years (yes = 1, no = 0)?"

The survey did not directly ask about language fluency. Instead, it asked respondents to report the language used in their workplace and language used in their homes. About 93 percent of respondents report that English is the main language in their workplaces. The rest of the respondents report that Spanish (6 percent) and other languages (1 percent) are the main language used in their workplaces. In addition, the survey asked respondents to report language used in their homes. About 85 percent report speaking English, 11 percent speak Spanish, and the remaining 4 percent speak a variety of different languages including Chinese, Vietnamese, and Russian. Because this paper focuses on language proficiency in English, I focused on those whose workplace language is English and created three different groups based on their home languages. The three groups are those who speak English at home, those who speak Spanish at home, and those who speak other languages at home. I reasonably infer that those speaking English at home and work have better command of English at work than do those speaking Spanish and other languages at home but speaking English at work.

The control variables include individual characteristics such as age and sex, job level characteristics such as unionization, job supervision, full time work, and occupational classification into seven major groups, and workplace characteristics such as independent workplace, size of workplace and industrial classification into eight main groups.

Findings

Table 1 compares average income levels among the five racial groups. It shows that an income penalty associated with being a minority is pervasive in contemporary California workplaces. While the average income for the entire sample of California workers is $42,145 per year, the annual incomes for Caucasians, African Americans, Hispanics, Asian and other minority groups are $48,329, $42,984, $28,394, $43,845 and $42,905 respectively. Compared to other groups, Caucasians have the highest income, whereas Hispanics make the lowest wage, which accounts for merely 58.75 percent of that for Caucasians. This result partially supports [H.sub.1] that average income for Hispanic workers is significantly lower than that for Caucasians. However, the income gap between Caucasians and Asian, in which the Asians made 90.72% of the income of Caucasian workers, is not significant. Similarly, income gap between African Americans and Caucasians is not significant without controlling for other independent mediating variables.

Sources of Income Penalty for Hispanic Workers

To account for the sources of income differentials among different racial groups, I produce Table 2 to show five different models; each controls for different groups of mediating factors. The income disparity between Caucasians and Hispanic workers persists in Models 1, 3, and 4, which suggests that variations in job and workplace characteristics are not adequate to explain why Hispanic workers make less money than do Caucasian workers. Model 2 presents striking results as the Caucasian-Hispanic income gap disappears when controlling for human capital indicators such as education, training, work tenure, and English fluency. This result supports [H.sub.2] that controlling for human capital indicators eradicates the income gap between Hispanic and Caucasian workers. In other words, Hispanic workers earn less because they have lower levels of human capital stock than do Caucasians.

To quantify this assertion, I compared Hispanic workers to Caucasian workers along crucial human capital dimensions. Table 3 shows that Hispanic workers on the average receive 2.58 years fewer years of education than do Caucasian workers. Breaking down the education level into five different categories, the percentages of Hispanic workers receiving less than high school or high school education are significantly higher than Caucasian workers by 21 and 9 respectively. In contrast, Hispanic workers receiving BA and graduate degrees are 13 percent and 17 percent, significantly lower than are Caucasian workers. Comparison of other human capital dimensions between Hispanics and Caucasians yields a similar pattern. Hispanic workers have shorter work tenure than do Caucasian. Fewer Hispanic workers participate in employer-paid and unpaid job training.

About 29 percent of Hispanics speak Spanish at home, in contrast to 0 percent of Caucasian workers who use Spanish at home. The evidence is striking that the source of the Hispanic-Caucasian income gap lies in the discrepancy in their human capital stock. Caucasian workers are more educated, receive more job training, have longer job tenure, and more cogently, have greater English fluency than are their Hispanic coworkers; all contribute to the higher income of Caucasians compared to Hispanics.

The results suggest that English fluency is a highly critical element of human capital for Hispanic workers. By merely adding the English fluency indicator to the other human capital factors as control variables, the income gap between Hispanic and Caucasian disappears. In particular, those who speak Spanish at home earned only 70.26 percent (Exp. (-.353) = 70.26%) of the income of their coworkers using English both at work and at home (see Model 2 in Table 2). In contrast, there is no significant income gap between those speaking other languages at home and those using English at home. Table 2 also shows that except for African American workers who made 73.79% (Exp (-.304) = 73.79%) of Caucasian income, income gaps between Caucasians and other minority groups disappear in Model 5 when all the mediating independent variables are put under control.

Comparing Hispanics with African American and Asian Workers

That Hispanic workers have a lower level of human capital stock--such as education, job training, work experience, and English proficiency--than their Caucasian coworkers may explain why they earn less than Caucasians. Comparing incomes between Caucasian and African workers yields a different pattern: none of the mediating independent controls from various sources can explain the income gap between them. This suggests that African American workers may encounter greater discrimination from employers than do Hispanics. What needs to be done is the work toward reducing or eliminating discriminatory wage practices; this suggests a systemic and political, in terms of law-making and compliance enforcement, solution, rather than one that individual workers can implement, e.g. seeking further education or other ways of increasing their human capital stocks.

Asian workers share many similarities with Hispanic workers in several important work profiles. Both groups have a considerable number of new immigrant workers who do not speak fluent English (Stolzenberg and Tienda 1997), and are highly heterogeneous in their ethnic backgrounds. However, the income comparison between Asians and Caucasians contrast starkly to that between Hispanics and Caucasians. First, the income gap between Asian workers and Caucasian workers is the smallest and statistically insignificant (see Table 1); whereas the gap between Hispanic and Caucasian workers is the largest and statistically significant (Table 1).

Second, although English fluency appears to be so vital to the incomes of Hispanic worker, the impact of language on income is completely absent for Asian workers. This finding concurs with a previous report that occupational inferiority for Hispanic workers is most pronounced when they have low English fluency and low schooling, a negative stereotype commonly held by many American employers for Hispanic workers (Stolzenberg 1990:151). As this profiling is absent for Asians, English proficiency is not a significant factor for Asian workers' income levels. In other words, my research suggests that it is a combination of employer stereotyping of low education and language fluency on the one hand, and a confirmation to this stereotype on the other, that produces income penalty for Hispanic workers. Despite the similar language barrier, Asian workers may receive much less of such a negative profiling, which explains the lack of income penalty for Asian workers. However, this allegation awaits for future scrutiny with pertinent data, particularly data from the employer side regarding how they perceive different racial groups in terms of possessing necessary job qualifications (Moss and Tilly, 2001).

Discussion

The turn of the century witnessed significant changes in the American population landscape. The Hispanic population has replaced the African American population to become the largest minority group. Yet, systematic studies on issues related and unique to Hispanic people are scarce. Analyzing data from a workforce survey from California, a state with the most diverse population composition, this paper made important contributions to the study of the Hispanic workforce. It identified English deficiency as a unique source of income penalty for Hispanic workers. Less educated Hispanic workers with English difficulties receive the largest income penalty.

To the extent that language proficiency facilitates coworker communication and enhances productivity, language fluency is considered an integral component of workers' human capital. Hence, language proficiency, much like education, should increase workers' income. This effect should be particularly pronounced for a group with great variation in their language proficiency. Hispanics and Asians are good cases in point as both groups are highly heterogeneous in their ethnic backgrounds and both have a considerable number of non-native English speakers. However, my research divulges that the effect of English fluency on income is a contingent one. Hispanic workers who speak Spanish at home are the only group that pays an income penalty for English deficiency. This finding strongly concurs with previous studies that Hispanic workers fitting the stereotype of less education and poor English skills incur the greatest loss in American labor market (Stolzenberg 1990). This line of work also holds great promise. For example, an employer-level survey can determine the existence and extent of employer profiling of different racial groups. A critical question awaits scrutiny as to the consequences of such employer profiling on employees confirming to that profiling. The evidence presented in this research seems to suggest that when employer profiling is in place, employees with such negative profiles are likely to endure a great deal of financial loss.

Pending future evidential support, this finding entails some preliminary implications for Hispanic workers. First, individual Hispanic workers can improve their income by increasing their human capital stocks, including gaining more education and greater English fluency. In this regard, this research uses language spoken at home as an indicator of English fluency with strong plausible assumption that language spoken at homes is highly correlated with English fluency at workplace. However, English fluency is also related to level of education, to amount of time in the United States, and to residence in ethnic enclaves versus more integrated settings (Portes and Macleod 1996). In these ways, language spoken at home is likely to be part of a bundle of indicators of language fluency. Thus future studies are needed to empirically assess the factor loadings on English fluency from language spoken at home and other factors. Results from these studies can be used to validate the use of language spoken at home as a proxy for English fluency at work. Second, it may take a long time and collective efforts from the entire Hispanic workforce, in conjunction with policy-makers, to eliminate the employer profiling that assigns negative stereotypes such as low education level and poor English skills to Hispanic workers. Until then, the chronic and idiosyncratic bias against Hispanic workers will continue to handicap their incomes.

Appendix
Item Constructions for Independent Control Variables

Variable names    Measuring Items              Coding Methods

Gender            Are you male or female?      Male=1
                                               Female=0

Age               How old were you on          Respondents' actual
                  your last birthday?          age in years

Full-time work    Are you currently            Full time=1
                  working for full time (35    Part time=0
                  + hrs/wk) or part time?

Job Supervisory   As part of your job, do      Agreement to each
                  you (1) supervise the        statement equals 1,
                  work of other employees?     and then results are
                  (2) Influence the pay or     summed up, thus
                  promotion of the people      producing a scale
                  you supervise? or (3) Hire   from 0 to 3.
                  and fire the people you
                  supervise?

Union             Do you currently belong      Yes=1
membership        to a labor union?            No=0

Respondents       What is your job title       A multiple dummy
occupations       called?                      variable including
                                               the following groups:
                                               managerial,
                                               professional and
                                               technical, service,
                                               secretary, machine
                                               operator, craft, and
                                               farming. Professional/
                                               technical is the
                                               reference group in
                                               regression

Workplace size    About how many people        1: fewer than 10
                  are employed where you       2: From 10 to 50
                  work?                        3: From 51 to 100
                                               4: From 101 to 1,000
                                               5: Over 1,000

Workplace         Is the place where you       1: Independent
Independency      work part of a larger        2: Yes, dependent
                  company?

Workplace types   Do you work for a            A multiple dummy
                  business, a government,      variable where
                  or a non-profit              business is reference
                  organization?                group

Workplace         What kind of business or     A multiple dummy
industries        industry do you work for     variable including
                  at this job?                 the following groups:
                                               agriculture,
                                               manufacturing, finance,
                                               personal services,
                                               professional services,
                                               retail, wholesale,
                                               transportation, and
                                               public administration.
                                               Public administration
                                               is the reference group

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Tomaskovic-Devey, Donald and Sheryl Skaggs. 2002. "Sex Segregation, Labor Process Organization, and Gender Incomes Inequality" American Journal of Sociology 108/1:102-128

U.S. Bureau of the Census. 2000. "Census 2000 Data for the United States" http://www.census.gov/census2000/states/us.html

U.S. Citizenship and Immigration Services. 2002. "Statistical Yearbook of the Immigration and Naturalization Service prior to the Fiscal Year 2002 edition" http://uscis.gov/graphics/shared/ aboutus/statistics/ybpage.htm

U.S. Bureau of the Census. 2000. "National Population Projections" http://www .census.gov/population/www/projections/natproj.html

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SONG YANG

University of Arkansas

Department of Sociology and Criminal Justice

Table 1
Income Differentials among the Four Racial Groups

                                      Percentage of      t test
                    Average Income      Caucasian       (compared
                    (St. Deviation)      income       to Caucasian)

Total                   42,145            87.20            --
(N=774)                (34,670)

African Americans       42,984            88.94           -1.04
(N=48)                 (60,189)

Hispanics               28,394            58.75           -7.00 ***
(N=206)                (23,807)

Asian                   43,845            90.72           -0.89
(N=50)                 (33,297)

Other Minorities        40,141            83.06           -1.23
(N=17)                 (31,762)

Caucasian               48,329             --              --
(N=445)                (34,144)

*** P < .001; F ratio = 12.287 (p < .001); adjusted R square = 5.6%

Table 2
Unstandardized Coefficients of OLS Regression of Income

Predictors                   Model 1      Model 2      Model 3

Constant                     8.777 ***    8.975 ***    9.461 ***
                             (.163)       (.195)       (.113)

Individual Characteristics

Hispanics                    -.168 **     -.044        -.276 ***
                             (.065)       (.075)       (.060)

Asians                       -.195        -.171        -.117
                             (.102)       (.116)       (.095)

Blacks                       -.251 *      -.242 *      -.335 ***
                             (.103)       (.104)       (.098)

Others                       -.045        -.013        -.148
                             (.166)       (.172)       (.168)

Caucasian                    --           --           --
  (reference group)

Male                         .394 ***     .409 ***     .250 ***
                             (.050)       (.053)       (.052)

Age                          .007 **      .007 **      .006 **
                             (.002)       (.002)       (.002)

Human Capitals

Graduate                     .903 ***     .709 ***
                             (.122)       (.160)

BA                           0.745 ***    .551 ***
                             (.118)       (.157)

Some college                 .391 ***     .203
                             (.108)       (.148)

High school                  .232 *       .058
                             (.112)       (.153)

Less than HS (ref.)          --           --           --

Employer-paid job            .258 ***     .246 ***
  training                   (.053)       (.055)

Self-paid job training       .019         .015
                             (.060)       (.062)

Work tenure                  .079 ***     .078 ***
                             (.018)       (.018)

Home language:                            -.353 **
  Spanish                                 (.130)

Home language: Others                     -0.106
                                          (.158)

Home language:               --           --           --
  English (ref.)

Job characteristics

Full time work                                         .891 ***
                                                       (.067)

Supervisory                                            .158 ***
                                                       (.023)

Union membership                                       .147 ***
                                                       (.060)

Occupation-managerial                                  -.073
                                                       (.084)

Occupation-secretary                                   -.379 ***
                                                       (.065)

Occupation-machine                                     -.684 ***
  operators                                            (.098)

Occupation-craft                                       -.469 ***
                                                       (.096)

Occupation-farming                                     -.779 ***
                                                       (.175)

Occupation-service                                     -.674 ***
                                                       (.082)

Occupation-PT (Ref.)         --           --           --

Workplace characteristics

Workplace size

Workplace
  independence

Non-profit public

Non-profit private

Profit (Ref.)                --           --           --

Industry-agriculture

Industry
  manufacturing

Industry-finance

Industry-personal
  service

Industry-professional
  service

Industry-retail

Industry-whole sale

Industry-transportation

Industry-public admin.       --           --           --
  (Ref)

Model [R.sup.2] (df)         34.8 (13)    32.2% (15)   51.8% (15)

Number of cases                 753          696          632

Predictors                   Model 4      Model 5

Constant                     9.973 ***    9.222 ***
                             (.174)       (.240)

Individual Characteristics

Hispanics                    -.315 ***    -.035
                             (.066)       (.068)

Asians                       -.187        -.259
                             (.112)       (.154)

Blacks                       -.293 **     -.304 **
                             (.111)       (.096)

Others                       -.066        -.187
                             (.180)       (.167)

Caucasian                    --           --
  (reference group)

Male                         .340 ***     .169 **
                             (.057)       (.053)

Age                          .012 ***     .003
                             (.002)       (.002)

Human Capitals

Graduate                                  .617 ***
                                          (.153)

BA                                        .381 *
                                          (.149)

Some college                              .152
                                          (.137)

High school                               .039
                                          (.138)

Less than HS (ref.)          --           --

Employer-paid job                         .156 **
  training                                -.000

Self-paid job training                    .027
                                          (.057)

Work tenure                               .035
                                          (.018)

Home language:                            -.228 *
  Spanish                                 (.109)

Home language: Others                     .012
                                          (.136)

Home language:               --           --
  English (ref.)

Job characteristics

Full time work                            .764 ***
                                          (.071)

Supervisory                               .133 ***
                                          (.023)

Union membership                          -.082
                                          (.071)

Occupation-managerial                     -.134
                                          (.082)

Occupation-secretary                      -.256 ***
                                          (.070)

Occupation-machine                        -.358 **
  operators                               (.120)

Occupation-craft                          -.272 *
                                          (.110)

Occupation-farming                        (.340)
                                          (.264)

Occupation-service                        -.349 ***
                                          (.095)

Occupation-PT (Ref.)         --           --

Workplace characteristics

Workplace size               .099 ***     .056 **
                             (.021)       (.020)

Workplace                    -.028        -.109
  independence               (.063)       (.057)

Non-profit public            -.165        -.251 **
                             (.086)       (.085)

Non-profit private           -.246 *      -.267 **
                             (.106)       (.094)

Profit (Ref.)                --           --

Industry-agriculture         -.442 **     -.132
                             (.151)       (.152)

Industry                     -.418 **     -.248
  manufacturing              (.152)       (.144)

Industry-finance             -.081        -.084
                             (.143)       (.132)

Industry-personal            -1.096 ***   -.713 **
  service                    (.254)       (.231)

Industry-professional        -.222        -.216
  service                    (.113)       (.111)

Industry-retail              -.795 ***    -.363 **
                             (.147)       (.140)

Industry-whole sale          -.611 **     -.142
                             (.226)       (.204)

Industry-transportation      -.244        -.063
                             (.144)       (.144)

Industry-public admin.       --           --
  (Ref)

Model [R.sup.2] (df)         27.3% (18)   59% (36)

Number of cases                 725          563

Numbers in parenthesis are standard errors
* P < .05; ** P < .01; *** P < .001 (two-tail test)

Table 3
Mean Value Differences in Human Capital
Factors between Caucasian and Hispanics

                                                        Hispanic-
                             Hispanic/N   Caucasian/N   Caucasian
Variables                      (St.d)       (St.d)      (t test)

Overall education            12.13/243     14.71/620    -2.58 ***
                               (3.45)       (1.89)      (-14.02)

Less than high school         .23/243       .02/620      .21 ***
                               (.42)         (.13)       (11.17)

High school                   .27/243       .18/620      .09 **
                               (.44)         (.38)       (2.99)

Some college                  .34/243       .35/620       -.01
                               (.48)         (.48)       (-.26)

BA                            .11/243       .24/620     -.13 ***
                               (.31)         (.43)       (-4.29)

Graduate                      .05/243       .22/620     -.17 ***
                               (.21)         (.41)       (-6.15)

Work tenure                   6.69/217     7.26/510     -.57 ***
                               (1.70)       (1.55)       (-4.41)

Employer-paid job training    .37/217       .64/510     -.27 ***
                               (.48)         (.48)       (-6.94)

Unpaid job training           .15/217       .25/510      -.10 **
                               (.36)         (.44)       (-2.95)

Speak English at both home    .69/176       .99/615     -.30 ***
  and work                     (.46)         (.11)      (-14.78)

Speak Spanish at home and     .29/176        0/615       .29 ***
  English at work              (.45)          (0)         (16)

Speak other language at       .17/176       .01/615      .16 ***
  home and English at work     (.13)         (.11)       (16.31)