wages of immigrant males

8/10/2019 Wages of Immigrant Males

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Training and the Earnings of ImmigrantMales: Evidence from the Canadian

Workplace and Employee Surveyn

Yoko Yoshida, McGill University

Michael R. Smith, McGill University

Objective. To improve on the existing research on earnings differentials betweenvisible minority immigrants and the native-born, and on the role of discriminationin producing that difference. To do this we introduce into the analysis: (1) access totraining and (2) training effects on earnings growth. Method. Using a panel dataset containing information on training we test cross-sectional models of access totraining, cross-sectional models of wage determination, and panel models ofwage growth. Results. Visible minority immigrants are disadvantaged in both ac-cess to training and earnings; education reduces the disadvantage; and they do betterthan the other two groups in wage growth. Conclusions. Some results are consistentwith a discrimination interpretation but, considered together, the complete sets ofresults are difficult to reconcile with any relatively straightforward discriminationaccount.

Immigration is a major issue in most rich societies. Fertility is below thereplacement rate so, without immigration, total population will decline inwestern Europe, North America, and Japan. This may undermine the fi-nancing of social programs in most countries affected.1 For immigrants toreplace the native-born they must be effectively integrated into employmentin their host societies. Changes in the sources of immigrants may havehindered that integration. Across Western societies they have been increas-ingly drawn from racial minority pools. Some research suggests significant

amounts of discrimination on the basis of race (e.g., Mason, 1997, 1999;

nDirect correspondence to Michael R. Smith, Department of Sociology, McGill Univer-sity, 855 Sherbrooke West, Montreal, Quebec, Canada, H3A 2T7 [email protected]. Professor Smith will provide information on data and coding. The raw data isregarded by Statistics Canada as confidential. Access to it can be secured through applicationto one of the Statistics Canada data centers located across Canada. For information on thedata center program, consult hhttp://www.statcan.ca/englihs/rdc/index.htmi. Helpful com-ments on early work on this article were received from Morton Weinfeld, participants inMcGills Social Statistics Seminar, and at the session Lanalyse des donnees denquetes: acquiset defis pour lavancement des connaissances en sciences sociales held at the meetings of the

Association canadienne francaise pour lavancement des sciences in Montreal in 2004.1S Sl b (2003) F l i i M D i l (2003)


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Coleman, 2003).2 Insofar as this is the case, it would imply a waste ofimmigrant talent; discriminatory employment practices reduce productivitybecause those subject to them are to some degree excluded from jobs forwhich their talent equips them.3

Wage-gap decomposition is the standard method for estimating theeffect on earnings of immigrant status and race. It assumes that earningsoughtto vary with human capital. Earnings differences between groups aftercontrols for education, experience, and language skills are attributed todiscrimination in either access to jobs or within comparable jobs. Differ-ences in earnings after controls for occupation, industry, and other jobcharacteristics are attributed to discrimination within comparable jobs.4

Research using this method finds a significant racial minority immigrantearnings disadvantage. For example, in the United States, similar earnings of

native-born whites and Asians is seen as evidence of discrimination because,on average, Asians have more education than native-born whites (Hirsch-man and Wong, 1984; Hurh and Kim, 1989; Tang, 1993, 2000). Researchin Canada using this method also suggests discrimination against racialminority immigrantsin Canada, and in the rest of this article, referred toasvisible minority immigrants(e.g., Li, 2000, 2001; Pendakur and Pendakur,1998; Reitz, 2001).5

A counterargument says that where markets are competitive, employerscannot afford to discriminate in either hiring or pay (Becker, 1957; Sowell,

2004:ch. 6). To do so is to forego profits and to risk being forced out ofbusiness by competitors who refrain from discriminating and hire the bestavailable, at the best price.6 In fact, recent research in both Canada and theUnited States suggests smaller average differences in earnings as compared tonative-born whites than had previously been reported (e.g., Swidinsky andSwidinsky, 2002; Zeng and Xie, 2004), and differences that are variableacross ethnic groups. Pay discrimination seems to be completely absent inthe highly competitive market for professional athletes (Singh, Sack, andDick, 2003).

2Most of this research in the United States is concerned with historically disadvantagedgroupsAfrican Americans and Latinosbut the argument for discrimination on the basisof race seems general. Some of this research does deal with immigrants.

3This is the position of the Conference Board of Canada (2001).4The utility of this method for establishing discrimination is contested. Darity and Mason

(2004) argue in favor of evidence from court cases and audit studies. This latter involvessending matched visible minority and nonvisible minority candidates to apply for jobs andcomparing the results. For a critique of audit studies, see Heckman (1998).

5This genreof research assumes that discrimination is notpresent if people with the same

human capital have access to the same jobs and the same pay. Institutional discriminationis a broader form of disadvantage (e.g., Healey, 2004:8689), often involving historicallyaccumulated disadvantages (e.g., concentration of residence in declining communities, poor

h l ) E l i h l f h i i ld i i l

Training and the Earnings of Immigrant Males 1219


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Still, there is a core general finding across most of the research: in ag-gregate, visible minority immigrantmalesare paid less than their native-borncounterparts because education and experience acquired in their country oforigin is less highly rewarded than education and experience acquired by

others (visible minority or white immigrant or native-born) in the hostcountry. Some studies suggest a rate of return to the overseas experience ofvisible minority immigrants that is close to zero. This lower rate of return tohuman capital is taken both as a gauge of discrimination and of a loss ofpotential productivity.

In assessing the evidence provided in support of the various claims aboutthe extent of earnings discrimination, a major issue is the adequacy of themeasurement of productivity. Employee productivity is assumed to varywith human capital. It is clear that the adequacy of measures of human

capital is highly variable and often downright inadequate. A large enoughsample to contain usable numbers of visible minority immigrants, for ex-ample, usually requires use of the Census or some other large nationwidesurvey, but the measures in these data sources were not constructed for thepurposes of testing theories about discrimination.

Consider the Canadian Census. Its measures of education (years, diploma,field of study) are excellent, but it has no question on experience, which hasto be measured as a maximum: age minus five or six minus years of ed-ucation. This overestimates average experience by a larger magnitude for

visible minority immigrants than for the native-bornand significantly so.In the data used here, the overestimate is 1.5 years relative to a mean of 18.5years for native-born white males, but 5 years relative to a mean of 16 yearsexperience for visible minority immigrant males. Census measures of lan-guage skills are also unsatisfactory. One question asks whether the respondentcan hold a conversation in English or French. This sets a very low perform-ance threshold.Simplyhaving conversational skills would not be enough formost professional and many managerial jobs, which require a capacity towrite.7 In this article we use a data set with good measures of education

(diploma) and experience (years employed), as well as a measure of languageskills (the coincidence of the language used at work and at home).

However, we focus on a different measurement issue. The wage-gap de-composition approach assumes that wage gaps explained by human capitaldifferences do not indicate discrimination but that those associated with jobcharacteristics (industry and occupation) do. Yet the form through whichexperience mostly increases human capital is training. Jobs differ in thetraining opportunities they provide. Most professional and many managerial

jobs provide career-long training. Many manual jobs do not. Part of the

differences in earnings explained by occupation, industry, and job charac-teristics may reflect training differences. This is likely to be pertinent in

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comparisons involving visible minority immigrants. Some will arrive with-out adequate skills in either of Canadas official languages. If so, in their firstyears of employment they are likely to be confined to jobs that make modestlanguage demandsdisproportionately jobs offering little training. If, be-

cause of language difficulties when they arrive in their host country, im-migrants find themselves in jobs offering fewer opportunities for training,the gap between their human capital and that of the native-born will tend toincrease (see Hum and Simpson, 2003).

Training and Discriminatory Pay Outcomes

The effects of training on earnings are not straightforward (Becker, 1964).

Generalskills are, by definition, portable. To retain those trained in them,employers must match the going rate for the skill. If they pay for general-skills training, there is a risk that those trained will be poached by otheremployers who, having evaded training costs, can pay more. Employers,then, will assign the cost of acquiring general skills to employees (possiblythrough very low pay during training). Specific skills are, by definition,nonportable; there is no market for them. Still, employers should act toprotect their investment in specific skills by paying a wage rate above thatavailable to the employee from alternative employers. They may, further,

concentrate the higher wage in the later part of an employees tenure withthem, thus providing an incentive for the employee to stay with the firm.Employer-provided training should, then, increase the trainees pay, butwhere specific skills are involved, by how much, and when, is less clear.

Careful research on training and on the pay changes that follow it mud-dies the picture still further. Barron, Berger, and Black (1997) found thatthose receiving employer-provided training mostly viewed it as general, thatthe employee productivity increases following training greatly exceeded theaccompanying pay increases, and that training was expensive. The clear

implication of this is that employers are likely to worry about their invest-ment in training because of both its cost and because of the high return ityields to them.

What might this imply for the visible minority immigrant wage setting?Discrimination might take one of the following forms. (1) As compared toothers, visible minority immigrants might have less access to training. Thiscould show up through a negative main effect after controls for humancapital, or through negative interactions with levels of education. We knowthat those with more education get more training. It is possible that the

effect of postsecondary education on the probability of being trained issmaller for visible minority immigrants than for others. (2) In cross-sectional



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visible minority immigrant status and training. (3) With the panel dataavailable to us from the data source we use, discrimination might be inferredwhere training is not followed by as large an increase in earnings for visibleminority immigrants as for whites. Again, with wage growth as the dependent

variable, negative interactions between training and visible minority immi-gration status would indicate this.

Analysis Based on a Different Data Set

In what follows we compare the access to training of visible minorityimmigrants, native-born whites, and immigrant whites, and the effect of

training on their earnings. The immigrant statuses are entered into analysesas dummy variables, with native-born whites as the default category. Theseimmigrant/visible minority statuses are our independent variable of inter-estcalled IVOI in what follows. Our analyses are both cross-sectional andlongitudinal. We exclude aboriginals and native-born visible minoritymembers from the analysis. In neither case does our sample contain enoughcases to support serious analysis. In any case, immigrants now make up thebulk of Canadas visible minority population.

Our data source is Statistics Canadas Workplace and Employee Survey

(WES), collected from managers in a stratified sample of workplaces, thenfrom a probability sample of employees within those workplaces. The em-ployee and workplace data can be matched. It is a panel survey. The work-place panel lasts six years. The employee panel is two years. We use cross-sectional data from both the workplace and employee surveys for 1999, andthe 1999/2000 employee panel with controls from the 1999 workplacesurvey. The 1999 survey generated 5,440 employer responses and 24,938employee responses.8 The 1999/2000 employee panelis a bit smaller. Theresearch cited earlier shows little or no earnings disadvantage to visible

minority immigrant women so we confine our analysis to men (e.g., Li,2000, 2001; Pendakur and Pendakur, 1998; Reitz, 2001; Swidinsky andSwidinsky, 2002). To eliminate the effects of limited participation in thelabor force, we confine our sample to full-time (34 or more hours per week),full-year (48 weeks or more of work in the survey year) employees.9

As a data source the WEShas several advantages. First, the sample hasenough visible minority immigrant males (about 650) to sustain a sensible

8Details of the survey are in Guide to the Analysis of the Workplace and Employee Survey

1999, Statistics Canada and at hhttp://stcwww.statcan.ca/english/sdds/2615.htmi. Linkingthe employer and employee responses reduces the usable Ns. There are workplaces for whichno employee responses exist and employees for which no workplace responses exist.

9Di i i i l k h f f li i d j b i hi h



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analysis. Second, response rates are very high indeedabout 93 percent forthe workplaces and 86 percent for the employees. Third, as well as questionson educational certification, there are direct questions on work experienceboth total or with the current employer.10 Years with the current employ-

erdurationworks best in models predicting training; total experience inmodels predicting earnings. Fourth, managers are usually more reliablesources of information on workplace characteristics (industry, managementpolicies) than are employees. Fifth, the surveys panel character provides thestandard advantages for the purposes of determining causality; it improvesthe plausibility of causal inferences without transforming them into cer-tainties. Sixth, and particularly relevant for our purposes, the survey containsdirect questions on training in the previous year: on whether employeesreceived employer-provided classroom training and/or on-the-job training,

and on the number of courses the respondent took.11 These remain quitecrude measures of training. There is some risk that they underestimatetraining effects on earnings.

To correct for sampling design effects, the standard errors used for in-ferential purposes were estimated using bootstrap weights supplied by Sta-tistics Canada. All p values in the tables are for a two-tailed test, whetherthere is a hypothesis or not. We take this into account in our discussion. Thedefault categories for the dummy variables are as follows: educationhighschool graduate; location of educationfully within Canada; IVOI

native-born whites.

Analysis

In what follows we address two questions. (1) What role does visibleminority immigrant status play in determining access to training? (2) Whatis the payoff to the training of visible minority immigrants as compared tothat of whites? Our analysis differs from the more common approach de-scribed earlier. Previous analyses use cross-sectional regression to estimate

10The experience question is: Considering all the jobs you have held, how many years offull-time working experience do you have? Clearly, this question will generate measurementerror. Still, the respondent is invited to sum work experience across jobs. Immigrants whowere out of the labor force while receiving language training and job hunting will adjust theirresponses. Duration is estimated from: When did you start working for this employer?

11The wording of the four questions we use is as follows. A preamble states: The next fewquestions deal with job-related training provided or paid by your employer. Then, 25. In

the past 12 months, have you received any classroom training related to your job? Itaccompanies this question with the following interviewer definition of what is included inclassroom training: All training activities which have pre-determined format, including a

d fi d bj i S ifi P b i d d/ l d Th



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portions of both immigrant categories to those of native-born whites.12 Asexpected, native-born whites have a higher wage rate than do visible mi-nority immigrants (0.05 level with a one-tailed test). White immigrants havethe highest rate of pay. They also have the most experience, reflecting theconcentration of white immigration in the earlier postwar decades, and arebetter educated than native-born whites. Visible minority immigrants haveless experience than the other two categories, but are morelikely to have a

university degree. They are less likely to have been trained in the previous

TABLE 1continued

WhiteNative

WhiteImm.w

VisMinImm.w

Information and cultural industries 4.16 4.58 1.45Finance and insurance 2.78 3.39 5.68Real estate, rental and leasing operations 1.44 1.53 1.84Business services 8.67 14.65 16.06Education and health services 9.6 9.66 6.58Retail trade and consumer services

(ref. level)17.32 11.02 24.14

Marital status (percent)Single never married nor in CL (ref. level) 18.42 11.39 nn n 20.18Legally married 59.9 74.24 nn n 73.1 nnn

Common law 15.01 7.75 nn n 1.75 nnn

Separated, divorced, widowed 6.66 6.62 4.98Company size (percent)

Less than 20 employees (ref. level) 27.81 28.2 29.812099 employees 29.9 24.61 n 29.69100499 employees 21.46 24.64 26.135001 employees 20.84 22.54 14.38 n

Foreign assets at workplace (percent)Foreign owned (foreign assets 450) 11.29 11.85 9.99

Incentive pay policy at workplace (percent)Incentive pay system 45.4 52.06 n 53.44n

Mean training expenditure per employee 372.21 287.61n 204.22nnn

Collective bargaining agreement (percent)Covered by CBA 29.49 25.41 16.5 nnn

wGroup means and percent are compared to those for white-native group (two-tailed test),except place of education and industry.

1Duration is the number of years that the respondents are working for the current employer.2Language match measures whether they use the same language at home and at work.3Mix education applies to those who had the early part of their education outside Canada.nSignificant at alpha50.1; n nsignificant at alpha50.05; n n nsignificant at alpha50.01.

12Th i l i f d i d i d f l All h



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year. There is, then, prima facie evidence of earnings disadvantage and,perhaps, discrimination. But there is one anomalous result: visible minorityimmigrants had the highest rate of wage growth over the previous two years.

Access to Training

Table 2 contains coefficients predicting the probability of on-the-jobtraining, classroom training, and the number of classroom courses taken.The goodness-of-fit measures indicate that most of the variance in the de-pendent variables is not explained by the independent variables, somethingthat is fairly common in logistic regressions.

Start with the main effectsthe effect of IVOI for high school graduates.Where the difference is significant, visible minority immigrants had lessaccess to training than native-born whites, after controls for human capital

TABLE 2

Determinants of Training n

Logistic Regression Poisson Regression

On-the-JobTraining

ClassroomTraining

Number ofCourses

N 9,935 9,935 9,935Pseudo-R2 0.0678 0.1033Log pseudo-likelihood 5478.56 5916.63 14723.62

Coeff. PValue Coeff. PValue Coeff. PValueIntercept 0.4719 0.085 2.2076 0.000 1.6481 0.000Duration 0.0729 0.133 0.0064 0.882 0.0062 0.604Duration2 0.0016 0.147 0.0008 0.271 0.0002 0.655

Mixed education 0.4462 0.552 0.1427 0.891 0.0031 0.977Foreign education 0.4394 0.363 0.4737 0.576 0.0723 0.619Language 0.2852 0.470 0.2704 0.765 0.0930 0.901Less than HS 0.3097 0.535 0.3830 0.455 0.3380 0.548College, etc. 0.0607 0.507 0.1700 0.670 0.2075 0.000University plus 0.2324 0.015 0.3976 0.031 0.2106 0.352

VisMinImm 0.5530 0.634 0.9967 0.096 0.7979 0.002WhiteImm 0.5489 0.005 1.1318 0.260 1.1987 0.218Less than HS *VisMinImm 0.7723 0.569 2.1097 0.669 2.2805 0.709Less than HS * WhiteImm 0.0632 0.834 0.0831 0.976 0.0793 0.977College, etc. *VisMinImm 0.7570 0.212 0.7766 0.699 0.3591 0.034College, etc.

*WhiteImm 0.4563 0.000 1.4832 0.556 1.0224 0.356

University plus *VisMinImm 0.3815 0.808 1.3823 0.376 0.8132 0.000University plus * WhiteImm 0.4997 0.690 1.2549 0.601 1.2028 0.463



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and workplace characteristics. This is consistent with a discrimination in-terpretation. The results for white immigrants qualify this. All three coef-ficients are negative, one significantly so. In two cases, the white immigrantnegative coefficient is larger than the corresponding visible minority immi-

grant coefficient. The other is about the same. This suggests an immigrantdisadvantage in access to training, irrespective of race.

The interaction terms suggest, though not overwhelmingly, that moreeducationeither college or universitypartially offsets the immigrantdisadvantage. For OJT there is a positive interaction between white immi-grant status and junior college or vocational training (College, etc.) andfor visible minority immigrants there are positive interactions betweennumbers of courses taken and both college and university education.

These effects can be seen more clearly in Table 3, which presents the

probabilities of access to on-the-job and classroom training, and the meannumber of courses taken, derived from the equations summarized in Table2. For native-born whites, there is a positive linear relation between edu-cation and training incidence. For visible minority immigrants, access to on-the-job training is lower for those with a high school diploma than for thosewithout one, as well for those with a university degree than for those with

junior college or vocational training. White immigrants certified at theCollege, etc. level have the same probability of being classroom trained asdo those with a university degree.Additionaleducation of immigrants seems

less straightforwardly related to training than is the case for native-bornwhites.

Equally interesting are the differences in probabilities of those with auniversity education as compared to those with secondary school or less. Fornative-born whites, the increases in probabilities from a secondary schooldiploma to a university degree are 5, 4, and 4 percent across the three formsof training. For white immigrants, the corresponding increases are 14, 12,and 17 percent, and for visible minority immigrants 11, 16, and 15 percent.The relative disadvantage of both immigrant categories falls as their level of

education rises.

Cross-Sectional Wage-Rate Differentials

The dependent variable in Table 4 is the log hourly wage rate. Each panelcontains main effects and interactions involving the IVOI and the trainingvariables. The coefficients in the first panel were generated after controls forhuman capital; those in the second panel after the addition of controls for

workplace and job characteristics. The usual earnings disadvantage of visibleminority immigrants is evident in the IVOI main effects. All the coefficients



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TABLE 3

Predicted Probability of Access to Training by Education and Visible

On-the-Job Training Classroom Training

LT HS HS COL UNIV 1

LT HS HS COL UN

WhiteNative 0.2344 0.2945 0.3072 0.3449 0.0702 0.0997 0.1161 0WhiteImm 0.1585 0.1942 0.2879 0.3339 0.0258 0.0345 0.1572 0

VisMinImm 0.2760 0.1936 0.3523 0.3073 0.0034 0.0393 0.0953 0

nGenerated from equations in Table 2.


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not as well rewarded as that of native-born whites or of white immigrants.For the other main effect, two of the training measures are positively as-sociated with earnings. On-the-job training is not. Finally, the relevantinteraction term shows that where human capital is controlled, the payoff totraining for visible minority immigrants is no different from that of native-born whites. The first panel of Table 4 shows that visible minority immi-grants earn less than white Canadians with similar education and experience,but differential returns to training seem not to be the source of that dis-

advantage.Consider, now, the second panel of Table 4, which contains coefficients

TABLE 4

Interactive Wage Models: Training and IVOI (Males) n

Controls for Human Capital

w

OJT* IVOI Classroom * IVOI Courses * IVOI

N 10,916 10,916 10,916R2 0.2139 0.2347 0.2255

Coeff. PValue Coeff. PValue Coeff. PValueIntercept 2.3762 0.000 2.3346 0.000 2.3730 0.000

VisMinImm 0.1030 0.092 0.0939 0.004 0.1185 0.112WhiteImm 0.0246 0.507 0.0858 0.622 0.0285 0.753

Train 0.0151 0.871 0.1541 0.000 0.0234 0.000

VisMinImm*Train 0.0473 0.840 0.0555 0.623 0.0433 0.691WhiteImm*Train 0.0160 0.913 0.1436 0.659 0.0044 0.919

All Controls Includedww

OJT* IVOI Classroom * IVOI Courses * IVOI

N 9,935 9,935 9,935R2 0.4263 0.4284 0.4282


VisMinImm 0.0035 0.975 0.0051 0.947 0.0362 0.608WhiteImm 0.0445 0.373 0.0759 0.328 0.0310 0.338

Train 0.0229 0.816 0.0548 0.000 0.0080 0.685VisMinImm*Train 0.0101 0.954 0.0124 0.860 0.0396 0.000WhiteImm*Train 0.0249 0.777 0.0986 0.748 0.0080 0.580

nPvalues for two-tailed test.wOther controls are educational degree, experience, experience squared, and language.wwOther controls are experience, experience squared, language, place of education, industry,company size, marital status, occupation, foreign ownership, incentive pay policy, training ex-penses per employee, collective bargaining, and region.



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viously significant training main effects (classroom training, number ofcourses) falls or both falls and becomes insignificant. This panel tells us thatvisible minority immigrants earn less than their white counterparts becausethey are employed in industries, occupations, and workplaces with lower

than average pay that provide less training. Within those industries, occu-pations, and workplaces there is, on average, no visible minority immigrantdisadvantage. Moreover, the one significant interactionvisible minorityimmigrant status and number of coursesis positive. It seems that, within

jobs that provide training, the training increases visible minority pay by asmuch or more than it increases the pay of native-born whites.

Tables 5a and 5b present rates of return by IVOI category. The first panelof Table 5a shows that, with comparable measured human capital, visibleminority immigrants receiving on-the-job or classroom training earned

about 5 percent less than native-born whites. Those enrolled in one coursein the previous year earned about 7 percent less. Interestingly, this differencedisappears as the number of courses increases. Among (the small number)taking four or more courses, visible minority earnings exceed those of native-born whites by 5 to 10 percent. The second panel of Table 5a shows thatsignificant effects disappear after industry, occupation, and workplace con-trols are added. With or without training, visible minority immigrants earnabout as much as native-born whites. However, the relative advantage ofvisible minority immigrants taking a lot of courses becomes even more

markedrising to almost 18 percent for the largest number of courses.Table 5b presents the same information differently. Given the initial pay

level without training (which we know to be lower among visible minorityimmigrants), it tells us how much higher is the pay in jobs that involvetraining. The result is fairly striking. Almost uniformly, across all combi-nations of controls and training kinds, visible minority immigrant trainingpayoffs are as large or larger than those of their white counterparts. For thosetaking two or more courses, the increases are very large indeed (between 10and 40 percent!).

This cross-sectional analysis informs us about the character of the jobsoccupied by visible minority immigrants. They are concentrated in indus-tries, occupations, and workplaces with below-average pay. This may bebecause their human capital is discounted, possibly indicating discrimina-tion, but the evidence on the payoff to training suggests that within in-dustries, occupations, and workplaces, discrimination is less evident, or maybe absent altogether. Once hired into jobs that provide training, their earn-ings equal or exceed those of both native-born and immigrant whites.

Training and Wage Growth



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TABLE 5a

Predicted Earnings Relative to Those of White Natives by


OJT Classroom

No Yes No Yes 0 1

WhiteNative

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

WhiteImm 1.0249 1.0414 1.0896 0.9438 1.0289 1.0334

VisMinImm 0.9021 0.9458 0.9104 0.9623 0.8883 0.9276

All Controls Includednn

OJT Classroom

No Yes No Yes 0 1

WhiteNative

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

WhiteImm 1.0455 1.0197 1.0788 0.9776 1.0314 1.0398

VisMinImm 0.9965 0.9864 0.9949 1.0073 0.9644 1.0033

nGenerated from equations in the first panel of Table 4.nnGenerated from equations in the second panel of Table 4.


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TABLE 5b

Predicted Earnings Relative to Those Without Traini

Controls for Human Capita

OJT Classroom

No Yes No Yes 0 1

WhiteNative

1.0000 1.0152 1.0000 1.1666 1.0000 1.0237

WhiteImm 1.0000 1.0316 1.0000 1.0105 1.0000 1.0282 VisMinImm 1.0000 1.0644 1.0000 1.2332 1.0000 1.0690


OJT Classroom

No Yes No Yes 0 1

WhiteNative

1.0000 0.9774 1.0000 1.0563 1.0000 1.0081

WhiteImm 1.0000 0.9533 1.0000 0.9572 1.0000 1.0162 VisMinImm 1.0000 0.9675 1.0000 1.0695 1.0000 1.0487

nGenerated from equations in the first panel of Table 4.nnGenerated from equations in the second panel of Table 4.


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earnings from 1999 to 2000. As before, the coefficients in the first panelwere generated after controls for human capital (and the 1999 wage); thosein the second panel after adding controls for industry, occupation, andworkplace characteristics. Across the two specifications, not surprisingly,training increases pay, with the sole exception of the classroom-trainingvariable.

More interestingly, the two specifications also show that over the one-yearperiod, the pay of visible minority immigrants tended to rise relative to that

TABLE 6

Training and the Growth in Log Earnings, 19992000 (Men) n


w

OJT Classroom Courses

N 10,496 10,496 10,496R2 0.4236 0.4232 0.4234


Train 0.0058 0.031 0.0011 0.652 0.0022 0.000VisMinImm 0.0249 0.000 0.0224 0.000 0.0272 0.000WhiteImm 0.0016 0.729 0.0017 0.756 0.0070 0.143

Train *VisMinImm 0.0036 0.697 0.0116 0.165 0.0007 0.686Train * WhiteImm 0.0365 0.000 0.0209 0.001 0.0038 0.030

All Controls Addedww

OJT Classroom Courses

N 9,554 9,554 9,554R2 0.4322 0.4323 0.4318


Train 0.0067 0.016 0.0088 0.000 0.0009 0.093VisMinImm 0.0248 0.001 0.0169 0.022 0.0257 0.000WhiteImm 0.0047 0.506 0.0041 0.576 0.0082 0.223

Train *VisMinImm 0.0039 0.696 0.0144 0.064 0.0013 0.500Train * WhiteImm 0.0314 0.000 0.0387 0.000 0.0047 0.010

nPvalues for two-tailed test.wOther controls in the models are hourly wage in 1999, educational degree, experience, ex-perience squared, and language.wwOther controls are hourly wage in 1999, educational degree, experience, experience squared,language, place of education, marital status, occupation, industry, company size, foreign own-ership, incentive pay policy, training expenses per employee, and region.



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spective of the nature of their jobs or their qualifications, visible minorityimmigrant wages grew faster than those of native-born whites. This mightindicate employer uncertainty on the quality of the overseas education andwork experience of visible minority immigrants and consequent caution in

hiring decisions. Because of that caution, the quality of those that get hiredmay be higher than that of their native-born white counterparts, causingfaster pay increases after hiring. Note that the return to training for visibleminority immigrants is equivalent to that of native-born whites, with theexception of classroom training, after the inclusion of all controls. In thatcase, the return to training of visible minority immigrants is significantlyhigher.

The case of white immigrants is different. Their pay did not grow relativeto that of native-born whites. Consistent with the interpretation above, this

might mean that employers have more confidence in their education andexperience and thus exercise less caution in their hiring so that their per-formance does not warrant a higher rate of wage growth. But their payoff totraining ishigher than that of native-born whites. How might this be ex-plained? Even the unusually high-quality industry and workplace data pro-vided by theWESis unlikely to capture all important attributes of different

jobs. It is possible that their higher payoff to training reflects the character ofthe jobs in which white immigrants are concentrated. But that is simplyspeculation.

Table 7 summarizes the net effects on earnings growth of the factorsdiscussed above. In the models in which only human capital is controlled,visible minority immigrants who had not been trained had a higher rate ofwage growth than their white counterparts. This advantage is a bit largerwhen the comparison is between those from each group who had receivedtraining. Adding controls for industry, occupation, and job characteristicsdoes not eliminate the earnings growth advantage of visible minority im-migrants when the comparison is among those who had not received train-ing. In that specification, visible minority immigrants receiving training had

lower wage growth than white immigrants, but better wage growth thannative-born whites.

Discussion

Human capital should not be viewed as a simple function of years ofexperience and level of education (even were the two factors typically meas-ured adequately). Employers deliberately upgrade human capital through

the training process. The data that has been available for almost all analysesof the pay determination of visible minority immigrantsanalyses upon



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TABLE 7

Predicted Growth in Earnings by Training Fo


OJT Classroom

No Yes No Yes 0 1

White Native 0.0148 0.0206 0.0160 0.0171 0.0138 0.0101 WhiteImm 0.0164 0.0587 0.0177 0.0398 0.0208 0.0268

VisMinImm 0.0397 0.0491 0.0384 0.0511 0.0410 0.0425


OJT Classroom

No Yes No Yes 0 1

White Native 0.0208 0.0141 0.0155 0.0243 0.0196 0.0187 WhiteImm 0.0161 0.0220 0.0196 0.0103 0.0115 0.0058

VisMinImm 0.0040 0.0068 0.0013 0.0070 0.0061 0.0057 nGenerated from equations in the first panel of Table 6.nnGenerated from equations in the second panel of Table 6.


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that it is useful to further explore this issue by incorporating the trainingprocess within it.

These are our main findings.

1. In aggregate, immigrants, whatever their race, get less training than thenative-born. Visible minority immigrants get the least.

2. This disadvantage in access to training falls considerably for those withcollege or university education. In fact, both the probability of class-room training, and the number of courses taken, are greater for visibleminority immigrants with a university education than for eitherequivalent white category.

3. As in other studies, controlling for human capital, in 1999 visibleminority immigrants earned less than their white counterparts. Their

wage disadvantage originates in lower returns to education and expe-rience. The cross-sectional analysis, however, provides no evidence of alower return to training.

4. In the cross-sectional analysis, controlling for industry, occupation,and workplace eliminates the earnings disadvantage of visible minorityimmigrants.

5. Panel analysis of wage growth indicates that, once employed, visibleminority immigrants pay tended to rise faster than was the case fornative-born whites.

6. The payoff to training for visible minority immigrants was as large orlarger than it was for native-born whites.

What is to be made of all this?Research on earnings by race and immigration status has largely been

animated by an interest in discrimination. For the most part, it finds dif-ferences consistent with discrimination. Two distinctions are useful inthinking about discrimination against visible minority immigrants, distinc-tions that are sometimes muddled together. First, there is the point at which

discrimination takes place. Employers may be reluctant to hire members ofvisible minorities in the first place. There can also be a tendency to dis-advantage them once hired with smaller pay increases and a reduced like-lihood of promotion. Second, there are the reasons for discrimination.Employers may discriminate on the basis oftaste. That is to say, they maymake decisions on grounds that are unrelated to a job candidates capacity todo a job. Or, discrimination may take the form of an unwarranted depre-ciation of the human capital acquired by immigrants in their countries oforigin, perhaps because of lack of information about the education and

experience that produced the human capital.13

This latter form of discrim-ination has been a particular focus of attention in Canadian research on the



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subject. Neither of these distinctions is mutually exclusive. There can bediscrimination both before and after hiring and there can be discriminationbecause of both taste and poor information. Still, for the purposes of whatfollows, these are useful distinctions.

Consider the findings from our research on training and earnings in lightof them. There areresults that are consistent with discrimination. Trainingincreases pay but visible minority immigrants are less likely to be trainedthan their native-born white counterparts with apparently equivalent levelsof human capital. As in other studies, visible minority immigrants earn lessthan their white counterparts with apparently similar education and expe-rience. Their human capital is depreciated. Those two facts are consistentwith discrimination. Lower pay after controls for human capital is consistentwith discrimination at either the point of hire or in subsequent pay and

promotion decisions. It is also consistent with discrimination on the basis oftaste or lack of information. On the other hand, unless we assume that thereis little training after the point of hire, lesser provision of training to visibleminority immigrants suggests some discrimination subsequent to employ-ment. If discrimination takes place after the point of hire, employer igno-rance of true human capital becomes a less plausible explanation fordifferential outcomes. Poor post hire treatment of visible minority immi-grant employees looks more like discrimination on the basis of taste. Still,much training is concentrated at the point of hire so, with respect to the

taste/ignorance variants of the discrimination argument, the evidence issomewhat equivocal.

Our other results pose some problems for at least some discriminationaccounts. The training disadvantage of visible minority immigrants tends todisappear for those with postsecondary education. Once trained, pay pre-miums are similar across the three IVOI categories (in both the cross-sectional and panel analyses).Ifemployers (or their agents) were inclined todiscriminate on the basis oftaste, why would they limit their actions to theless well educated and fail to extend their discriminatory actions to the pay

and promotion decisions that follow training? Also, ifemployers discrim-inate on the basis of taste, why is there a significant positive effect of visibleminority immigrant status on pay growth? It is difficult to reconcile theseresults witheitherdiscrimination after hiringordiscrimination on the basisof taste.

Visible minority immigrantsarehired into industries, occupations, work-places, and jobs that are less well paid (on average) than those of their whitecounterparts. This is linked to the depreciation by employers of their humancapital. The core question is, then, does the lower rate of return to human

capital of visible minority immigrants indicate discrimination?The answer depends on the character of the signal given by certification



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why positions with substantial training do not have significantly lowerstarting wages.

It is clear that different jobs imply radically different training experiences.

The cost associated with that training means that prudent employers wouldseek to avoid the risk of loss implied by hiring a candidate with hard-to-interpret credentials. For visible minority immigrant job candidates thisanxiety is compounded by the chance that the disjunction between qual-ification and competence may be very large indeed. This would help explainthe difficulties confronted by visible minority immigrants in generating re-turns to their human capital comparable to those achieved by whites,whether native-born or not.

Conclusion

Several methods have been used to examine possible discrimination in thelabor market (Darity and Mason, 2004). Even within the current legalcontext, help-wanted advertisements in newspapers may signal ethnic pref-erences, albeit subtly. Audit studies compare labor market outcomes for jobcandidates matched as far as possible except in race. There is the largevolume of statistical work using one or another form of the wage-gap

decomposition method. This latter method is the concern of this article.We argue that it is difficult to apply because it requires accurate, or at leastunbiased, measurement of human capital. Such measurement is usually notavailable in the standard data sets used. It may not be available in any largeand general data set.

It follows from this that the idea of a precise estimate of the quantity ofdiscrimination is probably a chimera. Still, we can make plausible inferencesabout the quantity of discrimination in the labor market by looking in detailat the correlates of labor market outcomes. Most relevant research does

contain evidence consistent with a discrimination account. Most impor-tantly, most studies, including this one, report negative main effects ofnonwhite status on pay. However, there is also a common finding that isanomalous for the discrimination account of visible minority immigrantearnings: the negative main effect is substantially reduced or sometimeseliminated altogether where an immigrants education is completedwithinhis or her host country. This is strong evidence that, in employer decisions,the signal provided by some overseas educations is as, or more, importantthan visible minority status.

By introducing into the analysis access, and payoff, to training we are ableto shed further light on the likely motivations of employers, which are at the



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growth, they seem, in fact, to do a bit better. The pay disadvantage of visibleminority immigrants comes from their initial jobs. Employer skepticismabout the quality of their human capital probably explains much of thisdisadvantage, a skepticism that is likely, in part at least, to be well founded.

However, once employers have improved information about the work ca-pacities of visible minority immigrants that is produced by employmentwithinin this caseCanada, they do not discriminate. This, we wouldargue, is reasonably close to the Becker/Sowell view of the outcomes ofcompetitive labor market functioning. It might even be thought of as reasonfor (very cautious) celebration!

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wages of immigrant males

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