economics of imigration

8/18/2019 Economics of Imigration

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Journal of Economic L i ter atu r e, Vol. XXXI I (December 1994 )Bor jas: The Economics of Immigration Journal of Economic L it eratur e Vol. XXXI I (December 1994 ), pp. 1667–1717

The Economics of Immigration

By GEORGE J . BORJAS Univer sit y of Cali fornia at San Di ego

and Nati onal Bureau of Economic Research

I am gratefu l t o Jul ian Betts, Daniel Hamermesh, James Rauch,

and Stephen Trejo for usefu l comment s, and to the Nati onal Sci -

ence Foundati on for r esearch suppor t.

1. Introduction

THERE HAS BEEN a resurgence of im-migration in the United States and in

many other countries. The United Na-tions estimates that over 60 million peo-ple, or 1.2 percent of the world’spopulation, now reside in a countrywhere they were not born (United Na-

tions 1989, p. 61). Although most immi-grants choose a “traditional” destination(over half typically go to the UnitedStates, Canada, or Australia), many othercountries are receiving relatively largeimmigrant flows. Nearly 11 percent of the population in France, 17 percent inSwitzerland, and 9 percent in the UnitedKingdom is foreign-born. Even Japan,which is thought of as being very homo-geneous and geographically immune to

immigrants, now reports major problemswith illegal immigration.

As a result of these changes in the “im-migration market,” the impact of immi-gration on the host economy is now be-ing debated heatedly in many countries. The political discussion is centeredaround three substantive questions.First, how do immigrants perform in thehost country’s economy? Second, what

impact do immigrants have on the em-

ployment opportunities of natives? Fi-nally, which immigration policy mostbenefits the host country?

The policy significance of these ques-tions is evident. For example, immi-grants who have high levels of productiv-ity and who adapt rapidly to conditionsin the host country’s labor market canmake a significant contribution to eco-

nomic growth. Natives need not be con-cerned about the possibility that theseimmigrants will increase expenditures onsocial assistance programs. Conversely, if immigrants lack the skills that employersdemand and find it difficult to adapt, im-migration may significantly increase thecosts associated with income mainte-nance programs as well as exacerbate theethnic wage differentials already in exist-ence in the host country.

Similarly, the debate over immigrationpolicy has long been fueled by the wide-spread perception that “immigranthordes” have an adverse effect on theemployment opportunities of natives.Which native workers are most adverselyaffected by immigration, and how largeis the decline in the native wage?

Finally, there is great diversity in im-migration policies across countries. Some

countries, such as the United States,1667


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award entry visas mainly to applicantswho have relatives already residing inthe country. Other countries, such asAustralia and Canada, award visas to per-sons who have a desirable set of socio-economic characteristics, and still other

countries, such as Germany, encouragedthe migration of “temporary” guest work-ers in the 1960s, only to find that thetemporary migrants became a permanentpart of the German population. Thechoice of the “right” immigration policycan obviously have a significant impacton economic activity both in the shortrun and in the long run.

The past decade witnessed an explo-sion in research on many aspects of the

economics of immigration. This litera-ture is motivated mainly by the variouspolicy concerns and provides valuable in-sights into all these issues. This paperdoes not attempt to provide an encyclo-pedic summary of the empirical resultsin the literature; instead, it surveys thethemes and lessons suggested by the on-going research. Perhaps the most impor-tant theme is that an assessment of the

economic impact of immigration re-

quires an understanding of the factorsthat motivate persons in the sourcecountries to emigrate and of the eco-nomic consequences of pursuing particu-lar immigration policies. As a result, themost important lesson is that the eco-

nomic impact of immigration will vary bytime and by place, and can be eitherbeneficial or harmful. Although the dis-cussion focuses on the experience of theUnited States (simply because most stud-ies in the literature use data drawn fromthe U.S. decennial Censuses), we willsee that much can be learned by compar-ing the U.S. experience to that of otherhost countries.

2. Immigrati on to the Uni ted States: ABr ief H istory

As Table 1 shows, the size of the immi-grant flow has fluctuated dramaticallyduring the past century. The First GreatMigration occurred between 1881 and1924, when 25.8 million persons enteredthe country. Reacting to the increase inimmigration and to the widespread per-

ception that the “new” immigrants dif-

TABLE 1LEGAL IMMIGRANT FLOW TO THE UNITED S TATES 1881–1990

DecadeImmigrant Flow

(in 1000s)

Immigrant Flow asPercentage of Change

in Population

Percentage of Populationthat is Foreign-Born at

End of Decade

1881–1890 5,246.6 41.0 14.71891–1900 3,687.6 28.3 13.61901–1910 8,795.4 53.9 14.61911–1920 5,735.8 40.8 13.21921–1930 4,107.2 24.6 11.61931–1940 528.4 5.9 8.81941–1950 1,035.0 5.3 6.91951–1960 2,515.5 8.7 5.41961–1970 3,321.7 13.7 4.71971–1980 4,493.3 20.7 6.21981–1990 7,338.1 33.1 7.9

Sour ces : U.S. Department of Justice. Immigration and Naturalization Service (1993, p. 25); U.S. Department of Commerce. Bureau of the Census (1975, pp. 8, 14; 1993b, p. 50).

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fered from the old, Congress closed thefloodgates in the 1920s by enacting thenational-origins quota system. This sys-tem restricted the annual flow fromEastern Hemisphere countries to

150,000 immigrants, and allocated the vi-sas according to the ethnic compositionof the U.S. population in 1920. As aresult, 60 percent of all available visaswere awarded to applicants from twocountries, Germany and the UnitedKingdom.

During the 1930s, only .5 million im-migrants entered the United States.Since then, the number of legal immi-grants has increased at the rate of about

one million per decade, and is now near-ing the historic levels reached in theearly 1900s. By 1993, nearly 800,000 per-sons were being admitted annually. There has also been a steady increase inthe number of illegal aliens. Demo-graphic studies conclude that around twoto three million persons were illegallypresent in the United States in the late1980s, and that the net flow of illegal

aliens is on the order of 200,000 to300,000 persons per year (U.S. GeneralAccounting Office 1993).

Table 1 also illustrates that the size of the immigrant flow has increased notonly in absolute terms, but also as a per-centage of population growth. In fact,the contribution of the Second Great Mi-gration to population growth is fast ap-proaching the level reached during theFirst Great Migration, when immigrationaccounted for 40 to 50 percent of thechange in population. As a result of thesetrends, the fraction of the populationthat is foreign-born rose from 4.7 to 7.9percent between 1970 and 1990.

The huge increase in immigration inrecent decades can be attributable partlyto changes in U.S. immigration policy.Prior to 1965, immigration was guided bythe national-origins quota system. The

1965 Amendments to the Immigration

and Nationality Act (and subsequent re-visions) repealed the national origin re-strictions, increased the number of avail-able visas, and made family ties to U.S.residents the key factor that determines

whether an applicant is admitted into thecountry. As a consequence of both the1965 Amendments and of major changesin economic and political conditions inthe source countries relative to theUnited States, the national origin mix of the immigrant flow changed substantiallyin the past few decades. As Table 2shows, over two-thirds of the legal immi-grants admitted during the 1950s origi-nated in Europe or Canada, 25 percent

originated in Western Hemisphere coun-tries other than Canada, and only 6 per-cent originated in Asia. By the 1980s,only 13 percent of the immigrants origi-nated in Europe or Canada, 47 percentin Western Hemisphere countries otherthan Canada, and an additional 37 per-cent originated in Asia.

In recent years, the debate over immi-gration policy led to the enactment of

two major pieces of legislation. Fueledby charges that illegal aliens were over-running the country, Congress enactedthe 1986 Immigration Reform and Con-trol Act (IRCA). This legislation gaveamnesty to three million illegal aliensand introduced a system of employersanctions designed to stem the flow of additional illegal workers.1 The 1990 Im-migration Act permits the entry of an ad-ditional 150,000 legal immigrants annu-ally. The legislated increase in the size of the immigrant flow makes it likely thatthe United States will admit a recordnumber of immigrants during the 1990s.

1 In 1986, the Border Patrol apprehended 1.8million illegal aliens. Although the number of an-nual apprehensions declined to about one millionfollowing the enactment of IRCA, they are nowback up to about 1.3 million, or 2.5 apprehensionsper minute (U.S. Department of Justice. I mmigra-

tion and Naturalization Service 1993, p. 156).

Bor jas: The Economics of I mmigrati on 1669


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TABLE 2NATIONAL ORIGIN COMPOSITION OF LEGAL IMMIGRANT FLOW TO UNITED S TATES,1931–1990

1931–40 1941–50 1951–60 1961–70 1971–80 1981–90

Number of Immigrants (in 1000s)

All Countries 528.4 1035.0 2515.5 3321.7 4493.3 7338.1

Europe 347.6 621.1 1325.7 1123.5 800.4 761.6 Germany 114.1 226.6 477.8 190.8 74.4 92.0 Greece 9.1 9.0 47.6 86.0 92.4 38.4 Ireland 11.0 19.8 48.4 33.0 11.5 32.0 Italy 68.0 57.7 185.5 214.1 129.4 67.3 Poland 17.0 7.6 10.0 53.5 37.2 83.3 United Kingdom 31.6 139.3 202.8 213.8 137.4 159.2

Asia 16.6 37.0 153.2 427.6 1588.2 2738.2 China 4.9 16.7 9.7 34.8 124.3 346.7 India 0.5 1.4 3.4 10.3 164.1 250.8 Iran 0 0.5 25.5 29.6 45.1 116.2 Japan 1.9 1.6 46.3 40.0 49.8 47.1 Korea 0 0.1 6.2 34.5 267.6 333.7 Philippines 0.5 4.7 19.3 98.4 355.0 548.8 Vietnam 0 0 0.3 4.3 172.8 280.8

America 160.0 354.8 996.9 1716.4 1982.7 3615.2 Canada 108.5 171.7 378.0 413.3 169.9 156.9 Mexico 22.3 60.6 299.8 453.9 640.3 1655.8 Cuba 9.6 26.3 78.9 208.5 264.9 144.6 Dominican Republic 1.2 5.6 9.9 93.3 148.1 252.0 Haiti 0.2 0.9 4.4 34.5 56.3 138.4

Africa 1.8 7.4 14.1 29.0 80.8 176.9

Oceania 2.5 14.6 13.0 25.1 41.2 45.2Percentage Distribution

Europe 65.8 60.0 52.7 33.8 17.8 10.4 Germany 21.6 21.9 19.0 5.7 1.7 1.3 Greece 1.7 .9 1.9 2.6 2.1 .5 Ireland 2.1 1.9 1.9 1.0 .3 .4 Italy 12.9 5.6 7.4 6.4 2.9 .9 Poland 3.2 .7 .4 1.6 .8 1.1 United Kingdom 6.0 13.5 8.1 6.4 3.1 2.2

Asia 3.1 3.6 6.1 12.9 35.3 37.3 China .9 1.6 .4 1.0 2.8 4.7 India .1 .1 .1 .3 3.7 3.4 Iran .0 .0 1.0 .9 1.0 1.6 Japan .4 .2 1.8 1.2 1.1 .6

Korea .0 .0 .2 1.0 6.0 4.5 Philippines .1 .5 .8 3.0 7.9 7.5 Vietnam .0 .0 .0 .1 3.8 3.8

America 30.3 34.3 39.6 51.7 44.1 49.3 Canada 20.5 16.6 15.0 12.4 3.8 2.1 Mexico 4.2 5.9 11.9 13.7 14.3 22.6 Cuba 1.8 2.5 3.1 6.3 5.9 2.0 Dominican Republic .2 .5 .4 2.8 3.3 3.4 Haiti .0 .1 .2 1.0 1.3 1.9

Africa .3 .7 .6 .9 1.8 2.4

Oceania .5 1.4 .5 .8 .9 .6

Source : U.S. Department of Justice. Immigration and Naturalization Service (1993, pp. 27–28).


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3. How Do I mmigrants Per form in the Host Countr y?

Many studies in the modern economicliterature on immigration focus on deter-

mining the trends in the skill level andearnings of the immigrant population inthe host country.2 These studies view thelabor market performance of immigrantsin the host country as a measure of theimmigrant contribution to the economy’sskill endowment and productivity. In ad-dition, the trends in immigrant skillshelp determine the impact of immigra-tion on the employment opportunities of native-born workers and on expenditures

in social insurance programs.

A. Aging and Cohort Ef fects

The pioneering work of BarryChiswick (1978) and Geoffrey Carliner(1980) analyzed how immigrant skillsadapted to the host country’s labor mar-ket by estimating the cross-section re-gression model:

logw i = X i φ + δA i + γ o I i + γ 1y i + εi , (1)where w i is worker i ’s wage rate; X i is avector of socioeconomic characteristicswhich might include education and re-gion of residence; A i gives the worker’sage or potential labor market experience;I i is a dummy variable indicating if theworker is an immigrant; and y i gives thenumber of years an immigrant workerhas resided in the United States (and is

set to zero for native-born workers). Inpractice, the model typically includeshigher-order polynomials in age andyears-since-migration, and the coeffi-cient vector (φ,δ) is allowed to vary be-tween immigrants and natives. For sim-

plicity, we restrict the discussion to thesimpler specification.

The coefficient γ 0 gives the percentagewage differential between immigrantsand natives at the time of arrival, while

the coefficient γ 1 gives the rate at whichthe earnings of immigrants rise relativeto the earnings of natives. The earlystudies of wage determination among im-migrant and native men in the UnitedStates reached a quick consensus: the co-efficient γ 0 was negative and the coeffi-cient γ 1 was positive.3 The essence of theresults is summarized in Figure 1, whichillustrates the predicted immigrant andnative age-earnings profiles implied by

Chiswick’s analysis of the 1970 Census.At the time of arrival, immigrants earnabout 17 percent less than natives. Be-cause immigrants experience faster wagegrowth, immigrant earnings “overtake”native earnings within 15 years after arri-val. After 30 years in the United States,the typical immigrant earns about 11percent more than a comparable nativeworker.

Two distinct arguments were used toexplain these results. At the time of arri-val, immigrants earn less than natives be-cause they lack the U.S.-specific skillsthat are rewarded in the American labormarket (such as English proficiency). Asthese skills are acquired, the humancapital stock of immigrants grows rela-tive to that of natives, and immigrantsexperience faster wage growth. The hu-

2 These questions are not restricted to the mod-ern literature. Paul Douglas (1919), for example,analyzed the occupational distribution of immi-grants who arrived during the First Great Migra-tion to determine if the newer immigrants were as

skilled as the old.

3 There is a widespread, though erroneous, per-ception that studies based on cross-section datafrom other countries and other time periods reachsimilar conclusions. However, Chiswick’s (1980)study of immigrants in Britain reports that years-since-migration has no impact on immigrant earn-ings. Similarly, both Francine Blau (1979) andBarry Eichengreen and Henry Gemery (1986) ana-lyze the economic mobility of immigrants who en-tered the United States at the turn of the 20thcentury, but reach conflicting conclusions. Blaufinds wage convergence between immigrants andnatives, while Eichengreen and Gemery find little

wage convergence between the two groups.



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man capital investment hypothesis, how-ever, does not by itself generate an over-taking point. After all, why would immi-grants accumulate more human capitalthan natives? The overtaking point was

instead interpreted in terms of a selec-tion argument: immigrants are “moreable and more highly motivated” thannatives (Chiswick 1978, p. 900), and im-migrants “choose to work longer andharder than nonmigrants” (Carliner1980, p. 89). This assumption was typi-cally justified by arguing that only themost driven and most able persons havethe ambition and wherewithal to packup, move, and start life anew in a foreign

country. The optimistic appraisal of immigrant

adjustment implied by the results sum-marized in Figure 1 was challenged byBorjas (1985), who argued that the posi-tive cross-section correlation betweenthe relative wage of immigrants andyears-since-migration need not indicatethat the wage of immigrants converges tothat of natives. The basic problem with

the “assimilationist” interpretation of the

regression in (1) is that it draws infer-ences about how the earnings of immi-grant workers evolve over time from asingle snapshot of the immigrant popula-tion. I t might be the case, however, thatnewly arrived immigrants are inherentlydifferent from those who migratedtwenty years ago. Hence we cannot usethe current labor market experiences of

those who arrived twenty years ago toforecast the future earnings of newly ar-rived immigrants.

Figure 2 illustrates the implications of this alternative hypothesis. For concrete-ness, consider a situation where thereare three separate immigrant waves, onewave arrived in 1950, the second in1970, and the last in 1990. Assume thatimmigrants enter the United States atage 20. The earliest cohort is assumed to

have the highest productivity level of anygroup in the population, including U.S.-born workers. I f we could observe theirearnings in every year after they arrive inthe United States, their age-earningsprofile would be given by the line PP inthe figure. Let’s also assume that the lastimmigrant wave (i.e., the 1990 arrivals) isthe least productive of any group in thepopulation. Their age-earnings profile is

given by the line RR in the figure. Fi-

Log Earningsin 1970

9.1

9.0

8.9

8.8

8.7

8.6

8.5

8.4

of Immigrants and Natives in theUnited States, 1970

Source : Chiswick (1978, Table 2, Column 3). All thevariables in the regression are evaluated at themeans of the immigrant sample, and immigrantsare assumed to enter the United States at age 20.

Figure 1. The Cross-Section Age-Earnings Profiles

20 25 30 35 40 45 50 55 60 65

Natives

Immigrants

Age

Wage

P

Age-Earnings Profile of ImmigrantsFigure 2. Cohort Effects and the Cross-Section

20 40 60

Q

R

C

1950 Cohort

1970 Cohortand Natives

1990 Cohort

Age

P

Q

R

C



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Each Census cross-section shows thatimmigrants who have been in the UnitedStates for several decades have higherwages than natives, while more recent

arrivals have lower wages. In 1990, forexample, immigrants who arrived in theUnited States between 1950 and 1960earned 19.6 percent more than natives,while immigrants who arrived between1985 and 1989 earned 31.7 percent less. The data, however, also support the hy-pothesis that there exist cohort effects inthe foreign-born population, with morerecent immigrant cohorts having rela-tively lower wage rates. For example, themost recent cohort enumerated in the1970 Census (i.e., the 1965–1969 arri-vals) earned only 16.6 percent less thannatives in 1970; the wage gap betweenthe most recent arrivals and natives grew

to 27.6 percent by 1980, and to 31.7 per-cent by 1990.5

Because of these cohort effects, thecross-section relationship between therelative wage of immigrants and years-

since-migration overestimates the wagegrowth actually experienced by a particu-lar cohort. The 1990 cross-section sug-gests that over a 20-year period (1970 to1990), the relative earnings of immi-grants grow by about 33 percentagepoints.6 In fact, the relative wage of the1965-1969 wave increased by only 18percentage points over the 20-year pe-riod, or about half of the cross-sectionrate of convergence.

The implications of the data summa-rized in Table 3 are clear and provoca-tive. I f we interpret the difference inwages between immigrants and nativesas a measure of relative skills, more re-cent immigrant waves are relatively lessskilled than earlier waves. Moreover, im-migrant wage growth is more sluggishthan suggested by the early cross-sectionstudies. I t is extremely unlikely that the

earnings of more recent cohorts will everreach parity with (let alone overtake) theearnings of natives.

observations. The percent wage differential be-tween immigrants and natives equals 100(e x − 1),where x is the difference in average log wages be-tween the groups. See Borjas (forthcoming) for amore detailed discussion of the data and of the

trends in immigrant earnings.

TABLE 3 PERCENTAGE WAGE DIFFERENTIAL BETWEEN

IMMIGRANT AND NATIVE MEN, 1970–1990

Group: 1970 1980 1990

All Immigrants .9 −9.2 −15.2Cohort: 1985–1989 Arrivals — — −31.7 1980–1984 Arrivals — — −27.8 1975–1979 Arrivals — −27.6 −17.8 1970–1974 Arrivals — −18.9 −9.3 1965–1969 Arrivals −16.6 -7.8 1.1 1960–1964 Arrivals −4.4 .1 9.0 1950–1959 Arrivals 5.6 5.7 19.6 Pre-1950 Arrivals 10.3 10.6 26.2

Sour ce: Author’s tabulations from the 1970, 1980, and

1990 Public Use Samples of the U.S. Census. Thestatistics are calculated in the subsample of menaged 25–64 who work in the civilian sector, who arenot self-employed, and who do not reside in groupquarters.

5 These results differ slightly from those re-ported by Edward Funkhouser and Trejo (forth-coming), who use CPS data from various supple-ments to describe the trend in immigrant skillsduring the 1980s. The CPS data indicate that thedecline in relative skills was reversed somewhat bythe late 1980s. The CPS, however, contains rela-tively small samples of immigrants. I n addition,

the national origin composition of immigrant co-horts is extremely unstable across CPS surveys.For instance, 21 percent of the cohort that immi-grated between 1982 and 1984 in the June 1988CPS is of Mexican origin, while the respective sta-tistic for th e same cohort in the November 1989CPS is 37 percent. These statistics suggest that thechange in the relative immigrant wage across theCurrent Population Surveys provides unreliablemeasures of both cohort effects and of the rate of wage convergence.

6 This statistic is calculated by comparing therelative wage of the immigrants who arrived in thelate 1980s with the relative wage of the immi-

grants who arrived in the late 1960s.



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Needless to say, these findings havegenerated a great deal of controversyand debate. (See, for example, Chiswick1986; Harriet Orcutt Duleep and MarkRegets 1992b; Robert LaLonde and

Robert Topel 1992; and Andrew Yuengert 1994.) Many of these studies(including the original work of Borjas,1985) point out that interpreting the in-tercensal trend in the relative wage of immigrants as a measure of relativechanges in skills implicitly assumes thatperiod effects influence the wage of im-migrants and natives by the same relativeamount. To see this point formally, con-sider the following generic model that

characterizes the analytical frameworknow used in the literature. Suppose wepool all the data in two cross-sections(such as the 1980 and 1990 Censuses)and estimate the regression equations:

logw i j = X j φi + δi A j + αy j + βC j + γ i π j + εi j , (2)

logw nl = X l φn + δn A l + γ n πl + εnl , (3)

where w i j gives the wage of immigrant j ;w nl gives the wage of native l ; X gives avector of standardizing socioeconomiccharacteristics; A gives the worker’s ageat the time of the Census; y gives thenumber of years that the immigrant hasresided in the United States; C is the cal-endar year of arrival in the UnitedStates; and π is a dummy variable in-dicating if the observation was drawnfrom the 1990 Census. To easily illus-

trate the identification problem, theage, years-since-migration, and calendaryear-of-arrival variables are entered lin-early.

The coefficients γ i and γ n give the pe-riod effects for immigrants and natives,respectively. The coefficient δn gives theaging effect for natives; the rate at whichnative earnings increase over the life cy-cle. The respective aging effect for im-

migrants is given by δi + α. The age-

earnings profiles of immigrants and na-tives converge if (δi + α) > δn (assumingimmigrants earn less than natives at thetime of arrival).7 Finally, the coefficientβ measures the cohort effect, the rate of

change in the entry wage across immi-grant cohorts.8 I t is well known that the key parame-

ters of the regression model in equations(2) and (3) are not identified. The years-since-migration variable is a linear com-bination of the period effect and the co-hort variable:

y i ≡ πi (1990 − C i ) + (1 − πi ) (1980− C i ) = 1980 − C i + 10π. (4)

In order to identify the period effects,the aging effects, and the cohort effect,therefore, a restriction must be imposedon the model. One possible restriction isthat the period effects are the same forimmigrants and natives, or:

γ i = γ n . (5)

Equation (5) implies that the relative

wage of immigrants and natives is inde-pendent of secular changes in the wagelevel. We implicitly imposed this restric-tion on the data when we interpreted theintercensal trends in Table 3 as changesin the relative skills of immigrants. Bynetting out the secular trend in the na-tive wage (i.e., by using a difference-in-differences estimator), we are simply leftwith the trend in immigrant productivity.Note, however, that the wage is the

product of the rate of return to skillstimes the worker’s human capital stock.

7 Although the regression model in (2) assumesthat the aging effect is the same for all immigrantcohorts, many of the empirical studies in the lit-erature relax this assumption.

8 The model assumes that there are no cohorteffects in the native population (perhaps due tochanges in the quality of education). E ven thoughthis is a standard assumption in the literature, theestimated cohort effects in the immigrant popula-tion may be sensitive to the existence of cohort

effects among native workers.



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I f period effects influence the price of skills differently for immigrants and na-tives, the intercensal change in relative

wages could be reflecting differences inprices rather than differences in humancapital.

There were historic changes in theU.S. wage structure during the 1980sand these changes did not affect all skillgroups equally (Frank Levy and RichardMurnane 1992). I n particular, there wasa sizable increase in the wage gap be-tween highly educated and less educatedworkers; and among workers within nar-

rowly defined occupation and industrycells. I t is unlikely that these changes inthe wage structure affected the earningsof immigrant and native workers by thesame percentage amount. The immigrantpopulation in the United States is rela-tively unskilled (at least in terms of edu-cational attainment). Because the rate of return to skills increased during the1980s, the relative wage of immigrants

would have fallen between 1980 and1990 even if immigrant skills had remained constant . I n other words, the

changes in the wage structure could ac-count for both the observed decline inthe relative wage of successive immi-grant cohorts and for the sluggish wagegrowth experienced by a particular co-hort as it entered the 1980s.

I t is unlikely, however, that changes inthe wage structure account for the down-ward trend in relative wages across suc-cessive immigrant cohorts or for the slowwage convergence between immigrants

and natives. Consider the trends in im-migrant educational attainment, a skillmeasure that is invariant to changes inthe wage structure. Table 4 documentsthe changes in the schooling distributionof immigrants and natives in the past twodecades. I n 1970, 39.6 percent of nativeswere high school dropouts; by 1990, only14.8 percent of natives lacked a highschool diploma. Among immigrants, 48.2

TABLE 4EDUCATIONAL A TTAINMENT OF IMMIGRANT AND NATIVE MEN, 1970–1990

1970 1980 1990

Group

Percent

High School Dropouts

Percent

CollegeGraduates

Percent


Percent

College Graduates

Percent


Percent

College Graduates

Natives 39.6 15.4 23.1 22.9 14.8 26.6Immigrants 48.2 18.9 37.4 25.3 36.9 26.6

Cohort: 1985–89 Arrivals — — — — 35.2 31.5 1980–84 Arrivals — — — — 40.4 24.1 1975–79 Arrivals — — 36.2 30.4 42.2 24.8 1970–74 Arrivals — — 44.0 24.9 42.7 24.1 1965–69 Arrivals 45.2 28.3 41.6 24.7 34.1 26.2 1960–64 Arrivals 44.8 21.1 34.7 24.8 27.5 27.9

1950–59 Arrivals 47.4 17.1 31.4 23.7 25.9 27.8 Pre-1950 Arrivals 51.7 15.0 35.3 21.6 25.2 31.8

Sour ce: Author’s tabulations from the 1970, 1980, and 1990 Public Use Samples of the U.S. Census. The statisticsare calculated in the subsample of men aged 25–64 who work in the civilian sector, who are not self-employed, andwho do not reside in group quarters.

1 LINE SHORT



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percent were dropouts in 1970, 37.4 per-cent in 1980, and 36.9 percent in 1990.Relative to natives, immigrants wereabout 21.7 percent more likely to behigh school dropouts in 1970, but are

now more than twice as likely to be highschool dropouts.Moreover, even though the percentage

of immigrant workers who are collegegraduates rose during the period, thepercentage of natives who are collegegraduates rose even faster. Immigrantswere more likely to be college gradu-ates in 1970 (18.9% for immigrants ascompared to 15.4% for natives). By1990, both groups had exactly the same

probability of being college graduates(26.6%). Therefore, changes in the“quantity” of immigrants’ human capitalare partly responsible for the decline inthe relative immigrant wage.

I t is also easy to show that changes inthe U.S. wage structure were not suffi-ciently large to account for a sizable partof the declining relative wage of immi-grants across successive waves. For ex-

ample, we know that the wage structurechanged in different ways for variousage-education groups, with groups withmore education and experience havinglarger wage growth between 1970 and1990. We can then use the wage growthobserved in 56 age-education cellsamong native workers to “deflate” thewage growth of immigrants in the sameage-education cells.9 To take into ac-count changes in wage inequality even

within age and education cells, LaLondeand Topel (1992) suggest using a defla-tor based on an immigrant’s ranking inthe native wage distribution. I f all work-ers who fall in the p th percentile of the

wage distribution are equally skilled,then we can use the wage growth experi-enced by natives in the p th percentile todeflate the wage growth of immigrantswho fall in the same percentile in the1970–1990 period.10

Table 5 reports the changes in the de-flated relative wage of immigrants be-tween 1970 and 1990. Regardless of which deflator is used, more recent im-migrant cohorts have substantially lower

relative wages than earlier cohorts. Themost recent cohort in 1970 earned 16.6percent less than natives at the time of arrival. The most recent cohort in 1990earned 29.5 percent less than natives if we use the deflator based on age-educa-tion cells, and 29.4 percent less if we usethe percentile deflator. The change inthe wage structure, therefore, accountsfor only 15 percent of the drop in the

relative immigrant wage between 1970and 1990. The cohort and aging effects calcu-

lated from the synthetic cohorts in theCensus data may be biased because thesample composition of a particular immi-grant cohort changes systematicallyacross Censuses. Perhaps one-third of immigrants in the United States eventu-

9 The eight age categories are: 25–29 years old;30–34; 35–39; 40–44; 45–49; 50–54; 55–59; and60–64. The seven education categories are: lessthan 8 years of schooling; 9 years; 10–11 years; 12years; 13–15 years; 16 years; and more than 16years. Define r s (t ) to be the wage growth experi-enced by the typical native worker in age group r and education group s between 1970 and year t (t =1980, 1990). The deflated wage is then given bylogw ̂l ,r s (t ) = log w l ,r s (t ) − ∆r s (t ), where logw l ,r s (t ) isthe log wage of person l in skill group r s in Censusyear t .

10

Neither deflator fully solves the problem of accounting for changes in the wage structure. Theage-education deflator, for example, ignores theincrease in inequality that occurred within age-education cells. The percentile deflator assumesthat immigrants and natives in the p t h percentileare perfect substitutes. This is unlikely to be true.Newly arrived immigrants might place badly in thenative wage ranking not because they are un-skilled, but because they are going through aninitial “testing” period. I n the end, therefore, animmigrant who initially places in the p t h percentilemay have skills that are comparable to those of natives in the (p + q )th percentile, where q > 0.

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ally return to their countries of origin(Robert Warren and Jennifer Peck1980). Suppose that the return migrantsare mainly composed of workers with

lower than average wages (i.e., the “fail-ures”). The intercensal tracking of a par-ticular cohort would reveal an improve-ment in relative wages even if no wageconvergence is taking place. Alterna-tively, if the return migrants are “suc-cesses,” the rate of wage convergencewould be underestimated. Because dataon the size and composition of the returnmigration flow is scarce, few studies sys-tematically analyze the selection mecha-

nism generating the return migrationflow (the limited available evidence isdiscussed in the next section). As a re-sult, the bias introduced by nonrandomreturn migration is typically ignored.

Even if there were no return migra-tion, Rachel Friedberg (1992) and JamesSmith (1992) have shown that the samplecomposition of a particular immigrantcohort changes over time because the

sample of working-aged immigrants in

later Censuses includes a larger numberof persons who migrated as children.11 The economic experiences of “immigrantchildren” may resemble those faced by

native workers. The inclusion of the im-migrant children in later Censuses thusbiases the estimated rate of wage conver-gence upward. A better measure of wageconvergence, therefore, is obtained bytracking a specific immigrant cohort, de-fined in terms of both year-of-migrationand age-at-arrival, across the variousCensuses.

Table 6 summarizes the trend in thepercent wage differential between a par-

ticular group of immigrants and similarlyaged natives, so that immigrants who ar-rived when they were between 25 and 34years old in the late 1960s are compared

TABLE 5PERCENTAGE WAGE DIFFERENTIAL BETWEEN IMMIGRANT, AND NATIVE, MEN, 1970–1990, DEFLATED BY

CHANGES IN WAGE S TRUCTURE

Using Age-Education

Deflator Using Percentile DeflatorGroup: 1970 1980 1990 1980 1990

All Immigrants .9 −9.4 −14.4 −8.6 −13.9

Cohort: 1985–1989 Arrivals — — −29.5 — −29.4 1980–1984 Arrivals — — −25.0 — −25.4 1975–1979 Arrivals — −25.2 −15.8 −26.2 −16.0 1970–1974 Arrivals — −17.5 −8.8 −17.9 -8.3 1965–1969 Arrivals −16.6 −8.2 −.2 −7.2 1.1 1960–1964 Arrivals −4.4 −1.0 6.0 .2 7.9 1950–1959 Arrivals 5.6 3.9 13.1 5.4 17.1

Pre-1950 Arrivals 10.3 4.7 16.0 10.2 23.2


11 An earlier study by Sherrie Kossoudji (1989)used the 1976 Survey of Income and Educationcross-section to estimate models of occupationalmobility which differentiate between persons whomigrated as children and those who migrated asadults. She finds that controlling for age-at-migra-tion leads to flatter occupational mobility profiles

among immigrants than among natives.



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to natives aged 25–34 in 1970, to natives35–44 in 1980, and to natives aged 45–54in 1990. About half of the wage conver-gence implied by the statistics presentedin Table 5 disappears after controllingfor age-at-migration. Consider, for exam-ple, the group of immigrants who arrivedbetween 1965 and 1969 and who were25–34 years old in 1970. They earned12.0 percent less than natives in 1970

and 2.5 percent less in 1990. Over a 20-

year period, therefore, the relative wageof this immigrant cohort increased by 10percentage points, in contrast to the 18percent growth suggested by the inter-censal comparison that does not controlfor age-at-migration and to the 33 per-cent growth implied by the 1990 cross-section.

Table 6 reveals that practically all im-migrants, regardless of when they ar-

rived in the country, experience the

TABLE 6PERCENTAGE WAGE DIFFERENTIAL BETWEEN IMMIGRANTS AND NATIVES, BY AGE GROUP

AND YEAR OF ARRIVAL

Actual Wage Actual Wage

Using Age-Education

DeflatorCohort/Age Group: 1970 1980 1990 1980 1990

1960–1964 Arrivals: 15–24 in 1970 — 1.1 4.2 .9 4.5 25–34 in 1970 3.1 −.3 −.2 .0 .1 35–44 in 1970 −6.0 −6.7 1.1 −6.7 1.4 45–54 in 1970 11.1 −10.8 — −10.9 —

1965–1969 Arrivals: 15–24 in 1970 — −4.6 −6.9 −6.2 −5.5 25–34 in 1970 −12.0 −5.9 −2.5 −5.4 −2.3 35–44 in 1970 −15.9 −15.3 −8.8 −15.5 −8.3

45–54 in 1970 −22.5 −21.1 — −21.6 —1970–1974 Arrivals: 25–34 in 1980 — −11.4 −11.8 −12.5 −10.4 35–44 in 1980 — −17.7 −16.4 −17.1 −15.6 45–54 in 1980 — −26.0 −20.7 −26.4 −20.0

1975–1979 Arrivals: 25–34 in 1980 — −21.3 −15.5 −21.2 −14.8 35–44 in 1980 — −24.9 −24.1 −24.2 −23.4 45–54 in 1980 — −29.8 −26.3 −29.8 −26.1

1980–1984 Arrivals: 25–34 in 1990 — — −18.6 — −18.2 35–44 in 1990 — — −25.3 — −24.5 45–54 in 1990 — — −34.0 — −33.0

1985–1989 Arrivals: 25–34 in 1990 — — −23.0 — −23.5 35–44 in 1990 — — −28.6 — −28.3 45–54 in 1990 — — −36.2 — −35.7




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same sluggish relative wage growth. Thisresult is significant because it suggeststhat more recent immigrant cohorts havenot had faster wage growth despite theirlower starting positions.12 In fact, immi-

grants who arrived during the 1970s ex-perienced the same wage growth asthose who arrived during the 1960s dur-ing their first decade in the UnitedStates. Immigrants who arrived between1975 and 1979 and were around age 30at the time of arrival earned 21.3 percentless than natives in 1980 and 15.5 per-cent less than natives in 1990, an in-crease of only 5.8 percentage points. This wage growth is similar to that expe-

rienced by similarly aged immigrantswho arrived between 1965 and 1969;they earned 12.0 percent less than na-tives in 1970 and 5.9 percent less in1980.

Many studies have confirmed thatthere has been an overall decline in therelative skills of successive immigrant co-horts. For example, Yuengert (1994, p.86) finds that the relative wage of the

immigrants who migrated in the late1960s was about 9 percentage pointslower than the relative wage of thosewho arrived in the 1950s; LaLonde and Topel (1992, p. 89) report a 22 percent-age point drop in the relative wage of immigrants cohorts between the late1960s and the late 1970s; and Funk-houser and Trejo (forthcoming, Table 6)report a 10 percentage point drop duringthe same period. There is also a consen-

sus that much of the decline is due tochanges in “observables.” Both Funk-houser and Trejo (forthcoming, Table 6)and LaLonde and Topel (1992, p. 89)conclude that at least two-thirds of the

decline can be attributed to changes inthe educational attainment of immi-grants relative to natives. Some studiesalso show that the changing national ori-gin mix of the immigrant flow (which ob-viously implies changes in the observableskills of immigrants) accounts for muchof the decline in skills across successivecohorts. This result will be discussed indetail below.

B. Wage Convergence Between Immigr ants and Ethni cally Simi lar Natives

The data summarized in the previoussection describe how the immigrantwage adjusts relative to that of the typi-cal native worker. Because recent immi-grant waves start off at such a disadvan-tage, it is not too surprising that their

earnings fail to reach parity with theearnings of the average U.S.-born worker(who is typically a white person of Euro-pean ancestry). A number of studies thusinvestigate if immigrant earnings con-verge to the earnings of U.S.-born work-ers who share the same ethnic back-ground. These intra-ethnic comparisonscan help assess if the “new immigration”will exacerbate the ethnic differences al-ready prevalent in the U.S. labor market.

There is, however, little consensus onwhether the relative skills of immigrantsdeclined within specific ethnic groups,or on whether the wage of immigrantsconverges to that of ethnically similar na-tives. Most studies typically focus onfour large ethnic groups: Mexican immi-grants, other Hispanic immigrants, Asianimmigrants (excluding the Middle East),and “white” immigrants (defined as per-

12 Duleep and Regets (1992b) use the 1970 and1980 Censuses to estimate correlations betweenwage growth and entry wages across national ori-gin groups. These correlations tend to be negative,leading them to conclude that the low entry wageof the immigrants who arrived in the late 1970sdid not represent their true “quality” because theywould have faster wage growth than earlier immi-grants. The additional data provided by the 1990Census indicates that the less-skilled cohorts whomigrated in the 1970s did not, in fact, experiencefaster wage growth than earlier waves.

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sons originating in Europe or Canada).13 The four native base groups are: Mexi-can-American natives (i.e., U.S.-bornpersons of Mexican ancestry); other His-panic-American natives (all other U.S.-born persons who report being of His-

panic ancestry); Asian-American natives(non-Hispanic persons whose race isAsian); and white natives (non-Hispanicwhites).

Table 7 summarizes the trends in thewage of immigrants in particular cohortsand age groups relative to ethnicallysimilar natives in the same age group, sothat Mexican immigrants aged 25–34 in1970 are contrasted with Mexican-American natives aged 25–34 in 1970,with Mexican-American natives aged 35–

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13 Kristin Butcher (1994) describes the processof wage convergence between black immigrantsand U.S.-born black workers, and finds that thelabor market experience of black immigrants re-sembles that of black natives who had moved outof their state of birth.

TABLE 7PERCENTAGE WAGE DIFFERENTIAL BETWEEN IMMIGRANTS AND NATIVES OF SAME E THNI C BACKGROUND

(Using Age/Education Deflator)

Mexican Other Hispanics Asian White

Cohort/Age Group 1970 1980 1990 1970 1980 1990 1970 1980 1990 1970 1980 19901960–64 Arrivals 15–24 in 1970 — −1.8 −5.1 — 20.3 29.2 — 1.9 .2 — 2.7 10.3 25–34 in 1970 −5.8 −9.6 −16.0 9.0 16.9 19.1 6.9 15.0 11.7 9.5 7.9 10.3 35–44 in 1970 −22.4 −19.7 −14.2 8.0 8.5 13.7 −15.3 4.1 17.1 4.5 3.9 14.3

1965–69 Arrivals 15–24 in 1970 — −11.7 −13.0 — .6 3.1 — 9.0 3.0 — .5 7.1 25–34 in 1970 −26.5 −16.5 −19.5 −15.8 −3.4 .1 −17.6 9.1 8.5 .3 2.9 12.3 35–44 in 1970 −32.5 −23.0 -29.2 −15.9 −9.8 −6.5 −15.2 −13.1 −5.6 −5.4 −6.2 9.4

1970–74 Arrivals 25–34 in 1980 — −19.5 −21.2 — −7.1 −1.0 — 2.7 5.3 — −2.9 8.1

35–44 in 1980 — −23.8 −29.3 — −11.7 −6.8 — −9.9 −3.9 — −6.9 3.01975–79 Arrivals 25–34 in 1980 — −33.8 −29.5 — −21.5 −16.7 — −19.7 −10.2 — −.6 11.7 35–44 in 1980 — −38.3 −36.7 — −22.4 −15.2 — −28.1 −25.8 — −1.8 4.2

1980–84 Arrivals 25–34 in 1990 — — −25.0 — — −19.7 — — −14.9 — — 12.4 35–44 in 1990 — — −39.6 — — −27.3 — — −28.8 — — 10.1

1985–89 Arrivals 25–34 in 1990 — — −33.9 — — −28.2 — — −24.3 — — 4.0 35–44 in 1990 — — −45.1 — — −36.2 — — −30.6 — — −1.2

Percent of ImmigrantPopulation Belongingto Particular EthnicGroup 9.7 18.5 26.2 11.4 13.1 16.1 8.6 16.4 21.7 62.4 36.8 21.5




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44 in 1980, and with Mexican-Americannatives aged 45–54 in 1990. There areinteresting differences in the directionand magnitude of cohort effects acrossthe various groups. The relative wage of

successive waves of Mexican immigrantsdeclined during the past two decades. I n1970, the typical Mexican immigrantaged 25–34 who had just arrived in theUnited States earned 26.5 percent lessthan the typical Mexican-American na-tive; by 1990, the latest wave of Mexicanimmigrants earned 33.9 percent less thantheir native counterparts. Note, how-ever, that the wage gap between Mexicanimmigrants and Mexican-American na-

tives underestimates the “true” economicstatus of Mexican immigrants in theUnited States. After all, Mexican-Ameri-can natives are themselves a relativelydisadvantaged group, earning 16 percentless than the typical U.S.-born worker in1990.

The relative wage of other Hispanicimmigrants and Asian immigrants alsofell across successive cohorts. I n contrast

to these groups, the relative wage of suc-cessive waves of European and Canadianimmigrants rose slightly between 1970and 1990. The most recent “white” arri-vals (aged 25–34 at the time of arrival)earned .3 percent more than white na-tives in 1970, but by 1990 they earned 4percent more.

The raw data thus suggest that somegroups experienced a decline in relativewages across successive cohorts, whilewhite immigrants experienced an in-crease. The data also indicate that mostnon-Asian cohorts experienced a 5 to 10percentage point increase in their rela-tive wage between 1970 and 1990. Forinstance, the Mexican immigrant whohad just arrived in the United States in1970 and was between 25 and 34 yearsold earned 26.5 percent less than thetypical Mexican-American native. By

1990, the wage gap had narrowed by only

7 percentage points. Similarly, the typi-cal white immigrant in the same agegroup who had just arrived in the UnitedStates in 1970 earned .3 percent morethan white natives, and this wage gap

grew to 12.3 percent by 1990. This rateof wage convergence allows white immi-grants to substantially outperform whitenatives after 20 years in the UnitedStates, but prevents Mexican immigrantsfrom reaching wage parity with Mexican-American natives.

Finally, there seems to be a structuralshift in the rate of wage convergence forAsian immigrants who migrated after1970. Asian immigrants who migrated in

the 1960s experienced a very high rate of wage convergence. The typical Asian im-migrant who arrived in the late 1960s(and was 25–34 years old at the time of arrival) earned 17.6 percent less thanAsian-American natives in 1970, andabout 9 percent more in both 1980 and1990. In contrast, a similarly aged Asianimmigrant who arrived in the late 1970searned 19.7 percent less than Asian-

American natives in 1980, and 10.2 per-cent less in 1990. In effect, this later co-hort of Asian immigrants has a rate of wage convergence which is half of thatexperienced by earlier immigrant waves.

Table 7 thus suggests that there is agreat deal of diversity in the economicexperiences of various immigrant groupsin the United States. In view of this di-versity, it is not surprising that there is agreat deal of disagreement in the litera-ture (which is mostly based on compari-sons of the 1970 and 1980 Censuses) asto whether there has been a decline inthe average skill level of successive im-migrant waves within ethnic group, andon whether there is wage convergencewith ethnically similar natives. For exam-ple, Smith (1992, p. 79) concludes thatthere is “very little within-cohort wageassimilation for [Mexican] immigrants

across their labor market careers,” and



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that there is “strong evidence of declin-ing labor market quality.” In contrast,LaLonde and Topel (1992, p. 82) con-clude that Mexican immigrants “showsubstantial assimilation” with “no signifi-

cant evidence of a decline in immigrantquality.” Even more striking, Yuengert’s(1994, p. 86) examination of the samedata reveals an increase in the skill levelof Mexican immigrant cohorts over time,with Mexicans who arrived between 1965and 1969 having 13 percent higher rela-tive earnings than those who arrived inthe 1950s.

There are many differences acrossthese studies which can potentially ex-

plain the disparity in results. Smith, forexample, stresses the importance of con-trolling for age-at-migration when esti-mating the rate of wage convergence, avariable that LaLonde and Topel and Yuengert ignore. In contrast, LaLondeand Topel stress the importance of con-trolling for the impact of changes in thewage structure on the wage of differentskill groups, a factor that Smith ignores.

The data summarized in Table 7 con-trols both for age-at-migration and forchanges in the wage structure, as well asextends the span of time studied by an-other decade (using the 1990 Census).

Although intra-ethnic comparisons arecommon in the literature, there are anumber of conceptual problems in thesestudies that have not been sufficientlyappreciated. Most obvious is the aggre-gation bias introduced by pooling immi-grants from different countries into aparticular “ethnicity” (such as creatingthe Asian group by combining personsfrom countries as diverse as India, Ja-pan, and Vietnam). Because immigrantgroups from different countries differsubstantially, it is doubtful that the com-posite “other Hispanic” or “Asian” re-sembles the average person in any of thenational origin groups making up the

ethnic category. Moreover, there are siz-

able changes in the national origin mix of the immigrant flow over very short timeperiods even within a particular ethnicgroup. As a result, we do not know howto interpret the cohort effects or the

changes in the rate of wage convergenceamong Asians or other Hispanics unlesswe deal directly with a more primitivedefinition of ethnicity (i.e., the one thatcoincides with national origin).

Moreover, the composition of the na-tive base in these broadly defined ethnicgroups is changing systematically overtime. In 1970, for example, there werefew adult Cubans in the other Hispanic-American native sample. By 1990, as the

U.S.-born children of the early Cubanwaves enter the labor market, the wageof the other Hispanic native base ispartly determined by the skill endow-ment of immigrant flows that arrived ageneration earlier. The comparison of Hispanic immigrants to Hispanic-Ameri-can natives in 1970 thus differs funda-mentally from the comparison of His-panic immigrants to Hispanic-American

natives in 1990.Most importantly, there is a sense inwhich these intra-group comparisonsmiss the point. What would we concludeif the relative wage of Mexican immi-grants converged to that of Mexican-American natives, or the relative wage of Asian immigrants converged to that of Asian-American natives? The fact re-mains that the wage of Mexican-Ameri-can natives is itself 16 percent below thatof the typical U.S.-born worker, whilethe wage of Asian-American natives is 12percent above. Intra-group convergenceis not an interesting phenomenon if wewant to identify the groups of nativeworkers who are most likely to be ad-versely affected by immigration, or if weare concerned about the impact of immi-gration policy on poverty rates, on thecosts of welfare programs, and on the

contribution of immigrants to the econ-



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omy’s skill endowment. The costs andbenefits of immigration are more closelyrelated to how immigrants perform rela-tive to the population average than tohow immigrants perform relative to a

nonrandom subset of the population.C. L anguage and the Pr ocess of Wage Convergence

Although there are many estimates of the rate of wage convergence betweenimmigrants and natives, we do not yetunderstand why some wage convergencetakes place. For the most part, the stud-ies investigating the differential accumu-

lation of human capital by immigrantsand natives focus on one single factor,the acquisition of “language capital” inthe host country.

The early work of Gilles Grenier(1984), and Walter McManus, WilliamGould, and Finis Welch (1983) con-cluded that U.S. immigrants who areproficient in the English language havehigher earnings than immigrants who arenot.14 Grenier reports that Hispanic im-

migrants who do not speak English pay a17 percent wage penalty, even after ad- justing for differences in education andother socioeconomic characteristics. Thiswage differential implies a $96,600 (in1993 dollars) increase in lifetime earn-ings for a Hispanic immigrant who be-comes proficient in the English language(McManus 1985). Presumably, profi-ciency in the host country’s language in-

creases immigrant earnings because bi-lingualism opens up many employmentopportunities.

There also seems to be a link betweenEnglish language proficiency and therate of wage convergence between immi-grants and natives. Chiswick (1991), for

example, documents that an additionalyear of residence in the United States in-creases the probability of English profi-ciency by about 3 percentage points in asmall sample of illegal aliens appre-

hended in Los Angeles. Moreover, add-ing variables measuring the worker’sEnglish skills to a cross-section earningsfunction reduces the coefficient of years-since-migration by 10 to 20 percent(Evelina Tainer 1988; Chiswick 1991).

In 1990, 47.0 percent of the immigrantstock in the United States did not speakEnglish very well (U.S. Department of Commerce 1993a, p. 129). Given the ap-parent high returns to English language

proficiency, it is worth asking why moreimmigrants do not pursue this humancapital investment. The rate of return tolanguage capital, however, may havelittle to do with the wage differentialbetween immigrants who are English-proficient and immigrants who are not.English proficiency and earnings mightbe correlated simply because more ableworkers are likely to speak English and

to earn more. Some studies correct forthe endogeneity of the language variableby using instrumental variable estima-tors, but these attempts are not convinc-ing. For example, Chiswick and Miller(1992, p. 265) use such instruments asthe worker’s veteran status, number of children, and the fraction of persons inthe state who speak the same language.I t is doubtful that this set of identifyinginstruments is correlated with Englishproficiency, but is not correlated withthe worker’s earnings capacity.

Even if language proficiency were ex-ogenous, the returns to language capitalare affected by the clustering of immi-grants in ethnic enclaves, such as the Cu-bans in Miami’s Little Havana and theMexicans in East Los Angeles. Immi-grants residing in these enclaves mightface low returns to language capital be-

cause most of their economic exchanges

14 Similar findings are reported in Carliner’s(1981) study of immigrants in Canada. DavidBloom and Grenier (1992) and Chiswick and PaulMiller (1992) compare the returns to language

capital in the United States and Canada.



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are with persons of the same ethnic (andlinguistic) background. For example, al-most half of the Cubans who arrived inthe Mariel boatlift in 1980 worked forCuban employers in 1986 (Alejandro

Portes 1987).15

McManus (1990) findsthat the wage gap between Hispanicswho are English proficient and Hispanicswho are not is 26 percent for workerswho live in a county that is only 10 per-cent Hispanic, but falls to 11 percent forworkers who live in a county that is 75percent Hispanic.

Although many studies measure thecorrelation between language capital andwage convergence, there are many other

variables which influence the assimila-tion process, such as the acquisition of formal education or on-the-job trainingin the post-migration period, invest-ments in geographic mobility within thehost country, and differences in jobsearch activities. Few studies, however,investigate how natives and immigrantsdiffer in these human capital invest-ments.16

4. National O ri gin and the Self- selection of I mmigrants

Why did the relative wages of succes-sive immigrant cohorts arriving in theUnited States decline? The empiricalevidence suggests that one single factor,the changing national origin mix of theimmigrant flow, can explain much of the

decline (Borjas 1992b; LaLonde and Topel 1992).

A. National Ori gin and the Decli ne in I mmigrant Skill s

Table 8 illustrates the huge differ-ences in educational attainment and

earnings across national origin groups in1990. Mean years of schooling rangefrom eight years for immigrants originat-ing in Mexico or Portugal, to about 15years for immigrants originating in suchdiverse countries as Austria, I ndia, Ja-pan, and the United Kingdom. Similarly,immigrants from El Salvador or Mexicoearn 40 percent less than natives, whileimmigrants from Australia or South Af-rica earn 30 to 40 percent more than na-

tives. These differences cannot be attrib-uted to the fact that some national origingroups have lived in the United Statesfor longer periods. There is substantialdispersion in both educational attain-ment and relative wages even among im-migrants who have been in the countrymore than 10 years.

In view of the post-1950 changes inthe national origin mix of immigrant

flows, it is not surprising that thesechanges “explain” the decline in relativewages across successive immigrantwaves. Borjas (1992b, p. 41) decomposesthe skill decline into a portion due tochanges in the national origin mix andinto a portion due to the changing skilllevel of immigrants from specific coun-tries. The changing national origin mixexplains over 90 percent of the declinein educational attainment and relative

wages across successive waves between1960 and 1980.

To some extent, the inter-group vari-ation in skills documented in Table 8mirrors the dispersion in skills across thepopulations of the various source coun-tries. There is, for example, a great dealof dispersion in educational attainmentacross countries (Robert Barro and Jong-Wha Lee 1993). Even if the immigrant

flow was randomly drawn from the popu-

15 Borjas (1990, ch. 10) and Ivan L ight andEdna Bonacich (1988) provide detailed studies of self-employment in the immigrant population.

16 An exception is given by Ann Bartel’s (1989)analysis of the internal migration decisions of for-eign-born workers in the United States. Bartelfinds that immigrants choose to reside in areaswhere there are other immigrants, and that theirinternal migration decision are much less sensitiveto regional wage differentials than those of na-

tives.



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lation of the source countries, the educa-tional attainment of immigrants who en-tered the United States in the 1980swould differ from that of earlier immi-grant waves.

To illustrate the importance of thiscompositional effect, Borjas (1992b) cal-culated the average schooling level of the country represented by the typicalimmigrant for a number of immigrantwaves. The typical immigrant who ar-rived between 1955 and 1960 originatedin a country where the average personhad 9.5 years of schooling. This statisticdeclined to 7.7 years for the 1975–1980flow. I f immigrants were randomly

drawn from the source country’s popula-tion and if the rate of return to schoolingis on the order of 7 percent, the declin-ing educational attainment of the typicalsource country would alone be responsi-ble for a 14 percent decline in relativewages across immigrant cohorts.

There is also a great deal of variationin other types of work-related skillsacross the various source countries, and

these skills are not equally transferableto the United States. Clearly, the kindsof skills workers acquire in highly devel-oped economies differ from those ac-quired in less-developed countries. I tseems likely that skills acquired in ad-vanced economies are more easily trans-ferable to the U.S. labor market. I n fact,there is a strong positive correlation be-tween immigrant earnings in the UnitedStates and the level of economic devel-opment in the country of origin, as mea-sured by the country’s per capita GNP(Guillermina Jasso and Mark Rosenzweig1986).

There has been a dramatic drop in theper capita income of the country repre-sented by the typical immigrant enteringthe United States (Borjas 1992b). Theaverage person who immigrated between1955 and 1960 originated in a country

which had a 1980 per capita GNP of

$6,823 (in 1980 dollars). By contrast, therespective statistic for the typical immi-grant who arrived in the late 1970s is$3,828. Because the elasticity of theearnings of immigrants in the United

States with respect to per capita GNP inthe source country is on the order of .04,immigrants who arrived in the late 1950swill earn about 4 percent more thanthose who arrived in the late 1970s, evenif the immigrant flow were randomly se-lected from the source countries.

B. The Self -Selecti on of the I mmigrant Flow

The immigrant flow, however, is notrandomly selected from the populationof the source countries. Borjas (1987) ar-gues that the self-selection of the immi-grant flow generates some of the nationalorigin differentials documented in Table8. Suppose that residents of country 0(the source country) consider migratingto country 1 (the host country). Assumealso that migration decisions are irre-versible so that no return migration oc-

curs. I f they choose to remain in thesource country, residents of the sourcecountry have an earnings distributiongiven by:

logw 0 = µ0 + ε0, (6)

where w 0 gives the worker’s earnings inthe source country; µ0 is the mean logearnings in the source country; and therandom variable ε0 measures deviations

from mean earnings, and is assumed tobe normally distributed with mean zeroand variance σ02.

I f the entire population of the sourcecountry were to migrate to the hostcountry, they would face the earningsdistribution:

logw 1 = µ1 + ε1, (7)

where µ1 is the mean log earnings in

the host country, and the random vari-



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able ε1 measures deviations from meanearnings, and is normally distributedwith mean zero and variance σ12. The cor-relation coefficient between the randomvariables ε0 and ε1 equals ρ.

The population mean µ1 need notequal the mean earnings of native work-ers in the host country. The averageworker in the source country, for in-stance, might be less skilled than the av-erage worker in the United States. Forconvenience, it is useful to assume thatthe typical person in both countries isequally skilled, so that µ1 also gives themean earnings of natives in the hostcountry. This assumption helps isolate

the impact of the selection process onthe skill composition of the immigrantflow.

Equations (6) and (7) summarize theearnings opportunities available to po-tential migrants in the source and hostcountries. The migration decision is de-termined by a comparison of earningsopportunities across countries, net of mi-gration costs (C ). Define the index func-

tion:I = log

w 1

w 0 + C ≈ (µ1 −µ0 −π) + (ε1 −ε0), (8)

where π = C /w 0 gives a “time-equivalent”measure of migration costs. A workermigrates to the host country if I > 0and remains in the source country other-wise.

Migration costs C will differ amongworkers. For instance, newly arrived im-

migrants may be unemployed while theylook for employment, suggesting thathigh-wage migrants might have highermigration costs. High-wage migrants,however, are more likely to have prior job connections and better informationabout job opportunities, suggesting anegative correlation between migrationcosts C and wages. The immigrant alsoincurs transportation costs. It is instruc-

tive to assume initially that the time-

equivalent migration costs, π, are con-stant in the population (so that migrationcosts are proportional to wages). Theprobability that a person migrates to thehost country can then be written as:

P = Pr {υ > (µ0 + π − µ1)}= 1− Φ(z ), (9)

where υ = ε1 – ε0, z = (µ0 + π – µ1)/σv ,and Φ is the standard normal distribu-tion function. I t is easy to show that:

∂P ∂µ0

0, and∂P ∂π

0), which gives the earnings of immigrants prior to their migration, andE (log w 1 | I > 0), which gives immigrantearnings in the host country. Because of the normality assumption, these condi-tional means are given by:17

17 To derive equation (11), note that: E (logw 0 I > 0) = µ0 + σ0E (ε0

∗ v ∗ > z ),where ε0∗ = ε0/σ0, v ∗ = v σv . Because the conditionalexpectation of a normal density is linear, we can write ε0∗ =

ρ0v v ∗

+ ξ, where ρ0v is the correlation between ε0 andv ,



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E (log w 0 I > 0)

= µ0 + σ0σ1σv

ρ −

σ0σ1

λ, (11)

E (logw 1 I > 0)

= µ1 + σ0σ1σv σ1σ0

− ρ λ, (12)

where λ = φ(z )/(1 − Φ(z )), and φ is thedensity of the standard normal. The vari-able λ is inversely related to the emigra-tion rate and is positive as long as somepersons find it profitable to remain inthe source country (P < 1).

Let Q 0 =E (0 | I > 0) and Q 1 =E (1 |I > 0). Inspection of equations (11) and

(12) indicates that there are three possi-ble types of selection characterizing theimmigrant flow:

Q 0 > 0 andQ 1 > 0

if and only if ρ > σ0σ1

andσ1σ0

> 1. (13)

Q 0 1. (14)

Q 0 0

if and only if ρ 0) = µ0 + σ0 ρ0v E (v ∗ v ∗ > z ).Equation (11) follows directly by noting thatρ0v = (ρσ0σ1 − σ0

2 )/σ0σv and λ = E (v ∗ v ∗ > z ). Equa-tion (12) can be derived in an analogous manner. I t isworth noting that the random variables ε0 andε1 can bedecomposed into observable and unobservable compo-nents so that the framework applies to selection in both

types of skill characteristics.



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skills, and thus serves an allocative rolein the sorting of persons across coun-tries. I t is worth noting that neither thedifference in mean earnings nor the levelof migration costs determines the type of

selection that characterizes the immi-grant flow.18 Although the first momentsdetermine the size and direction of theflow, they do not determine if immi-grants are drawn mainly from the upperor lower tails of the earnings distribu-tion.

The empirical evidence provides somesupport for the equilibrium skill sortingimplied by the model. Borjas (1990, ch.7) reports that measures of income in-

equality in the source country, which area rough proxy for the rate of return toskills, are negatively correlated with theearnings of immigrant men in the UnitedStates.19Holding constant a vector of ob-servable socioeconomic characteristics(including educational attainment andage), the point estimates suggest thatMexican immigrant men earn about 4percent less than British immigrants

simply because of the selectivity effectresulting from Mexico having a higherrate of return to skills than the United

Kingdom. Deborah Cobb-Clark (1993)finds a similar negative correlation be-tween the earnings of immigrant womenin the United States and measures of therate of return to schooling in the source

countries. Finally, Edward Taylor’s(1987) case study of migration in a ruralMexican village concludes that Mexicanswho migrated illegally to the UnitedStates are less skilled, on average, thanthe typical person residing in the village. This type of selection is consistent withthe fact that Mexico has a relatively highrate of return to skills.

The discussion provides an interestingexplanation of the decline in the relative

skills of immigrant cohorts admitted tothe United States in the postwar era.Prior to the 1965 Amendments, the allo-cation of visas was guided by the ethniccomposition of the U.S. population in1920, and thus favored immigration froma small number of Western Europeancountries. The 1965 Amendments re-pealed the national-origins quota systemand greatly increased the number of im-

migrants originating in Asian and LatinAmerican countries. The new immigra-tion, therefore, is more likely to origi-nate in countries where the populationtends to be less skilled, where skills areless easily transferable to the UnitedStates, and where the rate of return toskills is relatively high. All these factorscontribute to a decline in the relativeskills of successive immigrant waves.20

The self-selection model can be ex-

18 Although the discussion assumed that migra-tion costs (in time-equivalent terms) are constant,it is not difficult to incorporate liquidity con-straints or variable migration costs into the model.For instance, economic conditions might motivatethe least-skilled to migrate, but liquidity con-straints prevent the migration of these workers. The “best of the worst” will then move if the flow

is negatively selected. Similarly, if migration costsare correlated with earnings, the selection charac-terizing the immigrant flow may change in eitherdirection. I f, for example, migration costs are posi-tively correlated with earnings, the immigrant flowis more likely to be negatively selected. I t is easyto show that the correlation between migrationcosts and earnings can change the type of selec-tion only if the variance in migration costs is suffi-ciently high relative to the variance in skills.

19 Alan Barrett (1993) shows that immigrantswho enter the United States using a family reunifi-cation visa have relatively lower earnings whenthey originate in countries where the income dis-

tribution has a large variance.

20 Part of the national origin wage differentialsmay also arise from discrimination against particu-lar groups. The literature has not investigated thishypothesis seriously because the evidence on theHispanic/non-Hispanic or the Asian/white wagedifferential among nati ve wor kers does not lenditself to a simple discrimination interpretation.Cordelia Reimers (1983) finds that much of theHispanic/non-Hispanic wage differential is attrib-utable to differences in observable characteristics,while Chiswick (1983) shows that Asian groups ac-tually have higher wages than white workers, even

after controlling for observable characteristics.



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tended to incorporate the fact that mi-gration decisions are reversible. Returnmigration can arise for two distinct rea-sons. F irst, it may be the optimal resi-dential location plan over the life cycle.

In other words, workers reside in thehost country for a few years and then re-turn to their home countries after accu-mulating sufficiently large levels of hu-man capital or wealth. This mobilitypattern allows some workers to attainhigher utility or wealth than if the migra-tion decision was permanent. Alterna-tively, return migration flows may resultfrom mistakes in the initial migration de-cision.21 Potential immigrants are uncer-

tain about the economic conditions avail-able to them in the destination. As aresult, the actual outcomes experiencedin the host country’s labor market differfrom the expected outcomes that guidedthe immigration decision. As long as re-turn migration costs are relatively low,immigrants who experience worse-than-expected outcomes will return to theirhome country.

Borjas and Bernt Bratsberg (forthcom-ing) argue that regardless of which of these two factors generates return migra-tion, the implications for the skill com-position of the “surviving” immigrantstock are the same: return migration ac-centuates the selection that characterizesthe initial migration flow. The intuitionis illustrated in Figure 3 for the specialcase where earnings are perfectly corre-lated across countries.22 Suppose that

the immigrant flow is positively selectedso that all workers with skill level ex-ceeding v H emigrate. The worker withskill level v H is the “marginal” immi-grant; he is indifferent between migrat-ing and not migrating. As a result, the

immigrants with skill level in the neigh-borhood of v H are most susceptible toimproved opportunities in the sourcecountry or to adverse random shocks inthe host country’s labor market. The re-turn migrants are the “worst of the best.”I f the return migration flow is negativelyselected, the immigrants have skills be-low v L. The persons in the neighborhoodof v L are the marginal immigrants, andthe return migrants are the “best of the

worst.” The limited empirical evidence sup-

ports these theoretical implications. Fer-nando Ramos (1992) analyzes the returnmigration decisions of Puerto Ricans liv-ing in the United States. The joint studyof the Puerto Rican and U.S. Censusesprovides valuable information on thecharacteristics of Puerto Ricans living inthe United States, of Puerto Ricans who

remained in their homeland, and of Puerto Ricans who returned to PuertoRico after living in the United States fora brief period. The data indicate thatPuerto Rican “immigrants” in the UnitedStates are relatively unskilled, but that

21 Eliakim Katz and Oded Stark (1987) presenta model of how the immigrant flow is selectedbased on the assumption that there is asymmetricinformation in the migration decision (workersknow their skills and earnings in the source coun-try, but not in the host country).

22 The assumption that earnings are perfectlycorrelated across countries implies that we canwrite the wage structure for country i (i = 0,1) aslog w i = µ i + i s , where s is a random variabledescribing a worker’s skills; and i is the rate of return to skills. Ignoring migration costs, a resi-

dent of the source country migrates when µ1 +

1s > µ0 + 0s . We can rewrite this decision rule as(1 − 0) s > (µ0 − µ1). Thus, there exists a thresh-old level of skills that separates out the migrantsfrom the nonmigrants. Note that this result doesnot depend on the distribution of the random vari-

able s .

Figure 3. The Self-selection of Return Migrants

L

Negatively-SelectedImmigrant Flow

SkillsH

Positively-SelectedImmigrant Flow



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the return migrants are relatively moreskilled than the typical immigrant. Thetypical Puerto Rican who migrated to theUnited States prior to 1975 had 9.4 yearsof schooling, as compared to 10.8 years

for a Puerto Rican who never left PuertoRico. The Puerto Rican migration flow,therefore, is negatively selected. In con-trast, the typical Puerto Rican who re-turned to Puerto Rico after a stint in theUnited States had 9.8 years of schooling.

Borjas and Bratsberg (forthcoming)find a relationship between the rate of return migration for a particular nationalorigin group and the average earnings of the surviving stock of immigrants in the

United States. In particular, a high rateof return migration for the national ori-gin group increases the average earningsof the surviving immigrants when the im-migrant flow is positively selected (i.e.,originates in a country with a low rate of return to skills), and reduces the averageearnings of the surviving stock when theimmigrant flow is negatively selected(i.e., originates in a country with a high

rate of return to skills). Even though thereturn migration rates in the Borjas-Bratsberg study are measured with agreat deal of error, the empirical evi-dence suggests that return migrationdoes accentuate the selection of immi-grants at either tail of the skill distribu-tion. Bratsberg (1993) shows that the re-turn migration rate of foreign students inthe United States differs substantiallyacross source countries. For example,only about 3 percent of students origi-nating in Mexico or Germany choose toremain in the United States, as opposedto nearly 30 percent of students originat-ing in Israel, Poland, and Kenya. Thedata indicate that foreign students aremore likely to return to wealthier coun-tries and to countries which offer highrates of return to schooling.

Roy’s framework has also been ex-

panded to incorporate the idea that im-

migration decisions are made in a familycontext (Cobb-Clark 1990; Borjas andStephen Bronars 1991). The maximiza-tion of family income implies that theimmigrant flow contains some tied mov-

ers, persons who would not have mi-grated on their own but who migrate aspart of the household. This approach willlikely play a crucial role in under-standing skill trends among immigrantwomen, both in terms of cohort effectsand wage convergence. The early workof James Long (1980), based on the 1970Census cross-section, suggests that thelabor market experiences of immigrantwomen in the United States differ sub-

stantially from those of men. For exam-ple, the earnings of immigrant womenare negatively correlated with years-since-migration. Remarkably, there hasbeen little empirical research document-ing the skill trends among immigrantwomen since that early study.

C. The Host Count r y’s Demand for Immigrants

Even though Roy’s self-selectionmodel has influenced our thinking abouthow the immigrant flow is chosen fromthe source country’s population, it is im-portant to stress that the model onlygives the “supply side” of the immigra-tion market. Workers who wish to mi-grate to a particular host country can doso only if the host country’s governmentallows it. The immigration market is

highly regulated. Most countries havestrict policies describing the demo-graphic characteristics of persons whoare allowed to enter the country (such asskills, national origin, or family ties withcurrent residents). The size and skillcomposition of the immigrant flow,therefore, are jointly determined by thesupply-side considerations stressed inthe self-selection model as well as by fac-

tors which influence the host country’s



27/51

demand for immigrants (or, equivalently,the supply of visas).

In general, the supply of visas is deter-mined by the host country’s political andeconomic gains from immigration. For

instance, the returns to immigration willdepend partly on the benefits accruingfrom admitting workers who can special-ize in particular industries and occupa-tions, and will also be determined by theimpact of immigrant flows on the em-ployment opportunities of natives as wellas on the social fabric of the host coun-try. I t is also clear that there will be dif-ferential benefits from admitting skilledor unskilled immigrant flows, depending

on the skill composition of the nativework force and on the generosity of so-cial insurance programs.

Unfortunately, the literature does notyet provide a systematic analysis of thefactors that generate the host country’sdemand function for immigrants. Recentwork by Jess Benhabib (1993) constructsa demand curve by noting that nativesdiffer in their wealth, so that there will

be both winners and losers from thechoice of a particular immigration policy. The demand function for immigrants isthen an exercise in political economy,and depends on the extent to which thewinners can compensate the losers. Rich-ard Freeman (1993) conjectures that thedemand curve for immigrants might bemostly determined by discriminationagainst some national origin groups.

A promising exploration of the factorsthat shift the U.S. demand for immi-grants is given by Claudia Goldin’s(1994) study of the origins of the na-tional-origins quota system. In 1915,Congress enacted legislation requiringimmigrants to pass a literacy test, effec-tively reducing the demand for unskilledimmigrants. President Woodrow Wilsonvetoed the legislation. Legislators repre-senting districts with large immigrant

populations voted not to override Wil-

son’s veto (suggesting that their immi-grant constituents did not support a re-strictionist policy towards unskilledworkers).23 I n contrast, legislators repre-senting districts where wages were stag-

nant voted to override the veto (implyingthat their constituents had little to gain,and perhaps much to lose, from admit-ting more immigrants). Therefore, itseems as if further research on the politi-cal economy of immigration policy mightgreatly improve our understanding of theproperties of equilibrium in the immi-gration market.

5. I nternational D if fer ences in I mmigrant Per formance

The performance of immigrants in thehost country’s labor market has beendocumented in a number of other coun-tries, including Australia (John Beggsand Bruce Chapman 1991); Britain(Chiswick 1980); Germany (ChristianDustmann 1993; Jörn-Steffen Pischke1993); and Israel (Friedberg 1993).

These international comparisons help as-sess the impact of differences in immi-gration policy. The most extensive re-search has been conducted on theimmigrant experience in Canada, whichby the early 1990s had an annual immi-grant flow on the order of one percent of its population (Michael Baker andDwayne Benjamin 1994; Bloom,Grenier, and Morley Gunderson forth-coming; and Robert Wright and Paul

Maxim 1993).Until 1961, Canadian immigration pol-

icy, like that of the United States, per-mitted the entry of persons originating inonly a few countries, such as the United

23 Lindsay Lowell, Frank Bean, and Rodolfo DeLa Garza (1986) report that Congressmen repre-senting districts with large Hispanic populationswere more likely to oppose enactment of an earlyversion of the 1986 Immigration Reform and Con-trol Act (which made it illegal for employers to

hire illegal aliens).



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Kingdom, or of persons who were de-pendents of Canadian residents. Majorpolicy changes in 1962 and 1967 re-pealed the national origin restrictions,and shifted the emphasis towards skillsrequirements. Under current regula-

tions, applicants for entry into Canadaare classified into three classes: the fam-ily class (which includes close relatives of Canadian residents), assisted relatives(which includes more distant relatives of Canadian residents), and independentimmigrants. Visa applicants in the lasttwo classes are screened by means of a“point system.” Points are awarded ac-cording to such factors as the applicant’seducation, age, and occupation. Appli-

cants who get a passing score areawarded an entry visa.

As Table 9 shows, the point systemseems to have had a major impact on theskill level of immigrants in Canada. Inthe early 1960s, the typical immigrantentering Canada had about half-a-yearless schooling than the typical immigrantentering the United States. By the late1970s, the typical immigrant entering

Canada had almost one more year of

schooling than the typical immigrant en-tering the United States. In addition, thetypical immigrant entering

economics of imigration

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