U.S. Department of Labor 

Employment and Training Administration Occasional Paper 2005-10Occasional Paper 2005-10

 

Immigration and the Effects on the U.S. 

Labor Market 

(1960-2000) 

George J. Borjas 

Harvard University 

This report has been funded, either wholly or in part, with Federal funds from the U.S. 

Department of Labor, Employment and Training Administration under Contract Number 

AF-12985-000-03-30. The contents of this publication do not necessarily reflect the 

views or policies of the Department of Labor, nor does mention of trade names, 

commercial products, or organizations imply endorsement of same by the U.S. 

Government. 
U.S. Department of Labor 

Employment and Training Administration

Table of Contents 

1.0 Introduction ........................................................................................................... 1

2.0 The Skills of Immigrants ....................................................................................... 4

3.0 The Labor Market Impact of Immigration .............................................................. 8

4.0 Labor Market Adjustments .................................................................................. 13

5.0 The Economic Benefits from Immigration ........................................................... 17

6.0 Conclusion and Recommendations .................................................................... 19

Endnotes ....................................................................................................................... 22

Appendix A: The Model ................................................................................................ 25

Appendix B: Tables ...................................................................................................... 29

Appendix C: Figures ..................................................................................................... 39
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1.0 Introduction 

 There has been a resurgence of immigration in the United States and in many other 

countries. The United Nations estimates that over 175 million people, or roughly 3 percent of the 

world’s population, now reside in a country where they were not born (United Nations 2002). 

Although most immigrants choose a “traditional” destination (such as the United States, Canada, 

or Australia), many other countries are also receiving relatively large immigrant flows. Nearly 11 

percent of the population in France, 9 percent in Germany, 11 percent in Sweden, and 7 percent 

in the United Kingdom is foreign born. 

Not surprisingly, the impact of immigration on the host country’s labor market is now 

being heatedly debated in many countries. In the U.S. context, this concern has motivated a great 

deal of research that attempts to document how the U.S. labor market has adjusted to the largescale immigration in the past few decades. Three central questions have dominated much of the 

research: What is the contribution of immigration to the skill endowment of the workforce? How 

do the employment opportunities of native workers respond to immigration? And, who benefits 

and who loses? 

 The policy significance of these questions is evident. For example, immigrants who have 

high levels of productivity and who adapt rapidly to conditions in the host country’s labor market 

can make a significant contribution to economic growth. Conversely, if immigrants lack the 

skills that employers demand and find it difficult to adapt, immigration may increase the size of 

the population that requires public assistance and exacerbate ethnic and racial inequality. 

 Similarly, the debate over immigration policy has long been fueled by the widespread 

perception that immigration has an adverse effect on the employment opportunities of natives. 

Which native workers are most adversely affected by immigration and how large is the decline in 

the native wage? 

Finally, a key insight of economic theory is that immigration has distributional impacts, 

reducing the income of workers who compete with immigrants and raising the income of those 

who employ immigrants or purchase immigrant-provided services. Any assessment of the costs 

and benefits of alternative immigration policy proposals will depend directly on the magnitude of 

this redistribution. 

This report summarizes what is known about the impact of immigration on the U.S. labor 

market. The analysis makes extensive use of the microdata provided by the decennial censuses 

between 1960 and 2000. In order to more fully understand the source of the labor market trends 

that will be discussed below, it is useful to begin by providing a brief summary of U.S. 

immigration policy—as the policy changes have had a direct impact on the size and the skill 

composition of immigration in the past half century. 

The major impetus for the resurgence of large-scale immigration to the United States, 

particularly immigration from less developed countries, came from the 1965 Amendments to the 

Immigration and Nationality Act. Before 1965, immigration to the United States was guided by 
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the national origins quota system. This scheme greatly restricted the annual number of 

immigrants, and used quotas to allocate visas across countries. The number of visas given to 

each country was based on the ethnic composition of the U.S. population in 1920. As a result, 60 

percent of all available visas were awarded to applicants from only two countries: Germany and 

the United Kingdom. 

The 1965 Amendments repealed the national origins quota system. Along with 

subsequent minor legislation, the Amendments set a higher worldwide numerical limit for 

immigration and enshrined a new objective for allocating entry visas among the many applicants: 

the reunification of families. The United States now grants the bulk of the visas to persons who 

have relatives already residing in the country. In 2002, for example, 63.3 percent of all legal 

immigrants used family connections to enter the country. 

There has also been a substantial increase in illegal immigration. The latest wave of 

illegal immigration began in the late 1960s after the end of the Bracero Program, an agricultural 

guest worker program for Mexicans that was discontinued because of its perceived harm on the 

economic opportunities of competing native workers. To address the problems created by illegal 

immigration, Congress enacted the 1986 Immigration Reform and Control Act (IRCA). This 

legislation gave amnesty to 3 million illegal aliens and introduced a system of employer 

sanctions designed to stem the flow of additional illegal workers. This legislation obviously did 

not solve the illegal immigration problem. In its most recent published estimate, the Immigration 

and Naturalization Service (2003) reported that 7 million illegal aliens resided in the United 

States in January 2000, with 69 percent originating in Mexico. 

The 1965 policy shift had a historic impact on the number of immigrants admitted. Even 

though only 250,000 legal immigrants entered the country annually during the 1950s, almost 1 

million were entering by the 1990s. Figure 1 illustrates the impact of this resurgence of 

immigration on the immigrant presence in the labor market. In 1970, there were 3.2 million 

foreign-born workers in the labor market, accounting for 4.9 percent of the workforce. By 2000, 

there were 15.4 million foreign-born workers, accounting for 13.4 percent of the workforce.1

The 1965 Amendments also changed the national origin mix of the immigrant population. 

Over two-thirds of the legal immigrants admitted during the 1950s originated in Europe or 

Canada, 25 percent in Latin America, and 6 percent in Asia. By the 1990s, only 16 percent 

originated in Europe or Canada, 49 percent in Latin America, and 32 percent in Asia. 

Historically, immigrants have clustered in a small number of geographic areas. The top 

panel of figure 2 summarizes the clustering that occurs at the state level. In 2000, for example, 

68 percent of immigrants lived in the six main immigrant-receiving states—California, New 

York, Texas, Florida, Illinois, and New Jersey—and almost 30 percent lived in California alone. 

As a result of this geographic clustering, the bottom panel of the figure shows that the foreignborn share of the population increased very rapidly in California, from 10 percent in 1970 to 33 

percent in 2000, and increased from 9 to 20 percent in the five other major immigrant-receiving 

states. In contrast, the foreign-born share in the rest of the country rose only slightly, from 3 to 7 

percent. The two panels of figure 2 also reveal a trend that will be discussed in greater detail 

below: the 1990s witnessed a “spreading out” of immigration from the traditional immigrant-
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receiving states to other parts of the country. Between 1990 and 2000, for example, the share of 

foreign-born workers that lived in California declined from 33 percent to 29 percent, while the 

share of foreign-born workers that lived outside the six main immigrant-receiving states rose 

correspondingly from 27 to 32 percent. 

The labor market impact of immigration depends not only on the size and geographic 

location of the immigrant population, but also on the skills that immigrants bring to the labor 

market—and, particularly, on how the skills of immigrants compare with those of natives. The 

point that it is the relative skills of immigrants that matter can be easily grasped through an 

example. Suppose that all of the immigrants who entered the United States between 1940 and 

2000 had twelve years of schooling. The labor market impact of immigration, however, would 

differ greatly over time because most natives in 1940 were high school dropouts while most 

natives in 2000 had some college education. In 1940, the impact of immigration would be 

generated by an increase in the supply of high-skill workers; in 2000, the impact of immigration 

would be generated by an increase in the supply of low-skill workers. 

Table 1 documents the trend in the distributions of educational attainment for native and 

immigrant workers. The table shows a significant decline in the relative education of the foreignborn workforce between 1960 and 2000. In 1960, for instance, 59.4 percent of immigrant 

workers were high school dropouts and 9.7 percent were college graduates. This educational mix 

was only slightly worse than that of native men, where 49.3 percent were high school dropouts 

and 9.7 percent were also college graduates. By 2000, however, natives were more likely to have 

a college degree (28.2 percent versus 25.9 percent) and were far less likely to be high school 

dropouts (29.4 percent of immigrants lacked a high school diploma, as compared to only 7.2 

percent of natives). As a result of the relative increase in the number of immigrants who lack a 

high school diploma, figure 3 shows that the immigrant share in the population of workers who 

are high school dropouts rose from 6.1 percent in 1970 to 38.7 percent in 2000. Among college 

graduates, the increase was much more modest, from 5.7 percent to 12.4 percent. 

This report presents an empirical analysis of the labor market consequences of these 

trends in the number, geographic distribution, and skills of immigrants. The report addresses four 

central questions in the economics of immigration: 

1) What are the long-run trends in the relative performance of immigrants in the labor 

market? 

2) What is the impact of immigration on the labor market opportunities of native 

workers? 

3) How do native workers adjust to the labor market consequences of immigration? 

4) How large are the economic benefits accruing from the immigrant-induced increase 

in labor supply? 
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2.0 The Skills of Immigrants 

The skill composition of the immigrant population—and, particularly, how the skills of 

immigrant workers compares to those of native workers—is the key determinant of the economic 

impact of immigration on the United States. As argued above, the skill mix of immigrants 

determines which native workers are most affected by immigration. Unskilled immigrants will 

typically harm unskilled natives, while skilled immigrants will harm skilled natives. Skilled 

immigrants might also assimilate more quickly. They might be more adept at learning the tools 

and “tricks of the trade” that can increase the chances of economic success. Finally, the relative 

skills of immigrants determine the economic benefits from immigration. The United States 

benefits from international trade because it can import goods that are not available or are too 

expensive to produce in the domestic market. Similarly, the country benefits from immigration 

because it can import workers with scarce qualifications and abilities. Because of the crucial role 

that the relative skills of immigrants play in any analysis of the economic consequences of 

immigration, a great deal of research attempts to measure the level and trend in the relative skills 

of foreign-born workers in the United States. 

The empirical results that will be reported throughout this report are based on an analysis 

of data drawn from the 1960-2000 Integrated Public Use Microdata Series (IPUMS) of the U.S. 

census.

2

 These data contain information on the skills and labor market outcomes of millions of 

workers in the United States. In 1960 and 1970, the data provide a 1 percent random sample of 

the population. In 1980, 1990, and 2000, the data provide a 5 percent sample. All of the available 

observations in these large surveys are used in the analysis that follows. Throughout the study, 

persons who are not citizens or who are naturalized citizens are classified as immigrants; all 

other persons are classified as natives.3 In this section of the report, where the focus is on 

documenting trends in relative immigrant skills, the samples are restricted to persons aged 18 to 

64 who are not in the military and are not enrolled in school. 

Table 2 summarizes the trends in key labor market characteristics for immigrants and 

natives over the past forty years, reported separately for men and women. The top panel of the 

table shows that immigrants have only slightly lower employment rates than native men, and that 

the employment rate of both groups declined at roughly the same rate over the past few decades. 

By 2000, 88.7 percent of natives and 87.9 percent of immigrant men worked at some point 

during the year. The table also indicates that immigrant men tend to work slightly fewer hours 

per year than native men. 

The large differences between immigrant and native men tend to show up not in terms of 

employment, but in terms of wages. Table 2 also reports summary statistics on income earned in 

the calendar year prior to the census, as well as the log wage differential, which approximately 

measures the percent wage differential between immigrant and native men.4

 In 1960, immigrants 

had slightly higher annual earnings: about $25,300 as compared to $25,000 for natives. By 2000, 

however, immigrants had substantially lower annual earnings: natives earned $36,000 annually 

as compared to $29,300 for immigrants. In terms of hourly wage rates, the percent wage 

differential between immigrant and natives stood at +6.5 percent in 1960, declined to -7.3 

percent in 1980, and declined further to -19.1 percent by 2000. This decline in relative wages can 

be partly attributed to the decline in relative educational attainment documented earlier. 
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The bottom panel of the table reports the respective statistics for immigrant and native 

women. Immigrant women tend to have much lower employment rates than native women. In 

2000, for example, 77.2 percent of native women worked, as compared to only 64.2 percent of 

immigrant women. These large differences in employment rates imply that the wage trends for 

immigrant and native women are likely to be contaminated by selection biases arising from 

differences in the work decision. In fact, the trend in the log wage differential between 

immigrant and native women resembles that found among men, but the decline is not as steep. In 

1960, immigrant women earned 2.8 percent more than native women; by 2000, immigrant 

women earned 9.8 percent less. Because of the likely importance of the selection that generates 

the sample of working women, most of the evidence reported below focuses on describing and 

explaining the trends for working men. 

 Many studies in the modern literature on the economics of immigration focus on 

analyzing how the earnings potential of immigrant workers adapts to the host country’s labor 

market.5

 These studies typically estimate a regression model that relates a typical worker’s 

earnings to his labor market experience, years of residence in the United States, and other 

variables. By tracing out the evolution of earnings over time (as natives accumulate more labor 

market experience and immigrants accumulate both more labor market experience and more time 

in the United States), it is possible to determine if the earnings of immigrant and native workers 

converge over time. The original study by Chiswick (1978) estimated this type of model using 

the 1970 census cross-section. Chiswick found that immigrants earn about 17 percent less than 

natives at the time of arrival. Because immigrants experience faster wage growth, immigrant 

earnings “overtake” native earnings within 15 years after arrival. After 30 years in the United 

States, the typical immigrant earns about 11 percent more than a comparable native worker. 

 Two distinct arguments have been used to explain these results. At the time of arrival, 

immigrants earn less than natives because they lack the U.S.-specific skills that are rewarded in 

the American labor market (such as English proficiency). As these skills are acquired, the human 

capital stock of immigrants grows relative to that of natives, and immigrants experience faster 

wage growth. The human capital investment hypothesis, however, does not by itself generate an 

overtaking point. After all, why would immigrants accumulate more human capital than natives? 

The overtaking point was instead interpreted in terms of a selection argument: immigrants are 

“more able and more highly motivated” than natives (Chiswick, 1978, 900). This assumption 

was typically justified by arguing that only the most driven and most able persons have the 

ambition and wherewithal to pack up, move, and start life anew in a foreign country. 

 This optimistic appraisal of immigrant adjustment was challenged by Borjas (1985), who 

argued that the positive cross-section correlation between the relative wage of immigrants and 

years-since-migration need not indicate that the wage of immigrants converges to that of natives. 

The basic problem with this interpretation of the data is that it draws inferences about how the 

earnings of immigrant workers evolve over time from a single snapshot of the immigrant 

population. It might be the case, however, that newly-arrived immigrants are inherently different 

from those who migrated twenty years ago. Hence, we cannot use the current labor market 

experiences of those who arrived twenty years ago to forecast the future earnings of newlyarrived immigrants.6
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 In the past two decades, the literature has concentrated on measuring both the 

“assimilation” and “cohort” effects that lie at the core of these varying interpretations of the 

cross-section data. To identify these effects separately, we need to track specific immigrant and 

native workers over time, or use a series of repeated cross-sections (such as the various censuses) 

to track specific groups of immigrant and native workers. Because large longitudinal samples of 

foreign-born workers are relatively rare, the literature has focused instead on measuring the 

various effects using the repeated cross-section method. 

To illustrate the nature of the evidence, figure 4 begins by describing the trend in cohort 

effects over the past 40 years. The line labeled “1-5 years in U.S.” describes the trend in the 

relative wage of immigrants who—as of the time of each census—have been in the United States 

for 1 to 5 years. More precisely, this line illustrates the trend in the unadjusted log wage 

differential between the immigrants who arrived in the 5-year period prior to the census and 

native workers in each census since 1960. 

 The figure clearly shows that the relative wage of newly arrived immigrants declined 

precipitously between 1960 and 1990.7

 In 1960, the typical male immigrant who had just arrived 

in the United States earned -9.1 percent less than his male native counterpart. By 1990, a new 

arrival was earning -38.0 percent less than his male native counterpart. 

The figure indicates, however, that this trend was reversed in the late 1990s. By 2000, the 

newly arrived immigrant still had a sizable wage disadvantage, but substantially less than the 

disadvantage observed for the respective cohort 10 years earlier. In particular, the newly arrived 

immigrant in 2000 had a 32.2 percent wage disadvantage. Figure 4, therefore, summarizes an 

important trend in the relative skills of successive cohorts of immigrant men: a steep decline up 

through 1990 and a reversal of this trend in the 1990s. 

It is worth noting that the “uptick” observed in the late 1990s in the relative wage of 

newly arrived immigrants seems to be specific to that cohort, and does not indicate an overall 

improvement in the earnings of other cohorts. Figure 4 also illustrates the trend in the relative 

wage of two additional immigrant cohorts: those present in the United States for 5 to 10 years 

and those present in the country for 10 to 15 years.8

 These trend lines clearly indicate that the 

relative wage of immigrants who have been in the United States either 5 to 10 years or 15 to 20 

years did not increase in the 2000 census. Put differently, the increased wage of the cohort of 

immigrant men that arrived between 1995 and 1999 does not “transfer” to other cohorts, so it 

does not indicate a general improvement in the economic conditions facing immigrants. Rather, 

it represents an improvement in the economic opportunities available to that specific cohort. 

 Recent research by Borjas and Friedberg (2004) have documented that the uptick in 

cohort quality for immigrants who arrived in the late 1990s can be explained in terms of a simple 

story that has significant policy relevance. In particular, the entire uptick disappears when the 

relatively small number of immigrants who are employed as computer scientists and engineers is 

excluded from the analysis.9

 Figure 5 illustrates this basic result. The figure illustrates two basic 

trends. The first is simply the replication of the trend line first reported in figure 4 showing the 

steep decline and then the increase in the relative skills of newly arrived immigrant men. The 

second is the trend line obtained when the immigrant sample omits all workers who are classified 
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as computer scientists or engineers in any given census year. Prior to 1990, the two trend lines 

correspond with each other very closely (with the sample excluding high-tech workers, of 

course, having relatively lower earnings). There is, however, a significant break in the trend lines 

between 1990 and 2000. 

In particular, the comparison of the actual cohort effects to the counterfactual (i.e., a labor 

market where no foreign-born computer scientists or engineers had been admitted) indicates that 

the uptick is completely driven by the admission of a large number of foreign-born computer 

scientists and engineers in the late 1990s. In both 1980 and 1990, fewer than 5 percent of the 

newly arrived immigrants worked in these high-tech occupations. By 2000, however, 11.1 

percent of the newly-arrived immigrants worked in these occupations. 

Although the census data does not provide information on the type of visa immigrants use 

to enter the country, it is probably not a coincidence that this increase in the relative number of 

high-tech immigrants occurred at the same time that the size of the H-1B Visa Program grew 

substantially. This program allows employers to sponsor the entry of temporary workers in 

“specialty occupations.” In fact, most of the workers entering the country with an H-1B visa are 

employed either in computer-related occupations or in engineering (70 percent in 2000).10

Between 1990 and 1994, the number of H-1B visas hovered around 100,000 annually. In 1996, 

this number increased to 144,548, to 240,947 in 1998, and to 302, 326 in 1999.11 It seems, 

therefore, that this “importation” of high-tech workers through the H-1B Program reversed the 

long-standing trend of declining relative skills in successive cohorts of new immigrants. 

 The 1960-2000 census data can also be used to measure the extent of “economic 

assimilation,” the improvement in the relative wage of a specific immigrant cohort over time.12

In particular, one can use the decennial censuses: to calculate the wage differential between 

newly arrived immigrants and natives as of 1970; to recalculate the wage gap between these 

same two groups ten years later in the 1980 census; and to recalculate it again later in the 1990 

and 2000 censuses. Figure 6 summarizes the economic assimilation trends experienced by men 

who arrived in the United States when they were 25 to 34 years old. Table 3 summarizes the 

assimilation trends for immigrants who arrived at other points in the life cycle. 

 Consider first the group of immigrant men who arrived in the late 1960s when they were 

25 to 34 years old. Figure 6 shows that these immigrants earned 13 percent less than comparably 

aged native workers at the time of entry. Move forward in time 10 years to 1980, when both the 

immigrants and the natives are 35 to 44 years old. The wage gap between the two groups has 

narrowed to 3 percentage points. Move forward in time again to 1990, when both immigrants 

and natives are 45 to 54 years old. The wage gap between the same immigrants and natives has 

disappeared and immigrants now have a slight wage advantage of +1 percent. Finally, move 

forward in time to 2000, when the two groups are 54 to 65 years old. This immigrant cohort does 

not achieve any additional wage improvement and is at parity with native workers. Overall, the 

process of economic assimilation exhibited by this cohort reduced the initial wage disadvantage 

of these immigrants by about 10 percentage points over a 30-year period—with most of the 

growth occurring in the first 10 years after immigration. Because this immigrant cohort had a 

relatively high entry wage, the process of economic assimilation allowed the immigrants to 

narrow the wage disadvantage and catch up with natives. 
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With the exception of the immigrant cohort that entered in the late 1990s, the young 

immigrants who arrived after 1970, however, face a much bleaker future—simply because they 

start out with a much greater disadvantage. Consider those who arrived in the late 1970s. They 

entered the country with a 24 percent wage disadvantage. As with the earlier cohort, they too 

were able to narrow the gap by 10 percentage points in the first decade to 14 percent. But the 

2000 census does not reveal any further narrowing. Even after 20 years in the United States, 

these immigrants still earn 14 percent less than comparably aged natives. The evidence, 

therefore, suggests that most of the immigrants who arrived in the 1970s and 1980s will not 

accumulate sufficient human capital to close the wage gap. 

 Table 3 presents more detailed evidence on the rate of economic assimilation for other 

cohorts of immigrants. The results suggest that the assimilation experience is significantly slower 

for immigrants who arrive in the United States at older ages. For example, the entry relative 

wage of immigrants who arrived in the United States in 1980 when they were 45 to 54 years old 

was -31.9 percent. This group’s relative wage improved to only -27.6 percent by 1990. The table 

also indicates that immigrants who enter the United States as children have relatively little wage 

disadvantage. Those immigrants who entered the country when they were 5 to 14 years old in 

1970, for example, earned about 4 percent more than natives when they eventually entered the 

labor market. 

Finally, it is well known that there are substantial differences in economic status across 

national origin groups in the immigrant population. Table 4 summarizes the evidence from the 

2000 census. The table reports the average log wage differential between immigrant men in a 

particular national origin group and native workers for the 15 largest national origin groups (as 

of 2000). 

There are huge differences in economic performance across the national origin groups. 

The relative log wage of Mexican immigrants, for example, is -0.49, while that of Canadian 

immigrants is +0.30. Moreover, the last column of the table shows that these large differences 

remain even in the subsample of immigrants who have been in the United States fewer than 5 

years, so that differences in the average number of years that the group has lived in the United 

States cannot explain the national origin differentials. Finally, as the last two rows of the table 

document, the differences in economic status across national origin groups are strongly 

correlated over time. The correlation between the log wage gap (for all national origin groups, 

not just the 15 groups listed in the table) in 1980 and 1990 is .91, and the correlation is still .88 

between the wage gap in 1980 and 2000. The national origin differences documented in table 4, 

therefore, are numerically very large and remarkably stable. 

3.0 The Labor Market Impact of Immigration 

 What happens when immigration increases the supply of workers in a particular labor 

market? In his influential introductory textbook, Paul Samuelson (1964, 552) gives the intuitive 

answer implied by the standard model of the labor market: 

“After World War I, laws were passed severely limiting immigration. Only a 

trickle of immigrants has been admitted since then... By keeping labor supply 
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down, immigration policy tends to keep wages high. Let us underline this basic 

principle: Limitation of the supply of any grade of labor relative to all other 

productive factors can be expected to raise its wage rate; an increase in supply 

will, other things being equal, tend to depress wage rates.” 

 Samuelson was writing just before the enactment of the 1965 Amendments to the 

Immigration and Nationality Act, the major policy shift that initiated the resurgence of largescale immigration, leading him to make the point that immigration restrictions tended “to keep 

wages high.” He also stressed the mirror-image implication: as immigrants increase the supply of 

a particular type of labor (such as low-educated workers), the wage paid to that group falls. 

More generally, economic theory implies that immigration should lower the wage of 

competing workers and increase the wage of complementary workers, of workers whose skills 

become more valuable because of immigration. For example, an influx of foreign-born laborers 

reduces the economic opportunities for laborers—all laborers now face stiffer competition in the 

labor market. At the same time, high-skill natives may gain substantially. They pay less for the 

services that laborers provide, such as painting the house and mowing the lawn, and natives who 

hire these laborers can now specialize in producing the goods and services that better suit their 

skills. 

Similarly, an immigrant influx of high-skill workers, such as the high-tech workers who 

entered the United States through the H-1B Visa Program would be expected to lower the wage 

of competing high-skill workers already employed in the United States. This influx could benefit 

low-skill workers, as the pace of scientific discovery allows quicker (and cheaper) dissemination 

of technology products, and may increase the productivity of low-skill workers through the 

introduction of technology products that are more complementary with the types of skills and 

services that low-skill workers offer to employers. 

Because of the policy significance associated with determining the impact of immigration 

on the employment opportunities of native workers, a large literature developed in the past two 

decades attempting to measure this impact. The starting point for much of this literature is the 

fact that immigrants in the United States cluster in a small number of geographic areas. In 2000, 

for example, 38.4 percent of immigrants lived in four metropolitan areas (New York, Los 

Angeles, Chicago, and San Francisco), but only 12.2 percent of natives lived in the four 

metropolitan areas with the largest native-born populations (New York, Chicago, Los Angeles, 

and Philadelphia). 

Practically all empirical studies in the academic literature exploit this geographic 

clustering to define the empirical exercise that purports to measure the labor market impact of 

immigration.13 The typical study defines a metropolitan area (or state) as the labor market that is 

penetrated by immigrants. The study then goes on to calculate a cross-city correlation measuring 

the relation between the native wage in a locality and the relative number of immigrants in that 

locality. A negative correlation, indicating that native wages are lower in markets with many 

immigrants, would suggest that immigrants worsen the employment opportunities of competing 

native workers. 
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There is a great deal of dispersion in the findings reported by the various studies in this 

empirical literature. Nevertheless, there is a tendency for the estimated cross-city correlations to 

cluster around zero, helping to create the conventional wisdom that immigrants have little impact 

on the labor market opportunities of native workers, perhaps because “immigrants do jobs that 

natives do not want to do.” It would seem, therefore, that a fundamental implication of the 

standard textbook model of the labor market—that an increase in supply lowers wages—is 

soundly rejected by the data. 

Recent research, however, raises two questions about the validity of interpreting nearzero cross-city correlations as evidence that immigration has no labor market impact. First, 

immigrants may not be randomly distributed across labor markets. If immigrants tend to cluster 

in cities with thriving economies (and high wages), there would be a built-in positive correlation 

between immigration and wages.14 This positive correlation would certainly attenuate, and 

perhaps even reverse, whatever negative impact immigration might have had on wages in local 

labor markets. 

Second, natives may respond to the wage impact of immigration by moving their labor or 

capital to other cities. For example, native-owned firms see that cities in Southern California 

flooded by low-skill immigrants pay lower wages to laborers. Employers who hire laborers will 

want to relocate to those cities. The flow of jobs to the immigrant-hit areas cushions the adverse 

effect of immigration on the wage of competing workers in those localities. Similarly, laborers 

living in Michigan were perhaps thinking about moving to California before the immigrants 

entered that state. These laborers learn that immigration reduced their potential wages in 

California and may instead decide to remain where they are or move elsewhere. Moreover, some 

Californians might leave the state to search for better opportunities. 

The flows of capital and labor tend to equalize economic conditions across cities. As a 

result, inter-city comparisons of native wage rates will not be very revealing: capital flows and 

native migration diffuse the impact of immigration across the national economy. In the end all 

laborers, regardless of where they live, are worse off because there are now many more of them. 

Because local labor markets adjust to immigration, a number of recent studies have 

emphasized that the labor market impact of immigration may be measurable only at the national 

level.15 Borjas (2003) used this insight to examine the link between immigration and the 

evolution of wages for specific skill groups in the past few decades. His study indicates that by 

analyzing national trends in the labor market and by defining skill groups in terms of both 

educational attainment and work experience, one can make substantial progress in determining 

how immigration alters the employment and earnings opportunities of native workers. 

To see the usefulness of the empirical tactic of using both educational attainment and 

work experience to define skill groups, consider the following example. As we have seen, recent 

immigration increased the relative supply of high school dropouts substantially. The labor 

market implications of this increase clearly depend on how the distribution of work experience in 

the immigrant population contrasts with that of natives. After all, one particular set of native high 

school dropouts would likely be affected if all the new low-skill immigrants were very young, 

and a very different set would be affected if all the immigrants were near retirement age. In 
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essence, the methodological approach introduced by Borjas (2003) exploits the fact that similarly 

educated workers with very different levels of work experience are unlikely to be perfect 

substitutes (Welch 1979; Card and Lemieux 2001). 

The empirical analysis reported in this section again uses data drawn from the 1960, 

1970, 1980, 1990, and 2000 Public Use Microdata Samples (PUMS) of the decennial census. 

The men are classified into four distinct education groups: persons who are high school dropouts; 

high school graduates; persons who have some college; and college graduates. Work experience 

is defined as the number of years that have elapsed since the person completed school.16 The 

analysis is restricted to workers with 1 to 40 years of experience. Workers are then grouped into 

eight different experience groups, indicating if the worker has 1-5 years of experience, 6-10 

years, 11-15 years, and so on. There are, therefore, a total of 32 skill groups in the labor market 

(four education and eight experience groups). 

As suggested above, there is a great deal of variation in the relative penetration of 

immigrants across the various skill groups. Figure 7 reports the trends in the immigrant share 

over the period, where the immigrant share gives the fraction of the skill group that is foreign 

born at a particular point in time. 

It is well known that immigration greatly increased the supply of high school dropouts in 

recent decades. What is less well known, however, is that this supply shift did not affect equally 

all experience groups within the population of high school dropouts. As panel A of figure 7 

shows, immigrants made up about half of all high school dropouts with 10 to 20 years of 

experience in 2000, but only 30 percent of those with less than 5 years. In 1960, however, the 

immigration of high school dropouts increased the supply of the most experienced workers the 

most. Similarly, panel D shows that the immigrant supply shock for college graduates in 1990 

was reasonably balanced across all experience groups, generally increasing supply by around 10 

percent. But the supply shock for college graduates in 1960 was larger for the most experienced 

groups, while in 2000 it was largest for workers with 5 to 20 years of experience. 

It is instructive to illustrate the link that exists between the mean weekly earnings of 

workers in a particular skill group and the respective immigrant. In particular, the data allow the 

calculation of the wage growth experienced by each skill group in each decade and the 

corresponding change in the immigrant share. Figure 8 presents a scatter diagram relating these 

changes.17 The plot clearly suggests a negative relation between wage growth and immigration: 

weekly wages grew fastest for workers in those skill groups that were least affected by 

immigration. 

It is easy to use the data summarized in this scatter diagram to estimate a statistical model 

that relates changes in labor market outcomes for a particular group to the change in the 

immigrant share for that skill group (Borjas 2003, 1347-1351). In particular, let yijt denote the 

mean value of a particular labor market outcome for native men who have education i, 

experience j, and are observed at time t. Consider the regression model: 

(1) yijt = γ pijt + si

 + xj + πt + (si × xj) + (si × πt) + (xj × πt) + φijt, 
U.S. Department of Labor 

Employment and Training Administration 12 

where si is a vector of fixed effects indicating the group’s educational attainment, xj is a vector of 

fixed effects indicating the group’s work experience, and πt is a vector of fixed effects indicating 

the time period. The linear fixed effects in equation (1) control for differences in labor market 

outcomes across schooling groups, experience groups, and over time. The interactions (si

 × πt) 

and (xj

 × πt) control for the possibility that the impact of education and experience changed over 

time, and the interaction (si

 × xj) controls for the fact that the experience profile for a particular 

labor market outcome differs across schooling groups. The presence of these educationexperience interactions further implies that the impact of immigration on labor market outcomes 

is identified from changes that occur within education-experience cells over time. The analysis 

uses three alternative dependent variables: the mean of log annual earnings; the mean of log 

weekly earnings; and the mean of log weeks worked in the calendar year prior to the census.18

 Table 5 reports the regression coefficients obtained from the statistical analysis. To easily 

summarize the implications of the evidence, the table also reports what happens to various labor 

market outcomes when immigration increases the number of workers in a particular skill group 

by 10 percent.19 The table shows that immigration has a very strong effect on annual earnings. A 

10 percent increase in the size of the skill group reduces annual earnings by 7.1 percent among 

salaried workers. This change in annual earnings arises because immigration reduces both 

weekly earnings and annual hours worked. Weekly earnings fall by 3.7 percent among salaried 

workers and by 4.5 percent if one includes the self-employed. Further, annual hours of work fall 

by about 3.5 percent. In sum, immigration has an adverse effect on both the wages and 

employment of competing native workers. 

 It is worth stressing that the strong negative effects of immigration on the employment 

opportunities of native workers found at the national level differ substantially from the near-zero 

correlations that are typically found when comparing wages across local labor markets 

differentially penetrated by immigrants. As we will see in the next section, the difference in the 

results between the two approaches may be partly attributed to the fact that immigration alters 

native migration decisions. In particular, native net migration rates fall in those areas most 

penetrated by immigrants, effectively spreading the impact of immigration on local labor 

markets to other areas. 

The approach of examining how immigration affects labor market opportunities for 

specific schooling-experience groups can be expanded in one important way. The regression 

results presented in table 5 focus on estimating the “own-effect” of immigration—the impact of 

immigration on the wages of comparable native workers. The influx of immigrants into a 

particular skill group, however, will likely affect the earnings of workers in other skill groups. 

For example, the large immigrant influx of high school dropouts may well have a beneficial 

effect on earnings of native college graduates. 

The problem with estimating these “cross-effects” is that there are over 500 cross-effects 

that need to be estimated across the 32 skill groups in the analysis. As a result, any study of these 

cross-effects must narrow the scope of the problem by relying on a specific model derived from 

economic theory. The typical approach used in the labor demand literature (Hamermesh 1993) 

specifies a production function that delineates how various types of labor and capital interact in 
U.S. Department of Labor 

Employment and Training Administration 13 

the production process, and estimates the implied parameters by assuming that workers are paid 

the value of their contribution to the firm’s revenue (a standard result in labor markets that are 

competitive). 

The mathematical appendix A of this report describes the technical model used to 

estimate both the own-effects and cross-effects of immigration on wages. In general terms, the 

model assumes that the economy-wide production function can be represented in terms of a 

three-level CES technology, a specification that aggregates across different levels of work 

experience and education groups in order to form the national workforce (Borjas 2003, 1359-

1368). In this framework, similarly educated workers with different levels of work experience 

are aggregated to form the effective supply of an education group and workers across education 

groups are then aggregated to form the national workforce. 

The assumption that the aggregate economy can be represented in terms of a three-level 

CES production function greatly reduces the number of parameters that need to be estimated. In 

particular, there are now three different responses of interest: how immigration in a particular 

skill group (say high school graduates with 20 years of experience) affects the earnings of native 

high school graduates with 20 years of experience; how these immigrants affect the wage of 

younger and older high school graduates; and how these immigrants affect the wage of workers 

in different education groups. 

The evidence suggests an immigration-induced 10 percent increase in the number of 

workers in each skill group has the following effects: it reduces the wage of native workers in 

that same skill group by 3.5 percent; it reduces the wage of native workers who have the same 

education but who differ in their experience by 0.7 percent; and it increases the wage of native 

workers with different educational attainment by 0.5 percent. The implications of these estimated 

own- and cross-wage effects for the wage structure are best illustrated by using a particular 

example. In particular, consider what happened to the earnings opportunities of native workers as 

a result of the immigrant influx that entered the United States between 1980 and 2000. 

Table 6 summarizes the results of this simulation. As indicated by the last row of the 

table, the immigrant influx of the 1980s and 1990s lowered the wage of native workers, 

particularly of those workers at the bottom and top of the education distribution. The wage fell 

by 7.4 percent for high school dropouts and by 3.6 percent for college graduates. In contrast, the 

wage of high school graduates and workers with some college fell by around 2 percent. Overall, 

the immigrant influx reduced the wage of the typical native worker by 3.7 percent. 

It is worth pointing out that these wage impacts imply sizable reductions in annual 

earnings. In 2000, for example, the typical native man without a high school diploma earned 

$22,000 annually. This implies that immigration reduced this worker’s earnings by around 

$1,600. Similarly, the typical male college graduate earned $70,000, implying that immigration 

reduced this worker’s wage by over $2,500. 

4.0 Labor Market Adjustments 

 The evidence reported in the previous section indicates that immigration seems to have a 

substantial adverse impact on wages at the national level. This finding stands in stark contrast to 
U.S. Department of Labor 

Employment and Training Administration 14 

the near-zero correlations that are typically estimated between wages and immigration across 

local labor markets. The difference between the two sets of findings could be explained in terms 

of a model of regional labor market adjustments. In particular, native workers or native-owned 

firms may respond to the impact of immigration on a particular labor market by moving their 

resources elsewhere, thereby spreading the impact of immigration away from the immigranttargeted areas and towards the entire economy. This section of the report focuses on a particular 

adjustment mechanism—native migration decisions—and attempts to determine the extent to 

which the location decisions of native workers are influenced by immigration.20

 The empirical analysis is begun by describing how the immigrant supply shock affected 

different labor markets in the past few decades. As shown in the introductory section, much of 

the immigration influx over the past 40 years affected a relatively small number of states. The 

possible link between the immigrant supply shock and the native location decision can be shown 

in a number of different ways. For example, consider the trend in the relative number of native 

workers who choose to reside in different parts of the country. Borjas, Freeman, and Katz (1997) 

first documented that the fraction of the native population that had chosen to live in California 

stopped growing around 1970, at the same time that large-scale immigration began. This 

important trend is illustrated in the top panel of figure 9. The data clearly indicate the relative 

numbers of native workers living in California first stalling, and eventually declining, as the 

scale of the immigrant supply shock increased rapidly.21

The middle panel of figure 9 illustrates a roughly similar trend for the other immigrant 

states. As immigration increased in these states (the immigrant share rose from about 9 percent in 

1970 to 22 percent in 2000), the fraction of natives who chose to live in those states declined 

from 26.3 to 24.5 percent. 

Finally, the bottom panel of the figure illustrates the trend in the relatively nonimmigrant 

areas that form the rest of the country. Although immigration also increased over time in this 

region, the increase has been relatively small (the immigrant share rose from 3.1 percent in 1970 

to 7.5 percent in 2000). At the same time, the share of natives living in this region experienced 

an upward drift, from 64.5 percent in 1970 to 66.5 percent in 2000. Overall, therefore, the 

evidence summarized in figure 9 suggests a link between native location decisions and the 

immigrant supply shock. 

The link that may potentially exist between the location decision of native workers and 

the immigrant share within schooling-experience cells is also evident at more disaggregated 

levels of geography. All of the census data available between 1960 and 2000 can be used to 

calculate for each schooling-experience-region group the growth rate of the native workforce 

during each decade (defined as the log of the ratio of the native workforce at the decade’s two 

endpoints) and the decadal change in the immigrant share. The top panel of figure 10 presents 

the scatter diagram relating these decadal changes at the state level after removing decade 

effects. The plot clearly suggests a negative relation between the growth rate of a particular class 

of native workers in a particular state and immigration. The bottom panel of the figure illustrates 

the same pattern when the decadal changes are calculated at the metropolitan area level. In sum, 

the raw data clearly reveal that the native population grew fastest in those labor markets that 

were least affected by immigration. 
U.S. Department of Labor 

Employment and Training Administration 15 

 Finally, table 7 provides an alternative way of looking at the data that seems to directly 

link native migration decisions and immigrant supply shocks. Beginning in 1970, the census 

contains information not only on the state of residence as of the census date, but also on the state 

of residence five years prior to the census. These data can be used to construct net-migration 

rates for each of the skill groups in each geographic market. To easily summarize the basic 

trends linking migration rates and immigrant supply shocks, consider breaking up the United 

States into three regions: California; the other immigrant-receiving states; and the rest of the 

country. A native worker is then defined to be an internal migrant if he moves across these three 

regions in the five-year period prior to the census. 

 The differential trends in the net-migration rate across the three regions are revealing. 

Within each education group, there is usually a steep decline in the net migration rate into 

California, a slower decline in the net migration rate into the other immigrant states, and a slight 

rise in the net migration rate into the rest of the country. In other words, the net migration of 

natives fell most in those parts of the country most heavily hit by immigration. 

The other panels of table 7 show that the relative decline in net migration rates in the 

immigrant-targeted states arises both because of a relative decline in the in-migration rate and a 

relative increase in the out-migration rate. For example, the in-migration rate of natives into 

California fell from 9.5 to 7.1 percent between 1970 and 2000, as compared to a respective 

increase from 3.0 to 3.4 percent in the rest of the country. Similarly, the out-migration rates of 

natives from California rose from 8.3 to 9.1 percent in California, but fell from 3.2 to 2.8 percent 

in those states least hit by immigration. 

To more fully evaluate the link between native location decisions and immigration, 

consider the following regression model: 

(2) log Nijt = Xijt β + θ pijt + si

 + rj + πt + (si × πt) + (rj × πt) +(si × rj) + εijt , 

where Nijt gives the number of native workers belonging to skill group i (a particular 

combination of schooling-experience) and residing in region j at time t, pijt gives the immigrant 

share for that cell, si

 gives a vector of fixed effects indicating the skill group, rj is a vector of 

fixed effects indicating the region, πt

 is a vector of fixed effects indicating the time period, and X

is a vector of control variables. The various vectors of fixed effects absorb any region-specific, 

skill-specific, and time-specific factors that affect the evolution of the size of the native 

workforce in a particular labor market. Similarly, the interactions allow for decade-specific 

changes in the number of workers in particular skill groups or in particular regions caused by 

shifts in aggregate demand. Finally, the interaction between the skill and region fixed effects 

implies that the coefficient of the immigrant supply shock is being identified from changes that 

occur within a specific labor market. 

 The coefficient θ in this regression model can be used to calculate the derivative ∂N/∂M, 

or the change in the number of native workers in a particular labor market for each additional 

immigrant worker.22 The regression model was also estimated using the net migration, inmigration, and out-migration rates as alternative dependent variables. The regression models 

were estimated both at the state and metropolitan area levels. The regression coefficients, as well 
U.S. Department of Labor 

Employment and Training Administration 16 

as the derivative of interest, are summarized in table 8. The data indicate that, at the state level, 

each additional immigrant worker reduces the number of native workers by 0.2 persons, with 

half of this impact being due to a reduction in in-migration and the other half due to an increase 

in out-migration. At the metropolitan area level, the effects are larger, with each additional 

immigrant reducing the size of the native workforce by between 0.3 and 0.6 persons. 

 This statistical evidence suggests that there is an important link between native migration 

decisions and immigration that requires much further study. The native migration response helps 

to equalize conditions across local labor markets and diffuses the impact of immigration across 

the entire economy. These internal labor flows may help to explain some of the difference in the 

results obtained between national and local studies of the labor market impact of immigration. 

 It is also important to emphasize that not only does immigration alter the location 

decision of native workers, but it may also alter the location decision of immigrants as well. As 

noted earlier, immigrants have traditionally clustered geographically in a very small number of 

states and cities. As a result, there has been little study of the factors that determine the location 

decisions of immigrants. An important exception is the study by Bartel (1989), which suggests 

that the internal migration decisions of immigrant workers (perhaps because of the importance of 

ethnic enclaves in determining the geographic sorting of the immigrant population) are less 

responsive to regional wage differences than those of native workers. 

Some of the evidence presented earlier in this report suggests that the geographic 

clustering of immigrants weakened somewhat during the 1990s, as immigrants chose to settle in 

nontraditional destinations. Figure 11 illustrates the nature of this important trend in the 

settlement decisions made by new immigrant workers—immigrants who have been in the United 

States 1 to 5 years as of the census date. The fraction of newly arrived immigrants who chose to 

live outside the main six immigrant-receiving states steadily declined from 31.9 percent in 1960 

to 24.0 percent in 1990. This fraction, however, rose by a remarkable 16 percentage points (to 

40.4 percent) in the 1990s. The immigrant gain to the “rest of the country” was entirely due to 

the reduction in the fraction of newly arrived immigrants who chose to reside in California. 

Moreover, the spreading out of immigration into nontraditional states occurred not only 

among the newly arrived immigrants, but also among the immigrants who were already residing 

in the country. Table 9 reports the trends in the net-migration rates of immigrants who have been 

in the country for at least 5 years (as of the census date). The net migration rate of immigrants 

out of California fell from +4.1 percent in 1970 to -4.0 percent by 2000. In contrast, the net 

migration rate of immigrants into the nontraditional immigrant states rose from -2.2 percent in 

1970 to +6.5 percent by 2000, with much of the increase occurring between 1990 and 2000. 

As noted above, there has been very little study of the factors that determine internal 

migration rates of immigrants in the United States and of the factors that might account for the 

differential location decisions that many immigrants began to make in the 1990s. It is clear that 

this aspect of immigrant behavior has important consequences for labor markets. These 

consequences will likely receive much greater study in the next few years. 
U.S. Department of Labor 

Employment and Training Administration 17 

5.0 The Economic Benefits from Immigration 

The influx of immigrants in the labor market changes the “terms of trade” between 

workers and firms, and affects the incomes accruing to workers, to firms, and to the native 

population in total. Does the net impact of all these changes benefit the native population of the 

United States?23

To measure accurately these economic gains, one needs to list all the possible channels 

through which immigration transforms the economy: immigration changes the prices of goods 

and services, the employment opportunities of workers, the number of jobs in native-owned 

firms, and the number of jobs in immigrant-owned firms. This exhaustive list can then be used to 

estimate what GDP (Gross Domestic Product) would have been if the country had not admitted 

any immigrants. The comparison of this counterfactual GDP with the actual GDP yields the 

increase in national income directly attributable to immigration. This calculation can also 

determine how much of the increase in national income accrues to natives as opposed to being 

paid directly to immigrants in return for their services. 

Given the complexity of conducting such calculations, it should not be surprising that any 

estimate of the economic benefits from immigration requires a detailed model of the U.S. 

economy describing how the various sectors of the economy operate and are linked together. In 

this section, the simplest “textbook model” of the labor market is used to calculate the economic 

benefits that accrue from the employment of immigrants and to illustrate how these benefits have 

changed over the past few decades. In this particular model of the U.S. labor market, the 

immigrant influx increases the number of workers available. In the short run, the rest of the 

economy is unaffected by immigration. In particular, the capital stock of the United States—in 

terms of its land, machines, and other physical productive resources—remains as it was before 

the immigrants arrived. 

 We begin by specifying the aggregate production technology in the United States. 

Suppose that the technology can be summarized in terms of an aggregate production function 

with two inputs, capital (K) and labor (L), so that output Q = f(K, L). The workforce is composed 

of N native workers and M immigrant workers. Suppose that all capital is owned by natives, so 

that we ignore the possibility that immigrants might augment the host country’s capital stock. 

We will also ignore skill differentials among immigrant and native workers and assume that all 

workers are perfect substitutes in production (hence L = N + M). Finally, we will assume that the 

supplies of capital and of both native- and foreign-born labor are perfectly inelastic. 

 The aggregate production function exhibits constant returns to scale. As a result, the 

entire output is distributed to the owners of capital and to workers. The equilibrium in this 

economy prior to the admission of M immigrants requires that each factor price equals the 

respective value of marginal product. Suppose that the price of capital is initially r0 and the price 

of labor is w0. The price of the output is the numeraire (so that the input prices are measured in 

units of output). Before the admission of immigrants, therefore, the national income accruing to 

natives, QN, is the price of capital times the quantity used, plus the price of labor times the 

number of workers hired, or QN = r0 K + w0 N. 
 Figure 12 illustrates this initial equilibrium in the labor market. Because the supply of 

capital is inelastic, the area under the demand curve (which represents the marginal product of 

labor curve) gives the economy’s total output. Prior to the entry of immigrants, therefore, the 

national income accruing to natives QN is given by the trapezoid ABN0. 

 What happens to national income when immigrants enter the country? The supply curve 

shifts and the market wage falls to w1. National income is now given by the area in the trapezoid 

ACL0. Part of the increase in national income, however, is distributed directly to immigrants 

(who get w1M in labor earnings). Inspection of figure 12 thus reveals that the increase in national 

income accruing to natives, or the immigration surplus, is given by the triangle BCD. Because 

the market wage equals the productivity of the last immigrant hired, immigrants increase national 

income by more than what it costs to employ them. 

 The immigration surplus is given approximately by the area of the triangle BCD, which 

can be calculated as 1⁄2 × (w0 − w1) × M. By manipulating this formula, it is easy to show that the 

immigration surplus, as a fraction of national income, equals:24

(3) 2 Immigration surplus 1 , 2

sep GDP = −

where s is labor’s share of national income; e is the elasticity of factor price for labor (that is, the 

percentage change in the wage resulting from a one percent change in the size of the labor force); 

and p is the immigration share, the fraction of the workforce that is foreign born. 

 In addition to creating an immigration surplus, immigration also causes a redistribution of 

wealth from labor to capital. In terms of figure 12, native workers lose the area in the rectangle 

w0BDw1, and this quantity plus the immigration surplus accrues to employers. Expressed as a 

fraction of GDP, the net change in the incomes of native workers and employers is given by: 

(4) Change in native labor earnings

GDP

 = sep p (1 ), −

(5) Change in income of employers

GDP

 = 

1 1 .

2 sep p

⎛ ⎞ − − ⎜ ⎟ ⎝ ⎠

 Table 10 uses the data available from 1960 through 2000 to calculate the immigration 

surplus and the redistribution of wealth in each of those years. The simulation assumes that 

labor’s share of national income is 0.7 and that the factor price elasticity is -0.35 (as suggested 

by the evidence presented earlier). The table clearly shows that the resurgence of large-scale 

immigration has increased the size of the immigration surplus in recent decades, from about $1 

billion annually in 1960 to around $21.5 billion annually in 2000. 

 Although the immigration surplus is small, immigration causes substantial wealth 

redistribution. By 2000, the model predicts that immigration reduced the total earnings accruing 

to native workers by about 2.8 percent of GDP and increased the income accruing to native 

U.S. Department of Labor 

Employment and Training Administration 18 
U.S. Department of Labor 

Employment and Training Administration 19 

employers by 3.1 percent of GDP. In 2002 dollars, workers lose around $278 billion, while 

employers gain $300 billion. 

6.0 Conclusion and Recommendations 

This report documents how immigration altered the U.S. labor market in the past four 

decades. The evidence suggests a number of important empirical findings: 

• The relative wage of successive waves of immigrants declined from 1960 through 

1990, but increased during the late 1990s. This increase is mainly attributable to the 

large influx of immigrants who worked in the high-tech industry in the late 1990s, 

probably due to the expansion of the H-1B Temporary Worker Program. 

• The wage gap between immigrants and natives narrows as immigrants accumulate 

experience in the U.S. labor market. This type of “economic assimilation” narrows 

the wage gap at the time of entry by about 10 percentage points during an 

immigrant’s working life, with most of the narrowing occurring in the first 10 years 

after immigration. 

• There are substantial differences in economic performance across national origin 

groups in the immigrant population, and these differences are remarkably stable over 

time. 

• Immigration has an adverse impact on the labor market opportunities of native 

workers at the national level. A 10 percent immigrant-induced increase in the number 

of workers in a particular skill group reduces the wage of native workers in that group 

by between 3 and 4 percent. 

• Native workers in local labor markets penetrated by immigrants respond by adjusting 

their location decisions. About 2 fewer native workers choose to live in a particular 

state for every additional 10 immigrant workers who move to that state. This native 

migration response diffuses the adverse impact of immigration on wages across the 

national economy. 

• The 1990s witnessed a dramatic change in the geographic settlement pattern of 

immigrants, with more immigrants (both new arrivals and earlier immigrants) 

choosing to reside in areas that have not traditionally received many immigrants. 

• The net economic benefits from immigration are relatively small, around $20 billion 

per year. This small net benefit, however, conceals a substantial redistribution of 

wealth that takes place as immigration lowers wages and increases the income of 

persons who use or employ immigrant services. 

Although our understanding of how immigration alters labor market opportunities in a 

host country has increased substantially in the past 25 years, many questions remain unanswered. 

Some of the key questions that deserve much greater attention in future research include: 
U.S. Department of Labor 

Employment and Training Administration 20 

• There is a sizable wage gap between immigrants and natives, and this wage gap has 

been increasing over time. An important part of this wage gap can be attributed to 

differences in educational attainment between the two groups. Does the educational 

wage gap found between adult immigrants and adult natives persist among the 

children of these groups? If so, what factors determine the educational wage 

disadvantage of the children of immigrants? 

• The immigrant cohort that arrived in the late 1990s had higher relative wages than the 

cohort that arrived in the late 1980s, probably due to the expansion of the H-1B Visa 

Program, a program that allows the importation of large numbers of temporary highskill workers. Does the entry of the H-1B visa holders represent only a temporary 

increase in the relative wage of newly arrived immigrants, or will most of these visa 

holders be able to adjust status and obtain a permanent residence visa to remain in the 

United States? 

• The process of economic assimilation observed over the past four decades suggests 

that most immigrant cohorts can narrow the wage gap between immigrants and 

natives by around 10 percentage points. What factors, including English language 

proficiency or internal migration, are responsible for the relatively faster earnings 

growth experienced by immigrant workers? What policies can encourage faster 

accumulation of human capital by immigrant workers? What is the role of ethnic 

enclaves in promotion or hindering the assimilation process? 

• There is a great deal of variability in the labor market performance of different 

national origin groups, both in terms of wage levels and in terms of wage growth. 

What factors account for this variability in economic performance and assimilation 

rates? 

• Immigration has a substantial effect on the earnings and employment opportunities of 

native workers at the national level. What exactly is the nature of this adverse effect? 

Do native workers move to lower-paying jobs as immigrants “take over” particular 

labor markets? What government policies can be used to best offset the adverse 

impact of immigration? 

• Does the national impact of immigration differ across industries? In particular, is the 

impact of immigration stronger if natives work in nontradeable industries than in 

tradeable industries? What would a differential impact imply for the possible 

effectiveness of government policies in offsetting any adverse impact of immigration? 

• Immigrants began to settle in nontraditional geographic areas in the 1990s. What 

factors motivated immigrants to spread out across the country in the 1990s? What has 

been the impact of this geographic diffusion on the labor markets most affected by the 

new immigration? 

• Immigration generates a net gain at the national level on the order of around $20 

billion annually. How much of this immigration surplus and of the wage savings that 
U.S. Department of Labor 

Employment and Training Administration 21 

initially accrue to employers gets passed down to consumers in the form of lower 

prices? 

• Immigration has increased the supply of workers particularly at the lower and upper 

ends of the education distribution. Has this unbalanced supply shock altered the 

educational decisions of native workers? 

• There is very little current information on the return migration decisions of 

immigrants. How many immigrants choose to leave the United States after they 

arrive? What is the selection mechanism generating this outflow? 

• A number of current policy proposals wish to regularize the status of current illegal 

immigrants and establish a guest worker program to match U.S. employers with 

foreign workers. What will be the labor market impact of this expansion in the 

number of workers legally entitled to work in the U.S. labor market? 

In addition to these research topics, the growing importance of immigration in the U.S. 

labor market suggests a number of pilot projects that the ETA could carry out that would 

substantially increase our understanding of the labor market impact of immigration. These 

projects include: 

• Becoming proficient in the English language is an important step in the path of 

assimilation faced by the immigrant workforce. The ETA is already involved in 

conducting pilot projects to determine the value of English as a Second Language 

(ESL) training classes for immigrant workers. It would be useful to extend these types 

of projects to analyze the more general issue of the value and impact of training and 

various forms of human capital acquisition in the immigrant population. Some of the 

skills that immigrants bring to the United States may be specific to the labor market 

of the country of origin. As a result, many immigrants may require some on-the-job 

training to retool their skills in order to be more easily matched with the types of jobs 

available in the U.S. labor market. Similarly, many of the immigrants who lack a high 

school education (perhaps because of insufficient educational opportunities in the 

source country) may benefit greatly from completing a General Equivalency Diploma 

(GED) program in the United States. Pilot studies that would carefully document the 

costs and benefits of such training programs in the immigrant workforce would 

provide evidence that may be extremely valuable in any future discussion of the 

economic consequences of immigration. 

• The presence of an immigrant enclave is likely to have a strong impact on the 

assimilation process, potentially affecting immigrant assimilation in both positive and 

negative ways. On the one hand, the enclave may provide a “warming embrace” to 

the incoming immigrants. On the other hand, the enclave may discourage immigrants 

from investing in the types of skills that may be useful in the labor market that lies 

outside the enclave’s geographic borders. The influence of the enclave on the process 

of assimilation would be much better understood if there was a strong documentation 

of the training and occupation decisions made by immigrants who choose to locate 
U.S. Department of Labor 

Employment and Training Administration 22 

 

outside of ethnic enclaves. A pilot study of such immigrants would provide valuable 

information about the costs and benefits of ethnic enclaves. Such a pilot study may 

also determine whether the value of providing training programs or ESL classes to the 

immigrant population depends on the labor market setting where such training will 

eventually be used. 

• There is probably a great deal of back-and-forth migration between the United States 

and the country of origin for some immigrant groups—for example, Mexican 

immigrants living in Southern California or Texas. The persistence of this link to the 

source country may affect the economic performance of immigrants in the United 

States, not simply because of the discontinuity in U.S. labor market participation that 

such migration inevitably implies, but also because this type of migration may 

weaken the immigrants’ incentives to fully “invest” in the types of skills that 

American employers find useful (e.g., becoming English language proficient). Little 

is known about the extent and labor market consequences of these back-and-forth 

flows. A pilot study of immigrants belonging to a particular national origin group 

who reside in a city where such type of migration is likely to occur frequently may 

substantially increase our understanding of how such type of migration affects 

economic performance and assimilation incentives. 

• Immigrants often cluster in particular occupations (e.g., the overwhelming majority of 

taxi drivers in New York City are foreign born). Before these occupations were 

penetrated by immigrants, however, the jobs were held by native-born workers. A 

pilot study of an occupation that is in the process of becoming “foreignized” would 

provide extremely useful information and insights into the labor market consequences 

of immigration. Such a study could track the native-born workers currently employed 

in this occupation as the immigrant influx is occurring, and investigate the labor 

market activities and earnings of the native workers as their jobs are exposed to a 

sizable increase in the supply of competing workers. 

Endnotes

1

 All of the statistics reported in this section are calculated using data drawn from 1960–2000 Integrated Public Use 

Microdata Series of the U.S. census. The data is described in more detail below. 

2

 These data are available at the University of Minnesota’s IPUMS website: 

http://www.ipums.umn.edu/usa/index.html. 

3

 This definition implies that persons born abroad of American parents or persons born in American territories are 

classified as natives. Some of the variables reported in the census (such as annual earnings) refer to the year prior to 

the survey. To avoid confusion, the data are always referred to in terms of the census year. It is important to note 

that the census data used in this report contain all foreign-born persons enumerated by the census, which implies that 

it contain a large number of illegal immigrants. Due to the difficulty of identifying legal (and even visa) status in the 

foreign-born population, the census does not make any attempt at collecting information that would allow a 

researcher to identify the various groups. The analysis presented here does not attempt any type of differentiation 

between legal and illegal immigrants. One could also plausibly argue that it is the increase in the stock of workers—

regardless of legal status—that is the first-order determinant of the labor market impact of immigration. 4

 Income earned in the past year includes both earnings from salaried jobs and income from self-employment. 
U.S. Department of Labor 

Employment and Training Administration 23 

 

5

 Representative studies include Carliner (1980), Duleep and Regets (1996), LaLonde and Topel (1992), and 

Yuengert (1994). Borjas (1999) and Smith and Edmonston (1997) survey this extensive literature. 

6

 The cross-section correlation may also be contaminated by cohort effects if there is selective out-migration of 

immigrants, so that the trend in the earnings of “survivors” over time will not measure the actual earnings growth 

experienced by a particular immigrant cohort. 

7

 To interpret the trend in the relative wage of immigrants (both within and across cohorts) as a measure of relative 

changes in skills, one must assume that period effects influence the wages of immigrants and natives by the same 

relative amount. It is well known that there were historic changes in the U.S. wage structure during the 1980s and 

that these changes did not affect all skill groups equally (Katz and Murphy 1992). Borjas (1995a) shows that 

detailed controls for period effects do not explain the downward trend in the relative wage of successive immigrant 

cohorts; see also Lubotsky (2001). 

8

 The data reported in the 1960 census do not allow for the identification of specific immigrant cohorts (except for 

the immigrants who arrived between 1955 and 1960). 

9

 The occupation codes used to define the sample of computer scientists and engineers in each census are: 80-93 in 

1960; 3, 4, 6-23, in 1970; 44-59, 64, 229 in 1970 and 1980; 100-111, 132-153 in 2000. 

10 U.S. Immigration and Naturalization Service (2002). 

11 U.S. Immigration and Naturalization Service (various issues). 

12 It is believed that as many as one-third of the immigrants in the United States eventually return to their origin 

countries. Suppose that the return migrants are disproportionately composed of workers with lower than average 

wages. The inter-censal tracking of a particular immigrant cohort would then indicate an improvement in relative 

wages even if no wage convergence is taking place. Alternatively, if the return migrants are the “successes,” the rate 

of wage convergence would be underestimated. Because of data limitations, the selection mechanism generating the 

return migration flow is not well understood. An important exception is the work of Ramos (1992), who analyzes 

the return migration decisions of Puerto Ricans living in the United States. 13 Representative studies include Altonji and Card (1991), Borjas (1987), Card (1990), Grossman (1982), LaLonde 

and Topel (1991), and Schoeni (1997). Friedberg and Hunt (1995) survey the literature. 14 Borjas (2001) finds that new immigrants belonging to a particular schooling group tend to settle in those regions 

that offer the highest return for their skills. 

15 Borjas, Freeman, and Katz (1997) proposed the hypothesis that the labor market impact of immigration may only 

be measurable at the national level. 

16 The analysis assumes that the age of entry into the labor market is 17 for the typical high school dropout, 19 for 

the typical high school graduate, 21 for the typical person with some college, and 23 for the typical college graduate, 

and restrict the analysis to persons who have between 1 and 40 years of experience. 

17 The data summarized in the plot adjusts for decade effects as well as for interactions between the decade effects 

and education or experience. 

18 The regressions are weighted by the sample size used to calculate yijt. The standard errors are clustered by 

education-experience cells to adjust for possible serial correlation. The evidence presented in table 5 differs from 

that reported in Borjas (2003) in two important ways. First, the results presented here include data from the newly 

released 2000 census. Second, the regressions were estimated not only in the sample of wage and salary workers, but 

also in a sample that includes all persons who worked in the past calendar year are included in the calculation of the 

skill groups mean log annual earned income, log weekly earnings, and log annual hours worked. The measure of 

annual earned income, therefore, includes both income from salaried sources as well as self-employment income. 

19 It is easier to interpret the coefficient γ by converting it to an elasticity that gives the percent change in wages 

associated with a percent change in labor supply. In particular, let m give the relative number of immigrants (the 

ratio of the number of immigrants to the number of natives). One can show that the wage elasticity ∂ log w/∂m = 

γ(1–p)2. In 2000, the immigrant share for working men was 14.7 percent. Note that this elasticity gives the 

percentage change in the wage attributable to an immigrant-induced percent increase in labor supply. The wage 

elasticity—evaluated at the mean value of the relative number of immigrants—can be calculated by multiplying γ by 

approximately 0.7. 20 A number of studies examine if native migration decisions respond to immigrant supply shocks. These studies 

offer a cornucopia of findings, with some studies finding strong effects (e.g., Frey 1995; Borjas, Freeman, and Katz 

1997), and other studies finding little connection (Card 2001; Wright, Ellis, and Reibel 1997). See also Card and 

DiNardo (2000) and Filer (1992). 
U.S. Department of Labor 

Employment and Training Administration 24 

 

21 Obviously, other factors also account for California’s demographic trends during the last 20 years, such as the 

impact of the defense cutbacks of the late 1980s and the high-tech boom of the late 1990s. 

22 It is easy to show that ∂N/∂M = θ(1–p)2. Evaluated at the mean value of the immigrant share, the derivative of 

interest can be calculated by multiplying θ by approximately 0.7. 23 Borjas (1995b, 2001) and Johnson (1997) present a variety of models that can be used to calculate the economic 

benefits from immigration. Regardless of the model’s complexity, these studies typically find that the net gains from 

immigration to the native population are relatively small. 

24 See Borjas (1995b) for details.
Appendix A: The Model 

 This appendix summarizes the model used to measure the own-wage and cross-wage 

effects of immigration (Borjas 2003 presents additional details). Suppose the aggregate 

production function for the national economy at time t is: 

(1) 

1/

, v v vQ KL t Kt t Lt t = λ +λ ⎡ ⎤ ⎣ ⎦

where Q is output, K is capital, L denotes the aggregate labor input; and v = 1 – 1/σKL, with σKL 

being the elasticity of substitution between capital and labor (–∞ < v ≤ 1). The vector λ gives 

technology parameters that shift the production frontier, with λKt + λLt = 1. The aggregate Lt

incorporates the contributions of workers who differ in both education and experience. Let: 

(2) , 

1/

t it it

i

L L

ρ

⎡ ⎤ ρ

= θ ⎢ ⎥ ⎣ ⎦ ∑

where Lit gives the number of workers with education i at time t, and ρ = 1 – 1/σE, with σE being 

the elasticity of substitution across these education aggregates (–∞ < ρ ≤ 1). The θit give timevariant technology parameters that shift the relative productivity of education groups, with Σi θit

= 1. Finally, the supply of workers in each education group is itself given by an aggregation of 

the contribution of similarly educated workers with different experience. In particular: 

(3) , 

1/

it ij ijt

j

L L

η

⎡ ⎤ η

= α ⎢ ⎥ ⎣ ⎦ ∑

where Lijt gives the number of workers in education group i and experience group j at time t

(given by the sum of Nijt native and Mijt immigrant workers); and η = 1 – 1/σX, with σX being the 

elasticity of substitution across experience classes within an education group (–∞ < η ≤ 1). 

Equation (3) assumes that the technology coefficients αij are constant over time, with Σj αij = 1. 

The marginal productivity condition implies that the wage for skill group (i, j, t) is: 

(4) log wijt = log λLt + (1 – v) log Qt

 + (v – ρ) log Lt + log θit + (ρ – η) log Lit

 + log αij + (η – 1) log Lijt . 

U.S. Department of Labor 

Employment and Training Administration 25 
The marginal productivity condition in (4) can be rewritten as: 

(5) 1 log log , ijt t it ij ijt

X

w L =δ +δ +δ −

σ

where δt

 = log λLt + (1 – v) log Qt + (v – ρ) log Lt, and is absorbed by period fixed effects; δit = 

log θit + (ρ – η) log Lit, and is absorbed by interactions between the education fixed effects and 

the period fixed effects; and δij = log αij, and is absorbed by interactions between education fixed 

effects and experience fixed effects. 

 

The coefficients of the education-experience interactions in (5) identify the parameters 

log αij. The estimates of αij and σX permit the calculation of Lit, the CES-weighted labor 

aggregate for education group i. Let log wit be the mean log wage paid to the average worker in 

education group i at time t. The marginal productivity condition is: 

(6) 1 log log log . it t it it

E

w L =δ + θ −

σ

Note that σE cannot be identified if the regression included interactions of education-period fixed 

effects to absorb log θit. To identify σE, one can use the Katz and Murphy (1992) assumption that 

the technology shifters can be approximated by a linear trend that varies across education groups. 

 

The empirical implementation of the three-level CES technology described above does 

not use any data on the aggregate capital stock. Hamermesh (1993, 92) concludes that the 

aggregate U.S. economy can be reasonably described by a Cobb-Douglas production function, 

suggesting that σKL equals one. This restriction is used in the simulation reported in table 6. 

 

The first step in the empirical application of the model is to estimate equation (5) using 

the sample of 160 education-experience-time cells. The IV estimate of this equation is:*

(7) log wijt= δt

 + δit + δij – 0.341 log Lijt . 

 (0.135) 

The implied elasticity of substitution across experience groups is 2.9. This implied estimate of 

the elasticity of substitution and the (transformed) coefficients of the education-experience fixed 

effects is used to calculate the size of the CES-weighted labor aggregate for each education 

group. The IV regression estimate of the marginal productivity condition in (6) is:†

(8) log wit = δt

 + linear trend interacted with education fixed effects – 0.425 log Lit . 

 (0.362) 

The implied σE is 2.3. 

U.S. Department of Labor 

 

Employment and Training Administration 26 
The factor price elasticity giving the impact on the wage of factor y of an increase in the 

supply of factor z is defined by: 

(9) log , log

y yz

yz z

z y

d w QQ

s

d L QQ

ε= =

z

where sz is the share of income accruing to factor z; and Qy = ∂Q/∂Ly, Qz = ∂Q/∂Lz, and Qyz = 

∂2Q/∂Ly∂Lz. 

 

The three-level CES technology implies that the own factor price elasticity giving the 

wage impact of an increase in the supply of workers with education i and experience j is: 

(10) ,

1 11 11 1 , ij ij

ij ij ij

X X E i E KL L KL

s s

s

s s

⎛ ⎞⎛ ⎞

ε =− + − + − + ⎜ ⎟⎜ ⎟

σ σσ σσ σ ⎝ ⎠⎝ ⎠

where sij gives the share of income accruing to group (i, j); si

 gives the share of income accruing 

to education group i; and sL gives labor’s share of income. Similarly, the (within-branch) cross 

factor price elasticity giving the impact on the wage of group (i, j) of an increase in the supply of 

group (i, j′ ), with j ≠ j′, is: 

(11) ,

11 11 1 . ij ij

ij ij ij

X E i E KL L KL

s s

s

s s

′ ′

′ ′

⎛ ⎞⎛ ⎞

ε= − + − + ⎜ ⎟⎜ ⎟ ⎝ ⎠⎝ ⎠ σσ σσ σ

Finally, the (across-branch) cross factor price elasticity giving the impact on the wage of group 

(i, j) of an increase in the supply of group (i′, j′ ), with i ≠ i′ is: 

(12) ,

11 1 . i j

ij i j i j

E KL L KL

s

s

s

′ ′

′ ′ ′ ′

⎛ ⎞

ε= − + ⎜ ⎟ ⎝ ⎠ σσ σ

 The calculations of the factor price elasticities require information on the factor shares. 

Assuming that labor’s share of income is 0.7, one can use the 2000 census to calculate the share 

of total annual earnings accruing to each education-experience cell. These total annual earnings 

were then used to apportion the labor shares accruing to the various groups. 

 

 Table 6 uses the estimated elasticities to calculate the wage impact of the immigrant 

influx that entered the United States between 1980 and 2000. The marginal productivity 

condition for the typical worker in education group s and experience group x can be written as 

wsx = D(K, L11, . . . , L18, . . . , L41, . . . , L48). Assuming that the capital stock is constant, the net 

impact of immigration on the log wage of group (s, x) is:‡

U.S. Department of Labor 

Employment and Training Administration 27 
(13) ,, , log , sx sx sx sx sx sj sj sx ij ij

j x is j

wm m ≠ ≠Δ =ε + ε + ε ∑ ∑∑ m

where mij gives the percentage change in labor supply due to immigration in cell (i, j). Because 

the size of the native labor force in each skill group is shifting over time, define mij as: 

(14) ,2000 ,1980

,1980 ,2000 ,1980

, 0.5( )

ij ij

ij

ij ij ij

M M

m

NN M

− = + +

so that the baseline population used to calculate the percent increase in labor supply averages out 

the size of the native workforce in the skill cell and treats the preexisting immigrant population 

as part of the “native” stock.

Endnotes, Appendix A

 

*

 The instrument is log Mijt and the standard errors are clustered by education-experience group. 

†

 The instrument is the immigrant share in cell (i, j, t). 

‡

 The assumption of a constant capital stock implies that the resulting wage consequences should be interpreted as 

short-run impacts. 

U.S. Department of Labor 

Employment and Training Administration 28 
U.S. Department of Labor 

Employment and Training Administration 29 

Appendix B: Tables 

Table 1. Education Distributions of Native and Immigrant Workers 

Year 

1960 1970 1980 1990 2000

Natives

High school dropouts 49.3% 35.3% 20.6% 10.5% 7.2% 

High school graduates 31.2 39.9 42.7 36.8 32.9 

Some college 9.7 11.9 17.6 28.7 31.7 

College graduates 9.7 12.9 19.1 24.0 28.2 

Immigrants

High school dropouts 59.4% 44.6% 37.0% 30.6% 29.4% 

High school graduates 21.2 28.1 27.9 26.1 25.9 

Some college 9.7 12.1 14.5 19.7 18.7 

College graduates 9.7 15.2 20.6 23.6 25.9 

Notes: The statistics are calculated in the sample of persons aged 18-64 who worked at least 1 week in the year prior 

to the census and are not enrolled in school. 
U.S. Department of Labor 

Employment and Training Administration 30 

Table 2. Labor Market Characteristics of Natives and Immigrants 

Natives Immigrants 

1960 1970 1980 1990 2000 1960 1970 1980 1990 2000

Men 

 Employment rate (%) 95.3 94.1 90.8 90.2 88.7 93.9 93.8 89.8 89.1 87.9 

 Annual hrs. worked (1,000s) 2.17 2.19 2.10 2.12 2.18 2.089 2.11 2.02 2.03 2.05 

 Self-employment rate (%) 14.9 12.0 11.9 12.0 11.7 17.2 12.8 11.7 12.3 11.0 

 Unemployment rate (%) 4.7 3.5 6.2 5.8 4.6 4.6 3.2 5.8 6.7 5.4 

 Annual earnings ($1,000) 25.0 33.3 33.1 32.9 36.0 25.3 33.2 30.9 29.1 29.3 

 Hourly wage rate 12.5 16.4 16.7 16.0 16.9 13.2 17.0 16.2 14.7 15.0 

 Log wage differential --- --- --- --- --- .065 .016 -.073 -.138 -.191 

Women 

 Employment rate (%) 48.4 57.3 65.0 74.5 77.2 44.0 54.2 59.5 64.5 64.2 

 Annual hrs. worked (1,000s) 1.60 1.61 1.55 1.68 1.77 1.61 1.60 1.56 1.68 1.72 

 Self-employment rate (%) 4.0 3.5 4.1 6.2 6.4 4.7 4.0 4.0 6.7 6.9 

 Unemployment rate (%) 5.3 5.0 6.2 5.5 4.5 6.2 5.5 7.4 8.2 7.7 

 Annual earnings ($1,000) 11.7 15.1 14.7 17.4 20.8 11.7 15.1 14.6 16.9 19.1 

 Hourly wage rate 8.6 10.7 10.4 10.7 12.2 8.7 10.9 10.4 10.6 11.9 

 Log wage differential --- --- --- --- --- .028 .030 -.015 -.038 -.098 

Notes: The statistics are calculated in the sample of persons not enrolled in school aged 18-64 as of the census year. 

The employment rate gives the percent of persons who worked at least 1 week in the year prior to the census; annual 

hours worked and the self-employment rate are calculated in the sample of workers; the unemployment rate is 

calculated in terms of the labor force participant’s status in the survey week. The means of the earnings variables are 

calculated in the sample of workers aged 25-64 who are not enrolled in school and are employed in the civilian 

sector. 
U.S. Department of Labor 

Employment and Training Administration 31 

Table 3. Tracking Age Cohorts of Male Immigrants Across Censuses 

(Log Wage Differential Between Immigrants and Comparably Aged Natives) 

Year 

1960 1970 1980 1990 2000

1955-1959 arrivals 

15-24 in 1960 --- .056 --- --- --- 

25-34 in 1960 -.060 .066 --- --- --- 

35-44 in 1960 -.094 .016 --- --- --- 

45-54 in 1960 -.111 -.054 --- --- --- 

1965-1969 arrivals 

5-14 in 1970 --- --- --- .048 .040 

15-24 in 1970 --- --- -.058 -.059 -.096 

25-34 in 1970 --- -.128 -.030 .011 .006 

35-44 in 1970 --- -.192 -.145 -.082 --- 

45-54 in 1970 --- -.267 -.235 --- --- 

1975-1979 arrivals 

5-14 in 1980 --- --- --- --- -.001 

15-24 in 1980 --- --- --- -.106 -.135 

25-34 in 1980 --- --- -.236 -.141 -.138 

35-44 in 1980 --- --- -.255 -.229 -.172 

45-54 in 1980 --- --- -.319 -.276 --- 

1985-1989 arrivals 

15-24 in 1990 --- --- --- --- -.186 

25-34 in 1990 --- --- --- -.269 -.192 

35-44 in 1990 --- --- --- -.326 -.309 

45-54 in 1990 --- --- --- -.414 -.409 

1995-1999 arrivals 

25-34 in 2000 --- --- --- --- -.167 

35-44 in 2000 --- --- --- --- -.254 

45-54 in 2000 --- --- --- --- -.392 

Notes: The relative wage is calculated in the sample of working men aged 25-64 who are not enrolled in school and 

who worked in the civilian sector. The hourly wage rate is defined as the ratio of total income earned annually to 

annual hours worked in the calendar year prior to the census. 
U.S. Department of Labor 

Employment and Training Administration 32 

Table 4. Hourly Wage Differentials Across National Origin Groups, 2000 

Log wage differential 

Country of origin

Percent of immigrant workforce 

belonging to national origin group All immigrants New arrivals

Mexico 31.9 -0.485 -0.686 

China 4.7 0.008 -0.180 

India 4.4 0.303 0.226 

Philippines 3.8 -0.010 -0.242 

Vietnam 3.4 -0.194 -0.486 

El Salvador 2.9 -0.420 -0.633 

Cuba 2.8 -0.201 -0.525 

United Kingdom 2.7 0.326 0.369 

Canada 2.3 0.300 0.348 

Korea 2.2 -0.006 -0.093 

Russia 1.9 -0.007 -0.212 

Dominican Republic 1.9 -0.376 -0.470 

Guatemala 1.7 -0.461 -0.654 

Germany 1.6 0.202 0.249 

Jamaica 1.6 -0.130 -0.357 

Correlation between 

1980 and 1990 

relative wage --- 0.914 0.754 

Correlation between 

1980 and 2000 

relative wage --- 0.883 0.833 

Notes: The relative wages are calculated in the sample of working men aged 25-64 who are not enrolled in school 

and who worked in the civilian sector. The “new arrivals” arrived in the United States between 1995 and 1999. The 

hourly wage rate is defined as the ratio of total income earned annually to annual hours worked in the calendar year 

prior to the census. 
U.S. Department of Labor 

Employment and Training Administration 33 

Table 5. The Impact of Immigration on the Earnings and 

Labor Supply of Native Workers 

Regression coefficients 

Predicted impact of an 

immigrant influx that increases 

the number of workers in a skill 

group by 10 percent 

Dependent variable Salaried workers All workers Salaried workers All workers

Log annual earnings -1.008 -1.143 -7.1% -8.0% 

(.457) (.506) 

Log weekly earnings -.532 -.648 -3.7% -4.5% 

(.202) (.223) 

Log weeks worked -.475 -.495 -3.4% -3.5% 

(.393) (.359) 

Notes: The standard errors of the regression coefficients are reported in parentheses and are clustered by schoolingexperience group. The regressions have 160 observations. The last two columns are obtained from the regression 

coefficients by multiplying by 0.7. 
U.S. Department of Labor 

Employment and Training Administration 34 

Table 6. Wage Consequences of Immigration in the 1980s and 1990s, 

Allowing for Cross-Effects 

(Predicted Percent Change in the Weekly Wage) 

Education 

Years of experience 

High school 

dropouts 

High school 

graduates Some college College 

graduates 

1-5 -6.1% -1.4% -1.2% -2.9% 

6-10 -10.3 -2.6 -1.8 -2.9 

11-15 -12.7 -3.1 -2.9 -4.2 

16-20 -12.4 -2.9 -3.7 -4.6 

21-25 -9.5 -2.4 -3.2 -4.3 

26-30 -7.3 -2.1 -2.7 -3.9 

31-35 -4.4 -1.7 -2.1 -3.3 

36-40 -1.8 -0.5 -1.5 -4.1 

All workers -7.4 -2.1 -2.3 -3.6 

Notes: The averages reported in the bottom row are weighted averages of the within-education effects using the 

1980 native population as weights. The calculations assume that the capital stock is constant. 
U.S. Department of Labor 

Employment and Training Administration 35 

Table 7. Trends in Migration Rates of Native Workers 

Year 

Education group Region 1970 1980 1990 2000

Net migration rates 

 All workers California 1.2% -0.2% 1.1% -2.0% 

Other immigrant states 0.1 -0.2 -0.9 -0.8 

Rest of country -0.2 0.1 0.2 0.6 

 High school dropouts California -0.2 -2.2 -2.3 -5.4 

Other immigrant states 0.3 -0.2 -1.6 -1.4 

Rest of country -0.1 0.3 0.8 1.0 

 High school graduates California 0.3 -1.4 -0.8 -4.5 

Other immigrant states 0.0 -0.1 -0.8 -0.9 

Rest of country 0.0 0.2 0.4 0.7 

 Some college California 1.3 -1.0 0.3 -3.4 

Other immigrant states -0.4 -0.3 -0.9 -0.8 

Rest of country -0.2 0.4 0.3 0.9 

 College graduates California 4.4 3.3 4.5 1.9 

Other immigrant states 0.3 -0.3 -0.8 -0.4 

Rest of country -1.0 -0.4 -0.5 -0.1 

In-migration rates 

All workers California 9.5 9.7 9.3 7.1 

Other immigrant states 6.1 7.5 6.8 6.4 

Rest of country 3.0 3.7 3.6 3.4 

Out-migration rates 

All workers California 8.3 9.9 8.2 9.1 

Other immigrant states 6.0 7.7 7.8 7.2 

Rest of country 3.2 3.6 3.4 2.8 

Notes: This table breaks up the country into three distinct regions: California, the other immigrant states, and the rest 

of the country. The “other immigrant states” include Florida, Illinois, New Jersey, New York, and Texas. A native 

worker is defined to migrate if he moves from one of these regions to another in the 5-year period prior to the 

census. 
U.S. Department of Labor 

Employment and Training Administration 36 

Table 8. Regression Coefficients from Migration Analysis

Regression coefficient Predicted change in number of 

natives per additional immigrant 

Dependent variable State Metropolitan area State Metropolitan area

Log of native workforce -.278 -.836 -0.195 -0.585 

(.081) (.090) 

Native net migration rate -.282 -.404 -0.197 -0.283 

(.064) (.084) 

Native in-migration rate -.150 -.116 -0.105 -0.081 

(.042) (.055) 

Native out-migration rate .132 .288 0.092 0.202 

(.049) (.062) 

Notes: The standard errors of the regression coefficients are reported in parentheses and are clustered by skill-region 

cells. The regressions have 160 observations. The last two columns are obtained from the regression coefficients by 

multiplying by 0.7. The regressions also control for the skill-region cell’s unemployment rate and mean log weekly 

wage, and the regressions on the native workforce include the lagged value of the log of the native workforce, while 

the migration regressions include the lagged value of the growth rate of the native workforce. 
U.S. Department of Labor 

Employment and Training Administration 37 

Table 9. Migration Rates of Immigrants Who Have Been 

in Country More Than 5 Years 

Year 

Education group Region 1970 1980 1990 2000

Net migration rates 

All workers California 4.1% 2.3% 0.7% -4.0% 

Other immigrant states -0.2 -1.1 -1.2 -1.3 

Rest of country -2.2 -0.3 1.2 6.5 

High school dropouts California 3.2 0.0 -0.9 -6.7 

Other immigrant states -0.3 -0.6 -1.5 -1.6 

Rest of country -1.6 1.1 5.8 12.6 

High school graduates California 2.9 2.5 -0.1 -6.0 

Other immigrant states -0.4 -1.4 -0.9 -1.4 

Rest of country -1.1 0.4 1.7 8.4 

Some college California 6.1 1.1 1.5 -2.9 

Other immigrant states -1.8 -1.4 -1.1 -1.1 

Rest of country -2.6 0.9 -0.2 4.7 

College graduates California 7.0 7.9 4.2 2.2 

Other immigrant states 1.5 -1.4 -1.4 -1.1 

Rest of country -4.3 -2.9 -1.7 -0.3 

In-migration rates 

All workers California 7.8 6.2 4.7 3.2 

Other immigrant states 4.5 4.1 4.2 4.5 

Rest of country 5.4 7.4 9.6 12.7 

Out-migration rates 

All workers California 3.7 4.0 3.9 7.2 

Other immigrant states 4.7 5.2 5.5 5.8 

Rest of country 7.5 7.7 8.3 6.2 

Notes: This table breaks up the country into three distinct regions: California, the other immigrant states, and the rest 

of the country. The “other immigrant states” include Florida, Illinois, New Jersey, New York, and Texas. A native 

worker is defined to migrate if he moves from one of these regions to another in the 5-year period prior to the 

census. 
U.S. Department of Labor 

Employment and Training Administration 38 

Table 10. The Economic Benefits from Immigration, 1960–2000 

Year 

1960 1970 1980 1990 2000

Labor’s share of income 0.7 0.7 0.7 0.7 0.7 

Factor price elasticity -0.35 -0.35 -0.35 -0.35 -0.35 

Immigrant share 0.052 0.049 0.066 0.089 0.134

As percent of GDP 

Change in labor earnings -1.20% -1.14% -1.52% -1.99% -2.84%

Change in firm’s profits 1.23% 1.17% 1.57% 2.09% 3.05%

Immigration surplus 0.03% 0.03% 0.05% 0.10% 0.22%

In billions of 2002 dollars 

Change in labor earnings -$30.05 -$42.98 -$78.26 -$141.63 -$278.37 

Change in firm’s profits $30.87 $44.08 $81.03 $148.57 $299.82 

Immigration surplus $0.82 $1.10 $2.78 $6.94 $21.46 
Appendix C: Figures 

Figure 1. Immigration and the Workforce, 1960–2000 

Notes: The workforce is defined as the group of persons aged 18 to 64 who are not enrolled in school and who 

worked in the civilian sector at least 1 week in the year prior to each decennial census. 

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

1960 1970 1980 1990 2000

Year

Immigrant share

0

2

4

6

8

10

12

14

16

Number of immigrant

s

(in millions) 

Immigrant share

Number of immigrants

U.S. Department of Labor 

Employment and Training Administration 39 
Figure 2. The Geographic Settlement of Immigrants 

A. Trends in Clustering 

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

1960 1970 1980 1990 2000

Year

Fraction of immigrants living in area

California

Other imm. 

states

Rest of country

B. Trends in the Immigrant Share 

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

1960 1970 1980 1990 2000

Year

Immigrant share

California

Other imm. 

states

Rest of country

Notes: The workforce is defined as the group of persons aged 18 to 64 who are not enrolled in school and who 

worked in the civilian sector at least 1 week in the year prior to each decennial census. The “other immigrant states” 

include Florida, Illinois, New Jersey, New York, and Texas. 

U.S. Department of Labor 

Employment and Training Administration 40 
Figure 3. The Immigrant Share in the Workforce, By Educational Attainment 

0.0

0.1

0.2

0.3

0.4

1960 1970 1980 1990 2000

Year

Immigrant share

High school dropouts

College graduates

Some college

High school graduates

Notes: The workforce is defined as the group of persons aged 18 to 64 who are not enrolled in school and who 

worked in the civilian sector at least 1 week in the year prior to each decennial census.

U.S. Department of Labor 

Employment and Training Administration 41 
Figure 4. Trends in the Relative Wage of Male Immigrant Cohorts 

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

1960 1970 1980 1990 2000

Year

Log wage gap

1-5 yrs in US

5-10 yrs

10-15 

Notes: The relative wage is calculated in the sample of working men aged 25-64 who are not enrolled in school and 

who worked in the civilian sector. The hourly wage rate is defined as the ratio of total income earned annually to 

annual hours worked in the calendar year prior to the census. 

U.S. Department of Labor 

Employment and Training Administration 42 
Figure 5. The Effect of High-Tech Immigration on the Relative Wage 

of Newly Arrived Immigrant Men

-0.5

-0.4

-0.3

-0.2

-0.1

0

1960 1970 1980 1990 2000

Year

Log wage differential

All immigrants, 1-5 years

Excluding engineers and 

computer scientists

Notes: The relative wage is calculated in the sample of working men aged 25-64 who are not enrolled in school and 

who worked in the civilian sector. The hourly wage rate is defined as the ratio of total income earned annually to 

annual hours worked in the calendar year prior to the census.

U.S. Department of Labor 

Employment and Training Administration 43 
Figure 6. Economic Assimilation Experienced by Immigrant Men 

Notes: The relative wage is calculated in the sample of working men who are not enrolled in school and who worked 

in the civilian sector. The hourly wage rate is defined as the ratio of total income earned annually to annual hours 

worked in the calendar year prior to the census.

-0.3

-0.2

-0.1

0

0.1

1960 1970 1980 1990 2000

Year

Percent

Arrived in 1955-59

Arrived in 1965-69

Arrived in 1975-79

Arrived in 1985-89

Relative wage of immigrants who arrived 

when they were 25-34 years old

Arrived in 1995-99

U.S. Department of Labor 

Employment and Training Administration 44 
Figure 7. Immigration By Skill Group, 1960–2000

A. High School Dropouts B. High School Graduates

0

0.10.20.30.40.5

0 10 20 30 40

Years of experience

1960

19701980

1990

2000

Immigrant share

0

0.05

0.1

0.15

0 10 20 3

Years of experience

1960

1980

1990

2000

Immigrant share

C. Some College D. College Graduates

0

0.05

0.1

0.15

0 10 20 30 40

Years of experience

1960

1970

1980

1990

2000

Immigrant share

0

0.05

0.1

0.15

0.2

0 10 20 

Years of experience

Immigrant share

1960

1980

1990

2000

Notes: Within each education group, workers are aggregated into experience groups defined in 5-year intervals. The immigrant shares asample of men aged 18-64 who are not enrolled in school and who worked in the civilian sector at least 1 week in the calendar year preU.S. Department of Labor Employment and Training Administration 
Figure 8. Scatter Diagram Relating Wages and Immigration, 1960–2000

Note: Each point in the scatter represents the adjusted decadal change in the log weekly wage and the immigrant 

share for a native education-experience group. The statistics are calculated in the sample of men aged 18-64 who are 

not enrolled in school and who worked in the civilian sector at least 1 week in the calendar year preceding the 

census. 

-0.10

-0.05

0.00

0.05

0.10

-0.06 -0.04 -0.02 0 0.02 0.04

Change in immigrant share

Change in log weekly wage

U.S. Department of Labor 

Employment and Training Administration 46 
Figure 9. Immigration and the Geographic Sorting of the Native Population 

A. California 

B. Other Immigrant States 

6.0%

7.0%

8.0%

9.0%

10.0%

1950 1960 1970 1980 1990 2000

Year

Percent of natives 

living in region

6.0%

14.0%

22.0%

30.0%

38.0%

Immigrant share

Native trend

Immigration

23.5%

24.5%

25.5%

26.5%

1950 1960 1970 1980 1990 2000

Year

Percent of natives 

living in region

6.0%

12.0%

18.0%

24.0%

Immigrant share

Native trend

Immigration

C. Rest of the Country 

64.0%

65.0%

66.0%

67.0%

1950 1960 1970 1980 1990 2000

Year

Percent of natives 

living in region

0.0%

4.0%

8.0%

12.0%

16.0%

Immigrant share

Native trend

Immigration

Note: The statistics refer to men aged 18-64 who are not enrolled in school and who worked in the civilian sector at least one 

week in the calendar year preceding the census. The “other immigrant states” are Florida, Illinois, New Jersey, New York, and 

Texas.

U.S. Department of Labor 

Employment and Training Administration 47 
Figure 10. Scatter Diagram Relating the Growth Rate 

 of the Native Workforce and Immigration

A. State Level Data 

B. Metropolitan Area Level Data 

Notes: Each data point represents the decadal change (between 1960 and 2000) observed in the log size of the native 

workforce and the change in the immigrant share for a particular skill group in a particular geographic area. Both 

plots remove decade effects from the data. The statistics are calculated in the sample of men aged 18-64 who are not 

enrolled in school and who worked in the civilian sector at least 1 week in the calendar year preceding the census.

U.S. Department of Labor 

Employment and Training Administration 48 
Figure 11. Location Decisions of Newly Arrived Immigrants 

0.1

0.2

0.3

0.4

0.5

1960 1965 1970 1975 1980 1985 1990 1995 2000

Year

Share of all immigrants 

living in region

Other immigrant 

states

California

Rest of country

Note: The statistics are calculated in the sample of men aged 18-64 who are not enrolled in school and who worked 

in the civilian sector at least 1 week in the calendar year preceding the census. The “other immigrant states” include 

Florida, Illinois, New Jersey, New York, and Texas. 

U.S. Department of Labor 

Employment and Training Administration 49 
Figure 12. The Economic Benefits from Immigration 

U.S. Department of Labor 

Employment and Training Administration 50 

0 

Dollars 

Employment 

A

B

L=N+M 

S′

w0

w1 D

C

Demand 

S

N 

Notes: Prior to immigration, there are N native workers in the economy and national income is given by the 

trapezoid ABN0. Immigration increases the labor supply to L workers and national income is given by the trapezoid 

ACL0. Immigrants are paid a total of DCLN dollars as salary. The immigration surplus gives the increase in national 

income that accrues to natives and is given by the area in the triangle BCD.