International Migration, Self-Selection, and the Distribution of Wages:
Evidence from Mexico and the United States

August 2004
Daniel ChiquiarBank of Mexico
Gordon H. Hanson
University of California, San Diego andNational Bureau of Economic Research

Abstract. In this paper, we use data from the 1990 and 2000 Mexico and U.S. population
censuses to examine who migrates from Mexico to the U.S. and how their skills andeconomic performance compare to those who remain in Mexico. We use these data to test
Borjas’ (1987) negative-selection hypothesis that in countries with high returns to skill
and high earnings inequality (e.g., Mexico) those most likely to migrate to countries withlow returns to skill and low earnings inequality (e.g., the U.S.) are the less skilled. We
find that 1) Mexican immigrants, while much less educated than U.S. natives, are on
average more educated than residents of Mexico, and 2) were Mexican immigrants in the
U.S. to be paid according to current skill prices in Mexico they would tend to occupy the
middle and upper portions of Mexico’s wage distribution. These results are inconsistent
with the negative-selection hypothesis and suggest, instead, that in terms of observableskills there is intermediate or positive selection of immigrants from Mexico.

We thank Kate Antonovics, Julian Betts, Richard Carson, Gary Ramey, James Rauch,
Chris Woodruff and seminar participants at American University, the Banco de Mexico,
the CIA, El Colegio de Mexico, the University of Houston, the NBER, Occidental
College, Stanford University, UC Berkeley, UC San Diego, the University of Toronto,
and Yale University for helpful comments. The opinions in this paper correspond to theauthors and do not necessarily reflect the viewpoint of the Banco de México.

1. Introduction
In recent decades, rising immigration from poor countries has made the U.S. labor
force larger, younger, and less-skilled than it otherwise would have been (Borjas, 1999).
The shift in immigrant composition appears due in part to the 1965 Immigration Act,
which relaxed long-standing country-of-origin restrictions on immigrant admissions.
In an important body of work, Borjas (1987) argues that who migrates to the U.S.
from a particular country will depend on that country’s wage distribution. In countries
with high returns to skill and high wage dispersion, as in much of the developing world,
there will be negative selection of immigrants. Those with the greatest incentive to
migrate to the U.S. will be individuals with below-average skill levels in their homecountries. In countries with low returns to skill and low wage dispersion, as appears to be
the case in Western Europe, there will be positive selection of immigrants. Those withabove-average skill levels will have the greatest incentive to migrate. In support of thisselection hypothesis, Borjas (1987, 1995) finds that as sources for U.S. immigration haveshifted from Europe to Asia and Latin America, the economic performance of new
immigrants has deteriorated. Relative to earlier cohorts, recent immigrants earn lower
wages compared to natives at time of arrival and take longer for their earnings toconverge to native levels.1 These findings counter an earlier belief that, irrespective oforigin country, immigrants have high potential for earnings growth (Chiswick, 1978).2
Largely missing in the discussion of U.S. immigration is evidence from source
countries. Surprisingly, there is little work on how the skills of immigrants compare to
1

Identifying changes in the average quality of immigrant cohorts is complicated by changes in unobserved cohort
quality, immigrant assimilation, and labor-market disturbances that vary by skill group. See LaLonde and Topel (1992,
1997), Borjas (1999) and Butcher and DiNardo (2002) on how to deal with this issue.
2
Evidence of positive selection includes brain drain from poor countries (Beine et al., 2001; Carrington et al., 1998)
and the internal migration of skilled workers (Borjas, Bronars, and Trejo, 1992; Bound and Holzer, 2000).

1

the skills of non-migrating individuals in countries of origin. Such data are essential toevaluate the nature of migrant selection. One exception is Ramos (1992) who uses 1980
census data for the U.S. and Puerto Rico. Consistent with negative selection, nonmigrants in Puerto Rico are more educated than individuals migrating from Puerto Ricoto the U.S. and less educated than those migrating from the U.S. to Puerto Rico.3
In this paper, we use data from the 1990 and 2000 Mexico population censuses
and data on Mexican immigrants in the 1990 and 2000 U.S. population censuses to
examine who in Mexico migrates to the U.S. and how their earnings and observable skills
compare to those who remain at home. Mexico is the largest source country for U.S.
immigration, accounting for 31.3% of net new arrivals in the 1990’s. In 2000 the 9.2
million Mexican immigrants in the U.S. were equal to 9.4% of Mexico’s total population.
Mishra (2003) estimates that over the period 1970-2000 emigration raised average wages
in Mexico by 8.0%.4 Relative to the U.S., Mexico has high returns to schooling and highwage dispersion, making it an ideal candidate to test the negative-selection hypothesis.
Following Borjas (1991), we develop a simple model, presented in section 2, toshow that migrant selection in a country like Mexico may be negative, intermediate, or
positive, depending on the size of migration costs and how they vary with skill. A simple
test for negative selection is to compare the observable skills of those who migrate and of
those who do not. In section 3, we find that Mexican immigrants, while much lesseducated than U.S. natives, are more educated than residents of Mexico. Individuals with3

To compare Ramos’ results to ours, it is important to note that many costs of migrating to the U.S. that are relevantfor Mexico (binding quotas, border enforcement, bureaucratic delays) are not relevant for Puerto Rico.
4
In the U.S., many studies find that regional immigration inflows are only weakly correlated with wage changes forlow-skilled U.S. natives, suggesting immigration has little impact on U.S. wages (LaLonde and Topel, 1997; Smith andEdmonston, 1997; Borjas, 1999). However, Borjas, Freeman, and Katz (1997) and Borjas (2003) argue that commonlyused cross-area wage regressions require strong and unrealistic identifying assumptions. Using alternative approaches,
these two studies find that higher immigration depresses wages for low-skilled U.S. natives.

2

10 to 15 years of schooling are the Mexican cohort most over-represented in the U.S.5
This is suggestive evidence against the negative selection of Mexican immigrants interms of observable skill.6 However, schooling may not be a sufficient statistic for skill
or for potential earnings. To compare migrants and non-migrants, we would prefer to see
what each would earn in the same labor market, under a common price for skill.
Realized earnings of non-migrants, which reflect Mexican skill prices, and of migrants,
which reflect U.S. skill prices, are not very informative.
To evaluate the selection of Mexican immigrants in terms of observable skills, we
compare actual wage densities for residents of Mexico with counterfactual wage densities
that would obtain were Mexican immigrants paid according to skill prices in Mexico.
The difference between these actual and counterfactual wage densities nonparametricallysummarizes immigrant selection in terms of potential earnings.

To construct these

densities, we extend the framework in DiNardo, Fortin, and Lemieux (1996), as shown insection 4. The results, presented in section 5, suggest that were Mexican immigrants in
the U.S. paid according to Mexican skills prices they would fall disproportionately in the
middle and upper portions of Mexico’s wage distribution. These findings do not support
negative selection and suggest instead there is intermediate or positive selection of
Mexican immigrants. Also in section 5, we examine the robustness of these results.

2. Theory
In this section, we motivate the empirical analysis by developing a simple modelof migration. Borjas (1987) applies Roy (1951) to show that in countries with relatively

56

In related work, Feliciano (2001) finds that before 1990 average schooling was also higher for Mexican immigrants.
Case-study evidence supports this view. See Orrenius et al. (2004), Durand et al. (2001) and Cornelius et al. (2001).

3

high returns to skill and earnings inequality migrants tend to be negatively selected – they
are drawn primarily from the lower half of the skill distribution in their home country.
Borjas (1991) shows that this result depends on assuming migration costs are constantacross individuals. If migration costs are negatively correlated with earnings, negative
selection may be overturned. We apply this insight to show that if migration costs are
decreasing in skill migrants may be negatively or positively selected in terms of skill,depending on the size of migration costs and the shape of the skill distribution.
Individuals from Mexico, indexed by 0, choose whether or not to migrate to the
U.S., indexed by 1. For simplicity, we treat this as a one-time decision, though theextension to a dynamic setting is straightforward (see Sjaastad, 1962; Borjas, 1991).
Residents of Mexico face a wage equation given byln( w0 ) = µ 0 + δ 0 s(1)

where for Mexico w0 is the wage, µ0 is the base wage, s is the level of schooling, and δ 0
is the return to schooling. We focus on migrant selection in terms of observable skills, inthis case schooling.

Implicitly, we imagine there are random components to wage

determination, but for simplicity we leave such features in the background.

If thepopulation of Mexicans were to migrate to the U.S., they would face the wage equationln(w1 ) = µ1 + δ 1 s(2)where for Mexican migrants in the U.S. w1 is the wage, µ1 is the base wage, and δ 1 isthe return to schooling. Consistent with skill being scarce in Mexico, we assume thatδ 0 > δ 1 , or that the return to schooling is higher in Mexico than in the U.S.
Let C be migration costs and let π = C / w0 be migration costs in time-equivalent4

units (i.e., the number of labor hours needed to migrate to the U.S.). Combining (1) and(2), a resident of Mexico will migrate to the U.S. ifln(w1 ) − ln(w0 + C ) ≈ ln(w1 ) − ln(w0 ) − π > 0 .

(3)Borjas (1987, 1999) assumes that π is constant, implying that all individuals require thesame number of labor hours in order to migrate to the U.S. This assumption simplifies
the analysis, but may not be an accurate reflection of reality. We assume instead thattime-equivalent migration costs decrease with schooling, such thatln(π ) = µπ − δ π s .

(4)This corresponds to the case in Borjas (1991), in which the random component of
earnings is negatively correlated with the random component in migration costs. Why
might migration costs decrease with schooling? First, individuals migrating legally to the
U.S. must satisfy many bureaucratic requirements, involving extensive paperwork andrepeated interactions with U.S. immigration authorities. More-educated individuals may
be able to meet these requirements more easily.7 Second, a large service industry oflawyers and other specialists exists to help migrants manage the U.S. admissions process.
Given the cost of these services is more or less fixed, the time-equivalent cost of
migration will be lower for individuals with higher hourly wages. There is also a large
service industry oriented towards illegal immigrants (Orrenius, 1999). To enter the U.S.
successfully, an illegal entrant must cross the border, find transport to a safe location in
the U.S., and obtain counterfeit residency documents. These costs are also fixed,
implying higher-wage individuals require fewer effective labor hours to migrate to the7Over 90% of legal Mexicans immigrants in the U.S. are admitted under family reunification provisions of U.S.
immigration law. While obtaining legal assistance cannot change an individual’s eligibility for admission, it may help
an individual clear the queue for legal admission more quickly.

5

U.S. Third, credit constraints may raise migration costs for low-income individuals, who
are also likely to be less educated. Individuals may have to borrow to cover migration
costs. If lower-income individuals face higher borrowing costs, due to a higher expected
probability of default, they will face higher migration costs.8
Combining (3) and (4), Figure 1 shows ln( w0 ) and ln( w1 ) − π , which defines
the cutoff schooling level for who migrates to the U.S. in the case where δ π = 0 andµ 1 − µ 0 > e µπ . Here, time-equivalent migration costs are constant and small, whichcorresponds to the assumptions in Borjas (1987). He focuses on unobservable skills but
the analogy to observable skills is straightforward. In Figure 1, there is negative selectionof migrants: individuals with schooling less than s* migrate from Mexico to the U.S. andindividuals with schooling greater than s* remain in Mexico. Individuals with relatively
high levels of schooling are less likely to migrate to the U.S. due to the fact that the returnto schooling in higher in Mexico.
Figure 2 shows an alternative case in which δ π > 0 and µ 1 − µ 0 < e µπ .9 Here,time-equivalent migration costs are decreasing in schooling and are large. Individuals
with schooling in the interval (sL, sU) migrate from Mexico to the U.S. and those withschooling outside this interval remain in Mexico. The selection of migrants in terms ofobservable skills depends on the distribution of schooling in Mexico. There are threepossible cases: (a) negative selection: if the support for the schooling distribution goesfrom some value between sL and sU to some value greater than sU, migrants will have low
8
A related possibility is that more-educated individuals may face less uncertainty with regards to the U.S. wages theywould earn, making them more likely to migrate for any given wage differential.

6

schooling relative to those who remain in Mexico; (b) positive selection: if the support ofthe schooling distribution runs from some value below sL to some value between sL andsU, migrants will have relatively high levels of schooling; or (c) intermediate selection: if
the support of the schooling distribution goes from some value below sL to some value
above sU, migrants will have intermediate levels of schooling.

In case (c), fixedmigration costs preclude those with low schooling from migrating and high returns toschooling in Mexico dissuade those with high schooling from migrating, giving thosewith intermediate schooling the strongest incentive to migrate to the U.S.
One caveat is that our analysis ignores migration networks, which appear to beimportant in Mexico (Munshi, 2003; Woodruff and Zenteno, 2001). Individuals withfriends or relatives in the U.S. may face lower migration costs. A second caveat is that
we ignore unobservable skills. If the correlation between observable and unobservableskills is positive and strong, we expect our results would apply to migrant selection interms of unobservable skills, as well.

In the empirical analysis, we discuss how

migration networks and unobserved determinants of migration might affect our results.

3. Data and Preliminary Evidence
To compare outcomes for residents of Mexico and immigrants from Mexico, we
use 1% samples from Mexico’s 1990 and 2000 Census of Population and Housing andthe 5% Public Use Microdata Sample from the U.S. 1990 and 2000 Census of Populationand Housing. For much of the analysis, we focus on recent Mexican immigrants, definedas those arriving in the U.S. within the last 10 years. They reflect individuals admittedµ

If µ 1 − µ 0 > e π (migration costs are small), then even if migration costs are decreasing in schooling there is still
an unambiguous prediction for the negative selection of migrants. An additional assumption needed to obtain Figure 2

97

under current U.S. policy. One measurement issue is that many Mexican immigrants arein the U.S. illegally. The U.S. census shows 4.3 million Mexican-born individuals in the
U.S. in 1990 and 9.2 million in 2000. Of these, the Census Bureau estimates 1.0 million
were illegal immigrants in 1990 and 3.9 million were in 2000 (Costanzo et al., 2001). By
its own estimation, the Census Bureau undercounts illegal immigrants by 15-20%.
Others suggest the undercount rate is higher.

The Immigration and Naturalization

Service (2003) puts the number of illegal Mexicans at 2.0 million in 1990 and 4.8 million
in 2000, close to estimates by Bean et al. (2001) and Passel et al. (2004).10 In section 5
we assess how undercounting illegal immigrants may affect our results.

3.1 Summary Data
Overall, the accumulated outflows of individuals born in Mexico are large. Table
1 shows Mexican immigrants in the U.S. as a percentage of the total population born in
Mexico by age cohort for males and females. We take the total Mexico-born population
to be the sum of the Mexico-born population in Mexico and the Mexico-born populationin the U.S. Consider first the cohort of males born in Mexico who were 16-25 years old
in 1990 (and thus 26-35 years old in 2000). The fraction of this cohort residing in the
U.S. was 7.6% in 1990 and 17.5% in 2000, implying that during the 1990’s about 10% of
the cohort migrated to the U.S. Consider next the cohort of males who were 26-35 years
old in 1990. The fraction of this cohort residing in the U.S. rose from 10.9% in 1990 to
15.5% in 2000, implying a within-decade emigration rate of about 4.6%. Within-decadeµ − δ πsis that ∂ (ln w 1 − π) / ∂s = δ1 + δ π e π> δ 0 for small s.

10

Most estimates of the illegal immigrant population subtract from the enumerated immigrant population new legal
immigrant admissions (less estimated departures and deaths for these individuals). This residual foreign born
population is taken to be illegal immigrants. See Bean et al. (2001), Costanzo et al. (2001), and INS (2003).

8

migration rates decline for each succeeding cohort. This suggests migration rates from
Mexico to the U.S. are highest for young adults. Comparing the stock of individuals in
the U.S. for different cohorts at the same age suggests that migration rates are rising overtime. The share of males 16-25 years old who resided in the U.S. rose from 7.6% in 1990
to 12.0% in 2000, and rose also for every other age group. For women migration rates
and migrant stocks are lower, but patterns are similar.
Tables 2a-2d show means for age, schooling, labor-force participation, and hourly
wages for residents of Mexico, Mexican immigrants in the U.S., and, for comparison,other U.S. immigrants and U.S. natives.

We choose education categories that arereported in the U.S. census (Mexico reports more categories).

Fortunately, thesecategories correspond to modes for high grade of schooling completed in Mexico, which
occur at grade 6 (primary schooling), grade 9 (secondary schooling), and grade 12(preparatory schooling).11

Grogger and Trejo (2002) report that Mexican immigrants

who arrive in the U.S. before age 6 complete as much education as second-generation
Mexican-Americans. In contrast, those who arrive after age 15 complete much lessschooling. To focus on migrants likely to have been schooled in Mexico, we limit theimmigrant sample to individuals aged 21 or older at time of U.S. entry.
Tables 2a-2d reproduce the familiar facts that when compared to U.S. natives
Mexican immigrants in the U.S. are younger, are much less educated, and have much
lower hourly wages. In 1990, 68.3% of all Mexican immigrant men and 62.8% of recent

11

For Mexico, average hourly wages are calculated as monthly labor income/(4.5*hours worked last week); for the
U.S., average hourly wages are calculated as annual labor income/(weeks worked last year*usual hours worked perweek). For Mexico, we need to assume individuals work all weeks of a month, which could bias wage estimates
downwards. However, this does not affect the results in section 5 since in no exercise do we compare Mexico and U.S.
wage levels. To avoid measurement error associated with implausibly low wage values or with top coding of earnings,
we restrict the sample to be individuals with hourly wages between $0.05 and $20 in Mexico and $1 and $100 in the
U.S. (in 1990 dollars). This restriction is nearly identical to dropping the largest and smallest 0.5% of wage values.

9Mexican immigrant men had completed 9 or fewer years of school, compared to only
7.3% of U.S. native men. However, Mexican immigrants, and recent immigrants inparticular, compare favorably when we examine residents of Mexico. In 1990, 75.2% ofmale residents of Mexico had 9 or fewer years of schooling.

Beyond 9 years ofeducation, Mexican immigrants outperform Mexican residents in every category except
college graduates. Relative to male residents of Mexico, recent Mexican immigrant men
are less likely to have 9 or fewer years of education (62.8% versus 75.2%), more likely tohave 10-15 years of education (32.4% versus 16.3%), and less likely to have 16 plus
years of education (4.8% versus 8.4%). A similar pattern holds for women.
Over time, educational attainment among the Mexico-born has increased, but thishas not changed the gap in educational attainment between Mexican residents and
Mexican immigrants. In 2000, relative to male residents of Mexico, recent Mexican
immigrant men remain less likely to have 9 or fewer years of education (55.8% versus
69.4%), more likely to have 10-15 years (38.8% versus 19.3%), and less likely to have 16
plus years (5.4% versus 11.3%). Again, a similar pattern holds for women.
Tables 2a-2d give preliminary evidence against the negative-selection hypothesis.
In terms of observable skills, it is the moderately well educated, not the least educated,
who are most likely to migrate from Mexico to the U.S. One concern about this evidence
is that recent Mexican immigrants may have high levels of schooling in part because they
are relatively young and educational attainment in Mexico has been rising over time. To
control for age, Table 3 shows average schooling for 26-35 year old Mexican residents
and Mexican immigrants in the U.S. For this high-migration age cohort, it remains thecase that Mexican immigrants have high schooling relative to Mexican residents.

10

A related concern is that Mexican immigrants may obtain schooling after arrivingin the U.S., in which case the U.S. census would overstate educational attainment of
Mexican immigrants at the time they left Mexico. Additional schooling may take theform of degree-oriented learning, or, more commonly, English language classes. We
have dealt with this issue in part by restricting the sample to those who were age 21 years
or older at time of arrival in the U.S. Adults appear less likely to continue schooling in
the U.S. Some adult immigrants may further their education by satisfying a high-school
equivalency requirement through passing the General Education Development (GED)exam.12 The bunching of Mexican immigrants at exactly 12 years of education (relative
to Mexican residents) in Tables 2a-2d could be consistent with such behavior.
Available evidence indicates few Mexican immigrants pass the GED. Using the
CPS, Clark and Jaeger (2002) find that among Mexican immigrants who lack a highschool diploma and who completed their schooling abroad only 1.2% had passed the
GED. And among Mexican immigrants who completed some schooling in the U.S. (mostof whom arrived in the U.S. as young children) only 3.7% had passed the GED. Moregenerally, while Betts and Lofstrom (1998) find that school enrollment rates for adult
immigrants are higher than for adult U.S. natives, the same does not hold for immigrants
from Mexico (Borjas, 1996; Trejo, 1997). Table 3 shows schooling levels for 26-35
year-old Mexican immigrants who have been in the U.S. 0-3 years or 4+ years. In 1990
and 2000, earlier arrivals are not less likely to have 12 years of schooling, suggesting
adult immigrants are unlikely to continue formal education after arriving in the U.S.

12

On the returns to a GED, see Grogger and Trejo (2002), Clark and Jaeger (2002), Cameron and Heckman (1993),
and Murnane, Willett, and Tyler (2000).

11

3.2 Labor-Force Participation in Mexico and the United States
Returning to Tables 2a and 2b, there appear to be differences in labor-force
participation rates between residents of Mexico and Mexican immigrants in the U.S.
Table 4 reports the fraction of the population of Mexican residents and of recent Mexican
immigrants in the U.S. with positive labor earnings by year, age, and schooling cells.
This definition of labor-force participation reflects the sample of individuals for whom
we have observations on wages. For males 26-55 years of age with more than four yearsof education, labor-force participation rates in the two countries are similar. Participation
rates are somewhat higher for Mexican immigrant males in the oldest cohort (56-65
years), and in the cohort with least schooling (0-4 years).
However, labor-force participation rates for women differ markedly between
migrants and non-migrants.

Among women with 11 or fewer years of education,

immigrants are much more likely to have positive labor earnings. This could be due tomore elastic female labor supply, in which case higher wages in the U.S. would induce
higher rates of labor-force participation. Alternatively, women who are more likely towork at any wage level may be more likely to self-select into migration. In either case,
Mexican immigrant women in the U.S. who work may differ from the subpopulation ofthese women that would work were they to return to Mexico.13
This poses a problem for the empirical analysis.

Differences in labor–force

participation between migrant and non-migrant females may affect the pattern of migrant
selection we uncover from data on wage earners. We return to this issue in section 4.

13

See Baker and Benjamin (1997) for further discussion of immigrant male and female labor supply.

12

3.3 Returns to Observable Skill in Mexico and the United States
A primary motivation for individuals in Mexico to emigrate is to earn higher
wages. The model in section 2 assumes that the base wage (the wage of an individualwith minimal skill) is higher in the U.S. and that returns to skill are higher in Mexico.
Available evidence is consistent with these assumptions. For Mexican immigrants in the
U.S., estimated returns to education are low. In the 1980’s and 1990’s, an additional yearof schooling is associated with an increase in log wages for men of 0.025 to 0.032(Borjas, 1996; Trejo, 1997; Groger and Trejo, 2002).14 In Mexico in the 1990’s, anadditional year of schooling is associated with an increase in log wages for men of 0.076
to 0.097 (Chiquiar, 2003).15 Figure 3 shows kernel density estimates for wages of
Mexican immigrants and Mexican residents (individuals 21-65 years of age, where
immigrants were at least 21 years of age at time of U.S. entry and immigrated within theprevious 10 years). Not surprisingly, mean wages in Mexico are much lower.16
To summarize differences in returns to observable skills in the two countries, weestimate OLS wage regressions for four samples of men: residents of Mexico, recent
Mexican immigrants in the U.S. (those arriving in the last 10 years), all Mexican
immigrants in the U.S., and other U.S. immigrants.

Table 5 reports the results.

Unreported results for women are similar. The regressors are dummy variables forschooling, age group, marital status, residence in a metro area, region of residence, race(for other U.S. immigrants only), and year of entry into the U.S. (for immigrants only).

14

Bratsberg and Ragan (2002) estimate slightly higher returns to schooling (0.035) for a sample of U.S. immigrant men
from any country. In all samples, the estimated return to education for U.S. natives is roughly twice as large.
15
On the returns to education in Mexico, see also Cragg and Epelbaum (1996) and Ariola and Juhn (2003).
16
Figure 3 suggests wage dispersion is lower among Mexican immigrants in the U.S. than among residents of Mexico.
However, since immigrants tend to be more homogeneous than the population from which they came, it is natural forthem to exhibit lower wage dispersion. This is a direct implication of self-selection (Heckman and Honore, 1990).

13

Estimated returns to schooling for residents of Mexico are much higher than for
Mexican immigrants.17 In 2000, completing 12 years of schooling is associated with anincrease in hourly wages of 60.5 log points for men in Mexico but only 11.2 to 15.4 log
points for Mexican men in the U.S. The difference is even larger for the 13-15 years and16+ years of education categories. Returns to age are also higher for Mexican residents
than for Mexican immigrants. Table 5 confirms previous results that estimated U.S.
returns to education are lower for recent immigrants relative to earlier immigrants and for
Mexican immigrants relative to other immigrants (Borjas, 1996 and 1999).

3.4 Migration Networks, Internal Migration, and Return Migration
Unobserved characteristics surely matter for the migration decision. Individuals
may be more likely to migrate if they are highly motivated, have family or other contacts
in the U.S., or have access to credit or financial resources. In the census, information onthese features is lacking. If unobservables relevant to migration are correlated withschooling, the evidence in Table 2 might be misleading. To gain insight into how unobservables affect migration, we examine three groups of Mexican residents: individuals
from high-migration states, internal migrants, and return migrants from the U.S.
One important unobserved characteristic is access to migration networks. In
Mexico, there is strong historical persistence in regional migration behavior, whichsuggests migration networks are regionally concentrated. This appears due in part tohistorical accident. In the early 1900’s, Texas farmers began to recruit laborers in
17

If unobserved ability and schooling are correlated, estimates of returns to schooling may be biased. Also, selfselection into the labor force or into migration may introduce further biases. In unreported results, we estimated wage
regressions for Mexican-born men in Mexico and in the U.S., including the inverse Mills ratio derived from a probit
model of the migration decision. Since we lack an instrument for migration, identification is achieved through the non-14

Mexico. Given then small populations on the Texas-Mexico border, recruiters followed
the main rail line into Mexico, which ran southwest to Guadalajara, a major city in thecenter-west of the country. Early migrants came from rural areas near the rail line. They
helped later generations of migrants find jobs in the U.S. (Durand, Massey, and Zenteno,
2001). Emigration continues to be concentrated in central and western Mexico. The
correlation between the fractions of the Mexican state population migrating to the U.S. in
the 1950’s and in the 1990’s is 0.73 (Woodruff and Zenteno, 2001). This suggests
individuals in high-migration regions in Mexico may be the relevant comparison groupfor Mexican immigrants in the U.S. Table 3 shows educational attainment for individuals
in Mexico’s high-migration states.18 Average schooling levels in these states are below
those in the rest of country, indicating that comparing Mexican immigrants with residentsof high-migration states would yield stronger evidence against negative selection.
Other important unobserved characteristics are drive and motivation, which may
help an individual take the risk of moving abroad. Similar to emigrants, internal migrants
have made the decision to relocate. Part of what distinguishes internal and externalmigration is the much higher cost of migrating abroad. If it is unobserved drive, and not
migration costs, that shapes migration decisions, external and internal migrants shouldhave similar characteristics. Comparing Tables 2 and 3, internal migrants (adults who
resided in a different state five years previously) are more educated than Mexican
residents overall. However, relative to immigrants in the U.S. internal migrants areunder-represented among those with 10-15 years of schooling and over-represented

linear way in which the other regressors enter into the inverse Mills ratio and so depends on distributional assumptions.
With these concerns in mind, correcting for self-selection into migration has little effect on the coefficient estimates.
18
These states are Aguascalientes, Colima, Guerrero, Hidalgo, Jalisco, Guanajuato, Michoacán, Morelos, Nayarít,
Oaxaca, Queretero, San Luis Potosí, and Zacatecas. In 2000, 9.0% of the households in these states had sent migrants
to the U.S. between 1995 and 2000, as compared to 2.6% of households in the rest of the country.

15

among those with lower and higher schooling levels. This again suggests that externalmigrants are intermediately selected in terms of educational attainment.
Return migrants are individuals who have chosen not to reside in the U.S.
permanently. This may be by design – in migrating to the U.S. their plan may have been
to stay temporarily – or a result of their U.S. earnings being lower than expected. Borjas
and Bratsberg (1996) show that where migrants are positively (negatively) selected,
return migrants will be more (less) skilled than non-migrants but less (more) skilled thanpermanent migrants. The Mexican census asks whether an individual resided in the U.S.
five years ago, which gives some information on the return migrant population. In Table
3, returnee women fit the pattern of positive selection (they have schooling levels
between those of residents and U.S. immigrants) but men fit neither pattern (they havelower schooling levels than either residents or U.S. immigrants). However, extremely
small sample sizes for returnees make these results difficult to interpret.19

4. Migration Abroad and the Distribution of Wages in Mexico
In this section, we develop a framework to compare wage distributions forresidents of Mexico and immigrants from Mexico in the U.S. This exercise will allow us
to assess nonparametrically whether in terms of observable skills there is positive or
negative selection of individuals who migrate from Mexico to the U.S.
Wage distributions for Mexican residents and Mexican immigrants may differ
either because of differences in the distribution of skills between the two groups or
because of differences in the prices of skills in the two labor markets. To examine

19

The fraction of adults aged 21-64 years who reported living in the U.S. five years prior in 2000 was only
0.7% for males and 0.3% for females and in 1990 was only 0.2% for males and 0.1% for females.

16differences in the distribution of skills between Mexican residents and Mexican
immigrants, we compute the counterfactual wage density of Mexican immigrants in the
U.S., assuming they are paid according to Mexico’s wage structure, and compare it to theactual distribution of wages in Mexico.

Our framework does not address how the

distribution of unobserved characteristics might influence the distribution of wages. If,
holding age, education, and other observables constant, Mexican immigrants in the U.S.
have low unobserved ability relative to residents of Mexico, we will tend to understate
the extent of negative selection. By taking skill prices as given, our framework also fails
to address the general-equilibrium effects of Mexico-to-U.S. migration.

4.1 Counterfactual Wage Densities
Let f i(w|x) be the density of wages w in country i, conditional on a set ofobserved characteristics x.

Also, let Di be an indicator variable equal to 1 if theindividual is in the labor force and equal to zero otherwise. We further defineh(x|i=Mex,Di=1) as the density of observed characteristics among wage earners in
Mexico, and h(x|i=US,Di=1) as the density of observed characteristics among wageearning Mexican immigrants in the U.S. To begin, we suppress time subscripts. The
observed density of wages for individuals working in Mexico isg ( w | i = Mex, Di = 1) = ∫ f Mex ( w | x ) h( x | i = Mex, Di = 1)dx

(5)

Likewise, the observed density of wages for Mexicans working in the U.S. isg ( w | i = US , Di = 1) = ∫ f US ( w | x )h( x | i = US , Di = 1) dx(6)Differences in f Mex ( w | x) and f US ( w | x) capture differences in skill prices in the two17

countries.20 Differences in h(x|i=Mex,Di=1) and h(x|i=US,Di=1) capture differences inthe distribution of observed characteristics for Mexican resident workers and for Mexican
immigrant workers. The differences in the h(·) functions are due in part to differences in
the characteristics of Mexican immigrants and Mexican residents and in part todifferences in who participates in the labor force in the two countries.
Consider the density of wages that would prevail for Mexican immigrant workers
in the U.S. if they were paid according to the price of skills in Mexico:Mex
g US( w) = ∫ f Mex ( w | x ) h( x | i = US , Di = 1) dx(7)This corresponds to the distribution of wages for Mexican residents in (5), except that it
is integrated over the skill distribution for working Mexican immigrants in the U.S.
While this distribution is unobserved, we can rewrite it as
Mex
g US( w) = ∫ f Mex ( w | x ) h( x | i = US , Di = 1)

= ∫θ fMexh( x | i = Mex, Di = 1)dx
h( x | i = Mex, Di = 1)(8)

( w | x )h( x | i = Mex, Di = 1) dx

where

θ=

h( x | i = US , Di = 1)h( x | i = Mex, Di = 1)

(9)

DiNardo, Fortin, and Lemieux (1996) (henceforth DFL) show that acounterfactual density as in (7) can be estimated by taking an observed density (e.g., forwage earners in Mexico) and re-weighting it (e.g., to reflect characteristics of Mexican
immigrant workers) as in (8). To compute the weights, use Bayes’ Law to write,h( x ) =20h( x | i = US , Di = 1) Pr(i = US , Di = 1)
Pr(i = US , Di = 1 | x)

(10)

When the conditional expectation is linear in the observed characteristics, these terms are closely related to the

18

and,

h( x ) =h( x | i = Mex, Di = 1) Pr(i = Mex, Di = 1)
Pr(i = Mex, Di = 1 | x)

(11)

Combining (10) and (11) we can obtain an expression for θ that is a function of the ratioof the conditional probability that a Mexican-born individual works in Mexico to theconditional probability that a Mexican-born individual works in the U.S. DFL suggest
estimating these probabilities parametrically, using the estimates to calculate θ , and thenapplying the θ ’s to estimate a counterfactual wage density as in (8).
However, the counterfactual density in (8) is not precisely what we desire. The
weight, θ , as seen in (9), adjusts for differences in the distribution of skills between
Mexican immigrants and Mexican residents, conditional on Mexican immigrants working
in the U.S. and Mexican residents working in Mexico.

In order to compare wage

distributions for the two groups, we want to condition on common labor-force
participation behavior, which requires modifying the weight we use to construct thecounterfactual wage density. First, note that it is possible to write the joint probability of
migration and labor-force participation, conditional on x, as the product of the conditional
distribution of the participation outcome and the marginal of the migration outcome:Pr(i = US , Di = 1 | x) = Pr( Di = 1 | i = US , x) Pr(i = US | x)(12)

Pr(i = Mex, Di = 1 | x) = Pr( Di = 1 | i = Mex, x) Pr(i = Mex | x)

(13)

Given (9)-(13), we can write:θ=Pr( Di = 1 | i = US , x) Pr(i = US | x) Pr(i = Mex, Di = 1)
Pr( Di = 1 | i = Mex, x) Pr(i = Mex | x) Pr(i = US , Di = 1)

(14)

regression equation for wages on observable characteristics (Butcher and DiNardo, 2002).

19

Next, note that Pr(i = Mex, Di=1)/Pr(i = US, Di=1) is a constant given by the
sample proportions of Mexican resident and Mexican immigrant workers. Since θ isscaled to sum to one once we estimate wage densities, we can without loss of generality
set Pr(i = Mex, Di=1)/Pr(i = US, Di=1) = 1. Thus, the weight θ can be written as,θ = θ Pθ M

(15)

where:⎛ Pr( Di = 1 | i = US , x) ⎞⎛ Pr(i = US | x) ⎞⎟⎟ and θ M = ⎜⎜⎟⎟⎝ Pr(i = Mex | x) ⎠⎝ Pr( Di = 1 | i = Mex, x) ⎠

θ P = ⎜⎜

(16)

The first ratio – the conditional probability of a Mexican immigrant in the U.S. working
over the conditional probability of a Mexican resident working – adjusts Mexico’s wage
density in (8) to reflect U.S. labor-force participation rates for each realization of x. The
second ratio – the probability of a Mexican-born individual being in the U.S. over the
probability of a Mexican-born individual being in Mexico – adjusts the wage density of
Mexican residents to reflect the characteristics of Mexican immigrants.
The second ratio, θ M , is the appropriate weight to construct counterfactual wage
densities. The full weight, θ , adjusts for differences in observables and in labor-force
participation between Mexican immigrants and residents. In (8), we replace θ with θ M ,which yields the wage distribution that would obtain if immigrants were paid according
to Mexican skill prices and participated in the labor force as do Mexican residents.21
To compute θ M , we estimate Pr(i=US | x) parametrically by running a logit on
the probability of a Mexican adult being in the U.S. using the full sample of Mexican
For males, θ and θ M are close in value (male labor-force participation is similar in the two countries), and either
weight yields similar results. For females, labor-force participation rates differ in the two countries (see Table 4). The

21

20

immigrants and Mexican residents (and not just wage earners). Once we estimate thismodel, we can compute Pr(i=Mex | x) = 1 - Pr(i=US | x) and construct the relevant
weight θ jM for each observation j in the sample.

After computing the weights, weestimate the wage densities nonparametrically, using a kernel density estimator.
To characterize the nature of immigrant selection nonparametrically, we estimate
the difference between the wage density for Mexican immigrants and Mexican residents(under common skill prices and labor-force participation behavior), which isMexgUS( w ) − g Mex ( w ) = ∫ [ θ M − 1] f Mex ( w | x )h( x | i = Mex , Di = 1 )dx (17)

If there is negative selection of migrants in terms of observable skills, this difference
would show positive mass in the lower part of the wage distribution – indicating migrants
are over-represented among Mexico-born individuals with below-average skills – andnegative mass in the upper part – indicating migrants are under-represented among the
Mexico-born with above-average skills. In contrast, with positive selection there would
be negative mass for low wages and positive mass for high wages.

4.2 Comparing Migrant Selection over Time
During the 1990’s, shocks to the Mexican and U.S. economies included changes
in the return to education (evident in Table 5), a severe recession in Mexico, and a sharp
increase in U.S. expenditure on enforcement against illegal immigration (Hanson andSpilimbergo, 1999). Each of these events may have altered the incentive to migrate from
Mexico to the U.S. for individuals at different points in the skill distribution.

counterfactual wage density for females using θ as the weight puts more emphasis on earnings of women with low
schooling and so is more supportive of negative selection (in contrast to the reported results, using θMas the weight).

21

To evaluate how immigrant selection has changed over time, we cannot compare
the density difference in (17) for 1990 with that for 2000, as this would confound changes
in the composition of immigrant and resident populations with changes in skill prices. A
meaningful comparison across time requires holding skill prices constant. To do so, firstre-write the weighting function, θ M , using time subscripts:⎛ US ,90 ⎞ ⎛ Pr( i = US | x , t = 1990 ) ⎞⎟⎟⎟ = ⎜⎜⎝ Mex ,90 ⎠ ⎝ Pr( i = Mex | x , t = 1990 ) ⎠

θM⎜

(18)

This weight, as defined in (16), adjusts the characteristics of Mexican residents in 1990 toreflect those of Mexican immigrants in 1990. Second, define two weighting functions,⎛ US ,00 ⎞ Pr( i = US | x , t = 2000 )⎛ Mex ,00 ⎞ Pr( i = Mex | x , t = 2000 )
, θM⎜
⎟=
⎟=
⎝ Mex,90 ⎠ Pr( i = Mex | x , t = 1990 )⎝ Mex ,90 ⎠ Pr( i = Mex | x , t = 1990 )

θM⎜

(19)
The first adjusts the population of Mexican residents in 1990 to reflect Mexican residents
in 2000, and the second adjusts Mexican residents in 1990 to reflect Mexican immigrants
in 2000. These weights can be estimated using a simple logit, as described in section 4.1.
Putting (19) together with (17), we summarize nonparametrically immigrant
selection in 2000, evaluated at 1990 skill prices, with the following density difference:
Mex ,90
Mex ,90gUS,00 ( w ) − g Mex ,00 ( w ) =

[ (

∫θ

)

(

Mex ,00
M US ,00−θ M
Mex ,90
Mex ,90)] fMex( w | x ,t = 90 )h( x | i = Mex , Di = 1,t = 90 )dx(20)
Finally, we evaluate the change in migrant selection between 1990 and 2000 with thefollowing double difference in wage densities:

22Mex ,90
Mex ,90
Mex ,90(gUSMex,00,90 ( w ) − g Mex( w )) =,00 ( w )) − (gUS ,90 ( w ) − g

[ (

∫ [θ

)

(

)

(

) ]

Mex ,00
US ,90
M US ,00−θ M] − [θ M− 1 ] f Mex ( w | x ,t = 90 )h( x | i = Mex , Di = 1,t = 90 )dx
Mex ,90
Mex ,90
Mex ,90(21)
Equation (21) shows the change in immigrant selection between 1990 and 2000, based on
1990 skill prices. If negative selection of immigrants in terms of observable skills
increased (decreased) in the 1990’s, the double difference would have positive (negative)
mass below zero – indicating an increase (decrease) in the relative population of migrants
with below-average skill – and negative (positive) mass above zero – indicating adecrease (increase) in the relative population of migrants with above-average skill.

5. Empirical Results
We apply the methodology to the combined sample of Mexican residents andrecent Mexican immigrants in the U.S. (individuals born in Mexico who are 21-65 yearsof age) in 1990 and 2000. Immigrants are individuals 21 years or older at time of U.S.
entry and who have been in the U.S. for 10 years or less. To construct counterfactual
wage densities, we estimate a logit for Pr(i=US | x), using the sample of Mexican
residents and Mexican immigrants in each year. The model links the choice of migrating
to the U.S. to age and age squared, dummy variables for schooling and marital status, andinteractions of these variables. Logit results are shown in Appendix A1. We use these tocompute the weights, θ M , which we apply to the sample of wage-earning Mexican
residents to estimate counterfactual kernel densities of wages for Mexican immigrants in
the U.S. All estimates are based on a Gaussian kernel function.22

22

We first used Silverman’s (1986) optimal bandwidth, which minimizes the mean integrated squared error if the data
are Gaussian and a Gaussian Kernel is used. However, the resulting densities appeared to be excessively smoothed and23

5.1 Actual and Counterfactual Wage Density Estimates
The results for Mexican males in 1990 are in Figures 4a and 4b. Although thecounterfactual density for Mexican immigrants is close to the actual density of Mexican
residents, some clear differences are apparent.

Contrary to the negative-selection

hypothesis, it is not the lowest-wage males who exhibit a stronger tendency to migrate to
the U.S. For Mexican immigrants, there is less mass in the lower half of the wage density
and more mass in the upper half, when compared with the actual wage density of Mexico
residents. This is seen more clearly in Figure 4b, which shows the difference between the
counterfactual and actual wage densities. The immigrant-resident density difference isnegative from the left tail to just below zero, positive for middle and upper-middle wage
values, and negative for high wage values. This suggests that immigrant males are drawn
disproportionately from the middle and upper middle of Mexico’s wage distribution,rather than from the bottom half. Low-wage and high-wage individuals appear least
likely to migrate to the U.S. These counterfactual wage densities support intermediate
selection of immigrant men in terms of observable skills.
The results for females in 1990, shown in Figures 4c and 4d, contain even lesssupport for negative selection. Except for high wage values, the counterfactual wage
density for immigrant females lies to the right of the actual wage density for resident
females. The immigrant-resident density difference is negative for low wage values,
strongly positive for upper-middle wage values, and zero for high wage values. For
women, there appears to be moderate positive selection of immigrants.

could in fact suffer from bias if the data do not conform to the Gaussian assumption. In order to avoid large bias in ourestimates, we instead started with a bandwidth of 0.03 log wage units, and sequentially increased it until the resulting
densities looked relatively smooth. This implied bandwidths in our final estimates of 0.07 log wage units.

24

Figures 5a-5d show counterfactual wage densities for immigrants in 2000,
evaluated at Mexican skill prices in 2000. For women, wage densities are very similar to
1990, again showing moderate positive selection of immigrants. For men, intermediate
selection of immigrants again appears, but less strongly. Comparing Figures 4b and 5b,in 2000 immigrant men became somewhat less under-represented among low-wage
workers and somewhat more under-represented among high-wage workers
To compare changes in the pattern of immigrant selection more precisely, we
estimate the difference between immigrant (counterfactual) and resident (actual) wage
densities in 1990 and 2000, using 1990 skill prices. Figures 6a and 6b show the density
differences for 1990 and 2000 at 1990 skill prices (equations (17) and (20)) for males andfemales and Figures 6c and 6d show the density double differences (equation (21)) for
males and females. For women, the density differences in the two years are very similar,
causing the double density difference to fluctuate around zero. For men, the density
difference in 2000 has less negative mass for low wage values and more negative mass
for high wage values, as suggested by Figures 4b and 5b. The double density differenceis thus positive below the mean, zero around the mean, and negative above the mean.
This is consistent with male immigrants becoming less under-represented at low wage
values and more under-represented at high wage values during the 1990’s.
Summarizing our results, Mexican immigrants in the U.S. are drawn disproportionately from the middle and upper-middle of Mexico’s wage distribution. Those most
likely to migrate abroad would earn medium-to-high wages in Mexico and those least
likely to migrate would earn low or very high wages in Mexico. In terms of observableskills, males exhibit intermediate selection and females exhibit positive selection.

25

5.2 Parametric Results on Actual and Counterfactual Wage Distributions
An advantage of our nonparametric approach is that it characterizes differences inresident and immigrant wage densities across the entire distribution. A disadvantage isthat it is difficult to assess whether the density differences are statistically significant. In
this section, we provide a parametric analog to the density differences reported in the lastsection in order to gauge the statistical precision of these estimates. We see if thecounterfactual wage distribution for Mexican immigrants matches the actual wage
distribution for Mexican residents, when we summarize the distributions in terms ofquantiles. First, we sort wage observations for Mexican residents in a given year into
deciles.

Then, we calculate the share of the population in each decile, weighting

observations by θ M (which adjusts the Mexican resident population to reflect thecharacteristics of the Mexican immigrant population in the U.S.). For each decile, we test
whether the θ M -weighted shares are statistically different from 0.10. Negative selectionof immigrants in terms of observable skills would produce population shares above 0.10
for deciles below the median and below 0.10 for deciles above the median.
Table 6 reports the results. For males in 1990, immigrant population shares are
below 0.10 in the first four deciles, above 0.10 in the 5th to 9th deciles, and below 0.10 in
the 10th decile. All shares, except for the 4th decile, are statistically different from 0.10 at
a 5% level of significance. This pattern reinforces the results in Figure 4, which showsthat immigrants are over-represented in the upper middle of the wage distribution. For
males in 2000, results are similar, except that immigrants are no longer under-represented
in the 4th decile and are now under-represented in the 9th decile. This is consistent withthe results in Figure 6, which suggest that during 1990’s male Mexican immigrants

26became somewhat less positively selected. For females in 1990, immigrant population
shares are below 0.10 in the first four deciles, close to 0.10 in the 5th decile, and greaterthan 0.10 in the 6th to 10th deciles. All shares, expect for the 5th decile, are statisticallydifferent from 0.10 at the 5% level. This is also consistent with Figure 4, in which female
immigrants from Mexico appear to be positively selected. For females in 2000, theresults are similar. Table 6 suggests the differences between actual and counterfactual
wage densities in Figures 4 and 5, though small, are statistically significant.

5.3 Correcting for the Undercount of Illegal Immigrants
As discussed in section 3, the U.S. census appears to undercount illegal
immigrants. If undercounted immigrants have low education levels, then our results
could be biased against finding negative selection. We cannot remedy this problem
directly, since we lack data on which immigrants are illegal within our sample. However,
we do have data from other sources on the characteristics of illegal immigrants.
We repeat the exercises from the previous section, but now modify the immigrantsample so that the sampling weights for the logit analysis used to construct thecounterfactuals correspond to those of a synthetic sample in which there is no illegalimmigrant undercount. We re-weight the original sample so that: i) the new weights
reflect an increase in the total Mexican immigrant population in the U.S., corresponding
to the estimated number of uncounted illegal immigrants, and ii) the re-weighting
procedure increases the population size to a greater extent for education groups in which
out-of-sample evidence suggests that illegal immigrants are concentrated.
Given multiple estimates of the illegal undercount, we report results based on
both Census Bureau and INS estimates of the illegal Mexican population. The Census

27Bureau (Costanzo et al., 2001) estimates put the number of Mexican illegal immigrants
counted in the U.S. census at 1 million in 1990 and 3.9 million in 2000. Using Census
undercount rates of 20% in 1990 and 15% in 2000, the number of Mexican illegal
immigrants would be 1.3 million in 1990 – implying a 5.9% increase in the Mexican
immigrant population counted in the census – and 4.6 million in 2000 – implying a 7.4%
population increase. For the Census Bureau-based estimates, we re-weight the original
sample to increase the populations by these percentages. The INS (2003) puts the illegal
Mexican population at 2.0 million in 1990 and 4.8 million in 2000. These figures alreadytake into account undercount assumptions and imply a much higher undercount rate for1990.23 For the INS-based estimates, we re-weight the original sample to increase thepopulations by the implied percentages. We call the Census Bureau-based estimate thelow-undercount estimate and the INS-based estimate the high-undercount estimate.
To make this increased population have the characteristics of illegal immigrants,
we adjust the weights by schooling group. The “new” population is assumed to follow
the distribution of immigrants by schooling in Orrenius (1999), shown in Appendix A2.
This distribution is based on the Mexican Migration Project (MMP), a household survey
in rural Mexican communities over the period 1987-1997 where migration to the U.S.
tends to be high (Durand et al., 1996; Massey et al., 1994). A high fraction of migrants in
the MMP report having entered the U.S. illegally. Schooling levels of this group aremuch lower than of Mexican immigrants in the U.S. census or of Mexican residents in
the Mexico census. A second data source on illegal immigrants is the Survey of Newly
Legalized Persons in California, from 1989, which covered undocumented immigrants
23

The INS estimate of 4.8 million illegal Mexican immigrants in 2000 is based on an assumed undercount rate 10%,which is less than the 15% undercount rate in Costanzo et al (2001) for that year. However, the INS estimates are28

who were granted legal residence in the U.S. under the amnesty provision of the 1986
Immigration Reform and Control Act. Appendix A2 shows this population has higher
education levels than the MMP, suggesting migrants with low schooling may be overrepresented in the MMP. In our analysis, we use MMP education levels to give greater
weight to the possibility our results are biased against negative selection.
Figure 7 shows results of correcting for an illegal immigrant undercount. Using a
low undercount rate, differences between actual and counterfactual wage densities arevery similar to those in Figures 4 and 5. Using a high undercount rate, results are very
similar for 2000 and in 1990 show somewhat weaker patterns of intermediate selection
for males and of positive selection for females. Overall, results for either a low or highundercount rate have similar qualitative implications to those without the undercountcorrection. Immigrants in the U.S. are over-represented in the middle and upper middleof Mexico’s wage distribution. This suggests the undercount rate would need to be
implausibly large to overturn our main findings.24 Table 6 shows that, after controlling
for the illegal-immigrant undercount, differences between the actual and counterfactual
wage distributions are statistically significant. The θ M -weighted population shares bydecile are statistically different from 0.10 for nearly all deciles.

5.4 Robustness Checks
In section 3, we discussed the possibility that Mexican immigrants may obtain
additional education after arriving in the U.S. In Figure 8a, we restrict the immigrantsample to be individuals who arrived in the previous 3 years – a group unlikely to havebased on a different methodology for computing the residual foreign born population.

29

obtained U.S. schooling. The estimated density differences are very similar to those in
Figures 4 and 5. There again appears to be intermediate selection of immigrant men andpositive selection of immigrant women. An alternative way to address this issue would
be to adjust for the bunching of immigrants at 12 years of education. In unreported
results we combined individuals with 9 to 12 years of education into a single schoolingcategory. This addresses the concern that some immigrants with 12 years of schooling
may have obtained a GED, or, in response to cultural pressure, may overstate theireducational attainment. Again, these results are very similar to those reported.25
There remains the concern that education may be correlated with unobserved
characteristics that affect the migration decision, in which case our results might
exaggerate the extent of positive selection. Following the discussion in section 3, we
examine the importance of unobservables by restricting the sample of Mexican residents
to whom we compare Mexican immigrants. In Figure 8b, we construct immigrantresident density differences (evaluated at 1990 skill prices) using the sample of Mexican
residents who live in high-migration states (see note 18). Migration rates in these statesare 3.5 times those in the rest of the country, suggesting the presence of regionalmigration networks. Relative to Figures 4 and 5, Figure 8b shows stronger evidence of
positive selection for both men and women. Given Table 3, which shows that residentsof high-migration states have low schooling levels relatives to either Mexican immigrants
in the U.S. or to residents in Mexico overall, this does not come as a surprise.
In Figure 8c, we limit the sample of Mexican residents to individuals who moved

24

Other results based on data in which illegal immigrants are over-represented tend to support our findings. Using the
MMP, Orrenius and Zavodny (2004) find that migrants tend to come from the middle of the schooling distribution.
25
Results are also very similar when we expand the immigrant population to those at least 16 years old at U.S. entry.

30

between states in the previous five years. Similar to external migrants, internal migrants
may be highly driven or motivated, which may be important unobserved determinants ofthe migration decision. Relative to Figures 4 and 5, there is somewhat weaker evidence
of intermediate selection for men. Immigrant men are still over-represented in the middleof the wage distribution, but are now more under-represented in the upper tail. For
women, positive selection is modest in 1990, but appears quite strongly in 2000. Overall,the qualitative patterns are broadly similar to previous results.

Immigrants remain

concentrated in the middle or upper-middle of Mexico’s wage distribution.26
We have argued that it is important to hold constant how skill prices affect laborforce participation when comparing wage distributions across workers in different
countries. So far, we have used skill prices and labor-force participation in Mexico as thebasis for these comparisons. For evaluating immigrant selection, it is no less valid to use
U.S. skill prices and labor-force participation. As a consistency check on our results, weestimated the distribution of wages that would obtain if residents of Mexico were paid
according to U.S. skill prices and participated in the labor force as do Mexican
immigrants and then compared this to the actual wage distribution for Mexican
immigrants. In unreported results, we find that neither for males nor for females is there
support for negative selection of Mexican immigrants in terms of observable skills.
Resident males are under-represented in the middle and upper middle of the immigrantwage distribution and over-represented in the upper tail, consistent with intermediate
selection of immigrants. Resident females are under-represented in the upper middle partof the wage distribution, consistent with moderate positive selection of immigrants.

26

U.S. return migrants are another potential sample to whom to compare Mexican immigrants. However,sample sizes for this group (see Table 3) are very small, complicating estimation of wage densities.

31

6. Discussion
In this paper, we use data on residents of Mexico and Mexican immigrants in the
U.S. to test Borjas’ (1987) negative-selection hypothesis that the less skilled are thosemost likely to migrate from countries with high returns to skill and high earnings
inequality to countries with low returns to skill and low earnings inequality. Our results
are inconsistent with the negative selection of Mexican immigrants in the U.S. For
Mexican-born males, there is evidence of intermediate selection and for Mexican-born
females there is evidence of positive selection. Those most likely to migrate are young
adults with moderately high schooling. Were these immigrants to return to Mexico, theywould to tend to fall in the middle or upper portions of Mexico’s wage distribution.
To arrive at these results, we construct counterfactual wage densities for Mexican
immigrants in the U.S., in which we use their observed characteristics to project thewages they would earn in Mexico and under current patterns of wage determination in the
country. The results are robust to controlling for undercounts of illegal immigrants in the
U.S. census, for opportunities to gain education in the U.S., for changes in skill prices
over time, and for access to regional migration networks.
There are several important caveats to our results. We cannot say how the returnof Mexican immigrants in the U.S. would affect Mexico’s wage structure. Other work
gives some guidance on this issue. Using data for 1970-2000, Mishra (2003) estimates anelasticity of wages with respect to emigration in Mexico of 0.4. Given the concentrationof emigrants among moderately well-educated young adults, wage gains in Mexico havebeen largest among 25 to 40 year olds with 12 to 15 years of schooling. Wage changes
are not the only consequence of emigration. In 2003, remittances by immigrants in the32

U.S. were 2% of Mexico’s GDP. In high-migration regions, remittances appear to haveraised investments in small businesses (Woodruff and Zenteno, 2001).
Another caveat is that our findings against negative selection and in favor ofintermediate or positive selection are in terms of observable characteristics only. We
cannot say whether the unobserved skills of Mexican immigrants in the U.S. are high or
low relative to those who remain in Mexico. There is some evidence on the role ofunobservables in the migration decision. When we limit the comparison to external andinternal migrants, individuals who may posses similar unobserved characteristics, we
continue to find evidence against the negative selection of Mexican immigrants.
That immigrants from Mexico appear to be relatively high-wage individuals is
surprising. The estimated returns to education are higher in Mexico than in the U.S.,
suggesting that low-wage individuals from Mexico would have most to gain from
migrating to the U.S. Heterogeneity in migration costs is one way to reconcile thesefacts. The more educated may be better able to negotiate the migration process, they may
have better access to migration networks in the U.S., or they may be less subject to credit
constraints in financing migration. Also, higher discount rates or greater risk aversion
among the low-skilled may make migration abroad relatively less attractive for thisgroup.

Finally, a “Washington apples” effect (Alchian and Allen, 1964) may also

contribute to positive selection of migrants. Given long queues for U.S. legal admission,
two-thirds of Mexicans immigrate illegally on their first attempt. Enforcement against
illegal immigration may act as a head tax, penalizing the less skilled. If more-skilled andless-skilled illegal immigrants compete for jobs in the U.S., it will be efficient, given
fixed migration costs, to fill jobs with more skilled individuals first.

33

Appendix
A.1 Logit Estimation of the Probability of Migration to the U.S.

VariableYears
1 to 4of
5 to 8Schooling9
10 to 1112
13 to 15
16+
Age
Age^2
MarriedAge*
1 to 45 to 89
10 to 1112
13 to 1516+
Age^2*
1 to 45 to 89
10 to 1112
13 to 15
16+
Married*
1 to 45 to 89
10 to 1112
13 to 15
16+
2
Pseudo R

Males 1990
Coeff.
St. Error-1.7130.37-3.2320.329-3.8440.501-3.7560.604-3.208
0.443
-5.88
0.596
-3.537
0.679
0.164
0.019
-0.28
0.026
3.584
0.265
0.042
0.02
0.158
0.018
0.17
0.03
0.198
0.037
0.22
0.026
0.331
0.037
0.118
0.038
-0.0250.025-0.1670.024-0.1790.043-0.1970.053-0.256
0.037
-0.39
0.054
-0.11
0.05
-0.1470.068-0.4030.057-0.4920.076-0.877
0.087
-0.62
0.067
-0.3560.085-0.1930.107
0.046

Males 2000
Coeff.
St. Error-1.2060.392-1.2330.302-3.4430.409-1.6630.44-0.9070.339-4.3630.471-1.582
0.471
0.207
0.017
-0.328
0.022
4.734
0.204
-0.022
0.021
0.037
0.016
0.122
0.024
0.058
0.025
0.059
0.019
0.224
0.028
0
0.026
0.044
0.026
-0.031
0.021-0.1450.033-0.0160.035-0.0550.025-0.262
0.038
0.023
0.034
-0.2550.072-0.4150.054-0.5730.063-0.8690.074-0.5680.059-0.311
0.078-0.3240.08
0.065

Notes: This table shows logit estimation results in which the dependent variable is 1 if an
individual lives in the U.S. and 0 if an individual lives in Mexico. The sample is Mexicoborn men 21-65 years of age in the U.S. and Mexico population censuses in 1990 or34

2000. Regressors are age, age squared, dummy variables for schooling and marital
status, and interactions among these variables. Unreported results for women are similar.
For females, we also include as regressors the number of children born to the individual
and its interactions with age and schooling.

A.2 Educational Attainment for Mexican Illegal Immigrants

Years of Education
0 to 1
2 to 45 to 6
7 to 910 to 1213+
% of trips to U.S.
involving illegal entry

Mexico Migration Project
Migrants Non-migrants
27.1
23.3
32.6
22.1
23.1
22.5
8.9
11.7
4.3
8.1
4.1
12.3
60.8

Survey of Newly Legalized Persons
Years of Education
Immigrants
0
5.6
1 to 314.6
4 to 6
41.6
7 to 922.2
10 to 12
12.8
13+
3.2

--

Notes: Columns 1-3 are from Orrenius (1999). The sample is 5,478 head of household
males between 15 and 65 years of age, surveyed between 1987-1995 in 35 communities
in western Mexico. Columns 4 and 5 are based on California Health and Welfare Agency(1989). The sample is 4,091 Mexico-born individuals (men and women) residing in
California in 1989 who arrived in the U.S. as illegal immigrants and who were granted
permanent legal residence under the amnesty provision of the 1986 Immigration Reformand Control Act. Reported education levels are a weighted average of those granted
amnesty based on having arrived in the U.S. prior to 1982 (60% of those legalized) andthose granted amnesty as special agricultural workers (40% of those legalized).
The education categories shown above are as reported in the original data sources, which
do not exactly match the categories we report in Table 2. Since the Mexican census
reports the exact number of years of schooling completed, we are able to use the above
figures to re-weight the samples, as described in section 5.3.

35

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38

Table 1:
Share of U.S. Immigrants from Mexico in Population of Mexico, 1990 and 2000
(percent)

Age Cohort
Age in 1990 Age in 2000

Males
% Residing in U.S.
1990
2000

ChangeFemales% Residing in U.S.
1990
2000
Change--16 to 25

--

11.99

--

--

7.68

--

16 to 25

26 to 35

7.57

17.53

9.96

4.89

12.62

7.73

26 to 35

36 to 45

10.87

15.49

4.62

7.69

11.90

4.21

36 to45

46 to 55

9.18

12.21

3.03

7.47

10.44

2.97

46 to 55

56 to 65

7.00

8.64

1.64

6.44

8.36

1.92

56 to 65

--

5.70

--

--

5.84

--

--

Notes: For 1990 and 2000, this table shows Mexican immigrants in the U.S. as apercentage of the population of individuals born in Mexico (equal to the sum of the
Mexico-born residing in Mexico and the Mexico-born residing in the U.S.) by age andsex categories.

39

Table 2a: Summary Statistics for Males, 1990

Variable
Age
Highest Gradeof SchoolingCompleted(%)

0
1 to 45 to 890 to 910 to 1112
13 to 15
10 to 15
16+

Wage Earners (%)
Hourly wage
N

MexicoResidents
36.8
0.138
0.223
0.276
0.115
0.752
0.048
0.066
0.049
0.163
0.084
0.726
1.39
163798

U.S. Residents
Mex. Immigrants
Other
RecentAll
Immigrants
33.2
39.1
42.7
0.133
0.146
0.040
0.125
0.167
0.029
0.289
0.302
0.095
0.081
0.068
0.028
0.628
0.683
0.192
0.059
0.050
0.041
0.178
0.152
0.234
0.087
0.077
0.200
0.324
0.279
0.475
0.048
0.038
0.334
0.832
0.830
0.792
7.17
8.23
14.57
22207
39780
152047U.S.
Natives
40.0
0.005
0.005
0.039
0.024
0.073
0.069
0.333
0.284
0.686
0.242
0.833
14.27
609786

Table 2b: Summary Statistics for Females, 1990

Age
Highest Gradeof SchoolingCompleted(%)

Wage Earners (%)
Hourly wage
N0
1 to 45 to 890 to 910 to 1112
13 to 15
10 to 15
16+

MexicoResidents
36.7
0.188
0.228
0.274
0.100
0.790
0.044
0.077
0.045
0.166
0.044
0.207
1.34
178726

U.S. Residents
Mex. Immigrants
Other
RecentAll
Immigrants
34.3
40.4
43.8
0.138
0.146
0.048
0.142
0.169
0.036
0.298
0.318
0.107
0.074
0.064
0.031
0.652
0.697
0.222
0.047
0.044
0.045
0.174
0.156
0.298
0.084
0.073
0.207
0.305
0.273
0.550
0.042
0.031
0.229
0.440
0.465
0.597
6.08
6.57
10.19
18640
34381
168222U.S.
Natives
40.2
0.004
0.003
0.031
0.023
0.061
0.073
0.370
0.299
0.742
0.197
0.709
9.98
65022940

Table 2c: Summary Statistics for Males, 2000

Variable
Age
Highest Gradeof SchoolingCompleted(%)

0
1 to 45 to 890 to 910 to 1112
13 to 15
10 to 15
16+

Wage Earners (%)
Hourly wage
N

MexicoResidents
37.2
0.069
0.166
0.270
0.189
0.694
0.045
0.101
0.047
0.193
0.113
0.740
1.21
215804

U.S. Residents
Mex. Immigrants
Other
RecentAll
Immigrants
33.2
39.5
43.5
0.096
0.127
0.035
0.057
0.080
0.013
0.300
0.307
0.071
0.105
0.087
0.025
0.558
0.601
0.144
0.064
0.055
0.036
0.245
0.212
0.240
0.079
0.083
0.186
0.388
0.350
0.462
0.054
0.050
0.394
0.789
0.800
0.800
7.90
8.72
15.23
39073
80453
222605U.S.
Natives
41.4
0.005
0.002
0.021
0.017
0.045
0.053
0.328
0.308
0.689
0.267
0.824
14.88
652895

Table 2d: Summary Statistics for Females, 2000

Age
Highest Gradeof SchoolingCompleted(%)

Wage Earners (%)
Hourly wage
N0
1 to 45 to 890 to 910 to 1112
13 to 15
10 to 15
16+

MexicoResidents
37.0
0.092
0.179
0.28
0.174
0.725
0.04
0.112
0.042
0.194
0.080
0.321
1.24
235086

U.S. Residents
Mex. Immigrants
Other
RecentAll
Immigrants
34.8
40.5
44.0
0.113
0.133
0.042
0.066
0.087
0.017
0.301
0.315
0.079
0.101
0.085
0.025
0.581
0.620
0.163
0.054
0.049
0.037
0.228
0.204
0.272
0.079
0.079
0.209
0.361
0.332
0.518
0.058
0.048
0.320
0.418
0.463
0.605
6.90
7.30
11.57
35277
72967
247970U.S.
Natives
41.5
0.004
0.001
0.016
0.015
0.036
0.050
0.323
0.337
0.710
0.255
0.739
11.28
69348341

Table 3: Educational Attainment for Subsamples of the Mexico Born

Males

HighestGrade of
Schooling
(%)

0
1-4
5-8
9
0-9
10-11
12
13-15
10-15
16+

N
Females

HighestGrade of
Schooling
(%)

N

0
1-4
5-8
9
0-9
10-11
12
13-15
10-15
16+

High Emig. States
1990
2000
0.190
0.093
0.264
0.203
0.263
0.286
0.096
0.169
0.813
0.751
0.034
0.034
0.055
0.081
0.037
0.037
0.126
0.152
0.063
0.096
54,77878,330

Mexico Residents
Internal Migrants Return Emigrants
1990
2000
1990
2000
0.087
0.042
0.094
0.039
0.141
0.106
0.211
0.135
0.262
0.238
0.368
0.372
0.130
0.203
0.139
0.222
0.620
0.589
0.812
0.768
0.061
0.052
0.039
0.041
0.090
0.124
0.057
0.093
0.075
0.063
0.022
0.035
0.226
0.239
0.118
0.169
0.154
0.171
0.070
0.062
8,913
9,694
459
1,695

26-35 Year Olds
1990
2000
0.086
0.036
0.172
0.103
0.297
0.263
0.129
0.246
0.684
0.648
0.058
0.059
0.082
0.129
0.057
0.044
0.197
0.232
0.118
0.121
52,864 68,206

Mexican Immigrants in US, 26-35 Yrs Old
0-3 Yrs
4+ Yrs
0-3 Yrs
4+ Yrs
1990
1990
2000
2000
0.113
0.113
0.076
0.078
0.103
0.112
0.045
0.043
0.278
0.309
0.303
0.275
0.082
0.081
0.114
0.116
0.576
0.615
0.538
0.512
0.061
0.057
0.064
0.067
0.188
0.189
0.260
0.278
0.103
0.096
0.075
0.098
0.352
0.342
0.399
0.443
0.073
0.043
0.062
0.046
3,599
9,121
9,358
15,839

0.233
0.265
0.259
0.084
0.841
0.031
0.061
0.035
0.127
0.032
61,297

0.123
0.159
0.278
0.122
0.682
0.063
0.106
0.073
0.242
0.075
9,491

0.121
0.196
0.309
0.109
0.735
0.053
0.090
0.056
0.199
0.065
58,069

0.097
0.113
0.278
0.076
0.564
0.052
0.205
0.106
0.363
0.074
3,211

0.113
0.213
0.297
0.157
0.780
0.030
0.090
0.036
0.156
0.064
88,818

0.051
0.117
0.270
0.198
0.636
0.055
0.141
0.055
0.251
0.112
10,141

0.093
0.173
0.360
0.121
0.747
0.033
0.117
0.065
0.215
0.037
214

0.019
0.109
0.336
0.225
0.689
0.053
0.163
0.046
0.262
0.049
733

0.047
0.116
0.278
0.219
0.660
0.054
0.145
0.039
0.238
0.103
75,625

0.109
0.121
0.348
0.082
0.660
0.045
0.170
0.088
0.303
0.038
7,057

0.070
0.041
0.269
0.112
0.492
0.056
0.276
0.083
0.415
0.093
6,575

0.078
0.043
0.294
0.125
0.540
0.062
0.253
0.090
0.405
0.055
16,173

Years of
Schooling0
1 to 45 to 89
10 to 1112
13 to 15
16+
0
1 to 45 to 89
10 to 1112
13 to 15
16+
0
1 to 45 to 89
10 to 1112
13 to 15
16+
0
1 to 45 to 89
10 to 1112
13 to 15
16+

Table 4: Labor-Force Participation Rates
Residents of Mexico
Recent Mex. Immigrants
Age Category
Age Category
26-35 36-45 46-55 56-65
26-35 36-45 46-55 56-65(a) Males, 1990
0.63
0.65
0.61
0.50
0.81
0.83
0.81
0.67
0.72
0.72
0.66
0.54
0.86
0.85
0.82
0.70
0.79
0.81
0.75
0.58
0.86
0.85
0.82
0.65
0.84
0.85
0.78
0.59
0.85
0.86
0.79
0.66
0.87
0.88
0.77
0.60
0.85
0.85
0.74
0.72
0.85
0.88
0.81
0.59
0.86
0.84
0.81
0.66
0.85
0.89
0.83
0.60
0.87
0.87
0.76
0.70
0.86
0.91
0.85
0.67
0.87
0.83
0.89
0.72
(b) Males, 2000
0.60
0.61
0.57
0.45
0.73
0.77
0.74
0.59
0.70
0.69
0.64
0.49
0.77
0.82
0.72
0.55
0.78
0.78
0.75
0.54
0.82
0.79
0.76
0.66
0.84
0.84
0.79
0.56
0.81
0.81
0.82
0.75
0.87
0.88
0.81
0.60
0.80
0.79
0.80
0.63
0.86
0.87
0.78
0.51
0.83
0.82
0.79
0.58
0.82
0.91
0.82
0.54
0.85
0.88
0.86
0.55
0.86
0.90
0.87
0.66
0.86
0.87
0.78
0.69
(c) Females, 1990
0.09
0.09
0.08
0.07
0.41
0.43
0.36
0.13
0.10
0.11
0.09
0.07
0.44
0.52
0.42
0.20
0.16
0.19
0.16
0.10
0.46
0.50
0.44
0.23
0.28
0.29
0.27
0.10
0.46
0.52
0.53
0.28
0.36
0.40
0.33
0.14
0.46
0.46
0.47
0.43
0.47
0.48
0.40
0.20
0.47
0.57
0.45
0.24
0.57
0.56
0.48
0.27
0.52
0.53
0.58
0.30
0.57
0.63
0.53
0.31
0.53
0.62
0.50
0.34
(c) Females, 2000
0.16
0.19
0.16
0.13
0.36
0.44
0.34
0.19
0.21
0.22
0.20
0.12
0.41
0.44
0.40
0.18
0.25
0.29
0.27
0.17
0.38
0.46
0.41
0.19
0.33
0.39
0.36
0.19
0.41
0.51
0.45
0.23
0.41
0.48
0.43
0.22
0.40
0.51
0.35
0.25
0.47
0.51
0.45
0.27
0.43
0.50
0.48
0.24
0.57
0.67
0.52
0.23
0.53
0.55
0.56
0.34
0.67
0.71
0.63
0.35
0.50
0.46
0.54
0.22

44

Table 5: OLS Wage Regressions for Men, 1990 and 2000

Variable
High
Grade
of
Schooling

5 to 89 to 1112
13 to 15
16+Age

26 to 35
36 to 45
46 to 55
56 to 65

R Sqd.
N

MexicoResident

Mexican Immigrants
RecentAll

Other
Immigrant

MexicanResident

Mexican Immigrants
RecentAll

Other
Immigrant

1990
0.202
(0.006)0.359(0.008)0.551(0.010)0.741(0.012)1.096(0.009)0.110(0.007)0.210(0.008)0.240(0.009)0.168(0.011)
0.233
112,1111990
0.038(0.011)
0.071(0.014)0.115(0.013)0.182(0.016)0.416(0.020)0.060(0.012)0.081(0.015)0.075(0.019)0.092(0.029)0.063
17,9391990
0.022(0.010)0.080(0.010)0.202(0.008)0.339(0.009)0.690(0.008)0.116(0.010)0.208(0.011)
0.194(0.011)
0.095(0.012)
0.248
112,5522000
0.196(0.007)0.358(0.007)0.605(0.009)0.960(0.013)1.328(0.009)0.126(0.006)0.207(0.007)0.242(0.008)0.195(0.011)
0.354
142,1132000
0.006(0.011)
0.033(0.012)0.112(0.011)0.209(0.014)0.460(0.016)0.068(0.010)0.106(0.012)0.079(0.015)0.076(0.023)0.062
30,0362000
0.017(0.010)0.100(0.010)0.202(0.009)0.358(0.009)0.783(0.008)0.154(0.011)0.166(0.011)0.118(0.011)
0.046(0.012)0.212
164,8641990
0.070(0.008)0.130(0.011)0.163(0.010)0.257(0.012)0.487(0.016)0.059(0.012)0.094(0.014)0.055(0.015)-0.007(0.019)0.140
32,011

2000
0.039(0.007)0.079(0.008)0.154(0.007)0.283(0.009)0.493(0.011)
0.069(0.010)0.107(0.011)0.086(0.012)0.023(0.014)0.098
62,551

Notes: See notes to Table 5, below.

45

Table 6: Counterfactual Wage Deciles
Decile
1
2
3
4
5
6
7
8
9
10

1
2
3
4
5
6
7
8
9
10

Males 1990

Males 2000
Females 1990
Females 2000(a) Main Sample
0.090
0.096
0.080
0.085
[0.089, 0.092][0.094, 0.097][0.077, 0.083][0.083, 0.088]
0.092
0.097
0.083
0.087
[0.091, 0.094][0.096, 0.099][0.080, 0.086][0.085, 0.089]
0.095
0.100
0.088
0.090
[0.094, 0.097][0.099, 0.102][0.085, 0.091][0.087, 0.092]
0.099
0.102
0.093
0.093
[0.097, 0.100][0.100, 0.103][0.090, 0.097][0.091, 0.095]
0.102
0.104
0.099
0.099
[0.101, 0.104][0.103, 0.106][0.096, 0.102][0.097, 0.102]
0.105
0.104
0.109
0.105
[0.103, 0.106][0.103, 0.106][0.106, 0.112][0.103, 0.108]
0.107
0.110
0.111
0.115
[0.105, 0.108][0.108, 0.112][0.108, 0.114][0.113, 0.118]
0.110
0.108
0.115
0.111
[0.108, 0.112][0.106, 0.109][0.112, 0.119][0.109, 0.114]
0.106
0.099
0.117
0.110
[0.104, 0.108][0.097, 0.100][0.114, 0.120][0.107, 0.112]
0.093
0.081
0.104
0.104
[0.092, 0.095][0.079, 0.082][0.101, 0.108][0.101, 0.106](b) Correcting for Illegal Immigrant Undercount
0.096
0.093
0.089
0.082
[0.094, 0.098][0.091, 0.094][0.086, 0.092][0.080, 0.084]
0.097
0.095
0.089
0.085
[0.095, 0.098][0.094, 0.097][0.086, 0.092][0.083, 0.087]
0.098
0.099
0.093
0.089
[0.096, 0.100][0.098, 0.101][0.090, 0.096][0.086, 0.091]
0.100
0.101
0.096
0.092
[0.098, 0.102][0.100, 0.103][0.093, 0.100][0.090, 0.094]
0.102
0.104
0.099
0.099
[0.101, 0.104][0.102, 0.105][0.096, 0.102][0.097, 0.101]
0.104
0.105
0.106
0.107
[0.102, 0.106][0.103, 0.106][0.103, 0.109][0.105, 0.109]
0.105
0.111
0.106
0.116
[0.103, 0.107][0.110, 0.113][0.103, 0.110][0.114, 0.119]
0.106
0.109
0.109
0.113
[0.105, 0.108][0.108, 0.111][0.106, 0.113][0.111, 0.115]
0.102
0.101
0.110
0.112
[0.100, 0.104][0.099, 0.102][0.107, 0.114][0.110, 0.115]
0.090
0.083
0.101
0.105
[0.088, 0.092][0.081, 0.084][0.098, 0.104][0.103, 0.108]

Notes: 95% confidence intervals are in brackets. See notes to Table 6 for other details.

46

Notes to Tables 2a-2d, 3: The sample is individuals 21-65 years old (in the U.S.,
excluding group quarters; in Mexico, excluding those not born in the country). Residentsof Mexico in 1990 are the 1% microsample of the XII Censo General de Poblacion y
Vivienda, 1990, and in 2000 are a 10% random sample of the 10% microsample of the
XIII Censo General de Poblacion y Vivienda, 2000. Mexican and other immigrants arefrom the 1990 and 2000 5% U.S. PUMS; U.S. natives are from the 1990 and 2000 1%
U.S. PUMS. Immigrants in the U.S. are restricted to be individuals 21 years or older attime of entry into the country. Recent immigrants are individuals who entered the U.S.
during the period 1981-1990 for 1990 and 1991-2000 for 2000. Schooling variables
show the percentage of individuals whose high grade completed is that indicated andwage earners shows the percentage of individuals with positive labor earnings. Wagelevels are in 1990 U.S. dollars (see note 11 on how wages are constructed). Average
wages in Mexico are for those individuals with average hourly earnings between $0.05
and $20 and in the U.S. are for those individuals with average hourly earnings between$1 and $100. See note 18 for the list of high-emigration states. Internal (return) migrants
are individuals who lived in a different state (in the U.S.) five years previously.
Notes to Table 4: The table shows the percentage of the population that reported positive
labor earnings by year, age, and high year of schooling completed for residents of Mexicoand for recent Mexican immigrants in the U.S. (individuals who arrived in U.S. during
the previous ten years). Immigrants are those 21 years or older at time of U.S. entry.
Notes to Table 5: The table reports OLS results for the log average hourly wage (seenote 11 for details on wages). Heteroskedasticity-consistent standard errors are inparentheses. Samples are men 21-65 years of age, whose usual or average weekly hours
of work are 10-80, and who are residents of Mexico (columns 1 and 5), Mexican
immigrants in the U.S. who arrived during the previous ten years (columns 2 and 6), all
Mexican immigrants in the U.S. (columns 3 and 7), or non-Mexico-born U.S. immigrants(columns 4 and 8). Immigrant samples exclude individuals aged 20 years or less at timeof U.S. entry. The Mexican-resident sample includes individuals with hourly wages
between $0.05 and $20 and U.S.-resident samples include individuals with hourly wages
between $1 and $100 (in 1990 U.S. dollars). Regressors not shown are dummy variables
for marital status, metro residence, and region (all columns); dummy variables for race(column 4 and 8); and dummy variables for year of U.S. entry (columns 2-4, 6-8).
Notes to Table 6: This table reports the weighted share of the Mexico resident populationthat occupies a given decile of the wage distribution (95% confidence intervals are inbrackets). Unweighted shares are by definition 0.10. The weights used to calculate thereported shares are those defined in equation (16), which adjust the characteristics of the
Mexican resident population to reflect the characteristics of the Mexican immigrant
population in the U.S. Mexican residents are individuals 21-65 years of age with positive
labor earnings. Mexican immigrants are individuals 21-65 years of age who arrived in
the U.S. in the previous 10 years. The weights are calculated using the logit results
shown in Appendix A1. Part (a) shows results corresponding to Figures 4 and 5 (with noadjustment for the illegal-immigrant undercount) and part (b) shows results that
correspond to Figure 7b (the sample adjusted for the high illegal-immigrant undercount).

47

Figure 1lnwµ 0 + δ 0s
µ1 + δ1s − e µ πµ1 − e µ π
µ0

s*

s
Figure 2µ 0 + δ 0s

lnw

µ1 + δ1s − e µ π − δ π sµ0µ1 − e µ π

sL

sU

s

48

Figure 3:
Wage Densities for Residents of Mexico and Mexican Immigrants in the U.S.

(a) Males, 1990
Actual density: Mex Residents(b) Males, 2000

Actual density: Mex Imm in US

Actual density: Mex Residents

1

1

.9

.9

.8

.8

.7

.7

.6

.6

.5

.5

.4

.4

.3

.3

.2

.2

.1

.1

0

Actual density: Mex Imm in US

0

-3

-2

-1

0

1
lnw

Actual density: Mex Residents

2

3

4

5

-3

Actual density: Mex Imm in US

-2

-1

0

1
lnw

Actual density: Mex Residents

1

1

.9

.9

.8

.8

.7

.7

.6

.6

.5

.5

.4

.4

.3

.3

.2

.2

.1

.1

0

2

3

4

5

Actual density: Mex Imm in US

0

-3

-2

-1

0

1
lnw

2

(c) Females, 1990

3

4

5

-3

-2

-1

0

1
lnw

2

3

4

5

(d) Females, 2000

49

Figure 4: Actual and Counterfactual Wage Densities, 1990
(Based on skill prices in Mexico)(a) Males: Actual (Resident) and Counterfactual(Immigrant) Wage Densities
Actual 1990 density: Mex Reside(b) Males: Immigrant Wage Density– Resident Wage Density

Counterfactual: Mex Imm in US
.085

.6

C ounterfactual - actual density

.5

.4

.3

.2

0

.1

0

-.085

-3

-2

-1

0
lnw

Actual 1990 density: Mex Reside

1

2

3

-3

-2

-1

0
lnw

1

2

3

-3

-2

-1

0
lnw

1

2

3

Counterfactual: Mex Imm in US
.085

.6

C ounterfactual - actual density

.5

.4

.3

.2

0

.1

0

-.085

-3

-2

-1

0
lnw

1

2

3

(c) Females: Actual (Resident) and Counterfactual(Immigrant) Wage Densities(d) Females: Immigrant Wage Density– Resident Wage Density50

Figure 5: Actual and Counterfactual Wage Densities, 2000
(Based on Skill Prices in Mexico)(a) Males: Actual (Resident) and Counterfactual(Immigrant) Wage Densities
Actual 2000 density: Mex Reside(b) Males: Immigrant Wage Density– Resident Wage Density

Counterfactual: Mex Imm in US
.085

.6

Counterfactual - actual density

.5

.4

.3

.2

0

.1

0

-.085

-3

-2

-1

0
lnw

Actual 2000 density: Mex Reside

1

2

3

-3

-2

-1

0
lnw

1

2

3

-3

-2

-1

0
lnw

1

2

3

Counterfactual: Mex Imm in US
.085

.6

Counterfactual - actual density

.5

.4

.3

.2

0

.1

0

-.085

-3

-2

-1

0
lnw

1

2

3

(c) Females: Actual (Resident) and Counterfactual(Immigrant) Wage Densities(d) Females: Immigrant Wage Density– Resident Wage Density

51

Figure 6: Immigrant – Resident Wage Densities at 1990 Skill Prices

Row (I): Immigrant – Resident Wage Densities in 1990 and 2000
Row (II) (Immigrant – Resident Wage Density, 2000) – (Immigrant – Resident Wage Density, 1990)(a) Males: Single Density Differences
Male self selection in 2000(b) Females: Single Density Differences

Male self selection in 1990

Female self selection in 2000

.085

.085

0

0

-.085

-.085

-2

-3

-1

0
lnw

1

2

3

-2

-3

.015

-1

0
lnw

1

2

3

-1

0
lnw

1

2

3

.015

C hange in female self selection

C hange in m ale self selection

Female self selection in 1990

0

-.015

0

-.015

-3

-2

-1

0
lnw

1

2

(c) Males: Double Density Difference

3

-3

-2

(d) Females: Double Density Difference52

Figure 7: Actual – Counterfactual Wage Densities, Correcting for Illegal Immigrant Undercount(at 1990 Skill Prices)(a) Density Difference with Low UndercountMale self selection in 2000(b) Density Difference with High UndercountMale self selection in 1990

Male self selection in 2000

.085

.085

0

0

-.085

Male self selection in 1990

-.085

-3

-2

-1

0
lnw

Female self selection in 2000

1

2

3

-3

Female self selection in 1990

-2

-1

0
lnw

Female self selection in 2000

.085

.085

0

0

-.085

1

2

3

Female self selection in 1990

-.085

-3

-2

-1

0
lnw

1

2

3

-3

-2

-1

0
lnw

1

2

3

53

Figure 8: Actual – Counterfactual Wage Densities, Additional Results for Restricted Samples(at 1990 Skill Prices)(a) Very Recent Immigrants
Male self selection in 2000(b) Residents from High-Emigration States

Male self selection in 1990

Male self selection in 2000(c) Residents who are Internal Migrants

Male self selection in 1990

Male self selection in 2000

.085

.085

.085

0

0

0

-.085

-.085
-3

-2

-1

0
lnw

Female self selection in 2000

1

2

3

Male self selection in 1990

-.085
-3

Female self selection in 1990

-2

-1

0
lnw

Female self selection in 2000

1

2

3

-3

Female self selection in 1990

-2

-1

0
lnw

Female self selection in 2000

1

2

3

Female self selection in 1990

.085

.085
.085

0

0

0

-.085

-.085
-.085
-3

-2

-1

0
lnw

1

2

3

-3

-2

-1

0
lnw

1

2

3

-3

-2

-1

0
lnw

1

2

3

54