Illegal Migration from Mexico to the United States
March 2006
Gordon H. Hanson
University of California, San Diego
and National Bureau of Economic Research

Abstract. In this paper, I selectively review recent literature on illegal migration from
Mexico to the United States. I begin by discussing methods for estimating stocks andflows of illegal migrants. While there is uncertainty about the size of the unauthorized
population, new data sources make it possible to examine the composition of legal andillegal populations and the time-series covariates of illegal labor flows. I then consider
the supply of and demand for illegal migrants. Wage differentials between the UnitedStates and Mexico are hardly a new phenomenon, yet illegal migration from Mexico did
not reach high levels until recently. An increase in the relative size of Mexico’s workingage population, greater volatility in U.S.-Mexico relative wages, and changes in U.S.
immigration policies are all candidate explanations for increasing labor flows from
Mexico. Finally, I consider policies that regulate the cross-border flow of illegal
migrants. While U.S. laws mandate that authorities prevent illegal entry and punish firmsthat hire unauthorized immigrants, these laws are imperfectly enforced. Lax enforcement
may reflect political pressure by employers and other interests that favor open borders.

I thank Roger Gordon and seminar participants at the 2006 AEA meetings, Boston
University, MIT, Syracuse University, and Tufts University for helpful comments andJeff Lin and Maribel Pichardo for excellent research assistance.

1. Introduction
There is increasing interest by academics and policymakers in Mexican migration
to the United States. Mexico is the most important source country for U.S. immigration,accounting for 34% of total immigrant arrivals since 1990. In 2004, the 10.5 million
Mexican immigrants living in the United States were 31% of the U.S. foreign-born
population and equivalent to 10% of the total population of Mexico (see Figure 1).1 The
United States has not experienced a concentrated immigration wave of this magnitude
since the influxes from Germany and Ireland in the mid 19th century.2 For Mexico, thecontinuing population outflow is unprecedented.3 In both countries, the cross-border
flow of labor appears to have affected the structure of wages, the intra-national
distribution of population, and the pattern of industrial specialization.
Beyond its scale, the distinguishing feature of Mexican immigration is that most
new arrivals enter the United States illegally. In 2004, there were an estimated 5.9
million unauthorized Mexican immigrants in the United States, among a total
unauthorized population of 10.3 million (Passel, 2005). Thus, 56% of Mexican
immigrants appear to lack permission to be in the country, compared to 17% of all otherimmigrants. Large-scale illegal immigration in the United States is a relatively new
phenomenon. It has provoked political debate about whether to provide public services
to illegal immigrants, grant them status as legal residents, or militarize U.S. borders toprevent further illegal inflows. In Mexico, migration abroad has helped ease thecountry’s adjustment to rapid growth in its working-age population and to
macroeconomic shocks, though not without disrupting the families and communities
whose members have moved to the United States.
There is an emerging body of economic research on illegal migration from
Mexico to the United States. This literature has been made possible by the recent
availability of data sources on the cross-border movements of legal and illegal Mexican
migrants. Also prompting attention is the realization that, with unauthorized entrants
accounting for over half of Mexican immigrants and over three tenths of all U.S.
immigrants, any discussion of international migration in the United States or Mexico
ends up confronting the issue of illegality either explicitly or implicitly.
Much of the initial research on unauthorized labor flows was done by noneconomists.4 The principal themes of this body of work resemble those in the economics
literature on internal migration in developing countries (see Lucas, 1997; Rapoport andDocquier, 2003). Early waves of illegal migration from Mexico appear to have
1

Figure 1 reports the total number of Mexican immigrants in the United States (legal and illegal) as a shareof Mexico’s population, the total U.S. population, and the foreign-born U.S. population.
2
Over the period 1841 to 1860, Ireland accounted for 39% of U.S. immigration and over the period 1841 to
1890, Germany accounted for 30% of U.S. immigration (U.S. Department of Homeland Security, 2004).
3
The one episode that approaches the current outflow is the Mexican Revolution (1911-1920). Between
1911 and 1925, 680,000 legal immigrants from Mexico (or 5% of Mexico’s 1910 population) entered theUnited States (and were joined by a large number of illegal immigrants). By the late 1920s, many of these
individuals had returned to Mexico. See Cardoso (1980) and Alanis (1999).
4
For reviews of this literature, see Massey et al. (1994) and Espenshade (1995).

1

originated in rural areas of the country (Cornelius, 1992; Durand, Massey and Zenteno,
2001), involved households financing the migration of one or more members in return for
remittances from the migrants (Durand, 1996; Durand et al, 1996), and depended onfamily and community networks that helped migrants enter and find employment in the
United States (Massey et al., 1994; Massey and Espinosa, 1997).
Yet, internal migration and illegal international migration differ in important
respects. While policy barriers that restrict within-country regional labor flows are rare,
countries actively regulate the inflow of labor from abroad. The United States determines
the level of legal immigration through quotas on entry visas, which change infrequently
over time.5 The country implicitly sets the level of illegal immigration through selecting
the intensity with which it enforces borders against illegal entry. Key issues for the studyof illegal migration are how countries choose their border regulation policies and how
prospective migrants respond to these policies.
There is a large literature on U.S. immigration, which tends to focus on the labormarket consequences of immigrant inflows and the economic performance ofimmigrants.6 This body of work examines, among other questions, whether immigration
reduces wages for U.S. native workers, whether immigrants make relatively greater useof means-tested entitlement programs, and whether earnings, education, fertility, or otheroutcomes for immigrants converge to native levels over time.
Largely taken for granted in the U.S. literature is why foreign residents migrate to
the United States. One obvious reason is that U.S. real wages far exceed those in manyother countries. Large wage differentials, coupled with binding and slowly changing
quotas on U.S. legal immigration, create queues to enter the United States. Given
extended delays in clearing such queues, annual variation in the level of legal
immigration appears to be more or less insensitive to contemporaneous annual
fluctuations in U.S. or foreign economies. Perhaps as a result, the quantity of literatureon the consequences of U.S. legal immigration vastly exceeds that on its causes.7
With illegal immigration, the determinants of migrant flows and the highfrequency variation of these flows have attracted more attention. Geographic proximity
allows unauthorized migrants from Mexico to move to the United States relatively
quickly. The existence of well-established migration networks enables U.S. employers to
5

The current U.S. immigration quota system was established by the Hart-Cellar Act of 1965 and amended
by the Immigration Act of 1990. The 1990 law set a flexible cap for U.S. legal admissions at 675,000
individuals of which 480,000 are to be family-based, 140,000 are to be employment-based, and 55,000 are
to be “diversity immigrants.” Immediate relatives of U.S. citizens are not subject to immigration quotas.
6
For surveys of this literature, see Smith and Edmonston (1997) and Borjas (1999a, 1999b) and for recent
work in the area see Borjas (2003) and Card (2005). For analysis of these issues in the context of Mexican
immigration, see Smith (2003), Trejo (2003), Blau and Kahn (2005), Borjas and Katz (2005), Card andLewis (2005), and Duncan and Trejo (2005). There is a smaller literature on the consequences of
emigration for Mexico. See Mishra (2005) and Hanson (2005a).
7
Exceptions include Hatton and Williamson (2004) and Mayda (2004), who examine the correlates ofinternational migration flows. In research on internal migration, there is considerable work on the incentive
to migrate. See Greenwood (1997) and Lucas (1997) for reviews of the literature.

2

communicate changes in their demand for labor to prospective migrants in Mexico.
Migrants use these same networks to find jobs and housing in the United States. Shocks
to either the Mexico or U.S. economies may be transmitted into changes in cross-borderlabor flows with relatively short time lags, making illegal migration potentially quite
responsive to changes in binational business-cycle conditions.
Another feature that distinguishes legal and illegal migrant flows is theircomposition. While legal migrants face entry costs associated with queues in obtaining
visas, illegal migrants face costs associated with evading immigration authorities. Oncein the receiving country, the risk of detection may make some employers unwilling tohire illegal migrants, limiting their occupational prospects and reducing the returns toskill they perceive. Variation in migration costs and in receiving-country wage profiles
between legal and illegal migration suggest the characteristics of illegal migrants may
differ from those of legal migrants. Observed changes in the composition of U.S.
immigrants, which has received much attention in research on consequences of U.S.
immigration, could be partly a by-product of the rising share of unauthorized entrants inimmigrant inflows. For Mexico, the composition of migrant outflows matters not just for
the labor-market effects of emigration but also for the ties that migrants retain with thecountry. Illegal migrants appear to be more likely than legal migrants to send remittances
to family members in Mexico. Rising illegal migration from Mexico may be partly
responsible for the recent surge in remittances in the country, which rose from 0.1% of
GDP in 1990 to 2.2% of GDP in 2004 and now generate more foreign exchange in the
country than tourism or foreign direct investment (IADB, 2005).
In this paper, I selectively review recent literature on illegal migration from
Mexico to the United States. In section 2, I discuss methods for estimating stocks andflows of illegal migrants. While there is uncertainty about the size of the unauthorized
population, new data sources make it possible to examine the composition of legal andillegal populations and the time-series covariates of illegal labor flows. In section 3, I
consider the supply of and demand for illegal migrants. Wage differentials between the
United States and Mexico are hardly a new phenomenon, yet illegal migration from
Mexico did not reach high levels until recently. An increase in the relative size of
Mexico’s working-age population, greater volatility in U.S.-Mexico relative wages, andchanges in U.S. immigration policies are all candidate explanations for increasing laborflows from Mexico. In section 4, I consider policies that regulate the cross-border flowof illegal migrants. While U.S. laws mandate that authorities prevent illegal entry andpunish firms that hire unauthorized immigrants, these laws are imperfectly enforced. Lax
enforcement may reflect political pressure by employers and other interests that favor
open borders. In section 5, I discuss directions for further research.
My goal in this paper is not to conduct an exhaustive survey of work on illegal
migration but rather to highlight major findings in the literature, assess the state ofimportant debates, and identify unresolved issues, with an eye toward advancing
questions to help guide future work. As much of the literature is empirical, I will focus
on this strand of research, with occasional forays into theory. One topic I will not discussat much length is the economic consequences of illegal migration, in part because3literature on the consequences of immigration has been subject to several recent surveys(see note 6) and in part because there is little research on the specific aspects of these
consequences that are attributable to illegal immigration.
Though my focus is on the United States and Mexico, insights from the literatureare relevant for other regions, as well. Unauthorized migration has become a global
phenomenon. In the last two decades, there have been sizable flows of illegal migrants
from North Africa and Eastern Europe to Western Europe, from Indonesia to elsewhere
in Southeast Asia, and from neighboring countries to South Africa. The U.S.-Mexico
experience may be instructive for these and other cases regarding how to measure
unauthorized migration, estimate the causes and consequences of migration flows, andgauge the potential impacts of policy interventions.

2. Stocks and Flows of Illegal Migrants
Illegal immigrants account for a large and growing fraction of the U.S. foreignborn population.8 One may imagine that, as part of the underground economy,
unauthorized migrants are not easily subject to measurement. However, there is now
abundant evidence that illegal immigrants are represented in official household surveys,
including the U.S. Census of Population and Housing and the U.S. Current Population
Survey. Given known levels of U.S. legal immigration, the number of foreign-born
individuals enumerated in these sources is far too large for them all to be legal.
The most common method to estimate the number of illegal immigrants is to takethe difference between the measured immigrant population and the sum of past legal
immigrant inflows. Estimates using this residual approach suggest that stocks of illegal
immigrants have risen sharply over time. However, there is considerable variability inthe estimates, associated with differing assumptions about the magnitude of errors inenumerating legal and illegal immigrants in official data sources.
Knowing the overall share of immigrants who are unauthorized is not the same as
knowing which specific immigrants in a given data source are unauthorized. Fortunately,
there are now several micro-level surveys that provide information on individualmigration status. Either by design or default, migrants from Mexico account for a large
fraction of those represented in these data sources. While each survey has limitations,
their use in conjunction with large public data sets from Mexico and the United States
provides considerable detail on the population of legal and illegal migrants from Mexico
who are living or have lived in the United States.
A third data source on unauthorized migration is a by-product of U.S.
immigration policy. To prevent illegal immigration, the U.S. Border Patrol polices U.S.
borders and ports of entry, attempting to apprehend those seeking to enter the country
illegally. The Border Patrol compiles high-frequency data on apprehensions andenforcement, the vast majority of which occur along the U.S.-Mexico border. Data on
8This also appears to be the case in Western Europe (Boeri, Hanson, and McCormick, 2002).

4

border apprehensions and enforcement allow one to examine how attempts at illegal entry
vary with economic conditions in the United States and Mexico and to see which factors
are associated with the intensity of U.S. enforcement activities.
2.1 The Residual Foreign-Born Population
In the United States, there are two classes of legal immigrants who appear in
official data sources (i.e., are surveyed by the U.S. Census Bureau, the U.S. Bureau ofLabor Statistics, or other official entities). One is permanent legal immigrants, who havethe right to reside in the country indefinitely. Another is temporary legal immigrants,
who have the right to reside in the country for a defined time period, as specified by anentry visa (such as those for students, specific skill classes of workers, diplomats, andfamily members of temporary legal immigrants).9 Since government surveys do notscreen individuals based on their immigration status, illegal immigrants also appear in
official data, to the extent they make themselves available to be surveyed.
The standard method to estimate the number of illegal immigrants is to assume itis equal to the residual foreign-born population, which is given byt

U t = Ft − ∑ L s (1 − m s − e s ) − Tt ,

(1)

s =1

where Ut is the unauthorized foreign-born population in year t, Ft is the total foreign-born
population in year t, Ls is the number of permanent legal immigrants that entered in years≤t, ms is the mortality rate between year s and year t for legal immigrants entering inyear s, es is the emigration rate between year s and year t for legal immigrants entering inyear s, and Tt is temporary legal immigrants present in year t.
Table 1 reports estimates of the unauthorized foreign-born population by the U.S.
Bureau of the Census (Costanzo et al., 2003), Bean et al. (2001a, 2001b), Passel (2005),
and the U.S. Immigration and Naturalization Service (2001).10 In the most recent
projection, Passel (2005) estimates that between 2002 and 2004 the illegal immigrant
population rose from 9.3 to 10.3 million, for an average annual net inflow of 500,000
migrants, with 57% of these individuals coming from Mexico. This compares to an
average annual net illegal inflow during the 1990-to-2000 period of 581,000 migrants(with 58% of net new immigrants coming from Mexico), based on U.S. Census Bureau
estimates (Costanzo et al., 2001), and 350,000 migrants (with 79% coming from
Mexico), based on INS (2001) estimates.
Obviously, there are a host of assumptions involved in estimating (1). One mustassign mortality and emigration rates to each entry cohort and determine the fraction of
temporary immigrants admitted in previous years that are still in the country. In practice,9

The Census Bureau does not survey individuals on tourist or business visas or other short-term visitors.
See Bean at al. (1998) for a survey of the literature on estimating illegal immigrant populations.

105most discrepancies evident in Table 1 appear unrelated to differences in theseassumptions. Of greater importance are assumptions about measurement error in Ft.
To simplify matters, I re-express equation (1) in contemporaneous values as
Ft = L t + U t , where Lt is the total legal foreign-born population in year t. The U.S.
Census Bureau (and other entities that conduct household surveys) tends to undercountthe total population (with undercount rates for low-income households, which would
include many Mexican immigrants, believed to be relatively high), in which case the~measured foreign-born population, Ft , is less than the actual foreign-born population, Ft.
The total legal-immigrant population, Lt, in contrast, appears to be measured with greater
accuracy, since immigration authorities have records on how many entry visas they
award. Suppose the legal-immigrant population that is enumerated in the census is,~
L t = L t (1 − λ t ) + ε Lt ,(2)

where λt is the fraction of legal immigrants that go uncounted, and the unauthorizedimmigrant population enumerated in the census is,~
U t = U t (1 − μ t ) + ε U
t ,

(3)

where μt is the fraction of unauthorized immigrants that go uncounted and εLt and εUt are~~mean zero iid errors. L t and U t are not observed individually but are observed in terms~of their sum, Ft . While the U.S. Census Bureau estimates that it undercounts the
Hispanic population by 5%, Bean et al. (2001b) put the undercount rate for legal Mexican
immigrants at 2% to 4%. Similarly, while the U.S. Census Bureau estimates that it
undercounts the illegal immigrant population by 15%, the INS assumes an undercount of
10%, and Bean et al. (2001) use undercount rates of 15% to 25%. It is primarily thesedifferences in assumptions that account for variation in the estimates in Table 1.
Assumptions about undercount rates are based on comparisons of the enumerated
population with the population measured in post-enumeration surveys conducted in
specific localities. This requires one to assume that under-enumeration in these localities
is representative of the total U.S. population (see Bean et al. 1998). The U.S. Census
Bureau justifies its undercount assumptions based on results from its own postenumeration surveys, from which standard errors for population estimates are derived(Costanzo et al., 2001). The INS (2001) justifies a 10% undercount rate based on a casestudy of Mexican immigrants in Los Angeles County. Bean et al. (2001a, 2001b) justify
their range of undercount rates based on results in Van Hook and Bean (1998).
~
Given observed values of Ft and Lt and assumed values for λt or μt, which I
indicate by a tilde, the estimated value of the illegal-immigrant population in year t canbe approximated as6~~Ft − L t (1 − λ t )Û t =
.
~
1− μ
t

(4)

If true undercount rates fall over time and we fail to account for this, estimates of the~illegal-migration population will be biased upwards ( Û t > U t ).11 Consider values for Ft
and Lt in 1996 and 2001 in Bean et al. (2001a, 2001b). If we change the assumption for
the 1996-2001 period from a constant illegal undercount rate of 25% (which is at theupper end of the Bean et al. rates) to a reduction in the illegal undercount rate from 25%
to 15% (to the lower end of the Bean et al. rates),12 the estimated annual illegal net inflow
from Mexico would fall by 112,000 migrants (from 432,000 to 320,000), which is 30% of
the average annual estimated illegal inflow over the period. Since different authors tend
to use different post-enumeration surveys as the basis for selecting undercount rates,
there is little consensus in the literature about what has happened to the undercount ofillegal immigrants over time, other than it exhibiting a downward trend.
In Table 1, the span of estimates for the illegal-immigrant population in the
United States is wide. Between 1990 and 2004, the estimated average annual net inflow
of unauthorized immigrants ranges from 350,000 to 580,000 individuals, with 55% to
80% of these individuals coming from Mexico. Missing in the literature are attempts togauge how the validity of different assumptions about undercount rates vary across time.
The post-enumeration surveys (typically conducted by or on behalf of the U.S. Census
Bureau or BLS) that are the basis for the undercount assumptions are carried out in asmall number of communities around the time of the main survey. Since samples forpost-enumeration surveys differ across survey periods, there is little basis for making
time-series comparisons in undercount rates. Until large-scale public surveys ask about
individual migration status directly, estimating the size of the illegal-immigrant
population will remain a speculative enterprise.
2.2 Churning in Legal and Illegal Immigrant Populations

Estimates of the stock of illegal immigrants give little indication of how longunauthorized migrants are likely to remain without a legal resident visa. Each year, there
appears to be a large flow of individuals from the pool of illegal migrants to the pool ofpermanent legal immigrants. Many immigrants who obtain visas for legal permanent
residence (green cards) are at the time they obtain their visas residing in the United Statesillegally. Figure 2 shows the number of Mexican immigrants awarded legal permanent
residence and the fraction of these individuals who are adjusting status. Over the period
1992-2002, status adjusters accounted for 56% of new legal permanent immigrants from
Mexico. Some of those adjusting their visa status are temporary legal immigrants who

[

]

~
μt −μ
~
~
t
Ft − (λ t − λ t )L t − ε Lt − ε Ut .
~(1 − μ t )(1 − μ t )When the assumed undercount rate for illegal immigrants is low, this expression will be negative (there will
be upward bias in the estimated number of illegal immigrants). This effect will be exaggerated if theassumed undercount rate for legal immigrants is also low.
12
In this exercise, I assume the undercount rate for legal migrants is held constant at 2%.
11

Ignoring interactions in undercount rates, U t − Û t ≈7

have succeeded in obtaining permanent entry visas. However, for Mexican immigrants,
the majority of those adjusting status to permanent legal residence appear to have been
living in the United States as illegal immigrants (DHS, 2004).
Further evidence of churning in the population of legal and illegal immigrants isavailable in the New Immigrant Survey (NIS), which in its pilot form includes a random
sample of 1,134 immigrants who received U.S. legal permanent resident visas in 1996
(Jasso, Massey, Rosenzweig, and Smith, 2000). Based on the NIS data, Massey andMalone (2002) find that 54% of Mexican nationals who obtained a green card in 1996
reported having entered the United States illegally at an earlier date in time, either bycrossing the U.S. border (41%) or overstaying a temporary entry visa (13%). Overall,
21% of U.S. green-card recipients in 1996 reported having crossed the U.S. borderillegally and 11% reported having overstayed a temporary entry visa.
Transitions from illegal to legal residence status indicate that many individuals
queuing for U.S. green cards choose to do so as illegal immigrants, rather than waitingout the process as residents of their home countries. Between 1992 and 2004, 90% of
Mexican immigrants who obtained U.S. green cards qualified under family-reunification
provisions of U.S. immigration law. Since 1965, the United States has granted
unrestricted legal entry to the immediate relatives of U.S. citizens and restricted legal
entry, subject to annual immigration quotas, to more distant relatives of U.S. citizens andrelatives of U.S. permanent legal residents. Most applicants take several years or more toclear the queue for a green card.13 While the United States periodically attempts to limit
the granting of green cards to those applicants who either have valid temporary entry
visas or are residing abroad, the sheer volume of applications has made this provisiondifficult to enforce (Vaughan, 2003). Consequently, the U.S. government routinely
grants green cards to individuals who currently are residing in the country illegally.
Massey and Malone (2002) report that prior illegality is more common among those who
receive green cards under family-based immigration provisions than under employmentbased immigration provisions. The latter category requires sponsorship by a U.S.
employer and applies mostly to highly skilled individuals.
Churning in the illegal immigrant population suggests some unauthorized
migrants may view their visa status as mutable. In making the decision to migrate to theUnited States, individuals in Mexico who have relatives that are U.S. legal residents may
internalize the prospect of obtaining a U.S. green card in the future. They may consider
being an illegal immigrant as simply an intermediate step in becoming a legal permanent
resident. The possibility of transitioning from illegal to legal status may blur differencesin the expectations and behavior of legal and illegal migrants.

13

Though immediate relatives of U.S. citizens are not subject to immigration quotas, to obtain a green cardthey still must screened by immigration authorities, a process that can take as long as two years. The
screening process is more protracted for individuals who meet the qualifications for a green card but whose
preference category is subject to immigration quotas (e.g., more distant relatives of U.S. citizens andrelatives of U.S. legal residents) and can take five years or more (Martin, 2005).

8

A second means by which prospective migrants might expect to obtain a U.S.
green card is through a future amnesty for illegal aliens. In 1986, the U.S. Immigration
Reform and Control Act (IRCA) awarded permanent legal residence visas to illegal
immigrants who could demonstrate either (i) continuous U.S. residence since 1982, or (ii)
60 days of employment in U.S. agriculture since 1985. Over the next eight years, 1.6
million illegal immigrants received green cards under the first provision and 1.1 million
illegal agricultural workers received green cards under the second provision, with
Mexican nationals accounting for 2 million of the 2.7 million IRCA legalizations (INS,1996). While there is political opposition in the United States to another amnesty, there
have been numerous recent legislative proposals to legalize at least some unauthorized
migrants (Hanson, 2005). The prospect of a future amnesty is another factor that helps
diminish distinctions between legal and illegal migrants.
2.3 Composition of the Legal and Illegal Immigrant Populations

A longstanding conception of Mexico-to-U.S. migration is that it is driven by theneeds and rhythms of agriculture. According to this view, most migrants from Mexico
are from the countryside, come to the United States to work as farm laborers during peak
agricultural months, and return to their families in Mexico for the winter off-season.
Migrants would tend to be male, rural in origin, relatively uneducated, and residing in the
United States on an itinerant basis. While there is little doubt that at one time this viewof Mexican migration was accurate, the Mexican immigrant population in the UnitedStates has since become more heterogeneous and more permanent.
Large-scale emigration from Mexico began in the early 20th century. Railroad
construction in the late 1800s linked interior Mexico to the U.S. border, giving U.S.
employers improved access to Mexican labor (Cardoso, 1980). In the early 1900’s,
Texas farmers began to recruit laborers in Mexico. They followed the main rail line into
the country, which ran southwest through agricultural states in Mexico’s central andwestern regions. Early migrants from Mexico came primarily from nine Mexican statesin this area (Durand, Massey, and Zenteno, 2001).14 Migration expanded further in the
1940s, after the U.S. Congress enacted the Bracero Program (1942-1964), which allowed
U.S. employers to bring in workers from Mexico (and the Caribbean) to fulfill short-termlabor contracts (of less than a year in length). At the end of their contracts, workers wererequired to return to their home countries. The vast majority of braceros worked on U.S.
farms (Calavita, 1992). At its peak, from 1954 to 1960, 300,000 to 450,000 migrant
workers from Mexico entered the United States annually. The end of the Bracero
Program marked the beginning of large-scale illegal immigration from Mexico.15
After working in the United States, many braceros returned to Mexico where they
assisted later generations in migrating abroad. They helped establish informal networks
through which earlier migrants help new migrants enter the United States, find housing in
14

These nine states are Aguascalientes, Colima, Durango, Guanajuato, Jalisco, Michoacán, Nayarit, San
Luis Potosí, and Zacatecas. During the period 1944 to 1964, this group of states accounted for 55% of
migration from Mexico to the United States (Durand, Massey, and Zenteno, 2001).
15
On illegal immigration and guest-worker programs, see Epstein, Hillman, and Weiss (1999).

9U.S. cities, and obtain jobs with U.S. employers. Networks are often embedded inrelationships involving family, kin, or community of birth, which gives them a regional
component. Partly as a result, there is strong historical persistence in migration rates tothe United States across Mexican regions. Figure 3 plots emigration rates in the 1950s
against those in the 1990s across Mexico’s 32 states. Data for the 1950s are from
Woodruff and Zenteno (2001), who calculate the fraction of each Mexican state’s
population that migrated to the United States between 1955 and 1959 under the U.S.
Bracero Program. Data for the 1990s, taken from the 2000 Mexico Census of Populationand Housing, report the fraction of households in a state having a member migrate to theUnited States between 1995 and 2000. The correlation between state migration rates in
the 1995-2000 and the 1955-59 periods is 0.73. Figure 4 shows that most high-migration
states are located in central Mexico, which is neither close to the United States nor home
to Mexico’s poorest households. States on the U.S.-Mexico border tend to have low
emigration rates, as do states in low-income southern Mexico.
Perhaps as a result of migration networks, current generations of Mexican
immigrants in the United States tend to live near individuals from their home regions in
Mexico. For instance, Munshi (2003) finds that immigrants from the state of Jalisco aremuch more likely to live in Los Angeles or San Diego than immigrants from the state ofGuanajuato, who prefer Chicago or Dallas. Migrants reinforce networks by creatinghome-town associations that help members of their communities in Mexico make thetransition to living north of the border. Of 218 home-town associations formed by
Mexican immigrants enumerated in a 2002 survey of such organizations in southern
California, 87% were associated with one of the nine west-central states that dominatedmigration to the United States under the Bracero Program (Cano, 2004).
While migration networks are a consistent feature of cross-border labor flows, thecomposition of these flows is not. Since the 1960s, Mexico has urbanized, become amore educated nation, and incorporated women into the labor force in greater numbers.
Cornelius (1992) and Cornelius and Marcelli (2001) suggest these changes have shifted
the composition of Mexican migrants in the United States from sojourners, who follow
the harvest season through the rural United States and then return to Mexico at the end of
the year, to settlers, who have a permanent presence in U.S. communities. Resisting thisnotion, Durand, Massey, and Zenteno (2001) suggest instead that migration to the UnitedStates remains dominated by men from traditionally agricultural states in Western
Mexico. While migrants have become better educated and more urban, they retain strong
ties to Mexico, returning often and tending to avoid permanent U.S. settlement.
Until recently, it would have been difficult to muster much more than case-study
evidence to evaluate these claims. There are now several data sources on migrants from
Mexico that give details on an individual’s migration status.16 Perhaps the best known
16

A similar source is the National Survey of Demographic Dynamics (ENADID), conducted by thegovernment of Mexico in 1992 and 1997. The ENADID asks households in Mexico whether any of its
members have ever worked in or looked for work in the United States (and the year in which this occurred).
In the 1997 ENADID, 9% of individuals report having been to the United States and 21% of households
report having a member in the United States, up from 8% and 17% in 1992. As with the MMP, the10and most utilized source is the Mexican Migration Project (MMP).17 The MMP is ahousehold survey conducted in winter months (when seasonal migrants tend to return to
Mexico) in 1982 and over the period 1987 to 1997 in several dozen rural communities inwestern Mexico, chosen for having high rates of migration to the United States (Massey
et al., 1994; Durand et al., 1996). In each community and in each year, the MMP
surveyed a random sample of several hundred households, collecting information on pastmigration behavior of each household member.18 An advantage of the MMP is that itallows one to construct retrospective migration histories on a reasonably large sample ofindividuals.19 Among male household heads, 23% report having migrated to the UnitedStates within three years of being surveyed (during the period 1984 to 1996). Of thosereporting having migrated over the period 1970 to 1990, 89% state that on their first trip
to the United States they entered without documents (McKenzie and Rapoport, 2004).
The MMP is subject to several potential problems associated with how migrants
and communities are selected into the sample. Since communities included in the MMP
are chosen on the basis of being rural and having residents with high migration
propensities, they are unlikely to be representative of Mexico as a whole (McKenzie andRapoport, 2004). Within communities, the households surveyed are those with at least
one member remaining in Mexico, thus excluding households that have migrated to theUnited States in their entirety. And, within households, the migrants surveyed directly
are those who have returned to Mexico, for at least part of the year. There are no direct
observations on individuals residing in the United States.
One way to evaluate the issue of sample selection in the MMP is to compareindividuals in the survey with individuals in Mexico’s Census of Population and Housing
and with Mexican immigrants in the U.S. Census of Population and Housing. Table 2
gives summary statistics for working-age adults in the 1990 U.S. and Mexico censuses
and in the 1989, 1990, and 1991 MMP surveys. I consider three MMP subsamples: (a)all respondents, (b) those who report residing in the United States but who are present in
Mexico at the time of the MMP survey (seasonal migrants), and (c) those in the UnitedStates at the time of the MMP survey (permanent migrants), whose responses areprovided by other members of their household in Mexico.
While Mexican immigrants in the United States (census immigrants) and MMP
permanent migrants have relatively similar characteristics, they differ considerably from
MMP seasonal migrants. Males account for 65% of MMP seasonal migrants, but only
56% of census immigrants and MMP permanent immigrants. And, while age profiles are
similar among the three groups, educational attainment is not. Males with nine or more
years of schooling account for 52% of census immigrants and 48% of MMP permanent
ENADID only includes households with at least one member remaining in Mexico. See Durand, Massey,
and Zenteno (2001) and McKenzie and Rapoport (2004) for work using the ENADID and Cornelius andMarcelli (2001) and Durand, Massey and Zenteno (2001) for discussions of other surveys.
17
Recent papers using the MMP include Massey and Espinosa (1997), Reyes (1997), Munshi (2003),
Gathmann (2004), Orrenius and Zavodny (2004), and McKenzie and Rapoport (2004).
18
Different households are surveyed in different years, such that the MMP is a repeated cross-sections ofhouseholds and not a true panel.
19
On measurement error in retrospective data, see Beckett et al. (2001) and Smith and Thomas (2003).

11

migrants, but only 31% of MMP seasonal migrants. Employment patterns also differ
across groups. Among males, 16% of census immigrants and 9% of MMP permanent
migrants work in agriculture, compared to 31% of MMP seasonal migrants. Seasonal
migrants also appear to be less established in the United States. For males, 55% of MMP
seasonal migrants have spent more than five years in the United States, compared to 71%
census immigrants and 63% of MMP permanent migrants. For each of thesecomparisons, results are similar for females.
Over time, Mexican immigrants have shifted out of agriculture as a main industryof U.S. employment. Using data from the U.S. census, Card and Lewis (2005) show thatbetween 1990 and 2000 among recent Mexican immigrants (0-5 years in the UnitedStates) the share working in agriculture fell from 23% to 15% for men and from 13% to
7% for women. Among men, construction accounted for the largest growth in
employment shares, while among women retail trade showed the largest increase.
Other surveys of illegal immigrants from Mexico also suggest their characteristics
are more similar to permanent migrants (whether in the U.S. census or the MMP) thanseasonal migrants. The Legalized Persons Survey (LPS) covered illegal immigrants who
were granted permanent legal residence in the United States under the amnesty provisionof IRCA (eligibility for which required proof of U.S. residence from 1982 forward). The
LPS consisted of an initial survey in 1989 and a follow-up survey in 1992 of immigrants
qualifying for legalization.20 Among working-age adults in the LPS, shown in Table 2,
57% of Mexican immigrants are male, 37% had nine or more years of schooling, and
12% worked in agriculture.
In sum, data sources that include illegal immigrants are almost by definition
subject to sample-selection problems. Official sources, such as the population census, arelikely to undersample illegal immigrants, given their tendency to undercount low-income
households. Surveys that specifically target illegal immigrants, such as the MMP or the
LPS, explicitly select respondents on the basis of observed characteristics (e.g., residence
in a high-migration community, eligibility for an amnesty). Yet, despite conflicting
selection criteria, available data sources paint a consistent picture of Mexican immigrants
in the United States, suggesting they include a high proportion of women, areoverwhelmingly employed outside agriculture, have high education levels (relative to
non-migrants in Mexico), and have established a long-term U.S. presence.
One data source that is inconsistent with this conclusion is the MMP sample ofseasonal migrants – individuals who reside in the United States but return to Mexico in
the winter months. Since MMP seasonal migrants are selected on the basis of having
returned to Mexico, it is hardly surprising that they fit the profile of itinerant migrantlaborers. Relative to other samples of legal and illegal immigrants, MMP seasonal
migrants are disproportionately male, uneducated, and agricultural. While much of theliterature based on the MMP uses the information on seasonal migrants to examine
20

The initial survey was of 6,193 respondents, of whom 5,691 had received amnesty by 1992 (with most of
the rest awaiting decision). Of those granted amnesty, 82% were located for the follow-up interview. The
LPS sample excluded those granted amnesty under the Special Agricultural Worker provision of IRCA.

12

Mexico-to-U.S. migration, this sample appears unrepresentative of Mexican immigrants
in the United States. When examining results using the MMP one should be mindful thatthey may apply only to seasonal migrants who return to Mexico with high frequency andnot to the general population of Mexican migrants.
2.4 Attempted Illegal Immigration

The majority of unauthorized immigrants from Mexico enter the United States bycrossing the U.S.-Mexico border illegally.21 The U.S. government devotes vast resources
to policing U.S. borders, airports, and ports of entry. Between 1980 and 2004, real
expenditure on border enforcement increased by over six times and in 2005 will equal$2.2 billion (Hanson, 2005b). U.S. border-enforcement activities provide a glimpse into
the high-frequency properties of illegal immigration. The first line of defense against
unauthorized entry is the U.S. Border Patrol. Border Patrol officers on “linewatch” duty
patrol the border, maintain electronic surveillance of major crossing points along theborder, and staff traffic checkpoints along major highways near the border. Figure 5
shows annual Border Patrol officer hours devoted to linewatch duty from 1964 to 2003.
Officer hours increased dramatically in the 1990s, rising from 2.5 million in 1994 to 9.8
million in 2001. This increase was due primarily to stepped up enforcement efforts at
urban crossing points in California and Texas.
Concurrent with increased enforcement, apprehensions of those attempting illegal
entry have increased, rising from 280,000 a year during the 1970s to 930,000 a year
during the 1990s. Individuals apprehended by Border Patrol officers on linewatch duty
are typically captured while trying to enter the United States or just after entering thecountry. Linewatch apprehensions are thus correlated with the contemporaneous level ofattempted illegal immigration.22 However, apprehensions are likely to be a poor indicatorof the actual level of illegal inflows (Espenshade, 1995). Within a single month, one
individual may be apprehended multiple times. Those apprehended who agree to bedeported voluntarily are not processed by the U.S. justice system. For Mexican nationals,
voluntary deportation often involves little more than a bus ride across the border, leaving
them in position to attempt illegal entry again in the near future.23, 24
To gauge how apprehensions might be related to illegal immigration, consider thelevel of apprehensions as a function of the average probability of apprehension and thenumber of attempts at illegal entry. Extending Ethier’s (1986) model, let
A t = P(H t , M t ) M t ,

(5)

21

A second strategy, less common among Mexican immigrants but more common among those from othercountries, is to enter the United States on a temporary visa and then remain in the country after it expires.
22
Individuals apprehended in the U.S. interior, in contrast, could have crossed the border at a much earlier
date, making interior apprehensions less strongly correlated with current attempts at illegal entry.
23
Between 1990 and 2003, 95% of those the Border Patrol apprehended agreed to depart voluntarily.
24
A further issue is that the majority of those attempting illegal entry do not appear to be apprehended on
any given attempt. Using MMP data, Massey and Singer (1995) find that for trips to the United States in
the 1970s and 1980s the average probability of apprehension was 35%.

13

where At is the level of apprehensions, Mt is the number of attempts at illegal entry, andP(Ht,Mt) is the average probability an individual is apprehended on any given attempt tocross the border. The apprehensions probability is a function of Ht, the intensity withwhich authorities police the border, and the number of entry attempts. Greater
enforcement is likely to raise the apprehension probability, making P(·) increasing in Ht.
For a given level of enforcement, more total attempts are likely to reduce the probability
any single attempt results in capture (since enforcement resources are spread more thinly
across those attempting entry), making P(·) decreasing in Mt. 25α−αSuppose P(H t , M t ) = cH t 1 M t 2 , where c, α1, and α2 are positive constants, inwhich case log apprehensions can be expressed asln A t = α 0 + α1 ln H t + (1 − α 2 ) ln M t(6)Hanson and Spilimbergo (1999) use monthly data over the period 1968-1996 to estimate
equation (6), modeling attempted illegal entry as a reduced-form function of real wages
in Mexico, real wages in the United States, other indicators of economic conditions in thetwo countries, a time trend and monthly dummy variables.26 With an estimate of α1, we
can solve for a function that is an affine transformation of lnMt, given by,
α 0 + (1 − α 2 ) ln M t = ln A t − α1 ln H t(7)The expression on the right of (7) will positively covary with lnMt as long as α2 < 1.
Approximated attempts at illegal entry in (7) do not give an estimate of the level of illegal
immigration. However, they may indicate the variation across time and the magnitude oflog changes in attempted illegal entry.
Figure 6 shows estimates of (7), based on instrumental-variables estimates ofequation (6) (see note 26). Approximated attempts at illegal entry rise from the 1960s to
the mid 1980s, are stable from the mid 1980s to the mid 1990s, and then decline
somewhat in 2000 and 2001. Part of the 2001 decline may reflect a change in bordercrossing activity after the events of September 11th in the United States. In late 2001, theU.S. Border Patrol increased its vigilance at border crossings, which may have dissuadedsome migrants from crossing as frequently as they had in the past.27

25

For earlier work using apprehensions data, see Bean, et al. (1990) and Borjas, Freeman, and Lang (1991).
To deal with the possible correlation between enforcement and unobserved shocks to apprehensions,Hanson and Spilimbergo (1999) instrument for enforcement using U.S. government spending on national
defense and indicators for whether there is an upcoming U.S. presidential, congressional, or border-stategubernatorial election. Border enforcement tends to follow a political cycle, dropping during election years(Hanson, Robertson, and Spilimbergo, 2001). The reported coefficient estimate for α1 is the long-run
elasticity of apprehensions with respect to enforcement.
27
See Mary Jordan, "Mexicans Caught at Border in Falling Numbers," Washington Post, May 24, 2002.
26

14

The trend in Figure 6 is roughly consistent with Table 1 and results from previous
estimates of the U.S. unauthorized population. Illegal immigration appears to have risen
steadily after the end of the Bracero Program in 1965 and has been relatively stable athigh levels for the past two decades.28 I will return to data on border apprehensions andenforcement when evaluating factors that affect the level of illegal immigration and thepolitical economy of U.S. policy on illegal immigration.
2.5 Summary

Currently, no data source gives precise estimates of the size of the U.S. illegalimmigrant population over medium or long time spans. Government data-gathering
agencies have been wary of asking questions about an individual’s immigration status,
perhaps out of fear of dissuading illegal migrants from participating in surveys. The
result is gaps in our knowledge about unauthorized migrants, which the literature has
been able to partially fill in through other data sources.
The perspective that emerges from the data that are available is that Mexico-toU.S. illegal migration increased in the 1970s and 1980s and averaged around 200,000 to
300,000 net unauthorized entries per year in the 1990s and early 2000s. The populationof illegal immigrants from Mexico in the United States includes a substantial fraction of
women, is predominantly employed in non-agricultural jobs, and has schooling levels
that are comparable to or higher than non-migrating individuals in Mexico. Thoughmany migrants maintain ties with family members in their origin communities, a majority
appear to have settled in the United States on a medium or long-term basis.

3. The Supply of and Demand for Mexican Immigrants

Beginning with Sjaastad (1962), economists have viewed migration as aninvestment decision. An individual migrates if the expected discounted difference in thestream of income between the new and old location exceeds moving costs. The incentive to
migrate will vary across individuals according to differences in their expectations of future
earnings, discount rates, and perceived cost of migrating. The cost of unauthorized
migration includes transport to the border, the physical risks and monetary charges incurredin crossing the border illegally, the psychic penalty from leaving one’s friends and family
behind, and the time and monetary expense of settling in another country. To uncoversources of variation in the demand for and the supply of illegal migrants, recent work
estimates the sensitivity of migrant outflows from Mexico to variation in U.S. andMexican wages, border-crossing costs, and access to migration networks.
Also beginning with Sjaastad, economists tend to model the migration decision as
irreversible (Greenwood, 1997; Lucas, 1997). In many contexts, this assumption may be
28

One limitation of this exercise is that I assume border-crossing technology and border-apprehensions
technology have been stable over time. There is anecdotal evidence that both may have changed
considerably, especially since September, 2001. However, Hanson and Spilimbergo (1999) find noevidence of a structural break in the apprehensions function for the 1968 to 1996 period.

15

reasonable. U.S. legal immigrants, if they wish to keep their green cards valid, must makethe United States their permanent residence.29 Those migrating to the United States legally
would thus tend to be individuals expecting to stay in the country for an extended period oftime. In the context of illegal migration from Mexico, individuals may operate on shortertime horizons. The substantial round-trip migration documented by the Mexican Migration
Project indicates that for at least some Mexican nationals the cost of moving back and forthacross the border is sufficiently low to warrant making the trip annually.
Of interest to both Mexico and the United States is not just the volume of migrantflows but their composition. Widening differences in earnings between immigrants andnatives in the United States is cited as evidence that recent U.S. immigrants arenegatively selected in terms of skill (Borjas, 1999a). Following this line of thought, one
might expect negative selection to be especially strong among illegal immigrants. Recent
work examines migrant selection by comparing the characteristics of Mexican
immigrants in the United States with those of non-migrating individuals in Mexico.
3.1 The Incentive to Migrate from Mexico to the United States

A long line of research applies the general framework in Sjaastad (1962) toexamine the sensitivity of migration flows to the observed costs and benefits of
migrating. For prospective illegal migrants in Mexico, the costs include the four
components identified above and the benefits include gains in real income associatedwith moving from low-wage Mexico to the high-wage United States.
First, consider the components of illegal migration costs. Of these, transport costs tothe border are likely to be small and psychic costs difficult to evaluate. Border-crossing
costs and settlement costs are in principle measurable, though it is only the former that has
been subject to much in the way of formal research. To avoid capture by the U.S. Border
Patrol, migrants often purchase the services of a smuggler, known as a coyote.30 Coyotes
offer a range of services, from simply guiding migrants across the border to more complete
packages that include transport to an interior U.S. city, such as Houston, Los Angeles, orPhoenix.31 The MMP is one of the few data sources that asks migrants from Mexico about
their border-crossing behavior. Using MMP data, Orrenius (2001) documents that during

29

A green-card holder may lose U.S. permanent resident status by taking permanent residence abroad,remaining abroad without obtaining a re-entry visa, or by filing a foreign tax return as a non-immigrant.
Once a legal immigrant becomes U.S. citizens, he or she is free to enter and leave the country at will.
30
Coyotes also help migrants navigate unfamiliar terrain. Recent changes in U.S. enforcement strategy
have made it more difficult to cross the border in urban areas, encouraging migrants to enter the UnitedStates through the mountainous desert regions of Arizona and Eastern California, where temperature
extremes expose migrants to physical risks. Annual deaths of border crossers have increased from anaverage of 100 during the mid 1990s to 410 during the period 2000-2004 (Cornelius, 2005). With net
annual illegal immigration from Mexico since 2000 at approximately 300,000 individuals (see Table 1),
there appear to be around 1.4 deaths per 1,000 successful net unauthorized migrants.
31
For the more expensive, complete package of smuggling services, the coyote typically receives a portionof the fee up front and the remaining portion once the migrant is safely delivered to friends or family
members in the United States. See Charlie LeDuff, “The Crossing: A Special Report; A Perilous 4,000
Mile Passage to Work,” New York Times, May 29, 2001, p. 1.

16the period 1978 to 1996 69% of migrants reported hiring a coyote, as shown in Figure 7.32
During this period, the average price paid for coyote services varied between $385 and $715
( in 2000 dollars). Since 2001, when the Border Patrol became more vigilant in monitoring
U.S. border crossing points, coyote prices have risen. Based on a 2005 survey of return
migrants in rural areas of two Mexican states (that are also in the MMP), Cornelius (2005)
finds that between the 1996-1998 and 2002-2004 periods average coyote prices rose by 37%
from $1180 to $1680.33
One might be concerned that coyote prices based on MMP data are biased
downwards. As mentioned in the last section, the MMP surveys households in communities
with high rates of migration to the United States. Individuals in these communities may
have relatively good access to migration networks, making them less dependent on coyote
services or better able to negotiate lower prices from smugglers. Cornelius (2005) reportsthat 65% of migrants hiring a smuggler in 2004 report using friends or family to find acoyote. Results using the MMP itself are consistent with concerns about bias in observed
coyote prices. Gathmann (2004) finds that migrants with family members in the UnitedStates are less likely to use coyote services, and, among migrants who do hire a smuggler,
those with family members abroad pay lower prices. Yet, even if one accepts the highcoyote prices quoted in the press,34 border-crossing costs since 2001 appear to be no morethan $2,000, which is 35% of Mexico’s 2003 per capita GDP.
Next, consider the benefits to migration. To calculate the gross return to migration,
one would need to account for the expected length of stay in the United States, the expected
path of future earnings, and the discount rate applied to these earnings. In the absence ofdata on these components, much of the research on the decision to migrate takes a reducedform approach to modeling how the returns to migration affect behavior.35 Using
retrospective data from the MMP, Orrenius and Zavodny (2005) estimate the hazard thatyoung males migrate to the United States as a function of individual and household
characteristics and economic conditions in the two countries.36 In the MMP, most migrants
appear to be entering the United States illegally. The migration hazard is non-monotonic inschooling, increasing at low and medium schooling levels (primary and secondary school)and decreasing at higher schooling levels (preparatory school and beyond).37 The hazard isdecreasing in per capita GDP in Mexico and increasing in U.S. average wages. These
results suggest that migrants tend to be drawn from the middle of the schooling distribution
and that migration is more likely during periods when U.S. income is expanding relative toincome in Mexico. The migration hazard is also higher for individuals whose fathers have32

To reduce recall bias, I only show data for years within four of years of an MMP survey.
The increase in prices is based on respondents recollections in 2005 of prices they paid in previous years
and thus may be subject to recall bias. In 2005, 90% of respondents in Cornelius’ data (all of whom arefrom the states of Jalisco or Zacatecas) report using a coyote on their previous trip to the United States.
34
Recent articles in the popular press give a price range for coyote services of $1,500 $2,000 (“ManAccused of Smuggling Immigrants,” The Oregonian, September 17, 2005, p. B2; “US Immigration,” The
Economist, May 19, 2005; “Between Here and There,” The Economist, July 5, 2001; “Illegal Immigration;
Border Agents Understand a Complex Issue,” The San Diego Union-Tribune, May 26, 2005, p. B12).
35
In earlier work, Taylor (1987) examines migration behavior in a single rural community in Mexico.
36
See also Stark and Taylor (1989, 1991) and Massey and Espinosa (1997).
37
McKenzie and Rapoport (2004), who also use MMP data, obtain similar results.
33

17

migrated or whose siblings have migrated. This result could indicate the presence of family
migration networks, or it could indicate the presence of unobserved household
characteristics (e.g., unmeasured wealth) that influence migration behavior.
By treating migration as a function of age, education, and other individual
characteristics, Orrenius and Zavodny pick up variation across individuals in the incentive to
migrate. By also including macroeconomic conditions, they pick up time-series variation in
the migration incentive. Yet, since their specification is a reduced form, the coefficientestimates do not allow one to recover the elasticity of migration with respect to wage
differentials between the United States and Mexico. Further, since the MMP is restricted tocommunities with historically high propensities to migrate to the United States, the results
may not be informative about how prospective migrants in other regions of Mexico respondto changes in binational economic conditions.
To examine how the gross level of attempted illegal migration responds tochanges in U.S.-Mexican wages, Hanson and Spilimbergo (1999) estimate anapprehensions function, similar to that in equation (6). Using monthly data, they regressapprehensions at the U.S.-Mexico border on lagged apprehensions, current and lagged
linewatch enforcement hours, the real peso wage for production workers in Mexican
industry, real peso and real dollar U.S. wages (measured as the weighted average of wages
in U.S. industries that employ recent Mexican immigrants in large numbers), and othercontrols. They instrument for enforcement using U.S. government spending on national
defense and indicators for whether there is an upcoming U.S. presidential, congressional,
or border-state gubernatorial election (see note 26). Figure 8 shows a partial regression
plot of log apprehensions on log Mexican real average hourly earnings, based on Hanson
and Spilimbergo’s estimates. Border apprehensions appear to be very responsive tochanges in Mexican wages. A 10% decline in Mexican real wages is associated with a 68% percent increase in border apprehensions. Moreover, this effect is almost fully
realized within three months following a wage change, suggesting that shocks to the
Mexican economy are rapidly transmitted to changes in attempted illegal migration.
Over the past three decades, Mexico has experienced wide variation in real
income, as periodic devaluations of the peso have lead to bursts of inflation, which havecaused incomes to fall sharply. Figure 9, which plots relative per capita GDP in the
United States and Mexico, gives evidence of this volatility. During three separate
episodes in the last 30 years, Mexico’s per capita GDP declined by 5 log points or more(relative to the United States) within the space of three years. Each of these episodes wasfollowed by an increase in border apprehensions. Much of the research on inter-regionalmigration in the United States and other countries finds that it is labor earnings in thereceiving region, and not the sending region, that appear to drive migration flows(Greenwood, 1997). However, this does not appear to be the case in Mexico, where
income volatility appears to be a strong push factor for illegal migration.
While apprehensions are the only available high-frequency measure of grossattempts at illegal migration, these data have important limitations. Since individualmigrants may be apprehended multiple times in a given time period, the number of18

apprehensions may far exceed the gross number of migrants (Espenshade, 1995).
Controlling for border enforcement addresses this problem, but only if one has valid
instruments for enforcement (since enforcement is likely to be endogenous to shocks toattempted illegal migration) and if the impact of enforcement on apprehensions (controlling
for the incentive to migrate) is stable over time (see notes 26 and 28).
3.2 U.S.-Mexico Wage Differences

The reduced-form results of Orrenius and Zavodny (2005) and Hanson andSpilimbergo (1999) suggest that illegal migration flows are highly responsive to changes in
the return to migration. However, these results give no indication of the magnitude of the
returns themselves. As a crude approximation of the short-run gross return to migration, I
examine differences in hourly wages for men in Mexico and Mexican immigrant men in the
United States in 2000. I ask how long a migrant from Mexico would have to work in the
United States in order to recoup border-crossing costs, as approximated by the price of
coyote services. I focus on males, since, as Table 2 shows, there are large differences inlabor-force participation rates between women in Mexico and Mexican immigrant women in
the United States, which complicates comparing female wage outcomes across national
borders. By limiting the analysis to current wage differences and a single component of
migration costs, this exercise falls well short of a complete cost-benefit accounting of the
migration decision. Still, given large back and forth flows of labor across the U.S.-Mexico
border, the current U.S.-Mexico wage differential is likely to be the relevant gross return tomigration for at least some prospective migrants.
Table 3a reports average hourly earnings by age and schooling categories for males
in Mexico (based on the 2000 Mexico Census of Population and Housing) and for
immigrant males from Mexico in the United States (based on the 2000 U.S. Census ofPopulation and Housing).38 To increase the share of illegal immigrants among Mexican
immigrant men, I limit the sample to very recent immigrants (individuals residing in the
United States for 0-3 years). To adjust for cost of living differences between the countries, I
scale up Mexican hourly wages to achieve purchasing power parity with the UnitedStates, using the 2000 PPP adjustment factor for Mexico in the Penn World Tables.39
Not surprisingly, wages are substantially higher among Mexican immigrants in the
United States than among residents of Mexico. For 23-27 year-old males the PPP-adjusted
hourly wage differential varies from $7.01 for those with 0-4 years of schooling to $5.76 forthose with 13-15 years of schooling and to $7.82 for those with 16 or more years ofschooling. Given that migration propensities vary widely across regions of Mexico, one
might think that the average hourly wage for the country as a whole may not be the relevantalternative wage for most prospective migrants. Table 3b reports wage differentials between
38

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 per week). For Mexico, I need to assume individuals work all weeks of a month, which
could bias wage estimates downwards. To avoid measurement error associated with implausibly low wage
values or with top coding of earnings, I drop the largest and smallest 0.5% of wage values.
39
In 2000, Mexico’s PPP-adjusted price level was 61% of the U.S. price level.

19the United and States and high-migration states in Mexico, defined to be states with aboveaverage emigration rates in 2000.40 U.S.-Mexico wage differentials for high-migration
states are similar to those for Mexico as a whole.
In Table 3b, the log binational wage difference declines with schooling, which is inline with empirical research that has suggested estimated returns to schooling are higher in
Mexico. In the 1990s in the United States, an additional year of schooling is associatedwith an increase in wages for Mexican immigrant men of 2.5 to 3.2 log points (Borjas,
1996; Trejo, 1997; Grogger and Trejo, 2002). In 1990s in Mexico, in contrast, an
additional year of schooling is associated with an increase in wages for men of 7.6 to 9.7
log points (Chiquiar, 2003).41 Returns to experience also appear to be higher for Mexican
residents than for Mexican immigrants. Higher returns to education in Mexico are
consistent with the country having a low relative supply of skilled labor compared to theUnited States. Higher average wages in the United States (within schooling groups) areconsistent with the country having a higher relative level of TFP and higher relative
supplies of physical capital.
In 2000, a 23-27 year-old recent Mexican migrant with 5 to 8 years of schooling(the category just below the national mean level of schooling for Mexico) would recoup
border-crossing costs of $2,000 in 313 hours, or 7.8 weeks based on a 40-hour work
week. The speed with which migrants would seem to recover border-crossing costs
suggests that other costs to migration (including psychic costs and financing costs
associated with credit constraints) are large. In a simple static model, the equilibrium
wage differential between two countries is fixed by migration costs. To reconcile
persistent U.S.-Mexico wage differences with small border-crossing costs, there would
need to be positive unobserved migration costs (otherwise migration flows would belarger) and heterogeneity across individuals in these costs (otherwise migration flows
would be lumpier, with either everyone or no one in a skill group willing to migrate).
There are reasons to be skeptical about how well the observed U.S.-Mexico wage
differential captures the incentive to migrate. This differential reflects the binational
difference in returns to both observed and unobserved characteristics. If migrants are
positively selected in terms of unobserved skill, observed wage differences will tend tooverstate true wage differences. In the next section, we address the issue of which types ofindividuals in Mexico select into migration.
Another issue is that, since average wages for Mexican immigrants in the UnitedStates are a composite of wages for legal and illegal immigrants, they may overstate wages
an illegal immigrant would expect to earn. Kossoudji and Cobb-Clark (2001) use theLegalized Persons Survey (LPS) to compare wages for illegal immigrants before and after
they obtained green cards under the amnesty provision of the 1986 Immigration Reform and
40

These states are Aguascalientes, Colima, Durango, Guanajuato, Guerrero, Hidalgo, Jalisco, Michoacan,
Morelos, Nayarit, Oaxaca, San Luis Potosi, and Zacatecas.
41
On the returns to education in Mexico, see also Cragg and Epelbaum (1996) and Ariola and Juhn (2003).
If unobserved ability and schooling are correlated, estimates of returns to schooling may be biased. Also,
self-selection into the labor force or into migration may introduce further biases.

20

Control Act. Between 1989 and 1992, average hourly earnings for newly legalized
immigrant men (64% of whom were Mexican nationals) rose by 6 log points relative toearnings for Latino men in the NLSY, controlling for observable characteristics. Also, prior
to legalization, illegal-immigrant men had relatively slow wage growth. Not surprisingly,
illegal status is associated with lower wages and less opportunity for occupational
advancement (Koussoudji and Cobb-Clark, 1996 and 2000).42
What is surprising, perhaps, is that the estimated wage premium for legalization is sosmall. One might expect that being confined to the underground economy would
substantially limit workers’ employment prospects. Once legalized, they would enjoystrong wage growth. By this reasoning, a 6-log-point gain over three years is unimpressive.
One possibility is that the three years covered by the LPS is not long enough for individuals
to realize the wage benefits associated with gaining legal status. Legalization may open up
new opportunities to move between occupations or between regions, which migrants needmore time to exploit. Another possibility is that Koussoudji and Cobb-Clark’s estimate isbiased downwards. Their control group includes legal and illegal immigrants from Latin
America, as well as second and later generation Latin American immigrants. Suppose
illegal immigrants are more negatively selected in terms of unobserved skill than legal
immigrants (where overall legal immigrants may be positively or negatively selected interms of skill). In an economy where the return to skill is rising (as in the United States
during the 1990s), stronger negative selection of illegal immigrants in terms of unobserved
skill would tend to make their unobserved wage growth relatively small and lead one tounderestimate the wage premium due to legalization.
The differential in U.S. and Mexican wages in Table 3b may also miss importantsources of variation across individuals in the incentive to migrate. For instance, average
wages for Mexican immigrants in the United States may hide heterogeneity in expectations
about U.S. wages among prospective migrants. Individuals with previous U.S. labor-market
experience or who speak English well might have relatively strong wage expectations due tothe ease with which they expect to find a U.S. job or the high productivity they anticipate
having on the job. Though not specific to Mexico, Bleakley and Chin (2004) find that, for
immigrants from non-English-speaking countries that arrived in the United States aschildren, wages are higher for those with stronger English-language skills.43
Another source of heterogeneity in wage expectations among prospective migrants
is variation in access to migration networks. Individuals with friends or family that havemigrated abroad may have better information about how to find a job in the United States.
Munshi (2003), using data from the MMP, finds that Mexican migrants in the UnitedStates are more likely to be employed and more likely to be employed in a (higherpaying) nonagricultural job the larger is the U.S. population of residents from their origin
community in Mexico.44 He instruments for the time-varying size of the U.S. population
42

Rivera-Batiz (1999), who also uses the LPS, estimates larger wage differences between legal and illegal
immigrants. For other work on the wage consequences of IRCA, see Bansak and Raphael (2001).
43
Bleakley and Chin (2004) instrument for English-language ability using immigrant age at arrival, exploiting
the fact that younger children appear to learn new languages more easily than older children.
44
For other work on migration networks in Mexico, see Winters, de Janvry, and Sadoulet (2001).

21

from a migrant’s origin community in Mexico using lagged rainfall in the migrant’s origin
community (which presumably affects the marginal productivity of labor in Mexican
agriculture and so the incentive to migrate abroad). His results suggest that having a larger
network improves a migrant’s ability to assimilate economically in the United States.
Migration networks appear to be organized around families. Among nonagricultural(agricultural) workers, 78% (74%) received assistance in finding a U.S. job, and among thisgroup 47% (43%) received help from a relative and 47% (43%) received help from a friendor paisano (someone from their home region in Mexico). The small remaining fraction of
those receiving assistance relied on an employer, labor contractor or other source.
Even accounting for heterogeneity in wage expectations among migrants, thevolume of Mexico-to-U.S. migration is smaller than one might expect. U.S.-Mexico real
wage differentials are large today and have been large for over a century (Massey, Durand,
and Zenteno, 2001). The emerging literature on migration networks suggests that networks
arise in response to hidden migration costs associated with finding employment and getting
settled abroad. Carrington et al. (1996) develop a model of regional labor movements inwhich migration networks lead migrant flows to be sluggish initially, when the migrant
population is small, and then to accelerate over time, as migration costs endogenously fall in
response to past migration. Imperfect credit markets, which we discuss in the next section,
are another explanation for sluggish migration.
3.3 The Selection of Migrants from Mexico

In an important body of work, Borjas (1987, 1991) argues that who migrates tothe United States from a particular country will depend on that country’s wage
distribution. In a country with high returns to skill and high wage dispersion, as in
Mexico, there will be negative selection of migrants. Those with the greatest incentive to
migrate to the United States will be individuals with below-average skill levels in theirhome countries. In support of this idea, Borjas (1987, 1995) finds that as sources for U.S.
immigration have shifted from Europe to Latin America and Asia, 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 to converge to native levels. These findings counter an earlier belief that
immigrants tend to have high potential for earnings growth (Chiswick, 1978).
A simple test of the negative selection hypothesis is to compare the observableskills of migrants from Mexico with individuals in Mexico who choose not to migrateabroad. While selection on observables does not necessarily reflect selection on
unobservables, one might expect individuals’ observable and unobservable skills to bepositively correlated. Table 4 shows educational attainment for Mexican immigrants in
the United States and for residents of Mexico in 2000, based on census data from the twocountries. To help isolate the population of illegal immigrants, the table shows results
separately for very recent immigrants (0-3 years in the United States) and for longer-term
immigrants (4+ years in the United States). Section 2 suggests a relatively high fractionof very recent immigrants are likely to be illegal immigrants. To control for age, I limit
the sample to 28-37 year olds, which in Table 2 is the age cohort with the highest

22likelihood of migrating abroad. For comparison, the table also describes educational
outcomes for the full sample of working-age residents of Mexico and immigrants from
Mexico in the United States.
It is well known that Mexican immigrants in the United States are much lesseducated than U.S. natives (Borjas and Katz, 2005). However, Mexican immigrants,
whether recent or longer term, compare favorably when we examine residents of Mexico.
In 2000, 65% of male residents of Mexico had 9 or fewer years of schooling, compared to
54% of recent immigrants and 51% of longer-term immigrants. 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 more likely to have 10-15 years of education (40% versus 23%) and less likely tohave 16 plus years of education (5% versus 12%). A similar pattern holds for men in1990 and for women in either year. It appears that in Mexico individuals with moderate
to high education levels have the highest likelihood of migrating abroad, which isinconsistent with negative selection of migrants in terms of observable skills.
Other data on Mexican migration are also inconsistent with negative selection.
Using historical data from U.S. and Mexico population censuses, Feliciano (2001) findsthat average schooling of Mexican immigrants has been higher than for residents of
Mexico since at least 1940. Based on MMP data, in which the vast majority of
individuals who migrate to the United States do so illegally (at least on their firstattempt), Orrenius and Zavodny (2005) estimate that the probability a young adult malemigrates to the United States increases as schooling rises from low levels to levels aroundthe national mean (eight years) and then declines as schooling rises to levels above thenational mean. McKenzie and Rapoport (2004) obtain similar results, also using the
MMP.45 These findings are similar to literature on internal migration in Botswana, India,
the Philippines, and other developing countries, in which there appears to be an inverted
U-shaped relationship between rural household income and the likelihood that ahousehold finances urban migration for one or more of its members (Lucas, 1997).
Chiquiar and Hanson (2005) develop a more formal approach to evaluate theselection of Mexican immigrants in terms of observables. Let the observed density ofwages for individuals working in Mexico beg( w | i = MX) = ∫ f MX ( w | x )h ( x | i = MX)dx ,(8)and the observed density of wages for Mexicans working in the United States beg( w | i = US) = ∫ f US ( w | x )h ( x | i = US)dx ,(9)where f i(w|x) is the density of wages w in country i, conditional on a set of observed
characteristics x, and h(x|i) is the density of observed characteristics in i. Consider the45

On migrant selection in Mexico, see also Ibarraran and Lubotsky (2005).

23

density of wages that would prevail for Mexican immigrants in the United States if they
were paid according to the price of skills in Mexico:MX
g MX( w | x )h ( x | i = US)dx .
US ( w ) = ∫ f(10)

DiNardo, Fortin, and Lemieux (1996) show that, under the assumption that the
distribution of unobservables (conditional on the distribution of observables) is the samein the two countries, a counterfactual density as in (10) can be written asMXg MX( w | x ) h ( x | i = MX )dx ,US ( w ) = ∫ θ f

(11)

where
θ=

h ( x | i = US)
Pr(i = US | x ) Pr(i = MX) 46=.
h ( x | i = MX) Pr(i = MX | x ) Pr(i = US)(12)

Thus, the counterfactual density in (11) can be estimated by taking the observed density
for wage earners in Mexico and re-weighting it to reflect characteristics of Mexican
immigrant workers in the United States. The weight in (12) can be estimatedparametrically by running a logit on the probability of a Mexico-born adult being in the
United States, conditional on observed characteristics, using a sample that combines
Mexican immigrants and Mexican residents.
By comparing actual and counterfactual wage densities, we can nonparametricallysummarize the nature of migrant selection in Mexico. Consider the difference between
the actual wage density for residents of Mexico and the counterfactual wage density thatwould obtain were Mexican immigrants paid according to skill prices in Mexico:MXg MX( w ) = ∫ [θ M − 1] f MX ( w | x ) h ( x | i = MX )dx .
US ( w ) − g(13)

If there is negative selection of migrants in terms of observable skills, the difference in(13) 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– and negative 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.
Figure 10 shows estimates of the density difference in (13) for men and women in1990 and 2000, based on results in Chiquiar and Hanson (2005). The sample is workingage adults (21 to 65 years of age) who either reside in Mexico or are very recent Mexican
46

Chiquiar and Hanson (2005) show how to derive a weighting function similar to (12) that controls fordifferences in labor-force participation among workers in the two countries. To simplify the exposition, I
leave out the analytics behind this extension. The results in Figure 10 control for differences in labor-force
participation between Mexico and the United States.

24

immigrants in the United States. Immigrants are individuals 21 years or older at time of
U.S. entry and who have been in the U.S. for 3 years or less. (Again, very recent
immigrants are likely to include a relatively high fraction of illegal immigrants.) The
logit regression used to estimate the weights in (12) has as regressors dummy variables
for age, schooling, and marital status, and interactions of these variables.
Consistent with Table 4, it is not the lowest-wage individuals who exhibit astronger tendency to migrate to the United States. In either year, for Mexican immigrant
males there is greater mass in the middle of the wage density and less mass in either tail,when compared with the actual wage density of Mexico residents. The immigrantresident density difference is close to zero in the left tail to just below zero, positive for
middle wage values, and again close to zero in the right tail. This suggests that
immigrant males are drawn disproportionately from the middle of Mexico’s wage
distribution, rather than from the bottom half. Low-wage and high-wage individuals
appear to be relatively less likely to migrate to the United States. These counterfactual
wage densities support intermediate selection of immigrant men in terms of observableskills. The results for females contain even less support for negative selection. 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.
Table 4 and Figure 10 do not explicitly separate legal and illegal Mexican
immigrants, leaving it unclear how the two groups compare in terms of skill. What does
theory suggest about the self-selection of legal versus illegal migrants? Consider asimple extension of Borjas (1991), who adapts the Roy (1951) model.47 Let individuals
from Mexico, indexed by 0, choose whether or not to migrate to the United States,indexed by 1, where migration is a one-time decision (or, equivalently, a one-period
decision). Residents of Mexico face a wage equation given byln(w 0 ) = μ 0 + δ 0 s ,(15)where for Mexico w0 is the wage, μ 0 is the base wage, s is the level of schooling, and δ 0
is the return to schooling. If the population of Mexicans were to migrate legally to the
U.S., they would face the wage equationln(w 1L ) = μ1L + δ1L s ,(16)while if they were to migrate illegally they would face wage equation,ln(w 1I ) = μ1I + δ1I s .

(17)

47

For expositional simplicity, I consider a non-stochastic version of Borjas (1991), in which there is nounobserved component of skill. The extension to a stochastic setting is straightforward.

25

where w 1i is the wage, μ1i is the base wage, and δ1i is the return to skill for Mexican
migrants with status i. Higher levels of TFP and larger relative supplies of capital in the
United States suggest the base wage is lower in Mexico ( μ1i > μ 0 , for i=I,L); higher U.S.
relative supplies of human capital suggest that the return to schooling is higher in Mexico( δ1i < δ 0 , for i=I,L). Table 3 supports both assumptions. Kossoudji and Cobb-Clark(2001) find that in the United States the base wage and the return to schooling are higher
for legal than for illegal immigrants, which suggests μ1L ≥ μ1I and δ1L ≥ δ1I .
Migrant selection also depends on the structure and financing of migration costs.
Consider a setup similar to Borjas (1987), in which the migration cost for migration
status i, Ci, expressed in time-equivalent units, π i = C i / w 0 , is assumed to be constantacross individuals. Combining equations (15)-(17), a resident of Mexico will be willingto migrate to the United States under migration status i ifln(w 1i ) − ln(w 0 + C i ) ≈ ln(w 1i ) − ln(w 0 ) − π i > 0 .

(18)

Suppose that migrants finance migration costs by borrowing and that to secure a loan ofamount Ci a migrant must have collateral of amount γCi, γ>0. Collateral requirements
may reflect imperfect credit markets, which place initial wealth restrictions on which
agents are capable of migrating abroad, as in Rapoport (2002), McKenzie and Rapoport(2004), and Orrenius and Zavodny (2005).48
To capture the relationship between credit constraints and skill, suppose that
individual wealth in Mexico, Y0, is a linear function of skill, such that
Y0 = ρ 0 + σ 0 s .

(19)

Low-skill individuals will be unable to finance migration because they have insufficientcollateral to secure a loan. The lowest skill level for migration strategy i to be feasible isγC i − ρ 0
si =
,.
σ0

(20)

which is higher the larger are migration costs. Whether the least skilled legal migrant ismore or less skilled than the least skilled illegal migrants depends on the relative
magnitude of legal and illegal migration costs. In Cornelius (2005), recent bordercrossing costs for illegal migrants range from $1200 to $1700 (in 2000 dollars). The
current costs of legal migration include fees paid to the U.S. government to process a visa
application, which range from $700 and $1000 for family-sponsored immigration visas,48

I limit the analysis to partial equilibrium. Rapoport (2002) develops a general-equilibrium model ofcredit constraints and migration, based on the occupation-choice model in Banerjee and Newman (2003).

26

and the expense of hiring a lawyer or immigration specialist to handle the application
process, which typically ranges from $400 to $1000.49 Ignoring the time costs involvedin crossing the border illegally or in completing the bureaucratic steps needed to obtain agreen card, entry costs for legal migrants from Mexico appear roughly similar in value tothose for illegal migrants, in which case s I and s L would also be similar.
Based on (18), high-skill individuals will choose not to migrate because higher
returns to skill in Mexico make it more attract to stay at home. The highest skill level forwhich migration strategy i is attractive is
μ i − μ 0 − Cisi = 1,
δ 0 − δ1i

(21)

which is higher the larger the U.S.-Mexico difference in the base wage and the smaller
the U.S.-Mexico difference in the return to skill. Thus, larger differences in the return toskill between countries make negative selection of migrants in terms of skill more
pronounced. I assume parameter values are such that si > s i for i=I,L, which yields
positive levels of both legal and illegal migration. As long as the lower support for the
distribution of skill is less than min (s L , s I ) and the upper support of the distribution ofskill is greater than max (s L , s I ) , both legal and illegal migrants will tend to be drawn
from individuals with intermediate skill levels. If legal and illegal migration costs are
similar, Kossoudji and Cobb-Clark’s (2001) results on base wages and returns to skill for
legal and illegal migrants would suggest that s L ≥ s I , or that the highest skilled legal
migrants are at least as skilled as the highest skilled illegal migrants.
The simple theoretical model together with available empirical evidence suggeststhat legal migrants would be more positively selected than illegal migrants in terms ofskill. However, this outcome implicitly depends on all prospective migrants in Mexico
having an equal probability of being eligible for a U.S. legal immigration visa. There is
no reason to expect this to be the case. If more-skilled individuals are more (less) likely
to have family members who are U.S. citizens or U.S. legal permanent residents, they
will be more (less) able to obtain a U.S. green card, in which case there would be stronger(weaker) positive selection of legal migrants relative to illegal migrants. Also, thediscussion ignores dynamic considerations, in which individuals may migrate illegally
today in expectation of obtaining a U.S. green card in the future. As of yet, there is little
empirical research on which types of households in Mexico appear to have better accessto legal channels of obtaining a U.S. green card.
Combining (20) and (21), it is clear that as migration costs rise both legal andillegal migrants become more positively selected in terms of skill (i.e., the interval

49

On fees for immigration visas, see http://uscis.gov/ and http://www.usavisanow.com/.

27

(s i , si ) , for i=I,L, shifts to the right). In the MMP, Orrenius and Zavodny (2005) findthat as the level of U.S. border enforcement rises (U.S. linewatch enforcement hours
increase) the probability a young adult male migrates to the United States falls, with theeffect being stronger for individuals with lower schooling levels. This suggests thathigher migration costs disproportionately select lower-skilled individuals out of the
migrant pool. Similarly, McKenzie and Rapoport (2004) find that in MMP communities
with stronger U.S.-migration networks the inverted-U-shaped relationship between
migration and schooling is weaker. As migration costs fall (access to migration networks
improves) positive selection of migrants becomes less pronounced.
There is abundant evidence that Mexican immigrants in the United States are
disproportionately drawn from the middle of the distribution of observable skills in
Mexico. While there are no data that explicitly differentiate between the selection patternof legal and illegal migrants, there is intermediate selection among migrants in the MMP,
in which a very high fraction of migrants are illegal. Intermediate selection is consistent
with migrants facing credit constraints in financing the cost of migration, such that lowskill, low-income individuals are disproportionately selected out of the migrant pool.
Other factors that could contribute to intermediate selection include higher discount rates
greater risk aversion, or higher psychic migration costs among low-skilled, low-income
individuals, such that these individuals are less willing to migrate to the United State forany given binational wage differential. Credit constraints or some other factor is needed
to reconcile the facts that (a) the incentive to migrate appears to be strongest forindividuals with low schooling (as seen in Table 3), and (b) individuals with low
schooling have a relatively low probability of migrating abroad (as seen in Table 4).
3.4 Migration and the Supply of Labor in Mexico and the United States

As we have seen, the literature on labor flows from Mexico to the United States
focuses primarily on the correlation between migration and U.S.-Mexico earnings
differences. There is little work on the underlying causes on these wage differences orwhether migration is related to variation in these causal factors. The work that perhaps
most closely addresses these issues is Robertson (2000), who examines the correlation
between U.S. and Mexican wages over time. Using synthetic cohorts constructed fromhousehold data in the two countries over 1987-1997, he regresses the quarterly change in
Mexican wages for a given age-education-region cell on quarterly changes in U.S. wages
for the same age-education cell and on the lagged difference in U.S. and Mexican wages
for the cell. A shock that raises U.S. wages by 10% is associated with an increase inwages in Mexican interior cities by 1.8% and wages in Mexican border cities by 2.5%.50
Positive comovements in U.S. and Mexican wages are consistent with the two countries’

50

Robertson (2000) also finds that wage changes in Mexico are negatively correlated with the lagged U.S.Mexico wage difference, which suggests that over time wages in the two economies tend to converge. The
estimated convergence rates are very rapid, with equilibrium U.S.-Mexico wage differentials being reachedwithin one to two quarters. Rapid convergence seems at odds with rising levels of trade, investment, andmigration between the two countries, which suggests that integration of U.S. and Mexican markets is
incomplete and that wage convergence between the two countries is more gradual.

28

labor markets being at least partially integrated. Migration flows are one factor that may
contribute to labor-market integration, as are cross-border trade and investment flows.
To examine further the determinants of labor flows from Mexico to the UnitedStates, consider a simple two-country model in which the aggregate production function
for Mexico (indexed by 0) at time t is given by
1
υ⎤υ
⎞κ ⎥

⎡
⎛
⎢
Q 0 t = A 0 t ⎢K 0υt + ⎜⎜ ∑ Lκ0ht ⎟⎟ ⎥ ,
⎝h
⎠ ⎥
⎢
⎦
⎣

(22)

where A0t is total factor productivity, K0t is the supply of capital, L0ht is the supply oflabor of skill type h, υ ≤ 1 determines the elasticity of substitution between capital andlabor (ν = 1 – 1/σKL), and κ ≤ 1 determines the elasticity of substitution between laborskill types (κ = 1 – 1/σhh’, all h≠h’). Equating the wage to the marginal product of labor,ln w 0 ht = (1 − ν) ln Q 0 t + (υ − κ) ln L 0 t + ( κ − 1) ln L 0 ht + ln p 0 t ,(23)where L0t is the labor aggregate in (22) and p0t is the price of Mexico’s output. The
aggregate production function for the United States (indexed by 1) has an analogous
structure, with the added dimension that labor skill type h, L1ht, is an aggregate of theemployment of native and immigrant workers, who are imperfect substitutes, such that

(

)

1
η
η η
L1ht = L1ht + I ht ,(24)

where L1ht is the supply of native-born U.S. labor of skill type h, Iht is the supply of
immigrant labor of skill type h, and η ≤ 1 determines the elasticity of substitution
between native and foreign labor (η = 1 – 1/σLI) The marginal product of immigrantlabor in the United States (assumed to be the same for legal and illegal immigrants) is

ln w Iht = (1 − ν) ln Q1t + (υ − κ) ln L1t + ( κ − η) ln L1ht + (η − 1) ln I ht + ln p1t ,(25)

where L1t is the U.S. labor aggregate and p1t is the price of U.S. output.
If migration equalizes wages between the United States and Mexico, thenln w 0ht − ln P0 t = ln w Iht − ln P1t − ln c ht ,(26)

where Pkt is the consumer price index in country k (expressed in terms of a common
currency) and cht = 1 – Cht/(wIht/P1ht) is the migration cost for labor type h expressed as amarkup over the wage rate. Cht may be a function of U.S. border enforcement, or, if
migration networks exist, of the existing U.S. stock of immigrants from Mexico.
29

Using (23)-(26) to solve for the level of immigration of skill type h,ln I ht =

⎤
Q 0t
L
L
p
P
1 ⎡
+ ( κ − ν) ln 0 t + (η − κ) ln 0 ht − ln 0 t + ln 0 t − ln c ht ⎥ .
⎢(ν − 1) ln
1− η ⎣
Q 1t
L 1t
L1ht
p 1t
P1t
⎦
(27)

Immigration of skill type h workers from Mexico is (a) decreasing in Mexico-U.S.
relative GDP (which, in turn, is decreasing in Mexico-U.S. relative TFP and the MexicoU.S. relative supply of capital), (b) increasing in the Mexico-U.S. relative labor aggregate(as long as different labor skill types are more substitutable than are aggregate labor andcapital, such that κ – ν ≥ 0), (c) increasing in the Mexico-U.S. relative supply of skill typeh (as long as immigrant and native workers of the same skill type are more substitutable
than are labor of different skill types, such that η – κ ≥ 0), (d) decreasing in the MexicoU.S. terms of trade, (e) increasing in the Mexico-U.S. relative cost of living, and (f)decreasing in migration costs. All else equal, immigration rises when Mexico has slower
productivity growth, slower capital accumulation, faster labor-force growth, or negative
terms-of-trade shifts relative to the United States.
What is the relative contribution of the variables on the right of (27) to theincrease in Mexican migration to the United States? Unfortunately, existing literatureoffers few answers to such a question. For a preliminary take on the data, Figure 11 plots
Mexico-U.S. relative real GDP per worker, the Mexico-U.S. terms of trade, and the
Mexico-U.S. real exchange rate over the 1960-to-2000 period (based on the Penn World
Tables). Relative real GDP per worker is a crude proxy for a term that combines the first,
second, fourth, and fifth terms in (27) (i.e., all terms except the relative supply of labor
for skill type h and migration costs). Relative income declines in the 1980s and is flat in
the 1990s. Slow growth in Mexico’s GDP and rapid growth in Mexico’s labor force havecombined to make the U.S.-Mexico gap in income per worker larger in 2000 than it wasin 1980. Changes in the terms of trade appear to have mattered little for relative incomechanges. Over the last two decades, the Mexico-U.S. terms of trade have been stable,consistent with the two countries exporting similar types of manufactured goods.51
Changes in aggregate income may understate the contribution of binational
income differences to Mexico-U.S. migration. Relative average aggregate income may
hide variation in income across sectors or regions in Mexico that affect migrant outflows.
For instance, negative income shocks to Mexican agriculture or to Mexico’s highmigration states would likely increase migration abroad, even if positive income shocks
to other sectors or regions helped smooth income at the national level. Also, the relative
variability of income may affect migration, independent of changes in relative mean
51

Terms-of-trade changes in the Penn World Tables may understate true terms-of-trade changes. Within
industries, Mexico and the United States tend to specialize at different ends of the production chain, with
the United States focused more on capital-intensive component production and Mexico focused more onlabor-intensive product assembly. Such within-industry specialization may not be adequately reflected inconventional measures of the terms of trade, which fail to account for the fragmentation of production.

30

income. As movements in Mexico’s relative price level in Figure 11 indicate, Mexico’s
economy has recently been subject to a high degree of price volatility.
To characterize changes in relative labor supplies in Mexico and the UnitedStates, Figure 12 shows the relative size of the working-age population in the twocountries over the period 1970 to 2000. I take the population of Mexican nationals to be
the sum of individuals born in Mexico residing in either Mexico or the United States,
which I then divide into age cohorts by gender. To examine a population of U.S. natives
who are likely to be substitutable in production with Mexican labor, I restrict U.S. age
and gender cohorts to be individuals with a high-school education or less.
Over the 1980-to-1990 and 1990-to-2000 periods, there is a dramatic increase in
the supply of young Mexican nationals relative to the supply of young less-educated U.S.
natives. In 1980, Mexico’s supply of 20-29 year olds was just over half the size of the
U.S. population of less-educated 20-29 year olds. By 2000, Mexico’s supply was larger
than the U.S. supply. The change in relative supply occurred in part because a large
cohort of young Mexicans entered the labor force in the 1980s and 1990s and in part
because more U.S. natives now continue their education beyond high school. The
increase in relative labor supply is especially strong among women, for whom skill
upgrading in the United States has been most pronounced (Katz and Autor, 1999).
As further evidence of the contribution of relative labor-supply changes to
Mexico-U.S. migration, Figure 13 plots the stock of Mexican immigrants in the UnitedStates against the Mexico-U.S. relative supply of labor by gender and age cohort over the
1970-to-2000 period (where I use five-year age cohorts to increase the sample size). I
restrict U.S. natives to be either those with a high-school education or less or those withless than a high-school education, which roughly approximates a plot of ln(Iht) againstln(L0ht/L1ht) in (27).52 Across cohorts and time, there is a strong positive correlation
between Mexico-U.S. migration and the relative supply of Mexican labor. Figure 13
gives suggestive evidence that increases in the relative supply of Mexican labor
contribute to migration abroad, presumably by pushing down Mexican wages.
Surprisingly, the determinants of wage differences between the United States and
Mexico and their link to Mexico-U.S. migration flows are largely unexplored topics. A
cursory glance at the data suggests that a combination of slow growth in Mexico’s
economy and rapid growth in Mexico’s labor force have contributed to rising labor
outflows in recent years. However, many details about this story are unknown. In thelast two decades, Mexico has undertaken major economic reforms, which may havechanged the distribution of factor rewards or the distribution of regional incomes in amanner that increased the incentive to emigrate.53 The migration function in (27)52

To keep this approximation as literal as possible, I define the supply of Mexican labor to be residents of
Mexico and the supply of U.S. labor to be the sum of Mexican immigrants in the United States and lesseducated U.S. natives (where both variables are measured by age and gender cohort).
53
These reforms include a liberalization of foreign trade and investment (unilaterally over the period 1985
to 1989 and trilaterally with Canada and the United States under the North American Free Trade
Agreement in 1994), the privatization of state-run enterprises and the deregulation of industry (in the late1980s and early 1990s), and changes in the land-tenure system (in the early 1990s, which privatized land31

suggests the link between relative factor supplies and migrant flows depends on thesubstitutability of Mexican and U.S. labor, which we know little about (Borjas, 2003).54
And, while there is considerable micro-level evidence that migration networks affectindividual migration decisions, there are few estimates of the impact of these networks on
aggregate labor flows. Thus, the literature provides little guidance for thinking about
how GDP growth, population growth, skill upgrading, or other shocks to national
economies affect aggregate labor flows from Mexico to the United States.
3.5 Summary

Differences in earnings between the United States and Mexico are one factor that
contributes to Mexico-to-U.S. migration. Consistent with previous migration research inmany other contexts, labor outflows from Mexico increase when U.S.-Mexico income orwage differences increase. Illegal migration flows, measured either using survey data on
migrant-sending communities in Mexico or apprehensions at the U.S.-Mexico border, arequite responsive to relative income changes. Access to migration networks also appears
to facilitate cross-border labor flows.
The literature has made little progress in identifying how the underlying
determinants U.S.-Mexico wage differences affect binational migration. Anotherunresolved issue is why, given large income differences between the United States and
Mexico and small observed costs in crossing the border illegally, migration flows are notlarger. There appear to be unobserved sources of migration costs (or heterogeneity in theperceived benefits of migrating abroad) that are important enough to impede many
individuals from leaving Mexico. These unobserved costs appear to be especially large
for less-educated individuals, who have relatively low migration propensities despite
having relatively large apparent returns to migration.

4. Policies to Control Illegal Immigration

Many government policies play a role in determining the level of illegal
immigration. Some policies, including the enforcement of international borders and themonitoring of hiring practices by employers, affect illegal immigration directly. Bychanging the intensity with which they enforce borders or monitor employers, authorities
effectively regulate the inflow of illegal immigrants from abroad. In the United States,
enforcement policies are under the control of federal government agencies and can bechanged frequently, even on a day-to-day basis. Other policies, including quotas forpermanent or temporary legal immigration, the rights of immigrants to draw on public
assistance, and minimum-wage requirements, affect illegal inflows indirectly throughtheir impact on the expected reward from unauthorized migration. In the United States,

previously held by rural cooperatives). Aguayo (2005) finds that in the 1990s individuals from
communities more exposed land reform were more likely to migrate internally in Mexico.
54
Cortes (2005) and Ottaviano and Peri (2005) provide recent evidence that in the United States low-skilled
native and foreign labor are less than perfect substitutes.

32these and other such indirect policies are set by Congress and tend to change slowly overtime, which has made their impact on illegal immigration difficult to gauge.
In this section, I examine U.S. policies to control illegal immigration. I focus on
enforcement policies, as these have been the subject of most academic research.
Theoretical literature considers the incentives of countries to restrict illegal immigrationand the political economy of impediments to labor inflows from abroad; empiricalliterature examines the impact of enforcement policies on migrant flows. There is little
work on policies that restrict labor outflows, as such policies do not exist in Mexico andare uncommon except in highly authoritarian regimes.
4.1 U.S. Border and Interior Enforcement Policies

There is considerable academic and policy interest in the economic impact of U.S.
actions to prevent illegal immigration. The increase in the stock of illegal immigrants in
the United States, evident in Table 1, indicates that U.S. enforcement efforts have notsucceeded in stopping illegal entry from Mexico or other countries. The concomitant
increase in U.S. resources devoted to enforcement, seen in Figure 5, suggests that the lackof success is not for want of effort.
In political science and sociology literature, rising illegal immigration tends to beinterpreted as a result of policy failure (Andreas, 2000; Massey, Durand, and Malone,
2003; Cornelius, Tsuda, Martin, and Hollifield, 2004; Cornelius, 2005). Due to politicalconstraints, the United States has focused on border enforcement, rather than monitoring
U.S. employers, which appears ill-suited to curtail unauthorized entry in a country thatshares a 2,000-mile long land border with a poor neighbor. Further, the United States haschosen to concentrate enforcement resources in border cities, leaving less populated
corridors largely unpoliced through which illegal immigrants continue to enter thecountry in large numbers. Are U.S. enforcement policies ineffective? And, if so, why
have U.S. immigration authorities chosen these policies? I deal with research on the firstquestion in this section and on the second question in the next.
Each year, the U.S. Congress appropriates funds to the Department of Homeland
Security (DHS) for enforcement of U.S. borders, which falls under U.S. Customs andBorder Protection, and for enforcement of immigration laws in the U.S. interior, whichfalls under U.S. Immigration and Customs Enforcement. (Prior to 2002, both border andinterior enforcement belonged to the now-defunct Immigration and Naturalization
Service(INS) in the U.S. Department of Justice.)
The U.S. Border Patrol, the primary agency responsible for border enforcement, ispart of U.S. Customs and Border Protection. Border Patrol officers may be deployed tolinewatch duty, in which they attempt to apprehend unauthorized immigrants at the U.S.Mexico border; to entry points along the U.S.-Mexico and U.S.-Canada borders, at which
they monitor pedestrian and vehicular traffic entering the United States; or to traffic
checkpoints along major highways inland from border crossings, at which they conduct
inspections. Some of these activities (linewatch duty) are more oriented toward33

preventing illegal immigration, while others (traffic checks) are more oriented toward
preventing the smuggling of contraband. The broad scope of Border Patrol activities
suggests that DHS officials (and INS officials before them) have sufficient discretion inallocating resources to allow them to vary the intensity with which they enforce borders
against illegal immigration. Discretion creates an opportunity for political pressure to
influence enforcement activities over short time horizons (as well as through the moreprotracted congressional appropriations process).
The detection of illegal immigrants in the U.S. interior falls under U.S.
Immigration and Customs Enforcement (ICE). The activities of ICE agents (and of INS
agents before the creation of DHS) include attempts to apprehend illegal immigrants at
U.S. worksites, investigations of international smuggling operations, and prosecutions
and deportations of non-citizens who have been convicted of a felony in the UnitedStates. As with the Border Patrol, DHS officials have discretion in how ICE agents are
deployed, allowing them to vary the intensity of interior enforcement against illegal
immigration at the local, regional, and national level.
Boeri, Hanson, and McCormick (2002) document that U.S. immigration
authorities apprehend far more illegal immigrants at U.S. borders than in the U.S.
interior. Table 5, which shows “deportable aliens” located by U.S. immigration
authorities from 1992 to 2004, updates their data. (Deportable aliens include, primarily,
apprehended illegal immigrants, and, secondarily, legal immigrants subject to deportation
because they have committed a criminal offense.) Over the period, 93% of deportable
aliens were located by the Border Patrol, rather than by ICE or INS agents in the U.S.
interior. Of those apprehended by the Border Patrol, 97% were Mexican nationals. And,of apprehensions of Mexican illegal aliens, less than 1% occurred at U.S. worksites. The
vast majority of Mexican nationals apprehended were “seeking employment,” which
generally means they were caught trying to cross the U.S.-Mexico border illegally.
Apprehensions of illegal immigrants are a policy output. How might one measure
inputs into enforcement policy? For border enforcement, the primary inputs are officer
hours the Border Patrol spends policing the border and capital expenditure on
enforcement infrastructure. In Figure 5, between 1990 and 2003 officer hours devoted toborder enforcement increased by 3.8 times, from 2.5 million to 9.5 million. For interior
enforcement, measures of policy inputs are more difficult to obtain. Those that areavailable suggest immigration authorities devote a relatively small share of theirresources to the U.S. interior. Between 1999 and 2003, the number of man hours ICE
agents devoted to worksite inspections declined from 480,000 (or 9% of total INS agent
hours) to 180,000 hours (or 4% of total ICE agent hours) (GAO, 2005). Thus, in 2003
U.S. immigration authorities devoted 53 times as many officer hours to linewatch
enforcement as to worksite enforcement. One consequence of low worksite enforcement
is that few U.S. employers who hire illegal immigrants are detected or prosecuted. The
number of U.S. employers paying fines of at least $5,000 for hiring unauthorized workers
was only 15 in 1990, which then fell to 12 in 1994 to 2 in 1998 and to 0 in 2004. A

34

recent U.S. General Accounting Office study concludes, “The worksite enforcement
program has been a low priority under both the INS and ICE” (GAO, 2005).55
The emphasis of border enforcement over interior enforcement does not appear tobe due to illegal immigrants being difficult to locate once they are inside the UnitedStates. Several U.S. industries, including agriculture, construction, and restaurants andhotels, appear to employ large numbers of unauthorized workers. The U.S. Departmentof Labor (2005) reports that over the period 1999 to 2002 54% of the U.S. farm laborers
it surveyed were in the United States illegally. At harvest time, in the late summer andearly fall, many of these workers are plainly visible at farms in California, Texas,
Washington, and elsewhere in the western United States. U.S. immigration authorities
simply choose not to conduct large-scale raids on U.S. farms, construction sites, or otherplaces of business where illegal immigrants tend to work.
Beyond concentrating on border enforcement, U.S. immigration authorities target
their efforts at specific locations along the border. Figure 14 plots annual hours the U.S.
Border Patrol officers spent on linewatch duty by region along the border over the period
1977 to 2003. Officer hours increase sharply in the early 1990s in Texas, in the mid
1990s in Western California (San Diego), and in the late 1990s in Arizona. These
increases reflect successive Border Patrol operations near specific U.S. border cities,
including El Paso, San Diego, El Centro, and McAllen (Reyes, Johnson, and Van, 2002).
Operations involve increased patrols, constructing walls and barricades, and mounting
electronic surveillance equipment. Figure 14 also plots apprehensions by U.S. Border
Patrol officers on linewatch duty by border region. Following the increase in Border
Patrol activities in Western California, apprehensions declined in the region but shortly
thereafter increased in nearby Eastern California and Arizona. In response to greater
border security in San Diego, which in the 1970s and 1980s was the primary illegal entry
point along the border, prospective migrants appear to have shifted their attempts to enterthe United States to the more remote desert regions of the California-Mexico andArizona-Mexico borders. Border Patrol operations have succeeded in reducing illegal
border crossings at targeted locations, but not, as the increase in illegal immigration
during the 1990s reveals, at other locations along the border.
There is active debate in policy circles about the effectiveness with which U.S.
immigration authorities deploy the resources they have available. In their own defense,
authorities claim that they are overwhelmed by current levels of attempted illegal
immigration (GAO, 2001). Even with the increase in enforcement resources, authorities
suggest they have been unable to staunch in the inflow of unauthorized migrants because
the number of those attempting to enter the United States illegally has risen too fast. An
opposing line of argument is that immigration authorities deploy their enforcement
resources ineffectively for strategic motives (Cornelius et al., 2004). In response topolitical pressure from employers and other groups that benefit from illegal immigration,
goes the reasoning, U.S. immigration authorities choose not to enforce U.S. laws against
55

Further, since September, 2001, and the shift in government priorities toward preventing terrorist attacks,
the majority of time ICE agents spend on worksite enforcement is devoted to monitoring “criticalinfrastructure sites,” such as airports and power plants (GAO, 2005).

35

hiring illegal immigrants and to deploy the Border Patrol in a manner that allows large
numbers of illegal immigrants to enter the country.
Has increased U.S. border enforcement in fact made illegal immigration in the
United States more costly?56 Hanson and Spilimbergo (1999) find that, controlling forthe endogeneity of enforcement, border apprehensions increase as border enforcement
increases. This finding suggests that expanded enforcement makes crossing the bordermore difficult (since more of those attempting illegal immigration are being caught at theborder), but it does not reveal whether greater difficulty in border crossing detersprospective migrants from attempting to enter the United States illegally.
Gathmann (2004) provides more direct evidence of the consequences of expandedborder enforcement for migration. She uses MMP data to examine the correlates ofcoyote prices paid by migrants from Mexico to the United States and to estimate theimpact of coyote prices on migrant demand for smuggler services.57 The price a migrantpays to a smuggler is higher in years when border enforcement is higher. But theelasticity of coyote prices with respect to enforcement is small, in the range of 0.2 to 0.5.
During the sample period, a one-standard-deviation increase in enforcement would havelead to an increase in coyote prices of less than $40; in the mid 1990s average coyote
prices were $410. The estimated demand for smuggler services and the individual
probability of choosing to migrate to the United States are both quite responsive tochanges in coyote prices. However, given the small enforcement elasticity of coyote
prices, the observed increase in border enforcement over 1986 to 1998 appeared to reduce
the average migration probability among MMP respondents by only 10%.
Gathmann’s results, of course, are conditional on the pattern of borderenforcement that was realized over the sample period. By increasing enforcement insome border locations but not others, U.S. immigration authorities may have(intentionally or not) mitigated the impact of expanded enforcement on smuggler prices.
Gathmann documents that as the United States carried out its 1990s border buildup many
migrants shifted from higher-enforcement to lower-enforcement crossing points. We do
not know how coyote prices would respond to a border-wide increase in enforcement as
the United States has yet to carry out that experiment.
Another issue is that MMP data may lead one to underestimate the impact ofenforcement on smuggler prices. MMP communities have sent migrants to the UnitedStates for decades. Many families in these communities have long-term relationships
with coyotes. Prices coyotes charge long-term customers may be less responsive toshocks than prices they charge on the smuggling spot market used by migrants in the rest56

For earlier work on this question, see Espenshade (1994, 1995), Kossoudji (1992), Donato, Durand, andMassey (1992), and Massey and Singer (1995). For other recent work see Angelucci (2003).
57
In the estimation of coyote prices, Gathmann (2004) instruments for border enforcement using the drug
budget of the U.S. Drug Enforcement Agency (DEA). In the estimation of the demand for coyote services,
she includes both the smuggler price and the level of border enforcement as regressors, instrumenting forthe former with the average U.S. prison term for smugglers (which rises over the sample period) and for thelatter again with the DEA drug budget. Under the assumption of normality in the errors, she is able tocontrol for selection into migration (by exploiting MMP data on non-migrants).

36

of Mexico. Hence, in the rest of the country attempted illegal migration could more
responsive to border enforcement than Gathmann’s results suggest.
The United States has undertaken a massive increase in the resources that it
devotes to border enforcement. Yet, the apparent impact of this increase has beenmodest. While expanded border enforcement has reduced attempted illegal entry at what
used to be major crossing points in California and Texas border cities, it appears to have
had a small effect on deterring illegal immigration overall (measured either in terms ofchanges in smuggler prices or the average probability a Mexican national migrates to theUnited States). One possibility is that there are important non-convexities in enforcement
such that it only becomes an effective deterrent to illegal entry at high levels of resource
commitment. This is perhaps the implicit argument of those calling for further expansionof U.S. enforcement efforts.58 Another possibility is that U.S. enforcement strategies are
ineffective by design, due to the political economy of immigration control.
4.2 The Determination of U.S. Enforcement Policies

In the absence of economic distortions, the optimal immigration policy would be
to have open borders. All else equal, immigration raises national income by allowing
countries to use fixed factors more productively, making free immigration welfare
maximizing. In practice, countries may choose to restrict immigration because existing
distortions, such as the existence of social-insurance programs financed by non-lumpsum taxes, make a departure from free immigration the constrained optimum (Scholten
and Thum, 1996; Wellisch and Walz, 1998; Razin and Sadka, 1999).59 Or, governments
may choose to restrict immigration because they weight the welfare of different
individuals unequally, for whatever reason favoring those opposed to immigration(Foreman-Peck, 1992). For instance, if the median voter is a worker whose wages would
be reduced by immigration, politicians may choose to restrict immigration in order toenhance their future electoral prospects (Benhabib, 1996).
Policies to address illegal immigration enter a country’s choice set when legal
immigrant admissions are subject to binding restrictions and the enforcement of borders
against illegal entry is costly. Enforcement costs may be due to the expense of policing
the border or to agency costs associated with giving immigration authorities an incentive
to implement laws against unauthorized entry.
Using Becker’s (1968) crime-theoretic framework, Either (1986a,b) derives
conditions under which border enforcement raises national income. In the absence of
migration, wages in a home country exceed wages in a foreign country. Restrictions on
legal migration prevent legal labor flows from equalizing international factor prices,
creating an incentive for illegal migration. The home country interdicts illegal migrants
58

See Steven A. Camarota, “Use Enforcement to Ease Situation,” Arizona Republic, October 23, 2005.
See Hanson (2005b) and Hanson, Scheve, and Slaughter (2006) for evidence on how the public-financeconsequences of immigration affect individual preferences toward immigration in the United States. See
Sinn, Flaig, Werding, Munz and Hofmann (2003) and Razin and Sadka (2003) for analysis of the publicfinance consequences of immigration in welfare states similar to the European context.
59

37

through costly border enforcement. Let the probability of apprehension, 0 ≤ g ≤ 1, be anincreasing, convex function of expenditure on enforcement, E, which is financed by taxes
on skilled labor. The home-country wage for unskilled natives, w, is then related to thealternative foreign-country wage of illegal labor, w*, through the equalization of expected
wages that results from illegal migration:w * = g(E) w * + [1 − g (E )]w − k ,(28)where k is the cost of migration (which, distinct from Either (1986a), I suppose isincurred whether or not an individual is apprehended). Consider the impact of anincrease in enforcement on the home-foreign wage gap. Totally differentiating (28)[]dw dw *
1−=g' ( w − w * ) + k ' .
dE
dE 1 − g(29)

The expression in (29) is positive, implying greater enforcement increases the homeforeign wage difference, as long as the apprehension probability is increasing in
enforcement (g’ ≥ 0), the initial wage gap is positive (absent which there would be no
incentive to migrate), and migration costs (e.g., smuggler prices) are weakly increasing in
enforcement (k’ ≥ 0). In (29), one can see that changes in enforcement affect the wage
for unskilled workers in the home country through three channels: by lowering wages in
the foreign country (greater enforcement increases foreign labor supply, lowering theforeign wage), by lowering the probability that those attempting migration succeed inentering the country (greater enforcement means a given level of attempted illegal
immigration has a smaller impact on the home-country’s labor supply), and by raising thecost of crossing the border (greater enforcement raises coyote prices).
Ethier (1986a) identifies two environments in which interdicting illegal migrants
raises national income: the existence of downward wage rigidity in the home-country
market for unskilled labor, such that border enforcement lowers unemployment by
reducing illegal immigration; and the presence of market power in the home-country
demand for foreign labor, such that greater border enforcement lowers the wage the homecountry has to pay immigrant workers. In the absence of these or other distortions,enforcement is welfare reducing.60
One implication of Ethier’s work is that for border enforcement to be more than aresource drain on the home country it must affect outcomes in sending and receiving
labor markets. We have already seen evidence in Gathmann (2004) that in Mexico
60

Extending Ethier’s analysis, Bond and Chen (1987) introduce international capital mobility, Djajic (1987,
1999) puts migration in a dynamic setting, Bandyopadhyay and Bandyopadhyay (1998) and GaytánFregoso and Lahiri (200) consider interactions of trade policy or foreign aid and illegal migration, andWoodland and Yoshida (2005) examine border enforcement where illegal migrants are risk averse.
Another distortion that could potentially justify positive border enforcement is the presence of welfare
policies that result in net fiscal subsidies to illegal immigrants (through their use of public schools,
emergency health care, and other public services).

38

higher U.S. border enforcement is associated with modestly higher smuggler prices andmodestly lower migrant outflows. Are there measurable impacts of border enforcementin the United States? Hanson, Robertson, and Spilimbergo (2001) examine the effect ofborder enforcement on wages in U.S. and Mexican border regions. If enforcement
impedes illegal immigration, and if illegal immigrants depress wages in the regions inwhich they settle, then wages in receiving (sending) border regions will tend to rise (fall)
after an increase in enforcement. They use quarterly data for 1980 to 1997 on wages inimmigrant-labor-intensive industries in U.S. border states and among less-skilled workers
in Mexican border cities. For high-immigrant industries (apparel, textiles, food products,
furniture) in California and Texas, they find zero correlation between wages andenforcement of the Mexico-U.S. border in that state. They also find no evidence of apositive effect of border enforcement on the wages of workers with low-education levels(high-school dropouts or high-school graduates) in border regions of California or
Texas.61 For Mexico, the impact of U.S. border enforcement appears to be larger. In
Tijuana, which is the most active crossing point for illegal immigrants during the sample
period, greater U.S. enforcement at the city’s border with San Diego is associated withlower wages for less-skilled workers (up to six years of education).
There are two quite different interpretations of Hanson, Robertson, andSpilimbergo’s results. One is that that border enforcement deters illegal immigrants, but
illegal immigration has a minimal impact on labor markets in U.S. border regions. U.S.
border regions may adjust to influxes of illegal immigrants without large changes inwages either through U.S. native workers exiting these regions (Borjas, Freeman, andKatz, 1997) or through border economies shifting towards industries that are relatively
intensive in the use of immigrant labor (Card and Lewis, 2005). A second interpretationof their results is that border enforcement has a minimal impact on illegal immigration,
consistent with Espenshade (1994) and Massey and Singer (1995). It would still beconceivable that illegal immigration puts downward pressure on wages in U.S. borderregions, but, since border enforcement does not impede illegal immigration, there would
be zero correlation between enforcement and wages in U.S. border regions. Gathmann’s(2004) results suggest border enforcement does affect migration costs. And it is the casethat wages in Tijuana decline following increases in border enforcement, even if wages
don’t change in California or Texas.
The framework in Either (1986a,b) is a normative analysis of border enforcement.
Turning to positive questions, how does the U.S. Congress decide on the level of funding
for immigration control and how do U.S. immigration authorities allocate budgetedresources between enforcement and other activities for which they are responsible? In
the context of trade policy, Grossman and Helpman (1994) develop a framework inwhich endogenously determined industry campaign contributions affect import tariffs on
foreign goods. Facchini and Willmann (2005) extend the Grossman-Helpman model toconsider policies on international factor mobility. In their setup, governments restrict
factor inflows from abroad through a per-factor unit tax or quota. Facchini and Willmann
61
Both sets of results hold with or without instrumenting for border enforcement (to control for the INS setting
enforcement in response to economic conditions in U.S. or Mexican border areas) using data on U.S. political cycles(see note 26) and entry activity at other U.S. international boundaries (ports and Canadian border crossings).

39

make two important assumptions about the structure of immigration policy: (i) thereceiving-country government captures factor tax revenues or quota rents, which it
rebates to citizens, and (ii) individuals are organized according to their factor type andlobby the government on immigration policy. In equilibrium, each factor lobby offers thegovernment campaign contributions to support stronger (weaker) restrictions on inflows
of factors for which its members substitute (complement) in production.
Facchini and Willimann’s first assumption appears to be counterfactual. In thecontext of illegal immigration, the U.S. and other governments do not collect payments
associated with factor inflows. On the contrary, the government spends resources on
enforcement to impede the immigration of labor. Their second assumption has more
empirical support. Periodic attempts by the INS or ICE to increase interior enforcement
are met with political opposition. For instance, in 2005 the Western Growers
Association, a business lobby representing farmers in the western United States, issued astatement complaining that excessive enforcement was preventing farmers in Arizonafrom hiring sufficient immigrant labor to harvest their winter lettuce crop.62 In 1998, INS
raids of onion fields at harvest time in the state of Georgia prompted the U.S. Attorney
General, both Georgia U.S. senators, and three Georgia congressional representatives tocriticize the INS for injuring Georgia farmers.63 There is also historical evidence of antienforcement efforts by business groups. In the 1940s and 1950s, the district
commissioner of the U.S. Border Patrol in El Paso would routinely issue orders to stop
apprehending illegal immigrants during the agricultural harvest season (Calavita, 1992).
On occasions when the Border Patrol did increase enforcement activities, Texas farmers
often complained to their congressional representatives, who pressured the INS (throughformal written communication) to be less aggressive.
Lax worksite enforcement by the U.S. government is indirect evidence thatpolitical factors influence the intensity with which the country enforces against illegal
immigration. Hanson and Spilimbergo (2001) search for systematic evidence of such
effects. They estimate the sensitivity of border enforcement to relative price changes inindustries that use unauthorized immigrant labor intensively (apparel, perishable fruits
and vegetables, slaughtered livestock, construction). In theory, higher relative prices for
immigrant-intensive industries would increase the returns to lobbying for weaker borderenforcement. Controlling for macroeconomic conditions in the United States and
Mexico, they find that increases in the relative product price for an immigrant-intensive
industry today is associated with a decrease in border enforcement six to ten months in
the future. This finding suggests authorities relax enforcement when the demand for
undocumented workers increases. Enforcement also rises when overall labor-market
conditions in the United States tighten, which suggests that the U.S. government raises
enforcement when attempted illegal immigration is expected to be high.

62

See Miriam Jordan, “As Border Tightens, Growers See Threat to ‘Winter Salad Bowl,’” The Wall Street
Journal, March 11, 2005, p. 1.
63
See Mark Krikorian, "Lured by Jobs, Illegal Immigrants Risk Death at Border Crossings,” Santa Barbara
News-Press, April 25, 1999.

40

It appears, then, that enforcement softens when the specific sectors that use illegal
aliens intensively expand but not when the overall demand for labor is high. This issuggestive of a free-rider problem among special-interest groups, in which sectors that
benefit greatly from lower border enforcement, such as apparel and agriculture, lobby
politicians on the issue (and lobby more strongly when the gains to higher immigration
are greater), while sectors that benefit modestly are less politically active.
4.3 Summary

Restricting unauthorized immigration only makes sense for a country that is
subject to distortions that would be exacerbated by illegal labor inflows. By selecting theintensity with which it enforces U.S. borders against unauthorized entry and monitors theemployment practices of U.S. business, U.S. immigration authorities implicitly determine
the level of illegal immigration. The United States makes stark choices in its
enforcement polices. It heavily polices specific U.S. border cities, maintains a lighter
presence in less-populated areas, and weakly enforces U.S. worksites. There has been
little research by economists on the political economy of policies specifically related toillegal immigration. The work that does exist suggests that special interest groups areactive in attempting to influence U.S. enforcement practices.

5. Concluding Discussion

In the United States, unauthorized immigration accounts for one-third to one-halfof new immigrant inflows. Mexico is by far and away the largest source country for
those entering the United State illegally. An emerging body of academic research on
illegal migration from Mexico to the United States has made progress on many fronts.
Yet, the literature is at an early stage. Heightened policy interest in illegal immigration,
both in the United States and Mexico, suggests the many unanswered questions, some ofwhich I now highlight, will receive attention in the time to come.
Available measures of the stock and flow of illegal migrants are imprecise,hampered by the unwillingness of official government agencies to question individuals
about their immigration status. With the U.S. population of illegal immigrants now
exceeding 10 million individuals, one would expect that, at the very least, U.S.
government household surveys would conduct post-enumeration surveys that explicitly
ask individuals about their immigration status. This would allow researchers to estimate
the stock of illegal immigrants with much more precision. Of greater benefit would beincorporating questions about immigration status directly into the U.S. Census ofPopulation or U.S. Current Population Survey. There is precedent for U.S. government
surveys including questions about whether a respondent has a legal immigration visa.
For over a decade, the U.S. Department of Labor has conducted surveys of U.S. farmworkers in which it explicitly asks whether an individual is in the United States legally orillegally (e.g., U.S. Department of Labor, 2005).

41

A handful of data sources provide information about illegal immigrants from
Mexico in the United States. Immigrants from Mexico, whether legal or illegal, aredrawn disproportionately from the middle of the country’s schooling distribution. Overtime, illegal migrants appear to have become more likely to be female, to work outside of
agriculture, and to settle in the United States on a long-term basis. Largely absent in theliterature is analysis of the life-cycle behavior of migrants. Many individuals from
Mexico first enter the United States as illegal immigrants and over time gain a legal
permanent residence visa through sponsorship by a U.S. family member. One would
expect that how a prospective migrant responds to changes in U.S. or Mexico economic
conditions, or the extent to which a migrant already in the United States assimilates into
U.S. society, would depend on whether the individual expects to obtain a U.S. green cardin the future. Family sponsorship in the granting of entry visas may thus create a direct
link between receiving-country policies on legal immigration and the incentive for illegal
immigration. Individuals in Mexico with family members in the United States do appear
to be more likely to emigrate. However, we do not know whether this represents theeffect of migration networks, which lower the cost of migration, or prospects for
obtaining a green card, which raise the benefit to migration.
Consistent with research in many other contexts, Mexico-to-U.S. migration flows
are correlated with changes in relative incomes in the two countries. Attempted illegal
immigration appears to be particularly responsive to shocks to the Mexican economy,
with surges in apprehensions at the U.S.-Mexico border coming shortly after downturns
in Mexico. Yet, given the large magnitude of U.S.-Mexico wage differences and thesmall apparent cost of crossing the border illegally, the volume of migration flows from
Mexico to the United States is surprisingly low. Also, given the high relative return toeducation in Mexico, it is puzzling that Mexican immigrants exhibit intermediate
selection in terms of their observable skills. One would expect less-skilled Mexican
immigrants to have the strongest incentive to migrate abroad. Unobserved sources ofheterogeneity in migration costs, home bias in consumption, and credit constraints infinancing migration are all potential explanations for the volume and composition of
Mexican immigration. Research on these issues is just beginning to emerge.
Over the last two decades, the United States has greatly increased the resources it
devotes to controlling illegal immigration. The government has, in particular, beefed up
enforcement at specific U.S. border cities. While the United States has criminalized thehiring of illegal immigrants, the government devotes few resources to monitoring U.S.
worksites for the employment of unauthorized workers. The net effect of changes inenforcement policy (coupled with changes in U.S. and Mexico economic conditions) has
been increasing levels of illegal immigration. There is no formal political economy
theory of immigration control that would explain why the United States chooses border
over interior enforcement.64 The United States appears to be on the verge of granting anamnesty to at least some of the illegal immigrants residing in the country, which would
come two decades after an earlier legalization under the Immigration Reform and Control
64

Ethier (1986a) includes a normative analysis of border versus interior enforcement but takes thegovernment preference to deviate from zero enforcement as given. See Epstein and Weiss (2001) for an
extension of this analysis and consideration of an amnesty for illegal immigrants.

42

Act. There is also no formal theory that would explain why a country would choose toenact imperfect and costly enforcement against illegal immigration today and later grant
an amnesty to those that entered illegally. Given the importance of illegal immigration
for the U.S. labor market and for U.S. public finances, policies to control labor inflows
have been the subject of surprisingly little research.

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Table 1: Estimates of the U.S. Illegal-Immigrant Population, 1990-2004

Year

Total Population of Illegal Immigrants in the United States (millions)
Costanzo et al. (2003)
Bean et al. (2001a, 2001b)
INS
Undercount Rate
Undercount Rate
Passel(2001)
10%15%
20%
15% Median 25%(2005)

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004

3.500
4.025
4.204
4.492
4.750
5.146
5.581
5.862
6.098
6.488
7.000
-----

3.766
---------8.705
-----

4.430
---------10.242
-----

4.707
---------10.882----------------5.918---------------7.751---------------9.864----------------9.300-10.300

Population of Illegal Immigrants from Mexico in the United States (millions)
1990
1996
2000
2001
2002
2003
2004

2.040
-4.808-----1.008-3.872----------------------1.524-3.462-----2.543-4.510-----3.706-5.765--------5.300-5.900Notes: Costanzo et al. (2003) report estimates of the total U.S. unauthorized population using threealternative undercount rates for illegal immigrants (10%, 15%, or 20%). The INS (2001) uses a constantundercount rate for illegal immigrants of 10%, as does Passel (2005). The 1996 Bean el al. (2001a)
estimates are based on undercount rates of either (a) 15% for illegal immigrants and 3% for legal
immigrants, or (b) 25% for illegal immigrants and 5% for legal immigrants. The 2001 Bean et al. (2001b)
estimates are based on undercount rates of either (a) 15% for illegal immigrants and 0.5% for all legal
immigrants (2% for legal Mexican immigrants), or (b) 25% for illegal immigrants and 2% for all legal
immigrants (4% for legal Mexican immigrants). The median estimates for Bean et al. (2001a, 2001b) arefor the full set of reported of undercount rates, which vary depending on the year and sample.

54

Table 2a: Migrant and Non-Migrant Mexican Nationals, Males

Mexico Migration Project, 1989-1991
In Mexico at time of survey
Currently on
In US atAll
lasttime of
Respondents
migration
survey
48.7
64.7
55.9

1990
US
Census56.2

1990
MexicoCensus48.0

18 to 27
28 to 37
38 to 47
48 to 57
58 to 67

38.5
31.9
17.1
8.2
4.4

37.6
25.8
17.4
11.7
7.5

34.1
29.7
19.0
10.7
6.4

38.4
35.7
18.1
6.7
1.1

43.5
35.7
11.7
5.9
3.2

33.0
42.5
16.5
5.8
2.3

Males Aged 18 to 47
Years of
None
Schooling
1 to 45 to 89 to 11
12 to 1516 plus

9.6
10.3
28.0
17.2
31.7
3.2

9.2
17.3
30.1
24.7
11.1
7.7

5.2
23.4
32.5
18.5
12.9
7.5

3.8
24.3
41.4
19.4
9.3
1.8

2.1
15.3
34.3
23.8
19.1
5.6

4.0
19.7
39.1
18.4
18.3
0.5

Live in Urban Area
In Labor ForceWork in Agriculture
Has Migrated to US
Migrate US Last Year

91.9
91.0
15.5
---

74.8
85.2
23.9
---

80.6
95.8
28.9
50.3
2.5

74.5
98.6
31.2
100.0
0.1

89.2
94.1
9.1
100.0
0.1

-95.2
11.9
---

0 to 56 to 1011 to 2020 plus

28.8
23.4
35.7
12.2

-----

65.6
14.8
15.2
4.5

45.2
17.3
28.2
9.3

36.9
20.2
36.3
6.6

13.9
19.4
6.7
59.9

Sample size, 18-67
Sample size, 18-47

96,487
83,703

196,729
158,917

5,370
4,448

722
666

375
341

1,670
1,535

Percent Male
Age

Years
in US

55

1989
LPS
57.2

Table 2b: Migrant and Non-Migrant Mexican Nationals, Females

Mexico Migration Project, 1989-1991
In Mexico at Time of Survey
In US atAll
Currently ontime of
Respondents last migration
survey
51.3
35.3
44.1

1990
US
Census43.8

1990
MexicoCensus52.0

18 to 27
28 to 37
38 to 47
48 to 57
58 to 67

33.0
31.4
18.9
10.4
6.3

38.0
26.1
17.0
11.5
7.6

35.0
29.3
18.6
10.7
6.5

35.5
37.1
21.3
5.6
0.5

43.2
33.5
16.6
4.4
2.4

31.9
42.0
16.8
6.6
2.7

Females Aged 18 to 47
Years of
None
Schooling
1 to 45 to 89 to 11
12 to 1516 plus

8.5
9.8
28.3
16.2
34.0
3.3

12.7
18.8
31.4
24.8
8.0
4.4

6.6
25.9
35.4
16.8
11.9
3.5

3.0
18.7
56.2
12.4
7.6
2.2

5.1
13.8
35.5
15.2
28.3
2.2

4.6
18.7
43.0
17.2
15.9
0.6

Live in Urban Area
In Labor ForceWork in AgricultureChildren Ever Born
Own Children in HH
Has Migrated to US
Migrate US Last Year

92.7
56.7
7.9
2.6
1.7
---

75.8
26.6
2.3
3.3
1.6
---

80.6
28.8
2.4
-1.8
18.6
0.8

70.8
35.7
2.2
-1.0
100.0
0.0

88.8
47.5
6.6
-2.2
100.0
0.0

-61.3
4.1
-2.1
---

Years in US

0 to 56 to 1011 to 2020 plus

26.2
20.9
37.3
15.6

-----

58.1
17.5
18.7
5.7

44.3
17.6
28.9
9.2

41.5
16.5
29.2
12.7

10.7
11.1
4.7
73.5

Sample size, 18-67
Sample size, 18-47

76,51863,278

212,912
172,458

5,658
4,688

394
370

296
276

1,248
1,132

Percent Female
Age

56

1989
LPS
42.8

Notes: Table 2 gives summary statistics on working-age adults, either 18-47 or 18-67 years old. The
samples are residents of Mexico (1990 Mexico Census of Population and Housing); Mexico-born residentsof the United States (1990 U.S. Census of Population and Housing); respondents of the 1989-1991 Mexican
Migration Project Survey (all respondents, those residing in United States but in Mexico at the time of thesurvey, or those residing in United States at the time of the survey whose responses were given byhousehold members in Mexico); and illegal immigrants from Mexico in the United States who qualified foramnesty under the U.S. Immigration Reform and Control Act (1989 Legalized Persons Survey). The
definition of an urban area is a locality with more than 2,500 inhabitants.

Table 3a: Average Hourly Wages for Mexican Males, 2000

4

Years of Schooling Completed
5 to 89 to 1112
13 to 15

16+

Mexican
18 to 22
Immigrants 23 to 27
in U.S.
28 to 32
33 to 37
38 to 42
43 to 47
48 to 52

7.83
8.44
8.27
9.46
9.19
9.75
9.57

7.60
8.19
8.56
9.25
9.39
8.90
9.37

7.45
8.21
8.70
9.34
9.33
9.35
9.42

8.07
9.06
9.66
10.07
11.01
10.68
9.31

8.76
9.53
9.56
11.36
12.11
12.80
11.65

8.44
13.02
15.69
16.84
16.26
15.88
17.78

Residentsof Mexico

1.36
1.43
1.56
1.65
1.64
1.69
1.66

1.56
1.80
1.93
2.08
2.14
2.30
2.30

1.76
2.10
2.42
2.56
2.88
3.00
3.15

2.06
2.79
3.22
3.45
3.74
4.40
4.21

2.61
3.77
4.80
5.25
5.62
5.86
6.11

3.91
5.20
6.63
7.07
7.42
8.05
8.71

Age

18 to 2223 to 27
28 to 32
33 to 37
38 to 42
43 to 47
48 to 52

Notes: Table shows average hourly wages in 2000 U.S. dollars for Mexican immigrant males in the UnitedStates or male residents of Mexico who report working 20 to 84 hours a week (in either sample, the highest
and lowest 0.5% of wage values are excluded). Data for Mexico are a 10% random sample of the 10%
microsample of the XIII Censo General de Poblacion y Vivienda, 2000; data for the United States are fromthe 5% U.S. PUMS in 2000. Mexican immigrants are restricted to individuals who have resided in the
United States for three years or less. See the text on the calculation of wage values.

57

Table 3b: Log U.S.-Mexico Hourly Wage Differential, Males in 2000

Age

18 to 2223 to 27
28 to 32
33 to 37
38 to 42
43 to 47
48 to 52

18 to 2223 to 27
28 to 32
33 to 37
38 to 42
43 to 47
48 to 52

4

Years of Schooling Completed
5 to 89 to 1112
13 to 15

16+

Log U.S.-Mexico Wage Differential, All Males in Mexico
1.263
1.098
0.985
0.895
0.750
0.361
(0.022)(0.013)(0.015)(0.018)(0.042)(0.099)
1.281
1.043
0.904
0.746
0.507
0.387
(0.024)(0.015)(0.018)(0.018)(0.038)(0.048)
1.225
1.036
0.821
0.652
0.306
0.357
(0.029)(0.018)(0.024)(0.023)(0.047)(0.052)
1.258
1.025
0.823
0.613
0.339
0.294
(0.034)(0.023)(0.034)(0.031)(0.057)(0.059)
1.287
1.019
0.775
0.575
0.288
0.216
(0.035)(0.029)(0.045)(0.043)(0.068)(0.075)
1.265
0.905
0.708
0.498
0.275
0.118
(0.040)(0.037)(0.063)(0.063)(0.103)(0.116)
1.264
0.987
0.715
0.418
0.164
0.122
(0.050)(0.046)(0.096)(0.076)(0.122)(0.142)
Log U.S.-Mexico Wage Differential, Males in High-Migration States
1.188
1.061
0.989
0.912
0.676
0.381
(0.026)(0.016)(0.017)(0.025)(0.052)(0.128)
1.211
1.013
0.895
0.722
0.481
0.425
(0.027)(0.017)(0.019)(0.024)(0.050)(0.052)
1.144
1.011
0.824
0.657
0.361
0.415
(0.031)(0.020)(0.025)(0.028)(0.057)(0.053)
1.151
0.988
0.821
0.652
0.317
0.411
(0.035)(0.025)(0.034)(0.034)(0.061)(0.060)
1.206
0.966
0.792
0.601
0.185
0.291
(0.036)(0.030)(0.044)(0.046)(0.070)(0.073)
1.194
0.867
0.671
0.528
0.237
0.181
(0.041)(0.037)(0.061)(0.067)(0.101)(0.112)
1.193
0.961
0.727
0.292
0.284
0.227
(0.049)(0.045)(0.092)(0.080)(0.125)(0.137)

Notes: Table shows the log difference in average hourly wages (and standard errors) for Mexican
immigrant males and male residents of Mexico. The top half of the table includes all regions of Mexico;
the bottom half includes only high-migration states. See Table 3a and the text for details.

58

Table 4: Educational Attainment for the Mexico Born Population, 2000
21-65 Year Olds
28-37 Year Olds
Residents
Mex. Imm.
Residents
Mex. Immigrants in US
Of Mexico
in US
of Mexico
0-3 Yrs
4+ YrsMales

HighestGrade of
Schooling
(%)

0
1-4
5-8
9
0-9

0.069
0.166
0.270
0.189
0.694

0.127
0.080
0.307
0.087
0.601

0.036
0.103
0.263
0.246
0.648

0.076
0.045
0.303
0.114
0.538

0.078
0.043
0.275
0.116
0.512

10-11
12
13-15
10-15

0.045
0.101
0.047
0.193

0.055
0.212
0.083
0.350

0.059
0.129
0.044
0.232

0.064
0.260
0.075
0.399

0.067
0.278
0.098
0.443

16+

0.113

0.050

0.121

0.062

0.046

215,804

80,453

68,206

9,358

15,839

0
1-4
5-8
9
0-9

0.092
0.179
0.280
0.174
0.725

0.133
0.087
0.315
0.085
0.620

0.047
0.116
0.278
0.219
0.660

0.070
0.041
0.269
0.112
0.492

0.078
0.043
0.294
0.125
0.540

10-11
12
13-15
10-15

0.040
0.112
0.042
0.194

0.049
0.204
0.079
0.332

0.054
0.145
0.039
0.238

0.056
0.276
0.083
0.415

0.062
0.253
0.090
0.405

16+

0.080

0.048

0.103

0.093

0.055

235,086

72,96775,625

6,575

16,173N
Females

HighestGrade of
Schooling(%)

N

Notes: The sample is individuals 21-65 or 28-37 years old (in the U.S., excluding group quarters; inMexico, excluding those not born in the country). Residents of Mexico in 2000 are a 10% random sampleof the 10% microsample of the XIII Censo General de Poblacion y Vivienda, 2000. Mexican immigrants
are from the 2000 5% U.S. PUMS and are restricted to be individuals 21 years or older at time of entry into
the country who have been residing in the United States for 0-3 years or 4 or more years. Schoolingvariables show the percentage of individuals whose high grade completed is that indicated.

59

Table 5: Illegal Immigrants Located by U.S. Immigration Authorities, 1992-2004

1992

1993

1994

1995

1996

1997

Total deportable aliens located(millions)1.258 1.327

1.095

1.395

1.650

1.537 1.679 1.714 1.815 1.387 1.062 1.046 1.241

Share located by ICE/INS Agents

0.047 0.048

0.058

0.050

0.061

0.080 0.074 0.079 0.076 0.087 0.101 0.110 0.065

Share located by Border Patrol

0.953 0.952

0.942

0.950

0.939

0.920 0.926 0.921 0.924 0.913 0.899 0.890 0.935

Of Border Patrol deportable
aliens located, share that are:Mex. aliens seeking employment

0.888 0.884

0.874

0.895

0.907

0.906 0.899 0.901 0.910 0.875 0.861 0.870 0.860

Mex. aliens at U.S. worksite

0.011 0.010

0.013

0.013

0.008

0.010 0.006 0.003 0.002 0.003 0.005 0.006 0.005

Mex. aliens unspecified

0.076 0.079

0.082

0.069

0.068

0.067 0.073 0.068 0.064 0.089 0.095 0.070 0.070

Other aliens

0.026 0.026

0.031

0.023

0.017

0.018 0.021 0.028 0.024 0.033 0.039 0.053 0.065

1998

Source: U.S. Department of Homeland Security (http://uscis.gov/graphics/shared/statistics/yearbook/index.htm).

1999

2000

2001

2002

2003

2004

Share of All US Immigrants
Share of Mexico Population

Share of US Population

.3

.3

.2

.2

.1

.1

0

0
1960

1970

1980

1990

2000

Mexican Immigrants in the US
Figure 1

Legal Immigrants

Share Adjusting Status.8

Legal Immigrants.6200000.4150000.2

100000
0
1992

1994

1996

1998

2000

2002

US Legal Immigration from Mexico
Figure 2

2004

Share Adjusting Status

250000Zacateca.15

Share of Households Sending
Migrants to the US, 1995-2000

Michoaca

Guanajua.1
Hidalgo Morelos
NayaritDurangoAguascal

San Luis
Jalisco
GuerreroColimaQueretar

Oaxaca.05

Chihuahu

PueblaSinaloa
Veracruz
Tamaulip
MexicoTlaxcala
Baja CalDF Sonora
Baja Cal
YucatanCampeche
ChiapasQuintana
TabascoCoahuila
Nuevo Le

0
0

.02
.04
Share of Residents Migrating to US, 1955-1959.06

Figure 3: Migration Rates to the U.S. by Mexican States, 1950s and 1990s

Zacateca.15

Househlds w/ Migrant in US, 2000

Michoaca

Guanajua.1
DurangoAguascalMorelos
San Luis
HidalgoNayaritJalisco
GuerreroColimaQueretar
Oaxaca.05

Chihuahu

PueblaSinaloa
Veracruz
MexicoTlaxcala

Tamaulip
Coahuila
Baja Cal Nuevo Le

DF

SonoraBaja Cal
TabascoYucatanChiapas Campeche
Quintana

0
0

500

1000
1500
Kilometers to US Border

2000

2500

Figure 4: Migration Rates to the U.S. by Mexican State and Distance to the U.S.

62

Apprehensions (000s)

Officer Hours (000s)

1500

10000

1000

5000

500
2500

0

0
1965

1970

1975

1980

1985
Year

1990

1995

2000

2005

Figure 5: Linewatch Apprehensions and Enforcement by the U.S. Border Patrolln(Apprehensions) - 0.8*ln(Officer Hours)
1

0

-1

-2

-3
1970

1975

1980

1985
Year

1990

1995

2000

2005

Figure 6: Estimated Attempts at Illegal Entry along the U.S.-Mexico Border

63

Officer Hours (000s)

Apprehensions (000s)

7500

Migrants Using Coyotes

Coyote Prices (2000 USD)
700

.9
600

.8
500
.7

400
.6
1980

1984

1988
Year

1992

1996

Figure 7: Use of Smugglers by Migrants in the Mexican Migration Project

Coeff.=-0.60, Std. Error=0.10

Log Border Apprehensions

.6

0

-.6
-.25

0
Log Mexico Hourly Wage

.25

Figure 8: Border Apprehensions and Average Wages in Mexico

64

Coyote Prices (2000 USD)

Migrants Using Coyotes

1

Ln Apprehensions

Ln US-Mexico Per Capita GDP.2

Ln Apprehensions

.1
.75
0

.25

-.1

Ln US-Mexico Per Capita GDP

1.25

-.2
-.25
1975

1980

1985

1990
Year

1995

2000

2005

Figure 9: Border Apprehensions and U.S.-Mexico Relative Per Capita Income

65Male self selection in 2000

Male self selection in 1990

.085

0

-.085
-3

-2

-1

0
lnw

Female self selection in 2000

1

2

3

Female self selection in 1990

.085

0

-.085
-3

-2

-1

0
lnw

1

2

3

Figure 10: Immigrant (counterfactual) – Resident (actual) Wage Densities, 1990 and 2000
(counterfactual density for Mexican immigrants minus actual density for Mexican residents)66

ln Mexico/US price level
ln Mexico/US GDP per worker

ln Mexico/US terms of trade

.5

0

-.5
1960

1970

1980
Year

1990

2000

Figure 11: Mexico-U.S. Relative Incomes, Prices, and Terms of Trade

67

0

.25

.5

.75

1

1.25

Pop. born in Mexico/Pop. of US natives w/ up to HS education

70 80 90 00

70 80 90 00

70 80 90 00

70 80 90 00

20 to 29

30 to 39

40 to 49

50 to 59

Figure 12a: Mexico-U.S. Relative Male Population, 1970-2000

0

.25

.5

.75

1

1.25

Pop. born in Mexico/Pop. of US natives w/ up to HS education

70 80 90 00

70 80 90 00

70 80 90 00

70 80 90 00

20 to 29

30 to 39

40 to 49

50 to 59

Figure 12b: Mexico-U.S. Relative Female Population, 1970-2000

68

Figure 13: Mexico-U.S. Relative Labor Supply, 1970-2000

Ln Mexican Immigrants in US

14

13

12

11

10
-1

0
Ln Mex-US Relative Labor Supply

1

U.S. Native Population with Less than a High School Education

Ln Mexican Immigrants in US

14

13

12

11

10
-2

-1.5

-1
-.5
Ln Mex-US Relative Labor Supply

0

U.S. Native Population with up to and Including a High School Education

69

TexasArizona

Western California
Eastern California

Officer Hours in Thousands

5000

4000

3000

2000

1000

0
1975

1980

1985

1990
Year

1995

2000

2005

Figure 14a: Linewatch Enforcement by U.S. Border Patrol Region

Texas
Arizona

Western California
Eastern California

Apprehensions in Thousands

600

400

200

0
1975

1980

1985

1990
Year

1995

2000

2005

Figure 14b: Linewatch Apprehensions by U.S. Border Patrol Region70