Globalization, Labor Income, and Poverty in Mexico
October 2004
Gordon H. Hanson*
University of California, San Diego andNational Bureau of Economic Research

Abstract. In this paper, I examine changes in the distribution of labor income acrossregions of Mexico during the country’s decade of globalization in the 1990’s. I focus theanalysis on men born in states with either high-exposure or low-exposure to
globalization, as measured by the share of foreign direct investment, imports, or export
assembly in state GDP. Controlling for regional differences in the distribution ofobservable characteristics and for initial differences in regional incomes, the distributionof labor income in high-exposure states shifted to the right relative to the distribution ofincome in low-exposure states. This change was primarily the result of a shift in mass in
the income distribution for low-exposure states from upper-middle income earners tolower income earners. Labor income in low-exposure states fell relative to high-exposurestates by 10% and the incidence of wage poverty (the fraction of wage earners whoselabor income would not sustain a family of four at above-poverty consumption levels) in
low-exposure states increased relative to high-exposure states by 7%.

*

I thank Julie Cullen, Esther Duflo, Ann Harrison, Jim Levinsohn, and participants in the
NBER Conference on Globalization and Poverty for helpful comments. Jeffrey Lin
provided excellent research assistance.

1. Introduction
There is now an immense body of literature on how globalization affects labormarkets. Early research centered on the United States (Freeman, 1995; Richardson,
1995), motivated in part by an interest in understanding what caused marked changes in
the U.S. wage structure during the 1980’s and 1990’s (Katz and Autor, 1999).

A

common theme in this work is that globalization – especially in the form of global
outsourcing – has modestly but significantly contributed to increases in wage differentials
between more and less-skilled workers (Feenstra and Hanson, 1999 and 2003). A small
effect for international trade is perhaps not surprising, given the large size of the U.S.
economy and the still limited role that trade plays in U.S. production and consumption(Feenstra, 1998; Freeman, 2003). Later research shifted attention to other countries, andto the developing world in particular, which in the 1980’s began to lower barriers to tradeand capital flows aggressively.

The tendency for rising wage inequality to follow

globalization is not limited to the United States or other rich countries. Expanding tradeand capital flows have been associated with increases in the relative demand for skilledlabor in many economies, including Chile (Pavcnik, 2003), Columbia (Attanasio,
Goldberg, and Pavcnik, 2004), Hong Kong (Hsieh, 2003), Mexico (Feenstra and Hanson,1997), and Morocco (Currie and Harrison, 1997), just to list a few recent examples.1
In most research to date, the focus has been on the relationship between
globalization and earnings inequality. Fewer studies have examined how globalization
affects income levels. This comes as something of a surprise, given the long-standing
interest of developing-country research in how changes in policy affect the well-being of1

See Winters, McCulloch, and McKay (2004) and Pavcnik and Goldberg (2004) for surveys of theliterature on globalization and income in developing countries.

1the poor. The relative lack of attention on the impact of globalization on poverty is
perhaps partly attributable to methodology. The most established empirical techniques
for identifying the effects of economic shocks, such as globalization or technologicalchange, on earnings relate to estimating changes in the relative demand for labor ofdifferent skill types (Katz and Autor, 1999). The lack of attention may also reflect a
U.S.-bias in the type of questions being asked. The strong emphasis in U.S. research on
why earnings inequality has increased may have spilled over into research on othercountries, partially crowding out other issues.
In this paper, I examine how the distribution of income changed in Mexico during
country’s decade of globalization in the 1990’s. Taking the income distribution as theunit of analysis makes it possible to examine changes both in the nature of inequality –reflected in the shape of the distribution – and in the level of income – reflected in theposition of the distribution. Mexico is an interesting case because over the last two
decades the country has aggressively opened its economy to the rest of the world. This
process began with a unilateral liberalization of trade in 1985, continued with theelimination of many restrictions on foreign capital in 1989, and culminated with theNorth American Free Trade Agreement (NAFTA) in 1994 (Hanson, 2004).2
The one study of which I’m aware that attempts to estimate the impact of tradeliberalization on poverty in Mexico is Nicita (2004). He applies data from the Mexico’s
National Survey of Household Income and Expenditure to techniques developed by
Deaton and Muellbauer (1980) to construct an estimate of how tariff reductions have2

See Chiquiar (2003) for a discussion of recent policy changes in Mexico. For other work on the labormarket implications of globalization in Mexico, see Ariola and Juhn (2003), Cragg and Epelbaum (1996),
Farris (2003), Feliciano (2001), Revenga (1997), Hanson and Harrison (1999), and Robertson (2003). See
Hanson (2004) for a review of this literature. For work on trade reform and wage inequality in Latin
America, see Behrman, Birdsall, and Szekely (2003).

2affected household welfare.

This exercise involves estimating the impact of tariffchanges on domestic goods’ prices, the impact of changes in goods’ prices on the wages
of different skill groups, and income and price elasticities of demand for different goods,
and then combining these estimates to form an estimate of the change in real income due
to tariffs. During the 1990’s, tariff changes appeared to raise disposable income for all
households, with richer households enjoying a 6% increase and poorer households
enjoying a 2% increase. These income gains imply a 3% reduction in the number ofhouseholds in poverty. Income gains are larger in regions closer to the United States,
where tariff-induced price changes are larger. I will also find that proximity to world
markets matters for income changes. By focusing on prices for final goods, the Deaton
and Muellbauer (1980) approach taken by Nicita (2004) ignores other impacts ofglobalization, such as increases in foreign investment or increased trade in intermediate
inputs. The approach I develop, which cannot relate specific policy changes to changes
in income, does consider these other aspects of Mexico’s economic opening.
An obvious empirical challenge in studying income distributions, rather than
individuals or industries, is the paucity of degrees of freedom. Income distributions areaggregate entities, implying the number of observations is limited to the number of time
periods in the sample. In my case, I really have only two observations, 1990 and 2000,
corresponding to the most recent years in which Mexico conducted its census of
population and housing. My strategy for identifying the impact of globalization on
Mexico’s income distribution is to exploit regional variation in exposure to foreign tradeand investment. As discussed in section 2, geography and history have made states in
Mexico’s north and center highly exposed to globalization and left states in Mexico’s

3south relatively disengaged from the global economy. I take states with high exposure to
globalization to be the treatment group and states with low exposure to globalization to
be the control group (leaving states with intermediate exposure to globalization out of theanalysis). I then apply a difference-in-difference strategy by comparing the change in theincome distribution for high-exposure states to the change in income distribution for lowexposure states during Mexico’s globalization decade. Implicit in the analysis is theassumption is that labor is sufficiently immobile across regions of Mexico for regionspecific labor-demand shocks to affect regional differentials in labor income.
The analysis is complicated by a host of issues, three of which stand out. One isthat income distributions change both because the characteristics of the underlying
population of individuals change and because the returns to these characteristics change.
To identify the effects of globalization, I want to examine changes in returns tocharacteristics (in my case, inter-regional differences in these changes), while holding the
distribution of characteristics constant. To perform this exercise, I apply non-parametrictechniques from DiNardo, Fortin, and Lemieux (1996) and Leibbrandt, Levinsohn, andMcCrary (2004), which I describe in section 3. I also compare results from this approachto results from a more-standard parametric approach, both of which are presented in
section 4.

A second issue is that other shocks in the 1990’s may also have had

differential effects on regions with high versus low exposure to globalization. Candidate
shocks include the privation and deregulation of industry, the reform of Mexico’s landtenure system, and, most importantly, Mexico’s peso crisis in 1994. The potential forthese shocks to contaminate the analysis is an important concern, which I address by way
of discussing qualifications to my results in section 5.

4

A third issue has to do with measurement.

There are many components toincome, including labor earnings, capital returns, rental income, government transfers,
gifts, and remittances from family members abroad. Surveys that measure each of thesecomponents carefully, such as Mexico’s National Survey of Household Income andExpenditure, are not representative across the regions of the country (Cortes et al., 2003),
which makes it impossible to apply my estimation strategy to these data. Surveys that are
representative across Mexico’s regions, such as the Census of Population and Housing,measure labor income with relatively high precision, but lack complete data on otherincome categories. To ensure my data are regionally representative, I use the Mexican
Census, and to minimize the impact of measurement error, I focus the analysis on labor
income. Excluding other sources of income has the obvious drawback of limiting theanalysis to labor earnings, rather than to the full distribution of income.3
To preview the results, states with high-exposure to globalization began the
1990’s with higher incomes than low exposure states, even after controlling for regional
differences in the observable characteristics of individuals. During the 1990’s, lowexposure states had slower growth in labor income than high-exposure states. This took
the form of a left-ward shift in the income distribution of low-exposure states relative tohigh-exposure states.

The results of this income shift were (a) a decrease in averagelabor earnings of 10% for individuals from states with low exposure to globalization
relative to individuals from states with high exposure to globalization, and (b) an increasein the incidence of wage poverty (the fraction of wage earners whose labor income would

3

One interesting extension to my analysis would be to use Mexico’s National Survey of Household Income
and Expenditure to estimate the empirical relationship between labor income and poverty. One could thenuse this mapping to evaluate how the changes in labor income that I estimate (using data from the Census
of Population and Housing) may have affected poverty.

5not sustain a family of four at above-poverty consumption levels) in low-exposure statesof 7% relative to that in high-exposure states.

2. Regional Exposure to Globalization
2.1 Data Sources
Data for the analysis come from two sources. In 1990, I use the 1% microsampleof the XII Censo General de Poblacion y Vivienda, 1990, and in 2000 I use a 10%
random sample of the 10% microsample of the XIII Censo General de Poblacion y
Vivienda, 2000. The sample is working age men with positive labor earnings. I focus onmen, since labor-force participation rates for women are low and vary considerably overtime, ranging from 21% in 1990 to 32% in 2000. This creates issues of sample selection
associated with who supplies labor outside the home. Compounding the problem, many
women who report zero labor earnings may work in the family business or on the family
farm. For men, problems of sample selection and measurement error also exist but
appear to be less severe. Their labor-force participation rates vary less over time, risingmodestly from 73% in 1990 to 74% in 2000. Still, differences in labor-force participation
over time and across regions could affect the results reported in section 4. In section 3, I
discuss strategies to correct for self-selection into the labor force.

2.2 The Opening of Mexico’s Economy
In Mexico, the last two decades have not been a quiet period. Since 1980, thecountry has had three currency crises, bouts of high inflation, and several severe
macroeconomic contractions, the most recent of which occurred in 1995 following alarge devaluation of the peso that precipitated the country’s conversion from a fixed to a6

floating exchange rate. The liberalization of the country’s trade and investment policies
has been, in part, a response to this turmoil. Mexico’s currency crises and ensuing
contractions have had very negative consequences on the country’s poor. Table 1 showsthat poverty rates rose sharply after the 1995 peso crisis.
Mexico’s economic opening began in 1982, when the government responded to asevere balance of payments crisis by easing restrictions on export assembly plants known
as maquiladoras. In 1985, Mexico joined the General Agreement on Trade and Tariffs(GATT), which entailed cutting tariffs and eliminating many non-tariff barriers. In 1989,
Mexico eased restrictions on the rights of foreigners to own assets in the country. In
1994, NAFTA consolidated and extended these reforms. Partly as a result of these policychanges, the share of international trade in Mexico’s GDP has nearly tripled, rising from
11% in 1980 to 32% in 2002. Mexico is now as closely tied to the U.S. economy as it
has been at any point in its history. In 2002, the country sent 89% of its exports to andbought 73% of its imports from the United States.4
Mexico’s maquiladoras, shown in Figure 1, have been instrumental in the
country’s export conversion. Between 1983 and 2002, real value added in maquiladoras
grew at an average annual rate of 11%, making it the most dynamic sector in the country.
In 2002, these export assembly plants accounted for 45% of Mexico’s manufacturingexports and 28% of the country’s manufacturing employment (up from 4% in 1980).
Their concentration in northern Mexico in part accounts for the differential regional
impact of globalization in the country. A brief history of Mexico’s trade policy reveals
the origins of northern Mexico’s advantage in export production.

4

Concomitant with its economic opening, Mexico privatized state-owned enterprises, deregulated entry
restrictions in many industries, and used wage and price restraints to combat inflation.

7

In the 1940’s, Mexico adopted a strategy of import substitution industrialization.
To import most manufacturing products, firms had to obtain a license from thegovernment and to pay moderate to high tariffs. In 1965, Mexico softened its importsubstitution strategy by allowing the creation of maquiladoras (Hansen, 1981).5 Firms
could import free of duty the inputs, machinery, and parts needed for export assembly
operations, as long as they exported all output. To ensure firms abided by this rule, they
were required to buy a bond equal to the value of their imports that would be returned tothem once they had exported all their imported inputs in the form of final goods (hence
the term in-bond assembly plants). In contrast to other firms in the country, maquiladoras
could be 100% foreign owned. Bureaucratic restrictions on maquiladoras kept the sector
small until 1982, when the government streamlined regulation of the plants.
Initially, maquiladoras were required to locate within 20 miles of an international
border or coastline.

In 1972, the government relaxed these rules and allowedmaquiladoras to locate throughout the country.
concentrate near the United States.

However, the plants continued to

As seen in Figure 2, 83% of maquiladora

employment is still located in states on the U.S. border. Proximity to the U.S. market ismotivated in part by a desire to be near U.S. consumers, to whom maquiladoras export
most of their production, and in part by a desire to be near U.S. firms, who often manage
Mexican maquiladoras out of offices based in U.S. border cities.
U.S. trade policies initially gave maquiladoras an advantage over other Mexican
producers in exporting to the U.S. market. Prior to NAFTA, a U.S. firm that made5

The original motivation for this program was to create employment opportunities for Mexican workers
returning to the country after working in the United States as temporary farm laborers under the Bracero
Program. The U.S. government ended the Bracero Program in 1964 and the Mexican government wasconcerned that the returning workers would raise unemployment in border states.

8

components, shipped them to a plant in Mexico for assembly, and then reimported thefinished good only paid U.S. import duties on the value of Mexican labor and raw materials
used in assembly. NAFTA ended this special status for maquiladoras by giving all Mexican
firms duty free access to the U.S. market.6 Yet, as seen in Figure 1, NAFTA did little tostunt the growth of maquiladoras. In a purely legalistic sense, NAFTA did mean the end of
the maquiladora regime; it eliminated the "in-bond" arrangement under which maquiladoras
operated. However, Mexico's low wages continue to give the country a comparativeadvantage in the assembly of manufactured goods for the U.S. economy.

2.3 Regional Exposure to Globalization
Mexico’s trade and investment reforms have dramatically increased the opennessof its economy. These policies appear to have affected some parts of the country much
more than others. Figure 3 plots the share of state GDP accounted for by valued added inmaquiladoras during the 1990’s against distance to the United States. For three of the six
states that border the United States (Baja California, Chihuahua, Tamaulipas), themaquiladora share of GDP is over 18%. For two of the three others (Coahuila, Sonora) it
is over 8%. In the rest of the country, the maquiladora share of GDP is below 5%.
While maquiladoras are a large part of Mexico’s exports, they are by no means
the whole story. Export production also occurs in states with relatively large supplies ofskilled labor, which have attracted multinational auto companies (as in Aguascalientes)
and electronics producers (as in Jalisco). Figure 4 plots the share of foreign direct
investment in state GDP against the share of maquiladora value added in state GDP, both
6

With NAFTA, all firms in Mexico obtained duty-free access to the U.S. market as long as they comply
with NAFTA rules of origin. NAFTA also exposes maquiladoras to rules of origin (from which they had
been exempt previously), but now also allows the plants to sell goods on the Mexican market.

9averaged over the 1990’s. While border states show up as high in both categories, otherstates have attracted foreign direct investment in forms besides maquiladoras. These
include states in which Mexico’s most important industrial cities are located (Mexico
City, Federal District; Monterrey, Nuevo Leon; Guadalajara, Jalisco).
Beyond FDI and maquiladoras, some states are exposed to globalization by virtueof having ports and being large importers. This is seen in Figure 5, which plots FDI as ashare of state GDP against imports as a share of state GDP.7 A few states, such as
Yucatan and Sinaloa, have high imports, while attracting little in the way of FDI.
To categorize states as having high or low exposure to globalization, I use thethree measures described in Figures 3-5: the share of maquiladora value added in state
GDP, the share of FDI in state GDP, and the share of imports in state GDP (eachaveraged over the period 1993-1999). Using all three measures is important, since withthe exception of FDI and imports they are relatively weakly correlated across states, asreported in Table 2. Table 3 reports the globalization measures for Mexico’s 32 states,
where states are sorted according to their average rank across the three measures. I selectas states with high exposure to globalization those whose average rank across the threemeasures is in the top third (and that have at least one individual rank in the top third) andI select as states with low exposure to globalization those whose average rank is in thebottom third (and that have no single rank in the top third).
Of the 7 states with high-exposure to globalization, 5 share a border with theUnited States; of the 10 states with low-exposure to globalization, 5 are in southern
Mexico. Historically, Mexico’s north – with its more abundant mineral deposits, lower
population densities, and closer proximity to the United States – has been relatively rich,
7

In Mexico, there are no data on exports at the state level (other than data on maquiladora exports).

10

while Mexico’s south – with its higher population densities and larger indigenous
community – has been relatively poor. It is well known that since Mexico’s economic
opening the border region has enjoyed relatively high wage growth, widening regional
wage differentials in the country (Hanson, 2004). However, the recent success of theborder follows a period in which Mexico’s poorer regions had been catching up.
Chiquiar (2004) finds that from 1970 to 1985, the 15 years preceding Mexico’s entry into
the GATT, there was convergence in per capita GDP levels across Mexican states, andthat after 1985 this process broke down. For the period 1985-2001, there is strong
divergence in state per capita GDP levels. His results are reproduced in Figure 6.
Mexico’s globalization decade thus follows a period during which income differences
between high-exposure states and low-exposure states had been closing.
Finally, it is important to note that exposure to globalization is not simply a proxy
for the opportunity to migrate to the United States. Contrary to popular belief, migration
to the United States is not especially common among residents of Mexican states on the
U.S. border. Mexico’s high migration states are in agricultural regions in central andwestern Mexico, which have dominated migration to the United States for most of thelast century (Durand, Massey, and Zenteno, 2001).

Most of these states have low

exposure to FDI or to trade, as seen in Figures 7 and 8, which plot the fraction of the state
population migrating to the United States over the period 1995-2000 against the share of
FDI in state GDP or the share of imports in state GDP. This suggests that high exposure
to globalization does not indicate high exposure to emigration.
Proximity to the United States explains part of regional differences in exposure to
globalization, but it is clearly not the whole story. Other states have become more11

integrated into the global economy by virtue of having more skilled workers, bettertransportation infrastructure, or larger markets. These features, while present before
globalization took hold in Mexico, are not exogenous. They reflect the ability of thesestates to develop economically, which may in turn reflect the quality of their legal orpolitical institutions or other historical factors.

This suggests that my measure of

exposure to globalization may proxy for institutional quality or other regional
characteristics. Identifying the factors that determine regional variation in exposure to
global markets, while beyond the scope if this paper, is important. Without this mapping,
one cannot make policy recommendations. My findings will suggest that in Mexico
regions more exposed to globalization have done better in terms of income growth. But
the policy implications of this result are unclear as I leave unanswered the question of
how one goes about increasing regional exposure.

3. Empirical Methodology
The empirical analysis involves comparing changes in income distribution during
Mexico’s globalization decade between two groups of states, states with high exposure to
globalization and states with low exposure to globalization. In this section, I describe
non-parametric and parametric approaches for making these comparisons.

3.1 Estimating Counterfactual Income Densities
Let f(w|x,i,t) be the density of labor income, w, conditional on a set of observed
characteristics, x, in region i and time t. Define h(x|i,t) as the density of observed
characteristics among income earners in region i and time t. For regions, i=H indicateshigh exposure to globalization and i=L indicates low exposure to globalization; for time12

periods, t=00 indicates the year 2000 and t=90 indicates the year 1990. The observed
density of labor income for individuals in i at t is,g( w | i ,t ) = ∫ f ( w | x ,i ,t )h( x | i ,t )dx
Differences inf ( w | x , H ,t ) and(1)f ( w | x , L ,t ) capture differences in returns toobservable characteristics in regions with high versus low exposure to globalization;differences in h(x|H,t) and h(x|L,t) capture differences in the distribution of observed
characteristics in high versus low-exposure regions.
To evaluate the change in income distributions across time and across regions, I
would like to compare changes in f ( w | x , H ,t ) and f ( w | x , L ,t ) , while holding the
distribution of x constant. However, in the data I do observe not these conditional
densities, but the only marginal densities, g(w|x,H,t) and g(w|x,L,t). To evaluate these
densities, I apply techniques from DiNardo, Fortin, and Lemieux (1996). First, consider
the cross-time change in income distribution in the high-exposure region that is due tochanges in returns to observable characteristics, which can be written as∫ f ( w | x , H ,00 )h( x | H ,90 )dx − ∫ f ( w | x , H ,90 )h( x | H ,90 )dx(2)

Equation (2) evaluates the change in income distribution in high-exposure regions
between 1990 and 2000, fixing the marginal density of observables to be that in highexposure regions in 1990. Rewrite equation (2) as∫ [θH 90 → H 00

− 1 ] f ( w | x , H ,90 )h( x | H ,90 )dx

(3)

where

θ H 90 → H 00 =f ( w | x , H ,00 )f ( w | x , H ,90 )

(4)

13

Equation (3) is simply the observed marginal income density in high-exposure regions in
1990, adjusted by a weighting function. Given an estimate of the weighting function in(4), it would be straightforward to apply a standard kernel density estimator to equation(3). The key, then, to estimating the change in income distribution that is due to changes
in returns to observables is estimating the weighting function in (4).
Before turning to the weighting functions, consider the analog to equation (2) forregions with low exposure to globalization. The change in income distribution in lowexposure regions that is due to changes in returns to observables is∫ f ( w | x , L ,00 )h( x | H ,90 )dx − ∫ f ( w | x , L ,90 )h( x | H ,90 )dx(5)Equation (5) evaluates the change in income distribution in regions with low exposure to
globalization between 1990 and 2000, again fixing the marginal density of observables tobe that in high-exposure regions in 1990. To rewrite equation (5) in terms of themarginal density of income in high-exposure regions in 1990, apply the weights

θ H 90 → L00 =f ( w | x , L ,00 )f ( w | x , L ,90 )and θ H 90 → L 90 =f ( w | x , H ,90 )f ( w | x , H ,90 )(6)which yields∫ [θH 90 → L00− θ H 90 → L 90 ] f ( w | x , H ,90 )h( x | H ,90 )dx(7)

As in estimating equation (3), estimating equation (7) comes down to applying theappropriate weighting function to a standard kernel density estimator.
The changes in conditional income densities in equations (2) and (5) reflect inpart the impact of globalization and in part the impact of other aggregate shocks to the
Mexican economy. The difference between these changes amounts to a difference-indifference estimator, which evaluates the change in returns to observables in regions with14

high exposure to globalization relative to the change in returns observables in regionswith low exposure to globalization. Putting equations (3) and (7) together,(∫ f ( w | x,H ,00 )h( x | H ,90 )dx − ∫ f ( w | x,H ,90 )h( x | H ,90 )dx)−(∫ f ( w | x,L,00 )h( x | H ,90 )dx − ∫ f ( w | x,L,90 )h( x | H ,90 )dx) =∫ (θH 90→H 00)()− 1 − θ H 90→L00 − θ H 90→L90 f ( w | x, H ,90 )h( x | H ,90 )dx(8)Equation (8) shows the 1990-to-2000 change income distribution in high-exposure
regions relative to low-exposure regions, holding the distribution of observables constant.
I use equation (8) to evaluate the impact of globalization on income distribution in
Mexico. The choice of the high-exposure region in 1990 as the base case is purely
arbitrary and should not affect the density difference. To check the robustness of theresults, I will discuss estimates using other base cases.
To estimate the weighting functions in (4) and (6), I use Leibbrandt, Levinsohn,and McCrary’s (2004) extension of the DiNardo, Fortin, and Lemieux (1996). Applying
Bayes’ Axiom to the weighting equations yields,

θ H 90 → H 00 =f ( w | x , H ,00 )Pr( t = 00 ,i = H ) | w, x ) 1 − Pr( t = 00 ,i = H ) | x )=f ( w | x , H ,90 ) 1 − Pr( t = 00 ,i = H ) | w, x ) Pr( t = 00 ,i = H ) | x )θ H 90 → L00 =f ( w | x , L ,00 )Pr( t = 00 ,i = L ) | w, x ) 1 − Pr( t = 00 ,i = L ) | x )=f ( w | x , H ,90 ) 1 − Pr( t = 00 ,i = L ) | w, x ) Pr( t = 00 ,i = L ) | x )θ H 90 → L90 =f ( w | x , L ,90 )Pr( t = 90 ,i = L ) | w, x ) 1 − Pr( t = 90 ,i = L ) | x )=f ( w | x , H ,90 ) 1 − Pr( t = 90 ,i = L ) | w, x ) Pr( t = 90 ,i = L ) | x )(9)Each weighting function is the product of odds ratios. Consider the first weight. The
first ratio is the odds an individual is from a high-exposure region in 2000 (based on a15

sample of individuals from high-exposure regions in 1990 and 2000), conditional on
observables, x, and labor income, w. The second ratio is the (inverse) odds an individualis from a high-exposure region in 2000 (again, based on a sample of individuals fromhigh-exposure regions in 1990 and 2000), conditional on just on x. I can estimate theodds ratios by estimating two logit models. In each case, the dependent variable is a 0-1
variable on the outcome i=H and t=00 (based on a sample of i=H and t=90 or 00). For
the first logit model, the regressors are x and w; for the second, the regressor is x, alone.
The other weights can be estimated analogously.
After estimating the weights, I apply them to a standard kernel density estimatorto obtain estimates for the densities described by equations (3), (7), and (9). Theseestimates are for the difference in income densities, in the case of (3) and (7), and for the

double difference in income densities, in the case of equation (9).

3.2 A Parametric Analog
The advantage of the approach described in section 3.1 is that it characterizes thedifference in income across time periods and/or regions at all points in the distribution.
The disadvantage is that there are no standard errors for these density differences. To
examine the statistical significance of the results, I estimate a parametric analog toequation (8), which is simply a difference-in-difference wage equation.
I pool data on working age men in 1990 and 2000 from states with either highexposure or low exposure to globalization and then estimate the following regression,ln whst = α s + X hst ( β 1 + β 2Y 2000ht + β 3 Highhs ) + φ * Y 2000ht * Highhs + ε hst
(10)

16

where w is labor market earnings, X is a vector of observed characteristics, Y2000 is adummy variable for the year 2000, and High is a dummy variable for high-exposurestates. The regression includes controls for state fixed effects and allows returns toobservable characteristics to vary across regions and across time. The coefficient, φ,captures the differential change in earnings from 1990 to 2000 between states with highexposure and low-exposure to globalization.
Equation (10) is a standard difference-in-difference specification, which implies I
estimate the mean differential in wage growth between low-exposure and high-exposurestates. This approach ignores the possibility that the wage effect of being in a state withhigh exposure to globalization may not be uniform throughout the wage distribution. The
results presented in the next section will provide evidence consistent with this possibility.
A more elegant approach would be to estimate the regional differential in wage changes
non-parametrically, as in the framework derived by Athey and Imbens (2003).

3.3 Estimation Issues
Several estimation issues merit attention.
regions.

First, individuals self-select into

Individuals who have chosen to live in a state with high-exposure to

globalization may have relatively high drive or ambition and may have moved to the state
to take advantage of the opportunities globalization offers. Similarly, individuals who
have chosen not to leave states with low-exposure to globalization may have relatively
low drive or ambition. Given this pattern of selection, unobserved components of labor
income would tend to be positive for individuals in high-exposure states and negative forindividuals in low-exposure states. The estimation exercises in (9) and (10) would thenbe polluted by systematic differences in unobserved characteristics between regions. To

17

avoid this problem, I categorize individuals by birth state and not by state of residence.
In this way, I pick up earnings differences in where people live based on where they wereborn – a factor out of their control – and not on where they have chosen to reside – afactor in their control. Consistent with expectations, in 1990 83% of those born in highexposure states still lived in those states, compared to only 73% of those born in lowexposure states. In 2000, the figures were 82% and 70%.
A second estimation issue is that individuals self-select into the labor force. Thisis partly due to age. Over time, young workers enter the labor force and older workers
exit. To control for these movements, I limit the sample to the cohort of men who were
25 to 55 years old in 1990 (and 35 to 65 years old in 2000). Relatedly, if over the 1990’s
labor-market conditions improved by more in high-exposure states than in low-exposurestates, high-exposure states may have registered a larger increase the fraction of lowability individuals participating in the labor force.

Given this pattern of selection,

unobserved components of labor income may have increased by less in high-exposurestates and than in low-exposure states.8 To control for selection into the labor-force, I
follow Lee (2004) and trim low-wage earners across the four samples (i.e., for i=H,L and

t=90,00) such that the fraction included in the estimation is the same for each group.
A third estimation issue is that shocks other than globalization may have had
differential impacts on regions with high versus low exposure to globalization. One suchshock is the pesos crisis of 1995. After a bungled devaluation of the peso in 1994,
Mexico chose to float its currency, which proceeded to plummet in value relative to thedollar.

The ensuing increase in the peso value of dollar-denominated liabilities

8

This suggests that selection into the labor force would work against selection into regions, in terms of the
impact on unobserved components of earnings.

18

contributed to a banking collapse and a severe macroeconomic contraction. It is hard togauge whether the peso crisis would have hurt states with high exposure to globalization
more or less than states with low exposure. On the one hand, high-exposure states are
more specialized in the production of exports and the devaluation of the peso would haveincreased demand for their output. On the other hand, high-exposure states are better
integrated into Mexico’s financial markets and the banking collapse may have hurt themmore. Other important shocks in the 1990’s included a reform of Mexico’s land tenuresystem in 1992, the ongoing privatization of state-owned enterprises and deregulation ofindustries, and the ruling party’s loss of majority control in Mexico’s congress in 1997.
Again, it is hard to say whether these shocks would have helped or hurt high-exposurestates more. The existence of these other shocks leaves the results subject to the caveatthat factors other than globalization may have accounted for any differential change inincome distribution across regions of the country. I return to this issue in section 5.

4. Empirical Results
The sample for the analysis is men aged 25 to 55 in 1990 or 35 to 65 in 2000 who
were born in one of the seven Mexican states with high-exposure to globalization or in
one of the ten Mexican states with low-exposure to globalization.

The dependent

variable is log average hourly labor earnings.9 I also discuss results using log total labor
income as the dependent variable. Summary statistics are in Table 4.

9

For Mexico, average hourly wages are calculated as monthly labor income/(4.5*hours worked last week); for the
U.S., average hourly wages are calculated as annual labor income/(weeks worked last year*usual hours worked perweek). Assuming individuals work all weeks of a month could bias wage estimates downwards. To avoid
measurement error associated with implausibly low wage values or with top coding of earnings, I restrict the sample to
be individuals with hourly wages between $0.05 and $20 (in 2000 dollars). This restriction is nearly identical todropping the largest and smallest 0.5% of wage values.

194.1 Raw Income Distributions
To provide a starting point for the analysis, consider the raw distributions of labor
income in states with either high exposure or low exposure to globalization. Figure 9shows kernel density estimates for hourly labor earnings in 1990 and 2000. In both
years, the density for high-exposure states is shifted to the right compared to lowexposure states. Between 1990 and 2000, the difference between the wage densities inthe two groups of states appears to widen. Higher wages in high-exposure states reflect
in part the fact these states have a more highly educated labor force, as indicated by Table
4. Higher wages in high-exposure states may also reflect differences in the returns toobservable characteristics across states in Mexico.
To see what these distributional differences imply about differences in theincidence of poverty between regions, Figure 10 shows the cumulative distribution forwages in high-exposure and low-exposure states in the two years. The vertical line ineach graph shows the hourly wage needed to provide the minimum caloric intake for afamily of four with one wage earner working the mean number of annual labor hours inthat year.10 The peso value for the minimum caloric intake is from Cortes et al. (2003).
The poverty wage line in Figure 10 is not meant to provide an accurate indicator of thefraction of individuals living in poverty. By focusing on labor income, I ignore othersources of household earnings.

Government transfers, rental income, loans, in-kindreceipts, and remittances also supplement family income, suggesting that the implied
poverty wage threshold in Figure 10 is set too high – some families below this threshold10

In 1990, the implied poverty cutoff for the hourly wage was $1.16 in low-exposure regions and $1.25 in highexposure regions (in 2000 US dollars), and in 2000, it was $1.13 in low-exposure regions and $1.22 in high-exposure
regions. The poverty wage is lower in low-exposure regions because rural areas have lower prices for goods andbecause a higher fraction of the population in low-exposure regions lives in rural areas. The line shown in Figure 9 isthat for the log poverty wage in low-exposure regions (in log terms the poverty wage in high-exposure and lowexposure regions is nearly the same).

20will receive enough income from other sources to allow them to afford a consumption
level that is above the poverty cutoff. Still, the poverty wage is a useful benchmark for
gauging the potential for a worker to sustain a family at above-poverty consumption
levels on labor income alone (which is two-thirds of total income in Mexico).
In 1990, the fraction of workers earning less than the poverty cutoff wage in lowexposure regions (0.42) is twice that in high-exposure regions (0.21). In 2000, thedifference is even larger, with the fraction of workers below the poverty wage at 0.49 in
low-exposure regions and 0.22 in high-exposure regions. While it appears that poverty
increased more rapidly in low-exposure regions, the results in Figure 10 are inconclusive.
Since both the price of labor and the composition of labor is changing across regions andover time, we don’t know whether the apparent increase in the relative incidence ofpoverty in low-exposure regions is due to a deterioration in the returns to observablecharacteristics or to change in the relative composition of the labor force. To separate
these effects, I construct counterfactual income densities.

4.2 Counterfactual Income Distributions
To control for regional differences in the distribution of observablecharacteristics, I apply the weights in equation (9) to the kernel density for high-exposurestates in 1990. This produces the two sets of densities in Figure 11. The first graphshows the actual income density in 1990 for high-exposure states and a counterfactual
density that would obtain were workers in high-exposure states in 1990 paid according to
the returns to observable characteristics in low-exposure states in 1990, or∫ f ( w | x , L ,90 )h( x | H ,90 )dx = ∫θH 90 → L 90f ( w | x , H ,90 )h( x | H ,90 )dx

21Since the distribution of observable characteristics is the same in the actual andcounterfactual densities, comparing the two makes it possible to isolate the regional
differences in income densities that are attributable to regional differences in returns tocharacteristics. In Figure 11, the density for high-exposure states in 1990 is again rightshifted relative to low-exposure states, though the regional difference in incomes issmaller than in Figure 9. Thus, even before Mexico’s globalization decade, incomes
were higher across the distribution in high-exposure states. These income differences
may be due to high-exposure states historically having better infrastructure, being more
specialized in the high-wage manufacturing sector, or being less specialized in the lowwage agricultural sector, among other factors (see Chiquiar, 2003, for a more complete
discussion). This highlights the importance of controlling for initial income differences
between states when examining changes in income distributions over time.
The second graph in Figure 11 shows income densities in 2000, evaluated basedon the distribution of observable characteristics in high-exposure states 1990. For highexposure states, the resulting counterfactual density is what workers in high-exposurestates in 2000 would earn were they to have the observable characteristics of workers inhigh-exposure states in 1990, or∫ f ( w | x , H ,00 )h( x | H ,90 )dx = ∫θH 90 → H 00

f ( w | x , H ,90 )h( x | H ,90 )dx

For low-exposure states, the counterfactual is what workers in low-exposure states in2000 would earn had they the characteristics of high-exposure states in 1990, or∫ f ( w | x , L ,00 )h( x | H ,90 )dx = ∫θH 90 → L00

f ( w | x , H ,90 )h( x | H ,90 )dx

Comparing these counterfactuals isolates regional differences in income densities that are
due to differences in returns to characteristics, rather than to the distribution of22

observables. As in 1990, the density for high-exposure states in 2000 is right-shifted
relative to low-exposure states. Comparing the two years, it appears that differences inincome densities between high-exposure and low-exposure states have increased overtime, suggesting relative incomes have risen in the former.
To relate the counterfactual wage densities to poverty, Figure 12 shows the
cumulative distribution analogues to the counterfactual wage kernels in Figure 11. The
first graph in Figure 12 thus shows the cumulative density for wages in high-exposureand low-exposure states in 1990, based on the characteristics of workers in high-exposure
regions in 1990. Comparing this graph to the first graph in Figure 10, we again see thatthe fraction of workers below the poverty wage is higher in low-exposure states (0.32)
than in high-exposure states (0.21). However, the difference in the incidence of wage
poverty between the two groups of states in Figure 12 (0.32-0.21=0.11) is considerably
lower than in Figure 10 (0.42-0.21=0.21).

Holding constant the distribution ofobservable characteristics leaves the difference in cumulative distributions due todifferences in returns to observables. Again, the apparent higher initial incidence ofpoverty in low-exposure states highlights the importance of controlling for initialconditions when comparing changes in income distributions.
The second graph in Figure 12 shows the cumulative density for wages in highexposure and low-exposure states in 2000, based on the characteristics of workers inhigh-exposure regions in 1990. The fraction of workers earning less than the poverty
wage is 0.40 in low-exposure states and 0.22 in high-exposure states, which again is asmaller difference (0.40-0.22=0.18) than that for the actual wage distributions in Figure
10 (0.49-0.22=0.27). Putting the 1990-to-2000 change in the incidence of wage poverty

23

for low-exposure versus high-exposure states together yields a difference-in-difference
estimate of (0.40-0.32)-(0.22-0.21)=0.07. During Mexico’s globalization decade of the
1990’s, the incidence of wage poverty in low-exposure states appeared to increase
relative to that in high-exposure states by approximately 7%.
To explore these distributional changes in more detail, Figure 13 shows estimatesof equation (2) – the 1990-to-2000 change in income densities in high-exposure states –and of equation (5) – the 1990-to-2000 change in income densities in low-exposurestates, where all densities are evaluated based on the distribution of observables in highexposure states in 1990 (as shown in equations (3) and (7)). In low-exposure states, there
was a pronounced shift in mass from the upper half of the distribution to the lower half ofthe distribution. In high-exposure states, there was a modest shift in mass from the upper
part of the distribution to the middle of the distribution. While labor incomes in the
1990’s deteriorated in both regions, caused in part by Mexico’s peso crisis in 1995, thedeterioration was much less severe in states with high-exposure to globalization.
The change in regional relative incomes is seen more clearly in Figure 14, whichshows an estimate of equation (8), the change in income density in high-exposure states
relative to the change in income density in low-exposure states (evaluated for the
distribution of observable characteristics in high-exposure states in 1990). It is clear thatthe income of high-exposure states has increased relative to the income of low-exposurestates. This appears as shift in mass in the double density difference from the lower halfof the distribution to the upper half of the distribution. During Mexico’s globalization
decade, individuals born in states with high-exposure to globalization appear to havedone much better than individuals born in states with low-exposure to globalization.

24

These results appear to be robust to changing the sample of states with either
high-exposure or low-exposure to globalization. In unreported results, I experimented
with dropping high-exposure states one at a time from the sample and re-estimating theincome densities and with dropping low-exposure states one at a time and re-estimating
the densities. Both sets of results are very similar to those reported.

4.3 Additional Results
Throughout the analysis, we have evaluated labor income densities fixing the
distribution of observable characteristics to be those in states with high-exposure to
globalization in 1990. This choice of the base case is arbitrary and should not affect theresults. To examine the robustness of the findings, Figure 15 re-estimates the double
difference in income densities in equation (8), evaluating all densities based on the
distribution of observables in low-exposure states in 1990. Figure 15 is very similar to
Figure 14, confirming that the choice of base case does not matter for the results.
The income densities shown so far are for average hourly labor earnings. Ifchanges in wages affect individual labor supply, changes in hourly labor earnings may
understate changes in total labor income. To see if this might be the case, Figure 16
estimates of the double density difference in equation (8), evaluated in terms of totallabor income rather than average hourly labor income. Figure 16 is similar to Figure 14,
suggesting that regional changes in the distribution of total labor income mirror regionalchanges in the distribution of hourly labor income.
In the results so far, I have included the full sample of workers from low-exposureand high-exposure states in 1990 and 2000. One concern is that the nature of selfselection into work varies across states or across time. If labor-force participation differs

25between low-exposure and high-exposure states, then cross-section comparisons in wage
distributions may be contaminated by sample selection. If these differences are stable
over time, they may not pose a problem for comparing changes in wage distributions.
However, if labor-force participation changes differentially over time between lowexposure and high-exposure states, then sample selection may also contaminate thedifference-in-difference analysis. For males with 9 or more years of education, laborforce participation rates are very similar in low-exposure and high-exposure states. For
low-education males, labor-force participation rates are higher in high-exposure states
and these differences appear to increase over time. This suggests the data are missing
more low-wage workers in low-exposure state than in high-exposure states, which would
tend to compress the estimated difference in wage distributions for the two groups ofstates. Further, since the relative fraction of missing low-wage workers in low-exposurestates rises over time, my estimates would tend to understate the full extend of the change
in relative wages between the two groups of states over time.
To deal with sample selection associated with labor-force participation, I apply
Lee’s (2004) technique for trimming observations to make them comparable acrosssamples (which may vary by region, time, or some other dimension). The idea is that if
both wages and labor-force participation are monotonically increasing in the unobserved
component of wages, then it is possible to make two samples comparable in terms of the
distribution of unobservables by trimming low-wage observations in the group that has
higher labor force participation. We can’t add low-wage workers who don’t work into
the sample in the low-labor-force participation group, but we can drop from the sample
low-wage workers in the high-labor-force participation group (who presumably wouldn’t

26

work if they were to be placed in the other group). I trim low-wage workers from thehigh-labor-force participation group until I obtain two samples that are identical in termsof the fraction of wage earners included. Figures 17 and 18 redo Figures 13 and 14
applying Lee’s trimming procedure. It remains the case that wages deteriorate by morein low-exposure states. Income in high-exposure states increases relative to income inlow-exposure states, which appears as shift in mass in the double density difference fromthe lower half of the distribution to the upper half of the distribution. This is further
evidence that during Mexico’s globalization decade individuals born in states with highexposure to globalization have done relatively well in terms of their labor earnings.

4.4 Parametric Results
While the non-parametric results a strong increase in relative incomes in stateswith high exposure to globalization they give no sense of the statistical precision of theseestimates.

As a check on the statistical significance of the results, Table 5 shows

estimation results for equation (10). The dependent variable is log average hourly labor
earnings. The regressors are dummy variables for educational attainment, a quadratic inage, a dummy variable for the year 2000 and its interaction with the age and education
variables, a dummy variable for having been born in a state with high-exposure to
globalization and its interaction with the age and education variables, dummy variables
for the state, and the interaction of the year 2000 and high-exposure-to-globalization
dummy variables. This last variable captures the differential change in wage growth inhigh-exposure states relative to low-exposure states. Standard errors are adjusted forcorrelation across observations within the same state.

27

Panel (a) of Table 5 shows that during the 1990’s the cohort of individuals born instates with high exposure to globalization enjoyed labor earnings growth that was 7.9 to
9.2 log points higher than earnings growth for individuals born in low-exposure states.
These coefficients are precisely estimated. This is consistent with the counterfactual
density estimates and again suggests than individuals in high-exposure states enjoyedhigher growth in labor income that individuals in low-exposure states. The second twocolumns of Table 5 show results where the year200/high-exposure interaction isinteracted with a dummy variable for an individual having completed a secondary
education. This term allows relative earnings growth to be larger for more-educatedworkers. The interaction term is negative, but imprecisely estimated.
Panel (b) of Table 5 redoes the estimation, trimming observations across the
samples to account for possible self-selection into work. Estimated relative wage growthfor high-exposure states is higher using this estimation method, with individuals born inhigh-exposure states enjoying labor earnings growth 9.0 to 10.9 log points higher than for
individuals born in low-exposure states. In the second two columns, the interaction
between the year200/high-exposure interaction and the dummy variable for secondary
education is negative, precisely estimated, and similar in absolute value to the main effect(the year 2000/high-exposure interaction). This suggests that on average most of therelative wage growth for individuals born in high-exposure states went to individuals
with low levels of schooling. The income gains in moving from low-exposure to highexposure states appear to be largest for low-wage workers.

28

5. Conclusion
In this paper, I examine the change in the distribution of labor income acrossregions of Mexico during the country’s decade of globalization in the 1990’s. I focus theanalysis on men born in states with either high-exposure to globalization or in states with
low-exposure to globalization, as measured by the share of foreign direct investment,
imports, and export assembly in state GDP during the 1990’s. Mexican states with highexposure to globalization are located along the U.S. border and in the relatively skillabundant center-west region of the country; states with low exposure to globalization are
primarily located in more rural southern Mexico. I exclude from the analysis individuals
born in states with intermediate exposure to globalization.
Controlling for regional differences in the distribution of observablecharacteristics and for initial differences in regional incomes, the distribution of labor
income in high-exposure states shifted to the right relative to the distribution of income inlow-exposure states. This change in regional relative incomes was the result of a shift in
mass in the income distribution of low-exposure states from upper-middle income earners
to lower income earners. Labor income in low-exposure states fell relative to highexposure states by 8-12% and the incidence of wage poverty (the fraction of wage earners
whose labor income would not sustain a family of four at above-poverty consumption
levels) increased in low-exposure states relative to high-exposure states by 7%.
There are several possible interpretations of these results. One is that trade andinvestment liberalization raised incomes in states with high exposure to the global
economy relative to states with low exposure to the global economy. However, trade andinvestment reforms were by no means the only shocks to the Mexican economy during

29

the 1990’s. The Mexican peso crisis in 1995 was another important event. The results
are also consistent with states that were more integrated into the global markets being
better able to weather the large devaluation of the peso, the banking crisis, and thecontraction in economic activity that occurred in Mexico during the mid 1990’s. Highexposure states are relatively specialized in export production and would potentiallybenefit from a depreciation of the currency.
Other policy changes, such as the privatization and deregulation of Mexican
industry or the reform of Mexico’s land-tenure system, may also have had differential
regional impacts in Mexico. Privatization and deregulation appeared to weaken Mexico’s
unions and lower wage premiums enjoyed by workers in these sectors (Fairris, 2003).
Since more heavily unionized industries are concentrated in Mexico’s north and center,and relatively absent in Mexico’s south (Chiquiar, 2003), we might expect a loss in union
power to lower relatives incomes in states with higher exposure to globalization, contrary
to what we observe in the data. The reform of Mexico’s land-tenure system allowed
individuals to sell agricultural land previously held in cooperative ownership.

In

principal we might expect this opportunity to raise relative incomes in rural southern
Mexico, where agriculture accounts for a relatively high share of employment and output.
Again, this is contrary to what we observe in the data.
Another possibility is that income growth in high-exposure states merely reflects
continuing trends unrelated to globalization. This also does not appear to be the case. As
seen in Figure 6, poorer states, which includes seven of the ten states with low-exposure
to globalization, had faster growth in per capita income than richer states, which includes
six of the seven high-exposure states. The process of income convergence in Mexico

30

came to a halt in 1985, coinciding with the onset of trade liberalization. Since 1985,
regional incomes have diverged in the country. The pattern of income growth I uncoverdoes not appear to have been evident in the early 1980s or before.
A brief review of Mexico’s other policy reforms during the 1990’s does notsuggest any obvious reason why they should account for the observed increase in relative
incomes in states with high-exposure to globalization. Still, it is important to be cautious
about ascribing shifts in regional relative incomes to specific policy changes. In the end,
we can only say that I find suggestive evidence that globalization has increased relative
incomes in Mexican states that are more exposed to global markets.

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34

Table 1: Percent of Mexico’s Population with Per Capita Income below Threshold
Needed to Achieve Minimum Caloric Intake

Area

1992

1994

1996

1998

2000

Urban
Households

10.2

7.2

20.1

16.4

9.8

Rural
Households
29.5
30
Source: Cortés, et.al (2003).

43.3

43.8

34.1

Table 2: Correlation Matrix for Measures of Exposure to Globalization Across
Mexican States in the 1990’s

Maquiladora
Value Added/
State GDP

Foreign Direct
Investment/
State GDP

Share of State
Imports/ Population Migrating
State GDP
to US, 1995-2000

Maquiladora
Value Added/
State GDP
Foreign Direct
Investment/
State GDP

0.381

Imports/
State GDP

-0.0080.582

Share of State
Population Migrating
to US, 1995-2000-0.129-0.371-0.257

Notes: Shares of state GDP (maquiladora value added, foreign direct investment,
imports) are averages over the period 1993-1999. Correlations are weighted by stateshare of the national population (averaged over 1990 to 2000).

35

Table 3: Categorizing Mexican States by Exposure to Globalization in the 1990’s

High
ExposuretoGlobalization

Intermediate
StatesLow
ExposuretoGlobalization

State
Baja California
Chihuahua
Nuevo Leon
SonoraJaliscoTamaulipas
Aguascalientes

Average
Rank
30
28
28
27
25
25
25

Share of State GDP
Imports
Maquiladoras
0.018
0.246
0.018
0.214
0.027
0.023
0.034
0.088
0.027
0.029
0.013
0.181
0.014
0.046

FDI
0.070
0.030
0.039
0.015
0.018
0.035
0.015

DF
Coahuila
YucatanPueblaBaja California Sur
San Luis Potosi
Guanajuato
Sinaloa
Tlaxcala
Queretaro
DurangoTabascoMorelos
MexicoMichoacan

22
22
21
19
19
18
18
17
17
16
16
16
15
15
15

0.055
0.011
0.005
0.009
0.032
0.028
0.009
0.005
0.019
0.013
0.001
0.010
0.024
0.031
0.000

0.058
0.014
0.023
0.015
0.011
0.011
0.014
0.027
0.010
0.011
0.012
0.017
0.010
0.008
0.016

0.000
0.077
0.031
0.015
0.008
0.013
0.008
0.001
0.020
0.011
0.035
0.000
0.005
0.004
0.000

Zacatecas
Quintana Roo
NayaritColimaGuerrero
Veracruz
ChiapasCampeche
HidalgoOaxaca

15
12
10
9
9
8
6
5
4
2

0.003
0.006
0.006
0.002
0.004
-0.004
0.000
0.001
0.000
0.000

0.013
0.011
0.011
0.014
0.007
0.012
0.011
0.008
0.007
0.005

0.008
0.000
0.000
0.000
0.002
0.000
0.000
0.000
0.000
0.000

Notes: Shares of state GDP (foreign direct investment, imports, maquiladora value
added) are averages over the period 1993-1999.

36

Table 4: Summary Statistics

Year

Variable

1990

Age
0
1 to 56 to 89 to 11
12 to 15
16+
Wage
N

High Exposure to
Globalization
Mean St. Dev.
33.6
5.9
0.055
0.229
0.185
0.388
0.273
0.445
0.208
0.406
0.139
0.346
0.140
0.347
2.590
2.610
13,771Age
0
1 to 56 to 89 to 11
12 to 15
16+
Wage
N

43.0
0.036
0.178
0.259
0.207
0.142
0.177
2.656
11,807

High Gradeof
SchoolingCompleted

2000
High Gradeof
SchoolingCompleted

5.7
0.187
0.383
0.438
0.405
0.349
0.382
2.798

Low Exposure to
Globalization
Mean
St. Dev.
33.9
5.9
0.132
0.338
0.285
0.452
0.255
0.436
0.141
0.348
0.100
0.300
0.087
0.282
1.781
2.073
19,352
43.2
0.093
0.255
0.259
0.157
0.109
0.128
1.674
17,967

5.8
0.290
0.436
0.438
0.364
0.312
0.334
1.965

Note: Sample is men with positive labor earnings aged 25-55 in 1990 or 35-65 in 2000
born in states with either high-exposure to globalization or low-exposure to globalization.
Wages are average hourly levels in 2000 U.S. dollars.

37

Table 5: Regression Results

All
Workers

Workers w/
20-80 Hour
Work Week

All
Workers

Workers w/
20-80 Hour
Work Week(a) Full Sample of Workers
Year 2000*High Exposure

0.092(0.039)0.079(0.033)

Year 2000*High
Exposure*
Secondary Education
R
N

0.337
45,0120.373
42,2980.115(0.053)0.116(0.050)-0.050(0.042)-0.079(0.046)0.337
45,0120.373
42,298(b) Trimmed Sample to Account for Sample Selection
Year 2000*High Exposure

0.109(0.029)0.090(0.025)

Year 2000*High Exposure*Secondary Education
R
N

0.380
42,7110.417
40,2240.159(0.040)0.153(0.039)-0.106(0.031)-0.130(0.034)0.380
42,7110.418
40,224

Notes: The dependent variable is log average hourly labor earnings. In columns 1 and 3,
the sample is non-self-employed males born in states with high exposure to globalization
or states with low exposure to globalization; in columns 2 and 4, the sample includes only
the non-self-employed who report working 20-80 hours a week. Other regressors(quadratic in age, dummies for year of education, and their interactions with year 2000
dummy and with High Exposure dummy; year 2000 dummy variable; state dummy
variables) are not shown. Standard errors are in parentheses and are adjusted forcorrelation across observations within birth states. In panel (a), the sample is working
males in all states and time periods; in panel (b), I trim low-wage workers in high-laborforce-participation state/year groups until the fraction of wage-earners is the same in lowexposure and high-exposure states and in the two years.

38Maquila Employment

Maquila Value Added

1500
1250

1250

1000
750

750
500

500
250

250
1985

2000
1995
1990
Employment and Value Added in Mexico's Maquiladoras

Figure 1

Share of National Employment

1.00

0.95

0.90

0.85

0.80
1985

2000
1995
1990
Share of Maquiladora Employment in Border States

Figure 2

39

Value Addedd(Millions of 94$)

Employment(000s of Workers)

1000

Maquiladora Share of GDP, 1993-9.3

Baja Cal

Chihuahu.2
Tamaulip.1

SonoraCoahuila

AguascalDurangoJaliscoYucatanTlaxcala
PueblaSa n L uis Queretar
Zacateca Guanajua
Baja Cal
Morelos
MexicoGuerreroOaxaca Ta basco Chiapas Campeche
Sin alo aMichoaca
HidalgoVeracruz
Nayarit
Quintana
DF Colima

Nu evo Le

0
0

500

1000
1500
Kilometers to US Border

2000

2500

Figure 3.075

FDI Share of GDP, 1994-99

Baja Cal

DF.05
Nuevo LeT amaulip
Baj a oCal
Mexic
San Luis.025

Chihuahu

Mor elo s
T laxcJ ala
alis coQueretarAguas cal

T abasco
GuanajuaPuebla0

Sono raCoahuila

Quintana
Nayarit
Sinaloa
Guerr
Zaceroatec a Yuc ata nColimaDur angoMichoacheaCampecChiapas
HidalgoOaxacaVeracruz

0.1.2
Maquiladora Share of GDP, 1993-9.3

Figure 4

40

.075

FDI Share of GDP, 1994-99

B aja Cal

DF

.05
Nuevo LeT am aulipBo
aja Cal
Mexic
S an Luis.025

Chi huah uMorelos
T laxc ala

J alis c o

A guas c al
Queretar
Co ahuila
T abasGuanajua
P ueb
la c oQuintana
Nayarit
Guerrero Zac atec a
Colim aDur ango
Mi c ho ac a
Cam
Chiapashe
Hidalgo
Oaxac
a pec

0

S onoraY uc a tanS inaloa

V erac ruz

0

.02
.04
Import Share of GDP, 1993-99.06

Figure 541

Annual Growth 1970-1985 vs. Initial GDP
0.06

Growth 1970-1985

0.05
0.04
0.03
0.02
0.01
0
8.0

8.4

8.8

9.2

9.6

10.0

Log GDP 1970

Annual Growth 1985-2001 vs. Initial GDP
0.04

Growth 1985-2001

0.03
0.02
0.01
0.00
-0.01
-0.02
8.4

8.8

9.2

9.6

10.0

10.4

Log GDP 1985

Figure 6: Growth in Log GDP across Mexican States, 1970-2001

42

Zacateca.15

H ousehlds w/ Migrant in US, 2000

Michoaca

Guanajua.1

DurangoHidalgoAguascalNayaritMorelos LuisSan

Jalis co

GuerreroColimaQueretar
Oaxaca.05

Chihuahu

PueblaSinaloa
Veracruz
Coahuila

T amaulip
MexicoNuevo LeT laxcalaBaja CalDF

SonoraBaja CalYuc atanCampecChiapashe
Quintana
T abasco

0
.025
.05
FDI Share of GDP, 1994-99

0

.075

Figure 7

Zacateca.15

H ousehlds w/ Migrant in US, 2000

Mi cho aca

Guanajua.1

Dur ango
Aguascal
San Luis
HidalgoMorelos
NayaritJalisco

GuerreroColimaQueretar
Oaxaca.05

Chi huah uPueb laSinaloa
Veracruz
T amaulip
T laxcalaCo ahuila
MexicoBaja Cal
Baja CalCampeche
ChiapasQuintana

Nuevo Le
SonoraDF

Yuca tan
T abasco

0
0

.02
.04
Import Share of GDP, 1993-99.06

Figure 8

43

0

.2

.4

.6

Figure 9: Density of Hourly Labor Income in States withHigh Exposure and Low Exposure to Globalization, 1990 and 2000

-4

-2

0
lnw

4

Low 1990

0

.2

.4

.6

High 1990

2

-4

-2

0
lnw
High 20002
Low 2000

4

0

.2

.4

.6

.8

1

Figure 10: Cumulative Distribution of Hourly Labor Income in States withHigh Exposure and Low Exposure to Globalization, 1990 and 200

-4

-2

0
lnw

4

Low 1990

0

.2

.4

.6

.8

1

High 1990

2

-4

-2

0
lnw
High 2000

2

4

Low 2000

45

0

.2

.4

.6

Figure 11:
Counterfactual Income Densities, High and Low-Exposure States, 1990 and 2000

-4

-2

0
lnw

4

Low 1990

0

.2

.4

.6

High 1990

2

-4

-2

0
lnw
High 2000

2

4

Low 2000

46

0

.2

.4

.6

.8

1

Figure 12: Counterfactual Cumulative Income Distributions,High and Low-Exposure States, 1990 and 2000

-4

-2

0
lnw

4

Low 1990

0

.2

.4

.6

.8

1

High 1990

2

-4

-2

0
lnw
High 2000

2

4

Low 2000

47

-.06

-.04

-.02

0

.02

.04

Figure 13: Estimated Change in Labor Income Densities, 1990 to 2000

-2

-4

0
lnw

Density Diff: [00Hi-90Hi]2

4

Density Diff: [00Lo-90Lo]

Density Diff: [00Hi-90Hi]-[00Lo-90Lo], 90Hi base case
-.04
-.02
0
.02
.04
.06

Figure 14: Double Difference in Labor Income Densities

-4

-2

0
lnw

2

4

48

Density Diff: [00Hi-90Hi]-[00Lo-90Lo], 90Lo base case
-.02
-.01
0
.01
.02
.03

Figure 15: Double Difference in Labor Income Densities (Alternative Base Case)

-4

-2

0
lnw

2

4

Density Diff: [00Hi-90Hi]-[00Lo-90Lo], 90Hi base case
.06
.04
.02
0
-.02
-.04

Figure 16: Double Difference in Total Labor Income Densities

0

2

4

6

8

10

lnw

49

-.05

0

.05

.1

.15

Figure 17: Estimated Change in Labor Income Densities, 1990 to 2000,
with Observations Trimmed to Account for Selection into Work

3

2

1

0

lnw

Density Diff: [00Hi-90Hi]

Density Diff: [00Lo-90Lo]

Density Diff: [00Hi-90Hi]-[00Lo-90Lo], 90Hi base case
-.1
-.05
0
.05

Figure 18: Double Difference in Labor Income Densities,
with Observations Trimmed to Account for Selection into Work

0

2

1

3

lnw

50