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Do Political Boundaries Matter? The Spatial Distribution ofMigration Economies in Mexico
Emily A. Sellars
University of Wisconsin-Madison
March 18, 2011
***Preliminary***
Abstract: Mexico-US migration represents the largest sustained flow of migrant workers inthe contemporary world (Massey et al. 1998, 73). The benefits of this process have not beendistributed uniformly within Mexico; a large proportion of Mexican migrants continue comefrom specific regions, states, and cities. While many scholars have examined the economic, cul-tural, and social reasons for migration, few have considered the potential political dimensions.Do sub-national policies or institutions affect migration rates? Using data from the MexicanNational Population Council (CONAPO) and non-parametric econometric techniques, I showthat after controlling for the continuous progression of economic and social variables throughspace, several state-level fixed effects remain significant. I argue that it is difficult to attributethese border effects to any specific state policy due to the relatively short history of significantsub-national decision-making in Mexico and the strong path dependence of migration. One pos-sibility is that a known and unmeasurable factor explaining migration, the presence of migrantnetworks, also breaks across political boundary points.
It is hard to overstate the importance of migration to Mexicos economy and society. Over
eleven million Mexicans, more than 10% of the population, live and work abroad (World Bank
2010). The economic impact of migration is profoundly important to the citizens of Mexico and
the overall economy. The remittances sent from the United States to Mexico average over 20
billion dollars annually, comprising around 2% of Mexican GDP and in many years exceedingFDI inflows (World Bank 2010). Mexicos economic dependence on migration has been reflected
in national art, film, architecture, politics, and society, and a large and growing literature in the
social sciences has started to assess the economic and social costs and benefits of labor export
as a development strategy.
As with any economic flow, the costs and benefits of migration and remittances are dis-
tributed unevenly across the nation. Migration is not a random process, and push and pull
factors such as household income levels or demand for different types of labor in the destination
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country will influence which individuals can benefit from migrating. A great deal of work has
examined the educational, health, gender and race selectivity of migration, but this paper ex-
amines another source of migrant selection: geography. An important characteristic of Mexicanmigration is its spatial clustering. Since the beginning of the last century, 50-60% of Mexi-
can migrants to the United States have come from the Western region of Mexico, and 30-40%
have come from three states alone: Guanajuato, Jalisco, and Michoacan (Durand et al. 2001).
In 2009, these three states accounted for over a quarter of the total remittance inflows in the
country (Banco de Mexico 2010).
Recent work has examined the economic spillover effects of migration on the labor market
and local economy, and there are reasons to believe that the spillover effects exacerbate these
geographic inequalities. Examining the wage effects of migration, Hanson (2005) provides evi-
dence that high migration states saw a significantly higher increase in wages during the 1990s
due to migration rates. A recent World Bank study (2006) argues that the scope for economic
multiplier effects fueled by remittance inflows will be greatest at the regional rather than the
national level due to the upward pressures on the exchange rate and the likely tightening of mon-
etary policy nationally. Government policies designed to maximize the developmental potential
of remittance inflows exacerbate these inequalities. Guanajuato, Jalisco, Michoacan, and Za-
catecas receive nearly two-thirds of Mexicos federal remittance matching program funds though
they make up only one quarter of the nation in terms of population. Zacatecas alone, one of
Mexicos smaller states, receives a third of government remittance matching funds (Orozco and
Lapointe 2004).
While scholars have argued that the economic effects of migration will differentially affect
high and low migration states, it is not clear what causes a state to have a high emigration
rate. Early economic work on migration (e.g. Lee 1966, Todaro 1976) focused on the role of
wage differentials between sending and receiving regions, and these forces certainly contribute to
Mexican-US migration. However, the poorer states of Mexico, which should stand to benefit the
most from migration based on wage differentials alone, send only a small portion of migrants
to the United States. Dependence on migration is higher in the middle income states, and
particularly those clustered in the Center-West region of Mexico. As shown in Figure 1, the states
with the highest dependence on migration (as measured by the National Population Councils
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Migration Intensity Index) are clustered in the Central Western part of the nation. Examining
Figure 2, which maps migration behavior at the municipio-level (analogous to a county in the
US), it is impossible to make out where the high migration states begin and end. This raisesthe question: is there a political or institutional reason why states like Guanajuato, Jalisco,
and Michoacan send so many migrants to the United States or is it merely that these states
are located in a high migration region in Mexico? Do we see migration behavior segregate
between political jurisdictions or does it gradually diffuse across space along with socioeconomic
or cultural variables?
These are important questions for several reasons. Political jurisdictions such as municipal-
ities, states, and nations are important because they delineate the scope of different policies
and institutions. If there are important policy-related or institutional reasons for increased
or decreased migration rates or remittance flows, we would expect to see discrete breaks in
behavior at the boundaries of the relevant jurisdictions. Furthermore, the distribution of mi-
grant or remittance-receiving households matters for current political decision-making. Migrant
households often have different preferences over tax rates, infrastructure investment, and even
government regulation than non-migrant households (see Levitt 2001; Smith 2006; Suro and Es-
cobar 2006). Do we see Tiebout sorting of migrant and non-migrant households by jurisdiction?
Do certain state- or municipal-level policies significantly incentivize or discourage migration?
In this paper, I examine evidence for the political segregation of migration behavior in Mexico
using data from Mexicos National Population Council. I consider whether there are state
fixed effects have any added explanatory power after controlling for the continuous geographic
progression of socioeconomic, cultural, and social variables across Mexico. I show evidence
for significant spatial externalities between states after controlling for known determinants of
migration. To address this methodological difficulty, I fit a generalized additive model, which
includes a linear component (for the controls and fixed effects) and a smooth component to
capture the effect of continuous, geographic variables. I show that several state effects remain
significant after controlling for this continuous spatial aspect of the data. I then explore some
possible causes and effects of the differences in migration patterns between communities and
between states.
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1 Are there High-Migration States in Mexico?
Several socioeconomic and historical factors help to explain why a small set of Mexican states
account for so much of the total migration from Mexico (see Table 1). The most cited reason
for the high rate of migration from these states is history. Mexican migration is not a new
phenomenon but a process that has continued throughout the history of both nations. There
were massive labor movements from Mexico to the United States during the late nineteenth
century to meet the booming labor demand associated with the expansion of US railroads and
farming. American labor recruiters known as enganchadores would travel south along the
existing Mexican rail system and contract labor along the way. The recruiters would often
bypass the sparsely-populated and more prosperous border areas (where recruitment of migrant
labor was more difficult) in favor of the states of West Central Mexico where there were many
more willing laborers (Massey et al. 2002). Interestingly, this same group of states continues
to supply the largest number of migrants to the US, a fact that has not changed through many
dramatic shifts in politics and policy on either side of the border.
Migration from this region of Mexico has reinforced itself over time for several reasons, many
having to do with the inherent costs and uncertainty of international migration. Traveling acrossthe border requires a large upfront investment in transportation, housing, and communications
and is risky in the absence of professional or personal connections in the destination region.
When migrants travel to the US, most send remittances to family and friends in their home
community, thereby raising household incomes in directly and through multiplier effects on
demand for goods and services. As incomes rise, many individuals who wish to travel to the
US gain the ability to do so. Furthermore, migrants transfer more than just money back home.
Many authors (e.g.Massey and Espinosa 1997; Massey and Zenteno 1999; Palloni 2001; Munshi
2003) illustrate the importance of migrant social networks to finding housing and jobs in and
transportation to the United States. While migration from a community may begin within a
certain social network or socioeconomic group, over time more non-migrants are exposed to
these networks and benefit from the economic spillover effects of remittances, and this increases
access to migration opportunities (Massey et al. 1994; McKenzie and Rapoport 2007). Migration
has become an important and anticipated part of the life process for Mexican youth in many
regions. Kandel and Massey (2002) argue that in many high migration municipalities, a culture
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of migration has developed in which migration aspirations spread through the population and
across generations, which provides yet another mechanism for the path dependence of migration.
These models predict that high migration municipalities will remain dependent on migration andmigration in new sending regions will grow over time. Recent empirical work has supported these
claims (e.g. Durand et al. 2001; Massey et al. 2002; McKenzie and Rapoport 2007).
An additional reason why so many migrants come from West Central Mexico is its middle
level of development. States nearer to the border benefit from higher wages than the rest of
Mexico due to the inflows of foreign direct investment and border traffic from the US, and the
wage income divide has expanded since NAFTA and the Mexican economic reforms of the 1990s
(Hanson 2007). Individuals from these regions, and from other wealthy areas like Mexico City or
Monterrey, have less incentive to migrate to the US for work (though the drug violence in border
communities is changing this dynamic). Conversely, migration from poor, southern states like
Chiapas is constrained by the poverty of these communities and by the higher costs of migration
from these areas.
A reasonable hypothesis is that these historical and socioeconomic factors determine most
of the observed variation in migration from different Mexican states. One can think of factors
like access to nineteenth century Mexican railroads and socioeconomic status as progressing
smoothly across the Mexican space. By this logic, neighboring areas should exhibit similar
levels of migration. This would imply that there is little about the state itself (for example,
policies, state-specific culture, or institutions) that influences migration patterns. If states do
matter, we would expect there to be discrete breaks in migration patterns at political boundaries
in Mexico. The remainder of this section examines the empirical evidence for this assertion.
1.1 Data and Measurement
The main source of data for this paper is the Mexican National Population Councils (CONAPO)
2002 report on household-level migration behavior in Mexico, which matches information from
the Mexican census with data from CONAPOs other household surveys on the level of the mu-
nicipio. In particular, I use three measures of migration dependence as the dependent variable
throughout this paper: CONAPOs Migration Intensity Index1, the percentage of households in
1This is an index constructed from data on the percentage of households in each community in four categories:those that receive remittances, those that had at least one member move to the US during the previous five-year
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a municipality with a member who has moved to the US during the previous five-year period,
and the percentage of households receiving remittances from abroad regularly. A high score on
the Migration Intensity Index indicates greater levels of migration. For the spatial analysis, Imeasure the latitude and longitude of each municipio from the area centroid. For the state-
level regressions, I measure the latitude and longitude of each state capital. I exclude the two
Mexican states, Baja California and Baja California Sur, that are not contiguous with the rest
of Mexico as well as two island municipalities (Cozumel and Isla Mujeres, Quintana Roo) to
avoid fitting spatial model across a body of water.2 As control variables, I use logged population
and state GDP (in the state regressions) and CONAPOs Marginalization Index, which is con-
structed using local measures of poverty, illiteracy, and resource access (higher values indicate
more marginalization). I also include data on the percentage of FDI and maquila income in
state GDP. Table 2 reports the summary statistics for the municipio data.
1.2 State Effects or Geographic Correlation?
As a first attempt at determining whether there are significant state effects on migration
behavior, I ran an OLS regression controlling for a set of socioeconomic variables and includ-
ing state fixed effects. The excluded category is Chihuahua, which has a CONAPO index of
approximately 0 and migration rates that are close to the national average. In the second
model, The results are presented in Table 3. When controlling only for population and the
poverty/marginalization measure, the majority of state effects are significant in the expected
direction given their average migration rates. In the second model, I add the distance from each
municipio centroid to El Paso, Texas, a popular US gateway city for migrants during much of
the 20th century, and this distance squared. This is a common explanatory variable in the litera-
ture, and should capture some of the spatial effects related to the cost of migration. Adding this
distance measure reduces the number of state effects that are estimated as significant by about
one-half. Even after controlling for the distance to El Paso, all of the states with very high
migration according to the CONAPO Index have positive and significant estimated coefficients.
period, those that had a member move to and then return from the US during the previous five year period, andthose that had a member living in the US in 1995 who has since returned permanently to Mexico. The weightingscheme was determined using principal components analysis, and places somewhat more weight on the first twomeasures. See CONAPO 2002
2These observations represent a small part of the Mexican nation.
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One might still be concerned that the fixed effects are capturing something besides the impact
of state policies and institutions. As noted above, the states with the highest dependence on
migration are clustered in a specific region of the country. To explore the correlation betweenneighboring states further, I estimated a spatial lag model of state-level migration in Mexico (as
measured by the CONAPO Index). I use three different spatial weight matrices: a standard,
inverse Euclidean distance weighting3, a normalized (so that the rows sum to one) version
of this matrix, and a matrix that imposed weights of 0 for all pairs of states more than the
average distance apart. The results are presented in Table 4. As expected, the measure of
spatial autocorrelation, , is positive and significant in all specifications. For each weighting
matrix, I estimate the model first using only the spatial lagged term. The second version of the
model includes a series of known correlates of state level migration as controls. Adding these
controls does not change the magnitude or the significance of the estimates of. For the third
version of the model, I include the distance and squared distance to El Paso, Texas as described
above. As Table 4 shows, adding the distance variables causes the other control variables to lose
significance, but remains significant and positive.4
This result is not surprising when one considers how many of the explanatory variables
identified in the literature on migration, such as income levels, cultural preferences, and social
networks are likely to exhibit high geographic correlation. In order to differentiate between
the effect of these geographic variables and the political or institutional state effects, I fit the
municipio-level data using a generalized additive model (GAM), which estimates the coefficients
on the linear variables (the control variables and state effects) while fitting a smooth, two-
dimensional geographic spline over the geographic data using penalized, thin-plate regression
splines.5 This model can be written as E(y|X) = X+ Z+ s(lat, long), where X represents
the state fixed effects, Z represents the set of socioeconomic control variables, and s() is the
smooth spline component. Short of policy interventions or institutional effects, we might expect
important variables like average income, education, or access to transportation to transition
3This distance measure does not consider the curvature of the Earth or the actual travel distance between twopoints. Suggestions are welcome on more suitable spatial weight matrices given my data.
4A likelihood ratio test of the hypothesis that = 0 cannot be rejected at the 10% level when using thestandardized weight matrix with the full set of controls, but can be rejected using a Wald Test.
5For more information about generalized additive models, see Hastie and Tibshirani 1986. The model wasfit using the mgcv package in R. An earlier version of this paper used a methodological procedure developed byMarc Ratkovic (Ratkovic unpublished), which used a model selection algorithm to estimate the state fixed effects.
Those results are available upon request.
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smoothly over space from high migration to low migration areas. This method allows us to
control for the continuous progression of variables like these, highlighting the discontinuous
breaks at state boundaries that could be attributed to policies or institutions within the jurisdic-tion. I estimate the model using three measures of migration dependence: the CONAPO Index,
the percentage of households in the municipality receiving remittances, and the percentage of
households in the municipality with an emigrant member. I fit an unweighted version of each
model and a version weighting municipiosby their population, and I include the same control
variables as in the OLS case. The results are shown in Table 5.
Interestingly, several of the coefficients on the state effects are positive and significant, though
there are generally fewer significant state effects as compared to the OLS case (see Table 3).
These results provide evidence that state policies and politics may have an independent effect
on migration outcomes when controlling for other correlates of geography. However, these esti-
mates differ from the OLS estimates in important ways. Notably, there are significant, positive
coefficients on the fixed effects for several states (Nuevo Leon, Chiapas, Tabasco, and Veracruz)
with low average migration and negative significant estimated coefficients in the OLS model.
Because these states are located in a low migration region of the country, municipios in these
states might be predicted to have even lower migration rates given their location. One can
interpret the negative, significant coefficient on the Guerrero state effect similarly; Guerrero is
a high migration state, but it is located in a region with very high migration.
In the next section, I discuss the possible causes of these discontinuities and interpretations
of these state fixed effects.
2 Why Do Sub-National Political Boundaries Matter?
The results of the GAM model above suggest that there are discontinuities in migration
behavior between Mexican states across space. Interpreting the cause or even the meaning of
these state effects is tricky for several reasons. Unlike studies of the spatial segregation of ethnic
groups where observed spatial discontinuities can be attributed to the sorting or clustering of
ethnicities across political boundaries, spatial discontinuities in migration behavior could be
caused either by the sorting of more migratory individuals between jurisdictions or the effect
of some state policy, institution, or peer effect on migration decisions. An added challenge is that
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the models above are fairly atheoretical. The size or significance of the state effect coefficients
do not have a clear political or economic meaning; they merely suggest that location within
specific political boundaries has some effect beyond the geographic location. As described in theintroduction, membership in a political jurisdiction is meaningful because important decisions
are made at the level of the jurisdiction. For this reason, I first consider the evidence that the
state effects are capturing the results of state-level policies or initiatives that influence migration
outcomes. I also consider the possibility that certain known predictors of migration that cannot
be controlled for directly (notably, social networks) might also break at state boundaries because
common meeting places such as schools, post offices, or community centers tend to be divided
along political lines as well.
2.1 Policy Choices
Simple economic models of migration consider only the wage differentials between regions,
but this cannot explain the diversity of migration behavior in Mexico. The wage gap between
Mexico and the US remains high: over $20 per hour for workers in manufacturing (Bureau
of Labor Statistics 2007). However, Hanson (2006) shows that migration streams have ebbed
and flowed over time in response to policy choices, particularly US policy choices. Do Mexican
politics and policies have any role to play in shaping and mediating these flows?
In popular accounts, Mexican-US migration is portrayed as a large, uncontrollable economic
flow. From the perspective of the Mexican government, the ability to encourage or regulate
migration is constrained not only by the push and pull of labor supply and demand but by
the significant, asymmetric power relationship between Mexico and the United States (Durand
2007). Since the beginning of the twentieth century, Mexico has tried to regulate and moderate
emigration outflows through a variety of mechanisms with varying degrees of success. Fitzgerald
(2006) argues that the failures of Mexican emigration policy can be attributed to internal as
well as external factors. Over the twentieth century, localities with differing views on the value
of emigration passed contradictory laws regulating the issue of passports, the distribution of
bracero permits, and other migration-related bureaucratic procedures. During thebracero era,
the distribution ofbraceropermits became a common form of patronage as corrupt public officials
ignored official quotas and issued permits or arranged illegal migration in exchange for bribes
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(Fitzgerald 2006). It is possible that the presence or absence of state-level corruption influences
current migration patterns through the perpetuation of migration behavior over time. However,
these effects would have been most pronounced at the municipio- rather than at the state-levelbecause this is the level at which permits were issued.
State-level governments have at various points tried to control or encourage migration di-
rectly. During World War II, the governors of the high migration states of Guanajuato, Mi-
choacan, and Jalisco voted to ban the recruitment of workers from their territory due to the
pervasive fear of labor shortages (Fitzgerald 2006). These policies were largely unsuccessful.
More recently, the governments of several west-central Mexican states have employed policies
such as remittance matching programs and special migrant-related government agencies designed
to encourage emigration and the repatriation of US-earned income. The famous Tres por Uno
remittance matching program, which provided public money to match collective remittance do-
nations to public infrastructure, is the most famous of these. Because these matching programs
were initiated after the 2000 census, they cannot explain spatial discontinuities in the data used
in this paper. They do suggest the potential for state-level divergence in remitting income earned
abroad as the programs continue. As sub-national politics become more important, we may see
the state effects increase in magnitude and the dynamic effects of policies on migration.
State level policies on tax rates, public spending, and regulation might influence migration
and remittance behavior indirectly by changing the costs and benefits of migration. One regu-
larity seems to be that high migration states tend to have payroll tax rates of close to 0, but this
is likely to be the result of the high economic dependence on migration rather than the cause.
For most of the twentieth century, policy making in Mexico was extremely vertically integrated
through the hegemonic party system. Even today, states and municipalities have increasing
power to decide local spending decisions, but the vast majority of sub-national government
revenue is channeled through the federal government rather than collected independently. As
argued above, the high migration rates from Central-Western Mexican states predated this lat-
est wave of decentralization. In fact, there is evidence that these states had higher than average
migration rates prior to the establishment of the PRI (then the PNR)in 1929. There is a sur-
prisingly high correlation between the measures of migration dependence used in this paper and
data from a 1925 US Department of Labor Report on migration flows from the Mexican states
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during April 1924 (Foerster 1925). After weighting these data by state populations (estimated
at the post-revolution census of 1921), the correlation between the migration rate for a single
month in 1924 and CONAPOs Migration Intensity Index is over 0.5. Data on border entryand exit during the bracero years (taken from Gonzalez Navarro 1974 and then weighted by
population from the 1960 Mexican Census) is also strongly correlated with the data. The cor-
relation between the 1960 bracero participation rate from each state and the CONAPO Index
is larger than 0.7. The strong continuity in migration patterns over time rather than the effect
of a specific policy seems to better explain the variation in the data.
2.2 Do Social Networks Break at State Boundaries?
As highlighted in this paper, a major reason why migration is a path dependent process
is the role that the friends and relatives effect plays in easing migration constraints through
transfers of income and information (Hatton and Williamson 2005). In fact, Massey and Es-
pinosa (1997) find that the most important factor in predicting future migration from the 25
Mexican communities in their data set was the migration of a relative. McKenzie and Rapoport
(2007) argue that the expansion of these networks enables poorer individuals to access migra-
tion opportunities and eventually leads to decreased income inequality in high migration areas.
These networks can best explain the temporal continuity and spatial clustering of migration from
specific places in Mexico, but can they explain why state effects are significant when controlling
for the continuous component of geography?
One possibility is that the social networks themselves exhibit breaks at political boundaries.
This is possible because many of the places where people meet one another are government-
related, including public schools, community centers, libraries, or post offices. Even private
organizations often organize themselves along political boundaries. Many studies of social net-
works (e.g. Townsend 1994; Ligon, Thomas, and Worrall 2002) implicitly assume that these
networks are delineated by jurisdictional boundaries, though this may be related to the fact
that aggregate data are only available at the level of a political jurisdiction. Future work should
examine the spatial externalities of these migration networks to see whether a smaller density
of social linkages across political boundaries can account for some of the discontinuities in mi-
gration rates at political borders. The extent to which migration from a neighboring state (or
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neighboring community within a state) increases access to migration opportunities is not clear,
but this relationship would have strong implications for the dynamics of inequality in Mexico.
3 Conclusion
The Mexican economy will continue to depend heavily on US migration for the foreseeable
future. Now, as ever, these benefits are highly clustered in space. A small set of Mexican states
receive the bulk of remittance inflows. The evidence in this paper suggests that the diversity
in state-level migration behavior is not merely due to the geographic position of states in poor,
rich, or migratory regions in Mexico. Many state effects remain significant after controlling for
the continuous geographic progression of migration and factors that determine migration. While
there is little evidence linking these effects to a specific state policy, the spatial discontinuities
identified here merit further investigation. Political jurisdictions are the level at which many
important economic and social decisions are made, and the importance of sub-national decision
making in Mexico is increasing. Investigating the causes of state-level migration behavior is
especially important because the relationship between economic policy and migration patterns
is likely to be dynamic: as migrant groups increase in political voice, there will be furtherpressure to adopt migration-friendly policies at the state level. Over the longer term, this may
have profound implications for inter-state inequality in Mexico.
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Rapoport, Hillel and Frederic Docquier 2006. The Economics of Migrants Remittances inThe Handbook of the Economics of Giving, Altruism, and Reciprocity. Ed. Serge-ChristopheKolm and Jean Mercier Ythier. Boston, MA: Elsevier/North Holland
Ratkovic, Marc. Identifying the Effects of Political Boundaries: Simultaneous Variable Selec-tion and Curve Fitting through Mixed-Penalty Regularization. Unpublished
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Townsend, R. M. 1994. Risk and Insurance in Village India. Econometrica62, 539-591
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Work Bank 2006. Global Economic Prospects: Economic Implications of Remittances and Mi-gration. Washington, DC: International Bank for Reconstruction and Development/World Bank
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Table 1: Mexican States by CONAPO Migration Intensity Index Score
State % Remittance HH % Emigrant HH CONAPO Index Migration Intensity Grad
Zacatecas 13.03 12.18 2.58352 Very HighMichoacan 11.37 10.37 2.0595 Very HighGuanajuato 9.20 9.55 1.36569 Very HighNayarit 9.64 6.82 1.27041 Very HighDurango 9.70 7.31 1.09000 Very HighAguascalientes 6.69 6.66 1.03883 HighJalisco 7.70 6.53 0.88785 HighColima 7.34 5.62 0.80260 HighSan Luis Potosi 8.20 7.43 0.67344 HighMorelos 6.44 7.46 0.51921 HighGuerrero 7.86 6.79 0.42772 HighHidalgo 5.06 7.14 0.39700 High
Chihuahua 4.32 3.70 -0.00082 MediumBaja California 4.02 2.38 -0.00104 MediumQueretaro 3.71 4.81 -0.04158 MediumOaxaca 4.13 4.76 -0.26377 MediumSinaloa 4.60 3.58 -0.26620 MediumPuebla 3.28 4.02 -0.42263 MediumTamaulipas 3.64 3.02 -0.42994 MediumCoahuila 3.38 2.23 -0.47955 MediumSonora 3.16 1.59 -0.63929 LowNuevo Leon 2.46 1.91 -0.66630 LowVeracruz 2.74 3.20 -0.70717 LowTlaxcala 2.24 2.70 -0.73806 Low
Mexico 2.11 2.63 -0.74732 LowBaja California Sur 1.08 1.03 -0.86423 LowDF (Mexico City) 1.72 1.60 -1.08207 Very LowYucatan 1.41 1.02 -1.08207 Very LowQuintana Roo 0.99 0.71 -1.14632 Very LowCampeche 1.02 0.88 -1.19328 Very LowChiapas 0.76 0.79 -1.24572 Very LowTabasco 0.64 0.58 -1.27065 Very Low
Table 2: Summary Statistics of Mexican Municipio Data, N=2423
Mean Median Standard Deviation Minimum MaximumMigration Intensity Index .037 -.32 .988 -.879 6.40% of Households with Migrant Member 6.38 3.78 6.85 0 46.67% of Households Receiving Remittances 6.56 3.45 7.72 0 53.71Marginalization Index -.002 -.047 .988 -2.37 3.36
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Table 3: Regression of Migration Intensity Index on State Fixed Effects (Omitted Category:Chihuahua)
(1) (2)Log(pop) -.152*** -.148***
(.016) (.015)Marginalization Index -.084*** -.082***
(.025) (.021)
Marginalization Index2 -.087*** -.090***(.015) (.013)
Distance to El Paso . .049. (.053)
Distance2 . -.004**. (.002)
Intercept 1.80*** 1.65***(.160) (.213)
Significant State Effects:
Ags (.806****) Ags (.861***)Cam ( -.918***) Coah (-.555***)Coah (-.506***) Dgo (.697***)Chs (-.753***) Gto (.959***)Dgo (.749***) Jal (.842***)Gto (.915***) Mich (1.01***)Gro (.257**) Nay (.481*)Jal (.818***) NL (-.515**)
Mex (-595***) Sin (-.404**)Mich (.928***) Son (-.857***)
Nay(.500**) Tams (-.562**)NL (-.482***) Tlax (-.630**)
Oax (-.534***) Zac (1.65***)Pue(-.345***)QRoo(-.803**)
SLP(.323**)Sin (-.345*)
Son(-.835***)Tab (-.841***)
Tams (-.568***)Tlax (-.814***)Ver (-.692***)
Yuc (-.998***)Zac(1.084***)N 2423 2423Significant Fixed Effects 26/29 13/29
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Table4:SpatialAutoregres
sionofState-LevelMigrationIndex
WeightMatrixUsed:
Regular
Regular
Regular
Standardized
St
andardized
Standardized
SmallBandwidth
SmallBandwidth
SmallBandwidth
.331***
.326***
.230***
.822***
.824***
.654**
.291***
.290***
.227***
(.072)
(.068)
(.078)
(.172)
(.171)
(.312)
(.05
6)
(.054)
(.069)
Intercept
-.236***
10.06**
5.6
0
-.071
11.3
4**
3.83
-.272
**
9.65**
6.79
(.144)
(4.8
7)
(5.1
0)
(.151)
(5.4
8)
(5.4
1)
(.13
8)
(4.6
4)
(5.01)
ControlVariables:
log(pop)
.
.942**
.413
.
1.1
7**
.272
.
.797*
.447
.
(.458)
(.497)
.
(.509)
(.527)
.
(.441)
(.484)
log(GDP)
.
-.998**
-.490
.
-1.1
9**
-.336
.
-.895**
-.5
46
.
(.458)
(.491)
.
(.511)
(.521)
.
(.439)
(.479)
MargIndex
.
.423
.672
.
.051
.597
.
.428
.659
.
(.685)
(.655)
.
(.762)
(.698)
.
(.650)
(.636)
MargIndex2
.
-.124
-.129
.
-.103
-.122
.
-.110
-.1
21
.
(.120)
(.112)
.
(.136)
(.120)
.
(.114)
(.109)
FDI/GDP
.
-3.5
4
-8.2
1
.
-11.67
-14.22
.
-1.9
8
-5.4
6
.
(10.83)
(10.40)
.
(12.00)
(10.69)
.
(10.34)
(10.3
1)
ElPaso
.
.
.156
.
.
.248*
.
.
.064
.
.
(.133)
.
.
(.134)
.
.
(.140)
ElPaso2
.
.
-.008
.
.
-.013**
.
.
-.0
05
.
.
(.0
05)
.
.
(.005)
.
.
(.005)
N
32
32
32
32
32
32
32
32
3
2
LikelihoodRatioTest:
Prob(=0)
.000
.000
.006
.008
.009
.122
.00
0
.000
.002
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Table5:
GeneralizedAdditiveModelw
ithGeographicSplinesandSta
teFixedEffects
DependentVariable
Index
Index
%
Remittances
%Remittances
%EmigrantMembers
%EmigrantMembers
ControlVariables:
Log(pop)
-.115***
-.134***
-1.0
2***
-1.0
2***
-.485***
-.694***
(.016)
(.011)
(.120)
(.081)
(.113)
(.075)
MargIndex
-.030
-.050***
-.587***
-.640***
.130
0.02
(.025)
(.018)
(.198)
(.142)
(.185)
(.131)
MargIndex2
-.092***
-.094***
-.794***
-.764***
-.980***
-.882***
(.015)
(.010)
(.116)
(.079)
(.110)
(.073)
Intercept
1.1
4***
1.43***
15.4
4***
16.6
6***
11.6
7***
14.3
1***
(.152)
(.108)
(1.1
7)
(.827)
(1.1
0)
(.765)
SignificantStateEffects:
Chs(.471*)
Ags(.436***)
Coa(-3.80*)
Ags(2.1
4*)
D
F(-4.31**)
Ags(2.3
1**)
DF(-.460*)
DF(-.4
92***)
Chs(5.0
7**)
DF(-2.46***)
D
go(5.7
0**)
Col(2.6
3**)
Dgo(.884***)
Dgo(.574***)
Dgo(4.3
1**)
Dgo(3.3
7***)
Gto(3.6
8***)
DF(-3.65***)
Gto(.392**)
Gto(.290***)
G
to(4.2
9***)
Gto(3.0
5***)
G
ro(-3.43**)
Dgo(4.6
3***)
Gro(-.4
12**)
Jal(.391***)
H
go(4.0
3***)
Jal(1.9
8**)
Hgo(4.0
4***)
Gto(2.4
9***)
Hgo(.419***)
Mex(-.5
94***)
M
ex(-4.46***)
Mex(-3.31***)
Mex(-6.02***)
Gro(-2.15**
Mex(-.8
04***)
Mich(.411***)
Mich(2.1
7*)
Mich(2.9
4***)
M
ich(2.1
8*)
Jal(4.0
2***)
Mor(-.4
89**)
Mor(-.2
79**)
Mor(-3.3
2**
Nay(3.7
7***)
M
or(-2.75*)
Mex(-4.14***)
SLP(.534***)
Nay(.611***)
P
ue(3.19***)
NL(2.2
0*)
SLP(2.8
3**)
Mich(2.9
1***)
Ver(.340**)
NL(.383***)
S
LP(6.4
4***)
Pue(-1.4
2*)
V
er(2.5
6**)
Mor(-1.41*)
Zac(.587***)
Pue(-.3
12***)
Son(-6.14*)
SLP(4.1
8***)
Z
ac(2.9
4**)
Nay(4.8
1***)
Qro(-.2
60**)
Tab(4.2
4*)
Tams(1.91*)
NL(2.9
1***)
SLP(.349***)
V
er(4.7
4***)
Tlax(-4.51***)
Oax(-1.89**)
Tams(.253*)
Zac(6.3
3***)
Pue(-2.5
3***)
Tlax(-.7
35***)
Qro(-1.76**)
Zac(.983***)
SLP(1.8
0**)
Tams(2.12**))
Tlax(-5.08***)
Zac(6.2
6***)
N
2423
2423
2423
2423
2423
2423
Weighting?
no
yes
no
yes
no
yes
SignificantFixedEffects
11/30
16/30
13/30
14/30
11/30
19/30
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Table 6: Correlation Between Past and Present Migration Measures
CONAPO Index % Remittances % Emigrants 1924 Mig. 1960 Mig.CONAPO Index 1.0000% Remittances .9821 1.0000% Emigrants .9673 .9520 1.00001924 Mig. .5444 .5151 .4132 1.00001960 Mig. .7027 .6816 .6218 .8042 1.0000
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Figure 1: CONAPO Map of Migration Intensity by State (CONAPO 2002)
Figure 2: CONAPO Map of Migration Intensity byunicipio
(CONAPO 2002)
i l l i
: i i l l l l i i i .II
30
26
22
18
EstadosUnidosdeAmrica
Golfo deMxico
TrpicodeCncer
Belice
Escala1:15 000 000
Ocano Pacfico Guatemala
102 98 94106110 90 114
30
26
22
18
Go
lfodeC
alifo
rnia
(Mard
eCort
s)
200 0 200 km
: i i l l l l i i i .II
102 98 94106110 90 86114
30
26
22
18
EstadosUnidosdeAmrica
Golfo deMxico
TrpicodeCncer
Belice
Escala1:15 000 000
Ocano Pacfico Guatemala
102 98 94 106110 90114
30
26
22
18
Go
lfod
eC
alifo
rnia
(Mard
eCort
s)
200 0 200 km