Unbelievably preliminary. Cite this and you’re cactus.
Migration, Trade and FDI in Mexico
Patricio Aroca
Universidad Catolica del Norte, Antofagasta, Chile
W.F. Maloney
Office of the Chief Economist for Latin America
The World Bank
April 15, 2002
“Mexico wants to export goods, not people.”
Carlos Salinas de Gortari
I. Introduction
Mexican President Carlos Salinas de Gortari promoted NAFTA partly on the
grounds that it would reduce the incentives for Mexicans to migrate north.
Yet what
limited evidence there is does not strongly support this claim. As Razin and Sadka
(1997) note, theoretically trade and to a lesser degree capital flows, and migration can be
complements.
Markusen and Zahniser (1999), drawing on models by Feenstra and
Hanson (1995) and Markusen and Venables (1995) study the effects of NAFTA on the
convergence in the wages of unskilled workers between the two countries and they echo
the widely held view that NAFTA will do little to achieve wage convergence and hence
deter migration.
This paper attempts to measure the direct impact of foreign direct investment and
trade on migration. Ideally, we would answer the question using actual data on MexicanUS migration, but the illicit nature of these flows and generally poor quality of the data
makes such a direct approach difficult. The paper instead asks whether these variables
have had any impact on flows within Mexico where the census data permit careful
analysis. We find that they do and that both FDI and exports are substitutes for labor
flows- they deter migration. We then generate some tentative inferences about the impact
on Mexico-US migration and find it to be of important magnitude.
In the process, we also generate the first estimates of determinants of migration
flows within Mexico. In line with recent advances in the industrialized country literature,
we generate proxies for both the level of amenities and costs of living and find their
influence statistically significant. Contrary to much of the literature, we also find very
significant and very intuitively plausible signs on labor market variables. The signs on
all these variables confirm the often postulated liquidity effect- it takes resources to
move.
1
II. Methodology
We assume that the potential migrant faces j possible destinations where i is the
region of origin and k is the migration region chosen. The worker internal migration
decision is reflected by the sign of the index function
I * = Vk − Vi − C
where V can be interpreted as a indirect utility function in the context of random utility
theory (Domicech and McFadden, 1975 and Train, 1986), and C is a measure of costs.
We assume that utility is a function of a linear combination of location characteristics X
Vj = Xj β + εj
If the destination region is more desirable, measured along several dimensions, and if the
migrant has sufficient resources to move, then we should observe migration.
The
probability that the indicator will be larger than zero is equal to the probability that the
difference between V’s is greater than transport costs:
P ( I * > 0) = P(Vk − Vi − C > 0) = P (ε i − ε k ≤ X k β − X i β − C )
This specification nests many standard estimated functions (See Greenwood) including
Borjas’ (2001) where the only argument in the utility function is the wage. 1 The actual
specification depends on the assumptions about the error term.
The βs may be allowed to vary and in fact the literature tends to find a greater
role for destination variable than for origin variables.
This may be because of
asymmetric information about locales (Gabriel et.al 1993), or that the individual variables
are correlated with omitted variables that may have a greater impact on one end of the
migration move.
As an example, many variables could be correlated with unmeasured
1
*
Borjas argues that I = max{w j } − wi − C where I* is an indicator variable, w is the wage y C the
j
costs of transportation to the new locale. This function must satisfy wk = max{w j } , where j represents
j
all possible destinations, i the region of origin and k the region chosen.
2
wealth or liquidity that would determine whether the worker has the savings to pay the
fixed cost C of moving (See Vanderkamp 1972).
The matrix X contains the variables capturing the relative expected incomes (Y)
in the two areas(wages, unemployment, price indices), and the set of characteristics of the
region (amenities) that may also affect the migration decision. It is through Y that we
might expect the impact of FDI and trade. As the survey by Razin and Sadka (1997)
argues, migration and trade, and to a lesser degree FDI may be substitutes or
complements and hence there is no guarantee that NAFTA would necessarily, on purely
theoretical grounds, lead to lower migration.
Since we work with aggregate data, we follow Berkson’s (1944) setup as
elaborated by Ben-Akiva and Lerman (1985) and generalized in Gourieroux (2000). Here
F −1 ( P( I * > 0)) = X k β d − X i β o − C
where F is the probability function that is determined by the structure of the errors.
III. Data
Migration data: The 2000 census tabulates the question “In which state did you reside
five years ago?” (¿En qué estado vivía usted hace 5 años?) and then this response is
compared with the present state. As with most data of this type, this has the drawback of
obscuring migrants who may have left and returned in the five year period. Graph 1
shows the rates of Net Migration by states. The variable is calculated as net flows as a
fraction of the population in the initial period.
Moving Costs: Following the literature (again, see Greenwood), we approximate the
costs of transportation as a quadratic function of distance.2 This is a proxy for the costs
of migration that consist of the moving costs themselves, the opportunity costs of moving
Though we assume that the indirect utility function is linear and the weight of each variable is
similar in each region, this assumption can be easy relaxed to differentiate the origin (o) and
destination (d) parameters.
2
3
which rise with the length of the journey, and rising communication costs with the family
in the point of origin, including the increased costs of return visits. In general, the
literature expects a negative impact on migration but with decreasing effect.
Population: is the population by state in 1995 as reported by the Census. As Greenwood
(1997) summarizes, population is often used as a measure of the availability of public
goods. However, it is also true that larger states offer more “connection points” than
small states and will hence, in a random reallocation, attract more migrants. Shultz
(1982) is also correct in arguing that larger states may have smaller rates of outmigration
simply by virtue of having more places to migrate to within. The 1995 value is used to
eliminate any problems of simultaneity with migration flows that happen in subsequent
years.
Labor Market Variables: Expected Earnings. These correspond to the average of the rate
of unemployment and wages in 1995, 1996 and 1997, the three years that are most likely
to influence the decision to migrate.
Cost of Living: To some degree, the potential migrant should be deflating expected
wages. However, the effect may not be so straight forward. For instance, if a migrant
plans to return to a low cost area to retire, he may generate real savings measured in his
retirement destination faster by earning a lower real wage in a high cost area (See R.E.B.
Lucas for a survey of this literature 1997). Further, high cost of living may point to a
larger potential income over the long run even if not experienced (Spencer 1989, Pagano
1990). Certainly, in an intertemporal or intergenerational context, taking a lower real
wage in the US is still likely to offer the migrants descendents far better options than
would have been the case in Mexico.
The literature provides mixed evidence. Cameron and Muellbauer (1997) studying
UK migration find strong “deflator” effects that they argue dominate any expectation
effects. On the other hand, Thomas (1993), also looking at the UK, finds no impact of
regional house price difference on destination choice of any group except retirees.
4
Two indices were constructed and the methodologies are detailed in annex I. The
first is a hedonically estimated housing price that is the analogue to those used elsewhere
in the industrial country literature. Since food is likely comprise a larger share of the
consumption basket in LDCs, we also generate the cost of a basic food basket by state.
These were included both separately and as an average measure of the cost of living.
Amenities: Price indices, however, may also simply reflect amenities available in the
new area, implying a positive relation with migration decisions. Further, as Roback
(1982) showed, they affect equilibrium wages as well and hence should be included as
part of the net utility change of moving from one region to another. To attempt to control
for this, we extract the principal component of a set of variables that include health,
education, and infrastructure services (see Annex II).
Trade and Investment Variables
Foreign Direct Investment: This is aggregate per capita foreign direct investment from
1995-1999 reported to the government by the firms investing and was provided by the
Central Bank of Mexico.
The Federal District (DF- Mexico City) shows vastly higher
rates because much of the FDI destined for other states is registered at the headquarters of
the firm in the DF. We include a dummy variable in the regressions to account for this
measurement error. Graph 2 shows the incidence of FDI per capita by state. As is not
surprising, FDI is highly, although not exclusively concentrated along the northern border
with the US.
Maquila Value Added: This variable may be seen purely as a proxy for FDI, but since the
maquilas are primarily exporters, it can also be seen as a proxy for “maquila exports per
capita” which brings it closer to being a trade variable. Since it is collected from
industrial surveys, it does not suffer from the “headquarters” effect of the FDI variable.
The two variables are moderately correlated (.64) and hence are it may be a better for
FDI more generally. On the other hand, the government of Mexico tabulates 12 of the
5
states in an “other” other category and hence we lose substantial information. We run the
regressions with the substantially reduced sample.
Exports: This variable is provided by the Ministry of Finance (Hacienda) and is alleged
to capture exports per state. However, it may be that this represents interpolations by the
government based on employment in industry as measured by the IMSS (Pending
verification).
Imports: Provided by Bancomext.
effects.
This variable could have multiple and conflicting
It could simply reflect the degree of integration of a state with external
economies and hence proxy as well for exports. On the other hand, if it is seen as
representing competition for import substituting firms, the short run labor market impact
could be negative and hence it could conceivably lead to more migration.
None of the variables are ideal, but they are complementary in the sense of being strong
whether the other is weak. Together, we may get some reliable picture of the impact of
trade/investment variables.
IV. Results
In preliminary regressions, we estimated a multinomial logit model for aggregate
data. Though the results were consistent with the theory, the Fry and Harris test (1998)
suggest that the data violated the Independence of Irrelevant Alternative (IIA). Therefore,
we estimate a multinomial probit and follow Gourieroux’s (2000) weighted least square
procedure.3
3
Given that the restriction on the pseudo indirect utility function that imposes the condition that all
individuals in every state have an identical distribution of conditional probabilities may be strong, we
follow the route suggested by Pudney (1989) that includes an alternative-specific additive constant in an
otherwise invariant utility function. This constant can be interpreted as fixed effects associated with the
average individual in each state and therefore measures the unobservable individual characteristics that
individuals in each state use in making their decisions.
As in Davies et al. (2001) estimating model problematic, most likely due to the large number of parameters
and the likely correlation between origin characteristics and state fixed effects. 3 Where Davies et al
tackled the problem by respecifying arbitrarily the variable associated between state, joining the states that
6
Column1 and la present the standard regression including the labor market
variables. Overall, the specifications are very satisfactory with the coefficients on the
core variables are all statistically different from zero and of predicted sign. [The distance
measure is at the low end, but firmly within the usual range of -.02 to -.2 (Greenwood,
see also Gabriel et al 1996, Fields 1982, and Schultz, 1982) and the population variables
are also consistent with usual results.
Preliminary regressions found both the origin unemployment and wage level to be
insignificant as has been found frequently in the literature (Greenwood, Lucas 1997).
However, we then attempted to isolate two countervailing effects, one a substitution
effect among regions, and the other a wealth or liquidity effect that allows the worker to
cover the fixed cost of moving. The latter effect has been found for unemployment by
Goss and Schoening (1984) and Herzog et al. (1993) for the US who find that the
probability of moving decreases with the duration of unemployment, and the literature on
the wage effect is also extensive (See Stark and Taylor 1991 for a discussion of credit
constraints).
We generate relative wage (ln wj-ln wi) and relative unemployment
(Uj/Ui) variables, and then allowing free standing initial wage and unemployment
variables to capture credit constraint effects. In the complete sample, all but the free
standing unemployment term enter significantly although it is of predicted sign and
significant at the 10% level in the restricted regression. This overall strong performance
suggests that the poor results of origin labor market variables in many previous studies
arises precisely because they capture two contradictory tendencies.
The cost of living variable enters very significantly and of important magnitude in
the sector of origin. The strong positive coefficient on the destination is unexpected,
share similar unobservable characteristics, we follow a different approach. We proceed to estimate
stepwise based on the initial parameters of the model, and choosing the dummy variables according to their
contribution to the model.
7
although perhaps consistent with the view of cost of living being a measure of
expectations for future income growth as discussed earlier.
The amenities variable also enters with predicted signs, but again in the
relative/free standing format that suggests that amenities may be correlated with an
omitted credit constraint variable. It is not significant in the restricted sample although
the signs are as predicted.
Integration Variables
Columns 2a,3a,4a,5a drop the labor force variables and replaces them with the the
trade and investment variables in Z.
In all cases, we find evidence of a strong
substitution effect: FDI and trade reduce migration. In most cases, simply entering the
initial and final terms yielded symmetrical results. However, a free standing term was
significant or borderline in half the cases suggesting again, a liquidity/wealth effect so the
results are again reported in the constrained/free standing form. All other coefficients
remain relatively unchanged with the exception of the destination price term which
appears to almost double.
Columns 2b, 3b, 4b, and 5b add the labor force variables and confirms that some,
but not all of the effect of Z works through the labor market at the same time.
The
substitution effect diminishes by at least a factor of 2 in more cases and in the case of FDI
becomes insignificant.
The free standing terms also show
propensity to flip sign
suggesting that initial FDI was capturing the initial wealth/liquidity now capture by initial
wages and unemployment and that, minus this, local Z has a more powerful deterent
effect than destination an attractive effect. The fact that all Z retain an effect outside of
the contemporaneous labor market variables may reflect that there is an independent
disincentive effect to migrate, perhaps through expectations of future growth.
8
In short, Salinas was correct to suppose that NAFTA, through various channels,
would lead to reduced migration, at least within Mexico. The next section attempts to
quantify how large these effects might be in the context of US/Mexican migration.
V. Simulations
With the estimate above, we attempt to make some inferences about the impact of
foreign investment or Trade on migration to the US. The key assumption is that we can
treat the US as a “33rd Mexican State.” It is probably not too far out of sample to make
predictions. As table 3 shows, ratio of the per capita income of Mexico’s richest state,
the D.F. relative to its poorest is about 6.4 in nominal or 5.6 in real terms while the ratio
of the average for the US relative to the D.F. is only about 1.9 or 2.3 PPP adjusted. That
is, in terms of development the US is closer to Mexico City than Mexico City is to
Chiapas. Nor are wage differentials radically different. The ratio of the Hispanic real
wage in the US to the mean wage of the DF adjusted for PPP is roughly the same as the
ratio of the real DF wage to that in Chiapas.
There may be some concern about the importance of the border representing
fundamentally different “transport costs.” In fact, the evidence is strong that this is more
a difference in magnitude than kind. Donato, Durand and Massey (1992) argue that “our
data from Mexico reveal a fairly high probability of apprehension by INS combined with
a near-certain probability of ultimately entering the United States.”(p 152) and that
“every migrant who attempted a border crossing, whether apprehended or not, eventually
gained entry” p 155 italics theirs. This suggests that border control serves more as a tariff
than a quota.
The costs of movement are substantially different, but perhaps not so much as we
might think at first look. The present (2002) cost of a second class bus from Quintana
Roo to Coahuila, one of longer trips in our sample, was roughly $US100 compared to
9
very little between Mexico State and DF.4 Anecdotal evidence about the cost of direct
transport across the border in the 1980s was $150 (244 $US 2002 CHECK; Conover
1987 cited in Hanson and Spilimbergo 1999), again, not so far out of sample.5 That is
the Mex/US cost is roughly 2.5 time the ratio Max/Min within Mexico- not too far out of
sample. The cost of a Coyote or smuggler/guide appears to have held steady in real terms
since the 1960s at around $350 ( $2075 $US2002; Donato, Durand and Massey 1992) 6
although Crane et al (1990 cited in Hanson and Spilimbergo 1999) suggest that in 1993
only 8.3 percent of those apprehended by the INS had employed one. Clearly, the
premium for risk involved in crossing the border and for the multiple trips will widen the
gap substantially. Even if these gaps are wide, eq (1) suggests that the coefficients on the
components of the indirect utility functions should be separable from those of the cost of
travel. That is, the travel cost elasticity may not be suitably estimated for forecasting the
impact of a change in Mex/US travel costs, but the response to wages, FDI or exports
should not be affected.
Under these assumptions, we can think of the estimates above in two ways. First,
as corresponding to those of the aggregate average Mexican state vis a vis any other state
including the US. The implicit elasticities therefore capture the reduction in push to the
US and the increased attractiveness from the US of and increase in trade or investment.
Alternatively, and with the potential for greater richness, we assume that total
emigration from any state is the sum of migration to other states plus the US:
32
M i = ∑ mij + miUS for all i = 1,..., 32
j =1
32
M i = ∑ Popi * pij + miUS
j =1
White Star Bus line
Inflated from 1985 figure with CPI.
6
This showed little change with the Immigration Reform and Control Act of 1986 suggesting that it is a
fairly robust number. Figure was reached by inflating the 1960 figure by the CPI. Coyotes only get paid for
successful crossings.
4
5
10
32
and that further total emigration is fixed: M = ∑ M i . This implies that we are only
i =1
concerned about the redistribution of emigrants between the Mexican states and the US in
response to a change in the X variables.
This is consistent with models which postulate
a two step decision on the part of migrants- first to migrate, and then which destination to
choose. It is also only problematic to the degree that we believe that the impact on total
flows are not second order and/or that they are radically different in distribution among
“states” from the substitution effects. We can write
32
32
32
32




M = ∑  miUS + Popi ∑ pij  = InM US + ∑  Popi ∑ pij 
i =1 
j =1
i =1 
j =1


where InMUS is the total migration from México to the US.
From above, we know that the probability of migrating from state i to state j
depends on the characteristics in X, pij = Φ ( X β ) and hence that Φ −1 ( pˆ ij ) = zij = Xb . The
percentage change in immigration to the US we can obtain as:
∂ ln InM US
1
=−
∂ ln X k
InM US
32
∂Φ ( Xb)
para k = {i,j}
k =1 ∂ ln X k
32
∑ Popi ∑
i =1
Tables 4a and 4b present simulations of the impact of a 10% and 100 million
dollar increase in a given state on migration. The former is perhaps more interesting as
an elasticity while the other can answer “if we were to locate a plant in Mexico, what
state would show the greatest fall in migration.” The first column presents reduced
migration, the second increased return migration and column 4 presents the total effect.
Column 3 shows whether the effect occurred largely to retention of workers, or to
attraction.
11
States are ranked by order of effect. It is not surprising that DF would have the
largest impact in percentage terms since it has by far the largest FDI and Durango the
least given it has virtually none. However, locating a plant in Baja California Sur, will
have the largest effects with the DF ranked only in 9. The top five seem to gain their
power primarily through raising return migration from the US. Those with the greatest
potential to retain future migrants are generally further south the DF, Sn Luis Potosi and
Yucatan heading the list.
Somewhat depressingly, the poorest states of the South,
Chiapas, Gerrero, Oaxaca where we might like for reasons of poverty reduction to
encourage investment are not the ones that will most reduce migration. In fact, in the top
10, only Tlaxcala, Morelos and Hidalgo have below average per capita incomes.
Since the simulations depend on the magnitude of migration to the US which is
not well known, Figures XX present the range of values of percent reduction for a range
of values. The calculated elasticity of reduction in response to a 10% rise in investment
averages at the most likely level roughly 1.5% and ranges from 6% for the DF to around
.5 for Campeche. The impact of a $US 100 million project is highest in Baja California
Sur, reducing net migration by 3%, and lowest in Chiapas, with again, roughly .5%. The
average, at around 1.25% is implicitly above that of the previous exercise since the mean
level of FDI is roughly double that used in the simulation. Hence, we might expect
around a 2.5% reduction in the case of a 10% increase in this simulation.
VI. Conclusions
The paper first presents nice specification where, contrary to the literature, labor
market variables enter as predicted.
We argue that part of the traditional poor
performance of the origin state variables is that they capture a mix of deterrent effects,
and credit constraints. Higher wages reduce the incentive to leave, but make it more
possible at the same time. Structuring the specification to capture these too effects yields
well behaved coefficients.
12
Second, it created variables for costs of living and amenities that have generally
the predicted effects.
Third, it established that FDI, maquila value added, exports, and imports all have
the effect of reducing outmigration/encouraging inmigration. This suggests that Salinas,
in principal, was correct that increased trade and investment flows would reduce labor
flows to the US.
Fourth, it simulated the magnitude of the response to an increase in FDI flows
(will later do exports etc.). We find the impacts to be non-negligible, although not
immense (must check). Further, the impact varies greatly by the state where the
investment is located. Unfortunately, it is not always the case that investment in the
poorest states will yield the greatest fall in migration to the US.
13
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16
Annex I. Amenities Indexes
A measure of amenities across Mexican states was created by the National
Institute of Economic and Geographical Information using the 2000 General Census of
Population and Housing. However, it includes measures of labor market tightness as
well as migration variables(percentage of resident population who were born in other
states and percentage residing in other states) whose influence we are trying to separate
out.
We create a new index extracting principal component from : the percentage of
the population living in urban areas, mortality rates, health and education infrastructure,
and percentage of houses with electricity and without water’s drainage. The analysis
derived four significant factor loadings presented in table I.1. We select the first one, with
the strongest interpretation of amenities. There urbanization, houses characteristics,
houses characteristics and mortality rates are correlated in the expected way, that is, a
higher value of the factor is more desireable. The correlation with wages for this measure
is 0.37, while the correlation with the INEGI index is 0.77. Figure # plots both indexes.
In both cases Federal District has the highest values. However, although Mexico State
has the second value for INEGI, for our index is under the mean.
Table I.1 Factor Loadings
Variable |
1
2
3
4
Uniqueness
----------------+-----------------------------------------------------% Urbanization |
0.79310
0.41846
-0.15671
-0.03589
0.17004
Mortality 1
|
0.07249
0.86585
0.19479
0.14717
0.18544
Mortality 2
| -0.00736
0.92668
0.15626
-0.02567
0.11613
Mortality 3
| -0.74188
0.32925
0.17973
-0.39571
0.15232
Mortality 4
| -0.34750
0.25433
0.49986
-0.60661
0.19673
Mortality 5
| -0.35733
0.13565
0.40111
0.69386
0.21158
No.Hosp.per cap.|
0.49021
-0.35179
0.49150
-0.03319
0.39326
Beds in Hosp.
|
0.86768
-0.02527
0.42278
-0.09298
0.05910
Drs. per cap.
|
0.81367
-0.09606
0.51915
0.06900
0.05445
Nurses per cap. |
0.86742
-0.04268
0.41601
-0.01092
0.07258
Prim.Sch.(1)
|
0.00871
0.84803
-0.05665
0.12899
0.26092
High Sch.(1)
| -0.72544
0.31273
0.29428
0.10275
0.27878
No Drainage
| -0.76241
-0.28955
0.46076
0.12313
0.10743
Electricity
|
0.73367
0.41105
-0.35998
-0.02897
0.16235
(1) Students per teachers.
17
Created Factor
6
4
2
0
1
2
3
4
5
INEGI Welfare Index
6
7
Amenities measures
18
Annex II. Cost of living across states (Pato, more detail please)
The existing price indexes for Mexican states do not allow to make comparisons
of cost of living across states. For this reason we create a price index using the Expenses
Section of the ENIGHs 1992/8 (Encuesta Nacional de Ingresos y Gastos de los Hogares).
The disposable information given by the survey allows us to create two indexes: Food
and Housing7.
Price Indexes (relative to average of states)
1.8
1.6
1.4
1.2
1
Housing Price Index
Food Price Index
0.8
0.6
0.4
0.2
Yucatan
Zacatecas
Tlaxcala
Veracruz
Tamaulipas
Sonora
Tabasco
Sinaloa
Quintana Roo
San Luis Potosi
Puebla
Queretario
Oaxaca
Nayarit
Nuevo Leon
Morelos
Michoacan
Jalisco
Mexico
Hidalgo
Guerrero
Durango
Guanajuato
Chihuahua
Distrito Federal
Colima
Chiapas
Coahuila
Campeche
Baja California Sur
Aguascalientes
Baja California Norte
0
The first one is a Laspeyres Price Index where the price and basket (200 items) of
reference is the national average in 1992. The Housing Price Index was created using
Hedonic Prices for rented houses only. The houses’ characteristics include : community’s
size; number of rooms; kitchen; bathroom; electricity; telephone; water drainage and
potable water; and type of walls, floors and ceilings. Both indexes are plotted in Figure
##.
The ENIGH gives information on expenses for all items, but only unit price and quantity
consumed for food items. Nevertheless the survey also includes House Characteristics which
allow us to estimate also Housing.
7
19
Figure 1:
Net Migration 2000
N
W
E
S
Percentage
-5.69 - -2.72
-2.72 - -0.58
-0.58 - 1.87
1.87 - 5.12
5.12 - 11.37
600
0
600
1200 Miles
20
Figure 2
Per Capita FDI
(From 1994 to 2000)
Millions of Dollars
1 - 122
122 - 452
452 - 972
972 - 1812
1812 - 4315
N
W
E
S
600
0
600
1200 Miles
Graph 2
21
Percent Reduction in Im igration to USA if FDI is increased by 10% in a Mx State
16.00
14.00
12.00
Most Likely Sit uat ion
10.00
DF
CAMP
8.00
MEAN
6.00
4.00
2.00
0.00
A l t e r na t i v e s C u r r e n t I n m i gr a t i o n t o U S A
Percent Reduction in Inm igration to USA if US$ 100 Million Are Invested in a Mx
State
8.00
7.00
6.00
Most Likely Sit uat ion
5.00
BCS
4.00
CHIS
MEAN
3.00
2.00
1.00
0.00
A l t e r na t i v e s C u r r e n t I n m i gr a t i o n t o U S A
22
Table 1: Summary Statistics of Data
Statistics
Obs.
Emigrants from i to j
Population
Distance
Distance sq.
Prices
log Prices
Unemployment
Nominal wages
log Nominal wages
Amenities
FDI
log FDI
Exports
log Exports
Imports
log Imports
Maquila
log Maquila
•
Mean
992
992
992
992
992
992
992
992
992
992
992
992
992
992
992
992
306
306
St. Dev.
Min.
Max.
3,890
17,208
16
470,693
2,848,697
2,440,552
375,494
11,170,796
19.27
12.42
1
64.88
525.98
660.95
1.00
4209.00
100.00
16.55
66.99
138.15
4.59
0.16
4.20
4.93
2.99
1.04
1.46
6.64
1787
345
1208
3036
7.47
0.18
7.07
8.01
2.11
1.00
0.10
4.72
268.00
386.00
0.04
1617.00
4.35
2.15
-3.11
7.38
1452
2130
19.82
8578
6.14
1.69
2.98
9.05
844
1219
5.52
4176
5.51
1.84
1.7084
8.33
97,681
129,480
1,029
453,211
10.57
1.51
6.93
13.02
”.”
23
DF and Mexico State
(log Zi)
log of FDI i / Maquila i
/Exports i / Imports i
(log Zj-log Zi)
Relative
FDI/Maquila/Exports/Imports
Investment and trade
Log nominal wage i
Relative nominal wage
Unemployment i
Relative unemp.
Labor market
Amenities i
Relative Amenities
Log prices j
Log prices i
Population j
Population i
Distance sq.
Distance
-0.3449
-(4.48)
-(12.64)
(4.98)
(6.)
-0.4771
1.2064
0.6638
(7.45)
(11.73)
-(1.89)
1.0961
0.8140
-(1.19)
-(2.21)
-0.0936
-(3.64)
-0.0193
-0.1771
(.86)
(9.24)
-0.1084
0.1458
(1.2)
(3.92)
0.1001
0.2274
0.0086
-(1.09)
(5.37)
(3.82)
-0.0371
0.4376
(4.09)
(11.02)
0.0951
0.3916
(22.97)
(.78)
0.0874
(5.15)
0.0862
0.0072
(7.28)
0.0221
0.0010
(10.86)
-(11.21)
0.0006
-0.0669
-(17.72)
Reduced
sample
1b
-0.0464
1a
Basic
Table 2: Determinants of Migration
-(12.32)
-0.5002
(1.66)
0.0110
(8.01)
0.0358
(11.54)
0.1292
(2.69)
0.0062
(9.83)
0.7609
(4.11)
0.3740
(20.35)
0.0805
(5.67)
0.0252
(10.84)
0.0006
-(16.13)
-0.0435
2a
FDI
-(10.03)
-0.4044
-(3.6)
-0.0276
(.81)
0.0042
(6.88)
0.8698
(9.41)
0.7769
-(2.49)
-0.0408
-(4.45)
-0.1313
(10.7)
0.1188
(2.25)
0.0050
(5.13)
0.4114
(4.86)
0.4622
(21.12)
0.0804
(4.15)
0.0179
(11.74)
0.0007
-(18.64)
-0.0493
2b
(log formulation)
-(5.57)
-0.4197
(1.54)
0.0307
(5.69)
0.0762
(4.57)
0.1081
(.23)
0.0074
(6.88)
0.8827
(3.2)
0.4498
(9.85)
0.0767
-(.25)
-0.0021
(8.22)
-0.0011
-(11.61)
-0.0696
-(4.97)
-0.3702
-(1.9)
-0.0436
(1.79)
0.0280
(5.66)
1.6162
(5.17)
0.9037
-(2.)
-0.0956
-(2.16)
-0.1656
(3.24)
0.0783
-(.53)
-0.0177
(.73)
0.1242
-(.42)
-0.0822
(11.92)
0.0909
(1.13)
0.0101
(7.77)
0.0010
-(11.72)
-0.0672
Maquila
(reduced sample)
3a
3b
-(12.75)
-0.4991
(3.9)
0.0361
(8.76)
0.0536
(10.01)
0.1019
(3.21)
0.0077
(13.72)
0.9768
(2.56)
0.2188
(20.61)
0.0806
(6.33)
0.0272
(9.88)
0.0006
-(15.7)
-0.0422
4a
-(12.34)
-0.4664
(.37)
0.0036
(3.49)
0.0231
(5.17)
0.6261
(8.9)
0.6946
-(1.29)
-0.0208
-(3.94)
-0.1165
(8.98)
0.0967
(2.57)
0.0060
(5.91)
0.4892
(3.98)
0.3840
(22.24)
0.0838
(5.34)
0.0227
(10.68)
0.0006
-(17.7)
-0.0461
4b
Exports
-(13.96)
-0.5265
(.54)
0.0052
(10.34)
0.0632
(12.79)
0.1490
(2.66)
0.0059
(11.5)
0.8181
(3.38)
0.2808
(20.46)
0.0781
(7.97)
0.0338
(10.55)
0.0006
-(16.3)
-0.0425
5a
-(13.45)
-0.4926
-(4.3)
-0.0502
(3.12)
0.0235
(7.27)
1.0015
(7.94)
0.6905
-(.2)
-0.0032
-(3.79)
-0.1097
(11.82)
0.1334
(1.68)
0.0037
(5.35)
0.4313
(2.17)
0.2110
(22.35)
0.0845
(7.98)
0.0359
(11.19)
0.0006
-(18.26)
-0.0465
5b
Imports
24
Obs.
Note: All Zi expressed per capita
Constant
306
-(8.62)
-(14.57)
992
-13.0708
-11.5669
992
-(15.42)
-8.3110
992
-(14.61)
-13.0936
306
-(10.53)
-9.1115
306
-(9.24)
-14.2026
992
-(16.37)
-8.7184
992
-(14.25)
-11.4840
992
-(15.96)
-8.1921
992
-(15.08)
-13.0955
25
PPP Adjusted
by State CPI
5,393
8,349
2,368
3,617
15,226
20,268
6.43
5.60
1.93
1.45
Current $US
PPP Adjusted
by State CPI
372
575
218
375
551
825
2.53
2.20
3.11
2.08
Mexico (2)
Current $US
1,716
Current $US
U.S. (4)
29,451
20,856
40,870
1.96
Current $US
Notes (1) INEGI; (2) Author's estimation for male urban workers using ENEU (3)
Bureau of Economic Analysis (http://www.bea.doc.gov) ; (4) Bureau of Labor Statistics
(http://www.bls.gov/cps/cpsaat37.pdf) median earnings of Hispanic origin workers.
Average
Min.
Max.
Max./Min.
Average US / Max. Mexico
Monthly wage
Average
Min.
Max.
Max./Min.
Average US / Max. Mexico
GDP per capita
Table 3
Monthly wages and GDP per capita, Mexico and United States
Mexico (1)
U.S. (3)
26
27
Code
DF
MEX
VER
JAL
GTO
PUE
MICH
MOR
NL
OAX
GRO
HGO
TAMPS
QRO
COAH
BC
SIN
CHIS
TAB
SON
SLP
AGS
BCS
CHIH
TLAX
QROO
COL
ZAC
YUC
NAY
CAMP
DGO
Mexican States
09 DF
15 MÉXICO
30 VERACRUZ
14 JALISCO
11 GUANAJUATO
21 PUEBLA
16 MICHOACÁN
17 MORELOS
19 NUEVO LEÓN
20 OAXACA
12 GUERRERO
13 HIDALGO
28 TAMAULIPAS
22 QUERÉTARO
05 COAHUILA
02 BCN
25 SINALOA
07 CHIAPAS
27 TABASCO
26 SONORA
24 SAN LUIS POTOSÍ
01 AGUASCALIENTES
03 BCS
08 CHIHUAHUA
29 TLAXCALA
23 QUINTANA ROO
06 COLIMA
32 ZACATECAS
31 YUCATÁN
18 NAYARIT
04 CAMPECHE
10 DURANGO
Reduce
Increase
Emigration Inmigration
from (RE)
to (IM)
-12373
2346
-5610
5114
-4621
921
-3662
1868
-3197
1263
-2437
1286
-2765
925
-1460
2113
-1719
989
-2018
587
-1731
808
-1622
841
-1380
1079
-972
1374
-1357
914
-1079
1192
-1415
702
-1659
332
-1052
763
-1034
641
-1049
516
-697
800
-306
1158
-857
604
-684
680
-502
838
-509
779
-782
420
-821
307
-631
479
-582
494
-729
249
Table 4a: Impact of a 10% rise in FDI on Migration
RE + IM
-10028
-495
-3700
-1794
-1934
-1151
-1841
653
-730
-1431
-923
-781
-301
402
-443
113
-713
-1327
-289
-393
-533
104
852
-253
-4
336
270
-362
-513
-152
-88
-480
Total
Effect
IM - RE
14719
10724
5542
5529
4461
3722
3690
3572
2708
2604
2539
2463
2459
2346
2272
2271
2117
1991
1815
1675
1566
1497
1463
1461
1364
1340
1288
1202
1128
1109
1075
978
28
Code
BCS
MOR
COL
QRO
QROO
TLAX
AGS
DF
CAMP
HGO
NAY
TAB
ZAC
BC
GRO
GTO
MICH
PUE
SLP
TAMPS
OAX
COAH
MEX
JAL
SIN
DGO
VER
SON
YUC
NL
CHIS
CHIH
Reduce
Increase
Emigration Immigration
Mexican States
from (RE)
to (IM) RE
03 BCS
-1036
5778
17 MORELOS
-1839
3505
06 COLIMA
-1620
3410
22 QUERÉTARO
-1699
3059
23 QUINTANA ROO
-1170
2863
29 TLAXCALA
-1951
1940
01 AGUASCALIENTES
-1701
2000
09 DF
-2310
1380
04 CAMPECHE
-1290
2140
13 HIDALGO
-1735
1226
18 NAYARIT
-1350
1495
27 TABASCO
-1131
1422
32 ZACATECAS
-1430
1000
02 BCN
-787
1587
12 GUERRERO
-1396
927
11 GUANAJUATO
-1463
802
16 MICHOACÁN
-1399
720
21 PUEBLA
-1373
745
24 SAN LUIS POTOSÍ
-1421
689
28 TAMAULIPAS
-1101
980
20 OAXACA
-1099
914
05 COAHUILA
-1101
862
15 MÉXICO
-995
885
14 JALISCO
-1138
657
25 SINALOA
-1015
750
10 DURANGO
-1285
447
30 VERACRUZ
-1163
498
26 SONORA
-948
705
31 YUCATÁN
-1196
449
19 NUEVO LEÓN
-945
589
07 CHIAPAS
-899
467
08 CHIHUAHUA
-688
623
Table 4b: Impact of a 100 million investment in a Mexican state
+ IM
4741
1666
1790
1360
1693
-11
299
-931
850
-510
145
291
-430
800
-469
-661
-679
-628
-731
-121
-185
-239
-110
-481
-265
-838
-665
-243
-747
-356
-432
-66
Total
Effect
IM - RE
6814
5344
5030
4758
4033
3891
3701
3690
3431
2961
2845
2554
2430
2374
2323
2266
2120
2117
2110
2080
2014
1963
1880
1795
1766
1732
1661
1652
1644
1533
1365
1311
29
30