.
The Effect of Remittances and Migration on Human Capital:
Evidence from Mexico
Alfredo Cuecuecha
ITAM
February 2008
Abstract.- In this paper we disentangle the effects of migration and remittances on human capital using a large data base which includes information on
both the reception of remittances and the existence of migrants in the household. We are able to separate out the positive income effect of remittances from
the allocation effect that migration has on education. The allocation effect can
be positive or negative, depending on gender and time since the last return
of the migrant. For migrants that left less than five years ago, the allocation
effect is positive for males and negative for females. The allocation effect is
negative and dominates the income effect for the children and teenagers from
migrants that left more than five years ago. These results are based on using
instrumental variables, that exploit differences between municipalities in the
extent of migrant networks, as well as differences between municipalities in the
importance of remittances as percentage of income.
JEL Classification: F22, D91, J61,D84, O15, O24 Keywords: Remittances, Migration, Human Capital.
Remmitances. Cuecuecha. February 2008
1
2
Introduction
In recent years the amount of remittances of migrants to their native countries have
increased substantially to represent very large amounts: in Mexico they reach a total
of $20 billion during 2005, 3% of total GDP, while in El Salvador the importance is
even bigger as remittances reach as much as 25% of GDP. In this paper we focus on
determining how remittances and migration affect human capital accumulation. On
the one hand, remittances imply faster human capital accumulation, as they increase
the family’s income; that is the income effect. On the other hand, migration generates
changes in the household that can have negative (positive) effects on education. First,
parents migrating to other countries to remit money to their household leave their
children behind at home, reducing thereby the time children devote to education
(Hanson and Woodruff, 2003).1 Second, the household can reallocate household chores
following market incentives. For example, if the labor market pays more for males, the
household can increase education efforts on males and specialize females in household
chores. Third, the household can increase labor market participation of the children if
the migrant sent away does not send money back home. We refer to all these changes
as the allocation effect.
This is the first paper to disentangle allocation and income effects. Using a large
representative data set that contains detailed information on the exposure of households to migration and remittances, we show that the allocation effect is negative for
females and positive for males. We also find that the income effect is positive for
all individuals. The combined effect of migration and remittances is positive for all
individuals, which implies that for females the income effect dominates. We also show
that the allocation effect overtakes the income effect when migrants left for more than
five years.
Studies so far have estimated only the combined effect, but have not disentangled
the income and the supervision effect. Hanson and Woodruf (2003) proposed the
1
This is called in the literature the supervision effect.
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3
concept of supervision effect for the fact that children of migrating parents that receive
less attention from their parents devote less time to education, increase leisure time
and time devoted to household chores and even labor force participation. Using data
from the 2000 Mexican Census they find that for female head of household remittances
increase the education of girls, but not of boys. As an instrument for remittances
they use historical data on migration patterns from Mexico to the United States at
the state level. On the contrary, Borraz (2005), using the same data and instruments,
but focusing on communities with less than 2500 inhabitants, finds positive effects for
both boys and girls. He also includes the geographical distance from the municipalities
where individuals reside to the US as an instrument.
The literature on the effects of remittances on human capital also contains studies
that have used aggregate data or non-nationally representative data. Lopez-Cordoba
(2006) uses aggregate data on municipalities and finds that among Mexican rural
municipalities, remittances generate higher human development indicators. In particular, he finds that infant mortality, child illiteracy and some poverty measures
tend to reduce. His analysis controls for potential endogeneity of the remittance variable using as an instrument rainfall patterns at the municipal level. Mckenzie and
Rapoport (2005) use data from the Mexican Migration Project, which is primarily
rural and it is not representative of the entire country. They find a relation between
inequality in education and migration. They argue that remittances first increase
inequality as only well off households have access to migration, but that as poorer
households get access the situation reverses. However, they find a negative effect of
migration and remittances on 16 to 18 year old individuals.2
The remainder of the paper is organized as follows: the second section presents
2
The paper is also related to a larger literature that has look for the effects of remittances in other
indicators of development or reduction of poverty. Some authors have analyzed the relation between
remittances and migration with entrepreneurial activities (Durand, Parrado and Massey, 1996; Lindstrom, 1997; Dustman and Kirchkamp, 2002). Esquivel (2004) analyzes the effect of remittances
on poverty. Rodriguez and Cox (2005) analyze how remittances relate to labor force outcomes.
Mckenzie and Rapoport (2004 ) analyze how remittances are related to wealth. Others have studied
how remittances relate to spending patterns (Zarate-Hoyos,2004; Adams and Cuecuecha, 2007).
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4
the data, the third section presents our empirical strategy, which consists of using as
instruments the number of migrants in the municipality where the child lives, excluding the family of analysis, and the amount of remittances received in the municipality
where the child lives, excluding the family of analysis. The empirical section also
presents an analysis based on random matching estimators, and a non-parametric
analysis that demonstrates the existence of selection in unobservable components.
The fourth section concludes.
2
Data
The primary source of data for this paper is the 9.1% sample of the 2000 Mexican
census. Our sample consists of children and young adults between the ages 8 to
19 years old that belonged to households for which the information on remittances,
migration, education of the children, and education of the head of household was not
missing. We restrict attention to children from the head of household. After applying
all our selection criteria, we have approximately 720 thousand individuals, 15 to 19
years old. We also have 1.3 million individuals 8 to 14 years old.3
The census asks the households about the amount of remittances from other countries that they receive. We generated the indicator function r = {1, 0} for the event
the household receives remittances. The census also asks the households if anybody
from the family has migrated in the last five years. We generated the indicator
function m = {1, 0} for the event the household has or had a migrant during the
last five years. Tables 1 and 2 show that the two indicator functions partition the
data into four household types (S = {1, 2, 3, 4}): (1) households with migrants and
remittances, (2) households with migrants and no remittances, (3) households with
remittances and no migrants, (4) households with neither remittances nor migrants.
3
The total number of observations for 8 to14 years old individuals is 1.6 million, 50.65% are
males. The total number of observations for 15 to19 years old individuals is 1 million, 49.4% males.
If we only include children from the head, the numbers go down to 1.5 million individuals, 8 to 14
years old, and 800 thousand individuals 15 to 19 years old.
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5
In particular, 86% of the children in our sample belong to households that neither
receive remittances nor have migrants. Approximately 8% of the children belong to
households with migrants that receive no remittances, while 5% of the households
can be classified in the category no migrants and receive remittances. Finally, 1%
of households is classified in the category migrants and remittances. An explanation
for the existence of the category s = 3, is that the census authority deleted from the
migrant files all individuals who either migrated more than five years ago or did not
belong to the household at the moment of the migration. Consequently, the category
is formed by children that belong to potentially different categories of households: a.
in some households the migrants left more than five years ago and remit home money,
and the family still considers the migrants part of them; b. in some other households
the migrants where not part of the household at the moment of migration but they
still send money to their relatives. Because the status of the migrants in this type of
household is not clear, we decided to keep this type of households apart from those
of type s = 1. Moreover, to simplify our reference to this type of household, from
now on we will refer to this type of households as households that have long term
migrants (LTM) and receive remittances.4
Given that we are looking at children of different ages, we decided to standardize
the education years according to the age of the children. Define h as the standardized
¡
¢
education years given by h = H(a) − H(a) /sd(H(a)) with H(a) equal to the edu-
cation that a child with age a has, H(a) equal to the mean education for children with
age a , and sd(H(a)) equal to the standard deviation for children with age a. Tables
1 and 2 compare the standardized education outcomes for the children depending
on the type of household they live in. All teenagers (i.e. 15 to 19 years old) living
in households with migrants and/or remittances have at most the education of the
children from households type s = 4. The same applies for children 8 to 14 years old.
These differences are all significant at the 1% level. This result suggest a negative cor4
The census misses a fifth type of household comprised by those households that left entirely the
country and have no extended family members in Mexico that will provide information about them.
Remmitances. Cuecuecha. February 2008
6
relation between the accumulation of human capital and the reception of remittances
and/or migration to the US. However, they do not represent causal relations because
they may reflect the correlation between observed and/or unobserved characteristics
of the different household types and the education choices for their children.5
A potential concern with our classification is that if the individuals migrated
more than five years ago and do not remit money, then we can not identify such
households. However, if an individual has left for more than five years and has not
send money to the household, it implies that such individual are no longer part of the
household. The concern, however, comes from the fact that such type of households
could differ systematically from households of type s = 4. One would expect such
household to be poorer, since they lost some members and the members do not remit
money. This would specially be true for households where the migrant was the main
breadwinner and the separation took place more recently, giving less time to the
remaining members of the household for reorganization. If the children from these
households are poorer, then the average education of children for households with
no remittances and no migrants would be lower down and the average education
of children with migrants and remittances would be increased. Given that the first
group of households is larger, the effect of this misclassification would most likely be to
increase the average education of children with remittances and migrants. Following
the above idea, it should be the case that children and teenagers from households
with LT migrants and no remittances are more likely to be found as outliers in the
distribution of children with no remittances and no migrants. So in an attempt to
attenuate this problem we will perform an estimation eliminating children that have
a standardized education above +-2.5.
5
The exogenous characteristics of the children, their households and communities are presented
in the appendix.
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3
7
Empirical Methodology
3.1
Lee method
To disentangle the effects of remittances and migration we will use a method proposed
by Lee (1983). This is a two stage method, where it is assumed that there is a different
equation for the endogenous variable for each type of household. In the first stage,
the household selects its type, while in the second stage a different equation for each
household type is estimated. The households can choose from the following types:
(1) Have migrants with less than five years away from their households and receive
remittances; (2) Have migrants with less than five years away from their households
and receive no remittances; (3) Have long term migrants and receive remittances; (4)
Have no migrants (and remittances).
Assume that the household chooses the type that maximizes household utility and
chooses the education of their children conditional on their type. Assume that the
decision is taken in two stages. In stage one, the household chooses the action that
will maximize utility. In stage two, conditional on the action and the characteristics of the children, the household chooses the level of education for their children.
Consequently unobserved characteristics of the children and the household that influence the schooling decision are correlated with the actions taken by the household.
Moreover, if the household solves the problem by backward induction, in stage one,
the evaluation of the value that the family can achieve by being in each different
state includes a level of education for each children that can potentially be different
depending on the actions taken by the household. Let those levels of education be
equal to:
hs = X 0 β s + es
For each choice, there is a latent variable:
(1)
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Is = Z 0 αs + es
(2)
where Z includes all variables in X plus a set of instrumental variables. Utility
maximization implies that:
I = s if Is > Ij (j = 1, 2, 3, 4; j 6= s)
(3)
εs = MaxIj − η s (j = 1, 2, 3, 4; j 6= s)
(4)
Let:
If η s follows a type I extreme value distribution, Domencich and McFadden (1975)
show that εs has the following distribution function:
Fs (ε) = Prob(εs < ε) =
Define:
exp(ε)
P
exp(ε) + j6=s exp(Z 0 αj )
ε∗s = Js (εs ) = Φ−1 (Fs (ε))
(5)
(6)
where Φ represents the standard normal cdf. Equation 5 represents the first stage
of the Lee (1983) method. The second stage consists in estimating the conditional
expectation of equation 1, which Lee (1983) showed to be:
hs = X 0 β s + σ s ρs
ϕ (Js (εs ))
+ vs
Φs (εs )
(7)
where σ 2s = var(es ), ϕ is the pdf of the standard normal and ρs is the correlation
coefficient between es and ε∗s . Moreover, E(vs |X, Z) = 0.
In the estimation of the above model there are three main concerns: identification,
the unit of analysis and the appropriate standard errors. The identification of the
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9
model is done based on both the nonlinearity of the model and the existence of
instrumental variables. While the nonlinearity of the model could be enough to
identify the estimator, we use variables that are correlated with the choices of family
type but that are otherwise assumed uncorrelated with the education of the children.
Our instruments are: the migration rate in the municipality where the individual
lives, excluding the household of analysis; remittances as a fraction of income in
the municipality where the individual lives, excluding the household of analysis; an
interaction of the two mentioned instruments; and the capital to labor ratio in the
municipality.6 The intuition for using four instruments comes from the identification
condition that is obtained using a linear probability model. In a linear probability
model, each choice becomes a linear equation and hence a system of four endogenous
variables and a structural equation is formed. The intuition for using the migration
rate at the municipality level is that different authors have found that migration
networks help individuals to migrate (Durand, Parrado and Massey, 1996) and if the
household lives in a place with more migrants it is more likely that the migrant can
be exposed to migration. Hence variations across municipalities in the migration
network will identify our treatments. This type of identification has been used in
the literature by other authors who used historic state-level migration rates (see
Hanson and Woodruff, 2003; Mckenzie and Rapoport, 2004; or Borraz, 2005). Our
variable has variation between communities and inside the community since by not
considering the household of study, we generate a variable that varies depending on
whether the family is a migrant or not. The intuition for using remittances as a
fraction of the income in the municipality, excluding family i, is that in places where
remittances constitute a bigger fraction of the income of the population, it is more
likely that there would be more business dedicated to the reception of money.
6
7
The appendix shows the mean and standard deviations for the instruments. Moreover, the
relevance of these instruments is shown by their correlation with the choices of the individuals,
which is shown to be significant later in the results section.
7
While postal offices are one way through which individuals receive money orders in Mexico and
they are found, in principle, in each municipality, electronic transfers are by and large the main
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10
Consequently, we exploit differences across municipalities in the fraction of income
that represent the remittances to identify the effect of remittances. By excluding
the household of study of this measure, we generate a variable that will also have
variation within the municipalities. We obtained a third instrument by interacting
our two instruments. Our fourth instrument is the capital to labor ratio in the
community.8 These instrument are used conditioning in the family income, the gdp
per capita in the municipality and the investment over output ratio. The intuition for
the use of the fourth instrument is that conditional on output and investment in the
community, a larger capital to labor ratio must be correlated with better economic
conditions that will reduce migration or the need for remittances, while the correlation
between the capital labor ratio with education is controlled for using the municipality
and household characteristics included in our equation. This variable also provides
with variations at the level of communities.
A second issue in the estimation is about the unit of analysis. In principle, a household makes a decision and then the decision applies to all children of the household.
However, note that the objective of the Lee method is to control for the correlation
between unobserved factors that are correlated with the decision of the family and the
unobserved factors that determine the schooling level of a given children. If children
belonging to the same household have different unobserved children characteristics,
assigning the same predicted value for the selection correction to those children will
measure with error the true selection. Consequently, we treat each children as an
independent observation.
The final concern has to do with the standard errors to be used in the study. We
have at least three concerns about it. The first is that a two stage method needs to
take into account the estimation error that comes from the first stage. We bootstrap
the standard errors to solve this problem. The second is that the instruments are
mechanism used by individuals to remitt money. According to Banco de Mexico (2008), in 2006
94% of all transactions were done via electronic transfers, and 3% where done using money orders.
8
The appendix shows a linear probability model in which the validity of the instruments is
formally tested.
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11
measured at the level of the municipality which can generate correlation between
the observations. We clustered the standard errors by municipality. The third is
that we treat observations for all children as independent observations, but there are
children coming from same households. We will perform robustness checks clustering
the standard errors by household.
3.2
Average Treatment Effects on the Treated
Once estimated the equations for each type of household, we can now define the
effects that will allow us to disentangle the effects of migration and remittances. To
do so we will apply techniques related to the analysis of two treatments in individuals,
when the assignment to treatment is non-random. Following Lechner (2002) define the
average treatment effect of treatment k compared to treatment l on the participants
in treatment k as:?
θk,l = E (hk |s = k, x) − E (hl |s = k, x)
(8)
Where E (hk |s = k, x) is the standardized education years that children from
households that chose s = k have, conditional on the observed and unobserved characteristics that make the household select s = k. E (hl |s = k, x) represents the standardized education years that children from households that chose s = k would have,
should the household have chosen s = l. Pairwise treatment effects are sufficient to
identity specific questions when a situation involves more than one treatment (Lechner, 2002). The pairwise treatments will allow us to disentangle the income effect
and the allocation effect. Specifically, estimate five pairwise comparisons:
(a) θ2,4 = E[h2 |s = 2, x] − E[h4 |s = 2, x] finds the ”allocation of time” effect. We
do so by obtaining the expected value of education for children from households that
have migrants and no remittances, and obtaining a counterfactual level of education
for those children, should they live in households where migration is equal to zero.
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12
(b) θ1,2 = E[h1 |s = 1, x] − E[h2 |s = 1, x] finds the ”income effect”. We obtain
the expected value of education for children from households with remittances and
migrants, as well as the counterfactual level of education that those children would
have should they live in households with migrants and no remittances.
(c) θ1,4 = E[h1 |s = 1, x] − E[h4 |s = 1, x] finds the ”combined effect” of migration
and remittances. We accomplish this by obtaining the expected value of the education for children from households with migrants and remittances and obtaining the
couterfactual expected value of education for that type of children, should they live
in households with no remittances and no migrants.
(d) θ1,3 = E[h1 |s = 1, x] − E[h3 |s = 1, x] finds the effect of long term migration. We accomplish that by obtaining the expected value of education for children
from households with remittances and migrants, and estimating the counterfactual
expected level of education if the children would live in households with remittances
and with long term migrants.
(e) θ3,4 = E[h3 |s = 3, x] − E[h4 |s = 3, x] finds the combined effect of migration
and remittances in households with long term migrants. We do so by obtaining
the expected education for children from households with remittances and long term
migrants, and obtaining the counterfactual expected level of education if the children
would live in households with neither remittances nor migrants.
3.3
Results
Tables 5 and 6 present the results for the first stage of the Lee method. Table 5 shows
a multilogit model fitted on individual, household and community characteristics for
individuals between 15 to 19 years old. Table 6 shows the corresponding results for
children 8 to 14. Table 5 shows that the instruments work very well for teenagers 15
to 19, with all instruments being always significant at the 10% level. For children 8
to 14, not all the instruments are independently significant but they are all jointly
significant.
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13
Tables 7 and 8 show the results of the second stage. The most important factor
for these tables is the σ s ∗ ρs , which shows the importance of the selection into each
of the equations. The tables show that selection matters for both teenagers and
children, eventhough the results indicate that selection is more important in the case
of teenagers.9
Tables 9 and 10 show the ATT that are obtained using the coefficients from the
Lee method for all the individuals, and for males and females.10 Let´s first discuss
the combined effect of remittances and migration. Teenagers 15 to 19 years old that
live in households with remittances and migrants increase their education .35% (.028
schooling years), compared to the education they would have without remittances and
no migrants. The effect is found to be bigger for males (.74%) and non-significant for
females. Children 8 to 14 years old from households with remittances and migrants
increase their education 4.6% (.192 schooling years) compared to the education they
would have without remittances and no migrants. The effect for males is 3.3% and
4.2% for females.
In regard to the income effect, we find that teenagers that live in households
with migrants and remittances increase their education .29% (.023 schooling years)
compared to the education that they would have in households with migrants and
no remittances. The effect is bigger for males (.46%) and it is non-significant for
females. For children 8 to 14 the effect is considerably larger 3.2%, especially for
females (4.2%).
The allocation of time effect has mixed results. Teenagers that live in households
9
There are different potential explanations for this result. The first is that children 8 to 14 are in
shooling years that are supposed to be mandatory in Mexico, while most of teenagers 15 to 19 are in
schooling years that are not mandatory. A second explanation is that child work is allowed by law
only at age 14. A third explanation is that migration of the young starts as early as 14 years old.
A fourth potential explanation is that because primary school (6 to 12 years old) and junior high
school (13-15) are mandatory there are more public schools offering such levels at the municipality
level, than high schools and colleges offering public education at the municipality level. In all these
potential explanations, either the direct cost or the opportunity cost of attending school increases
at age 15. Further research into this issue is needed and is left for future work.
10
The multilogit models and second stage equations used for males and females are similar to
those shown in tables 5 to 8, except that exclude the sex variable. Results available upon request.
Remmitances. Cuecuecha. February 2008
14
with migrants and no remittances have a similar level of education to the one that
they would have if their households would be with no migrants. However, we find
that for males the effect is positive and almost a half of the total positive combined
effect, while for females the effect is found to be negative (-.45%). For male children
the effect is also found to be positive (.36%) while for females it is also found to be
negative (-.55%).
The effect of long term migration is also found to have mixed results. Teenagers
that live in households where the migrants left less than five years ago and receive
remittances have less education (-3.5%) compared to what education they would have
if they would live in households with remittances and where the migrants left more
than five years ago. This implies that over time the positive effects of migration and
remittances accumulate. On the other hand, children 8 to 14 years old that live in
households with migrants and remittances have more education (2.8%) compared to
what would they have if they would live in households with remittances and migrants
that left more than five years ago. These results apply for both boys and girls.
Finally the combined effect of remittances and migration for families with long
term migrants is found to be negative. Specifically, teenagers that live in households
with remittances and long term migrants have less education (-.73%) compared to
what would they have if they would live in households with no remittances and no
migrants. The effect is negative for males and females and also for children 8 to 14
years old (-.77%).
3.4
Selection in observables
We present this section for two main reasons: first as a robustness check on our
previous estimation, and because if observable characteristics are the main reason for
selection bias to exist, the propensity score matching (Rosenbaum and Rubin, 1983)
would be our best option to estimate the ATT. This method involves the estimation
of a propensity score that identifies the probability for an observation to receive the
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15
treatment, and later on matching individuals that are very similar in their probability
of receiving the treatment. Under the assumption that conditional on the propensity
score, the treatment is uncorrelated with the characteristics of the individuals, we
can identify the ATT. This characteristic is called the "Balancing Hypothesis" and
implies that for a given propensity score, exposure to treatment is random (Becker
and Ichino, 2002). Following (Heckman, et al,1996 ) we will limit testing of this
hypothesis to individuals that belong to the common support.11 We followed the
approach of calculating each pairwise ATT using individual models of the binary
relations implied for each pair analyzed.
12
Each pairwise comparison involves the
estimation of a different propensity score. We, however, fix the model to be estimated
in each case using a set of exogenous characteristics and instruments that were also
used in our instrumental variable estimation.13 Then, the ATT was estimated using
three different methodologies: stratification, nearest neighbor, and kernel.14
Table 11 presents our results for males and females 15 to 19 years old, while Table
12 presents the results for children 8 to 14 years old. Unlike our estimation using
the Lee method, the combined effect is found to be insignificant for males 15 to 19
years old, while it is found to be negative for females 15 to 19 years old. Similarly, the
combined effect is found to be insignificant for females 15 to 19 years old. In contrast,
the combined effect is found to be positive for males 8 to 14 years old, which is similar
to our estimation with the Lee method.
The income effect is found to be positive and significant for all our subsamples,
11
The estimation is done using Becker and Ichino (2002) STATA program.
An alternative methodology that can be used in the context of multiple treatments was also
implemented. Qualitatively similar results to those mentioned in the text were obtained. Those
methodologies are based on Lechner (2002). Results available from the authors upon request.
13
We also follow the approach of letting each pairwise probability to be estimated according
to models that will satisfy implications of the balancing hypothesis (Becker and Ichino: 2002).
Specifically, we specify a model under which we could partition the observations in the common
support into blocks that would satisfay the condition that treated and non-treated individuals have
equal means for all exogenous variables used in the estimation of the score. Results are qualitatively
similar to those presented using a common model for all pairwise probabilities. The appendix shows
the variables used in the models fitted to obtain the propensity scores.
14
The estimations are done using the STATA programs provided by Becker and Ichino(2002).
12
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16
just as it was found with the Lee method. The results from the random matching
estimators find a negative allocation effect for both males and females. This is similar
to what is found for females using the Lee method and different in sign for males.
The random matching estimations show that the effect of long term migration
(LTM) is non-significant for males 15 to 19 years old and negative and significant
for females 15 to 19 years old. These results agree with those founds with the Lee
method for females. The effect of LTM is found to be positive and significant for
males 8 to 14 years old and insignificant for females. These results also agree with
the Lee method results for males.
The combined effect for long term migrants is found to be insignificant for 15 to
19 years old individuals, which does not contradict the negative and significant effect
found with the Lee method. The combined effect for long term migrants is found to
be insignificant for females 8 to 14 years old, while it is found positive and significant
for 8 to 14 boys, which contrasts with the negative and significant effects found with
the Lee method.
3.5
The importance of selection
Given the discrepancy of results between the Lee and the random matching estimator
for the combined effect of migration and remittances, we decided to attempt to study
the type of selection implied by the exposure of migration and remittances and its
relation with the human capital of children and young adults. The type of selection
in unobservable characteristics is determined using the non-parametric methodology
of DiNardo. et al (1996) which has been applied to migration by Chiquiar and
Hanson(2005).
In order to do so, let us write the distribution of the standardized education h in
a way that we can see its relation with the type of household that we are analyzing:
Γi = Γ(h|s = i) =
Z
Φi (h|x) Ψ(x|s = i)dx
(9)
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17
where Φi (h|x) is the conditional distribution of education for children from households type s = i and exogenous characteristics x. Also Ψ(x|s = i) is the conditional
distribution of characteristics for households from type s = i. Therefore, the distribution of education of children from type i households is conformed by how households
optimally choose education for children with characteristics x, and the distribution
of characteristics among children of such households. Having defined the conditional
distribution of education, we can now define counterfactual distributions that will allow us to find how the exposure to migration and remittances can affect the education
distribution. Define the counterfactual conditional distribution as:
Γi,j =
Z
Φj (h|x) θi,j Ψ(x|s = j)dx
(10)
where θi,j is a reschaling factor given by:
θi,j =
Ψ(x|s = i)
Ψ(x|s = j)
(11)
θi,j is obtained using Bayes law (DiNardo, Fortin and Lemiuex , 1996; Chiquiar
and Hanson, 2005) and we write it as:
θi,j =
Ψ(x|s = i)
Pr (s = i|x) Pr(s = j)
=
Ψ(x|s = j)
Pr(s = j|x) Pr(s = i)
(12)
where Pr (s = i|x) is obtained using a parametric multilogit model, and Pr(s = i)
is obtained using the observed sampling rates.15 Γi,j can be understood as showing
how the distribution of education for children of households of type i would look
like if the optimal choices of education in their households would follow the optimal
decisions of households of type j. Define the difference in education distribution ∆i,j
as:
∆i,j = Γi,j − Γj =
15
Z
(θi,j − 1) Φj (h|x) Ψ(x|s = j)dx
(13)
The multilogit model used here is the same that is shown in the first stage of the Lee method.
Remmitances. Cuecuecha. February 2008
18
Notice that children with a weight larger than one contribute positively to the
difference. If θi,j > 1, we have that children with characteristics x have higher
probability of living in households of type i than probability of living in households
of type j. If we graph the above difference with the education of the children, we
can learn how the ratio of probabilities correlates with education. If the difference
∆i,j shows positive mass above the education mean, it implies that children with
larger probability of being in households of type i have education above the mean.
If the difference shows positive mass below the mean, then we would say that there
is negative correlation between the probability of being in households of type i and
education. If the difference shows in the middle of the distribution we would speak of
a sort of "selection from the middle" (Chiquiar and Hanson, 2005) in the sense that
children that have higher probability of living in households of type i are most likely
to be found in the middle of the education distribution of children from type j.
We estimated 3 differences:
∆1,4 =Difference between the counterfactual distribution of education for children
with the characteristics of households with migrants and remittances, and observed
distribution of education for children with the characteristics of households with no
migrants and no remittances, holding the conditional education distribution constant
and equal to that of children with no remittances and no migrants.
∆2,4 =Difference between the counterfactual distribution of education for children
with the characteristics of households with migrants and no remittances, and observed
distribution of education for children with the characteristics of households with no
migrants and no remittances, holding the conditional education distribution constant
and equal to that of children with no remittances and no migrants.
∆3,4 =Difference between the counterfactual distribution of education for children
with the characteristics of households with migrants that left more than five years
ago and no remittances, and observed distribution of education for children with
the characteristics of households with no migrants and no remittances, holding the
Remmitances. Cuecuecha. February 2008
19
conditional education distribution constant and equal to that of children with no
remittances and no migrants.
Figures 1 and 2 show the results of the counterfactual experiments for teenagers
15 to 19 years old and children 8 to 14 years old. For teenagers, the figures show
clearly that individuals with characteristics that will make them more likely to live in
households exposed to migration or remittances are more likely to be found above the
mean of the education distribution for children from households with no migrants and
no remittances. The only exception is that of households types s = 2, which are also
shown with some positive mass below the mean. For children, the type of selection
depends in the types of households being compared. Children from households type
s = 1, are seen to be selected from the middle of the education distribution of the
children with no migrants and no remittances. Children from households type s = 3,
seem to selected from the bottom of the education distribution of the children type
s = 4. Finally, children from households type s = 2, seem to be selected from the top
of the education distribution of children from households s = 4.
4
Conclusion
Remittances and migration can have effects of opposite sign in the education of children. We refer to the effects generated by the additional income brought by remittances as the income effect. Every other effect we called it the allocation effect. Using
the 2000 Mexico census we identify whether households have members in the US and
whether they receive remittances. This information allow us to disentangle the income and the allocation effects, as well as the combined effect of remittances and
migration on the education of children and teenagers in Mexico. We use instrumental
variables to accomplish our purpose and we have four main results:
First, we identify the income effect to be positive: on average children and
teenagers increase their levels of education above what would they have in the absence
Remmitances. Cuecuecha. February 2008
20
of remittances. The first experiment an increase of 3% in education years, while the
latter increase .3%.
Second, we identify the allocation effect to be non-negative for males and negative
for females. For male teenagers the effect is non-significant while for male children
the effect is an increase of .45% in education years, compared to the education they
would have with no migrants in the household. For females, it represents a reduction
between —.55% (children) and -.45% (teenagers).
Third, the combined effect of migration and remittances is found to be positive for
males and non-negative for females: it represents an increase of 4.6% for male children
and an increase of .74% for male teenagers, compared to the levels of education that
they would have with no migrants and no remittances in the household. For female
children the increase is 3.3%, while no effect is found for female teenagers.
Fourth, we also identify the combined effect of remittances and long term migration to be negative: teenagers have -.73% education years and children have -.77%
education years compared to what would they have with no remittances and no long
term migrants.
Our analysis also found that selection in unobservable characteristics is important
in the education equations. We found this importance using both parametric and
semiparametric techniques. This implies that the use of instrumental variables to
identify the effects of remittances and migration is the best strategy to follow.
Our results have important implications for research and policy. First, they imply that while remittances and migration can be positive for human capital due to
the additional resources that the household can acquire through them, factors that
reduce the sending of remittances or difficult the contacts between families must have
negative impacts on the education of children. Second, our finding that effects are
different by gender, being either not so positive for females or even negative, imply
that families can either be discriminating within the household, or that their decisions respond to market incentives by using resources selectively among household
Remmitances. Cuecuecha. February 2008
21
members. Further research is needed to better understand the effects found.
5
5.1
Appendix
1. Characteristics of children and teenagers
Tables A1 and A2 present the characteristics of the children and teenagers depending
on the household type in which they live. Teenagers have in average 17 years in all
households and half of them are males. Children are on average 11 years old and half
of them are males. There are significant differences in terms of household income,
education and age of the father, and the characteristics of the municipalities where
they live in. Teenagers and children that receive no remittances live in households
where the father is more educated and the family income is higher. They also live in
municipalities that have higher GDP per capita.
5.2
2. Instrumental variables and regions
Table A3 shows the mean and standard deviations for the instruments used in the
paper. Migrants represent around 2% of the population, while remittances represent
around 2% of municipality income. The capital labor ratio is calculated using gross
fixed investment by municipality divided by population employed in the municipality.
This data comes from INEGI (1999). The table also shows the fraction of children
that lives in the five regions in which the entire country is divided. These regions are
based on Chiquiar (2005). The table also shows the states included in each region.
5.3
3. Variables used in propensity score models
Table A4 shows the variables used in each of the models fitted for the estimation
of the propensity scores used to form the pairwise matches. Those variables are the
same used in most of the regressions presented in the paper, with the exception of
Remmitances. Cuecuecha. February 2008
22
regional dummies. The introduction of the regional dummies consistently generated
few matches and we decided to drop them from the propensity score models. Sex
was dropped from all regressions since the matches were always done conditional on
gender.
5.4
4. Alternative methods
Under the assumption of random assignment the average treatment effect on the
treated can be estimated using appropriate OLS equations. To illustrate, let suppose
we are interested in comparing children from households s = 1, 2, 3 with households
with s = 4. In other words, our control group are households with no remittances and
no migrants. An appropriate OLS equation would be:
hj = Xj0 β + Tj0 δ 4 + ej
(14)
Where hj is the standardized years of education of children j; Xj represents a
vector of exogenous characteristics; Tj is a vector of indicator functions for children
j and ej is the error term. Vector Tj is formed by three indicator functions: T 1 which
is an indicator for the event : household receives remittances and has migrants”;
T 2 an indicator function for the event ”the household has migrants and receive no
remittances”; and T 3 an indicator function for the event : ”the household receives
remittances and has no migrants”.
Each element δ k,4 from the vector δ 4 identifies the average treatment effect on the
treated (ATT) as follows16 :
E (hk |s = k, x) − E (h4 |s = k, x) = δ k,4
16
(15)
Under the assumption that the treatments are exogenous, the ATT is identical to the average
treatment effect in the population. At the mean of the entire sample, each element δ k,4 from the
vector δ 4 identifies the following difference:
E (hk |s = k, x, ) − E (h4 |s = 4, x) = δ k,4
Remmitances. Cuecuecha. February 2008
23
Note that unlike the Lee method, where constants and coefficients are allowed to
vary depending on the type of household studied, or the random matching estimators
where a semi-parametric method is used to obtain the ATT, equation 14 only allows
the ATT to occur in one parameter. Consequently, differences between this OLS
approach and the Lee method, as well as with the random matching estimator, come
not only from the assumption of random assignment, but also from the use of a
simpler model in the case of OLS. The assumption of random assignment can easily be
rejected in the context of migration because the literature on migration has showed the
importance of selection in observable and unobservable characteristics (Borjas, 1987),
particularly for the Mexican case (Chiquiar and Hanson, 2005). Consequently, the
coefficients δ k,4 from the OLS estimation are biased estimators of θk,4 . An alternative
estimation is the use of an instrumental variable approach and a linear probability
model.
This method estimates an structural equation given by equation 14, together
with three reduced form equations, one for each indicator variable Tj .The difference
between this IV estimation and the Lee method comes not only from the assumption
of a linear probability model, but also from the simpler model assumed in equation
14. A similar argument applies for the difference between the IV estimator and the
random matching estimator.
The instruments used are the same used in the Lee method and the estimation
of the propensity scores for the random matching. Hausman tests show the need
for instruments and all Sargan test performed show the validity of the instruments.
Table A5 shows the results from the estimation. The combined effect is found to
be positive and significant for all subsamples, as with the Lee method. The income
effect is found to be positive and significant for all subsamples. The allocation effect
is found to be negative and significant for all subsamples. The effect of long term
migration is found to be negative and significant for all subsamples. The combined
effect for LT migrants is found to be negative and significant.
Remmitances. Cuecuecha. February 2008
24
In conclusion, the effects found with the IV method are straightforward: the
combined effect is positive, the income effect is positive and the allocation effect is
negative. The income effect is then obtained positive with any of the methods. The
combined effect is found non-negative with any of the methods. The allocation effect
is found non-positive for females in any of the three methods. On the other hand,
the allocation effect for males is found with similar signs for the Lee method and the
random matching method, but not for the linear IV method. Given that the linear IV
imposes more restrictions in the model estimated our favorite estimations are those
obtained with the Lee method.
Remmitances. Cuecuecha. February 2008
25
[1] R. Adams and A. Cuecuecha. Remittances, Household Expenditure and Investment in Guatemala. World Bank, Working Paper, 2007.
[2] S. Becker and A. Ichino. Estimation of Average Treatment Effects Based on
Propensity Scores. Stata Journal, 2(4): 358-377. 2002
[3] F. Borraz. Assesing the Impact of Remittances on Schooling: The Mexican
Experience. Global Economy Journal, 5(1):1-32. 2005
[4] D. Chiquiar and G. Hanson. International Migration, Self Selection, and the
Distribution of Wages. Journal of Political Economy, 113(2):239-281. 2005.
[5] D. Chiquiar. Why Mexico’s regional income convergence broke down. Journal
of Development Economics, 77(1): 257-275. 2005.
[6] J. DiNardo, N.M. Fortin, and T. Lemieux. Labor Market Institutions and
the Distribution of Wages, 1963-1992: A Semiparametric Approach. Econometrica,
64(5): 1001-1044. 1996.
[7] J. Durand, E. Parrado, and D. Massey. Migradollars and Development: A
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[8] C. Dustmann, and O. Kirchkamp. The Optimal Migration Duration and Activity Choice after Remigration. Journal of Development Economics, 67(1):351-372.
[9] G. Esquivel and A.H. Pineda. Remittances and Poverty in Mexico: A Propensity Score Matching Approach. El Colegio de México. Working Paper. 2006.
[10] G. Hanson and C. Woodruff. Emigration and Educational Attainment in
Mexico. NBER. Working Paper. 2003.
[11] J. Heckman, H Ichimura, J Smith and P. Todd. Sources of Selection Bias in
Evaluating Social Programs: An interpretation of conventional measures and evidence
of effectiveness of matching as a program evaluation method. Proceedings from the
National Academy of Sciences USA, 93(23):13416-13420. 1996.
[12] M. Lechner. Some Practical Issues in the Evaluation of Heterogenous Labour
Market Programmes by Matching Methods. Journal of the Royal Statistical Society,
165 (1):59-82. 2002.
[13] Lee, Lung-Fei. 1983. Generalized Econometric Models with Selectivity.
Econometrica 51, no. 2: 507-512.
[14] D.P. Lindstrom. Economic Opportunity in Mexico and Return Migration
from the United States. Demography, 33(3):357-374. 1996.
[15] E. Lopez-Cordoba. Gloablization, Migration and Development: The Role of
Mexican Migrant Remittances. Mimeo, Inter American development Bank. 2004.
[16] D. Mckenzie and H. Rapoport. Network Effects and the Dynamics of Migration and Inequality: Theory and Evidence from Mexico. Stanford University and
Bar-Ilan University, Working Paper. 2004.
[17] D. Mckenzie and H. Rapoport. Migrant Network, Migration Incentives and
Education Inequality in Rural Mexico. Stanford University and Bar-Ilan University,
Working Paper. 2005.
[18] E. Rodriguez-Oregia and A. Cox-Edwards. The Effect of Remittances on
Labor Force Participation in Mexico. Universidad Iberoamericana, Working Paper.
2006.
[19] G. Zarate-Hoyos. Consumption and Remittances in Migrant Households:
Toward a Productive Use of Remittances. Contemporary Economic Policy, 22 (4):555565. 2004.
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26
[20] Banco de México. Familiy Remittances Datasheet [online]. Consulted on January 2008. Available at: http://www.banxico.org.mx/polmoneinflacion/estadisticas/
balanzaPagos/balanzaPagos.html
[21] INEGI. Censos Económicos 1999.
Remmitances. Cuecuecha. February 2008
Table 1, 2000 Mexican Census, 15 to 19 years old
Education years and standardized ed. yrs. of young adults by type of family
Differences with respect to families with no rem. and no mig. in parenthesis
All
Rem.and Rem. and No rem.
No rem.
Mig.
LT mig.
and Mig. and no mig.
All
Ed. yrs.
7.95
7.61
7.99
7.52
7.99
Standardized ed yrs.
-.01
-.11
.01
-.16
.01
Difference
(-.12)**
(-.0004)
(-.17)**
Males
Ed. yrs.
7.81
7.57
7.86
7.47
7.84
Standardized ed yrs.
-.06
-.12
-.18
-.04
-.04
Difference
(-.07)*
(.003)
(-.13)**
Females
Ed. yrs
8.11
7.66
8.15
7.58
8.16
Standardized ed yrs.
.05
-.11
-.13
.06
.07
Difference
(-.18)**
(-.004)
(-.21)**
N (males and females) 721,978
1,294
38,637
54,115
627,932
% (sample)
100
.18
5.35
7.49
86.97
% (weighted)
100
.21
4.32
8.89
86.58
Source: Calculations done by the author using the 9.1% public sample of the 2000
Mexico census. Sample includes only children of the head of household, for whom
information on their education, the education of the head, and the migration
information in the households is available. **1 % significance level.
27
Remmitances. Cuecuecha. February 2008
Table 2, 2000 Mexican Census, 8 to 14 years old
Education years and standardized ed. yrs. of children by type of family
Differences with respect to families with no rem. and no mig. in parenthesis
All
Rem. and Rem. & No rem.
No rem.
Mig.
LT mig. and Mig. and no mig.
All
Ed. yrs
4.14
4.13
4.15
3.93
4.16
Standardized ed. yrs
.001
.05
.002
-.097
.011
Difference
(.04)*
(-.01)*
(-.10)**
Males
Ed. yrs
4.09
4.11
4.11
3.93
4.11
Standardized ed. yrs
-.03
.05
-.03
-.11
-.03
Difference
(.003)
(.001)
(-.17)**
Females
Ed. yrs
4.20
4.15
4.19
3.94
4.23
Standardized ed. yrs
.04
.05
.04
-.08
.05
Difference
(-.001)
(-.01)?
(-.14)**
N (males and females) 1 ,2 9 8,3 6 2
2,405
69,555
102,880
1 ,1 2 3 ,5 2 2
% (sample)
100
.18
5.35
7.92
86.55
% (weighted)
100
.22
4.29
9.35
86.14
Source: Calculations done by the author using the 9.1% public sample of the
2000 Mexico census. Sample includes only children of the head of household,
for whom information on their education, the education of the head, and the
migration information in the households is available. **1 % significance level.
*5% significance level. ?10% significance level.
28
Remmitances. Cuecuecha. February 2008
Table 3, Multilogit model for Probability that a household is of type S
(First stage of Lee Method) Teenagers 15 to 19.
Household Type
Migration &
Migration &
LT Migrants
Remittances No Remittances
& Rem.
Age
-.022
-.011
-.001
(.028)
(.007)
(.005)
Sex
.012
-.0008
-.001
(.078)
(.021)
(.014)
Family Income
-5.60e-07*
1.94e-07
-1.80e-06***
(3.07e-07)
(1.49e-07)
(6.47e-07)
PIB pc
-.00001**
-1.19e-06**
-2.34e-06***
(6.67e-06)
(6.00e-07)
(4.59e-07)
FBK in
-6.04e-06**
.467*
-.389***
municipality
( 3.31e-06 )
(.273)
(.198)
Migration rate
37.52***
32.07***
-4.097***
in municipalitya
(1.145)
(.156)
(.423)
Remittances as %
8.09***
-24.25***
7.61***
of municipal incomea
(1.213)
(1.309)
(.127)
Mig rate in mun*
189.85***
125.81***
165.03***
Rem/income in mun
(16.683)
(10.36)
(12.417)
Capital/Labor
-.0008*
.001***
-.0003***
in municipality
(.0004)
(.00005)
(.00006)
Constant
-10.80
-5.43***
-3.30***
(.258)
(.031)
( .020)
N=715743. Pseudo R2 : 51%. The regression includes the education and age
of the head of household, as well as four region dummies.
*** Significant at 1% level; **Significant at 5%. * Significant at 10 %.
a Excludes members of household i.
29
Remmitances. Cuecuecha. February 2008
Table 4, Multilogit model for Probability that a household is of type S
(First stage for Lee method) Children 8 to 14.
Household Type
Migration &
Migration &
LT Migrants
Remittances No Remittances
& Rem.
Age
-.022
.006
.0002
(.028)
(.006)
(.004)
Sex
.012
.012
.013
(.078)
(.026)
(.016)
Family Income
-5.60e-07*
-1.84e-06
-1.26e-06
(3.07e-07)
(1.51e-06)
(7.85e-07)
PIB pc
-.00001**
-1.38e-06
-3.24e-06
(6.67e-06)
(1.38e-06)
(9.75e-07)
FBK in
-6.04e-06**
4.288***
-.218
municipality
( 3.31e-06 )
(.442)
(.339)
Migration rate
-234.45
-11.60
83.58
in municipalitya
(756.424)
(120.614)
(99.651)
Remittances as %
8.16***
-8.61***
6.63***
of municipal incomea
( 1.002)
(.203)
( .127)
Mig rate in mun*
23328.22
5574.50
-4325.06
Rem/income in mun
(58892.36)
(12825.97)
(10719.44)
Capital/Labor
-.001***
.00003
-.0005***
in municipality
( .0002)
(.0003 )
( .0001)
Constant
-10.80***
-2.45***
-2.69***
(.258)
( .043)
( .037)
N=1287929. The regression includes the education and age
of the head of household, as well as four region dummies.
*** Significant at 1% level; **Significant at 5%. * Significant at 10 %.
a Excludes members of household i.
30
Remmitances. Cuecuecha. February 2008
Table 5, Regressions for Standardized Education
(Second stage of Lee method) Teenagers adults 15 to 19.
Families with
Migration &
Migration &
LT Migrants
No Mig.
Remittances No Remittances
& Rem.
No Rem.
Age
-.059**
.007
-.002
-.006**
(.024)
( .005)
(.005)
(.002)
Sex
.005
-.036
-.108***
-.118***
(.086)
(.023)
(.014)
(.004)
Family Income
.00002**
1.96e-07*
4.09e-06***
5.74e-07***
(9.85e-06)
(1.00e-07)
(1.36e-06)
(1.35e-07)
PIB pc
-.00001**
-6.44e-06***
-5.16e-07
-1.02e-06***
(6.67e-06)
(2.29e-06)
(4.52e-07)
(3.64e-07)
FBK in
1.096
-.988
-.453*
-.371**
municipality
(.965)
(1.147)
(.235)
(.148)
Sigma i
-.264
-.494***
.202***
-.093**
(.316)
(.169)
(.061)
(.043)
Constant
.275
-.089
-.690***
-.206***
(.849)
(.139)
(.126)
(.043)
N
1278
53087
37992
619682
R2
.11
.07
.08
.05
All reg. include the ed. and age of the head of hh.
. Clustered s.e. by mun..**Significant
at 1% level; *Significant at 5%. a Instruments: K/L ratio in municipality, mig.
rate in mun.exc. hh i, and rem./gdp in mun. exc. hh i, interaction of
mig. rate in mun. and rem/gdp in mun. both exc. hh i.
31
Remmitances. Cuecuecha. February 2008
32
Table 6, Regressions for Standardized Education
(Second Stage Lee Method) Children 8 to 14.
Families with
Migration &
Migration &
LT Migrants
No Mig.
Remittances No Remittances
& Rem.
No Rem.
Age
-.039**
-.037***
.004
.002
( .016)
(.005)
(.003)
(.001)
Sex
.036
-.024*
-.085***
-.090***
( .059)
(.013)
(.010)
(.002)
Family Income
2.86e-06*
-9.34e-08
6.67e-07
3.38e-07***
(1.48e-06)
(1.90e-07)
(7.35e-07)
(9.96e-08)
PIB pc
-5.39e-06
-4.16e-07
5.65e-07
4.02e-07
(9.31e-06)
(3.16e-06)
(6.06e-07)
(3.16e-07)
FBK in
-.525
.160
-.015
-.1102845
municipality
(2.122)
(.630)
(.156)
(.115)
Sigma i
-.018
-.033**
.008
-.009
(.013)
(.013)
(.018)
( .006)
Constant
.097
-.138*
-.203***
-.210
(.161)
( .083)
( .042)
( .022)
N
1494
98670
68457
1109547
R2
.04
.05
.03
.03
All reg. include the ed. and age of the head of hh.
. Clustered s.e. by mun..**Significant
at 1% level; *Significant at 5%. a Instruments: K/L ratio in municipality, mig.
rate in mun.exc. hh i, and rem./gdp in mun. exc. hh i, interaction of
mig. rate in mun. and rem/gdp in mun. both exc. hh i.
Table 7, Predicted Standardized Education and Counterfactual Standardized Education
Obtained for the Pairwise Average Treatment Effect on the Treated.
(Lee Method) Teenagers adults 15 to 19.
Pairwise
E[zs |x,h=s] E[zk |x,h=s]
ATT
ATT
ATT.
comparison
(1)
(2)
(Difference 1-2)
Males
Females
Combined Effect
-.059
-.087
.028***
.059***
-.004
s=1; k=4
(.010)
(.007)
(.007)
(.010)
(.012)
Income Effect
-.059
-.083
.023***
.037***
.008
s=1; k=2
(.010)
(.008)
(.007)
(.011)
(.013)
Allocation Effect
-.162
-.163
.001
.032***
-.033***
s=2; k=4
(.001)
(.001)
(.0007)
(.0009)
(.001)
LT migration effect a
-.059
.195
-.254***
-.246***
-.266***
s=1;k=3
(.010)
(.008)
(.007)
(.011)
(.012)
b
Combined for LT
-.009
.044
-.053***
-.051***
-.058***
s=3;k=4
(.001)
(.001)
(.0005)
(.0008)
(.0007)
*** Significant at 1% level; *Significant at 5%. a Compares children in families with remittances
and migrants (s=1) with the education that children from those families would have if the migrants
of the familiy would have more than five years without returning. b Compares children in families
with remittances and migrants that left more than five years ago (s=3) with the education that
children from those families would have if no remittances and migrants would exist in the family.
Remmitances. Cuecuecha. February 2008
·
33
Table 8, Predicted Standardized Education and Counterfactual Standardized Education
Obtained for the Pairwise Average Treatment Effect on the Treated.
(Lee Method) Children 8 to 14.
Pairwise
E[zs |x,h=s] E[zk |x,h=s]
ATT
ATT
ATT.
comparison
(1)
(2)
(Difference 1-2)
Males
Females
Combined Effect
.082
-.109
.192***
.138***
.173***
s=1; k=4
(.007)
(.004)
(.005)
(.004)
( .006)
Income Effect
.082
-.053
.136***
.115***
.176***
s=1; k=2
(.007)
(.006)
(.005)
(.004)
(.007)
Supervision Effect
-.114
-.121
.007***
.015***
-.023***
s=2; k=4
( .0008)
(.0006)
(.0003)
(.0004)
( .0005)
Supervision for LTa
.083
-.034
.117***
.010***
.103***
s=1;k=3
(.007)
(.005)
(.004)
(.004)
(.005)
Combined for LTb
-.007
.024
-.032***
-.036***
-.031***
s=3;k=4
(.0007)
( .0006)
(.0002)
( .0004)
(.0003)
a
*** Significant at 1% level; *Significant at 5%. Compares children in families with remittances
and migrants (s=1) with the education that children from those families would have if the migrants
of the familiy would have more than five years without returning. b Compares children in families
with remittances and migrants that left more than five years ago (s=3) with the education that
children from those families would have if no remittances and migrants would exist in the family.
Table 9, Average Treatment Effect on the Treated, Pairwise comparisons
Random Matching Estimators, 15 to 19 years old
Males
Combined Income Allocation LT migration
Combined
Remittance
Effect
Effect
Effect
Effect
effect for LTM
Effect
1,4
1,2
2,4
1,3
3,4
Method
θ
θ
θ
θ
θ
θr
Stratification
-0.015
0.079
-0.181***
-0.025
-0.072
-0.028
(0.060)
(0.060)
(0.058)
(0.058)
(0.053)
(0.057)
Nearest
-0.050
0.164**
-0.169***
-0.102
-0.083
0.002
Neighbor
(0.084)
(0.079)
(0.060)
(0.080)
(0.065)
(0.078)
Kernel
-0.009
0.098*
-0.192***
-0.017
0.002
-0.036
(0.058)
(0.060)
(0.066)
(0.042)
(0.056)
(0.050)
N
8,938
8,938
377,811
8,938
177,803
186,741
Females
Stratification
-0.096
0.093
-0.238***
-0.100
-0.037
-0.099
(0.068)
(0.069)
(0.063)
(0.064)
(0.062)
(0.063)
Nearest
-0.177**
0.082
-0.243***
-0.191**
-0.111
-0.212**
Neighbor
(0.089)
(0.092)
(0.065)
(0.086)
(0.080)
(0.087)
Kernel
-0.081
0.145*** -0.237***
-0.093
0.001
-0.091
(0.060)
(0.052)
(0.068)
(0.067)
(0.060)
(0.062)
N
7,561
7,561
315,938
7,561
159,573
167,134
***Significant at 1%. ** at 5%. *at 10%. Source: 9.1% public sample of the 2000
Mexico census. Sample includes only children for whom information on the edu., and
age of the head of household is known.
Remmitances. Cuecuecha. February 2008
34
Table 10, Average Treatment Effect on the Treated, Pairwise comparisons
Random Matching Estimators. 8 to 14 years old
Males
Combined Income Allocation LT migration
Combined
Remittance
Effect
Effect
Effect
Effect
effect for LTM
Effect
1,4
1,2
2,4
1,3
3,4
Method
θ
θ
θ
θ
θ
θr
Stratification
0.082**
0.154***
-0.059
0.040
0.051
0.082**
(0.038)
(0.048)
(0.044)
(0.042)
(0.040)
(0.043)
Nearest
0.126**
0.159**
-0.076*
0.131**
0.080*
-0.011
Neighbor
(0.065)
(0.062)
(0.045)
(0.061)
(0.049)
(0.058)
Kernel
0.094**
0.144***
-0.084**
0.031
0.051
0.079**
(0.040)
(0.044)
(0.036)
(0.044)
(0.035)
(0.041)
N
16,203
16,203
668,716
16,203
310,073
326,276
Females
Stratification
-0.015
0.221*** -0.198***
-0.027
0.057
-0.015
method
(0.044)
(0.053)
(0.045)
(0.046)
(0.046)
(0.043)
Nearest
0.026
0.176*** -0.179***
-0.070
-0.034
-0.093
Neighbor
(0.061)
(0.066)
(0.047)
(0.063)
(0.053)
(0.058)
Kernel
0.001
0.194*** -0.206***
-0.016
0.047
0.021
(0.040)
(0.050)
(0.040)
(0.043)
(0.046)
(0.039)
N
15,423
15,423
650,017
15,423
295,241
310,664
***Significant at 1%. ** at 5%. *at 10%. Source: 9.1% public sample of the 2000
Mexico census. Sample includes only children for whom information on the edu.
and age of the head of household.
Remmitances. Cuecuecha. February 2008
35
Table A1, 2000 Mexican Census, 15 to 19 years old
Characteristics of young adults, their households and their municipalities
(Differences with respect to families with no mig. and no rem. in parenthesis)
All
Rem. and
Rem. &
No remi.
No rem.
Mig.
LT mig.
and Mig.
and no mig.
Age
16.8
16.7
16.8
16.79
16.8
(-.01)
(.00)
(-.002)
Males (%)
52.9
54.2
52.7
54.4
52.7
(.01)
(-.001)
(.01)**
Family income
5704.63
6214.39
4669.14
7081.1
5613.6
(annual pesos pc)
(600)
(-944)**
(1467)**
Education of HH
7.45
5.92
4.74
6.76
7.66
Head
(-1.74)**
(-2.92)**
(-.90)**
Age of HH head
43.9
48.8
51.11
41.9
43.7
(5.13)**
(7.37)**
(-1.83)**
% of children in hh
9.10
100
0
100
0
with mig.
% of children in HH
4.53
100
100
0
0
with rem.
GDP pc
10,376
6,835
8,455
6,431
10,779
in mun.
(-3,943)** (-4,347.74)** (-2323.37)**
Gross fixed investment
.037
.045
.035
.042
.037
/gdp in mun.
(.008)**
(-.002)**
(.004)**
N (thousands)
721,978
1,294
38,637
54,115
627,932
Source: Calculations done by the author using the 9.1% public sample of the 2000
Mexico census. Sample includes only children of the head of household, for whom
information on their education, the education of the head, and the migration
information in the households is available. **1 % significance level.
Remmitances. Cuecuecha. February 2008
36
Table A2, 2000 Mexican Census, 8 to 14 years old
Characteristics of children, their households and their municipalities
(Differences with respect to families with no mig. and no rem. in parenthesis)
All
Rem. and Rem. and
No rem.
No rem.
Mig.
LT mig.
and Mig. and no mig.
Age
10.94
10.87
10.94
10.88
10.95
(-.07)*
(-.007)
(-.06)**
Males (%)
50.7
51.2
51.9
50.7
50.6
(.01)
(.01)*
(.001)
Family income
4510
5303
3876
6055
4371
(annual pesos pc)
(931)
(-494)**
(1683)**
Education of HH
7.46
6.08
4.74
6.81
7.67
Head
(-1.58)**
(-2.91)**
(-.85)**
Age of HH head
43.8
47.9
51.11
41.7
43.7
(4.23)**
(7.37)**
(-1.99)**
% of children in hh
9.58
100
0
100
0
with mig. to the US
% of children in HH
4.51
100
100
0
0
with rem.
GDP per capita in mun.
5,717
3,544
3,805
4,374
5,964
(-2,420)** (-2,158)** (-1,589)**
Gross fixed investment/
.037
.047
.034
.042
.037
gdp in mun.
(.009)**
(-.002)**
(.005)**
N
1,298,362
1,294
38,637
54,115
627,932
Source: Calculations done by the author using the 9.1% public sample of the 2000
Mexico census. Sample includes only children of the head of household, for whom
information on their education, the education of the head, and the migration
information in the households is available. **1 % significance level.
Table A3, 2000 Mexican Census,
Instrumental variables and regions
8 to 14 yr old
15 to 19 yr old
Mig rate in municipality exc. hh i.
.02
.02
(.06)
(.06)
Rem /Income in municipality exc. hh i.
.02
.02
(.03)
(.04)
Capital/labor ratio in municipality
140.53
141.45
(188.31)
(190.54)
Border
11.49
11.75
North
11.92
11.70
Center
36.95
36.98
Capital
15.08
16.92
South
24.53
22.62
Source: 2000 Mexico census. Border: all Mexican states in the US border.
North: Baja Sur, Nayarit, Zacatecas, Aguascalientes, San Luis Potosí.
Center: Jalisco, Colima, Guanajuato, Michoacan, Queretaro, Hidalgo.
Puebla, Tlaxcala, Veracruz, Morelos. Capital: DF, Edomex. South: all others.
Remmitances. Cuecuecha. February 2008
37
Table A4
Variables used in the estimation of the pairwise propensity scores
Family income
Age of children
Education of head of household
Age of head of household
Gross fixed investment in municipality
GDP pc in municipality
K/L in municipality
Mig rate in mun. excluding family i
Rem/GDP in mun. excuding family i
Mig rate in municipality*Rem/GDP in mun. (excluding family i)
·
Table A5, Average Treatment Effect on the Treated, Pairwise comparisons
IV method-linear probability model
Combined Income Allocation LT migration
Combined
Effect
Effect
Effect
Effect
effect for LTM
θ1,4
θ1,2
θ2,4
θ1,3
θ3,4
Males 15-19
6.63**
6.92*
-.81***
7.45*
-.28***
(3.15)
(3.23)
(.27)
(3.16)
(.10)
8-14
5.36*
3.78
-.61***
4.13*
-.18**
(3.16)
(2.31)
(.22)
(2.26)
(.09)
Females 15-19
9.22**
9.67**
-.88***
10.11***
-.47***
(3.90)
(4.00)
(.29)
(3.90)
(.13)
8-14
7.95*
5.86*
-.74***
6.15**
-.37***
(4.23)
(3.00)
(.23)
(2.93)
(.12)
***Significant at 1%. ** at 5%. *at 10%. Source: 9.1% public sample of the 2000
Mexico census. Sample includes only children for whom information on the edu., and
age of the head of household is known.
Remmitances. Cuecuecha. February 2008
Figure 1:
38
Remmitances. Cuecuecha. February 2008
Figure 2:
39