migration, remittances and investment in human capital
TRANSCRIPT
Migration, Remittances and Investment in Human Capital:
The Case of Bangladesh
Abstract:
Migration and the consequent flow of remittances is part of the reality of a significant proportion of households in Bangladesh. However, the relationships relating to migration, remittance receipts and investment in education is a complex one. Using data from the 2010 Bangladesh Household Income and Expenditure Survey this paper investigates the relationship between school attendance at the secondary and post-secondary level and the receipt of household remittances. Propensity score matching suggests that, remittances combined with outmigration of a household member increases school attendance at both the secondary and postsecondary level but remittance receipts without outmigration from the household has a negative effect on school attendance. The simultaneous equation approach, by contrast, suggests that both migration and remittances have positive effects but the effect of remittance receipts is much more stronger. The study therefore suggests significant policy implication of remittances and migrations on human capital formation of the country.
I. Introduction
Migration and the consequent flow of remittances is part of the reality of a significant
proportion of households in Bangladesh. From the 1970s into the 21st century, international
outmigration was increasing steadily until it peaked at 875,055 in 2008 (Islam, 2012). As a
consequence, in 2012, remittance receipts from abroad amounted to the equivalent of 10.7 percent
of the GDP of Bangladesh (WDI, 2014). Internal migration too has been substantial. According to
the 2011 census, nearly 10 percent of the Bangladeshi population was living in a district other than
that of their birth. Thus, though internal remittance amounts are generally smaller in magnitude, they
affect a significantly larger proportion of households (Das et al, 2013). It follows that, given the
magnitude and pervasiveness of remittance flows, for many households, decision relating to human
capital investments (and specifically for this investigation, school attendance) will be affected by
migration and remittance receipts.
However, the relationships relating to migration, remittance receipts and investment in
education is a complex one. Firstly, decisions relating to school attendance by (school-aged)
members of the household are likely to be affected by both migration and remittances, but possibly
in different directions. According to the income effect (which we will refer to here as the remittance
effect), the receipt of remittance, by reducing liquidity constraints, is likely to increases the ability of
households to finance and support school attendance. By contrast, migration producing households
may face a migration effect whereby the removal of adult members of the household through migration
places additional burdens on younger members of the household that reduce the prospect of school
attendance. However, if migration is a premeditated decision of households, educational attainment
may rise due to the potential high return from education in the post-migration period. This is the
brain-gain effect suggested by Doquier and Rapoport (2009), and McKenzie and Rapoport (2010).
Secondly, all three decisions - migration, remittance flows and school attendance - may be either
simultaneously determined or related to common factors. For example, the decision to finance the
migration of a member of the household may be taken with the intention of obtaining additional
income to support school attendance by younger members. Alternatively, both the decision to
migrate and the decision on schooling might be part of a response to an income or related shock
experienced by the household. In short, any decision relating to school attendance is likely to be
related to both migration and remittance flows but these relationships are not straightforward.
The complexity in the education-income-migration relationship presents significant
challenges to investigators. Most studies investigating the relationship between remittances and
school attendance have either ignored migration or presumed it as necessarily preceding remittances.
(Thus, remittance-receiving and migrant-producing households are assumed to be identical).
However, in the specific case of Bangladesh, though most migrant producing households do in fact
receive remittances, remittance receiving households are not necessarily migrant producing
households (Das et al, 2013). This may be the case in many other countries as well. Therefore, for
some households the individual effect of remittances (presuming no migration) may be the relevant
factor while in other households it is the net of both migration and remittances that matter. This
study will attempt to discern both the combined effect of remittances and migration and the specific
effect of remittances for households in Bangladesh. More specifically, using data from the 2010
Bangladesh Household Income and Expenditure Survey (HIES) and applying a variety of statistical
techniques, this paper investigates the relationship between school attendance at the secondary and
post-secondary level and the receipt of household remittances when the household has experienced
no migration and when migration is also taken into account.
The remainder of this paper is organized as follows: the next section provides a brief review
of the complex relationships between remittances, migration and education as described in the
literature; he succeeding section describes the methodologies applied in this investigation; the
penultimate section presents the results of the investigation, and the last section concludes the
paper.
II. Literature Review
Although the literature on the development effects of remittances and migration is quite
large, their impacts on investment in education (in the form of school attendance or educational
attainment) have not often been a subject of investigation until recently. The existing studies can be
divided into three specific groups. The first group attempts to identify the nexus between
remittances and school attendance (the 'remittance effect'). In most empirical papers, the remittance
effect is found to be positive. For example, results from Cox Edwards and Ureta (2003) support the
view that remittances in El Salvador always have a larger effect than other types of income on
school retention. The greater positive effect of remittance-income and school attendance holds even
when the authors measure income from sources that are not directly correlated with parental
schooling or remittances. Therefore, "... relaxing the budget constraint of poor households does
have an effect on children’s school attainment, even if parents have low levels of schooling" (Cox
Edwards and Ureta 2003, 457). Boraaz (2005) uses household surveys and Census reports of Mexico
from 1992 to 2002 and confirms that children who live in remittance receiving households in cities
complete more years of schooling than children living in non-remittance receiving households.
Adams Jr. and Cuecuecha (2010) use a nationally-representative household dataset from Guatemala
to examine the effects of remittances (both internal and international) on the marginal spending
behaviour or households. According to their findings, internal and international remittance receiving
households spend 377 percent and 194 percent more, respectively, on education than non-
remittance receiving households. Acosta (2011) works with a dataset from El Salvador and finds that
the likelihood of staying in school for the children of remittance-receiving households is 5 to 7
percent higher than for non-remittance receiving households. After controlling for wealth, this
relationship remains positive but the impact of remittances goes down to approximately 2 percent.
Calero et al (2009), and Mansour et al (2011) find similar results for Ecuador and Jordan
respectively.
Instead of studying the overall effects of remittance on education, Yang (2003) and Alcaraz
et al (2012) examine the effects of exogenous economic shocks on remittances flows and education
in large-remittance receiving countries. Yang (2003) uses data that covers the period of the Asian
economic crisis and includes the remittance amounts sent by Filipino workers from different
countries. The author shows that remittance equivalent to 10 percent of the total household income
result in a 10.3 percentage point increase in school attendance for students whose age was between
17 to 21 years. Similar results are found for students from 10 to 16 years old. Alcaraz et al (2012)
investigate whether the fall in remittance flows (due to the global economic crisis) from the U.S. to
Mexico affected school attendance in the home country. Within a differences-in-differences
framework, the treatment and the control groups are composed of children of remittance-receiving
and non-remittance-receiving households respectively. Their findings suggest that the fall in
remittances due to the economic shock reduced school attendance and increased child labour in
Mexico. Kroeger and Anderson (2013) however found rather unconventional relationships between
remittances and school enrollment during a volatile period in Kyrgyzstan from 2005 to 2009. Using
the fixed effect estimation technique, their results suggested no correlation between the receipt of
remittances and school enrollment for children aged 6 to 18. When the same relationship was
estimated for a sub-group of older children aged from 14 to 18, they found that the school
enrollment of the older students was 2.4 percentage point lower, on average, in remittance receiving
households than non-remittance receiving households. There was no correlation between
remittances and school enrollment for older girls but the coefficient for older boys was negative
0.028. Kroeger and Anderson conclude that, when compared to females, male members of
households are more likely to migrate to find a better job. Therefore, the number of years of school
enrollment drops among older male students.
Although the effect of migration on school attendance is generally perceived as a negative
one, recent empirical evidence does not necessarily support that presumption. Mansuri (2006), for
example, finds a significant positive impact of migration on school enrollment in Rural Pakistan.
Using the standard OLS, OLS with fixed effects, and IV with fixed effects, Mansuri suggests that
children from migrant households are more likely to attend school and accumulate more years of
schooling than children from non-migrant households. Results from Kandel and Kao (2001) are
rather interesting. The first set of results suggest that the high level of US migration of a family
member is negatively associated with university aspirations but positively associated with a higher
grade point average (GPA) for Mexican children. Kandel and Kao hypothesised that a higher GPA
is associated with higher financial resources from migrant members that reduce the probability of
children's participation in the labour force.
The third set of literature examines the overall effects of migration and remittances on
educational attainment. Studies that examine the net effects of migration and remittances find that
the negative effect of migration on schooling eliminates the positive effect of remittances. McKenzie
and Rapoport (2011) define the overall impact as the sum of three important effects: i) the positive
effects of remittances on investment in education due to the release of budget constraints, ii) the
negative effect of migration because of unavailability of parents in the household, and iii) the
negative effect of migration prospects on education due to lower educational returns. One of the
problems of including remittances and migration simultaneously in an equation to determine
educational outcome is that there may be endogeneity and omitted variable bias (McKenzie and
Sasin, 2007; Calero et al, 2008). Hu (2011) gives a number of examples of possible sources of
endogeneity that may arise from migration. Migration of adult family members may take place to
finance the education of younger members of the family. Additionally, while remittances may
determine educational outcome, the opposite may very well be true. "... for example, an aunt may be
remitting to a favorite nephew to reward him for his school attendance. In that case, the nephew’s
schooling is determining the aunt’s remittances instead of the reverse" (Amuedo-Dorantes and
Pozo, 2010, 1751-1752). Omitted variable bias Natural derives from the fact that third, unmeasured,
variables may determine both the choice of schooling and migration. A natural disaster or lean
period of food production may increase the rates of migration and children's dropping out of school
simultaneously. Amuedo-Dorantes and Pozo (2010) argue that if remittances are negatively related
to expected household income, which in turn is positively related to school attendance, the estimate
of the effects of remittances on school attendance may produce results that are downward biased.
Hence most studies use different instrumental variable (IV) techniques in order to accommodate the
problems of endogeneity and omitted variable bias. Both Hu (2011), and Amuedo-Dorantes and
Pozo (2010) use the migration network as instruments. Hu studies the rural-urban migration in
China and its effect on high school attendance on children who are left behind. His analysis
highlights the negative effect of displaced adult household members on the high school attendance
of children in rural areas. The positive effect of remittances partially offset that loss by releasing the
liquidity constraints. These effects are stronger for girl child as well as for those from poor
households. Amuedo-Dorantes and Pozo (2010) find similar results for migrants in the Dominican
Republic. Overall, they find that a 10 percentage point increase in remittances tends to increase
school attendance by 3 percentage points. But once the children in migrants' households are
included in the estimation, remittances do not seem to have any significant positive impact on
children's school attendance. Hanson and Woodruff (2003), using Mexican data, argue that 10 to 15
year old children who live in households with migrants in the U.S. and a low level of parents'
education tend to complete an extra 0.23 years of schooling. In order to accommodate the
endogeneity that emerges from household migration, they use instruments including the interaction
between historical state migration patterns and household characteristics. Results from the IV
regression estimates suggest an extra 0.73 to 0.89 years of schooling for 10 to 15 year old girls who
live in migrant households and whose mothers have low education levels. However this difference
was not statistically significant for more than one year. These results are challenged by McKenzie
and Rapoport (2011). They use the IV-censored ordered probit model and show that living in a
migrant household actually reduces the chances of completing junior high school for boys and high
school for both boys and girls. Koska et al (2013), using the Egypt Labour Market Panel Survey
(ELMPS) and applying different econometric techniques (including OLS, fixed effects, and IV)
show that the migration effect has a larger impact than the remittance effect in Egypt. In other
words, the negative effect of migration seems to dominate the positive effect on human capital
investment (i.e., school enrollment).
We can draw a number of conclusions from the literature review:
i) There are compelling reasons to believe that the decision to migrate, remit funds and
educational attainment are strongly interrelated. Any estimation technique that
includes these variables should accommodate the issues of simultaneity, reverse
causality, and omitted variable bias.
ii) The relationship between remittances, migration and educational attainment is a
rather a complex one. Remittances can increase school attendance by removing the
liquidity constraints of migrants' households. Migration however has a high
opportunity cost that can result in a reduction in school attendance of children who
are left behind. The negative effect of migration may sometimes overwhelm the
positive effect of remittances.
iii) The effects of remittances and migration on educational attainment may vary across
gender, different age groups, communities and countries.
This paper will contribute to the existing literature by attempting to discern, separately and
combined, the effect of migration and remittances on school attendance for secondary school and
college/university-aged students. This investigation will attempt to do so by applying two separate
and complementary approaches: propensity score matching and estimating a simultaneous equation
system (both of which seek to address the inherent complications in the relationships). To our
knowledge, this is the first time that school attendance of university-aged students has been
investigated with respect to remittances and migration and also the first time that these investigative
methods have been used in combination.
III. Methodology
The complex nature of the relationships between school attendance, migration and remittance
receipts complicate any attempt to uncover the nature and extent of the relationship between school
attendance and remittance receipts, school attendance and migration and school attendance and the
combined effect or migration and the receipt of remittances. Several methods have been used to try
to address the issues of endogeneity, bi-causality and omitted variables in these relationships. In this
investigation we will apply two approaches both of which attempt to do so in very different ways.
III.a. Propensity Score Matching
At face value, one way to deal with the complex nature of the relationship between
migration, remittances and school attendance is to directly compare households that do experience
outmigration and/or remittance receipts with those that do not. Such an approach would seek to
determine if schooling outcomes of children from “treated” households (meaning those who receive
remittance and/or experience outmigration) differ significantly from households that have not had
either of these experiences. However, if, as is generally presumed, the decision to finance and
accommodate the outmigration of a household member and/or to obtain remittances from relatives
and friends living elsewhere is not independent of household characteristics, a simple comparison of
households that have experienced migration and/or remittances with those that have not will not
necessarily be a comparison between households with similar propensities – thus producing an
inherent bias in the estimation of the schooling effect. If the selection into migration and remittance
receipt is attributable purely to unobservable attributes then it may be impossible to correct for that
bias. However, if at least some (and preferably most) of this self-selection is related to observable
household attributes then propensity score matching allows us to partially or wholly correct for that bias
by more closely matching households across measurable attributes without.
In the current framework we wish to estimate the following relationship:
= 0 + 1 + 2 + 3 + (1)
where Aij a binary variable representing the school attendance or non-attendance of secondary or
post-secondary school aged students (the outcome of interest); Tj represent the treatment variable
which, for this investigation, will be either the receipt of remittances with no outmigration at the
household level or the receipt of remittances with outmigration at the household level; Xij represent
individual attributes; Wj represent household characteristics; and εij are iid residual errors. Thus β1 is
the coefficient of primary interest. If we assume that the determinants of treatment (whether a
household has experienced outmigration and/or receives remittances) are a set of covariate Yj that is
a subset of Wj then in an ideal world we could simply match each household that has received the
treatment of interest (remittance receipt only or remittance receipt and outmigration) with a
untreated household that exactly matches that household across each dimension of Yj. However this
would require an enormous sample of household, from which a control group is to be chosen, to
make such an approach feasible. Rosenbaum and Rubin (1983) argue that a nearly equivalent
approach (but requiring a much smaller, and feasible, sample of untreated households) is to match
households by a “propensity score” measured as the probability that a household will match the
criteria met by households that do receive the treatment (Z). This probability score is typically
determined by as a probit regression which can be described by the model:
() = ( = 1|) (2)
From the literature, the attributes generally shown to be associated with households that choose to
finance and accommodate out migration and receive remittances include: age, gender and education
of the household head, the number of adults and children in the household, the marital status of the
household head and indicators of the level of wealth and income of the household.1
Once a propensity score has been assigned to each household matching across treated and
control groups can be accomplished using several methods. For this investigation we utilize, and
present, the results from the following approaches.
Nearest neighbour matching – each united from the treated sample is matched
with the unit from the untreated sample that has the closest propensity score.
Radius matching – each unit from the treated sample is matched with the unit
from the untreated sample that has the closest propensity score within a specified
radius (or maximum range).
Kernel matching – each united from the treated sample is matched with several
units from the untreated sample using weights based on the distance in terms of
propensity scores.
Stratified matching – outcomes are compared across blocks of treated and
untreated units rather than individual units.
1 Income is, typically, not included directly because it is endogenous and it not easily disentangled from remittance
receipts.
Using these matching techniques we attempt to measure the average treatment effect on the treated, which
is the difference between the average outcome of the treated units versus what is estimated to be the
average outcome they would have achieved had they not been treated.
III.b. A Simultaneous Equations Model
A directly estimable version of equation (1) would have to include both treatments
(remittance receipt and out migration). Thus, school attendance of person i in household j (Aij) is
modeled as the linear outcome of the effect outmigration from the household (Mj), remittance
receipt (Rj), personal characteristics (Xij), and household characteristics (Wj). Since Aij is a binary
variable (school attendance or nonattendance) such a model would typically be measured as a probit
or logit model. The personal attributes could include the age of the individual, gender and the
number and age of siblings. Household characteristics could include the number of children
attending (or not attending school), the number of adults in the household, the age, gender and
education level of the household head, and various indicators of the household’s income and wealth.
= 0 + 11 + 12 + 2 + 3 + (3)
However, as noted previously, both M and R are endogenous variables and each can be
modeled as outcomes of specific individual, household and community factors. For migration the
community level variables would be indicators of network and institutional effects that tend to
generate migration (Hu, 2012). This would include past and present levels of migration from the
community and rural status. Remittance receipts (measured as the proportion of remittances income
in total income) are affected by levels of outmigration, as well as community level factors that
indicate past migration and remittance behaviour. This would include the past and present levels of
(per capita) remittance receipts. The Yj variables in these equations (see below) would be similar to
the variables used in the probit model for determining propensity scores above.
= 0 + 1 + 2 + (4)
= 0 + 1 + 2 + 3 + (5)
Clearly, equations (3)-(5) can be estimated as a set of simultaneously equation but not a
conventional one. The school attendance equation is properly estimated as binary probit or logit
regression. The migration equation would typically be measured as an ordered probit model (with
the number of migrants varying from 0 to 4 or 5) and the remittance equation as a tobit regression
given that negative values of remittances are not encountered.2 However the conditional mixed
process estimator (CMP), developed for STATA, by Roodman (2008, 2009) can be used to estimate
a system of equations in which the dependent variables take different forms (binary, discrete,
censored or continuous). The CMP is used to estimate equations e(3), (4) and (5) as a system of
equations. Equations (4) and (5) are identified by the inclusion of different community level
variables.
III.c. Data
Most of the data used in this investigation (for both the propensity score matching and the
regression analysis) was extracted from the Bangladesh Household Income and Expenditure Survey
22 It is possible to define negative values of remittance receipts by including households that send remittances to
other households but it is not clear that negative remittance receipts truly exist along a continuum with positive receipts (meaning that they can be explained by the same household and community attributes).
of 2010. Data on past remittance behaviour was derived from the Bangladesh Household Income
and Expenditure Survey of 2005.
IV. Results and Analysis
Table 1 below presents the results from the propensity score matching. Two groups of
potential students are considered: (1) those of secondary school age (14-18 years); and (2) those of
university age (19-24). The outcome variable is whether the potential student attended school or not.
Two treatments are considered: (a) the receipt of remittances with no outmigration (versus no
remittances and no outmigration in the control group); and (b) receipt of remittances and the
outmigration of a household member (versus no remittances and no outmigration).
Table 1: Average Treated Effect on the Treated
Age Group Treatment Group
Received
Post-
Secondary
(19-24)
Received
Received
*Balancing property within blocks were not satisfied
Noticeably propensity score methods gave both unanimous results and treatment estimates
of similar magnitudes across groups and treatments. For both the secondary-school-age population
and the post-secondary-school aged population, the propensity scores all suggest that the treatment
of outmigration combined with remittance receipts increased the likelihood of school attendance.
The effect was stronger at the secondary level. By contrast, the treatment of remittances receipts
without outmigration reduced the likelihood of school attendance. It would appear that outmigration
and remittance receipt is closely tied to investment in education but receipt of remittances
(presumably from someone outside the immediate family) holds no such attachment. However,
causality cannot be inferred here. On the one hand, it could be that receipt of extra income leads to
reduced incentives to invest in the future but it could also be that it is those households facing
difficulties, that may thus lead to the withdrawal of children from school or the choice of work over
school, who receive remittance from non-household members. At face value the co-determination
story seems more plausible.
Table 2 presents the results of the estimation of the probit regression with school attendance
as the dependent variable (equation 3 above). The results indicate that school attendance of
secondary-school aged students is positively and significantly related to: being female, the education
level of the household head, indicators of income and wealth, high remittance receipts relative to
income and outmigration of a household member. School attendance is negatively and significantly
related to the age of the potential student (suggesting the effect of attrition) and, surprisingly for
Bangladesh, being female. For college-aged participants school attendance is positively and
significantly related to the number of adults in the family (suggesting the effect of reduced
household responsibilities), the education level of the head of the household and the receipt of
remittances. Only the age of the participant had a significant negative relationship with school
attendance.
The probit model results suggest, contrary to the propensity score matching results, that
remittance receipts is the stronger positive determinant of school attendance. However, the probit
model does not as clearly differentiate between the situation where remittance are received without
outmigration and when they are received without migration.
Table 2: Results of Probit Model of School Attendence Estimated Coefficients
Explanatory Variables Secondary School
Number of siblings of post-secondary school age (19-24) 0.0797 -0.00676
(1.43) (-0.09)
Number of siblings of secondary school age (14-18) 0.0191 0.0726
(0.38) (1.29)
Number of siblings of primary school age (6-13) -0.0309 0.00849
(-0.96) (0.16)
(-0.91) (4.32)
0.144 ***
0.172 **
0.149 **
(1.70) (1.12)
0.0378 **
** p < 0.05,
*** p < 0.01
V. Conclusion
In this investigation we used two approaches to investigate the relationship between school
attendance of secondary school and college-aged household members in Bangladesh. Those two
approaches were propensity score matching and a simultaneous equation estimation using the
conditional mixed process (CMP) estimator. The first estimation method suggests, very strongly,
that remittances combined with outmigration of a household member increases school attendance at
both the secondary and postsecondary level but remittance receipts without outmigration from the
household has a negative effect on school attendance. The simultaneous equation approach, by
contrast, is much closer to the majority of the literature in suggesting that both migration and
remittances have positive effects but the effect of remittance receipts is much more statistically
significant. However, the second approach does is not as clearly able to untangle the remittance
effect from the combined remittance-migration effect.
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Migration, Remittances and Investment in Human Capital:
The Case of Bangladesh
III.c. Data
The Case of Bangladesh
Abstract:
Migration and the consequent flow of remittances is part of the reality of a significant proportion of households in Bangladesh. However, the relationships relating to migration, remittance receipts and investment in education is a complex one. Using data from the 2010 Bangladesh Household Income and Expenditure Survey this paper investigates the relationship between school attendance at the secondary and post-secondary level and the receipt of household remittances. Propensity score matching suggests that, remittances combined with outmigration of a household member increases school attendance at both the secondary and postsecondary level but remittance receipts without outmigration from the household has a negative effect on school attendance. The simultaneous equation approach, by contrast, suggests that both migration and remittances have positive effects but the effect of remittance receipts is much more stronger. The study therefore suggests significant policy implication of remittances and migrations on human capital formation of the country.
I. Introduction
Migration and the consequent flow of remittances is part of the reality of a significant
proportion of households in Bangladesh. From the 1970s into the 21st century, international
outmigration was increasing steadily until it peaked at 875,055 in 2008 (Islam, 2012). As a
consequence, in 2012, remittance receipts from abroad amounted to the equivalent of 10.7 percent
of the GDP of Bangladesh (WDI, 2014). Internal migration too has been substantial. According to
the 2011 census, nearly 10 percent of the Bangladeshi population was living in a district other than
that of their birth. Thus, though internal remittance amounts are generally smaller in magnitude, they
affect a significantly larger proportion of households (Das et al, 2013). It follows that, given the
magnitude and pervasiveness of remittance flows, for many households, decision relating to human
capital investments (and specifically for this investigation, school attendance) will be affected by
migration and remittance receipts.
However, the relationships relating to migration, remittance receipts and investment in
education is a complex one. Firstly, decisions relating to school attendance by (school-aged)
members of the household are likely to be affected by both migration and remittances, but possibly
in different directions. According to the income effect (which we will refer to here as the remittance
effect), the receipt of remittance, by reducing liquidity constraints, is likely to increases the ability of
households to finance and support school attendance. By contrast, migration producing households
may face a migration effect whereby the removal of adult members of the household through migration
places additional burdens on younger members of the household that reduce the prospect of school
attendance. However, if migration is a premeditated decision of households, educational attainment
may rise due to the potential high return from education in the post-migration period. This is the
brain-gain effect suggested by Doquier and Rapoport (2009), and McKenzie and Rapoport (2010).
Secondly, all three decisions - migration, remittance flows and school attendance - may be either
simultaneously determined or related to common factors. For example, the decision to finance the
migration of a member of the household may be taken with the intention of obtaining additional
income to support school attendance by younger members. Alternatively, both the decision to
migrate and the decision on schooling might be part of a response to an income or related shock
experienced by the household. In short, any decision relating to school attendance is likely to be
related to both migration and remittance flows but these relationships are not straightforward.
The complexity in the education-income-migration relationship presents significant
challenges to investigators. Most studies investigating the relationship between remittances and
school attendance have either ignored migration or presumed it as necessarily preceding remittances.
(Thus, remittance-receiving and migrant-producing households are assumed to be identical).
However, in the specific case of Bangladesh, though most migrant producing households do in fact
receive remittances, remittance receiving households are not necessarily migrant producing
households (Das et al, 2013). This may be the case in many other countries as well. Therefore, for
some households the individual effect of remittances (presuming no migration) may be the relevant
factor while in other households it is the net of both migration and remittances that matter. This
study will attempt to discern both the combined effect of remittances and migration and the specific
effect of remittances for households in Bangladesh. More specifically, using data from the 2010
Bangladesh Household Income and Expenditure Survey (HIES) and applying a variety of statistical
techniques, this paper investigates the relationship between school attendance at the secondary and
post-secondary level and the receipt of household remittances when the household has experienced
no migration and when migration is also taken into account.
The remainder of this paper is organized as follows: the next section provides a brief review
of the complex relationships between remittances, migration and education as described in the
literature; he succeeding section describes the methodologies applied in this investigation; the
penultimate section presents the results of the investigation, and the last section concludes the
paper.
II. Literature Review
Although the literature on the development effects of remittances and migration is quite
large, their impacts on investment in education (in the form of school attendance or educational
attainment) have not often been a subject of investigation until recently. The existing studies can be
divided into three specific groups. The first group attempts to identify the nexus between
remittances and school attendance (the 'remittance effect'). In most empirical papers, the remittance
effect is found to be positive. For example, results from Cox Edwards and Ureta (2003) support the
view that remittances in El Salvador always have a larger effect than other types of income on
school retention. The greater positive effect of remittance-income and school attendance holds even
when the authors measure income from sources that are not directly correlated with parental
schooling or remittances. Therefore, "... relaxing the budget constraint of poor households does
have an effect on children’s school attainment, even if parents have low levels of schooling" (Cox
Edwards and Ureta 2003, 457). Boraaz (2005) uses household surveys and Census reports of Mexico
from 1992 to 2002 and confirms that children who live in remittance receiving households in cities
complete more years of schooling than children living in non-remittance receiving households.
Adams Jr. and Cuecuecha (2010) use a nationally-representative household dataset from Guatemala
to examine the effects of remittances (both internal and international) on the marginal spending
behaviour or households. According to their findings, internal and international remittance receiving
households spend 377 percent and 194 percent more, respectively, on education than non-
remittance receiving households. Acosta (2011) works with a dataset from El Salvador and finds that
the likelihood of staying in school for the children of remittance-receiving households is 5 to 7
percent higher than for non-remittance receiving households. After controlling for wealth, this
relationship remains positive but the impact of remittances goes down to approximately 2 percent.
Calero et al (2009), and Mansour et al (2011) find similar results for Ecuador and Jordan
respectively.
Instead of studying the overall effects of remittance on education, Yang (2003) and Alcaraz
et al (2012) examine the effects of exogenous economic shocks on remittances flows and education
in large-remittance receiving countries. Yang (2003) uses data that covers the period of the Asian
economic crisis and includes the remittance amounts sent by Filipino workers from different
countries. The author shows that remittance equivalent to 10 percent of the total household income
result in a 10.3 percentage point increase in school attendance for students whose age was between
17 to 21 years. Similar results are found for students from 10 to 16 years old. Alcaraz et al (2012)
investigate whether the fall in remittance flows (due to the global economic crisis) from the U.S. to
Mexico affected school attendance in the home country. Within a differences-in-differences
framework, the treatment and the control groups are composed of children of remittance-receiving
and non-remittance-receiving households respectively. Their findings suggest that the fall in
remittances due to the economic shock reduced school attendance and increased child labour in
Mexico. Kroeger and Anderson (2013) however found rather unconventional relationships between
remittances and school enrollment during a volatile period in Kyrgyzstan from 2005 to 2009. Using
the fixed effect estimation technique, their results suggested no correlation between the receipt of
remittances and school enrollment for children aged 6 to 18. When the same relationship was
estimated for a sub-group of older children aged from 14 to 18, they found that the school
enrollment of the older students was 2.4 percentage point lower, on average, in remittance receiving
households than non-remittance receiving households. There was no correlation between
remittances and school enrollment for older girls but the coefficient for older boys was negative
0.028. Kroeger and Anderson conclude that, when compared to females, male members of
households are more likely to migrate to find a better job. Therefore, the number of years of school
enrollment drops among older male students.
Although the effect of migration on school attendance is generally perceived as a negative
one, recent empirical evidence does not necessarily support that presumption. Mansuri (2006), for
example, finds a significant positive impact of migration on school enrollment in Rural Pakistan.
Using the standard OLS, OLS with fixed effects, and IV with fixed effects, Mansuri suggests that
children from migrant households are more likely to attend school and accumulate more years of
schooling than children from non-migrant households. Results from Kandel and Kao (2001) are
rather interesting. The first set of results suggest that the high level of US migration of a family
member is negatively associated with university aspirations but positively associated with a higher
grade point average (GPA) for Mexican children. Kandel and Kao hypothesised that a higher GPA
is associated with higher financial resources from migrant members that reduce the probability of
children's participation in the labour force.
The third set of literature examines the overall effects of migration and remittances on
educational attainment. Studies that examine the net effects of migration and remittances find that
the negative effect of migration on schooling eliminates the positive effect of remittances. McKenzie
and Rapoport (2011) define the overall impact as the sum of three important effects: i) the positive
effects of remittances on investment in education due to the release of budget constraints, ii) the
negative effect of migration because of unavailability of parents in the household, and iii) the
negative effect of migration prospects on education due to lower educational returns. One of the
problems of including remittances and migration simultaneously in an equation to determine
educational outcome is that there may be endogeneity and omitted variable bias (McKenzie and
Sasin, 2007; Calero et al, 2008). Hu (2011) gives a number of examples of possible sources of
endogeneity that may arise from migration. Migration of adult family members may take place to
finance the education of younger members of the family. Additionally, while remittances may
determine educational outcome, the opposite may very well be true. "... for example, an aunt may be
remitting to a favorite nephew to reward him for his school attendance. In that case, the nephew’s
schooling is determining the aunt’s remittances instead of the reverse" (Amuedo-Dorantes and
Pozo, 2010, 1751-1752). Omitted variable bias Natural derives from the fact that third, unmeasured,
variables may determine both the choice of schooling and migration. A natural disaster or lean
period of food production may increase the rates of migration and children's dropping out of school
simultaneously. Amuedo-Dorantes and Pozo (2010) argue that if remittances are negatively related
to expected household income, which in turn is positively related to school attendance, the estimate
of the effects of remittances on school attendance may produce results that are downward biased.
Hence most studies use different instrumental variable (IV) techniques in order to accommodate the
problems of endogeneity and omitted variable bias. Both Hu (2011), and Amuedo-Dorantes and
Pozo (2010) use the migration network as instruments. Hu studies the rural-urban migration in
China and its effect on high school attendance on children who are left behind. His analysis
highlights the negative effect of displaced adult household members on the high school attendance
of children in rural areas. The positive effect of remittances partially offset that loss by releasing the
liquidity constraints. These effects are stronger for girl child as well as for those from poor
households. Amuedo-Dorantes and Pozo (2010) find similar results for migrants in the Dominican
Republic. Overall, they find that a 10 percentage point increase in remittances tends to increase
school attendance by 3 percentage points. But once the children in migrants' households are
included in the estimation, remittances do not seem to have any significant positive impact on
children's school attendance. Hanson and Woodruff (2003), using Mexican data, argue that 10 to 15
year old children who live in households with migrants in the U.S. and a low level of parents'
education tend to complete an extra 0.23 years of schooling. In order to accommodate the
endogeneity that emerges from household migration, they use instruments including the interaction
between historical state migration patterns and household characteristics. Results from the IV
regression estimates suggest an extra 0.73 to 0.89 years of schooling for 10 to 15 year old girls who
live in migrant households and whose mothers have low education levels. However this difference
was not statistically significant for more than one year. These results are challenged by McKenzie
and Rapoport (2011). They use the IV-censored ordered probit model and show that living in a
migrant household actually reduces the chances of completing junior high school for boys and high
school for both boys and girls. Koska et al (2013), using the Egypt Labour Market Panel Survey
(ELMPS) and applying different econometric techniques (including OLS, fixed effects, and IV)
show that the migration effect has a larger impact than the remittance effect in Egypt. In other
words, the negative effect of migration seems to dominate the positive effect on human capital
investment (i.e., school enrollment).
We can draw a number of conclusions from the literature review:
i) There are compelling reasons to believe that the decision to migrate, remit funds and
educational attainment are strongly interrelated. Any estimation technique that
includes these variables should accommodate the issues of simultaneity, reverse
causality, and omitted variable bias.
ii) The relationship between remittances, migration and educational attainment is a
rather a complex one. Remittances can increase school attendance by removing the
liquidity constraints of migrants' households. Migration however has a high
opportunity cost that can result in a reduction in school attendance of children who
are left behind. The negative effect of migration may sometimes overwhelm the
positive effect of remittances.
iii) The effects of remittances and migration on educational attainment may vary across
gender, different age groups, communities and countries.
This paper will contribute to the existing literature by attempting to discern, separately and
combined, the effect of migration and remittances on school attendance for secondary school and
college/university-aged students. This investigation will attempt to do so by applying two separate
and complementary approaches: propensity score matching and estimating a simultaneous equation
system (both of which seek to address the inherent complications in the relationships). To our
knowledge, this is the first time that school attendance of university-aged students has been
investigated with respect to remittances and migration and also the first time that these investigative
methods have been used in combination.
III. Methodology
The complex nature of the relationships between school attendance, migration and remittance
receipts complicate any attempt to uncover the nature and extent of the relationship between school
attendance and remittance receipts, school attendance and migration and school attendance and the
combined effect or migration and the receipt of remittances. Several methods have been used to try
to address the issues of endogeneity, bi-causality and omitted variables in these relationships. In this
investigation we will apply two approaches both of which attempt to do so in very different ways.
III.a. Propensity Score Matching
At face value, one way to deal with the complex nature of the relationship between
migration, remittances and school attendance is to directly compare households that do experience
outmigration and/or remittance receipts with those that do not. Such an approach would seek to
determine if schooling outcomes of children from “treated” households (meaning those who receive
remittance and/or experience outmigration) differ significantly from households that have not had
either of these experiences. However, if, as is generally presumed, the decision to finance and
accommodate the outmigration of a household member and/or to obtain remittances from relatives
and friends living elsewhere is not independent of household characteristics, a simple comparison of
households that have experienced migration and/or remittances with those that have not will not
necessarily be a comparison between households with similar propensities – thus producing an
inherent bias in the estimation of the schooling effect. If the selection into migration and remittance
receipt is attributable purely to unobservable attributes then it may be impossible to correct for that
bias. However, if at least some (and preferably most) of this self-selection is related to observable
household attributes then propensity score matching allows us to partially or wholly correct for that bias
by more closely matching households across measurable attributes without.
In the current framework we wish to estimate the following relationship:
= 0 + 1 + 2 + 3 + (1)
where Aij a binary variable representing the school attendance or non-attendance of secondary or
post-secondary school aged students (the outcome of interest); Tj represent the treatment variable
which, for this investigation, will be either the receipt of remittances with no outmigration at the
household level or the receipt of remittances with outmigration at the household level; Xij represent
individual attributes; Wj represent household characteristics; and εij are iid residual errors. Thus β1 is
the coefficient of primary interest. If we assume that the determinants of treatment (whether a
household has experienced outmigration and/or receives remittances) are a set of covariate Yj that is
a subset of Wj then in an ideal world we could simply match each household that has received the
treatment of interest (remittance receipt only or remittance receipt and outmigration) with a
untreated household that exactly matches that household across each dimension of Yj. However this
would require an enormous sample of household, from which a control group is to be chosen, to
make such an approach feasible. Rosenbaum and Rubin (1983) argue that a nearly equivalent
approach (but requiring a much smaller, and feasible, sample of untreated households) is to match
households by a “propensity score” measured as the probability that a household will match the
criteria met by households that do receive the treatment (Z). This probability score is typically
determined by as a probit regression which can be described by the model:
() = ( = 1|) (2)
From the literature, the attributes generally shown to be associated with households that choose to
finance and accommodate out migration and receive remittances include: age, gender and education
of the household head, the number of adults and children in the household, the marital status of the
household head and indicators of the level of wealth and income of the household.1
Once a propensity score has been assigned to each household matching across treated and
control groups can be accomplished using several methods. For this investigation we utilize, and
present, the results from the following approaches.
Nearest neighbour matching – each united from the treated sample is matched
with the unit from the untreated sample that has the closest propensity score.
Radius matching – each unit from the treated sample is matched with the unit
from the untreated sample that has the closest propensity score within a specified
radius (or maximum range).
Kernel matching – each united from the treated sample is matched with several
units from the untreated sample using weights based on the distance in terms of
propensity scores.
Stratified matching – outcomes are compared across blocks of treated and
untreated units rather than individual units.
1 Income is, typically, not included directly because it is endogenous and it not easily disentangled from remittance
receipts.
Using these matching techniques we attempt to measure the average treatment effect on the treated, which
is the difference between the average outcome of the treated units versus what is estimated to be the
average outcome they would have achieved had they not been treated.
III.b. A Simultaneous Equations Model
A directly estimable version of equation (1) would have to include both treatments
(remittance receipt and out migration). Thus, school attendance of person i in household j (Aij) is
modeled as the linear outcome of the effect outmigration from the household (Mj), remittance
receipt (Rj), personal characteristics (Xij), and household characteristics (Wj). Since Aij is a binary
variable (school attendance or nonattendance) such a model would typically be measured as a probit
or logit model. The personal attributes could include the age of the individual, gender and the
number and age of siblings. Household characteristics could include the number of children
attending (or not attending school), the number of adults in the household, the age, gender and
education level of the household head, and various indicators of the household’s income and wealth.
= 0 + 11 + 12 + 2 + 3 + (3)
However, as noted previously, both M and R are endogenous variables and each can be
modeled as outcomes of specific individual, household and community factors. For migration the
community level variables would be indicators of network and institutional effects that tend to
generate migration (Hu, 2012). This would include past and present levels of migration from the
community and rural status. Remittance receipts (measured as the proportion of remittances income
in total income) are affected by levels of outmigration, as well as community level factors that
indicate past migration and remittance behaviour. This would include the past and present levels of
(per capita) remittance receipts. The Yj variables in these equations (see below) would be similar to
the variables used in the probit model for determining propensity scores above.
= 0 + 1 + 2 + (4)
= 0 + 1 + 2 + 3 + (5)
Clearly, equations (3)-(5) can be estimated as a set of simultaneously equation but not a
conventional one. The school attendance equation is properly estimated as binary probit or logit
regression. The migration equation would typically be measured as an ordered probit model (with
the number of migrants varying from 0 to 4 or 5) and the remittance equation as a tobit regression
given that negative values of remittances are not encountered.2 However the conditional mixed
process estimator (CMP), developed for STATA, by Roodman (2008, 2009) can be used to estimate
a system of equations in which the dependent variables take different forms (binary, discrete,
censored or continuous). The CMP is used to estimate equations e(3), (4) and (5) as a system of
equations. Equations (4) and (5) are identified by the inclusion of different community level
variables.
III.c. Data
Most of the data used in this investigation (for both the propensity score matching and the
regression analysis) was extracted from the Bangladesh Household Income and Expenditure Survey
22 It is possible to define negative values of remittance receipts by including households that send remittances to
other households but it is not clear that negative remittance receipts truly exist along a continuum with positive receipts (meaning that they can be explained by the same household and community attributes).
of 2010. Data on past remittance behaviour was derived from the Bangladesh Household Income
and Expenditure Survey of 2005.
IV. Results and Analysis
Table 1 below presents the results from the propensity score matching. Two groups of
potential students are considered: (1) those of secondary school age (14-18 years); and (2) those of
university age (19-24). The outcome variable is whether the potential student attended school or not.
Two treatments are considered: (a) the receipt of remittances with no outmigration (versus no
remittances and no outmigration in the control group); and (b) receipt of remittances and the
outmigration of a household member (versus no remittances and no outmigration).
Table 1: Average Treated Effect on the Treated
Age Group Treatment Group
Received
Post-
Secondary
(19-24)
Received
Received
*Balancing property within blocks were not satisfied
Noticeably propensity score methods gave both unanimous results and treatment estimates
of similar magnitudes across groups and treatments. For both the secondary-school-age population
and the post-secondary-school aged population, the propensity scores all suggest that the treatment
of outmigration combined with remittance receipts increased the likelihood of school attendance.
The effect was stronger at the secondary level. By contrast, the treatment of remittances receipts
without outmigration reduced the likelihood of school attendance. It would appear that outmigration
and remittance receipt is closely tied to investment in education but receipt of remittances
(presumably from someone outside the immediate family) holds no such attachment. However,
causality cannot be inferred here. On the one hand, it could be that receipt of extra income leads to
reduced incentives to invest in the future but it could also be that it is those households facing
difficulties, that may thus lead to the withdrawal of children from school or the choice of work over
school, who receive remittance from non-household members. At face value the co-determination
story seems more plausible.
Table 2 presents the results of the estimation of the probit regression with school attendance
as the dependent variable (equation 3 above). The results indicate that school attendance of
secondary-school aged students is positively and significantly related to: being female, the education
level of the household head, indicators of income and wealth, high remittance receipts relative to
income and outmigration of a household member. School attendance is negatively and significantly
related to the age of the potential student (suggesting the effect of attrition) and, surprisingly for
Bangladesh, being female. For college-aged participants school attendance is positively and
significantly related to the number of adults in the family (suggesting the effect of reduced
household responsibilities), the education level of the head of the household and the receipt of
remittances. Only the age of the participant had a significant negative relationship with school
attendance.
The probit model results suggest, contrary to the propensity score matching results, that
remittance receipts is the stronger positive determinant of school attendance. However, the probit
model does not as clearly differentiate between the situation where remittance are received without
outmigration and when they are received without migration.
Table 2: Results of Probit Model of School Attendence Estimated Coefficients
Explanatory Variables Secondary School
Number of siblings of post-secondary school age (19-24) 0.0797 -0.00676
(1.43) (-0.09)
Number of siblings of secondary school age (14-18) 0.0191 0.0726
(0.38) (1.29)
Number of siblings of primary school age (6-13) -0.0309 0.00849
(-0.96) (0.16)
(-0.91) (4.32)
0.144 ***
0.172 **
0.149 **
(1.70) (1.12)
0.0378 **
** p < 0.05,
*** p < 0.01
V. Conclusion
In this investigation we used two approaches to investigate the relationship between school
attendance of secondary school and college-aged household members in Bangladesh. Those two
approaches were propensity score matching and a simultaneous equation estimation using the
conditional mixed process (CMP) estimator. The first estimation method suggests, very strongly,
that remittances combined with outmigration of a household member increases school attendance at
both the secondary and postsecondary level but remittance receipts without outmigration from the
household has a negative effect on school attendance. The simultaneous equation approach, by
contrast, is much closer to the majority of the literature in suggesting that both migration and
remittances have positive effects but the effect of remittance receipts is much more statistically
significant. However, the second approach does is not as clearly able to untangle the remittance
effect from the combined remittance-migration effect.
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Migration, Remittances and Investment in Human Capital:
The Case of Bangladesh
III.c. Data