Migration, Remittances and Investment in Human Capital
21
First Draft – Not to be quoted Migration, Remittances and Investment in Human Capital: The Case of Bangladesh Anupam Das * Department of Policy Studies Mount Royal University John Serieux * Department of Economics University of Manitoba Sayema Haque Bidisha * Department of Economics University of Dhaka 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.
Migration, Remittances and Investment in Human Capital
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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III.c. Data