Mental Health Effects of Internal Migration – Evidence from Urban Bangladesh
Incomplete, please do not quote without permission
Prabal K. De
(Corresponding author)
The City College of New York, CUNY
Daniel Dench
The Graduate Center, CUNY
ABSTRACT
We investigate the impact of internal migration on the mental health of migrant population in six
urban areas in Bangladesh. Specifically, we try to empirically test the social theories of selection
and stress by looking at the link between the migrants’ overall mental health status including
suicidal thoughts, and migration status and history. Contrary to most of the previous literature
that have found that migrants have higher levels of mental health problems, we find no
significant difference between the mental health statuses of migrants and native-born. However,
significant differences exist between men and women’s socio-economic and migration behavior
including motives for migration. This confirms previous research that gender plays an important
role in understanding the link between social factors and mental health outcomes. Additionally,
we test theories on the effects of migration duration. To mitigate the problem of migrant
selection, we exploit migration motivation comparing cases of voluntary migration with quasiinvoluntary migration.
Keywords: Internal migration, Mental health, Bangladesh
JEL classification: J61, I12, O15
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1. INTRODUCTION
Migration - the act of moving from the place of birth to another, either voluntary or involuntary,
individual or with the family, within the same country or across international borders - is
generally stressful and involves psychological costs relevant to the exit, the journey, or the
destination. Though economic costs of migration have been researched extensively,
psychological costs have been analyzed to a much lesser extent in the economics literature. This
leaves a gap in our understanding of the cos-benefit analysis of migration; because mental health
costs need to be factored into any cost-benefit analysis of migration either at a personal level, or
at the policy level. Though there is a sizeable literature on the mental health cost of international
migration on immigrants, particularly in the United States, there is surprisingly limited evidence
on the potential mental health costs of internal, often rural to urban, migration in developing
countries. This is unfortunate because domestic migration has also been a strong force in the
global demographic transition. Since 1900, rapid urbanization has led from 10% of the
population living in an urban environment to over 50%, and that percentage is rising quickly,
especially in the developing world (Grimm et al., 2008). Though urbanization cannot necessarily
be equated to migration as some migration takes place within rural areas or urban areas alone
(Lucas, 2015).
In this paper we study the impact of internal migration on migrants’ mental health in six
Bangladeshi cities. Bangladesh is an appropriate country to study as it has been experiencing a
rapid increase in urban population over the last two decades. A large part of this population
comes from rural areas either to seek economic opportunities, to escape consequences of natural
disasters or both. At the same time, urban infrastructure in cities has struggled to keep up with
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this inflow. This motivates our research question: What are the effects of migration to cities,
which both offers better opportunities and presents enormous challenges, on migrants’ mental
health? We have used the 2005 Urban Health Survey, which has well-documented information
on a sample of urban residents’ mental health, economic and demographic characteristics. Such
research can offer important insights into the ongoing global debate on migration, urbanization
and health policy.
Specifically, the first aim of this article is to compare mental health statuses of migrants
with that of the native-born population, hypothesizing that migrants have worse mental health
outcomes on average – a salient finding in the immigrant mental health literature in the Unites
States. The second aim is to test some of the theoretical relationships postulated in the literature.
For example, our data allows us to test the association between mental health and migration
motives, family separation, duration of migration and employment. Finally, we demonstrate how
some of these results vary remarkably along the gender lines. To the best of our knowledge, this
is one of the first pieces of research on the effect of domestic migration on mental health in
South Asia.
Using multiple regressions and matching estimators, we find no evidence that migrants
have overall worse mental health than natives. Owing to the potential selection into migration
depending on various omitted variables, our results cannot necessarily be deemed causal.
However, we perform several tests within the constraints set by our cross-section data to address
this concern. For example, we use matching estimators where individuals are matched on the
observed characteristics that are significant in determining mental health and found the same
result. Our result is also robust to introduction of mediation factors like involuntary migration,
age at migration and whether the migration has been rural to urban or urban to urban.
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Finally, we deal extensively with the gender dimension of migration. It has long been
documented that women often experience migration differently (Pedraza, 1991). It is also wellknown that gender affects some key economic variables such as labor force participation
significantly. This paper presents an opportunity to study variables like migration motivation,
labor force participation and mental health jointly. We do find that process of migration is
gendered as women migrants differ from men in terms of motivation for migration and the away
labor force participation affects mental health.
2. BACKGROUND
2.1. Domestic Migration Issues and Mental Health issues in Bangladesh
In Bangladesh, there has been a steady shift from agricultural to industrial production (the former
down from 32% to 19% and latter up from 21% to 28% as a share of GDP between 1980 and
2010) since its conception in 1971. Bangladesh experienced faster urbanization than South Asia
as a whole between 2000 and 2010. Over that period, the share of its population living in
officially classified urban settlements increased by 1.69 percent a year (World Bank, 2014).
Like many other developing countries, domestic migration to urban areas is a livelihood strategy
in Bangladesh. According to the current trends, more than 50 percent of the population in
Bangladesh will live in urban areas by the year 2025 (ESCAP, 2007). However, this has led to
quality of life issues due to severe infrastructure and service deficiencies, lack of land
administration, expansion of slums and social strife. For example, Dhaka, the largest and best
performing city in Bangladesh, was ranked 139 out of 140 cities by the 2015 livability index of
the Economist Intelligence Unit.
In much of the developing world mental health constitutes a major public health
challenge undermining the social and economic development. For example, According to one
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estimate, mental health disorders account for 13% of the global burden of disease (WHO, 2007).
Mental health support infrastructure in Bangladesh, like many other developing countries, is
inadequate by most accounts. According to a report by WHO in 2007, there was no mental health
authority, no day treatment mental health facilities in the country, and only 31 community-based
psychiatric inpatient units for a total of 0.58 beds per 100,000 population (WHO, 2007).
Research evidence on mental health disorders in Bangladesh is also thin. According to Hossain
et al. (2015), the overall prevalence of mental disorders varied from 6.5 to 31% among adults
and from 13.4 to 22.9% among children As far as mental health disorders in urban areas is
concerned, Izutsu et al. (2015) found that such disorders were worse in slum areas and were
correlated with education and working status.
2.2. Prior research on mental health and migration
There are two different ways in which the mental health impacts of migration have been
examined in the literature. First, the mental health outcomes of migrants are compared in the
destination communities. This is more common in the context of international migration where
mental health conditions of immigrants are routinely compared with those of natives. One strand
of this literature focuses on the incidence of clinical condition of schizophrenia among migrants.
Cochrane and Bal (1987) identify several pathways by which migrating individuals can have
schizophrenia at a higher rate than the native born population and find in the United Kingdom
that largest immigrant groups have higher incidence than natives. More recently, Dealberto
(2010) found similar results. More generally, in an extensive meta-study, Bhugra (2004) found
that migrants overall suffer more from mental health issues than natives. However, this line of
findings contradicts the phenomenon known as the “Healthy Migrant Effect” in the literature –
the phenomenon whereby migrants exhibit better health outcomes than natives when they first
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arrive at their destination country (one that is richer than their home countries), but lose that
advantage over time (Guarnaccia et al. (2005)). Though various Latino groups’ immigration
experiences have been the main focus of this literature in the United States new research has
started focusing on other immigrant groups keeping up with the changes in the demographic
composition. The results are mixed. Studying Vietnamese immigrants in the US from Ho Chi
Minh City and New Orleans, Fu and VanLandingham (2012) found that immigrants face both
mental health advantages and challenges. Finally, Gong et al. (2011) study whether human
agency factors (measured by migration reasons such planning and voluntariness) and timing
(measured by age at immigration) affect mental health outcomes among Asian immigrants in the
United States. We also account for some of these factors.
Such studies are rare in the context of internal migration with some exceptions like Lu et
al. (2007) and Chen (2011), who found that healthy migrant phenomenon was generally valid
among Chinese migrants with some caveats. The first study found that the urban migrants had
higher mental health scores than their urban counterparts, but lower than the rural residents.
Chen (2011) found the healthy migrant effect to be valid with respect to physical health, but not
mental health. The latter paper is closest to our work, though differs along several dimensions.
First, we have six large cities in Bangladesh allowing us to control for spatial variations (Chen
has noted this limitation in the study). Second, we exploit information on conditions surrounding
the migration experience to estimate the effects of causes of migration on mental health.
A second way to explore the mental health effects of migration has been to address the
counterfactual: what would have been the mental health status of a migrant if she had not
migrated? This is a difficult exercise because of its counterfactual nature. Stillman et al. (2009)
exploit a somewhat unique mechanism to address this. They study Tongan migration to New
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Zealand, where the migration treatment was randomly assigned via lottery. They found that
migration had positive effects on mental health and wellbeing of those that received the
migration treatment in comparison to those that wanted to migrate but were randomly assigned
not to migrate. They also found that between those who wanted to migrate and those who did
not, the former group had lower mental health status.
2. 3. Conceptual Framework - Migration and Mental Health
Most of the theoretical framework to analyze the mental health effects of migration is in the
context of international migration, and some of the issues involved like language and cultural
barriers may seem less relevant in the context of internal migration. However, they are not
inseparably attached to international migration. For example, more than one hundred languages
are spoken in India, while all but one country in Latin America speak a common language.
Therefore, we can adopt a theoretical framework similar to the one used to examine the mental
health effects of international migration and use it for studying such problems in the context of
domestic migration.
Theoretically, two forces influence the link between migration and mental health: Social
Selection and Social Stress (Guarnaccia, 1997). The first refers to the circumstances surrounding
migrants’ exit from home communities. It is widely documented that migrants, both internal and
international, are “selected groups” of people, in the sense that they cannot be treated as random
selection from their home communities; rather people who are more (or less) able than an
average member to leave. In terms of mental health status, it is often argued that only the
mentally strong and resilient people migrate. Social stress can be a function of several variables.
First, circumstances surrounding the departure can contribute to stress. This is particularly true in
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Bangladesh, where natural disasters have led to loss of assets and livelihoods and prompted
people to migrate. Even in the absence of dire circumstances, personal feelings of homesickness,
loss of roots and the possibility of facing challenges of new surroundings may make internal
migration stressful. The characteristics of the receiving community also affect the stress level.
Particularly in developing countries like Bangladesh, urban infrastructure facilities may not be
equipped to handle the influx of new users. The native born may also have feelings of animosity
and xenophobia towards the domestic migrants. Finally, the characteristics of migrants – their
human capital resources in terms of health and education, possible ties to social networks and
knowledge of the receiving society also play an important role in determining the stressfulness of
migration. It is also important to keep track of the “control” group when we treat migrants as
belonging to a “treatment” group. When natives form the control group, the social selection
phenomenon also needs to be interpreted differently. Migrants may be more resilient than their
peers in home communities; there is no reason to presume that such would be the case with
respect to their destination community peers also.
2.4. Hypothesis Development
Specifically, we test the following hypotheses:
H1: Mental health problems of domestic migrants in six cities in Bangladesh were not
significantly different from those of natives. If not rejected, this result would provide against one
part of the healthy migrant hypothesis.
H2: Mental health problems vary according to the degrees of voluntariness of migration.
H3: Over time, the mental health problems deteriorate for the domestic migrants the
longer the average duration of stay is.
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3. EMPIRICAL ANALYSIS
To test H1, we start with the following specification captured in equation (1):
_
log
where the subscripts refer to an indivudual i residing in city s, education level is measured in
years of completed education, Migrant, married, schooling, smoker, Islamic and employed are
binary variables. Finally,
captures city fixed effects (s = 1,2,..6). Equation (1) is estimated
with correct sampling weights using data on all individuals, migrants and non-migrants
clustering within the household. As a set of robustness checks for this model we use a covariate
matching method, inverse propensity score weighting, and coarsened exact matching in section
4.4.
For H2, we use the same specification as H1 except we split the migrant variable into two
migrant categories related to choice in migration. Specifically, we test if there is a difference
between migrants that moved for work or education (voluntary), and those that do so for familial
or other reasons (non-voluntary). For H3 we use all the same controls as H1 except that we test
only among migrants and we replace the migration explanatory variable with a quadratic for how
long it has been since the migrant moved to their current city in years.
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3.1. Data and Sample
The data comes from the Bangladesh Urban Health Survey (BUHS), a study conducted by the
National Institute of Population Research and Training (NIPORT) and Measure Evaluation,
University of North Carolina, Chapel Hill, USA. The study was representative of several slums
and non-slums in six urban districts with population over 45,000 – Dhaka, Barisal, Chittagon,
Khulna, Rasjhahi and Sylhet. As part of the survey, several mental health questions were asked
as detailed below. In addition, the study included questions on a plethora of household,
individual and community characteristics. For tables 1-6 we use the survey weights included
with the survey to improve external validity. Table 7, which trades external validity for a better
causal interpretation through balancing estimators, does not use the survey weights.
Table 1 presents descriptive statistics classified by migration status and gender. The first
categorization is the principal focus of this paper and second one is motivated by the fact that
gender plays an important role in many social, economic and health behaviors in Bangladeshi
society such as marriage patterns, workforce participation, smoking and alcohol use. The gender
dynamics also plays an important role in data collection – certain questions were asked to men
only as discussed below.
Please insert Table 1 here
In table 1, Columns (1) and (2) report the means of the variables used in the analysis for men
across migrant and non-migrant (people who respond “yes” to the question “Were you born
here?”) samples. Columns (4) and (5) report the same for women. Columns (3) and (6) report the
p-values from test of differences in means between migrant and non-migrant samples.
Comparing the means, we cannot reject the conjecture that there are significant differences
between migrant and non-migrant groups for both genders for many variables.
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Migration variables
There are several variables related to migration, including when they migrated to their current
area, the reason that they migrated, and the type of place they migrated from. The basic
definition of a migrant member is straightforward: we treat that individual as a migrant (and
assign the value 1 to the relevant binary variable in our analysis) if the response to the question
“Were you born here?” was negative.1
Please insert Table 2 here
Table 2 summarizes some of the migration characteristics for our sample. Panel A reports the
reasons behind migration by gender. Unsurprisingly, migration motivations differ between men
and women. Men are much more likely to have moved looking for work, for more work, for
service transfer, or for their own education than are women (combined 81.4% versus 29.2%).
Women are more likely to have moved for familial or for marriage reasons than are men
(combined 66.8% versus 16.0%). We get back to this issue in section 5 to test if less “voluntary”
motivation behind migration has differential effects on mental health.
Independent Variables
We follow Stillman, McKenzie, and Gibson’s (2009) lead in choice of independent variables,
including gender, age, age-squared, years of education, religion dummies, employment status,
and household income. Though smoking and alcohol consumptions are generally strongly
correlated with mental health, our data present an unusual challenge. To preserve cultural
sensitiveness, the survey asks such questions only to men and not to the women surveyed.
1
We have performed several cross-checks such as making sure that the individual has non-missing and meaningful
values for other migration-related questions to make sure that our coding is correct.
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Accordingly, we include them in the male sample and not in the female sample. We include
fixed effects dummies for the division in which they currently live. Among migrants we also
control for whether the migration was for involuntary reasons, either for familial, marriage, river
erosion, eviction or security reasons. We also control for whether someone lived in a village
before they migrated in some specifications.
Mental Health Variables
We use the average of 19 mental health variables that ask the individual in each household
whether or not in the last month they have been nervous, tense or worried; are easily frightened,
generally feel unhappy, find it difficult to make decisions, have headaches quite often, have
problems to think clearly, find it difficult to enjoy daily activities, lose interest in things,
constantly felt tired, have loss of appetite, have problems with sleep, have uncomfortable
feelings in stomach, experienced shaking of hands, felt tired, cry more than normal, daily
activities were suffering in any way, feel unable to play a useful part in life, have poor digestion,
thought of ending their life, and feel worthless. We code in the direction of absence of these
things taking the value of 0, while presence of these things takes the value 1, so that higher
values of the average mental health variable indicate worse mental health.
Please insert Table 3 here
Table 3 provides a summary of the main outcome variables of interest across (i) gender and (ii)
migrant and native groups of the sample. Results for the average mental health problem can be
interpreted as percentage of the possible problems that are asked that the responder reported they
had. It shows us that male migrants on average tend to have less mental health problems than
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women, while migrants tend to have more mental health problems than non-migrants, before
controlling for other characteristics.
4. RESULTS
4.1 Impact of Migration Status and Migration Duration on Mental Health
Regression estimates for equation (1) are summarized in table 4, where we show the coefficients
of the control variables as well as the respective standard errors. Despite the descriptive results,
after controlling for several confounding variables, the difference in mental health problems
between migrants and non-migrants shrinks to less than a 1% difference in the number of
problems that they report which is not statistically significant.
Please insert Table 4 here
Another interesting finding is employment decreases mental health problems for men, while
significantly increasing mental health problems for women even when controlling for average
income. This may also be a reflection of norms associated with working and gender, as reflected
in the average employment numbers showing men work at over 3 times the rate of women. In
addition there is direct evidence of stigma associated with women working. In our sample, over
60% of women believe that it is either not okay for married women to work in any circumstance
or that it is okay for married women to work only if their husband is not earning enough money2.
So it seems likely that women that chose to work do so out of necessity. This could be a
2
This percentage is the proportion of women that answer no to the question, “If the husband is making
enough money, do you believe that it is acceptable for married women to work outside the home to earn an
income?”
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reflection of differential expectations of what is considered “enough money” since income is
controlled for. It could be that while working, women maintain the majority of home
responsibility in addition to their employment responsibilities which contributes to increased
stress. Even in the US as recently as 2015, where employment for women is the norm rather
than the exception, women that work fulltime more frequently take on the majority of home
responsibilities than men (Pew, 2015). Another explanation is that women are working due to
unexpected expenses. This unexpected expense could be the real driver of higher mental health
issues rather than employment and therefore this omitted variable could bias the coefficient for
female employment in a positive direction (since unexpected expenses would likely have a
positive effect on mental health problems and be positively correlated with female employment).
The other variables take on mostly expected directions; those that have higher income, more
education, and those that are married have lower mental health issues.
4.2 Impacts of quasi-involuntary migration
Theories of work migration predict that individuals move to increase their utility or welfare
(Borjas, 1999). Indeed, in the BUHS, the main reason behind migration among men is workrelated as 83% of the report that they have moved either to look for work, or more work (Table
2). However, this is different for women, for whom the comparable number is only 27.5%.
Similar disparities exist with respect to education as a motive behind migration. On the other
hand, a large proportion of women move due to marriage and other familial reasons. We have
exploited this rich information to create three categories – native born as baseline, voluntary
migrants (those who have moved to find more work) and quasi-involuntary migrants (those who
have moved for familial reasons). We test the hypothesis that different motivations have
differential impacts on mental health. The results are presented in table 5.
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Please insert Table 5 here
In table 5, the first four rows show the correlation between voluntary and quasi-involuntary
migration (as compared to being non-migrants). While neither category of migration has
significantly different mental health than native born men, quasi-involuntary migrants have
significantly better mental health outcomes than voluntary migrants. While the direction of effect
is the same for female quasi-involuntary and voluntary migrants, their categories are not
significantly different from one another.
4.3 Impacts of migration duration
The dominant literature that relates mental health to migration is in the area of immigration into
the United States. One key finding in this literature is that the mental health status of immigrants
declines over time during their stay in the US. The reasons attributed to this phenomenon include
problems of acculturation and the wedge between the idea of ‘promised land’ and reality.
Though internal migration may present lower information barrier and problems of acculturation,
it is worthwhile to test if the average mental health status deteriorated over time for migrants. We
perform this analysis on a restricted sample consisting of only migrants. The results are
presented in table 6.
Please insert Table 6 here
We find no evidence that migrants’ mental health deteriorates over time. In a quadratic
specification the joint F-statistic on migration duration and migration duration2 falls well within
the region of failing to reject the null for men and for women.
4.4. Robustness Tests – Evidence from Matching Estimators
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Like any Ordinary Least Square (OLS) estimation the results above may suffer from omitted
variable bias. What we are really interested in is the effect that migration has on the mental
health of migrants in comparison to what their mental health would be if they had been born in
their current city of residence. Obviously we cannot observe the respondent in these two nonoverlapping states. Recall that in table 1 we found that there are some significant differences in
observable characteristics such as employment, income, and marital status and migration which
may confound the relationship between migration and mental health. We can do a better job of
matching individuals with like characteristics and seeing what the difference is between
individuals who are similar on all observable characteristics except for whether or not they are a
migrants. It should also be noted that this is a way of isolating the effects of migration on mental
health that are separate from observable pathways such as increased income or employment. We
use three strategies to obtain the treatment effect on the treated (ATT) which all produce about
the same results.
The first method, nearest neighbor matching is a method by which a function is applied that
measures the average distance between values of specified covariates of treated and untreated
cases. Treated observations are then matched with the value of the nearest neighbor algorithm
which are most similar. We have used the matching technique developed in Abadie and Imbens
(2002) and Abadie et al. (2004).
The second method is inverse propensity score weighting. First you estimate a model for the
probability that someone would receive the treatment (in this case belong to the migrant group).
Then you run a weighted regression of the outcome on the treatment variable where the weights
are 1 for the treated cases and p/1-p (where p is the probability of treatment) for the untreated
cases. This weights observations from groups in the non-treatment group that have
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characteristics that are similar on average to the treatment group more heavily and those that
have characteristics that are less like the treatment group on average less heavily (Morgan and
Winship, 2014).
The third and final method, coarsened exact matching, works by grouping values of continuous
variables so that the values within groups are not substantially different from one another and
then looking for exact matches between treated and control units. This method is
computationally more efficient than the nearest neighbor matching because it simply
concatenates the values of the coarsened variables, so that each combination of coarsened
variables has exactly one value, sorts and groups the treatment and control units into strata. To
calculate the ATT, weights which equal 1 for the treated unit and (number of control units
matched to treated units/number of treated units matched to control units)*(number of treated
units within strata/number of control units within strata) (Iacus et al., 2011). Table 7 provides the
results of these three methods.
Please insert Table 7 here
As we can see from the reported numbers, while magnitudes of ATT are all similar, migration
does not seem to have a significant effect on the average mental health status of the migrants
using any of the three methods. Pruning, which is identified by King and Nielsen (2015) as a
problem for propensity score estimators (deleting control observations that are not along the
support of the distribution) is not issue in our dataset because an adequate number of control
observations across the distribution of the probability of treatment are available.
4.5. The Healthy Migrant Hypothesis?
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One plausible explanation for our results is that migrants are selected from individuals that are
physically healthier on average and therefore the lack of difference in mental health status is
biased by the omission of physical health status towards a negative effect of migration on mental
health problems. One question asks, “In general, how is (NAME) health? Very healthy,
somewhat healthy, somewhat unhealthy, or unhealthy?” We hesitate to include this as a covariate
in our models because it is not specifically referring to physical health and therefore variation in
this variable may be due to variation in mental rather than physical health. So this would be a
little like having a component of a variable be used as an explanatory variable. However, if
health is significantly better on average in migrant population than conditioning on covariates we
would expect migrants to report being very healthy on average at a higher rate than nonmigrants. This is examined in table 8. We use the same model specification as table 4 and table 5
except that the dependent variable in this case is whether or not they responded “Very Healthy”
to the question on general health. In contrast to the healthy migrant hypothesis, we find that
migrants report being very healthy at a lower rate than non-migrants. This is truer for voluntary
migrants than for involuntary migrants. Therefore in this sample, the healthy migrant hypothesis
seems implausible in explaining our not finding positive effects of migration on mental health
problems.
5. DISCUSSION
Using data from a large scale urban health survey spanning six Bangladeshi cities, we
estimated the impact of internal migration on the mental health of the migrants compared to the
native-born by gender. We analyzed an average of multiple indicators of mental health problems
including suicidal tendencies, thereby providing a nuanced view of the effects of internal
migration on mental health. We have examined several different aspects of migration and how
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such aspects might affect men and women differently. Migration is a gendered phenomenon in
many developing countries including Bangladesh. This was reflected in the reasons behind
migration. A majority of the male migrants moved due to employment reasons whereas female
migrants did for familial reasons. In this paper we have studied whether such differences in
motivation affect the mental health statuses of those migrants by creating a quasi-voluntary
migration category. We also examine an oft-studied question in the international migration
literature – Do migrants mental health cost increase the longer they stay at the destination
communities? In a sensitivity analyses, we controlled for the migrant selection by employing two
matching estimators that assign higher weights to similar individuals.
This study builds on the existing literature on the link between economic decision making
and mental health, including the sub-filed of migration and mental health. This also contributes
to the small, but growing field of literature that links domestic migration with mental health. This
is also one of the first systematic studies on the link between internal migration in Bangladesh
and mental health outcomes in large cities. Given the rapid urbanization and precarious mental
health support infrastructure in cities, this is a pressing issue in Bangladesh like many other
developing countries. Our study covers six populous regions and covers more than 50% of the
urban population of Bangladesh.
There are two principal findings of this study. First, for most of the cases, internal
migration has no significant impact on either overall mental health problems. Second, mental
health status does not deteriorate with time spent in the destination city, nor does motivation
behind migration play a significant role. The former finding differs from a large set of literature
that finds migration linked with harmful outcomes, including higher rates of depression and
lower quality of overall mental health. We also find that employment has different effects on
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men than on women, with lower mental health problems in employed men than in unemployed
men, and higher mental health problems in employed women than in employed women even
after controlling for income.
Our findings come with several caveats. Most of the limitations of this study stem from
the nature of the data that we have at our disposal. The dataset is somewhat unique. It is not easy
to find systematic mental health data in large sample surveys in developing countries that also
include demographic, economic and migration (including migration history variables).
Unfortunately, the dataset lacks either any longitudinal information or any information on
sending communities. This created severe handicap on identifying any causal impacts on
migration, both at the cross-section level or at the level of changes in metal health status. We
have dealt with issues in a number of ways. We have controlled for a large number of variables
to ameliorate selection bias. We have also included city fixed effects to control for spatial
variabilities in health and other infrastructure. Finally, we have examined the healthy migrant
hypothesis and have found it unlikely to explain our results.
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22
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23
Figure1: Map
M of Banglladesh with sttudy areas hig
ghlighted
U
Nationss http://www..un.org/Deptss/Cartographiic/map/profilee/banglade.pddf
Source: United
24
Table 1: Descriptive Statistics
Variable
age
married
Years of Schooling
Islamic Faith
Currently Smoke Cigarettes
Currently Drink Alcohol
Employed
Log Income
In General Health is Very Healthy
Observations
Standard deviations in brackets
Migrant
Male
Non-Migrant
34.52
[10.89]
0.74
[0.44]
6.96
[5.01]
0.87
[0.34]
0.51
[0.50]
0.11
[0.32]
0.92
[0.28]
2.02
[0.88]
0.37
[0.48]
8497
32.89
[11.02]
0.62
[0.49]
7.32
[4.68]
0.82
[0.39]
0.49
[0.50]
0.17
[0.37]
0.86
[0.35]
1.84
[0.81]
0.44
[0.50]
7482
P-value
0.000
0.000
0.129
0.041
0.443
0.001
0.000
0.004
0.001
Migrant
Female
Non-Migrant
32.02
[10.47]
0.83
[0.37]
5.24
[4.62]
0.86
[0.35]
----0.29
[0.45]
1.69
[0.87]
0.28
[0.45]
8913
30.94
[10.58]
0.71
[0.45]
6.25
[4.70]
0.84
[0.37]
----0.23
[0.42]
1.71
[0.92]
0.34
[0.47]
6354
25
Total
P-value
0.000
0.000
0.000
0.588
----0.005
0.667
0.000
32.71
[10.81]
0.74
[0.44]
6.36
[4.83]
0.85
[0.36]
----0.57
[0.49]
1.83
[0.88]
0.35
[0.48]
31246
Table 2: Migrant Characteristics
Main Reason For Moving
looking for work
for more work
service/for transfer
for own education
for children education
for familial
for marriage
buy new land/house
look after property
for river erosion
for eviction
for security
other
Moved from village
Time since migration to current
area
Observations
26
Male
Female
43.9%
15.5%
13.8%
8.2%
0.4%
15.6%
0.4%
0.1%
0.2%
1.1%
0.1%
0.7%
0.1%
50.1%
21.1%
2.5%
3.9%
1.8%
1.4%
36.6%
30.2%
0.3%
0.0%
1.3%
0.2%
0.9%
0.0%
52.4%
12.1
8,497
13.7
8,913
Table 3: Prevalence of Mental Health Problems in the past month*
Variable
Male
Migrant
Non-Migrant
feel nervous, tense, worried
easily frightened
feel unhappy
find it difficult to make decisions
had headaches quite often
have problem to think clearly
find it difficult to enjoy daily activities
lose interest in things
constantly feel tired
loss of appetite
problem with sleep
uncomfortable feelings in stomach
experienced hands shaking
problems with sleep
0.56
[0.50]
0.10
[0.30]
0.23
[0.42]
0.15
[0.36]
0.39
[0.49]
0.19
[0.39]
0.30
[0.46]
0.15
[0.35]
0.30
[0.46]
0.27
[0.45]
0.27
[0.44]
0.19
[0.39]
0.09
[0.29]
0.23
[0.42]
0.53
[0.50]
0.11
[0.31]
0.25
[0.43]
0.19
[0.39]
0.40
[0.49]
0.21
[0.41]
0.29
[0.45]
0.17
[0.38]
0.30
[0.46]
0.27
[0.45]
0.28
[0.45]
0.20
[0.40]
0.11
[0.31]
0.21
[0.41]
27
P-value
0.172
0.220
0.197
0.026
0.479
0.296
0.649
0.153
0.955
0.955
0.619
0.655
0.046
0.439
Migrant
Female
Non-Migrant
0.62
[0.49]
0.20
[0.40]
0.32
[0.47]
0.21
[0.41]
0.57
[0.50]
0.35
[0.48]
0.50
[0.50]
0.26
[0.44]
0.52
[0.50]
0.37
[0.48]
0.33
[0.47]
0.21
[0.41]
0.19
[0.40]
0.36
[0.48]
0.61
[0.49]
0.19
[0.40]
0.29
[0.45]
0.21
[0.41]
0.57
[0.49]
0.33
[0.47]
0.48
[0.50]
0.28
[0.45]
0.50
[0.50]
0.34
[0.47]
0.32
[0.47]
0.22
[0.42]
0.16
[0.36]
0.30
[0.46]
Total
P-value
0.353
0.908
0.017
0.995
0.824
0.195
0.224
0.416
0.254
0.018
0.284
0.309
0.001
0.000
0.58
[0.49]
0.15
[0.36]
0.27
[0.45]
0.19
[0.39]
0.48
[0.50]
0.27
[0.44]
0.39
[0.49]
0.21
[0.41]
0.41
[0.49]
0.31
[0.46]
0.30
[0.46]
0.21
[0.40]
0.14
[0.35]
0.28
[0.45]
cry more than normal
daily activities suffering
thought of ending life
unable to play a useful part in life
suffer from poor digestion
Average of all listed problems
Observations
Standard deviations in brackets
0.03
[0.17]
0.12
[0.32]
0.03
[0.16]
0.14
[0.35]
0.14
[0.35]
0.21
[0.19]
8497
0.03
[0.18]
0.11
[0.31]
0.02
[0.15]
0.13
[0.34]
0.14
[0.34]
0.21
[0.20]
7482
28
0.423
0.690
0.000
0.640
0.739
0.554
0.16
[0.37]
0.18
[0.39]
0.10
[0.30]
0.21
[0.41]
0.12
[0.33]
0.31
[0.24]
8913
0.16
[0.36]
0.15
[0.36]
0.08
[0.28]
0.22
[0.41]
0.13
[0.33]
0.30
[0.23]
6354
0.448
0.134
0.000
0.585
0.811
0.086
0.10
[0.30]
0.14
[0.35]
0.06
[0.23]
0.18
[0.38]
0.13
[0.34]
0.26
[0.22]
31246
Table 4: Determinants of Mental Health Problems including Migration Status
(1)
(2)
Dep Var. Average Mental Health
Problem
VARIABLES
Male
Female
migrate
age
age2
married
Years of schooling complete
Islamic faith
Currently Smoke Cigarettes
Currently Drink Alcohol
Employed
Log income
-0.14
[0.87]
-0.42*
[0.21]
0.0072**
[0.003]
-0.13
[1.06]
-0.62***
[0.09]
0.91
[1.26]
1.23
[0.70]
3.62***
[1.01]
-0.62
[1.34]
-1.35
[0.84]
13,369
-0.63
[0.84]
1.24***
[0.31]
-0.013**
[0.005]
-1.20
[0.86]
-0.83***
[0.10]
2.17
[2.75]
3.92***
[0.98]
-2.21***
[0.62]
12,604
(3)
(4)
Dep Var: Suicidal
Male
Female
0.11
[0.09]
-0.0079
[0.03]
-0.000032
[0.000]
-0.017
[0.11]
-0.029**
[0.01]
0.043
[0.13]
0.067
[0.10]
0.22
[0.12]
-0.41**
[0.15]
-0.083
[0.04]
13,369
-0.010
[0.07]
0.055*
[0.02]
-0.00081*
[0.000]
-0.27***
[0.07]
-0.055***
[0.01]
0.072
[0.18]
0.11*
[0.05]
-0.092
[0.06]
12,604
Observations
Robust standard errors in brackets
*** p<0.001, ** p<0.01, * p<0.05
Note: Smoking and alcohol consumption variables are not included in the Female sample because such
questions were not asked of them. City Fixed Effects are included in the model but not shown here.
29
Table 5: Determinants of Mental Health Problems including Migration
(1)
(2)
Dep Var. Average Mental Health
Problem
VARIABLES
Male
Female
Voluntary migrant
0.29
-0.29
[0.89]
[1.17]
Involuntary migrant
-1.93
-0.76
[1.10]
[0.84]
age
-0.43*
1.24***
[0.21]
[0.32]
age2
0.0073**
-0.013**
[0.00]
[0.00]
married
-0.16
-1.16
[1.05]
[0.85]
Years of schooling complete
-0.62***
-0.83***
[0.09]
[0.10]
Islamic faith
0.88
2.15
[1.28]
[2.75]
Currently Smoke Cigarettes
1.23
[0.70]
Currently Drink Alcohol
3.73***
[1.01]
Employed
-0.74
3.81***
[1.31]
[1.01]
Log income
-1.39
-2.22***
[0.83]
[0.61]
Observations
13,369
12,604
Robust standard errors in brackets; *** p<0.001, ** p<0.01, * p<0.05
Note: Smoking and alcohol consumption variables are not included in
the Female sample because such questions were not asked of them. City
Fixed Effects are included in the model but not shown here.
30
Table 6: Migration Duration and Mental Health
(1)
(2)
Dep Var. Average Mental Health Problem
VARIABLES
Male
Female
Migration Duration
Migration Duration2/10
Lived in a village before Migration
age
age2
married
Years of schooling complete
Islamic faith
Currently Smoke Cigarettes
Currently Drink Alcohol
Employed
Log income
-0.045
[0.10]
-0.002
[0.03]
0.65
[0.95]
-0.069
[0.27]
0.0026
[0.00]
-0.48
[1.42]
-0.70***
[0.12]
2.67
[1.96]
0.73
[1.07]
2.52
[1.73]
-3.64
[1.88]
-1.12
[1.20]
0.16
[0.19]
-0.03
[0.06]
-3.24*
[1.49]
1.10**
[0.39]
-0.012*
[0.01]
-2.12
[1.58]
-0.96***
[0.15]
1.97
[3.47]
3.55**
[1.16]
-2.50***
[0.69]
Joint F-test P-value for Migration
0.4555
0.5114
duration variables
Observations
8,263
8,015
Robust standard errors in brackets, *** p<0.001, ** p<0.01, * p<0.05
31
Table 7: Effect of Migration on Mental Health of the Migrant – Results from Matching Estimators and
Inverse Propensity Score Weighting (Average Treatment Effects on the Treated)
ATT-Matching
ATT-Weighting
Coarsened Exact Matching
Observations
Men
Women
0.0011
0.001
(0.003)
(0.004)
0.53
-0.71
(0.40)
(0.49)
0.46
-0.66
(0.42)
(0.42)
13369
12604
Note: Note: Matching variables are age, married, Islam, Years of schooling, employment status,
household income and region.
32
Table 8: Determinants of perceived general health including Migration Status
(1)
(2)
-3
-4
Dep Var. general health: very
Dep Var. In general health:
healthy
very healthy
VARIABLES
Male
Female
Male
Female
migrate
-0.22***
-0.11**
--[0.04]
[0.04]
Voluntary migrant
---0.24***
-0.19**
[0.05]
[0.07]
Involuntary migrant
---0.12*
-0.086
[0.05]
[0.05]
Observations
13,369
12,604
13,369
12,604
Robust standard errors in brackets
*** p<0.001, ** p<0.01, * p<0.05
Note: Includes all the same covariates as table 4 and table 5. Only selected coefficients shown here.
33