-
7/30/2019 Mental health and poverty in developing countries.pdf
1/14
Social Science & Medicine 65 (2007) 467480
Mental health and poverty in developing countries:
Revisiting the relationship
Jishnu Das, Quy-Toan Do, Jed Friedman, David McKenzie, Kinnon Scott
World Bank, Development Economics Research Group, MSN MC 3-311, 1818 H Street, Washington, DC, USA
Available online 25 April 2007
Abstract
The relationship between poverty and mental health has received considerable attention in the recent literature.
However, the associations presented in existing studies typically rely on limited samples of individuals and on proxy
indicators for poverty such as education, the lack of tap water, or being unemployed. We revisit the relationship between
poverty and mental health using data from nationally representative household surveys in Bosnia and Herzegovina,
Indonesia and Mexico, along with special surveys from India and Tonga.
As in previous studies, we find that individuals who are older, female, widowed, and in poor health are more likely to
report worse mental health outcomes. Individuals living with others with poor mental health are significantly more likely to
report worse mental health themselves. The size of the coefficients and their significance are comparable across the five
countries. In contrast to previous studies, the relationship between higher education and better mental health is weak or
non-existent. Furthermore, there is no consistent association between consumption poverty and mental health in two
countries mental health measures are marginally worse for the poor; in two countries there is no association; and in onecountry mental health measures are better for the poor compared to the non-poor. Moreover, the sizes of the coefficients
for both education and consumption poverty are small compared to other factors considered here.
While the lack of an association between consumption poverty and mental health implies that poor mental health is not
a disease of affluence, neither is it a disease of poverty. Changes in life circumstances brought on, for instance, by illness
may have a greater impact on mental health than levels of poverty. Effective public health policy for mental health should
focus on protecting individuals and households from adverse events and on targeted interventions following such adverse
changes.
r 2007 Elsevier Ltd. All rights reserved.
Keywords: Mental health; Poverty; Household concordance; Socioeconomic gradients; Developing countries; Comparative
Introduction
The relationship between poverty and mental
health holds great interest for both health and
economic policy makers. Empirical findings from
developed countries suggest that, for most mental
health disorders, the association between low socio-
economic status and psychiatric morbidity is strong
and significant (Kessler, Chiu, Demler, & Walters,
2005; WHO International Consortium in Psychia-
tric Epidemiology, 2000).
This relationship has been found to hold, in some
cases even more strongly, in low-income countries.
ARTICLE IN PRESS
www.elsevier.com/locate/socscimed
0277-9536/$- see front matterr 2007 Elsevier Ltd. All rights reserved.
doi:10.1016/j.socscimed.2007.02.037
Corresponding author. Tel.: +1 0202 473 2781.
E-mail addresses: [email protected] (J. Das),
[email protected] (Q.-T. Do), [email protected]
(J. Friedman), [email protected] (D. McKenzie),
[email protected] (K. Scott).
http://www.elsevier.com/locate/socscimedhttp://localhost/var/www/apps/conversion/tmp/scratch_8/dx.doi.org/10.1016/j.socscimed.2007.02.037mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_8/dx.doi.org/10.1016/j.socscimed.2007.02.037http://www.elsevier.com/locate/socscimed -
7/30/2019 Mental health and poverty in developing countries.pdf
2/14
In 11 developing-country community-based studies,
significant associations between poverty indicators
and common mental disorders were found in all but
one study (Patel & Kleinman, 2003). Univariate
odds ratios (henceforth OR) predicting prevalence
of a common mental disorder include low education(OR 3.3), earning less than one-quarter of the
minimum wage (OR 3.9), and not working
(OR 3.6) in Brazil; having no electricity
(OR 1.47) or tap water (OR 2.2) in Indonesia;
arguing with spouse for economic reasons
(OR 10) in Pakistan; and being unemployed
(OR 2.9) or living in an overcrowded situation
(OR 2.1) in Zimbabwe. Another multi-country
review of studies in Zimbabwe, two sites in Brazil,
and in Chile showed strong associations between
income terciles and the prevalence of common
mental disorders, with OR for those in the highestterciles ranging from 0.46 to 0.50 relative to those in
the lowest terciles (Patel, Araya, de Lima, Ludermir,
& Todd, 1999). Higher monthly income and formal
education were also associated with reduced odds of
mood disorders in rural Ethiopia (Awas, Kebede, &
Alem, 1999).
This paper revisits the association between mental
health and socio-economic outcomes in a number of
low and middle-income countries through the
analysis of recently available household survey data
from Bosnia and Herzegovina, Indonesia, India,Mexico and Tonga. These data differ from those
used in previous studies in important ways. First,
the samples are drawn from a sampling frame of
households rather than (for instance) a sampling
frame of patients in health clinics (Patel et al., 1998).
The latter may result in biased estimates of
population-wide morbidity and the association with
socio-economic characteristics if the use of health
clinics is different among the general population
compared to those with mental disorders. Second,
mental health measures are collected for all adults in
the household; these allow us to examine the
concordance of mental health outcomes among
different members of the household with important
implications for treatment. Third, detailed expendi-
ture modules in each of these multi-purpose surveys
can be used to construct household consumption
measures. Household per capita consumption is the
preferred monetary-based welfare measure for
poverty analysis among economists and hence is
particularly germane for discussions of mental
health and poverty in the developing world (Deaton
& Zaidi, 2002; Ravallion, 1994).
Why mental health and poverty might be associated
with one another?
Conceptually, there are a number of potential
channels that may lead to a higher prevalence of
mental health disorders among the poor. Under thesocial causation hypothesis, poverty may lead to
mental health disorders through pathways such as
stress or deprivation (Johnson, Cohen, Dohren-
wend, Link, & Brook, 1999; Miech, Caspi, Moffitt,
Wright, & Silva, 1999), or lower the likelihood of
individuals receiving effective treatment (WHO,
2001). Under the drift or selection hypothesis,
causation may run the other way, as poor mental
health can impoverish people through lower em-
ployment and higher health costs (Bartel & Taub-
man, 1986; Miranda & Patel, 2005). The
relationship may also simply reflect third factorsrelated to both poverty and mental health. For
example, poor people are more vulnerable and may
be more likely to experience stressful life experiences
such as exposure to violence and poor physical
health, which are recognized risk factors for mental
health disorders (Patel & Kleinman, 2003). This
points to the importance of multivariate analysis,
which allows one to control for the influence of
many of these third factors when examining the
povertymental health relationship.
This general framework suggests several waysthrough which the mental healthpoverty relation-
ship may differ in developing countries compared to
developed countries. The first concerns the relative
availability of mental health services in developing
countries compared with the developed world
the proportion of individuals with mental
health disorders receiving treatment is much higher
in developed countries. WHO World Mental
Health Survey Consortium (2004) reports that
49.764.5% of serious cases are treated in developed
countries, compared to 14.623.7% in Mexico,
Colombia, and Lebanon, while only 0.510% of
mild cases received treatment in developing coun-
tries. The relative availability of competent yet
costly mental health services can result, as in the
US, in a situation where middle-class individuals
with mild disorders in rich countries receive treat-
ment, whereas the poor with severe disorders do
not. This differential access to treatment will then
lead to an association between mental health and
poverty. In contrast, with equally poor access
over at least the bottom three-quarters of the
income distribution in developing countries, the
ARTICLE IN PRESS
J. Das et al. / Social Science & Medicine 65 (2007) 467480468
-
7/30/2019 Mental health and poverty in developing countries.pdf
3/14
relationship between mental health and povertyshould be less strong.
The relationship between mental health and
poverty may also be weaker in developing countries
due to the more flexible nature of employment,
especially in the informal sector in developing
countries. Self-employment, agricultural work, and
other jobs with less rigid attendance requirements
are more common in developing countries. If
depression, anxiety, and other mental health dis-
orders make it more difficult for individuals to keep
regular working hours, mental health problemswould be expected to lead to more of an unemploy-
ment-related fall into poverty in developed coun-
tries than in developing countries. Additionally,
larger family and village social support systems in
developing countries may act to both lower the risk
of developing mental health disorders and help
insure individuals against poverty should they
develop a disorder.
Data and methods
Data and context
Multi-purpose surveys combining a mental health
component with extensive socio-economic measures
and information on all household members were
fielded in Bosnia and Herzegovina (hereafter
Bosnia), Indonesia, India, Mexico and Tonga.
Interviews were conducted face-to-face by trained
interviewers in the local languages. These household
surveys are multi-stage probability samples repre-
sentative of the national population in Bosnia,
Indonesia and Mexico with data on over 5400
households in Bosnia and over 10000 households inIndonesia and Mexico. The Indian and Tongan
surveys were special purpose surveys. The data in
India are from a longitudinal study of 300 house-
holds (1600 individuals) in the capital, Delhi; the
sample of households is no different in observable
attributes from a representative sample of house-
holds in the city (Das & Sa nchez-Pa ramo, 2003).
The Tongan respondents in 230 households were
chosen randomly from villages in which some
individuals had applied for an emigration lottery
(Stillman, McKenzie, & Gibson, 2006).These five countries span a range of continents,
levels of development, and cultural settings, allow-
ing us to determine the extent to which associations
with mental health are similar in very different
contexts. Table 1 provides the GDP per capita and
available health infrastructure indicators for each
country, and compares these with the OECD, low
income, and middle income averages.1 India and
Indonesia have similar health care expenditure per
capita to the low income country average, at less
than 1% of the OECD average. Bosnia and Tonga
are slightly poorer than the average middle income
country and have health care expenditure equal to
35% of the OECD average, while Mexico is an
upper-middle income country, with health expendi-
ture still only 10% of the OECD average.
The prevalence of mental health disorders is likely
to be particularly high in Bosnia, due to lingering
ARTICLE IN PRESS
Table 1
Context
GDP per capita US$ Health expenditure per
capita (US$)
Hospital beds per 1000 Physicians per 1000
Bosnia 1325 168 3.1 1.3
India 512 27 0.9 0.6
Indonesia 872 30 n.a. 0.1
Mexico 5876 372 1.0 1.5
Tonga 1615 102 3.2 0.3
OECD 28055 3509 3.0 6.0
Low income average 428 29 n.a. n.a.
Middle income
average
1937 115 2.0 n.a.
Source: Latest year available from 2001 to 2003 from World Bank Central Database.
1The World Bank classifies 54 countries as low income, based
on GNI per capita of less than $875 in 2005, and a further 98
countries as middle income, with GNI per capita between $876
and $10,725 in 2005.
J. Das et al. / Social Science & Medicine 65 (2007) 467480 469
-
7/30/2019 Mental health and poverty in developing countries.pdf
4/14
effects of the 199195 war, and possibly in
Indonesia due to the 199798 financial crisis (Fried-man & Thomas, 2006), although comparison of
mental health prevalence across countries is notor-
iously difficult. Both Mexico and Tonga have high
rates of international migration. However, in both
countries there is evidence that migrants have higher
levels of education than those who do not migrate
(Chiquiar & Hanson, 2005; McKenzie, Gibson, &
Stillman, 2006). As a result, it does not seem that
the magnitudes of migration among the poor are
high enough for any possible non-random migra-
tion by mental health status amongst poor house-holds to affect the inferences drawn in this paper.
Given the variation in country contexts and cultural
backgrounds, our analysis will look at deviations
from country means, examining the relationship
between different socioeconomic characteristics and
mental health status within a country.
Each of the surveys fielded a widely used mental
health screening instrument designed to measure
mental health status of the general population.
Table 2 lists the survey instrument used by country.
The Tonga survey used the Mental Health Inven-
tory (MHI-5) of Veit and Ware (1983), which has
been used in over 50 countries as part of the
International Quality of Life Assessment project. It
has been shown to perform well in a number of
settings in detecting major depression, general
affective disorders, and anxiety disorders (Berwick
et al., 1991). The Mexican and Indonesian surveys
used variants of the General Health Questionnaire
(GHQ) of Goldberg (1972), which displays similar
psychometric properties to the MHI-5 (McCabe,
Thomas, Brazier, & Coleman, 1996). The Bosnia
survey used the Center for Epidemiological Studies
Depression Scale of Radloff (1977), a 20-question
self-reported depression scale. The Indian surveyuses the most comprehensive instrument, the 90
question Symptom Checklist 90 Revised.2
These screening surveys were translated and
back-translated to ensure accuracy and extensively
tested in the field to ensure comprehension among
study subjects. In four countries (Bosnia, Indonesia,
Mexico and Tonga), the surveys were fielded on the
first visit to the household. In India, the 90 question
SLC-90R was fielded 1 year after the longitudinal
survey was initiated to ensure some degree of
comfort between the respondents and field workers.The mental health modules in each of the utilized
surveys ask respondents the frequency in the last
month of a similar range of internal states (e.g.
feeling sad or blue, feeling anxious or nervous)
or related behaviors (e.g. difficulty falling asleep,
distracted from everyday activities).3 The fre-
quency of such states or behaviors is recorded on a
four-point scale that ranges from never or
almost never to very often. To score the
individuals survey response, a low ordinal value
(1 point) is assigned to categorical responses of
infrequency and high ordinal values (up to 4 points)
to the categories indicating greatest frequency. The
average response across all questions constitutes the
respondents mental health score, often known as
ARTICLE IN PRESS
Table 2
Overview of datasets employed
Country Year of survey Number of Obs. Level of
representation
Mental health
survey instrument
Mental health measure
Mean SD
Bosnia 2001 12956 National CESD 1.495 0.502
India 2003 784 7 neighborhoods
in New DelhiaSCL-90R 1.535 0.416
Indonesia 2000 25470 National GHQ derived 1.413 0.508
Mexico 2002 19798 National GHQ derived 1.341 0.358
Tonga 2005 714 Special sample of
migrant sending
villages
MIH-5 1.745 0.337
aIndistinguishable from a representative sample of the day.
2As opposed to relatively lengthy diagnostic interviews such as
the Comprehensive International Diagnostic Interview (Kessler
et al., 2005), the more common mental health instruments
included in socio-economic surveys attempt to measure general
psychological distress and are not intended to diagnose specific
manifestations of mental illness per se.3The recall period in the Indian survey using the SCL-90R was
1 week instead of 1 month.
J. Das et al. / Social Science & Medicine 65 (2007) 467480470
-
7/30/2019 Mental health and poverty in developing countries.pdf
5/14
the Global Severity Index or GSI, which is higher
for those reporting worse mental health.4
The Global Severity Index weights all questions
equally, in accordance with the approach widely
employed in the existing literature across a number
of settings and countries. One question is whetheran alternative index could account for more of the
variation in mental health status across individuals
if a different weighting scheme were used. One
natural approach is to reweight with the first
principal component of the different questions used
in constructing the index, which weights each
question in order to provide maximum discrimina-
tion across individuals. This approach will give
questions which vary the most across individuals
higher weight. We find the correlation between the
mental health score obtained by equal weighting
and by principal components to be 0.9932 in India,0.9996 in Tonga, 0.9959 in Indonesia, 0.9980 in
Mexico, and 0.9984 in Bosnia. As a result, any
change in the index from re-weighting will be
minimal, and so we follow the existing literature in
constructing the GSI with equal weights.
Table 2 presents the mean of the raw scores
across the surveys, as well as various characteristics
of each survey. The main mental health scores
across all surveys fall in the narrow range of
approximately 1.351.50 indicating the average
response to any particular mental health measureto be somewhere between almost never and
rarely or infrequently. The standard deviation
around this mean is also relatively similar in the
general range of .35.50. Tonga is the exception
where the average question response is higher at
1.75 and with slightly less dispersion at a standard
deviation below .35.5
Given similar distributions of the mental health
score across the five countries, the score is
standardized around the mean of each country
and expressed in units of standard-deviations to
enhance comparability and facilitate the interpreta-
tion of the results. The standardized GSI, formally
defined as GSIindividualmean(GSI)country/standard
deviation(GSI)country, is the outcome variable forthe analysis in this paper. The relative magnitudes
of different factors are then directly compared
across countries.
Statistical methods
The analysis explores the co-variation of the
standardized GSI with a range of potentially related
factors at the level of the individual and
household. In order to parsimoniously explore
predictors of poor mental health, similar groups
of characteristics measured consistently acrosseach data set are identified. These characteristics
vary either at the individual level, such as age,
gender, marital status, or education, or the
household level, such as household size or total
household expenditures.6 For the larger surveys,
community level characteristics are accounted
for either through a community-level fixed effect
or by including the average individual mental health
score for the entire community as an additional
explanatory variable. The predictive power of each
of these characteristics is estimated in a separatenational level ordinary least square regressions7
using STATA/SE Version 9.0. In each of these
regressions, the respondents are restricted to those
aged 1580 and standard errors are clustered to
correct for possible response dependency at the
household level.
Selected results are graphically summarized in
accompanying figures. Associations between the
mental health score and continuous control vari-
ables are depicted by gradients estimated with a
partial linear model. In this approach, all covariates
except the one depicted are modeled in a parametric
ARTICLE IN PRESS
4
Typically a cut-off score indicating the likely presence ofpsychological disorders is determined by mental health profes-
sionals through supplementary validation exercises. These
exercises were not available for all studies included here.5The exact questionnaire content varies across countries.
However for every country but Tonga there are three similar
questions: whether the respondent has recently felt sad, felt
anxious, and had trouble sleeping. Limiting overall mental health
score to these three questions yields similar yet slightly elevated
scores and greater dispersion around the mean score. The mean
response for the 3-question subset and the overall mean score are
highly correlated with coefficients ranging from .84 to .90 across
the datasets. Due to these close correlations, and in order to
include Tonga in the analysis, the analysis focuses on the
comprehensive measure.
6Most of the surveys use an extensive expenditure module to
capture not just monetary expenditures, but also the value of
goods produced for home production, gifts, and the value of
owner-occupied housing. This provides a comprehensive indica-
tor of consumption welfare. The Tongan survey uses per capita
household income instead of consumption.7Qualitatively similar results are obtained using logistic
regression to examine the odds that the individual will be in the
worst 20%, 10% or 5% of the population in terms of mental
health scores; hence, the associations found with mental health
outcomes are not purely driven by those with sub-clinical
symptoms that are of less important than clinical illness.
J. Das et al. / Social Science & Medicine 65 (2007) 467480 471
-
7/30/2019 Mental health and poverty in developing countries.pdf
6/14
fashion while the depicted variable is allowed to
vary in a non-parametric fashion.8 Associations
between mental health and discrete covariates are
conveyed graphically by their 95% confidence
intervals.
Results
Figs. 1 and 2 summarize the findings from the five
countries; Fig. 1 presents mental health associations
with continuous variables and Fig. 2 with discrete
variables. The parametric version of these relation-
ships using ordinary least squares are presented in
Table 3.
Demographic influences
Several of the empirical regularities with regard to
demographic influences identified in previous re-
search are reproduced in the data here (Andrews,
Henderson, & Hall, 2001; Awas et al., 1999; Kessler
et al., 2005; Patel et al., 1999; Weissman et al., 1996;
WHO International Consortium in Psychiatric
Epidemiology, 2000). Age, gender, and marital
status are all significant predictors of individual
mental health in the direction of influence found
earlier.
Mental health measures are positively and sig-
nificantly associated with age in every country but
Tonga (Fig. 1A). However the magnitude of the age
gradient varies substantially across countriesin
four of the five countries mental health worsens with
age, while in Tonga the association between mental
ARTICLE IN PRESS
-1
-0.5
0.5
0
1
Mentalhealthscore
20 40 60 80
Age
Bosnia Indonesia
India Mexico
Tonga
Age
-1
-0.5
0
0.5
1
Mentalhealthscoree
0 5 10 15
Years of formal schooling
Bosnia Indonesia
Mexico Tonga
Education
-1
-0.5
0
0.5
1
1.52
-1
-0.5
0
0.5
1
1.5
2
Mentalhealthscore
-2 0 2 4 6Household average mentalhealth score
Bosnia Indonesia
India Mexico
Tonga
Mental health of other household members
Mentalhealthscoree
-4 -2 0 2 4 6
Community average mental health score
Bosnia Indonesia
Mexico
Mental health of other community members
-4 -2 0 2 4 6-4 -2 0 2 4 6
Fig. 1. Mental health score by selected characteristics (continuous). (A) Age, (B) Education, (C) Mental health of other members and (D)
Mental health of other community members.
8See Yatchew (1998) for a description of these semi-parametric
methods and Lokshin (2005) for programming implementation.
J. Das et al. / Social Science & Medicine 65 (2007) 467480472
-
7/30/2019 Mental health and poverty in developing countries.pdf
7/14
health and age is an inverted-U shape whereby
mental health scores improve at advanced ages.
Bosnia exhibits the steepest age gradient by far.
One of the most pronounced demographic
regularities is that the odds of experiencing anydisorder and specifically of experiencing affective
(mood), anxiety, and somatoform disorders are
significantly higher among females.9 Fig. 2A repli-
cates this regularity. In four of the five national
settings, mental health measures are worse among
women, although the magnitude of the female
penalty varies widely from a low of 0.16 standard
deviations in Indonesia to a high of 0.49 standard
deviations in Mexico. Tonga is again the exception,
where women report significantly lower level of
distress to the order of 0.2 standard deviations.
A third consistent finding in the literature is that
respondents who are separated, divorced or wi-
dowed report worse mental health compared to
those who are married (Andrade, Walters, Gentil, &
Laurenti, 2002; Andrews et al., 2001; Kessler et al.,
2005; Weissman et al., 1996; WHO International
Consortium in Psychiatric Epidemiology, 2000).
Again, in a majority of countries examined here,
widows are indeed worse off, although the relative
deprivation of widows varies over the national
setting and in the India data widows report
lower levels of distress than non-married individuals
(Fig. 2B).
Physical health and mental health
Similarities between these data and previously
reported results are also evident in the positive
association between mental and physical health
(Kessler et al., 1994; Kessler et al., 2005; Bijl et al.,
2003). Poorer physical health, as measured by a
binary variable based on self-assessed general health
status (poor health 0), is strongly associated with
worse mental health outcomes in all countries with
sufficient data. As observed in Fig. 2C, the
coefficients are large and precisely estimated. The
lowest magnitude is for India at 0.42 standard
deviations; while in Bosnia, an individual reporting
poor health also reports a mental health score 1.1
standard deviations higher than someone in good
physical health.
Socioeconomic status and mental health
In sharp contrast to these results, which replicate
those reported earlier in the literature, associations
between socio-economic measures and mental
ARTICLE IN PRESS
Marital status (compared to non-married)
-1
-0.5
0
0.5
1
Married
Widowed
Married
Widowed
Married
Widowed
Married
Widowed
Married
Widowed
Bosnia India Indonesia Mexico Tonga
Female
-1
-0.5
0
0.5
1
Bosnia India Indonesia Mexico Tonga
Poor physical health
-1
-0.5
0
0.5
1
1.5
Bosnia India Indonesia Mexico Tonga
Per capita household expenditures
-1
-0.5
0
0.5
1
2nd
Quartile3rd
Quartile
Top
Quartile
2nd
Quartile3rd
Quartile
Top
Quartile
2nd
Quartile3rd
Quartile
Top
Quartile
2nd
Quartile3rd
Quartile
Top
Quartile
2nd
Quartile3rd
Quartile
Top
Quartile
Bosnia India Indonesia Mexico Tonga
A B
C D
Fig. 2. Mental health score by selected discrete characteristics. (A) Female, (B) Poor physical health, (C) Marital status (compared to non-
married) and (D) Per capita household expenditures.
9In one important exception, males tend to have higher odds of
substance use disorders (Andrews et al., 2001).
J. Das et al. / Social Science & Medicine 65 (2007) 467480 473
-
7/30/2019 Mental health and poverty in developing countries.pdf
8/14
health deviate considerably from expected patterns.
Figs. 1B and 2D depict how mental health
scores vary with years of formal schooling (Fig. 1)
and quartiles of household per capita consumption
(Fig. 2).
For either socio-economic measure there
is no clear pattern across the five countries. In
three of the five countriesIndia, Mexico, and
Bosniaeducation is significantly and negatively
associated with worse mental health. There
is no association across years of education
in Indonesia and in Tonga the estimated relation-
ship is U-shaped, with mental health first
improving as education increases and then worsen-
ing. Of equal interest are the relatively small
magnitudes of the schooling coefficients compared
to the demographic and physical health measures
reported abovethe association between educa-
tion and mental health, where it is significant at all,
is small.
ARTICLE IN PRESS
Table 3
Correlates of mental health
Tonga India Mexico Bosnia Indonesia
(1) (2) (3) (4) (5) (6) (7) (8)
Age 0.00285 0.00799* 0.00269*** 0.00263** 0.0127*** 0.0123*** 0.00266*** 0.00270***
(0.0043) (0.0041) (0.0010) (0.9910) (0.00095) (0.00096) (0.00067) (0.00070)
Female dummy 0.221*** 0.356*** 0.488*** 0.486*** 0.298*** 0.300*** 0.164*** 0.161***
(0.069) (0.078) (0.020) (0.020) (0.016) (0.016) (0.015) (0.015)
Married dummy 0.449*** 0.104 0.0329 0.0250 0.123*** 0.133*** 0.172*** 0.173***
(0.11) (0.12) (0.030) (0.029) (0.030) (0.031) (0.022) (0.023)
Widowed dummy 0.604** 0.421* 0.169*** 0.161*** 0.272*** 0.290*** 0.0919** 0.0914**
(0.25) (0.23) (0.052) (0.052) (0.051) (0.052) (0.042) (0.042)
Poor physical health 0.417*** 0.940*** 0.943*** 1.076*** 1.115*** 0.663*** 0.672***
(0.12) (0.060) (0.060) (0.13) (0.12) (0.028) (0.028)
Years of education 0.0164 0.0197*** 0.0219*** 0.00976*** 0.0113*** 0.00306 0.00322
(0.019) (0.0031) (0.0033) (0.0016) (0.0018) (0.0019) (0.0021)
HH PCEquartile 2 0.0119 0.00644 0.520** 0.0443* 0.0323* 0.0317* 0.00122 0.00229
(0.072) (0.094) (0.025) (0.026) (0.019) (0.017) (0.018) (0.019)
HH PCEquartile 3 0.0701 0.0396 0.0695*** 0.0617** 0.0501*** 0.0460*** 0.00372 0.00560
(0.072) (0.089) (0.026) (0.028) (0.019) (0.018) (0.019) (0.020)
HH PCEquartile 4 0.156** 0.0180 0.0467* 0.0264 0.0621*** 0.0332* 0.0224 0.0232
(0.076) (0.097) (0.028) (0.030) (0.018) (0.018) (0.021) (0.023)
Household size 0.00715 0.0140 0.00361 0.00262 0.0118** 0.00892* 0.000318 0.000034
(0.010) (0.016) (0.0054) (0.0054) (0.0060) (0.0052) (0.0024) (0.0026)
Old dependents 0.504*** 0.389 0.0685 0.0451 0.355*** 0.352*** 0.0448 0.0742
(0.17) (0.24) (0.054) (0.055) (0.035) (0.034) (0.045) (0.047)
Young dependents 0.586*** 0.0288 0.0706 0.0841* 0.0758 0.0469 0.0857** 0.115***
(0.15) (0.16) (0.050) (0.050) (0.047) (0.046) (0.038) (0.040)
HH mental health 0.484*** 0.321*** 0.178*** 0.161*** 0.472*** 0.511*** 0.194*** 0.174***
(0.038) (0.053) (0.014) (0.014) (0.018) (0.015) (0.012) (0.012)
Community mental health 0.119*** 0.187*** 0.124***
(0.0093) (0.011) (0.0069)Primary to high school 0.149*
(0.084)
High school or more 0.197*
(0.10)
District fixed effects No No No Yes No Yes No Yes
Constant 0.155 0.330* 0.386*** 0.355*** 0.634*** 0.656*** 0.183*** 0.211***
(0.25) (0.17) (0.055) (0.058) (0.049) (0.049) (0.035) (0.038)
Observations 681 747 17926 17926 11766 11766 19584 19584
R2 0.32 0.18 0.19 0.21 0.61 0.60 0.15 0.16
Robust standard errors in parentheses clustered at the household level.***po0.01, **po0.05, *po0.1.
J. Das et al. / Social Science & Medicine 65 (2007) 467480474
-
7/30/2019 Mental health and poverty in developing countries.pdf
9/14
Any general relationship between mental health
and household per capita expenditures is even more
tenuous. Across the five countries only twoTonga
and Bosniaexhibit a negative gradient between
household per capita consumption and individual
mental health. The largest gradient is observed inTonga, where individuals in the top quartile report
on averaging a mental health score that is 0.15
standard deviations below the other three quartiles.
However, to claim that the poor report worsens
mental health in Tonga one would have to adopt an
expansive definition of poverty that includes the
bottom three quartiles of the distribution. Bosnia
also exhibits a negative gradient, albeit much
smaller in magnitude. In Indonesia and India, there
is no association between per capita expenditures
and mental health outcomes and in Mexico, there is
a significant positive gradient suggesting that mentalhealth outcomes are better for the poor.
Furthermore, for all five countries, the magni-
tudes of the estimated socioeconomic coefficients
are much smaller than the coefficients for any other
factor presented here. For example, in Bosnia one
more year of education is associated with a 0.01
standard deviation improvement in mental health,
while moving from the bottom to the top quartile of
the per capita expenditure distribution improves
mental health by 0.03 standard deviations (Table 3).
These pale in comparison with the associatedworsening in mental health from being female
(0.30 standard deviations), being widowed (0.29
standard deviations), having poor physical health
(1.12 standard deviations), and having a one
standard deviation worse mental health of other
members in the household (0.51 standard devia-
tions). Similar magnitudes are seen in other
countries, suggesting that the relative importance
of consumption poverty in the determination of
mental health is actually quite slight.
Spatial clustering of individuals with poor mental
health
In addition, the household-based nature of these
data enables a unique exploration of the co-location
of poor mental health within the household and
communityfew other studies in the developing
country literature are able to look at the covariation
of poor mental health among household and
community members. Fig. 1C depicts the non-
parametric regression lines of individual mental
health on the average mental health score of other
members in the household (excluding the individual
herself).10 There is a strong positive association
between an individuals psychological well-being
and others in his or her constituent household. This
association exists at all levels of mental health and is
one of the most powerful predictors of the mentalhealth score on the rough order of gender or
physical health and certainly more influential than
any socio-economic measure. The community aver-
age mental health score also influences an indivi-
duals mental well-being even after adjusting for
household average mental health (Fig. 1D). The
degree of association is roughly half as large as the
association at the household level in the three
countries that allow for community level measures
(see Table 3).11 Determining the possible reasons for
such observed co-location are beyond the scope of
inquiry, but can include the negative externalities ofpoor mental health on family and neighbors, genetic
predisposition towards poor mental health within
families, and the uneven geographic distribution of
mental and other health services.
Discussion
Summary of main results
Household surveys in five low and middle-incomecountries covering Latin America, Eastern Europe,
East Asia and the Pacific, and South Asia reveal
significant associations between mental health
scores and gender, the physical health of the
respondent, his/her marital status, and the mental
well-being of other members in the respondents
household and community. These relationships hold
(with occasional deviations) across all the countries
with roughly comparable magnitudes. In contrast,
there is no consistent relationship between mental
health scores and socio-economic measures such as
the respondents education or the per capita
expenditure of the household in which the respon-
ARTICLE IN PRESS
10Hence individuals living alone, representing 3.2% of the
pooled data, are not included in the analysis.11An important distinction in low-income countries is between
urban and rural areas. Rapid urbanization and economic
restructuring are defining forces in much of the developing world
and may lead to unique stressors (Blue & Harpham, 1996). In
these data, urban residents report worse average mental health
scores. However, controlling for residence through district fixed
effects leaves the interpretation of the regression coefficients
unchanged. (The Indian data are exclusively an urban sample and
the urban/rural distinction in Tonga has less meaning.)
J. Das et al. / Social Science & Medicine 65 (2007) 467480 475
-
7/30/2019 Mental health and poverty in developing countries.pdf
10/14
dent resides. These summary results provide the
setting for a discussion centered around: (a) the
potential use of mental health modules in multi-
purpose household surveys and (b) the implications
for policy and research on mental health.
Measuring mental health in multi-purpose surveys
The consistency of the magnitudes and signs
obtained across the five countries suggest that
mental health screening questionnaires can be
incorporated into large and nationally representa-
tive standard household surveys such as the Living
Standards Measurement Survey (Scott, Massagli,
Kapetanovic, & Mollica Lavelle, 2005). Further-
more, the associations between mental health scores
and individual/household characteristics are very
similar in surveys where questionnaires were fieldedon a first visit to households and where they were
fielded after a period of acquaintanceship. Finally,
shorter modules (such as the GHQ-12) reveal
similar associations as the longer SCL-90R, which
took one hour to field for non-literate respondents.
Indeed, the depression and anxiety components of
the SCL-90R are found to contain most of the
relevant information for the nine dimensions
covered under the full questionnaire (Das & Das,
2006).
Implications for public health interventions
On a more substantive note, effective public
health policy requires an understanding of the
mechanisms that determine poor mental health
and, in turn, the implications of poor mental
health for the individual and his/her family. The
descriptive analysis here provides suggestive evi-
dence for what these mechanisms may be and
therefore a potential role for public health inter-
ventions.
The lack of any relationship between conven-
tional economic welfare measures and mental health
outcomes across a diverse sample of developing
countries suggests that poverty, per se, i s n o t a
strong determinant of poor mental health. A
straightforward equity rationale for public invest-
ments in mental health is undermined by the
frequently higher relative prevalence among the
poor of other health problems such as tuber-
culosis and malaria, as well as continuing
financing gaps for these illnesses. The lack of a
relationship between consumption poverty and
mental health is certainly not, however, supportive
of arguments that suggest no scope for public
interventions towards improving mental health.
Instead, we argue that resources should be targeted
towards improving the mental health of those who
have experienced adverse events, and note also thedistinction between severe and common mental
disorders.
Two of the strongest factors associated with poor
mental health are poor physical health and widow-
hood. Related papers on India, Indonesia and
Tonga confirm that, more generally, changes in life
circumstances brought on by positive or negative
events have long-lasting implications for
mental health. In India, women who report child-
loss (either through miscarriages, abortions or
death) are at significantly higher risk of mental
health problems compared to those without;indeed, the female penalty observed in the India
data is entirely driven by the difference between
men and women in households that experienced
the loss of a child (Das & Das, 2006). In Indonesia,
the mental health of the population worsened
dramatically following the economic crisis of the
nineties; however, although consumption levels
recovered by 2000, mental health did not (Friedman
& Thomas, 2006). Finally, in Tonga, indivi-
duals who were selected by a lottery to emigrate
(and randomly received a positive incomeshock) reported significantly better mental health
outcomes after emigration (Stillman et al., 2006).
These findings are also consistent with the
studies that report worsened mental health out-
comes in populations that have suffered conflict
or disasters (Mollica et al., 1999; Mollica et al.,
2001; Lopes Cardozo et al., 2004; United Nations
High Commissioner for Refugees (UNHCR),
2005).
The Indian and Indonesian studies suggest that
the trauma from adverse events may persist long
after the recovery of more traditional measures of
welfare and there may very well be real individual
and household costs to this persistence. Examples of
such costs along the health dimension previously
identified in the literature include lower adherence
to dietary recommendations and medication re-
gimes among diabetics with depressive symptoms
compared to diabetics without (Ciechanowski,
Katon, & Russo, 2000) high co-morbidity rates for
smoking and psychiatric disorders, with smoking
twice as common among the mentally ill compared
to the mentally healthy population (Lasser et al.,
ARTICLE IN PRESS
J. Das et al. / Social Science & Medicine 65 (2007) 467480476
-
7/30/2019 Mental health and poverty in developing countries.pdf
11/14
2000) and an association between maternal
mental health and child welfare, with maternal
depression significantly increasing the odds that a
child will experience growth faltering (Harpham,
Huttly, De Silva, & Abramsky, 2005; Patel, Rah-
man, Jacob, & Hughes, 2004). Strong evidence ofsuch costs in other dimensions of welfare such as
education also exist (Kessler, Foster, Saunders, &
Stang, 1995).
If individuals who have suffered adverse events
(or shocks as in the economic literature) are
particularly likely to report worse mental health,
traditional measures of poverty such as per capita
household expenditure are insufficient to fully
understand the association between mental health
and poverty. These poverty measures do not
account for the risk and uncertainty that households
face; alternative measures that incorporate risk andvulnerability could yield significantly different
results.
Focusing on shocks and mental health out-
comes suggests a dual role for policy. First,
addressing the causes of poor mental health is a
viable policy alternative for which there is already a
strong global consensus in place. Few would argue
that decreasing child mortality or improving
physical health should not be a global priority; that
such investments also have an effect on mental
health strengthens an already existing case. Second,there may be a role for targeted treatments
to the directly affected in the aftermath of an
adverse event if such treatments lead to improved
outcomes. The clustering of mental health outcomes
within households provides evidence that such
treatments targeted at the level of the household
may have larger benefits than those targeted to
individuals.
An important limitation of this study and of the
household-survey-based methodology is our inabil-
ity to differentiate common from severe mental
disorders. A clear distinction has been remarked
on in the literature, especially in the context of
findings that the annual prevalence of common
mental disorders exceeds 10% in many countries,
and is as high as 16.9% in Lebanon, 17.8% in
Colombia, 20.4% in Ukraine, and 26.3% in the
US (WHO World Mental Health Survey
Consortium, 2004). Severe mental health problem
(such as schizophrenia), brought on by bio-
genetic causes and possible interactions with envir-
onment, require a separate policy response. In
several low-income countries, the institutional
capacity for treating such disorders is very poor
with frequent human-rights violations of the
severely mentally ill (WHO, 2001). Neither does it
appear that the private sector is capable of
providing the required responsedoctors tested
on the handling of a patient with depression inDelhi had to be above average competence to
have a better than even chance of not harming the
patient; even those in the highest quintile of
competence provided a harmless treatment only
58% of the time (Das & Hammer, 2005). The long-
term treatment required for such disorders and the
high costs imposed on households suggest that
these are the types of disorders where the lack
of insurance markets requires clear government
intervention.
A second limitation of this study is that, in the
absence of an experimental setup, the associationspresented are consistent with multiple interpreta-
tions. For instance, the concordance of mental
health outcomes within households could reflect
unobserved household-level shocks, assortative
matching (where those in poor mental health are
more likely to marry each other), genetic links
between parents and children, or a contagion
effect, whereby caring for a mentally ill person in
the household in turn affects the mental well-being
of others. Longitudinal data and experimental
mental health interventions are needed to try andseparate these channels.
These results ask for a more nuanced under-
standing of the relationship between poverty and
mental health. Two potential avenues for further
research suggested by these findings concern the
long- and short-term effects of negative and positive
shocks to mental health, as well as the link between
mental health outcomes and broader measures of
welfare that incorporate risk and vulnerability in
their construction.
Acknowledgement
We would like to thank Alison Buttenheim and
Le Dang Trung for expert research assistance. All
errors accrue to the authors. The views presented
herein do not reflect the views of the World Bank or
any of its affiliates and only reflect on the authors.
Appendix
Correlates to severe mental health (see Table A1).
ARTICLE IN PRESS
J. Das et al. / Social Science & Medicine 65 (2007) 467480 477
-
7/30/2019 Mental health and poverty in developing countries.pdf
12/14
Table A1
Marginal effects from probit estimation of being in the worst 10% or worst 5% of mental health scores
Tonga India Mexico Bosnia
(1) (2) (3) (4) (5) (6)
Worst 10% Worst 10% Worst 10% Worst 10% Worst 10% Worst 10%
Age 0.0000237 0.000972 0.000463*
0.000103 0.000810***
0.000235***
(0.00049) (0.0010) (0.00026) (0.00017) (0.00016) (0.000053)
Female dummy 0.0120 0.0449** 0.0791*** 0.0371*** 0.0216*** 0.00636***
(0.011) (0.021) (0.0056) (0.0036) (0.0039) (0.0017)
Married dummy 0.0260* 0.0169 0.0140* 0.0113** 0.0131*** 0.00234
(0.014) (0.032) (0.0075) (0.0048) (0.0047) (0.0016)
Widowed dummy 0.0883 0.0431 0.0339** 0.0234* 0.0496** 0.00757
(0.13) (0.036) (0.016) (0.012) (0.024) (0.0062)
Years of education 0.00127 0.00442*** 0.00213*** 0.000281 0.000168
(0.0011) (0.00079) (0.00051) (0.00024) (0.00010)
HH incomequartile 2 0.00108 0.0244 0.00566 0.00295 0.00369 0.00100
(0.010) (0.021) (0.0065) (0.0043) (0.0032) (0.0012)
HH incomequartile 3 0.00768 0.0157 0.0139* 0.00333 0.00470 0.000936
(0.0096) (0.024) (0.0077) (0.0048) (0.0031) (0.0014)
HH incomequartile 4 0.000399 0.0213 0.00239 0.00289 0.00824*** 0.00177*
(0.012) (0.027) (0.0082) (0.0054) (0.0027) (0.0011)
Household size 0.00292* 0.00647 0.000644 0.000736 0.00187* 0.000471
(0.0016) (0.0045) (0.0014) (0.00087) (0.00099) (0.00047)
Old dependents 0.0860** 0.0262 0.0117 0.0116 0.0230*** 0.00758***
(0.042) (0.061) (0.015) (0.0085) (0.0059) (0.0022)
Young dependents 000465* 0.102** 0.0142 0.00302 0.00771 0.00756*
(0.025) (0.050) (0.014) (0.0092) (0.0095) (0.0041)
HH mental health 0.0326*** 0.0486*** 0.0228*** 0.0100*** 0.0222*** 0.00653***
(0.0059) (0.0091) (0.0025) (0.0015) (0.0021) (0.00096)
Primary to high school 0.0184
(0.024)
High school or more 0.0276
(0.026)Poor physical health 0.0933** 0.176*** 0.0964*** 0.156*** 0.0941***
(0.043) (0.019) (0.015) (0.035) (0.023)
Community mental health 0.0150*** 0.00850*** 0.0110*** 0.00252***
(0.0022) (0.0013) (0.0019) (0.00071)
Observations 681 747 17926 17926 11766 11766
Note: Marginal effects are the change in probability associated with a discrete change in dummy variables from 0 to 1 and with an infinitesi
Robust standard errors in parentheses.***po0.01, **po0.05, *po0.1.
-
7/30/2019 Mental health and poverty in developing countries.pdf
13/14
References
Andrade, L., Walters, E. E., Gentil, V., & Laurenti, R. (2002).
Prevalence of ICD-10 mental disorders in a catchment area in
the city of Sao Paulo, Brazil. Social Psychiatry and Psychiatric
Epidemiology, 37(7), 316325.
Andrews, G., Henderson, S., & Hall, W. (2001). Prevalence,comorbidity, disability and service utilization. Overview of
the Australian National Mental Health Survey. British
Journal of Psychiatry, 178, 145153.
Awas, M., Kebede, D., & Alem, A. (1999). Major mental
disorders in Butajira, southern Ethiopia. Acta Psychiatrica
Scandinavia (Supplement), 397, 5664.
Bartel, A., & Taubman, P. (1986). Some economic and
demographic consequences of mental illness. Journal of Labor
Economics, 4(2), 243256.
Berwick, D. M., Murphy, J. M., Goldman, P. A., Ware, J. E., Jr.,
Barsky, A. J., & Weinstein, M. C. (1991). Performance of a
five-item mental health screening test. Medical Care, 29(2),
169176.
Bijl, R. V., de Graaf, R., Hiripi, E., Kessler, R. C., Kohn, R.,
Offord, D. R., et al. (2003). The prevalence of treated and
untreated mental disorders in five countries. Health Affairs
(Millwood), 22(3), 122133.
Blue, I., & Harpham, T. (1996). Urbanization and mental health
in developing countries. Current Issues in Public Health, 2(4),
181185.
Chiquiar, D., & Hanson, G. (2005). International migration, self-
selection, and the distribution of wages: Evidence from
Mexico and the United States. Journal of Political Economy,
113(2), 239281.
Ciechanowski, P. S., Katon, W. J., & Russo, J. E. (2000).
Depression and diabetes: Impact of depressive symptoms on
adherence, function, and costs. Archives of Internal Medicine,160(21), 32783285.
Das, J., & Das, V. (2006). Mental health in urban India: Patterns
and narratives. The World Bank. Processed.
Das, J., & Hammer, J. (2005). Which doctor? Combining
vignettes and item response to measure clinical com-
petence. Journal of Development Economics, 78,
348383.
Das, J., & Sa nchez-Pa ramo, C. (2003). Short but not sweet: New
evidence on short duration morbidities from India. Policy
Research Working Paper 2971, World Bank, Development
Research Group, Washington, DC.
Deaton, A., & Zaidi, S. (2002). Guidelines for constructing
consumption aggregates for welfare analysis. Living Stan-dards Measurement Study Working Paper No. 135, The
World Bank.
Friedman, J., & Thomas, D. (2006). Mental health during a crisis.
Mimeo, The World Bank.
Goldberg (1972). The detection of psychiatric illness by ques-
tionnaire. London: Oxford University Press.
Johnson, J. G., Cohen, P., Dohrenwend, B. P., Link, B. G., &
Brook, J. S. (1999). A longitudinal investigation of social
causation and social selection processes involved
in the association between socioeconomic status and psychia-
tric disorders. Journal of Abnormal Psychology, 108(3),
490499.
Kessler, R. C., Chiu, W. T., Demler, O., & Walters, E. E. (2005).
Prevalence, severity, and comorbidity of 12-month DSM-IV
disorders in the National Comorbidity Survey Replication.
Archives of General Psychiatry, 62(6), 617627.
Kessler, R. C., Foster, C. L., Saunders, W. B., & Stang, P. E.
(1995). Social consequences of psychiatric disorders, I:
Educational attainment. American Journal of Psychiatry,
152(7), 10261032.
Kessler, R. C., McGonagle, K. A., Zhao, S., Nelson, C. B.,Hughes, M., Eshleman, S., et al. (1994). Lifetime and 12-
month prevalence of DSM-III-R psychiatric disorders in the
US. Results from the National Comorbidity Survey. Archives
of General Psychiatry, 51(1), 819.
Lasser, K., Boyd, J. W., Woolhandler, S., Himmelstein, D. U.,
McCormick, D., & Bor, D. H. (2000). Smoking and mental
illness: A population-based prevalence study. JAMA, 284(20),
26062610.
Lokshin, M. (2005) Semi-parametric difference-based
estimation of partial linear regression models. Stata ado-
file with help and documentation. Washington, DC: World
Bank [Available at: /http://econ.worldbank.org/programs/
poverty/toolkitS].
Lopes Cardozo, B., Bilukha, O. O., Crawford, C. A., Shaikh, I.,
Wolfe, M. I., Gerber, M. L., et al. (2004). Mental health,
social functioning, and disability in postwar Afghanistan.
JAMA, 292(5), 575584.
McCabe, C. J., Thomas, K. J., Brazier, J. E., & Coleman, P.
(1996). Measuring the mental health status of a population: A
comparison of the GHQ-12 and the SF-36 (MHI-5). British
Journal of Psychiatry, 169, 516521.
McKenzie, D., Gibson, G., & Stillman, S. (2006). How Important
is Selection? Experimental vs. non-experimental measures of
the income gains from migration, World Bank Policy
Research Working Paper no. 3906.
Miech, R. A., Caspi, A., Moffitt, T. E., Wright, B. R. E., & Silva,
P. A. (1999). Low socioeconomic status and mentaldisorders: A longitudinal study of selection and causation
during young adulthood. American Journal of Sociology,
104(4), 10961131.
Miranda, J. J., & Patel, V. (2005). Achieving the millennium
development goals: Does mental health play a role? PLoS
Medicine, 2(10), 962965.
Mollica, R. F., McInnes, K., Sarajlic, N., Lavelle, J., Sarajlic, I.,
& Massagli, M. P. (1999). Disability associated with
psychiatric comorbidity and health status in Bosnian refugees
living in Croatia. JAMA, 282(5), 433439.
Mollica, R. F., Sarajlic, N., Chernoff, M., Lavelle, J., Vukovic, I.
S., & Massagli, M. P. (2001). Longitudinal study of
psychiatric symptoms, disability, mortality, and emigrationamong Bosnian refugees. JAMA, 286(5), 546554.
Patel, V., Araya, R., de Lima, M., Ludermir, A., & Todd, C.
(1999). Women, poverty and common mental disorders in
four restructuring societies. Social Science & Medicine, 49(11),
14611471.
Patel, V., & Kleinman, A. (2003). Poverty and common mental
disorders in developing countries. Bulletin of the World Health
Organization, 81(8), 609615.
Patel, V., Pereira, J., Coutinho, L., Fernandes, R., Fernandes, J.,
& Maan, A. (1998). Poverty, psychological disorder, and
disability in primary care attenders in Goa, India. British
Journal of Psychiatry, 172(June), 533536.
Patel, V., Rahman, A., Jacob, K. S., & Hughes, M. (2004). Effect
of maternal mental health on infant growth in low income
ARTICLE IN PRESS
J. Das et al. / Social Science & Medicine 65 (2007) 467480 479
http://econ.worldbank.org/programs/poverty/toolkithttp://econ.worldbank.org/programs/poverty/toolkithttp://econ.worldbank.org/programs/poverty/toolkithttp://econ.worldbank.org/programs/poverty/toolkit -
7/30/2019 Mental health and poverty in developing countries.pdf
14/14
countries: New evidence from South Asia. British Medical
Journal, 328(7443), 820823.
Radloff, L. S. (1977). The CES-D scale: A self-report depression
scale for research in the general population. Applied
Psychological Measurement, 1, 385401.
Ravallion, M. (1994). Poverty comparisons. Fundamentals in pure
and applied economics, Vol. 56. Chur: Harwood AcademicPress.
Scott, K.M., Massagli, A., Kapetanovic, R., Mollica Lavelle, J.
(2005). Measuring mental health in post-conflict societies: An
assessment of the Bosnia and Herzegovina experiment,
mimeo, World Bank.
Stillman, S., McKenzie, D., & Gibson, J. (2006). Migration and
mental health: Evidence from a natural experiment. BREAD
Working Paper No. 123.
United Nations High Commissioner for Refugees (UNHCR).
(2005). Refugees by the Numbers. Retrieved January 17,
2006, from /http://www.unhcr.ch/cgi-bin/texis/vtx/home/
opendoc.htm?tbl=BASICS &id=3b028097c&page=basics#
NumbersS.
Veit, C. T., & Ware, J. E., Jr. (1983). The structure of
psychological distress and well-being in general
populations. Journal of Consulting and Clinical Psychology,
51, 730742.
Weissman, M. M., Bland, R. C., Canino, G. J., Faravelli, C.,
Greenwald, S., Hwu, H. G., et al. (1996). Cross-national
epidemiology of major depression and bipolar disorder.JAMA, 276(4), 293299.
WHO International Consortium in Psychiatric Epidemiology.
(2000). Cross-national comparisons of the prevalences and
correlates of mental disorders. Bulletin of the World Health
Organization, 78(4), 413426.
WHO. (2001). The World Health reportMental health: New
understanding, new hope. World Health Organization.
WHO World Mental Health Survey Consortium. (2004).
Prevalence, severity, and unmet need for treatment of mental
disorders in the World Health Organization World Mental
Health Surveys. JAMA, 291(21), 25812590.
Yatchew, A. (1998). Nonparametric regression techniques in
economics. Journal of Economic Literature, 36(2), 669721.
ARTICLE IN PRESS
J. Das et al. / Social Science & Medicine 65 (2007) 467480480
http://www.unhcr.ch/cgi-bin/texis/vtx/home/opendoc.htm?tbl=BASICS&id=3b028097c&page=basics#Numbershttp://www.unhcr.ch/cgi-bin/texis/vtx/home/opendoc.htm?tbl=BASICS&id=3b028097c&page=basics#Numbershttp://www.unhcr.ch/cgi-bin/texis/vtx/home/opendoc.htm?tbl=BASICS&id=3b028097c&page=basics#Numbershttp://www.unhcr.ch/cgi-bin/texis/vtx/home/opendoc.htm?tbl=BASICS&id=3b028097c&page=basics#Numbershttp://www.unhcr.ch/cgi-bin/texis/vtx/home/opendoc.htm?tbl=BASICS&id=3b028097c&page=basics#Numbershttp://www.unhcr.ch/cgi-bin/texis/vtx/home/opendoc.htm?tbl=BASICS&id=3b028097c&page=basics#Numbershttp://www.unhcr.ch/cgi-bin/texis/vtx/home/opendoc.htm?tbl=BASICS&id=3b028097c&page=basics#Numbershttp://www.unhcr.ch/cgi-bin/texis/vtx/home/opendoc.htm?tbl=BASICS&id=3b028097c&page=basics#Numbershttp://www.unhcr.ch/cgi-bin/texis/vtx/home/opendoc.htm?tbl=BASICS&id=3b028097c&page=basics#Numbershttp://www.unhcr.ch/cgi-bin/texis/vtx/home/opendoc.htm?tbl=BASICS&id=3b028097c&page=basics#Numbers