mental health and poverty in developing countries.pdf

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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).
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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

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J. Das et al. / Social Science & Medicine 65 (2007) 467480468


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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


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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.



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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.



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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.



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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


Poor physical health

-1

-0.5

0

0.5

1

1.5


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


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).



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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.



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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.)



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



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



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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.


13/14

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