Poverty and Perceived Stress: Evidence From Two ... · education, food insecurity, housing, social class, and financial stress (Lund et al., 2010). Given the adverse effects of poverty

POVERTY AND PERCEIVED STRESS IN ZAMBIA

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Poverty and perceived stress: evidence from two unconditional cash transfer programmes in Zambia

[Version accepted for publication in Social Science and Medicine:

http://www.sciencedirect.com/science/article/pii/S0277953617300308]

Lisa Hjelm1

Sudhanshu Handa1,2

Jacobus de Hoop1

Tia Palermo1

on behalf of the Zambia CGP and MCP Evaluation Teams

1 UNICEF Office of Research – Innocenti

Piazza SS. Annunziata, 12,

50122 Florence, Italy

2 Carolina Population Center, University of North Carolina at Chapel Hill

206 West Franklin St., Rm. 208 Chapel Hill, NC 27516, USA

Email addresses:

Lisa Hjelm: [email protected]; Sudhanshu Handa : [email protected], [email protected]; Jacobus de Hoop: [email protected]; Tia Palermo: [email protected]

Corresponding author:

Lisa Hjelm, UNICEF, Eastern and Southern Africa, PO Box 44145-00100 Nairobi, Kenya [email protected]

http://www.sciencedirect.com/science/article/pii/S0277953617300308

mailto:[email protected]







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Abstract

Introduction: Poverty is a chronic stressor that can lead to poor physical and mental health.

This study examines whether two similar government poverty alleviation programs reduced

the levels of perceived stress and poverty among poor households in Zambia.

Methods: Secondary data from two cluster randomized controlled trials were used to

evaluate the impacts of two unconditional cash transfer programs in Zambia. Participants

were interviewed at baseline and followed over 36 months. Perceived stress among female

caregivers was assessed using the Cohen Perceived Stress Scale (PSS). Poverty indicators

assessed included per capita expenditure, household food security, and (nonproductive) asset

ownership. Fixed effects and ordinary least squares regressions were run, controlling for age,

education, marital status, household demographics, location, and poverty status at baseline.

Results: Cash transfers did not reduce perceived stress but improved economic security (per

capita consumption expenditure, food insecurity, and asset ownership). Among these poverty

indicators, only food insecurity was associated with perceived stress. Age and education

showed no consistent association with stress, whereas death of a household member was

associated with higher stress levels.

Conclusion: In this setting, perceived stress was not reduced by a positive income shock but

was correlated with food insecurity and household deaths, suggesting that food security is an

important stressor in this context. Although the program did reduce food insecurity, the size

of the reduction was not enough to generate a statistically significant change in stress levels.

The measure used in this study appears not to be correlated with characteristics to which it

has been linked in other settings, and thus, further research is needed to examine whether this

widely used perceived stress measure appropriately captures the concept of perceived stress

in this population.

Key words: perceived stress, unconditional cash transfer, food security


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Stress is a determinant of poor mental health, a leading cause of years lived with

disability in high-, middle- and low-income countries (Vos et al., 2015), and it is an important

determinant of overall well-being. Therefore, it is important to measure stress as an outcome

in its own right (Haushofer & Shapiro, 2016; Kling, 2007). Stress and mental health are both

closely linked to poverty; studies from low- and middle-income countries have revealed a

link between poor mental health and socioeconomic status (SES) indicators such as

education, food insecurity, housing, social class, and financial stress (Lund et al., 2010).

Given the adverse effects of poverty on mental health, this study hypothesized that a poverty-

alleviation program (an unconditional cash transfer) would reduce poverty among poor

households in Zambia and subsequently reduce stress.

There are several hypothesized mechanisms through which poverty may influence

mental health, including chronic stress, malnutrition, substance abuse, social exclusion, and

exposure to trauma and violence. Known as the social causation hypothesis, it has been

studied extensively (Johnson et al., 1999; Lund et al., 2011). Further, in what is known as the

social drift hypothesis, people with mental illness are at increased risk of experiencing

poverty through increased health expenditures, reduced productivity, and stigma related to

mental health (Lund et al., 2011). Thus, poverty and poor mental health mutually reinforce

each other (Lorant et al., 2003; Lund et al., 2011). Poverty and low SES may also affect an

individual’s exposure to stress and stressful life events as well as his or her ability to cope

with stress, as fewer social and psychological resources are usually available to overcome

stressful events (Adler et al., 1994; Cohen, 1988; Cohen & Janicki‐Deverts, 2012; Hamad et

al., 2008).

Stress as a mechanism that links poverty and health merits further investigation.

Psychological stress, described as the experience of environmental demands exceeding the

ability to cope with the situation (Lazarus & Folkman, 1984), is associated with a range of


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physical and mental health states. It has been linked to depressive disorder, depressive

symptoms, cardiovascular disease, and the risk of progressing from HIV infection to AIDS

(Cohen et al., 2007). Experimental studies have shown that acute and chronic stressors can

produce biological stress reactions, including excessive inflammation (McEwen & Seeman,

1999). Over the course of a lifetime, these reactions may contribute to morbidity and

mortality disparities and increased levels of cortisol, particularly for stressors of an

uncontrollable nature (Miller et al., 2007). Poverty-induced chronic stress has also been

hypothesized to accelerate the natural aging of the immune system (referred to as

immunosenescence) (Aiello & Dowd, 2013). Studies have demonstrated that individuals of

lower SES show an increased antibody response to persistent herpes viruses, which may be

due to differential exposure to stress (Aiello & Dowd, 2013) and reduced resources to cope

with it (Kristenson et al., 2004). It is hypothesized that increased stress, caused by a range of

poverty-associated factors, continuously activated stress-related autonomic and

neuroendocrine responses, thus impairing immunity and ultimately leading to poor health

outcomes (Aiello & Dowd, 2013). Maternal perceived stress also has been associated with

low birth weight and poor childhood nutritional status (Dole et al., 2003; Lobel et al., 1992;

Rondó et al., 2013; Torche, 2011).

The majority of studies that examine the relationship between stress, SES (Cohen &

Janicki‐Deverts, 2012; Matthews et al., 2010), and stressful life events are associated with

higher levels of perceived stress (Dowd et al., 2014; van Eck et al., 1998). These variables

have been less studied in sub-Saharan African countries, where food insecurity (Pike & Patil,

2006) and HIV infection (Garcia et al., 2013) are more widespread, which may have

implications for variation in stress levels by SES. A South African study found that perceived

stress was related to subjective social status but not to other socioeconomic indicators, such

as education, employment, and income (Hamad et al., 2008). A Kenyan study among farmers


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demonstrated that elevated levels of cortisol and self-reported stress were induced by the

absence of rain, which caused a negative income shock (Chemin et al., 2013). Another study

found a reduction in self-reported stress due to unconditional cash transfers (a positive

income shock), but no impact on cortisol levels (Haushofer & Shapiro, 2016). A key issue in

all of these studies is the use of measures of stress that have not been validated in sub-

Saharan Africa. Therefore, these measures for stress may not be appropriate in low-income

settings of sub-Saharan Africa.

This study posited that cash transfer programs aimed at improving food security and

smoothing consumption would lead to reduced stress levels in a poor- and food-insecure

setting in sub-Saharan Africa. Cash transfer programs directly aim at poverty alleviation and

not improving outcomes in mental health and related areas. Thus, impacts of the cash transfer

must first work through household-level outcomes—such as food security, economic

security, time use and labor decisions, and general stress levels. Then, the impacts make their

way to individual-level outcomes, such as physical and mental health, perceived stress,

expectations, and outlook.

To date, certain studies in Kenya and Malawi have demonstrated that social cash

transfers have improved mental health in the form of fewer depressive symptoms (as

measured respectively by the Center for Epidemiologic Studies Depression Scale [CES-D]

and the General Health Questionnaire [GHQ-12]). Evidence from Malawi suggests that the

effects of cash transfers on depressive symptoms depend on program design, specifically the

combinations of conditions and transfer amounts. Other studies have reported mixed impacts

on cortisol levels of cash transfer beneficiaries, such as protective impacts among Mexican

children and no impacts among adults in a Kenyan sample (Baird et al., 2013; L. Fernald &

Gunnar, 2009; Haushofer & Shapiro, 2016; Kilburn et al., 2015).


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Current evidence on the relationship between cash transfer programs and perceived

stress is mixed. There are examples of studies that have examined this relationship in Latin

America (Ozer et al., 2011; Schady & Paxson, 2007) and Africa (Haushofer & Shapiro,

2016). In Mexico, participation in the Oportunidades program was associated with lower

depression, and reduced perceived stress [measured by the Perceived Stress Scale (PSS)] was

found to be the mediating factor in women (Ozer et al., 2011). In contrast, in Ecuador,

participation in an unconditional cash transfer program had no significant effect on perceived

stress (measured using a four-item version of the PSS) or on symptoms of depression (Schady

& Paxson, 2007). In Kenya, participation in a cash transfer program reduced perceived stress

(measured by the Cohen PSS) but not cortisol levels in the overall sample. Nonetheless, some

reductions in cortisol were seen among subsamples, such as among female recipients and

among participants who received lump-sum transfers compared to those receiving a monthly

transfer (Haushofer & Shapiro, 2016). On a related note, two additional studies examined the

impacts of loan access and the provision of health care on perceived stress. A Kenyan study

found that health care receipts reduced perceived stress (Chemin et al., 2016). A South

African study found that, among individuals who were initially not offered a small loan, a

second chance to receive the loan increased the levels of perceived stress (Fernald et al.,

2008).

As outlined above, the evidence to date on poverty alleviation and perceived stress is

mixed, and therefore the present study aimed to investigate (1) whether participation in a cash

transfer program reduced poverty-related outcomes and perceived stress and (2) which

individual- and household-level characteristics are associated with higher levels of perceived

stress. To investigate these questions, data from longitudinal impact evaluations of two

government cash transfer programs in Zambia were used. It is important to note that neither

program was designed to address stress, but rather to address food insecurity and extreme


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poverty. Nevertheless, given the theoretical link between poverty and stress, and the fact that

food insecurity is a widespread problem in this population, it is of policy and public health

interest to assess the link between the programs and perceived stress.

Method

Interventions

The Zambia Child Grant Program (CGP) is a government-run unconditional cash

transfer program targeting households with a child under the age of five. The CGP’s

objectives include supplementation of household income, increased enrollment and

attendance in primary school, reduced child morbidity, productive assets, food security, and

improved mortality and nutrition. Districts for program implantation were targeted by the

government because of their high rates of mortality, morbidity, stunting, and wasting among

children aged 0–3 years. Households included in the program receive an amount equivalent to

11 USD per month, which is estimated to be sufficient to cover the cost of one meal per

person per day in an average-sized household. Households “age-out” or graduate from the

program when the index child turns five, although in practice this was not implemented until

after the 36-month evaluation survey was conducted (American Institutes for Research,

2011).

Similar to the Zambia CGP, the Zambia Multiple Category Cash Transfer Program

(MCP) is another government-run unconditional cash transfer program in Zambia, also

implemented by the Ministry of Community Development, Mother and Child Health. The

objectives of the MCP are to assist the most vulnerable households in the society, allowing

them to meet their basic needs related to health, education, food, and shelter. The program

targets households that fall into any of the following categories: female headed and keeping

orphans, having a disabled member, headed by an elderly and keeping orphans, or special


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cases of critically vulnerable households that are not included in any of the aforementioned

categories (American Institutes for Research, 2012).

Sample data

This study used secondary data that were collected as part of the impact evaluations of

the CGP and the MCP. These impact evaluations were randomized controlled trials, designed

and implemented by The American Institutes for Research and the University of North

Carolina at Chapel Hill under contract to UNICEF-Zambia.

The CGP impact evaluation comprised 2,515 households at baseline from 90

communities (randomized into treatment and control arms) in three districts – Kaputa,

Kalabo, and Shang’ombo – for a total evaluation sample size of 14,565 individuals. Baseline

data were collected in December 2010, and follow-up data were collected in September and

October 2012 (24 months), June and July 2013 (30 months), and September and October

2013 (36 months). The MCP impact evaluation took place in 92 communities (randomized to

treatment and control arms) within two districts, Luwingu and Serenje. MCP baseline data,

including 3,078 households and 15,630 individuals, were collected in November and

December 2011, and follow-up data were collected in November and December 2013 (24

months) and November and December 2014 (36 months).

In the subsample for analysis in the present study, data were used from the

observations of female caregivers of children (main household survey respondents). These

caregivers were observed at baseline and 36 months for the MCP and at baseline, 30 months,

and 36 months for the CGP. The official evaluation reports demonstrated balance at baseline

(indicating successful randomization of the treatment and control arms) and no evidence of

selective attrition between study arms (American Institutes for Research, 2011, 2012). Data

collections and analyses plans went through ethical review at the American Institutes for


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Research in Washington, DC, and at the University of Zambia Ethical Review Committee.

Informed consent was obtained from all study participants.

Measures

This study used the PSS to measure psychological stress. The PSS was developed on

the basis of the concept of stress as an interaction between environmental demands and the

individual’s capacity to cope (Cohen et al., 1983). Originally developed with 14 items, its

creators later refined it to 10 items (the PSS10) of which 6 are negatively phrased and 4 are

positively phrased (Cohen, 1988). These items consider the degree to which individuals

experience their lives as unpredictable, uncontrollable, and overloading (Cohen et al., 1983).

This scale is one of the most frequently used measures of perceived stress and has been

validated in many countries around the world; it is increasingly being used in sub-Saharan

Africa (e.g., Garcia et al., 2013; Hamad et al., 2008; Lemma et al., 2012), but to current

knowledge, it has never been validated there. The PSS has been associated with depressive

symptomatology in a number of Western countries using the original English language

questionnaire (Cohen et al., 1983; Eisenbarth, 2012; Hewitt et al., 1992) and in other regions

and languages (e.g., Andreou et al., 2011; Chaaya et al., 2010; Wang et al., 2011).

Consistent with previous research, principal component analysis resulted in two

factors in this study’s samples, one consisting of the negatively worded items and the other

consisting of the positively worded items (Cohen, 1988). However, the two subscales were

not closely correlated. The “negative” subscale showed more variation in relation to

happiness and optimism compared to the “positive” subscale, and the “positive” subscale did

not show consistent associations. Thus, this study concluded that the positively worded items

did not perform well in this setting, and a consolidated stress scale including both positively

and negatively worded items would not be suitable as a single measure of perceived stress in


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these two samples. This study therefore included only the six negatively worded PSS items.

The questions included in the final scale were:

In the last 4 weeks, how often have you been upset because of something that

happened unexpectedly? In the last 4 weeks, how often have you felt that you were

unable to control the important things in life? In the last 4 weeks, how often have you

felt nervous and “stressed”? In the last 4 weeks, how often have you found that you

could not cope with all things that you had to do? In the last 4 weeks, how often have

you been angered because of things that were outside of your control? In the last 4

weeks, how often have you felt difficulties were piling up so high that you could not

overcome them?

Likert-type responses ranged from 0 (never) to 4 (very often/always). The scale was

constructed by adding the total score from each question resulting in a scale ranging from 0 to

24. Cronbach’s α was 0.84 for the CGP and 0.83 for the MCP sample, indicating the high

internal reliability in each sample. For MCP, 3% of the analysis had missing values for the

PSS, and 6% for CGP. There was no systematic difference in treatment arm, age, education,

or marital status between women who responded and those with missing values. Due to their

relatively small number, these observations were dropped from the analysis.

Because this study hypothesized that cash transfers could alleviate stress through the

poverty pathway, it also examined whether the program affected the following poverty-

related outcomes: household consumption expenditures, food security, and the number of

nonproductive assets owned. Food security was measured using the previously validated

(Knueppel et al., 2010; Maes et al., 2009) Household Food Security Access Scale (HFIAS)

(Coates et al., 2007). The scale consists of nine items capturing different severities of food

security, from worrying about not having enough food to not eating at all because of lack of

food. The questions referred to the past 4 weeks. Severe food insecurity was calculated based


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on the occurrence of the more severe experiences of food insecurity included in the HFIAS

questionnaire, for example, if there was no food to eat of any kind because of a lack of

resources.

Monthly consumption expenditure per capita in Zambia kwacha (ZMW) was

calculated using an expenditure module, which was adopted from the Zambian Living

Conditions Monitoring Survey, covering a broad range of expenditure categories and

including more than 200 items. At follow-up, expenditures were deflated to baseline year

ZMW values, 2010 for CGP and 2011 for MCP.

The asset ownership indicator is the sum of nonproductive assets owned from a list of

10 assets (clock, watch, mobile phone, DVD, television, radio, sofa, table, mattress, and bed)

in the CGP and 7 assets in the MCP (clock, watch, mobile phone, radio, sofa, table, and

mattress).

Covariates

Individual-level control variables included age of the respondent, whether the

respondent had ever attended school – and subsequently – highest grade attained in school;

and whether the respondent was married, never married, divorced, or widowed. Control

variables at household level included the total number of household members, number of

household members of different age groups (0–5, 6–12, 13–18, 19–35, 36–55, 56–69, and

70+ years), and the district where the household was located. It also included the poverty

status at baseline in terms of food insecurity, log of per capita expenditures, and asset

ownership (described above). To measure stressful life events, we examined any death in the

household (recall period since last follow-up survey).

Statistical analyses

This study first examined sample characteristics, including descriptive statistics and

covariates for balance between treatment and control samples at baseline; then two sets of


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multivariate analyses were performed. In the first set of multivariate analyses, we

investigated treatment effects on perceived stress and poverty-related indicators using

ordinary least square (OLS) regressions. These analyses were conducted using cross-sections

from the latest follow-up waves at which perceived stress data were collected (36-months

follow-up). To estimate program impacts, regressions with a treatment indicator (1=

treatment, 0=otherwise) were run. The covariates listed above were controlled to improve the

precision of the estimates. Additionally, regressions without controls were run, and it was

concluded that adding controls did not significantly change the results. This study used

baseline values of control variables because the program may have affected them, and thus,

using contemporaneous values may have underestimated the treatment effect on outcomes of

interest. Control variables with missing values were replaced with −1, and then an indicator

(+1) variable was added (0=otherwise) to control for missing information (which is why

control variables in Table 1 may have fewer observations than regressions in Tables 2 and 3).

To estimate treatment impacts, these analyses relied on the successful randomization of the

program, which created statistically equivalent treatment and control groups. As there was no

pretreatment measure of the key outcome indicator, the estimated treatment effect assumes

that this measure was balanced at baseline, which is consistent with the results of the balance

tests reported in Table 1 over a range of outcomes.

In the second set of analyses, this study examined determinants of perceived stress by

estimating associations between perceived stress and household- and individual-level

characteristics using OLS regressions on the cross-sectional control samples at 36 months.

Treatment individuals in this latter analysis were excluded as the program may have

mitigated some of the risk factors for stress in the treatment arm. As a robustness check for

the determinants of stress analysis, individual fixed-effects OLS regressions were run using

observations from the CGP control sample at 30 months and 36 months. As these regressions


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control for unobserved characteristics, which may simultaneously affect both risk factors and

perceived stress, they may reduce any remaining bias in the estimates. All standard errors for

clustering at the community level were adjusted (the level of program randomization). The

data were analyzed using Stata Version 14.

Results

Of the 2,515 households included in the CGP at baseline, 2,273 had a female

caregiver who was observed at baseline and had completed the PSS questionnaire at 30 and

36 months. In the MCP, of the 3,077 households included at baseline, 2,490 households had a

female caregiver who was observed at baseline and had completed the PSS questionnaire at

36 months.

Table 1 describes the covariates and the poverty-related outcome variables at baseline,

and examines the balance between the treatment and control groups of the two samples at

baseline. Treatment and control arms were balanced (i.e., there were no statistically

significant differences between treatment and control arms) at baseline for both samples.

Although the average age for women interviewed in the CGP households was approximately

30 years, women in MCP households were considerably older with an average age of 52

years. The poverty-related outcome variables illustrate the deprived background of the

households benefitting from these programs. Per capita monthly expenditure at baseline was

40 ZMW for households in the CGP and 49 ZMW for households in the MCP, roughly

equivalent to 8 USD (or 26 cents per person per day) for CGP households and 10 USD (or 33

cents per person per day) for MCP households. Approximately 90% of the CGP households

and 81% of the MCP households were severely food insecure at baseline.

[Insert Table 1 about here]

Within the individual items making up the PSS, among the women in the CGP and

MCP control groups, respectively 6% and 22% had been upset because something happened


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unexpectedly; 8% and 19% had felt that they were unable to control the important things in

life; 11% and 23% had felt nervous and stressed;12% and 21% had found that they could not

cope with all things that they had to do; 12% and 21% had been angered because things were

outside their control; and 18% and 27% had felt that difficulties were piling up so high that

they could not overcome them fairly often or very often/always in the 4 weeks preceding the

survey.

Table 2 shows the program impacts on perceived stress and poverty-related indicators

for the CGP in Panel A and MCP in Panel B. The results indicate that the program had no

statistically significant impact on perceived stress (columns 1 and 2). However, the program

was successful in reducing poverty-related outcomes. It increased the monthly per capita

expenditures by 10 ZMW (17 ZMW in the MCP; column 2), an increase of 20% (28% in

MCP). It also reduced household food insecurity by three points (0.5 SD) on the HFIAS

[three points (0.6 SD) in the MCP; column 3] and increased nonproductive assets by 0.7

items (0.4 in the MCP; column 4) on average.


Table 3 presents the results examining the determinants of perceived stress using only

observations from the control groups. In the OLS regressions (columns 1 and 3), it was found

that the only covariate associated with perceived stress was household food insecurity, which

was associated with stress levels that were 0.15 points higher in the CGP and 0.27 points

higher in the MCP on average. After controlling for unobserved factors in the fixed effects

model in the CGP, this study found that death in the household was also associated with

stress levels that were 1.63 points higher on average (column 2). Age, educational attainment,

household consumption expenditures, and assets were not associated with stress levels in

these samples.



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Discussion

This study examined whether participation in an unconditional cash transfer program

reduced perceived stress. Although the program was successful in reducing poverty as

measured by three different household-level indicators, this study found no program impacts

on perceived stress levels. These findings are similar to those from Ecuador, where

participation in an unconditional cash transfer program had no effect on perceived stress

(Schady & Paxson, 2007). However, the findings of this current study contrast with

quantitative findings from Kenya and Mexico, which found that cash transfers reduced

perceived stress (as measured by the PSS) (Haushofer & Shapiro, 2016; Ozer et al., 2011).

When this study examined correlates of perceived stress, only food insecurity and

household deaths were related to higher levels of stress, which suggests that the experience of

not having enough food in the household is a key source of stress compared to other aspects

of poverty that were examined. High levels of food insecurity are a persistent challenge in

sub-Saharan Africa (FAO et al., 2015). In sub-Saharan African rural settings, food insecurity

may be a more prominent source of insecurity than in other locations, particularly in

subsistence farming settings with distinct seasonal variations in the accessibility of food

(Hadley & Patil, 2008). The insecurity of not knowing where the next meal will come from,

having to deprioritize other necessities, and the shame of not having enough food for the

children, causes stress among those that are living in food insecurity (Hadley et al., 2012).

Hunger and food insecurity have been identified as important stressors in sub-Saharan Africa

(Pike & Patil, 2006), and the experience of food insecurity as a stressful event raises the

levels of perceived stress (Addo et al., 2011). The stress of food insecurity potentially

explains the link between household food insufficiency and increased risk for depression that

has been found in a number of studies in sub-Saharan Africa (Hadley et al., 2008; Maes et al.,

2010; Palermo et al., 2013; Tsai et al., 2016), particularly in Zambia (Cole & Tembo, 2011).


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There are several possible explanations regarding why this study did not find an

association between stress and consumption expenditures, asset ownership, or cash transfer

receipt. One is that relative poverty or the perceived SES could be more important in

determining stress levels than absolute levels of poverty (measured by expenditures and asset

ownership), and this is supported by research done in South Africa (Hamad et al., 2008).

Several studies from high-income countries have shown that relative experience of

deprivation can be a source of stress and cause ill health (Adler et al., 1994; Jennifer Beam

Dowd et al., 2008; Schulz et al., 2012). The possibility that the program did affect perceived

social status remains; however, our study did not measure this outcome. The measures of

poverty included in this study are in more absolute terms, and thus do not reflect a person’s

subjective perception of their rank in society, which may be a larger comparative driver of

stress.

A second possible explanation could be that the grant amount was not sufficient to

affect stress levels. The cash may work its way along the pathway from poverty, which it did

affect, but may not have been enough to continue along the pathway to affect stress.

Considering the effect sizes, it can be concluded that the treatment effect on food insecurity is

large, with a 2.9-point reduction on the food insecurity scale for CGP beneficiaries. However,

although the association between food security and perceived stress was statistically

significant, the coefficient was small. In the CGP, a one-point decrease on the food insecurity

scale resulted in a 0.12-point reduction on the PSS. Consequently, the hypothesized treatment

effect on perceived stress through reduced food insecurity would result in a 0.35-point (2.9

0.12) or 0.08-SD reduction in perceived stress for CGP beneficiaries. The corresponding

figure for the MCP would be a 0.8-point or 0.17 SD reduction in perceived stress, which is

slightly higher but still not large enough to be detected in this study sample. Beyond the size

of the grant, other aspects of program implementation such as predictability of payment could


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also affect stress. Yet, in both the programs, payments were made regularly and on-time;

therefore, this is unlikely to explain the lack of impacts on PSS.

A third theory is that the PSS10, which was first developed in a Western setting, does

not adequately capture stress in a poor, rural setting in sub-Saharan Africa. It has been argued

that the concept of “stress” may not be used and understood the same way in all cultural

contexts (Pike & Patil, 2006) and that a context-specific stress scale based on local conditions

and expectations may better capture the experience of stress (Ice et al., 2012). The possible

inadequacy of the PSS to capture stress in this context could also explain why age and

education, which are factors that are typically associated with levels of perceived stress in

other settings (Cohen & Janicki‐Deverts, 2012; Dowd et al., 2014; Remor, 2006), showed no

consistent findings in this study’s samples. Although there was no association between age

and perceived stress in these samples, the relatively older women in the MCP households

report more stress. This finding may be a function of stress increasing with age or with the

targeting criteria for the MCP, which targets households in potentially demanding situations,

such as caring for orphans and disabled household members. In contrast, CGP beneficiaries

are targeted based on the sole criteria of having a child under the age of five in the household

(in very poor geographic areas).

Nonetheless, our study did find that one measured stressful life event—a death in the

family—was associated with higher perceived stress, which is confirmed by findings

elsewhere (Cohen et al., 1983). Thus, the PSS10 may be better able to pick up stressful life

events in this setting but is less sensitive to chronic daily stress. Chronic daily stress is a key

hypothesized mediator for poverty alleviation programs, and it is therefore important to

measure in impact evaluations of cash transfer programs (Haushofer & Shapiro, 2016; Kling,

2007).


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Finally, the divergence between findings on food insecurity and PSS may be

explained by the fact that HFIAS includes items that capture both material outcomes (having

enough to eat) and mental outcomes (worrying about food), and the material outcomes may

drive the impact on the overall scale. To explore this hypothesis, this study estimated

program impacts on each individual item in the scale separately and found significant impacts

on each item (results not displayed). The key difference, of course, is that the mental items in

the HFIAS relate specifically to food, whereas they are more general in the PSS. This

underscores the idea that food insecurity is the critical dimension of well-being in this

population.

Limitations

There are some limitations to this study. Although the PSS is widely used and

validated globally, it has never, to our knowledge, been validated in a sub-Saharan African

setting. Further, psychometric problems mean that this study was not able to use all 10 items

on the PSS10 scale; instead, this study used a subset of six questions because the positively

worded question items performed poorly in this setting.

The key outcome used in this paper was only collected at follow-up. Thus, causal

inference relies on the assumption of baseline equivalence in the mean value of the stress

indicator across study arms. This limitation is mitigated by the fact that in both studies

assignment to study arm was done randomly, and baseline equivalence is established among a

range of other indicators including a key determinant of stress—household food security.

A final limitation concerns the potential generalizability of these findings to other

rural populations in sub-Saharan Africa. The two cash transfer programs targeted ultra-poor

households with unique demographic profiles, households with a pre-school child in the

CGP, and households with a disabled member or an elderly or female head caring for orphans

in the MCP. The combination of absolute poverty and demographic vulnerability may result


19

in unique stressors which may not be relevant for other types of poor households in rural sub-

Saharan Africa.

Future Studies

The findings of this study can explain how different aspects of poverty may

differentially affect mental health in low-income countries, with stress as a potential pathway

between poverty and mental health. Future studies, particularly in sub-Saharan Africa and

specifically Zambia, should investigate the concept of stress and how poverty relates to

different measures of stress. The inconsistency in findings of the present study compared to

those of previous studies indicates that the stress scale is inadequately measuring stress in this

context. The PSS should be validated in a sub-Saharan African setting, and a more regionally

appropriate scale should be developed.

Acknowledgements

The CGP and MCP impact evaluations were commissioned by the Government of Zambia (GRZ)

through the Ministry of Community Development, Mother and Child Health to the American

Institutes of Research (AIR) and the University of North Carolina at Chapel Hill (UNC) and funded

by a consortium of donors including DFID, UNICEF, Irish Aid, and the Government of Finland.

Palermo, Handa, and Hjelm received additional funding from the Swedish International Development

Cooperation and de Hoop received additional funding from the US Department of Labor to the

UNICEF Office of Research - Innocenti for analysis of the data and drafting of the manuscript.

The members of the CGP evaluation team, listed by affiliation and then alphabetically within

affiliation are:

Principal Investigators: David Seidenfeld (AIR) and Sudhanshu Handa (UNC); AIR: Juan Bonilla,

Rosa Castro Zarzur, Leah Prencipe, Dan Sherman, David Seidenfeld; UNICEF-Zambia: Charlotte

Harland Scott, Paul Quarles van Ufford; Government of Zambia: Vandras Luywa, Stanfield Michelo,

Manzunzo Zulu; DFID-Zambia: Kelley Toole; Palm Associates: Alefa Banda, Chiluba Goma, Liseteli


20

Ndiyoi, Gelson Tembo, NathanTembo); UNC: Sudhanshu Handa; UNICEF Office of Research –

Innocenti: Sudhanshu Handa, Tia Palermo, Amber Peterman, Leah Prencipe.

The members of the MCP evaluation team, listed by affiliation and then alphabetically within

affiliation are:

Principal Investigators: David Seidenfeld (AIR) and Sudhanshu Handa (UNC); AIR: Juan Bonilla,

Alvaro Ballarin Cabrera, Thomas De Hoop, Gilbert Kiggundu, Nisha Rai, Hannah Reeves, Joshua

Sennett, Dan Sherman, Jonathan Sokoll, Amy Todd, Rosa Castro Zarzur; Palm Associates: Alefa

Banda, Liseteli Ndiyoi, Nathan Tembo; UNC: Sudhanshu Handa; UNICEF Office of Research -

Innocenti: Tia Palermo, Amber Peterman, Leah Prencipe


21

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Table 1. Household characteristics of the Zambia Child Grant Programme (CGP) and Multiple Category Cash Transfer Programme (MCP) samples, baseline

Panel A: CGP N All Control

(proportion /mean)

Treatment (proportion

/mean) P-value

Characteristics of women

Age 2,272 29.79 29.64 29.95 0.65 Ever attended school 2,271 0.71 0.70 0.73 0.40 Highest grade completed 2,261 3.93 3.70 4.17 0.09 Married 2,266 0.73 0.72 0.74 0.73 Never married 2,266 0.10 0.10 0.11 0.78 Divorced 2,266 0.10 0.11 0.09 0.10 Widowed 2,266 0.06 0.06 0.07 0.87 Household demographics

Household size 2,273 5.71 5.65 5.77 0.51 Number of people ages 0 - 5 2,273 1.91 1.92 1.90 0.68 Number of people ages 6 - 12 2,273 1.27 1.27 1.27 1.00 Number of people ages 13 - 18 2,273 0.56 0.53 0.60 0.15 Number of people ages 19 - 55 2,273 1.87 1.83 1.90 0.18 Number of people ages 56 or older 2,273 0.09 0.10 0.09 0.95 Material well-being

Household food insecurity access scale (HFIAS) (0-24)a

2,235 15.23 15.4 15.05 0.55

Severely food insecure households 2,243 0.90 0.90 0.90 0.90 Total household expenditure per

person in the household 2,271 39.82 38.87 40.79 0.46

Assets owned (0-10) 2,273 0.82 0.73 0.92 0.12 Minimum N 2235 1127 1106

Panel B: MCP

Characteristics of women

Age 2,490 51.62 51.26 51.98 0.50 Ever attended school 2,481 0.61 0.63 0.60 0.42 Highest grade completed 2,458 3.03 3.09 2.98 0.64 Married 2,474 0.29 0.30 0.29 0.83 Never married 2,474 0.05 0.06 0.04 0.01 Divorced 2,474 0.14 0.14 0.14 0.96 Widowed 2,474 0.51 0.50 0.53 0.37 Household demographics

Household size 2,490 5.16 5.18 5.14 0.84 Number of people ages 0 - 5 2,490 0.75 0.73 0.77 0.51 Number of people ages 6 - 12 2,490 1.33 1.28 1.38 0.19 Number of people ages 13 - 18 2,490 0.98 1.03 0.94 0.11 Number of people ages 19 - 55 2,490 1.38 1.41 1.34 0.46 Number of people ages 56 or older 2,490 0.72 0.74 0.71 0.55 Material well-being

Household food insecurity access scale (HFIAS) (0-24)a

2,431 14.68 14.61 14.75 0.76

Severely food insecure households 2,459 0.81 0.78 0.83 0.09 Total household expenditure per

person in the household 2,490 48.63 48.79 48.48 0.91

Assets owned (0-7) Minimum N

2,490 2431

0.54

0.58 1207

0.51 1224

0.35

Note: P-values are reported from Wald tests on the equality of means of Treatment and Control for each variable. Standard errors are clustered at the community level a One question was dropped from the Household Food Security Access Scale (HFIAS) at baseline


30

Table 2. Treatment effect on stress and indicators of poverty in Zambia’s Child Grant Programme (CGP) and Multiple Category Cash Transfer Programme (MCP), 36-months follow-up

Panel A: CGP

Perceived Stress Scale

(0-24)a

Expenditure per capitab

Household Food Insecurity Access

Scale (0-27)b

Number of non-productive assets

ownedb,c (1) (2) (3) (4)

Treatment effect

0.07

10.43***

-2.86***

0.72***

(t-statistic)

(0.21)

(4.32)

(-7.63)

(6.29)

Number of observations

2,273

2,273

2,269

2,272

Control mean (sd) 7.60 (4.20) 50.98 (36.97) 13.50 (5.20) 0.86 (1.44)

Treatment mean (sd) 7.70 (4.03) 62.52 (37.56) 10.54 (4.84) 1.71 (1.80)

Panel B: MCP

Treatment effect

-0.42

16.68***

-3.02***

0.37***

(t-statistic)

(-1.17)

(4.76)

(-6.94)

(5.73)

Number of observations

2,490

2,490

2,490

2,490

Control mean (sd) 9.92 (4.73) 60.54 (40.53) 14.50 (5.54) 0.49 (0.87)

Treatment mean (sd) 9.58(4.64) 76.87(53.62) 11.52(5.11) 0.84(0.97) Note: Impact estimated based on OLS regressions with and without controls. T-statistics are based on standard errors clustered at the community level. *** p<0.001, ** p<0.01, * p<0.05. a Control variables include age, education and marital status of the woman as well as poverty status at baseline (expenditure per capita, food insecurity and asset ownership), district, household size and demographic composition of the household. b Control variables include poverty status at baseline (expenditure per capita, food insecurity and asset ownership), district, household size and demographic composition of the household. c Number of assets owned range from 0-10 in the CGP and 0-7 in the MCP.


31

Table 3. Fixed-effect and cross-sectional OLS regressions of individual and household characteristics associated with the perceived stress scale, control groups only

CGP MCP

Cross-sectional OLS

regression (1)

Fixed-effect regression

(2)

Cross-sectional OLS regression

(3)

Age 0.02 - 0.01

(1.58) - (0.64) Education (attended school) 0.48 - -0.16

(1.74) - (-0.52) Any death in the household 0.53 1.63** -0.61

(0.58) (2.91) (-0.84) Household Food Insecurity Access Scale (0-27) 0.15** 0.12**

0.27***

(3.21) (2.77) (6.36) Expenditure per capita 0.01 0.00 -0.00

(1.78) (1.04) (-0.78) Number of non-productive assets owneda -0.01 -0.23

-0.08

(-0.05) (-1.37) (-0.42)

Constant 4.82*** 6.42*** 6.63***

(4.28) (9.18) (5.40)

Number of women 1,139 1,145 1,227 R-squared 0.09 0.04 0.18 Observations 1,145 2,285 Notes: Robust t-statistics in parentheses based on standard errors clustered at the community level. Cross-sectional models control for marital status, district, household size and demographic composition of the household. *** p<0.001, ** p<0.01, * p<0.05

Documents

Poverty and Perceived Stress: Evidence From Two ... · education, food insecurity, housing, social class, and financial stress (Lund et al., 2010). Given the adverse effects of poverty