PIERI-12506

FINAL REPORT DRAFT 1

Presented to

Partnership for Economic Policy (PEP)

By

Damilola Olajide

& Adaku Ezeibe

Olusegun Sotola

Kafilah Gold

Olufunke Olufemi

Florence Adebayo

NIGERIA

April 2016

Randomised evaluation of unconditional cash transfer scheme for

the elderly in Ekiti State Nigeria

1. ABSTRACT

Many countries in the developing world have implemented non-contributory old age pensions, however, evidence of the impacts on the elderly in Sub-Saharan

Africa is scarce. This paper provides evidence from a randomised evaluation of an unconditional, non-contributory pension scheme targeted at the elderly in Ekiti State,

Nigeria. Our goal is to examine the extent to which such a program can serve as an instrument to improve the wellbeing of beneficiaries and their households. We used

data collected from 6,326 eligible beneficiaries and 18,954 household members across 112 electoral wards in Ekiti State. The randomization of beneficiaries was done

at the ward level, where 3,230 beneficiaries were eligible to receive an unconditional and non-contributory cash transfer and 3,096 beneficiaries were kept

as controls. Treated beneficiaries self-reported better quality of life, mainly in terms of

more stable mental health, higher perceptions of happiness and capabilities,

improvement in household decision making and family relationships, and better

health status. Also, family members of treated beneficiaries increased their labour market participation but there is no evidence that treated beneficiaries increased

their own labour market participation. Finally, household vulnerability decreased. Our findings provide evidence-based support for demand side interventions aimed

at improving the welfare of poor households. Key terms: Randomised control trial; Aging; Non-contributory elderly pensions; Ekiti State, Nigeria.

Acknowledgements

We are grateful for comments from the participants at the 10th Partnership for Economic Policy (PEP) General Meeting and 2015 Human Capital and Aging Workshop at the Harvard T.H. Chan School of Public Health, Harvard University. This work was carried out with financial and scientific support from the PEP, with funding from the Department for International Development (DFID) of the United Kingdom (UK Aid), and the Government of Canada through the International Development Research Center (IDRC). Any errors and omissions are our own. We are grateful also to the officials of the implementation agency, Ekiti State Ministry of Labour, Productivity and Human Development, Ado Ekiti.

2. INTRODUCTION

2.1. Context Description

Nigeria is the most populous country in Africa with an estimated population of 173.6

million in 2013 (National Bureau of Statistics, 2014). In Sub-Saharan Africa (SSA), one

in four persons is a Nigerian. Since the end of the Nigerian civil war (1967-1970),

Nigeria’s population has tripled, increasing from 56.1m in 1970 to 170.1m in 2010. As

in many other countries, the Nigerian population has been witnessing the

demographic transformation, aging. This has been largely due to declining fertility

and rising longevity. The fertility rate has dropped to 5.2% in 2014 from 6.1% in 1990,

whilst the birth rate has fallen from 6% to 3.8% in the same period, and a decline in

child mortality rate; from 116 in 1990 to 74 in 2010 (National Population Commission,

NPC 2011; World Bank 2015). As such, life expectancy also increased from 46.7 years

in 2005 to 52.6 years in 2014 (CIA World Factbook, 2014).

The proportion of the elderly population in Nigeria (i.e. aged 65 and above)

increased from 2.8% in 1970 to 3.2% in 2011, with the majority (55%) being female. The

elderly population is projected to increase from the current 6.5m reach 10.8m in

2025, representing an increase of 66% (NPC, 2011). The aging population represents

a key challenge for Nigeria in its capacity to provide decent living conditions and

improve the well-being of the elderly. Over half of the Nigerian population live in the

rural areas, with the majority being the elderly. Rural-urban migration in Nigeria has

been increasing at a rate of 3.5% per year, representing one of the highest in Africa.

Also, most of the elderly population have worked in the informal sector all their

productive lives, mainly in subsistence agriculture, and therefore have no access to

formal employment related pensions or other types of retirement benefits.

Moreover, alternative means of livelihood at old age has been severely limited, as

most elderly people do not have savings to rely upon and are faced with

vulnerabilities arising from social and economic shocks. For many, family members,

children, and relatives remain the most reliable source of livelihood at old age.

According to NPC (2011), 70% of the elderly aged 65 and above live with their

children or relatives, whilst 10% live alone. Even for those who live alone, they are still

largely dependent on their children and relatives for their well-being. These statistics

suggest an increasing aging population Nigeria will be accompanied by increasing

responsibilities amongst the elderly in the future.

Generally, one of the policy responses of governments to the aging challenge in

developing countries has been the provision of a social protection scheme such as

non-contributory pension targeted at the elderly with no access to formal retirement

pensions. This has provided a way to improve the well-being of the elderly as well as

alleviate old age poverty (Dethier et al, 2011). The Mexico’s Adult Majores program is

an example (Galiani et al 2014).

The policy response of the federal government of Nigeria however, has been one of

lack of political will. Several programs have been proposed but have not been

implemented. For example, the National Policy on the Care and Wellbeing of the

Elderly in Nigeria was finalised in 2003 but successive administrations have failed to

implement it (Holmes et al 2012a). Also, the Pension Reform Act 2004 established the

National Social Insurance Trust Fund ignored the elderly (Eze 2010). Moreover, the

Old Peoples Act of 2009 was designed to provide basic welfare and recreational

facilities, maintain and protect the rights of older persons, amongst other goals.

However, the informal sector which is the largest employer of labor in Nigeria and

perhaps, the most in need of the social security was excluded (Obashoro-John

2011).

Likewise, the Federal government program called “In Care of The Poor Program”,

provided conditional cash transfer to households considered as the poorest of the

poor, was implemented only in selected one-third (12) of the States in the

federation, and covered only a few of the targeted households. Holmes et al

(2012b) found that many households were not paid and the benefits have not been

sustained beyond a year.

The present study focuses on the Ekiti State Social Security Scheme for the elderly,

which was implemented in October 2011. Ekiti state is located in South West Nigeria,

one of the smallest states in Nigeria, with a population of 2.4 million. The Ekiti State

Scheme provided an unconditional and non-contributory cash transfer, which was

motivated by the government’s concern for the well-being of the elderly in the state.

The scheme was motivated by the government’s concern for the well-being of the

elderly in the state. The concern of the State government was that the majority of

the elderly citizens of the state (aged 65 years and above) are unable engage in

rigorous economic activities, thereby leaving them vulnerable to social and

economic shocks at old age. The Ekiti state old-age pensions scheme was unique, as

it was the first of its kind to be implemented at the regional level in Nigeria and in

West Africa.

There is scope to learn from the local and regional context of the scheme. Also, the

implementation has raised concerns about the sustainability of the program. This is

because Ekiti state relies largely on the monthly allocation from the Federal

government, which is the lowest amongst the 36 states. The monthly allocation

accounts for over 75% of the state’s total monthly revenue whilst the tax revenue

base accounts for less than 5%. Understanding the impact of the scheme will inform

evidence-based policy making in the context of old age social pensions in Nigeria

and SSA generally.

2.2. Program description

The intervention considered in this study was the Ekiti State (Nigeria) social security

scheme for the elderly. The Ekiti scheme was an unconditional, non-contributory

pension scheme targeted at the elderly of the state. It pays a monthly cash transfer

of N5,000 (approximately $32 USD) for eligible beneficiaries, who not receiving any

pensions, and whose monthly income is less than N3,000 ($19 USD). The intervention

was the first of its kind to be implemented at the regional level in Nigeria and in West

Africa.

This study was undertaken in collaboration with the implementation agency, Ekiti

State Ministry of Labour, Productivity and Human Development. The program was

initially piloted during October 2011 – September 2012. Ekiti State comprises 16 local

government areas and 163 electoral wards.1 Figure A1 in the Appendix shows the

Map of Ekiti state with 16 local government areas. Ekiti state is largely a

homogenous and an agrarian society, with most of the inhabitants undertaking

informal sector activities such as subsistence agriculture, market trading, and a

sizable public service sector. The elderly accounts for 3.6% of the population, with a

dependency ratio at 6.1% in 2006 based on the population census (NPC 2011).

According to the then State governor, Dr. Kayode Fayemi, the program was

intended “to improve the well-being of the elderly citizens in the State and to serve

as a poverty reduction strategy through provision of regular income payments”.2

However, the amount of the cash transfer represents about 28% of the national

minimum wage of N18,000, about 23% of the average monthly cost of living in

Nigeria (NGN22,094), and just around a dollar a day in line with the international

poverty line. Even as the size of the benefit appears to be small, it was more than

enough for the elderly beneficiaries in the State.

2.3. Research Question and Outcomes

A key challenge facing public finance decisions in developing context relates to

how fiscal policy such as cash transfers can be used to address issues of elderly

poverty and their vulnerability to shocks. A major aspect of this challenge is to assess

the effectiveness of different public finance options in an environment of

increasingly constrained budget. With limited economic opportunities available to

the elderly and limited access to formal contributory pensions, non-contributory

pensions provide a policy option, as a source of income to the elderly and their

households.

A descriptive analysis based on qualitative data obtained from household survey of

the beneficiaries in the Brazilian and South African non-contributory pension

schemes show that the pension benefits were shared amongst household members,

have impact on reducing poverty and household vulnerability, as well as promoting

old people’s daily functioning (DFID and HelpAge International 2003).

The core research question in this study was ‘to what extent can demand side

interventions such as non-contributory pensions targeted at the elderly be used as

instrument to improve the wellbeing of the beneficiaries and their households? The

central objective therefore, was to examine the effectiveness of the Ekiti non-

contributory cash transfer program targeted at the elderly on indicators of wellbeing

of the beneficiaries and their household members.

The main outcomes of interest in this study comprise three groups, namely primary,

and secondary outcomes. Figure 2.1. shows the classification of the outcomes. The

primary outcomes comprised indicators of wellbeing or quality of life and household

vulnerability, which are observed at the beneficiary level. Indicators of wellbeing or

quality of life include mental health as measured by depression symptoms; self-

1 Electoral wards are the smallest administrative units in Nigeria. 2 ‘The Ekiti State progressive welfare program’, Olu Adewusi, The Nation Newspaper,

November 12, 2012.

assessed health; perceptions of happiness, abilities and capabilities; personal

relationships; and community life.

Indicators of household vulnerability include child labour, migration from household,

food-shortage in household and exposure/experience of shocks such as crime.

Others are financial inclusion, beneficiary labour supply, and health behaviour.

Secondary outcomes comprised household member outcomes observed at the

household member level. These include household labour supply; income,

expenditure and savings – in both total and per capita terms; and physical activities.

Section 3.4. provides details of how the outcome variables were derived.

Table 2.1. Classification of Outcomes

Primary outcomes

(Observed at beneficiary level)

Secondary

(Observed at household member

level)

Indicators of wellbeing or quality of life,

including:

(i) Mental health – depression symptoms;

(ii) Self-assessed health;

(iii) Perceptions of happiness, abilities, and

capabilities;

(iv) Personal relationships – with children,

grandchildren, relatives, and other

family members;

(v) Perceptions of disability and difficulties

in doing daily activities;

(vi) Community life (e.g. peer networking,

community participation).

Indicator of household member level

outcomes include:

(i) Household labour supply;

(ii) Income, expenditure, and

savings (total and per

capita); and

(iii) Physical activity – moderate

and rigorous activities.

Indicators of household vulnerability,

including:

(i) Migration from household;

(ii) Child labour – using children for

hawking;

(iii) Food shortage in household; and

(iv) Experience of shock - e.g. crime.

Others, include:

(i) Financial inclusion – use of financial

services;

(ii) Beneficiary labour supply;

(iii) Health behaviour

2.4. Literature review

Several previous studies have examined the effectiveness of non-contributory cash

transfer programs for the elderly in developing countries3. Most of these previous

studies emanated from the non-contributory social security schemes implemented in

Latin America such as Mexico, Ecuador, and Chile (e.g. Dethier et al 2011; Barrientos

2003; Schwarzer and Querino 2002; Bertranou and Grushka 2002), and the South

Africa’s (SA) old age pension program (e.g. Duflo 2000, 2003; van der Berg 2002;

Edmond 2006, Edmond et al 2005; Case and Deaton 1998). The findings from these

studies have been generally favourable to the old-age pension schemes.

Specifically, a key finding from the previous studies is that the schemes reduce old

age poverty and inequality in beneficiary households, and a means of investment in

human and social capital. The SA program showed that in a society where a

significant proportion of its elderly citizens receive old-age pensions and the benefit

level is sufficiently high, such pensions will play a very crucial role in the reduction of

poverty (van der Berg 2002).

Dethier et al (2011) examined the impact of a minimum pension on old age poverty

and its budgetary cost in 18 Latin American countries. They found that the old age

pensions lead to significant reduction in both relative and absolute poverty and at

reasonable cost. In relative terms, the reduction ranged between 17% in Colombia

to 75% in Costa Rica, whilst the reduction ranged between 2% in Brazil to 24% in

Costa Rica in absolute terms.

Another key result from the previous studies, especially those emanating from the SA

program is that income received from a non-contributory cash transfer program has

important redistributive effects, as the beneficiary households can use their benefits

in different ways, including food consumption, education of children, household

labour supply, saving, etc. (e.g. Duflo 2003; Case and Deaton 1998). This suggests

that beneficiary outcomes can be examined on various dimensions of elderly

welfare or multiple beneficiary outcomes.

Additionally, previous studies such as Duflo, (2003) and Carvalho (2008) have

consistently found that significant treatment effects of a non-contributory old age

pension scheme can be achieved even within a short-period (6 months) following

implementation, particularly where the beneficiaries spend their benefits on basic

needs, such as food and medicines. The authors also found that a non-contributory

old age pension scheme can also result in unintended effects (positively or

negatively). Specifically, they found significant increase in the number of cigarettes

smoked amongst the beneficiaries, but a decline in alcohol consumption.

A number of impact evaluation studies of elderly pension programs has emerged

recently, particularly from interventions implemented in Latin America. Joubert

(2014) used data from Chile to examine how pension schemes can affect the

incentive to work in the informal sector. The study found that State-provided non-

3 For a comprehensive summary of previous studies, see Barrientos and Lloyd-Sherlock (2002)

and Aguila et al (2010).

contributory benefits led to a reduction in labour force participation at older ages

and a transfer of workers from the formal to the informal labour market.

Galiani et al (2014) used the quasi-experimental method of regression discontinuity

design (RDD) to examine the impact of Mexico’s non-contributory cash transfer for

the elderly on economic security and well-being of the beneficiaries and their

families. The authors found that mental health of the elderly beneficiaries improved

significantly. Also, the program did not generate significant negative effects on the

labour supply of the working age members in beneficiary households. However, the

beneficiaries reduced their participation in the labour market.

Generally, earlier evaluation studies on non-contributory pensions suffer from several

shortcomings and methodological limitations relating to identification of program

impact. First, some have used standard regression analysis comparing beneficiaries

before and after receipt of benefit. This approach raises the problem of causality, as

several other factors could have explained the observed differences. Another

common approach has been to compare beneficiary with non-beneficiary

outcomes to estimate impact. This approach induces selectivity bias, potentially

arising if the outcome of interest is correlated with eligibility for the program (e.g.

Gertler et al 2011, Khandker et al 2010).

More recent studies have tended to use quasi-experimental approaches such as

RDD. RDD exploits the information at an eligibility threshold to identify program

impact. However, the estimates of program impact are valid only around the

threshold. Also, the impact may differ for those beneficiaries that are farther away

from the threshold but are eliminated. As such, the statistical power of the analysis is

significantly weakened, as the sample size to identify impact is reduced

considerably (for a comprehensive review, see, Lee and Lemieux 2010).

Finally, most of the previous studies have examined the impact of programs

implemented at the national level. However, most recent regional (as opposed to

national level) and pilot non-contributory schemes have been implemented with

local resources in four SSA countries (HelpAge International 2012)4. There has been

little evidence of their effectiveness. The extent of the impact may vary according

to the level of implementation. To our knowledge, only Aguila et al (2010) have

examined the impact of an unconditional and non-contributory old age pension

scheme implemented at the regional level in the State of Yucatan, Mexico. But the

authors also relied on quasi-experimental approach to identify impact. The use of

randomised controlled trial to evaluate a non-contributory pension scheme for the

elderly in developing context has been missing.

4 The programs comprise two state level schemes in Nigeria – Ekiti State (2011) and Osun

State (2012), two schemes in Kenya (2006 and 2008), Uganda (2011) and Zambia (2007))

(HelpAge International 2012). See also, HelpAge International’s Pensions Watch database on

non-contributory social pensions; available at http://www.pension-watch.net/.

2.5. Contributions to the literature

The present impact evaluation of the Ekiti cash transfer program for the elderly

makes important contributions to the literature. Firstly, to our knowledge, the present

study is the first randomised control trial (RCT) or randomised evaluation of

unconditional and non-contributory elderly pensions in developing country context.

RCT involves randomised assignment of the eligible beneficiaries into treatment and

control groups, albeit before the implementation the intervention commenced.

By construction therefore, random allocation to beneficiaries and controls solves the

problems of attributing causality and selection bias through. Also, random allocation

addresses the issue of the appropriate counterfactual to the extent that the

beneficiaries in the treatment group are similar in baseline characteristics to those in

the control group, such that the only difference between the two groups can be

attributed to the intervention (Gertler at al 2011; Duflo et al 2006).

Secondly, very little is known about the effectiveness of recently implemented non-

contributory pensions in SSA, on the beneficiaries and by extension, their household

members Understanding the effectiveness of these programs is important for policy-

making and accountability, particularly in an environment with high poverty levels

and multiple demands on increasingly constrained budgets.

We propose to address these and other specific questions in the context of the Ekiti

State social security scheme for the elderly. Addressing these questions and other

related questions will fill important knowledge gaps, particularly for evidence based

policy-making for roll-out or expansion, accountability and justifying resources

allocation, and informing donor decisions whether to co-fund or otherwise.

3. EVALUATION DESIGN

3.1. Sampling

The sample design followed two stages, namely; (i) determining the primary

sampling units required for randomisation; and (ii) power calculation for

survey sample. The sampling was initially designed to be representative of the

elderly population in Nigeria, including Ekiti State, and to generate sufficient

statistical power for external validity. In order to achieve these, the original

plan was to use the beneficiary level as the primary sampling unit for

randomisation. But individual randomisation was not feasible from the point of

view of program implementation and more importantly, the need to avoid

possible contamination of the program impact potentially arising from

individual randomisation (see, Miguel and Kremer, 2004). Therefore, cluster

randomisation was adopted, involving randomisation at a higher level, using

the beneficiary register at the electoral ward level. In Nigeria, the electoral

ward is the smallest administrative unit and the population is highly

homogenous at this level. 5

As at the time of undertaking this study, Ekiti state has 163 electoral wards,

consisting of 18,642 eligible beneficiaries on the government register. Cluster

randomization implies assigning the electoral wards into treatment or control,

and all eligible individuals within each ward in the treatment group received

the intervention.

For the purpose of power calculation however, cluster randomization also

required a calculation of the intracluster correlation coefficient (ICC or rho)

for outcomes of interest (e.g. Kerry and Bland, 1998). The ICC was important

in order to account for potential correlation of shocks to outcomes of interest

within the wards or clusters. The elderly based ICC was calculated for States

in Nigeria, using several variables from the 2010/11 General Household Survey

panel (National Bureau of Statistics, NBS 2012). The variables included total

household income, per capita income, and per capita health expenditure.

Using the Optimal Design software (Raudenbush 2011), the calculation showed

that for a standardized effect size of 0.20 and ICC=0.05, 100 clusters were

required with at least 35 beneficiaries in each ward in order to achieve a

power of 0.80 and at 5% significance level (see, Figure A2 in the Appendix).

The calculated ICC was higher for some of the outcomes, suggesting the

need for more clusters or more eligible beneficiaries in each cluster. Allowing

for an ICC=0.10, 120 clusters was needed, each having at least 60 eligible

beneficiaries. However, largely because of the eligibility threshold imposed by

the government, there were too few wards that have 60 eligible beneficiaries

in the government register.6

The randomisation problem was solved by removing the eligibility criteria

imposed by the administrators, which allowed an oversampling of the

number required initially and undertake the baseline survey. The ICC could

then be re-calculated post-survey in order to obtain the desired sample size,

using the baseline data on the outcomes of interest. The oversampling led to

a sample size of n=6,720 (120 wards x 60 beneficiaries) eligible beneficiaries

surveyed.

The oversampling approach enabled us to sample all of the wards with the

required number of beneficiaries, so that the random assignment could be

performed on all the wards. We identified 112 wards that have the required

number of beneficiaries. The final sample consisted of n=6,326, representing

5 The electoral wards are now known as registration areas. 6 The low number of beneficiaries in some of the wards could be attributed to the fact that

people changed wards in order to become eligible, but according to the program

administrators, each ward should at least have 60 beneficiaries.

94.1% of the total of n=6,720 originally proposed.7 A total n=24,176 household

members of the eligible beneficiaries were also interviewed at the baseline,

suggesting an average of 4 members per beneficiary household.

3.2. Random allocation

The random allocation of the electoral wards was carried out after the

completion of the baseline survey. It involved distribution of eligible

beneficiaries into treatment and control groups by the electoral wards, using

STATA codes for randomisation. The randomised assignment produced 56

wards comprising n=3,230 eligible beneficiaries in the treatment group,

representing 51.1% of the total, and 56 wards comprising n=3,096

beneficiaries (or 48.9%) in the control group. Figure 1 shows the random

allocation of the experimental sample.

Table 1 shows the results of the randomised assignment and the distribution of

the eligible beneficiaries into treatment and control wards. As the table

shows, the distribution of the eligible beneficiaries was fairly balanced

between the treatment and control wards. The share of the total (percent) by

wards was generally comparable between the treatment and control wards,

ranging from minimum of around 1% to and maximum of about 2%, with the

exception of a ward in the control wards (Idamudu) in which only 15

beneficiaries were found on the register (0.47%).

The beneficiaries in the treatment areas were formally informed during the

month of October 2013, and payment of cash benefits of N5,000 began in

November 2013, following the random assignment. Payments were made at

designated payment centres monitored by the officials of the

implementation agency. In agreement with the government, eligible

beneficiaries in the control areas would receive their cash benefits after the

completion of the second follow-up survey.

3.3. Data collection at the baseline and follow-ups

The timeline for the intervention was a year (12 months), which began in

November 2013 and ended in October 2014. Data were collected at three

stages of survey, namely: baseline, first follow-up in six months following the

baseline survey, and the second follow-up in six months following the first

follow-up.

7 The difference between the figures was largely due to discrepancies in the government

register. A considerable number of multiple registrations was discovered across the wards,

which were removed from the list.

The baseline survey carried out during July – September, 2013, before the

implementation of the program and random assignment. The first follow-up

survey took place during June – September, 2014, after a minimum of six

months have elapsed since implementation. The second follow-up survey

took place during April – July, 2015, after a minimum of six months have

elapsed since the first follow-up.

Questionnaire was the main survey instruments used for data collection. The

questionnaire survey followed the format of the Nigeria’s Living Standards

Measures Study of the General Household Survey-Panel 2010/11 developed

at the World Bank (NBS 2012).8 The questionnaires contained five modules,

namely: (i) General beneficiary information module, which collected

information on the respondent’s identity and relationship with the beneficiary,

amongst others; (ii) Household member module, which collected information

on household members of the eligible beneficiaries; (iii) General household

module, which collected information on the beneficiary household

characteristics. Heads of household were interviewed, defined as the person

(beneficiary or otherwise) who could provide the required information. (iv)

Beneficiary module in which the elderly persons were interviewed individually,

including widows/widowers; and (v) Program implementation module, in

which individuals in the treatment areas were interviewed in order to collect

specific information regarding program implementation (e.g. how benefits

were collected).

3.4. Construction of Outcome Variables

Table A.2 in the Appendix presents details of how the main outcome variables were

constructed. Largely because several variables were essentially measuring different

aspects of the same outcome, most of the outcome variables were composite

variables constructed from the response options provided in the survey

questionnaires. Previous evaluation studies have also used composite variables

derived in this way (e.g. Galiani et al 2014, Notenbaert et al 2013, Golla et al, 2011,

FANRPAN 2006, Sheikh and Yesavage 1986).

For example, mental health (defined as depression symptoms and lack of self-

esteem) was measured on the Geriatric Depression Scale developed by Sheikh and

Yesavage (1986), based on the elderly respondent’s answer to yes=1 or no=0 in 15

related questions. The responses were then summed to give a total score for the

individual. Higher score indicates higher probability of suffering mental health. Similar

approach was used to measure other outcomes such as perception of happiness,

abilities and capabilities, self-reported heath, etc. The scores from each of these

variables were further summed up to derive what we called well-being or quality of

life indicator.

8 The questionnaires were translated into the local Yoruba language to ensure that

enumerators and interview team face no difficulty when they communicate with the elderly

people.

Similarly, child labour was constructed based on a set of 10 questions including

whether the child worked to support family, frequency of work, whether the child

has stopped working; both for males and females in the household. Higher score

implied a higher probability of a child working.9 Other composite variables such as

food shortage, migration of a household member to neighbouring towns and

villages, and victim of crime were combined with child labour to derive an indicator

for household vulnerability, comprising 24 questions. In this study, household

vulnerability is defined at the inability of a household member to cope with social,

economic or environmental shocks when they occur.

Finally, the beneficiary labour supply indicator was constructed from the elderly

responses to the question of whether they worked in the last six months, stopped

work, hours of work, worked for pay or unpaid, and the share of beneficiaries

working for pay compared to the share undertaking unpaid work, such as working in

the family farm or business, or helping taking care of children. In analysis, each of the

composite variables was standardised by centring them about the mean.

4. DESCRIPTIVE STATISTICS

4.1. Baseline and Balancing

Balancing requires that the treated and control wards were similar at the baseline.

The balancing assumption was tested on both beneficiary and household level data

sets. For a given variable, the test was carried out based on observing a statistical

significance in the mean difference between the treatment and control wards, using

the Donar and Klar (2000) clustered-adjusted chi2 and t-tests.

Table 4.1 presents the baseline summary statistics for selected individual and

household characteristics of beneficiaries, and showing the balancing test. For the

beneficiary level characteristics, statistically significant differences in characteristics

between the treatment and control areas at the baseline were observed only in

three out of almost 120 variables. For the household member data (Table 4.2), there

were no statistically significant differences between the treatment and control areas

across the baseline characteristics, except for a category of occupation. On the

basis of these results, it was concluded that the treated and untreated groups were

comparable in their baseline characteristics, suggesting that the two groups were

balanced.

9 The definition of child labour adopted in this study differs from the usual definition that treats

the child as victim of deprivation, in which their ability to have basic education is constrained

and such work is considered as dangerous and harmful. Rather, child labour was defined in

line with Bass (2004), in terms of undertaking work, paid or unpaid, to support the family. This

definition has been used in evaluation studies as a measure of household vulnerability (e.g.

Pronyk et al 2008).

4.2. Program Implementation and compliance

Compliance relates to when all of beneficiaries in the treatment group actually

received the intervention. In practice however, it is usually the case that the reverse

prevailed. This raises an issue of non-compliance in program implementation. This

may be due to certain factors, including administrative failure, corruption, limited

resources or cheer manipulation.

Table 4.3. presents the outcome of the program implementation in order to

ascertain the compliance in the Ekiti elderly pensions program. In this study,

compliance was assessed in terms of how many months has a beneficiary received

the cash payment, and mode of collection and payments. As at the end of the

second follow-up, all beneficiaries in the treatment wards have received a

maximum of 12 payments.

Tab 4.3. Program implementation and compliance

Mode of payment and collection Share of total (%)

Collection:

Self 79.8%

Spouse – husband/wife 3.4%

Child-son/daughter 15.4%

Relatives/friend 0.4%

Other-neighbour/in-law 1.0%

Payment:

Designated centres 61.4%

Home visits 35.0%

Hospital 3.0%

Other - 0.6% Source: Authors’ calculation

Also, most of them (80%) collected the cash by themselves and at designated

centres (61%). Around a third (35%) received the cash through the home visits by

government payment officials. The beneficiaries in this category were mainly those

who were frail or too weak to go to the designated payment centres. These figures

provide a strong indication of compliance. 10

4.3. Attrition

10 The strong compliance has been due largely to aggressive monitoring on the part of the

research team and other government officials. Also, officials of the implementation agency

underwent a two-day workshop designed to improve their capacity for the implementation

of the program. The workshop was jointly sponsored by the UNDP and Ekiti State government.

Attrition rate relates to the proportion of the total beneficiaries who were interviewed

initially but were lost in the follow-up surveys. Knowing the attrition rate provides re-

contact rate in the follow-ups. Attrition tends to result from several factors including

relocation, migration to other cities, death, change of address, or outright refusal by

respondents to make themselves available for follow-up interview.

Table 4.4 shows the distribution of attrition between the survey groups (treatment

and control) and the sources of the attrition for the first- and the second follow-up.

The figures in parentheses indicate the percentage of the total. The re-contact rate

in both the first and second follow-ups were very high. In the baseline, a total of

n=6,326 elderly people were interviewed, comprised n=3,178 eligible beneficiaries in

the treatment group and n=3,148 in the control group, representing 50.1% and 49.9%

of the total, respectively.

In the first follow-up, the number of beneficiaries lost was n=57, representing attrition

rate of 0.9% (less than 1%) or re-contact rate of 99.1%. In the second follow-up

however, the number of beneficiaries lost increased to n=329, representing attrition

rate of 5.2% or re-contact rate of approximately 95%. In both follow-ups, death was

the main source attrition, accounting for 75% and 89% in the first and second follow-

up, respectively. The remaining were respondents were those that could not be

located

Table 4.4: Attrition in the follow-up survey

Surveyed groups

Treatment Control Total

Baseline

3,178

(50.2%)

3,148

(49.8%)

6,326

(100%)

Attrition at first follow-up:

35

(1.1%)

22

(0.7%)

57

(0.9%)

Sources

Death

27

(77%)

16

(72.7%)

43

(75%)

Unlocated

8

(23%)

6S

(27.3%)

14

(25%)

Attrition at second follow-up:

93

(4.9%)

236

(5.4%)

329

(5.2%)

Sources

Death

80

(86%)

212

(90%)

292

(89%)

Unlocated

13

(14%)

24

(10%)

37

(11%) Source: Authors’ calculation from data

Attrition rate showed an interesting pattern between the treatment and

control groups in the first and second follow-ups. In the first follow-up,

respondents in the treatment wards were in the majority in both sources of

attrition. In the second follow-up however, respondents in the control wards

were in the majority in both sources of attrition.11

Furthermore, it was examined whether the observed attrition was random. A

probit regression analysis was carried out to examine the correlates of the

attrition. In particular, attrition was regressed on the treatment assignment

and other baseline characteristics such as age, sex, marital status,

educational level, etc. Table 4.4. presents the probit regression results, for

both the first and second follow-ups.

As the table shows, the coefficient on the treatment assignment variable

(treat) was insignificant in the first follow-up, but became negative and highly

statistically significant in the second follow-up. These results suggest that

attrition rate was statistically the same in both the treatment and control

wards at the first follow-up, but became significantly lower in the treatment

wards than in the control wards.

However, the coefficients on age and working were statistically significant at

the 95% or higher, suggesting that attrition was mainly determined by age in

the first follow-up, but both age and being in a treatment ward reduced the

probability of attrition. Age was expected to be a significant factor. The raw

data showed that all but 1 amongst the dead was above 86 years old. On

the basis of these results, it is concluded that whilst attrition was largely

random in the first follow-up, being in a treated ward was also an important

determinant of the likelihood of being lost to follow-up.

5. EVALUATION RESULTS

5.2. Empirical Model

The program impacts were estimated using Intention to Treat (ITT) model, which compares averages amongst beneficiaries between the treatment and the control areas. The estimated ITT model was of the form:

ijki

K

k

kijij eXTy 1

1 (1)

11 The extent of the ‘unlocated’ respondents could have been much higher without their

contact information, which were used to track them, especially those in the control group.

where yij was the outcome of interest for individual i in ward j; was the expected

mean outcome without treatment; Tij was an indicator variable being in treatment area

(treatment dummy), taking the value of 1 if the beneficiary was randomized into the

treated ward, and value 0, otherwise; X ki were a K vector of baseline characteristics,

observed at household or individual level, included as controls to reduce

unexplained variation (residual variance); and eij was an error term assumed to be

independent and identically distributed (i.i.d.) (i.e. independent across sample

members within experimental areas, with mean zero and constant variance). 1 is

the key parameter of interest, the estimated treatment impact. It represents the

difference in expected outcomes for the treatment and control areas. Equation (1)

was estimated adjusting the standard errors for clustering at the ward level and

sampling weights.

5.3. Estimates of Program Impacts

This section presents estimates from three versions of equation (1). The first compared

the average values in treatment and comparison areas, averaged over those who

received the treatment and those who did not, without the inclusion of X ki control

variables. The baseline controls were included in the second estimation while the third estimation also controlled for anticipation effect, potentially arising if the beneficiaries in the control areas change their behaviour in anticipation of receiving the intervention (e.g. Todd and Wolpin 2006, Attanasio et al 2005).12

5.3.1. Simple Difference in the Means

Tables 4a and 4b report simple mean comparison for household members and beneficiary specific outcomes, using the follow-up data, respectively. The difference in means across all of the household member level outcomes were highly statistically significant. All of the household member labour supply indicators were higher on average in the treated areas than in control areas, and the difference was statistically significant at the 95% level. For income and expenditure outcomes, all household expenditure outcomes were lower whilst household income outcomes were higher in the treated areas, and the average treatment effects were statistically significant. These statistics are puzzling, as we expect both income and expenditure to be higher in the treated localities than in the control localities. However, the results seem to suggest that members the beneficiary households were not spending the increased income made possible by the cash transfer. Health care costs (total and per capita) were higher in the treated areas and the difference was highly statistically significant, as the increase in household income enabled more household members to use health care services. Additionally, household members in the treated areas were more physically active than their counterparts in the control areas and the differences in all of the variables were statistically significant.

12 Anticipation effect was controlled for by treating it as a confounding factor on the observed outcomes. A

variable called anticipation was generated as an inverse probability weight from a regression of the probability

of being selected into the beneficiary group on a set of covariates, including age duration - measured as number

of days above 65 years, and baseline characteristics (sex, marital status, educational level), and labour supply

outcomes.

Table 4b reports the comparisons of average outcomes across groups for beneficiary-specific outcomes. Each of the components of household vulnerability score was significantly lower for households in treated areas, and the differences were statistically significant, except for migration in which the difference was insignificant. Also, labor supply score was lower amongst the beneficiaries in the treatment areas than beneficiaries in the control areas, but the difference was statistically not different from zero. However, there was a significant effect in health behaviour (smoking and alcohol consumption habits). The health behaviour score was lower amongst the beneficiaries in the treated areas than beneficiaries in the control areas. Also, there the total non-food expenditure and the components, showed statistically significant average treatment effects, except the share of medical expenses in the total.

Finally, there were significant treatment effects in the indicators of quality of life and the standardised overall measure of quality of life, except mental health score in which there was no statistical difference between the beneficiaries in the treated areas and in the control areas. Other components of the quality of life score exhibited statistically significant mean differences.13

5.3.2. Impact of Ekiti Cash Transfer Program on Beneficiary Household Members

Tables 5 reports the estimated impact of the program on beneficiary household member outcomes without and with control variables that account for any difference in observable characteristics at baseline. Firstly, the program had highly significant positive impacts on labour supply in beneficiary households. Specifically, the average number of hours of work per week and the total number of hours worked were approximately 5 and 13 hours higher in households in treated areas than in households in untreated areas, respectively. Secondly, the cash transfer program also had highly significant positive impacts on income related outcomes in beneficiary households. Log of total household income was 0.254 higher in households in treated areas than in households in control areas, representing an increase of 3.3%.14 Also, the log of household per capita income was higher on average in household in treated areas than in households in control areas, representing an increase of about 1%. In term of household expenditure, the cash transfer program had highly significant

negative impacts on expenditure outcomes in beneficiary households. Log of total household expenditure was 0.193 lower in households in treated areas than in

households in untreated areas, representing a decline of 1.8%. Similarly, the log of per capita expenditure was 0.274 lower on average in households in treated areas

than in households in untreated areas, representing a decrease of 3.6%. The results

on the expenditure outcomes were unexpected. It is unclear whether the reduction

represented household savings. However, the anticipation effects were significant

for expenditure variables. Also, the cash transfer program also had highly significant

10 It should be noted that the outcome measures are self-reported, thus prone to errors. The use of standardised composite measures minimises the magnitude of such errors.

14 Since the dependent variable y was in logs and x is the independent variable, the percentage was calculated as, yˆ / y , where yˆ = dy/dx.

positive impacts on the capability of household members to undertake physical

activities. On average, ability to undertake moderate and rigorous physical activities was 41.3% and 22.7% higher in households in treated areas than in households in

untreated areas, respectively. However, there was no evidence of the impact of the cash transfer program on health care costs for the family members of beneficiaries.

5.3.3. Impact of Ekiti Cash Transfer Program on Household Vulnerability Table 6 reports the impact of the cash transfer program on measures of household vulnerability in the treated areas compared to beneficiaries in the control areas. The cash transfer program had a highly statistically significant impact on the overall measure of household vulnerability. Overall vulnerability score was 0.216 points lower in households in treated areas than household in control areas, representing a reduction of 3.4 %. All of the components of the overall vulnerability score made

significant contributions to the result, except experience of shock by crime. Specifically, child labour was 0.097 points lower in households located in treated areas than households located in treated areas, - a reduction of 2.3%. Food shortage in household was 0.423 points lower in beneficiary households in the treated areas than beneficiary households in the control areas – a reduction of 32.5%. It is possible that the additional money available in the beneficiary households was spent on food. Migration score was 0.412 points lower in beneficiary households in treated areas than beneficiaries in control areas – a reduction of 45.8%. There was no evidence of anticipation effect on any of the vulnerability indicators.

5.3.4. Impact of Ekiti Cash Transfer Program on Beneficiary Labour Supply, Financial Inclusion and Non-food Expenditures

Table 7 reports the impact of the Ekiti cash transfer program on other beneficiary outcomes, including own labour supply. The cash transfer program had no impact on the labour force participation amongst the beneficiaries, similarly for health behaviour (drinking and smoking), and medical expenditure. The variable financial inclusion measured the use of varieties of formal and informal financial services by the beneficiaries in the last six months. The financial inclusion score amongst was 1.38 points higher for beneficiaries in the treated areas thank for beneficiaries in the control areas – an increase of about 33%. The estimate also exhibited a very strong evidence of anticipation effect, reflecting the possibility that in anticipation of the receipt of the cash benefit, eligible beneficiaries in the control group were using financial services such as opening accounts in banks, registering with informal associations such as cooperatives and their age group associations.15 Moreover, the log of the money spent by the beneficiaries on other household members (e.g. grand children) was 0.544 higher on average for beneficiaries in the treated areas than for beneficiaries in untreated areas – an increase by approximately 7%. Finally, the log total expenditure on all

15 Indeed, most of the beneficiaries initially rushed to open bank accounts based on the rumour that the

government officials would only give the cash benefit to those who have accounts. In this situation,

contamination may be an issue on this result.

non-food items was 0.169 significantly higher amongst the beneficiaries in the treated areas than beneficiaries in untreated areas – an increase by 2.3%.

5.3.5. Impact of Ekiti Cash Transfer Program on Quality of Life among Beneficiaries

Table 8 reports the quality of life indicators as measured by mental health, perceptions of happiness, household decision making and family relationships, self-assessed health, and difficulties in undertaking activities of daily life. As the table shows, the overall quality of life score improved significantly amongst beneficiaries as a result of the cash transfer program. Specifically, the quality of life score was 2.98 points higher for beneficiaries in the treated areas than for beneficiaries in the untreated areas – an increase of approximately 9%. All of the component indicators made significantly contributions to the impact on the overall quality of the life score, except mental health which appeared to be stable, and difficulties in undertaking activities of daily life The perceptions of happiness and capabilities score was 1.786 points higher for beneficiaries in the treated areas than for beneficiaries in the untreated areas – an increase of approximately 24%. Also, an index score measuring household decision-making and family relationships was 0.493 points higher for beneficiaries in treated areas than for beneficiaries in untreated area, representing an increase of approximately 8%. Similarly, health status score was 0.495 points higher for beneficiaries in the treated areas than for beneficiaries in control areas, representing an increase of approximately 5%.

6. DISCUSSION

One of the policy responses of governments to the aging wave in developing countries has been the provision of social protection schemes such as a non-contributory pension targeted at the elderly citizens with no access to formal retirement pensions. It is believed that this will provide a way to improve the well-being of the elderly as well as alleviate old age poverty. The perspective of this study was built around the traditional institutional structure in Sub Sahara Africa as essential for an understanding of the pathways through which non-contributory cash transfer can enhance the living standards of elderly beneficiaries and their households. The traditional African support system is based on social relationships encompassing family and kinship networks in which the main source of support is the household and family members. The need to care for the children, elderly, and other vulnerable people in the household constrains the extent to which economically active family members could participate in the labour market. Where the economically active family members have to work, the elderly provide care for the children. The context of African traditional institutional setting was conceptualised within a framework of the interrelationships between the ‘Bourdieusian’ capitals, in which the additional income from the elderly pension provides an economic capital, which in return enhances social and cultural capitals. In this context, the additional pension income helps to support other relatively younger and more economically active members of the beneficiary household to become more productive and improve their capacity to participate in income generating activities. These may manifest in several ways, including food security in household, support for children education,

building social networks and community participation. The results showed no evidence suggesting that direct beneficiaries increased their own labour market participation, and there was no evidence of anticipation effect. This result is consistent with recent findings by Galiani et al (2014). The negative coefficients on the estimates can be associated with what the authors called time-compositions effect, in which elderly people refer leisure to work. However, it contradicts the response of the beneficiaries that they were able to work more. It seems they make distinction between paid and unpaid work. Future research is needed to understand the mechanisms underlying this behavioural response of the beneficiaries to the cash transfer. To some extent, our findings seem to provide an empirical support for the interaction amongst ‘Bourdieusian’ capitals embedded in the social order and familial African traditional institutions. Even as the beneficiaries of the elderly pension did not directly increase their labour supply, they brought with them ‘economic capital’ through

which social and cultural capitals. For household member outcomes, we see that labour market participation improved significantly in beneficiary households, thereby enhancing their social capital (e.g. status in society) and provide economic capital (e.g. increased income) that can be re-invested in cultural capital (e.g. education of children). Due largely to the short period of the study, we are yet to see evidence of the impact of the program on health related outcomes.

We also see that household vulnerability, as measured by child labour (using children to hawk goods), food shortage in household, and migration way from home, decreased significantly amongst the beneficiaries in treated localities. Additionally, we found evidence suggesting that the non-contributory cash transfer improved the quality of life of the beneficiaries, as measured by stable mental health, perceptions of happiness and capabilities, household decision making and family relationships, and better health status. These results are consistent with previous findings that non-

contributory old age pensions reduce household vulnerability as well as promoting old people’s daily functioning (DFID and HelpAge International 2003). A major puzzle in this study however, relates to the odd findings in household expenditure between treated and untreated groups. It is unclear whether the lower expenditures imply savings. Our investigation via a focused group discussion with selected beneficiaries and non-beneficiaries showed that the lower expenditure may be reflecting the uncertainty associated with the people’s believe (or lack of trust in the government) that the transfer was for a short-term and decided not to spend their increased income. Also, we see the anticipation effects were positive and significant. It is possible also that the beneficiaries reduced their expenditure in anticipation of a possible stoppage of payment by the government. This happened in Osun state Nigeria (a neighbouring state), where the government paid cash transfer to a few people for just three months and stopped the program.

How valuable was the cash transfer to the beneficiaries?

In terms of how they spend the cash benefit in a month, a greater proportion (almost 67%) responded that the cash benefit was either saved or spent on other household members. In terms of informal work 35% reported that the cash benefit enabled them to look after their grandchildren or helping their children in household shores. Again, these qualitative evidence supports the view that the cash transfer provides a

sort of economic capital for the beneficiary households, thereby contributing to household capital formation in those households.

7. CONCLUSIONS & POLICY IMPLICATIONS

The world is experiencing a rapid growth in aging population. Such rise is mainly due to improved living conditions, high incomes, and demographic changes. As in other countries in Sub Saharan Africa, the aging population is a key challenge for Nigeria in its capacity to provide decent living conditions and improve the well-being of the elderly. Over half of the Nigerian population live in the rural areas, with a considerable proportion being the elderly. Most of the elderly population have worked in the informal sector all their productive lives, mainly in subsistence agriculture, and therefore have no access to formal employment related pensions or other types of retirement benefits.

A key challenge facing public finance decisions in developing countries with respect to the rapidly aging population relates to how fiscal policy such as cash transfers can be used to address the issues of elderly poverty and their vulnerability to shocks. A major aspect of this challenge is to assess the effectiveness of different public finance options in an environment of increasingly constrained budget.

This study was motivated by the fact that developing countries, especially those in Africa, have large informal sectors. Also the traditional support systems based on family and kinship networks in Sub-Saharan Africa is very strong. The household and family members are still the main source of support. Thus, the elderly population can be an important factor in order to allow the younger members of the households accumulate human capital through the former unpaid services to the family.

Our findings show that providing such demand side interventions will benefit not only the elderly who receive those benefits, but also their immediate families and other household members. Our findings are in line with the growing recognition of the role of older generation in the care of younger generation and in the relationship between care and development. Elderly population, enable other family members to undertake productive work in the labour market by means of taking care of the child and other household related activities. In this way, providing social protection to the elderly can help to address the intergenerational transfer of poverty by driving people away from informal work. A well-designed and implemented cash transfer scheme can also help strengthen household productivity and capacity for income generation. Small but reliable and regular flows of transfer income would help poor households to accumulate productive assets, build social and human capital, and reduce vulnerability of members to socioeconomic shocks. Given the short period of the follow-up, there is a limitation to which the results can be considered as conclusive. In the second follow-up, similar questions and estimation will be repeated with richer data to examine some dynamic aspects into the analysis.

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Table 4.1: Baseline Summary Statistics: Beneficiary Individual & Household characteristics

Treatment

(n=3,178)

Control

(n=3,148)

Difference

(T-C) P-value

t-

statistic

Household member

characteristics

Age 79.63 79.03 0.600 0.067 1.839

Sex (male=1) 0.308 0.313 -0.005 0.845 -0.196

Marital status

Married –

monogamous/polygamous 0.53 0.521 0.009 0.798 0.256

Never married 0.0031 0.0016 0.0015 0.277 1.09

Cohabiting/informal union 0.0003 0.001 -0.0007 0.407 -0.831

Divorced/Separated 0.022 0.022 0 0.984 0.012

Widowed 0.445 0.455 -0.01 0.777 -0.284

Living arrangement

Spouse-husband or wife 0.267 0.316 -0.049 0.262 -1.125

Children/grandchildren 0.255 0.284 -0.029 0.366 0.906

Spouse, children/grandchildren 0.159 0.123 0.036 0.268 1.111

Friend's family 0.025 0.014 0.011 0.108 1.615

Relative's family 0.164 0.141 0.023 0.478 0.711

Alone 0.113 0.093 0.020 0.318 1.001

Other-with tenants 0.010 0.02 -0.010 0.138 1.488

Educational background & level

completed

Read and write 0.113 0.104 0.009 0.552 0.595

Attended school 0.148 0.138 0.010 0.695 0.393

Level completed

None 0.239 0.223 0.016 0.516 0.651

Primary school 0.184 0.178 0.006 0.738 0.335

Secondary/modern school 0.246 0.247 -0.001 0.996 -0.006

Teacher training/NCE 0.119 0.106 0.013 0.362 0.914

Technical/vocational 0.084 0.079 0.005 0.06 0.531

Religious 0.069 0.103 -0.034 0.091 -1.701

Degree/higher degree 0.006 0.012 -0.0063 0.026 -2.251

Other-OND, HND, etc. 0.053 0.0053 0.0477 0.991 1.011

Occupational status:

subsistence-crop farmer 0.400 0.403 -0.0027 0.92 -1.004

subsistence-livestock farmer 0.066 0.085 -0.019 0.194 -1.303

subsistence-mixed crop &livestock 0.090 0.096 -0.006 0.707 -0.376

subsistence-fisher, hunter, etc. 0.015 0.018 -0.003 0.562 -0.581

market oriented-cash crop 0.026 0.045 -0.019 0.11 -0.605

market oriented-livestock 0.079 0.023 0.056 0.003 3.664

livestock farmer 0.017 0.012 0.005 0.345 0.947

street/market sales person 0.074 0.078 -0.004 0.577 -0.559

no answer/don’t know 0.184 0.187 -0.003 0.803 -0.249

shop sales 0.029 0.042 -0.013 0.325 0.987

other-not mentioned 0.020 0.017 0.003 2.111 0.02

Self-assessed general health:

Very good 0.045 0.057 -0.012 0.421 -0.806

Good 0.369 0.336 0.033 0.488 0.694

Fair 0.446 0.464 -0.018 0.62 0.497

Bad 0.101 0.108 -0.007 0.797 -0.258

Very bad 0.040 0.035 0.005 0.698 0.388

Notes: The summary statistics in this table was based on the baseline data. Figures for the

difference were obtained from chi2 and t-tests, which assesses whether the means of the two

groups are statistically different.

Table 4.2: Baseline summary statistics: Household members

Treatment

(n=11,934)

Control

(n=12,242)

Difference

(T-C) P-value t-statistic

Household member

characteristics

Household size-number 4.885 5.489 -0.604 0.0012 -3.272

Age 50.085 51.7 -1.615 0.733 -0.342

Sex (male=1) 0.474 0.477 -0.003 0.86 0.176

Marital status

Married –

monogamous/polygamous 0.653 0.631 0.022 0.399 0.845

Never married 0.206 0.229 -0.023 0.236 -1.188

Cohabiting/informal union 0.001 0.013 -0.012 0.531 -0.628

Divorced/Separated 0.122 0.013 0.109 0.655 0.448

Widowed 0.128 0.125 0.003 0.854 0.184

Relationship to beneficiary

Son or daughter 0.401 0.401 0 0.987 0.016

Grandchild 0.124 0.135 -0.011 0.604 -0.519

Distant relative or friend 0.085 0.088 -0.003 0.794 -0.261

Sibling-brother/sister 0.031 0.028 0.003 0.823 0.224

Spouse-wife or husband 0.301 0.229 0.072 0.911 0.113

Son or daughter in-law 0.034 0.028 0.006 0.557 0.588

Other-neighbour 0.024 0.021 0.003 0.516 0.651

Educational background & level

completed

Read and write 0.496 0.503 -0.007 0.726 -0.352

Attended school 0.472 0.475 -0.003 0.718 0.362

Education level completed

None 0.284 0.268 0.016 0.304 1.03

Primary school 0.199 0.193 0.006 0.61 0.511

Secondary/modern school 0.316 0.332 -0.016 0.323 -0.99

Teacher training/NCE 0.087 0.096 -0.009 0.403 -0.838

Technical/vocational 0.024 0.025 -0.001 0.853 -0.185

Religious 0.003 0.0064 -0.0033 0.0042 -2.892

Degree/higher degree 0.038 0.0381 -0.0001 0.997 -0.004

Other-OND, HND, etc. 0.013 0.008 0.005 0.016 2.439

Occupational status:

None 0.519 0.515 0.004 0.824 0.223

Subsistence-crop farmer 0.098 0.106 -0.008 0.775 -0.286

Subsistence-livestock farmer 0.017 0.014 0.003 0.811 0.286

Subsistence-mixed

crop/livestock 0.012 0.011 0.001 0.954 0.057

Subsistence-fisher, hunter, etc. 0.012 0.007 0.005 0.61 0.511

Market oriented-crop farmer 0.043 0.055 -0.012 0.509 -0.661

Market oriented-livestock farmer 0.016 0.019 -0.003 0.769 -0.294

Professional 0.014 0.008 0.006 0.655 0.441

Civil servant 0.037 0.028 0.009 0.613 0.506

Artisan 0.047 0.04 0.007 0.765 0.3

Market oriented-mixed

crop/livestock 0.008 0.006 0.0015 0.863 0.173

Street/market sale 0.156 0.143 0.013 0.675 0.42

Shop sales person 0.020 0.047 -0.027 0.077 -1.776

Forces-police, civil defence 0.003 0.0013 0.0015 0.602 0.523

Table 4.4: Probit regression results for attrition at follow-up

(Follow-up 1) (Follow-up 2)

VARIABLES (Dependent: Attrition (yes=1)) Attrition1 Attrition2

Treat (treatment at baseline) -0.0524 -0.0809

(0.132) (0.0915)

Age (yrs) 0.0730*** 0.0368***

(0.00879) (0.00491)

Sex (male=1) 0.0831 -0.0151

(0.162) (0.0759)

Marital status: Ref=Married-monogamy/polygamy

Never married - -

Cohabiting/informal union - -

Divorced/separated 0.551 -0.210

(0.386) (0.220)

Widowed 0.0910 -0.0297

(0.172) (0.0636)

Attended school (yes=1) 0.121 0.0472

(0.241) (0.125)

Read and write (yes=1) -0.123 -0.0591

(0.247) (0.140)

Own house (yes=1) 0.167 0.0820

(0.145) (0.0760)

Living arrangement: (ref= spouse-husband or wife)

children/grandchildren -0.128 -0.0864

(0.217) (0.0917)

spouse, children/grandchildren -0.115 0.0316

(0.190) (0.103)

friend's family relative's family 0.158 0.130

(0.315) (0.211)

alone -0.223 -0.0441

(0.221) (0.136)

other-with tenants -0.0112 -0.0711

(0.250) (0.121)

Major occupation: (ref= subsistence-crop farmer)

subsistence-livestock farmer -0.0582 0.230**

(0.235) (0.112)

subsistence-mixed crop & livestock -0.400* -0.118

(0.237) (0.0927)

subsistence-fisher, hunter, etc. -0.234 0.141

(0.414) (0.210)

market oriented-cash crop 0.371 0.0802

(0.324) (0.121)

market oriented-livestock 0.206 -0.341*

(0.286) (0.188)

livestock farmer 0.322 -0.208

(0.301) (0.208)

street/market sales person 0.211 0.251**

(0.197) (0.111)

no answer/don’t know -0.430** -0.0345

(0.180) (0.0930)

shop sales - -0.186

(0.161)

other-not mentioned - 0.0320

(0.222)

Health behaviour:

Drinking alcohol (yes=1) -0.436** -0.153**

(0.216) (0.0713)

Smoking (yes=1) 0.0103 -0.322

(0.238) (0.229)

Self assessed general health: (ref.=very good)

Good 0.251 0.0158

(0.307) (0.170)

Fair 0.178 0.00235

(0.290) (0.161)

Bad 0.157 -0.105

(0.327) (0.189)

Very bad - 0.0105

(0.196)

Constant -8.798*** -4.541***

(0.842) (0.399)

Log-likelihood -199.30 -1124.70

Observations 5,280 5,928

Note: Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1; Missing figures

are the variables not used due to multicollinearity.

32

Table 5a: Average treatment effect of main outcome variables: Beneficiary

and beneficiary household level: First follow-up

First follow-up

Outcomes

Treatment

(T)

Control

(C)

Difference

(T-C) p-value

t-

statistic

Beneficiary specific:

Overall Quality of life score 41.015 37.92 3.095 0 4.449

Mental health - depression symptoms 8.187 8.158 0.029 0.828 0.217

Perceptions of happiness, abilities

and capabilities 8.758 8.199 0.559 0.0022 3.101

Perceptions of disabilities and

difficulties in activities of daily life 1.43 1.381 0.049 0.743 0.33

Personal relationships - children,

family, etc. 5.755 5.009 0.746 0 4.526

Community life - peer networking,

participation, etc. 9.22 9.288 -0.068 0.157 -1.422

Self-assessed general health 9.309 8.715 0.594 0.014 2.491

Beneficiary household vulnerability:

Overall vulnerability score 6.729 7.356 -0.627 0.08 -1.761

Child labour score 4.344 4.006 0.338 0.026 2.247

Food shortage in household 1.77 2.138 -0.368 0.04 -2.069

Experience of shock - crime, etc. 0.043 0.052 -0.009 0.541 -0.613

Migration from household 1.000 0.763 0.237 0.044 2.029

Others:

Financial inclusion 2.86 2.012 0.848 0.01 2.586

Labour supply score 12.097 11.732 0.365 0.373 0.893

Notes: See Table A1 for the construction of the outcome variables. Estimates were cluster-

adjusted robust standard errors: *** p<0.01, ** p<0.05, * p<0.1.

33

Table 5b: Average treatment effect of main

outcome variables: Beneficiary and beneficiary

household level: Second follow-up

Second Follow-up

Outcomes

Treatment

(T)

Control

(C)

Difference

(T-C)

p-

value

t-

statistic

Beneficiary specific:

Overall Quality of life score 34.412 33.011 1.401 0.0038 2.925

Mental health - depression

symptoms 8.437 8.994 -0.557 0.0004 -3.623

Perceptions of happiness, abilities

and capabilities 8.478 7.93 0.548 0.0046 2.868

Perceptions of disabilities and

difficulties in activities of daily life 1.446 1.896 -0.45 0.018 -2.387

Personal relationships - children,

family, etc. 2.088 1.9 0.188 0.0025 3.064

Community life - peer networking,

participation, etc. 6.797 6.092 0.705 0 4.56

Self-assessed general health 3.574 3.296 0.278 0.012 2.538

Beneficiary household vulnerability:

Overall vulnerability score 6.711 5.692 1.019 0.0005 3.542

Child labour score 3.307 3.718 -3.122 0.0049 -2.848

Food shortage in household 0.596 1.057 -0.461 0.0001 -3.944

Experience of shock - crime, etc. 0.205 0.201 1.117 0.904 -0.121

Migration from household 1.318 1.478 -0.16 0.013 -2.507

Others:

Financial inclusion 3.167 2.295 0.872 0 4.279

Labour supply score 6.042 7.433 -1.391 0.491 -1.98

Notes: See Table A1 for the construction of the outcome variables. Estimates were cluster-

adjusted robust standard errors: *** p<0.01, ** p<0.05, * p<0.1.

34

6a. Average Treatment Effect for Household Members: First

follow-up

First follow-up

Outcomes

Treatment

(T)

Control

(C)

Difference

(T-C)

p-

value t-statistic

Labour supply Share of adults working 0.501 0.465 0.036 0.037 2.102

Total hours worked per week 9.782 7.804 1.978 0.0006 3.49

Total hours worked in hhold 68.47 54.63 13.84 0.0004 3.56

Income, expenditure, savings Total hhold income - all sources (Naira) 18000.00 16000.00 2000 0.094 1.69

Total hhold expenditure-all items (Naira) 13000.00 14000.00 -1000 0.0028 -3.022

Total savings in household - all sources (Naira) 4400.00 1800.00 2600 0 5.343

Income per capita 4700.00 4000.00 700 0.007 2.721

Expenditure per capita 3400.00 3600.00 -200 0.053 -1.944

Saving per capita 1200.00 1200.00 0 0.853 0.185

Physical activities

0 Days unable to perform normal tasks 4.277 4.806 -0.529 0.060 -1.889

Currently able to perform moderate activities 5.402 5.349 0.053 0.970 0.037

Currently able to perform rigorous activities 5.539 5.527 0.012 0.925 0.095

Distance currently work without getting tired 1.808 1.701 0.107 0.045 2.015

6b. Average Treatment Effect for Household Members:

Second follow-up

Second Follow-up

Outcomes

Treatment

(T)

Control

(C)

Difference

(T-C)

p-

value

t-

statistic

Labour supply Share of adults working 0.544 0.49 0.054 0.0001 4.102

Total hours worked per week 9.289 6.601 2.688 0 6.216

Total hours worked in hhold 65.022 46.208 18.814 0 6.5

Income, expenditure, savings Total hhold income - all sources (Naira) 23000.00 17400.00 5600 0 6.432

Total hhold expenditure-all items (Naira) 18000.00 15000.00 3000 0 4.703

Total savings in household - all sources (Naira) 5200.00 1900.00 3300 0 5.838

Income per capita 6100.00 4100.00 2000 0 6.413

Expenditure per capita 4600.00 3700.00 900 0 4.933

Saving per capita 1700.00 1200.00 500 0.0005 3.54

Physical activities Days unable to perform normal tasks 5.680 12.990 -7.31 0.000 -13.901

Currently able to perform moderate activities 5.673 5.634 0.039 0.967 0.042

35

Currently able to perform rigorous activities 5.52 5.508 0.012 0.967 0.042

Distance currently work without getting tired 2.019 1.538 0.481 0.000 8.836

Table 7a: Program impacts on household

member outcomes: First follow up

Treatment

effect without

control vars.

Treatment

effect with

control vars.

Labour supply:

Hours worked per wk (average)

5.869***

(1.099)

4.770***

(0.924)

Total hrs worked in household

15.056***

(3.859)

13.16***

(3.379)

Income and expenditure:

Ln total hhold income

0.332***

(0.058)

0.251***

(0.0307)

Ln total hhold expenditure

-0.214***

(0.059)

-0.203***

(0.0443)

Ln per capita income

0.515***

(0.057)

0.0823***

(0.0200)

Ln per capita expenditure

-0.268***

(0.034)

-0.280***

(0.034)

Health care cost

Ln total health care cost in hhold

0.047

(0.026)

0.057

(0.017)

Ln per capita health care cost

0.087*

(0.037)

0.087

(0.015)

Physical activity:

Days unable to do normal tasks /week

-0.608*

(0.267)

0.0943

(0.0637)

Capable of doing moderate activities

0.791***

(0.052)

0.413***

(0.0225)

Capable of doing rigorous activities

0.176***

(0.012)

0.123***

(0.0109) Notes:

Rows represent dependent (outcome) variables in a regression model. The second

column reports the estimated program impact (i.e. receipt of cash benefit) in a

treated ward on the dependent variable from a regression model that contained

no control variables. The third column reports the estimated program impact of

receiving the cash transfer in a treated ward on the dependent variable from a

regression model containing baseline control variables. The asterisks *, **, and ***

36

indicate level of statistical significance at the0 0.10, 0.05, and 0.01 level,

respectively.

Table 7b: Program impacts - household

vulnerability: Second follow up

Treatment

effect without

control vars.

Treatment

effect

with

control

vars.

Anticipation

effect

Child labour

-0.091**

(0.039)

-0.097**

(0.040)

-0.077

(0.213)

Food shortage in household

-0.410***

(0.078)

-0.423***

(0.086)

-0.754

(0.545)

Migration

-0.260**

(0.126)

-0.412**

(0.161)

0.464

(1.161)

Experience of shocks by crime

-0.298

(0.323)

-0.303

(0.434)

0.989

(0.961)

Overall vulnerability score

-0.190***

(0.051)

-0.216***

(0.056)

-0.137

(0.278)

Sample size 6,268 4,181 4,181

Notes: Rows represent dependent (outcome) variables in a regression

model. The second column reports the estimated program impact (i.e.

receipt of cash benefit) in a treated ward on the dependent variable from

a regression model that contained no control variables. The third column

reports the estimated program impact of receiving the cash transfer in a

treated ward on the dependent variable from a regression model

containing baseline control variables. The asterisks *, **, and *** indicate

level of statistical significance at the 0.10, 0.05, and 0.01 level, respectively.

Table 7c: Program impacts – labour supply,

health behaviour, financial inclusion & all non-

food expenditure

Treatment

effect without

control vars.

Treatment

effect with

control vars.

Anticipation

effect

Beneficiary labour supply

-0.064

(0.039)

-0.085

(0.924)

1.463

(1.954)

37

Health behaviour

-0.131**

(0.056)

-0.003

(0.046)

-0.619

(0.435)

Financial inclusion

0.305**

(0.118)

1.383***

(0.439)

3.871***

(1.892)

Ln amount spent on household

members

0.467***

(0.075)

0.544***

(0.061)

0.130

(0.415)

Ln medical expenditure

0.023

(0.059)

0.057

(0.054)

-0.163

(0.506)

Ln total all non-food expenditure

0.100**

(0.042)

0.169***

(0.055)

-0.695**

(0.341)

Sample size 4,519 4,519 4,519

Notes: Rows represent dependent (outcome) variables in a regression model.

The second column reports the estimated program impact (i.e. receipt of

cash benefit) in a treated ward on the dependent variable from a regression

model that contained no control variables. The third column reports the

estimated program impact of receiving the cash transfer in a treated ward

on the dependent variable from a regression model containing baseline

control variables. The asterisks *, **, and *** indicate level of statistical

significance at the 0.10, 0.05, and 0.01 level, respectively.

Table 7d: Program impacts – quality of life

Treatment

effect without

control vars.

Treatment

effect with

control vars.

Anticipation

effect

Mental health

0.059

(0.175)

0.202

(0.197)

-0.414

(1.149)

Perception of happiness &

capabilities

0.279***

(0.057)

1.786***

(0.272)

0.539

(1.513)

Household decision making &

family relationships

0.598***

(0.151)

0.493***

(0.145)

-1.448*

(0.777)

Health status

0.663**

(0.267)

0.495***

(0.123)

0.464

(1.161)

Difficulties in doing activities of

daily life

-0.302**

(0.144)

0.0059

(0.066)

0.028

(1.1514)

Overall quality of life score

2.281***

(0.461)

2.983***

(0.056)

-0.952*

(0.543)

Sample size 6,268 4,181 4,181

Notes: Rows represent dependent (outcome) variables in a regression model.

The second column reports the estimated program impact (i.e. receipt of cash

benefit) in a treated ward on the dependent variable from a regression model

that contained no control variables. The third column reports the estimated

38

program impact of receiving the cash transfer in a treated ward on the

dependent variable from a regression model containing baseline control

variables. The asterisks *, **, and *** indicate level of statistical significance at

the 0.10, 0.05, and 0.01 level, respectively.

39

List of Figures

Figure 1: Random assignment

APPENDIX

Figure A1: Map of Ekiti State Nigeria, showing the 16

LGAs Source: Ekiti State government.

Stage 3

Surveyed sample

(n=6,720)

Experimental

sample

(n=6,326 from

112 wards)

Stage 1 Treatment group:

(3,178 from 56 wards)

Comparison group:

(3,148 from 56 wards)

Stage 2

40

Figure A2: Power and sample size calculation

41

Table A1: Distribution of eligible beneficiaries by wards

Treatment Control

Electoral

No. of

eligible Electoral

No. of

eligible

wards beneficia

ries Percent wards

beneficiaries Percent

afao_araromi 59 1.8 afao_kajola 60 1.89

aisegba_2 60 1.83 agbado_1 59 1.86

aramoko_2 59 1.8 agbado_oyo 52 1.64

are 59 1.8 aisegba_1 60 1.89

awo 57 1.74 aramoko_1 60 1.89

ayegbaju 60 1.83 araromi 61 1.93

efon_1 60 1.83 Araromi/bolorunduro 61 1.93

efon_3 59 1.8 ayetoro_1 57 1.80

efon_7 60 1.83 dalemore 60 1.89

efon_8 60 1.83 eda_oniyo 64 2.02

efon_9 60 1.83 efon_2 60 1.89

erijiyan 58 1.77 efon_6 60 1.89

erinmope_2 59 1.8 ekamarun 53 1.67

erinwa_2 38 1.16 ekameta 57 1.80

idamudu_2 60 1.83 erinwa_1 59 1.86

ido_ajinare 60 1.83 erio 59 1.86

ifaki_2 59 1.8 ewu 61 1.93

igbaraodo_2 60 1.83 idamudu_1 15 0.47

igede_2 60 1.83 ifaki_1 60 1.89

ijero_b 60 1.83 igbaraodo_1 57 1.80

ijero_d 60 1.83 igbole_aye 55 1.74

ijesamodu 60 1.83 igogo_2 58 1.83

ikogosi 60 1.83 ijigbo 58 1.83

ikole_north 60 1.83 ikole_west_1 51 1.61

ikun_2 59 1.8 ikole_west_2 60 1.89

ilawe_1 60 1.83 ikun_1 60 1.89

ilawe_3 62 1.89 ilapetu_ijao 61 1.93

ilawe_5 60 1.83 imesi 60 1.89

ilawe_7 60 1.83 inisa 53 1.67

iloro/ijurin 51 1.55 ipole_iloro 60 1.89

iludun_2 58 1.77 ipoti_c 60 1.89

ilumoba 60 1.83 ire_1 60 1.89

ipoti A 57 1.74 ire_2 60 1.89

Iropora/esure/eyio 60 1.83 irona 54 1.71

iye_2 60 1.83 itapa_iyemero 60 1.89

iye_3 60 1.83 itapa_osin 59 1.86

iyin_1 54 1.64 iye_1 62 1.96

42

kota_1 60 1.83 iyin_2 60 1.89

obadore_3 55 1.67 obadore_1 46 1.45

obadore_4 61 1.86 odo_ayedun 60 1.89

ode_1 61 1.86 odo_ise_1 58 1.83

ode_2 60 1.83 odo_ise_3 61 1.93

odo_emure_1 57 1.74

Odoayedun/ayebo

de 30 0.95

odo_emure_2 46 1.4 ogbontioro_1 60 1.89

odo_emure_4 61 1.86 ogbontioro_2 60 1.89

odo_ise_2 63 1.92 oke_osun 63 1.99

odose 66 2.01 okesa 36 1.14

ogbonjana 59 1.8

Omuoke/omuodoij

elu 56 1.77

ogotun_2 62 1.89 oraye_3 46 1.45

okeyinmi 50 1.52 orin/ora 60 1.89

omuo_oke_1 58 1.77 orun_2 47 1.48

omuo_oke_2 60 1.83 otun_1 60 1.89

osi 60 1.83 otun_2 59 1.86

osun 63 1.92 oye_2 60 1.89

oye_1 60 1.83 ugele_aroku 60 1.89

settlement 55 1.67 usi 59 1.86

Total 3,285 100 Total 3,167 100

Source: Authors’ calculation. Note: The difference in the totals was due to discrepancies in the government register such as multiple registrations which we removed.

43

Table A2 Construction of outcome variables. Outcome groups Components of outcome Specific Response points to construct outcome

questions

A. Household member level

1. Total health care i) Cost of service used Q20, Q23, The values of the variables are summed to generate total health care cost. The log is used cost ii)cost of extra medicines Q29, Q33 in analysis. iii)cost of transportation to health provider iv)hospitalisation cost

B. Beneficiary level

2. Hhold i) Child labour Q38-Q43 i. Child labour: vulnerability ii) Food shortage Q44-Q47 Q38-male child work : yes=1, no=0; iii) Victim of crime Q48-Q50 Q39- frequency of occurrence,: 1 point each but in reverse order so that the more (from General iv) Hhold member migration-labour Q51 – Q57 frequent the more vulnerably; 1-6, 2-5, 3-4, 4-3, 5-2, and 6-1 (the least frequent) Household mobility Q62 Q40-reason for not working: point in the following order 1-3, 2-2, 3-0 and 4-1. Characteristics Q41-Q43 (Same as above for female). data) Food shortage: Q44-number of days any member of hhold eats less than three times daily: convert number of days directly to points. E.g. 0=0.

Q45-number of days adults reduce food consumption because of children: convert

number of days directly to points. E.g. 5 days=5 points. Q46- Hhold faced with food shortage: Yes=1, no=0 point. Q47- main cause of food shortage: 1 point for each answer.

ii. Crime Q48- Any member of hhold victim of crime: yes=1, no=0 Q49-nature of crime: 1 point each Q50-frequency of occurrence: Allocate 1 point in reverse order: 1-7, 2-6, 3-5, 4-4, 5-3, 6-2 and 7-1 (the least frequent).

iii. Hhold member migration

44

Q51-any hhold member migration: yes=1, no=0 Q52-number of members who migrated: convert number of members directly to points.

E.g. 5 members=5 points.

45

Q53-reason for migration: 1 point each Q54-initial migrants returning home: yes=1, no=0 Q55-number returning home: this time, reverse the number. 5 or more – 0, 4=1, 3=2, 2-3, 1-4, and 0-5. Q56-reason for returning home. 0 point

Interpretation: The higher the score the more vulnerable.

v)

3. Labour supply - vi) Working Q70 – Q73 Q70-currently working: yes=1, no=0 (to measure vii) Days worked Q71-major occupation: four categories defined; displacement viii) Hrs spent at work Q72-number of days worked last month: <10 days =1 point, 10 to 15 days =2points, 15 effect, labour ix) Reason(s) not working to 20 days=3points, and >20 days=4 points. market x) The share of beneficiaries working for Q73-Hours spend in a day at work: four categories defined; <10 hrs =1 point, 10 to 20 participation) pay compared to the share that are hrs =2points 20 to 40 hrs=3points, and >40 hrs=4 points. working in the informal unpaid work such as family farm, or helping watch Q74-Reasons not working = not used over children shops. Interpretation: to measure displacement effect and labour force participation. The hire the better.

4. Health behaviour i) Alcohol consumption Q75 – Q85 i. Alcohol consumption ii) Smoking Q75 – whether drink any alcohol beverage: yes=1, no=0 Q76 - type of alcohol drink: 1 point for each mentioned Q77 – reason not drinking: never drink 1-3, stopped drinking occasionally 2-2, occasionally 3-1, other 1 point. Q74 - alcohol quantity: I point for non-zero figure Q84 - Expenses on alcohol: 1 point for a non-zero value

ii. Smoking Q79 – ever smoked at all: yes=1, no=0 Q79– ever smoked cigarette nowadays: yes=1, no=0 Q80 – number smoked: I point for each. Q81 – expenditure on cigarette: check, the higher the more points Q82 – whether smoked local tobacco, yes=1, no=0 Q83- quantity smoked: I point for each number Q84- amount spent on tobacco: check, the higher the more points Q85- reason for not smoking: never smoked 0, stopped smoking 0, occasionally 1 point.

Collated the total points. The higher the worse heath behaviour.

5. Non-food i) medical expenses=doctor’s visit + Q86-Q91 These expenditure items are used to calculate the shares in total expenditure:

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expenditure medicine (i) medical expenditure share= (doctor’s visit+medicine)/total expenditure. pattern ii) All non-food items, and (ii) non-food expenditure share= All non-food items/ total expenditure.

iii) Expenditure on household members (iii) hhold member expenditure share= hhold member/ total expenditure.

6. Community i) Peer networking Q92 -Q93 The relevant variables are used to construct community activity score. The higher the activities ii) Feeling of inclusion in the community Q145 –Q148 score the better. iii)Participation in community activities

7. Financial i) Use of formal and informal financial Q93 –Q106 The main financial variables were used to construct the financial inclusion score. The inclusion institutions higher the score, the more financially inclusive. ii) Savings

iii) Borrowings

8. Quality of life i) Mental health-depression and lack of Q107-Q121 Q107-Q121 - Each question carries 1 point. self-esteem Q122-Q126 How scores are awarded:

ii) Perception of happiness, disabilities & Q127-Q130 (i) For depression symptoms: capabilities Q131-Q137 Answers are used to develop a short version of the Geriatric Depression Scale (GDS)

iii) Hold decision making &relationship Q138-Q148 (e.g. Sheikh & Yesavage, 1986). Scoring: score 1 point for each one selected. A score of with family members Q156-Q160 0 to 5 is normal. A score greater than 5 suggests depression.

iv) Self-reported health

v)Difficulties in undertaking activities of (ii) For perception of happiness and capabilities daily life Answers are used to develop a short version of the Geriatric Depression Scale (GDS).

vi)Care giving Score 1 point for each one selected. However, the higher score the better, suggesting greater happiness and capabilities.

(iii) Hhold decision making and family relationship Scores increased with better relationship: good=2, regular=1, and bad=0. The higher the better. (iv) Self-reported Health Scores increased with better health. The higher the better. Scores are awarded as follows "very good" =5, "well"=4, "moderate" =3, “poor"=2 "very bad"=1 "no answer/don’t know"=0, and accordingly. The higher score, the better.

(v) Difficulties in undertaking activities of daily life including caregiving The variables here have 4 categories: No; Yes; Can’t do it; and Won’t do it. Score 1 were awarded if YES difficulty, and 0 for the remaining category. Therefore, the lower

the value the better.

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