1
Impact Evaluation Report: Egypt’s
Takaful Cash Transfer Program
Second Round Report
Hoda El Enbaby, Dalia Elsabbagh, Dan Gilligan, Naureen Karachiwalla, Bastien
Koch, and Sikandra Kurdi
MENA REGIONAL PROGRAM WORKING PAPER 40
SEPTEMBER 2022
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CONTENTS
Executive Summary .......................................................................................................... vii
Background And Methodology ....................................................................................... vii
Takaful Impact Results .................................................................................................. viii
Recommendations .......................................................................................................... ix
1. Introduction ................................................................................................................... 11
1.1 Context For The Initiation Of The Takaful And Karama Program ............................. 11
1.2 Evidence Of Impact From The First-Round Evaluation Of The Takaful And Karama
Program ........................................................................................................................ 12
1.3 Covid-19 And The Government Of Egypt’s Response ............................................. 12
1.4 The Second Round Of Impact Evaluation Of The Takaful And Karama Program .... 13
1.5 Outline Of This Report ............................................................................................. 14
2. Takaful And Karama Program ...................................................................................... 14
2.1 Program Description ................................................................................................ 14
2.2 Targeting ................................................................................................................. 14
2.3 Program Experience ................................................................................................ 15
3. Impact Evaluation Design ..................................................................................... 18
3.1 Motivation For The Regression Discontinuity Approach ........................................... 18
3.2 Sample Selection .................................................................................................... 19
3.3 Heterogeneity Analysis ............................................................................................ 19
3.4 Regression Discontinuity Validity ............................................................................. 20
4. Sample And Survey Data ...................................................................................... 25
4.1 Sample .................................................................................................................... 25
4.2 Data Collection ........................................................................................................ 26
4.3 Survey ..................................................................................................................... 27
5. Summary Statistics For The Impact Analysis Sample ........................................ 28
5.1 Household Characteristics ....................................................................................... 28
6. Impact Of The Takaful Program ............................................................................ 29
6.1 Variables And Outcomes ......................................................................................... 29
6.2 Household Total Expenditure And Poverty .............................................................. 31
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6.3 Components Of Household Consumption ............................................................... 33
6.3.1 Household Composition ....................................................................................... 37
6.4 Assets, Savings, And Debt ...................................................................................... 39
6.5 Household Labor ..................................................................................................... 44
6.6 Child Schooling And Child Labour ........................................................................... 45
6.7 Household, Mother, And Child Dietary Diversity ...................................................... 48
6.8 Mother And Child Anthropometry, Overweight And Wasting, And Morbidity And
Treatment ...................................................................................................................... 49
6.9 Antenatal And Postnatal Care And Infant And Young Child Feeding (Iycf) Practices52
6.10 Women’s Decision-Making And Gender Norms ..................................................... 53
6.11 Mental Health ........................................................................................................ 58
6.12 Shocks And Coping Strategies .............................................................................. 59
6.13 Covid-19 ................................................................................................................ 62
6.14 Robustness To Alternate Definition Of Beneficiary Status ..................................... 63
7. Conclusions And Recommendations ............................................................................ 1
About The Authors.............................................................................................................. 5
Acknowledgments .............................................................................................................. 5
References .......................................................................................................................... 5
TABLES
Table 2.3.1. Self-reported Transfer Amounts………………………………………………. 16
Table 2.3.2. Challenges in Applying to Takaful…………………………………………….17
Table 2.3.3. Challenges in Receiving Transfers…………………………………………….17
Table 3.4.1. Beneficiary status as determined by the PMT threshold…………………..21
Table 4.2.1 Distribution of Sample by Governorate...……………………………………...26
Table 5.1.1 Summary Statistics of Household Demographic Characteristics…………29
Table 6.1.1. Current beneficiary versus ever beneficiary status…………………………30
Table 6.2.1 Impacts of Takaful program on household consumption expenditure…..32
Table 6.2.2. Impacts of Takaful program on household poverty measures……………33
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Table 6.3.1. Impacts of Takaful program on food consumption expenditure by
category…………………………………………………………………………………………….35
Table 6.3.2. Impacts of Takaful program on non-food consumption expenditure by
category…………………………………………………………………………………………….36
Table 6.3.3. Household Composition………………………………………………………….38
Table 6.4.1. Impacts of Takaful program on asset indices………………………………...40
Table 6.4.2. Impacts of Takaful program on individual agricultural assets …………….41
Table 6.4.3. Impacts of Takaful program on livestock…………………………………….42
Table 6.4.4. Impacts of Takaful program on household savings and debt…………… .44
Table 6.5.1. Impacts of Takaful program on employment outcomes…………………….45
Table 6.6.1. Impacts of Takaful program on enrolment and attendance………………..47
Table 6.7.1. Number of food groups consumed by households …………………………48
Table 6.7.2 Impacts of Takaful program on dietary diversity
outcomes……………………………………………………………………………………………49
Table 6.8.1. Impacts of Takaful program on mother anthropometrics outcomes……..50
Table 6.8.2. Impacts of Takaful program on child anthropometrics outcomes………..51
Table 6.8.3. Impacts of Takaful program on child stunting and wasting……………….52
Table 6.9.1. Impacts of Takaful program on antenatal care (ANC) outcomes…………53
Table 6.9.2. Impacts of Takaful program on Infant and Young Child Feeding (IYCF)
practices……………………………………………………………………………………………53
Table 6.10.1 Impacts of Takaful program on women's decision-making, all women…55
Table 6.10.2. Impacts of Takaful program on women's decision-making for women
with some formal education……………………………………………………………………..56
Table 6.10.3. Impacts of Takaful program on women's decision-making for women
with no formal education…………………………………………………………………………57
Table 6.10.4. Impacts of Takaful program on gender norms ………………………………58
Table 6.11.1. Impacts of Takaful program on mental health indicators………………….59
Table 6.12.1 Impacts of Takaful program on shocks experienced by household……..60
Table 6.12.2. Impacts of Takaful program on coping methods for shocks experienced
by the household………………………………………………………………………………….61
Table 6.13.1. Impacts of Takaful program on experiences with the COVID-19
pandemic……………………………………………………………………………………………63
Table 6.14.1. Impacts of Takaful program duration (self-reported)………………………65
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FIGURES
Figure 2.3.1. Program Satisfaction…………………………………………………………….16
Figure 3.1.1 Intuition Behind Regression Discontinuity…………………………………...19
Figure 3.4.1: Probability of having received a Takaful transfer in the past two months,
by PMT score (Self-Reported)…………………………………………………………………..23
Figure 3.4.2: Probability of ever having received a Takaful transfer, by PMT score
(Self-Reported)…………………………………………………………………………………….24
Figure 3.4.3: Number of months transfers were received (Self-Reported)……………..24
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ACRONYMS
AEU Adult Equivalent Unit
BMI Body Mass Index
CAPI Computer-Assisted Personal Interview
CAPMAS Central Agency for Public Mobilization and Statistics
CCT Conditional cash transfer
CPI Consumer Price Index
DHS Demographic Household Survey
GAD Generalized Anxiety Disorder
GDP Gross Domestic Product
HH Household
HIECS Household Income, Expenditure, and Consumption Survey
IFPRI International Food Policy Research Institute
IRB Internal Review Board
IYCF Infant and Young Child Feeding
IYCN Infant and Young Child Nutrition
MoSS Ministry of Social Solidarity
PCA Principal components analysis
PMT Proxy Means Test
RD Regression Discontinuity
RDD Regression Discontinuity Design
TKP Takaful and Karama Program
WHO World Health Organization
WHZ Weight-for-Height Z-score
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EXECUTIVE SUMMARY
BACKGROUND AND METHODOLOGY
Egypt introduced the Takaful and Karama Program (TKP), a pair of targeted cash
transfer schemes in March 2015. Takaful and Karama was designed as a conditional cash
transfer program providing income support targeted to the poor and most vulnerable; namely
poor families with children (under 18 years of age), poor elderly (aged 65 years and above)
and persons with severe disability. Originally implemented as an unconditional cash transfer,
the program is now a conditional cash transfer program, but the conditionalities have yet to
be monitored. Starting July 2017, households received EGP60 for each child under 6 years
old, EGP80 for each child in primary education, EGP100 for children in preparatory educa-
tion, and EGP140 for secondary education. As of June 2017, 90% of TKP beneficiaries were
women.
In 2018, the International Food Policy Research Institute (IFPRI) completed the first
round of impact evaluation of TKP, based on household survey data collected after
the first 15 months of the program. The evaluation found that TKP substantially improved
wellbeing for poor households, increasing household consumption per adult equivalent by
8.4 percent. and reducing the probability that a beneficiary household is poor (< USD1.90
per capita per day) by 11.4 percentage points, which is comparable to several of the well-
known, large-scale programs in Latin America where consumption impacts are on the order
of 7-8 percent.
In the period between the first-round evaluation and the data collection for this report
in January 2022, Egypt faced an enormous economic shock in the COVID-19 pan-
demic with a complete loss of tourism, which before the crisis was responsible for
12% of GDP and 10% of employment (IMF, 2021). Partial lockdowns and restrictions on
large public gatherings further reduced economic activity. Tax revenues decreased substan-
tially, and international investors withdrew. The Ministry of Social Solidarity expanded target-
ing of Takaful and Karama, adding an additional 411,000 households to the beneficiary list.
Egypt undertook several measures to respond to the economic crisis posed by the pan-
demic, including expansion in some safety net programs and introduction of some new so-
cial protection programs.
This follow-up evaluation was designed to assess whether impacts estimated from
the first round have been sustained and whether longer duration of treatment has led
to impacts on additional outcome variables. The follow-up evaluation focuses only on
Takaful. The evaluation assesses the program’s impact on indicators that have been cov-
ered by the first evaluation and adds new insights on some outcomes, such as COVID-19
related impacts and responses. Data from a household survey collected in January and Feb-
ruary 2022, and administrative data on registration into the program are used for the impact
evaluation.
This follow-up impact evaluation was conducted using a regression discontinuity
(RD) design similar to the first round but using a different sample of households
much more tightly concentrated around the 4500 threshold to address concerns
about the smaller discontinuity. Regression discontinuity is the most scientifically rigorous
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methodology available given the program’s approach to targeting and timing of the evalua-
tion.
TAKAFUL IMPACT RESULTS
Households invested in assets, particularly productive assets. There are large and sig-
nificantly positive effects on household asset holdings. Beneficiary households own more as-
sets in general, and importantly, they own more productive and livestock assets. Specifically,
households invested in tractors, plows, and irrigation. With regards to livestock, households
purchased buffaloes, cattle, goats, and sheep, and reduced their investments in smaller ani-
mals such as chickens, geese, pigeons, and ducks. These large and lumpy productive and
livestock investments are important for future income generation. Many households cannot
afford to make these larger investments and the Takaful program may have given house-
holds the ability to redirect funds for these investments.
Takaful households reduced their debt burdens. We see statistically significant differ-
ences between beneficiaries and non-beneficiaries with beneficiary households carrying
substantially less debt. This is also a positive result as it frees households from being forced
to pay down loans for specific items rather than using the money in more beneficial ways,
and because it reduces the burden of interest.
There were no measurable impacts of the Takaful program on household consump-
tion or poverty. Compared to the first round, statistical power of the analysis was more
limited with results not being statistically distinguishable from zero, although the confidence
intervals do rule out that the impacts on consumption could have been as large as those
found in the first round. Importantly, we note that the consumption aggregates are calcu-
lated per adult equivalent unit (AEU), which adjusts for the amount of consumption needed
by adult and child household members (children are given less weight). In particular, house-
holds reduced their consumption of grains, eggs, oils and fats, and fruits. Households also
spent less on construction and communications.
Changes in household demographics may partly explain the lack of impact on con-
sumption. We find that Takaful beneficiaries had more household members, and particu-
larly more children 0-5 years old and 6-11 years old. The higher number of household mem-
bers ages 6-11 years old is likely an artifact of the PMT score construction which was not
completely near the 4500 cutoff. We also see some suggestive evidence that Takaful in-
duced increases in number of children born in the past 5 years, including among households
that already had at least 2 children at the time of registration.
Takaful changed household labor patterns. We see a small difference in the types of oc-
cupations among beneficiaries. Beneficiaries are significantly more likely to be employed in
the informal sector.
Takaful improved school enrollment and attendance. Children of primary school age
were almost 9 percentage points more likely to be enrolled in school and children of prepara-
tory school age were 21 percentage points more likely to be enrolled in school. Further, we
see improvements in attendance rates among secondary school children. Secondary school
children who are enrolled in school attend more regularly, and the result is driven by in-
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creased participation among girls. This positive result will have implications for future gener-
ations – increased education among women is associated with several positive outcomes for
their children (better nutrition, higher learning levels, higher earning potential, etc.).
Impacts on nutrition outcomes are weak and mixed. Household level dietary is reduced
household level dietary diversity in one specification. One the other hand, beneficiary house-
holds had lower rates of wasting among children 6-23 months of age. Mother’s anthropomet-
ric outcomes were also not affected.
Women’s ability to make decisions within their households did not differ between
beneficiaries and non-beneficiaries. While the first-round evaluation found decreases in
measures of women’s decision-making power, particularly among women with no formal ed-
ucation, we do not find this same pattern in our analysis. Overall, there are no impacts in any
domain of decision-making. For the sample of women who have some formal education,
there is a slight negative effect on decision-making regarding taking children to the doctor,
and for women without any formal education there is a slight positive impact on deciding
what food to cook. However, overall, Takaful did not have any substantive impact on
women’s decision-making within their households. We also see some evidence of higher lev-
els of gender positive norms among beneficiary households. We do not find any effects on
mental health – worries, generalized anxiety, or self-esteem.
Takaful contributed to households’ ability to cope with shocks. When faced with shocks
in the past five years, the predominant method Takaful households used to cope was selling
gold/jewelry. We also see a reduction in borrowing to cope with shocks. This is a positive re-
sult since it is coping strategy with less potential for long-term negative impacts than others
such as child labor or having daughters marry early.
RECOMMENDATIONS
Takaful should be continued and even possibly extended. The program enabled house-
holds not to resort to coping with shocks in negative ways. Particularly considering increas-
ingly frequent global shocks like COVID-19 and the Russian invasion of Ukraine, social pro-
tection programs, including cash transfer programs like Takaful, could be an effective way to
protect against large-scale shocks since the infrastructure to reach people is largely in place.
Proceed with plans for recertification and graduation of beneficiaries who have achieved
self-sufficiency while using a generous cut-off for self-sufficiency given that many house-
holds have not managed to substantially increase their consumption despite increased pro-
ductive assets.
Improve communication regarding exclusion restrictions, program length, and recerti-
fication so that beneficiaries understand that they will not be excluded from the program for
formal sector work with income below a certain threshold and to ensure that beneficiaries
are not surprised by sudden changes in program status or unnecessarily worried about the
short-term continuity of the transfers.
Consider greater coordination with communication campaigns related to family plan-
ning if the behavioral response by families of having more children is confirmed and seen as
in conflict with other national policy goals.
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Continue to work towards a comprehensive social protection strategy that helps to
continue protecting the poor as well as contributing to achieving longer-term developmental
goals. Coordinating with the Ministry of Education to provide high quality public service deliv-
ery will magnify the impacts of increased school enrollment and with the Ministry of Health
regarding diets and nutrition.
Complementary programming would also be beneficial. In general, complementary pro-
gramming on issues such as nutrition practices or financial training need to be quite inten-
sive to be impactful. There are currently programs that are implemented by the Government
of Egypt on these topics, particularly a nation-wide nutrition campaign. However, it would be
worth considering pairing these programs and intensifying them by leveraging Takaful to link
to already vulnerable households.
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1. INTRODUCTION
1.1 Context for the initiation of the Takaful and Karama Program
Since 2014, Egypt has undertaken a broad program of macroeconomic reforms designed to
reduce government spending, liberalize the economy, promote employment and economic
growth, and strengthen the social safety net. These reforms included a devaluation of the ex-
change rate, substantial reductions in energy subsidies, changes to the tax code, and freez-
ing of public sector hiring (World Bank 2019). The government also initiated a series of
changes to social programs, in part to help smooth the effects of the economic reforms and
to improve the functioning of the social safety net through changes in program designs and
improved targeting.
Egypt has long provided substantial social support. Major programs include a large social
solidarity pension and a system of broad food subsidies initiated after World War II. These
reach millions of households, but they are costly and inefficient as a form of redistribution.
The food subsidy alone cost 1.6% of GDP and reached 70% of the Egyptian population
(Ministry of Finance, 2017). In 2015, the government reformed food subsidies, instituting a
ration card that served as a voucher for discounted purchases of selected food items
(Moselhy, 2017; Ecker et al. 2016). During the macroeconomic reforms, the government in-
creased the size of voucher payments to help offset the negative impacts of the reforms
(Breisinger et al. 2018).
In the context of these reforms, Egypt introduced the Takaful and Karama Program (TKP), a
pair of targeted cash transfer schemes in March 2015. Takaful and Karama was designed as
a conditional cash transfer program providing income support targeted to the poor and most
vulnerable; namely poor families with children (under 18 years of age), poor elderly (aged 65
years and above) and persons with severe disability. The introduction of the program repre-
sented a significant step on behalf of the Egyptian government to increase the share of so-
cial spending reaching poor households. Takaful and Karama is implemented by the Ministry
of Social Solidarity (MoSS), and co-financed by the Government of Egypt and the World
Bank. Takaful (Solidarity), the larger of the two programs, is a family income support
scheme. The program was initially rolled out as an unconditional cash transfer, but planned
education and health conditionalities were introduced in 2018. Continued receipt of Takaful
transfers was conditioned on school children aged 6-18 years maintaining attendance of at
least 80% of the school days, and on mothers and children below 6 years completing three
visits to health clinics per year, in addition to maintaining child growth monitoring records,
and attending nutrition awareness sessions. Takaful transfers start from a basic amount of
EGP325 per household, per month, which increases depending on the number of children in
the households and their educational level. At the beginning of the program, household re-
ceived EGP60 for each child in primary education, EGP80 for each child in preparatory edu-
cation and EGP100 in secondary education. The nominal value of the transfers was in-
creased over time. For example, starting July 2017, households received EGP60 for each
child under 6 years old, EGP80 for each child in primary education, EGP100 for children in
preparatory education, and EGP140 for secondary education. Households can receive bene-
fits for up to three children only, who are usually the oldest three children in the households.
Karama (Dignity) is an unconditional income support scheme targeted at the poor elderly
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and persons with severe disability, and orphans. Orphans were added as Karama beneficiar-
ies in 2017 and receive EGP350 per beneficiary. Karama monthly transfers for poor elderly
and person with disability started at EGP350 per beneficiary and were increased in July
2017 to EGP450 per beneficiary. Karama also has a maximum of three beneficiaries per
household (World Bank, 2015). Families can be entitled to both Takaful and Karama bene-
fits. As of June 2017, 90% of TKP beneficiaries were women.
1.2 Evidence of impact from the first-round evaluation of the Taka-
ful and Karama Program
In 2018, the International Food Policy Research Institute (IFPRI) completed the first round of
impact evaluation of TKP, based on household survey data collected after the first 15
months of the program. The evaluation was conducted using a regression discontinuity de-
sign, a methodology that provides rigorous estimates of program impact for programs like
TKP targeted using a proxy means score with a fixed threshold for household eligibility. The
evaluation found that TKP substantially improved wellbeing for poor households, increasing
household consumption per adult equivalent by 8.4 percent. and reducing the probability that
a beneficiary household is poor (< USD1.90 per capita per day) by 11.4 percentage points.
The consumption impact of Takaful is comparable to several of the well-known, large-scale
programs in Latin America. A review of conditional cash transfer programs in Latin America
(Fizbein et al, 2009) found that impacts on household expenditure ranged from 7-10 percent
among four programs in Brazil, Mexico, Colombia, and Honduras that providing transfers of
similar size to Takaful as a share of household expenditure. The first-round impact evalua-
tion also showed that Takaful increased the value of household food consumption and im-
proved the quality of household diets.
The first round of evaluation also identified some limitations in the program’s impact. For ex-
ample, there were no significant impacts of Takaful on school enrollment or heath care utili-
zation, which may be explained by the absence of conditionalities at the time of the evalua-
tion. Also, estimates showed a negative and significant impact of the program on women’s
control over decision making, which was driven primarily by households in Lower Egypt and
by women with less than primary education. The first-round evaluation did not find any
measurable impacts of the Karama program, due to methodological challenges introduced
by variation in the eligibility threshold as program leaders sought to expand its coverage.
1.3 COVID-19 and the Government of Egypt’s response
In the period between the first-round evaluation and the data collection for this report in Jan-
uary 2022, Egypt faced an enormous economic shock in the COVID-19 pandemic. The pan-
demic had several immediate, severe negative effects on the economy. For example, Egypt
experienced a complete loss of tourism, which before the crisis was responsible for 12% of
GDP and 10% of employment (IMF, 2021). Partial lockdowns and restrictions on large public
gatherings further reduced economic activity. In addition, tax revenues fell and the country
experienced capital outflows of more than $15 billion as investors withdrew from emerging
markets. Despite these challenges, Egypt was one of the few emerging markets to experi-
ence positive, though modest, economic growth in 2020. Economic growth was projected to
further accelerate in 2021, to 5.5% (World Bank 2022).
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Egypt undertook several measures to respond to the economic crisis posed by the pan-
demic, including expansion in some safety net programs and introduction of some new so-
cial protection programs. These changes included an increase in the social pension and new
transfers targeted to pregnant women, women with young children, individuals with disabili-
ties, the elderly, and informal and self-employed workers, such as in the tourism and agricul-
ture sectors (Gentilini, 2022). In addition, the Ministry of Social Solidarity expanded targeting
of Takaful and Karama, adding an additional 411,000 households to the beneficiary list. With
this expansion, through July 2021, the project has reached 3.37 million direct project benefi-
ciaries (including 75% women), while 11.85 million direct and indirect beneficiaries benefited
from the project. Not surprisingly, these changes in eligibility rules used to expand the TKP
created some challenges in measuring impacts of the program, which we discuss below.
1.4 The second round of impact evaluation of the Takaful and
Karama Program
In 2018, the World Bank contracted IFPRI to conduct the first round of impact evaluation of
the Takaful and Karama Program, in conjunction with the Ministry of Social Solidarity and
with funding from the United Kingdom Foreign and Commonwealth Office (UK FCO). For the
second round, IFPRI has been contracted by the World Bank again, this time with funding
from the United States Agency for International Development (USAID) for the data collection.
As with the first-round evaluation, IFPRI has worked in conjunction with the Ministry of Social
Solidarity in conducting this second-round impact evaluation of the Takaful program.
The follow-up evaluation focuses only on Takaful, which is the largest of the two programs,
and where more human capital accumulation is expected with the implementation of the con-
ditionalities. The main objective of conducting a second round of evaluation is to assess
whether impacts estimated from the first round have been sustained and whether longer du-
ration of treatment has led to impacts on additional outcome variables. This evaluation round
also follows the implementation of conditionalities, which were not in place when the first
evaluation was conducted. The second round of evaluation rigorously assesses the pro-
gram’s impact on indicators that have been covered by the first evaluation, such as house-
hold consumption, poverty, asset ownership, as well as other measures of well-being, such
as the prevalence of overweight and obesity in adult women and children, as well children’s
education and health. This second round of evaluation also adds new insights on some out-
comes, such as COVID-19 related impacts and responses. Data from a household survey
collected in January and February 2022, and administrative data on registration into the pro-
gram are used for the impact evaluation.
The Takaful program continues to be targeted using a Proxy Means Test (PMT), which is an
index of well-being based on household demographics, income, housing quality, assets, and
other characteristics. Households with a PMT score below a preset threshold are considered
eligible for the program, while those above it are not. The PMT cut-off point as well as the
PMT function used by MOSS have been changed between both evaluations. The design of
the program creates a quasi-experiment which IFPRI researchers utilized to rigorously as-
sess the impact of the program.
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1.5 Outline of this Report
The remainder of this report is organized as follows Chapter 2 provides a brief overview and
updates about the Takaful Program. Chapter 3 presents the impact evaluation design. Chap-
ter 4 describes the evaluation survey and sample. Chapter 5 summarizes beneficiary and
non-beneficiary household characteristics, providing context to the study. Chapter 6 presents
the impact estimates for Takaful. Finally, Chapter 7 concludes and provides policy recom-
mendations.
2. TAKAFUL AND KARAMA PROGRAM
2.1 Program Description
Egypt launched the Takaful and Karama Program in March 2015. The initiation of the pro-
gram followed the implementation of a series of economic reforms that were implemented in
Egypt starting 2014. The program aimed to protect the most vulnerable population groups
from the impacts of the economic reforms, as well as to improve the targeting of social pro-
tection in Egypt (World Bank, 2015). Takaful and Karama is a conditional cash transfer pro-
gram that seeks to provide income support to the poor and most vulnerable; namely poor
families with children (under 18 years of age), poor elderly (aged 65 years and above) and
persons with severe disability. The program has two main sub-programs: Takaful, and
Karama.
“Takaful” (Solidarity) is a family income support scheme aimed at protecting poor families
with children. The program is de jure conditioned on school attendance and health outcomes
but de facto, the conditionalities are not enforced. Receiving the cash transfers is conditional
on attendance of at least 80% of the school days by children aged 6–18 years, and on re-
ceiving three health monitoring visits per year, by mothers and children below 6 years in ad-
dition to maintaining child growth monitoring records and attending nutrition awareness ses-
sions. Takaful transfers start from a basic amount of EGP 325 per household, per month,
and increase depending on the number of children in the households and their educational
level, with a maximum amount of EGP 625 Transfers were originally delivered on a quarterly
basis but were shifted to monthly transfers in 2017.
With the outbreak of COVID-19 and the global economic crisis that followed, MOSS has
been expanding Takaful to 411,000 more households to shield more households from the
loss of employment and the inflationary pressures.
2.2 Targeting
When the program was first launched, targeting beneficiaries combined geographical target-
ing with a Proxy Means Test (PMT) mechanism. With respect to the geographical targeting,
the program was first launched in the poorest districts within the poorest governorates in
Egypt. Currently, the program is available in all of Egypt’s governorates. The PMT is used to
identify the poor, based on selection criteria and a set cutoff score, based on the poverty line
derived from Egypt’s Household Income, Expenditure and Consumption Survey (HIECS).
The cutoff score for Takaful has been changed several times throughout the life of the pro-
gram but the cutoff faced by the overwhelming majority of applicants is 4,500 points. Any
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household can apply to the program if they meet the following criteria: 1) the household head
is 35 years or older, 2) the household’s monthly income is less than EGP 400 per month
from the public or private sector, 3) the household does not benefit from social insurance, 4)
the household has children, and 5) the household resides in Egypt (Al-Masry Al-Yom, 2022).
The PMT uses poverty predictors from the 2012–2013 Household, Income, Expenditure and
Consumption Survey (HIECS). A PMT formula was developed based on the following crite-
ria:
Household head characteristics (e.g., gender, marital status, age, education, work
status etc.)
Household members characteristics (e.g., number of elderly, children, members en-
rolled in education, illiterate, employment situation)
Money transfers received by household (e.g., pensions, remittances, allowances,
etc.)
Housing unit characteristics (e.g., type of unit, ownership, ceiling, flooring, water con-
nection, etc.)
Ownership of assets (e.g., private car, internet connection, refrigerator, etc.)
The PMT formula is based on a regression with the logarithm of per capita annual expendi-
ture (adjusted to regional differences, price variations and age differences of HH members)
as the dependent variable (El-Sheneity, 2014). Different PMT models are used for the differ-
ent geographic regions in Egypt; namely urban Upper Egypt, rural Upper Egypt, urban Lower
Egypt, rural Lower Egypt, Metropolitan, and Frontier governorates, however the same PMT
eligibility threshold is used for all regions.
2.3 Program experience
Satisfaction from Takaful program seems to have declined between 2018 and 2022. Most
applicants seem to have a positive experience and are satisfied with the program. Yet, in
2018, 68.1% of surveyed households were very satisfied from the program and 89.2% were
either very satisfied or somewhat satisfied (from a nationally representative sample). Mean-
while, in 2022, only 48.7 percent of our surveyed households were very satisfied with Taka-
ful, and 76.1 percent were either very satisfied or somewhat satisfied, see Figure 2.3.1.
16
Figure 2.3.1. Program Satisfaction
Source: Authors’ calculations
The average transfer amount reported by beneficiaries in the past 2 months was EGP 432
per month. On average, administrative data shows that households have been receiving
payments for 3.8 years.
Table 2.3.1. Self-reported Transfer Amounts
Mean
Standard devi-
ation
Average amount last 2 months – EGP
431.5
(65.5)
No of months since first transfer
42.2
(20.9)
No of years since first acceptance
3.5
(1.7)
Note: Standard deviations are reported in parentheses
The evaluation investigated the challenges that households experienced during the registra-
tion process, as well as in receiving the transfers. Table 2.3.3 shows the challenges that reg-
istrant households faced while applying for Takaful. The most common challenge among
registrants was that queues for program application were long, which was reported by 20
percent of Takaful beneficiaries and 30 percent of non-beneficiaries. The second most com-
mon challenge was preparing the needed documents, which required time and effort. Regis-
trants who did not end up as Takaful beneficiaries were more likely to report this issue (20
percent), compared to Takaful beneficiaries (14 percent). Meanwhile, 64 percent of Takaful
beneficiary household reported no challenges, while 43 percent of non-beneficiaries also re-
ported no challenges.
48.7
27.4
4.9
7.5
11.6
Program Satisfaction
Very satisfied Somewhat satisfied
Neither satisfied nor dissatisfied Somewhat unsatisfied
Very unsatisfied
17
Table 2.3.2. Challenges in Applying to Takaful
Variable
Takaful Benefi-
ciaries
Takaful Non-Benefi-
ciaries
The social workers were not helpful in explaining the
needed documents
0.13
0.25
(0.33)
(0.43)
Getting the needed documents require a lot of money
0.10
0.18
(0.30)
(0.38)
Getting the needed documents require traveling
0.08
0.10
(0.27)
(0.30)
Getting the needed documents is difficult and time con-
suming
0.14
0.20
(0.35)
(0.40)
There were long queues for program application
0.20
0.30
(0.40)
(0.46)
The time to travel to apply was prohibitive
0.04
0.05
(0.20)
(0.22)
The application is time consuming
0.04
0.09
(0.21)
(0.29)
The application form was too difficult
0.02
0.06
(0.15)
(0.23)
Other challenges
0.01
0.04
(0.07)
(0.19)
No challenges met during application to TKP
0.64
0.43
(0.48)
(0.49)
Number of households
2,543
3,932
Note: Standard deviations are reported in parentheses
In terms of receiving the transfers, the only commonly reported issue is that the working
hours of payment delivery units were not convenient, which was reported by 15 percent of
Takaful households.
Table 2.3.3. Challenges in Receiving Transfers
Variable
Takaful Beneficiaries
Requires traveling for long distances
0.02
(0.14)
Travel costs are costly
0.05
(0.23)
Informal facilitation fees need to be paid to receive payment
0.01
(0.10)
Did not know where or how to receive it
0.01
(0.08)
Payments are regularly delayed
0.07
(0.25)
Do not know when the payment should be received
0.01
(0.11)
Lost the card and found difficulty in renewing it
0.01
(0.07)
Lost the pin code and found difficulty in renewing it
0.01
(0.07)
Working hours at payment delivery units are not convenient
0.15
(0.35)
Other challenges
0.01
(0.09)
Number of households
2,543
Note: Standard deviations are reported in parentheses
18
3. IMPACT EVALUATION DESIGN
3.1 Motivation for the Regression Discontinuity Approach
Impact evaluation is a valuable tool for policy makers because it allows them to understand
the causal impact of programs - i.e., the amount of difference in household welfare caused
by the program being evaluated rather than any other factor.
As recognized in the baseline impact evaluation of Takaful and Karama (Breisinger et al.,
2018), the ideal impact evaluation approach to use to measure the impacts of the Takaful
program is regression discontinuity. Other common strategies that economists use to iden-
tify impact such as a randomized control trial or differences-in-differences were not feasible
due to inability to collect pre-program data or randomly assign participants to treatment and
control groups. A third common approach, matching, was also discarded because of the sig-
nificant role of self-selection in determining which households applied to the program and the
large number of observable characteristics already included in the PMT score. By contrast,
regression discontinuity works well, as it takes advantage of the program’s targeting ap-
proach which uses a strict cutoff in the PMT score.
Figure 3.1.1 shows the intuition behind the regression discontinuity evaluation approach:
households just above and just below the cutoff are compared, while controlling for the un-
derlying relationship between the outcome and the PMT score variable. As highlighted in
the baseline evaluation, this approach is recognized as a rigorous impact evaluation strategy
in the international literature. One disadvantage, however, is that the impact estimated using
the regression discontinuity approach should be interpreted as the average impact of the
program specifically for households in the neighborhood of the cutoff. In practice, this
means that the estimated impacts of Takaful are for the relatively better off among all Taka-
ful beneficiary households.
Note that another option considered was defining the “treatment” group as those currently
receiving Takaful transfers, and the “control” group as those not currently receiving transfers.
However, there are many factors that determine whether a household is receiving transfers.
The first is of course whether they meet the eligibility criteria: primarily, a PMT score below
4500 points when the household applied. That criterion is exogenous, meaning that the
household cannot do anything to influence it (and we can check whether that is the case in
the data) so there are no confounding factors that would influence the results. Accordingly, it
is considered exogenous and impacts using this eligibility criterion allows for a causal inter-
pretation of results. Since our sample comprises households who applied to Takaful several
years ago, they were eligible at the 4500 point cutoff. However, the cutoff has changed since
then and household status has also changed for many households. Some households grad-
uated from the program and are no longer receiving transfers, some households were newly
eligible and added to the program and are now receiving transfers even though they were
previously ineligible, and some households who stopped receiving transfers applied again
under a new ID and are now receiving transfers. These factors are not exogenous – they are
factors that households can affect, and household who can affect these changes are differ-
ent from those who cannot, making them an invalid comparison group. Consequently, using
a definition of whether the household is a current beneficiary would not result in a causal in-
terpretation of estimates.
19
Figure 3.1.1 Intuition Behind Regression Discontinuity
Source: Authors’ illustration
3.2 Sample Selection
In order to measure the medium-term impact of Takaful, we constrained our sample for this
evaluation round only to households that had registered in May 2016-December 2016. Eligi-
ble households in this sample – i.e., households with PMT scores below the 4500 cutoff-
started receiving Takaful transfers in late 2016 or early 2017 given the several month delay
between registration and program entrance. This means that households still receiving
transfers as of the second-round evaluation survey had been in the program for approxi-
mately five years.
Not all households continued in the program, however, and some households that had origi-
nally been excluded from the program eventually re-registered and were included. However,
we define our “treatment” and “control” groups based on the original eligibility in 2016 in or-
der to maintain a causal identification strategy. Whether households met the eligibility crite-
ria( a PMT score below 4500 points) when the household applied is exogenous, meaning
that the household cannot do anything to influence it (and we can check whether that is the
case in the data) so there are no confounding factors that would influence the results. Ac-
cordingly, impacts using this eligibility criterion allows for a causal interpretation of results.
Some households graduated from the program and are no longer receiving transfers, some
households were newly eligible and added to the program and are now receiving transfers
even though they were previously ineligible, and some households who stopped receiving
transfers applied again under a new ID and are now receiving transfers. These factors are
not exogenous – they are factors that households can affect, and household who can affect
these changes are different from those who cannot, making them an invalid comparison
20
group. Consequently, using a definition of whether the household is a current beneficiary
would not result in a causal interpretation of estimates.
3.3 Heterogeneity analysis
In recognition of the limitation of the regression discontinuity approach, an associated com-
plementary analysis of administrative data is planned to estimate the degree to which house-
holds farther from the cutoff point may have benefited differently from the Takaful program
than households in the immediate neighborhood of the cutoff. The heterogeneity analysis
portion of the evaluation report is expected to be available by December 2022 depending on
the timing of the administrative data collection.
3.4 Regression Discontinuity Validity
The remainder of this chapter provides details about the regression discontinuity specifica-
tion used for this impact evaluation and shows tests used to ensure that the regression dis-
continuity estimate is valid for estimating an impact in this particular sample. Readers less
interested in the technical details may skip to Chapter 4.
The general justification for use of a regression discontinuity design based on the Proxy
Means Test (PMT) was presented in the baseline evaluation report and remains relevant.
The use of a Proxy Means Test to create an indicator of well-being for program registrants
and use of a threshold PMT score to determine program eligibility provides the conditions
needed to measure impact using a regression discontinuity (RD) design.
While the eligibility cutoff changed several times in recent years, our sample was selected to
include only households that initially registered for Takaful between May 2016 and Decem-
ber 2016. This sample was specifically selected such that the 4500 cutoff was relevant for
determining household eligibility. The 4500 cutoff was applied from September 2016 to April
2017 and during this period in the Takaful program, registrants who applied in May 2016-Au-
gust 2016 and who were not eligible according to the previous cutoff of 4296 were automati-
cally included in the program when the cutoff increased.
Nevertheless, the 4500 cutoff is not perfectly predictive of program participation. In contrast
to the baseline evaluation when approximately 90% of registrants below the threshold were
current beneficiaries, in round 2, only approximately 50% of registrants who were below the
threshold are still listed in the MoSS database as beneficiaries. This is because MoSS used
data from other departments to ensure that all eligibility criteria were met (for example, not
receiving remittances, not owning a car, etc.). Based on the original household registration,
the strictness of the threshold was very strong with less than 2% of applicants with PMT
scores above the 4500 cutoff being beneficiaries, and more importantly, we learned that
many households that were initially rejected re-registered later for the program under a dif-
ferent application number. Nevertheless, the household PMT score at the time of registra-
tion relative to the 4500-cutoff remained a substantial determinant of program participation.
21
Table 3.4.1 demonstrates the probability of ever having been a beneficiary, being a current
beneficiary (defined as receiving a transfer in the past two months), and the average amount
received if a household’s PMT score is less than or equal to 4500. When the PMT score is
less than or equal to 4500, the household is almost twice as likely to have ever received
transfers from the Takaful program, was 26% more likely to report a transfer in the past two
months (indicating that some households who were beneficiaries are no longer beneficiaries)
and received 109 EGP more than non-beneficiary households. Additionally, beneficiary
households received transfers for almost two years longer than non-beneficiary households
(non-beneficiary households are coded as zero months). Another potential definition of hav-
ing been “treated”, is to consider households who are currently beneficiaries but and have
also received at least 24 transfers.
1
While we know the number of transfers received, we do
not know whether a household began receiving transfers, stopped receiving them, and then
started receiving them again, or whether they have not recently received transfers. We do
know, however, that 17% of households reported ever having ever received a transfer but
are no longer receiving them. No households who received ever having received a transfer
reported that they do not currently receive transfers. This alternative definition captures the
combination of more recent transfers and what could be considered a “sufficient” history sub-
stantial period of receiving transfers equal to at least 40% of the maximum possible period of
transfer receipt. Using this definition, households whose PMT score fell below the 4500 point
threshold were 32% more likely to be treated compared to those below the threshold. Over-
all, the PMT threshold is a very strong predictor for all three outcomes, meaning that RD is a
valid strategy. As a result, as in the baseline evaluation, we employ a ‘fuzzy’ regression dis-
continuity design. We estimate local linear regressions on either side of the cutoff with robust
bias-corrected confidence intervals (Calonico et al, 2014).
1
25% of households have a PMT score below 4500, are still receiving transfers, and have had 36 or more months of transfers.
26% of households have a PMT score below 4500, are still receiving transfers, and have had 24 or more months of transfers.
This indicates that the choice of 24 months versus a longer period would not change the treated sample very much.
22
Table 3.4.1. Beneficiary Status as Determined by the PMT Threshold
(1)
(2)
(3)
(4)
HH ever re-
ceived trans-
fers from Taka-
ful
HH reported a Taka-
ful transfer in the
past two months
Average amount of
Takaful transfers re-
ceived in the last two
months
Number of monthly
Takaful transfers
ever received
PMT score
≤ 4500
0.481***
0.264***
108.5***
23.17***
(0.0128)
(0.0133)
(5.973)
(0.699)
Mean Dep.
Var.
0.568
0.393
169.2
23.97
R
2
0.236
0.470
0.065
0.196
F-statistic
293.56***
82.28***
67.82***
226.93***
N
6449
6475
6475
6449
The RD estimation strategy, as described above, is a local linear regression model that iden-
tifies impacts around the threshold of participation in the program. The estimating equation is
as follows.
The treatment effect is estimated as the difference in the expected value of the outcome
conditional on the PMT score on either side of the threshold relative to the change in proba-
bility of participation (P) at the cutoff:
(1)
where represents the impact of the Takaful program on a particular outcome. To estimate
E[Y|score] and E[p|score] we use local unweighted linear regressions including strata fixed
effects to account for the minor differences in the formula used for calculating the PMT score
variable in different regions and cluster standard errors at the community level.
We estimate two separate specifications: one based on current beneficiary status, and one
based on having ever received Takaful transfers. We define a household as a current benefi-
ciary if they reported receiving a Takaful transfer in the past two months, and as ever having
been a beneficiary if they reported ever receiving Takaful transfers. As a robustness check,
we also use the number of months that a household reports receiving transfers as the “treat-
ment”. We do this for a select number of our main outcomes only, since the estimates for
program participation on average are easier to interpret and participation in the program is
our main object of interest rather than intensity of participation.
Figure 3.4.1 shows the probability of being a Takaful participant in the past two months,
among those who registered, by PMT score. The vertical line denotes the 4500 threshold.
While approximately 45% of households above the threshold are receiving transfers, nota-
bly, almost 25% of households above the threshold are also receiving transfers. This finding
is important in interpreting our results because the size of the difference in probability of re-
ceiving transfers is relatively small at around 20%. In the first-round evaluation of Takaful,
the equivalent difference was about 55% and that larger difference enabled us to estimate
effects more precisely. The small size of this discontinuity compared to the baseline evalua-
tion means that our statistical power is still lower than ideal, despite the sampling approach
23
in the second-round evaluation being designed to maximize statistical power. The implica-
tion of this reduced statistical power for our impact evaluation is that while larger impacts are
still discernable, the confidence intervals on our estimates are large enough that there may
be small but meaningful impacts that we are not able to statistically distinguish them from
zero. Figure 3.4.2 shows the discontinuity in whether a household has ever been a Takaful
recipient. While 80 percent of households below the threshold report that they have received
Takaful transfers, approximately 25 percent above the threshold also report ever having re-
ceived transfers. Finally, Figure 3.4.3 shows that there is also a discontinuity in the duration
of transfers received by households. The outcome variable is the number of months that a
household reported receiving Takaful transfers. Below the 4500 point threshold, the average
number of months is approximately 35 months, while above the threshold the average is
about 10 months. This is a fairly large gap in the duration of transfers.
Figure 3.4.1: Probability of having received a Takaful transfer in the past two
months, by PMT score (Self-Reported)
Notes: Estimated using rdplot with uniform kernel and 30 equally spaced bins.
24
Figure 3.4.2: Probability of Ever Having Received a Takaful Transfer, by PMT Score
(Self-Reported)
Notes: Estimated using rdplot with uniform kernel and 30 equally spaced bins.
Figure 3.4.3: Number of Months Transfers Were Received (Self-reported)
25
3.5 Regression Discontinuity Specification
To estimate the treatment effect described in equation (1), we use the ‘rdrobust’ package in
Stata, developed by Calonico, Cattaneo, and Titiunik (2014).
There were two main decisions to be made when estimating this equation: the choice of
bandwidth and the kernel. As will be described in Chapter 4, we chose to sample from the
beginning from a very narrow window around the 4500 cutoff to maximize statistical power.
Consistent with the baseline evaluation in which we found that when the initial sample is al-
ready concentrated around the cutoff, data-driven bandwidth selection is not feasible, we
chose to set the bandwidth such that all data collected was used. Equally consistent with
our choices in the baseline evaluation, we used a uniform kernel.
Following the suggestion of Calonico et al. (2014), we report conventional, bias-corrected,
and robust bias-corrected estimates. The conventional results approximate a linear regres-
sion both above and below the cutoff. The concern, however, is that if the true underlying
relationship is non-linear, the conventional estimates may be affected by leading bias
caused by the non-modeled curvature of the relationship. The bias-corrected point estimate
allows for quadratic regressions on each side of the cutoff and the robust estimate shows the
bias-corrected point estimate together with recentered confidence intervals. The confidence
interval in the robust bias-corrected estimates is more robust to the choice of bandwidth than
the conventional approach. To the extent that we are confident in the assumption that the
underlying relationship between the PMT score, and the outcome variable is linear, however,
the conventional estimates remain valid.
4. SAMPLE AND SURVEY DATA
4.1 Sample
The sampling strategy was designed to provide a sample of Takaful households that had
had the opportunity to receive transfers for approximately five and half years with PMT
scores as close as possible to the 4500 threshold to maximize the statistical power. To
make the data collection feasible, we targeted 12 households per village.
We began with administrative data on all registrants in the program. After extracting all
households that registered in the period of May 2016-December 2016 and removing any
overlaps with ongoing Forsa or Haya Karima interventions, we selected villages in which it
was possible to find 16 Takaful registrant households within the smallest possible distance of
the 4500 cutoff. To identify 540 villages for a sample size comparable to the baseline evalua-
tion, we ended up using a window of PMT scores from 4448 to 4562 (for geographic repre-
sentativeness, we also allowed the inclusion of two villages in the frontier region for which 14
Takaful registrant households with PMT scores from 4444 to 4564 could be found). The ini-
tial sample included an extra four households per village as replacement households, to ac-
count for anticipated difficulties with locating households based on registration data.
Compared to the baseline evaluation, the sample for the second-round evaluation has a dra-
matically narrower bandwidth: 62 compared to 600. This narrower bandwidth is expected to
increase the precision of our estimates, partially making up for the large loss in statistical
power resulting from the much smaller discontinuity in program participation at the cutoff.
26
4.2 Data Collection
Data was collected by the survey firm El-Zanaty and Associates between January 8 and
February 13, 2022. The field staff consisted of 8 teams of 1 male interviewer and 4 female
interviewers each. Households were interviewed by female interviewers, with male interview-
ers collecting the community questionnaire.
The main enumerator training took place in July 2021, but as the data collection was consid-
erably delayed due to security clearance issues, a refresher training was held for four days
in January 2022 prior to the start of the data collection.
Our initial sample for the Takaful and Karama analysis components consisted of 6,480
households. Due to the anticipated difficulties in locating all households listed in the regis-
tration data, the replacement households in each community were used. Within the main
sample, 77% of households were located and surveyed. Considering only households which
could be located and including the replacement households used in these cases, the overall
response rate was 94.6%. The final sample size was 6,475 households. Table 4.2.1 shows
the distribution of our sample by governorate.
Table 4.2.1 Distribution of Sample by Governorate
Governorate
Number
Percent
Cairo
72
1.11
Alexandria
72
1.11
Suez
12
0.19
Kalyubia
96
1.48
Kafr El-Sheikh
132
2.04
Gharbia
24
0.37
Menoufia
36
0.56
Behera
552
8.53
Ismailia
96
1.48
Giza
276
4.26
Beni Suef
1,105
17.07
Fayoum
828
12.79
Menya
1,311
20.25
Assuit
336
5.19
Souhag
780
12.05
Qena
420
6.49
Aswan
192
2.97
Luxor
120
1.85
New Valley
15
0.23
Total
6,475
100
27
4.3 Survey
Data was captured in CAPI (computer assisted personal interview).
The household survey instrument consisted of the following modules:
a) Household Roster: ages, educational attainment, and disability status
b) Children’s Schooling: enrollment, grade level, school type, and tuition payments
c) Employment: time spent in small business or agriculture, unemployment, time spent
in wage employment, sector, and average monthly wage
d) Housing Conditions: number of rooms, building materials, water and sanitation facili-
ties
e) Household Assets, Debt, and Income from Sources other than Wages
f) Household Program Participation: including both Takaful and Karama participation
and transfers from other government programs
g) Shocks: description of type and severity of shocks in past 3 years and coping strat-
egy employed
h) Food Consumption: 7 day recall period
i) Nonfood Consumption: 30 day recall period
j) Dietary Diversity: for mother/ caretaker, one child age 6-23 months, and one child
age 24-59 months
k) Women’s Use of Antenatal and Postnatal Care: only for women with children under 5
years
• Infant and Young Child Feeding Practices: only for women with child under 2
years
l) Mental Health and Preferences: depression, anxiety, risk, ambiguity, and time prefer-
ences
m) Intrahousehold Decision-Making: Who makes decisions on various aspects of house-
hold affairs
n) Anthropometry: for woman or caretaker of children under 18, one child 6-23 months,
and one child 24-59 months
o) COVID-19
p) Cognition: Digit span forwards and backwards
q) Mental Health of Mother: depression
Compared to the baseline evaluation, four modules were dropped (agriculture, health, infant
and young child feeding knowledge, and Raven’s test) and four modules were added (P, S,
T and V), while there was also substantial revision of modules B and M to include additional
outcomes and reflect lessons learned from the baseline evaluation.
Households were read an informed consent statement in which the purpose of the data col-
lection was explained, respondents were told that they did not have to participate in the inter-
view and could stop at any time, and we emphasized that individual-level data would not be
shared with MoSS or impact program participation and that it would be stored on an en-
crypted, password protected file. All COVID-19 protocols set out by the Government of Egypt
were strictly adhered to.
The community questionnaire included responses informed leader of the community on
shocks faced recently by the community as a whole as well as services offered at the near-
est health center.
28
Internal Review Board (IRB) approval was sought and received through the International
Food Policy Research Institute’s IRB. Questionnaires and a protocol for data collection and
protection were submitted along with certificates in data collection for human subjects for all
PIs and researchers involved.
5. SUMMARY STATISTICS FOR THE IMPACT
ANALYSIS SAMPLE
5.1 Household Characteristics
This section will provide a picture of the characteristics of households within our sample. Ta-
ble 5.1.1 displays means and standard deviations (in parentheses) of several demographic
characteristics of Takaful beneficiaries (column 1) and non-beneficiaries (column 2).
The total number of household members in both samples is just under five. Of these, about
2.5 are children between the ages of 0-18, with beneficiary households having slightly more
children. This result is expected, of course, since Takaful targets households with children.
The PMT score is also as expected – below 4,500 for beneficiaries and above 4,500 for non-
beneficiaries.
Household heads are also younger in the beneficiary sample at 40 years old versus 42 years
old for non-beneficiaries. Heads of households are almost exclusively male and the propor-
tions are the same in the two samples. The main household demographics are extremely
similar between the two groups: levels of education of the household head and their spouse
hardly differ and there is no consistent pattern to show that some households may be much
worse off in these pre-determined characteristics. This result is important and encouraging
because it means that the non-beneficiary households serve as a valid control group for the
beneficiary households. Characteristics that are pre-determined and would not be affected
by the program should indeed not be substantially different between the two groups.
In examining assets as a measure of wealth, however, it is clear that there are differences
between beneficiaries and non-beneficiaries. We examine indices of livestock assets, pro-
ductive assets, household durables, and total assets, and in all cases, Takaful beneficiaries
have lower index scores. This result is also expected since the beneficiaries are indeed
poorer – they fall below the 4,500 threshold whereby they are eligible to receive the pro-
gram.
29
Table 5.1.1 Summary Statistics of Household Demographic Characteristics
Variable
(1)
Takaful Beneficiar-
ies
(2)
Takaful Non-Beneficiar-
ies
Number of members in the household
4.97
4.82
(1.12)
(1.26)
Total children 0-18 in the household
2.77
2.53
(1.19)
(1.38)
PMT Score
4,492.91
4,502.27
(22.03)
(21.90)
Age of household head
40.19
42.29
(8.22)
(9.17)
Household head is male
0.96
0.96
(0.20)
(0.21)
Household head did not attain any education
0.34
0.33
(0.47)
(0.47)
Household head attained primary education level
0.14
0.11
(0.34)
(0.31)
Household head attained preparatory education
level
0.10
0.08
(0.30)
(0.27)
Household head attained secondary education
level
0.39
0.44
(0.49)
(0.50)
Spouse did not attain any education
0.39
0.37
(0.49)
(0.48)
Spouse attained primary education level
0.10
0.09
(0.30)
(0.28)
Spouse attained preparatory education level
0.14
0.13
(0.35)
(0.33)
Spouse attained secondary education level
0.32
0.34
(0.46)
(0.47)
Index of Livestock Assets
-0.06
0.04
(0.94)
(1.57)
Index of Productive Assets
-0.01
0.01
(1.27)
(1.41)
Index of Durable Assets
-0.09
0.06
(1.39)
(1.39)
Index of all household assets
-0.05
0.03
(1.48)
(1.66)
Number of households
2,543
3,932
Note: Standard deviations are reported in parentheses. Asset index is based on principal component analysis of reported
household assets, segregated into livestock assets, productive assets, household durable goods, and livestock.
6. IMPACT OF THE TAKAFUL PROGRAM
6.1 Variables and Outcomes
In this chapter, we report the impact estimates of the Takaful program. We report on our
main outcomes of household expenditure, poverty, household composition, labor supply, as-
sets, savings, and debt, child schooling, mother’s and children’s anthropometry, dietary di-
versity, household, mother, and child dietary diversity, antenatal care and infant and young
child feeding (IYCF) practices; maternal overweight and obesity, women’s control over deci-
sion-making and shocks and coping strategies, including COVID-19.
30
In each table, we report three estimates. The first row reports the standard coefficients esti-
mated without any corrections, i.e., the base, standard specification. The second row reports
estimates that have been bias-corrected. Finally, the third row reports estimates that are
bias-corrected and robust. Estimates marked with stars are statistically significant, meaning
that we are confident that there is a non-zero impact for these results. For other results, the
estimate is not measured precisely enough to determine whether there is an impact.
As stated in Chapter 3, we define two separate treatment variables. The first is an indicator
variable equal to one if the household has received a transfer from Takaful in the past two
months. This variable is interpreted as indicating the household is a current beneficiary. Due
to the way that the sample was drawn (i.e., households who applied in 2017), these are
households who were recipients in 2017 and reported being current recipients. The second
variable is an indicator variable equal to one if the household has ever been a Takaful bene-
ficiary, which can be thought of as more of a persistence interpretation of the program. Both
variables are self-reported by the household. The correct variable to use differs depending
on the outcome. For outcomes that would only be affected by the current state of household
liquidity such as consumption, schooling, or diets, the correct treatment variable is whether
the household is currently a Takaful beneficiary. For outcomes that take time to change,
such as assets holdings and anthropometry, the correct treatment variable is whether the
household has ever been a Takaful participant. Consequently, for most outcomes we report
only the treatment variable of currently being a Takaful participant. For other outcomes, we
report both and compare the consistency of the two.
Table 6.1.1 shows the concordance between the two variables. We see that, as expected, all
households who have received transfers in the past two months are also recorded as ever
having received a transfer. For those who are not currently receiving transfers, 28.6% of
households report having received a Takaful transfer in the past. This means that these
households stopped receiving the program at some point.
Table 6.1.1. Current Beneficiary Versus Ever Beneficiary Status
Has never received
Takaful transfers
Has received Takaful trans-
fers at some point
Total
Has not received Takaful trans-
fers in the past 2 months
2,788.0
(71.4%)
1,118.0
(28.6%)
3,906.0
(100.0%)
Has received Takaful transfers
in the past 2 months
0.0
(0.0%)
2,543.0
(100.0%)
2,543.0
(100.0%)
Total
2,788.0
(43.2%)
3,661.0
(56.8%)
6,449.0
(100.0%)
Because we are using a regression discontinuity impact evaluation strategy, all results
should be interpreted as the program impact on a household near the cutoff point.
We first note that there are outcomes directly related to spending of the transfers received,
and other outcomes that are related to the way in which the transfers and other income are
spent. The first type of outcomes are decisions that households make in how to spend trans-
fers. Households can either increase (or decrease) consumption, save the transfers, pay
down debts, or invest in assets (durables or productive assets). We examine each below.
31
The second type of outcomes are the result of the decisions made in spending. For example,
if a household spends more on food, dietary diversity could increase, or antenatal and post-
natal outcomes could improve. If a household decreases debt, perhaps households can af-
ford to keep children in school for longer. We will examine these types of outcomes in the
second part of Chapter 6.
6.2 Household Total Expenditure and Poverty
We do not detect statistically insignificant impacts of Takaful and Karama on household ex-
penditure or on moving households over the poverty line. Table 6.2.1 reports total monthly
consumption, monthly food consumption, and monthly non-food consumption in Adult Equiv-
alent Units (AEU) using the standard definition in the literature.
2
While two coefficients are
negative and statistically significant at the 10% level, we do not interpret this as an overall
negative effect. For the same outcome, the other estimates are not statistically significant,
and the overall effect size is quite small. Additionally, the standard errors are large, indicat-
ing that we do not have sufficient power to detect precise effect sizes.
Comparing these estimates to the first round of the Takaful evaluation, these are in the op-
posite direction. In the first round, consumption increased by 7% overall, and this was pri-
marily driven by an increase in food consumption (an increase of 8%). Recall that this sam-
ple of respondents comprises households who may have graduated from the program but
reapplied and are now receiving transfers even though their PMT score is recorded in our
application data as being below the threshold. Also recall that the difference in the probability
of receiving Takaful at the threshold based on the PMT score is only 20%, reducing our abil-
ity to detect smaller effect sizes. It is also important to note that estimated impacts on total
consumption value (not in AEU) are also consistent with Table 6.2.1; we do not detect any
effects on consumption.
Importantly, estimating no effect on consumption is not an outlier in the literature. There are
not many studies estimating long-term effects on consumption, but the studies that do show
mixed results with a couple of studies showing continuing improvements, and others show-
ing no effects. In a review of cash transfer studies conducted by Bastagli et al (2016), only 2
out of 7 evaluations that studied long term consumption effects showed positive results. In
the Egyptian context, inflation may have played a role. More recently, Haushofer and
Shapiro (2018) show that while a cash transfer program in Kenya had positive short-term ef-
fects on consumption, the effects did not persist in the longer term. Instead, households in-
vested in assets. Investment in assets is common in the literature on cash transfers and will
be discussed further below. We note that current consumption is indicative of where house-
holds might fall in the distribution of basic versus longer-term needs. Once households be-
come less poor, they do not have to put most of their transfer income into consumption but
rather, can put more money towards productive investments, which would increase their fu-
ture income. This is a natural progression that is a very positive potential impact of cash
transfer programs.
2
Adult equivalent units in our analysis are defined as giving a weight of 1 to the first adult, 0.7 to additional adults and, 0.3 to
children under age 18 (Hagenaars, de Vos, and Zaidi, 1994).
32
Table 6.2.1 Impacts of Takaful Program on Household Consumption Expenditure
Treatment variable: household received Takaful transfers in the past two months
(1)
(2)
(3)
Monthly food consump-
tion expenditure
Monthly non-food con-
sumption expenditure
Monthly total (food & non-
food) consumption ex-
penditure
Conventional
-0.0176
-0.0555
-0.0338
(0.0717)
(0.0744)
(0.0608)
Bias-corrected
-0.105
-0.143
*
-0.106
*
(0.0717)
(0.0744)
(0.0608)
Robust
-0.105
-0.143
-0.106
(0.0924)
(0.0974)
(0.0785)
Mean Dep.
Var.
6.791
6.526
7.396
N
6475
6475
6475
Standard errors clustered at the village level. The full sample (N=6475) consists of 2539 households who reported a Takaful
cash transfer in the two months prior to being interviewed, and 3936 households who did not. Model details: Linear Trend on
PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (ur-
ban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. Consumption
aggregates shown are winsorized at the 2nd and 99th percentiles, calculated as Adult Equivalent Units (AEU), and transformed
using Inverse Hyperbolic Sine (IHS). * p < 0.10, ** p < 0.05, *** p < 0.01.
Next, we turn to poverty. Table 6.2.3 shows impact estimates on poverty outcomes using
whether a household received a Takaful transfer in the past two months as our treatment in-
dicator. We examine whether the household is living under USD 1.90 per day and under
USD 3.20 per day (the World Bank definitions of poverty and extreme poverty, respectively).
We also examine whether the household is under the Egyptian 2017/2018 poverty line (de-
fined at the regional level, whereby each region has a different poverty line that is relevant to
that context).
In interpreting our results on poverty, it is necessary to keep in mind first that our results are
sensitive to the exact poverty line chosen and secondly to realize that our sample comprises
a population that is already near the regional poverty line (in fact, that is how the threshold
was selected). All the coefficients are positive, but only one is marginally statistically signifi-
cant. We do not interpret this as strong evidence that there was an increase in poverty, how-
ever. These findings are consistent with the findings on consumption.
33
Table 6.2.2. Impacts of Takaful Program on Household Poverty Measures
Treatment variable: received a transfer in the past two months
(1)
(2)
(3)
Household living
under US$1.90 per
person per day
Household living un-
der US$3.20 per per-
son per day
HH living under
2017/2018 regional
poverty line
Conventional
0.0156
0.0350
0.0866
(0.0340)
(0.0734)
(0.0610)
Bias-corrected
0.0422
0.0833
0.106
*
(0.0340)
(0.0734)
(0.0610)
Robust
0.0422
0.0833
0.106
(0.0468)
(0.0995)
(0.0801)
Mean Dep. Var.
0.0604
0.413
0.811
N
6475
6475
6475
Standard errors in parentheses. Linear Trend on PMT Score, Uniform Kernel, Bandwidth=63, Strata dummies included (Fron-
tier and Upper Egypt (rural) combined to single stratum); excluded category: Metropolitan. (1) and (2) show the World Bank's
International Poverty Line at US$1.90 (2011 PPP) and the Lower Middle Income Class Poverty Line at $3.20 (2011 PPP), re-
spectively. The national-level core CPI was used to express 2011 PPP figures in 2022 EGP values. Similarly, the regional pov-
erty line in (3) uses the latest regional poverty lines available (2017/2018) and was compared to our consumption survey data
collected in 2022 which was deflated to 2017/2018 values.
*
p < 0.10,
**
p < 0.05,
***
p < 0.01.
6.3 Components of Household Consumption
We next examine the impact of the Takaful program on the value of (log) expenditure of spe-
cific food groups. There are 12 food groups (grains, potatoes, vegetables, fruits, meat, eggs,
fish, legumes, dairy, oils and fats, sweets, and other foods) and we also examine impacts on
expenditures incurred on food consumed outside of the household. The treatment indicator
is whether the household is a current Takaful beneficiary.
Table 6.3.1 reports the results of log expenditures per AEU. We find that there are statisti-
cally significant negative effects on four of the food groups: grains, fruits, eggs, and dairy.
Unfortunately, these food groups have high nutritional value, though we note that expendi-
tures on meat, fish, vegetables, and legumes are not affected by the program, and these
items are also high in nutritional content. For fish, the evidence is weakly suggestive of a
positive impact. Encouragingly, there are no effects of the program on the consumption of
sweets and salty snacks or drinks and beverages, which are low in nutritional content. There
is also no impact on expenditures on food consumed outside the household. There is a neg-
ative impact on the amount spent on oils and fats, which could be interpreted as a positive
result, depending on what types of oils and fats are being consumed (processed versus un-
processed).
The magnitude of these differences is large. There is a more than 20% decrease in the con-
sumption of grains, 77% for fruit, 66% for eggs, and 57% for dairy. Since many of the coeffi-
cients are negative (even though not statistically significant), these patterns are consistent
with the effects on food consumption expenditure, with a negative but statistically insignifi-
cant coefficient.
We also examine the impact of the Takaful program on the log expenditure per AEU of spe-
cific non-food groups. Table 6.3.2. presents the results, again using current beneficiary sta-
tus as the treatment variable. We do not see many changes in patterns of non-food con-
sumption as a result of the Takaful program. There is weak and suggestive evidence that ex-
penditures were lower on construction and communications/entertainment, but the results
34
are not consistently statistically significant. Schooling expenditures, while not statistically sig-
nificant, have a large coefficient. We will return to this result in subsequent sections below
but recall the previous result that the number of school-aged children in the household in-
creased. Consequently, the amount spent per child may have decreased. The effect on ex-
penditure on clothes is also large and statistically insignificant. Note again that these are in
AEU so the spending per person would have decreased. Notably, the magnitudes of the co-
efficients on healthcare and medicines are large and are also negative. It is possible that
household members required less healthcare and medicine as a result of being healthier.
We will also return to this result in later sections. Encouragingly, there are no effects on
smoking expenditures as a result of the program.
35
Table 6.3.1. Impacts of Takaful Program on Food Consumption Expenditure by Category
Standard errors clustered at the village level. The full sample (N=6475) consists of 2539 households who reported a Takaful cash transfer in the two months prior to being interviewed, and 3936
households who did not. Model details: Linear Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (urban), Lower Egypt
(rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. Consumption aggregates shown are winsorized at the 2nd and 99th percentiles, calculated as Adult
Equivalent Units (AEU), and transformed using Inverse Hyperbolic Sine (IHS). * p < 0.10, ** p < 0.05, *** p < 0.01.
Treatment variable: household received Takaful transfers in the past two months
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Monthly Expendi-
tures on Grains per
AEU- log values
(screened)
Monthly Expendi-
tures on Potatoes
per AEU - log values
(screened)
Monthly Expenditures
on Vegetables per
AEU - log values
(screened)
Monthly Expendi-
tures on Fruits per
AEU- log values
(screened)
Monthly Expendi-
tures on Meat per
AEU - log values
(screened)
Monthly Expendi-
tures on Eggs per
AEU- log values
(screened)
Monthly Expendi-
tures on Fish per
AEU -log values
(screened)
Conventional
-0.111
-0.0823
0.129
-0.467
*
0.00396
-0.0234
0.355
(0.101)
(0.122)
(0.0925)
(0.255)
(0.308)
(0.245)
(0.245)
Bias-corrected
-0.221
**
-0.191
0.0478
-0.775
***
-0.480
-0.660
***
0.474
*
(0.101)
(0.122)
(0.0925)
(0.255)
(0.308)
(0.245)
(0.245)
Robust
-0.221
*
-0.191
0.0478
-0.775
**
-0.480
-0.660
**
0.474
(0.133)
(0.160)
(0.118)
(0.333)
(0.405)
(0.333)
(0.330)
Mean Dep.
Var.
4.056
2.683
3.941
2.111
3.829
1.762
0.909
N
6475
6475
6475
6475
6475
6475
6475
(8)
(9)
(10)
(11)
(12)
(13)
Monthly Expenditures on
Legumes per AEU - log
values (screened)
Monthly Expenditures
on Dairy per AEU - log
values (screened)
Monthly Expenditures
on Oils and Fats per
AEU- log values
(screened)
Monthly Expenditures on
Sweets/Salty Snacks per
AEU- log values
(screened)
Monthly Expenditures on
Drinks/Beverages per
AEU- log values
(screened)
Monthly Expenditures
Outside the HH per
AEU- log values
(screened)
Conventional
0.355
-0.148
-0.408
*
0.118
0.0139
0.000164
(0.245)
(0.236)
(0.225)
(0.102)
(0.0893)
(0.121)
Bias-corrected
0.474
*
-0.0221
-0.570
**
0.0587
-0.0347
-0.116
(0.245)
(0.236)
(0.225)
(0.102)
(0.0893)
(0.121)
Robust
0.474
-0.0221
-0.570
*
0.0587
-0.0347
-0.116
(0.330)
(0.309)
(0.299)
(0.131)
(0.116)
(0.154)
Mean Dep.
Var.
0.909
2.194
2.676
3.855
3.171
3.081
N
6475
6475
6475
6475
6475
6475
36
Table 6.3.2. Impacts of Takaful Program on Non-food Consumption Expenditure by Category
Treatment variable: household received Takaful transfers in the past two months
(1)
(2)
(3)
(4)
(5)
(6)
Monthly Expenditures
on school (total) per
AEU - log values
(screened)
Monthly Expendi-
tures on Transpor-
tation per AEU - log
values (screened)
Monthly Expendi-
tures on Rent and
Utilities per AEU-
log values
(screened)
Monthly Expendi-
tures on Communi-
cations and Enter-
tainment per AEU -
log values (s
Monthly Expendi-
tures on Personal
Care and Hygiene
Items per AEU - log
values (sc
Monthly Expendi-
tures on Smoking
per AEU - log val-
ues (screened)
Conventional
0.379
0.115
-0.0183
-0.0460
0.0247
0.0284
(0.305)
(0.139)
(0.0755)
(0.184)
(0.109)
(0.336)
Bias-corrected
0.200
0.262
*
-0.0303
-0.332
*
-0.116
-0.284
(0.305)
(0.139)
(0.0755)
(0.184)
(0.109)
(0.336)
Robust
0.200
0.262
-0.0303
-0.332
-0.116
-0.284
(0.400)
(0.191)
(0.0994)
(0.239)
(0.141)
(0.445)
Mean Dep. Var.
4.152
0.346
4.667
2.305
3.779
1.645
N
6475
6475
6475
6475
6475
6475
(7)
(8)
(9)
(10)
(11)
(12)
Monthly Expenditures
on Clothes per AEU -
log values (screened)
Monthly Expendi-
tures on Construc-
tion per AEU - log
values (screened)
Monthly Expendi-
tures on HH Dura-
bles per AEU - log
values (screened)
Monthly Expendi-
tures on Occasions
per AEU - log val-
ues (screened)
Monthly Expendi-
tures on Healthcare
per AEU - log val-
ues (screened)
Monthly Expendi-
tures on Medicine
per AEU - log val-
ues (screened)
Conventional
0.263
-0.0739
*
0.0257
-0.00615
-0.0362
-0.146
(0.224)
(0.0439)
(0.0334)
(0.0475)
(0.222)
(0.160)
Bias-corrected
-0.287
-0.0747
*
0.0269
-0.0451
-0.150
-0.219
(0.224)
(0.0439)
(0.0334)
(0.0475)
(0.222)
(0.160)
Robust
-0.287
-0.0747
0.0269
-0.0451
-0.150
-0.219
(0.307)
(0.0564)
(0.0433)
(0.0643)
(0.292)
(0.210)
Mean Dep. Var.
2.553
0.0554
0.0350
0.0648
1.949
3.231
N
6475
6475
6475
6475
6475
6040
Standard errors clustered at the village level. The full sample (N=6475) consists of 2539 households who reported a Takaful cash transfer in the two months prior to being interviewed, and 3936
households who did not. Model details: Linear Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (urban), Lower Egypt
(rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. Consumption aggregates shown are winsorized at the 2nd and 99th percentiles, calculated as Adult
Equivalent Units (AEU), and transformed using Inverse Hyperbolic Sine (IHS). * p < 0.10, ** p < 0.05, *** p < 0.01.
37
6.3.1 Household Composition
To dig deeper into the consumption effects we estimate, we examine household composition
since the consumption results are in AEU, meaning that the denominator depends on house-
hold size. Consequently, we investigate whether changes in household composition could
also be affecting our estimates. We estimate the impacts of Takaful on the total number of
household members, and split the number of household members into age groups as well
(children 0-5, children 6-11, and adolescents 13-18 years old). The results are presented in
Table 6.2.2. In Panel A our treatment variable is an indicator equal to one if the household
received transfers from Takaful in the past two months, and in Panel B our outcome variable
is an indicator equal to one if the household has ever received a Takaful transfer. The latter
can be interpreted as more of a cumulative effect; the effect even if a household is not cur-
rently receiving transfers. Results are consistent across panels.
In Panel A Column 1, we find that the number of household members has increased by ap-
proximately 0.3 members in Takaful households (0.2 in Panel B). The increase in household
members is primarily driven by an increase in household members aged 6-11 (Column 3).
These results raise the question of whether Takaful households are more likely to have a
child (extensive margin) or are having more children as a result of the program (intensive
margin). To dig into this question further, we restrict the sample to households who already
had two or more children at the time of registration and examine whether, among that sam-
ple, there are increases in the number of children aged 0-3 (Column 5) or 0-5 years old (Col-
umn 6). Takaful beneficiaries who already had two or more children at registration are in-
deed more likely to have more children. However, this result should not be too strongly inter-
preted because households just under the threshold at registration we more likely to have 2
or more children (the discontinuity is significant at the 10 percent level).
The interpretation of these results is not straightforward. Increases in fertility could possibly
be driven by different factors: the desire to have more children coupled with the newfound
ability to afford them, or a mistaken view that further transfers will be received if there are
more children in the household (i.e., households do not know that Takaful pays transfers for
a maximum of two children). We do not have data on the latter, but we do see that the aver-
age desired number of children is 3.52 and the average number of children delivered is 3.68,
which is neither a large difference nor indicative of the transfers enabling households to fulfill
their desire for more children.
38
Table 6.3.3. Household Composition
Panel A: Received Takaful transfers in the past two months
Number of household members
Number of household members (HHs
who had 2 or more children at regis-
tration)
(1)
(2)
(3)
(4)
(5)
(6)
Number of total
household members
0-5 years old
6-11 years old
12-18 years old
0-3 years old
0-5 years old
Conventional
0.314
*
0.178
0.221
*
0.0385
0.165
*
0.194
(0.187)
(0.134)
(0.131)
(0.102)
(0.0996)
(0.134)
Bias-corrected
0.450
**
0.252
*
0.287
**
0.0610
0.244
**
0.378
***
(0.187)
(0.134)
(0.131)
(0.102)
(0.0996)
(0.134)
Robust
0.450
*
0.252
0.287
0.0610
0.244
*
0.378
**
(0.251)
(0.180)
(0.175)
(0.141)
(0.138)
(0.181)
Mean Dep. Var.
4.876
0.811
1.156
0.542
0.844
0.914
N
6475
6475
6475
6475
3895
3895
Panel B: Ever received Takaful transfers
Conventional
0.182
*
0.0996
0.122
*
0.0223
0.203
**
0.239
**
(0.0988)
(0.0710)
(0.0691)
(0.0540)
(0.0909)
(0.112)
Bias-corrected
0.259
***
0.138
*
0.156
**
0.0362
0.229
**
0.288
**
(0.0988)
(0.0710)
(0.0691)
(0.0540)
(0.0909)
(0.112)
Robust
0.259
**
0.138
0.156
*
0.0362
0.229
*
0.288
*
(0.132)
(0.0957)
(0.0929)
(0.0746)
(0.124)
(0.153)
Mean Dep. Var.
4.876
0.811
1.156
0.542
0.844
0.914
N
6449
6449
6449
6449
2770
2275
Standard errors clustered at the village level. The treatment variable in Panel A is an indicator variable equal to one if the household reported a Takaful cash transfer in the two months prior to
being interviewed, and the treatment variable in Panel B is an indicator variable equal to one if the household reported ever having received a transfer. Model details: Linear Trend on PMT
Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier
(rural), excluded category: Metropolitan. * p < 0.10, ** p < 0.05, *** p < 0.01.
39
6.4 Assets, Savings, and Debt
In this section, we examine three other choices households can make with regards to spend-
ing transfer and other income. In addition to consumption, households can either invest in
assets, increase their savings, or reduce their debts. Assets, savings, and debt are cumula-
tive and reflect a history of past investments. Consequently, we use the treatment status in-
dicator equal to one if the household reports that they have ever received a Takaful transfer
in addition to the indicator for being a current beneficiary.
We construct four indices using principal components analysis: 1) an index of total house-
hold assets including durable assets (such as furniture, television), productive assets (those
that can enable increased income generation), and livestock assets (such as cows, chick-
ens) comprising 37 items; 2) household durable assets comprising 20 items, 3) an index of
productive assets comprising 8 items, and 4) an index of livestock assets comprising 9 types
of livestock. Table 6.4.1 displays the impact estimates on these outcomes, using the treat-
ment indicator of receiving a Takaful transfer in the past two months in Panel A, and using
whether the household ever received a Takaful transfer in Panel B. In Panel A we see that
there is a statistically significant impact on total assets and this effect appears to be driven
by investments in productive assets (using the conventional estimates). While the bias-cor-
rected and robust specifications show a negative value for total assets and for durable as-
sets, the magnitudes are extremely small and not economically meaningful so we do not in-
terpret this as a negative effect. For productive assets, the conventional estimate is positive
and statistically significant and the bias-corrected and robust estimators are not significant
but they are also positive and relatively large. In Panel B we see that the estimates are con-
sistent, but the coefficients are smaller. We conclude that households used Takaful transfers
to invest in productive assets, and that there is suggestive evidence that more recent partici-
pation in the program matters more for asset ownership.
40
Table 6.4.1. Impacts of Takaful Program on Asset Indices
Panel A: Household received Takaful transfer in past two months
Asset index for
(1)
(2)
(3)
(4)
Total
assets
Durable
assets
Productive as-
sets
Livestock as-
sets
Conventional
0.517
**
-0.0514
0.467
**
0.349
(0.218)
(0.226)
(0.189)
(0.230)
Bias-corrected
-0.00418
-0.0254
0.167
0.0643
(0.218)
(0.226)
(0.189)
(0.230)
Robust
-0.00418
-0.0254
0.167
0.0643
(0.310)
(0.300)
(0.261)
(0.356)
N
6474
6474
6474
6474
Panel B: Household ever received a Takaful transfer
Conventional
0.278
**
-0.0111
0.249
**
0.191
(0.115)
(0.120)
(0.0991)
(0.122)
Bias-corrected
-0.00703
-0.00594
0.0838
0.0346
(0.115)
(0.120)
(0.0991)
(0.122)
Robust
-0.00703
-0.00594
0.0838
0.0346
(0.163)
(0.160)
(0.137)
(0.189)
N
6449
6449
6449
6449
Standard errors clustered at the village level. The treatment variable in Panel A is an indicator variable equal to one if the
household reported a Takaful cash transfer in the two months prior to being interviewed, and the treatment variable in Panel B
is an indicator variable equal to one if the household reported ever having received a transfer. All indices are constructed based
on the first principal component from principal component analysis (PCA). The index that includes all assets uses dummies for
assets ownership. The same for the durables and productive assets indices. The livestock index is composed using a count of
the livestock owned by the household. Model details: Linear Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The fol-
lowing strata indicators are included as covariates: Lower Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper
Egypt/Frontier (rural), excluded category: Metropolitan. * p < 0.10, ** p < 0.05, *** p < 0.01. Note that the mean dependent vari-
able is not reported as the outcomes are indices constructed to have mean zero.
We next delve further into the specific types of assets that households were investing in –
agricultural assets in Table 6.4.2 and livestock assets in Table 6.4.3. A substantial proportion
of households invested in large and lumpy assets such as tractors, machine and animal
plows, and drip irrigation. These particular investments enable households to make their ag-
ricultural production more efficient and diversify their labor into other income generating ac-
tivities. As with the asset indices, it appears that more recent transfers are associated with
higher investments in these productive assets.
Livestock investments were also lumpy, with larger animals being purchased. Households
decreased the number of small animals they owned (chickens, geese, pigeons, and ducks)
and increased their stock of buffaloes, cattle, goats, and sheep. The purchase of buffaloes
and cattle is consistent with the result that households invested in animal plows. These
larger animals can also result in increased efficiency of agricultural production, further free-
ing up time for other income-generating activities. We will explore labor outcomes in the next
section.
41
Table 6.4.2. Impacts of Takaful Program on Individual Agricultural Assets
Panel A: household received a Takaful transfer in the past two months
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Tractor
Machine pulled
plow or har-
rower
Animal pulled
plow
Mechanical wa-
ter pump
Animal or man-
ual powered
water pump
Drip irrigation
network
Rice winnower
Ox cart or don-
key cart
Conventional
0.0107
**
0.00887
*
0.0149
*
0.00941
0.000739
0.00609
*
-0.000397
0.0139
(0.00452)
(0.00526)
(0.00867)
(0.00877)
(0.00350)
(0.00359)
(0.000402)
(0.0113)
Bias-corrected
0.0114
**
0.00969
*
0.00728
-0.00398
0.000243
0.00840
**
-0.00167
***
-0.0118
(0.00452)
(0.00526)
(0.00867)
(0.00877)
(0.00350)
(0.00359)
(0.000402)
(0.0113)
Robust
0.0114
*
0.00969
0.00728
-0.00398
0.000243
0.00840
-0.00167
-0.0118
(0.00582)
(0.00731)
(0.0121)
(0.0122)
(0.00482)
(0.00547)
(0.00166)
(0.0153)
Mean Dep. Var.
0.000927
0.00124
0.00402
0.00386
0.00124
0.000463
0.000154
0.00834
N
6474
6474
6474
6474
6474
6474
6474
6474
Panel B: household ever received a Takaful transfer
Conventional
0.00570
**
0.00474
*
0.00794
*
0.00503
0.000402
0.00325
*
-0.000210
0.00745
(0.00238)
(0.00278)
(0.00458)
(0.00465)
(0.00186)
(0.00191)
(0.000212)
(0.00596)
Bias-corrected
0.00592
**
0.00505
*
0.00371
-0.00222
0.000128
0.00439
**
-0.000885
***
-0.00639
(0.00238)
(0.00278)
(0.00458)
(0.00465)
(0.00186)
(0.00191)
(0.000212)
(0.00596)
Robust
0.00592
*
0.00505
0.00371
-0.00222
0.000128
0.00439
-0.000885
-0.00639
(0.00308)
(0.00387)
(0.00641)
(0.00650)
(0.00257)
(0.00291)
(0.000887)
(0.00810)
Mean Dep. Var.
0.000927
0.00124
0.00402
0.00386
0.00124
0.000463
0.000154
0.00834
N
6449
6449
6449
6449
6449
6449
6449
6449
Standard errors clustered at the village level. The treatment variable in Panel A is an indicator variable equal to one if the household reported a Takaful cash transfer in the two months prior to being
interviewed, and the treatment variable in Panel B is an indicator variable equal to one if the household reported ever having received a transfer. Model details: Linear Trend on PMT Score; Uni-
form Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded
category: Metropolitan. * p < 0.10, ** p < 0.05, *** p < 0.01.
42
Table 6.4.3. Impacts of Takaful Program on Livestock
Panel A: household received a Takaful transfer in the past two months
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Buffaloes/ Cat-
tle
Goats/Sheep
Donkeys/ mules
Horses
Chicken/geese/
pigeons/ducks
Turkeys
Rabbits
Conventional
0.0823
*
0.152
**
0.0252
0.00136
-0.436
0.0133
0.00741
(0.0441)
(0.0740)
(0.0204)
(0.00136)
(0.955)
(0.0155)
(0.0310)
Bias-corrected
0.0163
0.0537
-0.00855
0.000959
-1.956
**
0.0233
0.0121
(0.0441)
(0.0740)
(0.0204)
(0.00136)
(0.955)
(0.0155)
(0.0310)
Robust
0.0163
0.0537
-0.00855
0.000959
-1.956
0.0233
0.0121
(0.0618)
(0.0824)
(0.0307)
(0.000912)
(1.336)
(0.0260)
(0.0479)
Mean Dep. Var.
0.0513
0.0539
0.0188
0.000154
2.599
0.00154
0.00618
N
6474
6474
6474
6474
6474
6474
6474
Panel B: household ever received a Takaful transfer
Conventional
0.0442
*
0.0795
**
0.0135
0.000723
-0.154
0.00707
0.00396
(0.0233)
(0.0387)
(0.0108)
(0.000721)
(0.503)
(0.00823)
(0.0165)
Bias-corrected
0.00807
0.0264
-0.00471
0.000494
-0.976
*
0.0122
0.00640
(0.0233)
(0.0387)
(0.0108)
(0.000721)
(0.503)
(0.00823)
(0.0165)
Robust
0.00807
0.0264
-0.00471
0.000494
-0.976
0.0122
0.00640
(0.0327)
(0.0430)
(0.0163)
(0.000485)
(0.706)
(0.0138)
(0.0255)
Mean Dep. Var.
0.0513
0.0539
0.0188
0.000154
2.599
0.00154
0.00618
N
6449
6449
6449
6449
6449
6449
6449
Standard errors clustered at the village level. The treatment variable in Panel A is an indicator variable equal to one if the household reported a Takaful cash transfer in the two months prior to being
interviewed, and the treatment variable in Panel B is an indicator variable equal to one if the household reported ever having received a transfer. Model details: Linear Trend on PMT Score; Uniform
Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category:
Metropolitan. * p < 0.10, ** p < 0.05, *** p < 0.01.
43
The literature has shown strong precedent for cash transfer programs increasing invest-
ments in assets, and productive assets in particular. Bastagli et al (2016) find that out of 8
impact evaluations of cash transfer programs, 3 had positive and statistically significant ef-
fects on agricultural productive asset accumulation. The rest either had positive but statisti-
cally insignificant effects, or null effects. Additionally, 6 of the studies also show increases in
agricultural input adoption, which are also considered productive investments. Finally, 12 out
of 17 studies showed that cash transfers increased livestock holdings. Accordingly, there is
substantial evidence that cash transfers are used to build up productive assets as invest-
ments in the future.
One consideration in this context is whether assets were received by other programs rather
than the Takaful program necessarily leading to asset investments. There are many pro-
grams that provide productive assets to the poor. For our results to be biased, it would need
to be the case that the asset transfers are also targeted to Takaful participants, or at least
with similar characteristics to beneficiaries whose PMT score is below 4500. To check
whether this might be a concern, we run the same specification as above but include a con-
trol variable for whether the household received any other source of income from: family
members or friends, religiously motivated support, divorce allowance, cash transfers from
other organizations, a private insurance fund, or rental earnings. Unfortunately, we do not
have specific information on whether the household received asset transfers, so this variable
serves as a proxy. When we include the control variable, the results barely change.
Households also reduced the amount of debt they carried. Table 6.4.4 shows that there are
no differences in rates of saving among beneficiary households (column 1). In column 2, the
outcome is a dummy variable equal to one if the respondent says that the amount they had
in savings increased since July 2020. The coefficients are positive but not statistically signifi-
cant. We do see that beneficiary households have lower levels of debt and the effect is sta-
tistically significant and meaningful in size. There is no evidence that beneficiary households
had different levels of debt with regards to instalment payments (column 4).
44
Table 6.4.4. Impacts of Takaful Program on Household Savings and Debt
Treatment variable: household received a Takaful transfer in the past two months
(1)
(2)
(3)
(4)
(5)
Amount of
savings
(EGP)
Amount of sav-
ings increased
or remained the
same between
July 2020 and
Feb 2022
Total amount of
debt currently
owed to anyone -
formal/informal
lenders (EGP),
IHS
Amount owed
of debt owed
for purchases
on credit
(EGP)
Total
amount of
debt owed
to informal
lenders or
owed for
purchases
on credit
Conventional
-209.6
0.340
-1.151
-332.4
-4326.0
*
(157.6)
(0.701)
(0.716)
(748.4)
(2359.7)
Bias-cor-
rected
-255.0
1.030
-1.835
**
-218.7
-5479.2
**
(157.6)
(0.701)
(0.716)
(748.4)
(2359.7)
Robust
-255.0
1.030
-1.835
*
-218.7
-5479.2
(241.0)
(0.934)
(0.965)
(966.5)
(3342.1)
Mean Dep.
Var.
53.53
0.909
3.219
827.7
4430.5
N
6475
88
6475
6475
6475
Treatment variable: household ever received a Takaful transfer
(1)
(2)
(3)
(4)
(5)
Amount of
savings
(EGP)
Amount of sav-
ings increased
or remained the
same between
July 2020 and
Feb 2022
Total amount of
debt currently
owed to anyone -
formal/informal
lenders (EGP),
IHS
Amount owed
of debt owed
for purchases
on credit
(EGP)
Total
amount of
debt owed
to informal
lenders or
owed for
purchases
on credit
Conventional
-111.4
0.122
-0.640
*
-174.1
-2315.4
*
(83.83)
(0.228)
(0.379)
(398.1)
(1254.0)
Bias-cor-
rected
-132.4
0.345
-0.977
**
-101.4
-2855.7
**
(83.83)
(0.228)
(0.379)
(398.1)
(1254.0)
Robust
-132.4
0.345
-0.977
*
-101.4
-2855.7
(127.9)
(0.322)
(0.511)
(513.9)
(1770.0)
Mean Dep.
Var.
53.53
0.909
3.219
827.7
4430.5
N
6449
88
6449
6449
6449
Standard errors clustered at the village level. The treatment variable in Panel A is an indicator variable equal to one if the
household reported a Takaful cash transfer in the two months prior to being interviewed, and the treatment variable in Panel B
is an indicator variable equal to one if the household reported ever having received a transfer. Model details: Linear Trend on
PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (ur-
ban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. * p < 0.10, ** p <
0.05, *** p < 0.01.
6.5 Household Labor
In this latter part of this chapter, we examine outcomes that are the product of the decisions
made as to how to spend transfer and other income. We begin with labor supply. Depending
on how households are choosing to use their transfers, labor supply patterns may be differ-
ent.
In Table 6.4.1 we report impact estimates on the likelihood that a household member aged 5
or older engages primarily in unpaid work, participates in agricultural activities for household
45
consumption, works in the formal sector, and works in the informal sector. The share of
household members who participate primarily in unpaid work is already very low at 0.4% and
we see no impacts on this outcome. Only 4% of household members engage in agricultural
activities for household consumption and there are also no impacts of the program on this
outcome. While there is a negative and statistically significant coefficient on the likelihood
that beneficiaries are engaged in formal labor, the evidence is not strong. There is, however,
a higher likelihood that beneficiary households are engaged in informal labor. However, we
do see a difference in formal and informal labor participation, with a movement away from
the formal sector to the informal sector. Column 5 shows that the average monthly wages of
all household members slightly decreased as well.
Table 6.5.1. Impacts of Takaful Program on Employment Outcomes
Treatment variable: household received a Takaful transfer in the past two months
(1)
(2)
(3)
(4)
(5)
Household
member par-
ticipates in un-
paid work
Household member
participates in agri-
cultural activities for
own household con-
sumption
Household
member par-
ticipates in
formal work
Household
member partic-
ipates in infor-
mal work
Total monthly
wage income
(EGP) all HH
members
Conventional
0.00627
0.0126
-0.0396
0.0246
-366.5
*
(0.00650)
(0.0170)
(0.0272)
(0.0216)
(191.2)
Bias-corrected
0.00840
-0.0119
-0.0570
**
0.0546
**
-225.4
(0.00650)
(0.0170)
(0.0272)
(0.0216)
(191.2)
Robust
0.00840
-0.0119
-0.0570
0.0546
*
-225.4
(0.00846)
(0.0221)
(0.0384)
(0.0297)
(255.1)
Mean Dep. Var.
0.00410
0.0398
0.0408
0.973
1848.1
N
26096
26096
7213
7213
6475
Standard errors clustered at the village level. Models run at the household member-level. Details: Linear Trend on PMT Score;
Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (urban), Lower
Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. Samples in (1) and (2) in-
clude all household members over age 5. Samples in (3) and (4) include household members who participate in economic ac-
tivities. The sample in column (5) includes individuals who report earning a wage, either from formal or informal employment. *
p < 0.10, ** p < 0.05, *** p < 0.01.
6.6 Child schooling and Child Labour
In this section, we examine the impacts of the Takaful program on children’s schooling and
labour. Depending on the amount of liquid cash and the extent to which children are needed
to help at the home or family business, school enrolment and attendance could be affected.
Table 6.6.1 first presents results on children’s enrollment in school by level (nursery, pri-
mary, preparatory, secondary, and university or higher).
3
In column 1 the sample is all chil-
dren of school going age, and in columns 2-6 the samples are children who are of the age
range for that level. There are no impacts of Takaful on enrollment in school of 6-18 year
olds. However, 96% of 6-18 year olds are enrolled in some level of schooling so we do not
expect to see much of an impact on such a high base. While very few children attend
nursery, 87% and 84% of children attend primary and preparatory school, respectively. For
both levels, we see positive and significant impacts on enrollment. The probability of a child
of primary school age being enrolled in primary school increased by about 6-8 percentage
points and the probability of a child of preparatory school age being enrolled in preparatory
school increased by 2-3 percentage points. These are economically meaningful effects –
3
Primary school comprises grades 1-6, preparatory comprises grades 7-9, secondary comprises either grades 10-12 (general
secondary) or grades 10-14 (technical secondary).
46
they suggest that almost all primary school aged children in the beneficiary group were at-
tending primary school and most were attending preparatory school. There are no significant
effects on enrollment at the secondary school level, and there are actually decreases in en-
rollment at the university level or above. However, this decrease can be explained by the
changing demographics of the household. With younger households in the beneficiary sam-
ple, there are fewer university-age students residing in these households.
With regards to attendance, we see that across all levels of schooling there is no impact of
the program. However, there is suggestive evidence that secondary school students attend
school more regularly as a result of the program. In particular, it is girls who are driving the
result (see column 10). This is an encouraging result as secondary school participation for
girls is a substantial driver of income, empowerment, and their children’s outcomes (Glewwe
2002; Hanushek and Zhang 2009).
47
Table 6.6.1. Impacts of Takaful Program on Enrolment and Attendance
Treatment indicator: household received Takaful transfers in the past two months
Currently enrolled in:
(1)
(2)
(3)
(4)
(5)
(6)
Any level
Nursery
Primary
Preparatory
Secondary
University or
higher
Conventional
0.0296
-0.0191
0.0870
**
0.210
***
-0.0156
-0.0647
(0.0218)
(0.0306)
(0.0439)
(0.0801)
(0.136)
(0.0701)
Bias-corrected
0.0179
-0.0374
0.0615
0.315
***
0.0391
-0.148
**
(0.0218)
(0.0306)
(0.0439)
(0.0801)
(0.136)
(0.0701)
Robust
0.0179
-0.0374
0.0615
0.315
***
0.0391
-0.148
(0.0286)
(0.0381)
(0.0576)
(0.112)
(0.186)
(0.0964)
Mean Dep. Var.
0.969
0.0257
0.873
0.840
0.647
0.0403
N
11314
4700
8354
2699
1936
818
Currently attending secondary:
(7)
(8)
(9)
(10)
Regularly attending
(all levels)
All
Boys
Girls
Conventional
0.0337
0.161
0.148
0.101
(0.0506)
(0.127)
(0.151)
(0.177)
Bias-corrected
0.0471
0.266
**
0.152
0.338
*
(0.0506)
(0.127)
(0.151)
(0.177)
Robust
0.0471
0.266
0.152
0.338
(0.0696)
(0.172)
(0.207)
(0.231)
Mean Dep. Var.
0.882
0.838
0.802
0.886
N
10967
1253
717
536
Standard errors clustered at the village level. Models run at the household member-level. Details: Linear Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata
indicators are included as covariates: Lower Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. Sample in (1) includes
all 6-18 year-old children. Samples in (2)-(7) include children who fall within the age range of that level of education. Samples in (8)-(10) include children currently attending secondary
school. * p < 0.10, ** p < 0.05, *** p < 0.01.
48
6.7 Household, mother, and child dietary diversity
In this section, we examine household, mother, and child dietary diversity. These outcomes
can be affected by spending decisions on food, non-food, and other expenditures and in-
vestments. We first look at the entire household, and then dietary diversity of the mother and
children.
Table 6.7.1 shows that most households consumed between 9 and 11 food groups out of
12, which is reasonably high. In that respect, we may not actually expect to see much more
improvement.
Table 6.7.1. Number of Food Groups Consumed by Households
<8
8
9
10
11
12
Total
Number of households
329
670
1,365
1,966
1,814
331
6,475
Percentage of households
5.1
10.3
21.1
30.4
28.0
5.1
100.0
In Table 6.7.2 we present impact estimates on the dietary diversity of the household, of
mothers in the household, as well as the dietary diversity a randomly selected child aged 6-
23 (up to 2 years old) months and one aged 24-59 months (between 2 and 5 years old). The
outcome variables are sums of the number of different food groups consumed in the past 7
days. For households there are 12 groups,
4
for mothers, there are 9 groups,
5
for children
aged 6-23 months there are 7 food groups,
6
and for children aged 24-59 months there are 8
food groups.
7
In general, dietary diversity at the household level is reasonable – on average, 10 groups out
of 12. However, for mothers and young children, it is relatively low. Out of 9 food groups,
mothers consume an average of 4, children 6-23 months old consume an average of 3.3 out
of 7 groups, and children aged 24-59 months consume an average of 5 of 8 groups.
The impacts on household dietary diversity are negative, but not significant except when us-
ing the robust specifications. Also, while there is not strong evidence of statistical signifi-
cance, there is a negative coefficient for dietary diversity of children aged 24-59 with a large
effect size at 0.8 food groups.
4
The 12 food groups are: cereals, potatoes and tubers, vegetables, fruits, meat, eggs, fish, legumes nuts and seeds, dairy, oils
and fats, sweets, and spices condiments and beverages.
5
The 9 food groups are: starchy foods, dark green leafy vegetables, vitamin A rich fruits, other fruits and vegetables, organ
meat, meat and fish, eggs, legumes nuts and seeds, and milk and milk products.
6
The 7 food groups are: grains, roots, and/or tubers, legumes, nuts and/or seeds, milk and/or milk products, flesh foods, eggs,
vitamin A rich fruits and/or vegetables, and other fruits and/or vegetables.
7
The 8 food groups are: grains, roots, and/or tubers, legumes, nuts and/or seeds, milk and/or milk products, flesh foods, eggs,
vitamin A rich fruits and/or vegetables, other fruits and/or vegetables, and foods cooked in oil/fat.
49
Table 6.7.2 Impacts of Takaful Program on Dietary Diversity Outcomes
Treatment variable: household received Takaful transfers in the past two months
(1)
(2)
(3)
(4)
Household die-
tary diversity
score (0-12)
Mother's dietary
diversity score (0-
9)
Children's dietary di-
versity score (0-7), 6-
23 months old
Children's dietary di-
versity score (0-8), 24-
59 months old
Conventional
-0.287
-0.267
0.684
-0.273
(0.226)
(0.293)
(1.039)
(0.370)
Bias-corrected
-0.707
***
-0.419
0.133
-0.811
**
(0.226)
(0.293)
(1.039)
(0.370)
Robust
-0.707
**
-0.419
0.133
-0.811
(0.294)
(0.401)
(1.436)
(0.494)
Mean Dep. Var.
9.772
3.842
3.344
4.909
N
6475
3340
483
2468
Standard errors clustered at the village level. The full sample (N=6475) consists of 2539 households who reported a Takaful
cash transfer in the two months prior to being interviewed, and 3936 households who did not. Sample in (1) is all households,
sample in (2) includes adult women who have children under 5 years (60 months) of age, and samples in (3) and (4) are chil-
dren of that age range. Model details: Linear Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata
indicators are included as covariates: Lower Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier
(rural), excluded category: Metropolitan. * p < 0.10, ** p < 0.05, *** p < 0.01.
6.8 Mother and child anthropometry, overweight and wasting, and
morbidity and treatment
In this section, we examine anthropometry measures for mothers and children health out-
comes, which are influenced by dietary diversity. We first look at mother’s body mass index
(BMI), then at child anthropometry, including overweight and wasting. Since anthropometric
outcomes reflect a series of prior decisions and investments, we again present results using
both treatment variables: whether the household received a Takaful transfer in the past two
months and whether the household ever received a Takaful transfer.
We first examine anthropometry outcomes of mothers, estimating impacts the mother’s body
mass index (BMI), as well as indicators for whether the mother is considered overweight
(BMI between 25 and 30) or obese (BMI above 30).
8
Table 6.8.1 displays the results. There
are no statistically significant impacts on women’s BMI, nor on the likelihood of a mother be-
ing overweight or obese with either treatment variable. It is reassuring that women are not
becoming more overweight as a result of the program. However, we do note that the aver-
age BMI for all mothers in the sample is 28.96, which means that the average mother is in-
deed overweight and is bordering on obese. This finding is important for integrated program-
ming within Takaful and for any complementary programming on diets and weight.
8
Body Mass Index (BMI) is calculated as a person’s weight in kilograms (or pounds) divided by the square of height in meters
(or feet).
50
Table 6.8.1. Impacts of Takaful Program on Mother Anthropometrics Outcomes
Panel A: household received a Takaful transfer in the past two months
(1)
(2)
(3)
Mother's Body
Mass Index (BMI)
Mother is overweight
(BMI between 25-30)
Mother is obese (BMI
above 30)
Conventional
0.160
-0.109
0.0926
(0.736)
(0.0788)
(0.0774)
Bias-corrected
-0.681
-0.0516
-0.0119
(0.736)
(0.0788)
(0.0774)
Robust
-0.681
-0.0516
-0.0119
(0.978)
(0.105)
(0.103)
Mean Dep. Var.
28.96
0.486
0.350
N
5711
5711
5711
Panel B: household ever received a Takaful transfer
(1)
(2)
(3)
Mother's Body
Mass Index (BMI)
Mother is overweight
(BMI between 25-30)
Mother is obese (BMI
above 30)
Conventional
0.0454
-0.0548
0.0459
(0.390)
(0.0416)
(0.0410)
Bias-corrected
-0.397
-0.0219
-0.0101
(0.390)
(0.0416)
(0.0410)
Robust
-0.397
-0.0219
-0.0101
(0.518)
(0.0553)
(0.0544)
Mean Dep. Var.
28.96
0.486
0.350
N
5690
5690
5690
Standard errors clustered at the village level. The treatment variable in Panel A is an indicator variable equal to one if the
household reported a Takaful cash transfer in the two months prior to being interviewed, and the treatment variable in Panel B
is an indicator variable equal to one if the household reported ever having received a transfer. Model details: Linear Trend on
PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (ur-
ban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. * p < 0.10, ** p <
0.05, *** p < 0.01.
In Table 6.8.2 we look at children’s anthropometry for children aged 6-23 months old 24-59
months old. We examine BMI, whether the child is overweight, and the height-for-age Z-
score for the two randomly selected children in the household. There are no program im-
pacts on any of these outcomes. Children are much less likely to be overweight than moth-
ers.
Next, we examine stunting and wasting, in Table 6.8.3. Stunting is an indicator equal to one
if the height-for-age Z-score is two standard deviations below the population mean, defined
by the WHO. It is a measure of whether a child is considered “short”. Wasting an indicator
equal to one if the weight-for-height Z-score is two standard deviations below the population
mean and is a measure of whether a child is considered too “thin”. Both height and weight
are indicators of whether the child receives proper nutrition in turn determines many out-
comes like cognition and schooling performance, and recovery from illness. There are no
program impacts on any of the outcomes when the treatment variable is that the household
is currently a Takaful beneficiary. However, when we look at whether the household has
ever been a beneficiary, we see a reduction in wasting for children aged 6-23 months. The
effects are also large in magnitude and are economically meaningful. This result is an en-
couraging finding of the program – sustained investments in people can lead to meaningful
longer-term benefits.
51
Table 6.8.2. Impacts of Takaful Program on Child Anthropometrics Outcomes
Panel A: household received a Takaful transfer in the past two months
(1)
(2)
(3)
(4)
(5)
(6)
BMI
(0-23 months)
BMI
(24-59 months)
Overweight (0-
23 months)
Overweight (24-
59 months)
HAZ score
(0-23 months)
HAZ score
(24-59 months)
Conventional
0.341
-0.567
-0.205
-0.0337
-0.586
-0.104
(0.986)
(0.479)
(0.180)
(0.0731)
(0.972)
(0.360)
Bias-corrected
-0.670
-0.806
*
-0.289
-0.0397
-0.399
-0.0430
(0.986)
(0.479)
(0.180)
(0.0731)
(0.972)
(0.360)
Robust
-0.670
-0.806
-0.289
-0.0397
-0.399
-0.0430
(1.397)
(0.611)
(0.256)
(0.0985)
(1.340)
(0.475)
Mean Dep. Var.
0.353
0.358
0.125
0.0672
-0.108
-0.639
N
904
2408
905
2412
886
2392
Panel B: household ever received a Takaful transfer
(1)
(2)
(3)
(4)
(5)
(6)
BMI
(0-23 months)
BMI
(24-59 months)
Overweight (0-
23 months)
Overweight (24-
59 months)
HAZ score
(0-23 months)
HAZ score
(24-59 months)
Conventional
0.164
-0.266
-0.0965
-0.0162
-0.276
-0.0620
(0.443)
(0.233)
(0.0802)
(0.0360)
(0.438)
(0.177)
Bias-corrected
-0.257
-0.396
*
-0.146
*
-0.0196
-0.213
-0.0376
(0.443)
(0.233)
(0.0802)
(0.0360)
(0.438)
(0.177)
Robust
-0.257
-0.396
-0.146
-0.0196
-0.213
-0.0376
(0.629)
(0.297)
(0.115)
(0.0485)
(0.601)
(0.233)
Mean Dep. Var.
0.353
0.358
0.125
0.0672
-0.108
-0.639
N
900
2399
901
2403
882
2383
Standard errors clustered at the village level. The treatment variable in Panel A is an indicator variable equal to one if the household reported a Takaful cash transfer in the two months
prior to being interviewed, and the treatment variable in Panel B is an indicator variable equal to one if the household reported ever having received a transfer. Models run at the household
member-level. Details: Linear Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (urban), Lower Egypt
(rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. * p < 0.10, ** p < 0.05, *** p < 0.01.
52
Table 6.8.3. Impacts of Takaful Program on Child Stunting and Wasting
Panel A: Household received Takaful transfers in the past two months
(1)
(2)
(3)
(4)
Child Stunted
(6-23 months)
Child Stunted (24-
59 months)
Child Wasted (6-
23 months)
Child Wasted
(24-59 months)
Conventional
-0.0411
-0.0440
-0.225
0.00879
(0.190)
(0.0838)
(0.145)
(0.0532)
Bias-corrected
-0.180
-0.0673
-0.236
-0.0166
(0.190)
(0.0838)
(0.145)
(0.0532)
Robust
-0.180
-0.0673
-0.236
-0.0166
(0.257)
(0.112)
(0.202)
(0.0696)
Mean Dep. Var.
0.134
0.105
0.0673
0.0374
N
886
2392
892
2377
Panel B: Household ever received Takaful transfers
Conventional
-0.0212
-0.0208
-0.0996
*
0.00176
(0.0847)
(0.0411)
(0.0590)
(0.0258)
Bias-corrected
-0.0869
-0.0325
-0.116
**
-0.0108
(0.0847)
(0.0411)
(0.0590)
(0.0258)
Robust
-0.0869
-0.0325
-0.116
-0.0108
(0.115)
(0.0551)
(0.0822)
(0.0341)
Mean Dep. Var.
0.134
0.105
0.0673
0.0374
N
882
2383
888
2368
Standard errors clustered at the village level. The treatment variable in Panel A is an indicator variable equal to one if the
household reported a Takaful cash transfer in the two months prior to being interviewed, and the treatment variable in Panel B
is an indicator variable equal to one if the household reported ever having received a transfer. Models run at the household
member-level. Details: Linear Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are in-
cluded as covariates: Lower Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded
category: Metropolitan. * p < 0.10, ** p < 0.05, *** p < 0.01.
6.9 Antenatal and postnatal care and infant and young child feeding
(IYCF) practices
This section explores outcomes related to antenatal and postnatal care and infant and young
child nutrition (IYCN) practices. These outcomes are also influenced by spending decisions
and cash liquidity since they have cost implications. We first explore whether the program
caused more mothers to get antenatal and postnatal care during their last pregnancy. The
sample includes adult females with children born between July 2020 and February 2022. Ta-
ble 6.9.1 reports the results.
There are no impacts of the program on whether the mother of the index child received ante-
natal care (ANC) during her last pregnancy, the number of ANC sessions received, whether
she took iron supplements, gave birth in a safe place, or received postnatal care within two
days of giving birth. Many of the coefficients on receipt of antenatal and postnatal care are
positive but none are statistically significant. We interpret this as suggestive evidence of a
positive impact, but we are not able to reject that the coefficients are zero.
53
Table 6.9.1. Impacts of Takaful Program on Antenatal Care (ANC) Outcomes
Treatment variable: household received Takaful transfers in the past two months
(1)
(2)
(3)
(4)
(5)
Mother received
ANC during last
pregnancy
Number of ANC
sessions re-
ceived during
last pregnancy
Took iron sup-
plements during
last pregnancy
Gave birth in a
save place
(public or pri-
vate facility)
Received post-
natal care
within 2 days of
giving birth
Conventional
0.184
2.123
-0.0145
0.0425
0.0461
(0.136)
(1.873)
(0.188)
(0.137)
(0.206)
Bias-corrected
0.157
1.968
-0.175
-0.0606
0.0192
(0.136)
(1.873)
(0.188)
(0.137)
(0.206)
Robust
0.157
1.968
-0.175
-0.0606
0.0192
(0.184)
(2.632)
(0.273)
(0.190)
(0.300)
Mean Dep. Var.
0.899
6.640
0.766
0.900
0.654
N
829
745
820
829
829
Standard errors clustered at the village level. This sample includes adult women with children born between July 2020 and Feb-
ruary 2022. Model details: Linear Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are
included as covariates: Lower Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded
category: Metropolitan. * p < 0.10, ** p < 0.05, *** p < 0.01
Next, we examine IYCN practices, specifically, whether someone helped put the baby to
breast just after birth, whether anything but breast milk was given to the baby within the first
three days, whether the child was given colostrum, and the age at which the mother stopped
breastfeeding the child. The sample includes women with a baby less than two years old.
Table 6.9.2 contains the results.
Table 6.9.2. Impacts of Takaful Program on Infant and Young Child Feeding (IYCF)
Practices
Treatment variable: household received Takaful transfers in the past two months
(1)
(2)
(3)
(4)
Someone helped
mother put the baby to
the breast after birth
Baby received only
breast milk during
the first 3 days
Mother gave
baby colostrum
Age at which
mother stopped
breastfeeding (in
months)
Conventional
0.193
-0.272
0.0427
-1.983
(0.196)
(0.181)
(0.102)
(1.378)
Bias-corrected
0.112
-0.0987
0.158
-1.214
(0.196)
(0.181)
(0.102)
(1.378)
Robust
0.112
-0.0987
0.158
-1.214
(0.281)
(0.254)
(0.146)
(2.032)
Mean Dep. Var.
0.466
0.718
0.937
4.420
N
1205
1204
1204
1099
Standard errors clustered at the village level. This sample includes adult women with children <2 years. Model details: Linear
Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower
Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. * p <
0.10, ** p < 0.05, *** p < 0.01.
6.10 Women’s decision-making and gender norms
Women’s control over decision-making within the household was measured using a series of
9 questions asking women to describe their ability to influence decisions on a scale of 1-4,
corresponding to ability to influence decisions “to a great extent” (4), “a medium extent” (3),
54
“a small extent” (2), or “not at all” (1). The domains were: wage employment, major and mi-
nor household expenditures, how to use cash transfers, what food can be cooked, getting
medical treatment and buying clothes for herself, taking a child to the doctor, and children’s
schooling. Table 6.10.1 reports the results of indicator variables equal to one if the woman
can influence decisions “to a great extent” on these domains in columns 1-9. We see that
there is no impact of the program on any of these outcomes. Some coefficients are positive
and some are negative, so there are no clear patterns in the results. While one coefficient is
statistically significant, give the number of tests for significance being conducted, we should
not interpret this as a negative effect. The main conclusion is that the program did not, on
average, have any impact on women’s ability to make household decisions.
In addition, an index for women’s control over decision-making was constructed using princi-
pal component analysis (PCA). Column 10 in Table 6.10.1 reports this result and we see that
there is no impact of the program on the index either, which is not surprising given that there
were no impacts on any of its components.
In the first round Takaful evaluation, we found negative and statistically significant impacts of
the program on women’s decision-making, both in the overall index and in the domains of
using government subsidies, taking a child to the doctor, and children’s schooling. These im-
pacts are further explored in detail in Elenbaby et al (2021), where four findings emerge.
First, the decreases in women’s decision-making are concentrated among women who did
not have any formal education. Second, men began to participate more in household deci-
sions; decision-making became more joint. Third, women who were working out of necessity
stopped working as a result of the transfers. Fourth, qualitative evidence found that the pro-
gram was generally well-perceived and that the pattern of results can be explained partly by
a relaxation of the household’s budget constraint (there were more decisions to make about
what to spend money on) and partly by the context. In Egypt, the relevant contextual factor is
that men are perceived and expected to be primary decision-makers in the household, so
the joint decision-making resulting from the transfers is not perceived poorly.
55
Table 6.10.1 Impacts of Takaful Program on Women's Decision-Making, All Women
Treatment variable: household received a Takaful transfer in the past two months
Woman can make own decisions to a great extent on:
(1)
(2)
(3)
(4)
(5)
Wage employ-
ment
Major house-
hold expendi-
tures
Minor house-
hold expendi-
tures
How to use
cash transfers
What food can
be cooked
every day
Conventional
0.00336
0.100
0.00547
0.0958
0.0710
(0.0607)
(0.0654)
(0.0744)
(0.0780)
(0.0621)
Bias-corrected
0.0342
0.0901
-0.102
-0.0271
0.0477
(0.0607)
(0.0654)
(0.0744)
(0.0780)
(0.0621)
Robust
0.0342
0.0901
-0.102
-0.0271
0.0477
(0.0819)
(0.0885)
(0.0983)
(0.105)
(0.0817)
Mean Dep. Var.
0.218
0.286
0.605
0.516
0.742
N
6473
6473
6473
6473
6473
Woman can make own decisions to a great extent on:
(6)
(7)
(8)
(9)
(10)
Getting medical
treatment for her-
self
Buying
clothes for
herself
Taking a
child to a
doctor
Children's school-
ing
Women’s de-
cision- making
index
Conventional
0.0905
0.0631
-0.00451
-0.00961
0.0446
(0.0714)
(0.0712)
(0.0725)
(0.0752)
(0.163)
Bias-corrected
0.0444
0.0886
0.0332
-0.170
**
-0.135
(0.0714)
(0.0712)
(0.0725)
(0.0752)
(0.163)
Robust
0.0444
0.0886
0.0332
-0.170
-0.135
(0.101)
(0.0958)
(0.0977)
(0.104)
(0.215)
Mean Dep. Var.
0.507
0.450
0.572
0.478
-0.0866
N
6473
6473
6473
6055
6055
Standard errors clustered at the village level. The full sample (N=6475) consists of 2539 households who reported a Takaful
cash transfer in the two months prior to being interviewed, and 3936 households who did not. Model details: Linear Trend on
PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (ur-
ban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. * p < 0.10, ** p <
0.05, *** p < 0.01.
Consequently, we next split the sample into women who have some formal education and
those who have no formal education to examine whether the pattern uncovered in Elenbaby
et al (2021) still holds. There is no evidence that the pattern was maintained – there are no
effects of the program on the decision-making of women when they have some formal edu-
cation. However, for women with no formal education, decision-making regarding what foods
to cook every day increases by about 20 percentage points, which is large. There are no
systematic patterns in the results, so we interpret these findings as the program having a
small positive impact on this one domain of women’s decision-making among women who
have no formal education.
56
Table 6.10.2. Impacts of Takaful Program on Women's Decision-Making for Women
with Some Formal Education
Treatment variable: household received Takaful transfer in the past two months
Woman can make own decisions to a great extent on:
(1)
(2)
(3)
(4)
(5)
Women deci-
sion-making in-
dex
Wage employ-
ment
Major house-
hold expendi-
tures
Minor house-
hold expendi-
tures
How to use
cash transfers
Conventional
0.0807
0.0402
0.151
*
0.0286
0.0884
(0.195)
(0.0735)
(0.0820)
(0.0895)
(0.0937)
Bias-corrected
-0.179
0.0604
0.120
-0.114
-0.0896
(0.195)
(0.0735)
(0.0820)
(0.0895)
(0.0937)
Robust
-0.179
0.0604
0.120
-0.114
-0.0896
(0.260)
(0.101)
(0.111)
(0.119)
(0.126)
Mean Dep. Var.
-0.0866
0.218
0.286
0.605
0.516
N
4365
4540
4540
4540
4540
(6)
(7)
(8)
(9)
(10)
What food can
be cooked
every day
Getting medical
treatment for her-
self
Buying clothes
for herself
Taking a child
to a doctor
Children's
schooling
Conventional
0.00806
0.114
0.122
-0.0441
-0.0131
(0.0793)
(0.0884)
(0.0893)
(0.0881)
(0.0910)
Bias-corrected
-0.00997
0.0153
0.147
0.0582
-0.133
(0.0793)
(0.0884)
(0.0893)
(0.0881)
(0.0910)
Robust
-0.00997
0.0153
0.147
0.0582
-0.133
(0.104)
(0.123)
(0.122)
(0.117)
(0.125)
Mean Dep. Var.
0.742
0.507
0.450
0.572
0.478
N
4540
4540
4540
4540
4365
Standard errors clustered at the village level. Sample includes women who have some formal education. Model details: Linear
Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower
Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. * p <
0.10, ** p < 0.05, *** p < 0.01.
57
Table 6.10.3. Impacts Of Takaful Program on Women's Decision-Making for Women
with No Formal Education
Treatment variable: household received Takaful transfers in the past two months
Woman can make own decisions to a great extent on:
(1)
(2)
(3)
(4)
(5)
Women deci-
sion-making in-
dex
Wage employ-
ment
Major house-
hold expendi-
tures
Minor house-
hold expendi-
tures
How to use
cash transfers
Conventional
-0.0373
-0.0829
-0.0156
-0.0458
0.107
(0.277)
(0.104)
(0.113)
(0.122)
(0.132)
Bias-corrected
0.00505
-0.0217
0.0119
-0.0806
0.123
(0.277)
(0.104)
(0.113)
(0.122)
(0.132)
Robust
0.00505
-0.0217
0.0119
-0.0806
0.123
(0.368)
(0.140)
(0.154)
(0.167)
(0.184)
Mean Dep. Var.
-0.0866
0.218
0.286
0.605
0.516
N
1690
1933
1933
1933
1933
(6)
(7)
(8)
(9)
(10)
What food can
be cooked
every day
Getting medical
treatment for her-
self
Buying clothes
for herself
Taking a child
to a doctor
Children's
schooling
Conventional
0.207
**
0.0415
-0.0640
0.0830
-0.00492
(0.105)
(0.130)
(0.124)
(0.122)
(0.132)
Bias-corrected
0.195
*
0.124
-0.0410
0.0119
-0.243
*
(0.105)
(0.130)
(0.124)
(0.122)
(0.132)
Robust
0.195
0.124
-0.0410
0.0119
-0.243
(0.138)
(0.180)
(0.169)
(0.175)
(0.187)
Mean Dep. Var.
0.742
0.507
0.450
0.572
0.478
N
1933
1933
1933
1933
1690
Standard errors clustered at the village level. Sample includes women who have no formal education. Model details: Linear
Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower
Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. * p <
0.10, ** p < 0.05, *** p < 0.01.
Another aspect of women’s empowerment concerns gender norms. In the questionnaire, we
ask several questions regarding gender norms, including whether men should make the im-
portant household decisions, whether men should help with housework if a woman works
outside the home, whether a husband should allow his wife to work outside the home,
whether a woman has the right to express her opinion even if she disagrees with a man, and
whether a woman must accept if her husband beats her. We also ask the female respondent
whether she has money that she can use for whatever she wants, how often she talks to a
non-household family member, and about her mobility. We construct a mobility index using
PCA with four questions on how often the woman can go to the market, to a friend or family
member’s house that is an hour away, to a place outside the village, and to see a healthcare
provider. Table 6.10.4 shows the results. We see that Takaful beneficiaries seem to have
more gender-equal norms compared to non-beneficiaries. Using both definitions of participa-
tion, the impact estimates for the gender norms index is positive and statistically significant
using conventional estimates. Other variables are not statistically significant. We take this as
some evidence, but not strong evidence, that Takaful improved gender norms.
58
Table 6.10.4. Impacts of Takaful Program on Gender Norms
Panel A: Household received Takaful transfers in the past two months
(1)
(2)
(3)
(4)
Norms and attitudes
around gender
(higher index=more
liberal)
Do you have your own
money that you can use
for what you want to use
it?
How often do you
talk to a family mem-
ber not living in the
HH?
Mobility in-
dex
Conventional
0.465
**
0.0191
0.106
0.0712
(0.224)
(0.0482)
(0.195)
(0.185)
Bias-corrected
0.176
-0.0632
0.232
-0.0874
(0.224)
(0.0482)
(0.195)
(0.185)
Robust
0.176
-0.0632
0.232
-0.0874
(0.297)
(0.0635)
(0.254)
(0.242)
Mean Dep. Var.
2.76e-09
0.105
6.021
-0.157
N
5832
6473
6473
6203
Panel B: Household ever received Takaful transfers
Conventional
0.245
**
0.00916
0.0605
0.0257
(0.115)
(0.0256)
(0.104)
(0.0988)
Bias-corrected
0.0900
-0.0336
0.126
-0.0632
(0.115)
(0.0256)
(0.104)
(0.0988)
Robust
0.0900
-0.0336
0.126
-0.0632
(0.153)
(0.0336)
(0.135)
(0.129)
Mean Dep. Var.
2.76e-09
0.105
6.021
-0.157
N
5813
6449
6449
6179
Standard errors clustered at the village level. The full sample (N=6475) consists of 2539 households who reported a Takaful
cash transfer in the two months prior to being interviewed, and 3936 households who did not. Model details: Linear Trend on
PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (ur-
ban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. * p < 0.10, ** p <
0.05, *** p < 0.01.
6.11 Mental Health
In this section, we examine program impacts on the mental health of the survey respondent.
This was almost always the head of the household, who is predominantly male. It is now
widely documented that cash transfers have the potential to improve mental health out-
comes (Ridley et al 2020). The poor face a lot of uncertainty and stressors and face constant
worry. These issues can then lead to poor mental health in the form of depression and anxi-
ety. The evidence base is mixed, with some transfer programs having large effects on men-
tal health and others having no impacts (negative impacts are very rare).
In Table 6.11.1 we examine three outcomes related to mental health. The first is a worry
scale calculated from 12 questions regarding the extent of worry about having enough
money to purchase essentials, having enough money to purchase other items, being able to
find consistent work, having the ability to sufficiently feed the family, being able to repay
debt, being able to repay installment payments, the death of a household member, illness of
a family member, being able to pay medical bills, being able to pay education expenses, a
natural disaster, or that the family’s economic situation will become worse. The choices were
not worried at all (1 point), a bit worried (2 points), quite worried (3 points), or extremely wor-
ried (4 points). We summed responses over all 12 questions and the outcome is represented
in Column 1. In Column 2 we present results on general anxiety using the Generalized Anxi-
ety Disorder (GAD-7) scale (Spitzer et al, 2006). The GAD-7 includes questions regarding
various indications of generalized anxiety rather than specific worries. In Column 3, we esti-
mate impacts on self-esteem. Self-esteem is important in feeling optimistic about the future
and in feelings of agency, helping people to make decisions in a better way. We use the
59
Rosenberg Self-Esteem Scale as our measure (Rosenberg, 1965). In all three measures, we
see that there are no significant impacts of the Takaful program.
Table 6.11.1. Impacts of Takaful Program on Mental Health Indicators
(1)
(2)
(3)
Worry score
General Anxiety Disorder
(GAD-7) score
Rosenberg Self-Esteem
score
Conventional
0.546
-0.212
0.356
(1.413)
(0.789)
(0.888)
Bias-corrected
1.412
-0.316
-0.146
(1.413)
(0.789)
(0.888)
Robust
1.412
-0.316
-0.146
(1.900)
(1.044)
(1.157)
Mean Dep. Var.
17.63
7.309
37.27
N
6473
6473
6473
Standard errors clustered at the village level. The full sample (N=6475) consists of 2539 households who reported a Takaful
cash transfer in the two months prior to being interviewed, and 3936 households who did not. Worry score sums the level of
worry over 12 items, ranges between 0 and 36. General Anxiety Disorder (GAD-7) score sums anxiety level over 7 items,
ranges between 0 and 21. Rosenberg Self-Esteem score sums 10 self-esteem items, ranges between 0 and 50. Model details:
Linear Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates:
Lower Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. *
p < 0.10, ** p < 0.05, *** p < 0.01
6.12 Shocks and coping strategies
In this section we examine the impacts of the Takaful program on shocks experienced by
households, and the types of ways that households cope with shocks. We examine shocks
in general and shocks specifically caused by COVID-19. This type of social protection pro-
gram may help shield households from certain types of shocks, and/or may help households
to better cope with shocks. We first look at the number of shocks experienced by the house-
hold in the past five years in Table 6.12.1.
We report whether the household experienced any shock, the number of shocks experi-
enced, and whether any of the shocks experienced were a result of COVID-19. Shocks are
relatively common, with 61% of households having experienced a shock in the past 5 years
and 23% of households reporting that a shock experienced in 2020 or 2021 was due to the
pandemic. The probability of experiencing a shock, the number of shocks, and the probabil-
ity of a shock being due to the pandemic do not differ between recipient and non-recipient
households. This result is expected because we do not expect that the program would
change the likelihood of a shock, but rather, the way that households cope with shocks.
60
Table 6.12.1 Impacts of Takaful Program on Shocks Experienced by Household
Treatment variable: household received Takaful transfer in the past two months
(1)
(2)
(3)
Household experienced
any shocks in past 5
years
Number of shocks
in past 5 years
Household experienced a
shock in 2020/21 due to
COVID-19
Conventional
0.0567
0.0515
0.0397
(0.0805)
(0.119)
(0.0659)
Bias-corrected
0.0714
0.0559
0.0691
(0.0805)
(0.119)
(0.0659)
Robust
0.0714
0.0559
0.0691
(0.105)
(0.152)
(0.0871)
Mean Dep. Var.
0.605
0.764
0.231
N
6473
6475
6475
Standard errors clustered at the village level. The full sample (N=6475) consists of 2539 households who reported a Takaful
cash transfer in the two months prior to being interviewed, and 3936 households who did not. Model details: Linear Trend on
PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (ur-
ban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. * p < 0.10, ** p <
0.05, *** p < 0.01.
We then look at potentially detrimental ways that households tend to cope with shocks: sell-
ing productive assets, household goods or jewelry, borrowing money from relatives, a bank,
or NGO, eating less or lower quality food, reducing spending on school or healthcare, chang-
ing occupations, using savings, or having children work outside the home. The impact esti-
mates for these outcomes are reported in table 6.12.2. There are no statistically significant
impacts on most of these outcomes. The main coping strategy was selling gold or jewelry,
but the magnitude of the impact is quite small. There is also weak evidence that borrowing
from relatives, traders, and banks was lower. Unfortunately, there is also weak and sugges-
tive evidence that households responded to shocks by eating less food and eating less nutri-
tious food to reduce food expenditures.
Reassuringly, very few households engaged in extremely harmful processes like having their
daughters marry early (this is very rare in the data so not reported in the table), spending
less on health and education, or having children work outside the home.
61
Table 6.12.2. Impacts of Takaful Program on Coping Methods for Shocks Experienced by the Household
Treatment variable: household received a Takaful transfer in the past two months
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Sold productive
asset
Sold household
good
Sold gold/ jew-
elry
Borrowed money
from relatives
Borrowed
money from
trader or bank
Took a loan from
NGO/institution
Ate less food to
reduce expenses
Conventional
0.00307
0.000491
0.0218
*
-0.109
-0.0124
0.0139
0.0207
(0.00512)
(0.00735)
(0.0119)
(0.0865)
(0.0266)
(0.0213)
(0.0824)
Bias-corrected
0.00807
-0.0136
*
0.0245
**
-0.159
*
-0.00343
0.0150
0.144
*
(0.00512)
(0.00735)
(0.0119)
(0.0865)
(0.0266)
(0.0213)
(0.0824)
Robust
0.00807
-0.0136
0.0245
*
-0.159
-0.00343
0.0150
0.144
(0.00697)
(0.0113)
(0.0144)
(0.116)
(0.0368)
(0.0296)
(0.112)
Mean Dep. Var.
0.00102
0.00204
0.00587
0.321
0.0263
0.0128
0.310
N
3914
3914
3915
3919
3917
3914
3922
(8)
(9)
(10)
(11)
(12)
(13)
(14)
Ate lower qual-
ity food to re-
duce expenses
Reduced
spending on ed-
ucation
Reduced
spending on
health care
Adult household
member temporarily
took job elsewhere
Forced to
change occupa-
tion
Used savings
Children started
working outside
the home
Conventional
0.0680
-0.0399
0.00139
-0.00471
0.00272
0.0113
-0.00317
(0.0733)
(0.0367)
(0.0166)
(0.00730)
(0.00787)
(0.0213)
(0.00825)
Bias-corrected
0.0409
0.00783
-0.0103
-0.00422
0.00338
-0.0135
-0.0106
(0.0733)
(0.0367)
(0.0166)
(0.00730)
(0.00787)
(0.0213)
(0.00825)
Robust
0.0409
0.00783
-0.0103
-0.00422
0.00338
-0.0135
-0.0106
(0.101)
(0.0511)
(0.0206)
(0.0104)
(0.0109)
(0.0311)
(0.0115)
Mean Dep. Var.
0.221
0.0470
0.0102
0.00204
0.00204
0.0135
0.00255
N
3921
3914
3914
3914
3914
3916
3914
Standard errors clustered at the village level. The full sample (N=6475) consists of 2539 households who reported a Takaful cash transfer in the two months prior to being interviewed, and 3936
households who did not. Model details: Linear Trend on PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (urban), Lower
Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. * p < 0.10, ** p < 0.05, *** p < 0.01.
62
6.13 COVID-19
The COVID-19 pandemic has disrupted life around the globe since early 2020 when its exist-
ence was first announced. Millions of people globally have died or become severely ill, lost
their livelihoods, gone hungry, lost years of learning, and suffered mental health problems,
among many other negative effects. In this section, we examine the ways in which house-
holds in our sample suffered due to the pandemic, as well as the ways in which they coped
with the negative effects.
Table 6.13.1 presents the results. On average, one third of households were negatively im-
pacted by COVID-19 in some way, and there were many negative shocks experienced. More
than half of households experienced job loss and/or a pay cut, and 16% of households expe-
rienced were actually infected with COVID-19.
9
Of those infected, two thirds sought medical
care, and 44% needed to borrow money to cover the associated medical costs. There are no
impacts of the program on the likelihood of being negatively affected by the pandemic, nor
the type of negative effect suffered, nor the coping strategies used to address negative ef-
fects. The interpretation is that the Takaful program did not lead to less suffering from
COVID-19, and also did not particularly cushion any negative effects experienced by house-
holds.
9
We should not take this figure at face value as there may be substantial misreporting. The availability of testing for COVID-19
was generally sparse in most of the country.
63
Table 6.13.1. Impacts of Takaful Program on Experiences with The COVID-19 Pan-
demic
Treatment variable: household received a Takaful transfer in the past two months
(1)
(2)
(3)
(4)
(5)
Household
was affected
by COVID-19
Household experi-
enced job loss
Household
experienced
pay cut
Household
was in-
fected with
COVID-19
Household
borrowed to
cope with
COVID-19
Conven-
tional
0.0280
-0.0115
-0.0975
0.0679
-0.0000475
(0.0798)
(0.153)
(0.157)
(0.110)
(0.0637)
Bias-cor-
rected
-0.102
-0.0413
-0.311
**
0.0722
-0.0445
(0.0798)
(0.153)
(0.157)
(0.110)
(0.0637)
Robust
-0.102
-0.0413
-0.311
0.0722
-0.0445
(0.106)
(0.202)
(0.211)
(0.152)
(0.0844)
Mean Dep.
Var.
0.321
0.555
0.590
0.161
0.206
N
6473
2075
2076
2075
6472
(6)
(7)
(8)
(9)
Household lent money to help another
household cope with COVID-19
Household
member(s)
migrated for
work before
the pan-
demic
Household
member in-
fected with
COVID-19
sought
medical
care
Household
borrowed
money to
cover COVID-
19 medical
care costs
Conven-
tional
-0.00151
-0.00239
0.0379
0.0199
(0.0103)
(0.00422)
(0.357)
(0.364)
Bias-cor-
rected
0.00286
-0.00636
-0.284
0.495
(0.0103)
(0.00422)
(0.357)
(0.364)
Robust
0.00286
-0.00636
-0.284
0.495
(0.0152)
(0.00479)
(0.487)
(0.494)
Mean Dep.
Var.
0.00556
0.000927
0.673
0.435
N
6472
6470
352
352
Standard errors clustered at the village level. The full sample (N=6475) consists of 2539 households who reported a Takaful
cash transfer in the two months prior to being interviewed, and 3936 households who did not. Model details: Linear Trend on
PMT Score; Uniform Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (ur-
ban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category: Metropolitan. * p < 0.10, ** p <
0.05, *** p < 0.01.
6.14 Robustness to alternate definition of beneficiary status
In this report, we have focused on two ways of defining a Takaful beneficiary: whether the
household reports ever having received a Takaful transfer and whether the household has
received a Takaful transfer in the past two months. We believe that these are the two most
relevant definitions of beneficiary status. The first definition can be interpreted as whether
any program participation had an impact on the outcomes. The second definition can be in-
terpreted as whether recent transfers have had an impact on the outcomes. For some out-
comes, one is more relevant than the other and for other outcomes, both definitions could be
relevant. Accordingly, we have presented our results as such.
64
Another way to think about beneficiary status is intensity of treatment, and rather than desig-
nate a household as a beneficiary or non-beneficiary, focus on the number of months that
the household received transfers. Those who have never received a transfer are coded as
zero months. The interpretation here is whether a higher intensity of treatment, a longer du-
ration of transfers, has an effect on the outcomes considered. We consider this definition as
less informative because the interpretation is difficult. The transfers may have been received
for several years and then stopped, or they could have been received for the same number
of months but more recently. The “treatment” would be the same, but we could not distin-
guish between the two. Consequently, our main results focus on the first two definitions.
Nonetheless, in Table 6.14.1 we use this alternate definition and estimate impacts on our
main outcomes: total consumption, non-food consumption, food consumption, savings, debt,
and investments in durable, productive, and livestock assets. We see that all of the results
are in the same direction and of similar statistical significance. Accordingly, all three defini-
tions result in consistent estimates and the results should be considered robust.
In Table 6.14.2, we consider another alternative definition: whether a household is a current
beneficiary (received transfers in the past 2 months) and received at least 24 transfers. This
definition captures recent transfers and a longer duration of transfers. Once again, the re-
sults are robust to this definition and suggest that our results are robust to this alternative
definition as well. It also reflects the fact that few households (17%) experienced disruptions
in transfers and that stopping to receive transfers during the past few years did not substan-
tially affect household behaviour.
65
Table 6.14.1. Impacts of Takaful Program Duration (Self-reported)
Standard errors clustered at the village level. The full sample (N=6475) consists of 2539 households who reported a Takaful cash transfer in the two months prior to being interviewed, and 3936
households who did not. Consumption aggregates shown are winsorized at the 2nd and 99th percentiles, calculated as Adult Equivalent Units (AEU), and transformed using Inverse Hyperbolic Sine
(IHS). Asset indices are constructed based on the first principal component from principal component analysis (PCA). The index that includes all assets uses dummies for assets ownership. The
same for the durables and productive assets indices. The livestock index is composed using a count of the livestock owned by the household. Model details: Linear Trend on PMT Score; Uniform
Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category:
Metropolitan. * p < 0.10, ** p < 0.05, *** p < 0.01
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Monthly food
consumption
expenditure
Monthly non-
food con-
sumption ex-
penditure
Monthly total
(food & non-food)
consumption ex-
penditure
Amount of
savings
(EGP)
(IHS)
Total amount of debt
currently owed to infor-
mal lenders or for pur-
chases on credit (IHS)
Durables
Productive
assets
Livestock
Conventional
-0.000202
-0.000671
-0.000419
-0.00235
-0.0133
-0.000237
0.00534
**
0.00408
(0.000814)
(0.000848)
(0.000692)
(0.00146)
(0.00813)
(0.00257)
(0.00212)
(0.00261)
Bias-corrected
-0.00124
-0.00172
**
-0.00129
*
-0.00271
*
-0.0177
**
-0.000124
0.00173
0.000691
(0.000814)
(0.000848)
(0.000692)
(0.00146)
(0.00813)
(0.00257)
(0.00212)
(0.00261)
Robust
-0.00124
-0.00172
-0.00129
-0.00271
-0.0177
-0.000124
0.00173
0.000691
(0.00105)
(0.00111)
(0.000891)
(0.00185)
(0.0111)
(0.00341)
(0.00293)
(0.00404)
Mean Dep. Var.
6.791
6.526
7.396
0.0956
3.717
2.51e-09
6.72e-09
-3.06e-09
N
6449
6449
6449
6449
6449
6449
6449
6449
66
Table 6.14.2. Impacts of Takaful program receipt in last 2 months, and having received at least 24 transfers overall
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Monthly food
consumption
expenditure
Monthly non-
food consump-
tion expenditure
Monthly total
(food & non-
food) consump-
tion expenditure
Amount of
savings
(EGP)
(IHS)
Total amount of debt
currently owed to in-
formal lenders or
owed for purchases
on credit (IHS)
Durables
Productive
assets
Livestock
Conventional
-0.0146
-0.0459
-0.0280
-0.171
-0.937
-0.0425
0.386
**
0.289
(0.0594)
(0.0614)
(0.0503)
(0.106)
(0.592)
(0.187)
(0.155)
(0.190)
Bias-corrected
-0.0868
-0.118
*
-0.0872
*
-0.202
*
-1.316
**
-0.0203
0.132
0.0483
(0.0594)
(0.0614)
(0.0503)
(0.106)
(0.592)
(0.187)
(0.155)
(0.190)
Robust
-0.0868
-0.118
-0.0872
-0.202
-1.316
-0.0203
0.132
0.0483
(0.0765)
(0.0805)
(0.0650)
(0.135)
(0.809)
(0.249)
(0.214)
(0.294)
Mean Dep. Var.
6.791
6.526
7.396
0.0956
3.717
2.51e-09
6.72e-09
-3.06e-09
N
6475
6475
6475
6475
6475
6474
6474
6474
Standard errors clustered at the village level. The full sample (N=6475) consists of 2539 households who reported a Takaful cash transfer in the two months prior to being interviewed, and 3936
households who did not. Consumption aggregates shown are winsorized at the 2nd and 99th percentiles, calculated as Adult Equivalent Units (AEU), and transformed using Inverse Hyperbolic Sine
(IHS). Asset indices are constructed based on the first principal component from principal component analysis (PCA). The index that includes all assets uses dummies for assets ownership. The
same for the durables and productive assets indices. The livestock index is composed using a count of the livestock owned by the household. Model details: Linear Trend on PMT Score; Uniform
Kernel; RD Bandwidth=63. The following strata indicators are included as covariates: Lower Egypt (urban), Lower Egypt (rural), Upper Egypt (urban), Upper Egypt/Frontier (rural), excluded category:
Metropolitan. * p < 0.10, ** p < 0.05, *** p < 0.01
1
7. CONCLUSIONS AND RECOMMENDATIONS
This report has presented the findings from a second-round evaluation of the Takaful cash transfer pro-
gram. The first-round evaluation reported on data collected in 2017, and this second round evaluation
uses data collected in 2021. The goal of this second evaluation is to assess whether there may have
been medium term and sustained impacts for Takaful beneficiaries who have been in the program for
several years.
Takaful is a cash transfer program that provides income support to the poor and most vulnerable;
namely poor families with children (under 18 years of age) and the poor elderly (aged 65 years and
above). It is implemented by the Ministry of Social Solidarity (MoSS) and co-financed by the Govern-
ment of Egypt and the World Bank. Targeting for the program uses a combination of geographical tar-
geting and application of a Proxy Means Test (PMT), an index of well-being based on household de-
mographics, income, housing quality, assets and other characteristics. In poor districts, potentially eli-
gible households were registered and interviewed to collect information for the PMT. Households with a
PMT score below a preset threshold, 4,500 points, were considered eligible for the program and re-
ceived transfers.
A household survey for the impact evaluation was conducted in early 2022 by the firm El-Zanaty and
Associates. The sample for the evaluation includes 6,473 households. The evaluation sample was se-
lected from the administrative database of registrants for the program and the sample was first re-
stricted to households who had been in the program for the longest. Subsequently, households who fell
within 63 points above or below the 4500 threshold were sampled.
The impact evaluation was designed using a regression discontinuity (RD) methodology, which is effec-
tive for measuring the impact of programs that use a threshold level of a continuous measure of well-
being, like a PMT score, to determine access to the program. The RD approach compares outcomes
for beneficiaries just below the threshold for eligibility to outcomes for non-beneficiaries just above the
threshold. Because the specific level of the eligibility threshold is not within the control of program appli-
cants, whether households near the threshold end up below it or above is nearly random and cannot be
affected by their actions. Consequently, the application of the threshold PMT score creates a quasi-ex-
periment locally around the threshold that is used to measure impact of the program.
Because the PMT score is not a perfect predictor of program participation (some households above the
threshold participate in the program and some households below the threshold do not participate) we
use a ‘fuzzy’ regression discontinuity design. The fuzzy RD adjusts for the fact that the threshold PMT
score does not perfectly predict participation by estimating the model in two stages: the first stage pre-
dicts the probability of participating in the program as a function of being below the eligibility threshold
on the PMT score and the second stage measures impact as the change in the level of the outcome
variable that is due to the difference in predicted probability of participating in the program as a result of
the use of the threshold level of the PMT score.
Households have four major ways to spend transfers and income: consumption, savings, debt reduc-
tion, or investments in assets. We first examine these four choices. We then examine outcomes that
2
may depend on the choice of where transfers and income were used, such as dietary diversity, school-
ing, infant and young child feeding practices, ante- and post-natal care, and anthropometry. We also
examined women’s empowerment, gender norms, mental health, and shocks. Given the prominence of
the COVID-19 pandemic and the resulting severely negative impacts on poor households, we also col-
lected data on how households were affected by the pandemic and the strategies they used to cope
with it.
We do not detect effects on food and non-food consumption expenditures per adult equivalent unit
(similar to per capita) compared to non-beneficiaries. This result contrasts with the results in the first-
round evaluation, which found a statistically significant increase in the value of monthly food consump-
tion per AEU by 8.3 – 8.9%. It is worth highlighting that this is not a precise estimate as the statistical
power of the second-round evaluation is reduced compared to the first round, so it is possible that there
is a positive but smaller impact on consumption than in the first round.
The difference in food consumption is driven by a few food categories: grains, fruits, eggs, oils, and
fats. Some of these groups are nutritious (grains, fruits, and eggs) but the reduction in oil and fat con-
sumption should not have a negative nutritional impact. Encouragingly, consumption of unhealthy
snacks and beverages, as well as food consumed outside of the household, has not increased.
The decrease in non-food consumption expenditures is due to a decrease in construction expenditures
and a decrease in expenditures on communications, such as phones and televisions. Rather than pur-
chasing these items, it is possible that households instead were investing in their capacity to generate
future income. We see increases in asset holdings, and in particular, productive assets. These assets
include both agricultural technologies and livestock. Beneficiary households invested in large items
such as tractors, plows, irrigation, buffaloes, and cows. These are lumpy expenditures that many
households may not be able to save for and should generate a persistent increase in income. Benefi-
ciary households also had lower levels of debt than non-beneficiary households.
We find that beneficiary households had more children 6-11 years old due to a slight imbalance across
the 4500 cutoff at the time of registration. Additionally, we find suggestive evidence that beneficiary
households had more children in the past 5 years as a result of receiving the transfers. This result may
explain part of the null effect on consumption.
Beneficiary households did not save differentially compared to non-beneficiary households, but they did
lower their debt owed to informal lenders and paid off their installment payments. Additionally, they in-
vested in assets, primarily productive assets in the form of items for small businesses and livestock.
These behaviors indicate that households may have moved out of the stage of needing to increase
consumption because minimum needs are not being met and may have transitioned into the next
phase where they are able to build their assets and improve their future income.
In terms of labor supply, we see a difference between beneficiary and non-beneficiary households.
Beneficiary households are significantly more likely to be engaged in informal labour and significantly
less likely to be engaged in formal labor This result may possibly be driven by the increased productive
asset holdings of households. The assets purchased may have enabled households to start their own
businesses.
3
There are very encouraging results with regards to children’s schooling. The likelihood that children are
enrolled in both primary and preparatory school increased substantially, by between 4 to 5 percentage
points. This is also an investment in households’ future – these children should go on to earn more and
may further push their families out of poverty. Girls attending secondary school are also more likely to
attend school regularly as a result of the program.
There are mixed results when it comes to child nutrition, and the results depend on the age of the child.
Dietary diversity is lower for 2-5 year olds among beneficiaries, but wasting is reduced for children aged
6-23 months for those who have ever received transfers. It is possible that the increased number of
children led households to focus their attention on fewer foods.
The previous evaluation of Takaful found a decrease in women’s decision-making power among
women who had no formal education. We test whether this pattern still holds, and we find that it does
not. There continues to be no impact of the program on women who have some formal education, but
now, beneficiary women with no education are more likely to be able to influence decisions regarding
what food can be cooked every day. We do not interpret these results as a large positive shift in
women’s decision making, but it is encouraging that women’s decision-making was not reduced by the
program. We also see some evidence of higher levels of gender positive norms among beneficiary
households. We do not find any effects on mental health – worries, generalized anxiety, or self-esteem.
The households in our sample were indeed exposed to shocks, including COVID-19 and its resulting
repercussions on movement and availability of foods. However, households did not use harmful coping
strategies such as pulling children out of school and having them work, having daughters marry early,
or reducing food consumption. The only response from beneficiary households is that they are more
likely to sell gold/jewelry to cope with shocks.
This evaluation has shown that the Takaful program caused several positive shifts in households.
There were investments in physical and human capital. The overall message is that while some as-
pects of household behavior were either not affected by the program or even had negative effects,
these investments may reflect shorter term decisions that households believe will pay off in the future.
Several policy recommendations emerge from these findings:
Takaful should be continued and even possibly extended. The program enabled households not to
resort to coping with shocks in negative ways. Particularly in light of increasingly frequent global shocks
like COVID-19 and the Russian invasion of Ukraine, social protection programs, including cash transfer
programs like Takaful, could be an effective way to protect against large-scale shocks since the infra-
structure to reach people is largely in place.
Proceed with plans for recertification and graduation of beneficiaries who have achieved self-suf-
ficiency while using a generous cut-off for self-sufficiency (and generous duration of exposure to the
program) given that many households have not managed to substantially increase their consumption in
spite of increased productive assets.
Improve communication regarding exclusion restrictions, program length, and recertification so that
beneficiaries understand that they will not be excluded from the program for formal sector work with in-
come below a certain threshold and to ensure that beneficiaries are not surprised by sudden changes
in program status or unnecessarily worried about the short-term continuity of the transfers.
4
Consider greater coordination with communication campaigns related to family planning if the
behavioral response by families of having more children is seen as in conflict with other national policy
goals.
Continue to work towards a comprehensive social protection strategy that helps to continue pro-
tecting the poor as well as contributing to achieving longer-term developmental goals. Coordinating with
the Ministry of Education to provide high quality public service delivery will magnify the impacts of in-
creased school enrollment.
Complementary programming would also be beneficial. In general, complementary programming
on issues such as nutrition practices or financial training need to be quite intensive to be impactful.
There are currently programs that are implemented by the Government of Egypt on these topics, partic-
ularly a nation-wide nutrition campaign. However, it would be worth considering pairing these programs
and intensifying them by leveraging Takaful to link to already vulnerable households.
5
ABOUT THE AUTHORS
Hoda El Enbaby is a PhD Candidate at Lancaster University. Dalia Elsabbagh is a Senior Re-
search Assistant at IFPRI, based in Cairo, Egypt. Dan Gilligan is a Senior Research Fellow and Dep-
uty Director of the Poverty, Health, and Nutrition Division at IFPRI, based in Washington, DC. Naureen
Karachiwalla is a Research Fellow in the Poverty, Health, and Nutrition Division (PHND) at IFPRI,
based in Washington, DC. Bastien Koch is a Research Analyst at IFPRI, based in Washington, DC.
Sikandra Kurdi is a Research Fellow in the Development Strategy and Governance Division at
IFPRI.
ACKNOWLEDGMENTS
This impact evaluation was managed by the World Bank and funded by the United Kingdom Foreign
and Commonwealth Office (UK FCO) and the United States Agency for International Development
(USAID). We gratefully acknowledge very helpful consultations on the design and conduct of the evalu-
ation with the Ministry of Social Solidarity of the Government of Egypt, in particular Her Excellency Dr.
Nivine El-Kabbag, Minister of Social Solidarity, Mr. Raafat Shafeek, Minister of Social Solidarity Advisor
for Strengthening Social Safety Net Programs and Executive Director of Takaful and Karama Condi-
tional Cash Transfer Program, and Eng. Amal Helmy, Database Administration and Quality Assurance
Specialist, and advice from Dr. Heba Ellaithy, Professor of Statistics at Cairo University, and Dr. Hania
Sholkamy, Professor of Anthropology at American University of Cairo. The impact evaluation has also
benefitted substantially from valuable comments from the World Bank team led by Ms. Nahla Zeitoun
and including Dr. Imane Helmy and Ms. Matuna Mostafa. We are grateful to Dr. Fatma El-Zanaty and
the research firm El-Zanaty and Associates for their role in collecting the household survey data. We
would also like to thank Prof. Matias D. Cattaneo, for his consultation and support. Finally, we thank the
participants of the study for their time. Address for correspondence: Naureen Karachiwalla, n.karachi-
walla@cgiar.org.
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The Egypt Strategy Support Program (EgSSP) is managed by the International Food Policy Research Institute (IFPRI) and is financially supported by the
USAID through the “Evaluating Impact and Building Capacity (EIBC)" project. This publication has been prepared as an output of the EIBC project and has
not been independently peer reviewed. Any opinions expressed here belong to the author(s) and are not necessarily representative of or endorsed by IFPRI.
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