An Unconditional Government Social Cash Transfer in Africa Does not Increase Fertility

1. unite for
children
An Unconditional Government Social Cash
Transfer in Africa Does not Increase Fertility
Tia Palermo
UNICEF Office of Research – Innocenti / Stony Brook University (SUNY)
With Sudhanshu Handa, Amber Peterman, Leah Prencipe, David Seidenfeld on
behalf of the Zambia CGP Evaluation Team d
April 1, 2016
Population Association of America Annual Meeting, Washington, DC

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Introduction: The rise of ‘cash’ in sub-
Saharan Africa . . .
• Explosion of Social Cash Transfers (SCTs):
 718 million people enrolled in SCTs globally (Honorati et al. 2015)
 Approximately half (21) SSA countries had an unconditional
cash transfer (UCT) in 2010 -- this doubled (40) by 2014
• Programs are ‘home-grown’:
 Target on poverty and vulnerability; greater role of community
 Unconditional or ‘soft conditions’
 Larger evidence base on impacts than any other region: more
countries, more topics

3. 3
Coverage of select Government programs
64000 69000 80000
150000 163000 170000 182000 190000
250000
310000
455000
1100000
1125000
0
200000
400000
600000
800000
1000000
1200000
 Not included (due to scale): CSG in South Africa (>11 million recipients)

4. 4
The Transfer Project
• Who: Community of research, donor and implementing partners –
focus on coordination in efforts and uptake of results
 UNICEF, FAO, UNC, Save the Children, National Governments
• Mission: Provide rigorous evidence on of government-run large-scale
(largely unconditional) SCTs
• Motivation:
 Income poverty has highly damaging impacts on human
development
 Cash empowers people living in poverty to make their own
decisions on how to improve their lives
• Where: Ethiopia, Ghana, Kenya, Lesotho, Malawi, South Africa,
Tanzania, Zambia and Zimbabwe

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Overview of programs & evaluations
• All programs unconditional, with exception of Tanzania (schooling, health)
• Longitudinal qualitative studies in Ghana, Malawi, Tanzania, Zimbabwe
Country (program)
Targeting
(in addition to poverty,
ultra-poor)
Transfer size (%
of baseline
consumption)
Methodology
Years of data
collection
Ghana (LEAP)
Elderly, disabled or
OVC
~7 Longitudinal PSM 2010, 2012
Ghana (LEAP 1000)
Pregnant women,
child<2
16 RDD 2015, 2017
Kenya (CT-OVC) OVC 22 RCT 2007, 2009, 2011
Malawi (SCTP) Labour-constrained 18 RCT 2011, 2013, 2015
Tanzania (PSSN) Food poor ~ RCT 2015, 2017
Zambia (CGP) Child 0-5 27 RCT
2010, 2012, 2013,
2014
Zambia (MCTG)
Female, elderly,
disabled, OVC
21 RCT 2011, 2013, 2014
Zimbabwe (HSCT)
Food poor, labour-
constrained
20
Longitudinal matched
case-control
2013, 2014, 2016

6. 6
Select research agenda
• Impacts on:
• Pathways and heterogeneous impacts
• Mythbusting research
•Increase fertility
•Create dependency (reduce labor force participation)
•Wasteful alcohol and tobacco spending
•Too costly
•Fully consumed, rather than used for investment
• Food security
• Productive activities
• Resilience
• Education
• Nutrition and health
• Safe transitions to
adulthood
• Stress, mental health
• Time preferences
• Women’s empowerment
(savings, investment,
decision-making)

7. 7
Current study aim
Examine impacts of
Zambia Cash Grant
Programme (CGP) on
fertility

8. 8
Theory
• Couples may update goals for quality and quantity of children
(Becker, 1960) based on change to economic situation induced by
cash transfer (Todd et al. 2012).
•Increase quantity if children are “normal goods” – recent empirical
evidence to support this (Black et al. 2013)
• Period-specific decisions such as contraceptive use may change in
response to perceptions of link between transfer and child in
household (Stecklov et al. 2007; Todd et al. 2012).
• Contraceptive use may increase through increased income to access
services or women’s increased ability to exert preferences
(empowerment).
• Transfers may delay sexual debut, pregnancy and marriage among
adolescents

9. 9
Existing evidence: government
programmes
• Largely do not increase fertility—experimental evidence from
Kenya, Malawi, Mexico, Nicaragua (Stecklov et al. 2007;
Stecklov & Winters 2011)
•Exceptions: (1) positive impact on fertility in Honduras [2-4
percentage point increase in probability of birth (Stecklov et al.
2007)] and (2) non-experimental study from Mexico [5% increase in
fertility (Arenas et al. 2015)]
• Cash transfers increase birth spacing
•South Africa: HR=0.66 (Rosenberg et al. 2015)
•Nicaragua: HR=0.68 (Todd et al. 2012)
• Transfers delay sexual debut and first pregnancy among
adolescents: Kenya and South Africa (Handa et al. 2015;
Heinrich et al. 2012)

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Data: Zambia Child Grant Programme
(CGP) Evaluation
• Implemented by the Ministry of Community Development and Social
Services (MCDSS) starting in 2010
• Geographically targeted to households with child under 5 years in
three districts (Kalabo, Shangombo, and Kaputa)
• Unconditional transfer: 60 Kwacha per month (12 USD) per
household
• Six stated program goals: 1) income, 2) food security, 3) productive
assets, 4) reduce child malnutrition, 3) primary school enrollment and
attendance, 6) reduce under 5 child mortality and morbidity
• Evaluation (2,500 households)
•Randomized Control Trial with 90 clusters (45 T, 45 C)
•Baseline (2010), 24-month (2012), 36-month (2013) and 48-month
(2014)

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Measures
Outcomes
 Total live births
 Ever pregnant
 Ever had miscarriage/still
birth/abortion
 Contraceptive use
 Household counts of children aged 0-
4 years (also 0-1, 2-4, 0-4)
Treatment variable
 Household receives unconditional cash
transfer (bi-monthly)
Controls
 Age, educational attainment, marital
status, log of household size, district, log
of distance to nearest food market,
prices
Zambia, credit: Amber Peterman

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Statistical analyses
Individual-level analyses
• Cross sectional linear probability models (LPM): currently pregnant,
ever pregnant, ever had miscarriage/stillbirth/abortion
• Cross-sectional Poisson models: number of children born alive
Household-level analyses
• Difference-in-differences Poisson models: total children 0-1 year and
0-4 years old in household

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Results: sample characteristics
Table 1.Baseline individual- and community-level characteristics by CGP
treatment
(1) (2) (3) (4)
All
(n=2675)
Control
(n=1326)
Treatment
(n=1349)
Difference
Age in years 28.2 28.28 28.11 -0.17
(0.17) (0.26) (0.21) (0.33)
Highest grade attained
(baseline)
3.62 3.4 3.83 0.42***
(0.13) (0.19) (0.18) (0.26)
Divorced/separated/widow
ed (baseline)
0.13 0.15 0.12 -0.03**
(0.01) (0.01) (0.01) (0.02)
Never married (baseline) 0.27 0.27 0.28 0.02
(0.02) (0.02) (0.02) (0.03)
Married/co-habiting
(baseline)
0.59 0.59 0.6 0.01
Standard errors in parenthesis.
* p<0.1 ** p<0.05; *** p<0.01

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Results: outcome means (all women)
Table 2. Means women's fertility outcomes by CGP treatment status
(1) (2) (3) (4)
Panel A: 24-month
All
(n=2669)
Control
(n=1324)
Treatment
(n=1345)
Difference
Currently pregnant 0.11 0.11 0.12 0.01
(0.01) (0.01) (0.01) (0.02)
Ever Pregnant 0.83 0.85 0.81 -0.04**
(0.01) (0.01) (0.01) (0.02)
Every miscarried, aborted, had
stillbirth
0.12 0.14 0.1 -0.04***
(0.01) (0.01) (0.01) (0.02)
total #children ever born alive 3.24 3.32 3.15 -0.16
(0.06) (0.09) (0.07) (0.11)
Standard errors in parenthesis.
* p<0.1 ** p<0.05; *** p<0.01

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Results: Program impacts on fertility
Poisson models
0.700
0.750
0.800
0.850
0.900
0.950
1.000
1.050
1.100
1.150
24 months 36 months 48 months 24 months 36 months 48 months 24 months 36 months 48 months
All women Main respondent Women <25 years
Total fertility - Incident risk ratio

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Results: impacts on other fertility-
related outcomes
Currently pregnant Ever pregnant
Ever miscarried,
aborted, had still birth
24 months 1.9 -2.5 -2.7
36 months 0.4 -1.6 0.7
48 months 0.1 -0.2 -2.1
-6.0
-4.0
-2.0
0.0
2.0
4.0
6.0
percentagepointimpact
Linear probability models; 90% CI

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Total Children in Household
 Difference-in-differences Poisson model estimates
 *p<.10; robust standard errors in parentheses
Table 5: Impact of CGP on Household-Level Child Counts, Ages 0-4 years, By
Gender, Poisson Models
All Female Males
(1) (2) (3) (4) (5) (6) (7) (8) (9)
24 mo 36 mo 48 mo 24 mo 36 mo 48 mo 24 mo 36 mo 48 mo
Treat*Time 0.01 0.04 0.02 0.01 0.06* 0.05 0.02 0.02 -0.01
(0.023) (0.026) (0.034) (0.030) (0.036) (0.046) (0.032) (0.034) (0.047)
N 4,815 4,976 4,942 4,815 4,976 4,942 4,815 4,976 4,942

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Conclusions
• No impacts of a cash transfer programme (targeted to families with
child <5 years) on fertility over a four-year period.
• First study to evaluate fertility impacts of an unconditional cash
transfer as reported from fertility histories of individual women in
Africa using an experimental design
• Findings consistent with existing evidence in the region

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• Article published: Palermo, Tia, et al. "Unconditional government social cash
transfer in Africa does not increase fertility." Journal of Population Economics
(2015): 1-29.
http://link.springer.com/article/10.1007/s00148-016-0596-x
• Transfer Project website: www.cpc.unc.edu/projects/transfer
• Briefs: http://www.cpc.unc.edu/projects/transfer/publications/briefs
• Facebook: https://www.facebook.com/TransferProject
• Twitter: @TransferProjct Email: tmpalermo@unicef.org
For more information
Ghana, credit: Ivan Griffi

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Transfer Project is a multi-organizational initiative of the United Nations Children’s Fund
(UNICEF) the UN Food and Agricultural Organization (FAO), Save the Children-United
Kingdom (SC-UK), and the University of North Carolina at Chapel Hill (UNC-CH) in
collaboration with national governments, and other national and international researchers.
Current core funding for the Transfer Project comes from the Swedish International
Development Cooperation Agency (Sida), as well as from staff time provided by UNICEF,
FAO, SC-UK and UNC-CH. Evaluation design, implementations and analysis are all funded
in country by government and development partners. Top-up funds for extra survey rounds
have been provided by: 3IE - International Initiative for Impact Evaluation (Ghana, Malawi,
Zimbabwe); DFID - UK Department of International Development (Ghana, Lesotho,
Ethiopia, Malawi, Kenya, Zambia, Zimbabwe); EU - European Union (Lesotho, Malawi,
Zimbabwe); Irish Aid (Malawi, Zambia); KfW Development Bank (Malawi); NIH - The United
States National Institute of Health (Kenya); Sida (Zimbabwe); and the SDC - Swiss
Development Cooperation (Zimbabwe); USAID – United States Agency for International
Development (Ghana, Malawi); US Department of Labor (Malawi, Zambia). The body of
research here has benefited from the intellectual input of a large number of individuals. For
full research teams by country, see: https://transfer.cpc.unc.edu/
Acknowledgements

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• Black, D. A., Kolesnikova, N., Sanders, S. G., & Taylor, L. J. (2013). Are children
“normal”? The review of economics and statistics, 95(1), 21-33.
• Handa, S., Peterman, A., Huang, C., Halpern, C. T., Pettifor, A., & Thirumurthy, H.
(2015). Impact of the Kenya Cash Transfer for Orphans and Vulnerable Children on
Early Pregnancy and Marriage of Adolescent Girls. Social Science & Medicine, 141,
36-45.
• Heinrich, C., Hoddinott, J., Samson, M., Mac Quene, K., van Nikerk, I., & Renaud, B.
(2012). The South African Child Support Grant Impact Assessment. South Africa:
Department of Social Development, South African Social Security Agency, UNICEF.
• Palermo et al. (2015). Unconditional Government Social Cash Transfer in Africa does
not increase Fertility. Innocenti Working Paper 2015-09.
• Rosenberg, M., Pettifor, A., Nguyen, N., Westreich, D., Bor, J., Barnighausen, T., . . .
Kahn, K. (2015). Relationship between receipt of a social protection grant for a child
and second pregnancy rates among South African women.
• Stecklov, G., Winters, P., Todd, J., & Regalia, F. (2007). Unintended effects of poverty
programmes on childbearing in less developed countries: experimental evidence from
Latin America. Population Studies, 61(2), 125-140.
• Todd, J. E., Winters, P., & Stecklov, G. (2012). Evaluating the impact of conditional
cash transfer programs on fertility: the case of the Red de Protección Social in
Nicaragua. Journal of Population Economics, 25(1), 267-290.
References