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unite for children
An Unconditional Government Social Cash Transfer in Africa Does not Increase Fertility
Tia PalermoUNICEF 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, 2016Population Association of America Annual Meeting, Washington, DC
2
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
Coverage of select Government programs
Nigeria
Zimba
bwe
Rwanda
Ghana
Leso
tho
Malawi
Botswan
a
Zambia
Namibi
a
Mozam
bique
Kenya
Tanza
nia
Ethiop
ia0
200000
400000
600000
800000
1000000
1200000
64000 69000 80000150000 163000 170000 182000 190000
250000310000
455000
1100000 1125000
Not included (due to scale): CSG in South Africa (>11 million recipients)
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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
5
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
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
Current study aim
Examine impacts of Zambia Cash Grant Programme (CGP) on fertility
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
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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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MeasuresOutcomes 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
12
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
13
Results: sample characteristicsTable 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/widowed (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
15
Results: Program impacts on fertility
Poisson models
24 months 36 months 48 months 24 months 36 months 48 months 24 months 36 months 48 monthsAll women Main respondent Women <25 years
0.700
0.750
0.800
0.850
0.900
0.950
1.000
1.050
1.100
1.150
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
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
perc
enta
ge p
oint
impa
ct
Linear probability models; 90% CI
17
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 moTreat*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
19
• 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: [email protected]
For more information
Ghana, credit: Ivan Griffi
20
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