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

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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

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

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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

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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)

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Current study aim

Examine impacts of Zambia Cash Grant Programme (CGP) on fertility

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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

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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 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

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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

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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 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

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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