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unite for
children
Lucia Ferrone, UNICEF Office of Research—Innocenti
On behalf of the Transfer Project
November 18, 2016
...
2
Rise of social protection in Africa:
Non-contributory Govt programming triples over
last 15 years
Source: Cirillo & Teba...
3
Typologies of programs & target
groups
Source: Cirillo & Tebaldi 2016 (Social Protection in Africa: Inventory of Non-Con...
4
Key feature of the African Model
• Programs tend to be unconditional (or with ‘soft’ conditions)
• Targeting tends to be...
5
The Transfer Project
Who: Community of research, donor and implementing partners – focus
on coordination in efforts and ...
6
Transfer Project Countries
Ethiopia, Ghana, Kenya, Lesotho, Malawi,
Madagascar, South Africa, Tanzania,
Zambia and Zimba...
7
Overview of programs & evaluations connected
with Transfer Project
Country
(program)
Targeting
(in addition to poverty)
...
8
Unique demographic structure of recipient
households: Missing prime-ages
Malawi SCT Zimbabwe HSCT
0.01.02.03.04
Density
...
9
Program
Female
beneficiarie
s
(%)
Female-
headed
households
(%)
Ghana LEAP 44 60
Ghana LEAP 1000 100 11
Kenya CT-OVC 85 ...
10
How can cash transfers impact child
poverty?
 Direct impact on monetary poverty:
• Immediate increase in consumption
•...
11
Reductions on poverty measures
10 10
1.4
12
11
10
0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
Ethiopia
(24 months)
Kenya
(24 m...
12
Across-the-board impacts on food security
Ethiopia
SCTP
Ghana
LEAP
Kenya
CT-
OVC
Lesotho
CGP
Malawi
SCTP
Zambia
MCTG
Za...
13
School enrollment impacts (secondary age
children): Comparable to those from CCTs in LAC
8
3
7
8
15
8
9
12
6
9
6
10
0
2...
14
Significant impacts on spending on school-age
children
(uniforms, children’s shoes and clothing)
11
26
30
23
32
11
5
0
...
15
Young child health and morbidity
Regular impacts on morbidity, but less consistency on
care seeking
Ghana
LEAP
Kenya
CT...
16
Role of supply side:
 Example 1: Skilled attendance at birth improved in Zambia
CGP, only among women with access to q...
17
Conclusion and way forward
 UCTs can effectively reduce child poverty in different
dimensions: monetary and MD poverty...
18
For more information
• Transfer Project website: www.cpc.unc.edu/projects/transfer
• Briefs: http://www.cpc.unc.edu/pro...
19
Acknowledgements
Transfer Project is a multi-organizational initiative of the United Nations Children’s Fund
(UNICEF) t...
20
Additional slides: General approach
to modeling
• Probit or ordinary least squares (OLS) multivariate
regressions
• Bas...
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Child Poverty Research Day: Reducing Economic Poverty - Lucia Ferrone, 'Social Protection for SDG 1: Evidence and Experiences from the Transfer Project'

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Child Poverty Research Day: Lucia Ferrone
Institute of Development Studies, Brighton.
18th November 2016

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Child Poverty Research Day: Reducing Economic Poverty - Lucia Ferrone, 'Social Protection for SDG 1: Evidence and Experiences from the Transfer Project'

  1. 1. unite for children Lucia Ferrone, UNICEF Office of Research—Innocenti On behalf of the Transfer Project November 18, 2016 GLOBAL COALITION TO END CHILD POVERTY – IDS, Brighton Social Protection for SDG 1: evidence and experiences from the Transfer Project
  2. 2. 2 Rise of social protection in Africa: Non-contributory Govt programming triples over last 15 years Source: Cirillo & Tebaldi 2016 (Social Protection in Africa: Inventory of Non-Contributory Programmes): www.ipc- undp.org/pub/eng/Social_Protection_in_Africa.pdf
  3. 3. 3 Typologies of programs & target groups Source: Cirillo & Tebaldi 2016 (Social Protection in Africa: Inventory of Non-Contributory Programmes): www.ipc- undp.org/pub/eng/Social_Protection_in_Africa.pdf
  4. 4. 4 Key feature of the African Model • Programs tend to be unconditional (or with ‘soft’ conditions) • Targeting tends to be based on poverty & vulnerability •OVC, labor-constrained, elderly • Important community involvement in targeting process • Payments tend to be manual, ‘pulling’ participants to pay-points •Opportunity to deliver complementary services
  5. 5. 5 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) social cash transfers (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
  6. 6. 6 Transfer Project Countries Ethiopia, Ghana, Kenya, Lesotho, Malawi, Madagascar, South Africa, Tanzania, Zambia and Zimbabwe
  7. 7. 7 Overview of programs & evaluations connected with Transfer Project Country (program) Targeting (in addition to poverty) Sample size (HH) Methodology LEWIE Youth Years of data collection Ghana (LEAP) Elderly, disabled or OVC 1,614 Longitudinal PSM X 2010, 2012, 2016 Ghana (LEAP 1000) Pregnant women, child<2 2,500 RDD 2015, 2017 Ethiopia (SCTP) Labour-constrained 3,351 Longitudinal PSM X 2012, 2013, 2014 Kenya (CT-OVC) OVC 1,913 RCT X X 2007, 2009, 2011 Lesotho (CGP) OVC 1,486 RCT X 2011, 2013 Malawi (SCTP) Labour-constrained 3,500 RCT X X 2011, 2013, 2015 South Africa (CSG) Child <18 2,964 Longitudinal PSM X 2010, 2011 Tanzania (PSSN) Food poor 801 RCT X 2015, 2017 Zambia (CGP) Child 0-5 2,519 RCT X 2010, 2012, 2013, 2014 Zambia (MCTG) Female, elderly, disabled, OVC 3,078 RCT X 2011, 2013, 2014 Zimbabwe (HSCT) Food poor, labour- constrained 3,063 Longitudinal matched case- control X X 2013, 2014, 2017
  8. 8. 8 Unique demographic structure of recipient households: Missing prime-ages Malawi SCT Zimbabwe HSCT 0.01.02.03.04 Density 0 20 40 60 80 Age (years) Zambia SCT (Monze) 0.01.02.03.04 Density 0 20 40 60 80 Age (years) 0.01.02.03.04 Density 0 20 40 60 80 100 Age (years) 0.02.04.06 Density 0 20 40 60 80 100 Age (years) Kenya CT-OVC Zimbabwe HCSTMalawi SCTP
  9. 9. 9 Program Female beneficiarie s (%) Female- headed households (%) Ghana LEAP 44 60 Ghana LEAP 1000 100 11 Kenya CT-OVC 85 85 Malawi SCTP 84 84 Zambia CGP 99 - Zambia MCTG 75 - Zimbabwe HSCT 68 68 And three of five beneficiary HH are female-headed Approximately two-thirds of beneficiaries are female Figures for female-headed households may reflect evaluation sample, rather than beneficiary sample. Zambia studies did not collect information on headship. Who gets the cash?
  10. 10. 10 How can cash transfers impact child poverty?  Direct impact on monetary poverty: • Immediate increase in consumption • Long term effects: household economic strengthening, resiliency and financial wellbeing; productive impacts.  Indirect impact on different dimensions of poverty: • Education • Food security/Nutrition • Health outcomes Impact on MD poverty
  11. 11. 11 Reductions on poverty measures 10 10 1.4 12 11 10 0 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 Ethiopia (24 months) Kenya (24 months) Lesotho (24 months) Malawi (36months) Zambia MCTG (36 months) Zambia CGP (48 months) Zimbabwe (12 months) Increase in household income (%) Reduction in poverty head count (pp) Reduction in poverty gap (pp) Solid bars represent significant impact, shaded insignificant. Impacts are measured in percentage points, unless otherwise specified
  12. 12. 12 Across-the-board impacts on food security Ethiopia SCTP Ghana LEAP Kenya CT- OVC Lesotho CGP Malawi SCTP Zambia MCTG Zambia CGP Zim HSCT Spending on food & quantities consumed Per capita food expenditures         Per capita expenditure, food items       Kilocalories per capita   Frequency & diversity of food consumption Number of meals per day    Dietary diversity/Nutrient rich food       Food consumption behaviours Coping strategies adults/children     Food insecurity access scale    Green check marks represent significant impact, black are insignificant and empty is indicator not collected
  13. 13. 13 School enrollment impacts (secondary age children): Comparable to those from CCTs in LAC 8 3 7 8 15 8 9 12 6 9 6 10 0 2 4 6 8 10 12 14 16 18 20 Primary enrollment already high, impacts at secondary level. Ethiopia is all children age 6-16. Bars represent percentage point impacts; all impact are significant.
  14. 14. 14 Significant impacts on spending on school-age children (uniforms, children’s shoes and clothing) 11 26 30 23 32 11 5 0 5 10 15 20 25 30 35 Ghana (LEAP) Lesotho (CGP) Malawi (SCTP) Zambia (MCTG) Zambia (CGP) Zim (HSCT) small hh Zim (HSCT) large hh
  15. 15. 15 Young child health and morbidity Regular impacts on morbidity, but less consistency on care seeking Ghana LEAP Kenya CT-OVC Lesotho CGP Malawi SCTP Zambia CGP Zimbabwe HSCT Proportion of children who suffered from an illness/Frequency of illnesses       Preventive care    Curative care     Enrollment into the National Health Insurance Scheme  Vitamin A supplementation  Supply of services typically much lower than for education sector. More consistent impacts on health expenditure (increases)
  16. 16. 16 Role of supply side:  Example 1: Skilled attendance at birth improved in Zambia CGP, only among women with access to quality maternal health services  Example 2: Anthropometry in Zambia CGP improved among households with access to safe water source  Example 3: Impacts on schooling enrollment in Kenya CT- OVC are largest among households which face higher out of pocket costs (uniform/shoes requirement, greater distance to school) [program offsets supply side barrier]
  17. 17. 17 Conclusion and way forward  UCTs can effectively reduce child poverty in different dimensions: monetary and MD poverty alike  Proven effective social protection tool in many domains  Many myths about UCTs disproven by evidence  But: they need investment on the supply side to complement the impact in critical domains such as health, sanitation & water, etc  What we need to know more:  Impact on child MD poverty (we are working on it!)  Impacts in different settings: universality of SDGs. Can the model be exported?  Social Protection in emergency settings  What’s the impact of combined interventions?
  18. 18. 18 For more information • 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 Ghana, credit: Ivan Griffi
  19. 19. 19 Acknowledgements 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/
  20. 20. 20 Additional slides: General approach to modeling • Probit or ordinary least squares (OLS) multivariate regressions • Baseline balance/ successful randomization in all countries • For “once occurring outcomes” use endline cross section and drop those who had already reported outcome at baseline • For outcomes changing over time (mental health, education, aspirations), use difference-in-difference models • Control for baseline individual, household, community characteristics & cluster standard errors • Weight for probability of appearing in sample (among all eligible youth in any given household)

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