Presented by Amber Peterman at EU/EFTA meeting in March 2018.

1. unite for
children
Results for Poverty Reduction
through Social Protection in Africa
Amber Peterman
On behalf of the UNICEF Office of Research—Innocenti
Transfer Project Team
EU/EFTA meeting; March 2018, Stockholm

2. 2
Source: Cirillo & Tebaldi 2016 (Social Protection in Africa: Inventory of Non-Contributory Programmes): www.ipc-
undp.org/pub/eng/Social_Protection_in_Africa.pdf
Rise of social protection in Africa:
Non-contributory Gov’t programming triples over last 15 years

3. 3
Percentage of total population covered by at least
one social protection benefit (2015, SDG indicator 1.3.1)
Source: World Social Protection Report 2017-2019: Universal social protection to achieve the Sustainable Development
Goals, ILO, Geneva, 2017.

4. 4
Evidence from Africa: Recent reviews
Source: Bastagli et al. (2016). Cash transfers: What does the evidence say? ODI, London; Hidrobo
et al. (2018) Social Protection, Food Security and Asset Formation World Development 101: 88-103.
Bastagli et al. (2016): 201 studies
0 20 40 60
Caloric intake
Food consumption
Livestock
Productive assets (non-farm)
Productive assets (farm)
Savings
Number of indicators
Sub-Saharan Africa East Asia & Pacific Europe & Central Asia
LAC MENA South Asia
Hidrobo et al. (2018): 91 studies

5. 5
Transfer Project: Partners & motivation
 Created 2009 as an Institutional Partnership between UNICEF,
FAO, Save the Children, University of North Carolina
 Originally 6 countries in SSA, but expanded given high demand
 Working in close collaboration with national counterparts,
including national governments and research institutions
 Objectives:
1. Provide evidence on the effectiveness of cash transfers in
achieving impacts for children & households
2. Inform the development and design of policy & programs
3. Promote learning across the continent on the design &
implementation of cash transfer evaluations and research

6. 6
The Transfer Project & From Evidence to Action
Ethiopia, Ghana,
Kenya, Lesotho,
Malawi, Madagascar,
South Africa,
Tanzania, Zambia &
Zimbabwe
From Evidence to Action, Open access: http://www.fao.org/3/a-i5157e.pdf

7. 7
Overview of Transfer Project Evaluations
Country
(program)
Targeting
(in addition to
poverty)
Sample
size
(HH)
Methodology LEWIE Youth
Years of data
collection
Ethiopia (SCTP) Labour-constrained 3,351 Longitudinal PSM X 2012, 2013, 2014
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
Kenya (CT-OVC) OVC 1,913 RCT X X 2007, 2009, 2011
Lesotho (CGP) OVC 1,486 RCT X 2011, 2013
Madagascar
LUL/TMDH
School-age or child <5 2799 RCT 2016, 2017
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
Tanzania (PSSN
Cash Plus)
Food poor 2209 RCT X 2017, 2018, 2019
Zambia (CGP) Child 0-5 2,519 RCT
X 2010, 2012, 2013,
2014, 2017
Zambia (MCTG)
Female, elderly,
disabled, OVC
3,078 RCT X 2011, 2013, 2014
Zimbabwe (HSCT)
Food poor, labour-
constrained
3,063
Longitudinal case-
control
X X 2013, 2014, 2017

8. 8
Total consumption pc
Food security scale (HFIAS)
Overall asset index
Relative poverty index
Incomes & Revenues index
Finance & Debt index
Material needs index (5-17)
Schooling index (11-17)
Anthropometric index (0-59m)
-.2 0 .2 .4 .6 .8
Effect size in SDs of control group
36-month results
Broad Impacts from two Zambian programsMCP
CGP
Source: Handa et al. (2018). Can Unconditional Cash Transfers raise long-
term living standards? Journal of Development Economics 133: 42-65

9. 9
Consumption
Food Security
Assets
Finance & Debt
Income & Revenue
Subjective well-being
Children’s
Material Needs
Schooling
Child Nutrition
-.1 .1 .3 .5 .7 .9
Effect size in SDs of the control groupSource: UNC (2016) Malawi Endline Impact Evaluation Report.
Broad Impacts from Malawi SCTP
30-month results

10. 10
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)
Percentagepointimpact
(48 months)
Solid bars represent significant impact, shaded insignificant.
Impacts are measured in percentage points, unless otherwise specified

11. 11
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
Pc food expenditures (overall)        
Pc 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
Source: Hjelm 2016 (The impact of cash transfers on food security): https://transfer.cpc.unc.edu/wp-
content/uploads/2015/09/The-Impact-of-Cash-Transfers-on-Food-Security.pdf

12. 12
No evidence cash transfers increase
beneficiary spending on ‘undesirable’ goods
(e.g. alcohol & tobacco)
 Alcohol & tobacco represent <2% of budget share across 7
evaluations (data comes from detailed consumption modules
with over 120 food items)
 No positive impacts on alcohol & tobacco spending (consistent
with meta-analysis on cash transfers & temptation goods).
 In Lesotho, impacts are negative (decreases in spending)
 Alternative measures in 4 evaluations yield same result:
 “Has alcohol consumption increased in this community over the
last year?”
“Is alcohol consumption a problem in your community?”
Source: Handa et al. 2017 (Myth-Busting? Confronting Six Common Perceptions about Unconditional Cash
Transfers as a Poverty Reduction Strategy in Africa): https://www.unicef-irc.org/publications/899/

13. 13
School enrollment impacts (secondary age children):
Same range as those from CCTs in Latin America
8
3
7
8
16
8
7 7
1
9
6
10
0
2
4
6
8
10
12
14
16
18
20
Percentagepointimpact
Primary enrollment already high, impacts at secondary level. Ethiopia is all children age 6-16.
Bars represent percentage point impacts - Solid bars represent significant impact, shaded not significant.

14. 14
Significant increase in material needs for school-age
children’s clothing, shoes, blanket
11
26
30
23
32
26
0
5
10
15
20
25
30
35
Ghana
(LEAP)
Lesotho
(CGP)
Malawi
(SCTP)
Zambia
(MCTG)
Zambia
(CGP)
Zimbabwe
(HSCT)
Percentagepointimpact
Lesotho includes shoes and school uniforms only, Ghana is schooling expenditures for ages 13-17. Other
countries are shoes, change of clothes, blanket ages 5-17.
Bars represent percentage point impacts - Solid bars represent significant impact, shaded not significant.
“I do not lack food that much
nowadays because the money
from Mtukula Pakhomo is there
to use to support us. Life has
changed. It has helped in
school, I have food, have
bought a change of clothes. In
the past I had only one set of
clothes that when I come from
school I could wash it at night
and wear it the next day. (Now)
the uniform is in good
condition and not torn up.”
~ Male youth in beneficiary household (on
recently transitioning to secondary school)

15. 15
Resilience

16. 16
Productive investments
Ethiopia
SCTP
Ghana
LEAP
Kenya
CT-OVC
Lesotho
CGP
Malawi
SCTP
Zambia
CGP
Zambia
MCTG
Zim
HSCT
Tropical Livestock Units (TLU)        
Any livestock ownership        
Any agricultural asset ownership        
Expenditure on crop inputs     
Value of harvest (local units)      
Green check marks represent significant impact, red adverse impact,
black are insignificant and empty is indicator not collected
 Overall impacts mask impacts on specific livestock/activities
 Households substitute out of casual paid labor (off farm) to on farm/small
businesses – no evidence that households systematically decrease work
participation
 Positive impacts on savings, networks, decreases in credit constraints vary by
country
Source: Handa et al. 2017 (Myth-Busting? Confronting Six Common Perceptions about Unconditional Cash
Transfers as a Poverty Reduction Strategy in Africa): https://www.unicef-irc.org/publications/899/

17. 17
Household-level multipliers
0.69 0.72
0.61
0.47
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Malawi
(SCTP, 36
months)
Zambia
(MCTG, 36
months)
Zambia
(CGP, 36
months)
Zimbabwe
(HSCT, 48
months)
Householdmultiplier
Transfer Multiplier
• Compare value of transfer to
measured ‘benefits’ in
monetary terms (spending,
savings, increases in assets,
income)
• Only significant impacts
considered, deflated to
baseline values
• Pathways for multipliers are
diverse (Ex. Agriculture in
MCTG, non-farm business in
CGP)

18. 18
Community-level impacts
• In 5 evaluations, tested for inflation using a basket of 10
commonly purchased goods
• No inflationary impacts found (*exception price of beef in
Lesotho)
• Why?  Supply expanded to meet demand, beneficiaries
relatively small proportion of population
• Local Economy Wide Impact Evaluation (LEWIE) models
collect information on non-beneficiaries and local
businesses/markets at baseline to simulate community-level
impacts of transfers

19. 19
Local Economy Wide Impact Evaluation
(LEWIE) estimates
Source: Taylor E, Thome K, Filipski M. “Local Economy-Wide Impact Evaluations of Social Cash Transfer
Programmes.” In “From Evidence to Action.” Eds. Davis B, Handa S, Hypher N, Winder Rossi N, Winters P,
Yablonski J. 2016. Oxford: Oxford University Press.
1.35
2.52 2.50
1.81
1.34
2.23
1.27
1.79 1.73
0
0.5
1
1.5
2
2.5
3
Ethiopia
SCTPP
(Abi-Adi)
Ethiopia
SCTPP
(Hintalo)
Ghana
LEAP
Kenya
CT-OVC
(Garissa)
Kenya
CT-OVC
(Nyanza)
Lesotho
CGP
Malawi
SCTP
Zambia
CGP
Zimbabwe
HSCT

20. 20
‘Psychology’ of poverty
 Poverty has psychological consequences (myopia, stress,
impatience, cognitive overload)
 Poor, sub-optimal decisionmaking which reinforces poverty
 New work using ‘lab in the field’ experiments soliciting responses of
how long participants are willing to wait for payoffs
 Results in Malawi & Zambia indicate cash transfers can alleviate
some of these negative mental states, including reduced stress &
willingness to wait for payouts
 Some measurement issues still to tackle: Perceived stress scales not
necessarily ‘calibrated’ to rural Africa context, biomarkers still costly &
problematic to collect

21. 21
Conclusions & what’s next?
 Large-scale government unconditional cash transfers have
strong, positive impacts on:
 Poverty, food security & expenditures
 Human capital
 Resiliency-related outcomes (assets, productive investment)
 Beyond beneficiaries: Local economies
 Design matters: Amount of transfer, regularity or payments
 Cash is important, but not sufficient: Supply side limitations
(health & education)
 Next frontier: “cash plus” programming & evaluation to examine
synergies
 Policy uptake important for understanding how to maximize
impact on poverty & human capital for poor populations

22. 22
Tack
Asante
Zikomo
Grazie
Obrigado!
Ghana LEAP 1000
(© Michelle Mills)

23. 23
Transfer Project is a multi-organizational initiative of the United Nations Children’s Fund (UNICEF)
the UN Food and Agriculture 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) to UNICEF Office of Research, 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

24. 24
• Transfer Project website: www.cpc.unc.edu/projects/transfer
• Facebook: https://www.facebook.com/TransferProject
• Twitter: @TransferProjct
Transfer Project: For more information
©FAO/Ivan Grifi

25. 25
Responding to
the critics
with evidence
Translated into 8
languages!
Available for download: http://www.fao.org/documents/card/en/c/fe894cf7-a0ba-4c13-8ec6-
3c6d09b5e7b7

26. 26
Age pyramids: Labor constrained
populations
0-5
6-10
11-15
16-20
21-25
26-30
31-35
36-40
41-45
46-50
51-55
56-60
61-65
66-70
71-75
76-80
81+
0-5
6-10
11-15
16-20
21-25
26-30
31-35
36-40
41-45
46-50
51-55
56-60
61-65
66-70
71-75
76-80
81+
15 10 5 0 5 10 15 15 10 5 0 5 10 15 15 10 5 0 5 10 15 15 10 5 0 5 10 15
15 10 5 0 5 10 15 15 10 5 0 5 10 15 15 10 5 0 5 10 15 15 10 5 0 5 10 15
Ethiopia SCTPP Ghana LEAP Kenya CT-OVC Lesotho CGP
Malawi SCT Zambia CGP Zambia MCTG Zimbabwe HSCT
males females
Population (%)

27. 27
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)