unite for

children

The role of cash transfers in sub-Saharan Africa in

poverty alleviation:

Evidence from the Transfer Project

Richard de GrootUNICEF Office of Research—Innocenti

On Behalf of the Transfer Project Team

UNU-WIDER Conference

5-7 July 2017: Maputo, Mozambique

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

▪ Programs tend to be unconditional (or with ‘soft’ conditions)

▪ Targeting is based on poverty and vulnerability (OVC, labor-

constraints, elderly)

▪ Important community involvement in targeting process

▪ Payments tend to be manual (‘pulling’ beneficiaries to pay-

points)

▪ Opportunity to deliver complementary services

Key features of the African ‘Model’

4

Transfer Project: Partners & motivation

▪ Created 2009 as an Institutional Partnership between FAO,

UNICEF, Save the Children, University of North Carolina at

Chapel Hill

▪ 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 and households

2. Inform the development and design of cash transfer policy and

programs

3. Promote learning across the continent on the design and

implementation of cash transfer evaluations and research

5

The Transfer Project & From Evidence to Action

Ethiopia, Ghana,

Kenya, Lesotho,

Malawi, Madagascar,

South Africa,

Tanzania, Zambia and

Zimbabwe

6

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 .8Effect size in SDs of control group

36-month results at a glance

Broad Impacts from two Zambian programsMCP

CGP

Source: Handa et al. (2016). Can Unconditional Cash Transfers

Lead to Sustainable Poverty Reduction? Working Paper.

7

Reductions on poverty measures

10 10

1.4

12

11

10

00.0

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 inhouseholdincome (%)

Reduction inpoverty headcount (pp)

Reduction in poverty gap (pp)

Solid bars represent significant impact, shaded insignificant.

Impacts are measured in percentage points, unless otherwise specified

8

Across-the-board impacts on food securityEthiopia

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

9

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 by Evans & Popova (2017) 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/

10

School enrollment impacts (secondary age children):

Same range as those from CCTs in Latin America

8

3

78

15

89

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 - Solid bars represent significant impact, shaded not significant.

11

Significant increase in share of households who

spend on school-age children’s uniforms, shoes and

other 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) largehh

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.

12

Resilience

©FAO/Ivan Grifi

13

Productive investmentsEthiopia

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/

14

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

15

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

EthiopiaSCTPP(Abi-Adi)

EthiopiaSCTPP(Hintalo)

GhanaLEAP

KenyaCT-OVC(Garissa)

KenyaCT-OVC(Nyanza)

LesothoCGP

MalawiSCTP

ZambiaCGP

ZimbabweHSCT

16

Conclusions and what’s next?

▪ Large-scale government unconditional cash transfers have

strong, positive impacts on:

▪ Poverty, food security and 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 and education)

▪ Next frontier: “cash plus” programming and evaluation to

examine synergies

▪ Policy uptake important for understanding how to maximize

impact on poverty and human capital for poor populations

17

Tack

Asante

Zikomo

Grazie

Obrigado!

Ghana LEAP 1000

(© Michelle Mills)

18

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

19

• 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

For more information

©FAO/Ivan Grifi

20

▪ A number of fledgling government programs and growing practice in SSA on cash transfers (2008)

▪ Some with plans for scaling up

▪ Most with models that were different from the well-known Latin American programs

▪ Little evidence from SSA

▪ A few programmes rolling out quantitative evaluations

▪ Others with evaluations but not rigorous methodology

▪ limited documentation and sharing on lessons, experience and impact evaluation

▪ Transfer Project: Responding to high demand for evidence to:

1) answer policy and program questions and

2) to influence and inform scale-up

In the beginning…

21

Overview of Transfer Project Evaluations

Country

(program)

Targeting

(in addition to

poverty)

Sample

size

(HH)

Methodology LEWIE YouthYears of data

collection

Ghana (LEAP)Elderly, disabled or

OVC1,614 Longitudinal PSM X 2010, 2012, 2016

Ghana (LEAP

1000)Pregnant women,

child<22,500 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 RCTX 2010, 2012, 2013,

2014, 2017

Zambia (MCTG)Female, elderly,

disabled, OVC3,078 RCT X 2011, 2013, 2014

Zimbabwe (HSCT)Food poor, labour-

constrained3,063

Longitudinal

matched case-

controlX X 2013, 2014, 2017

22

Scaled up cash transfers are affordable in SSA

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In % of general government total expenditure In % of GDP

Simulations show average cost 1.1% of GDP

or 4.4% of total Government spending

23

Age pyramids: Labor constrained

populations

0-56-1011-15

16-2021-25

26-3031-35

36-4041-45

46-5051-55

56-6061-65

66-7071-75

76-8081+

0-56-1011-15

16-2021-25

26-3031-35

36-4041-45

46-5051-55

56-6061-65

66-7071-75

76-8081+

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 (%)

24

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)

25

Responding to

the critics

with evidence