unite for children

Lucia Ferrone, UNICEF Office of Research—Innocenti On behalf of the Transfer Project

November 18, 2016GLOBAL COALITION TO END CHILD POVERTY – IDS, Brighton

Social Protection for SDG 1: evidence and experiences from the Transfer Project

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

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

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

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 GovernmentsMission: 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

Transfer Project Countries

Ethiopia, Ghana, Kenya, Lesotho, Malawi, Madagascar, South Africa, Tanzania, Zambia and Zimbabwe

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-

controlX X 2013, 2014, 2017

8

Unique demographic structure of recipient households: Missing prime-ages

Malawi SCT Zimbabwe HSCT

0.0

1.0

2.0

3.0

4D

ensi

ty

0 20 40 60 80Age (years)

Zambia SCT (Monze)

0.0

1.0

2.0

3.0

4D

ensi

ty

0 20 40 60 80

Age (years)

0.0

1.0

2.0

3.0

4D

ensi

ty

0 20 40 60 80 100Age (years)

0.0

2.0

4.0

6D

ensi

ty

0 20 40 60 80 100Age (years)

Kenya CT-OVC

Zimbabwe HCSTMalawi SCTP

9

ProgramFemale

beneficiaries

(%)

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

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

Reductions on poverty measures

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)

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

10 10

1.4

1211

10

0

Solid bars represent significant impact, shaded insignificant.Impacts are measured in percentage points, unless otherwise specified

12

Across-the-board impacts on food securityEthiopia

SCTPGhana LEAP

Kenya CT-

OVC

Lesotho CGP

Malawi SCTP

Zambia MCTG

Zambia CGP

Zim HSCT

Spending on food & quantities consumed  

Per capita food expenditures ü ü ü ü ü ü ü ü Per capita expenditure, food items ü ü ü ü ü ü Kilocalories per capita ü üFrequency & diversity of food consumption   Number of meals per day   ü ü ü Dietary diversity/Nutrient rich food ü ü ü ü ü üFood consumption behaviours   Coping strategies adults/children ü ü ü ü Food insecurity access scale   ü ü ü

Green check marks represent significant impact, black are insignificant and empty is indicator not collected

13

School enrollment impacts (secondary age children): Comparable to those from CCTs in LAC

02468

101214161820

8

3

78

15

89

12

6

9

6

10

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

Significant impacts on spending on school-age children(uniforms, children’s shoes and clothing)

Ghana

(LEAP)

Leso

tho (C

GP)

Malawi (S

CTP)

Zambia

(MCTG)

Zambia

(CGP)

Zim (H

SCT) small

hh

Zim (H

SCT) large

hh05

101520253035

11

2630

23

32

11

5

15

Young child health and morbidityRegular 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 ü ü ü ü ü üPreventive care ü ü üCurative care ü ü ü üEnrollment into the National Health Insurance Scheme üVitamin A supplementation ü

Supply of services typically much lower than for education sector.More consistent impacts on health expenditure (increases)

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

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

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

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

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)