Cash transfers and human capital development: Evidence, gaps and potential

Sudhanshu Handa on behalf of the Transfer ProjectUNICEF Office of Research-Innocenti and UNC

Presented at the Transfer Project Annual Workshop: Addis Ababa, April 2016

2

No CTs

After 2004

Prior to 2004

No data

Transfer Project

Cash Transfer programs in sub-Saharan Africa: The ‘quiet’ revolution

Households covered andpercent of population: Government programs

64000 69000 80000

150000 163000 170000 182000 190000250000

310000

455000

11000001125000

0

200000

400000

600000

800000

1000000

1200000

2.0% 3.2%2.5% 34.8% 4.6%

21.5% 5.4%46.8%

5.1%

4.6%

2.0%

Not included (due to scale): CSG in South Africa (>11 million recipients)

0.2%

5.2%10.6%

Percent of population are estimates assuming average household size and one beneficiary per household (child grant and pension designs)

Programs tend to be unconditional (or with ‘soft’ conditions), with exception of Tanzania (conditional on schooling, health)

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’

Unique demographic structure of recipient households: Missing prime-ages

Malawi SCT Zimbabwe HSCT

0.0

1.0

2.0

3.0

4

Density

0 20 40 60 80Age (years)

Zambia SCT (Monze)

0.0

1.0

2.0

3.0

4

Density

0 20 40 60 80

Age (years)

0.0

1.0

2.0

3.0

4

Density

0 20 40 60 80 100

Age (years)

0.0

2.0

4.0

6

Density

0 20 40 60 80 100

Age (years)

Kenya CT-OVC

Zimbabwe HCSTMalawi SCTP

Labor-constrained criterion selects unique households: Example from Zambia

Zambia SCT Households Rural Ultra-Poor LCMS 2010

0.0

1.0

2.0

3.0

4.0

5.0

6D

ensity

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100105

Age (years)

0.0

1.0

2.0

3.0

4.0

5.0

6D

ensity

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Age (years)

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?

How much do programs pay? Transfer as share of beneficiary pre-program consumption

0

5

10

15

20

25

30

35

40

A critical threshold

% o

r p

er c

apit

a co

nsu

mp

tio

n

Overview of programs & evaluations connected with Transfer Project

Country (program)

Targeting (in addition to poverty)

Sample size (HH)

Methodology LEWIE YouthYears 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 RCTX

2010, 2012, 2013, 2014

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, 2016

Cas

h T

ran

sfer

Mediators

• Future expectations

• Attitudes towards risk

• Information

Household

Consumption

• Food Security

• Material well-being

Investment

• Crop production

• Livestock

• Assets

Time-use

• Use of services

• Caring practices

• Labor

Income

Income

Young Child

• Nutrition

• Illness

Older Child

• Schooling

• Material well-being

• Work

• HIV risk

• Mental health

Adult Care-giver

• Self-assessed welfare

• Health

• Distance/quality of facilities

• Prices

• Shocks

• Infrastructure

Moderators

• Services

• Norms

How does cash affect the household and its members?

LEVEL 1

LEVEL 2

Summary of results based on 7 rigorous impact evaluations

Domain of impact Evidence

Food security, extreme poverty

Alcohol & Tobacco

Subjective well-being

Secondary school enrollment

Spending on school inputs (uniforms, shoes, clothes)

Health

Spending on health

Nutritional status

Increased fertility

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 head

count (pp)

Reduction in poverty gap (pp)

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

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

No evidence cash is ‘wasted’ on alcohol & tobacco

Alcohol/tobacco represent 1% of budget share

Across 7 countries, no positive impacts found on alcohol/tobacco:

Data comes from detailed consumption modules covering over 250 individual items

In Lesotho negative impacts on alcohol consumption (possible decrease through decrease in poverty-related stress?)

Alternative measurement approaches yield same result:

“Has alcohol consumption increased in this community over the last year?”

“Is alcohol consumption a problem in your community?”

Beneficiaries are happier too: Consistent impacts on subjective well-being

41%

9%

20%

6%

12%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Ghana LEAP(happiness)

Zambia CGP(happiness,

women)

Malawi SCT (QoLscore)

Kenya CT-OVC(QoL score)

Zimbabwe HSCT(SWB scale)

**

***

**

**

**

Impacts are percentage changes, countries not shown did not collect data on subjective well-being

What about the kids?

School enrollment impacts (secondary age children): Equal to 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; all impact are significant.

Grade 3 math test – Serenje District, ZambiaMore kids in school but school quality still a challenge

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

Solid bars represent significant impact, shaded insignificant.Impacts are measured in percentage points; 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.

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

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)

Green check marks represent positive protective impacts, black are insignificant and red is risk factor impact. Empty is indicator

not collected

Budget shares and expenditure impacts on health

8.7

5.8

2.1

3

1.4

5

3.9

0

1

2

3

4

5

6

7

8

9

10

Ghana(LEAP)

Kenya (CT-OVC)

Lesotho(CGP)

Malawi(SCTP)

Zambia(CGP)

Zambia(MCTG)

Zimbabwe(HSCT)

Ethiopia

NS

Solid bars represent significant impact, shaded insignificant.Impacts are measured in percentage points (top figure). Bars represent % of budget share at baseline - Malawi figures represent treatment means.

No impacts on young child nutritional status (anthropometry)

Evidence based on Kenya CT-OVC, South Africa CSG, Zambia CGP, Malawi SCTP, Zimbabwe HSCT However, Zambia CGP 13pp increase in IYCF 6-24 months

Some heterogeneous impacts If mother has higher education (Zambia CGP and South Africa CSG) or

if protected water source in home (Zambia CGP)

Possible explanations… Determinants of nutrition complex, involve care, sanitation, water,

disease environment and food

Weak health infrastructure in deep rural areas

Few children 0-59 months in typical OVC or labor-constrained household

No fertility incentives!

Malawi & Kenya: DD Probit models predicting Pr(child aged 0-1 in household)

Zambia: DD Poisson models estimating number of children 0-1 years in household

0.700

0.800

0.900

1.000

1.100

1.200

1.300

All Male Female

Zambia

Total children 0-1 year - Incident risk ratio

-60

-50

-40

-30

-20

-10

0

10

20

30

40

50

All Girls Boys All Girls Boys

Malawi SCTP Kenya CT-OVC

per

cen

tage

po

int

imp

act

Impacts on number of children, ages 0-1, in the household, Malawi, Kenya

Scaled up cash transfers are affordable in SSAPlausible simulations show average cost 1.1% of GDP or 4.4% of spending

0%

5%

10%

15%

20%

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In % of general government total expenditureIn % of GDP

Emerging evidence that effect of cash larger depend on supply side factors

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]

What determines type and size of impacts?

Predictability of transfers (Allows planning, consumption smoothing)

Size of transfer and protection from inflation (Rule of thumb of 20% of mean consumption of target population)

Context (Supply of health and education, user fees)

Who you target (Labor-constrained; households with more adolescents/OVC and fewer pre-school children)

Evidence, potential, gaps

Evidence: Cash transfers are protective—they work

Potential: Programs are affordable, can contribute to inclusive growth strategy

Gaps: Health and nutrition effects on 0-5 years inconsistent

Few households with young children targeted are reached under current approaches

Health infrastructure not as well developed as schooling, attitudes and other factors at play in demand for health

Summary of results based on 7 rigorous impact evaluations

Domain of impact Evidence

Food security, extreme poverty

Alcohol & Tobacco

Subjective well-being

Secondary school enrollment

Spending on school inputs (uniforms, shoes, clothes)

Health

Spending on health

Nutritional status

Increased fertility

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 @ashudirect

Email: Ashu Handa, shanda@unicef.org

For more information

Photo credits: Ghana LEAP 1000 coverphoto, Ivan Grifi (2015) Kids in Malawi, slide 16, Darlen Dzimwe (2014) Math test Zambia MCTG, slide 18, Ashu Handa (2013)

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