Transcript Slide 1

The Impact of Cash Transfer Programs in subSaharan Africa: Evidence from the Transfer Project
Sudhanshu Handa
UNICEF Office of Research-Innocenti
Outline
1. Overview on development oriented cash transfers in SSA
2. Summary of impacts – claims and ‘myth busters’
3. The future agenda for programming and research
https://transfer.cpc.unc.edu/
Social Protection is thriving in Africa
• Focusing on cash transfer programs alone
– >120 programs across the continent of all kinds
(Garcia & Moore 2012)
– ~30 long-term development programs in 20
countries
• Programs are ‘home-grown’
– Target on poverty and vulnerability; greater role of
community
– Unconditional or ‘soft conditions’
– Larger evidence base on impacts than any other
region: more countries, more topics
Cash Transfer Programs in Sub-Saharan Africa:
The ‘quiet’ revolution
No CTs
After 2004
Prior to 2004
No data
3
Households covered in selected programs
(RSA excluded; ETH direct beneficiaries only)
350000
300000
250000
200000
150000
100000
50000
0
Rapid scale-up in last year
Transfer Project: Regional initiative to study
impact of CTs in SSA
• Led by UNICEF with partner governments
– FAO on productive impacts and local economy
• Rigorous evaluations of national programs
– Technical support, design, analysis, policy dialogue
• SSA now has largest evidence base on cash
transfers anywhere in the world
– Over a dozen rigorous evaluations
– In SSA, we no longer need to refer to the “Latin
American evidence on CCTs”
Zambia SCT (Monze Evaluation)
.06
.04
Kenya CT-OVC
0
.01
.02
.02
Density
.04
.03
Unique demographic structure of recipient households
in OVC and labor-constrained models (missing prime-ages)
0
0
20
40
age at baseline
60
20
80
40
60
Age in Wave 1
80
100
.04
.04
0
Zimbabwe HSCT
.02
0
0
.01
.01
.02
Density
.03
.03
Malawi SCT
0
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
age
0
20
40
60
age
80
100
Labor-constrained criterion selects
unique households: Zambia
.05
.04
.03
.02
0
.01
0
.01
.02
.03
Density
.04
.05
.06
Age Distribution of Rural Severly Poor: Zambia LCMS 2010
.06
Age Distribution of Monze, Zambia SCT Beneficiaries
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100105
agey
Zambia SCT Households
0
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
agey
Rural Ultra-Poor LCMS 2010
Suspicious characters
Kaputa District – 20+ hours
by car from Lusaka
Kalabo Districts – 12+ hours by car
from Lusaka
Beneficiary, Migori District, Kenya
(missing generation family)
Beneficiary, Serenje District, Zambia
(missing generation family)
(household’s water source)
How much do programs pay? Benefit structure
and level in selected programs (US$)
# members
Ghana
LEAP
Malawi
SCT
MOZ
PSA
8
2.83
7
10
2
9.50
3.66
9
15
3
11
4.83
11
20
4+
13.25
6.17
13.50
25
Beneficiary consumption
pp per day
0.62
0.34
0.50
0.85
1 person
Zimbabwe Kenya CTHSCT
OVC
Zambia
SCT
15 flat
12 flat
0.70
0.30
ZAM: $24 if disabled member
MLW: 0.83 and 1.67 top-up per child in primary and secondary school
respectively
Among other things, impact depends on transfer size
40
Widespread impact
35
Selective impact
30
% or per capita consumption
25
20
15
10
5
0
Ghana
2010
Kenya Burkina
CT-OVC
(big)
TASAF
2012
Kenya RSA CSG Malawi Lesotho
CT-OVC
2014
CGP
(2010)
Ghana
2015
Kenya
CT-OVC
(small)
Zim
(HSCT)
Zambia Zambia Malawi
CGP
MCP
2007
Claim: Cash is ‘wasted’ on alcohol and
tobacco
• Alcohol & tobacco represent 1 percent of
budget share
• Across seven countries, no positive impacts
observed on alcohol and tobacco
– Data comes from detailed consumption modules
covering over 250 individual items
• Alternative measurement approaches yield
same result
– “Has alcohol consumption increased in this
community over the last year?”
Big impacts on food security, consumption or
diet diversity
Ghana*
10pp reduction in proportion of children missing a meal for an
entire day
Ethiopia
12% increase in diet diversity; 150 calories per week increase
in food (6%)
Lesotho
11pp reduction in proportion of children who had to eat fewer
meals because of food shortage; reduction by 1.5 in number
of months hhld had extreme shortage of food
Malawi
30% increase in consumption; 60pp increase in proportion of
households eating meat or fish (diet diversity)
Kenya
10% increase in consumption (and improved diet diversity)
Zambia CGP
30% increase in consumption (and improved diet diversity)
Zimbabwe
8% increase in consumption; 10% increase in diet diversity
Claim: Cash not used for productive activity or
investment, just a ‘hand-out’
School enrollment impacts among secondary age
children strong, equal to those from CCTs in Latin
America
20
18
16
14
12
10
8
6
4
2
0
All
Girls only
Primary enrollment already high, impacts at secondary level
Grade 3 math test – Serenje District, Zambia
Households invest in productive activities:
impact varies by country, transfer size, target group
Zambia
Ethiopia Malawi
Zim
Lesotho
Kenya
Ghana
Agricultural inputs
+++
+++
+++
NS
++
NS
+++
Agricultural tools
+++
+++
+++
+++
NS
NS
NS
Agricultural
production
+++
+++
NS
NS
++
NS
NS
Livestock ownership
+++
+++
+++
+++
++
Small
NS
Non farm enterprise
+++
NS
NS
+++
NS
+FHH
NS
NS=not significant
Less impact
Stronger impact
Mixed impact
Claim: Leads to laziness
.02
0
.01
Density
.03
.04
“I used to be a slave to ganyu (labour) but now I’m a bit free.”
-elderly beneficiary, Malawi
0
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
age
Reduction in casual wage labor, shift to on farm
and more productive activities
adults
Zambia
Kenya Malawi Ethiopia
ZIM
Lesotho
Ghana
Agricultural/casual wage
labor
---
---
---
---
---
--
NS
Family farm
+++
+++
NS
+++
NS
NS
+++
Non farm business (NFE)
+++
+++
NS
NS
NS
NS
NS
Non agricultural wage
labor
+++
NS
+++
NS
NS
NS
Shift from casual wage labour to family business—
consistently reported in qualitative fieldwork
Congo, Democratic Republic
Zimbabwe
Burundi
Liberia
Eritrea
Niger
Malawi
Central African Republic
Madagascar
Mali
Togo
Guinea
South Sudan
Mozambique
Guinea-Bissau
Comoros
Ethiopia
Sierra Leone
Burkina Faso
Uganda
Rwanda
Benin
Tanzania, United Republic of
Zambia
Côte d'Ivoire
Kenya
The Gambia
Senegal
Mauritania
Sao Tome and Principe
Lesotho
Cameroon
Chad
Sudan
Djibouti
Nigeria
Ghana
Cape Verde
Congo Brazzaville
Swaziland
Angola
Namibia
South Africa
Mauritius
Botswana
Gabon
Seychelles
Equatorial Guinea
Social cash transfer expenditure estimates
Claim: Cannot be scaled up—not affordable
Plausible simulations show average cost 1.1% of GDP or 4.4% of spending
20%
15%
10%
5%
0%
In % of general government total expenditure
In % of GDP
Claim: Lead to inflation
• In six countries, tested for inflation in
intervention versus control communities using
basket of ten goods
– No inflationary effects found
• Why not?
– Beneficiaries small share of community, typically
15-20 percent
– Poorest households, low purchasing power, don’t
buy enough to affect market prices
In fact, cash transfers lead to multiplier effects
in local economy!!
Multiplier: Amount generated in local economy by every $1
transferred
3
2.5
2
1.5
1
0.5
0
Kenya
(Nyanza)
Ethiopia
(Abi_adi)
ZIM
Zambia
Kenya
(Garissa)
Lesotho
Ghana
Ethiopia
(Hintalo)
Can it really be this good?
• Impacts on health inconsistent
– Health expenditures increase, some increase in
curative care, no consistent effects on illness
• No impacts on child nutritional status
– In RSA, Zambia, positive impacts when mother has
completed primary school
– Determinants of nutrition complex, involve care,
sanitation, water, disease environment and food
– Weak health infrastructure in deep rural areas
Meanwhile, emerging evidence that transfers enable
safe-transition of adolescents into adulthood:
Impacts on sexual debut among youth
50%
-7 pp impact**
40%
-13 pp impact***
44%
45%
-6 pp impact**
36%
32%
35%
28%
27%
30%
-11 pp impact***
25%
17%
20%
15%
11%
10%
5%
0%
Kenya (N=1,443)
Malawi (N=1684)
Treat
Zimbabwe (N=787)
Control
South Africa, girls (N =
440)
 Kenya and Zimbabwe impacts driven by girls, Malawi driven by boys. Zambia no impacts!
And beneficiaries are happier too-consistent impacts on subjective well-being
Ghana LEAP
16pp increase in proportion reporting ‘yes’ to “Are you
happy with your life?”
Malawi SCT
20pp increase in proportion ‘very satisfied’ with their life
Kenya CT-OVC*
6% increase in Quality of Life score
Zambia CGP
45% increase in proportion who believe ‘they are better
off than 12 months ago’
Zambia Monze*
10pp increase in proportion who feel ‘their life will be
better in 2 years”
Zimbabwe
12% increase in subjective well-being scale
What is on the future agenda?
How can Sida help?
• Programming
– Explicit links with social services (health insurance,
school fee waivers) and where appropriate,
agricultural and business services
– Building comprehensive social protection system
with cash transfers as the core
– Policy and operational integration (MIS,
targeting…)
– Keep peddling or else you fall off
What is on the future agenda?
How can Sida help?
• Research and Evidence
– Do we need yet another impact evaluation?
• Each country wants to know if it works in their context
• Well designed set of studies inform program design and
operations
– Rigorous research sends strong signal of professionalism
– Keep peddling…
• The LAC model continues to dominate the discourse in
certain places
• African model and evidence still not mainstreamed
The (Innocenti) Transfer Project Team
** Missing in action: Frank Otchere
[Resilience/poverty]
Amber Peterman
[Gender/youth]
Michelle Mills
[Research
dissemination]
Jacob de Hoop
[Education/child
labor]
Luisa Natali
[Zambia/labor]
Sudhanshu (Ashu)
Handa
[Basically everything]
Lisa Hjelm
[Food security/
poverty & stress]
Tia Palermo
[Gender/youth]
Leah Principe
[Zambia/ECD]
Audrey Pereira
[violence/ maternal
health]
Richard de Groot
[Education &
nutrition/Ghana]