Richard de Groot presents at the Young Lives Conference on the Ghana LEAP cash transfer program

1. Heterogeneous Impacts of an Unconditional Cash
Transfer Programme on Schooling: Evidence from the
Ghana LEAP Programme
Richard de Groota, Sudhanshu Handaa, Michael Parkb, Robert Osei Darkoc, Isaac Osei-
Akotoc, Garima Bhallab, Luigi Peter Ragnod
a UNICEF Office of Research – Innocenti
b University of North Carolina at Chapel Hill, Department of Public Policy
c Institute of Statistical, Social and Economic Research, University of Ghana – Legon
d UNICEF Ghana Country Office
Adolescence, Youth and Gender: Building Knowledge for Change Conference
Oxford, September 8, 2016

2. Introduction
 Findings from Ghana Livelihood Against Poverty
(LEAP) programme
 What is the effect of an unconditional cash transfer
programme on schooling outcomes for children?
 Do parents display compensating or reinforcing
behaviour towards lower ability children (‘marginal’
child)?
 Is there a need for conditionality in this case? (i.e.
strict rules attached to receipt of transfer)

3. Ghana LEAP programme
 Ghana’s flagship social protection programme
 Initiated in 2008, currently reaching >150,000
households (and counting!)
 Cash payments and health insurance
 Three demographic groups:
 Orphaned and vulnerable children (OVC’s), elderly and
people with a disability (PWD)
 Soft conditions for OVC households
 Transfer level: 7% of household consumption

4. Methods
 Core: Difference-in-Difference (DD) with
Propensity Score Matching (PSM)
 Treatment group (N=699) pulled from 2010
extension in 3 regions (Brong Ahafo, Central &
Volta)
 Comparison group (N=914) pulled from national
survey
 Identical survey instruments, teams and field work
methods  Ideal conditions for PSM!

5. Methods
 Inverse Probability Weighting (IPW)
 IPW uses inverse of the propensity score:
𝑝𝑠
1−𝑝𝑠
 Higher score, more similar to LEAP household
 Covariate adjustment
 Cluster fixed effects
 Sample size (children 5 – 17 years):
Comparison Treatment
Baseline (2010) 1,239 979
Follow-up (2012) 1,076 869

6. Methods
 Schooling indicators:
 Current enrolment
 Attendance (missed any school in last 7 days)
 Analyze effect by subgroups:
 Age group (5 – 12 and 13 – 17)
 Sex
 Cognitive ability (Raven’s test)

7. Ghana LEAP - Impacts on school enrollment
 Impacts on enrollment large for older boys
-15
-10
-5
0
5
10
15
20
25
30
35
All children
5 - 17 years
All Boys Girls All Boys Girls
Children 5 - 12 years Children 13 - 17 years
-0.7
-4.9**
1.1
8.1**
20.3***
1.3
0.4

8. Ghana LEAP - Impacts on any missed school
 Impacts on attendance for younger children and older girls
-25
-20
-15
-10
-5
0
5
10
15
All children
5 - 17 years
All Boys Girls All Boys Girls
Children 5 - 12 years Children 13 - 17 years
-8.5***
-10.5***
-13.0***
-8.3**
-5.4
0.4
-9.8*

9. Impacts on school enrollment by cognitive ability
 Higher impact among older children with low
cognitive ability (the ‘marginal’ child)
-20
-10
0
10
20
30
40
All children 5 - 17
years
Children 5 - 12
years
Children 13 - 17
years
Boys 5 - 17 years Girls 5 - 17 years
Low cognitive ability High cognitive ability
-6.4**
22.0***
4.1*
8.5*
5.4*

10. Impacts on any missed school by cognitive ability
 Strong impact observed among children with low
cognitive ability  compensating behavior of
parents
-30
-25
-20
-15
-10
-5
0
5
10
15
20
25
All children 5 - 17
years
Children 5 - 12
years
Children 13 - 17
years
Boys 5 - 17 years Girls 5 - 17 years
Low cognitive ability High cognitive ability
-8.7**
-12.8*** -12.8**

11. Pathways of impact
 How did LEAP create an increase in enrolment and
attendance?
 Analyze effects on schooling inputs:
 Significant increases in schooling expenditures (books,
uniforms, total schooling expenditures)
 LEAP may have loosened constraints on out-of-
pocket educational expenditures

12. Conclusion
 Ghana LEAP showed strong impacts on children’s
schooling
 Important to move beyond average treatment effects
 Strong impacts on adolescent boys  positive
elasticity of demand for schooling
 Significant increase in enrolment for older children
with low cognitive ability
 Suggests that parents demonstrate compensating
behavior towards their children, if given the chance

13. School enrollment impacts among secondary age children
strong, equal to those from CCTs in Latin America
0
2
4
6
8
10
12
14
16
18
20
Notes: Bars represent percentage point impacts. Enrollment for primary aged children already high, thus
impacts reported at secondary age enrollment
Cross-country impacts on secondary age school enrollment
(percentage point impacts similar or above Latin American CCTs)
Zimbabwe: No impacts on enrollment (crowding out due to Basic Education
Assistance Module - BEAM). However, 7 pp increase in attendance.