Frank Otchere presents how the Malawi Cash Transfer Program helped households to build resilience

1. Unconditional Cash Transfer and
Household Resilience: Results from the
Malawi Cash Transfer Program
Frank Otchere
Sudhanshu Handa
University of North Carolina – Chapel Hill
CSAE CONFERENCE 2017
University of Oxford, UK
March 21, 2017

2. Background and relevance
‘Resilience’ is increasing becoming the reference concept in
development discourse

3. Background and relevance contd.
Variously defined:
 ...capacity of a system to absorb disturbances and reorganize while
undergoing change ~ Resilience Alliance (2002)
 …ability of countries, communities and households to manage change, by
maintaining or transforming living standards in the face of shocks ~ DFID
(2011)
 …the capacity over time of a person, household or other aggregate unit to
avoid poverty in the face of various stressors and in the wake of myriad
shocks ~ Barrett and Constas (2014)
Resilience is a latent construct that seeks to measure the
capacity of households to anticipate and prevent, or withstand
(idiosyncratic) shocks and stressors to their livelihoods without
compromising quality of life [food security]

4. • Poor
Resilience-poverty interaction
• Non-poor
Income

5. • Poor
• Less resilient
Resilience-poverty interaction contd
• Poor
• More resilient
• Non-poor
• Less resilient
• Non-Poor
• More resilient
Resilience
Income
A
B
C
D

6. Objective and contribution
Examine the impact of an unconditional cash transfer program
on resilience
 Partly address the question of whether cash transfers only serve to alleviate
poverty today or has long term development effects
 Empirically test the relationship between the measure of resilience and actual
coping mechanisms to shocks
We add to the literature by exploring the pathways B and C
instead of only A in traditional impact evaluation of UCT
programs;
Our empirical test of the reliability of the resilience measure
provide an alternative to targeting and program designs to
improve on welfare gains (vis-à-vis PMT score targeting) .

7. Overview of the Malawi SCTP
The MSCTP is a flagship program of the Malawi government
targeted at ultra-poor, labor-constrained households.
Started in 2006 as a pilot; scale up in 2009, reaching over
163,000 households in 18/28 districts by December 2015
Transfer size:
 Varies with household size; but ~20 per cent of monthly household real per
capita consumption
Additional ‘schooling bonus’ based on number of hh members
enrolled in primary or secondary school

8. IE Design, Data and Results
Mixed methods experimental study designed for impact
evaluation prior to scale up of the SCT in 2012
Quantitative component is a cluster-randomized longitudinal
study of 1678 beneficiary households and 1853 control
households:
 Three waves of data: 2013, 2014, 2015
 Several modules including food consumption, agricultural & livestock
production, labor supply, non-farm enterprise operation, household
asset, social networks, operational model (to track implementation)
Treatment and control arms balanced at baseline (about 100
indicators); no differential overall attrition at endline; evidence
of selective attrition at endline corrected with IPW.

9. IE Design, Data and Results contd.
Program impact between 2013 and 2015 estimated using DD
25,000
30,000
35,000
40,000
45,000
50,000
55,000
Baseline Midline Endline
MalawiKwacha
Treatment Control
Per Capita Consumption Food security
79.6
93.6
81.6 81.6
70
75
80
85
90
95
Baseline Midline Endline
PercentageofHouseholds
Treatment Control

10. IE Design, Data and Results contd.
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Baseline Midline Endline
Agric Asset Ownership Index
Control Treatment
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Baseline Midline Endline
Proportion of Households in Debt
Control Treatment
0
0.02
0.04
0.06
0.08
0.1
0.12
Baseline Midline Endline
TLU Owned
Control Treatment
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
Baseline Midline Endline
TLU Consumed
Control Treatment

11. A number of approaches exist for measuring resilience:
 FEG/HEA, IFAD, KIMETRICA, ACCRA, Tulane, Tufts, CRS, FAO RIMA
Common thread: Resilience is a latent construct
 Leverages several dimensions of the household livelihood, external support
and ability to respond to shocks
The FAO RIMA Model is the most widely used
 Resilience has several pillars/domains including productivity, asset
ownership, social safety nets, access to credits, debts and labour constraints
 Modeled using Multiple Indicator and Multiple Outcome Model (MIMIC) – a
type of SEM
Turning to the resilience: how do we measure?

12. RIMA pillars and model structure
RCI
AST
AC
SSN
PC Food
SI
E1
E3
E2
 Three pillars (AST, SSN, AC) are identified as the formative indicators
determining resilience and, contemporaneously, resilience should
predict PC Food consumption and Simpson’s Index of dietary
diversity
 Each pillar estimated using factor analysis on a number of indicators

13. Domain FAO suggested indicators SCTP Equivalents/Proxies
Outcome
Indicators
Average per person daily income, Average
per person daily expenditure, Food
consumption score/other nutrition proxy,
dietary diversity and food frequency score,
dietary energy consumption
V1. Per capita food expenditure
V2. Simpson’s Diversity Index
AST Agric assets, Non-Agric Assets, TLU, Land
owned
V3. ‘Wealth’ index of agric assets, durable
goods, housing & household characteristics
V4. Per capita TLU owned
V5. Per capita Total Land Cultivated
SSN Amount of cash and in-kind assistance, Social
Networks, Frequency of assistance,
Formal/Informal Transfers
V6. Log of total in-kind transfers
V7. Log of value of free maize
V8. Credit Constraint,
V9. Perceived available support in times of
need
AC Diversity of income sources, Educational level
(household average), Employment ratio,
Available coping strategies
V10. Number of income sources
V11. Ratio of FTW to NFTW,
V12. Not Crop production only household
Pillar variables and SCTP equivalents

14. Estimation results
Baseline Endline
Resilience
Quintiles C T Total C T Total
Lowest 21.96 24.12 22.99 27.86 12.92 20.73
Second 22.40 18.93 20.75 19.15 15.40 17.36
Middle 18.83 19.22 19.02 17.88 19.73 18.76
Fourth 17.70 18.69 18.17 17.30 22.79 19.91
Highest 19.10 19.04 19.08 17.82 29.15 23.22
Total 100.00 100.00 100.00 100.00 100.00 100.00

15. Impact on Resilience
Dependent Endline Baseline Baseline Endline Endline
Variable Impact Treatment
Mean
Control
Mean
Treated
Mean
Control
Mean
(1) (2) (3) (4) (5)
Full Sample 12.432*** 42.144 41.493 58.457 45.076
(7.67)
N 6,472 1,556 1,686 1,532 1,698
Baseline poorest 50% 14.516*** 28.249 28.114 54.380 38.462
(9.87)
N 3,283 780 853 785 865
Baseline Small Households 11.797*** 48.970 48.854 62.482 49.456
(6.28)
N 3,188 782 826 753 827
Baseline Labour Constrained
Households
13.144*** 41.806 40.952 58.189 44.073
(7.88)
N 5,236 1,302 1,369 1,231 1,334

16. Resilience and coping with shocks
0
.2.4.6.8
1
0 20 40 60 80 100
Resilience Capacity Index
bandwidth = .8
Running mean smoother
 For the full sample: both C and T:
 Evidence of positive coping mechanisms to idiosyncratic shocks increasing
with resilience

17. Resilience and coping with shocks contd.
For only C households: we examine if baseline
resilience predicts endline food security

18. Conclusions
We show here that unconditional cash transfer
programs can improve resilience
 Cash transfers do not only serve as handouts but beneficiaries are able to
make the optimal judgements that incorporate their own vulnerability into
account
Resilience is a reliable predictor of future food
security and can therefore be used more frequently
for profiling and ranking when treatments are to be
prioritized
UCTs should be considered one of the key policy
options for improving resilience

19. END
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