cash transfers and household resilience
TRANSCRIPT
Unconditional Cash Transfer and Household Resilience: Results from the Malawi Cash
Transfer Program
Frank OtchereSudhanshu Handa
University of North Carolina – Chapel Hill
CSAE CONFERENCE 2017University of Oxford, UK
March 21, 2017
Background and relevance ‘Resilience’ is increasing becoming the reference concept in
development discourse
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]
• Poor
Resilience-poverty interaction
• Non-poor
Inco
me
• Poor• Less resilient
Resilience-poverty interaction contd
• Poor• More resilient
• Non-poor• Less resilient
• Non-Poor• More resilient
Resilience
Inco
me
A
B
C
D
Objective and contributionExamine 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) .
Overview of the Malawi SCTPThe 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
IE Design, Data and ResultsMixed 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.
IE Design, Data and Results contd.Program impact between 2013 and 2015 estimated using DD
Baseline Midline Endline25,000
30,000
35,000
40,000
45,000
50,000
55,000
Treatment Control
Mal
awi K
wac
ha
Per Capita Consumption Food security
Baseline Midline Endline70
75
80
85
90
95
79.6
93.6
81.6 81.6
Treatment Control
Perc
enta
ge o
f Hou
seho
lds
IE Design, Data and Results contd.
Baseline Midline Endline-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Agric Asset Ownership Index
Control Treatment
Baseline Midline Endline0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Proportion of Households in Debt
Control Treatment
Baseline Midline Endline0
0.02
0.04
0.06
0.08
0.1
0.12
TLU Owned
Control TreatmentBaseline Midline Endline
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
TLU Consumed
Control Treatment
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?
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
Domain FAO suggested indicators SCTP Equivalents/ProxiesOutcome 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 expenditureV2. 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 ownedV5. 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 transfersV7. Log of value of free maizeV8. 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 sourcesV11. Ratio of FTW to NFTW, V12. Not Crop production only household
Pillar variables and SCTP equivalents
Estimation results
Baseline EndlineResilience 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
Impact on Resilience
Dependent Endline Baseline Baseline Endline EndlineVariable Impact Treatment
MeanControl 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 1,698Baseline poorest 50% 14.516*** 28.249 28.114 54.380 38.462
(9.87) N 3,283 780 853 785 865Baseline Small Households 11.797*** 48.970 48.854 62.482 49.456
(6.28) N 3,188 782 826 753 827Baseline 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
Resilience and coping with shocks
0.2
.4.6
.81
Sha
re o
f pos
itive
cop
ing
resp
onse
s
0 20 40 60 80 100Resilience 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
Resilience and coping with shocks contd.
For only C households: we examine if baseline resilience predicts endline food security
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
END
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