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

THANK YOU