How to Find the Poor: Field Experiments on Targeting Abhijit Banerjee, MIT
Why is targeting hard? • Targeting entails a different set of challenges in developing countries because governments lack reliable data on incomes • Several methods used to address this problem entail a tradeoff between information and local preferences: ▫ Proxy-means testing (PMT): government collects data on hard-to- hide-assets to proxy for consumption ▫ Community-based targeting: allow local community discretion to decide who is poor ▫ Self-selection: allow people to apply –and make the application process costly May be then do a PMT
Which of these works best? • The Indonesian government wanted to know • Not an easy question: • Different programs are targeted differently but they are also different in fifty other ways • Also within each model what is the optimal design? ▫ Community targeting by the whole community Or just by the elites? ▫ How costly should the application process be? One extreme is National Rural Employment Guarantee Act (NREGA) Another extreme is a one time application cost • Evidence from experiments
Proj ect 1: PMT vs. Community Targeting • This study examined a special, one-time real transfer program operated by the government of Indonesia ▫ Beneficiaries would receive a one-time, US$3 transfer (PPP$6) ▫ Goal of program was to target those with per-capita consumption less than PPP$2 per day • Sample consists of 640 sub-villages (rural and urban) across 3 provinces in Indonesia
The PMT Method • Government chose 49 indicators, encompassing the household’s home (wall type, roof type, etc), assets (own a TV, motorbike, etc), household composition, and household head’s education and occupation • Use pre-existing survey data to estimated district- specific formulas that map indicators to PCE • Government enumerators collected asset data door- to-door • PMT scores calculated, and those below village- specific (ex-ante) cutoff received transfer
The Community Method • Goal: have community members rank all households in sub-village from poorest (“ paling m iskin ”) to most well- off (“ paling m am pu ”) • Method: ▫ Community meeting held, all households invited ▫ Stack of index cards, one for each household (randomly ordered) ▫ Facilitator began with open-ended discussion on poverty (about 15 minutes) ▫ Start by comparing the first two cards, then keep ranking cards one by one • Also varied who was invited (elites or everyone)
Hybrid • Hybrid combined community with PMT verification of very poor • The idea was that the community could pre-screen those to be put in to the PMT
Time Line Fund Distribution, com plaint form s Endline Survey Baseline Survey Targeting & interviews • late Feb and early • Nov to Dec 2008 • Dec 2008 to Jan 2009 with the sub- Mar2009 village heads • Feb 2009
Distribution of Per Capita Cons. • PMT centered to the left of community methods— better performing on average • However, community methods select slightly of the very poor (those below PPP$1 per day) • On net, beneficiaries have similar average consumption
MISTARGET ivk = α + β 1 COMMUNITY ivk + β 2 HYBRID ivk + γ k + ε ivk • Using the $2 per (1) capita expenditure Full cutoff per day, 3 Sample: population Community treatment 0.031* percentage point (or (0.017) 10 percent) increase Hybrid treatment 0.029* (0.016) in mistargeting in Observations 5753 community and Mean in PMT treatment 0.30 hybrid over the PMT
Community S atisfaction: Endline Is the method applied Are you satisfied with Are there any poor to determine the P2K08 activities in HH which should be targeted households this sub-village in added to the list? appropriate? general? (0=no, 1 = yes) (1=worst,4=best) (1=worst,4=best) Community treatment 0.161*** 0.245*** -0.189*** (0.056) (0.049) (0.040) Hybrid treatment 0.018 0.063 0.020 (0.055) (0.049) (0.042) Observations 1089 1214 1435 Mean in PMT treatment 3.243 3.042 0.568 Number of HH that Number of HH that Number of complaints should be added from should be subtracted in the comment box list from list Community treatment -0.578*** -0.554*** -1.085*** (0.158) (0.112) (0.286) Hybrid treatment 0.078 -0.171 -0.554** (0.188) (0.129) (0.285) Observations 1435 1435 640 Mean in PMT treatment 1.458 0.968 1.694
Understanding the differences • Summary of results so far: ▫ Community methods did slightly worse based on PPP$ poverty line. No differences in average consumption ▫ Hybrid and community do exactly equally well ▫ Villagers overwhelmingly happier with community • Why might PMT be different? 1. Elite capture – No 2. Effort (community gets tired) – Yes 3. Different concept of poverty – Yes 4. Different information (community less accurate that PMT) – Unlikely
1. Elites • Elite subtreatment: ▫ In half of community/ hybrid villages, only local small group of elites (neighborhood leader + a few others) invited to meetings ▫ In other villages, whole community invited • Results: ▫ No change in average mistargeting rates ▫ No change in mistargeting rates for households who are family members of elites ▫ Only result is that in community treatment, elite connected households even less likely to be on list than PMT – this “reverse discrimination” is eliminated to some degree in elite treatment
2. Community Effort • To rank 75 households, must make 365 pairwise comparisons • Community members may get fatigued throughout the meeting • To investigate this, we randomized the order in which households were considered • We find that those ranked early in the meeting were ranked more accurately – in fact, more accurately than PMT
3. Does the Community Have a Different Concept of Poverty? • Communities could be identifying who is poor, but have a different view of what constitutes poverty • We therefore estimate: ▫ RANKCORR vkw = α + β 1 COMMUNITY vk + β 2 HYBRID vk + γ k + ε vkw where RANKCORR is the rank correlation in each village between targeting rank list and: ▫ Consumption (u g ) ▫ Community survey ranks (u c ) ▫ Sub-village head ranks (u e ) ▫ Self assessments (u s )
Impact on different welfare metrics Community Subvillage Self Consumption survey ranks head survey Assessment (u g ) (u c ) ranks(u e ) (u s ) Community -0.065** 0.246*** 0.248*** 0.102*** (0.033) (0.029) (0.038) (0.033) Hybrid -0.067** 0.143*** 0.128*** 0.075** (0.033) (0.029) (0.038) (0.033) Observations 640 640 640 637 Mean in PMT treatment 0.451 0.506 0.456 0.343 • Community methods have lower correlation between targeting rank list and consumption, but higher correlation between targeting rank list and self-assessment • Hybrid closer to community preferences than PMT, but less so than community method
Unpacking the local welfare metric • To investigate this, we regress survey ranks and community ranks in a wide variety of household characteristics, conditional on per-capita consumption
Key findings • Household equivalence scales: ▫ Community accounts for household economies of scale ▫ Community treats kids as more costly than adults (e.g., greater “burden”) • Networks for smoothing shocks ▫ Community counts elite-connected households as better off than indicated by consumption ▫ Community counts those who have high share of savings in banks as better off, even though total savings doesn’t affect rank ▫ Those with family outside the village also counted as richer
Key differences • Discrimination ▫ No discrimination against ethnic minorities • “Laziness” or “deservingness” ▫ Those with only primary education treated as poorer, conditional on actual consumption ▫ Widows, disabled, and those with serious illness all treated as poorer, conditional on actual consumption
4. Is community information worse? Survey rank Survey rank (1) (2) Rank per capita consumption within 0.132*** 0.088*** village in percentiles (0.014) (0.012) Rank per capita consumption from 0.368*** PMT within village in percentiles (0.014) Individual PM Variables YES • Unlikely: in fact, community members have additional information about consumption over the PMT • Controlling for PMT score, a one percentile increase in consumption rank is associated with a 0.132 percentile increase in individual household ranks of the community
Conclusions • Decentralizing to the community: ▫ Increased local satisfaction with the outcomes ▫ Produced only slightly worse targeting based on per- capita consumption • Reason is primarily that local communities have a different welfare function than the central government
PMT vs. S elf-selection + PMT • One way to do so is to impose program requirements that are differentially costly for the rich and the poor • Welfare programs with labor requirements (WPA, NREGA) ▫ Food schemes with lower quality food ▫ Unemployment schemes with weekly requirements to visit unemployment office during work hours • Many self selection mechanisms rely on the idea that “time” is more costly for the rich than the poor
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