Targeting Outcomes, Redux Coady, Grosh, and Hoddinott (forthcoming in World Bank Research Observer) Presentation at Reaching the Poor Conference Washington, DC. February 2004 Full paper at

What is targeting? A system for concentrating benefits among the neediest. Targeting may mean that some are not served at all, or that all are served but with different benefit levels. For the transfer programs reviewed in this study “ needy ” refers to income poverty. In other applications, need might defined differently.

Targeting Involves Two Tradeoffs Benefits vs Costs Errors of inclusion vs errors of exclusion When information is imperfect actions that reduce one usually increase the other Administrative Incentive Political economy Private (e.g. transactions costs) Social (e.g. stigma) Higher efficiency

Questions for Review What are the targeting outcomes observed in practice? Are there systematic differences across targeting methods or program types? What are implications for design and implementation of targeted interventions?

New database 122 programs from 48 countries Search Criteria Developing/transition country Principal objective is poverty reduction Includes cash and in-kind transfers, targeted price subsidies, public works jobs, social funds Excludes contributory SI (pensions/UI), micro-credit, supplementary feeding targeted by nutritional status; fee waivering, emergency feeding Documentation includes method and outcome Relatively recent ( ) Search Strategy Journals (90-03), books, “ grey literature ”

Targeting Options Considered Individual Assessment Means (3 rd party verification; in-office, none) Proxy means Community-based Categorical/Group Geographic Demographic (kids, elderly, other) Other Self-Selection By purchase of commodity Work requirement Community Bidding

Evaluation of Targeting Performance The classic meta-analysis problem: defns, methods and presentation differ substantially Most studies reviewed have one of following Proportion of transfers to bottom deciles Proportion of beneficiaries in bottom deciles Proportion of transfers/beneficiaries who are “ poor ” Solution: construct an indicator of the ratio of “ actual ” to “ neutral ” Proportion of transfers accruing to target relative to population share of this group

Definition of Indicator % of benefits % of population 60% of benefits to poorest 40 percent  % of benefits to poorest quintile  % of benefits to poorest decile  2.5

Some summary statistics Mean number of targeting methods per program is 2, range is 1 to 5 Performance ranges from.28 to 4.00 Median is 1.25 Excluding food subsidies, median is 1.30 One quarter of programs regressive Excluding food subsidies, 16% of programs regressive Theil ’ s – between group variation low: By method – 20% of total By region – 28% By program type – 36%

Notes on descriptive statistics Hierarchy of “ aggregate ” methods Individual>Categorical>Self-selection Lot of variation within “ aggregates ” Work requirement has highest median, but universal food subsidy the lowest Some methods have both low medians and low iqr, hence limited potential – universal food subsidies and community bidding Rest have high iqr, so good potential, but uncertain outcomes Conclusion - details of design and implementation extremely important

Regression Analysis Identifying interesting associations in the data Dependant variable: log (performance) Independent variables Country GDPpc 1995 PPP: capacity Gini coefficient: potential gains, identification Voice/Governance: Kaufman et al (1999) Targeting methods (choices) Robustness: performance (order, levels, median) Robust t-stats

Findings from regression analysis Methods with good results: Means testing, geographic targeting, work requirement Methods with good potential, high variance: Proxy means testing, demographic targeting to children, community-based Methods with low potential: Demographic targeting to elderly, community bidding, self-targeting food subsidies BUT implementation and circumstances matter a LOT

Caveats Diversity of raw performance measures Ignoring distribution of “ leakage ’ Possible bias in the sample of programs Static measure: entry-exit rules Welfare impact depends on more than targeting (coverage, other objectives) Focus on benefit side

4 messages “ targeting can work ” – mean is 1.25, top ten from 2-4, wide range of circumstances But doesn ’ t always one quarter of programs regressive, No clearly preferred method 80% of variation within method A weak ranking developed: Means, geographic, work requirement Proxy means, community based, demo to kids Demographic targeting to elderly, community bidding, self-selection of commodities