Does Food Aid Really Have Disincentive Effects? New Evidence From Sub-Saharan Africa

1. Does Food Aid Really Have Disincentive Effects? New Evidence From Sub-Saharan Africa • Awudu Abdulai, University of Kiel (Germany) • Christopher B. Barrett, Cornell University • John Hoddinott, International Food Policy Research Institute

2. Outline of Presentation • Dependency and disincentive effects • Why such claims might be misleading • Our contribution • Micro-level evidence: Ethiopia • Macro-level evidence: sub-Saharan Africa • Summary and implications

3. Dependency and Disincentive Effects • Widespread concern about “dependency syndrome” • @ micro level: income transfers reduce incentives to work and invest, “crowd out” private transfers • @ macro level: food aid expands supply faster than demand, hurting producer prices and thereby discouraging production/investment. • May build preference for imported foods • Also, bails out governments, letting poor policies persist

4. Dependency and Disincentive Effects The Disturbing, Broad Pattern

5. Dependency and Disincentive Effects Adverse Product Market Effects

6. Why Such Claims Might Be Misleading Alternative, Positive Impacts • Factor Market Incentive Effects: • Improving household nutrition and healthy, thereby expanding labor supply • Relieving seasonal liquidity constraints • “Crowding In” public investments (roads, SWC, etc.) • Risk Management Effects: • Reduce downside risk, encourage tech. adoption and higher expected gains (moral hazard) • Targeting and Human Rights: • Is “dependency” as life support really a bad thing?

7. Problem of spurious correlation in observational data vs. Need to control for placement effects based on observable characteristics (geographic and indicator targeting) and for potential endogeneity of food aid flows. Why Such Claims Might Be Misleading The Statistical/Methodological Problem

8. Our Contribution • In a literature and policy debate replete with anecdotes and strong opinions, fill an empirical void. • Reminder of the difference between correlation and causality • Emphasize the importance and the impact of effective targeting.

9. Micro-level evidence: Ethiopia Data: - Use 3 rounds of Ethiopian Rural Household Survey, 1994-95 - ~1470 households in 15 villages in main agro-ecological zones - Food aid distribution in this period relied heavily on geographic and indicator targeting and on self-targeting FFW Frequency of Food Aid Receipt: 48% of households never received food aid in these three rounds 28% received it only one round 18% received it in two of three rounds 6% received it prior to all three rounds

10. Micro-level evidence: Ethiopia When we look at the unconditional correlation between food aid receipt and lagged or contemporaneous labor supply, investment and private sharing behaviors, the effects are clearly negative. Paired t-tests of access to food aid and 1995 behaviors

11. Micro-level evidence: Ethiopia But when we control properly for targeting on observables and for the prospective endogeneity of food aid, negative effects vanish and the contemporaneous effects actually turn positive. This is true when we estimate probit and Tobit regressions as well as when we use Maddala’s “treatments” model (an IVLS estimator) and compare those against straight OLS estimates. With proper controls, the apparent disincentive effects vanish or become muted to the point of becoming trivial.

12. Table 7: Estimated impact of food aid on labor supply and mutual support in 1995, treating access as endogenous Micro-level evidence: Ethiopia Table 7: Estimated impact of food aid on labor supply and mutual support in 1995, treating access as endogenous

13. Macro-level evidence: Sub-Saharan Africa VAR specifications for food production and food aid with country-specific effects and controls for rainfall, conflict and natural disasters: Estimated in first-differences using GMM to control for non-stationarity and endogeneity due to fixed effects, with optimal lag length determined at m=4 for both eqns.

14. Macro-level evidence: Sub-Saharan Africa Data: 1970-2000 annual observations for 42 countries Food aid flows - WFP Food production per capita - FAO Rainfall - Tyndall Centre Conflicts, natural disasters - CRED No control for intertemporal change in soils, production technologies, etc.

15. Macro-level evidence: Sub-Saharan Africa Summary of the Main Results • Granger causality tests suggest bidirectional causality between food aid and food production. • Without controls for fixed effects, rainfall and conflicts, food aid has negative impact on food production. • But with proper controls in place, food aid appears to have exerted a modest positive lagged impact on food production in SSA. • Suggests that any possible disincentive effect due to depressed product prices induced by food aid shipments must be more than offset by positive risk management and factor availability/price effects. • Food output negatively affects food aid levels with a lag. • Rainfall (+) and disasters (-) have expected effects.

16. Macro-level evidence: Sub-Saharan Africa IRFs identify the predicted time-path of responses to a one-off increase in food aid shipments of one kilogram per capita

17. Summary and Implications • Using more advanced econometric methods to control for possible confounding effects, we see that food aid’s purported disincentive effects largely disappear at both micro and macro levels of analysis • … stimulative effects may even exist! • Food aid critical for emergency relief and potentially for safety nets. • Severity of resource constraints mean that even an inefficient resource can have favorable impacts.