1. Welfare Dynamics in Rural Kenya and Madagascar: Preliminary Quantitative Findings Chris Barrett Cornell University March 15, 2004 BASIS CRSP Project Annual Team Meeting Nyeri, Kenya

2. Why is poverty so persistent in rural Africa? The design of appropriate strategies to combat persistent poverty depend on its origins. Is poverty something … … all people naturally grow out of in time (unconditional convergence)? … implies laissez-faire /macro focus. … some people grow out of in time (conditional convergence)? … implies need for cargo nets. … some people can be trapped in perpetually (poverty traps due to multiple equilibria)? … implies need for safety nets and cargo nets.

3. Outline I.Theory and Its Implications II.Economic Mobility and Poverty Dynamics III.Why Economic Immobility? IV.Conclusions and Policy Implications

4. Pov. line W2W2 W2W2 Well-being t+1 Well-being t Brief theoretical background: The slow convergence possibility Welfare Dynamics With Unconditional Convergence Key: unique, common path dynamics with a single stable dynamic equilibrium Welfare Dynamics With Conditional Convergence Low group High group Chronic poverty region ` Transitory poverty region Welfare Dynamics With Multiple Dynamic Equilibria Key: unique path dynamics with a single stable dynamic equilibrium for distinct groups or individuals Key: nonlinear path dynamics with multiple stable dynamic equilibria and at least one unstable dynamic equilibrium (threshold effect)

5. Why bother with the theory? These three alternative theoretical foundations for understanding persistent poverty carry very different policy implications. - need for/design of safety nets for asset protection - need for/methods of targeting cargo nets - need for patience So need to get a firmer handle on (i) the nature of persistent poverty. (ii) what causes observed poverty traps? (iii) how can we move thresholds and/or path dynamics? Those are the objectives of this project.

6. Economic Mobility and Poverty Dynamics Ultra-Poverty Transition Matrices As measured against $0.50/day per capita income poverty line Poor in Subsequent PeriodNon-Poor in Subsequent Period Poor in Initial Period 2000-2002 Dirib Gombo 100.0% 70.8% 1989-2002 Madzuu 60.7% 1997-2002 Fianarantsoa 82.8% 2000-2002 Dirib Gombo 0.0% 11.2% 1989-2002 Madzuu 20.2% 1997-2002 Fianarantsoa 10.3% 2000-2002 Ng’ambo 86.5% 1997-2002 Vakinankaratra 58.5% 2000-2002 Ng’ambo 9.0% 1997-2002 Vakinankaratra 7.4% Non-Poor in Initial Period 2000-2002 Dirib Gombo 0.0% 11.3% 1989-2002 Madzuu 10.1% 1997-2002 Fianarantsoa 6.9% 2000-2002 Dirib Gombo 0.0% 6.8% 1989-2002 Madzuu 9.0% 1997-2002 Fianarantsoa 0.0% 2000-2002 Ng’ambo 0.0% 1997-2002 Vakinankaratra 22.3% 2000-2002 Ng’ambo 4.5% 1997-2002 Vakinankaratra 11.7% Kenya rural poverty line ~ $0.53 Madagascar poverty line ~ $0.43 Poverty deepest where agroecology and markets least favorable (“remote rural areas” or “less favored lands”)

7. Estimated annual gross (net) poverty exit rates Estimate using mobility transition probability: PR t = m t PR 0 SiteGrossNet Dirib Gombo: 0.0% (0.0%) Madzuu:2.2% (1.0%) Fianarantsoa:2.3% (0.7%) Vakinankaratra:2.4% (-4.2%) Ng’ambo:5.2% (4.1%) Considerable persistence of ultra-poverty with low rates of net exit from poverty Economic Mobility and Poverty Dynamics

8. Moving beyond headcount measures We want to know the directions and magnitudes of welfare change, not just discrete movements relative to an arbitrary poverty line. Annual average percent change in income, by site and resurveying interval Key point: Short panels may exaggerate economic mobility. Much year- on-year change is random. Economic Mobility and Poverty Dynamics

9. Filtered vs. unfiltered income change regressions Unfiltered: Y = A`[r + ε R ] + U + ε T + ε M (2) dY = dA `[r + ε R ] + A`[dr +dε R ]+ dε T + dε M (4) includes measurement error … negative bias Filtered: E{Y} = A`r + U (3) E{dY} = E{dA}`r + A`E{dr} (5) omits true stochastic component of income … positive bias Regress dY on Y, E{dY} on E{Y}, or both to bracket? Economic Mobility and Poverty Dynamics

10. Site-specific filtered and unfiltered income change regressions: It clearly makes a difference Economic Mobility and Poverty Dynamics

11. Summary of Findings on Economic Mobility and Poverty Dynamics -Considerable persistence of ultra-poverty with low rates of net exit from poverty -Poverty deepest where agroecology and markets least favorable (“remote rural areas” or “less favored lands”) -Stochastic component of income appears substantial -Not at all clear whether the conditional convergence or poverty traps hypotheses, or both, best explain these data.

12. Why Economic Immobility? Explanation 1: Risk-taking and asset/consumption smoothing Wealth-dependent risk management among northern Kenya pastoralists Consumption smoothing a luxury enjoyed by the wealthiest third.

13. If income variability increases with wealth, so should returns on assets. Indeed, the income-herd size relation exhibits increasing returns, consistent with risk-based poverty traps: Why Economic Immobility?

14. Explanation 2: Barriers to entry into higher-return activities - educational attainment and rationing (social networks) - lack of credit and liquid savings (negligible credit access) … limited capacity to enter higher-return businesses or even to buy livestock - pastoralist mobility depends on herd size … expected result is nonlinear asset dynamics, with rapid accumulation beyond key thresholds

15. Why Economic Immobility? Herd Dynamics in Southern Ethiopia Asset Index Dynamics Highland Kenya/Madagascar The asset data appear consistent in the Kenya sites with multiple equilibria, but in the Madagascar sites, low-level conditional convergence seems to fit better.

16. Why Economic Immobility? Same with the income data. Multi-modal income distribution in Madzuu. 2002 Income Distribution in Madzuu Consistent with qualitative evidence: - Importance of non-farm salaried employment, incl. to agricultural intensification - Fragility of non-poor status, esp. to health shocks

17. Why Economic Immobility? But unimodal distribution in Madagascar reflective more of conditional convergence with significant geographic grouping. Implied dynamic real income equilibria: Vakinankaratra ~ $0.61 Fianarantsoa ~ $0.33 Latter seems a geographic poverty trap

18. Conclusions and Policy Implications 1) Reject the unconditional convergence hypothesis. 2) Qualitative and quantitative evidence most consistent with poverty traps hypothesis in rural Kenya. Need safety nets for asset protection critical for (i) risk management and (ii) to prevent collapse into poverty (for health shocks, natural disasters such as drought/floods, etc.). 3) Poverty traps seem to exist due to missing financial markets and (i) excessive risk exposure and/or (ii) significant barriers to entry to remunerative livelihoods. 4) Conditional convergence apparent at community level in both countries. Cargo nets needed for asset building among poor and for remote communities (i.e., indicator and geographic targeting). 5) Transition technologies, improved market access, etc. key.

19. Misaotra! Asante! Thank you!