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Markus Goldstein Alaka Holla Michael O’Sullivan Africa region gender team/DECRG

It’s a question of power What we know from impact evaluation about gender in ag & psd interventions in Africa and why we don’t know more. Markus Goldstein Alaka Holla Michael O’Sullivan Africa region gender team/DECRG.

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Markus Goldstein Alaka Holla Michael O’Sullivan Africa region gender team/DECRG

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  1. It’s a question of powerWhat we know from impact evaluation about gender in ag & psd interventions in Africa and why we don’t know more Markus Goldstein AlakaHolla Michael O’Sullivan Africa region gender team/DECRG

  2. How we might think about the gender impacts of policy/interventions • Male vs female beneficiaries (i.e. who gets the program) • Spillover benefits: effects on those of different genders w/in the household of the beneficiary (whoever the beneficiary is) • Differences in male versus female sensitivity/responsiveness to an intervention

  3. How we think about our IE results • We are testing the hypothesis: the gender difference is not statistically different from zero • Two ways this can happen: • 1. There might or might not be a gender difference - we can’t tell – the estimates are so noisy as to be indistinguishable  NO information for policy • 2. The difference is actually zero (well estimated)  policy relevant result

  4. We don’t know much • Scattered evaluations • Methods are often improvised ex-post, making it harder to show causal links or they are experiments to test a particular point rather than policy • Agricultural & PSD interventions are harder to evaluate than, e.g. health and education • More data intensive (crop/plot data, firms) • Interventions often multi-sectoral/multi-faceted • For ag: Infrastructure placement does not make finding a control group easy

  5. We do know a bit about PSD(although not much) • I’ll discuss some evidence from impact evaluations in small business finance • Credit: access and take-up • Investment and income responses • Impacts within the household • Suggestive evidence, but not designed for gender effects • Know less about • Effectiveness of BDS for women entrepreneurs • Effects on enterprise outcomes (by gender) • Spillovers to non-enterprise outcomes and total welfare effects • What policy interventions can close gaps? Is it desirable to do so?

  6. The interventions • Interest free savings accounts in rural banks in Kenya (Dupas & Robinson) • Better information for lenders, and the borrowers know this in Malawi (Gine, et al) • Changing advertising content, interest rates, and extending credit to marginal borrowers in South Africa (Karlan, et al)

  7. Savings accounts in Kenya Numbers in red = statistically significant

  8. Fingerprinting borrowers in Malawi Numbers in red = statistically significant

  9. South Africa: Content of loan advertisements Numbers in red = statistically significant

  10. South Africa: Extending credit to marginally ineligible applicants

  11. And agriculture?

  12. Land certification- Ethiopia • Large land certification program, joint ownership for spouses • Reductions in perceived insecurity, big increases in land investment, and increased rental market activity • Gender: Female-headed HHs with certificates were 20% more likely than male headed hh to make soil & water conservation investments in land & spent 72% more time on these investments • (Deininger, Ali, Alemu, 2008)

  13. Reducing exposure to risk - Malawi • Rainfall insurance, tied to loans for HYV • Fewer farmers take loan when tied to insurance • Gender: No significant difference • (Giné and Yang 2008)

  14. Technological adoption - Kenya • Provided credit, agricultural extension and export facilitation services to farmers to adopt and market export crops • Farmers more likely to grow export crops but no overall impact on farm input usage, HH income or harvest value • Gender: No significant difference • (Ashraf, Giné, and Karlan 2008)

  15. Increasing fertilizer use - Kenya • Put in place mechanisms to get farmers to commit to buying fertilizer at harvest rather than later • Small, well-timed discounts can outperform large (not-timed) subsidies • Gender: No significant difference • (Duflo, Kremer and Robinson, 2009)

  16. So that’s a lot of insignificant results Why?

  17. Unpacking gender insignificance • We are testing the hypothesis: the gender difference is not statistically different from zero • Two ways this can happen: • 1. We can’t tell – the estimates are so noisy as to be indistinguishable  NO information for policy • 2. The difference is actually zero (well estimated)  policy relevant result

  18. What separates the two is statistical power

  19. So where we do we go? • To do this right, to really build the evidence on gender differences, we need bigger surveys, gender incorporated in the design of evaluations • This means: • that people doing the evaluations pay attention to this (not obvious) • more money • The results get used • And thus, it is all a question of power…

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