“engendering” your Impact Evaluation

1. “engendering” your Impact Evaluation Markus Goldstein The World Bank • Cross-Country Workshop for Impact • Evaluations in Agriculture • and Community Driven DevelopmentAddis Ababa, April 13-16, 2009

2. 4 Steps to engendering your IE • Start conceptually  knowing what to look for • Design the data collection right  knowing how to measure it • Make sure the right analysis gets done  actually doing it • Make sure the results get used  knowing what to do with it

3. 1. Start conceptually • Start from an understanding of what existing gender issues are in your target population • Think about causal chain of the project and how it might be different for men and women and then design an evaluation to capture this

4. Example of thinking through the causal chain by gender • e.g. farmers targeted for export crop adoption… • male farmers  inc income  more ag prod/diversification • female farmers  inc income  men take over OR more autonomy, household spending changes, fertility changes, etc. • Bottom line is that given existing welfare gaps in terms of hours worked, autonomy, etc between men and women we can expect effects to be different

5. Keep in mind direct and indirect beneficiaries • This will be important for thinking through the causal chain and designing the evaluation • Gender differences amongst the effects on direct beneficiaries • e.g. the effects of providing irrigation on female vs male farmers’ yields • Gender differences amongst household members of beneficiaries • e.g. (non-head) male vs female agro-processing income in households where the head gets extra extension

6. A note on gender: Female headship • It is important in Africa • Approx 1/3 of households in Kenya & Ghana • But this is not only the place we find women – go beyond the head of household • e.g. Even in male headed households in middle & southern Ghana, parts of Burkina, women have their own farms. Western Kenya, generally they do not

7. Getting the right data:we need better data • Get the information at the right level: individual vs household • Most existing rural surveys collect information at the household level • Gender disaggregation (particularly looking at indirect beneficiaries) will require more disaggregated data • Implication here is that you want to think about e.g. plot level data, individual level interviews (for some variables)

8. 2. Getting the right data • Intrahousehold information • Think about getting variables that capture change in intrahousehold dynamics (e.g. who controls sales, who makes plot decisions, who keeps proceeds) • Look for effects on children • (a) & (b) are not easy (e.g. Ghana property rights) • Detailed questions • Composition of survey team • Pretest your survey! (esp. cross-country)

9. 2. Getting the right data • Use causal thinking to inform the questions you ask – but look for unintended consequences (e.g. fertility in Peru)  towards a more robust cost benefit analysis • Get the sample size right. You will want to examine results by gender  bigger sample, need to do power calculations

10. 2. Getting the right data • If budget allows, combine qualitative and quantitative • Qualitative work to understand existing conditions (X gender) • Quantitative baseline • Qualitative work to understand program implementation (X gender) • Quantitative endline • Qualitative work to understand results (X gender)  inform policy conclusions

11. 3. Doing the gender analysis • Program assignment can be important here – think this through in advance and in the analysis • Who chooses to participate (self selection issues) can also be important and policy relevant – look at this (qualitative data can help) • Think both about gender treatment interactions • All else equal, what was the effect of the program across genders • Versus the idea that the production process (for example) might be different by gender

12. 3. Doing the gender analysis • Look closely to check out the difference between zero difference between genders and an indeterminate difference between genders • Most of all, make sure it gets done

13. 4. Making use of the results • Zeros (if measured right) are informative, put them in the write up • Put the results in the context of existing gender gaps (your baseline data can be useful here) • Draw the policy implications by gender as well • Disseminate the results widely – and “genderized” results may have a broader audience than the straight program effects – this may be another way to “sell” the program

14. To summarize: 4 Steps to engendering your IE • Start conceptually – think through gaps and causal chain • Design the data collection to capture gender differences • Make sure the gendered analysis gets done • Make sure the results get used for policy

15. Thank youmerciabrigadome wo daase