Rural micro-finance in Morocco Lessons from an on-going randomized study

1. Rural micro-finance in MoroccoLessons from an on-going randomized study Tanguy Bernard Agence Française de Développement (AFD) Dakar, February 2, 2010

2. Outline • The planned study • Implementation difficulties • Some lessons

3. 1. Planned study

4. Context • Al Amana: the largest Morrocan micro-credit institution (495,000 active clients in 2005). • Extension of activities to rural areas. • 2004/2005: peri-urban areas • 2006 - … : remote rural areas • Progressive extension to remote areas: a unique opportunity to assess impact of micro-finance (mostly undocumented thus far): • Interesting for the program itself (~accountability, learning) • Interesting for all such type of actions in developing countries.

5. Partnership • Operator: • Al Amana, committed to the evaluation study • Willing to affect its operations for IE purpose • Research team: • Top-level experts on the conduct of impact evaluation • J-PAL, PSE, INSEE-CREST • Funder: • AFD, covering all (direct) costs • Interested in learning more from impact evaluation studies. •  A winning combination!

6. Basic set-up • Al amana extention to rural areas in 2006: • Four consecutive waves to set up 73 antennas throughout the country. • Each antenna reachs ~10 villages • IE design: • In each antenna, draw pairs of comparable villages on basis of distance, size, pop density, altitude etc. • In each pair, randomly assign control status to one village, for one year. • Impact measure: • Compare those who have borrowed in treatment villages, to those who would have borrowed in control group.

7. Central zone Always served Zone B Zone A Treatment or Control: random allocation

8. Dealing with imperfect compliance • Power of estimation relies on take-up rates of micro-credit products. • Non-borrowers not informative for Treatment on the Treated (ATT). • If take-up rate low, large data collection effort may still lead to limited capacity to observe impact. • Raise power by surveying those with higher probability to borrow. • Wave 1: Survey 100 hh, wait for 5 months, identify best predictors of participation. • Later waves: run mini-survey (best predictors) on 100 hh, apply parameters from wave 1 model, select 25 hh with higher probability. • A very purposive sampling based on significant data collection effort.

9. Summary: planned steps • Prepare questionnaires • Study sites from wave 1 and split into three zones • Select tretament and control from peripheric zone • Full questionnaire on 100 hh over 10 sites from wave 1 (2000 hh) • Define predictive model for take-up. • Mini-survey and selection of hh to receive full survey • Full baseline survey on selected hh. • Al Amana offers micro-credit in treatment villages. • Midline survey and intermediary results • Al amana offers micro-credit in contrôle villages. • Final survey and final results

10. Well-designed randomization

11. 2. Implementation difficulties

12. Low take-up rate • Much weaker than expected. In itself an interesting question. Reasons? • Product is ill-adapted to farm economy? Too expensive? Etc. • Product is new, there is learning curve? Depends on others’ behavior? There are other sources of credit

13. Steps taken • Al Amana: increase take-up rate: • Delay expansion (few months) after wave 1, to better calibrate take-up model. • Keep control zones virgin for two years (instead of one). • Increase sensibilization in survey zones • Weekly follow-up with agents in survey zones. • Raise incentives for agents (transport + per-contract incentive). • Remove women quotas. • Research team, AFD: • Study on take-up rates (qualitative) • increase sample size.

14. Take-up rate, sample size and MDE

15. Total number of surveys • Mini-survey (waves 2, 3 and 4), one round only • = 15236 households • Full survey • Baseline: Wave 1 (100/village) + waves 2, 3 and 4 (25/ village) • = 6939 households • Midline: Wave 1 only (100/village) • = 1516 households • Endline: Wave 1 (100/village) + waves 2, 3 and 4 (25+9/village) • = 6318 households

16. Data quality issues • Significant issues with data entry: quality not satisfactory • Re-enter entire database from baseline • Change data entry operator for endline. • Some issues with data collection • Add controlers external to data collection firm • Random resample of households to assess quality • Raise price paid to data collection firm • Drop some pairs of villages (low data quality (2), low credit take-up (1), contagion (5), misplacement (3)) • Some issues from matching hh database and Al Amana Database • Rounds of data collection initially meant to occur at same time, but could not.

17. 3. Lessons

18. What is the treatment? • Treatment varies by length of take-up • Possible to ‘control’ for it, but difficult for interpretation. • Type of treatment varied thoughout implementation • Initially group based, then individuals • Repayment period also extended during the program • Removal of women quotas

19. What is the population? • At the beginning, representative on all Morocco, then a number of villages were dropped. • Only those villages that have a ‘pair’, not all villages. • Only remote rural areas. • A very particular population selected by model, difficult to replace in general population.

20. What is the impact on borrowers? • Minimum detectable effect: 20% • Impact on specific populations can be detected? • Impact expected in the short run • Also need outcome indicators that are likely affected in SR,and that will clearly affect poverty in medium run  Need a clear theory of impact mechanisms • Take-up rates interesting per se, worth studying. • Cf: How to give access to the poor (credit, insurance etc.)

21. Overall… • Doing an impact evaluation can be difficult… • Even with top research team, commited operator and funder • Reports rarely mention these difficulties that are however important determinants of the lessons that can be generated… • This is especially the case for programs with • Low take-up • Not matured ‘products’ • Mature  possibility to assess take up rates and expected impact size through previous M&E, literature, experts knowledge

22. Operational learning vs General learning? • Operational learning • Projects are complexe. Impact depends on several components and means to implement  Black-box effect • MDE: what is the impact that one finds satisfactory? • Cf Cost-benefit analysis • Cf results from other robust evaluations with similar obectives • Use project as it is implemented • General learning • Interested in identifying mechanisms, how relieving constrains leads to behavioral response that affects final outcome. • Need « simple » treatment: just the active ingredient  Affect design of project • Sometimes difficult to reconcile these diverging expectations. • One needs to know, right from the beginning what the expected lessons are.  Preparation is the key!

23. Further thoughts… • Use local research centers to conduct impact evaluations • May help refine question and intepret results • Can help support local research capacities • Eventually build mixed research teams • Link up with data collection exercise by national statistical agency • E.g. The Beninese integrated modular survey