1. Evaluating a Microfinance Expansion in Egypt David Mckenzie

2. Intervention Snapshot The World Bank is providing a financial intermediary loan of $300 million to the government of Egypt to foster micro and small enterprise growth. As part of this financial access initiative, the Social Fund for Development (SFD), Egypt’s apex organization, has established an initiative with the Egyptian Postal Authority to broaden access to finance through the already established postal network. SFD will on-lend to microfinance organizations who are interested in expanding in new villages through the local post office infrastructure

3. Village Entrance

4. Impact evaluation key challenges Intervention is at village level, not individual level (introduce microfinance to particular villages) Take-up for microfinance can often be low. Many of the key outcomes are quite variable (e.g. business profits) All three things reduce power/increase the sample size the study needs to detect an effect.

5. Example illustration Consider trying to measure increase in profit from 300 to 400 per month (33% increase), with s.d. = 300, one baseline and one follow-up, with rho = 0.5. 100% take-up, individual level randomization: – Need 142 firms treated, 142 control. 10% take-up – individual level randomization – Need 14186 T, 14186 control. 100% take-up, village-level randomization, with 50 firms surveyed per village – Need 838 T, 838 control with intra-village corr = 0.10. 10% take-up, village level randomization, with 50 firms surveyed per village – Need T= 83,638, C = 83,638 if intra-village corr = 0.10.

6. Solutions? Village-level randomization – Have to live with this, can’t randomize at individual firm level, and are interested in measuring some village-level outcomes Low take-up – Conduct screening survey to oversample firms most likely to borrow – Will also do encouragement of these firms, by making sure they have all information and knowledge needed to apply for loans Variability in key outcomes – Measure multiple times to average out noise

7. Experimental Design SFD wants to initially expand in 10 villages SFD/MFIs chose 25 villages in which MFI was equally happy to expand lending and agreed to choose 10 villages at random from these 25 villages for the lending expansion. To mitigate the risk of the MFI pulling out in one of the predetermined treatment villages, we can randomize pair- wise

8. Listing The goal of the listing exercise was to narrow our population to the people most likely to become microfinance clients: business owners (and then also screen on interest in a loan among this group) We sent enumerators to each of the 25 villages to conduct a listing exercise to identify at least 100 business owners in each village After completing the listing, we sent out enumerators to revisit 100 business owners in each village and conduct the full survey Very low refusal rates – Only 2/13427 refused to be listed – Only 66/2565 refused to be in business survey (2.5%)

9. Village Maps

10. Cluster Map

11. Business Owners in Menya, Egypt 23% of the 13,411 households we listed have a business The median age of business owners in our sample is 45 years old. 89% are male 91% are married The mean family size is 5 people The mean reported monthly family consumption is $220 94% of business owners’ households have a television, 86% have a fridge, 69% have a mobile phone, and 5% have a computer Half of business owners have never attended school, less than a quarter have completed secondary schooling, and only four percent have completed more than secondary schooling.

12. What are these businesses doing? About 60% agricultural – Mostly cultivating land, some livestock 40% non-agricultural – Huge range – from grocery stores, taxi drivers, vegetable sellers, etc. etc. 59% have no paid workers, 40% no workers of any kind Only 35% have tax identification card, 92% keep no business records. Lots of seasonal variation in profits: the best month accrues more than 3 times the profit of an average month, and the worst month accrues a little more than one quarter of an average month’s profit.

13. Table 1: Summary Statistics on Balance Between Treatment and Control ControlTreatmentp-values Number of Business Owners15161009 Number of Households1500999 Proportion Male0.900.880.38 Proportion Ever Attended School0.550.530.49 Mean Age46470.45 Mean Years doing business13 0.90 Proportion who own an Agricultural Business0.650.610.36 Proportion who own a Non-Agricultural Business0.370.420.57 Mean Profit120512720.66 Mean Consumption125614120.30 Total Number of Employees1.912.050.65 Ever Borrowed From a MFI0.080.070.35 Land Size(square meters)410541710.89 Land Value1738881539270.40 Building Value14596101080.15 Assets Value42269420980.98

14. Access to Finance in Menya, Egypt 54% of business owners say that they want loans 4% of business owners currently have a loan from an MFI 8% have ever received a loan from an MFI 96% of those who applied for a loan from an MFI are granted a loan Less than 1% of people report ever taking a loan from an informal money lender. 1.82% of business owners have a current account 20% of people report ever saving. Of those who save, 80% save at home and 20% save in a ROSCA – locally known as a gam’ya 76% of respondents respond that they don’t know what an Islamic loan is, and another 19% percent believe that an Islamic loan is either cost free or very low cost.

15. Grocery store in Sandafa Al-Far

16. Financial Institution in Menya, Egypt There are 22 financial institutions that operate throughout the 25 villages in our sample Village populations span from 5,000 to 30,000 people, and the median population is 15,500 people 63% of active borrowers are women The oldest microfinance organization lending operations began in 1995, and the mean age of a microfinance organization is 7 years. The median interest rate is between 12.5 and 13% The mean outreach level is 6 villages, and four MFIs outreach to over 15 villages each. The median number of outstanding loans in each village is 132 (i.e only 9 per 1000 population).

17. Next steps Need to get implementation of loans underway in selected villages + select more villages for surveying Baseline data + administrative records on take-up can provide quick feedback to project on who is getting access and who isn’t. – Then can modify as needed. Follow-ups to not only look at individual outcomes, but outcomes on financial competition – does expansion for example lead to other providers lowering their interest rates?