Micro-credit and Poverty: The Role of Programme Placement Bias(*) Twyeafur Rahman

1. Micro-credit and Poverty: • The Role of Programme Placement Bias(*) • Twyeafur Rahman • Robert E. Wright • (*) DSA Scotland Mini Conference, University of Strathclyde, Glasgow, May 30, 2014

2. Overview • What is micro-credit? • Empirical evaluation of the impact of microcredit on poverty • Programme placement bias • Evidence from Bangladesh • Concluding Comments

3. (1) What is Micro-credit? • “Small loans for poor people” • Has been around for some time in both high- and low-income countries • Has grown rapidly in low-income countries • Particularly in Bangladesh where it has been spurred on by M. Yunus who established the Grameen Bank

4. View that the formal banking and financialsector in low-income countries does not benefit the poor since: • Require significant collateral • Have a preference to high-income, big loan clients • Have lengthy and bureaucratic lending procedures • High transaction costs

5. Outcome is that the poor often turn to informal sector (money lenders/”loan sharks”) who tend to: • Charge excessively high interest rates • Undervalue collateral • Let racist/sexist attitudes guide lending decisions • Employ threats and violence to ensure repayment

6. This suggests that the failure of both the formal and informal financial sectors to provide affordable credit to the poor is often viewed as a one of the main factors that in fact reinforces poverty. • Somewhat counter-intuitive • Make poor people “better-off” by getting them in debt! • Micro-credit is a response to this “market failure”

7. “Micro-credit is essentially the dispersion of small collateral-free loans to groups of jointly liable borrowers in order to foster income generation and hence poverty reduction” J.Morduch • The loan is supposed to be invested in capital or skills development and not just spent on consumption. • The loans must be paid back with interest • Mandatory savings • Group lending is central as is lending primarily to groups of women • “Social collateral” i.e. joint default risk

8. Does micro-credit lead to lower poverty? • There is a strong belief that it does: “Micro-credit Miracle” • Yunus receiving the Nobel Peace Prize • Development budgets being redirected to micro-credit and away from more traditional development activities (such as infrastructure) • Problem: Short-run versus long-run effects • Evaluation of micro-credit programmes has not been rigorous and many of these studies are very weak from a scientific rigour point of view • These programmes need to be rigorously evaluated

9. (2) Empirical Evaluation of the Impact of Micro-credit on Poverty • Basic model: • Prob (Poor=1) = f(XP, XH, XV) • XP = Vector of micro-credit programme variables • XH =Vector of household-level and individual-level socio-economic characteristics • XV = Vector of village-level characteristics

10. Problems with this Approach—Lots! • Self-selection of participants • Non-successful applicants • Lack of suitable control group. What is the relevant control group? • Participation of non-eligible participants • No reaching the “poorest poor” • Unobserved heterogeneity • Programme placement bias

11. Need quasi-experimental design • With or without matching • Problem: Banks are only interested in paying for surveys of those who they have loaned money to.

12. Quasi Experimental Design • Group 1: Village with no micro-credit outlet • Group 2: Village with micro-credit outlet • Applied for loan • Application was successful • Group 3: Village with micro-credit outlet • Applied for loan • Application was not successful • Group 4: Village with micro-credit outlet • Did not apply for loan

13. Some hypotheses that could be tested with this design. There are others: • H1: Micro-credit reduces poverty, non-random outlet placement, applicant self-selection • Poverty(1) > Poverty(2) > Poverty(3) > Poverty(4) • H2: Micro-credit reduces poverty, random outlet place, applicant self-selection • Poverty(1) = Poverty(4) > Poverty(2) > Poverty(3)

14. H3: Micro-credit reduces poverty, random outlet place, no applicant self-selection • Poverty(1) = Poverty(4) = Poverty(2) > Poverty(3) • H4: Micro-credit doesn’t reduces poverty, random outlet place, no applicant self-selection • Poverty(1) = Poverty(4) = Poverty(2) = Poverty(3) • Or maybe! • Poverty(1) = Poverty(4) = Poverty(2) < Poverty(3)

15. Aside: • “Pipe-line borrowers” • Compare participants who have got a loan but have not got the money yet with those who got the loan and have the money • Assume unobservables are the same for both

16. (3) Programme Placement Bias • Concerns the decision-making process of deciding where to place branches • View 1: Main goal of micro-credit institutions is to reduce poverty by providing loans to poor people. Therefore, branches are placed in regions where the rate of poverty is “high(er)”. • View 2: Main goal of micro-credit institutions is to make profit by providing loans to poor people. Therefore branches are placed in regions where the rate of poverty is “low(er)”. • PROBLEM: Both will likely bias the estimates of the impact of micro-credit on poverty

17. Testable: • Let i = 1, 2, 3, …, N “regions” • Prob(Bi = 1) = α1X1i + α2X2i + α3X3i + … + αkXki + ei • If programme placement is random: • H0: α1 = α2 = α3 =αk = 0

18. Considering the branch placement as a random process which can lead to serious bias in measuring outcome of micro-credit programme effectiveness (Pitt, Rosenzweig, and Gibbons, 1993). • Outcome of micro-credit might be biased due to non-random programme placement. (e.g. Murdoch, 1998 and Chowdhury, Ghosh and Wright, 2005). • MFIs in Bangladch are more likely to place their branches in the relatively developed areas where better transport facilities and government and private infrastructures are available (Sharma and Zeller, 1999). • MFIs are more likely to place their branches where literacy is low and flood etc risk is high (Sharma and Zeller, 1999).

19. (4) Evidence from Bangladesh • Data: • ASA (Association of Social Advancement) • Founded 1978 in Bangladesh, now international • Owner/Founder: Shafiqual Haque Chowdhury • 2,936 branches • Branches “surveyed” in November/December 2013 • Response rate was 100%!

20. Information on the branch location specific characteristics such as schools, hospitals, local market centre and Grameen Bank through surveying branch managers’ Information on number of borrowers and repayment rate and outstanding loan amount for each branch through the ASA Head Office, Dhaka. Information about the poverty ratio, literacy rate, population, access to roads, area of cultivable land, number of market centres from the Bangladesh Bureau of Statistics. Information of risk from flooding, draught, cyclone, tornados, earthquake and river erosion) from SWISS Agency for Development and Co-operation and MoD Mgt., Bangladesh.

21. Poverty Map: Bangladesh • There are four layer of administrative Units in Bangladesh. • Division (7)>Districts (64)>Thana(517)>Unions (6766) • (Sources: BBS, 2001 and 2005)

25. (5) Concluding Comments • Some evidence of programme placement bias • Further analysis required • Qualitative research needed • “Marginal branch” • Endogeneity