PPS232S.01 Microeconomics of International Development Policy

1. PPS232S.01Microeconomics of International Development Policy 7. CAPITAL

2. Credit and Microfinance In the fall of 2006, the Nobel price for peace was awarded to Muhammad Yunus, founder of Bangladesh’s Grameen Bank. All of a sudden, the policy world (and students) appeared to have discovered the one policy instrument to end poverty.

3. Credit and Microfinance In truth, the Nobel committee and the policy world had merely discovered something that applied microeconomists had known for a while: The Folk Theorem, when combined with endogenously formed groups, means informal enforcement of social norms and contracts.

4. Credit and Microfinance Idea: When i has an outstanding loan with an MFI and –i cannot borrow for as long as i has not repaid her loan, if –i consists of i’s peers, then we should expect i to be more likely to repay than otherwise when the group consists of (i,-i). This is obviously an example of a decentralized mechanism (Platteau, 1994a and 1994b).

5. Credit Market Failures The bulk of the theory behind credit market failures comes from a seminal article by Stiglitz and Weiss (1981). S r* D Credit Rationing

6. Credit Market Failures This is similar to the efficiency wage story of Shapiro and Stiglitz (1984): we observe a price that is different from the market-clearing price, and this is inconsistent with the notion of a competitive equilibrium. Why does this happen?

7. Credit Market Failures Stiglitz and Weiss explain this based on asymmetric information: 1. Adverse selection: If lenders increase r, the composition of the pool of borrowers may change so as to include riskier borrowers, who are more likely to default. Thus, profit maximization entails an interest rate lower than the market-clearing rate (with caveat from Emran and Stiglitz, 2007).

8. Credit Market Failures 2. Moral hazard: If lenders increase r, then borrowers are likely to choose riskier entrepreneurial projects, which also have a higher expected payoff. Thus, once again, to curb this risk-taking behavior, banks maintain an interest that is lower than the market-clearing rate. Consequence: Many individuals are rationed out of the formal credit market.

9. Credit Market Failures Credit rationing is not a distinguishing feature of developing countries (pawn shops on Guess Rd.; loan shark ads on TV in Florida…) This can also apply to the subprime mortgage market débâcle, which caused the current financial crisis. How? Stiglitz and Weiss were talking about the formal credit market… but the argument applies to the informal credit market. How so? Does it apply to the same extent?

10. ROSCAs The acronym stands for rotating savings and credit association (ROSCA): a group of individual who know each other form a group in which they pool their resources so as to avoid borrowing from a moneylender who charges prohibitive rates (also known as “tontine”). Every period, one (randomly selected) member of the ROSCA gets to borrow.

11. ROSCAs Upon repayment (which is enforced through peer pressure), another randomly selected member gets to borrow, and so on and so forth until every member has borrowed. When everyone has borrowed, the “life” of the ROSCA has ended and it can be either dissolved or start over again.

12. ROSCAs Typically, ROSCA members are women who save in a ROSCA so as to protect their savings from their husbands, who want to spend the money on immediate consumption of what we refer to as “adult” goods (Anderson and Baland, 2002).

13. Digression: Tax on Cash People do not always borrow from banks, moneylenders, ROSCAs, or MFIs for the obvious reasons, however. Baland, Guirkinger, and Mali (forthcoming) develop a theoretical model in which borrowing serves as a (false) signal of being liquidity constrained to avoid having to lend to proximate members of one’s social network.

14. Digression: Tax on Cash Individuals have savings S in their bank account and request a loan b < S. That means they have to repay principal (b) and interest (rb) at the end of the loan period. Yet, they could have used their savings, in which case they would have foregone ib < sb (since i < s if the bank is to be profitable.) So something else must be going on.

15. Digression: Tax on Cash It turns out that in West Africa, especially Cameroon, people who have money in savings get asked for loans from relatives and friends when these relatives and friends know about the existence of these savings. To fend off these loan requests, they borrow from the bank: rb - ib is thus the premium they pay to signal that they are poor so as to avoid having to lend to relatives and friends.

16. Group Lending Besley and Coate (1995): game-theoretic model of MFIs like the Grameen Bank which identifies two effects. 1. Positive effect: Successful group members may step in to help a member whose project fails repay her loan. 2. Negative effect: Everyone defaults simultaneously, including those who normally would have repaid.

17. Group Lending Besley and Coate (1995) also define the notion of “social collateral” (as opposed to physical collateral, e.g., one’s house or plot of land). Social collateral is essentially one’s social network. In a group lending situation, where peer pressure can serve to enforce repayment, default can mean loss of one’s social network – people stop talking to you when they have to step in and repay your debt.

18. Group Lending Advantages of group lending: 1. Group lending with joint responsibility mitigates adverse selection directly when participants inform the bank about a potential joiner’s “type” (i.e., riskiness). Group lending, however, also mitigates adverse selection indirectly…

19. Group Lending: Digression Recall Akerlof’s (1970) “lemons” story. Two types of cars on the market: good cars (g) and bad cars (b). The proportion of g is α and the proportion of b is 1- α. Each g is worth $1,000 and each b is worth $500 to their respective owners, so that the average value (to the owner) of a car on the market is µ = α1000+(1- α)500 = 500α + 1000

20. Group Lending: Digression But then, 500 < µ < 1000. The average buyer only knows the average value µ, so that is her willingness to pay for a car on this market. But then, the owners of good cars will either get out of the market because they don’t receive the price they think their cars are worth, or they will sell at µ and cross-subsidize the bad cars.

21. Group Lending Group lending allows to do pre-empt the “lemonification” of the credit market. That is, since groups form endogenously (this is the crucial component), one should observe positive assortative matching, i.e., similar-typed borrowers form groups together. That is, safe borrowers group together, and risky borrowers group together.

22. Group Lending At the beginning, the bank does not know which type each group belongs to, so that safe borrowers end up cross-subsidizing risky borrowers. After a little while, the bank has a good idea of the average risk within each group, and can start tailoring its interest rates to each group, thereby ending the cross-subsidization (i.e., no more lemons story!)

23. Group Lending 2. More obviously, group lending mitigates moral hazard. Group members monitor each other’s project choice and can penalize those who choose bad projects (ex ante moral hazard). In addition, group lending circumvents ex post moral hazard, i.e., the possibility that a member may choose not to repay the lender, since group members monitor their peers at a lower cost than the bank can.

24. Empirical Evidence There exists a wide body of empirical evidence on credit and microfinance in developing countries, so the following inevitably has to be very selective. In addition, the following does not get into the details of the (many) studies on the impacts of microfinance. If you have an interest in the topic, you are strongly encouraged to go to the source.

25. Empirical Evidence Kochar (1997) Are households as credit-rationed as everyone likes to think? Uses the All-India Debt and Investment Survey and looks at whether households (i) exhibit a positive demand for formal credit; and (ii) obtain a loan conditional on exhibiting a positive demand for formal credit.

26. Empirical Evidence Kochar (1997) In addition, she controls for an important factor, i.e., is formal credit really cheaper than informal credit? In many cases, the answer is “no”. These three binary variables allow her to estimate the various stages of selection.

27. Empirical Evidence Kochar (1997) Empirical Results: Pr(Access to formal credit) = 40 percent Pr(Demand for formal credit) = 35 percent Pr(Formal credit cheaper) = 39 percent

28. Empirical Evidence Kochar (1997) The big result, however, is this: Pr(Access to formal credit | Positive demand for formal credit and formal credit cheaper) = 75 percent. Thus, rationing is only 25 percent (quite far from the previous estimates of 60 percent!)

29. Empirical Evidence Anderson and Baland (2002) Study ROSCAs. Results indicate that ROSCAs are predominantly made up of women – especially married women – who wish to protect their savings from their husbands’ immediate consumption needs.

30. Empirical Evidence Wydick (1999) Uses data from Guatemala to assess the effect of peer monitoring, social ties, and group pressure on group performance. Peer monitoring indeed deters moral hazard, group pressure only has a small effect, and social ties have no effect – the latter result being the really surprising one.

31. Empirical Evidence Karlan and Zinman (2007) Randomized controlled trial. They generate over 60,000 randomized direct mail offers for credit cards in South Africa and also observe the lender’s information directly. Menu of offers designed to tease out the effects of moral hazard and adverse selection.

32. Empirical Evidence Karlan and Zinman (2007) “Identification problem” of applied contract theory (Chiappori and Salanié, 2003) Suppose you observe a cross-section of contracts and you observe inefficiencies within these contracts. Then, it becomes really difficult to tell whether this is because of moral hazard or adverse selection.

33. Empirical Evidence Karlan and Zinman (2007) For example, sharecropping. You observe the contracts signed (sharecropping or fixed rent), and you observe that share tenancy is associated with lower productivity. This could be a manifestation of the well-known “Marshallian inefficiency” (i.e., moral hazard) story.

34. Empirical Evidence Karlan and Zinman (2007) But this could also mean that people who are inherently (and unobservably) less productive (if only because they have lower managerial ability) select into sharecropping at a higher rate than into fixed rent. Bottom line: it is really difficult to tease out these effects, short of having experimental or panel data.

35. Empirical Evidence Karlan and Zinman (2007) So, Karlan and Zinman carefully set up their randomized trial experiment so as to isolate the effects of moral hazard and adverse selection. They find evidence of adverse selection primarily among women, and of moral hazard primarily among men. About 20 percent of defaults are due to one or the other.

36. Empirical Evidence Ahlin and Lin (2007) Study the performance of 112 MFIs across 48 countries over five to nine years (longitudinal data with attrition) with regards to macro indicators. Ask the question of whether MFI performance is dependent on macro conditions.

37. Empirical Evidence Ahlin and Lin (2007) Their main result is that macroeconomic conditions (e.g., economic growth) have positive effects on sustainability of MFIs as well as repayment rates. As a result, the economy at large should be taken into account when evaluating the performance of MFIs (either good or bad performances).

38. Empirical Evidence Fafchamps et al. (2011) Randomly give cash and in-kind grants to male- and female-owned micro-enterprisesin Ghana. “First, while ATEs of in-kind grants are large and positive for both males and females, the gain in profits is almost zero for women with initial profits below the median, suggesting that capital alone is not enough to grow subsistence enterprises owned by women. Second, for women we strongly reject equality of cash and in-kind grants; only in-kind grants lead to growth in business profits. Results for men also suggest a lower impact of cash, but differences between cash and in-kind grants are less robust. The difference in the effects of cash and in-kind grants is associated more with a lack of self-control than with external pressure.”

39. Empirical Evidence Giné et al. (2011) Run an RCT designed to test the impacts of better forms of borrower identification in Malawi (here, fingerprinting). “Improved personal identification enhances the credibility of a lender by allowing it to withhold future loans from past defaulters and expand credit for good borrowers. Fingerprinting led to substantially higher repayment rates for borrowers with the highest ex ante default risk, but had no effect for the rest of the borrowers. The change in repayment rates is driven by reductions in adverse selection (smaller loan sizes) and lower moral hazard (for example, less diversion of loan-financed fertilizer from its intended use on the cash crop).”

40. Empirical Evidence Dupas and Robinson (2011) Run an RCT designed to test the impacts of savings technology in Kenya. “Simple informal savings technologies can substantially increase investment in preventative health, reduce vulnerability to health shocks, and help people meet their savings goals. The two main barriers that keep people from saving on their own appear to be transfers to others and ‘unplanned expenditures’ on luxury items. Providing people with a designated safe place to keep money was sufficient to overcome these barriers for the majority of individuals, through a mental accounting effect.” This is consistent with Baland et al. (forthcoming) and Fafchamps et al. (2011).

41. Some Concluding Thoughts on Microfinance A January 2011 article in the New York Times: “Microcredit was once extolled by world leaders like Bill Clinton and Tony Blair as a powerful tool that could help eliminate poverty, through loans as small as $50 to cowherds, basket weavers and other poor people for starting or expanding businesses. But now microloans have met with political hostility in Bangladesh, India, Nicaragua and other developing countries.”

42. Some Concluding Thoughts on Microfinance The CGDev’s David Roodman in October 2010, reporting on a conference on microfinance: “A highlight was Esther Duflo’s report on results from a microcredit impact study in Morocco. This is the first randomized test of microcredit in a rural setting, and in a setting that was ‘virgin’ microcredit territory, to boot. The study design was generally similar to J-PAL’s Hyderabad study in that a microcreditor agreed to randomize the order in which it rolled out its program into new territory. Take-up was far from universal: 16% of people who could borrow did so. After 24 months, indicators of poverty such as household spending and school enrollment had not budged.”

43. Some Concluding Thoughts on Microfinance Jonathan Morduch, in January 2011, about how microfinance research is not as bad as medical research: “The questions tend to be far more focused. Does access to microfinance increase business profit? Business investment? Household consumption? Those microfinance hypotheses usually stem from a clear theoretical model and should show up in clear patterns. That’s different from medical studies, in which a much greater range of plausible hypotheses exist (along with a greater range of incorrect hypotheses). So lots of stuff gets tested in the medical literature, and ‘effects’ may emerge that pass standard levels of statistical significance but which are caused by odd outliers or other features common to small data sets – and which turn out to be wrong.”

44. Some Concluding Thoughts on Microfinance A few things to keep in mind about microfinance: A lot of the opposition may be politically driven. This is especially true in Bangladesh, where Yunus had once mentioned that he wanted to form his own political party. What is the measure of effectiveness? Repayment rates and default rates are to microfinance what enrollment rates are to education, i.e., easy to measure, but uninformative about welfare effects.

45. Some Concluding Thoughts on Microfinance Not everyone is an entrepreneur. To take myself as an example, the reason why I became an academic was because I saw my two entrepreneur parents deal with risk and uncertainty for most of their lives. A lot of those loans end up used to finance consumption. Marginal (does microfinance reduce poverty?) vs. inframarginal (does microfinance make poor people less poor?) question. The case for more T in experiments.

46. Some Concluding Thoughts on Microfinance Lack of local regulations on microfinance. Many governments have simply chosen to ignore the microfinance sector. Ultimately, microfinance is here to stay. The poor still request those loans, the MFIs still make them, all this without waiting for the result of a group of academics’ intellectual games. In other words, the institution has passed the market test. What else is needed?

47. What About Micro-Insurance? We began the semester by discussing how departures from the First Fundamental Theorem of Welfare were the results of market failures. Microfinance responds to an important market failure (i.e., the failure of capital and credit markets), but in most cases, underdevelopment is the result of several market failures. This is why we often insist on the lack of silver bullets in development policy.

48. What About Micro-Insurance? Recently, development economists have started looking at insurance market failures. Recall from our discussion of land tenancy that sharecropping – which in many cases is likely to be inefficient – often emerges as a direct consequence of the lack of insurance markets. So if we could somehow fix the insurance market, the thinking goes, maybe we could eliminate a number of inefficiencies.

49. What About Micro-Insurance? For this reason, development economists have recently (since about 2008) started thinking about micro-insurance, or the provision of insurance services to people who are otherwise uninsured or uninsurable. The idea is to develop institutions and insurance products that can be used to insure the poor against the risk they most commonly face: crop, livestock, weather, health etc.

50. What About Micro-Insurance? Cole et al. (2011): “We use a series of randomized field experiments in rural India to test the importance of price and non-price factors in the adoption of an innovative rainfall insurance product. We find that demand is significantly price sensitive, but even if insurance were offered with payout ratios similar to those in the United States, widespread coverage would not be achieved. We identify key non-price frictions that limit demand: lack of trust, liquidity constraints, particularly among poor households, and limited salience.”