Microfinance

1
Microfinance
2
Overall motivation for microfinance

• Lack of access to financial instruments (savings,
  credit) is a key obstacle to poor families
  seeking to improve their own lives
• Many investments that are good for households'
  long-run prospects require large up-front costs
• e.g., tuition for education, capitalization of
  small enterprises
• But it is often difficult to pay such up-front
  costs
• Savings mechanisms are inefficient or nonexistent
• Credit mechanisms poor
• Microfinance institutions seek to fill this gap,
  by bringing financial services to the poor and
  previously unserved

3
Microfinance common elements

• Focus on providing financial services to those
  excluded from the formal banking sector
• Most common credit
• More recently savings
• New frontier insurance
• Credit mostly intended to finance self-employment
  activities
• Provide small loans (as small as 75), to be
  repaid over several months to a year
• Many dispense with collateral requirements
• Key for poor households with few assets
• How are microfinance lenders able to do this?

4
Asymmetric information problems

• Adverse selection individuals who know they are
  likely to default select into borrowing pool,
  raising default rates and interest rates for
  everyone
• Hidden type, hidden information
• Moral hazard
• Individuals exert less effort than the lender
  would desire, raising default rates and interest
  rates for all
• Ex-ante less effort is exerted to make the
  project succeed
• Ex-post even if project succeeds, may
  voluntarily default
• Hidden action

5
Group liability lending

• Widely-publicized mechanism of Grameen Bank for
  dispensing with collateral requirements
• a.k.a. joint liability
• Idea make everyone in a group of 5 borrowers
  jointly liable for repaying each of the loans to
  group members
• If group doesn't repay each loan, no-one in group
  gets subsequent loans
• Not the same as group lending

6
Group liability in theory

• Why does it work?
• Helps solve asymmetric information problem that
  usually exists between lenders and borrowers (and
  that is costly for conventional lenders to deal
  with)
• Adverse selection
• Moral hazard
• Reduces adverse selection
• groups will only form if all have confidence in
  individuals' repayment
• people generally know each other beforehand,
  members will be selected for their reliability
  as borrowers
• Reduces moral hazard
• creates incentives for within-group monitoring
  and enforcement
• In the end, key is to reduce transaction costs
  for lenders, allowing them to serve borrowers
  with very small loans

7
Group liability drawbacks

• What drawbacks might group liability have?
• Increases tension among members
• Leads to voluntary dropouts
• Can harm social capital among members
• More costly for clients who are good risks,
  because they are more likely to pay off loans of
  their peers
• Bad clients can free ride off good ones
• Makes it more difficult to attract and retain
  good clients
• As groups mature, loan sizes typically diverge
• Smaller clients may not want to guarantee larger
  loans of other group members
• Overall group liabilitys beneficial effect on
  repayment may reduce client base (and poors
  overall access to finance)
• Also bank profitability may be lower

8
Gine and Karlan (2006)

• Field experiment in the Philippines
• Microfinance bank where borrowers (all women)
  organized into joint-liability groups of 20
• 169 pre-existing centers randomized into
• Treatment converted to individual-liability
  centers
• Control no change from joint liability
• Findings
• No impact on repayment rate
• Attracts new clients to individual-liability
  centers
• Caveats
• Groups were still formed under joint liability
  (so still benefit from joint structures impact
  on adverse selection)
• Only moral hazard is affected by experiment
• Next step form groups under individual
  liability, and test effects

9
Karlan and Zinman (2007) Motivation

• Credit markets thought to be imperfect due to
  asymmetric information problems
• Adverse selection
• Moral hazard
• Policy responses
• Microlending resolve adverse selection, moral
  hazard via joint liability, group lending
• Subsidies for lenders if moral hazard, asymmetric
  information problems make private sector lending
  unprofitable for the poorest sectors
• Appropriate policies depend on understanding
  extent of these asymmetric information problems

10
Karlan and Zinman (2007)

• Goal quantify importance of various information
  asymmetries in a credit market
• Adverse selection
• Repayment burden
• Moral hazard
• Typically assumed to be unobservable
• Experiment with a consumer lender to the working
  poor in South Africa
• Randomization used to separately identify these
  effects
• offer interest rate identifies adverse
  selection
• contract interest rate identifies repayment
  burden
• dynamic repayment incentive identifies moral
  hazard

11
Experimental design
12
Findings

• Evidence of moral hazard
• Dynamic repayment incentive has significant
  effects on default
• No evidence of adverse selection or repayment
  burden overall
• But analysis by gender reveals
• adverse selection for females
• Repayment burden for males
• See Tables 4, 5

13
Table 4
14
Table 5
15
Discussion items

• Why is it important for loan supply decision to
  be blind to the experimental offer rates?
• And was it, in fact?
• Why present results for the standardized index of
  three default measures? (Kling, Liebman, and Katz
  2007)
• Ditto the seemingly unrelated regression (SUR)
• How to interpret results by gender?

16
Savings
17
The role of savings

• Transform a series of small payments into a
  usably large lump sum (Rutherford 1999)
• For investment
• As buffer stock (self-insurance)
• Less costly than credit no need to pay for
  lenders risk

18
Barriers to savings

• Problems with self-discipline
• While understanding the need to save for the
  future, individuals cant resist the temptation
  to spend now
•  
• Strong social pressures to share accumulated
  assets with others who have immediate needs
• Reflective of informal insurance/risk-sharing
  arrangements
• High transactional or informational costs
• Distance to branches, unfamiliarity with formal
  financial institutions, difficulty filling out
  forms, etc.
• A barrier to formal savings

19
Informal savings

• In the absence of formal savings mechanisms,
  households in poor countries have developed a
  variety of informal means to save
• Cash savings at home
• But vulnerable to temptation, theft, and pressure
  to share with others
• Asset accumulation and decumulation
• E.g., livestock
• Czukas, Fafchamps and Udry (1998)
• Rosenzweig and Wolpin (1993)
• But this comes at an efficiency cost
• ROSCAs
• but some innovative MFIs are starting to offer
  formal savings

20
SafeSave

• Helping overcome transactional cost barriers to
  savings
• New program in Dhaka, Bangladesh
• Deposit collectors visit people in their homes
• Clients may deposit as little as one taka
  (0.015) when the collector calls at their house
  each day
• Accounts with balances above 1,000 taka (15)
  earn 6 interest.
• Clients may withdraw up to 500 taka per
  day (7.50) at their doorstep, or up to 5,000
  taka per day (75) at the branch office
• 22,000 clients, with average savings balance of
  22

Source http//www.safesave.org/
21
Commitment savings

• Do people need help with self-control, with
  committing to savings?
• Ashraf, Karlan, and Yin (2006), Tying Odysseus
  to the Mast
• Randomized offer of commitment savings product to
  customers of a rural bank in Philippines
• Customers pre-commit to save a certain amount or
  for a certain time period before withdrawal
• Withdrawals not allowed before pre-committed
  amount or time period, except for emergencies

22
Table 1
23
Table 2
24
Table 3
25
Table 5
26
Table 6
27
Comments on Ashraf, Karlan, and Yin

• Effects may be due to helping overcome
• Self-discipline problems
• But also helps resist pressure to share with
  others
• More research needed on whether this is
  substitution from other forms of saving (other
  banks, or physical asset holdings)
• A follow-up paper indicates that savings do not
  seem to be sustained in longer term

28
Risk and insurance
29
Agenda

• Risk-coping mechanisms
• Townsend (1994), Udry (1994), and related
  literature
• A field experiment in Malawi insurance, credit,
  and technology adoption

30
Micro-level responses to risk

• How do households cope with risk?
• In rich countries, people have insurance
• Fire insurance, home insurance, auto insurance,
  life insurance, medical insurance
• These insulate people from the potentially
  ruinous effects of catastrophic shocks
• In poor countries, formal insurance markets tend
  not to exist or to be very limited
• The poor have to rely on informal insurance
• A vast literature in development economics
  illustrates the ingenious ways poor households
  insure themselves from adverse shocks
• A theme idiosyncratic risk is easier to cope
  with than aggregate risk

31
Poverty and vulnerability a vicious circle
Poverty
Vulnerability
32
Ways to cope with risk

• Ex ante smooth income
• Ex post smooth consumption

33
Smoothing income

• Choose a safe production technology farm a food
  crop like cassava rather than a cash crop like
  coffee
• Avoid risky new investments, transitions to
  different technologies (Malawi example)
• Diversify income sources
• Diversify farming plots spatially
• References Morduch (1992, 1995, 1999)
• Note all of these are costly (reduce average
  income, even while making income more stable)

34
Smoothing consumption

• Reciprocal transfers (informal insurance)
• Coate and Ravallion (1993), Townsend (1994), Udry
  (1994), Ligon (1998), Banerjee and Newman (1993)
• Credit Udry (1994)
• Asset sales Rosenzweig and Wolpin (1993)
• Savings Paxson (1992)
• Labor supply Kochar (1999)
• Migration by family members Rosenzweig and Stark
  (1989)
• Remittances Yang (2008), Yang and Choi (2007)

35
Theory risk-sharing between households

• Basic result if there is a Pareto-efficient
  allocation of risk across households, one
  householdss consumption should not depend on
  idiosyncratic shocks
• 2 households, indexed by i1,2
• Uncertain income, separable utility
• Pareto efficient allocation of risk between
  households 1 and 2 implies
• Any two households marginal utilities are
  proportional
• consumption moves in tandem
• If utility is CARA

36
Empirical implication

• Consumption depends only on mean village income
  (and households weight in the Pareto program),
  and not on idiosyncratic shocks
• Consumption should comove within villages
• Empirical test regress household consumption on
  idiosyncratic shocks, controlling for village
  income (or village fixed effects in panel
  setting), and idiosyncratic shocks should not
  have effect
• Townsend (1994), Ravallion and Chaudhuri (1997)
  find high degree of comovement in consumption
  across Indian ICRISAT households, even with
  substantial idiosyncratic income variation
• But can reject full risk-sharing (idiosyncratic
  shocks do have some effect)

37
Insurance, Credit and Technology Adoption Field
Experimental Evidence from Malawi
Xavier Gine World Bank Dean Yang University of
Michigan
38
A technology adoption puzzle

• Green Revolution high-yield crop varieties have
  led to significant increases in agricultural
  productivity worldwide
• But there is enormous variation in the extent to
  which households have adopted these new
  technologies
• In Malawi, hybrid maize adoption has lagged
  behind Kenya, Zambia, and Zimbabwe
• Need to look beyond credit constraints even when
  credit offered, only 33 of farmers took up a
  loan for improved seeds

39
Credit or insurance as the key barrier?

• In observational data, the relative importance of
  credit constraints and imperfect insurance may be
  confounded
• Example widely-observed correlation between
  wealth and adoption of new technology
• May be because wealthier farmers have better
  access to credit
• But wealthier households may also have better
  access to (formal and informal) insurance
  mechanisms
• Disentangling the two explanations is crucial to
  good policymaking
• Needed exogenous variation in insurance

40
Technology adoption, risk, and credit

• Key question Does risk inhibit adoption of new
  technologies?
• High-yielding varieties have higher yields but
  may also be riskier
• So households unwilling to bear fluctuations in
  their consumption may decide not to adopt
• Downside risk of adoption may be exacerbated when
  adoption requires credit
• Failure of crop is compounded by the consequences
  of default
• Problem absent or imperfect insurance markets

41
This paper

• A field experiment where insurance was allocated
  randomly
• Question of interest
• Does providing insurance against a major source
  of risk increase farmers willingness to take out
  a loan to adopt a new technology?
• Adoption decision whether or not to take out a
  loan for improved groundnut and maize seeds

42
Harvesting groundnuts
43
Weather insurance and loan take-up in theory

• Risk-averse farmers choose between traditional
  seeds, and taking out loan for improved seeds
• Improved seeds have higher mean yield, but are
  riskier
• Consider attractiveness of bundling loan with
  weather insurance (at actuarially fair rate)
• Loans subject to limited liability in case of
  default, lender can only seize the value of
  production
• Under certain conditions, farmers might take the
  uninsured loan if offered, but prefer the status
  quo (traditional seeds) to the insured loan
• Basic idea limited liability provides implicit
  insurance
• Insurance premium may exceed benefit from
  insurance
• Rosenzweig and Wolpin (1993) welfare gain from
  actuarially-fair weather insurance is minimal

44
Simple model

• Output from traditional seeds YT
• Output from improved seeds YH, YL with
  probabilities p, 1-p
• Output positively covaries with rainfall
• Farmers are offered loans to purchase improved
  seeds (repayment R) lender can only confiscate
  production, but cannot seize assets (so there is
  a consumption floor)
• CRRA utility u(c) c1-s/(1-s)
• Farmers are heterogeneous in risk aversion (si)
  and low-state income from improved seeds (YL,i)
• Some farmers offered loan bundled with
  actuarially fair rainfall insurance policy (loan
  forgiven if low state occurs)
• Does rainfall insurance raise loan take-up?

45
What farmers take up the loan?

• Find coefficient of relative risk aversion
  sTU(YL) such that farmer whose si sTU is
  indifferent between traditional seeds and
  uninsured loan for hybrid seeds
• Farmer takes up the uninsured loan if si lt sTU
• Find analogous cutoff for insured loan, sTI(YL)
• Cutoffs will be function of income from improved
  seeds in low state, YL
• See Figure 1

46
Figure 1
47
Key partners in project

• Rural lenders
• Malawi Rural Finance Company (MRFC)
• Opportunity International Bank of Malawi (OIBM)
• National Smallholder Farmers Association of
  Malawi (NASFAM)
• Contact with farmers
• Insurance Association of Malawi
• Underwrites insurance
• World Bank / University of Michigan
• Technical advice on design of insurance policy
• Design of randomized evaluation

48
Experimental design

• Joint liability loans for clubs of 10-15
  farmers
• Participation is individual farmer decision
• Randomization across 32 localities
• Treatment farmers offered hybrid seed loan with
  insurance against poor rainfall
• 393 farmers
• Control farmers offered hybrid seed loan only
  (no insurance)
• 394 farmers

49
Loan details

• Farmers given option to purchase either groundnut
  package only, or both groundnut and maize
• Seeds and fertilizer for planting 1 acre
  (groundnut) or ½ acre (maize)
• Initial deposit of 12.5 of principal
• Repayment due in 10 months
• 27.5 interest rate (33 annual interest rate x
  10/12)
• Maize repayment
• Uninsured 36
• Insured 40-43
• Groundnut repayment
• Uninsured 34
• Insured 36-38

50
Weather insurance policy

• Farmers insured against poor rainfall as measured
  at nearest weather station
• Paid continuous amount depending on shortfall
  below 1st trigger, up to maximum amount for
  rainfall at or below 2nd trigger
• Insurance premium actuarially fair price
  17.5 surtax

51
Insurance payout structure
payout
2nd trigger (corresponds to crop failure)
1st trigger
rainfall during phase
52
Project locations
53
Orientation meeting, October 2006
54
Simple treatment-control comparison

• Take-up rate for uninsured loan 33.0
• Take-up rate for insured loan 17.6

55
Regression specification

• For farmer i in group j
• Yij a bIj fXij eij
• Yij takeup indicator
• Ij treatment indicator
• Xij vector of control variables (collected at
  baseline)
• Standard errors reported
• clustered at locality level
• bootstrapped

56
Impact of insurance on take-up
Table 3 Impact of insurance on take-up of loan for hybrid seeds Table 3 Impact of insurance on take-up of loan for hybrid seeds Table 3 Impact of insurance on take-up of loan for hybrid seeds Table 3 Impact of insurance on take-up of loan for hybrid seeds
(Ordinary least-squares estimates)

Dependent variable Respondent took up loan for November 2006 planting season Dependent variable Respondent took up loan for November 2006 planting season Dependent variable Respondent took up loan for November 2006 planting season Dependent variable Respondent took up loan for November 2006 planting season

(1) (2) (3) (4)

Treatment indicator -0.154 -0.141 -0.132 -0.128
0.109 0.082 0.082 0.074
Clustered s.e. p-value 0.155 0.085 0.107 0.082
Bootstrapped p-value 0.198 0.116 0.140 0.120

Region fixed effects Y Y Y
Linear control variables Y
Indicators for 5-year age categories Y
Land quintile indicators Y
Income quintile indicators Y
Education quintile indicators Y

Mean dependent variable 0.253 0.253 0.253 0.253
Observations 787 787 787 787
R-squared 0.03 0.13 0.15 0.17

significant at 10 significant at 5 significant at 1 significant at 10 significant at 5 significant at 1 significant at 10 significant at 5 significant at 1
57
Implied interest rate elasticity

• For a farmer placing zero value on insurance,
  effective annual interest rates for groundnut
  loan were
• 27.5 for uninsured loan
• 37.8 to 44.4 for insured loan (varied according
  to location)
• The 13-percentage-point decline in take-up (from
  baseline 33.0) ? a 39.4 decline
• Increase in effective interest rate due to
  insurance 37.5 to 61.3
• Implied interest rate elasticity of credit demand
  ranging from 0.64 to 1.05

58
Additional testable predictions from theory

• Take-up rates for insured vs. uninsured loan
  suggest that sample tends to have lower levels of
  YL
• In this range of YL, there is another theoretical
  implication to test
• YL should be positively correlated with take-up
  of insured loan
• But not correlated with take-up of uninsured
  loan
• But how to measure YL?
• Assume farmers with higher socio-economic status
  have higher YL
• Regress take-up on education, income, and wealth
• Separately for farmers offered insured and
  uninsured loans

59
Figure 1
60
Other determinants of take-up
Table 5 Determinants of take-up in treatment and control groups Table 5 Determinants of take-up in treatment and control groups Table 5 Determinants of take-up in treatment and control groups Table 5 Determinants of take-up in treatment and control groups Table 5 Determinants of take-up in treatment and control groups
(Ordinary least-squares estimates) (Ordinary least-squares estimates)

Dependent variable Respondent took up loan for November 2006 planting season Dependent variable Respondent took up loan for November 2006 planting season Dependent variable Respondent took up loan for November 2006 planting season Dependent variable Respondent took up loan for November 2006 planting season Dependent variable Respondent took up loan for November 2006 planting season Dependent variable Respondent took up loan for November 2006 planting season

Treatment group (insured loan) Treatment group (insured loan) Treatment group (insured loan) Treatment group (insured loan) Treatment group (insured loan) Treatment group (insured loan) Control group (uninsured loan) Control group (uninsured loan) Control group (uninsured loan) Control group (uninsured loan) Control group (uninsured loan) Control group (uninsured loan)
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Years of schooling 0.014 0.011 -0.001 -0.002
0.005 0.005 0.008 0.009
Net income (MK 100,000) 0.098 0.075 0.004 0.003
0.059 0.053 0.010 0.010
House quality 0.041 0.027 0.011 0.011
0.027 0.030 0.022 0.022
Land owned 0.001 0.001 0.001 0.001
0.003 0.003 0.002 0.002
Risk aversion (self-reported) -0.008 -0.008 -0.015 -0.015
0.006 0.006 0.004 0.004

Region fixed effects Y Y Y Y Y Y Y Y Y Y Y Y

Mean dependent variable 0.176 0.176 0.176 0.176 0.176 1.176 0.330 0.330 0.330 0.330 0.330 0.330
Observations 393 393 393 393 393 393 394 394 394 394 394 394
R-squared 0.074 0.078 0.073 0.058 0.061 0.101 0.274 0.274 0.274 0.274 0.286 0.287

F-stat Joint signif. of first 4 indep. variables F-stat Joint signif. of first 4 indep. variables F-stat Joint signif. of first 4 indep. variables F-stat Joint signif. of first 4 indep. variables F-stat Joint signif. of first 4 indep. variables 3.446 F-stat Joint signif. of first 4 indep. variables F-stat Joint signif. of first 4 indep. variables F-stat Joint signif. of first 4 indep. variables F-stat Joint signif. of first 4 indep. variables F-stat Joint signif. of first 4 indep. variables 0.113
P-value 0.03 P-value 0.98

significant at 10 significant at 5 significant at 1 significant at 10 significant at 5 significant at 1 significant at 10 significant at 5 significant at 1 significant at 10 significant at 5 significant at 1
Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions. Notes -- Standard errors clustered by localities in square brackets. Dependent variable equal to 1 if respondent took up loan for November 2006 planting season, and 0 otherwise. Omitted region indicator is for Kasungu. See Appendix for variable definitions.
61
Other potential explanations

• Complexity
• Risk priming
• Differential default cost perceptions

62
In sum

• Take-up is lower for loans bundled with insurance
  against poor rainfall (priced actuarially fairly)
• Compared with identical loans that are uninsured
• Potential explanation
• Farmers already implicitly insured by limited
  liability inherent in loan contract
• Reduces value of the formal, explicit insurance
• Among farmers offered the insured loan, take-up
  is higher among farmers with higher education,
  income, and wealth
• But not among farmers offered the uninsured loan
• Perhaps because higher-status farmers have higher
  default costs

63
Ongoing related research

• Current projects continue to examine the nature
  of constraints that Malawian farmers face in
  financial markets
• Credit
• How important are difficulties in enforcement in
  limiting credit supply in rural areas?
• In particular, can improvements in identification
  technology raise loan repayment rates?
• Savings
• How important are imperfect savings mechanisms in
  explaining low input use on farms?
• How important are transactions costs in
  explaining low utilization of formal savings
  mechanisms?