Exploring Elasticities of Demand in Consumer Credit Market

1. Elasticities of Demandfor Consumer Credit:Evidence and Implications Jonathan Zinman Dartmouth College Dean S. Karlan Yale University MIT Poverty Action Lab USAID BASIS/CRSP Researcher/Practitioner Conference March 23, 2006

2. What We Do • Estimate elasticities of demand with respect to (Karlan-Zinman 2005b): • Price • Maturity (repayment period) • Using randomized trials conducted by a South African consumer lender • Part of larger set of experiments with this Lender conducted in 2003 and 2004 • Karlan-Zinman (2005a) on information asymmetries • Bertrand-Karlan-Mullainathan-Shafir-Zinman (2005) on psych-inspired marketing of loans • Karlan-Zinman (2006) on derationing and impacts

3. Microfinance/Practical Motivations • These experiments designed to make methodological and empirical contributions to the design and implementation of microfinance policy and initiatives • Are there underlying frictions that motivate intervention (KZ 2005a)? • What is the nature of liquidity constraints? (KZ 2005b, KZ 2006) • Are there decision-making biases that motivate intervention (BKMSZ 2005)? • How do borrowers respond to incentives? Are lenders pricing and assessing risk efficiently? (KZ 2005a, KZ 2005b, BKMSZ 2005: KZ 2006)? • Does expanding access to credit produce measureable impacts? If not why not? (KZ 2006)

4. Microcredit/Practical Motivations:This Paper • Outreach: • How reach poor? • Are they more price elastic? (Dehijia et al) • Are they less price elastic? (Attanasio et al) • Do maturity elasticities dwarf price elasts? • Sustainability: • Can MFIs that are trying to become self-sufficient raise revenues by raising prices (AM)? • What about defaults? (asymmetric information problems) • Companion paper on this: Karlan-Zinman (2005)

5. General Economic Motivation • These elasts widely recognized as among most parameters in: • Macro • Finance • Development • Implications for: • Monetary and fiscal policy • Optimal contracting • Nature of liquidity constraints

6. Market Setting: The Lender • Very profitable consumer lender • Established (20+ years) • 100+ branches throughout South Africa • All loan applications, underwriting done face-to-face

7. Market Setting: Loan Product • Rates: 11.75% per month for first-time borrowers • 98% of our offers below standard rates • Small (modal is $150) • Fixed repayment schedules • No collateral • Term loans • 1, 4, 6, 12 & 18 month loans available • 80%+ are four-month repayment schedules • Monthly equal principal payments • Interest charged over original balance • No additional fees • Example • R1000 loan for 4 months, 10.00% rate • R350 monthly payment

8. Market Setting: Borrowers • Working poor and middle class • Must have verifiable employment • Lots of rejected applicants (50% of first-timers)

9. Borrowers: Loan Usage • Variety of uses (Table 1b): • School Fees • Retire Other Debt • Investment in household enterprise • Housing • Family and Events (holidays, funerals) • Vehicles • Consumption (necessities, durables)

10. Market Setting:Competition and Regulation • Quasi-competitive “cash loan” market: • Many competitors for 1 month loans (high risk lenders) and 12+ month loans (banks). • Little if any competition in Lender’s niche (4 months) • Negotiation on loan terms: • none on interest rates (important for identifying a/s) • little if any on maturity • loan size is negotiated. • Regulated market: • Usury deregulation allowed institutions to supplant loan sharks as dominant players in this market • Debt burdens and lending practices regulated

11. Preview of Findings:Price Elasticity • Demand curve is downward sloping with respect to price: • Relatively flat over wide range of rates below the Lender’s standard ones • Very steep on a small sample of rate above the Lender’s standard one • Some evidence that elasticity increases with income

13. Related Work: Price Elasts • Earlier generation of studies (Hall 1988) found essentially inelastic demand • But identification issues (Browning-Lusardi 1996) • Starting with Gross and Souleles (2002) in US, new generation of (quasi-) experimental studies have found nontrivial elasticities ranging from -0.73 to << unity • Alessie et al (2005): Italy • Dehijia et al (2005): Dhaka slums

14. Preview of Findings:Maturity Elasticity • Maturity sensitivity is huge, dwarfs price sensitivity • Increasing maturity by 20% (i.e., by one month) increases the amount borrowed by 15% • Interest rate would have to drop to essentially zero (from an average of ~ 200% APR) to have the same effect • Elast only significant for young, poor

15. Related Work: Maturity Elasts • Juster and Shay (1964) • Hypothetical survey questions in USA • Attansio et al (2004) • Show formally that liquidity constraints produce maturity elasticities: • Longer maturity » Lower monthly payments » Smaller amount of current resources devoted to debt service » Can move cons’n forward in time • Flip side: longer maturity permits larger loan amount, c.p. • USA car loans 1984-1995 • Find results almost exactly paralleling ours • Combo of quasi-experimental and structural identification

16. Identification Strategy • Random assignment of interest rates and maturity “suggestions” • Motivation: interest rate is endogenous, even in panel data • Demand correlated with opportunity set (potentially time varying) • Supply decisions correlated with unobserved riskiness • Hard to know what we’re measuring in non-experimental studies.

17. Identification Strategy:Price Elasticity • Randomly assign rates • Conditional on observable risk • 50,000+ offers sent at wide range of rates from 3.25% to 11.75% simple per month • These offers all at or below Lender’s standard rates (11% on average) • “Pre-approved” solicitations via direct mail • All prior clients (borrowed in past 24 months)

18. Empirical Strategy: Price Elasticity Then estimate: Y = f(r, X) Where: • Y is a measure of demand: • Takeup • Unconditional loan amount • Conditional loan amount • X are randomization conditions (margins of heterogeneity) • observable risk • Timing of mailer • (demogrphics, including interactions with rates, when we are estimating heterogeneity)

19. Price Elasticity: Core Findings • Downward-sloping but flat demand curve throughout wide range of rates below standard ones: • No estimates < -0.5 • VERY price sensitive in the 600 offers made at rates > standard • Some evidence that elasticity increases with income • Profits: lowering rates does: • Reduce defaults by alleviating asymmetric problems • Increase gross revenues via borrowers choosing longer maturities • But these factors NOT enough to moivate rate cuts: price insensitivity effect dominates • Rate increases a non-starter: kinked demand + asymmetric information

20. What Explains the Kinkat Standard Rates? • Selection (on discounting, rates of return)– everyone in sample is prior borrower • But Lender has several standard rates, so this would require heterogeneity and time-varying selection • Competition (high-rate guys borrow elsewhere) • Anecdotally competition thin in Lender’s niche • No evidence of this in credit bureau data, but noisy • KZ (2006) lends support to this explanation • Wait for normal rates to return? No– opposite. • Non-standard preferences? • Prospect theory, fairness

21. Towards Macro Implications Can our estimates inform understanding of aggregate response to a rate change? • Does direct mail understate price elasticity due to lack of attention/information? • Within-sample exploration suggests not much • Do have measures conditional on borrowing • Does cheaper credit from the Lender crowd-out (or –in) other sources? • No evidence it does, but credit bureau data is noisy

22. Towards Macro Implications • Does cheaper credit cause the Lender’s borrowers to substitute borrowing now for borrowing later? • If anything, MORE borrowing over medium-run • Goodwill? • Asymmetric Information? • Debt trap? • External Validity? • Cash loan market is important in aggregate…. • But are Lender’s borrowers representative?

23. Maturity Elasticity:Empirical Strategy • Relatively small fraction of borrowers is eligible for longer maturities (6- and 12-month, vs. modal 4-month) • Randomize direct mail “suggestions” in direct mailers via example loans • Two observably identical borrowers are shown loans with the same rate and principal, but randomly assigned maturity • Suggestions orthogonal to the interest rate • Suggestions nonbinding • Loan officers instructed to ignore the offer letter • Use suggestion to instrument for maturity

24. The Power of Suggestion • Why might this work? Psychology. • Power of suggestion: other subtle cues seem to impact demand in this sample (BKMSZ 2005) • Power of “default option” (USA savings literature) • Did work; i.e., we have a first-stage • Each additional suggested month increases actual months by 0.11 months

25. Maturity Elasticities: Core Findings • Next we instrument for maturity using the suggestion in 2SLS estimation of ln(loan size) on maturity, price, risk, and other observables • Findings: • Huge maturity elasts • They dwarf price elasts • One month maturity increase has same effect as dropping interest rate 890 basis points (almost to zero) • Sig only for relatively young (sometimes) and poor • Same patterns and order of magnitudes as Attanasio et al find using: • Very different methodology • In a very different setting

26. What Drives Maturity Elasticities? • Neoclassical consumer choice under liquidity constraints • Certainly intuitive in our setting • Alternative explanation: cognitive bias • Stango-Zinman (2006) find “payment-interest bias”

27. What Drives Price Elasts? • What drives differences across markets, studies? • Strict neoclassical economics says differences due to methodology, and unobserved heterogeneity in: • Preferences • “Returns”, broadly defined (e.g., ~ of shocks) • IO of credit markets

28. What Drives a Price Elast?Some New Insights • Our work suggests there are some additional margins to consider: • Product type (potential interactions with maturity elasticity on term loans) • Prior borrowing status • Marketing • Bertrand, Karlan, Mullainathan, Shafir, and Zinman (2005) find that “behavioral marketing” can dull price sensitivity • Changes in rates may matter, not just levels

29. Summing Up:Practical Implications Practitioners and Policymakers: • Ignore maturity sensitivity at great peril • Ignore other “non-standard” factors at (potentially great) peril • Can use randomized trials to pin down optimal contracting and outreach strategies • Method used by USA credit card companies on ongoing basis • KZ planning extensions/replications • Hope to integrate this into normal operations of MFIs; if nothing else multiple trials would be informative