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Elasticities of Demandfor Consumer Credit:

Evidence and Implications

Dean S. KarlanYale University

MIT Poverty Action Lab

Jonathan ZinmanDartmouth College

USAID BASIS/CRSP Researcher/Practitioner Conference March 23, 2006

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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

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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)

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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)

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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

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Market Setting: The Lender

• Very profitable consumer lender

• Established (20+ years)

• 100+ branches throughout South Africa

• All loan applications, underwriting done face-to-face

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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

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Market Setting: Borrowers

• Working poor and middle class– Must have verifiable employment

• Lots of rejected applicants (50% of first-timers)

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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)

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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

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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

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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

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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

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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

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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.

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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)

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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)

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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

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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

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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

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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?

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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

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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

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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

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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”

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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

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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

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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