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Journal Article•DOI•

Credit Elasticities in Less-Developed Economies : Implications for Microfinance

Dean Karlan1, Jonathan Zinman2•
Yale University1, Dartmouth College2
01 May 2008-The American Economic Review (American Economic Association)-Vol. 98, Iss: 3, pp 1040-1068
TL;DR: In this article, the assumption of price inelastic demand using randomized trials conducted by a consumer lender in South Africa was tested and it was found that the demand curves are downward sloping, and steeper for price increases relative to the lender's standard rates.
Abstract: Policymakers often prescribe that microfinance institutions increase interest rates to eliminate their reliance on subsidies. This strategy makes sense if the poor are rate insensitive: then microlenders increase profitability (or achieve sustainability) without reducing the poor's access to credit. We test the assumption of price inelastic demand using randomized trials conducted by a consumer lender in South Africa. The demand curves are downward sloping, and steeper for price increases relative to the lender's standard rates. We also find that loan size is far more responsive to changes in loan maturity than to changes in interest rates, which is consistent with binding liquidity constraints.

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Karlan and Zinman (2008), "Credit Elasticities….", American Economic Review, Web Appendix:
Price Sensitivity of Takeup: Other Nonlinearities?
Mean dependent variable (applied = 1): 0.18 0.17 0.06 0.06
(1) (2) (3) (4)
Interest Rate <= Lender’s Standard Rate for Low Risk (7.75) -0.01014*** -0.00233* -0.00209
(0.00260) (0.00125) (0.00133)
7.75<r<=9.75 -0.01649*** -0.00210** -0.00189**
(0.00355) (0.00084) (0.00092)
9.75<r<=11.75 -0.00393* -0.00243*** -0.00226***
(0.00223) (0.00067) (0.00071)
Interest Rate > Lender’s Standard Rate for High Risk (11.75) -0.00322*** -0.00424***
(0.00071) (0.00088)
Interest Rate <= Lender’s Standard Rate for Medium Risk (9.75) -0.00084
(0.00275)
Interest Rate > Lender’s Standard Rate for Medium Risk (9.75) -0.01420***
(0.00439)
Sample low risk medium risk high risk
high risk, prior
rate = 11.75
Pseudo R-squared 0.002 0.004 0.002 0.002
Number of observations 6,424 4,896 42,490 35,605
* p<0.10, ** p<0.05, *** p<0.01.
Each row presents slopes for a different interest rate interval on the demand curve, each column is a sub-sample based on risk
category. Bold cells show the slope for the interval of rates directly above the standard (prior) rate for that risk class. The Lender
did not make any offers > 11.75 to low risk clients. Probit marginal effects with robust standard errors clustered within branch.
Interest rate units in percentage points (e.g., 8.0). For medium risks, small sample sizes prevent us from estimating the slope for
9.75<r<=11.75 (23 obs) and r>11.75 (41 obs) slopes separately. Column 4 drops clients for whom the prior rate might be different
than the standard rate for their risk category at the time of the mailer. We do not observe the prior rate directly, but based on the
Lender’s policy rules we know it can differ from the mailer rate only for a subset of high-risk clients since: a) clients can improve
from high risk only after a minimum of six successfully repaid loans; b) lower-risk clients revert to high-risk after 6 months of not
borrowing.
1
Karlan and Zinman (2008), "Credit Elasticities….", American Economic Review , Web Appendix:
Maturity Elasticity 1st-Stage: Effects on Takeup?
Estimator:
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Maturity shown (linear) -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.000 -0.001 -0.000
(0.002) (0.003) (0.003) (0.003) (0.003) (0.002) (0.003) (0.003) (0.003) (0.003)
Maturity shown = 6 -0.012 -0.012
(0.018) (0.018)
Maturity shown = 12 -0.007 -0.007
(0.016) (0.016)
Interest rate -0.001 -0.000 -0.001 -0.001 -0.000 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001
(0.005) (0.006) (0.006) (0.005) (0.006) (0.006) (0.005) (0.006) (0.006) (0.005) (0.006) (0.006)
Income split? Low High Low High Low High Low High
Number of observations 3,083 1,431 1,652 3,083 1,431 1,652 3,083 1,431 1,652 3,083 1,431 1,652
* p<0.10, ** p<0.05, *** p<0.01.
OLS Probit
Probit results are marginal effects. Robust standard errors clustered on branch. Sample includes low- and medium-risk clients who received a maturity suggestion. All
specifications include controls for loan amount shown, risk, and mailer wave.
2
Karlan and Zinman (2008), "Credit Elasticities….", American Economic Review, Web Appendix:
How Big is the Power of Pure Suggestion?
Dependent variable:
1= actual loan is long
maturit
y
1= actual loan is maturity ~=
p
rior maturit
y
(1) (2)
Long maturity (6 or 12) shown on letter 0.135***
(0.037)
(Maturity ~= prior maturity) shown on letter 0.152***
(0.042)
Pseudo R-squared 0.08 0.08
Number of observations 493 488
* p<0.10, ** p<0.05, *** p<0.01.
Results are probit marginal effects with standard errors clustered on branch. This table shows more
estimates of the impact of our randomly assigned maturity suggestion on maturity choice. The sample frame
includes those who received a suggestion (i.e., an example loan featuring a 4-, 6-, or 12-month maturity) and
tookup a loan with a standard maturity (so thirteen loans with 1 and 18 month maturities are dropped).
Column 2 drops the five cases where the last maturity was 1 month. Both probits also include controls for
loan amount shown, risk, wave, and the randomly assigned interest rates.
3
Karlan and Zinman (2008), "Credit Elasticities….", American Economic Review , Web Appendix:
More Results on the Maturity Elasticity 1st-Stage: The Power of Pure Suggestion, by Proxies for Financial Sophistication
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Maturity Shown (linear) 0.157*** 0.061 0.163*** 0.051 0.195*** 0.020
(0.051) (0.049) (0.048) (0.064) (0.055) (0.048)
1 = 6-month maturity shown -0.180 0.096 0.212 -0.281 -0.204 0.202
(0.314) (0.477) (0.348) (0.338) (0.293) (0.367)
1= 12-month maturity shown 1.226*** 0.484 1.294*** 0.379 1.519*** 0.175
(0.401) (0.398) (0.380) (0.515) (0.430) (0.387)
# prior loans prior loans # prior loans prior loans low high low high age age age age
<=median > median <=median > median education education education education <=median > median <=median >median
Number of observations 289 204 289 204 220 273 220 273 270 223 270 223
* p<0.10, ** p<0.05, *** p<0.01.
Sample splits on proxies for relatively less (more) sophistication in odd (even) columns. OLS with linear maturity chosen as the dependent variable, and standard errors clustered on branch. This table
shows more estimates of the impact of our randomly assigned maturity suggestion on maturity choice. The sample frame includes those who received a suggestion (i.e., an example loan featuring a 4-, 6-,
or 12-month maturity) and tookup a loan with a standard maturity (so thirteen loans with 1- and 18-month maturities are dropped). All regressions also include controls for loan amount shown, risk, wave,
and the randomly assigned interest rates. Education is predicted from occupation. Comparable results for income splits are shown in Table 8 of the paper.
Proxy:
4
Karlan and Zinman (2008), "Credit Elasticities….", American Economic Review , Web Appendix:
Maturity Elasticities of Loan Demand: Censoring Robustness Checks
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Maturity (linear) 0.161*** 0.168*** 0.155*** 0.161*** 0.168*** 0.155*** 0.157** 0.214*** 0.050 0.139*** 0.198*** 0.058
(0.011) (0.009) (0.018) (0.009) (0.011) (0.014) (0.062) (0.072) (0.126) (0.052) (0.067) (0.100)
Interest Rate -0.035 -0.053** 0.011 -0.035 -0.053* 0.011 -0.036 -0.041 0.011 -0.046* -0.055 0.011
(0.027) (0.026) (0.038) (0.026) (0.029) (0.043) (0.029) (0.038) (0.038) (0.028) (0.037) (0.035)
Log(Loan Amount Shown) 0.443*** 0.390*** 0.369*** 0.443*** 0.390*** 0.369*** 0.445*** 0.356*** 0.408*** 0.447*** 0.369*** 0.437***
(0.047) (0.056) (0.069) (0.038) (0.054) (0.058) (0.061) (0.076) (0.113) (0.067) (0.071) (0.137)
Income split? No
Low
incom
e
High
incom
e
No
Low
incom
e
High
incom
e
No
Low
incom
e
High
incom
e
No
Low
incom
e
High
incom
e
1- and 18-month loans in? No No No No No No No No No Yes Yes Yes
N 493 239 254 493 239 254 493 239 254 506 243 263
* significant at 10%; ** significant at 5%; *** significant at 1%.
Robust standar
d
error
s
clustere
on
b
ranch. Log(loa
n
size) is the dependen
t
variable; result
s
ar
e
simila
r
fo
r
leve
l
loa
n
size. The sampl
e
fram
e
includes thos
e
who receive
d
a suggestio
n
(i.e, an
example loan featuring a 4-, 6-, or 12-month maturity) and tookup a loan with a standard maturity (so thirteen loans with the relativelyrare 1- and 18-month maturities are dropped). Here we use
categorical measures of suggested maturity as the instrument; results are similar if we use the linear instrument. The “loan amount shown” was in all cases the client’s loan size on their most
recent prior loan. All specifications also include controls for risk category, mailer wave, the contract interest rate and whether it was valid for one year. High- and low-income are split on
median gross income at time of loan approval. Columns 1-3 reproduce the OLS estimates presented in the paper (Table 9, Columns 1-3). Columns 4-6 show tobit results with maturity not
instrumented (tobit IV is equivalent to 2SLS in our case). Columns 7-9 reproduce the 2SLS results from the paper (Table 9, Columns 4-6). Columns 10-12 include the relatively rare 1-month
and 18-month loans in the sample: there are 13 of these loans.
OLS Tobit Instrumental Variables
5
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Daniel Kahneman, Amos Tversky
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Mental Accounting and Consumer Choice

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Richard H. Thaler1•
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TL;DR: It’s time to get used to the idea that there is no such thing as a “right answer” to everything.
Abstract: A new model of consumer behavior is developed using a hybrid of cognitive psychology and microeconomics. The development of the model starts with the mental coding of combinations of gains and losses using the prospect theory value function. Then the evaluation of purchases is modeled using the new concept of “transaction utility.” The household budgeting process is also incorporated to complete the characterization of mental accounting. Several implications to marketing, particularly in the area of pricing, are developed. This article was originally published in Marketing Science, Volume 4, Issue 3, pages 199--214, in 1985.

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National Bureau of Economic Research1, Federal Reserve System2, Harvard University3
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ThalerRichard
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Frequently Asked Questions (3)
Q1. What are the contributions in this paper?

For instance, this paper showed that interest rate > Lender 's standard rate for low risk ( 7.75 ) -0.01014 −0.00233 −0 0.00424 − 0.00223 − 0.00424− 0. 

Controls include: credit risk category mailer wave, quadratics in internal credit score, external credit score, and gross income at time of pre-approval (but not net income since this is only available for wave 3 individuals), number of prior loans with Lender, gender, number of dependents, marital status, quadratic in age, rural residence, education, province, and branch fixed effects. 

Controls for unconditional specifications include: quadratics in internal credit score, external credit score, and gross income at time of pre-approval (but not net income at time of pre-approval since this is only available for wave 3 individuals), months since last loan with Lender, number of prior loans with Lender, gender, number of dependents, marital status, quadratic in age, rural residence, education, province, and branch.