Financial Inclusion and Contract Terms

MITIGATING THE RISKS OF FINANCIAL INCLUSION
EXPERIMENTAL EVIDENCE FROM MEXICO
Sara G. Castellanos1 Diego Jiménez Hernández2 Aprajit Mahajan3 Enrique Seira4
1
Banco de México
2
Stanford University
3
University of California, Berkeley
4
Instituto Tecnológico Autónomo de México
Oct 5, 2018
HKUST SEMINAR

Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Motivation
− Increased interest in expanding access to financial services.
− Considerable work on innovative approaches to inclusion – e.g. Micro-Finance.
− Much less known about inclusion by large financial institutions whose scale
potentially important for expanding financial access:
◦ In 2009 Mexico had 2.3M total MF clients.
◦ Study financial product by large Mexican bank with 1.3M clients at the time.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 1/34

Today
− Use large Mexican bank’s experience to describe challenges of ﬁnancial inclusion.
− Examine formal sector credit for borrowers with limited credit history.
◦ Combine observational & experimental data in Mexican credit card market.
− Large scale RCT on population identiﬁed as marginal borrowers by the bank.
◦ Results representative of large population (> 1M) of borrowers by construction.
◦ Discuss relevance for broader population.

Research Questions and Preview of Findings
(1) New to Formal Credit Borrowers (NTB) are credit constrained (3X relative to U.S.).
(2) How much risk do NTB represent?
◦ High turnover: 1/3 default or cancel over study period.
◦ Large revenue variation.
◦ Default, revenue difficult to predict.
(3) Can changes in interest rates and minimum payments reduce default?
◦ Reducing interest rates or increasing minimum payments (experimentally)
• Have small impacts on default ( R = +0.20 MP = +0.02)
• But decrease bank revenues significantly.
◦ Substantively small effects on default.
◦ Coda: Bank discontinued card; moved away from NTB borrowers.
(4) What explains high default? (in progress)
◦ Large negative shocks.

Presentation Outline
1 Motivation
2 Related Literature
3 Data
4 Market Facts
5 Experiment
6 Explaining Default
7 Conclusions

1 Motivation
3 Data
4 Market Facts
5 Experiment
7 Conclusions

Related Literature
− Asymmetric Information and Credit Constraints:
Gross and Souleles (2002), Karlan and Zinman (2009),Adams et al (2009), Einav et al (2012),
Dehejia et al (2012); Attanasio et al (2008), Karlan and Zinman (2016).
− Sub-optimal Contract Choice and Consumer Protection:
Bar-Gill (2004), Ausubel (1999), Durkin (2000); Kőszegi & Heidhues (2010), Meier and Sprenger
(2010); Laibson (2006b); Melzer (2011), Bertrand and Morse (2011) Agarwal et al (2015).
− Financial Inclusion:
Demirguc-Kunt et al (2012); Dabla-Norris et al (2015); Dupas et al (2018).

1 Motivation
3 Data
4 Market Facts
5 Experiment
7 Conclusions

Datasets
(1) Bank Data (Experimental Sample):
◦ Monthly card level data from 03/07 to 05/09.
• Basic demographics, stratum indicators.
• Credit limit, debt, purchases, payments, fees, card status.
(2) Credit Bureau Data:
◦ Loan level data matched to experimental sample annually from 06/07 to 06/09.
◦ Loan level data for 06/10 representative of the entire credit bureau population.
• Credit limit, payment history, opening and closing dates for loans, amount due, amount in
arrears, closure reasons, credit score and demographics.
(3) Social security Data:
◦ Individual-level, monthly information (from 10/10 to 05/15).
◦ IMSS-reported monthly wage, employment status in the formal sector.
◦ Limited matching (∼ 18%) with experimental data.
(4) Survey Data: ENIGH, MxFLS

Experimental Sample: Product
− Product: Store credit card offered by large Mexican bank (“Bank A”) for borrowers
with limited credit history.
◦ Started in 2002 – 1.3 million clients nationwide by 2009.
◦ Accounts for 14% of all first-time formal sector loan products in 2010.
Type of first loan (Credit bureau 2010)
0.25.5.75
Fractionofindividuals
Credit cardPersonal loan Credit line Real estate Auto Other
Experimental type of cards

Experimental Sample: Description
− Sample frame: Cardholders who had paid at least minimum payment for past 6
months up to 01/07. (∼ 1M)
◦ Stratiﬁed random sample of 162,000 cardholders.
◦ First Credit Card (57%); First formal sector credit product (47% ).
− Strata: Partitioned borrowers into 9 strata.
◦ Card Tenure (01/07)∈ { 6-11 months, 12-23 months, 24+ months}.
◦ Repayment History (01/07)∈ {“minimum-payers”, “medium payers”, “full-payers”}.
◦ Sample 18,000 borrowers from each stratum.
◦ Use stratum weights to make population statements.
− Experiment: Varied interest rates and minimum payments for 144,000
cardholders.
◦ Treatments: Full-block design of 4 interest rate levels and 2 minimum payment levels.

Summary Statistics
Panel A. Experimental Sample
Payments in pesos (March 2007) 711 (1,473)
Purchases in pesos (March 2007) 338 (1,023)
Debt in pesos (March 2007) 1,198 (3,521)
Credit limit in pesos (March 2007) 7,879 (6,117)
(%) Cardholders for whom experiment card is ﬁrst card 57
(%) Cardholders who default between Mar/07 - May/09 17
Panel B. Matched Credit bureau Data
Credit score (June 2007) 645 (52)
Amount in arrears given that it is positive (June 2007) 9,738 (49,604)
(%) Cardholders with any arrears (June 2007) 22
Panel C. Demographics
(%) Male 52
(%) Married 62
(%) Cardholders matched in SS data 18
Age (March 2007) 39 (6)
Monthly income in pesos (10/11)a
13,855 (11,244)
Observations 162,000
a Income only available for formal sector workers (∼ 18%).

1 Motivation
3 Data
4 Market Facts
5 Experiment
7 Conclusions

Market Facts: NTB Borrowers
(1) NTB borrowers are Credit Constrained.
◦ Use Gross and Souleles, (2002) methodology: Sample credit constrained (3X U.S.).
(2) High and Unpredictable Exit Rates:
◦ 1/3 of sample exits (defaults or cancellations) during 26 month study.
◦ Default hard to predict
• Use ML tools.
• Observables at application.
• Observables in the beginning of experiment (March 2007).
(3) Variable and Unpredictable Bank Revenues:
◦ Construct revenue measure per card, and show that it is hard to predict using:
• Use ML tools.
• Observables at application.
• Observables in the beginning of experiment (March 2007).
(4) Client “poaching” consistent with ﬁrst lender externality (Stiglitz, 1993).
◦ NTB borrowers ex post revealed as good clients are “poached”.
◦ Rough calculation: ﬁrst lender loses around twice the mean revenue per switcher.

NTB Borrowers Are Credit Constrained
− Use Gross and Souleles, (2002) to document credit constraints. Card i in month t:
∆Debti,t = δt +
12
j=0
βj∆Limiti,t−j + γ Xi,t + i,t
− Object of Interest in θ ≡
12
j=0
βj.
− Weaknesses:
◦ Only observe formal sector debt.
◦ {∆Limiti,t−j}j likely endogenous.
• Banks use “timing rules” to evaluate accounts for credit limit (time since last revision).
• Need to assume: Time since last revision aﬀects debt only through change in credit limits.
• =⇒ Instrument for {∆Limiti,t−j }j using months since last change (dummies).

NTB borrowers are credit constrained
6-11 months 24+ months
All Minimum Full
(1) (2) (10)
Panel A. Bank’s debt and limit
ˆθOLS 0.32*** 0.69*** 0.03**
(0.04) (0.06) (0.01)
ˆθIV 0.73*** 2.14*** -0.08
(0.14) (0.32) (0.14)
Observations 1366035 118687 186338
Mean dependent variable 70 184 23
(2292) (3631) (1272)
Amount Due/Credit Limit 0.52 0.72 0.3
(2.96) (0.34) (2.82)
Median utilization 0.5 0.81 0.2
Notes: Errors are clustered at the individual level. Each cell represents a different regression. Column (1) estimates incorporate stratum weights. All
regressions include time fixed effects and the total number of credit line increases and decreases. The first row shows the “baseline” estimates; the second
row shows the instrumental variable estimates. *: p < .05; **: p < .01; ***: p < .001 respectively.
− ˆθUS ≈ 0.13
− Large variation across strata.

Signiﬁcant Card Exit
Cumulative Exits: Control group
0.1.2.3.4.5
Proportionofaccounts
Jan/07 Jul/07 Jan/08 Jul/08 Jan/09 Jul/09
Fecha
Client cancelled Revoked by bank Other
Note: Other includes lost cards and cardholder deaths
− 19% of control group defaulted over experiment.
− Another 16% cancelled card.
− Similar rates for similar populations in other data.

Measuring Bank Revenues
− Using monthly purchases, payments, debt to construct bank revenue measure.
− Deﬁne revenue for card i:
Revi ≡ PV(Pay - Buy)i − Debt03/07,i + αiPV(Debt05/09,i) (1)
− Strong assumptions on borrower behavior outside the experiment window.
◦ Subtract March 2007 debt.
◦ Assume fraction of May 2009 debt is repaid (adjusting for card exit).

Bank Revenues: High Variability
− Non-monotonic relationship between credit scores and revenue proxy.
0.02.04.06.08.1
Fractionofcardholders
-20 -10 0 10 20
NPV of Revenue (MXN thousand pesos)
25thpercentile
50thpercentile
75thpercentile
1234
NPVofRevenue(MXNthousandpesos)
525 575 625 675 725
Credit Score in June 07
95% CI lpoly smooth
kernel = epanechnikov, degree = 0, bandwidth = 6.99, pwidth = 10.48
Revenue by strata Credit score distribution
− Average (median) monthly revenue per card (over study): 168 (144) pesos (s.d. 282 pesos).

Revenue and Default are Diﬃcult to Predict
Table 1: Predicting Revenues and Default
Revenue Bank Revocations
Benchmark Linear Regression Random Forest Benchmark Random Forest
(1) (2) (3) (4) (6)
Panel A. Public information available at the moment of application
ρ(predicted, realized) 0.00 0.09 0.15 0.00 0.29
Out of sample root MSE 6201 6180 6140 0.43 0.41
Out of sample MAE 4354 4370 4364 0.18 0.17
Out of sample R-squared 0.00 0.01 0.02 0.00 0.01
AUC - ROC Curve - - - 0.50 0.58
Panel B. March 2007 public information
Out of sample MAE 4354 4595 4364 0.19 0.17
AUC - ROC Curve - - - 0.50 0.58
Panel C. March 2007 public and private information
Out of sample MAE 4354 4066 3732 0.18 0.15
AUC - ROC Curve - - - 0.50 0.62
− Caveat: Sample is only successful applicants.
− Estimation Details

1 Motivation
3 Data
4 Market Facts
5 Experiment
7 Conclusions

Experimental Design
− Given limited screening, can ex-post contract terms to affect behavior?
◦ Use RCT by Bank A to answer this.
− Treatment arms: Bank ran 8 Arm (+C) RCT with 18,000 borrowers per arm. 8
interest rate and minimum payment combinations:
◦ Annual Interest rate: r ∈ {15%, 25%, 35%, 45%}
◦ Monthly Minimum payment: MP ∈ {5%, 10%} of Amount Due.
− Borrowers informed about new contract terms in March 2007 statement . No
other information; not informed about RCT or when terms would end.
◦ Borrower awareness: Initial non-response to MP changes.
− 26 Month Experiment: Consumers remained in assigned arm March 2007 – May
2009. No pre-announced end of terms.
− Control: Interest rates and MP varied at mkt conditions (∼ 55%, 4%).
− (r = 45%, MP = 5%) arm most similar to mkt contract . Show two contrasts to
simplify exposition:
◦ Effect of interest rate decrease: (r = 45%, MP = 5%) vs. (r=15%, MP = 5%).
◦ Effect of minimum payment increase: (r = 45%, MP = 5%) vs. (r = 45%, MP=10%).
− Use sampling weights to be representative of eligible population in the bank.

Eﬀect on Default
− Large ∆ in contract terms =⇒ small ∆ in default relative to base rate.
− Interest rate decrease (45% ↓ 15%) =⇒ ↓ 2.6 percentage points over 26 months
( = +0.20).
◦ Smaller than previous studies.
Table 2: ATE on Default
Sep/07 May/09
(1) (2)
r = 15, MP = 5 0.000 -0.026*
(0.001) (0.008)
r = 45, MP = 10 -0.000 0.005
(0.000) (0.007)
Constant (r = 45, MP = 5) 0.016*** 0.193***
(0.000) (0.006)
Observations 143,916 143,916
R-squared 0.000 0.001
*: p < .05; **: p < .01; ***: p < .001 respectively. Regressions include
stratum dummies and stratum weights.
− No substantive (or stat sig) eﬀect of minimum payment increase on default.
− Doubling minimum payment (5% ↑ 10%) =⇒ ↑ default .5 pp over 26 months
( = .02).
◦ Smaller than previous studies (all observational).

Effect on Default: Variation Over Time
-0.04
-0.02
0.00
0.02
TreatmentEffect(prop)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: interest rate
Dependent variable: cumulative default
-0.04
-0.02
0.00
0.02
Treatment: minimum payment
Dependent variable: cumulative default
− Effect of ∆r ≈ 0 first year.
− Decline small, only statistically significant in last months.
− Effect of ∆ MP relatively constant (≥ 9 months).

Effect on Default: Variation Across Strata
Table 3: Stratum Treatment Effects on Default (May 2009)
Min.Pay, 6-11M Full Pay,24+M Min.Pay,24+M
(3) (4) (5)
r = 15, MP = 5 -0.018 -0.001 -0.037**
(0.015) (0.006) (0.012)
r = 45, MP = 10 0.018 0.001 -0.004
(0.015) (0.006) (0.012)
Constant (r = 45, MP = 5) 0.346*** 0.040*** 0.182***
(0.011) (0.004) (0.009)
Observations 15,978 16,000 15,987
R-squared 0.001 0.000 0.001
Columns (3),(4) and (5) estimate the endline regressions for three different strata – (a) “Min Payers, 6-11M” borrowers
who were with the bank for between 6 and 11 months in January 2007 and were in the lowest payment category ;(b) “Full
Payers,≥24M” who had been with the bank for more than 2 years by January 2007 and had were in the highest payment
category; (c) “Min Payers,≥24M” borrowers who had been with the bank for more than 2 years by January 2007 and were
in the lowest payment category. *: p < .05; **: p < .01; ***: p < .001 respectively.
− Oldest borrowers & best baseline repayment history (least constrained): No effects.
− Newest borrowers & poorest baseline repayment history (most constrained): No effects.

Effect on Cancellations
Table 4: Effects on Cancellations
Sep/07 May/09
(1) (2)
r = 15, MP = 5 -0.008** -0.035***
(0.002) (0.004)
r = 45, MP = 10 0.007 0.017**
(0.003) (0.005)
Constant (r = 45, MP = 5) 0.051*** 0.134***
(0.002) (0.002)
R-squared 0.001 0.002
*: p < .05; **: p < .01; ***: p < .001 respectively. Regressions include
stratum fixed effects and stratum weights. Estimation Details
− Cancellations: 13% over 26 month study (default: 19%).
− Larger reductions in r =⇒ card more attractive to borrowers.
◦ Interest rate decrease (45% ↓ 15%) =⇒ ↓ 3.5 percentage points (pp) over 26 months ( = +0.39).
− Ambiguous apriori effect of ∆MP on cancellations.
◦ Doubling minimum payment (5% ↑ 10%) =⇒ ↑ cancellations 1.7 pp over 26 months ( = +0.12).
− Effect of ∆r, ∆MP on cancellations much stronger than on default.

Eﬀect on Cancellations: Variation Over Time
-0.04
-0.02
0.00
0.02
0.04
Treatment: interest rate
Dependent variable: cumulative cancellations
-0.04
-0.02
0.00
0.02
0.04
Treatment: minimum payment
Dependent variable: cumulative cancellations

Effect on Cancellations: Variation Across Strata
− Oldest borrowers with best baseline repayment history (least constrained): No effects.
− Newest borrowers with poorest baseline repayment history (most constrained): Largest effects.
Min.Pay, 6-11M Full Pay,24+M Min.Pay,24+M
(3) (4) (5)
r = 15, MP = 5 -0.039*** -0.011 -0.040***
(0.008) (0.011) (0.010)
r = 45, MP = 10 0.002 0.022 0.017
(0.009) (0.012) (0.011)
Constant (r = 45, MP = 5) 0.095*** 0.150*** 0.142***
(0.007) (0.008) (0.008)
Observations 15,978 16,000 15,987
R-squared 0.003 0.001 0.003
Columns (3),(4) and (5) estimate the endline regressions for three different strata – (a) “Min Payers,
6-11M” borrowers who were with the bank for less than six months in January 2007 and were in the
lowest payment category ;(b) “Full Payers,≥24M” who had been with the bank for more than 2 years
by January 2007 and had were in the highest payment category; (c) “Min Payers,≥24M” borrowers
who had been with the bank for more than 2 years by January 2007 and were in the lowest payment
category.*: p < .05; **: p < .01; ***: p < .001 respectively.

Effect on Revenues
− Revenues increasing in interest rate ( = +1.54).
− Revenues decreasing in minimum payments ( = −0.16).
− =⇒ Departures from (45, 5) arm ↓ bank revenues
− =⇒ Bank A’s standard terms maximize profits
Table 5: Treatment Effects on Bank Revenues
Standard dependent variable Selected strata in May/09
May/09 Min.Pay, 6-11M Full Pay,24+M Min.Pay,24+M
(1) (2) (3) (4)
r = 15, MP = 5 -2,859*** -3,426*** -514*** -3,113***
(212) (222) (123) (164)
r = 45, MP = 10 -469*** -488* -23 -522**
(41) (245) (130) (176)
Constant (r = 45, MP = 5) 2,768*** 1,708*** -185 3,291***
(110) (172) (96) (133)
Observations 143,916 15,978 16,000 15,987
R-squared 0.035 0.027 0.003 0.042
− Best paying stratum generates zero revenues.
− Largest revenues from long-term borrowers with poorest baseline repayment history.
− Revenue Graph

Eﬀect on Proximate Determinants of Revenues
− Can dig deeper into revenue eﬀects by examining (monthly) data on purchases,
payments and debt.
− Account for attrition (card exit)
◦ Use Lee, (2009) bounds. Assumptions For Lee Bounds
◦ Lee bounds after imputing zero for all outcomes for cancelled cards.
• Imputing zeros for defaulted cards less defensible.
• Details for Zero Imputations

Proximate Effects
− Interest rate reductions had
◦ mixed effects on purchases: ∈ [−0.37, +0.25].
◦ small negative effects on payments: ∈ [+0.04, +0.39].
◦ a modest negative effect on debt: ∈ [+0.35, +0.74].
− Doubling the minimum payment had
◦ small positive effect on purchases: ∈ [+0.15, +0.68]
◦ small positive effects on payments: ∈ [+0.01, +0.37]
◦ small negative effect on debt: [−0.44, −0.01]
− Detailed Analysis for Purchases
− Detailed Analysis for Payments
− Detailed Analysis for Debt

1 Motivation
3 Data
4 Market Facts
5 Experiment
7 Conclusions

Explaining Default
− Previous sections document that:
◦ NTB borrowers default at high rates.
◦ Large experimental changes in contract terms have muted eﬀects on default.
− What explains underlying default rates?
− We document:
(1) Default reduces subsequent access to formal sector credit.
(2) Formal sector terms (interest rates and duration) Informal sector terms.
(3) Default correlated with unemployment (controlling for individual FE)
• Use monthly employment status from IMSS (≈ 20% subsample).

1 Motivation
3 Data
4 Market Facts
5 Experiment
7 Conclusions

Summary and Conclusions
− Increasing emphasis on expanding financial access.
− Little known about expanding credit by large formal sector financial organizations
whose size suggests important role in expanding access.
− Examine large Mexican bank’s effort at catering to NTB population with credit
card – constituted 14% of all first-time formal sector loan products in 2010.
− NTB population: credit-constrained, high default and cancellation rates.
− Construct measure of bank revenue per borrower: low and variable.
− Used ML methods to argue that screening borrowers ex-ante only weakly
predictive of default and subsequent revenue.
− Next, use large national level RCT and find that large changes in interest rates and
minimum payments have muted effects on default.
− Bank discontinued card.
− Work in Progress: Explaining large baseline default rates. Using matched
individual level employment data.

Related Literature: Asymmetric Information and Credit Constraints
− Gross and Souleles (2002)
− Karlan and Zinman (2009)
− Adams et al (2009)
− Einav et al (2012)
− Dehejia et al (2012
− Attanasio et al (2008)
− Karlan and Zinman (2016)

Related Literature: Consumer Protection
− Melzer (2011): Evaluated welfare effects of payday loans by using distance to a
state that allows payday lending as a source of exogenous variation. Finds that
access to payday loans leads to difficulty in paying other bills (rent, utilities,
mortgage) so welfare effects are likely low.
− Bertrand and Morse (2011): Information intervention RCT with payday borrowers.
Borrowers informed of fees (in dollar terms) accumulated for typical repayment
profiles reduced borrowing by 11% four months after treatment.
− Agarwal et al (2015): Find that the CARD act regulation that limited fees led to a
decline in borrowing costs for lower credit score borrowers.

Consumer Protection and Sub-Optimal Choice
− Bar-Gill (2004), Warren (2008): Policy pieces arguing for
− Ausubel (1999): Evidence of adverse selection from RCT on solicitation for
pre-approved credit cards. Also, some evidence of behavioral issues – the
“underestimation hypothesis” – consumers underestimate current and future
borrowing.
− Ausubel and Shui (2005): Use RCT on solicitations; estimate β/δ model, ﬁnd
β = .8
− Koszegi and Heidhues (2010): Model of ﬁrms interacting with possibly
time-inconsistent agents. Equilibrium contracts will have front-loaded payments
and high fees and penalties.
− Meier and Sprenger (2010): Find positive correlations between survey elicited
measures of time preferences and credit card borrowing on both the extensive
and intensive margins.
− Gabaix and Laibson (2006): Argue that hidden costs (“shrouding”) may be an
equilibrium phenomenon in an economy with myopic (or unaware) consumers.

Financial Inclusion
− Demirguc-Kunt et al (2012)
− Dabla-Norris et al (2015)
− Dupas et al (2018)

Sampling Weights
Cardholder’s payment behavior
Total
Minimum payer Part-balance payer Full-balance payer
(1) (2) (3) (4)
Months of credit card use
6 to 11 months 9.8 1.6 0.6 12
12 to 23 months 10.7 1.7 0.7 13
24+ months 61.5 9.8 3.8 75
Total 82 13 5 100
Return to slide Return to Study Design Slide

Bank Revenue Calculation
− Deﬁne
Amount Due[t, t + 1] =Amount Due[t − 1, t] − Payments[t − 1, t]
+ Purchases[t − 1, t] + Fees[t − 1, t] +
r
12
Debt[t − 1, t]
− Manipulating,
Payments[t − 1, t] − Purchases[t − 1, t] =Amount Due[t − 1, t] − Amount Due[t, t + 1]
+ Fees[t − 1, t] +
r
12
Debt[t − 1, t]
and summing card inception (t = 0) to exit (t = T) and discounting each period by β
T
t=0
βt
Payments[t − 1, t] − Purchases[t − 1, t]
= Amount Due[−1, 0] − βT
Amount Due[T, T + 1]
+ (β − 1)
T −1
t=0
βt
Amount Due[t, t + 1]
+
T
t=0
βt
Fees[t − 1, t] +
r
12
Debt[t − 1, t]
− Adjust since (a) T (card exit) not observed for all cards; (b) 0 corresponds to start of experiment, not
card exit.
Return to slide

Large variance in revenue
6 to 11 months, minimum payers
0.02.04.06
-20 -10 0 10 20
25thpercentile
50thpercentile
75thpercentile
-4-20246
525 575 625 675 725
95% CI lpoly smooth
24+ months, full payers
0.1.2.3.4
-20 -10 0 10 20
25thpercentile
50thpercentile
75thpercentile
-20246
525 575 625 675 725
95% CI lpoly smooth

Credit score of experimental sample (2007) and market (2016)
0.02.04.06.08.1
Fractionofindividuals
400 500 600 700 800
Credit score
Market data (PL) Experiment cards

Estimation Details for Table 1
− Note: Each column in each Panel is a different prediction method. The first row in each
panel represents the correlation between the predicted value and the realized value for a
test sample. The R-squared is 1 minus the ratio of the variance of the prediction errors
relative to the variance of the dependent variable.
− Variables: Panel A uses variables measured at the moment of application. The prediction
variables are the state, zip code, marital status, sex, date of birth, number of prior loans,
number of prior credit cards, number of payments in the credit bureau, number of banks
interacted with, number of payments in arrears, number of payments in arrears for credit
cards, the length in months of the relationship in the credit bureau, the date of last time in
arrears, and the date of last time in arrears for a credit card. Panel B uses all variables from
Panel A, but measured in March 2007. In addition, we use the credit score which is
measured in June 2007 (this is our oldest credit score measure). Panel C uses all variables in
Panel B, and in addition it uses purchases, payments, debt, and amount due, all measured
in March 2007.
− Overview: We we separate the control group into two samples: the test sample (25%) and
the training sample (75%). We construct different predictors using the training sample, and
evaluate predictive success by comparing the predicted outcome to the true observed
outcome for the test sample.
Return to slide

Credit limit and duration of the card in the market
Meaninitialcreditlimitfortheexperiment
.15
.2
.25
.3
.351(cardclosesbefore27months)
0 30,000 60,000 90,000 120,000
Credit limit in pesos
95% CI lpoly smooth
Return to slide

Quantifying The First Lender Externality
− Regress realized revenues on June 2007 credit scores for all cards that did not
attrit during the experiment.
− Predict revenues for the entire sample of cards using the estimates above and
compute the diﬀerence between predictions and realized values for the entire
sample.
− The average of this diﬀerence for the sub-sample that cancelled cards during the
experiment is our estimate of the revenue lost by the bank over the 27 months.
Return to slide

Other Elasticities
− Elasticity of loan demanded with respect to the interest rate.
− D. Karlan and Zinman, (2016) Mexico: = −2.9 (29 Months)
− Dehejia, Montgomery, and Morduch, (2012) Bangladesh: ∈ (−.73, −1.04)
− Attanasio, Goldberg, and Kyriazidou, (2008) USA: ≈ 0 (poorer households)
− D. S. Karlan and Zinman, (2008) South Africa: = −0.32
− Gross and Souleles, (2002) USA: = −1.3
− Return to (Debt, Purchase) Slide.

Other Default Elasticities
− Elasticity of Default with respect to the Interest rate. +0.20
◦ Lower than the delinquency elasticity of 1.8 implied by D. Karlan and Zinman, (2016).
No default elasticities shown.
◦ Lower than the default elasticity of 0.39 implied by the interventions in D. S. Karlan and
Zinman, (2009).
− Elasticity of Default with respect to minimum payment increase: +0.02
◦ Smaller than = .20 for delinquency in Keys and Wang, (2016)
◦ Smaller than = .06 in d’Astous and Shore, (2015) revocation rates.
− Return to Default Slide.

Documenting Credit Constraints
−
∆Debti,t = δt +
T
j=0
βj∆Limiti,t−j + γ Xi,t + i,t (2)
− θ ≡
T
j=0
βj
Table 6: Documenting Credit Constraints: θ
6-11 months 24+ months
(1) (2) (4) (8) (10)
All Minimum Full Minimum Full
Panel A. Bank’s debt and limit
Baseline 0.32 0.69 0.23 0.33 0.03
(0.04) (0.06) (0.03) (0.06) (0.01)
IV 0.73 2.14 0.47 0.62 -0.08
(0.14) (0.32) (0.37) (0.19) (0.14)
Observations 1366035 118687 170791 146291 186338
Mean dependent variable 70 184 59 95 23
SdDep 2292 3631 1756 2863 1272
Mean changes in limit -104 -141 -105 -100 -120
SdInd 1460 1532 1486 1446 1956

Borrower Initiated Cancellations
Minimum-payers
-1
0
1
2
3
Cancelledbyclient
Minimumpayment
-6
-4
-2
0
Cancelledbyclient
Interestrate
Full-payers
-1
0
1
2
3
Cancelledbyclient
Minimumpayment
-3
-2
-1
0
1
2
Cancelledbyclient
Interestrate

Bank initiated Revocations
Minimum-payers
-2
0
2
4
Revokedbybank
Minimumpayment
-3
-2
-1
0
1
Revokedbybank
Interestrate
Full-payers
-1
-.5
0
.5
1
Revokedbybank
Minimumpayment
-1.5
-1
-.5
0
.5
Revokedbybank
Interestrate

Effect on Purchases
-.5
0
.5
1
TreatmentEffect(ε)
Treatment: Interest rate
Dependent variable: purchases
-.5
0
.5
1
TreatmentEffect(ε)
Treatment: Minimum payment
− Treatment effect coefficient normalized by control mean in each period.
− Return to Purchase slide.

Effect on Purchases: Across Strata and Time-.50.511.5
TreatmentEffect(ε)
Min. Payers w/ 6-11 M Full Payers w/ 24+ M
-.50.511.5
TreatmentEffect(ε)
− Treatment Effect Coefficient normalized by Control Mean in each period.
− Minimal response from long-term “full payers”.
− Return to Purchases Slide

-.2
0
.2
.4
.6
.8
TreatmentEffect(ε)
-.2
0
.2
.4
.6
.8
TreatmentEffect(ε)
− Bounds with purchases for all borrowers who cancelled set to 0.
− Return to Purchase slide.

Eﬀect on Purchases/Amount Due
Monthly Purchases
Amount Due
(1) (3)
Short Term (6m) Long Term (27m)
r = 15, MP = 5 0.0194*** -0.0025
(0.0031) (0.0040)
r = 45, MP = 10 0.0211*** 0.0150**
(0.0037) (0.0032)
Constant (r = 45, MP = 5) 0.0762*** 0.0888***
(0.0021) (0.0019)
R-squared 0.003 0.005
Lee Bounds IR [0.0168, 0.0194] [-0.0542, 0.0046]
Lee Bounds MP [0.0203, 0.0393] [0.0088, 0.0770]
Lee Bounds IR [ -0.38, -0.33] [ -0.08, 0.92]
Lee Bounds MP [ 0.27, 0.52] [ 0.10, 0.87]
− Using Purchases
Amount Due
as outcome.
− Dropping ≈ 5% of observations with 0 amount due.
Return to Purchases Slide

Eﬀect on Purchase/Amount Due Across Time
− Point Estimates and Lee Bounds.
-.05
0
.05
.1
TreatmentEffect(Ratio)
Dependent variable: purchases / amount due
-.05
0
.05
.1
Dependent variable: purchases / amount due
− Using Purchases
Amount Due
as outcome.
− Return to Purchases Slide

Eﬀect on Fraction Paid
Monthly Payment
Amount Due
(1) (3)
Short Term (6m) Long Term (27m)
r = 15, MP = 5 -0.0024 -0.0113***
(0.0012) (0.0016)
r = 45, MP = 10 0.0289*** 0.0249***
(0.0011) (0.0015)
Constant (r = 45, MP = 5) 0.1152*** 0.1053***
(0.0016) (0.0011)
R-squared 0.009 0.013
Lee Bounds IR [-0.0055, -0.0021] [-0.0435, -0.0027]
Lee Bounds MP [0.0277, 0.0402] [0.0173, 0.0609]
Lee Bounds IR [ 0.03, 0.07] [ 0.04, 0.62]
Lee Bounds MP [ 0.24, 0.35] [ 0.16, 0.58]
− Dropping ≈ 5% of observations with 0 amount due.
Return to Payments Slide

Effect on Fraction Paid Across Time
− Point Estimates and Lee Bounds.
-.05
0
.05
.1
Dependent variable: paym_amt_due
-.05
0
.05
.1
Dependent variable: paym_amt_due
− Persistent, constant effect of MP change.
− Limited effect of r changes.
− Return to Payments Slide

Eﬀect on Normalized Monthly Payments
− Point Estimates and Lee Bounds
-.4
-.2
0
.2
.4
.6
TreatmentEffect(ε)
Dependent variable: payment
-.4
-.2
0
.2
.4
.6
TreatmentEffect(ε)

Eﬀect on Monthly Payments: Across Strata and Time-.20.2.4.6.8
TreatmentEffect(ε)
-.20.2.4.6.8
TreatmentEffect(ε)
− Return to Payments Slide 1
− Return to Payments Slide 2

Eﬀect on Payments (Cancellations set to 0)
-.2
0
.2
.4
.6
TreatmentEffect(ε)
-.2
0
.2
.4
.6
TreatmentEffect(ε)
− Bounds informative for most of experiment.

Cancellation Estimation Details
− Column (1) is estimated for client-initiated cancellations 6 months after the start
of the intervention and the remainder are for cancellations at the end of the
experiment (27 months). Columns (3),(4) and (5) estimate the endline regressions
for three diﬀerent strata – (a) “Min Payers, <12” borrowers who were with the bank
for less than six months in January 2007 and were in the lowest payment category
;(b) “Full Payers,>24M” who had been with the bank for more than 2 years by
January 2007 and had were in the highest payment category; (c) “Min
Payers,>24M” borrowers who had been with the bank for more than 2 years by
January 2007 and were in the lowest payment category.
Return to Cancellation Table

Assumptions for Lee, (2009) Bounds
− (YA, YB, ) potential outcomes under treatments A and B .
− (SA, SB) potential sample selection indicators. e.g. If card remains in sample under
treatment A but exits sample under treatment B then (SA = 1, SB = 0).
− Need to assume SA ≥ SB
− In our context, need card exit to be more likely under B than A. Reasonable e.g. when
S(r%,m) ≥ S(45%,m) ∀ r < 45%, ∀m
but not necessarily others.
− If SA ≥ SB, then obtain sharp bounds on ATE for the “always in sample” sub-population
E (YA − YB|SA = 1, SB = 1) = E (YA − YB)
− Bounds on ATE for sub-population of cards that would not exit under treatment A or B.
− Compute these period-by-period (t = 1 . . . 27).
− Return to Proximate Determinants Slide

Imputing Zeros for Card Exits
− For purchases and payments in period t impute Yt = 0 for all periods t ≥ s after
card cancels (St = 0 ∀ t ≥ s).
− Eliminates attrition by cancellers.
− Since card has been closed with no outstanding balance , plausible to set
outcomes to zero (purchases, payments and debt).
− Setting revoked cards to zero less defensible.
− Return to Proximate Determinants Slide

Purchases
(1) (3) (5)
Short Term (6m) Long Term (27m) Long Term w/Zeros
r = 15, MP = 5 99*** 65*** 75***
(15) (7) (6)
r = 45, MP = 10 75*** 92*** 62***
(9) (9) (6)
Constant (r = 45, MP = 5) 401*** 415*** 341***
(6) (10) (8)
Observations 134,385 87,093 105,180
R-squared 0.002 0.004 0.003
Lee Bounds IR [ 49, 101] [ -192, 104] [ -56, 85]
Lee Bounds MP [ 75, 107] [ 65, 352] [ 51, 231]
Lee Bounds IR [ -0.38, -0.18] [ -0.38, 0.69] [ -0.37, 0.25]
Lee Bounds MP [ 0.19, 0.27] [ 0.16, 0.85] [ 0.15, 0.68]
− ↓ interest rates =⇒ ↑ purchases somewhat, bounds wide (include zero).
◦ Low relative to other elasticities.
− ↑ minimum payments =⇒ ↑ purchases.
◦ Robust, unexpected.

Effect on Purchases: Variation Across Time
− Monthly Point Estimates and Lee Bounds.
-200
0
200
400
TreatmentEffect(MXN)
-200
0
200
400
− Bounds relatively tight for initial 6 months.
− Persistent positive effect of MP on purchases.
− Even upper bounds suggest relatively small effects.
− Results (Regressions, Graphs) with purchases as fraction of amount due as LHS.
− Results normalized by control mean in each period.
− Variation across strata and time.
− Bounds with cancellations set to zero.

Eﬀect on Monthly Payments
Monthly Payments
(1) (3) (5)
Short Term (6m) Long Term (27m) Long Term (w/ Zeros)
r = 15, MP = 5 -27* -64*** -26*
(12) (9) (8)
r = 45, MP = 10 154*** 53* 25
(13) (18) (15)
Constant (r = 45, MP = 5) 638*** 628*** 515***
(8) (5) (5)
Observations 134,385 87,093 105,180
R-squared 0.003 0.003 0.002
Lee Bounds IR [ -103, -24] [ -267, -17] [ -134, -14]
Lee Bounds MP [ 153, 184] [ 9, 301] [ 7, 193]
Lee Bounds IR [ 0.06, 0.24] [ 0.04, 0.64] [ 0.04, 0.39]
Lee Bounds MP [ 0.24, 0.29] [ 0.01, 0.48] [ 0.01, 0.37]
− ↓ interest rates =⇒ ↓ payments (debt related).
− ↑ minimum payments =⇒ ↑ payments.
◦ No heterogeneity in signs (unlike Keys and Wang, 2016).

Eﬀect on Monthly Payments: Variation Across Time
− Monthly Point Estimates and Lee Bounds.
-400
-200
0
200
400
-400
-200
0
200
400
− Limited response to MP increase in ﬁrst two months (consistent with inattention).
− Results (Regressions, Graphs) with amount paid as fraction of amount due as LHS.
− Results normalized by control mean in each period.
− Variation across strata and time.
− Bounds with cancellations set to 0.

Eﬀect on Debt
Debt
(1) (3) (5)
Short Term (6m) Long Term (27m) Zeros
r = 15, MP = 5 -270* -604*** -417***
(83) (62) (42)
r = 45, MP = 10 25 -789*** -691***
(46) (89) (69)
Constant (r = 45, MP = 5) 1,409*** 2,114*** 1,732***
(11) (49) (35)
Observations 134,385 87,093 105,180
R-squared 0.001 0.005 0.004
Lee Bounds IR [ -397, -266] [-1,576, -474]
Lee Bounds MP [ 22, 106] [ -971, 326]
Lee Bounds IR [ 0.28, 0.42] [ 0.34, 1.12] [0.34, 0.74]
Lee Bounds MP [ 0.02, 0.08] [ -0.46, 0.15] [-0.44,-0.00]
− ↓ interest rates =⇒ ↓ debt.
◦ Recall ↓ interest rates =⇒ purchases ↑ (?), payments ↓
◦ Debt compounds at lower rates.
◦ Compare to other papers
− ↑ minimum payments =⇒ ↓ debt.
◦ Larger than Keys and Wang, (2016) (and less heterogeneity)

Eﬀect on Debt: Variation Across Time
-1
-.5
0
.5
TreatmentEffect(ε)
Dependent variable: debt
-1
-.5
0
.5
TreatmentEffect(ε)
− Normalized by control mean.
− Interest rate eﬀects robustly negative for most of experiment.

Eﬀect on Debt: Variation Across Strata and Time
-1000
-500
0
500
1000
1500
Treat.Effect(MXN)
-1000
-500
0
500
1000
1500
Treat.Effect(MXN)
− No evidence of perverse diﬀerential responses (contra Keys and Wang, 2016)

NPV of bank revenue
-3000-15000150030004500
NPVofrevenue
I:15%
P:5%
I:15%
P:10%
I:25%
P:5%
I:25%
P:10%
I:35%
P:5%
I:35%
P:10%
I:45%
P:5%
I:45%
P:10%
Mean Std. Deviation

NPV of bank revenue
6 to 11 months minimum payers
-3000
-1500
0
1500
3000
4500
NPVofrevenue
I:15%
P:5%I:15%
P:10%
I:25%
P:5%I:25%
P:10%
I:35%
P:5%I:35%
P:10%
I:45%
P:5%I:45%
P:10%
Mean Std. Deviation
24+ months full payers
-3000
-1500
0
1500
3000
4500
NPVofrevenue
I:15%
P:5%I:15%
P:10%
I:25%
P:5%I:25%
P:10%
I:35%
P:5%I:35%
P:10%
I:45%
P:5%I:45%
P:10%
Mean Std. Deviation

Probability of getting a loan against default
New credit card between t and t + 6 New credit between t and t + 6
OLS OLS
(3) (6)
Default -0.1145*** -0.1466***
(0.0035) (0.0045)
Constant 0.1498*** 0.2126***
(0.0014) (0.0016)
R-squared 0.0048 0.0060
Dependent Variable Mean 0.1443 0.2056
− Strong negative eﬀect of default on subsequent credit (≈ 70% decline).
− Back to Explaining Default

Formal Sector Terms Dominate Informal Terms
Interest rate Loan amount Loan duration in years
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Formal credit -94*** -108** -7.08 6,184.3*** 4,926*** 3,934*** 0.554*** 0.544*** 0.491***
(31) (48) (38) (288) (484.3) (659.3) (0.034) (0.058) (0.104)
Age -0.483 97.86*** 0.005***
(1.45) (10.73) (0.002)
Monthly expenditure 0.014* 0.382*** 0.000
(0.007) (0.060) (0.000)
Car -26 -760*** -0.059***
(16) (130) (0.020)
Washing machine -43 110 0.007
(36) (226) (0.040)
Appliances 28 -364* -0.023
(31) (198) (0.034)
Constant 291*** 336*** 152*** 3,658*** 564 4699*** 0.520*** 0.333** 0.436***
(19) (125) (41) (134) (960) (762) (0.021) (0.149) (0.122)
Education dummies No Yes No No Yes No No Yes No
Sample dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes
Individual FE No No Yes No No Yes No No Yes
Dependent variable mean 254 254 231 5022 5022 5061 0.732 0.732 0.732
Dependent variable SD 503 503 423 6,938 6,938 7,023 0.757 0.757 0.757
Observations 2,427 880 202 8,810 2,992 423 4,257 1,522 301
R-squared 0.006 0.036 0.860 0.063 0.171 0.661 0.083 0.119 0.646

Unemployment Increases Default
default
j
it = αi + γs,t +
k≥1
βj
k × 1( months unemployedit = k) + εit (3)
-.050.05.1
increaseinprobability(β)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Months since last employed
dep. var >1m >2m >3m >6m

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