1. Li Liao, Xiumin Martin, Ni Wang, Zhengwei Wang, and Jun Yang
Carrot or stick? Evidence from a pair of natural field experiments testing lender information sharing hypotheses
Li Liao, Xiumin Martin, Ni Wang, Zhengwei Wang, and Jun Yang
IFS, SWUFE, July 20, 2018

2. Motivation
Consumer loans have increased significantly in both developed countries and emerging markets, aided by recent advancements in lending technologies.
It is an important and imminent issue on how to improve credit allocation while expanding credit to unscored borrowers, especially when legal enforcement of loan repayment is weak.
Important policy implications for regulators on borrower information sharing among lenders (e.g., in the US, online marketplace lending outcomes are not required to be reported to credit rating agents).
Inform private lenders the benefits/costs of sharing information.

3. Institutional background--credit registry/bureaus in China
Central credit registry under People’s Bank of China, covering only 25% of population;
Sesame score issued by Sesame Credit (under Alibaba), based on 500 million consumers who use Alibaba’s Taobao and T-Mall market places, plus 400 million registered users of Alipay (支付宝);
Online marketplace lenders generate their own credit scores using proprietary models. Unlike Sesame score, this score is not shared among lenders.
On May 23, 2018, the establishment of 百行征信 (8 including Alibaba and Tencent); On June 28, 15 lenders started information sharing with百行征信, 120 consumer financing institutions signed information sharing agreement with百行.

4. Research questions
Knowing that lenders share loan repayment information, do borrowers change their behavior?
Loan take-up decision
Repayment/default decision
How information sharing (via central credit registry) affects adverse selection and borrowers’ moral hazard in the consumer credit market?

5. Main results
We document casual effect of the awareness of credit reporting to the central credit registry on mitigating borrower moral hazard, reducing default likelihood of new borrowers (default rate reduces by % given unconditional default rate of 10%--given the 4,000 RMB size, this can save up to 228 RMB per loan).
We find mixed evidence on the effect of the awareness of credit reporting on adverse selection of borrowers (the resulting composition change of borrowers)—repeat (new) borrowers are less (more) likely to take up the loan.
Policy implications: establishing central credit registry/bureaus, educating borrowers about credit reporting, text messaging is inexpensive and effective.

6. Literature: sharing borrower repayment/default information
Theories:
Sharing default information reduces moral hazard, but sharing more detailed information as well may attenuate such effect (Padilla and Pagano, 2000).
Bulow and Rogoff (1989): dynamic, no (sovereign) debt can be sustained in equilibrium if the only punishment for default is the denial of future credit (reputation costs).
Empirical evidence—lenders’ perspective
Reduces asymmetry information and improves screening (Pagano and Jappelli, 1993).
Reduces banks’ informational rents (Padilla and Pagano, 1997).
Asymmetric information and competition affect lenders’ willingness to share borrower information (Brown and Zehnder, 2010).
Establishing public credit registry reduces lenders’ credit to firms on which they have negative information, in anticipation of other lenders’ reaction to the negative news (Hertzberg et al., 2011). Lenders may also distort information to retain information rent (Giannetti et al. 2017).

7. Literature: information sharing (2)
Empirical evidence—borrowers’ perspective
Information sharing improves lenders’ screening and group borrowers’ payment incentives (De Janvry, McIntosh, and Sadoulet 2010);
Information sharing improves loan performance without disentangling moral hazard from adverse selection (Doblas-Madred and Minertti 2013);
Offering low interest rate for future loans improves borrowers’ repayment incentives, (Karlan and Zinman 2009)—not about information sharing.
Our contribution
Our field experiments fix the credit supply (conducted after loan approval), we can focus on the effect of information sharing on credit demand--borrower behavior;
Natural field experiments distinguish causation from correlation (e.g., establishing a public credit registry changes the equilibrium, both supply and demand);
We identify borrower moral hazard and adverse selection channels;
We are the first to examine the effect of information sharing on borrowers’ take-up decisions.

8. Institutional background – LHP
Founded in China in 2014; matching individual borrowers with institutional lenders;
As of August 2017, LHP made nearly 8 million loans totaling billion Yuan (/6.2);
Using own risk model, collecting fees (4-5% per month) for bearing credit risks;
Two types of lenders for LHP--financial institutions (RLs) and other lenders (NRLs):
RLs are required to report repayment information (+ or -) to the central credit registry;
NRLs are not required to report, and do not do so;
Typical borrowers: Male in late 20s, employed with an average income of 4,000 Yuan/month, average Sesame score of 602 (low end of “fair” grade), and good education (averaging to 3-year junior college).
Typical loans: 3-month maturity, amount of 4,500 Yuan, 1-1.2% monthly interest rate.

9. Loan application Experiment 1 new borrowers 85% repeat borrowers 15%
Approved
11%
Rejected
89%
Approved
70%
Rejected
30%
MSG (rn >= 0.5)
RL (odd bd)
50%
Experiment 1
No MSG (rn < 0.5)
NRL (even bd)
50%
MSG (rn >= 0.5)
No MSG (rn < 0.5)

10. Experiment 1 Funds deposited
If approved, a lender is assigned to the borrower.
The borrower receives text message confirming approval
Funds deposited
If the loan defaults, LHP starts debt collection process
Approval based on the internal model
If taken out, a loan contract is formed
Payment due date
Timeline
The borrower applies for a loan
The borrower decides whether to take up the loan
The borrower repays or defaults on the loan
First randomized experiment
Credit warning
(RL and NRL)
No MSG: Loan approved. Use app to input bank account information.
MSG: Loan approved. Use app to input bank account information. Repayment/default of the loan will be reported to the central credit registry

11. (different borrowers)
Experiment 2
(different borrowers)
Loan approved. Use app to input bank account information
If approved, a lender is assigned to the borrower.
The borrower receives text message confirming approval
Second randomized experiment
Credit warning
(RL only)
If the loan defaults, LHP starts debt collection process
Approval based on the internal model
If taken out, a loan contract is formed
Payment due date
Timeline
The borrower applies for a loan
The borrower decides whether to take up the loan
The borrower repays or defaults on the loan
No MSG: Funds deposited.
MSG: Funds deposited. Repayment/default of the loan will be reported to the central credit registry

12. Adverse selection model
Two types of borrowers: h (credit quality) and l (credit quality); risk neutral; no strategic default;
Both types borrow a fixed amount of F at an interest rate of r > 0;
Borrower’s project fails with a probability of 0≤ 𝑝 ℎ < 𝑝 𝑙 ≤1;
Value of the loan is g(F) ≥ 𝐹 1+𝑟 if a project succeeds and zero otherwise;
If succeeds, pays principal and interest, rewards for enhancing credit report is 𝑐 ℎ >0 and 𝑐 𝑙 >0;
If fails, penalty for adverse effect on future access to credit card or mortgages is 𝑠 ℎ >0 and 𝑠 𝑙 >0;
𝜶 is the probability of reporting loan repayment to the central credit registry.
𝜕 𝑈 ℎ 𝜕𝛼 = (1− 𝑝 ℎ )𝑐 ℎ − 𝑝 ℎ 𝑠 ℎ
𝑈 ℎ = 1− 𝑝 ℎ (𝛼 𝑐 ℎ +𝑔(𝐹)−𝐹 1+𝑟 )− 𝑝 ℎ 𝛼 𝑠 ℎ ≥ 𝑈 ℎ 0 (reservation utility).
𝜕 𝑈 𝑙 𝜕𝛼 = 1− 𝑝 𝑙 )𝑐 𝑙 − 𝑝 𝑙 𝑠 𝑙
As reporting probability increases, adverse selection will be mitigated (exacerbated) if the change in the expected net benefit is greater (smaller) for high-credit-quality borrowers than low-quality borrowers.

13. Adverse selection and moral hazard
For h type borrowers, the default probability is 𝑝 ℎ 𝑒 ℎ = 𝑝 ℎ − 𝑒 ℎ (e.g., 𝑝 ℎ ′ 𝑒 ℎ =−1);
The cost of exerting effort is 0.5 𝑒 ℎ 2 ;
The expected utility of high-type borrowers is
𝑈 ℎ = 1− 𝑝 ℎ 𝑒 ℎ 𝛼 𝑐 ℎ +𝑔 𝐹 −𝐹 1+𝑟 − 𝑝 ℎ 𝑒 ℎ 𝛼 𝑠 ℎ − 1 2 𝑒 ℎ 2 ≥ 𝑈 ℎ 0
𝐹𝑂𝐶: 𝑒 ℎ ∗ = 𝛼(𝑐 ℎ + 𝑠 ℎ )+𝑔 𝐹 −𝐹 1+𝑟 and 𝑝 ℎ 𝑒 ℎ ∗ = 𝑝 ℎ − 𝛼(𝑐 ℎ + 𝑠 ℎ )−𝑔(𝐹)+ 𝐹 1+𝑟
𝜕 𝑈 ℎ 𝜕𝛼 = (1− 𝑝 ℎ 𝑒 ℎ ∗ ) 𝑐 ℎ − 𝑝 ℎ 𝑒 ℎ ∗ 𝑠 ℎ
𝜕 𝑈 𝑙 𝜕𝛼 = 1− 𝑝 𝑙 𝑒 𝑙 ∗ )𝑐 𝑙 − 𝑝 𝑙 𝑒 𝑙 ∗ 𝑠 𝑙
As reporting probability increases, adverse selection will be mitigated (exacerbated) if the change in expected net benefit is greater (smaller) for high-credit-quality borrowers than low-quality borrowers.
As reporting probability increases, given the composition of borrowers who take up loans, moral hazard will be mitigated.

14. Summary statistics for Experiment 1
Variable N
Mean
Min
p25
p50
p75
Max
St. Dev.
Default
6,803
0.071 1
0.256
take-up
8,281
0.822
0.383
MSG
0.469
0.499 RL
0.568
0.495
Amount (Yuan)
3,736.75
2,000
4,000
6,000
1,750.16
Maturity (months)
3.094 3 6
0.522
Interest rate (monthly)
0.060
0.032
0.050
0.070
0.072
0.013
New
0.552
0.497
Sesame score
653.68
584
625
651
678
753
37.38
LHP score
674.21
532
640
675
706
772
40.98
Female
0.237
0.425
Age
29.641 20 25 28 33 55
6.193
Junior college or above
0.441
Below junior college
0.368
0.482

15. Experiment 1: New vs. repeat borrowers
New borrowers
Diff
Variables N
Mean
Default
3,339
0.044
3,464
0.096
-0.052***
Take-up
3,707
0.901
4,574
0.757
0.143***
MSG
0.489
0.453
0.037*** RL
0.558
0.575
-0.017
Amount (Yuan)
4,561.1
3,068.6
1,492.5***
Maturity (months)
3.197
3.01
0.186***
Interest rate (monthly)
0.052
0.067
-0.016***
Sesame score
659.5
649.0
10.48***
LH score
703.46
650.50
52.96***
Female
0.246
0.23
0.016*
Age
29.253
29.956
-0.703***
Junior college or above
0.468
0.419
0.049***
Below junior college
0.299
0.424
-0.125***

16. Summary statistics for Experiment 2
Variable N
Mean
Min
p25
p50
p75
Max
St. Dev.
Default
2,804
0.069 1
0.254
MSG
0.253
0.435
Amount (Yuan) 3,
2,000
4,000
6,000 1,
Maturity (months) 3
Interest rate (monthly)
0.061
0.042
0.052
0.072
0.013
New
0.522
0.5
Sesame score
584
623
647
674
748
36.896
LHP score
610
640
675
712
772
40.449
Female
0.241
0.428
Age
29.845 20 25 28 33 55
6.278
Junior college or above
0.429
0.495
Below junior college
0.400
0.490

17. Probit regression: Dependent variable = take-up
Repeat borrowers
New borrowers
(1)
(2) RL
-0.090***
-0.011
(-9.730)
(-0.862)
MSG
0.003
0.028**
(0.373)
(2.180)
Sesame score
-0.000**
-0.002***
(-2.343)
( )
Interest rate
-0.615*
-4.524***
(-1.697)
(-5.045)
Female
0.000
0.014
(0.030)
(0.941)
Age
-0.000
-0.001
(-0.104)
(-1.171)
Education dummies
(base group: borrowers with education missing)
Junior college or above
0.013
0.015
(1.160)
(0.819)
Below junior college
0.033***
0.050***
(2.878)
(2.712)
Observations
3,707
4,574
Pseudo R2
0.0436
0.0384
Repeat borrowers are 9% less likely to default on RL loans (unconditional take-up rate: 90%)
New borrowers are 2.8% more likely to take up with message (unconditional take-up rate: 76%)

18. Probit regression: Dependent variable = default
Repeat borrowers
New borrowers
(1)
(2) RL
-0.015**
0.003
(-2.160)
(0.326)
MSG
0.007
-0.027***
(1.087)
(-2.833)
Sesame score
-0.000
-0.001***
(-1.462)
(-5.470)
Interest rate
0.660**
1.399*
(2.537)
(1.745)
Female
-0.019***
-0.014
(-2.646)
(-1.207)
Age
0.000
0.001
(0.565)
(1.265)
Education dummies
(base group: borrowers with education missing)
Junior college or above
-0.013
-0.035***
(-1.467)
(-2.601)
Below junior college
-0.004
-0.006
(-0.457)
(-0.434)
Observations
3,339
3,464
Pseudo R2
0.0237
0.0392
Repeat borrowers are 1.5% less likely to default on RL loans (unconditional default rate: 4.4%)
New borrowers are 2.7% less likely to default with message (unconditional default rate: 9.6%)

19. Probit regression: Dependent variable = default
Experiment 2
Experiment 1 (RL only)
Repeat borrowers
New borrowers
(1)
(2)
(3)
(4)
MSG
-0.012
-0.057***
0.007
-0.035***
(-1.266)
(-3.969)
(0.809)
(-2.747)
Sesame score
0.000
-0.001***
-0.000
(0.137)
(-3.818)
(-1.232)
(-4.227)
Interest rate
0.539
2.607**
0.733**
0.447
(1.477)
(2.009)
(2.376)
(0.441)
Female
-0.023**
-0.030*
-0.016*
-0.013
(-2.476)
(-1.841)
(-1.802)
(-0.833)
Age
0.001
(1.566)
(1.101)
(1.348)
(1.071)
Education dummies (base: borrowers with education missing)
Junior college+
-0.014
-0.024
-0.021**
-0.043**
(-1.165)
(-1.005)
(-2.065)
(-2.331)
Junior college
-0.004
(-0.342)
(0.059)
(-1.495)
(-0.746)
Observations
1,340
1,464
1,781
1,973
Pseudo R2
0.0294
0.0587
0.0344
0.0392
New borrowers receiving credit warning message after taking up loans are 5.7% less likely to default (unconditional default rate: 10%)
New borrowers receiving credit warning message before taking up loans are 3.5% less likely to default with message (unconditional default rate: 9.6%)
The difference between the two coefficient estimates are not statistically different (pooled regression)

20. Exp. 2 Difference in observables (post match: Mahalanobis distance (1:3))

21. Conclusion Our research contributes to the debate on
Whether information sharing should be achieved via a public registry (mandated information sharing) or private credit bureaus;
The benefits and costs of lenders’ information sharing policy;
Information sharing mitigating moral hazard suggests policies to improve the efficiency of consumer lending should combine loan repayment reporting with subsidies on loan interest rates, guarantees, or improving screening strategies.