Learning in the Limit:

Income Inference from Credit Extension

Xiao Yin

3/5/2024

University College London
Large Spending Responses to Credit Limit Extension

• Credit limit underpins how much consumers can borrow for consumption.

• more than 1/3 of consumers have positive consumption debt.

Gross and Souleles (2002), Zinman (2009), Fulford (2015), Vihriälä (2020), Gathergood and Olafsson (2022), etc.

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Large Spending Responses to Credit Limit Extension

• Credit limit underpins how much consumers can borrow for consumption.

• more than 1/3 of consumers have positive consumption debt.

Gross and Souleles (2002), Zinman (2009), Fulford (2015), Vihriälä (2020), Gathergood and Olafsson (2022), etc.

• How does credit limit affect consumption?

debtor: lower borrowing capacity

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Large Spending Responses to Credit Limit Extension

• Credit limit underpins how much consumers can borrow for consumption.

• more than 1/3 of consumers have positive consumption debt.

Gross and Souleles (2002), Zinman (2009), Fulford (2015), Vihriälä (2020), Gathergood and Olafsson (2022), etc.

• How does credit limit affect consumption?

debtor: lower borrowing capacity

saver: preference for smoother

⇒ limit ↘ ability for consumption smoothing ⇒ precautionary saving

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Large Spending Responses to Credit Limit Extension

• Credit limit underpins how much consumers can borrow for consumption.

• more than 1/3 of consumers have positive consumption debt.

Gross and Souleles (2002), Zinman (2009), Fulford (2015), Vihriälä (2020), Gathergood and Olafsson (2022), etc.

• How does credit limit affect consumption?

debtor: lower borrowing capacity

saver: preference for smoother

⇒ limit ↘ ability for consumption smoothing ⇒ precautionary saving

• expect non-trivial spending effects only if (almost) borrowing constrained.

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Large Spending Responses to Credit Limit Extension

• Credit limit underpins how much consumers can borrow for consumption.

• more than 1/3 of consumers have positive consumption debt.

Gross and Souleles (2002), Zinman (2009), Fulford (2015), Vihriälä (2020), Gathergood and Olafsson (2022), etc.

• How does credit limit affect consumption?

debtor: lower borrowing capacity

saver: preference for smoother

⇒ limit ↘ ability for consumption smoothing ⇒ precautionary saving

• expect non-trivial spending effects only if (almost) borrowing constrained.

• In reality: $1 credit limit ⇒ $0.4 consumption (Agarwal et al. 2017)

• strong reaction for high-liquidity consumers.

Gross and Souleles (2002), D’Acunto et al. (2020), Aydin (2022), etc.

• Open question: consumption responses to credit limit extensions.

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Information Content of Credit Supply

• What could go wrong?

standard estimation: random shocks to borrowing limits.

assumption: random to the consumers.

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Information Content of Credit Supply

• What could go wrong?

standard estimation: random shocks to borrowing limits.

assumption: random to the consumers.

• How do consumers think about credit supply?

bank decision-making rarely random.

e.g. results of economic conditions.

⇒ inattentive consumers treat credit supply as signals.

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Information Content of Credit Supply

• What could go wrong?

standard estimation: random shocks to borrowing limits.

assumption: random to the consumers.

• How do consumers think about credit supply?

bank decision-making rarely random.

e.g. results of economic conditions.

⇒ inattentive consumers treat credit supply as signals.

• Do consumers infer information from credit limit extension?

if yes, debt/spending decisions?

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Experimental Design

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Experimental Design

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Experimental Design

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Experimental Design

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Data

• Survey data

without surveys: 4,281 in total, 2,331 treated.

with surveys: 6,057 in total: 3,356 treated.

• expectations of future income, saving, consumption, default, etc.

• Bank account data:

demographics, transaction histories, etc.

two types of income

• salary (70.48%): deposited monthly income/social security insurance.

• business income (29.52%): net inflow labeled as business operations.

• Credit registry:

interest-incurring unsecured debt from all financial institutions

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Summary Statistics

Mean SD N Mean SD Diff t-stats N

(1) (2) (3) (4) (5) (6) (7) (8)

Control Treatment

Age

Female

37.91

0.43

10.25

0.50

1588

1588

37.62

0.41

9.50

0.49

-0.29

-0.02

-0.74

-1.01

1875

1875

College

Income

0.46

9.69

0.50

8.74

1588

1588

0.47

9.48

0.50

6.99

0.02

-0.21

0.92

-0.65

1875

1875

Saving

Debt

Debt|Debt>0

Limit

Δ Limit

139.05

7.40

16.54

23.25

12.01

149.84

13.37

15.76

27.19

9.09

1588

1588

711

1588

1588

139.91

7.01

16.99

22.33

11.73

138.71

10.10

8.81

26.53

8.20

0.86

-0.39

0.45

-0.92

-0.29

0.15

-0.79

0.54

-0.81

-0.82

1875

1875

774

1875

1875

Spending

Income

7.77

9.61

13.57

9.60

1588

1588

7.94

9.54

12.92

7.75

0.17

-0.07

0.32

-0.19

1875

1875

Liquid Wealth

Total Wealth

155.59

431.39

266.48

900.12

1588

1588

150.71

426.85

188.84

678.23

-4.88

-4.54

-0.53

-0.14

1875

1875

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Evolution of Spending and Debt

Total Spending Unsecured Debt

• Spending is from a single institute – coverage?

• Focus on main users:

1. answered one to:

How many banks do you usually use for transaction purposes?

2. at least 15 spending transactions each month

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MPB and MPCL

Δ B

(2)

Panel A: 3 Months

Δ C

(4)

Δ B

(6)

Panel B: 6 Months

Δ C

(8)

Treatment 0.106***

(0.008)

0.212***

(0.031)

0.179***

(0.014)

0.393***

(0.048)

N 8037 8037 8037 8037

• Over six months, each CNY increase in credit limit increases

• spending by 0.393 CNY (Agarwal et al. 2017)

• unsecured debt by 0.179 CNY (Agarwal et al. 2017, Aydin 2022)

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Elicit Belief Changes with Surveys

• Theories for consumption responses to limit changes:

limit ⇒ improves consumption smoothing ⇒ lower saving

• Test theories: do subjective expectations align?

• Post-experiment surveys:

• spending:

Over the next 12 months, how much would you most likely spend on average every month (excluding

investment and purchases over durable goods including housing, cars, etc.)?

• income:

Over the next 12 months, conditional on not switching jobs, what’s the level of total income you are

most likely to get?

• Similar questions for wealth, hours to work, unemployment, default prob, etc.

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Changes in Expectations

Δ E[C]

(1)

Δ E[Y]

(2)

Δ E[Liq. W]

(3)

Δ E[Total W]

(4)

Treatment 0.267**

(0.117)

0.323***

(0.078)

0.001*

(0.000)

0.001

(0.001)

N 8037 8037 8037 8037

• After positive limit shocks, consumers expect

• higher spending and income.

• unchanged total saving.

• Inconsistent with conventional economic theories.

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Changes in Expectations

Δ E[Hours]

(1)

E[p(unemp.)]

(2)

E[p(default)]

(3)

Δ E[Limit - 6M]

(4)

Δ E[Limit - 5Y]

(5)

Treatment 0.000

(0.000)

-0.185*

(0.107)

-0.053

(0.094)

67.656

(881.117)

-323.508

(1437.581)

N 8037 8037 8037 8037 8037

• No reported higher defaults

• Unchanged hours planned to work

⇒ no change in labor supply

• lower unemployment probability

⇒ higher labor demand

• Not expecting higher credit limits.

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What Does This Tell Us?

• Existing theories: higher limits

• reduce the need for precautionary saving

Agarwal et al. 2017, Aydin 2022

• increases self-employment or labor mobility

Doornik et al. 2021, Herkenhoff et al. 2021

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What Does This Tell Us?

• Existing theories: higher limits

• reduce the need for precautionary saving

Agarwal et al. 2017, Aydin 2022

• increases self-employment or labor mobility

Doornik et al. 2021, Herkenhoff et al. 2021

• Survey evidence:

• more spending financed by higher income

• higher income from higher productivity/labor demand

• unchanged savings and hours planned to work

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What Does This Tell Us?

• Existing theories: higher limits

• reduce the need for precautionary saving

Agarwal et al. 2017, Aydin 2022

• increases self-employment or labor mobility

Doornik et al. 2021, Herkenhoff et al. 2021

• Survey evidence:

• more spending financed by higher income

• higher income from higher productivity/labor demand

• unchanged savings and hours planned to work

• Consistent with Inferring info from credit supply

• consumers imperfectly informed about economic conditions

Mankiw and Reis 2002, Reis 2006, Coibion and Gorodnichenko 2012, Andre et al. 2022

• infer (heter. impacts of) macro conditions from pro-cyclical credit supply

Bassett et al. 2014, Fishman et al. 2020, Weitzner and Howes 2023

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Inference about Macroeconomy

• Should update expectations about macroeconomic variables

Q: How much will the overall Chinese economy/unemployment rate change (in percentage relative to the current level) over the next year?

• Larger income expectation changes if more uncertain

Q: How confident are you in evaluating whether the overall economy is functioning well

at the moment?

• low Macro Uncertainty if answered very confident

• study changes in income expectations by Macro Uncertainty

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Income Expectation Changes by Macro Sensitivity

E[Δ GDP] E[Δ Unemp Rate] E[Δ Y] E[Δ Y]

Macro Uncertainty

Low High

(1) (2) (3) (4)

Treatment 0.046*** -0.231*** 0.199 0.446**

(0.017) (0.063) (0.231) (0.228)

N 2310 2310 943 1367

• Consistent with inferring macro conditions

• limit increase ⇒ expansionary economy

• low macro uncertainty ⇒ insignificant expectation changes

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Heterogeneity by Income Volatility

• Distinguishing between learning and other stories:

• learning about income only when income is volatile

• Zero income variations ⇒

1. unchanged income expectations

2. smaller debt/spending changes

• Estimate ATE respectively for

1. zero income variations previous year

2. positive income variations previous year

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Heterogeneity by Income Volatility

Δ B

(1)

Δ C

(2)

E[Δ Y]

(3)

Δ B

(4)

Δ C

(5)

E[Δ Y]

(6)

Panel A: SD(Y) = 0 Panel B: SD(Y) > 0

Treatment 0.081***

(0.025)

0.249***

(0.083)

-0.031

(0.030)

0.172***

(0.018)

0.433***

(0.060)

0.610***

(0.118)

N 1165 1152 1141 4326 1068 3984

• Much smaller effects when income has zero variations

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Conclusion

• Credit extension increases consumer income expectation.

• clean identification with RCT.

• Expectations with respect to labor demand/productivity.

• no changes in expected labor supply.

• Consumers associate credit supply with macro movements.

• significant changes in macro expectations.

• unchanged income expectations if low macro uncertainty

• Things for the future:

• supply-driven credit cycle with learning.

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