Empirical Household Finance fileRecent Work: Online Data Sources \Sticking to Your Plan: Hyperbolic Discounting and Credit Card Debt Paydown" Theresa Kuchler, 2015 \Debt and the Consumption

$Page 1: Empirical Household Finance fileRecent Work: Online Data Sources \Sticking to Your Plan: Hyperbolic Discounting and Credit Card Debt Paydown" Theresa Kuchler, 2015 \Debt and the Consumption$
Empirical Household Finance

Theresa Kuchler (NYU Stern)

Overview

Three classes:

1. Questions and topics on household finance

2. Recent work: Online data sources

3. Recent work: Administrative data sources

1

Recent Work: Online Data Sources

• “Sticking to Your Plan: Hyperbolic Discounting and Credit CardDebt Paydown” Theresa Kuchler, 2015

• “Debt and the Consumption Response to Household IncomeShocks” Scott Baker, 2014

• “Disentangling Financial Constraints, Precautionary Savings, andMyopia: Household Behavior Surrounding Federal Tax Returns”Baugh, Brian, Itzhak Ben-David, and Hoonsuk Park, 2013

• “Harnessing Naturally Occuring Data to Measure the Responseof Spending to Income” Michael Gelman, Shachar Kariv,Matthew D. Shapiro, Dan Silverman and Steven Tadelis. Science345(6193) (2014), 212-15

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2

“Sticking to Your Plan: Hyperbolic Discounting and Credit Card DebtPaydown”

Theresa Kuchler, 2015

3

Goal and Data

Question: Why do people hold large credit card balances at highcost over substantial time horizons?

Goal: Understand the role of present bias in debt paydown

Data: Sample of users of financial management website

• Account balances

• Spending on credit and debit card

• Income

• Planned paydown

4

Goal and Data






• Income

• Planned paydown

4

Goal and Data






• Income

• Planned paydown

4

Present Bias - Definition

Present Bias

• More impatient in short-run (βδ) than long-run (δ)

Ut = u(ct) + β

∞∑τ=t+1

δτu(cτ )

→ Time inconsistent

• Two present-biased types− Fully Aware → Sophisticated

− Unaware of changing preferences → Naive

→ Two features of present bias:− Extent of short-run impatience (β)

− Sophistication

5


Present Bias


Ut = u(ct) + β

∞∑τ=t+1

δτu(cτ )





− Sophistication

5


Present Bias


Ut = u(ct) + β

∞∑τ=t+1

δτu(cτ )





− Sophistication

5

Overview

1. Relate features of present bias to spending patterns

− Level of short-run impatience

− Awareness of future short-run impatience/sophistication

2. Assess who sticks to plan of debt reduction

Patterns of debt paydown are

• Consistent with present bias/hyperbolic discounting

• Inconsistent with alternative explanations (e.g., creditconstraints, unrealistic expectations)

6

Overview

1. Relate features of present bias to spending patterns− Level of short-run impatience






6

Overview







6

Overview







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Overview







6

Overview







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Short-run Impatience and ConsumptionOver the Paycycle

Setup: Consumer receives regular paycheck every 2 weeks

Fraction

consumed

Week 1 Week 2

Paycheck

received

7



Fraction

consumed

Week 1 Week 2

Paycheck

received

7



Fraction

consumed

Week 1 Week 2

Paycheck

received

Impatient in short-run

Consumers with high short-run impatience• Highly value current consumption

• Consume higher fraction immediately after receiving paycheck

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Fraction

consumed

Week 1 Week 2

Paycheck

received

Very impatient in short-run


Consumers with high short-run impatience• Highly value current consumption

• Consume higher fraction immediately after receiving paycheck

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Fraction

consumed

Not impatient in short-run

Week 1 Week 2

Paycheck

received




Consumers with no short-run impatience• Smooth consumption over paycycle

• Consumption spending does not depend on when paycheckarrives

7



Fraction

consumed


Week 1 Week 2

Paycheck

received




→ Higher short-run impatience reflected in sensitivity ofconsumption to paycheck

Framework

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Sophistication and Effect of Resources

Setup: Consumer sometimes receives higher net paycheck, e. g. bonus

Fraction

consumed

Sophisticated

Week 1 Week 2

Paycheck

received

Low resources

+ Bonus

Fraction

consumed

Naive

Week 1 Week 2

Paycheck

received

+ Bonus

Sophisticated consumer aware of• Future propensity to overconsume, relative to long-run

preferences• Resources being partially consumed rather than further passed on

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Fraction

consumed

Sophisticated

Week 1 Week 2

Paycheck

received

Low resources

+ Bonus

Fraction

consumed

Naive

Week 1 Week 2

Paycheck

received

+ Bonus

Sophisticated consumer aware of• Future propensity to overconsume, relative to long-run

preferences• Resources being partially consumed rather than further passed on

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Fraction

consumed

Sophisticated

Week 1 Week 2

Paycheck

received

Low resources

High resources

+ Bonus

Fraction

consumed

Naive

Week 1 Week 2

Paycheck

received

+ Bonus

Higher paycheck

→ Higher consumption, lower marginal propensity to (over)consume

→ More resources further passed on rather than consumed

→ More worthwhile to act patiently/pass on resources

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Fraction

consumed

Sophisticated

Week 1 Week 2

Paycheck

received

Low resources

High resources

+ Bonus

Fraction

consumed

Naive

Week 1 Week 2

Paycheck

received

+ Bonus

→ Sophisticates less sensitive to paycheck when resources high

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Fraction

consumed

Sophisticated

Week 1 Week 2

Paycheck

received

Low resources

High resources

+ Bonus

Fraction

consumed

Naive

Week 1 Week 2

Paycheck

received

Low resources

High resources

+ Bonus

→ Level of resources affects trade-off for sophisticates only

→ Sophistication reflected in effect of resources

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Estimating Sensitivity to Paycheck

Estimate separately for each user

log(Eit) = αi + payweekitγ1i +X ′itψi + εit

where Xit includes month FE and day of week FE

Obs. Mean Std. Dev.25th

pctile

50th

pctile

75th

pctile

%

statistically

> 0

%

statistically

< 0

Short-Run Consumables 516 0.061 0.211 -0.081 0.049 0.199 9.3 1.4

Restaurants&Entertainment 516 0.046 0.201 -0.086 0.052 0.172 5.0 1.0

Sensitivity to Paycheck Receipt

→ Extent of sensitivity as proxy for level of short-run impatience

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pctile

50th

pctile

75th

pctile

%

statistically

> 0

%

statistically

< 0





9






pctile

50th

pctile

75th

pctile

%

statistically

> 0

%

statistically

< 0





9






pctile

50th

pctile

75th

pctile

%

statistically

> 0

%

statistically

< 0





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Effect of Resources and Sophistication

log(Eit) = αi + payweekitγ1i + resourcesitγ2i

+ resourcesit ∗ payweekitγ3i +X ′itψi + εit

Sophisticated ⇔ Negative effect of resources on sensitivity (γ3i)

Naive ⇔ Non-negative effect of resources on sensitivity (γ3i)

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Effect of Resources and Sophistication



Sophisticated ⇔ Negative effect of resources on sensitivity (γ3i)

Naive ⇔ Non-negative effect of resources on sensitivity (γ3i)

10

Methodology: Estimating Effect ofResources?



• Endogeneity?− Spending Eit affects resourcesi,t+1 directly− Spending Eit potentially affects “taste for consumption” in t+ 1,

i.e. εi,t+1

• Solution− Measure resources at beginning− Instrument for level of resources→ Simulated balances as simulated instrument

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• Endogeneity?

− Spending Eit affects resourcesi,t+1 directly− Spending Eit potentially affects “taste for consumption” in t+ 1,

i.e. εi,t+1


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i.e. εi,t+1


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i.e. εi,t+1


11

Methodology: Simulated Instrument

Idea: Isolate exogenous part of variation, filter out endogenous part

Need: Source of exogenous variation

• Policy change, e.g., tax change

• Discontinuity /kink in allocation formula

• Fixed timing of payments

→ What would have happened if only exogenous variation, noendogenous response?

→ Use simulated /hypothetical variable as instrument for actual

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Estimating Effect of Resources:Simulated Balances

Endogeneity of resource level and spending

→ Instrument for resources usingHypothetical balances based on regular, expected payments

• Regular payments: same amount, regularly (7, 14 or 30 days)

• Idea: Affect available resources, but independent of past orfuture discretionary spending

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13






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Regular Payments and Simulated Balances

paycheck paycheck paycheck paycheck

14



- rent - rent

14


paycheck

Actual Resources

paycheck paycheck paycheck

- rent - rent

14


Simulated balance


- rent - rent

14


Simulated balance


- rent - rentSlope:

Average daily non-regular

expenditure

over sample period

14

Effect of Present Bias - Joint Pattern

Sophisticated

No impatience

β→1

High impatience

β low

Naive

No impatience

β→1

High impatience

β low

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Sophisticated

Sensitivity to Paycheck

•Low resources

No impatience

β→1

High impatience

β low

Naive


•Low resources

No impatience

β→1

High impatience

β low

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Sophisticated


•Low resources

•Higher resources

No impatience

β→1

High impatience

β low

Naive


•Low resources

•Higher resources

No impatience

β→1

High impatience

β low

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Sophisticated

No impatience

β→1

High impatience

β low

Naive

No impatience

β→1

High impatience

β low

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Sophisticated

Planned Paydown

Debt Paydown

No impatience

β→1

High impatience

β low

Naive

No impatience

β→1

High impatience

β low

• Sophisticated :Follow plan to pay downPay down less the more impatient

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Sophisticated

Planned Paydown

Debt Paydown

No impatience

β→1

High impatience

β low

Naive

Planned Paydown

No impatience

β→1

High impatience

β low

• Naive:

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Sophisticated

Planned Paydown

Debt Paydown

No impatience

β→1

High impatience

β low

Naive

Planned Paydown

No impatience

β→1

High impatience

β low

Debt Paydown

• Naive:Repeatedly plans to pay off next period

17


Sophisticated

Planned Paydown

Debt Paydown

No impatience

β→1

High impatience

β low

Naive

Planned Paydown

No impatience

β→1

High impatience

β low

Debt Paydown

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Sophisticated


Planned Paydown

No impatience

β→1

High impatience

β low

Debt Paydown

Naive


Planned Paydown

No impatience

β→1

High impatience

β low

Debt Paydown

• Sophisticated :Pay down less the more impatient

• Naive:Delays irrespective of plans and impatience level

17

Regression Results - Debt Paydown

Paydowni = µ0 + Sensitivityiµ1n + PlannedPaydowniµ2n

+ Sensitivityi ∗ Sophistiµ1s

+ PlannedPaydowni ∗ Sophistiµ2s

+X ′iλ+ νi

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Methodology: Estimated Regressors




+X ′iλ+ νi

• Usually: regressors are observables

• Sensitivityi, Sophisti are estimated

− Estimated with noise− First stage regression informative of estimation error

→ Need to correct standard errors

→ Bootstrap standard errors

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+X ′iλ+ νi






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+X ′iλ+ νi






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Methodology: Bootstrap StandardErrors

• Idea

− Draw sample from data with replacement

− Estimate regression specification for each sample draw

− Compute standard errors based on sample draws

• Good news: Bootstrap Package in Stata

• Bad news: Exact specification depends on specifics of estimationat hand

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Methodology: Bootstrap StandardErrors

• Idea

− Draw sample from data with replacement

− Estimate regression specification for each sample draw

− Compute standard errors based on sample draws

• Good news: Bootstrap Package in Stata

• Bad news: Exact specification depends on specifics of estimationat hand

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+X ′iλ+ νi

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+ Sensitivityi ∗ Sophistiµ1s + PlannedPaydowni ∗ Sophistiµ2s +X′iλ + νi

Short-run

Consumables

Restaurant&

Entertainment

Short-run

Consumables

Restaurant&

Entertainment

Sensitivity 8.511 -3.293 6.461* 7.547*

(0.241) (0.613) (0.099) (0.083)

Planned paydown 0.179* 0.280*** 0.129 0.177**

(0.056) (0.002) (0.133) (0.033)

Sensitivity * Sophisticated -33.293*** -13.820 -10.179* -17.774***

(0.004) (0.188) (0.082) (0.006)

Planned paydown * Sophisticated 0.371* 0.086 0.391* 0.295

(0.098) (0.666) (0.065) (0.119)

Sophisticated 4.157 -8.474 -1.099 -7.299

(0.469) (0.127) (0.797) (0.077)

Constant -12.035*** -7.636* -8.585*** -6.217**

(0.000) (0.064) (0.000) (0.010)

Controls Y Y Y Y

Number of individuals 516 516 516 516

Paydown 90 Days Paydown 180 Days

P-values of bootstrapped standard errors in parentheses. Significance: ∗ (p<0.10), ∗∗ (p<0.05), ∗∗∗ (p<0.01).

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+ Sensitivityi ∗ Sophistiµ1s + PlannedPaydowni ∗ Sophistiµ2s +X′iλ + νi

Short-run

Consumables

Restaurant&

Entertainment

Short-run

Consumables

Restaurant&

Entertainment

Sensitivity 8.511 -3.293 6.461* 7.547*

(0.241) (0.613) (0.099) (0.083)

Planned paydown 0.179* 0.280*** 0.129 0.177**

(0.056) (0.002) (0.133) (0.033)

Sensitivity * Sophisticated -33.293*** -13.820 -10.179* -17.774***

(0.004) (0.188) (0.082) (0.006)

Planned paydown * Sophisticated 0.371* 0.086 0.391* 0.295

(0.098) (0.666) (0.065) (0.119)

Sophisticated 4.157 -8.474 -1.099 -7.299

(0.469) (0.127) (0.797) (0.077)

Constant -12.035*** -7.636* -8.585*** -6.217**

(0.000) (0.064) (0.000) (0.010)

Controls Y Y Y Y

Number of individuals 516 516 516 516

Paydown 90 Days Paydown 180 Days

P-values of bootstrapped standard errors in parentheses. Significance: ∗ (p<0.10), ∗∗ (p<0.05), ∗∗∗ (p<0.01).

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Debt Paydown and Present Bias



• Inconsistent with alternative explanations:− Direct relation between reduction in sensitivity and paydown

− Impatient users drive difference btw sophisticated and naive

− Credit constraints: exclude when likely to be constrained

− Habits, non-separabilities

− Unrealistic expectations, overoptimism

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• Inconsistent with alternative explanations:

− Direct relation between reduction in sensitivity and paydown





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22

“Debt and the Consumption Response to Household Income Shocks”

Scott Baker, 2014

23

Overview of Paper

• Present new data set and validate data coverage

• Estimate how sensitivity of consumption to income depends onhousehold debt

24

Data

• Online personal finance website (Mint.com)

• Data updated automatically via financial institution

→ Highly reliable and complete

• Complete balance sheet and spending for many users

• Long daily panel

• Transaction level data

→ Unparalleled in traditional data sources

• One of several papers using online sources→ Uniquely suited to analyzing households during GreatRecession

25

Data

• Online personal finance website (Mint.com)

• Data updated automatically via financial institution

→ Highly reliable and complete

• Complete balance sheet and spending for many users

• Long daily panel

• Transaction level data

→ Unparalleled in traditional data sources

• One of several papers using online sources→ Uniquely suited to analyzing households during GreatRecession

25

Data Validation

• User survey

− Confirms individual level coverage

• Benchmark to aggregate

− Census Retail Sales

− Consumer Expenditure Survey

− Housing wealth (Zillow.com)

− 2010 Survey of Consumer Finance

26

Data Validation

• User survey

− Confirms individual level coverage

• Benchmark to aggregate

− Census Retail Sales

− Consumer Expenditure Survey

− Housing wealth (Zillow.com)

− 2010 Survey of Consumer Finance

26

Sensitivity to Income and Debt

• Previous literature:

− Consumption responds to income shocks

• This paper:

− Micro-level

F Estimate effect of debt on consumption response to income

F Disentangle possible channels (credit constraints)

− Macro-level

F Quantify effect of debt levels during Great Recession

27




• This paper:

− Micro-level



− Macro-level


27




• This paper:

− Micro-level



− Macro-level


27

Identifying Effect of Debt Levels onResponse to Income Shocks

Empirical Challenge: Identifying Income Shocks

• Reverse causality: debt levels can affect incomee.g., credit check required for some jobs

• Other shock affecting income and debt simultaneouslye.g., health shock, divorce

→ Need exogenous change in income

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28






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• Link users to employers based on paycheck deposit

• Identify shocks to employers

− Firm layoffs, acquisitions, large write-offs, earnings surprises

− SEC filings, news reports

• Establish validity of income shocks

− Firm shocks un-anticipated by consumers and investors (Table 2)

− Shocks affect subsequent firm returns

− Firm shocks affect employee income (Table 3)

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29

Results

• Income shocks increase consumption spending

• Higher debt associated with higher consumption response toincome shocks

• Effect of debt consistent with credit constraints

− Weaker effect when likely less constrained (high credit score,unused credit, liquid assets)

− Stronger effect when likely constrained (credit limit decline,interest rate)

→ Empirical support for common assumption

30

Results

• Income shocks increase consumption spending

• Higher debt associated with higher consumption response toincome shocks

• Effect of debt consistent with credit constraints

− Weaker effect when likely less constrained (high credit score,unused credit, liquid assets)

− Stronger effect when likely constrained (credit limit decline,interest rate)

→ Empirical support for common assumption

30

Conclusion

• Introduces new dataset

− Careful data validation

• Effect of debt on sensitivity of consumption to income shocks

− Exploits unique data features to identify shocks

− Quantifies effect of debt

− Evidence for credit constraints as driving factor

− Quantifies macro effect during great recession

31

Conclusion

• Introduces new dataset

− Careful data validation

• Effect of debt on sensitivity of consumption to income shocks

− Exploits unique data features to identify shocks

− Quantifies effect of debt

− Evidence for credit constraints as driving factor

− Quantifies macro effect during great recession

31

“Harnessing Naturally Occuring Data to Measure the Response ofSpending to Income”

Michael Gelman, Shachar Kariv, Matthew D. Shapiro, Dan Silvermanand Steven Tadelis

Science, 2014

32

Overview

• Present online data as new tool to measure economic activity

• Use data to test spending response to anticipated income receipt

33

Measuring Economic Activity - Data

Literature so far: Surveys

• e.g. Consumer Expenditure Survey (CEX)

• Expensive, infrequently fielded

• Administrative records

• No info on income and spending simultaneously

Data in this paper: Online transaction level data

• Check− financial aggregation website− 1.5 million active users in 2012

• Comprehensive income and spending

• Sample− 75,000 randomly selected Check users− 300 consecutive days in 2012 and 2013− Overrepresents male and young

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Research Question

• Theory:

Consumers should smooth consumption

→ Timing of (anticipated) income irrelevant for spending

• But:

Previous findings indicate increased spending after receipt ofanticipated income

• This paper:

Estimate response of spending to income

35

Research Question

• Theory:



• But:


• This paper:


35

Research Question

• Theory:



• But:


• This paper:


35

Estimation

xict =

Sun∑j=Mon

δjc +

6∑k=−7

βkcIi(Paidt−k) + εict

where

• xict ratio of individual i’s spending in category c at date t

• δjc day-of-week fixed effect

• Ii(Paidt−k) indicator for income receipt at time t− k

→ βkc captures increase in spending relative to average daily spending

36

Estimation

xict =

Sun∑j=Mon

δjc +

6∑k=−7


where





36

Estimation

xict =

Sun∑j=Mon

δjc +

6∑k=−7


where





36

Results1

.8.6

.4.2

0−.2

Fraction of daily average spending

−7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6 −7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6

Days since check arrival

−7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6

Fig. 2. Response of spending to income: Alternative components of spending. (A) Total spending. (B) Nonrecurring spending. (C) Fast food and

coffee shop spending. The solid line represents regression coefficients from Eq. 1. The dashed lines are 95% confidence intervals. Estimates are based on

5,371,244, 5,371,244, and 5,173,594 total observations from 23,985, 23,985, and 23,021 users for panels (A), (B), and (C), respectively.

→ Response of spending to receipt of anticipated income

→ But: Driven by coincident timing of regular income and spending

37

Results1

.8.6

.4.2

0−.2


−7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6 −7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6


−7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6

Fig. 2. Response of spending to income: Alternative components of spending. (A) Total spending. (B) Nonrecurring spending. (C) Fast food and

coffee shop spending. The solid line represents regression coefficients from Eq. 1. The dashed lines are 95% confidence intervals. Estimates are based on

5,371,244, 5,371,244, and 5,173,594 total observations from 23,985, 23,985, and 23,021 users for panels (A), (B), and (C), respectively.

→ Response of spending to receipt of anticipated income

→ But: Driven by coincident timing of regular income and spending

37

Results1

.8.6

.4.2

0−.2


−7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6 −7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6


−7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6

Fig. 3. Response of nonrecurring spending to income: Liquidity ratio. (A) Low liquidity. (B) Medium liquidity. (C) High liquidity. The solid line

represents regression coefficients from Eq. 1. The dashed lines are 95% confidence intervals. Estimates are based on 1,784,460, 1,809,839, and 1,769,968

total observations from 7956, 7956, and 7955 users for panels (A), (B), and (C), respectively. The liquidity ratio is defined as the average daily balance of

checking and savings accounts normalized by daily average spending.

→ Excess sensitivity concentrated among liquidity constrainedindividuals

38

Results1

.8.6

.4.2

0−.2


−7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6 −7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6


−7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6

Fig. 3. Response of nonrecurring spending to income: Liquidity ratio. (A) Low liquidity. (B) Medium liquidity. (C) High liquidity. The solid line

represents regression coefficients from Eq. 1. The dashed lines are 95% confidence intervals. Estimates are based on 1,784,460, 1,809,839, and 1,769,968

total observations from 7956, 7956, and 7955 users for panels (A), (B), and (C), respectively. The liquidity ratio is defined as the average daily balance of

checking and savings accounts normalized by daily average spending.

→ Excess sensitivity concentrated among liquidity constrainedindividuals

38

Conclusion

• Present new data to address existing question in Economics→ Good fit for publication in Science

• Confirm previous findings of excess spending in response toanticipated income

• Refine findings− Response largely due to regular spending− Excess spending concentrated in liquidity constraint individuals

• Leverage detail and scope of data

39

Conclusion





39

Conclusion





39

Conclusion





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40

“Disentangling Financial Constraints, Precautionary Savings, andMyopia: Household Behavior Surrounding Federal Tax Returns”

Baugh, Brian, Itzhak Ben-David, and Hoonsuk Park, 2013

41

Overview

Question: Why do people increase spending following tax refunds?

Goal: Disentangle the role of

• Financial Constraints

• Precautionary Savings

• Myopia

Setting: Filling of tax return versus receipt of refund

• At filling: Learn amount of tax refund

• At receipt: Receive tax refund (deposited into account)

42

Overview

Question: Why do people increase spending following tax refunds?Goal: Disentangle the role of



• Myopia




42

Overview

Question: Why do people increase spending following tax refunds?Goal: Disentangle the role of



• Myopia




42

Data

• Transaction-level bank account and credit card data

• Anonymous data provider

• 27,591 (of 500,000) households who

− Use tax filing service− Complete and clean data

43

Hypotheses

• Precautionary savings− Hold buffer stock for uncertain future shocks− Learning about refund = reduction of uncertainty→ Decrease buffer stock /increase spending

• Financial constraints− Smooth consumption but cannot borrow against future income− At payment receipt, increase consumption, but smooth over all

future periods→ Persistent increase in spending following receipt

• Myopia− Consumers are short-sighted, spend what they have− Increase spending following payment receipt, but do not smooth

over future periods→ Short-term increase in spending following receipt

44

Hypotheses






44

Hypotheses






44

Estimation

Xht = α+

4∑k=−2

bkI(filingweekk)+

12∑k=−2

ckI(refundweekk)+γt+δh+εht

where

• bk capture increased spending after filing

• ck captuer increased spending after receipt of refund

Identify filing and refund date

• Filing date: date of charge for tax software

• Refund date: date of refund deposited into account

• Identify via text description of transactions

45

Estimation

Xht = α+

4∑k=−2

bkI(filingweekk)+

12∑k=−2

ckI(refundweekk)+γt+δh+εht

where

• bk capture increased spending after filing

• ck captuer increased spending after receipt of refund

Identify filing and refund date

• Filing date: date of charge for tax software

• Refund date: date of refund deposited into account

• Identify via text description of transactions

45

ResultsFigure 6: Time Series of Week Dummy Coefficients Surrounding Filing and Refund

→ No spending increase at filing (but: increased credit card usage

→ Substantial spending increase following refund receipt46

Additional Results

Differences in response by financial constraints?

• Split sample by income− Higher spending only on credit cards following filing for high and

low income households− Higher spending following receipt primarily for low income

households

• Split sample by financial slack− Use interest received and paid as proxy for asset and debt balances− Larger spending response following receipt for low financial slack

households

47

Additional Results




households


households

47

Additional Results




households


households

47

Conclusion

• Innovative way to disentangle information content ofunanticipated income from cash flow

• Findings consistent with previous literature

• Distinguish between alternative theories by exploiting detail indata and corresponding differences in predictions

48

Documents

Empirical Household Finance fileRecent Work: Online Data Sources \Sticking to Your Plan: Hyperbolic Discounting and Credit Card Debt Paydown" Theresa Kuchler, 2015 \Debt and the Consumption