Second Chance: Life without Student Debt Marco Di Maggio 1 , Ankit - - PowerPoint PPT Presentation

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Motivation Setting Data Main Results Conclusions Second Chance: Life without Student Debt Marco Di Maggio 1 , Ankit Kalda 2 and Vincent W. Yao 3 1 Harvard Business School & NBER 2 Indiana University 3 Georgia State University Oct 2019 Di Maggio,

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Motivation Setting Data Main Results Conclusions

Second Chance: Life without Student Debt

Marco Di Maggio1, Ankit Kalda2 and Vincent W. Yao3

1Harvard Business School & NBER 2Indiana University 3Georgia State University

Oct 2019

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Motivation Setting Data Main Results Conclusions

Student Debt Delinquencies on the Rise

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Motivation Setting Data Main Results Conclusions

Policy Makers Worry about Student Debt Defaults

The newly appointed Chairman of the Federal Reserve stated “You do stand to see longer-term negative effects on people who can’t pay off their student loans. It hurts their credit rating, it impacts the entire half of their economic life, as this goes on and as student loans continue to grow and become larger and larger, then it absolutely could hold back growth.”

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Motivation Setting Data Main Results Conclusions

Policy Debate

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Motivation Setting Data Main Results Conclusions

Policy Debate

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Three main findings

Discharged borrowers reduce their credit balances, by both reducing their demand for credit and limiting the use of the existing account. They are also significantly less likely to default on other accounts, file for bankruptcy and experience medical defaults. These borrowers’ geographical mobility increases, as well as, their probability to change job and ultimately their income.

In contrast to most existing literature, we show the importance of relaxing long-term constraints.

Motivation Setting Data Main Results Conclusions

This Paper

We use plausibly exogenous variation to quantify the effects of student debt forgiveness on credit and labor market outcomes for distressed borrowers (don’t quantify costs).

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Discharged borrowers reduce their credit balances, by both reducing their demand for credit and limiting the use of the existing account. They are also significantly less likely to default on other accounts, file for bankruptcy and experience medical defaults. These borrowers’ geographical mobility increases, as well as, their probability to change job and ultimately their income.

In contrast to most existing literature, we show the importance of relaxing long-term constraints.

Motivation Setting Data Main Results Conclusions

This Paper

We use plausibly exogenous variation to quantify the effects of student debt forgiveness on credit and labor market outcomes for distressed borrowers (don’t quantify costs). Three main findings

Di Maggio, Kalda and Yao Student Debt Relief 6 / 32

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They are also significantly less likely to default on other accounts, file for bankruptcy and experience medical defaults. These borrowers’ geographical mobility increases, as well as, their probability to change job and ultimately their income.

In contrast to most existing literature, we show the importance of relaxing long-term constraints.

Motivation Setting Data Main Results Conclusions

This Paper

We use plausibly exogenous variation to quantify the effects of student debt forgiveness on credit and labor market outcomes for distressed borrowers (don’t quantify costs). Three main findings

Discharged borrowers reduce their credit balances, by both reducing their demand for credit and limiting the use of the existing account.

Di Maggio, Kalda and Yao Student Debt Relief 6 / 32

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These borrowers’ geographical mobility increases, as well as, their probability to change job and ultimately their income.

In contrast to most existing literature, we show the importance of relaxing long-term constraints.

Motivation Setting Data Main Results Conclusions

This Paper

We use plausibly exogenous variation to quantify the effects of student debt forgiveness on credit and labor market outcomes for distressed borrowers (don’t quantify costs). Three main findings

Discharged borrowers reduce their credit balances, by both reducing their demand for credit and limiting the use of the existing account. They are also significantly less likely to default on other accounts, file for bankruptcy and experience medical defaults.

Di Maggio, Kalda and Yao Student Debt Relief 6 / 32

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In contrast to most existing literature, we show the importance of relaxing long-term constraints.

Motivation Setting Data Main Results Conclusions

This Paper

We use plausibly exogenous variation to quantify the effects of student debt forgiveness on credit and labor market outcomes for distressed borrowers (don’t quantify costs). Three main findings

Discharged borrowers reduce their credit balances, by both reducing their demand for credit and limiting the use of the existing account. They are also significantly less likely to default on other accounts, file for bankruptcy and experience medical defaults. These borrowers’ geographical mobility increases, as well as, their probability to change job and ultimately their income.

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Motivation Setting Data Main Results Conclusions

This Paper

We use plausibly exogenous variation to quantify the effects of student debt forgiveness on credit and labor market outcomes for distressed borrowers (don’t quantify costs). Three main findings

Discharged borrowers reduce their credit balances, by both reducing their demand for credit and limiting the use of the existing account. They are also significantly less likely to default on other accounts, file for bankruptcy and experience medical defaults. These borrowers’ geographical mobility increases, as well as, their probability to change job and ultimately their income.

In contrast to most existing literature, we show the importance of relaxing long-term constraints.

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Motivation Setting Data Main Results Conclusions

Student Loan Market: Background

Student loans are basically split into federal loans and private student loans. The rate for federal loans is fixed and set by Congress. In almost all cases, federal student loans have better terms than the heavily advertised and expensive private student loans. Many people with private student loans, like those who took on subprime mortgages, end up shouldering debt that they never earn enough to repay.

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We use “Clerical Errors” made by the trust in this process that wiped

ff loans for students whose paperwork were not in order/went missing

Trust lost a series of collection lawsuits against the borrowers they were trying to collect from because they failed to prove that they

wned the debt in the first place.

We hand-collect a unique dataset with information about these lawsuits, including borrower identities and addresses among other things, and merge this with credit bureau data to examine a comprehensive set of outcomes.

Motivation Setting Data Main Results Conclusions

Setting: Random Debt Discharge

National Collegiate Student Loan Trust is the largest holder of private student debt with 800,000 private student loans totaling $12 billion

Purchases student loans from lenders, securitizes them and allows investors to invest in bonds

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Trust lost a series of collection lawsuits against the borrowers they were trying to collect from because they failed to prove that they

wned the debt in the first place.

We hand-collect a unique dataset with information about these lawsuits, including borrower identities and addresses among other things, and merge this with credit bureau data to examine a comprehensive set of outcomes.

Motivation Setting Data Main Results Conclusions

Setting: Random Debt Discharge

National Collegiate Student Loan Trust is the largest holder of private student debt with 800,000 private student loans totaling $12 billion

Purchases student loans from lenders, securitizes them and allows investors to invest in bonds

We use “Clerical Errors” made by the trust in this process that wiped

ff loans for students whose paperwork were not in order/went missing

Di Maggio, Kalda and Yao Student Debt Relief 8 / 32

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We hand-collect a unique dataset with information about these lawsuits, including borrower identities and addresses among other things, and merge this with credit bureau data to examine a comprehensive set of outcomes.

Motivation Setting Data Main Results Conclusions

Setting: Random Debt Discharge

National Collegiate Student Loan Trust is the largest holder of private student debt with 800,000 private student loans totaling $12 billion

Purchases student loans from lenders, securitizes them and allows investors to invest in bonds

We use “Clerical Errors” made by the trust in this process that wiped

ff loans for students whose paperwork were not in order/went missing

Trust lost a series of collection lawsuits against the borrowers they were trying to collect from because they failed to prove that they

wned the debt in the first place.

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Motivation Setting Data Main Results Conclusions

Setting: Random Debt Discharge

National Collegiate Student Loan Trust is the largest holder of private student debt with 800,000 private student loans totaling $12 billion

Purchases student loans from lenders, securitizes them and allows investors to invest in bonds

We use “Clerical Errors” made by the trust in this process that wiped

ff loans for students whose paperwork were not in order/went missing

Trust lost a series of collection lawsuits against the borrowers they were trying to collect from because they failed to prove that they

wned the debt in the first place.

We hand-collect a unique dataset with information about these lawsuits, including borrower identities and addresses among other things, and merge this with credit bureau data to examine a comprehensive set of outcomes.

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Motivation Setting Data Main Results Conclusions

Credit Bureau Data

Using identities and addresses from the court filings data, we requested Equifax to merge it to their anonymous data on individuals that has two components to it. Credit Data: covers 260 million borrowers and over 1.8 billion single loans, and is updated monthly.

Includes loan level information on account type, originations, balances, credit limits, monthly payments and performance.

Income Data: covers 30 million individuals and has information on income, employer, industry of employment, job titles among other variables.

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Motivation Setting Data Main Results Conclusions

Distribution of Legal Settlements

Calculated by authors based on the sample used in the paper.

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Motivation Setting Data Main Results Conclusions

Empirical Methodology

Difference-in-differences framework where individuals involved in the failed collection lawsuits (whose debt is discharged) constitute our treatment group. For every treated individual, we find a matched control group comprising all individuals who reside in the same ZIP code, are of same age, carry similar student loan amounts, and crucially, who defaulted

n their student loans as well (but their debt is not discharged).

In robustness checks, we also provide evidence that the results hold when we take advantage of the different discharge timing within the sample of treated individuals.

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Motivation Setting Data Main Results Conclusions

Empirical Methodology

Formally, the main specification is the following: Outcomei,j,t = α + β × (Treatedi × Postt ) + µi + γi×τ + εi,j,t (1) We include individual and County by event-month fixed effects. The Post dummy is purposely capturing several months (36) after the discharge because for some of our outcomes we would expect a lagged reaction.

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(sd of 11,600)

Dependent Var Student Loan Student Loan Credit Score Accounts Balance (1) (2) (3) DebtRelief × Post

0.65***
5319.04***

7.03*** (0.05) (148.94) (0.98) Individual FE Yes Yes Yes County x Event-Month FE Yes Yes Yes Observations 6,010,381 6,010,381 6,010,381 R2 0.8 0.84 0.58

Credit Score increases for borrowers experiencing discharge

Motivation Setting Data Main Results Conclusions

Student Debt Relief Validation

Debt relief is associated with an average decline of student loan balance of $6,855

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Dependent Var Student Loan Student Loan Credit Score Accounts Balance (1) (2) (3) DebtRelief × Post

0.65***
5319.04***

7.03*** (0.05) (148.94) (0.98) Individual FE Yes Yes Yes County x Event-Month FE Yes Yes Yes Observations 6,010,381 6,010,381 6,010,381 R2 0.8 0.84 0.58

Credit Score increases for borrowers experiencing discharge

Motivation Setting Data Main Results Conclusions

Student Debt Relief Validation

Debt relief is associated with an average decline of student loan balance of $6,855 (sd of 11,600)

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Credit Score increases for borrowers experiencing discharge

Motivation Setting Data Main Results Conclusions

Student Debt Relief Validation

Debt relief is associated with an average decline of student loan balance of $6,855 (sd of 11,600)

Dependent Var Student Loan Student Loan Credit Score Accounts Balance (1) (2) (3) DebtRelief × Post

0.65***
5319.04***

7.03*** (0.05) (148.94) (0.98) Individual FE Yes Yes Yes County x Event-Month FE Yes Yes Yes Observations 6,010,381 6,010,381 6,010,381 R2 0.8 0.84 0.58

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Motivation Setting Data Main Results Conclusions

Student Debt Relief Validation

Debt relief is associated with an average decline of student loan balance of $6,855 (sd of 11,600)

Dependent Var Student Loan Student Loan Credit Score Accounts Balance (1) (2) (3) DebtRelief × Post

0.65***
5319.04***

7.03*** (0.05) (148.94) (0.98) Individual FE Yes Yes Yes County x Event-Month FE Yes Yes Yes Observations 6,010,381 6,010,381 6,010,381 R2 0.8 0.84 0.58

Credit Score increases for borrowers experiencing discharge

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Motivation Setting Data Main Results Conclusions

Main Results

Debt Behavior Delinquency and Bankruptcy outcomes Mobility and Income

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Motivation Setting Data Main Results Conclusions

Debt Balances

Dependent Var Total Balance Credit Card Auto Mortgage (Ex. Stud) Balance Balance Balance (1) (2) (3) (4) DebtRelief × Post

4,303.21***
369.44***
226.81***
888.24***

(652.21) (28.99) (69.58) (163.55) Individual FE Yes Yes Yes Yes County x Event-Month FE Yes Yes Yes Yes Observations 6,010,381 6,010,381 6,010,381 6,010,381 R2 0.8 0.83 0.77 0.86

Average debt (ex student loans) declines by 26% relative to the mean before the discharge Decline occurs across different components of debt

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Motivation Setting Data Main Results Conclusions

Total Debt: Dynamics

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−10000 −5000 Distance from Debt Discharge (Months) Total Debt (Ex Student Loans) −20 −15 −10 −5 5 10 15 20

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Motivation Setting Data Main Results Conclusions

How borrowers reduce debt: Credit Cards & Home Loans

Dependent Var Account Utilization Payment Account Origination Payment Opening Opening Amount (1) (2) (3) (4) (5) (6) DebtRelief × Post

0.002**
0.018***

12.58***

0.001**
9,402.83**

38.98** (0.001) (0.004) (1.99) (0.0004) (3,799.04) (13.10) Individual FE Yes Yes Yes Yes Yes Yes County × Event-Month FE Yes Yes Yes Yes Yes Yes Observations 6,010,381 6,010,381 1,299,622 6,010,381 2,042,908 1,291,613 R2 0.095 0.607 0.58 0.36 0.95 0.89

Account openings and origination amount (conditional on openings) decline while payments increase Similar results for auto loans

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Motivation Setting Data Main Results Conclusions

How borrowers reduce debt: Credit Utilization

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−0.04

−0.02 0.00 0.02 Distance from Debt Discharge (Months) Credit Card Utilization −20 −15 −10 −5 5 10 15 20

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Motivation Setting Data Main Results Conclusions

Credit Demand

Dependent Var Total Multi–Inquiry Inquiries Indicator (1) (2) DebtRelief × Post

0.24***
0.02***

(0.050) (0.005) Individual FE Yes Yes County x Event-Month FE Yes Yes Observations 6,010,381 6,010,381 R2 0.56 0.45

Demand less credit as reflected by decline in inquiries.

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Motivation Setting Data Main Results Conclusions

Main Results

Debt Behavior

Debt declines as borrowers demans less credit and make higher payments

Delinquency and Bankruptcy outcomes Mobility and Income

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Motivation Setting Data Main Results Conclusions

Delinquency Extensive Margin

Dependent Var All DLQ Credit Card Auto Mortgage Accounts DLQ DLQ DLQ (Ex. Stud) Accounts Accounts Accounts (1) (2) (3) (4) DebtRelief × Post

0.11***
0.10***
0.01**
0.01***

(0.020) (0.020) (0.004) (0.003) Individual FE Yes Yes Yes Yes County x Event-Month FE Yes Yes Yes Yes Observations 6,010,381 6,010,381 6,010,381 6,010,381 R2 0.74 0.74 0.7 0.76

Borrowers are 11% less likely to become delinquent on other loans. Less likely to file for bankruptcy, be foreclosed upon and default on medical bills

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Motivation Setting Data Main Results Conclusions

Delinquency: Dynamics

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−0.2 −0.1 0.0 0.1 Distance from Debt Discharge (Months) All Delinquent Accounts (Ex Student Loans) −20 −15 −10 −5 5 10 15 20

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Motivation Setting Data Main Results Conclusions

Main Results

Debt Behavior

Debt declines as borrowers lower credit demand and increase payments

Delinquency and Bankruptcy outcomes

Less likely to become delinquent and file for bankruptcy

Mobility and Income

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Motivation Setting Data Main Results Conclusions

Mobility & Income

Dependent Var Mobility Job Change Moving to Moving to Income ($) Different Higher Paying Industry Industry (1) (2) (3) (4) (5) DebtRelief × Post 0.005*** 0.004** 0.003** 0.01** 79.72*** (0.001) (0.002) (0.001) (0.004) (31.330) Individual FE Yes Yes Yes Yes Yes County x Event-Month FE Yes Yes Yes Yes Yes Observations 6,010,381 967,411 967,219 245,114 471,547 R2 0.31 0.19 0.17 0.34 0.57

Higher geographic and job mobility Earn $3,000 more over three years following discharge

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Motivation Setting Data Main Results Conclusions

Income: Dynamics

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−200

200 400 Distance from Debt Discharge (Months) Income ($) −20 −15 −10 −5 5 10 15 20

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Motivation Setting Data Main Results Conclusions

Main Results

Debt Behavior

Debt declines as borrowers lower credit demand and increase payments

Delinquency and Bankruptcy outcomes

Less likely to become delinquent and file for bankruptcy

Mobility and Income

Earn $3,000 higher total income over three years following discharge

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Motivation Setting Data Main Results Conclusions

Plausible Mechanisms

Liquidity constraints

Most borrowers might’ve stopped paying off their loan - debt relief less likely to impact disposable income Control group might still be subject to wage garnishment - there might be differential impact on disposable income But we find similar results for sample of only treated borrowers Less likely to be the driving mechanism

Credit Score Changes

Controlling for changes in credit scores yields similar/stronger estimates Less likely to be important in our setting

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Motivation Setting Data Main Results Conclusions

Plausible Mechanisms

Delinquency Flag Removal

Delinquency flag on credit file may limit employment opportunity set if employers check for it Flag removal on debt discharge may allow borrowers to access labor market more freely

Debt Overhang

High levels of debt for delinquent borrowers may reduce their incentives to look for better opportunities Relieving debt will lower this friction Find stronger results for higher levels of debt relief - mobility and income results concentrated among borrowers experiencing higher than median levels of debt relief (where avg relief is 12k)

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Motivation Setting Data Main Results Conclusions

Other Results and Robustness

Durable consumption as measured by car purchases increases Similar results using sample of only treated individuals, i.e. using different discharge timing within the sample of treated individuals as the source of variation. Similar results using sub-sample of individuals for which we observe income data Robust to different specifications (e.g. balanced panel one year around treatment, double clustering etc)

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They are also significantly less likely to default on their accounts, above and beyond their student loan accounts. Significantly more likely to move, change job and experience an increase in income. Although our analysis shows significant benefits, we are not able to estimate the welfare consequences of these policies.

Motivation Setting Data Main Results Conclusions

Concluding Remarks

Borrowers experiencing debt relief are significantly more likely to engage in deleveraging, by both reducing their demand for credit and limiting the use of the existing account.

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Significantly more likely to move, change job and experience an increase in income. Although our analysis shows significant benefits, we are not able to estimate the welfare consequences of these policies.

Motivation Setting Data Main Results Conclusions

Concluding Remarks

Borrowers experiencing debt relief are significantly more likely to engage in deleveraging, by both reducing their demand for credit and limiting the use of the existing account. They are also significantly less likely to default on their accounts, above and beyond their student loan accounts.

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Although our analysis shows significant benefits, we are not able to estimate the welfare consequences of these policies.

Motivation Setting Data Main Results Conclusions

Concluding Remarks

Borrowers experiencing debt relief are significantly more likely to engage in deleveraging, by both reducing their demand for credit and limiting the use of the existing account. They are also significantly less likely to default on their accounts, above and beyond their student loan accounts. Significantly more likely to move, change job and experience an increase in income.

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Motivation Setting Data Main Results Conclusions

Concluding Remarks

Borrowers experiencing debt relief are significantly more likely to engage in deleveraging, by both reducing their demand for credit and limiting the use of the existing account. They are also significantly less likely to default on their accounts, above and beyond their student loan accounts. Significantly more likely to move, change job and experience an increase in income. Although our analysis shows significant benefits, we are not able to estimate the welfare consequences of these policies.

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Motivation Setting Data Main Results Conclusions

Summary Statistics

Panel A: Statistics of the Sample Variable Mean

St. Dev.

Min Median Max Number of Accounts 6.599 4.704 5 25 Total Debt ($) 25,690.01 39,779.00 12,652 293,080 Total Debt (Ex Student Loans, $) 16,300.29 130,076.15 7,930 293,080 Number of Accounts (Ex Student Loans) 2.901 3.284 2 25 Credit Card Accounts 2.116 2.589 1 13 Auto Accounts 0.538 0.757 3 Mortgage Accounts 0.115 0.421 3 Credit Card Balance ($) 1,132.53 2,431.23 18,017 Auto Balance ($) 3,943.34 7,021.75 31,877 Mortgage Balance ($) 4,422.04 22,359.52 175,443 Credit Card Utilization 0.341 0.338 0.258 1 Auto Loan Origination Amount ($) 20,629.78 12,724.36 550 17,339 77,868 Mortgage Origination Amount ($) 214,839.02 186,797.58 22,900 154,777 507,750 All Delinquent Accounts (Ex Student Loans) 1.302 1.864 1 17 Total Past-Due Amount (Ex Student Loans, $) 2,213.92 4,891.69 907 54,455 Mobility (1/0) 0.035 0.183 1 Income ($) 2,376.71 1,636.62 830.21 2,531.19 9,588.85 Credit Score 535.25 74.44 300 530 836

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Motivation Setting Data Main Results Conclusions

Summary Statistics

Panel B: Different Population and Samples All All Delinquent Sample Borrowers Student Student Treated (1% CCP) Loan Loan Individuals Population Population Number of Accounts 11.23 11.26 8.90 9.29 Total Debt ($) 22,271.52 36,105.21 40,634.51 49,943.09 Credit Card Accounts 11.84 11.28 4.61 2.96 Auto Accounts 0.95 1.09 0.78 0.63 Mortgage Accounts 0.80 0.71 0.23 0.19 Credit Card Balance ($) 51.78 134.70 269.37 1829.39 Auto Balance($) 16,954.98 16,595.81 14,353.55 4,464.43 Mortgage Balance ($) 186,211.67 194,967.58 134,257.00 6,469.94 Credit Card Utilization 0.43 0.64 0.98 0.37 Delinquent Accounts 0.44 0.83 3.44 5.15 Total Past-Due Amount ($) 1,471.48 2,580.82 14,847.59 6,028.63 Age 49.32 37.79 39.52 34.75

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