Second Chance: Di Maggio, Kalda & Yao (2026)

What this is. The paper’s core results, datasets, and theory: enough to know what it found without reading all 44 pages. To replicate or extend it, read the full source at the original.

TL;DR

Using hand-collected court-filing data matched to Equifax credit bureau records, the paper exploits a plausibly random debt discharge shock: National Collegiate Student Loan Trusts lost paperwork for thousands of borrowers, causing courts to dismiss collection lawsuits and discharge the debt. Treated borrowers (debt discharged) are compared with similar defaulted borrowers whose cases were not dismissed. Debt relief leads to lower balances and delinquency rates on other accounts, higher geographic and job mobility, and approximately $3,000 more income over three years. Both the treated group (via debt relief) and the control group (via wage garnishment and collections) contribute to these differential outcomes.

The paper builds on Dobbie and Song (2015), a benchmark for debt-relief effects on credit and labor outcomes via chapter 13 bankruptcy, but examines private student debt discharge outside bankruptcy. It also builds on Dobbie and Song (2020), a targeted credit-card debt relief experiment, and finds faster and broader effects for student debt discharge.

Core results

Magnitudes and significance are as reported; **/*** = 5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Debt relief reduces student loan balances by approximately $7,400 and credit scores improve by 6.8 points	Table II, p. 524	SL balance: -$7,404.56*** (SE 340.5); credit score: +6.81*** (SE 1.23); N=1,283,639
R2	Total non-student debt balances fall by more than $4,500 for treated borrowers	Table III, p. 528	-$4,600.88*** (SE 387.55); credit card -$618.76***; mortgage -$1,564.60***; auto loan not significant
R3	Treated borrowers deleverage actively: credit card utilization falls, credit limits decline, new account opening falls, repayments rise	Table IV, p. 529	Utilization -0.023*** (SE 0.007); credit limit -$971.82**; new accounts -0.002***; monthly payment +$13.57**
R4	Delinquency rates on other accounts fall by 3 percentage points (12% relative decline); bankruptcy and foreclosure also decline	Table V, p. 531	Total delinquency -0.03*** (SE 0.003); bankruptcy -0.04*** pp; foreclosure -0.03*** pp; medical default -0.1*** pp
R5	Geographic mobility rises 0.3 pp; job change probability rises 0.3 pp; industry switching rises 0.3 pp	Table VI, p. 532	Mobility dummy +0.003*** (SE 0.001); job change +0.003** (SE 0.001); new industry +0.003* (SE 0.002)
R6	Monthly income rises by approximately $80 (1 pp higher income growth); cumulative gain over three years approximately $3,000	Table VI, p. 532	Income (level): +$79.98*** (SE 31.99); % change in income: +0.01** (SE 0.004)
R7	Control group drives credit outcomes via liquidity constraints (wage garnishment): estimates are larger when control group is more likely to be garnished	Table VII, p. 537	Panel A (adjudicated controls): total balance -$5,099.56*** vs baseline -$4,600.88***; delinquency -0.029***
R8	Treated group drives labor outcomes via debt overhang: fraction of variable pay rises +3.1 pp and hours worked rise by 1.20/week for treated borrowers post-discharge	Table VIII, p. 539	Variable pay share: +0.031*** (SE 0.015) all jobs; +0.042*** (SE 0.018) no-job-change sample; hours: +1.20** (SE 0.510) for hourly workers

Overall (paper’s conclusion). A $7,400 debt-relief shock translates into a $4,600 reduction in non-student indebtedness, a $3,000 income gain over three years, and a $4,700 decline in the amount of debt in delinquency. Effects persist for at least two years and are not transitory.

Datasets used

Dataset	Role in paper	Wiki page
LexisNexis court filings (all U.S. civil courts, 2010–2017)	Hand-collected National Collegiate lawsuit data: borrower identity, court, filing date, outcome	no page yet
Equifax credit bureau (matched anonymized panel)	Monthly credit outcomes: balances, delinquency, credit score, account types for treatment/control borrowers	no page yet
Equifax employment and income verification (payroll data, 5,000+ U.S. firms)	Labor market outcomes: monthly gross earnings, hours, job type, tenure, employer	no page yet

Sample: 9,878 treated borrowers; 6,388 control borrowers; 1,283,639 borrower-month observations in the credit data. Lawsuits cover 2010–2017.

Theory / model

The paper has no structural model; it is a reduced-form causal study. It tests three hypotheses derived from debt-overhang and liquidity-constraint models (pp. 519-521):

• Hypothesis 1 (p. 520): Student debt discharge leads to a relative decline in other debt balances. Mechanism: discharge removes a delinquent account from the credit report, relaxes credit constraints, and protects future wages from garnishment, reducing the incentive to borrow further. Building on Herkenhoff, Phillips, and Cohen-Cole (2021), who show credit access improving self-employment, the paper tests whether student debt discharge relaxes credit constraints.
• Hypothesis 2 (p. 520): Student debt discharge leads to a relative decline in the likelihood of default and experiencing distress. Mechanism: improved financial condition reduces delinquency on all accounts and provides positive spillovers to other creditors.
• Hypothesis 3 (p. 521): Student debt discharge leads to a relative increase in mobility and income. Mechanism: debt overhang (analogous to the corporate finance problem) and liquidity constraints distort labor supply decisions; discharge removes these frictions. Melzer (2017) documents debt overhang reducing homeowner investment; here the same mechanism is tested for student-debt borrowers in the labor market.

The paper formalizes the borrower’s budget constraint (p. 517) to clarify what is and is not observable:

$$c_{i,t} = (1 - s^u_{i,t}) \cdot \gamma_{i,t-1} + (1 - G_i(t_d))(1 - s_{i,t})(Y^o_{i,t} + Y^h_{i,t} + Y^f_{i,t}) - \sum_{l=1}^{A} (1 + r_{i,l}) \cdot b_{i,t,l}$$

where $c_{i,t}$ is consumption of individual $i$ in period $t$; $\gamma_{i,t-1}$ is wealth at $t-1$; $G_i(t_d)$ is a piecewise garnishment function equal to rate $g$ if $i$ is in the control group after default date $t_d$, zero otherwise; $Y^o_{i,t}$ is observed wage income; $Y^h_{i,t}$ is hidden wage income; $Y^f_{i,t}$ is financial income; $s^u_{i,t}$ is the savings rate out of assets; $s_{i,t}$ is the savings rate out of income; $r_{i,l}$ and $b_{i,t,l}$ are interest rates and balances for loans of type $l$ (p. 517-518). The paper explicitly notes it cannot observe consumption, savings, or hidden income, which limits the set of testable predictions.

Identification. The source of variation is the plausibly random loss of paperwork by National Collegiate Student Loan Trusts. Courts dismissed collection lawsuits against borrowers whose chain of title could not be proved. This documentation loss is argued to be orthogonal to borrower characteristics (p. 516): the same trust held loans it could and could not prove, and the distinction was driven by clerical errors, not by borrower type. The paper verifies balance on pre-treatment observables and the absence of pre-trends across all outcomes (Figure 2, pp. 525-527).

Method

The empirical strategy uses a difference-in-differences (DiD) estimator building on difference-in-differences and panel-fe.

Treatment and control groups. Treated borrowers are those whose National Collegiate cases were dismissed and debt discharged. Control borrowers were sued by the same trusts but their debt was not discharged during the sample period (either the trust did not lose the paperwork or the case was not adjudicated by end of sample). Both groups defaulted on loans owned by the same trust and were subject to collection by the same agency, making the groups likely to be similar on unobservables (p. 518).

Standard-error treatment. Standard errors are clustered at the zip code level throughout, allowing within-neighborhood error correlation across borrowers (p. 519). Robustness checks use individual-level clustering and double clustering by zip code and calendar month (Internet Appendix Table IA.I).

Treated-only robustness. To address concerns that the control group is confounded by wage garnishment, the paper re-estimates the main specifications using only treated borrowers, exploiting the staggered timing of discharges as the source of variation (Table IX, p. 541). Results are qualitatively similar and economically meaningful, supporting the validity of the treated group’s contribution.

Empirical specifications

Baseline DiD (equation 1, p. 518). The main estimating equation is:

$$\text{Outcome}_{i,j,t} = \alpha + \beta \cdot (\text{DebtRelief}_i \times \text{Post}_t) + \mu_i + \gamma_{j \times t} + \epsilon_{i,j,t} \tag{1}$$

• $\text{Outcome}_{i,j,t}$ is the outcome variable for borrower $i$, filing year $j$, calendar year-month $t$.
• $\text{DebtRelief}_i$ = 1 for treated borrowers (debt discharged), 0 for control.
• $\text{Post}_t$ = 1 after debt discharge and 0 before.
• $\mu_i$ are individual fixed effects.
• $\gamma_{j \times t}$ are filing-year by calendar-year-month fixed effects (ensuring treated and control borrowers are compared within the same filing-year cohort and calendar time).
• $\epsilon_{i,j,t}$ is the error term.
• Standard errors clustered at zip code level. N = 1,283,639 borrower-month observations (credit data).

Dynamic event-study (equation 2, p. 519). To assess pre-trends and persistence:

$$\text{Outcome}_{i,j,t} = \alpha + \sum_{\tau=-5}^{9} \beta_\tau \cdot (\text{DebtRelief}_i \times \text{Post}_\tau) + \mu_i + \gamma_{j \times t} + \epsilon_{i,j,t} \tag{2}$$

• $\tau$ indexes event-quarters relative to discharge.
• $\tau = -5$ captures all months before five quarters pre-treatment and $\tau = 9$ captures all months nine or more quarters post-treatment.
• Coefficients $\beta_\tau$ are plotted with confidence intervals (Figure 2, pp. 525-526).
• Pre-trend coefficients are indistinguishable from zero across all outcomes; post-discharge coefficients diverge persistently for at least two years.

Outcome variable groups and samples:

• Credit outcomes (R1-R4): full sample, N = 1,283,639; outcomes include SL balance, credit score, total non-SL balance, credit card balance, mortgage balance, credit card utilization, credit limit, account openings, monthly payments, delinquency rates, bankruptcy, foreclosure, medical default.
• Labor outcomes (R5-R6): subsample with Equifax employment data; N = 211,716 (job change), 197,874 (new industry), 106,580 (income level), 91,230 (income growth).
• Mechanism tests (R7): subsample with adjudicated control-group cases (Table VII Panel A, N = 1,028,559); and control borrowers with prior collections on file (Table VII Panel B, N = 838,295).
• Wage composition (R8): subsample of workers in Equifax payroll data (Table VIII, N = 39,459 all jobs; 28,653 no-job-change; 22,128 hourly).

The same two-way FE structure (individual + filing-year x YM) applies in all specifications; the only variation across tables is the outcome variable and the sample restriction.

When to read the full paper

Use the original DOI if you are: replicating (code in Supporting Information); extending the design to other debt types or populations; examining mechanism tests (wage composition, above/below median debt relief, liquidity-constraint heterogeneity); or auditing a specific coefficient. The locators above point you to the exact table. For “what did this paper find,” the table above is the intended default.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(1). This distillation was extracted by an LLM on 2026-05-31 and is not human-verified or independently reproduced. The CC BY 4.0 licence (confirmed via Crossref) permits reproduction with attribution. No verbatim PDF is hosted in this batch; the canonical source is the publisher DOI.

Attribution (CC BY 4.0). Di Maggio, Marco, Ankit Kalda, and Vincent Yao. “Second Chance: Life with Less Student Debt.” The Journal of Finance 81, no. 1 (February 2026): 507–550. DOI: 10.1111/jofi.70002. © 2025 The Author(s). Licensed under CC BY 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.