This paper reports results from a randomized field experiment that offered distressed credit
card borrowers more than $50 million in debt forgiveness and over 27,500 additional months
to repay their debts. The experimental variation effectively randomized debt write-downs and
minimum payments for borrowers at eleven large credit card issuers. Merging information from
the experiment to administrative tax and bankruptcy records, we find that the debt write-downs
increased debt repayment and decreased bankruptcy ling. The debt write-downs also increased
formal sector employment for the most financially distressed borrowers. In contrast, we find little
impact of the lower minimum payments on debt repayment, bankruptcy, or employment. We
show that this null result can be explained by the positive short-run effect of increased liquidity
being o set by the unintended, negative effect of exposing borrowers to more default risk. We
conclude that debt relief is more effective than debt restructuring for distressed credit card
borrowers, even when these borrowers are liquidity constrained.

We estimate the impact of charter schools on early-life labor market outcomes using administrative data from Texas. We find that, at the mean, charter schools have no impact on test scores and a negative impact on earnings. No Excuses charter schools increase test scores and four-year college enrollment, but have a small and statistically insignificant impact on earnings, while other types of charter schools decrease test scores, four-year college enrollment, and earnings. Moving to school-level estimates, we find that charter schools that decrease test scores also tend to decrease earnings, while charter schools that increase test scores have no discernible impact on earnings. In contrast, high school graduation effects are predictive of earnings effects
throughout the distribution of school quality. The paper concludes with a speculative discussion of what might explain our set of facts.

Over 20 percent of prison and jail inmates in the United States are currently awaiting trial,
but little is known about the impact of pre-trial detention on defendants. This paper uses the
detention tendencies of quasi-randomly assigned bail judges to estimate the causal effects of
pre-trial detention on subsequent defendant outcomes. Using data from administrative court
and tax records, we find that being detained before trial significantly increases the probability of
a conviction, primarily through an increase in guilty pleas. Pre-trial detention has no detectable
effect on future crime, but decreases pre-trial crime and failures to appear in court. We also find
suggestive evidence that pre-trial detention decreases formal sector employment and the receipt
of employment- and tax-related government benefits. We argue that these results are consistent
with (i) pre-trial detention weakening defendants’ bargaining position during plea negotiations,
and (ii) a criminal conviction lowering defendants’ prospects in the formal labor market.

Credit reports are used in nearly all consumer lending decisions and, increasingly, in hiring decisions in the labor market, but the impact of a bad credit report is largely unknown. We study the effects of credit reports on financial and labor market outcomes using a difference-in-differences research design that compares changes in outcomes over time for Chapter 13 filers, whose personal bankruptcy flags are removed from credit reports after 7 years, to changes for Chapter 7 filers, whose personal bankruptcy flags are removed from credit reports after 10 years. Using credit bureau data, we show that the removal of a Chapter 13 bankruptcy flag leads to a large increase in credit scores, and an economically significant increase in credit card balances and mortgage borrowing. We study labor market effects using administrative tax records linked to personal bankruptcy records. In sharp contrast to the credit market effects, we estimate a precise zero effect of flag removal on employment and earnings outcomes. We conclude that credit reports are important for credit market outcomes, where they are the primary source of information used to screen applicants, but are of limited consequence for labor market outcomes, where employers rely on a much broader set of screening mechanisms.

This paper develops a new test for identifying racial bias in the context of bail decisions –a high-stakes setting with large disparities between white and black defendants. We motivate our analysis using Becker’s (1957) model of racial bias, which predicts that rates of pre-trial misconduct will be identical for marginal white and marginal black defendants if bail judges are racially unbiased. In contrast, marginal white defendants will have a higher probability of misconduct than marginal black defendants if bail judges are racially biased against blacks. To test the model, we develop a new estimator that uses the release tendencies of quasi-randomly assigned bail judges to identify the relevant race-specific misconduct rates. Estimates from Miami and Philadelphia show that bail judges are racially biased against black defendants, with substantially more racial bias among both inexperienced and part-time judges. We also find that both black and white judges are biased against black defendants. We argue that these results are consistent with bail judges making racially biased prediction errors, rather than being racially prejudiced per se.

We estimate the causal effect of parental incarceration on children’s medium-run outcomes
using administrative data from Sweden. Our empirical strategy exploits exogenous variation
in parental incarceration from the random assignment of criminal defendants to judges with
different incarceration tendencies. We find that the incarceration of a parent in childhood
leads to significant increases in teen crime and pregnancy and a significant decrease in early-life
employment. The effects are concentrated among children from the most disadvantaged families,
where teen crime increases by 18 percentage points, teen pregnancy increases by 8 percentage
points, and employment at age 20 decreases by 28 percentage points. In contrast, there are no
detectable effects among children from more advantaged families. These results imply that the
incarceration of parents with young children may increase the intergenerational persistence of
poverty and criminal behavior, even in affluent countries with extensive social safety nets.

This paper tests for bias in consumer lending decisions using administrative data from a
high-cost lender in the United Kingdom. We motivate our analysis using a simple model of
bias in lending, which predicts that profits should be identical for loan applicants from different
groups at the margin if loan examiners are unbiased. We identify the profitability of marginal
loan applicants by exploiting variation from the quasi-random assignment of loan examiners.
We find significant bias against both immigrant and older loan applicants when using the firm’s
preferred measure of long-run profits. In contrast, there is no evidence of bias when using a
short-run measure used to evaluate examiner performance, suggesting that the bias in our setting
is due to the misalignment of firm and examiner incentives. We conclude by showing that a
decision rule based on machine learning predictions of long-run profitability can simultaneously
increase profits and eliminate bias.