Financial Consequences of Pretrial Detention: Slutzky & Xu (2025)

What this is. The paper’s core results, identification design, and estimating equations: enough to understand what was found and how, without reading all 45 pages. To replicate or extend, read the original at https://doi.org/10.1093/rfs/hhaf009.

TL;DR

This paper asks whether pretrial detention, which holds individuals in jail before trial because they cannot afford bail, causes subsequent household financial distress. Using Maryland criminal court data (2000-2016) matched to bankruptcy filings from Gross, Notowidigdo, and Wang (2014), foreclosure, and judgment lien records, it exploits the quasi-random assignment of court commissioners to cases as an instrument for detention decisions. A more lenient commissioner reduces the probability of detention, and this variation is unrelated to defendant characteristics. The main finding is that pretrial detention causally increases household insolvency, raising chapter 7 bankruptcy rates by 0.79 percentage points (30% of the mean) and judgment lien rates by 0.56 percentage points (35% of the mean) within three years. Foreclosures increase significantly (2.9 pp, 23% of the mean) only in ZIP codes with declining house prices, consistent with home equity acting as a liquidity buffer. The financial burden falls primarily on family members, not defendants themselves, suggesting cohabiting relatives (parents, partners) posting or guaranteeing bail bear the cost. Commercial bail bonds play a partial role, but insolvency effects persist even in samples that largely eliminate income-loss and criminal-record channels.

Core results

Magnitudes and significance are as reported; \*\* = 5%, \*\*\* = 1%. All regressions instrument detention with the residualized leave-out mean commissioner leniency measure (first-stage F-stat exceeds 6,000 unless noted).

#	Result	Locator	Magnitude
R1	Pretrial detention raises chapter 7 bankruptcy at the 1-3 year horizon; null for chapter 13	Table 5, p. 3353	+0.44 pp at 1 year**, +0.76 pp at 2 years**, +0.79 pp at 3 years** (30% of 2.65% mean); chapter 13 insignificant at all horizons
R2	Pretrial detention raises judgment lien rates at the 3-year horizon	Table 6, p. 3355	+0.56 pp at 3 years** (35% increase relative to 1.6% mean); insignificant at shorter horizons
R3	Foreclosure effects are null overall but large in areas with declining house prices	Table 7, p. 3356	Full sample: +0.92 pp at 3 years (insignificant); negative-HPI subsample: +2.9 pp** at 3 years (23% of mean); null in positive-HPI subsample
R4	Overall household insolvency (bankruptcy + lien + foreclosure) rises by 2.4 pp at 3 years	Table 8, p. 3359	+1.7 pp** at 2 years, +2.4 pp*** at 3 years (16% of 14.8% mean); clean zero in backward-looking placebo tests (Figure 8)
R5	Insolvency burden falls on family members, not defendants	Table 9, p. 3362	InsolventDef: +0.0027 (insignificant, mean 1.6%); InsolventFamily: +0.0075** (22% of 3.4% mean); InsolventUnrelated: +0.0123 (insignificant)
R6	Commercial bail bonds amplify but do not fully explain the insolvency effect	Table 10, p. 3365	ROR/commercial-bond sample: +4.2 pp***; excluding commercial bonds: +1.9 pp*; effect persists when income-loss and conviction channels are controlled
R7	Insolvency effects are stronger for younger defendants and mortgaged, shorter-maturity properties	Table 11, p. 3367	Young defendants (age < 30): +2.8 pp*; mortgage-financed properties: +3.9 pp**; short-maturity: +4.1 pp***; consistent with older relative bearing bail costs
R8	Failure to appear magnifies the insolvency effect for defendants and family members via bond forfeiture	Table 13, p. 3369	FailToAppear x Detained interaction: +0.0308*** (InsolventDef, col 2); +0.0088*** (InsolventFamily, col 3); insignificant for InsolventUnrelated (col 5); OLS result, causal interpretation limited

Overall (paper’s conclusion). Pretrial detention imposes significant household financial costs that extend beyond the defendant to cohabiting family members, most plausibly through the direct cost of posting or guaranteeing bail. The 3-year bankruptcy effect (0.79 pp) is comparable in magnitude to Dobkin et al. (2018), who find that hospital admissions raise bankruptcy rates by 0.4-1.4 pp. Home equity cushions households from insolvency: the foreclosure effect is concentrated in areas with declining house prices, where households cannot tap equity to meet liquidity shocks. Commercial bail bonds play a partial but not exclusive role. The findings add to the literature on the collateral damage of the criminal justice system and are relevant to ongoing debates about bail reform.

Theory / model

The paper has no formal economic model. The identification logic and tested hypotheses are as follows.

Hypotheses tested. Three potential channels link pretrial detention to household insolvency (pp. 3330-3332):

1. Liquidity shock from bail costs (cash bond, commercial surety bond fee, property bond pledge) borne immediately by the defendant or family.
2. Loss of current income from prolonged detention, reducing the defendant’s ability to contribute to household obligations.
3. Long-run income reduction through higher conviction rates and reduced future formal employment, documented by Gupta, Hansman, and Frenchman (2016) and Dobbie, Goldin, and Yang (2018).

The empirical tests are designed to separate these. The commercial bail bond subsample test (Table 10) restricts to cases where defendants obtain immediate release via commercial bonds, which filters out the income-loss channel while preserving the liquidity-shock channel. The finding that insolvency effects persist in this sample (R6) is consistent with the bail-cost mechanism.

Double-trigger hypothesis for foreclosure. Foote, Gerardi, and Willen (2008) show that both a liquidity shock and negative home equity are needed for foreclosure. The heterogeneous foreclosure effects (R3) are explicitly framed as a test of this: the liquidity shock from pretrial detention triggers foreclosure only when home equity is insufficient to buffer it (pp. 3356-3358).

Identification. Assignment of commissioners to cases in Maryland is treated as quasi-random conditional on court-by-year, ZIP-code-by-year, month, day-of-week, sex, race, and charge fixed effects. The exclusion restriction requires that the commissioner’s leniency affects household financial outcomes only through the pretrial detention decision. The paper provides two sets of evidence: (i) the residualized instrument is uncorrelated with all observable defendant and case characteristics (Table 2, column 3; joint F-test p-value = 0.499), and (ii) the first-stage coefficient on the instrument is large and symmetric across subsamples defined by race, sex, age, and geography (Table 3).

Method

The paper applies an instrumental variables design built on two existing approaches: the leave-out commissioner leniency instrument of Dahl, Kostol, and Mogstad (2014) and Dobbie, Goldin, and Yang (2018), applied to a new outcome domain (household finance). The technique genealogy runs through instrumental-variables and panel-regression.

First stage: residualized leave-out mean (p. 3345, Equation 3). For each commissioner $j$ in year $t$, the leniency instrument is constructed as:

$$\text{ReleasedRIV}_{ctj} = \left(\frac{1}{n_{tj} - n_{itj}}\right)\left(\sum_{k=0}^{n_{tj}} \text{Released}^*_{klt} - \sum_{c=0}^{n_{itj}} \text{Released}^*_{ict}\right) \tag{3}$$

where $n_{tj}$ is the total number of cases commissioner $j$ sees in year $t$, $n_{itj}$ is the number involving defendant $i$, and $\text{Released}^*_{ict}$ is the residual release decision after partialling out defendant and case characteristics $X_{ict}$ (Equation 2):

$$\text{Released}^*_{ict} = \text{Released}_{ic} - \gamma \, X_{ict} = \text{ReleasedRIV}_{ctj} + \epsilon_{ict} \tag{2}$$

Leaving out cases involving the focal defendant avoids the mechanical correlation that would arise if the instrument included the defendant’s own case. The instrument is then transformed into $\text{DetainedRIV} = 1 - \text{ReleasedRIV}$ so that results are reported as effects of detention rather than release.

Second stage (p. 3344, Equation 1). The primary estimating equation is:

$$Y_{ict} = \beta_0 + \delta \, \text{Released}_{ic} + X_{ict}\beta + \epsilon_{ict} \tag{1}$$

where $Y_{ict}$ is the cumulative insolvency indicator (bankruptcy, judgment lien, or foreclosure) for individual $i$ in case $c$ in year $t$, $\text{Released}_{ic}$ is the endogenous binary treatment variable, and $X_{ict}$ is a vector of case- and defendant-level controls. In the 2SLS version, $\text{DetainedRIV}$ instruments for $\text{Detained}_{ic}$.

First-stage strength. The first-stage F-statistic is 13,526 in the main sample (Table 3, col 1; Table 4B), well above the Stock and Yogo (2005) and Olea and Pflueger (2013) thresholds. The coefficient on $\text{ReleasedRIV}$ in the first stage is approximately -0.946 (Table 3, col 1), meaning a one-unit increase in the leniency instrument shifts the probability of release by 94.5 percentage points. The coefficient is stable across subsamples (range 0.898 to 1.082, Table 3, cols 2-10).

Empirical specifications

Outcome variables. The insolvency indicators are defined as cumulative dummy variables: $Y_{ict}^{\tau} = 1$ if a bankruptcy, judgment lien, or foreclosure occurs within $\tau$ periods of the initial hearing date. Horizons are 3 months, 6 months, 1 year, 2 years, and 3 years. Bankruptcy data (PACER) cover 2000-2008; foreclosure and lien data (Maryland Judiciary + ZTRAX) cover 2000-2016.

Fixed effects and standard errors. All regressions include court-by-year, ZIP-code-by-year, month, day-of-week, sex, race, and charge fixed effects. Standard errors are clustered at the commissioner level.

Bankruptcy specifications (Table 5). The 2SLS regression instrumenting $\text{Detained}_{ic}$ with $\text{DetainedRIV}_{ctj}$ yields:

Horizon	Coefficient on Detained	SE	Mean
3 months	+0.0003	(0.0013)	0.0029
6 months	+0.0005	(0.0015)	0.0057
1 year	+0.0044**	(0.0021)	0.0110
2 years	+0.0076**	(0.0031)	0.0198
3 years	+0.0079**	(0.0037)	0.0265

First-stage F-stat: 10,004. N = 306,722.

Judgment lien specifications (Table 6). Same specification, first-stage F-stat = 13,526, N = 502,546. The coefficient becomes significant at the 3-year horizon (+0.0056**, SE = 0.0023), consistent with liens being a last resort after other repayment channels are exhausted.

Foreclosure specifications (Table 7). Full sample (N = 275,325): coefficient at 3 years = +0.0092 (SE = 0.0074), insignificant. Negative-HPI ZIP codes (N = 107,357): +0.0291** at 3 years (SE = 0.0123). Positive-HPI ZIP codes (N = 166,155): -0.0033 (SE = 0.0089), null.

Overall insolvency (Table 8). Combines bankruptcy, lien, and foreclosure for the ZTRAX-matched sample (N = 275,325). Coefficient at 3 years: +0.0242*** (SE = 0.0090), mean = 0.1479 (SD = 0.3550).

Family spillover specifications (Table 9). 2SLS (same instrument as benchmark, first-stage F = 6,305.53). Insolvency events are matched using both addresses and full names, separating defendant’s name (InsolventDef), family members’ names (InsolventFamily), same last name (InsolventName), and unrelated individuals (InsolventUnrelated). N = 275,325, all horizons 3 years.

Placebo tests. Figures 4, 5, and 8 plot coefficients at backward-looking horizons (-3 years, -2 years, -1 year, -6 months, -3 months). In all cases the coefficients are small and statistically indistinguishable from zero, confirming the identifying assumption that the instrument is uncorrelated with pre-existing insolvency trends.

Datasets used

Dataset	Role in paper	Wiki page
Maryland Judiciary public access database	Criminal case records: 1.08 million cases 2000-2016; commissioner ID, release decisions, bail types, charge categories	Maryland Judiciary
PACER (Public Access to Court Electronic Records)	Consumer bankruptcy filings 2000-2011: 318,000 filings in Maryland; chapter 7 and chapter 13 type, filing date, address	PACER
ZTRAX (Zillow Transaction and Assessment Database)	Real estate transactions 1993-2020: 9 million Maryland transactions; foreclosure events post-2007, property-level matching	ZTRAX (licensed)
Maryland Judiciary civil court records	Judgment lien filings 2000-2020: 386,938 lien filings; plaintiff/defendant address, filing date	Maryland Judiciary
Federal Housing Finance Agency HPI	ZIP-code-level annual house price index; used to split sample into negative/positive HPI growth subsamples	No page yet

Sample: over 500,000 criminal cases in Baltimore City, Montgomery County, and Prince George’s County District Courts. 78% from Baltimore City. 81% Black defendants, 83% male, median age 30.

When to read the full paper

Read the original if you are: studying the economics of the bail system or pretrial detention reform; working on household insolvency and liquidity shocks more broadly; replicating or extending the leave-out commissioner leniency instrument to other outcomes or jurisdictions; or testing the double-trigger hypothesis for foreclosures with a new source of liquidity shocks. Tables 5-9 contain the headline regressions; the Internet Appendix (available on the RFS website) contains robustness tests including a lagged IV, non-residualized IV, apartment-inclusive sample, and absorbing-state tests.

Attribution and rights

Source: peer-reviewed, The Review of Financial Studies 38(11), November 2025. This distillation was extracted by an LLM on 2026-06-06 and is not human-verified or independently reproduced. The paper is paywalled (OUP standard publication reuse rights); no verbatim reproduction. Access the original at https://doi.org/10.1093/rfs/hhaf009.

Citation: Slutzky, Pablo, and Sheng-Jun Xu. “The Financial Consequences of Pretrial Detention.” The Review of Financial Studies 38, no. 11 (2025): 3329-3373. DOI: 10.1093/rfs/hhaf009.