New York City jails: COVID discharge policy, data transparency, and reform

Abstract

During the early stages of the COVID-19 pandemic in 2020, Mayor Bill de Blasio ordered the release of individuals incarcerated in New York City jails who were at high risk of contracting the disease and at low risk of committing criminal reoffense. Using public information, we construct and analyze a database of nearly 350,000 incarceration episodes in the city jail system from 2014-2020, paying special attention to what happened during the week of March 23-29, 2020, immediately following the mayor's order. In concordance with de Blasio's stated policy, we find that being discharged during this focus week is associated with a lower probability of readmission as compared to being discharged during the same calendar week in previous years. Furthermore, comparing the individuals discharged during the focus week of 2020 to those discharged during the same calendar week in previous years, we find that the former group was, on average, slightly older than the latter group, although the difference is not large. Additionally, the individuals in the former group had spent substantially longer in jail than those in the latter group. With the release of long-serving individuals demonstrated to be feasible, we also examine how the jail population would have looked over the past six years had caps in incarceration been in place. With a cap of one year, the system would experience a 15% decrease in incarceration. With a cap of 100 days, the reduction would be over 50%. Because our results are only as accurate as New York City's public-facing jail data, we discuss numerous challenges with this data and suggest improvements related to the incarcerated individual's age, gender, race, and more. Finally, we discuss the policy implications of our work, highlight some opportunities and challenges posed by incarceration caps, and suggest key areas for reform. One such reform might involve identifying and discharging low-risk individuals sooner in general, which might be feasible given the de Blasio administration's actions during the early stages of COVID-19.

Fig 1. Jail population in New York City, 2014–2020.

(A) Incarcerated population over time. (B) Histogram of weekly net change in jail population. The week of March 23—29, 2020 is when Mayor Bill de Blasio ordered discharge of individuals at high risk of contracting COVID-19 and at low risk of reoffending.

Fig 2. Duration of New York City incarceration episodes, 2014–2020.

(A) Histogram of all episodes. Because the frequencies vary over many orders of magnitude, we use a pseudo-log scale on the vertical. (B) Distributions of duration for individuals discharged March 23—29, separated by year and shown as box plots. The distribution for 2020 appears to have a substantially higher median value. (C) Cumulative distribution function for the data in panel B, with years 2014—2019 combined. From a Kolmogorov-Smirnov test (see text) we conclude that for March 23—29 discharges, the individuals discharged in 2020 tended to have served more time than individuals in previous years.

Fig 3. Age at discharge for New York City incarceration episodes, 2014–2020.

(A) Histogram for all episodes. Because the frequencies vary over many orders of magnitude, we use a pseudo-log scale on the vertical. (B) Distributions of age for individuals discharged March 23—29, separated by year and shown as box plots. (C) Cumulative distribution function for the data in panel B, with years 2014—2019 combined. From a Kolmogorov-Smirnov test (see text) we conclude that for March 23—29 discharges, individuals discharged in 2020 tended to be older than in previous years, with a modest median diference of approximately two years.

Fig 4. Effect on historical jail population of hypothetical incarceration caps in New York City, 2014–2020.

(A) Time series of the jail population for several values of the incarceration cap D. The top curve (pink) represents the true historical jail population. (B) Effect of incarceration cap on the jail system as a function of D, which we place on a log axis. If incarcerations were limited to one year, the system would experience a 15% decrease in person-days of incarceration. With a cap of D = 100 days, the reduction would be over 50%. Additionally, as the cap becomes more stringent (smaller), we see a drastic reduction in the number of pre-trial detainees and a substantial proportional decrease for sentenced state-ready individuals. Status codes are as follows: CS = City Sentenced; CSP = City Sentenced with VP Warrant; DE = Detainee; DEP = Detainee Parole Violator; DNS = Detainee Newly Sentenced to State Time; DPV = Detainee Technical Parole Violator; SCO = State Prisoner Court Order; SSR = Sentenced State Ready; NA = Missing Data.