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ABSTRACT
This study examines the efficiency of local jails in the United States in 2016 using Data Envelopment Analysis with metafrontiers. Metafrontiers envelop jails that have a similar production technology and yield more accurate efficiency estimates. Using an input-oriented model with variable returns to scale, the results suggest that, on average, jails could reduce or reallocate their inputs by 37% given their output level. For managers, the findings provide evidence that jail managers can improve efficiency by carefully assessing how they allocate and use their resources. For policy makers, the results indicate that opportunities exist for implementing cost-saving approaches to maximize taxpayer dollars.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes
1. Here, the author indicates incarceration because all the studies to date have focused on prisons rather than jails. There are only eight studies that have examined the efficiency of prisons and there are no studies available that have examined the efficiency of jails.
2. Production technologies is understood as the inputs and the environment in which a firm (a jail in this case) operates.
3. Slacks are the additional improvement needed by an inefficient unit to achieve efficient. This could be done by either decreasing outputs and/or increasing inputs.
4. Non-discretionary inputs are those inputs that cannot be controlled by the prison managers.
5. They are also difficult to quantify from an operational perspective.
6. Scale efficiency compares constant (CRS) and variable returns (VRS) to scale and provides a measure on whether the firm is operating at its optimal size (Bogetoft & Otto, Citation2010).
7. There are a few examples where multiple outputs can be accommodated in a cost-efficiency model using SFA (see Perelman & Santín, Citation2011).
8. Cost function models examine efficiency from a cost perspective.
9. Given the challenge of finding accurate data on market prices for public sector institutions, the authors argued that could not have access to them.
10. The DMU (Decision Management Unit) is the unit of analysis. In the case of the current study is local jails.
11. This is the efficiency frontier.
12. The Annual Jail survey collects data from a nationally representative sample of local jails on jail inmate populations, jail capacity, and related information.
13. Bureau of Justice Statistics. The survey data were collected in 2015.
14. This is because linear programming models like DEA require that all the data be strictly positive to prevent infeasible solutions in the model.
15. The expected length of stay in 2015 was 41 days and it is calculated by multiplying the average daily population by the number of days in a year divided by the number of annual admissions to the jail.
16. At 12 million admissions per year, jails have almost 20 times more admissions than state and federal prisons combined (Subramanian, Delaney, Roberts, Fishman, & McGarry, Citation2015).
17. This is calculated as the sum of weekly admissions and releases divided by the average daily population in the jail (Bureau of Justice Statistics, Citation2016).
18. Convicted people currently incarcerated are who have been convicted but are awaiting sentence.
19. There may be cases in which the inmate is sentenced and serves the sentence in the jail instead.
20. The table with normalized data is not reported but is available upon request.
21. This value is for Fiscal Year 2014.