Solutions to Post-Incarceration Employment and Entrepreneurship: The Role of Businesses and Universities Assistant Professor, Decision, Risk and Operations Columbia Business School

1. Measuring the E↵ect of the Experience of
Incarceration on Reo↵ending
Susanna M. Makela, Daniel S. Nagin and Jos´e R. Zubizarreta
Carnegie Mellon University and Columbia University
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 1 / 10

2. Does the experience of incarceration a↵ect future
o↵ending?
• 1% of the adult population in the US is incarcerated —highest
incarceration rate in the world
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 1 / 10

3. Does the experience of incarceration a↵ect future
o↵ending?
• 1% of the adult population in the US is incarcerated —highest
incarceration rate in the world
• Main mechanisms through which incarceration impacts o↵ending:
Incapacitation
General deterrence (threat of incarceration)
Specific deterrence (experience of incarceration)
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 1 / 10

4. Does the experience of incarceration a↵ect future
o↵ending?
• 1% of the adult population in the US is incarcerated —highest
incarceration rate in the world
• Main mechanisms through which incarceration impacts o↵ending:
Incapacitation
General deterrence (threat of incarceration)
Specific deterrence (experience of incarceration)
• Theory of specific deterrence is murky
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 1 / 10

5. Evidence concerning the experience of incarceration
• Recent review of the empirical literature (Nagin et al. 2009):
Evidence points to either no e↵ect or mildly criminogenic e↵ect
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 2 / 10

6. Evidence concerning the experience of incarceration
• Recent review of the empirical literature (Nagin et al. 2009):
Evidence points to either no e↵ect or mildly criminogenic e↵ect
• Issues with the existing literature:
Nearly all use non-experimental data
Hidden bias may persist even after rigorous statistical control
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 2 / 10

7. Evidence concerning the experience of incarceration
• Recent review of the empirical literature (Nagin et al. 2009):
Evidence points to either no e↵ect or mildly criminogenic e↵ect
• Issues with the existing literature:
Nearly all use non-experimental data
Hidden bias may persist even after rigorous statistical control
• Findings from five recent studies with stronger designs:
Green and Winik (2010) find no e↵ect of incarceration
Loe✏er (2013) finds no lasting e↵ect
Nagin and Snodgrass (2013) find no e↵ect of incarceration
Mueller-Smith (2014) finds long term criminogenic e↵ect
Azer and Doyle (2015) find that juvenile incarceration increases adult
incarceration
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 2 / 10

8. Our contribution
• Estimate the e↵ect of incarceration on recidivism using extensive
administrative data and new statistical methods
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 3 / 10

9. Our contribution
• Estimate the e↵ect of incarceration on recidivism using extensive
administrative data and new statistical methods
• Data:
All individuals sentenced in 1998, 1999 and 2000 in the Pennsylvania
criminal courts (approximately 120,000 individuals)
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 3 / 10

10. Our contribution
• Estimate the e↵ect of incarceration on recidivism using extensive
administrative data and new statistical methods
• Data:
All individuals sentenced in 1998, 1999 and 2000 in the Pennsylvania
criminal courts (approximately 120,000 individuals)
• Methods:
Matching approach for approximating a randomized encouragement
design while strengthening the encouragement —the instrumental
variable (Zubizarreta 2012, Zubizarreta et al. 2013, Yang et al. 2014,
Zubizarreta and Kilcioglu 2016)
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 3 / 10

11. A natural experiment in Pennsylvania
• In Pennsylvania, criminal o↵enders are randomly assigned to judges,
and judges vary in their punitiveness
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 4 / 10

12. A natural experiment in Pennsylvania
• In Pennsylvania, criminal o↵enders are randomly assigned to judges,
and judges vary in their punitiveness
Because of randomization, caseloads tend to be equivalent across
judges
No di↵erences across judges in the tendency of their caseloads to
recidivate
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 4 / 10

13. A natural experiment in Pennsylvania
• In Pennsylvania, criminal o↵enders are randomly assigned to judges,
and judges vary in their punitiveness
Because of randomization, caseloads tend to be equivalent across
judges
No di↵erences across judges in the tendency of their caseloads to
recidivate
There is significant variation across judges in their proclivity to use
incarceration
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 4 / 10

14. A natural experiment in Pennsylvania
• In Pennsylvania, criminal o↵enders are randomly assigned to judges,
and judges vary in their punitiveness
Because of randomization, caseloads tend to be equivalent across
judges
No di↵erences across judges in the tendency of their caseloads to
recidivate
There is significant variation across judges in their proclivity to use
incarceration
• Following Green and Winik (2010), Loefler (2013), and Nagin and
Snodgrass (2013), we capitalize on these two facts and define as an
instrumental variable
H :=
(
1 if the o↵ender is randomly assigned to a harsh judge
0 if the o↵ender is randomly assigned to a lenient judge
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 4 / 10

15. Balance on the covariates, imbalance on the instrument
Covariate/Instrument Harsh Lenient Std.
judge judge dif.
Age 29.42 29.37 0.00
Female 0.19 0.19 0.00
White 0.35 0.35 0.00
O↵ense Gravity Score 4.41 4.31 0.05
Prior Record Score 1.78 1.77 0.01
Probability of Jail 0.57 0.29 2.25
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 6 / 10

16. Visualizing the matched design
• Near matching on the o↵ense gravity score, far matching on the
probability of going to prison:
2 4 6 8 10 12
0.10.20.30.40.50.6
Near/far matching for a random sample of 50 matched pairs
Offense gravity score
Probabilityofprison
Harsh judge
Lenient judge
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 7 / 10

17. References
• Aizer, A. & Doyle, J.J., (2015). Juvenile Incarceration, Human Capital, and Future Crime: Evidence from Randomly
Assigned Judges. Quarterly Journal of Economics, 18, 33–87.
• Baiocchi, M., Small, D.S., Lorch, S. & Rosenbaum, P.R. (2010). Building a stronger instrument in an observational
study of perinatal care for premature infants. Journal of the American Statistical Association, 105, 1285–1296.
• Green D.P. & Winik, D. (2010). Using random judge assignments to estimate the e↵ects of incarceration and probation
on recidivism among drug o↵enders. Criminology, 48, 357–387
• Loe✏er, C.E. (2013). Does imprisonment alter the life-course? Evidence on crime and employment from a natural
experiment. Criminology, 51, 137–166.
• Mueller-Smith, M.G. (2014). The Criminal and Labor Market Impacts of Incarceration. Working Paper.
• Nagin, D.S., Cullen, F.T., & Jonson, C.L. (2009). Imprisonment and reo↵ending. In M. Tonry, ed., Crime and Justice:
An Annual Review of Research, Chicago: University of Chicago Press.
• Nagin, D. S., & Snodgrass, G. M. (2013). The e↵ect of incarceration on re-o↵ending: Evidence from a natural
experiment in Pennsylvania. Journal of Quantitative Criminology, 29, 601–642.
• Yang, F., Zubizarreta, J.R., Small, D.S., Lorch, S.A., & Rosenbaum, P.R. (2014). Dissonant conclusions when testing
the validity of an instrumental variable. Submitted.
• Zubizarreta, J.R., (2012). Using mixed integer programming for matching in an observational study of acute kidney
injury after surgery. Journal of the American Statistical Association, 107, 1360–1371.
• Zubizarreta, J.R., Small, D.S., Goyal, N.K., Lorch, S.A., & Rosenbaum, P.R. (2013). Stronger instruments via integer
programming in an observational study of late preterm birth outcome. Annals of Applied Statistics, 7, 25–50.
• Zubizarreta, J. R., & Kilcioglu, C., “designmatch: Construction of Optimally Matched Samples for Randomized
Experiments and Observational Studies that are Balanced by Design.”
Jos´e R. Zubizarreta (Columbia) E↵ect of Incarceration on Reo↵ending 04/29/2016 10 / 10