Incarceration and the Transition to Adulthood Gary Sweeten Arizona State University Robert Apel University at Albany June 4, Crime and Population Dynamics Summer Workshop

After Incarceration 240,000 youths under age 24 are released from secure adult or juvenile facilities each year Two-thirds of ex-prisoners are re-arrested in three years Nearly one quarter are re-incarcerated in three years Relegation to secondary labor market: lower wages, less wage growth, instability Less educational attainment Disruption of marital unions

Research Questions Does incarceration have a causal effect on crime, employment, education, relationships and fertility? Do juvenile and adult incarceration have different effects? How do causal effects develop over time? (decay vs. growth)

Fundamental Problem The biggest hurdle in estimating a causal effect of incarceration is selection bias The justice system reserves incarceration for the most serious offenders. Incarcerees differ significantly from the general population, from self-reported offenders, from arrestees, from convicts, and from probationers.

Selection: Incarcerated sample is more involved in crime

Panel Models Fixed effects models eliminate selection bias attributable to time-stable unobservables –Identification: within-individual change Remaining problems: –Bias due to omitted dynamic variables –Bias due to varying effects of time-stable variables Adolescence is a time of great change. For many outcomes of interest, there is little to no pre-period variation (e.g. marriage, employment, dropout)

Panel Models In this paper, we employ difference-in- difference fixed effects models to assess the effects of incarceration. –Identification: within-individual change, contrasted between groups –Contrast groups: un-incarcerated, arrested, convicted Advantage: eliminates bias attributable to time-stable unobservables, and bias due to time-varying unobservables with equivalent effects on the compared groups

Propensity Score Matching We also employ propensity score matching to assess the effects of incarceration. –Identification: unincarcerated individuals matched to incarcerated based on propensity to be incarcerated Advantages: highlights common support issue, allows assessment of multiple outcomes over multiple years once balance is demonstrated

Data: Two Samples National Longitudinal Survey of Youth 1997 –First eight waves, –8,984 youths years old as of 12/31/1996 Incarceration at Incarceration at Full sample Two pre observations One treatment observation One post observation No previous incarceration 8,984 6,708 6,395 6,269 6,218 8,984 8,968 8,369 7,872 7,692 Number incarcerated116135

Re-alignment of Data Pre-treatment waves: used for contrast in fixed effects d-in-d models, for propensity score estimation, assessment of balance Treatment wave, average of up to 3 waves during which individual was 16 or 17 (18 or 19 for older sample) Post-treatment waves: treatment effect assessed during waves after the age of interest

Key Measures: Treatment and Response Variables Treatment Self-reported incarceration of any length Response variables (all self-report) Criminal behavior, illegal earnings, arrest Formal employment, hours, earnings High school dropout, GED, grades completed Marriage, cohabitation, fertility

Incarcerated at 16-17Incarcerated at Dependent Variable Vs. All Non- Incarcerated (N=6,218) Vs. Arrested at (N=646) Vs. Convicted at (N=273) Vs. All Non- Incarcerated (N=7,692) Vs. Arrested at (N=803) Vs. Convicted at (N=401) Crime Prevalence Arrest prevalence Employed in formal job Hours per week (  10) High school dropout Highest grade completed Had a child.335 (.037)*.273 (.021)*.033 (.032).571 (.202)*.053 (.011)* (.061)*.013 (.006)*.079 (.042)+.133 (.037)*.008 (.036).491 (.232)*.041 (.015)* (.075)*.009 (.009).037 (.047).095 (.047)* (.043).407 (.317)*.039 (.023) (.099)*.020 (.011)+.300 (.031)*.234 (.017)* (.030).408 (.103)*.097 (.016)* (.062)*.016 (.011).061 (.035)+.136 (.028)* (.031).270 (.110)*.066 (.020)* (.074)*.021 (.011)+.035 (.038).129 (.034)*.059 (.034)+.285 (.122)*.068 (.022)* (.084)*.027 (.011)* Random-Effects Models of Pre-Incarceration Differences between Treated and Untreated Individuals, by Age of First Incarceration + p <.10, * p <.05)

Fixed Effects Difference-In-Difference Results, Incarcerated vs. Convicted Dependent Variable T=1 b (s.e) T=2 b (s.e.) T=3 b (s.e.) T=4 b (s.e.) Crime Prevalence, Crime Prevalence, Arrest Prevalence, Arrest Prevalence, Formal Employment, Formal Employment, Hours per week (/10), Hours per week (/10), High school dropout, High school dropout, Highest grade completed, Highest grade completed, Had a child, Had a child, (.067).036 (.060).029 (.057) (.048) (.052)* (.047)* (.318) (.193)*.303 (.048)*.228 (.041)* (.114)* (.107)*.043 (.039).077 (.035) (.080) (.069) (.068) (.054)* (.062) (.053)* (.341) (.329).243 (.058)*.248 (.047)* (.135)* (.121)*.056 (.045).062 (.040) (.081).100 (.091) (.069) (.072)* (.063)* (.070) (.353) (.282)*.344 (.058)*.299 (.061)* (.137)* (.160)*.205 (.046)*.095 (.053) (.124).152 (.126).001 (.097) (.095) (.088)* (.093) (.449) (.350)+.188 (.082)*.200 (.081) (.193) (.211)*.178 (.065)*.136 (.069)* + p <.10, * p <.05)

Propensity Score Matching In simple comparisons, less than 40% of 206 background variables were balanced between incarcerated and unincarcerated groups Type of matching: up to 3 nearest neighbors within.01 on propensity score metric Using just 32 predictors for juvenile incarceration (58 for adult) 98% of background variables were balanced (91% for adults) Support: 5 of 116 (4.3%) incarcerated juveniles and 9 of 135 (6.7%) incarcerated adults went unmatched

Propensity Score Matching Estimates Dependent Variable T=1 b (s.e) T=2 b (s.e.) T=3 b (s.e.) T=4 b (s.e.) Crime Prevalence, Crime Prevalence, Arrest Prevalence, Arrest Prevalence, Formal Employment, Formal Employment, Hours per week (/10), Hours per week (/10), High school dropout, High school dropout, Highest grade completed, Highest grade completed, Had a child, Had a child, (.067)*.157 (.059)*.230 (.058)*.151 (.054)* (.055) (.046)+.326 (.215).214 (.175).229 (.066)*.184 (.062)* (.196) (.178)* (.072).052 (.087).084 (.062).121 (.068)+.146 (.063)*.055 (.047) (.052) (.051)+.430 (.211)* (.210).157 (.071)*.199 (.068)* (.223) (.215)* (.092).030 (.110) (.067).171 (.070)*.130 (.059)*.037 (.049) (.050) (.059).259 (.217).203 (.251).210 (.071)*.237 (.076)* (.236)* (.257)* (.110) (.155).032 (.179).212 (.079)*.086 (.059).138 (.070)* (.070)* (.076).080 (.301).108 (.340).231 (.087)*.239 (.101)* (.276)* (.311)* (.150) (.211) + p <.10, * p <.05)

Conclusions The correlation between incarceration and life transitions is causal for some outcomes, but a selection artifact for others Crime and arrest: possibly short-term criminogenic causal effect (mixed evidence) Employment: reduced participation in formal job market, short-term for adult incarceration –But, for those who find employment, there appears to be no effect of incarceration on other features of work Education: consistent negative effects that grow over time Family transitions: no evidence of casual effect