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Will the real nonparticipants please stand up?

Will the real nonparticipants please stand up?. Exploring selection bias and treatment contamination in employment programs. agenda. Reentry context in New York State Knowledge base: work, finances, and crime Aims and research questions Research findings Practice and research implications

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Will the real nonparticipants please stand up?

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  1. Will the real nonparticipants please stand up? Exploring selection bias and treatment contamination in employment programs

  2. agenda • Reentry context in New York State • Knowledge base: work, finances, and crime • Aims and research questions • Research findings • Practice and research implications • Ongoing and future research

  3. “…reducing the madness OF AN INCARCERATION SOCIETY…”* • 25% decline in NY prison population • Declining crime rates in NYC • Rockefeller drug laws reform • Cuomo: reduce fiscal burden on the state • 11 recent, 4 planned prison closings • Reinvest in preventive and rehabilitative services *Cuomo, 2014

  4. Prison reduction strategies • Council on Community Re-Entry and Reintegration • State council to help nonviolent offenders • $5 million proposed FY14 investment in programs: • Workforce Investment Board: Oneida County • TASC Case management and reentry services • Strategies include job training and supported work

  5. Jobs program logic model • Services • Life skills • Transitional jobs • Job coaching • Job development • Supportive services • Increased • Employment • Income • Soft skills • Work readiness • Stability • Increased • Employment • Job retention • Reduced recidivism • Reduced recidivism Adapted from Redcross et al. (2012)

  6. Weak evaluation support • Programs do not appear to improve work outcomes or reduce crime • Experimental studies: Most jobs programs show modest or null effects • Positive effects observed among: • Older former prisoners • High-risk prisoners

  7. labor force participation • Before prison • Rising unemployment • High levels of work instability • High job turnover • After release • Initial boost in formal employment • Rates decline to pre-prison levels within three years

  8. Labor force nonparticipation • Low opportunity cost to crime • Limited job options available • Limited formal work experience • Weak labor market skills • High opportunity cost to formal employment • Low hourly wage, reduced leisure time • Garnished wages (e.g., child support, legal debts) • Risk detection at workplace

  9. Research questions, Part 1 • Do respondents exhibit distinct arrest trajectories before entering prison? • Do participants differ from nonparticipants along prior arrest trajectories? • Do employment programs improve men’s post-release employment and recidivism outcomes?

  10. Research questions, Part 2 • Is labor force non-participation associated with increased recidivism risk? • Is labor force participation associated with higher quality employment? • What factors break the association between employment and reduced offending?

  11. Research aims • Examine whether evaluation findings reflect • Men’s selection into employment services • Contamination from participation in similar programs • Examine whether effects persist after controlling for • Prior criminal record • Work experience • Participation in programs that offer overlapping content

  12. Serious and violent offender reentry initiative (SVORI) • Target Population: • Adult male prisoners under 35 years old • Convicted of violent or serious drug offenses • States designed services to fit local context • Intent-to-treat design • Propensity score weights: SVORI service receipt

  13. data sources FBI National Crime Information Center • Lifetime adult arrest records • Spanning state lines and agency reporting systems SVORI Evaluation baseline interviews • Conducted in prisons during month before release • Demographics, background, criminal history, prison experiences, physical/mental health

  14. Descriptive statistics (N = 1,575)

  15. Trajectory model Predictor variables, final model: • Age at each arrest: Linear and squared terms • Indicator of arrests during 10 years before SVORI term • State indicator for prison site • Age at release from prison • Lifetime adult arrest record: Natural log transformation Outcome variable: • Predicted probability of group membership

  16. Pre-svori arrest trajectories High (38.9%) Probability of arrest each year Middle (45.1%) Low (16.0%) Age at arrest

  17. Criminal history: Trajectory groups

  18. Demographics: trajectory groups

  19. Key findings

  20. Participation: trajectory groups

  21. Propensity score matching Multilevel logistic regression model • Stata xtmelogit • SVORI treatment condition, Prison site (state) • Individual-level characteristics Matching techniques • Stata psmatch2 • Radius matching with caliper (.2 SD/ln odds) • Common support condition, ties permitted

  22. Criminal history: participation

  23. Demographics: trajectory groups

  24. Prison site: participation

  25. State profiles • Nonparticipants: Indiana, Kansas, Maryland • Older (M = 32.5 vs. 28.6 years old) • Higher statewide recidivism rate (45% vs. 34%) • Higher lifetime arrests • Higher proportion African American • Higher proportion drug offenders • Lower proportion violent offenders • Lower proportion property offenders • Less likely to have worked before entering prison

  26. Duration model measures Duration models • Indicators of three employment services • Indicator of multiple service receipt • Demographic characteristics • Indicator for work before prison • Criminal history

  27. Hazard ratios: Time to first arrest

  28. Strengths and limitations • Trajectory model • Possible bias due to varying length of criminal records • Unobserved heterogeneity • Propensity score model • Lingering observed heterogeneity • Unobserved heterogeneity • Limited common overlap • Duration model • Variation in quality and quantity of services received • Official data: timing and observation

  29. Part 2: Job quality model • norawikoff.wordpress.com

  30. Implications: research • Direction of the work-crime relationship • Factors that contribute to labor force exit • Low wages, debts, garnishments, financial strain • Factors that increase labor force attachment

  31. Implications: practice • Program design • Offer intensive programs to a select few • Use desistance “signals” to identify participants • Program evaluation • Model the selection process (not cream-skimming) • Service delivery

  32. Other research • SEED for Oklahoma Kids (SEED OK) • Test of universal Child Development Accounts • Experimental study design • Better Futures Enterprises (Twin Cities, MN) • Social enterprise providing subsidized housing and supported employment to homeless men • Pay-for-success agreements with nonprofits and governmental agencies

  33. future research: Pathways to Desistance • Young serious offenders • Rigorous study design

  34. Acknowledgements This research is supported by a research grant from the National Institute of Justice: NIJ Graduate Research Fellowship • Grant Award #: 2013-IJ-CX-0042 • Project Period: 11/1/13 – 7/31/14

  35. Contact information Nora Wikoff Brown School of Social Work Washington University in St. Louis nwikoff@go.wustl.edu [c] 314-703-8731 norawikoff.wordpress.com

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