WILL THE REAL
NONPARTICIPANTS
PLEASE STAND UP?
Exploring selection bias
and treatment
contamination in
employment programs
Nora Wikoff, MSLS, MSW
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
“…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
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
JOBS PROGRAM LOGIC MODEL
Services
Life skills
Transitional jobs
Job coaching
Job development
Supportive services
Increased
Employment
Income
Increased
Employment
Job retention
Soft skills
Work readiness
Stability
Reduced
recidivism
Reduced
recidivism
Adapted from Redcross et al. (2012)
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
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
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
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 postrelease employment and recidivism outcomes?
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?
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
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
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
DESCRIPTIVE STATISTICS (N = 1,575)
Variable
Definition
N/M
% / SD
Age
At release from prison
29.6
(7.3)
Sentence length
Time served by date of release
2.6
(2.6)
Education
Less than high school diploma
633
40.2
High school diploma
228
14.5
GED
456
29.0
Trade certificate, Some/more college
256
16.3
African American
872
55.4
Hispanic, Multi-racial, Other
186
11.8
White
515
32.7
1,040
66.1
Ever held job for 2/more years
496
38.1
Fired from one job
316
26.4
Fired from more than one job
339
28.4
Racial/ethnic status
Employment
Job termination
Worked last 6 months before prison
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
PRE-SVORI ARREST TRAJECTORIES
Probability of arrest each year
High (38.9%)
Middle (45.1%)
Low (16.0%)
Age at arrest
CRIMINAL HISTORY: TRAJECTORY GROUPS
(High) n = 616
(Mid.) n = 706
(Low) n = 253
N/M
% (SD)
N/M
% (SD)
N/M
% (SD)
Age at first arrest***
15.4
(3.4)
16.3
(4.5)
18.4
(8.2)
Lifetime arrests***
22.7
(13.5)
11.3
(6.5)
3.2
(2.1)
Lifetime convictions***
6.2
(5.5)
5.0
(4.5)
2.8
(3.3)
Times in prison***
1.9
(1.6)
1.2.
(1.3)
0.7
(1.1)
Age at release***
28.8
(6.5)
30.3
(7.3)
29.6
(8.8)
Sentence length***
2.0
(1.6)
2.6
(2.5)
3.9
(4.1)
Parole violation
124
24.5
164
27.8
42
20.2
Violent offense***
202
33.1
286
40.6
156
62.2
Property offense*
164
26.8
168
23.9
44
17.5
Drug offense***
268
43.9
234
33.2
39
15.5
SVORI term:
DEMOGRAPHICS: TRAJECTORY GROUPS
(High) n = 616
(Mid.) n = 706
(Low) n = 253
N/M
% (SD)
N/M
% (SD)
N/M
% (SD)
Less than HSD
298
48.5
263
37.3
72
28.5
High school diploma
79
12.9
108
15.3
41
16.2
GED
174
28.3
191
27.1
91
36.0
Trade school/some coll.
63
10.2
144
20.4
49
19.3
African American
394
64.2
376
53.3
102
40.3
Hispanic, multi-racial, other
56
9.1
85
12.0
45
17.8
White
164
26.7
245
34.7
106
41.9
Worked before prison***
363
58.9
500
70.8
177
70.2
Education ***
Race***
KEY FINDINGS
PARTICIPATION: TRAJECTORY GROUPS
(High)
n = 616
(Mid.)
n = 706
(Low)
n = 253
Percentages engaged in each type of employment-focused program
Participation in each type of program
49.7
58.1
61.3
Education programs (e.g., GED, literacy, college
classes)
40.7
46.6
54.2
Work readiness or job training programs
19.3
22.5
24.5
Prison job (work release or prison industry)
6.3
8.6
8.7
Participation in more than one program
7.3
9.3
12.0
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
CRIMINAL HISTORY: PARTICIPATION
Unm.
Total
Non.
Emp.
Educ.
Job
269
1,304
658
152
515
102
Age at first arrest***
16.1
16.3*
16.6
16.5
15.6
17.6
Lifetime arrests***
2.3
2.3***
2.5
2.2
2.2
2.4
Lifetime
convictions***
5.1
5.1
5.3
4.8
4.9
5.4
Times in prison***
1.3
1.5***
1.7
1.3
1.2
1.5
Age at release***
28.5**
29.8**
*
30.9
28.5
28.1
31.5
Sentence length***
4.3***
2.2***
1.9
2.5
2.4
2.6
Parole violation
15.2***
25.3
27.1
21.7
21.6
28.4
Violent offense***
53.3***
38.6
36.4
39.7
43.1
30.7
Property offense*
25.2
23.8
24.4
25.8
22.7
19.8
26.3**
36.3
36.0
39.1
34.5
38.6
SVORI term:
Drug offense***
DEMOGRAPHICS: TRAJECTORY GROUPS
Unm.
Total
Non.
Emp.
Educ.
Job
269
1,304
658
152
515
102
Education ***
*
***
Less than HSD
47.6
38.7
37.2
24.3
45.0
26.5
High school diploma
13.4
14.7
19.6
11.2
7.6
13.7
GED
24.5
29.9
26.1
35.5
34.8
38.2
Trade school/some coll.
14.5
16.6
17.1
28.9
12.6
21.6
Race***
***
African American
51.7
56.2
58.1
53.9
55.5
42.2
Hispanic, multi, other
20.4
10.0
8.8
11.8
11.8
9.8
White
27.9
33.7
33.1
34.2
32.6
48.0
56.3***
68.1
67.8
71.1
66.2
84.3
Worked before
prison***
PRISON SITE: PARTICIPATION
Unm.
Total
Non.
Emp.
Educ.
Job
State site ***
269
1,304
658
152
515
102
Iowa
8.1
11.2
9.7
29.6
10.9
8.8
Indiana
4.8
11.0
13.4
5.3
10.3
2.0
Kansas
7.0
3.8
5.2
0.7
2.9
1.0
Maryland
11.8
16.5
21.4
8.6
10.5
18.6
Missouri
14.8
3.2
2.6
1.3
3.9
2.9
Nevada
9.2
9.3
8.5
14.5
10.7
2.9
Ohio
11.8
3.9
2.6
5.3
5.8
2.0
Oklahoma
5.9
5.7
4.7
6.6
6.2
6.9
Pennsylvania
4.8
7.9
6.4
5.9
8.7
21.6
South Carolina
11.4
23.7
22.3
19.7
26.0
28.4
Washington
10.3
3.8
3.2
2.6
4.1
4.9
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
DURATION MODEL MEASURES
Duration models
Indicators of three employment services
Indicator of multiple service receipt
Demographic characteristics
Indicator for work before prison
Criminal history
HAZARD RATIOS: TIME TO FIRST ARREST
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
PART 2: JOB QUALITY MODEL
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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
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
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
FUTURE RESEARCH:
PATHWAYS TO DESISTANCE
Young serious offenders
Rigorous study design
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
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