Economic self-sufficiency and life 

stability one year after starting a 

social enterprise job 

November 14, 2014

Dana Rotz

Nan Maxwell

Adam Dunn

Submitted to: 

REDF

221 Main Street, Suite 1550

San Francisco, CA 94105

Project Officer: Christina Garcia

Contract Number: REF 11-011

Submitted by:

Mathematica Policy Research

505 14th Street, Suite 800

Oakland, CA 94612-1475

Telephone: (510) 830-3700

Facsimile: (510) 830-3701

Project Director: Nan Maxwell

Reference Number: 40004.700
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iii

DISCLAIMER

This report was prepared for REDF. The views expressed are those of the authors and 

should not be attributed to REDF. 
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ACKNOWLEDGMENTS MATHEMATICA POLICY RESEARCH

v 

ACKNOWLEDGMENTS

The authors wish to recognize the contributions and support of others in this research. First 

and foremost, we would like to thank the organizations and their social enterprises that 

steadfastly supported our efforts to reach study participants for follow-up surveying. They 

generously shared information when appropriate and took proactive steps in reaching out to 

potential respondents. At REDF, the advice and steady guidance of our project officer, Christina 

Garcia, greatly enhanced our ability to execute the study, and Tracy Lam-Hine headed the cost 

capture project that provided the cost information used in the study. The authors would also like 

to acknowledge several people at Mathematica Policy Research who contributed to the study. 

Melissa Dugger ably led the follow-up survey efforts and oversaw the diligent staff in our 

Survey Operations Center. Leonard Brown, Lauren Greczyn, Shelley Jacobs, and staff in our 

survey department deserve kudos for helping us boost response rates, and Sheryl Safran was 

instrumental in ensuring that survey data met the highest standard for quality. Barbara Carlson 

verified the weights. Kathryn Gonzales provided outstanding technical and research assistance. 

Josh Haimson and Anne Gordon provided essential feedback at various points in the study. 

Sheena Flowers and Autumn Parker helped prepare this report, and Maura Butler and Mike 

Donaldson provided editorial assistance. 
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CONTENTS MATHEMATICA POLICY RESEARCH

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CONTENTS

EXECUTIVE SUMMARY ......................................................................................................................... XVII

I INTRODUCTION.............................................................................................................................. 1

A. Social enterprises in the study................................................................................................... 2

B. Research design........................................................................................................................ 3

1. Data collected ..................................................................................................................... 4

2. Samples .............................................................................................................................. 5

3. Analysis............................................................................................................................... 8

C. Structure of report.................................................................................................................... 10

II THE SOCIAL ENTERPRISE EXPERIENCE ................................................................................. 11

A. The SE experience .................................................................................................................. 11

B. Economic self-sufficiency after the SE job began ................................................................... 16

C. Life stability after the SE job began......................................................................................... 18

III ECONOMIC SELF-SUFFICIENCY AND LIFE STABILITY FOLLOWING AN SE JOB................. 21

A. More economically self-sufficient after the SE job began ....................................................... 22

B. Greater housing stability after the SE job began..................................................................... 24

C. Worker health and optimism declined ..................................................................................... 24

D. Changes in economic self-sufficiency and life stability ........................................................... 26

IV IMPACT OF THE SE ON ECONOMIC SELF-SUFFICIENCY AND LIFE STABILITY .................. 28

A. Chrysalis employment services............................................................................................... 29

B. Impact of SE on employment and housing: confirmatory analysis.......................................... 32

C. Impact of SE on self-sufficiency and life stability: exploratory analysis .................................. 33

1. Economic self-sufficiency.................................................................................................. 33

2. Life stability ....................................................................................................................... 34

V COSTS AND BENEFITS OF THE SE EXPERIENCE................................................................... 36

A. Benefits of the SE experience ................................................................................................. 39

B. Costs of the SE experience..................................................................................................... 42

C. Putting the benefits and costs of the SE experience in context .............................................. 44

APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES ...............................................A.1

APPENDIX B. COST-BENEFIT ANALYSIS ..............................................................................................B.1

APPENDIX C. COLLECTING AND ANALYZING DATA ...........................................................................C.1
CONTENTS MATHEMATICA POLICY RESEARCH

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APPENDIX D. TABLES..............................................................................................................................D.1

APPENDIX E. FOLLOW-UP SURVEY ......................................................................................................E.1

REFERENCES...........................................................................................................................................R.1
TABLES MATHEMATICA POLICY RESEARCH

ix

TABLES

I.1 Description of SEs............................................................................................................................ 3

I.2 Organizations included in each study .............................................................................................. 6

I.3 Measures used to capture outcomes............................................................................................... 9

II.1 Employee characteristics (raw percentages, except where noted) ............................................... 12

II.2 Work in the year after the SE job began (raw percentages).......................................................... 17

II.3 Economic self-sufficiency and life stability before and after the SE job began (raw 

percentages, except where noted)................................................................................................. 19

III.1 Income in the year before and after the SE job began (regression-adjusted percentages, 

except where noted)....................................................................................................................... 23

III.2 Housing in the year before and after the SE job began (raw percentages)................................... 24

III.3 Physical and mental health (regression-adjusted percentages, except where noted) .................. 25

IV.1 The SE experience and self-sufficiency (propensity-score weighted percentages, except 

where noted) .................................................................................................................................. 34

V.1 Anticipated costs and benefits associated with the SE.................................................................. 38

V.2 Benefits associated with the SE experience by domain (dollars) .................................................. 41

V.3 Total benefits per employee at each SE (outcomes study, dollars) .............................................. 42

V.4 Per-employee SE costs (outcomes study, dollars)........................................................................ 44

V.5 Benefits per dollar spent by SE (outcomes study, dollars except where noted)............................ 46
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FIGURES MATHEMATICA POLICY RESEARCH

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FIGURES

I.1 Following individuals for one year.................................................................................................... 5

I.2 Derivation of samples for the outcomes study................................................................................. 7

I.3 Derivation of samples for the impact study...................................................................................... 8

II.1 Employment supports while working at the SE (raw percentages) ............................................... 13

II.2 Life stability supports while working at the SE (raw percentages)................................................. 14

II.3 Satisfaction with SE experience (raw percentages) ...................................................................... 15

II.4 Reason for leaving the SE, reported one year after SE job began (raw percentages) ................. 16

II.5 Months of continuous work in the year the SE job began (raw percentages) ............................... 18

III.1 Employment before and after the SE job began (regression-adjusted percentages).................... 23

III.2 Attitudes and optimism before and after the SE job began (regression-adjusted 

percentages) .................................................................................................................................. 26

IV.1 Receiving employment services at Chrysalis................................................................................. 30

IV.2 Profile of individuals entering the Chrysalis labor pool (propensity-score weighted 

percentages) .................................................................................................................................. 31

IV.3 Services provided to treatment and control group members (propensity-score weighted 

percentages) .................................................................................................................................. 31

IV.4 Impact of the SE experience on current employment (propensity-weighted percentages)........... 32

IV.5 Impact of the SE experience on stable housing (propensity-weighted percentages).................... 33

IV.6 The SE experience and health (propensity-scored percentages, except where noted)................ 35

V.1 Monetary value of benefits per SE employee served (dollars) ...................................................... 40

V.2 SE costs (dollars)........................................................................................................................... 43

V.3 Benefits per dollar spent (dollars) .................................................................................................. 45

V.4 Social ROI: Comparing nonbusiness costs and benefits (percentages) ....................................... 47
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LIST OF ACRONYMS MATHEMATICA POLICY RESEARCH

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LIST OF ACRONYMS

ABE adult basic education 

CalTrans California Department of Transportation 

CalWORKs California Work Opportunity and Responsibility to Kids 

CBA cost benefit analysis

CDCR California Department of Corrections and Rehabilitation 

CEO Center for Employment Opportunities 

CHP Community Housing Partnership 

CNCS Corporation for National and Community Service 

CRC Community Resource Center

DBL double bottom line 

GED General Educational Development (tests) 

MJS Mathematica Jobs Study

MSA Metropolitan statistical area

NA not available 

n.a. not applicable 

OLS ordinary least squares 

ROI return on investment 

SE social enterprise

SIF Social Innovation Fund 

Taller Taller San José

TANF Temporary Assistance for Needy Families

VoA Volunteers of America

Weingart Weingart Center for the Homeless
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GLOSSARY MATHEMATICA POLICY RESEARCH

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GLOSSARY

Table A lists definitions for terms that we use multiple times throughout the report. Because 

the glossary is a centralized resource for readers to look up the definitions of terms we use, it 

precludes the need to define terms repeatedly and improves the exposition of the text. We 

generally list terms alphabetically within each (alphabetically ordered) section, although we first 

list terms that are used to define other terms in a section.

Table A. Definition of terms

Term Definition 

General terms

Business mission

Current employment

Double bottom line

Homeless

Social enterprise (SE)

Social mission

Stable housing

Goal of the business achieving financial viability.

Worked for pay in the week prior to the survey.

Goal of achieving both a business and social mission.

Housing in an emergency shelter, emergency voucher housing, transitional housing, or 

permanent housing for people that were previously homeless or sleeping outside or in 

public in the past year.

A mission-driven business focused on hiring and assisting people who face barriers to 

work.

Goal of the business providing employment to people who might not otherwise have a job.

Housing included using their own owned or rented home in the past year.

Populations and samples

Outcomes study

Full sample

Noninstitutional 

sample

Impact study

Full propensity-score 

sample

Noninstitutional 

propensity-score 

sample

Comparison group

Treatment group

Cost benefit analysis

(CBA)

CBA population

Impact study CBA

Outcomes study 

CBA

Society (as a whole)

Taxpayers

The group of people who responded to the follow-up survey or were in jail or prison at the 

time of surveying.

The group of people who responded to the follow-up survey.

Participants from Chrysalis who responded to the follow-up survey or were in jail or prison 

at the time of surveying and who had propensity scores below 0.90 and above 0.10 (see 

Appendix B). Includes both those who were hired by the SE and those who were not.

Participants from Chrysalis who responded to the follow-up survey and who had 

propensity scores below 0.90 and above 0.10 (see Appendix B). Includes both those who 

were hired by the SE and those who were not.

Individuals who entered Chrysalis labor pool and consented to be in the study, but were 

not hired by the SE.

Individuals who entered Chrysalis labor pool, consented to be in the study, and were hired 

by the SE.

Individuals who were hired into one of the six SEs participating in the CBA between April 

1, 2012, and March 31, 2013.

The CBA study defining benefits as the impact of SE employment at Chrysalis, which is 

determined by comparing changes in outcomes for SE workers with changes in outcomes 

for individuals in its labor pool that did not work in an SE. 

The CBA study defining benefits using the differences in outcomes for SE workers 

between the time they began SE employment and about one year later.

All entities potentially affected by the SE, which provides an estimate of total costs and 

benefits. It includes the SE as a business, SE workers, the friends and families of SE 

workers, and taxpayers not directly affiliated with the SE.

Entities that are not directly affected by the SE, that is government and the community.
GLOSSARY MATHEMATICA POLICY RESEARCH

xvi

Term Definition 

Statistics and data 

Ashenfelter dip

Confirmatory analysis

Difference in 

difference

Exploratory analysis

Internal validity

External validity

Fixed-effect models

Mathematica Jobs 

Study (MJS) 

data/MJS database

Moderate evidence

Multivariate 

regression analysis

Ordinary least 

squares

Propensity score

Regression-adjusted 

mean (or percentage)

Statistical significance

A graphic representation of how entrants to employment programs typically faced a recent 

negative event affecting their employment prospects, driving them to seek the program. 

Ashenfelter (1978) demonstrated how this phenomenon falsely enhances the effects of 

employment programs because individuals would have experienced improved outcomes 

even without a programmatic intervention.

Examining data to research hypotheses already in place. In this study, it is the analysis 

designed to address the primary research questions raised in the subgrantee evaluation 

plan to the Corporation for National and Community Service (CNCS). 

Comparing a treatment and a comparison group (first difference) before and after the 

intervention (second difference).

Examining data for trends or characteristics that would allow for exploration of new 

hypotheses or relationships not previously considered or known to be of interest. In this 

study, it is the analysis that addresses questions or trends that are not the primary research 

questions raised in the subgrantee evaluation plan.

The extent to which the study’s findings can approximate the truth about cause and effect 

relationships. Internal validity exists when observed changes can be attributed to an 

intervention and not to other causes.

How well the results from the study apply to different settings.

A generalization of the difference-in-difference approach that holds constant the average 

effects of each individual by looking at deviations from the means within each time period.

Information collected from the intake, baseline, exit, or follow-up surveys.

Evaluation designs with strong internal validity but weaker external validity (CNCS 2013). 

Moderate evidence comes from studies able to demonstrate that a program produces 

changes among participants (or groups or sites) but unable to demonstrate how well the 

program would work among groups other than those included in the study. CNCS considers 

that studies with moderate evidence might need to address a few minor threats to internal 

validity.

A statistical technique that uses several explanatory variables to predict the outcome of a 

response variable.

A method for estimating the unknown parameters in a linear regression model. 

The probability of an individual receiving treatment. For this study, the treatment was 

defined as employment in the SE and estimated based on demographic characteristics, 

employment history, and barriers to employment.

Means or percentages that account for differences in the characteristics and employment 

barriers of the treatment and comparison groups using a regression adjustment.

The probability of a type I error (that is, that the estimated relationship is due to chance, also 

known as a false positive) is less than or equal to 5 percent. Also written as p ≤ 0.05. If the 

probability of a type I error is less than or equal to 10 percent (p < 0.10), we say it is 

marginally statistically significant.

Studies 

Cost benefit analysis

(CBA)

Impact study

Outcomes study

Compares the costs and benefits of SE employment for SE workers, the SE, and taxpayers 

(those who are not involved in the SE), and society as a whole (total benefit).

Compares information collected at the time an individual entered the labor pool for SE 

employment at Chrysalis and one year later for those who were employed in the SE 

(treatment group) with those who were not (comparison group). Estimates the impact of SE 

employment on outcomes.

Compares information collected before the SE job began and one year later to determine 

whether changes in outcomes over time are associated with SE employment.
EXECUTIVE SUMMARY MATHEMATICA POLICY RESEARCH

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EXECUTIVE SUMMARY

In 2011, REDF placed social enterprise (SE) employment at the heart of its five-year 

strategy to transform how people with many employment barriers transition into the workforce. 

SEs are mission-driven businesses focused on hiring and assisting people who face barriers to 

work.1 In support of its strategy to leverage these organizations, REDF launched a new portfolio, 

with funding from the Social Innovation Fund (SIF) of the Corporation for National and 

Community Service (CNCS) and support from corporations, foundations, and individuals. REDF 

also committed to conducting an evaluation to support the success of the SE approach and 

selected Mathematica Policy Research to design and implement the evaluation. The evaluation, 

which is called the Mathematica Jobs Study (MJS), is structured to address the general research 

question, How do social enterprises serve individuals with multiple barriers to employment? Its 

focus is economic self-sufficiency and life stability for SE workers hired from April 1, 2012, 

through March 31, 2013. The analysis looks at participants’ employment as the primary indicator 

of self-sufficiency, although the study also examines participants’ income and support from 

government. In addition, the study examines five outcomes related to life stability: (1) housing 

(most important), (2) recidivism, (3) physical health, (4) mental health, and (5) substance abuse. 

The MJS contains four integrated components: (1) an implementation study of eight 

organizations that received REDF SIF funding in January 2012; (2) an outcomes study of the 

change in economic self-sufficiency and life stability for SE workers in seven organizations, 

from the period before they started the job until one year later; (3) a quasi-experimental impact 

study that complements the outcomes study by offering stronger internal validity (a more 

rigorous estimate of the effect of SE employment) at the expense of external validity (ability to 

generalize results); and (4) a cost benefit analysis (CBA) that assessed whether the net value of 

the SE to society as a whole—which includes the SE workers, the SE business enterprise, and 

taxpayers (those not directly involved in the SE)—outweighed its costs. This report provides 

results of the outcomes and impact studies and the CBA. It is a follow-up to the interim report 

(Maxwell et al. 2013), which provides results of the implementation study.

Section A of this executive summary provides a brief overview of the SEs and their workers; 

Section B describes the methods used to conduct the research and istudy limitations; Section C 

highlights the lessons learned from the research findings; and Section D describes the issues 

needing further research.

A. Social enterprises and their workers

Substantial differences exist in the SEs in the seven host organizations included in the MJS 

outcomes study (Table 1). In 2013, these SEs contained seven business lines—(1) cafés, (2) 

street cleaning, (3) temporary staffing (including lobby services), (4) retail services, 

(5) construction/ maintenance services, (6) janitorial services, and (7) pest control. The 

businesses varied greatly in size and maturity. One employed nearly 500 people annually, and 

 

1 In a previous report (Maxwell et al. 2013), we defined SEs as “businesses that sell goods and services that the 

marketplace demands in order to intentionally employ individuals who would otherwise face bleak prospects of ever 

getting a job.” The shift in wording reflects REDF’s ongoing evolution of thought on which aspects of SEs are most 

important to nurture.
EXECUTIVE SUMMARY MATHEMATICA POLICY RESEARCH

xviii

two employed only 10 to 12; some were well established, and others used REDF money to start. 

Target populations differed, although all employed four types of economically disadvantaged 

individuals: (1) those with mental health disabilities, (2) those who are homeless, (3) parolees or 

those who were formerly incarcerated, and (4) young adults not enrolled in school or 

participating in the labor market.

Table 1. Description of social enterprises

H ost SE business 2013 Year SE 

organization line(s) workers started Target population 

1. Buckelew

Cafés 

Janitorial services

18

23

1986

2009 Mental health disabilities

2. CEO Street cleaning 108 2011 Parolees

3. Chrysalis

Temporary staffing

Street cleaning 500 1991 Formerly incarcerated, homeless

4. CHP

Lobby services

Maintenance 

services

55

30 2007 Homeless

5. CRC Retail 36 2012 REDF-defined barrier

6. Taller Construction 12 2007 Age 18–28 and not in school or the labor market

7. Weingart Pest control 10 2012 Homeless

Source: Maxwell et al. (2013).

Note: A REDF-defined barrier includes low income, mental illness, homelessness, status as a parolee, and for 

youth, not being in school or the labor market. Number of workers is a 2013 estimate.

SE = social enterprise; CEO = Center for Employment Opportunities; CHP = Community Housing Partnership; CRC = 

Community Resource Center.

The SE workers in these organizations faced multiple employment barriers (Table 2). 

Indicators of economic self-sufficiency were low at the time the SE job began: one-quarter had 

never held a job before the SE job began, and only 23 percent of monthly income came from 

work, with the remainder coming from government programs (71 percent) or transfers from 

others (5 percent). Indicators of life stability were also low: about 85 percent did not have stable 

housing (own or rent their home for the entirety of a year), and nearly 70 percent had been 

convicted of a crime and sentenced to jail. Nearly 30 percent lacked a high school diploma. 

Although differences in barriers existed in populations served by each organization, barriers 

were high at each.
EXECUTIVE SUMMARY MATHEMATICA POLICY RESEARCH

xix

Table 2. Employee characteristics (raw percentages, except where noted)

All Buckelew CEO Chrysalis CHP CRC Taller Weingart 

Average age (in years)* 41 37 33 44 41 46 25 49

Never employed* 25 46 16 25 29 26 23 0

No high school diploma* 29 10 51 27 17 6 38 0

Ever convicted* 69 25 100 71 61 20 62 20

Unstable housing (past year) 85 84 90 83 90 77 82 100

Income from wages 23 18 32 17 30 35 79 69

Source: Maxwell et al. (2013).

Note: An asterisk (*) indicates a significant likelihood that values differ across organizations. Unstable housing is 

defined as not owning or renting a home throughout the past year. Income from wages is the percentage of 

monthly income from wages or salary.

CEO = Center for Employment Opportunities; CHP = Community Housing Partnership; CRC = Community Resource 

Center; Taller = Taller San Jose; Weingart = Weingart Center for the Homeless.

The SE intervention, in practice, consisted of the following components: 

1. Employment. On average, SE employees reported working 24 hours per week for 24 weeks 

(576 hours), or about one-third of a year of work experience for someone working 35 hours 

per week. A great deal of variation existed in the level of SE work experience, however. 

Nearly 6 percent worked fewer than 8 hours, and about 27 percent worked more than 960 

hours, or the equivalent of about half a year of full-time work experience.2

2. Employment supports and services while employed. Almost all (97 percent) reported 

receiving supports and services designed to help them sustain employment. More than 90 

percent said they received job-readiness skills training (job search assistance and career 

counseling); more than 90 percent said they received training to build soft, vocational, or 

technical skills; and almost 80 percent said they received work supports (clothing, 

transportation, or housing assistance). 

3. Life stability supports while employed. About two-thirds said they received life stability 

supports, including financial education; food security (for example, food pantries, reducedprice meals, nutritional education); help to avoid relapse of behaviors such as drug abuse or 

criminal activity; and access to public benefits, and tax preparation. 

4. Postemployment support. About two-thirds said they continued to receive services after 

leaving the SE, including access to an employment counselor and access to a life counselor.

2 As described in Appendix A, the SE workers examined in this study include everyone that was hired by the 

SE, to capture the population that the SE was intended to serve. 

 
EXECUTIVE SUMMARY MATHEMATICA POLICY RESEARCH

xx

B. Research methods

This research drew information mainly from a baseline survey of individuals shortly before 

they started their SE job or shortly after they were referred to the SE labor pool, and from a 

follow-up survey about one year later. The outcomes study drew information from 282 SE 

workers, and the impact study drew information from 154 SE workers at Chrysalis and 37 

individuals who entered its labor pool but were not hired. Costs and benefits were estimated for 

workers from all organizations except Buckelew. 

The outcomes analysis was designed to assess the extent to which SEs and host 

organizations improved workers’ self-sufficiency and life stability, whereas the impact analysis 

allows us to assess the effect of SE employment on these measures. The outcomes analysis 

provides evidence of changes from the time an individual started the SE job until one year later. 

The impact analysis estimates the difference between the outcomes of SE workers at Chrysalis 

and those who entered the Chrysalis labor pool but did not work in the SE. Although results of 

the analyses suggest we might be cautiously optimistic about the SE experience, we must be 

careful interpreting the results. The outcomes study analysis is limited to individuals who worked 

in an SE, so it is difficult to discern what they would have experienced if they had not been 

offered this opportunity. Although the impact study analysis includes a comparison group, both 

the participants and the comparison group are small samples of individuals in the labor pool of 

one of the SE organizations. Furthermore, we captured benefits from the SE experience in only 

five domains and only as they occurred in the first year after the SE job began. 

C. Lessons learned

Taken together, results from the outcomes study, impact study, and CBA provide a 

comprehensive examination of how SEs might influence the employment and life stability of 

individuals they employ as well as their value to different stakeholders. We highlight four 

overarching themes that emerged from the research.

1. Workers gained economic self-sufficiency one year after the SE experience began. 

A central goal of the SE was to build economic self-sufficiency through employment by 

providing work experience in the SE and helping workers find employment when the SE job 

ended. Such work experience was considered critical because, prior to starting the SE job, 25 

percent of SE workers reported they had never held a job, 37 percent reported not holding a job 

in the prior year, and 84 percent were not currently employed (Maxwell et al. 2013). As a result, 

economic self-sufficiency was low, with 71 percent of individuals’ monthly income coming from 

government transfers. 

Evidence suggests that the SE experience may have helped workers gain that experience. In 

the year following the start of the SE job, 93 percent of SE employees had worked for at least 

one month, 84 percent had worked continuously for at least 3 months, 67 percent worked 

continuously for at least 6 months, 51 percent worked continuously for at least 9 months, and 35 

percent worked continuously for all 12 months. Because a review of transitional employment 

studies (Sattar 2010) suggests that 6 months of employment improved wages, long-term job 

retention, and labor force attachment for people with employment barriers, this distribution 

suggests that about two-thirds of SE workers might realize gains after leaving the SE. 
EXECUTIVE SUMMARY MATHEMATICA POLICY RESEARCH

xxi

Furthermore, the outcomes study indicates that about one year after their SE jobs began, 51 

percent of SE workers were employed, representing a 33 percentage point increase in 

employment. Our impact study suggests that although some of the increase in employment may 

have occurred in the absence of SE work, the SE experience is likely responsible for part of the 

change. We estimated that SE employment led to a 19 percentage point increase in employment 

after one year. These estimated changes were significant in the outcomes study but only 

marginally statistically significant in the impact study. In addition, individuals who were not 

incarcerated about one year after the SE job began spent about two-thirds of that year working, 

with about 67 percent working for six or more continuous months. Evidence from the outcomes 

study suggests that the SE workers improved their income at one year after their SE jobs began. 

Total monthly income increased by 91 percent, from $653 to $1,246. 

2. The SE helped workers stabilize their lives. 

Results from the outcomes study suggested that housing, the study’s main measure of life 

stability, stabilized in the year after the SE job began. The percentage of SE workers renting or 

owning a home or apartment during any part of the past year increased from 49 to 81 percent, 

and the percentage who reported stable housing (living in a home or apartment that they rented 

or owned) throughout the year increased from 15 to 53 percent. Both increases for SE workers

are statistically significant over time, but the percentage increase in stable housing was not 

significant in the impact study, perhaps because of small sample sizes. Despite these gains, more 

than one-third of SE workers who were not incarcerated about one year after the SE job began 

reported at least one period of homelessness (defined as living outside or in public, in an 

emergency shelter, or in housing designated for homeless individuals) in the past year, a rate that

was relatively unchanged from the year before SE employment. Both the treatment and 

comparison groups reported that levels of physical health and mental health declined one year 

after the SE job began. However, when changes in physical health were compared with a 

comparable group of individuals who did not work in the SE in the impact study, declines were 

smaller for the treatment group. SE workers showed no difference in reported levels of mental 

health and higher levels of physical health.

3. Support after leaving the SE is associated with increased self-sufficiency and life stability. 

About two-thirds of workers reported postemployment supports. Sixty-two percent 

continued access to employment case management and job retention services, about 41 percent 

received non-employment case management or other types of services to help them with other 

barriers to work or life stability, and 9 percent reported receiving some other type of services. 

Receiving postemployment support was associated with a 21 percentage point increase in 

the likelihood of housing stability, a $428 increase in total monthly income, and a medium to 

large decrease in the depression index one year after the SE job began. Such relationships cannot 

be interpreted as being causally linked to SE employment in the scope of this research. Observed 

associations might be driven by unobserved characteristics (for example, motivation) that could 

not be controlled for in these studies and could affect both the receipt of services and outcomes. 
EXECUTIVE SUMMARY MATHEMATICA POLICY RESEARCH

xxii

4. The SE experience adds value to society. 

SEs in REDF’s portfolio provide a net benefit to society: for every dollar spent by the SE, 

the SE returned $2.23 (outcomes study) or $1.34 (impact study) in total benefits. Furthermore, 

taxpayers may have an incentive to support SEs, as each dollar spent by the SEs included in this 

research generated taxpayer savings of $1.31 (outcomes study) to $0.41 (impact study) 

Additionally, the gains to taxpayers from the SE experience are substantially larger than the 

subsidies provided to them by the government and other funders. Workers, however, actually 

experience small net monetary losses from SE employment: their gains in economic selfsufficiency and life stability are offset by reductions in government transfers and public subsidies 

for housing. The SEs, as business enterprises, roughly break even. 

The returns on investment (ROIs) estimated for the SEs in this study tend to be at least as 

large—if not larger than—estimates from programs similar to the SE experience. Some programs 

oriented toward individuals with substantial barriers to employment actually find ROIs of less 

than one, implying that their benefits do not outweigh their costs (see, for example, Schochet et 

al. 2008 or Cave et al. 1993). The Ready, Willing, and Able job training program (Sirios and 

Western 2010), which provided employment opportunities to homeless individuals, produced an 

ROI of 21 percent, which is slightly lower than the ROI estimated in our impact study. A largescale examination of the benefits of subsidized employment programs for individuals with high 

barriers to employment (Bell and Orr 1994) produced a range of ROI values (dependent on the 

program and assumptions); the ROI from our impact study fell within this range.

D. Looking forward: issues for further exploration

The MJS provides evidence that larger and more established SEs might produce value to 

society and a net benefit to taxpayers in the first year after SE jobs begin, although the smaller 

and newer SEs we studied do not appear to produce such benefits. Our impact study provides 

moderate evidence that the SE experiences increase economic self-sufficiency and life stability, 

and the associated CBA indicates that the benefits of the SE experience exceed its costs. 

Moreover, we can triangulate these findings based on similar results from our outcomes study 

and its associated CBA, as well as the implementation study (Maxwell et al. 2013).

This evidence suggests that further exploration of SE employment is warranted. A larger 

study incorporating more SEs and participants would help to more precisely estimate effects, and 

following study participants for a longer period would help determine if results continue over 

time. Such a study could also help identify which kinds of organizations are more successful in 

generating impacts and which populations benefit most. A randomized controlled trial in which 

individuals are randomly assigned to become SE workers could further increase the quality of 

causal evidence available on the SE experience, and an accompanying CBA could determine 

whether SEs are an efficient use of public and private resources.
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I. INTRODUCTION MATHEMATICA POLICY RESEARCH

1 

I. INTRODUCTION

In 2011, REDF, a San Francisco-based intermediary, initiated a five-year strategy to help atrisk youth and adults transition into the workforce. This strategy seeks to expand social 

enterprise (SE) employment in California and develop a national SE model to address the 

difficulties faced by people with serious employment barriers, including challenges related to 

homelessness, mental health, addiction, and incarceration.3 For this report, we use REDF’s 

current definition of an SE: a mission-driven businesses focused on hiring and assisting people 

who face barriers to work.4

 By developing participants’ skills, the SE seeks to help workers 

achieve sustained employment and earnings gains (economic self-sufficiency) and improve the 

quality of their lives. These potential benefits often come with additional business expenses, 

because SEs may pay workers more than the value of what they produce, or provide employee 

supports to help workers overcome employment barriers and transition to unsubsidized 

employment. The Social Innovation Fund (SIF) of the Corporation for National and Community 

Service (CNCS) helps support REDF’s effort, as do other philanthropic sources, including 

corporations, foundations, and individuals. 

REDF committed to conducting an evaluation to support the success of its approach. It 

selected Mathematica Policy Research to design and implement an evaluation that provides a 

moderate level of evidence (CNCS 2013) on the impact of the SE experience in organizations 

receiving REDF SIF funding in January 2012.5 Mathematica’s evaluation, called the 

Mathematica Jobs Study (MJS), has four key components: 

1. An implementation study of eight organizations that documents the implementation of the 

SE model. 

2. An outcomes study of individuals hired to work in an SE in one of seven organizations, that 

analyzes outcomes about one year after the SE job began. 

3. An impact study with a quasi-experimental design that studies individuals referred to an SE 

labor pool in a single organization and that estimates the impact of the SE experience on a 

worker’s employment and housing about one year later. 

 

3 The selection of the SE model was supported by a review of 27 rigorous research evaluations of paid work 

interventions (Sattar 2010) that suggested paid employment was effective at improving wages, long-term job 

retention, and educational attachment for people with employment barriers.

4 A previous report (Maxwell et al. 2013) defined SEs as “businesses that sell goods and services that the 

marketplace demands in order to intentionally employ individuals who would otherwise face bleak prospects of ever 

getting a job.” The shift in wording reflects REDF’s ongoing evolution of defining an SE. 

5 CNCS defines as moderate evidence evaluation designs with strong internal validity but weaker external

validity. It comes from “studies able to show that a program produces changes among participants (or groups or sites), 

but [unable to] demonstrate how well the program would work among . . . groups [other than] those included in the 

study, or [that] may have a very limited number of threats to internal validity unaddressed.” It includes “cut-off score 

matched group designs” as an example of evaluation designs that may produce moderate evidence. As Appendix A 

shows, this study meets the criteria for moderate evidence by having “at least one well-designed and wellimplemented experimental or quasi-experimental study supporting the effectiveness of the practice, strategy, or 

program, with a small sample size or other conditions of implementation or analysis that limit generalizability.” 
I. INTRODUCTION MATHEMATICA POLICY RESEARCH

2 

4. A cost benefit analysis (CBA) that estimates the value of the SE experience for society as a 

whole, which includes the SE workers, the SE as a business, and taxpayers not directly 

involved with the SE (the government and community). 

This report provides results of the outcomes, impact, and cost benefit studies. Mathematica’s 

interim report (Maxwell et al. 2013) provides findings from the implementation study and, in the 

process, provides a description of the characteristics of workers, operations, and outputs from the 

SEs that received some support from REDF’s SIF grant. The rest of this chapter describes the 

SEs included in these three studies (Section A); presents an overview of the research designs of 

the outcomes, impact, and cost benefit studies, including their data, samples, and analytic 

methods (Section B); and provides a road map to this report (Section C). 

A. Social enterprises in the study

REDF sees its SIF portfolio as a laboratory for identifying and understanding the 

components of successful SEs. It observed organizations developing or operating SEs in their 

own way, and funded many business models, hoping to draw the strongest components from 

each. REDF’s funding came with an expectation that investments would support the SE to (a) 

help individuals gain one year of work experience, develop soft and hard skills, achieve stability 

in their lives, and transition to unsubsidized employment outside the SE; and (b) meet a double 

bottom line that achieves financial viability of the SE (business mission) while providing 

employment to and building the skills of people who might not otherwise have a job (social 

mission). Substantial differences existed across the SEs in the seven host organizations in 

REDF’s SIF portfolio in January 2012 that became part of the MJS outcomes study (Table I.1).6

In 2013, SEs in these organizations offered work in seven business lines (cafés, street cleaning, 

temporary staffing, retail services, construction/maintenance services, janitorial services, and 

pest control). The SEs varied greatly in size (one employed nearly 500 people annually, whereas 

two employed only 10 to 12) and maturity (some were well-established, and others used REDF 

money to start). Target populations also differed, although all SEs hired individuals from one or 

more of four populations of economically disadvantaged individuals: (1) those with mental 

health disabilities, (2) those who are homeless, (3) parolees or formerly incarcerated individuals, 

and (4) young adults who are neither enrolled in school nor participating in the labor market. 

 

6 Each organization is described at http://www.redf.org/what-do-we-do/invest. The MJS does not include the 

SEs run by Goodwill of Silicon Valley, which entered the portfolio after January 2012, and Urban Strategies, which 

had limited capacity to participate. The Coalition for Responsible Community Development is not included in the 

outcomes and impact studies or CBA, because its SIF-supported SE began shortly before the end of the MJS study 

period. It was a full participant in the implementation study.
I. INTRODUCTION MATHEMATICA POLICY RESEARCH

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Table I.1. Description of SEs

Host 

organization SE business line 

2013 

workers 

Year SE 

started Target population 

1. Buckelew

Cafés 

Janitorial services

18

23

1986

2009 Mental health disabilities

2. CEO Street cleaning 108 2011 Parolees

3. Chrysalis

Temporary staffing

Street cleaning 500 1984 Formerly incarcerated, homeless

4. CHP

Lobby services

Maintenance 

services

55

30 2007 Homeless

5. CRC Retail 36 2012 REDF-defined barrier

6. Taller Construction 12 2007 Age 18–28 and not in school or the labor market

7. Weingart Pest control 10 2012 Homeless

Source: Maxwell et al. (2013).

Note: A REDF-defined barrier includes low income, mental illness, homelessness, parolee status, and for youths,

not being in school or the labor market. Number of workers is a 2013 estimate. 

CEO = Center for Employment Opportunities; CHP = Community Housing Partnership; CRC = Community Resource 

Center; Taller = Taller San Jose; Weingart = Weingart Center for the Homeless.

B. Research design 

The MJS examined the outcomes, impacts, costs, and benefits associated with SE 

employment for workers hired between April 1, 2012, and March 31, 2013.7

 The outcomes study 

answers the following question: How do economic self-sufficiency and life stability change for 

individuals after they begin work in a social enterprise? It provides the foundation for 

understanding the social mission of the SEs by focusing on how an individual’s life has changed 

about one year8

 after starting an SE job. Economic self-sufficiency is the focus of the outcomes 

study, with employment the key outcome, although income and income source (wage and salary 

or government) are also considered. The study also examines life stability, focusing on a measure 

of housing stability (using only housing owned or rented home by the individual in the past 

year). Other outcomes include recidivism, physical health, mental health, and substance abuse. 

The impact study builds on the outcomes study by assessing how an individual’s life might 

change because of the SE experience. It answers a second question: How does working in a 

social enterprise change an individual’s employment and life stability? Although the outcomes 

study provides preliminary evidence on the impacts of the SE experience, it does not contain a 

group of individuals similar to SE employees for comparison with SE workers. The impact study 

uses a quasi-experimental design to compare outcomes of individuals that did and did not work 

in an SE and provides moderate evidence to CNCS on how the SE changed an individual’s life 

 

7 Workers who applied before or after this time frame were not included in the study, and those entering during 

this time frame were included on a rolling basis.

8 An average of 405 days elapsed between completion of the baseline and follow-up surveys, with the period 

ranging between 8 and 23 months. Because 80 percent of those in our sample completed the follow-up survey 

between 10 and 18 months after the baseline, we refer to this period as about one year after SE employment began 

for ease in exposition. 
I. INTRODUCTION MATHEMATICA POLICY RESEARCH

4 

about one year after seeking employment there. It was designed to address impacts on two 

primary outcomes (confirmatory analysis): (1) whether the respondent was currently employed 

(defined as employed in the last week) and (2) whether he or she was in stable housing in the 

past year (defined as owning or renting a home throughout the year). The exploratory portion of 

the study focuses on four types of secondary outcomes: (1) alternative employment and housing 

measures, (2) income, (3) recidivism, and (4) health. 

The CBA answers a third research question: What is the value of spending an additional 

dollar on an SE? It assesses value by dividing the present value of the benefits of the SE 

experience by the present value of its costs. It uses the results of the outcomes and impact studies 

to quantify benefits and information from the financial records of the organization and SE to 

quantify costs and estimate value from the perspectives of society as a whole, which includes SE 

workers, the SE as a business, and taxpayers not directly involved with the SE (the government 

and community). 

1. Data collected

The MJS collected extensive information on individuals who started or were referred to SE 

employment from April 1, 2012, to March 31, 2013, for the outcomes and impact studies and the 

benefits portion of the CBA. Data were collected on individuals at as many as four points in time 

(Appendix A): 

1. Intake information was collected from people who requested employment services at an 

SE’s host organization. It includes demographic and background information. 

2. A baseline survey was administered to individuals referred to or hired by an SE and 

obtained detailed information on individuals’ work history and employment barriers. 

3. An exit survey was conducted when workers left the SE or after six months, whichever 

came first. Information from this survey is used only to ground information from the followup survey in this report (for example, to determine the time since the last survey). 

4. A follow-up survey followed MJS study participants about one year after they completed 

the baseline survey and had a 51 percent response rate.9

 It obtained information on 

outcomes that is used in both the confirmatory and exploratory analyses. 

Data collection instruments were structured to capture changes in and measures of the 

economic self-sufficiency and life stability of respondents immediately before they started the 

SE job or entered the labor pool, and about one year later. Instruments were identical for all 

organizations included in the study. As Figure I.1 illustrates, the intake process and baseline 

survey captured information about the individual before the SE job began or at the time the 

individual entered the labor pool for an SE job; the follow-up survey captured outcomes one year 

later. About 24 percent of SE employees were still working at the SE at the time of the follow-up 

survey (the dotted line in the figure). 

 

9 Appendix A provides details on the differences in response rates across organizations and in characteristics of 

respondents and nonrespondents. All analyses use weights to adjust for such differentials; Appendix A describes 

how these weights were constructed. 
I. INTRODUCTION MATHEMATICA POLICY RESEARCH

5 

Figure I.1. Following individuals for one year

Enter 

organization

Hired by SE or

Enter labor pool

Follow-up

survey

Individual 

and 

organization

interactions

Intake Baseline

survey

Leave SE or

organization

One year after hire 

or enter pool

Data 

collection 

instrument

Note: The figure highlights how the evaluation timing is coordinated with data collection and services requested 

and received by study participants. Individuals who enter the organization and request employment 

services are asked a series of questions as part of the intake process, which is used to capture background 

information on participants. When individuals are hired into the SE or placed in the labor pool at Chrysalis, 

they complete a baseline survey, which establishes employment history, economic self-sufficiency, and life 

stability in the prior year. Not all participants had left the SE or had severed ties with the organization at the 

time of the follow-up survey, which occurred about one year after the SE job began or the individual 

entered the Chrysalis labor pool. This possibility is indicated in the figure by the extension of the dotted line 

after the follow-up survey/one-year marker. 

REDF staff captured financial information on the costs SEs incurred to fulfill their business 

and social missions. These costs covered expenditures on supervision for employees or client 

services such as mental health support, transportation, shelter, or food assistance. Appendix B 

provides details. 

2. Samples

SE workers at the seven organizations listed in Table I.1 form the sample for the outcomes 

study and provided the information that was used to describe the SE experience in Chapter II. 

Because REDF selected organizations that hosted SEs with a range of characteristics, the results 

from the outcomes analysis reflect those for workers with a variety of SE experiences. In 

contrast, the impact analysis uses information from individuals who entered the labor pool for an 

SE job at only Chrysalis. Although the external validity (generalizability or the ability to apply 

results to other SEs or transitional jobs programs) from such an analysis is limited, because it 

does not use information from individuals across the broad spectrum of organizations, the ability 

to make inferences with comparable treatment and comparison groups provides a higher degree 

of internal validity (the ability to draw a causal conclusion) and allows us to estimate the impact 

from the SE experience. The CBA draws information about benefits from both the outcomes and 

impact studies and cost information from six of the seven organizations.10 Table I.2 summarizes 

the organizations that are part of each study. 

 

10 Cost information was not available from Buckelew.
I. INTRODUCTION MATHEMATICA POLICY RESEARCH

6 

Table I.2. Organizations included in each study

 

Implementation 

study 

Outcomes 

study 

Impact 

study CBA

Buckelew X X

Center for Employment Opportunities X X X

Chrysalis X X X X

Community Housing Partnership X X X

Community Resource Center X X X

Coalition for Responsible Community Development X

Taller San José X X X

Weingart Center for the Homeless X X X 

We used two groups of individuals in the outcomes and impact studies: (1) individuals that 

either responded to a follow-up survey or were incarcerated at the time of the survey, and (2) 

survey respondents only. The distinction between the two groups arises because we can identify 

the work status, housing status, recidivism, and wage and salary income of those we could 

identify as being incarcerated but have more detailed information on individuals who responded 

to the survey. As a result, we can use both sets of individuals in analysis of the five 

aforementioned outcomes but must use the more restrictive set of survey respondents for other 

analyses. 

Figure I.2 shows the path for how individuals entered the two samples used in the outcomes 

study. The 242 SE workers who responded to the follow-up survey make up the noninstitutional 

sample, so called because no one was incarcerated at the time of that survey.11 The full sample

includes individuals in the noninstitutional sample plus the 40 study participants who were 

incarcerated at the time of the follow-up survey. Both the full and noninstitutional samples were 

weighted to correct for differences in sampling probabilities and response rates across SEs. With 

the weighting, the distribution of the characteristics of individuals in the samples is more similar 

to the distribution of individuals hired by the SEs. Appendix A provides details of this process. 

 

11 We purposely did not attempt to survey any incarcerated individuals.
I. INTRODUCTION MATHEMATICA POLICY RESEARCH

7 

Figure I.2. Derivation of samples for the outcomes study 

Hired by SE

Responded to follow-up survey 

(noninstitutional sample)

Full sample

Incarcerated

Note: The noninstitutional sample contains individuals who completed the follow-up survey, and the full sample 

includes those individuals as well as those found to be incarcerated at the time of follow-up survey. 

Figure I.3 shows how individuals entered the samples used in the impact analysis. These 

samples contain individuals who completed a follow-up survey and were hired by the SEs at 

Chrysalis (the treatment group) and survey respondents who were not hired (the comparison 

group). The 138 people in the treatment group and 32 people in the comparison group who 

responded to the survey form the starting point for building the impact study sample. We used 

regression analysis to develop cutoff scores to identify individuals in the treatment and 

comparison groups most likely to be similar at the time they were placed into the Chrysalis labor 

pool and removed individuals from the samples whose cutoff scores were outside designated 

ranges. The resulting noninstitutional propensity-score sample contains 55 individuals in the 

treatment group and 28 individuals in the comparison group. We built a corresponding full 

propensity-score sample in a parallel fashion. We first added the group of incarcerated 

individuals to the survey respondents, providing a pool of 154 individuals in the treatment group 

and 37 individuals in the comparison group. A similar procedure then removed individuals who 

were notably different from the average individual outside of their (treatment/comparison) group. 

The procedure produced a full propensity-score sample of 59 individuals in the treatment group 

and 32 individuals in the comparison group. Both the full and noninstitutional propensity-score 

samples were weighted by the probability of being in the sample (the propensity score) in all 

analyses. With the weighting, baseline values of outcomes, demographics, and background 

variables demonstrate baseline equivalence across almost all variables considered. Appendix A 

provides further details of this process. 
I. INTRODUCTION MATHEMATICA POLICY RESEARCH

8 

Figure I.3. Derivation of samples for the impact study 

Entered Chrysalis 

labor pool

Hired=treatment (T)

Not hired=comparison (C)

Responded 

to follow-up 

survey

(T & C)

Incarcerated

(T & C)

Noninstitutional 

propensityscore sample 

(T & C)

T & C members 

with very high or 

low propensity 

scores

Responded 

to follow-up 

survey

(T & C)

Full 

propensityscore sample 

(T & C)

T & C members 

with very high or 

low propensity 

scores

[Note: The noninstitutional propensity-score sample contains individuals who completed the follow-up survey, and 

the full propensity-score sample adds individuals found to be incarcerated at the time of follow-up survey. 

The analysis excludes some individuals (identified by the white boxes) because their propensity score was 

either very high or very low. Appendix A provides a detailed discussion of this process. 

3. Analysis 

We used both descriptive (means and percentage distributions) and multivariate analysis to 

assess how the SE experience might affect economic self-sufficiency and life stability. The 

former analysis describes changes for SE workers. In the outcomes study, it describes changes in 

economic self-sufficiency, life stability, and attitudes toward work between the time the SE job 

began and about one year later. In the impact study, it assesses whether changes between when 

individuals were placed in the SE labor pool and one year later were greater for SE workers than 

those who did not work in the SE. Analysis uses a two-tailed t-statistic to determine whether 

changes were statistically significant (p ≤ 0.05). 

We used multivariate regression analysis to answer research questions posed in the 

outcomes and impact studies and to quantify the benefits for the CBA. In the outcomes study, we 

assessed whether the SE experience is associated with increases in economic self-sufficiency or 

life stability. We also explored which characteristics of the SE experience are associated with 

larger changes using multivariate analysis. In the impact study, we focused on whether the SE 

experience increased current employment and housing stability in confirmatory analyses; we 

assessed other measures of economic self-sufficiency and life stability in exploratory analysis. 
I. INTRODUCTION MATHEMATICA POLICY RESEARCH

9 

We use regression-adjusted averages to show the results of these analyses, which provide the 

average value that would occur at one year, after accounting for variation in factors such as 

demographic characteristics and employment barriers. 

Table I.3 shows the key outcome measures used in the outcomes and impact studies and, for 

the impact study, whether the analysis using the information was confirmatory (shown in red) or 

exploratory (in black). As this table shows, we use information from the full samples when it is 

available and information from the noninstitutional sample when it is not. Appendix A provides 

a detailed discussion of our multivariate methods. 

Table I.3. Measures used to capture outcomes

Outcomes 

study Impact study 

Outcome Information captured relative to follow-up survey date Full

Noninstitutional FullNoninstitutional

Economic self-sufficiency 

Employment 

Work for pay in the last week X X

Work for pay in the last month X X

Work continuously for six months during the past year X X

Share of time spent in work during the past year X X

Income 

Total income in past month X X

Wage and salary income in past month X X

Share of income from work in past month X X

Share of income from government in past month X X 

Life stability

Housing Stable housing in the last year X X

Homeless in past year X X

Recidivism Arrested in past year X X

Health

Depression index X X

Physical health rated as excellent X X

Substance abuse in the past year X X 

Notes: Timing is relative to when the follow-up survey was completed or the individual was identified as being 

incarcerated. Red indicates outcomes in confirmatory analysis. Stable housing indicates the individual only 

owned or rented a home. Homeless indicates the individual used an emergency shelter, emergency 

voucher, transitional housing, or permanent housing for the previously homeless or slept outside or in 

public. The depression index is an inventory of feelings indicative of clinical depression in the past week.

Our CBA assigned dollar values to the benefits estimated from the multivariate analysis in 

the outcomes and impact studies and compared them with the dollar values of the costs of 

operating the SE as measured by information from SEs’ financial records. Appendix B provides 

details of how we quantified both the benefits and the costs. 
I. INTRODUCTION MATHEMATICA POLICY RESEARCH

10

C. Structure of report

The remainder of this report proceeds as follows. Chapter II provides an overview of the SE 

experience to provide context for interpreting our results. The next three chapters of the report 

each address findings from one of the studies. Chapter III presents findings from the outcomes 

study. It describes economic self-sufficiency and life stability of SE workers about one year after 

they began their jobs and identifies which components of the SE experience might be associated 

with improved outcomes. Chapter IV presents findings from the impact study and provides 

estimates of the impact of the SE experience on economic self-sufficiency and life stability about 

one year after individuals entered the Chrysalis labor pool. Chapter V presents findings from the 

CBA, describes the costs of operating an SE with both business and social missions, and 

estimates how these costs are associated with the benefits of SE employment. 

The report also includes five appendices. The first two provide detailed information about 

data collection and analyses: Appendix A details the outcomes and impact studies, and Appendix 

B provides a technical discussion of the CBA. Appendix C defines the variables used in the 

analyses, and Appendix D provides the main data tables on which we base figures in the text. 

Appendix E presents a copy of the follow-up survey. 
II. THE SOCIAL ENTERPRISE EXPERIENCE MATHEMATICA POLICY RESEARCH

11

II. THE SOCIAL ENTERPRISE EXPERIENCE

REDF’s funding and technical assistance were expected to increase SE employment and to 

improve the ability of SEs to provide workers with a positive work experience and opportunities 

to build more stable lives.12 Section A of this chapter summarizes the SE experience provided to 

workers and how the workers assessed that experience. The next two sections examine economic 

self-sufficiency (Section B) and life stability (Section C) in the year after the SE job started. 

Key chapter findings

• The range in hours worked in the SE was large. On average, employees worked 576 hours in the SE; 

however, nearly 6 percent worked fewer than 8 hours, and 27 percent worked more than 961 hours. 

• SE workers said they received a variety of services and supports. While working, almost all 

received job readiness and skills training, close to 80 percent received work support services, and about 

two-thirds received supports that fostered life stability. About two-thirds continued receiving services 

after leaving the SE. 

Between the time the SE worker was hired and one year later:

• SE workers gained economic self-sufficiency. Employment increased from 16 to 51 percent, and 

monthly wage and salary income increased from an average of $216 to $777. The share of monthly 

income from the government decreased from 77 to 24 percent (measured for nonincarcerated 

individuals only). 

• SE workers gained housing stability. Only 15 percent of SE workers lived in stable housing prior to 

the SE job starting, whereas 53 percent lived in stable housing in the year after the SE job began.

A. The SE experience

The SE workers hired from April 1, 2012, through March 31, 2013 faced multiple 

employment barriers (Table II.1). Indicators of economic self-sufficiency were low at the time 

the SE job began: one-quarter had never held a job before the SE job began, and only 23 percent 

of the average individual’s monthly income came from work. Indicators of life stability were 

also low: about 85 percent did not have stable housing, and nearly 70 percent had been convicted 

of a crime and sentenced to jail. Nearly 30 percent lacked a high school diploma. Although 

differences in barriers existed in the populations targeted and served by each organization, 

barriers were high within each organization. 

 

12 REDF defined employment as working 32 hours within four weeks. 
II. THE SOCIAL ENTERPRISE EXPERIENCE MATHEMATICA POLICY RESEARCH

12

Table II.1. Employee characteristics (raw percentages, except where noted)

All 

workers Buckelew CEO Chrysalis CHP CRC Taller Weingart 

Average age (in years)* 41 37 33 44 41 46 25 49

Never-employed* 25 46 16 25 29 26 23 0

No high school diploma* 29 10 51 27 17 6 38 0

Ever convicted* 69 25 100 71 61 20 62 20

Unstable housing (past 

year) 85 84 90 83 90 77 82 100

Income from wages 23 18 32 17 30 35 79 69

Source: Maxwell et al. (2013). 

Note: Analysis weighted to correct for survey nonresponse. An asterisk (*) indicates a significant likelihood that 

values differ across organizations. Unstable housing is defined as not owning or renting a home during the 

year. Income from wages is the percentage of income in the past month from wages or salary.

CEO = Center for Employment Opportunities; CHP = Community Housing Partnership; CRC = Community Resource 

Center; Taller = Taller San Jose; Weingart = Weingart Center for the Homeless. 

Survey results suggest that some variation existed in exposure to each of the four 

components of the SE experience: (1) employment, (2) employment supports, (3) life stability 

supports, and (4) postemployment services (Appendix D, Tables D.1–D.2). With respect to 

employment, SE workers reported they worked, on average, 24 hours per week for 24 weeks, 

which is equivalent to a little more than three months of full-time work. However, 

• About half worked more than 18 weeks, and half worked less, 

• Nearly 6 percent worked fewer than 8 hours, which is virtually no exposure to the SE, and 

• About 27 percent worked more than 961 hours, which is the equivalent of about half a year 

of full-time work experience. 

Furthermore, about 29 percent said they had worked in the SE prior to the start of the MJS, 

which suggests that the amount of time spent in the SE is actually more than reported for these 

workers (our measures of time worked in the SE include only the most recent stint of SE 

employment).

In addition to work experience, almost all SE workers (97 percent) reported receiving, 

through the SE or host organization, employment supports while employed (Figure II.1). 

Specifically, they said they received the following: 

• Job-readiness skills training (more than 90 percent), with 86 percent saying they received 

job search assistance and close to 70 percent reporting receiving career counseling

• Skills training (over 90 percent), with more than 80 percent reporting soft-skills and 64 

percent reporting receiving vocational skills training 

• Work supports (almost 80 percent) of clothing, transportation, or housing assistance 
II. THE SOCIAL ENTERPRISE EXPERIENCE MATHEMATICA POLICY RESEARCH

13

Figure II.1. Employment supports while working at the SE (raw percentages)

91

86

72

91

81

64

55

24

79

66

46

12

0

20

40

60

80

100 Percentage

Source: MJS database, noninstitutional sample, Appendix D, Table D.2.

Note: Analysis weighted to correct for survey nonresponse. Percentages have not been regression adjusted. 

Light blue bars reflect the percentage of SE workers saying they received a specific support or service. 

Dark blue bars reflect the percentage who said they received any of the supports or services in the group 

that is designated by the light blue bars to its right.

ABE = adult basic education; GED = General Educational Development. 

Most SE workers (64 percent) also said they received life stability supports through the SE 

or host organization while employed in the SE (Figure II.2), although the level of such supports 

was lower than the level of employment supports received:

13

• About half reported receiving some type of financial education 

• About 28 percent reported receiving help to gain food security (for example, through food 

pantries, reduced-price meals, or nutritional education) 

• About 24 percent reported receiving help to avoid relapsing into behaviors such as drug 

abuse or criminal activity 

• About 20 percent said they received help to access public benefits; about the same 

percentage that said they received help with tax preparation 

Finally, about two-thirds said they received postemployment services after leaving the SE, 

with 62 percent having access to an employment counselor and 41 percent having access to a life 

counselor (not shown). 

 

13 The survey classified supports as related to life stability or employment based on the most likely targets of 

these supports. Alternative classifications may be feasible.
II. THE SOCIAL ENTERPRISE EXPERIENCE MATHEMATICA POLICY RESEARCH

14

Figure II.2. Life stability supports while working at the SE (raw percentages)

64

50

28 24 21 20 16 15 12 12

0 

20

40

60

80

100

Percentage

Source: MJS database, noninstitutional sample, Appendix D, Table D.2.

Note: Analysis weighted to correct for survey nonresponse. Percentages have not been regression adjusted. 

Light blue bars reflect the percentage saying they received a support or service, and dark blue bars 

reflect the percentage who said they received any of the supports or services in the group.

Two pieces of information suggest that workers held a positive view of their SE experience. 

First, they expressed high levels of satisfaction with their work experiences (Figure II.3): 

• Almost all (about 96 percent) were satisfied with their job contributing to society 

• More than 90 percent were satisfied with the feedback they received

• More than 85 percent were satisfied with their job location, independence, and type of work 

• About three-quarters were satisfied with the support received, number of hours they worked, 

and job security 

• About two-thirds were satisfied with the challenge of the job, salary, and level of 

responsibility 

• More than half were satisfied with opportunities for advancement and benefits offered 
II. THE SOCIAL ENTERPRISE EXPERIENCE MATHEMATICA POLICY RESEARCH

15

Figure II.3. Satisfaction with SE experience (raw percentages)

96

91 87 87 85 81

75 73

68 68 68

55 50

0 

20

40

60

80

100

Percentage

Source: MJS database, noninstitutional sample. Appendix D, Table D.1.

Note: Analysis weighted to correct for survey nonresponse. Percentages have not been regression adjusted. 

Satisfaction was measured on a four-point scale: satisfied, somewhat satisfied, somewhat dissatisfied, or 

very dissatisfied. Bars indicate the percentage of survey respondents that said they were satisfied or 

somewhat satisfied. 

Second, workers who had left the SE generally reported positive reasons for leaving the SE 

(Figure II.4): 

• About 51 percent left for another job or training opportunity or were still working at the SE 

• About 28 percent left because the program ended or they were terminated (we cannot 

distinguish between groups)

• About 21 percent left because they were dissatisfied or did not want a job 

• About 10 percent left for family or personal reasons 

• Fewer than 3 percent said they left because of either incarceration or drug use, although this 

percentage might be understated, because the question was not asked of those who were 

incarcerated 
II. THE SOCIAL ENTERPRISE EXPERIENCE MATHEMATICA POLICY RESEARCH

16

Figure II.4. Reason for leaving the SE, reported one year after SE job began 

(raw percentages)

51

28

21

10 9

3 2 1

0

20

40

60

80

100

At SE or left: 

job or training

Left: program 

end or 

terminated

Left: 

dissatisfied or 

did not want 

job 

Left: family or 

personal 

reasons 

Left: other 

reasons

Said never 

worked at SE

Left: drug use Left: 

incarcerated

Percentage

Source: MJS database, noninstitutional sample, Appendix D, Table D.1.

Note: Analysis weighted to correct for survey nonresponse. Multiple answers permitted. Bars show the 

percentage of participants who reported the reason for leaving the SE. The percentage reporting each 

reason for leaving the SE is about the same about one year after starting work (this table) and at the time 

the worker left the SE or six months of work, whichever was first (see Maxwell et al. 2013). 

B. Economic self-sufficiency after the SE job began

A central goal of the SE was to build economic self-sufficiency through employment by 

providing work experience in the SE and helping workers find employment when the SE job 

ended. This work experience was considered critical because, prior to starting the SE job, 25 

percent of SE workers reported they had never held a job, 63 percent reported working sometime 

in the prior year, and 16 percent were currently employed (Maxwell et al. 2013). 

Information collected in the follow-up survey enabled us to construct several measures of 

work experience one year after the SE job began for the noninstitutional sample: whether 

participants were working for pay the week before the survey, whether they worked continuously 

for a six-month period, and the share of that year they spent working (Table II.2). The first 

measure may also be constructed for all members of our full sample and shows that about 51 

percent of all SE workers were employed about one year after starting the SE job. That 

percentage varied across organizations; however, 84 percent of SE workers at Weingart and 41 

percent of SE workers Buckelew were employed about one year after they began their job. 

Employment outcomes are stronger for SE workers who were not incarcerated. Consider the 

following outcomes for SE workers, about one year after starting: 

• About 62 percent were employed, ranging from 86 percent of SE workers at Weingart to 45 

percent at Buckelew. 

• About 67 percent had worked continuously for six months during the year, ranging from 

about 92 percent at CHP to 28 percent at Buckelew. 

• They had spent about 67 percent of their time working since they were hired by the SE, 

ranging from about 97 percent at Weingart to about 47 percent at Buckelew. 
II. THE SOCIAL ENTERPRISE EXPERIENCE MATHEMATICA POLICY RESEARCH

17

Table II.2. Work in the year after the SE job began (raw percentages) 

All Buckelew CEO Chrysalis CHP CRC Taller Weingart 

Full sample

Currently employed 51 41 44 49 72 70 65 84

Noninstitutional sample

Currently employed 62 45 73 56 78 71 75 86

Worked continuously for six

months in last year 67 28 75 64 92 58 75 84

Share of time spent in work 

during past year 67 47 73 65 89 61 76 97

Source: MJS database.

Note: Analysis weighted to correct for survey nonresponse. Percentages have not been regression-adjusted. 

Currently employed means worked for pay in the last week. 

CEO = Center for Employment Opportunities; CHP = Community Housing Partnership; CRC = Community Resource 

Center; Taller = Taller San Jose; Weingart = Weingart Center for the Homeless. 

One should use caution in interpreting the employment measures after the SE job began, 

presented here and throughout the report, because the measures could capture both SE and 

unsubsidized employment. For example, someone who worked at the SE for the past eight 

months would be classified as currently employed and working more than half of the past year. 

Thus, our employment measures should be thought of as a mixture of the outputs of SE 

employment (which are directly influenced by the SE) and outcomes associated with SE 

employment (less proximal effects).14

Our results suggest that the SEs succeeded in obtaining work experience for individuals. About 

one year after the SE job began, more than half of all SE workers were currently employed. 

When we examine the more complete set of information from those SE workers who were not 

incarcerated, about 62 percent reported current employment, about 67 percent had worked 

continuously for at least six months in the prior year, and about 67 percent of that year was spent 

working. The level of work experience, however, seems to vary by organization (Table II.2). 

To clarify the extent of continuous work experience gained, we examined the percentage of 

SE workers who worked continuously for every monthly interval during the year after they were 

hired (Figure II.5). This analysis is available only for individuals who were not incarcerated. In 

the year after the SE job began, the results were as follows: 

• 93 percent worked for at least one month 

• 84 percent worked continuously for at least 3 months 

• 67 percent worked continuously for at least 6 months 

• 51 percent worked continuously for at least 9 months 

• 35 percent worked continuously for all 12 months 

 

14 Daniel Bloom’s testimony before the U.S. House Committee on Ways and Means, Human Resources 

Subcommittee, on July 30, 2014 (http://www.mdrc.org/sites/default/files/Bloom_testimony.pdf), places this finding 

within the broader context of evaluation results on subsidized jobs. 
II. THE SOCIAL ENTERPRISE EXPERIENCE MATHEMATICA POLICY RESEARCH

18

Figure II.5. Months of continuous work in the year the SE job began (raw 

percentages)

93 90

84 79

73

67

61

55 51 47

41

35

0 

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Percentage

Months worked continuously

Source: MJS database, noninstitutional sample. 

Note: Analysis weighted to correct for survey nonresponse. Percentages have not been regression adjusted. Bars 

show the percentage who worked continuously for at least the number of months indicated.

One anticipated benefit of employment was increased economic self-sufficiency as income 

from wages and salary increases and income from the government decreases. The MJS data 

suggest that economic self-sufficiency might have increased in both ways. Within the full 

sample, monthly wage and salary income increased significantly, from an average of $216 to 

$777 (Table II.3). The more extensive information for the noninstitutional sample suggests that 

the share of monthly income from the government decreased significantly, from 71 to 24 percent. 

C. Life stability after the SE job began

Another key goal of the SE was to help workers stabilize their lives outside the workplace. 

Because SE workers frequently faced employment barriers stemming from issues in their 

personal lives (Table II.1), gaining stability in these areas could improve their chances of 

obtaining and retaining employment. The MJS data suggest that housing stability increased in the 

year after the SE job began. Only 15 percent of SE workers lived in stable housing in the year 

prior to their SE job: one year later, 53 percent did (Table II.3). Other indicators of life stability 

are less encouraging. Nearly one-quarter were arrested in the year after the SE job started, and no 

significant changes in substance abuse occurred.15 A lower percentage of SE workers reported 

being in excellent physical health, and a higher percentage reported being depressed, although 

these results might be driven by time-related factors that would affect the individuals irrespective 

of SE employment, as Chapters III and IV will discuss. 

 

15 This arrest rate is substantially higher than that reported by Maxwell et al. (2013), who used data on 

individuals six months after beginning SE employment. This difference does not necessarily represent an increase in 

the arrest rate over time. Rather, the field locating efforts conducted for the follow-up surveying included searches 

of the incarceration status of individuals, which provided more-complete information on arrests in the analysis for 

this report than was available for the earlier report. 
II. THE SOCIAL ENTERPRISE EXPERIENCE MATHEMATICA POLICY RESEARCH

19

Table II.3. Economic self-sufficiency and life stability before and after the SE 

job began (raw percentages, except where noted)

Before SE job 

began 

One year after 

SE job began Difference 

Full sample 

Sample size 282 282 n.a.

Economic self-sufficiency

Currently employed 17.9 51.2 33.3*

Average monthly wage and salary income $215.7 $777.3 $561.6*

Life stability

Stable housing in past year 15.4 53.2 37.8*

Arrested since hired at SE n.a. 24.9 n.a.

Noninstitutional sample

Sample size 242 242 n.a.

Economic self-sufficiency

Share of monthly income from:

Work 22.5 69 46.5*

Government transfers 71.3 23.8 −47.5*

Life stability

Health

Self-reported excellent physical health 31.2 21.9 −9.3*

Depression index (standard deviation) 0.0 0.3 0.3*

Substance abuse

Drank four or more drinks 22.5 26.9 4.4

Used marijuana 17.2 17.9 0.7

Used hard drugs 4.9 3.4 −1.5

Source: MJS database, full (all SE workers) and noninstitutional samples (SE workers who were not incarcerated), 

Appendix D, Tables D.9, D.10, D.11, and D.14.

Note: Analysis weighted to correct for survey nonresponse. Percentages have not been regression adjusted. 

Stable housing indicates that the individual used only his or her own home as housing in the past year. The 

depression index is an inventory of feelings indicative of clinical depression in the past week. It was 

normalized to have a mean of 0 and standard deviation of 1 at time of hire. 
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III. ECONOMIC SELF-SUFFICIENCY AND LIFE STABILITY FOLLOWING AN SE 

JOB 

REDF allocated funds toward its portfolio organizations as a laboratory to identify the 

components of successful SEs. In the previous chapter, we described the SE experience, 

including employment, employment supports received while employed, life stability supports 

received while employed, and postemployment services. Each component was designed to 

increase workers’ economic self-sufficiency and life stability, primarily by reducing barriers to 

work. These components might lead to a number of changes for workers: more-stable 

employment and housing, less reliance on government programs for support, less recidivism, and 

improved physical and mental health. 

In this chapter, we present results of the outcomes study and answer the following research 

question: How do economic self-sufficiency and life stability change after individuals begin work 

in a social enterprise? We present a general picture of the changes in economic self-sufficiency

and life stability of SE workers during the year after their SE job began. We focus on two main 

outcomes—current employment and housing stability—to present a clearer picture of individual 

outcomes and to decrease the possibility of reporting spurious associations, which could occur 

with additional primary outcomes. To broaden our analysis, we examine other aspects of 

workers’ economic well-being (monthly income), life stability (recidivism and physical and 

mental health), workers’ attitudes about work and optimism about the future, and associations 

between components of the SE experience and self-sufficiency and life stability. 

Key chapter findings

In the year after the SE job began, SE workers experienced the following:

• Greater economic self-sufficiency. The percentage currently employed increased by 33 

percentage points (from 18 to 51 percent), total monthly income increased by 91 percent, and the 

share of income from government transfers decreased. 

• Greater housing stability. The percentage living in stable housing throughout the year increased 

by 28 percentage points (from 16 to 44 percent). In addition, a greater percentage of SE workers 

who were not incarcerated reported using more stable sources of housing. 

• A decline in self-reported health and optimism. Fewer reported their physical health as excellent 

and more reported a higher level of depression symptoms. In addition, fewer reported feeling 

optimistic about the future.

Specific program components might be associated with economic self-sufficiency and life stability. 

• Supports after leaving the SE were associated with increased housing stability and monthly 

income, and decreased depression. 

• Duration of SE employment was associated with increased housing stability and decreased

depression.

Properly accounting for the Ashenfelter dip is one of the main challenges of describing 

outcomes of an employment program using a strategy without a comparison group of similar 

individuals who did not participate in the program, as we do in this chapter. The Ashenfelter dip 

describes a graphic representation of how entrants to employment programs typically faced a 

recent negative event affecting their employment that drove them to seek the program. 

Ashenfelter (1978) demonstrated how this phenomenon falsely enhances the effects of 

employment programs because individuals would have experienced improved outcomes even 
III.. ECONOMIC SELF-SUFFICIENCY AND LIFE STABILITY FOLLOWING AN SE JOB MATHEMATICA POLICY RESEARCH

22

without the program.16 Our impact analysis, presented in the next chapter, helps correct for the 

Ashenfelter dip and similar effects by using a comparison group of similarly situated individuals 

who did not have an SE job. 

Although we cannot correct for the Ashenfelter dip in the outcomes analysis, we can control 

for other sources of bias to help improve the validity of our results. We use a fixed-effects model 

to estimate the relationship between SE employment and outcomes. The fixed-effects 

specification controls for worker characteristics that do not vary over time by analyzing 

deviations from the average level of an outcome for an individual.17 It produces more-precise 

estimates than the average change in outcomes over time presented in Chapter II. Our regression 

also controls for the unemployment rate, allowing us to account for difference in general 

economic conditions between when an individual started his or her SE job and one year later. 18

This control may also mitigate the bias from the Ashenfelter dip, as some of the negative events 

leading to an individual taking an SE job may relate to the overall strength of the economy. We 

report results from these estimations using regression-adjusted means. 

Section A reports results of this analysis for changes in economic self-sufficiency—

employment and income—between when the SE job began and one year later. Section B reports 

results for changes in housing stability; and Section C reports results for changes in health, 

attitudes toward work, and optimism about the future. Appendix A provides more information 

about the statistical model and procedures. Section D describes how components of the SE 

experience are associated with economic self-sufficiency and life stability, based on an ordinary 

least squares analysis. 

A. More economically self-sufficient after the SE job began

The fixed-effects analysis showed significant improvement in employment for SE workers, 

using a variety of measures of employment status (Figure III.1). Most importantly, using our full 

sample of individuals, 51 percent (regression adjusted) were currently employed roughly one 

year after their SE job commenced, compared with 18 percent who were employed before the SE 

job began, a 33 percentage point increase. Results are similar for the noninstitutional sample of 

individuals not incarcerated one year after beginning an SE job, with a 31 percentage point

increase in those reporting having been employed at some point in the past month one year after 

the SE job began (from 32 to 63 percent) and a 28 percentage point (from 39 to 67 percent) 

increase in the percentage of those reporting continuous employment for at least six months in 

the past year. 

 

16 For a more recent discussion, see Andersson et al. (2013).

17 Because the fixed-effects model allows us to control for all time-invariant individual characteristics,

including those that are observed (for example, race) and unobserved (for example, determination), it is preferable to 

using ordinary least squares with a more limited set of observed controls.

18 Because the unemployment rate generally decreased over the year examined, this adjustment decreased the 

magnitude of change associated with the program. 
III.. ECONOMIC SELF-SUFFICIENCY AND LIFE STABILITY FOLLOWING AN SE JOB MATHEMATICA POLICY RESEARCH

23

Figure III.1. Employment before and after the SE job began (regressionadjusted percentages)

18 19

36

51

62 67

0

20

40

60

80

100

Worked last week (full)* Worked last month 

(noninstitutional)*

Worked continuously for six 

months in past year 

(noninstitutional)*

Percentage

Before SE job began One year after SE job began

Source: MJS database, full sample (full) and noninstitutional sample (noninstitutional). 

Note: Analysis weighted to correct for survey nonresponse; regression-adjusted mean percentages estimated 

from the fixed-effects model. 

*At hire and one year differ at the p ≤ 0.05 level, as measured by a two-tailed t-test.

Increases in economic self-sufficiency one year after the SE job began are also reflected by 

changes in different measures of income—monthly wage and salary income, total monthly 

income, and the share of income from work versus the share from government programs (Table 

III.1). Monthly wage and salary income increased by $570 (268 percent) for all SE workers, 

from $213 before the SE job began to $783 one year later. Information from the noninstitutional 

sample suggests that this increase helped increase total monthly income by $593 (91 percent), 

from $653 to $1,246 and reduced reliance on government transfer payments by 46 percentage 

points, from 71 to 25 percent (all estimates at one year are regression adjusted). 

Table III.1. Income in the year before and after the SE job began (regressionadjusted percentages, except where noted)

Before SE job 

began 

One year after 

SE job began Difference 

Full sample

Wage and salary income in last month $213 $783 $570*

Noninstitutional sample

Total income in past month $653 $1,246 $593*

Percentage of income from . . .

Work 22 67 45*

Government 71 25 −46*

Source: MJS database, full and noninstitutional samples.

Note: Analysis weighted to correct for survey nonresponse; regression-adjusted means and percentages 

estimated from the fixed-effects model. Arrested in past year was not measured at hire. 

*At hire and one year differ at the p ≤ 0.05 level, as measured by a two-tailed t-test.
III.. ECONOMIC SELF-SUFFICIENCY AND LIFE STABILITY FOLLOWING AN SE JOB MATHEMATICA POLICY RESEARCH

24

B. Greater housing stability after the SE job began

Our fixed-effects analysis also suggests that SE workers’ lives stabilized in the year after the 

SE job began. On our second main outcome measure, housing stability, 44 percent of workers 

reported stable housing in the year after the SE job began, as compared to 16 percent in the year 

before (a regression-adjusted increase of 28 percentage points; not shown). Information available 

from the noninstitutional sample indicated that significantly more SE workers reported using 

stable sources of housing at some point in the year after the SE job began (Table III.2). A greater 

percentage used a stable home or apartment for housing (from 49 to 81 percent), and fewer used

an emergency shelter or voucher (from 14 to 9 percent), a psychiatric hospital, or a substance 

abuse rehabilitation center (the combined measure decreases from 16 to 7 percent).19 Despite 

these gains, more than one-third reported at least one period of homelessness in the past year—

living outside or in public, in an emergency shelter, or in housing for the homeless; this rate was 

relatively unchanged from the year before hire (not shown). 

Table III.2. Housing in the year before and after the SE job began (raw 

percentages)

Year before SE 

job began 

Year after SE 

job began 

Stable home/apartment 49 81*

Home of family member/friend 40 36

Transitional housing 27 24

Jail/prison/juvenile detention 25 6*

Psychiatric hospital, rehabilitation center 16 7*

Emergency shelter/voucher 14 9

Outside or in public 10 13

Permanent housing for previous homeless 9 14

Hotel or motel 6 7

Halfway home 6 5 

Source: MJS database, noninstitutional sample, Appendix D, Table D.6.

Note: Analysis weighted to correct for survey nonresponse. Percentages have not been regression-adjusted. 

*At hire and one year differ at the p ≤ 0.05 level, as measured by a two-tailed t-test.

C. Worker health and optimism declined

Information from the follow-up survey allows us to examine additional life stability

outcomes, such as changes in health following SE employment, for the noninstitutional sample. 

Results from the fixed-effects analysis indicate the following (Table III.3):

• A lower percentage of SE workers reported excellent physical health. The (regressionadjusted) percentage reporting excellent health declined by 9 percentage points, from 31 to 

22 percent. 

 

19 Fewer reported using a jail or prison as housing, decreasing from 25 to 6 percent (regression adjusted) in the 

noninstitutional sample; however, we estimate that 25 percent of individuals were in jail or prison at some point 

during the last year if we include the 40 individuals who were incarcerated at the time of the follow-up survey and 

who did not provide information on the sources of housing in the year following SE employment. 
III.. ECONOMIC SELF-SUFFICIENCY AND LIFE STABILITY FOLLOWING AN SE JOB MATHEMATICA POLICY RESEARCH

25

• More SE workers reported symptoms of depression one year after their SE job began.

Self-reported symptoms of depression increased significantly by 0.31 standard deviations on 

our depression index scale, indicating that SE workers, on average, exhibited more

symptoms of depression at one year after hire than before the SE job began.20 

• Rates of alcohol or substance abuse counseling in SE workers did not change. No 

statistically significant difference existed between the percentage who were in counseling 

for substance abuse treatment in the year before they started their SE job and the year 

following it. This finding is in itself not negative, as workers may be receiving maintenance 

counseling to prevent a reoccurrence of substance abuse.21

Table III.3. Physical and mental health (regression-adjusted percentages, 

except where noted) 

Before SE job 

began 

One year after 

SE job began Difference 

Self-report that physical health is . . .

Excellent 31 22 −9*

Very good or good 58 55 −3

Poor or fair 11 23 12*

Depression index (standard deviations) −0.01 0.30 0.31*

Substance abuse

In counseling for substance abuse in past year 22 17 −5

No prior counseling at hire (in past year) n.a. 7 n.a.

Prior counseling at hire (in past year) n.a. 50 n.a.

Source: MJS database, noninstitutional sample.

Note: Analysis weighted to correct for survey nonresponse; regression-adjusted percentages estimated from a 

fixed-effects (health, depression, substance abuse, and in counseling) or ordinary least squares estimation 

(no prior and prior counseling). The depression index is an inventory of feelings indicative of clinical 

depression in the past week. It was normalized to have a mean of zero and standard deviation of one at 

time of hire. Arrested in the past year was not available before the SE job began, which precluded its 

inclusion in this analysis. 

*At hire and one year differ at the p ≤ 0.05 level, as measured by a two-tailed t-test.

One goal for the SE experience was to create a work environment that would support 

positive attitudes toward work and optimism about the future. We are able to observe changes in 

these attitudes and optimism in the noninstitutional sample (those who completed a follow-up 

 

20 The scale is reported in units of standard deviation, which is equivalent to standard effect size. Typically, 

effects measured on this scale that fall in the range of 0.20 are described as small, whereas those in the range of 0.21 

to 0.50 are described as medium (Cohen 1988). Reporting fewer symptoms of depression when the SE job began 

may be related to a greater level of optimism at that time (see Section C). We cannot distinguish between a unifying 

factor influencing both an increase in depression and a decrease in optimism, or whether a decrease in one may be 

driving a decrease in the other.

21 This possibility is further supported by the characteristics of workers in counseling for substance abuse. 

Only 7 percent of SE workers who reported no counseling in the year before hire started to receive this counseling in 

the year after their SE job began (compared to half of those with prior counseling). This suggests that the 

overwhelming majority of those reporting substance abuse counseling after hire had substance abuse concerns in the 

recent period before SE hire.
III.. ECONOMIC SELF-SUFFICIENCY AND LIFE STABILITY FOLLOWING AN SE JOB MATHEMATICA POLICY RESEARCH

26

survey). Although most SE workers remained positive about work and optimistic about their 

future one year after the SE job began, the positive outlook was slightly less evident one year 

after hire than at the time of hire (Figure III.2 shows the measures with significant changes). 

Figure III.2. Attitudes and optimism before and after the SE job began 

(regression-adjusted percentages)

83

88

95

72 69

83

0

10

20

30

40

50

60

70

80

90

100

Would like a job even if does not 

need money*

Believes will be established in a 

career in five years*

Believes will be removed from illegal 

activity in five years*

Percentage

Before SE job began One year after SE job began

Source: MJS database, noninstitutional sample.

Note: Analysis weighted to correct for survey nonresponse; regression-adjusted mean percentages estimated 

from a fixed-effect model. Items shown with an asterisk (*) all have significant differences (p ≤ 0.05 level), 

as measured by a two-tailed t-test between the period before the SE job began and one year later.

One potential explanation for the findings in this section—a decrease in self-reported 

physical health, an increase in depression, and a less optimistic outlook—may be a variant of the 

Ashenfelter dip: A person’s optimism is temporarily high, so they enroll in an employment 

program. Later, optimism regresses to its usual level. In this case, the negative findings about 

optimism and depression at one year may be related to this fading of the heightened optimism 

from enrollment.22 When we revisit these outcomes in the next chapter, we use a design that is 

able to remove this potential bias from the analysis by providing a comparison group. 

D. Changes in economic self-sufficiency and life stability 

We now turn our attention to whether specific components of the SE experience might be 

associated with economic self-sufficiency and life stability one year after the SE job began. We 

use the workers’ description of SE employment (number of hours worked in a typical week and 

weeks worked), employment supports received, life stability supports received, and 

postemployment services received as independent variables in a multivariate analysis of 

outcomes about one year after the SE hire to make this assessment (Appendix D, Table D.15). 

 

22 There may be other competing explanations. For example, the stress on workers from greater exposure to 

the labor market may increase their depression at one year. Our research design does not allow us to sort out 

competing explanations or determine whether multiple factors are influential.
III.. ECONOMIC SELF-SUFFICIENCY AND LIFE STABILITY FOLLOWING AN SE JOB MATHEMATICA POLICY RESEARCH

27

We focus our discussion on components of the SE experience that show statistically significant 

relationships with more than one outcome. Such relationships cannot be interpreted as causal, 

because they might be driven by unobserved characteristics (for example, motivation) that affect 

both the SE experience and outcomes. Nonetheless, examining these associations may suggest 

promising strategies that can be used to inform policy or practice. We see the following effects: 

• Duration of SE employment is associated with increased housing stability and 

incrementally less depression. Each additional week worked in the SE was associated with 

a small increase in housing stability and a slight decrease in the depression index one year 

after the SE job began. Those working a total of six months had an 8 percentage point 

increase in the likelihood of having stable housing and a 0.18 standard deviation decrease in 

the depression index (a small effect) one year after the SE job began, as compared with

those that worked for only one week. 

• Supports after leaving the SE are associated with increased housing stability, increased 

monthly income, and incrementally less depression. Workers who reported receiving 

postemployment supports (of any kind) were associated with a 21 percentage point greater

likelihood of housing stability, a $428 increase in total monthly income, and a 0.68 standard 

deviation decrease in the depression index (a medium to large effect) one year after the SE 

job began. 

Although overall depression increased among workers (as noted in Section C), those working for 

a longer duration or receiving supports after leaving the SE were able to buffer this increase to 

some extent. The findings in this section highlight certain aspects of the SE experience that may 

be related to larger improvements in the positive findings identified for SE employment (income 

and housing stability) and to reductions in the negative findings identified, specifically those 

related to depression. In light of our finding that workers, on average, reported incrementally 

more depression at about one year after the SE job began, offering supports to workers after 

leaving the SE seems to be an especially promising strategy to cushion the transition of SE 

workers in to the wider labor market. 
IV. IMPACT OF THE SE ON ECONOMIC SELF SUFFICIENCY AND LIFE STABILITY MATHEMATICA POLICY RESEARCH

28

IV. IMPACT OF THE SE ON ECONOMIC SELF-SUFFICIENCY AND LIFE STABILITY

Key chapter findings

The SE experience at Chrysalis may increase economic self-sufficiency and life stability:

The strength of the evidence on the impact of SE employment provided by our outcomes 

study is limited because it lacks a comparison group. Without information on a group of 

individuals who are similar to SE employees but who did not work in an SE, it is hard to say 

what changes in outcomes are due to the SE experience and what changes would have occurred 

even if individuals did not have an SE job. A comparison group is needed to provide stronger 

evidence of the impact of the SE. The hiring process at Chrysalis provided us with both a group 

of SE workers and a comparison group that did not work in the SE. At Chrysalis, individuals 

seeking employment assistance with the most significant barriers to employment can enter the 

SE labor pool. Because about one in five of these individuals were never hired, we can use them 

as a comparison group, generating stronger causal evidence on the impact of the SE experience. 

Because the comparison group was eligible to receive some of the same services as the treatment 

group, comparisons between these two groups allows us to understand the impact of adding SE 

employment to the set of services individuals receive from Chrysalis. In contrast, our outcomes 

study looked at changes over time associated with the receipt of services and SE employment. 

• The confirmatory analysis suggests that employment increased by 51 percent within our most 

inclusive sample. This impact is somewhat smaller than the pre-post change in outcomes, but it is 

marginally statistically significant.

• The exploratory analysis suggests that the SE experience increased by 24 percentage points the 

likelihood of working continuously for six months at some point in the year after entering the SE 

labor pool. 

• The exploratory analysis suggests that self-reported physical health increased, with a statistically 

significant impact. 

This quasi-experimental design impact study complements the outcomes study by offering 

stronger internal validity at the cost of decreased external validity and sample size. It provides a 

more plausibly causal estimate of the impact of the SE experience than the outcomes study does, 

because it uses a comparison group to account for factors that influenced both treatment and 

comparison group members over time. For example, we would expect employment before an SE 

job to be especially low because of the Ashenfelter dip. But if similar dips occurred in both the 

treatment and comparison groups before they entered the labor pool, our comparison-group 

design would correct for these issues. The drawback of this approach is that the impact study 

estimates are relevant to a smaller group of individuals: SE employees at Chrysalis instead of SE 

employees at the seven organizations in the MJS outcomes study. The impact study also uses a 

much smaller sample size than the outcomes study. As a result, many of our estimates are 

imprecise. Thus, the analysis presented in this chapter must consider both the size and statistical 

precision of estimated effects. To achieve this objective, we use an estimate’s p-value, which 
IV. IMPACT OF THE SE ON ECONOMIC SELF SUFFICIENCY AND LIFE STABILITY MATHEMATICA POLICY RESEARCH

29

tells us the likelihood that the true impact is different from zero.23 If p < 0.05, we say the result is 

statistically significant; if p < 0.10, the result is said to be marginally statistically significant.24 

Our confirmatory analysis captures the impact of the SE experience on current employment 

and housing stability in the year after individuals enter the Chrysalis labor pool. Our exploratory 

analysis captures the impact of the SE experience and several other measures of economic selfsufficiency and life stability, including total monthly income, monthly wage and salary income, 

the share of income from work, the share of income from government transfers, arrests, and 

health (physical, mental, and substance abuse). All analyses combine propensity-score methods 

with ordinary least squares to estimate impacts (see Appendix A for details).25 As in Chapter III, 

we present results using regression-adjusted means, which account for differences in the 

characteristics and employment barriers of the treatment and comparison groups when they 

entered the pool. All estimates of impacts are captured roughly one year after entering the 

Chrysalis labor pool. 

The rest of this chapter describes our impact study population and our estimated impacts of 

the SE experience. Section A provides greater detail on how individuals enter the labor pool at 

Chrysalis and become SE employees, individuals’ characteristics, and services received from 

Chrysalis. Section B provides findings relating to the confirmatory analysis, and Section C 

provides results related to the exploratory analysis.

A. Chrysalis employment services

Chrysalis provides many services to individuals who request employment assistance. One 

such service is SE employment. Individuals who request employment services proceed through 

the organization in a predetermined manner, as depicted in Figure IV.1. They first receive an 

initial assessment and then move into a variety of training and counseling programs. Individuals 

with the highest employment barriers (as determined by a set referral and hiring process and 

guided by quantitative data) enter the Chrysalis labor pool (Maxwell et al. 2013). 

As SE work shifts become available, members of the labor pool are offered work at the 

discretion of SE management. Those hired out of the labor pool form our treatment group, and 

those who never work at a Chrysalis SE form our comparison group. Typically, individuals not 

hired from the labor pool were either deemed ineligible for employment because they violated 

Chrysalis’ sobriety requirement or left the labor pool voluntarily for another activity (including 

other employment). The implementation evaluation (Maxwell et al. 2013) suggests that 

individuals who were not hired out of the labor pool by the SE may be more ready for work, on 

average, than those who were hired. This is consistent with staff-reported perceptions of the 

process and the mission of the SE, to give employment experience to those facing the highest 

barriers. This likely selection of the hardest-to-employ individuals for SE employment could lead 

us to underestimate the effects of the SE experience. 

 

23 A p-value is based on the estimated impact and its precision (its standard error). 

24 Note that p < 0.05 implies 95 percent confidence that the true impact estimate is not zero. Although these 

cutoffs provide a way to categorize the available evidence, estimates with p > 0.10 should not be completely 

ignored, as they can still provide interesting and suggestive results.

25 Ordinary least squares is more appropriate for the impact study, because we are comparing outcomes 

between treatment and comparison group members; we are not comparing outcomes for one individual over time. 
IV. IMPACT OF THE SE ON ECONOMIC SELF SUFFICIENCY AND LIFE STABILITY MATHEMATICA POLICY RESEARCH

30

Figure IV.1. Receiving employment services at Chrysalis 

Intake

• Determine 

eligibility

• Meet with 

employment 

specialist (ES)

Initial service 

provision

• Assess skill level 

and establish 

goals

• Complete core 

curriculum on job 

preparation and 

related skills

• Complete other 

courses 

recommended by 

ES

• Clients mentored 

by ES and 

community 

volunteers

• Short-term 

mental-health 

counseling

Job search and 

initial training

• Self-directed job 

search

• Access to funds 

for additional 

vocational training

Ongoing support

• Assistance finding 

permanent, fulltime employment

• Support groups

• Career seminars

Enter 

Chrysalis 

Enterprise 

labor pool

Work at 

SE

Do not 

work at 

SE

Clients with 

significant 

barriers

Clients with less substantial barriers

Treatment Group: 

offered and accept at 

least one SE shift

Comparison Group: 

never offered SE shift or did 

not accept employment offer

Source: Maxwell et al. (2013).

Both SE workers and those in the comparison group appear to have substantial employment 

barriers (Figure IV.2), with no significant differences existing between the groups. This 

highlights the fact that individuals in the labor pool face substantial barriers to employment, with 

the treatment group containing even more hard-to-serve individuals than the comparison group. 

When they entered the Chrysalis SE labor pool, relatively few workers appeared to be 

economically self-sufficient. Among the treatment group, only 2 percent were employed, and 81 

percent of the average individual’s income came from the government. Few members of the 

labor pool appeared to be meeting life stability objectives. Among the treatment group, 17 

percent had stable housing throughout the prior year, 81 percent had arrest records, 21 percent 

lacked a high school diploma, and 10 percent had a health condition impeding their ability to 

work. The barriers faced by those in the Chrysalis labor pool were similar to those faced by 

individuals served by other organizations in REDF’s SIF portfolio; although they tended to have 

lower earnings, were more likely to be black and less likely to be white, and were older 

(Maxwell et al. 2013). 
IV. IMPACT OF THE SE ON ECONOMIC SELF SUFFICIENCY AND LIFE STABILITY MATHEMATICA POLICY RESEARCH

31

Figure IV.2. Profile of individuals entering the Chrysalis labor pool 

(propensity-score weighted percentages)

1

87

21

61

9 5 2

81

17

81

21

10

0

20

40

60

80

100

Worked in past 

week

Share income 

from government 

transfers 

Stable housing in 

past year

Ever arrested No high school 

diploma

Physical or mental 

health condition

Percentage 

Comparison group Treatment group

Source: MJS database, full propensity-score sample.

Note: Propensity-score-weighted percentages. Stable housing in past year is defined as an individual using only 

his or her own owned or rented home as housing in the year before entering the labor pool. None of the 

above differences is statistically significant at the p = 0.05 level.

Because Chrysalis provides employment and life stability supports to all clients, members of 

the treatment and comparison groups received many of the same services (Figure IV.3).26

Almost all individuals in both groups received skills training and employment transition 

supports; a statistically significant greater percentage of the treatment than the comparison group 

received work supports (91 versus 63 percent) and help in stabilizing their lives (83 versus 39 

percent). 

Figure IV.3. Services provided to treatment and control group members 

(propensity-score weighted percentages)

94

63

39

100

56

98

91

83

100

74

0

20

40

60

80

100

Any skills training Any work supports* Any life stability 

supports*

Any employment 

transition supports

Any services after 

leave program

Percentage

Comparison group Treatment group

Source: MJS database, noninstitutional propensity-score sample, Appendix D, Table D.2

Note: Propensity-score-weighted percentages.

 

26 We did not distinguish in our survey between services received from the host organization or the SE.
IV. IMPACT OF THE SE ON ECONOMIC SELF SUFFICIENCY AND LIFE STABILITY MATHEMATICA POLICY RESEARCH

32

* Treatment and comparison group significantly different at the p ≤ 0.05 level, two-tailed test.

B. Impact of SE on employment and housing: confirmatory analysis

The confirmatory analysis, around which the quasi-experimental design was designed, 

focuses on whether the SE experience increased current employment or housing stability in the 

past year as previously noted in the outcomes study. Because of the small sample size and 

examination of a single SE, we should view the results of the impact study in conjunction with 

results from the outcomes study. The analyses complement each other, allowing us to be more 

confident in our overall conclusions. 

Indeed, our impact analysis supports the conclusion in the outcomes study that SE 

employment can improve economic self-sufficiency, as SE employees are more likely to be 

employed one year after entering the Chrysalis labor pool than those who were not hired by 

Chrysalis (Figure IV.4). Both the treatment and the comparison group had large increases in 

employment between the time individuals entered the labor pool and one year later: fewer than 2 

percent of individuals in both groups worked the week prior to entering the labor pool and the 

regression-adjusted employment rates stood at 56 percent for the treatment and 37 percent for the 

comparison group one year later. The 19 percentage point increase in employment one year after 

hire for SE workers as compared to the comparison group is large and substantively significant; 

however, the p-value is only 0.094, implying that the difference is only marginally statistically 

significant, likely because of our small sample. 

Figure IV.4. Impact of the SE experience on current employment (propensityweighted percentages)

Source: MJS database, full propensity-score sample. Appendix D, Table D.16 for multivariate analysis. 

Note: Propensity-score-weighted percentages reported at entry into the labor pool; propensity-score-weighted, 

regression-adjusted percentages reported one year later. Impact shows the estimated coefficient from 

regression analysis. 

Impacts were smaller and not significant for increases in housing stability (Figure IV.5).27

We find that housing becomes more stable for individuals in both the treatment and comparison 

 

27 The results of the outcomes and impact studies may differ for at least three reasons. First, the outcomes study 

may be subject to biases not present in the impact study. Second, employment at Chrysalis may impact individuals’ 

lives differently than employment at the average SE in REDF’s portfolio. Finally, our outcomes study compares 
IV. IMPACT OF THE SE ON ECONOMIC SELF SUFFICIENCY AND LIFE STABILITY MATHEMATICA POLICY RESEARCH

33

groups and that the SE experience did not lead to significantly larger gains. The percentage in 

stable housing increased from 17 to 51 percent for the treatment group and from 21 to 38 percent 

for the comparison group (estimates at one year are regression-adjusted means). Although we 

estimate that the SE experience led to a 12 percentage point (32 percent) increase in stable 

housing, the difference is not statistically significant. 

Figure IV.5. Impact of the SE experience on stable housing (propensityweighted percentages)

21

38

17

51

12

0

20

40

60

80

100

Before SE job began One year after SE job began Impact

Percentage

Comparison group Treatment group

p = 0.287

Source: MJS database, full propensity-score sample. Appendix D, Table D.17 for multivariate analysis.

Note: Propensity-score-weighted percentages reported at entry into the labor pool; propensity-score-weighted, 

regression-adjusted percentages reported one year later. Impact shows the estimated coefficient from 

regression analysis. 

C. Impact of SE on self-sufficiency and life stability: exploratory analysis

The MJS database contains a plethora of measures that allow us to explore in greater depth 

whether the SE experience impacts economic self-sufficiency and life stability. For this analysis, 

we use the same measures of self-sufficiency and life stability as we did in the outcomes study 

(Chapter III) and typically use the noninstitutional propensity-score sample, because the followup survey provided the additional measures to assess differences between treatment and 

comparison groups about one year after they entered the Chrysalis SE labor pool. 

1. Economic self-sufficiency

We use two gauges of economic self-sufficiency: employment and income. The measures of 

employment suggest positive impacts of the SE experience (Table IV.1). SE employees were 24 

percentage points (59 percent) more likely to have worked continuously for six months at some 

point in the year after entering the SE labor pool, with the difference being statistically 

significant (p = 0.045). They also spent a greater proportion of the time after labor pool entry 

working: an SE worker spent 66 percent of the year after entering the SE labor pool working, 

 

individuals before and after they entered SE employment and received a range of services, whereas our impact study 

isolates the changes in outcomes associated with SE employment alone.
IV. IMPACT OF THE SE ON ECONOMIC SELF SUFFICIENCY AND LIFE STABILITY MATHEMATICA POLICY RESEARCH

34

compared with 47 percent for those not employed in the SE (p = 0.079 for the difference). The 

SE experience did not have a statistically significant impact on any measure of income.28

Table IV.1. The SE experience and self-sufficiency (propensity-score 

weighted percentages, except where noted) 

 

Outcomes one year after 

entering labor pool 

Estimated impact of 

the SE experience 

Outcome Comparison Treatment Impact p-value 

Employment

Worked continuously for six months in past year 41 66 24 0.045

Share of past year spent employed 47 66 19 0.079

Income 

Total monthly income $1,173 $1,032 −$141 0.500

Share of income from work 61 67 5 0.704

Share of income from government 40 24 −16 0.151

Source: MJS database, noninstitutional propensity-score sample, Appendix D, Table D.16 for estimated impact.

Note: Numbers show the propensity-score-weighted, regression-adjusted percentages for outcomes about one 

year after entering the labor pool, the impact estimated from the regression analysis, and the p-value 

associated with the impact estimate. 

2. Life stability 

We also examined the impact of the SE experience on life stability using the same five 

gauges of life stability as in the outcomes study: (1) housing, (2) recidivism (full sample only), 

(3) physical health, (4) mental health, and (5) substance abuse (Figure IV.6). We found that the 

SE experience did not impact homelessness, arrest rates, participation in substance abuse 

counseling, or mental health. However, we did find that the SE experience was associated with 

increases in self-reported physical health. Adjusting for characteristics at labor pool entry, 29 

percent of the treatment and 11 percent of the comparison group reported excellent physical 

health one year after entering the SE labor pool, a difference that is statistically significant (p = 

0.024). 

 

28 It is standard in the literature to take the natural log of income in regressions, where this value is the 

dependent variable of interest. As income can be zero, and ln(0) is undefined, we explored several transformations 

of our income measure. Our results were sensitive to the specification of the income regression (that is, whether we 

used income, ln(income), ln(income + 1), or ln(income + 0.01) as our dependent variable of interest). However,

results were similarly insignificant and imprecise across a variety of specifications.
IV. IMPACT OF THE SE ON ECONOMIC SELF SUFFICIENCY AND LIFE STABILITY MATHEMATICA POLICY RESEARCH

35

Figure IV.6. The SE experience and health (propensity-scored percentages, 

except where noted)

A. Depression index 

0.10

0.32

0.01

0.38

0.06

0.0

0.2

0.4

0.6

0.8

1.0

Before SE job began One year after SE job 

began

Impact

Standard deviations

Comparison group Treatment group

p = 0.872

B. Percent in drug or alcohol counseling in last year

32

20

27 26

7 

0 

20

40

60

80

100

Before SE job began One year after SE job began Impact

Percentage

p = 0.411

C. Percent reporting excellent health 

23

11

25 29

18

0 

20

40

60

80

100

Before SE job began One year after SE job began Impact

Percentage

p = 0.024

Source: MJS database, noninstitutional propensity-score sample, Appendix D, Table D.19 for impact estimates.

Note: Propensity-score-weighted percentages reported at entry into the labor pool; propensity-score-weighted, 

regression-adjusted percentages reported one year later. Impact shows the estimated coefficient from 

regression analysis. The depression index is an inventory of feelings indicative of clinical depression in the 

past week. It was normalized to have a mean of zero and standard deviation of one at time of hire. Scale 

reported in units of standard deviation.
V. COSTS AND BENEFITS OF THE SE EXPERIENCE MATHEMATICA POLICY RESEARCH

36

V. COSTS AND BENEFITS OF THE SE EXPERIENCE

Our CBA provides additional context for interpreting the results from the outcomes and 

impacts studies. Although both of these studies help us understand how individuals’ lives 

changed during and after SE employment, the CBA examines the relative efficiency of SEs by 

comparing the outcomes and impacts of SE employment (found in Chapters III and IV) with the 

costs of operating an SE. This chapter presents two measures of the ROIs of SEs. The main 

analysis compares the business and social costs of operating an SE to its business and social 

benefits and builds an understanding of the overall returns of operating an SE. A secondary 

analysis focuses on estimating the ROI to converting a preexisting business into an SE by 

analyzing the social element of SE operations while ignoring the business element of the SE.29

By allowing us to assess whether the benefits of SE employment outweigh its costs, we can 

examine whether SEs are an efficient way to improve the lives of individuals, rather than simply 

assessing whether they do improve lives. We conducted two CBAs to assess this efficiency: (1) 

an outcomes study CBA and (2) an impact study CBA. Because the impact study CBA is based 

on stronger causal evidence, it provides a more plausible estimate of the benefits of SE jobs per 

dollar spent by these programs. Because the outcomes study CBA is based on data from a larger 

number of SEs, it provides estimates of benefits per dollar spent for a larger group of SEs. 

 

Key chapter findings

• SEs in REDF’s portfolio have benefits that outweigh their costs. ROIs of 123 percent 

(outcomes study) or 34 percent (impact study) are as large as or larger than those associated with 

a number of similar programs. 

• Public funders have an incentive to support the SE. The average dollar spent by an SE 

generated $1.31 (implied by the outcomes study) or $0.41 (implied by the impact study) in benefits 

to taxpayers. The nonbusiness benefits of SE employment far outweigh nonbusiness costs.

• SE workers incur small financial losses from SE employment. After SE employment, they earn 

more and have enhanced life stability, but these gains do not exceed losses from reduced 

government transfers and increased expenditures on housing in the year after starting the SE job. 

• The SEs as enterprises roughly break even. For each dollar it spends, the SE receives, on 

average, $0.97 in revenues. 

The CBA explores the value of the average dollar spent by the SEs over the period analyzed 

from four perspectives: (1) society as a whole (the total benefits of the SE’s expenditures), (2) 

SE workers (benefits to individuals served by the SE’s social mission), (3) the SE itself (as a 

business venture), and (4) taxpayers not directly involved with SE (benefits to the community, 

excluding those directly benefiting from the SE).30 SE workers may benefit from the SE 

experience as their lives stabilize and they gain economic self-sufficiency. The SE itself benefits 

based on the revenues it makes from selling goods and services in the market and the subsidies it

receives in support of its social mission. Taxpayers (excluding SE workers, their friends, and 

their families) can also benefit from the SE: as individuals become more economically self-

 

29 All details on the CBA calculations can be found in Appendix B.

30 We do not explicitly discuss benefits of SE employment to the friends and relatives of SE employees; 

however, we account for these changes when calculating the total benefit of SE employment to society as a whole.
V. COSTS AND BENEFITS OF THE SE EXPERIENCE MATHEMATICA POLICY RESEARCH

37

sufficient, they pay more in taxes and receive fewer government transfers, implying cost savings 

for the government, which are in turned passed on to citizens (either in the form of lower taxes or 

increases in other services provided). Our measure of benefits to society as a whole combines the 

benefits accruing to all three parties, as well as those enjoyed by the friends and relatives of SE 

workers (included to capture any changes in income received by SE workers from these 

individuals).

The benefits and costs that accrue to each of these parties differ. Indeed, a benefit to one 

party often comes at a cost to another. Table V.1 shows, for each benefit measure, whether we 

expect the SE to increase (+), reduce (−), or have no impact (0) on benefits to a particular party. 

For each cost measure, it shows whether we expect a specific party to bear a cost (+) or not (0) 

for providing the SE experience. A question mark (?) indicates that we cannot anticipate how the 

SE will affect the benefit or cost. These directions of the anticipated benefits and costs are based 

on the assumption that the SE increases self-sufficiency and helps stabilize the lives of its 

workers. These outcomes would lead us to expect, in the income domain, workers’ earnings to 

increase, the money (including housing) they receive from the government to decrease, and the 

transfers they get from friends and relatives to decrease. The total net benefit to workers is 

ambiguous because their earnings gains could be offset by reductions in government benefits and 

decreased monetary or other support from friends and relatives. That is, improved selfsufficiency requires an individuals to spend their own earned money instead of receiving monies 

from the goverment, which could produce net monetary losses. Similarly, for taxpayers not 

directly involved with the SE, the net benefit is ambiguous. Although we anticipate that 

taxpayers will gain because SE workers pay higher taxes and receive fewer benefits after SE 

employment, taxpayers also provide subsidies to the SE, thus decreasing the net benefit of SEs to 

taxpayers. Net benefits to the SE itself will be positive if total revenues exceed total costs; 

otherwise, they will be negative. Finally, if the sum of all benefits exceeds the total costs of 

operating an SE, society as a whole gains from SE employment.

The CBA also considers whether providing funds to SEs is efficient from the perspective of 

a policymaker focused only on the social benefits and costs of the SE experience. This additional 

analysis ignores the costs that SEs incur and the revenues they accrue that are related to 

providing goods or services. The exercise allows us to assess whether the benefits of adding a 

social mission to a business outweigh the associated costs.
V. COSTS AND BENEFITS OF THE SE EXPERIENCE MATHEMATICA POLICY RESEARCH

38

Table V.1. Anticipated costs and benefits associated with the SE

Variable 

Society 

as a 

whole 

SE 

worker 

SE as an 

enterprise 

Taxpayers 

not directly 

involved 

with SE 

Friends and 

relatives of 

SE workers 

Total benefits ? ? + ? +

+: Benefits will increase as a result of changes in variable

−: Benefits will decrease as a result of changes in variable

?: Benefits may increase or decrease as a result of changes in variable

Income

Work + + 0 0 0

Taxes and government transfers 0 − 0 + 0

Other income 0 − 0 0 +

Housing

Amount paid for housing ? − 0 + 0

Quality of life + + 0 0 0

Criminal activity + 0 0 + 0

Health 

Overall health + + 0 0 0

Substance abuse + + 0 0 0

SE revenues

Business revenues + 0 + 0 0

Grant money 0 0 + − 0

Total costs + 0 + 0 0

+:Costs are positive for these entities

Costs associated with business mission + 0 + 0 0

Costs associated with social mission + 0 + 0 0

Net present value

+: Positive net present value

?: Net present value cannot be predicted 

? ? ? ? + 

Notes: Table shows the anticipated benefits and costs of the SE experience assuming it builds self-sufficiency and 

stabilizes lives. See Appendix B for details on the assumed beneficiaries within each domain.

The CBA uses the estimates discussed in Chapters III and IV to measure the benefits of the 

SE. For the outcomes study, we use our fixed-effects specifications to quantify changes in 

outcomes over time with the average, regression-adjusted change from these specifications 

serving as our measures of benefits. For the impact study, we use the estimated effect of the SE 

experience as our measure of benefits. Where available, we use estimates from the full sample; 

otherwise, we use estimates based on the noninstitutional sample. We capture benefits in five 

domains: (1) income (changes in wage and salary income, government taxes and transfers, and 

other income), (2) housing (the cost of housing and a quality of life index based on an 

individual’s housing status), (3) criminal activity (arrest rate), (4) health (self-assessed health 

status and participation in substance abuse counseling), and (5) revenue (financial information 

provided by REDF). 

Our measures of costs reflect the perspective of the SE as an organization and include 

measures of both parts of its double bottom line: the costs associated with providing goods and 

services in the market (business mission) and with helping the target population (social mission).
V. COSTS AND BENEFITS OF THE SE EXPERIENCE MATHEMATICA POLICY RESEARCH

39

We define its costs as those faced by the SE and treat subsidies to it as a benefit for the SE and 

an offsetting negative benefit for taxpayers. Note that the differences between a cost and a 

benefit are somewhat arbitrary, and many negative benefits could be reframed as costs. For this 

study, costs are outlays by the SE or host organization on behalf of the SE. Benefits include all 

other changes associated with the SE job, which isolates costs as those occurring from the 

perspective of the SE. We systematically transformed both benefits and costs into present value 

dollars per employee served, taking into account time preferences and standardizing both into per 

employee units.31

Although the CBA results shed light on the costs and benefits of the SE experience, they 

should be interpreted cautiously. Many assumptions were required to generate the measures of 

benefits per dollar spent. Additionally, our measures of benefits are limited in several ways. We 

use only five domains and a subset of the possible constructs within those domains to measure 

benefits. Furthermore, our benefits are based on our outcomes and impact studies, which have 

their own limitations (see Appendix A). Our measures of costs also suffer from potential biases. 

Cost measures that vary based on the phase of SE development may omit important elements not 

recorded on balance sheets and may be subject to reporting errors, misclassification errors, or 

other accounting issues (see Appendix B for a more detailed discussion). Because our estimates 

of benefits and costs required many assumptions, we explored how our estimates changed when 

we varied our assumptions regarding how we translated outcomes and impacts to benefits, how 

we discounted different values to account for time preference, and how benefits evolved from the 

start of our study onward. These analyses are detailed in Appendix B and suggest that our broad 

findings hold under a variety of modeling choices. 

 Appendix B provides a full description of this process. 

The rest of this chapter provides details on the results of each component of the CBA and an 

assessment of the benefits generated by the SE per dollar spent on these programs. Section A 

provides an overview of our benefit estimates, and Section B describes the costs per worker of 

providing the SE experience. In Section C, we combine these elements to estimate the ROI in the 

SE and the benefits to various parties per dollar spent. 

A. Benefits of the SE experience

Results from both the outcomes and impact CBAs suggest that SEs (particularly those that 

are large and developed) generate benefits for society as a whole, although SE workers 

themselves actually incur small losses (Figure V.1). The outcomes study CBA suggests that 

every employee hired by the SE creates a benefit worth, on average, $22,632 to society as a 

whole, $13,250 to taxpayers not directly involved with the SE, and $9,822 to the SE itself.32

Benefits are negative (−$165) for SE workers. The negative benefit occurs even though, as 

Chapters III and IV show, SE employees gain in many ways from their experience. It is simply 

the case that monetary losses associated with decreased government transfers and increased 

outlays on housing (the costs of improved self-sufficiency) are not fully offset by increases in 

wage and salary income and other benefits. Also note that we exclude from this analysis any 

 

31 Benefits reaped in subsequent years were discounted using an 8 percent interest rate, so they could be 

compared with costs incurred earlier to derive net present values. 

32 We assume that all benefits of SE employment have been realized by one year after individuals began their 

SE job or entered the Chrysalis labor pool. See Appendix B for details.
V. COSTS AND BENEFITS OF THE SE EXPERIENCE MATHEMATICA POLICY RESEARCH

40

benefits of the SE experience that we cannot convert into monetary quantities. For example, 

although we include income changes, we cannot include in our analysis other benefits related to 

the increases in employment in the year after the SE job began. If employment leads individuals 

to assess themselves as happier (or less happy), it will lead us to underestimate (or overestimate) 

the benefits of SE employment. 

The impact study CBA suggests that the benefits of the SE employment are somewhat 

smaller than estimated in the outcomes study (because the estimated impacts of SE employment 

reported in Chapter IV are smaller than those implied by the pre-post changes in outcomes 

reported in Chapter III). When the benefits and costs are monetized and summed, employment 

for one SE worker at Chrysalis produces, on average, $8,745 in benefits to society as a whole, 

$6,593 in benefits to SEs, $2,751 in benefits to taxpayers not directly involved with the SE, and 

losses of $443 for workers. Three factors might cause differences in benefits across the outcomes 

and impact studies. First, benefits in the outcomes study might be overestimated, perhaps 

because of the Ashenfelter dip (discussed in Chapter III), or because of another factor that would 

have led outcomes to improve in the absence of SE employment. Second, Chrysalis, the only SE 

in the impact study, could produce smaller benefits than other SEs. This difference might relate 

to differences in the population served by the SE or differences in the program itself.33 Finally, 

note that benefit calculations for the outcomes study are based on changes associated with the 

full set of services provided by the SE and host organization, whereas our impact study CBA 

estimates benefits are based on changes derived from only the SE employment experience.

Figure V.1. Monetary value of benefits per SE employee served (dollars)

$22,632

$8,745

-$165 -$443

$9,822

$6,593

$13,250

$2,751

-$5,000

$0

$5,000

$10,000

$15,000

$20,000

$25,000

Outcomes study Impact study

Society as a whole SE worker SE Taxpayers not directly involved with SE 

Source: MJS database and cost capture project, Appendix B, Table B.4.

Note: For the outcomes study, estimated benefits use the full sample (stable housing, work income) or 

noninstitutional sample (all other benefits), excluding Buckelew. For the impact study, estimated benefits 

use the full propensity-score sample (stable housing, work income) or noninstitutional propensity-score 

sample (all other benefits). Benefits of SE employment to the friends and relatives of SE workers are 

excluded from this graph but are included in benefits to society as a whole. 

 

33 Both of the first two factors may be at play. The pre-post change in employment at Chrysalis is larger than 

the effect estimated in the impact study, and pre-post estimates for Chrysalis tend to be slightly smaller than those 

for all SEs combined. Appendix B provides SE-specific measures of costs and benefits from the outcomes study. 
V. COSTS AND BENEFITS OF THE SE EXPERIENCE MATHEMATICA POLICY RESEARCH

41

To explore the source of the losses incurred by SE workers, Table V.2 tabulates the costs 

and benefits of the SE experience by domain. In both the outcomes and impact studies, housing 

is the force that drives losses for SE workers. Both the outcomes and impact studies imply 

housing stability increases with SE employment. Although our calculation of benefits accounts 

for the improved quality of life associated with this change, it also accounts for the associated 

increase in the amount individuals pay for housing, which drives the net losses they incur (see 

Appendix B for details). That is, improved self-sufficiency requires an individual to spend more 

of his or her money on housing, which is largely responsible for the net losses. 

Table V.2. Benefits associated with the SE experience by domain (dollars)

Variable 

Benefit to 

society as a 

whole 

Benefit to 

SE worker 

Benefit to 

SE

Benefit to taxpayers not 

directly involved with SE 

Outcomes study 

Income $6,254 $3,774 $0 $2,755

Work 6,254 6,254 0 0

Government transfers 0 −2,755 0 2,755

Transfers from others 0 275 0 0

Stable housing 612 −3,070 0 3,682

Criminal activity 10,126 0 0 10,126

Health −869 −869 0 0

SE revenues 6,509 0 9,822 −3,313

Impact study 

Income $1,257 −$235 $0 $1,647

Work 1,257 1,257 0 0

Government transfers 0 −1,647 0 1,647

Transfers from others 0 155 0 0

Stable housing 75 −1,332 0 1,407

Criminal activity 503 0 0 503

Health 1,125 1,125 0 0

SE revenues 5,786 0 6,593 −807

Source: MJS database and cost capture project, Appendix B, Table B.4. 

Note: For the outcomes study, estimated benefits use the full sample (stable housing, work income) or noninstitutional sample (all other benefits), excluding Buckelew. For the impact study, estimated benefits use 

the full propensity-score sample (stable housing, work income) or noninstitutional propensity-score sample 

(all other benefits). Benefits of SE employment to the friends and relatives of SE workers are excluded.

Notable variation in benefits across SEs (Table V.3) suggests the enterprises’ values may 

vary across all organizations. Because some organizations contributed very few individuals to 

our samples (in particular, Taller and Weingart), SE-specific estimates should be interpreted with 

caution, as they are estimated less precisely. Nevertheless, this variation may reveal important 

differences in benefits across SE models. The largest benefits accrue from the SEs run by Taller 

and Weingart, each of which generates more than $45,000 in benefits per employee hired. But 

even ignoring the smallest SEs, we see substantial variation. CHP generates the largest benefit 

among the larger SEs, creating benefits worth $36,378 per employee served. The benefits to 

society as a whole produced by the SE at CEO are the smallest, at $18,195 per employee. 
V. COSTS AND BENEFITS OF THE SE EXPERIENCE MATHEMATICA POLICY RESEARCH

42

Table V.3. Total benefits per employee at each SE (outcomes study, dollars)

Social enterprise 

Benefit to 

society as a 

whole 

Benefit to SE 

worker Benefit to SE 

Benefit to taxpayers not 

directly involved with SE 

CEO $18,195 $1,684 $8,052 $9,171

Chrysalis 21,871 −206 6,593 15,473

CHP 36,378 11,449 7,465 17,577

CRC 20,883 −4,823 18,801 7,111

Taller 65,780 6,994 73,947 −15,578

Weingart 46,042 27,293 41,245 −10,751

Source: MJS database and cost capture project, Appendix B, Table B.4. 

Note: For the outcomes study, estimated benefits use the full sample (stable housing, work income) or noninstitutional sample (all other benefits), excluding Buckelew. For the impact study, estimated benefits use 

the full propensity-score sample (stable housing, work income) or noninstitutional propensity-score sample 

(all other benefits). Benefits of SE employment to the friends and relatives of SE workers are excluded.

CEO = Center for Employment Opportunities; CHP = Community Housing Partnership; CRC = Community Resource 

Center; Taller = Taller San Jose; Weingart = Weingart Center for the Homeless. 

The benefits of the SE experience accruing to SE workers, the SE, and taxpayers not directly 

involved with the SE also vary substantially by SE. For example, whereas CHP produced an 

$11,449 benefit to workers, employees at CRC lost $4,823 when they worked in that SE. 

Taxpayers gained the most per employee from the SEs hosted by Chrysalis and CHP, each 

generating more than $10,000 in benefits per employee. The smaller SEs (at Taller and 

Weingart) generated losses for taxpayers (that is, the benefits produced exceeded grant money 

paid to the organizations), possibly because they are less mature than other SEs. Some SEs 

appear to produce proportionally more benefits for SE workers, and others tend to produce larger 

benefits for the SE itself. The SE experience will benefit different groups based on the associated 

changes across domains. For example, SEs leading to larger changes in housing and smaller 

changes in work income will produce larger gains for taxpayers but smaller gains for employees. 

B. Costs of the SE experience

SEs face costs in fulfilling both their business and the social missions. Across all SEs in the 

outcomes study CBA, the total cost of running an SE was approximately $10,136 per employee 

hired (Figure V.2). These costs were primarily directed toward business activities, with $7,738 

per employee funding business expenses, such as capital and materials. SEs spent an additional 

$2,398 per employee fulfilling their social missions, including money spent on work and life 

supports provided to employees, and other costs SEs would not face if they did not try to employ 

individuals with substantial barriers (for example, training).34 Costs were somewhat lower for 

Chrysalis, the impact study site. Chrysalis spent $6,506 per employee in total, with $1,080 per 

employee funding the SE’s social mission. This difference is likely due, at least in part, to the 

fact that Chrysalis’ SEs are relatively well established. These businesses, established in the 

1990s, did not have to pay many of the start-up costs faced by the newer SEs. 

 

34 Although SEs may pay their workers more than what they would have been paid by other firms, we 

categorize wages as expenses associated with SEs’ business mission, rather than their social mission, unless a firm 

specifically imputed in its income statements the extra cost paid to workers associated with an SE’s social mission.
V. COSTS AND BENEFITS OF THE SE EXPERIENCE MATHEMATICA POLICY RESEARCH

43

Figure V.2. SE costs (dollars) 

$7,738

$5,426

$2,398

$1,080

$0

$2,000

$4,000

$6,000

$8,000

Outcomes study Impact study

Business mission Social mission

Source: MJS database, cost capture project data and employee counts. Appendix B, Table B.1.

Per-employee costs vary substantially across the organizations in our study (Table V.4). 

Total costs ranged from $7,477 to $81,624 per employee, with CEO, Chrysalis, and CHP having 

relatively low costs, and CRC, Taller, and Weingart having higher costs. Business costs 

accounted for most of the dispersion, ranging from $74,446 per employee at Taller to $4,631 at 

CEO. Social costs varied less widely in an absolute sense. CHP spent the least on its social 

mission ($621 per employee), and Weingart spent the most ($13,726). The share of costs 

attributable to the SE’s social mission also varied greatly, from 9 percent at Taller to 50 percent 

at CRC. This variation is due to the services received by SE employees provided both by the SE 

and the host organization that are unavailable to those who are not SE workers. If host 

organizations provide many services to all of their clients (SE workers and non-SE workers 

alike), it may drive down the social costs of the SE.

Several other factors might explain why costs vary across SEs. First, we captured costs over 

a limited period of time (18 months), which could imply differences in the types of costs 

captured. For example, costs in some organizations may contain development or one-time 

expenses (such as opening or closing a business line or moving to a transitional employment 

model) accruing during our time frame. Second, costs will differ with different business and 

service delivery models. For example, the average length of SE employment ranged from 3.2 

months at CEO to 7.6 months at Weingart (Appendix B, Table B.1), and per-employee costs may 

vary with program length. Further, because business models and industries differ, some SEs bear 

higher training costs as they train workers for more highly skilled occupations or bear higher

business expenses from higher operating costs (Maxwell et al. 2013). 

$10,000
V. COSTS AND BENEFITS OF THE SE EXPERIENCE MATHEMATICA POLICY RESEARCH

44

Table V.4. Per-employee SE costs (outcomes study, dollars)

Per-employee costs 

Organization Total Business mission Social mission 

CEO $8,049 $4,631 $3,417

Chrysalis 6,506 5,426 1,080

CHP 7,477 6,855 621

CRC 19,913 9,923 9,990

Taller 81,624 74,446 7,177

Weingart 48,731 35,006 13,726

Source: MJS database, cost capture project data and employee counts, Appendix B, Table B.1.

CEO = Center for Employment Opportunities; CHP = Community Housing Partnership; CRC = Community Resource 

Center; Taller = Taller San Jose; Weingart = Weingart Center for the Homeless. 

C. Putting the benefits and costs of the SE experience in context

Our measures of benefits per dollar spent by the SEs allow us to examine the value of SE 

investments to each party. These per-dollar benefits are calculated by dividing the total benefit of 

the SE experience to a specific party by the cost per employee (spent by the SE). We also 

estimate the ROI of the SE experience to society as a whole (the percentage return on investment 

in the SE). These transformations of benefits and costs lead to the following results from the 

outcomes study (Figure V.3): 

• Society as a whole sees a positive ROI. Each dollar spent by the SE produces benefits 

worth $2.23 for society as a whole. This production implies a return on investment of 123 

percent, or that each dollar the SE spends produces benefits that offset the SE’s initial 

expenditure, as well as an additional $1.23 of gains. 

• Costs slightly exceed benefits to the average SE. The typical SE in the outcomes study 

suffers a small monetary loss. For each dollar it spends, it receives $0.97 in revenues, 

implying that revenues are 3 percent smaller than costs (or that profit is −3 percent). 

• Dollars spent on SE workers produce negative benefits. Consistent with the negative 

estimated benefit, the average dollar spent by the SE does not create a monetary benefit for 

workers. Each dollar spent by the SE is associated with a loss to workers of $0.02. This 

occurs because although the earned income of SE workers rises, the workers face cuts in 

government transfers and increased housing costs.

• Taxpayers enjoy substantial gains from SE expenditures. Taxpayers not directly 

involved with the SE gain benefits worth $1.31 for every dollar spent by the SE, meaning 

that every dollar spent by the SE eases taxpayer burden by $1.31. Taxpayer gains stem 

primarily from benefits associated with decreases in criminal activity and government 

transfers (Appendix Table B.5). 
V. COSTS AND BENEFITS OF THE SE EXPERIENCE MATHEMATICA POLICY RESEARCH

45

Figure V.3. Benefits per dollar spent (dollars)

2.23

1.34

-0.02 -0.07

0.97 1.01

1.31

0.42

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

Outcomes study Impact study

Dollars

Society as a whole SE worker SE Taxpayers not directly involved with SE 

Source: MJS database and cost capture project, Appendix B, Table B.4. 

Note: For the outcomes study, estimated benefits use the full sample (stable housing, work income) or 

noninstitutional sample (all other benefits), excluding Buckelew. For the impact study, estimated benefits 

use the full propensity-score sample (stable housing, work income) or noninstitutional propensity-score 

sample (all other benefits). Benefits of SE employment to the friends and relatives of SE workers are 

excluded from this graph but are included in benefits to society as a whole. 

These aggregated estimates mask the substantial heterogeneity in the overall ROI and 

benefits per dollar spent across SEs (Table V.4). Some SEs did not generate value for society as 

a whole, whereas others did. The lowest measure of benefits per dollar spent, for Taller, was only 

0.81, suggesting each dollar spent only created $0.81 in benefits. This value corresponds to an 

ROI of −19 percent, or a 19 percent loss to society on investments made by the SE. Even though 

Taller produced the highest total benefits per employee ($65,780, see Table V.2), it had the 

highest costs ($81,624, Table V.4), leading to its low ROI. Conversely, the highest ROI was 387 

percent at CHP, implying each dollar spent created $3.87 in benefits over the initial expenditure. 

This organization had both above-average benefits per employee ($36,378, see Table V.2) and 

the second-lowest costs of all the SEs ($7,477 per employee, see Table V.4). 

The per-dollar benefits for the SE, workers, and taxpayers not directly involved with the SE

also varied across SEs. Half of the SEs, in particular those that were older or larger, made a small 

financial profit or broke even, whereas others faced substantial losses despite hefty subsidies 

(Appendix B). Per-dollar benefits to workers and taxpayers varied far more than per-dollar 

benefits to SEs. Per-dollar benefits for SE workers ranged from −$0.24 at CRC to $1.53 at CHP 

(Table V.5). Similarly, taxpayers did not benefit on net from expenditures by Taller and 

Weingart but gained more than $1.00 for every dollar spent by Chrysalis, CEO, and CHP. 

Although this variation is quite large, some of it likely stems from our small sample sizes. 

Estimates for all organizations are based on a small number of SE workers; this problem is 

particularly relevant for CRC, Taller, and Weingart. Therefore, measures of benefits per dollar 

spent by SE should be interpreted with particular caution. 
V. COSTS AND BENEFITS OF THE SE EXPERIENCE MATHEMATICA POLICY RESEARCH

46

Table V.5. Benefits per dollar spent by SE (outcomes study, dollars except 

where noted) 

Benefits per dollar spent by group 

Organization 

Society as a 

whole (ROI) SE workers SE

Taxpayers not directly 

involved with SE 

CEO $2.26 (126%) $0.21 $1.00 $1.14

Chrysalis 3.36 (226%) −0.03 1.01 2.38

CHP 4.87 (387%) 1.53 1.00 2.35

CRC 1.05 (5%) −0.24 0.94 0.36

Taller 0.81 (−19%) 0.09 0.91 −0.19

Weingart 0.94 (−6%) 0.56 0.85 −0.22

Source: MJS database and cost capture project, Appendix B, Table B.4. 

Note: Estimated benefits use the full sample (stable housing, work income) or noninstitutional sample (all other 

benefits), excluding Buckelew. Benefits of SE employment to the friends and relatives of SE workers are 

excluded from this table but are included in benefits to society as a whole.

CEO = Center for Employment Opportunities; CHP = Community Housing Partnership; CRC = Community Resource 

Center; Taller = Taller San Jose; Weingart = Weingart Center for the Homeless. 

Estimates from our impact study CBA confirm the value of the SE experience. Although 

small sample size remains an issue, this CBA is based on more plausibly causal estimates of the 

benefits of the SE experience. Impact estimates suggest that each dollar spent by Chrysalis 

produces $1.34 for society as a whole and $1.01 of revenues for the SE. That dollar also eases 

taxpayer burden by $0.42 but leads SE workers to lose $0.07. Thus, the impact study CBA 

suggests that Chrysalis is a profitable business that saves taxpayers money and benefits society 

as a whole. 

Finally, we considered the benefit of the SE experience from a purely social perspective, 

ignoring the SEs’ business mission. Some decision makers may care little about the business 

aspect of the SE, preferring to focus only on the costs of assisting the SE population and the 

benefits accruing to SE workers, their friends and family, and taxpayers. We calculate a social 

ROI of the SE experience by defining costs as those that the SE faces from serving the target 

population, beyond the standard costs of operating the SE and defining benefits by excluding the 

business revenues received by the SE (those associated with providing goods and services in the 

market). This value provides a measure of the ROI from adding a social component to a 

preexisting business. From this perspective, ROIs in SEs are quite high (Figure V.4). The 

outcomes study implies that each dollar spent toward the SEs’ social missions creates $6.72 in 

benefits for society as a whole, for an ROI of 572 percent. The impact study implies smaller but 

still large gains. Each dollar spent toward the SEs’ social mission generates $2.74 of value, for 

an ROI of 174 percent. 
V. COSTS AND BENEFITS OF THE SE EXPERIENCE MATHEMATICA POLICY RESEARCH

47

Figure V.4. Social ROI: Comparing nonbusiness costs and benefits 

(percentages)

572%

174%

0%

200%

400%

600%

800%

Outcomes study Impact study

ROI

Source: MJS database and cost capture project, Appendix B, Table B.4. 

Note: For the outcomes study, estimated benefits use the full sample (stable housing, work income) or 

noninstitutional sample (all other benefits), excluding Buckelew. For the impact study, estimated benefits 

use the full propensity-score sample (stable housing, work income) or noninstitutional propensity-score 

sample (all other benefits). 
APPENDIX A

COLLECTING AND ANALYZING DATA ON OUTCOMES 
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APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.3 

This appendix describes the Mathematica Jobs Study (MJS) database and its use in the 

analysis presented in this report. The database contains information on individuals who requested 

employment services between April 1, 2012 and March 31, 2013 from one of the seven 

organizations hosting an REDF-supported social enterprise (SE): Buckelew, Center for 

Employment Opportunities (CEO), Chrysalis, Community Housing Partnership (CHP), 

Community Resources Center (CRC), Taller San José (Taller), and Weingart Center (Weingart).

The database was first collated in 2013 for analysis presented in the interim report on REDF’s 

SEs (Maxwell et al. 2013) and initially contained information collected by organization staff at 

three points in time (Table A.1) when a person: (1) entered the organization and requested 

employment services (intake); (2) was referred to or hired by the SE (baseline survey); and (3) 

left the SE or after six months of employment, whichever came first (exit survey). In fall 2013 

and winter 2014 the database was expanded to include information from the MJS follow-up 

survey, which collected information on individuals about one year after the baseline survey was 

administered (Appendix E provides a copy of the survey instrument). 

Table A.1. MJS data collection points

Population of 

interest Field dates Period covered Information 

Intake

Baseline 

survey

Exit 

survey

Followup 

survey

Individuals hired by 

and referred to SE

Individuals hired by 

and referred to SE

Individuals hired by 

SE 

Individuals hired by 

and referred to SE

April 1, 2012 to 

March 31, 2013

April 1, 2012 to 

March 31, 2013

May 22, 2012 to 

August 29, 2013

September 4, 

2013 to March 

21, 2014

Adult working life until 

survey date

Adult working life until 

survey date

Total time or first six 

months working at SE, 

which ever greater

Date of last survey 

(exit or baseline) to 

present

Demographic and background 

characteristics

Work history and employment 

barriers

SE work experience and changes 

in employment barriers

Employment, housing, income, 

criminal activity, health, substance 

abuse, education and training, 

attitudes toward work; future plans; 

demographics; and SE work

Notes: The follow-up survey was designed to be conducted about one year after the baseline survey; in practice, 

an average of 13 months elapsed between the baseline and follow-up surveys. Some follow-up survey 

questions assessed activities over the past 12 months instead of since the date of the last survey.

In this report, we drew on the MJS data in three ways. First, we used information collected 

at baseline and follow-up on individuals who worked at SEs to determine the changes in 

outcomes over time associated with SE employment (the outcomes study). Second, we used 

information from a single organization (Chrysalis) on individuals who were employed in the SE 

(treatment group) and those who were eligible to work at the SE but never hired (comparison 

group) (the impact study) to estimate the effects of SE employment on employment and life 

stability. Finally, we quantified the benefits estimated in the outcomes and impact study and 

linked those benefits to costs in cost benefit analysis (CBA), as explained in Appendix B. 

In this appendix, we describe the processes used to conduct the follow-up survey and the 

methods used to analyze information from it for the outcomes and impact studies. The interim 

report (Maxwell et al. 2013) provides equivalent information for data collection for intake and 

baseline and exit surveys. Section A provides details on the population in the MJS; Section B 

explains our follow-up survey efforts; Section C discusses the analytic methods used for the 

outcomes and impact studies; and Section D discusses the limitations of our approaches. 
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.4 

A. The MJS populations and samples

The MJS database contains information on individuals hired into an SE at Buckelew, CEO, 

Chrysalis, CHP, CRC, Taller, and Weingart between April 1, 2012 and March 30, 2013, the 

sample for the outcomes study. It also contains information on individuals who entered the 

Chrysalis labor pool during the same period, the sample for the impact study.1 Figure A.1 

illustrates our success in collecting data for both groups (Maxwell et al. 2013 provides details). 

The sample built for the outcomes study (panel A) was derived from all individuals hired into an 

SE and the sample built for the impact study (panel B) was derived from individuals at Chrysalis 

who were referred to the labor pool from which SE workers were hired. All of these individuals 

were taken through the informed consent process for enrolling in the MJS. All 609 who 

consented to be in the study (84 percent) completed a baseline survey. The 527 (84 percent) that 

were eventually hired into an SE all provided (or had staff provide) information on that 

experience through an exit survey. The remaining 82 individuals in the Chrysalis labor pool who 

were not hired into an SE serve as the comparison group for our impact study. 

Only slight differences exist in the distributions of the target population and MJS study 

participants across organizations (Table A.2). No statistically significant differences (p ≤ 0.05) 

were found between the distributions of the target population and MJS samples across 

organizations in chi-squared tests of the overall distributions of employees across organizations 

or two-tailed t-tests of each organization’s employment share. The majority of individuals in the 

target population and samples were hired by Chrysalis with no more than 5 percent coming from 

CRC, Taller, or Weingart.

 

1 The impact study is restricted to individuals from Chrysalis because it was the only organization that 

collected baseline data on individuals who were eligible to work at an SE but did not do so.
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.5 

Table A.2. Population and samples (percentages, except where noted) 

Target population MJS sample 

Potential survey sample 

(eligible for follow-up) 

Number

MJS 

inclusion

Outcomes 

study Number

MJS 

inclusion

Outcomes 

study Number

MJS 

inclusion

Outcomes 

study

Buckelew 52 7.2 8.6 41 6.7 7.8 41 7.0 8.2

CEO 119 16.5 19.7 94 15.4 17.8 94 16.2 18.8

Chrysalis 452 62.6 55.2 383 62.9 57.1 356 61.2 54.8

Treatment 333 46.1 55.2 301 49.4 57.1 274 47.1 54.8

Comparison 119 16.5 0.0 82 13.5 0.0 82 14.1 0.0

CHP 46 6.4 7.6 42 6.9 8.0 42 7.2 8.4

CRC 31 4.3 5.1 31 5.1 5.9 31 5.3 6.2

Taller 13 1.8 2.2 13 2.1 2.5 13 2.2 2.6

Weingart 9 1.2 1.5 5 0.8 0.9 5 0.9 1.0

Total 722 100.0 100.0 609 100.0 100.0 582 100.0 100.0

Source: MJS database. 

Notes: Unweighted analysis. Neither two-tailed t nor chi-squared tests found statistically significant (p ≤ 0.05) 

differences between the target and MJS samples. Both Chrysalis and Weingart numbers changed from the 

interim report (Maxwell et al. 2013). Of the 106 MJS participants Chrysalis identified as in their labor pool 

and not hired into an SE, 24 were found to have worked at the SE in the past and ineligible to be in the 

comparison group and 48 individuals did not consent to be in the MJS. We computed the Chrysalis 

comparison target population based on the assumption that 22.6 percent had worked in an SE in the past 

(same rate as those who consented). Weingart follow-up surveying included two additional MJS 

participants who completed a baseline survey and for whom staff completed an exit survey but did not 

include the four additional participants that did not complete the baseline survey despite being employed by 

Weingart’s SE during the study period. 
A.6 

APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

Figure A.1. MJS target population and samples

Panel A: Outcomes study

Hired by SE

Target 

population

(n=603)

Consented 

and 

completed 

baseline 

survey

(n=603)

Completed exit Survey

(n=527)

Completed 

follow-up 

survey

(n=242)

Incarcerated 

(n=242)

Deceased

(n=3)

Worked at SE

(n=527)

Sample for 

follow-up

(n=500)

Panel B: Impact study

Entered 

Chrysalis 

labor pool

Target 

population

(n=452)

Consented 

and 

completed 

baseline 

survey

(n=383)

Completed 

exit survey

(n=301)

Completed 

follow-up survey

(n=170;

138 treatment, 

32 comparison)

Incarcerated 

(n=21;

16 treatment,

5 comparison)

Deceased

(n=1

[treatment])

Worked at 

Chrysalis

(n=301)

Sample for 

follow-up

(n=274)

Did not work at 

Chrysalis

Comparison Group

(n=82)

Source: MJS database.

SE = Social Enterprise.
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.7 

B. Follow-up survey

Individuals in both the outcomes and impact studies were potentially eligible for the followup survey, with one notable exception: we randomly sampled 91 percent of individuals who 

worked at Chrysalis for the follow-up survey for budgetary reasons. We omitted individuals 

served by this organization instead of randomly sampling SE workers across organizations 

because Chrysalis workers comprised about half of all SE workers in the target population (Table 

A.2). Removing 9 percent of its workers thus maximized our average within-organization 

statistical power, subject to our constraint to survey only 500 individuals hired by SEs. In total, 

we attempted to survey 582 individuals, 500 of whom could inform the outcomes study (as they 

were hired into an SE), 354 of whom could inform the impact study (as they were members of 

the Chrysalis labor pool),2

 and 274 who could be included in both studies. This section of the 

appendix provides details on our follow-up survey efforts and the resulting samples, including 

how we located respondents, our response rates, missing data, the characteristics of our sample, 

and the weights we developed to increase the representativeness of our samples. 

1. Locating potential respondents

We used a hard copy of the survey (administered by telephone) and a program developed for 

data entry to record information from the 582 MJS participants selected for the follow-up survey. 

All respondents received a $20 gift card as a token of appreciation upon survey completion. To 

capture information about one year after participants completed the baseline survey, we initiated 

calls at three different time periods (Table A.3). Individuals who completed the baseline survey 

toward the beginning of the MJS period were called first. 

Table A.3. Timing of follow-up survey calls

Group Dates of baseline survey 

Advanced letter 

sent Calling started Calling ended 

I April 1, 2012 to September 30, 2012 August 30, 2013 September 4, 2013 March 14, 2014

II October 1, 2013 to December 31, 2012 September 27, 2013 October 1, 2013 March 14, 2014

III January 1, 2013 to March 31, 2012 October 29, 2013 November 2, 2013 March 14, 2014

Calling potential respondents with valid contact information continued until March 14, 

2014, and surveys were taken for individuals who initiated contact through March 21, 2014. An 

average of 405 days elapsed between completion of the baseline and follow-up surveys, with the 

period ranging between 8 and 23 months (Table A.4). Individuals completing the baseline survey 

early in our study period (that is, group I) were typically contacted to complete the follow-up 

survey more than one year after they began work at an SE; individuals administered the baseline 

later in the study period (group III) typically provided follow-up survey information less than 

one year after the baseline survey.

 

2 We attempted to survey individuals for the outcomes study based on their hire by an SE. A small number of 

these individuals (less than one percent), reported never working at the SE in the follow-up survey. Despite this, we 

refer to the group of individuals hired by the SEs as “SE employees” throughout this report.
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.8 

The time elapsed between surveys is of interest because some of the questions on the followup survey asked respondents about activities “since the last survey.” Given that 64 percent of SE 

workers (individuals in the outcomes study) completed an exit survey (36 percent had an SE or 

host-organization staff member complete this survey), the average individual had previously 

provided information for the MJS only about 8 months prior to completion of the follow-up 

survey. Because individuals in the impact study’s comparison group did not complete an exit 

survey, our last formal contact with individuals in that population (the baseline survey) was 

about 14 months prior to the follow-up survey. 

Table A.4. Follow-up survey timing (percentages, except where noted)

Outcomes 

study 

respondents 

Impact study respondents 

Treatment 

group 

Comparison 

group 

Number of respondents 242 138 32

Last interviewed at baseline 36.4 27.2 100.0

Last interviewed at exit 63.6 72.8 0.0

Average number of months since last survey 8.3 7.6 13.8

0-5 months 29.8 32.6 0.0

6-11 months 50.0 52.2 37.5

12-17 months 17.8 13.0 43.8

18-23 months 2.5 2.2 18.8

More than 23 months 0.0 0.0 0.0

Average time since left social enterprise (months) 4.3 3.6 n.a.

Still employed 21.6 27.9 n.a.

0-5 months 33.9 33.1 n.a.

6-11 months 27.5 23.4 n.a.

12-17 months 8.3 5.6 n.a.

18-23 months 2.3 3.2 n.a.

More than 23 months 0.0 0.0 n.a.

Average time since baseline interview (months) 13.2 13.1 13.8

0-5 months 0.0 0.0 0.0

6-11 months 41.3 41.3 37.5

12-17 months 50.8 52.2 43.8

18-23 months 7.9 6.5 18.8

More than 23 months 0.0 0.0 0.0

Source: MJS database.

Note: Analysis is unweighted.

Because our target population was difficult to track—people with histories of homelessness, 

unstable housing, or incarceration—we developed and used three different levels of tools to 

reach, locate, and survey study participants. Each level used an increasing amount of resources to 

locate and survey an increasingly difficult portion of the sample and complete the number of 

surveys needed to achieve a 55 percent response rate in each organization. We used only level I 

tools in some organizations, started with level II tools in organizations in which we believed it 

would be difficult to achieve response targets, and deployed level III tools in organizations that 

did not have a 55 percent response rate when two weeks remained in the survey period. Once we 
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.9 

completed the number of surveys needed at an organization, we moved telephone and locating

resources from that organization to those without the desired completion rate but we continued to 

administer surveys if a respondent called in. The tools are described below and in Table A.5. 

Level I tools. The tools in the first level were developed to reach and interview participants 

who were expected not to require a high level of effort, although the tools did include both 

extensive in-house and field locating efforts (Table A.5). Such efforts included: 

• Structuring the baseline survey to obtain information which would help locate participants

(for example, the baseline survey obtained information on up to three different ways to 

contact the respondent, as well as friend and relative contact information, and places the 

respondent liked to frequent) 

• Asking for updates on contact information in the exit survey 

• Sending holiday cards in December 2012 and “happy summer” cards in August 2013 to 

remind participants about the study, maintain contact, and identify those with outdated 

address information 

• Verifying and updating all contact information by processing it through a database of public 

records (a subscription-based product called Accurint)

• Sending a pre-survey notification letter to inform participants of our plan to reach them via 

telephone to complete a survey 

• Removing cases from the active sample when we uncovered a deceased or incarcerated 

participant3

 

Cases in which incorrect contact information could not be updated were sent to trained web 

locators to conduct web searches using customized search strings that provide optimal results. 

We used this information, in conjunction with information from the baseline and exit surveys, to 

create a profile on an individual without current contact information. Field locators used these 

profiles to locate participants. 

Level II tools. The second level of tools involved partnering with the SE host organizations 

to reach participants in an attempt to capitalize on the SEs’ relationships with participants built 

prior to follow-up. These tools were developed in conjunction with SE host organizations, 

although REDF greatly facilitated their development by encouraging organizations to work with 

us. Level II tools were created and applied at the outset for participants from Chrysalis, which 

hired about half of the participants. They were also applied at other organizations when 

responses to level I tools dropped. Examples of level II tools include hiring staff from the 

organizations as field locators to find participants and encourage survey completion; obtaining 

updated contact information from the organizations; reaching out to participants during contacts 

with the organization (for example, post-SE employment support); and providing organization 

staff with monetary incentives to reach out to participants (for example, through email blasts) 

about survey completion. 

 

3 If the release date from prison or jail was during the fielding period, participants were suspended from 

locating efforts until their release date. Our primary means of identifying an incarceration was through the Victim 

Information and Notification Everyday database (www.vinelink.com). 
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Level III tools. We developed and applied our third level of tools, which involved using 

Facebook, during the final push for survey respondents (two weeks prior to the end of the survey 

period). Because we did not gain consent from participants to directly engage them in survey 

efforts using social media (for example, to “friend” them on Facebook), we used social media 

only to help locate participants. Facebook profiles allowed us to identify additional information 

about nonrespondents4 and send personalized emails to nonrespondents with privacy settings that 

allowed us to access contact information. In addition, some organizations posted a message on 

their Facebook page that asked nonrespondents to complete the survey. 

Table A.5. Activities undertaken at each level of survey effort

Level I. Used traditional survey methods 

Obtained extensive contact information on baseline and exit surveys

Identified and updated contact information with "holiday cards" for address updates

Updated all contact information before advance letters were sent and periodically during survey period

Developed profiles of nonrespondents using web searches and information from baseline and exit surveys

Used profiles to help field locators find respondents 

Used mapping software (zip stream) to assign cases to appropriate field staff 

Identified incarcerated individuals weekly and monitored release dates to remove temporarily or permanently from 

survey efforts

Sent email blasts and letters to nonrespondents

Provided a $20 incentive for respondents

Heavily monitored staff productivity and locating efforts using administrative data on success rates 

Monitored response rates weekly by organization and moved resources mid-way through surveying to ensure each 

organization achieved a minimum response rate 

Level II. Obtained assistance from SE host organization 

Hired organization staff who knew program and population as field locators 

Had organizations provide updated contact information for nonrespondents, including current work status in the SE 

Provided staff at organizations with incentives ($20 gift card per completed survey) linked to their activities (for 

example, email blasts, phone calls)

Integrated information about the follow-up survey into organization's support services (for example, retention staff 

reminded participants about the study; provided phone numbers, provided phones or on-site gift cards to 

respondents completing a survey)

Had funder impress to the organization the importance of the follow-up survey and efforts to help reach and survey 

participants

Level III. Used Facebook for locating 

Asked organizations to post reminder messages about follow-up surveying on their Facebook page

Used Facebook to find additional information that might facilitate locating

Sent personalized email to nonrespondents who had Facebook access

 

4

 Anyone not successfully surveyed—except for those located who refused to be surveyed, the incarcerated, 

and the deceased—was referred to Facebook locating. Locators confirmed two to four points of contact (for 

example, name, location, date of birth, or family member) before adding information from Facebook to the locating 

profile or sending an email through Facebook.
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2. Response to the follow-up survey

We describe the results of the follow-up survey in two ways: results of the effort and rates of 

response (Table A.6). Results include completed interviews, partially completed interviews, and 

so forth. Our efforts yielded 274 completed questionnaires, which form the basis of what we call 

the noninstitutional sample. Of the 274 completed questionnaires, 32 were from the comparison 

group, leaving information from 242 respondents for the outcomes study. The 32 surveys from 

the comparison group and 138 surveys from Chrysalis SE workers (the treatment group) are 

available for the impact study. Rates of response can be used to describe the success of the 

operational aspects of the survey and the representativeness of our sample. The 274 completed 

surveys represent a 51 percent response rate.5 Response rates varied by organization (Table A.6), 

and this variation was statistically significant (not shown in table). The smallest organizations 

(Taller and Weingart) had response rates over 70 percent; larger organizations had rates that 

ranged from 58 (CRC) to 44 (CEO) percent. 

 

5 The response rate is calculated using the Council of American Survey Research Organizations definition: the 

number of completed (C) or partially completed (P) surveys divided by the number of eligible respondents in the 

sample (R): Survey Response Rate = [(C+P) / R] x 100. Eligible individuals were those who were not deceased nor 

N j incarcerated. We also computed weighted response rates, with weights estimated as w = , where an MJS j Rj

participant from organization j with a target population of Nj and number of eligible respondents Rj receives a weight 

of w. The weighted response rates revealed similar patterns.
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

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Table A.6. Survey efforts (percentages unless otherwise reported)

Survey results (numbers of cases) 

Attempted

Completed 

surveys Incarcerated Dead 

Buckelew 41 21 1 0

CEO 94 32 20 1

Chrysalis 356 170 21 1

Treatment 274 138 16 1

Comparison 82 32 5 0

CHP 42 21 2 0

CRC 31 18 0 0

Taller 13 8 1 1

Weingart 5 4 0 0

Totals 582 274 45 3

Outcomes study 500 242 40 3

Impact study 356 170 21 1 

Survey response/information rates 

Noninstitutional sample 

(responded to survey) 

Full sample 

(responded to survey + incarcerated) 

Buckelew 52.5 53.7

CEO 43.8 55.9

Chrysalis 50.9 53.8

Treatment 53.7 56.4

Comparison 41.6 45.1

CHP 52.5 54.8

CRC 58.1 58.1

Taller 72.7 75.0

Weingart 80.0 80.0

Totals 51.3 55.1

Outcomes study 53.0 56.7

Impact study 50.9 53.8

Total possible respondents 534 579

Outcomes study 457 497

Impact study 334 355

Source: MJS database.

Note: Unweighted analysis.

Our survey efforts also identified 45 participants who were incarcerated and 3 participants 

who were deceased. Although we did not attempt to collect information from individuals that we 

discovered were incarcerated, knowing a person was in jail or prison at follow-up provides 

valuable information. In particular, our confirmatory analysis for the impact study requires 

information for two key outcomes (as discussed in Chapter III): whether the participant was 

employed in the week before the survey and whether they were in stable housing since the 

baseline survey. As we can posit that incarcerated individuals spent some time living in non-
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

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stable housing since baseline (that is, jail or prison) and did not work in the past week for pay, 

these individuals can be included in some analyses. Together with the noninstitutional sample, 

we have a full sample of 319 individuals, 282 of whom are in the outcomes study and 191 of 

whom are in the impact study. Our full sample information rate (response rate accounting for 

incarcerated individuals) was 55 percent (319/[582 - 3]) and varies by organization, with the 

smallest SEs having the highest rates. Of note, a 10 percentage point differential exists between 

CEO’s response rate and full sample information rate, due to its relatively high incidence of 

incarcerated participants. Differences in the information rates across the organizations mean that 

the distribution of the full sample among the organizations significantly differs from the 

distribution of the target population (Table A.2), which we correct for with weighting (see 

Section A.5 of this appendix). For the impact study, we also must consider differences in 

information rates by treatment status. That is, differences arise because our treatment group had 

higher information rates than the comparison group (56 versus 45 percent, a statistically 

significant difference). This difference largely appears to be driven by demographic differences 

between the samples, which we explore in detail in Section B.4 of this Appendix.6

3. Approach to missing data 

Nonresponse can also be item-specific; that is, respondents may fail to provide information 

on all questions. As Table A.7 shows, rates of missing data for key variables were generally low. 

Almost all respondents reported employment and about 90 percent completed the housing 

module of our survey, allowing us to estimate if they were stably housed in the year before 

follow-up. Less than 8 percent of the noninstitutional sample was missing information related to 

health, substance abuse, criminal activity, and receipt of transfer payments. Some variables had 

relatively high rates of missing data, notably total income (nearly 20 percent in the 

noninstitutional sample for the outcomes study). This variable drew information from several 

different questions, leading to the high rate of missing data (that is, missing information on any 

source of income makes the total income variable missing).7 

 

6 We further demonstrate in Section B.4 that differences in final sample characteristics between the treatment 

and comparison groups can be controlled for in a regression framework. 

7 Within our impact study, we saw only one statistically significant difference in rates of missing data by 

treatment-comparison group status. Given the large number of variables tested, we would anticipate one such 

significant difference to occur simply by chance (1 in 20 tests of significance at the 5 percent level will be 

statistically significant by chance even if no differences actually exist between two groups). 
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Table A.7. Missing data (percentages, except where noted) 

Data element

Outcomes study Impact study 

Noninstitutional 

sample Full sample 

Noninstitutional 

sample Full sample 

Key outcomes

Employed last week 0.4 0.4 0.0 0.0

Stable housing in past year 10.3 8.9 12.4 11.0

Other outcomes of interest

Employment duration and continuity 4.1 17.7 4.7 15.2

Total Income 19.4 30.9 21.8 30.4

Wage and salary income 7.0 6.0 10.6 9.4

Percentage of income from work 22.7 33.7 25.9 34.0

Percentage of income from government 22.7 33.7 25.9 34.0

Arrested in past year 0.0 0.0 0.0 0.0

Depression index 0.0 14.2 0.0 0.0

Physical health 0.0 14.2 0.0 11.0

Substance abuse counseling in past year 0.4 14.5 0.0 11.0

Attitudes toward work 0.4 14.5 0.0 11.0

Facilitating factors

Education and training

Enrollment status 0.4 14.5 0.6 11.5

Training completed 0.8 14.9 1.2 12.0

Attitude toward work 0.4 14.5 0.6 11.5

Counselor assessed needs 1.2 1.1 0.0 0.0

Lagged indicators

Worked in week before intake 0.4 0.8 0.6 0.5

Stable housing in year before baseline 3.7 3.2 2.9 2.6

Total income 7.4 6.7 2.9 2.6

Wage and salary income 0.4 0.4 0.0 0.0

Percentage of income from work 16.5 17.0 14.1 13.6

Percentage of income from government 16.5 17.0 14.1 13.6

Arrest record 4.5 4.6 4.7 5.2

Depression index 0.4 0.4 0.6 0.5

Physical health 0.0 0.0 0.0 0.0

Substance abuse counseling past year 0.0 0.0 0.0 0.0

Demographics (from intake)

Age 0.4 0.4 0.0 0.0

Veteran status 3.7 3.2 3.5 3.1

Hispanic 1.7 1.8 2.9 3.1

Marital status 0.4 0.4 0.6 0.5

Number dependents 1.2 1.4 0.6 0.5

English fluency 1.2 1.1 0.0 0.0

Source: MJS database.

Note: Analysis is unweighted. Elements without missing data are not reported. 
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

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4. Sample characteristics 

The noninstitutional and full samples are non-random subsamples of the target population 

because (1) not all individuals consented to be in the MJS, (2) we randomly dropped 27 SE 

workers from Chrysalis out of the follow-up noninstitutional sample,8

 (3) we were not able to 

locate all participants, and (4) when located, not all participants agreed to answer survey 

questions. We compared our samples and the target population to understand differences 

between them in various measures of life stability, education, and attitudes, as well as differences 

in demographics from the intake data,9 using both descriptive and multivariate analyses. The 

descriptive analyses used two-tailed t-tests to test for statistically significant differences in 

averages and chi-squared tests to determine statistically significant differences in distributions. If 

significant differences in the distributions existed, we used two-tailed t-tests to test for 

statistically significant differences between categories in the distribution.

Some notable differences emerge in the descriptive analysis (Table A.8). As compared to the 

target population, individuals in the noninstitutional and full samples for the outcomes study 

were more likely to meet REDF’s definition of SE employment (working 32 hours within a fourweek period), to have been employed prior to the baseline survey, to have higher levels of 

education, and to be less likely to have used temporary housing prior to baseline. Individuals in 

the noninstitutional sample were under-represented at CEO and less likely to have been arrested 

or convicted of a crime. Differences also exist between those in the MJS from Chrysalis and 

those included in the impact study. Individuals in the noninstitutional and full samples had more 

stable housing before requesting employment services, and individuals in the noninstitutional 

sample are less likely than those in the MJS to have been arrested in the past.

 

8 Although individuals were randomly selected from Chrysalis to be dropped, this selection resulted in a 

change in the composition of individuals across SEs and thus must be accounted for.

9

Maxwell et al. (2013) demonstrated that MJS participants were similar to the target population. 
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

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Table A.8. Target population and sample differences (percentages, except 

where noted) 

Outcomes study Impact study 

Target 

population

Noninstitutional 

sample

Full 

sample MJS

Noninstitutional 

sample

Full 

sample

Number of people 603 242 282 383 170 191

SE work experience

Hired by SE 100.0 100.0 100.0 80.2 81.1 80.5

Met REDF hours requirement 83.8 87.9* 88.1* 61.8 65.7 65.8

Life stability at baseline

Worked for pay in the ... before intake

Week 16.7 18.1 17.4 17.8 17.3 15.9

Month 28.8 33.6* 33.0* 30.1 32.1 31.2

6 months 49.3 53.8 53.6* 53.8 52.4 53.4

Year 61.1 66.0* 65.5* 66.8 65.5 66.5

Worked continuously in the past...

Year 40.8 45.3 43.0 49.1 49.1 49.5

More than 1 year but fewer than 2 years 10.5 11.2 10.0 13.2 13.8 13.3

Two years or more but fewer than 5 years 15.9 16.4 15.6 13.2 14.4 14.9

More than 5 years 20.8 18.1 20.7 16.4 16.2 16.0

Never 11.9 9.1 10.7 8.1 6.6 6.4

Housing was temporary in the past...

Week 38.2 36.3 38.0 45.7 38.8* 41.6*

Month 41.2 37.6 39.5 48.1 39.4* 42.7*

6 months 50.8 45.7* 48.3 54.1 44.8* 48.6*

Year 58.3 52.6* 54.6 60.5 50.9* 54.1*

Criminal activity

Arrested 80.9 75.1* 77.9 80.8 76.2* 78.8

Convicted and sentenced to jail or prison 69.4 62.9* 67.8 70.7 67.3 70.4

If ever convicted, most recent conviction

In past year 6.8 5.6 6.7 4.6 2.8 4.0

More than 1 year but fewer than 2 years 

ago 13.9 10.6 11.2 14.6 12.1 11.9

More than 2 years but fewer than 5 years 

ago 38.3 34.5 36.3 33.5 27.1 28.6

More than 5 years ago 41.1 49.3 45.8 47.3 57.9* 55.6*

English language ability

Percentage native English speaker 96.4 97.1 96.8 95.9 97.6 97.4

Percentage good English (if not native) 47.1 20.0 42.9 46.7 0.0 20.0

Percentage fair English (if not native) 41.2 60.0 42.9 40.0 75.0 60.0

Percentage poor English (if not native) 11.8 20.0 14.3 13.3 25.0 20.0

General support

Needs a lot of support 58.7 62.3 63.6* 80.0 81.7 83.7

Needs some support 30.5 28.0 26.5 18.8 17.2 15.3

Needs no support 10.8 9.7 9.8 1.2 1.2 1.1

Income below 200 percent of federal poverty 

level 97.8 97.5 97.5 100.0 100.0 100.0

Don’t know income 1.8 2.1 2.2 0.0 0.0 0.0
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

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Table A.8 (continued)

Outcomes study Impact study 

Target 

population

Noninstitutional 

sample

Full 

sample MJS

Noninstitutional 

sample

Full 

sample

Education and training at intake

No high school diploma 26.9 21.2* 23.6 24.6 22.8 21.8

High school diploma/graduate/GED 44.4 41.1 42.2 47.0 45.5 47.9

At least some college 28.7 37.7* 34.2* 28.5 31.7 30.3

Demographic characteristics at intake

Male 80.9 73.6* 76.6* 78.3 74.0 76.3

Average age (years) 40.6 41.7 40.9 43.5 44.3 43.9

Hispanic 16.3 17.0 17.6 19.9 20.7 21.2

Race

Black 59.7 55.6 56.8 66.4 65.7 65.3

White 24.4 27.2 24.8 20.8 20.1 19.5

Other race or refused 15.9 17.2 18.3 12.8 14.2 15.3

Marital status

Single 74.9 71.8 72.9 73.8 70.8 70.9

Married or in a domestic partnership 11.2 13.9 13.0 11.2 14.9 14.3

Divorced or widowed 8.3 8.0 7.6 10.0 8.9 9.0

Separated 5.6 6.3 6.5 4.9 5.4 5.8

Dependents

Average number of dependents 0.89 0.85 0.89 0.73 0.79 0.78

No dependents 55.3 55.9 54.0 63.5 63.1 63.0

Military

Veteran 5.7 5.2 4.8 7.7 8.0 7.6

If veteran, percentage served (all that 

apply)

Gulf War period (8/90 to 3/95) or later 12.1 16.7 15.4 12.9 15.4 14.3

5/75 to 7/90 54.5 50.0 53.8 58.1 61.5 64.3

Before 5/75 (Vietnam era or earlier) 18.2 25.0 23.1 22.6 23.1 21.4

Missing 15.2 8.3 7.7 6.5 0.0 0.0

Organization

Buckelew 8.7 8.8 7.9 0.0 0.0 0.0

CEO 19.2 12.6* 17.6 0.0 0.0 0.0

CHP 7.7 8.8 8.3 0.0 0.0 0.0

CRC 5.2 7.5 6.5 0.0 0.0 0.0

Chrysalis 55.5 57.3 55.0 100.0 100.0 100.0

Taller 2.2 3.3 3.2 0.0 0.0 0.0

Weingart 1.5 1.7 1.4 0.0 0.0 0.0

Source: MJS database.

Note: Unweighted estimates. An asterisk (*) indicates a significant (p < 0.05) difference between the target 

population (outcomes study) or MJS (impact study) and other samples as measured by a two-tailed t-test 

or a chi-squared statistic test for significant differences in distributions (for example, race) and a two-tailed 

t-test to test for significant differences between each category in distributions with significant differences. 
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

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The descriptive analysis provides a broad view of how samples differ but does not show 

which variables might predict sample inclusion or parse out what drives differences in response 

rates across and within grantees. We used a probit analysis to assess which characteristics are 

most strongly associated with an individual from the target population or MJS also being in our 

samples (indicated by Yijs = 1 if participant i attached to an SE at organization j is in sample s).10

This multivariate analysis controls for the characteristics collected at intake (Xi, including 

indicators for key variables being missing for individual i) and either organization fixed effects 

(γisj, for the outcomes study) or treatment status (indicated by Hiredi=1 for the impact study). 

The specifications for the outcomes (equation 1) and impact (equation 2) studies are:

(1) ()|1Pr( ) ijs XY i Xiss ijs ++Φ== γθα

(2) Pr( 1| , ) ( ) Y X Hired is i = =Φ + + αθ β s s i X Hiredi

where Φ represents the standard normal cumulative distribution function. Statistically significant 

probit coefficients on the group indicators suggest differences in the characteristics of the group 

from the target population. We report results as marginal effects evaluated at the mean values of 

the independent variables. 

Our multivariate analyses for the outcomes study show that, compared to the target 

population (Table A.9): 

• Individuals who met REDF’s definition of employment are 11 percentage points more likely to 

be in the noninstitutional sample and 12 percentage points more likely to be in the full sample. 

• Men, individuals who are not married or in a domestic partnership, and those that used 

temporary housing are less likely to be in both samples. 

• More educated individuals and individuals assessed by organization staff to need more 

support are more likely to be in both the noninstitutional and full samples. 

• Individuals employed by Taller and Weingart are more likely to be in the noninstitutional 

sample than those from Chrysalis. Individuals employed by CEO, CRC, CHP, and Taller are 

more likely to be in the full sample.

Fewer factors predict inclusion in the samples used for the impact study. Most importantly, 

treatment status does not significantly predict sample inclusion, suggesting that the treatmentcomparison group differences in response rates are not large or problematic once we account for 

observable differences. Additionally, some of the differences in the samples will be mitigated by 

our weighting scheme, developed in the next section, which is designed so that the demographic 

characteristics of individuals in the samples more closely mimics those of individuals in the 

target population.

 

10 Using a logit model instead to predict the propensity score does not lead to changes in our overall 

conclusions.
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

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Table A.9. Probit estimation of sample membership 

Outcomes study Impact study 

Non- Noninstitutional institutional 

sample Full sample sample Full sample 

Sample

Target 

population

Target 

population MJS MJS

Sample size 603 603 383 383

Work experience

Hired by SE n.a. n.a. 0.087

[0.067]

0.068

[0.068]

Met REDF definition of employment 0.111*

[0.027]

0.120*

[0.024]

n.a. n.a.

Life stability at baseline

Worked for pay in the week before intake -0.036

[0.084]

-0.049

[0.059]

-0.004

[0.073]

-0.068

[0.075]

Worked for pay in the year before intake 0.087

[0.082]

0.108

[0.066]

-0.044

[0.062]

0.006

[0.062]

Temporary housing in the past week 0.028

[0.038]

0.076*

[0.027]

0.030

[0.083]

0.045

[0.083]

Temporary housing in the past year -0.122*

[0.041]

-0.120*

[0.049]

-0.191

[0.084]

-0.172

[0.085]

Ever arrested -0.059

[0.035]

-0.134

[0.074]

-0.070

[0.107]

-0.077

[0.107]

Ever convicted of a crime -0.003

[0.029]

0.088

[0.064]

0.028

[0.092]

0.089

[0.092]

Needs a lot of support 0.123*

[0.062]

0.227*

[0.037]

0.094

[0.073]

0.144)

[0.074]

Needs little support -0.026

[0.080]

-0.060

[0.076]

0.083

[0.277]

0.116

[0.265]

Income below 200 percent of federal 

poverty level

0.005

[0.141]

-0.133

[0.126]

n.a. n.a.

High school diploma/graduate/GED 0.062

[0.041]

0.065*

[0.014]

-0.120

[0.078]

-0.166

[0.079]

Some college or more education 0.182*

[0.050]

0.183*

[0.024]

-0.050

[0.065]

-0.045

[0.066]
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

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Table A.9 (continued)

Outcomes study Impact study 

Non- Noninstitutional institutional 

sample Full sample sample Full sample 

Demographics

Male -0.159*

[0.031]

-0.138*

[0.034]

-0.127

[0.068]

-0.086

[0.068]

Age 0.001

[0.001]

0.001

[0.001]

0.003

[0.003]

0.002

[0.003]

Hispanic -0.091

[0.068]

-0.076

[0.083]

-0.058

[0.093]

-0.045

[0.094]

Black -0.076

[0.053]

-0.062

[0.049]

-0.068

[0.084]

-0.043

[0.083]

Other race or refused 0.043

[0.037]

0.074*

[0.032]

0.021

[0.098]

0.096

[0.096]

Married or in a domestic partnership 0.142*

[0.037]

0.105*

[0.049]

0.139

[0.090]

0.135

[0.091]

No dependents -0.007

[0.024]

-0.021

[0.028]

0.005

[0.058]

-0.003

[0.058]

Veteran -0.007

[0.041]

-0.021

[0.035]

0.048

[0.108]

0.012

[0.110]

Organization (Chrysalis is the comparison group)

Buckelew -0.009

[0.047]

0.038

[0.054]

n.a. n.a.

CEO -0.025

[0.045]

0.166*

[0.046]

n.a. n.a.

CHP -0.021

[0.040]

0.069*

[0.032]

n.a. n.a.

CRC 0.0372

[0.051]

0.085*

[0.036]

n.a. n.a.

Taller 0.327*

[0.045]

0.393*

[0.047]

n.a. n.a.

Weingart 0.393*

[0.119]

0.217

[0.165]

n.a. n.a.

Source: MJS database.

Note: Unweighted estimates. Acronyms defined at beginning of report. Robust standard errors in brackets. 

Appendix C provides definitions of variables. An asterisk (*) indicates a significant coefficient (p < 0.05) as 

measured by a two-tailed t-test. 

5. Nonresponse weights

We developed and applied nonresponse weights to reduce selection bias that could arise 

from differences in observed characteristics between the noninstitutional and full samples and 

the target population. For our outcomes study, in sample s (s = noninstitutional sample, full 

sample), observation i (associated with organization j) receives weight
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.21

(3) 1

Pr( 1| )

outcomes

ijs

ijs i

w

Y X = =

where Y and X are defined as before and Pr(Yijs=1|Xi) is estimated using equation (1). Likewise, 

we estimate weights for the impact study of

(4) 1

Pr( 1| , )

impact

is

is i

w

Y X Hired = =

using equation (2) to estimate Pr(Yijs=1|Xi). All weights were further normalized to have an 

average value of one (within each study and sample), allowing their sum to reflect the number of 

observations in the data. 

This weighting scheme gives observations more influence as their prominence in the sample 

becomes rarer. For example, Table A.9 indicates that individuals who lived in temporary housing 

in the year before intake were less likely to respond to the noninstitutional sample. Thus, we give 

the survey respondents that lived in temporary housing in the year before intake more weight 

than we give to respondents who did not use temporary housing; the response of the former is 

relatively rare and should be given greater weight, so that the overall prevalence of temporaryhousing use in the noninstitutional sample is closer to that in the target population. 

C. Analytic methods

We use both descriptive and multivariate methods to understand the outcomes and impacts 

associated with SE employment. This section outlines the methods used for each study. Unless 

otherwise noted, all data used in analyses presented in our report and appendices are weighted to 

be representative of the target population, as described in Section A.5. 

1. Outcomes study

We used simple averages and frequency distributions to describe each outcome of interest 

and compared its value at follow-up with its value at baseline. This allows us to assess the 

change that occurred between the time an individual was hired into the SE and about one year 

later. This analysis describes the outcomes following SE employment and changes in them and 

provides a context for interpreting multivariate outcomes and impact analyses. We used twotailed t-tests to test for statistically significant differences between baseline and follow-up 

measures in averages and chi-squared tests to determine statistically significant differences in 

distributions. If significant differences in the distributions existed, we used two-tailed t-tests to 

test for statistically significant differences between the individual categories in the distribution. 

We used a regression framework to identify average changes in outcomes given the 

characteristics of individuals at baseline and the economic environment at baseline and followup. All regressions accounted for the correlation of individual outcomes within an organization 

by clustering the standard errors at that level. To control for unchanging measured and 

unmeasured characteristics that might be correlated with improved outcomes, we used a fixedeffects specification to estimate the average change in outcomes:

(5) Yit = α + βPostit + φi + θuit + εit
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.22

where Yit is SE worker i’s outcome measure at baseline (t = b) or follow-up (t = f), Post takes a 

value of one if an observation occurred at follow-up and zero if at baseline, and φ is an 

individual-level fixed-effect. uit is the unemployment rate prevailing in the area where the SE 

operates when an individual began SE employment (t = b) or one year later (t = f).11 β is 

interpreted as the average change in an outcome from baseline to follow-up, holding factors that 

do not vary over time and the unemployment rate constant. We used ordinary least squares 

(OLS) for both continuous (for example, earnings) and binary (for example, employment) 

outcomes because non-linear models (such as a probit or logit) typically perform poorly when 

fixed-effects are included in the specification (see Nickell 1981).

We also used regression analysis to examine which organizations, program components, or 

types of workers are associated with larger improvements in outcomes following SE 

employment. The specification of interest is:

(6) =+ + + + + + αλ δ θ η η ε 1 1

outcomes Y Y EX u u if ib i ib ib if i

where Ei is a series of measures capturing the SE employment experience (such as services 

received or indicators for organization).12 In these specifications, we omit the individual fixedeffects specified in equation (5) but control for the individual’s characteristics ( X ib ) at baseline, 

including age, gender, race/ethnicity, indicators of baseline employment barriers, time between 

the baseline and follow-up surveys, and a cohort indicator (that is, an indicator quarter entered 

the SE).13 

All regressions presented were estimated using OLS, but our findings are robust to 

alternative assumptions about functional form (for example, using a probit specification for 

binary variables). In the estimation, a statistically significant δ indicates that a component of the 

SE experience predicts improved outcomes for SE workers and a significant θ coefficient 

indicates the characteristics of workers most likely to see improvement after SE employment. 

2. Impact study

We examined the impact of SE employment using propensity score methods. Prior to 

implementing these methods in a regression framework, we conducted three validation exercises. 

We first discussed the hiring process at Chrysalis with the organization and REDF staff to 

determine the face validity of using individuals in Chrysalis’ labor pool who never become 

Chrysalis employees as a comparison group. We then estimated the propensity score and 

assessed the overlap in its distribution between the treatment and comparison groups. Finally, 

we examined whether the treatment and comparison groups demonstrated baseline equivalence.

 

11 We normalize the unemployment rate to have a mean of zero and standard deviation one in this regression. 

12 Outcome variables may be more easily interpreted as a change outcome (for example, number of arrests 

since baseline) instead of the level of the outcome (number of times arrested). Because baseline levels of the 

outcome variable are included as a regressor, either interpretation is valid when interpreting all coefficients except β. 

13 We do not include controls for barriers or characteristics at one year in these regressions. These variables are 

potentially influenced by SE employment. Thus, including them in our regression could introduce additional biases. 
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.23

Based on conversations with REDF and Chrysalis staff, we concluded that our comparison 

group of individuals who entered Chrysalis’ labor pool but were never hired has face validity. 

Whether the SE hired individuals in the Chrysalis labor pool was determined by set referral and 

hiring processes. This starts with employment specialists referring individuals that they believe 

have the most significant employment barriers to the SE (Maxwell et al. 2013). Once referred, 

individuals participate in an orientation and assessment and become part of the labor pool. As SE 

work shifts become available, members of the labor pool are offered work. While in the labor 

pool, individuals can call or go to the SE office to ask if shifts are available, although this checkin is not required for employment consideration. Staff suggested that the individuals hired out of 

the labor pool were no less capable than those who were not hired. In fact, the implementation 

evaluation suggests that the individuals in our comparison group may be more work-ready than 

those in the treatment group, which could lead us to underestimate the effects of the SE 

experience. 

Our second validation test used a propensity score regression, a probit estimation model, and 

individuals’ baseline characteristics to predict whether an individual was hired into the SE at 

Chrysalis (that is, became part of the treatment group): 

(8) p = Pr�Hiredi�Y�ıb,Xib

p � = Φ(α + λY�ıb + θXibp ) 

where Y� is a vector of outcomes measured at baseline and p is the propensity score derived from 

this model. We used a set of baseline variables (Xp) selected to maximize overlap and baseline 

equivalence of the sample implied by the propensity score regression. In particular, because it is 

important that the employment histories of those in the treatment and comparison groups are as 

similar as possible, Xp includes several measures of employment.

The results from this regression are presented as the anticipated changes in probabilities 

associated with a change in the variable of interest (evaluated at average covariate values) in 

Table A.10. Patterns on the work attitudes and education variables suggest that individuals in the 

treatment group (that is, hired into the SE) were less able than the comparison group in both the 

full and noninstitutional samples. That is, individuals with lower educational attainment and less 

positive attitudes toward work were significantly more likely to be in the treatment group. 

However, patterns of life stability suggest that individuals with a more stable life (working in the 

month before intake) might be more likely to be hired into SE employment for the full sample. 

Furthermore, when the coefficients on all life stability measures are tested jointly, they are 

statistically significant for both samples, suggesting that positive life-stability factors have joint 

predictive power for being in the treatment group. 
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.24

Table A.10. Propensity score regression: in treatment group 

Noninstitutional 

sample Full sample 

Sample size 170 191

Life stability before baseline survey

Employed in week before intake

Employed in month before intake

Employed in year before intake 

Stable housing during year before baseline

Arrested (1-9 times) at baseline 

Arrested (>9 times) at baseline

Excellent self-reported physical health at baseline

Depression index

Substance abuse counseling in past year

Monthly income/100

Health insurance

Participation in social assistance programs

Share of income from government transfers

Share of income from family or friends

p-value of joint test of all barriers 

0.080

[0.048]

0.080*

[0.038]

-0.021

[0.026]

0.012

[0.030]

0.042

[0.030]

0.110*

[0.044]

0.021

[0.023]

-0.004

[0.014]

-0.089

[0.066]

0.004

[0.004]

-0.006

[0.028]

0.095

[0.112]

0.039

[0.063]

-0.146

[0.001]

0.028

0.068

[0.054]

0.126*

[0.043]

-0.033

[0.026]

0.015

[0.032]

0.040

[0.031]

0.100*

[0.044]

0.036

[0.022]

-0.010

[0.013]

-0.060

[0.052]

0.008

[0.004]

-0.024

[0.030]

0.055

[0.091]

0.083

[0.071]

-0.090

[0.138]

0.001

Education and attitudes at baseline

High school diploma/graduate/GED

Some college or more education

Believe a job is just a way of earning money

Would like a job even if did not need money

-0.087*

[0.037]

-0.073*

[0.035]

0.061*

[0.024]

-0.033*

[0.017]

-0.094*

[0.037]

-0.087*

[0.036]

0.065*

[0.023]

-0.039*

[0.017]
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.25

Table A.10 (continued)

Noninstitutional 

sample Full sample 

Demographic characteristics at baseline

Male

Age 

Hispanic

Black (white is reference)

Other race (white is reference)

Married or in a domestic partnership

No dependents

Native English speaker

0.011

[0.030]

-0.001

[0.001]

-0.065

[0.040]

0.006

[0.038]

-0.060

[0.072]

0.013

[0.028]

0.036

[0.024]

-0.048*

[0.017]

0.042

[0.043]

0.000

[0.001]

-0.048

[0.036]

0.004

[0.037]

-0.032

[0.053]

0.008

[0.034]

0.046

[0.025]

0.050

[0.115]

Source: MJS database.

Notes: Analysis is weighted to correct for survey nonresponse. Numbers represent the predicted change in 

probability associated with a characteristic when all other variables (marginal effects) are at their mean 

values with robust standard errors reported in brackets. Appendix C provides definitions of variables. An 

asterisk (*) indicates a significant coefficient (p < 0.05) as measured by a two-tailed t-test. 

For propensity score regressions to yield reliable results, the distribution of the propensity 

score for treated and comparison individuals must overlap. This implies some underlying 

randomness to who received the treatment and makes causal inference possible. We used the 

regression results presented in Table A.10 to estimate individual-specific propensity scores and 

plotted their distributions in Figure A.2 to assess overlap. Although not perfect, overlap is 

reasonable. For the noninstitutional sample, the propensity score ranges from 0.16 to 1.00 in the 

treatment group and from 0.00 to 0.95 in the comparison group. This suggests that the propensity 

score has a relatively similar range across the treatment and control groups (that is, the 

distributions overlap). Moreover, only four members of the comparison group have propensity 

scores outside of the [0.10, 0.90] range. A large number of treated individuals do have propensity 

scores above 0.90, which might be expected given the relative prominence of this group. Similar 

results hold for the full sample and the propensity score it implies. 
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.26

Figure A.2. Probability of Chrysalis employment given characteristics

Noninstitutional sample Full sample

0 20 40 60

Frequency

0 .2 .4 .6 .8 1 Propensity Score

Hired Not Hired

0 20 40 60 80

Frequency

0 .2 .4 .6 .8 1 Propensity Score

Hired Not Hired

Despite the reasonable overlap between the groups, we omit observations with propensity 

scores above 0.90 or below 0.10. Crump et al. (2006) suggest that this omission can increase

precision (particularly valuable given our small sample size). The resulting noninstitutional 

sample for the impact study contains 55 individuals hired by Chrysalis (treatment group) and 28 

individuals who were not hired (comparison group). The full sample contains 59 and 32 

members of these groups. We call these samples the noninstitutional propensity score sample 

and full propensity score sample, respectively.

In our final validation test, we consider the baseline equivalence of the treatment and 

comparison groups, using both the standard sampling weights developed in Section B.5 and the 

(inverse) propensity-score-weights implied by the regression in Table A.9. 14 This allows us to 

assess whether the study will provide moderate evidence of an impact of SE on employment and 

housing. Following the What Works Clearinghouse (WWC 2014), we suppose that if our 

treatment and comparison groups exhibit baseline equivalence, our study can provide moderate 

evidence on the impacts of SE employment as defined by CNCS (2013). We further use the 

WWC’s definition of baseline equivalence: two groups exhibit baseline equivalence with respect 

to a variable (X) if the normalized difference (the difference in the group-specific means of X

divided by the pooled standard deviation of X, or g-value) between the groups is less than 0.25. 

In general, g-values less than 0.05 in absolute value are considered negligible, and g-values from 

0.05 to 0.25 are considered moderate; variables demonstrating a difference of this size must 

serve as controls in a regression framework. Variables with g-values above 0.25 should be 

examined and could potentially lead a study to fail to provide moderate evidence (WWC 2014). 

Within the whole of the impact study samples, baseline equivalence appears to be an issue 

(Panel A of Tables A.11 and A.12). Many normalized differences exceed 0.25, including those 

related to our confirmatory outcomes. But when we select our propensity-score samples, balance 

is reasonable. Both the noninstitutional propensity-score and full propensity-score samples 

demonstrate baseline equivalence based on all variables considered (Panel B of Tables A.11 and 

 

14 The propensity score weight corrects for both survey nonresponse and selection into the treatment group.
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.27

A.12): g-values are small, and those for the confirmatory outcomes are well within established 

bounds. When we weight by the propensity-score (Panel C), most differences shrink further. 

Only one normalized difference at baseline is over 0.25 in the propensity-score weighted data, 

the proportion of individuals arrested 10 or more times in the full propensity-score sample. This 

difference is 0.30 standard deviations, suggesting that care should be taken in drawing 

conclusions on outcomes related to criminal history. Overall, however, our findings suggest we 

can be confident that our propensity score method can provide CNCS-defined (CNCS 2013) 

moderate evidence on the impact of SE employment at Chrysalis in most domains. Furthermore, 

although demographic and other background variables are generally not required to be balanced 

across treatment and comparison groups at baseline, their balance lends further credibility to a 

study and we show reasonable balance among these variables. In the samples selected for 

overlap, all baseline differences for these are smaller than 0.25 and most are below 0.10. 

These tests suggest we can confidently use the propensity-score-weighted data to analyze 

the impacts of SE employment. For all outcomes, we use OLS to estimate models of the form: 

(9) impact Y Hired Y X if =+ + + + αβ λ θ ε i ib ib i

where variables are defined as before. Ximpact is a subset of the controls used in the outcomes 

analysis. They still account for demographic and baseline characteristics of individuals but were 

chosen to generate a slightly more parsimonious regression model because of the smaller impact 

study sample size. In this specification, β can be interpreted as the effect of SE employment (at 

Chrysalis) on outcome Y at follow-up, holding demographic characteristics and baseline levels of 

Y constant. Results are largely robust to using a probit specification for binary outcomes. 
A.28

APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

Table A.11. Balance in the impact study: noninstitutional sample (percentages, except where noted) 

Panel A: 

noninstitutional sample 

Panel B:

propensity-score noninstitutional 

sample 

Panel C:

propensity-score noninstitutional 

sample, weighted by propensity score 

Hired Not hired

Normalized 

difference 

(g-value) Hired Not hired

Normalized 

difference 

(g-value) Hired Not hired

Normalized 

difference 

(g-value)

Sample size 138 32 170 55 28 83 55 28 83

Outcome variables available at baseline

Confirmatory

Employed last week (intake) 19.3 2.7 0.43* 2.9 2.9 0.00 2.3 1.7 0.03

Stable housing during last year 18.4 16.5 0.05 17.8 15.9 0.04 18.7 13.0 0.10

Exploratory

Arrested 1-9 times 58.8 60.7 -0.02 61.0 65.0 -0.05 59.6 62.4 -0.02

Arrested 10 or more times 25.1 12.4 0.27 18.6 9.8 0.15 20.7 6.9 0.23

Monthly income 547 491 0.10 485 484 0.00 448 473 -0.03

Monthly wage and salary income 167 110 0.15 142 100 0.08 122 86 0.09

Share of income from work 17.3 15.2 0.06 17.3 15.3 0.04 18.5 13.4 0.09

Share of income from government 80.1 76.1 0.11 80.0 80.8 0.00 79.2 81.1 -0.02

Excellent physical health 34.1 23.8 0.20 27.0 27.2 0.00 25.2 23.1 0.03

Depression index (standard deviations) -0.03 0.08 -0.10 0.05 0.03 0.01 0.02 0.11 -0.05

Substance abuse counseling in past year 22.0 28.4 -0.12 28.3 32.5 -0.05 27.5 32.3 -0.05

Facilitating factors at intake

Education and training

High school diploma or GED 45.2 51.8 -0.11 54.1 47.5 0.08 53.6 46.6 0.08

Some college or more education 25.9 34.5 -0.18 24.9 36.8 0.08 25.9 43.1 -0.20

Training completed 53.4 57.6 -0.07 62.4 59.1 0.04 60.5 66.9 -0.07

Attitudes

Believe a job is just a way of earning money 21.9 11.5 0.23 12.7 8.9 0.08 9.5 6.5 0.08

Would like a job even if did not need money 82.9 87.5 -0.10 83.5 85.8 -0.03 86.0 89.1 -0.05

Demographics from intake

Male 77.9 77.5 0.01 81.0 74.3 0.11 79.3 73.4 0.08

Average age 43.2 43.7 -0.03 42.6 43.9 -0.07 44.2 44.4 0.00

Hispanic (of any race) 0.16 0.34 -0.40 27.1 36.1 -0.11 35.6 42.4 -0.07

Black 16.1 34.2 0.37 49.1 46.6 0.03 45.5 44.2 0.02

White 70.3 50.6 -0.18 27.9 27.3 0.01 28.5 22.3 0.08

Married or domestic partnership 18.0 26.5 -0.15 11.6 14.6 -0.05 14.3 14.8 0.00

Without dependents 11.2 16.5 0.02 65.2 57.5 0.10 62.9 57.0 0.07

Native English speaker 63.8 62.7 -0.18* 100.0 100.0 n.a. 100.0 100.0 n.a.

Source: MJS database. 

Note: Appendix C provides definitions of variables. Item-specific nonresponse reduced the number of individuals in some cells. Estimates are weighted to represent target 

population (see Section A.5). An asterisk (*) indicates a significant difference between the treatment and comparison group (p < 0.05) as measured by a two-tailed t-test.
A.29

APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

Table A.12. Balance in the impact study: full sample (percentages, except where noted) 

Panel A: 

Panel B: Panel C:

propensity-score noninstitutional propensity-score noninstitutional 

noninstitutional sample sample sample, weighted by propensity score 

Normalized Normalized Normalized 

difference difference difference 

Hired Not hired (g-value) Hired Not hired (g-value) Hired Not hired (g-value)

Sample size 154 37 191 59 32 91 59 32 91

Outcome variables available at baseline

Confirmatory

Employed last week (intake)

Stable housing during last year

Exploratory

Arrested 1-9 times

Arrested 10 or more times

Monthly income

Monthly wage and salary income

Share of income from work

Share of income from government

Excellent physical health

Depression index (standard deviations)

Substance abuse counseling in past year

20.1

17.8

55.3

28.2

549

167

17.0

79.9

34.1

-0.06

23.8

2.3

14.8

59.7

14.7

466

99

15.2

76.1

22.6

0.13

25.4

0.46*

0.08

0.05

0.30

0.15

0.19

0.06

0.11

0.24

-0.20

-0.02

3.1

16.7

57.3

22.8

470

98

11.7

83.6

19.5

-0.01

30.1

2.6

17.2

62.1

10.8

448

75

10.5

85.6

26.2

0.06

26.6

0.02

0.00

-0.07

0.22

0.04

0.06

0.03

-0.03

-0.10

-0.05

0.05

2.4

16.9

59.6

20.9

4,356

91

14.58

81.42

20.2

0.02

25.9

1.3

21.2

55.1

6.4

395

58

8.85

86.90

20.5

0.08

22.3

0.07

-0.05

0.05

0.30*

0.06

0.11

0.12

-0.10

0.00

-0.03

0.06

Fac ilitating fa ctors at intake

Education and training

High school diploma or GED

Some college or more education

Training completed

Attitudes

Believe a job is just a way of earning 

money

Would like a job even if did not need 

money

45.5

26.7

53.0

21.6

80.6

52.7

34.2

60.5

9.9

86.8

-0.14

-0.15

-0.14

0.28

-0.15

51.0

27.3

56.5

8.0

83.8

47.7

37.1

59.0

7.5

84.6

0.11

0.04

-0.02

0.01

-0.01

51.3

28.1

57.0

6.0

85.3

54.3

36.8

57.8

5.2

89.6

-0.03

-0.10

0.00

0.03

-0.09

Demographics from intake

Male

Average age

Hispanic (of any race)

Black

White

Married or domestic partnership

Without dependents

Native English speaker

78.5

43.3

16.6

69.6

18.7

10.6

65.0

97.7

74.1

43.3

29.6

53.7

24.4

17.3

61.9

97.8

0.10

0.00

-0.33

0.32

-0.12

-0.21

0.06

0.00

80.0

42.6

27.2

48.9

27.5

11.3

66.5

98.7

70.0

43.0

31.6

52.4

25.0

16.2

55.8

97.4

0.16

-0.02

-0.07

-0.05

0.03

-0.10

0.15

0.08

75.8

43.8

30.8

49.9

26.3

15.3

61.3

98.6

67.2

43.7

35.7

49.3

28.3

15.4

57.7

98.0

0.11

0.01

-0.05

0.01

-0.01

0.00

0.04

0.03

Source: MJS database. 

Note: Weighted analysis. Appendix C provides definitions of variables. Item-specific nonresponse reduced the number of individuals in some cells. An asterisk (*) indicates a 

significant difference between the treatment and comparison group (p < 0.05) as measured by a two-tailed t-test.
APPENDIX A. COLLECTING AND ANALYZING DATA ON OUTCOMES MATHEMATICA POLICY RESEARCH

A.30

D. Key data limitations

Although the methods used to collect and analyze data for the outcomes and impact studies 

meet standards for rigor, our results still have limitations. In the outcomes study, we face 

limitations from at least four sources:

1. Findings are based only on a small set of SEs, all funded by REDF. About half of the SE 

employees worked for SEs hosted by a single organization (Chrysalis). Other SEs, or other 

transitional work programs, would have different funding structures, locations, and resources. 

As a result, the findings of our outcomes study cannot be used to describe SEs outside of 

REDF’s portfolio. 

2. Study results are based only on individuals in the target population whom we could locate 

and who chose to participate in our survey. Although our use of nonresponse weights 

mitigates this concern, it does not completely resolve it. For example, selection into 

participation in the evaluation or nonresponse at the follow-up survey might cause the 

analytic sample to differ in unobservable ways from the group of people who entered the 

organization for services, potentially resulting in biased estimates. 

3. Causal inferences cannot be made based on results of the outcomes study. Even though the 

analyses of SE outcomes provide interesting insights into the experiences following SE 

employment, all analyses for the outcomes study are descriptive, because of the lack of a 

plausible comparison group for much of the sample. Results cannot be interpreted to make 

causal inferences about the impact of SE employment on post-program outcomes. 

4. Interpreting the employment and earnings outcome measures must be done with caution 

because they could capture SE employment. They therefore should be thought of as a 

mixture of the outputs of SE employment (which are directly influenced by the SE) and 

outcomes associated with SE employment (less proximal effects).

The impact study faces similar limitations, although it gains internal validity at the expense 

of external validity. That is, limitation (1) is even more notable for the impact study, as this 

portion of our analysis includes only a single (albeit large) SE. But our impact study does 

provide moderate causal evidence, reducing limitation (3). As such, we believe the effects 

estimated by the impact study are due at least in part to SE employment; however, changes in 

outcomes may also be due in part to other factors. In addition, it must be noted that our sample 

for the impact study is quite small. Even in the full sample, inference is based on a comparison 

group comprised of only 37 individuals. 

Finally, the accuracy and reliability of the information obtained cannot be verified for either 

the outcomes or impact studies. Responses to all questions are subject to individuals correctly 

recalling and truthfully reporting answers to survey questions. Truthful and accurate reporting 

can be particularly problematic for sensitive topics, such as those in our surveys (for example, 

arrest record, housing, income, and mental health). The survey questions were designed to 

minimize these types of bias, but without reliability tests we cannot rule out the possibility that 

individuals did not truthfully and correctly answer all questions. 
APPENDIX B

COST-BENEFIT ANALYSIS
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APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.3 

A social enterprise (SE) is a mission-driven businesses focused on hiring and assisting 

people who face barriers to work. It strives to meet a double bottom line (DBL): achieve 

financial viability (business mission) and provide employment to people who might not have a 

job otherwise (social mission). The social mission creates costs in addition to the standard 

business operating costs, as SEs often subsidize the employment of their workers (by paying 

them more than the value of what they produce) or provide them with services and supports to 

overcome employment barriers and transition to employment outside the SE (Maxwell et al. 

2013). We use the experiences of individuals who were hired into one of the six SEs 

participating in the cost-benefit analysis (CBA) between April 1, 2012 and March 31, 2013 (the 

CBA population) to develop a CBA that links the costs of SE employment to its benefits and 

provides estimates of the return on investment (ROI) to SE employment. Six organizations 

participated in the CBA: Center for Employment Opportunities (CEO), Chrysalis, Community 

Housing Partnership (CHP), Community Resources Center (CRC), Taller San José (Taller), and 

Weingart Center (Weingart). Each organization housed and supported one or more SEs, received 

funding from REDF to develop or expand one or more SEs, and participated in the Mathematica 

Jobs Study (MJS) that is described in Appendix A.15

This appendix provides details on the CBA and ROI calculations. Section A describes the 

data used to estimate both costs and benefits and the overarching assumptions made in 

conducting the CBA. Section B describes how we estimated the per-employee costs associated 

with providing a worker SE employment and Section C describes how we converted the benefits 

of SE employment, described in Chapters III and IV of the report, into dollar amounts. Section D 

provides details on how we estimated the ROI associated with SE employment and explores the 

robustness of the ROI to alternative assumptions. The final section (Section E) discusses some of 

the limitations associated with our approach. 

 

A. Structuring the cost-benefit analysis

We focus the CBA on answering the question, What is the value of spending an additional 

dollar on an SE? We approached this question from four perspectives: society as a whole, SE 

workers, the SE, and taxpayers not directly involved with the SE (that is, the government and the 

community). Society encompasses not only workers, the SE, and taxpayers, but also other 

potential beneficiaries like friends and family. To answer the question posed by the CBA, we 

calculated the per-employee costs and benefits of SE employment and used them as the basis for 

computing the ROI of SE employment from each perspective. We structured our analysis from 

the point of view of an organization deciding whether to hire an additional SE worker on March 

31, 2012, the day before the first members of the CBA population began SE employment. 

We drew data from three sources: (1) the Cost-Capture Project undertaken by REDF 

provided information on costs, (2) the Mathematica Jobs Study (MJS) database described in 

Appendix A provided information on benefits, and (3) employee counts from the organizations 

provided a measure of employment and a way to develop a standard unit of analysis across 

databases. 

 

15 Buckelew was omitted from the CBA because it left REDF’s portfolio before cost data could be collected. 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.4 

1. The Cost-Capture Project

The CBA drew information on costs from REDF’s Cost-Capture Project. This initiative was 

launched by REDF in September 2013 to provide accurate information on the costs of running an 

SE. REDF asked each organization to provide information on costs that accrued between April 

2012 and September 2013 using its balance sheets and other key data sources. Organizations 

provided monthly information for the period, unless otherwise noted (Section B.1 of this 

appendix). The period roughly corresponds to the period during which the CBA population

worked in SEs: most SE employment spells lasted one to six months (Maxwell et al. 2013) and

September 2013 falls six months after the last person in the CBA population began work. Costs 

were defined as expenditures needed both to run a business (business mission) and to provide 

employment to individuals with multiple employment barriers (social mission). Thus, the data 

collected allows us to capture both business costs (that would have been incurred even without 

hiring individuals with employment barriers) and social costs (incurred by intentionally 

employing such workers). We augmented this organization-provided information with 

information from REDF on major capital expenditures (tracked by REDF for other purposes). 

The Cost-Capture Project also included information on revenues the SE received for goods and 

services sold (business mission) and revenues received for its social mission (for example, grant 

money or other subsidies from the government, REDF, or other organizations). 

2. The MJS database 

Most of the benefits of SE employment included in the CBA were estimated using the MJS 

database, which contains information on workers hired into SE employment or requesting 

employment services between April 1, 2012 and March 31, 2013 (see Appendix A for details). 

Information on individuals was obtained shortly before they started SE employment (or as they 

requested employment services) and about one year later. Key outcomes from SE employment 

that we use to capture benefits include earnings, transfer payments received from the 

government, housing, criminal activity, and health. We assessed benefits in two different ways. 

First, we determined the differences in outcomes for SE workers between the time they began SE 

employment and about one year later. We call this our outcomes study CBA, and it builds an 

understanding of how individuals’ lives changed during the SE employment experience. Second, 

we determined the impact of SE employment at Chrysalis by comparing changes in outcomes for 

SE workers to changes in outcomes for individuals that entered its labor pool but did not work in 

an SE. We call this our impact study CBA.

3. Employment counts

Information about benefits from the MJS database is available on a per-employee (that is, 

individual) basis, but information from the Cost-Capture Project is in aggregate terms. To 

calculate the ROI, we must translate numbers into the same units. We do this by translating costs 

and revenues from the Cost-Capture Project into measures of per-employee costs, which capture 

the average cost of hiring an SE worker, and per-employee measures of revenues (that is, the 

average revenue from hiring an SE worker). To translate total costs and total revenue into peremployee units, we need estimates of the total employment over the period. We estimated total 

employment by summing employment counts obtained from each organization for each month 

from April 2012 to September 2013 to estimate the number of person-months of employment 

(that is, the number of individuals employed each month). CEO provided 1,051 person-months 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.5 

of employment, Chrysalis provided 5,083, CHP provided 1,069, CRC provided 292, Taller 

provided 67, and Weingart provided 115. We transformed person-months of employment to the 

number of people employed based on information from the MJS database on average 

employment duration (see Section B). 

4. Key assumptions

We used the following general assumptions to ground our CBA, although we test how 

changing some of these assumptions would affect our analysis (Section D). Assumptions specific 

to either calculation of costs or monetization of benefits are discussed in Sections B and C. 

Throughout, we make the general high-level assumptions, some of which have assumptions 

subsumed within them: 

1. All costs of SE employment are internalized by the organization and reflected in the 

expenditures provided. 

- Workers face a zero opportunity cost of SE employment. Only 19 percent of SE workers 

were employed in the week prior to beginning work, rationalizing this choice.

1. Our measures of benefits fully capture the benefit of SE employment. For example, all 

benefits from reduced criminal activity are captured by our measure of decreased costs of 

incarceration. 

- Benefits outside those captured in this study are negligible. 

- All benefits of SE employment can be measured approximately one year after an 

individual begins work (that is, our baseline model assumes benefits stop accruing after 

the date of the follow-up survey). 

2. Funds spent by SE(s) come from an organization’s cash reserves and do not require 

additional resources (for example, fundraising expenses or interest payments). 

3. An 8 percent16 annual discount rate accurately converts costs and benefits into April 2012 

terms. 

B. Developing cost estimates

We used the following five-step process in our computations, although assumptions and 

steps were sometimes organization specific: 

1. Depreciate capital costs and incorporate them as a flow (the cost of use) and not as a stock 

(the cost of acquisition). 

2. Classify expenditures into costs associated with the organization’s business or social 

missions using the descriptions provided in the Cost-Capture Project data. Expenditures 

 

16 The 8 percent discount rate is higher than the rate typically used to evaluate government programs (for 

example, Schochet et al. 2006) for three reasons. First, such CBAs often take the perspective of the government 

funding a program and base discount rates on yields on long-term government bonds. Our CBA takes the 

perspective of a non-profit organization that faces much higher costs of borrowing, implying a higher discount rate. 

Second, SEs have both a business and a social mission and the discount rates used by businesses typically reflect the 

costs of both equity and debt financing, which increases the effective rate of time preference. Third, REDF’s social 

ROI analysis (REDF 2013) suggests an 8 percent discount rate is warranted.
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.6 

associated with providing goods and services in the marketplace are considered business 

costs and expenditures associated with serving the CBA population were considered social 

costs. If an SE classified costs as related to business or social missions, we use their 

delineation, making exceptions only as noted in Section B.1 of this appendix. 

3. Adjust cost data in the Cost-Capture Project to accurately capture the costs of running an 

SE. To do this we:

- Exclude (social) expenditures on services provided to the CBA population that were not 

contingent upon SE employment (for example, case management provided to all of the 

organization’s clients). 

- Include costs of running the SE borne by the host organization (for example, 

administrator time spent on planning) even if they were not reported as line-item 

expenditures. 

4. Estimate average per-employee monthly costs by (a) summing the discounted monthly 

expenditures across months to create a measure of total costs, and (b) dividing by the 

number of person-months of employment from April 2012 to September 2013 (see Section 

A.3 of this appendix). 

5. Estimate average per-employee costs by multiplying the per-employee monthly cost (step 4) 

by the average number of months individuals worked at the SE (Maxwell et al. 2013). 

1. Organizational nuances

The type and form of cost information in the Cost-Capture Project varied by organization: 

some organizations provided estimates that carefully delineated between social and business 

costs, while others did not. Such differences produced slight variations in our method to calculate 

the per-employee social and business costs for each organization. 

Center for Employment Opportunity. Multiple partners worked with CEO’s Californiabased SE, which was developed using REDF grant money in 2012. The SE was originally 

structured with the City of Oakland contracting with Volunteers of America (VoA) to provide 

services and supports and VoA contracting with CEO to provide employees. Rubicon, a local 

nonprofit, initially provided some supports for the employees, although that contract was 

discontinued in July 2012 and CEO directly provided those services thereafter. This complex 

structure of funding required that financial information be obtained from several sources.

• The City of Oakland provided information on funds from the California Department of 

Transportation (CalTrans) and the California Department of Corrections and Rehabilitation

(CDCR) that were routed through VoA to the SE. 

• Rubicon provided information on a grant from Tipping Point to support training for SE workers. 

• REDF provided information on funds for the social mission to CEO for work supports and 

training to SE workers and two lump-sum disbursements to VoA to support SE workers.17

 

17 Funds were released on two dates but supported operations for one year. Because funding was based on the 

number of individuals who worked at least 32 hours in four weeks, we assumed funds were spent monthly in 

proportion to the number of individuals who met this requirement. 

 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.7 

Because each set of financial data was available only from a single perspective (for example, we 

know the amount of money REDF provided to CEO but not CEO’s expenditure), we assumed 

costs and revenues are equal (that is, no entity made a profit or loss). We also assumed that

business costs are captured by the money that the City of Oakland received from CalTrans, and 

social costs are captured by the sum of Rubicon’s Tipping Point expenditures, REDF’s funding, 

and the funds received by Oakland from CDCR. 

Chrysalis. Because Chrysalis started its SE in the 1980s, our cost estimates capture those 

associated with running a mature SE. Chrysalis provided balance sheets with monthly 

information for line-item-level direct and indirect expenditures associated with running two SE 

business lines. It also provided expenditures associated with the SE’s social mission, including 

an estimate of the subsidy paid to workers at the SE (that is, the extent to which wages exceed 

the value of the labor employed) and estimated costs borne by both Chrysalis and SE staff 

associated with supervising and working with the CBA population. 

Community Housing Partnership. CHP provided monthly financial information on the 

costs associated with its two SEs (one closed and one expanded during the study period) and on 

the costs of the social services given to SE workers by the SEs, including imputed costs borne by 

CHP to support SE operations. 18 CHP staff estimated costs and included expenditures on both 

the SEs’ business and social missions. We estimated the costs associated with the business and 

social missions by assuming half of the imputed host organization expenditures supported each 

line of business. Other costs were designated as business costs if they were associated with either 

line of business and social costs if they supported social services.

19 Because CHP staff estimated 

that the SEs provide some services directly to workers that they would have received from CHP 

had they not been SE workers, we adjusted the social-mission costs to reflect these cost 

savings.20

Community Resources Center. During April 2012 to September 2013, CRC developed and 

refined its SE and hired its first cohort of workers in May 2012. Our costs therefore capture those 

associated with developing and running the SE, but omit start-up and research-related 

expenditures borne prior to April 2012. CRC provided line-item financial data for all 

expenditures or revenues that accrued during the period, as opposed to monthly data. This 

aggregation of data presented a challenge because expenditures made at different points in time 

should be discounted at different rates. To account for temporal variation, we assumed that any 

fixed costs were distributed evenly across the period and that any variable costs were 

 

 

18 We created aggregate measures when we found small inconsistencies between CHP line items and totals.

19 We made one exception to the designation of costs as business or social: the line item for “tech training/out” 

was associated with one of the lines of business from February to June 2013. We assumed that these expenses 

related to training SE workers, which would not be necessary if the SE did not hire a population with large barriers 

to employment. We therefore classified these expenditures as associated with the SE’s social mission.

20 Specifically, staff estimated that 50 percent of employees in one business line were CHP clients and that the 

SE provided 30 percent of the social services that would have been provided by CHP and that 40 percent of 

employees in the other business line were CHP clients and the SE replaced about 40 percent of their services. This 

implies that the social costs were overestimated by about 15 percent (0.30*0.50) in one business line and 16 percent 

(0.40*0.40) in the other. We therefore reduced social costs each month by between 15 and 16 percent, with the 

precise reduction determined by the number of workers employed at each business line in the month.
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.8 

proportional to the number of SE workers in a given month (see Section A).21

Taller San José. During April 2012 to September 2013, Taller’s SE changed from one 

providing permanent employment to one providing transitional jobs with social service supports. 

Costs therefore include expenditures associated with transforming the SE into a transitional 

employment program. Financial data included business costs, which we adjusted for increased 

costs due to the SE’s social mission. Social costs allocated for the additional expenditures 

required to serve the CBA population were available beginning in July 2012 and were imputed 

for April to June 2012 as the average monthly cost from the remainder of 2012. 

We created cost 

measures using approximately 50 different line items on expenses categorized into business and 

social missions. CRC’s SE had substantial losses, even though it estimated relatively low social 

costs and operated other very similar enterprises with high profit margins that did not hire 

individuals with employment barriers. We therefore assumed that if the SE did not purposefully 

hire these workers, the revenues generated by its SE (excluding any grant money or subsidies) 

would cover its variable business costs. We adjusted the total variable business costs downward 

and social costs upward, while holding total costs constant, to match this assumption. 

Weingart Center. Weingart’s SE first booked business revenue in May 2012, which means 

cost estimates include running an SE during start up but exclude start-up and research-related 

expenditures incurred prior to April 2012. Financial data were delineated by line item and 

included SE outlays and revenues, including costs incurred by Weingart in support of the SE (in 

particular, wrap-around services provided as part of the SE). 

2. Cost summary

Table B.1 provides summarizes the SE costs captured for each organization. The peremployee cost to provide an individual with SE employment averaged $9,855 ($7,539 for 

business mission and $2,316 for social mission), with a range from $6,506 at Chrysalis to 

$81,624 at Taller. Variations in costs across organization could arise with differences in:

• The specific costs captured, described above and summarized in the second column of Table B.1. 

• Average employment durations, which ranged from 3.2 months at CEO to 7.6 months at 

Weingart, as shown in the third column of Table B.1. 

• Business costs (fourth column of Table B.1), which are largely determined by the nature of 

the products or services sold. These costs had greater variation than did social costs both on a 

per-employee and per-employee-month basis. Taller spent the most per employee month on 

business costs ($21,896) while CEO ($1,461) and Chrysalis ($1,447) spent the least. 

• Social costs (fifth column of Table B.1), which stem from differences in the social supports 

provided to employees.22

 

21 We used line-item descriptions to classify costs as fixed or variable with respect to the number of workers 

hired.

 CRC spent the most per employee-month on their social mission 

($2,220) while CHP spent the least ($160). 

22 These costs of social supports are consistent with a description of their provision (Maxwell et al. 2013).

CRC, Weingart, and Taller provided wrap-around services and had higher social costs. Chrysalis and CHP provided 

a more limited set of services (other than those provided regardless of SE employment) and had lower social costs.
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.9 

Table B.1. Per-employee cost of SE employment (dollars, expect where noted)

Components of per-employee 

costs

Per-employee costs 

(monthly costs*average 

months of employment) Average 

months of 

employment

Average peremployee monthly 

cost

Organization Costs captured

Business 

mission

Social 

mission

Business 

mission

Social 

mission Total

Total Individual organization 

costs

3.7 2,054 625 7,738 2,398 10,136

CEO Running a new SE 3.2 1,461 1,078 4,631 3,417 8,049

Chrysalis Running a mature SE 3.8 1,447 288 5,426 1,080 6,506

CHP Running the SE, closing 

one business line and 

expanding another

3.9 1,776 161 6,855 621 7,477

CRC Developing and running 

the SE 

4.5 2,205 2,220 9,923 9,990 19,913

Taller Running the SE, 

transitioning to a 

transitional employment 

program 

3.4 21,896 2,111 74,446 7,177 81,624

Weingart Developing and running 

the SE 

7.6 4,606 1,806 35,006 13,726 48,731

Source: MJS database, cost-capture data, employee count data. 

Note: We used a weight for each organization proportional to the size of its CBA population to compute average 

costs across organizations.

C. Monetizing benefits

In this section, we describe how we translated each benefit into a monetary value, focusing 

on the assumptions required to transform outcomes and impacts into a dollar-denominated 

measure of benefits of SE employment. We then summarize the per-employee benefits included 

in the CBA. 

1. Individual benefit calculations

We capture benefits for the CBA in five domains: income, housing stability, criminal 

activity, health, and revenue generated for the SE. All benefits except revenue are measured with 

information from the MJS database. Revenue is measured with information from the Cost 

Capture Project. 

Income. We measure income along three dimensions: earnings, taxes paid and government 

transfers received by SE workers, and other income (mostly transfers received from friends and 

relatives). Because all income measures are captured in monetary terms and at the individual 

level, we need not convert them into per-employee dollar amounts. They are, however, provided 

on a monthly basis (for the month prior to the follow-up survey) and must therefore be 

aggregated to reflect the total change in income during the year following the start of SE 

employment. 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.10

1. Earnings. Monthly wage and salary income at the time of follow-up survey is our main 

measure of earnings. We assumed that changes in this measure occurred immediately after 

the individual began SE employment (with their SE earnings) and lasted one year, the 

approximate duration between our baseline and follow-up interviews.23

2. Taxes and government transfers. We assumed that earnings were taxed at a combined 

federal, state, and local rate of 15 percent, which means 85 cents of every dollar earned by 

workers goes to the individuals and 15 cents is a benefit to taxpayers not directly involved 

with the SE (see Emerson et al. 2000). Reductions that occurred in income received by the 

SE worker from the government (after SE employment) reflect a smaller transfer from the 

government to the SE worker that does not change the societal benefit of SE employment 

(assuming negligible changes in administrative costs associated with the transfers), but 

reduces the benefit to the worker and increases the benefit to the taxpayer. We assumed any 

changes occurred immediately after the individual began SE employment and lasted one 

year. 

3. Other income. Other income received by the worker is mostly money given to them by 

friends and relatives. Changes in this measure do not affect the benefit of SE employment to 

society as a whole as they reflect a transfer from friends and relatives to the SE worker. We 

assumed that changes in income occurred immediately after the individual began SE 

employment and lasted one year.

Housing. We estimate the benefits from stable housing in two ways: (1) housing 

expenditures, that is, reduced government expenditures on emergency housing as a benefit to 

taxpayers not directly involved with the SE and corresponding increases in housing outlays paid 

by SE workers, and (2) increased quality of life for the SE worker from having stable housing. 

1. Housing expenditures. We examined the types of shelter that individuals in unstable 

housing used in the week before SE employment to produce a snapshot of the housing used 

by those not owning or renting their home. Assuming each housing category reported was 

used equally over the period covered (for example, individuals spent half their time in a 

location if they reported staying in two different locations during the week), 19 percent of the 

nights were spent in transitional housing, 5 percent were spent in emergency housing, and 5 

percent were spent in permanent housing, with 23 percent of nights spent in their own 

home.24

Spellmen et al. (2010) quantified the costs of emergency shelters, transitional housing, 

permanent supportive housing, and fair-market rents for families and individuals in Houston 

 We therefore estimated that moving the average unstably housed individual into 

their own home for 100 days left the individual with 77 additional days in their own home, 

19 fewer days in transitional housing, 5 fewer days in emergency housing, and 5 fewer days 

in permanent supportive housing. 

 

23 We make this assumption because we can only observe changes for approximately one year after the 

baseline survey. See Section D of this appendix for a discussion of the robustness of our results to alternate 

assumptions on the persistence of benefits.

24 Unstably housed individuals who did not spend time in their own home, emergency shelters, transitional 

housing, or permanent housing mostly spent time in homes of friends and relatives, which we assume has a zero 

housing cost.
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.11

Texas in 2006. We adjusted these costs for increases in housing costs over time (1 percent 

from 2006 to 2012–2013) and cost differences between Houston and the three California 

housing markets in which SEs operated.25 Table B.2 shows the estimates from Spellman et 

al. (2010) and our transformed measures. For example, it costs $2,616 to house an individual 

in emergency housing for one month in San Diego but would cost that individual only 

$1,199 to rent a home themselves. 

We used these estimates to calculate the costs of unstable housing. For example, an SE 

worker in San Diego who did not solely use stable housing spent 23 percent of their time in 

their own home. With rent of $1,199 per month, this implies the average unstably housed 

individual spends $276 per month on rent. They also spend about 19 percent of their time in 

transitional housing, costing the government about $616 per month (19 percent times a 

monthly cost of $3,241); 5 percent of their time in emergency shelters, costing $131 per 

month (5 percent times a monthly cost of $2,616); and 5 percent of their time in permanent 

supportive housing, costing the government $119 (5 percent times a monthly cost of 

$2,272). Having the individual use only their own housing each month therefore implies a 

benefit of $865 to taxpayers not directly involved with the SE and a loss of $923 to the 

individual ($1,199 – $276). This implies a net loss of $58 per month to society as a whole. 

We performed similar calculations for each city and for individuals with and without 

families (Table B.2). 

 

25 Relative costs of housing by location (CNN Money 2014) show an increase of 94 percent for individuals 

living in and around Los Angeles, 89 percent in the San Diego area, and 184 percent in the San Francisco area. 

Housing prices were inflated over time using the index for Dallas, Texas (S&P/ Case-Shiller Dallas Home Price 

Index 2014) and transforming the 2006 average to April 2012 to December 2013 averages. 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.12

Table B.2. Monthly housing costs for the homeless and fair market rents 

(dollars)

 

Cost for Houston 

(Spellman et al. 

2010)

2012–2013 costs by city

San Diego

Los 

Angeles

San 

Francisco

Individual

Emergency shelter 1,335 2,616 2,548 3,829

Transitional housing 1,654 3,241 3,157 4,744

Permanent supportive housing 1,211 2,372 2,311 3,472

Family

Emergency shelter 1,391 2,726 2,655 3,990

Transitional housing 3,211 6,292 6,129 9,210

Permanent supportive housing 799 1,566 1,525 2,292

Fair market rents

Individual (one bedroom) 612 1,199 1,168 1,755

Family (two bedroom) 743 1,456 1,418 2,131

Monthly benefits of stable housing: taxpayers not 

directly involved with SE

No family 442 865 843 1,266

Has family 720 1,410 1,374 2,064

Increased monthly costs for stable housing: SE 

worker

No family 471 923 900 1,351

Family 572 1,121 1,092 1,641

Source: Spellmen et al. (2010), CNN Money (2014), S&P/Case-Shiller Dallas Home Price Index (2014).

2. Quality of life. We adopted techniques typically applied to estimating the economic value 

of health conditions to capture changes in quality of life from gaining stable housing. As in 

Cutler and Richardson (1997, 1998), we used an ordered probit regression with controls for 

the types of housing used26

26 We captured housing between follow-up survey and the last preceding survey (Appendix A provides details).

 and demographic characteristics to predict an individual’s 

response to the question: “If you were to consider your life in general these days, how happy 

or unhappy would you say you are, on the whole? Please use a scale of 1 to 7 to rate your 

happiness with 1 being very happy and 7 being not at all happy.” The regression took the 

form: 

(1) WB∗ = bH + dX + e 

where WB* is an individual’s true (unobserved) well-being, H is a series of indicators for 

using a given type of housing other than one’s owned or rented unit, X is a set of 

demographic variables, and e is a normally distributed error term. Although we cannot 

observe WB*, we can approximate it using WB, based on the answer to the question above. 

To avoid confusion which might occur if lower numbers were associated with higher wellbeing, we assign WB=7 to responses of “very happy,” 6 to responses of “happy,” and so on. 

 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.13

The ordered probit model allows us to estimate two sets of coefficients. First, it produces 

estimates of b and d, or the relationships between our variables of interest and the 

unobservable index of well-being, WB*. Second, it produces estimates of cut-off values that 

provide a map between WB* (the unobservable well-being index) and WB (our observable 

measure of well-being). These cut-off values (c1, c2, c3, c4, c5, and c6) tell us: 

(2) 

WB=1 if WB* ≤ c1 WB=2 if c1 < WB* ≤ c2 WB=3 if c2 < WB* ≤ c3 WB=4 if c3 < WB* ≤ c4

WB=5 if c4 < WB* ≤ c5 WB=6 if c5 < WB* ≤ c6 WB=7 if c6 < WB*.

Thus, b tells us the relationship between the well-being index and housing, d tells us how 

other characteristics are associated with well-being, and c allows us to understand how this 

index relates to our survey question. Note that positive values of b and d imply that a 

variable is associated with increased well-being.

Table B.3 shows the estimates from this ordinal probit regression. Although few coefficients 

are statistically significant, some intuitive patterns emerge. Individuals who lived with 

family or friends, who slept outdoors or in public, or who lived in a mental hospital, 

substance abuse rehabilitation facility, jail, or prison are less happy than stably housed 

individuals. Results show no evidence that individuals who used transitional housing, 

permanent housing for previously homeless individuals, halfway houses, or hotels and 

motels as shelter are less happy than stably housed individuals. 

We used the results presented in Table B.3 to build a quality-of-life index based on each 

individual’s housing. Following the health economics literature (Cutler and Richardson 

1997, 1998), we assigned a value of 1.00 to individuals who were stably housed, assumed 

that housing categories associated with increased happiness (Table B.3) increased the 

quality of life index, and valued a one percentage point increase in the index at $1,000 per 

year. Indices range from 1.00 to 0.80, suggesting a maximum improvement in quality of life 

associated with housing of 20 percentage points (worth $20,000 per year). An individual 

who lived with friends or family because they lacked their own home would improve their 

quality of life by 19 percentage points if they moved into their own home, for example. 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.14

Table B.3. Well-being and housing (ordered probit coefficients)

Rating on happiness scale 

Sample size 273

Used...as housing since last survey

Home of family member or friend -0.458*

[0.148]

Jail, prison, or juvenile detention -0.352

[0.244]

Transitional housing 0.054

[0.115]

Psychiatric hospital, substance abuse treatment facility, or other related 

facility

-0.495*

[0.155]

Emergency shelter or voucher 0.175

[0.181]

Outside or in public -0.388*

[0.161]

Hotel or motel 0.411*

[0.096]

Halfway home for those with criminal history or similar facility -0.032

[0.423]

Permanent housing for previously homeless people 0.206

[0.226]

Other (group home, nonpsychiatric hospital, and write-in options) -0.292

[0.181]

Any missing or refused responses -0.221*

[0.093]

Male -0.182

[0.099]

Age -0.002

[0.008]

Hispanic 0.142

[0.160]

Race

Black 0.104

[0.169]

White 0.022

[0.162]

Married or in a domestic partnership -0.066

[0.100]

Dependents 0.100

[0.098]

Native English speaker 0.085

[0.150]
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.15

Table B.3 (continued)

Rating on happiness scale 

Cutoff points

c1

-2.043*

[0.613]

c2

-1.615*

[0.606]

c3

-1.077

[0.643]

c4

-0.641

[0.652]

c5

-0.184

[0.651]

c6

0.380

[0.690]

Source: MJS database, combined noninstitutional outcomes study and impact study samples.

Notes: Higher scores indicate greater well-being. Estimates are unweighted. Coefficients reflect the change in the 

ordered probit index for happiness that occurs with a given change in the variable (see equation [1]). The 

cutoff points reflect the relationship between the well-being index and responses to our question on wellbeing (see [2]). Regression models also include indicators for missing values, time since last survey, and 

the last survey being the baseline survey. (See Appendix A for details). Appendix C provides variable 

definitions. Robust standard errors are in brackets. An asterisk (*) indicates a significant coefficient (p <

0.05) as measured by a two-tailed t-test.

Criminal activity. Because arrests were the crime-related outcomes most likely observed in 

the MJS database and are the least likely to be subject to non-reporting biases, we used arrests to 

estimate benefits of a reduction in criminal activity. We converted estimates of arrests into a 

monetary benefit using the anticipated governmental savings from not having to house a 

prisoner. The costs of prison (Legislative Analyst’s Office n.d.), relative costs of jail (Urban 

Strategy Council 2007), and numbers of California inmates housed in prisons and jails 

(California Department of Corrections and Rehabilitation 2013) suggest that the average personyear of imprisonment in California costs taxpayers approximately $30,000. Maxwell et al. (2013) 

shows the average arrest is associated with 15.7 months sentenced to jail or prison for SE 

workers and the California Penal Code 2933 suggests that inmates serve half of their sentenced 

time. These facts imply that each averted arrest saves taxpayers $19,566. Assuming a maximum 

of one averted arrest per SE worker, we thus estimate that a one percent decrease in the arrest 

rate between baseline and follow-up is associated with a gain of $196 to taxpayers not directly 

involved with the SE (per SE employee). We further assume that arrests were averted six months 

after the average individual began SE employment for the purpose of discounting benefits. This 

time horizon corresponds to approximately the midpoint of the period in which we have data on 

the behavior of the average member of the survey sample. 

Health. We estimate the benefits from health in two ways: (1) gains in overall quality of life 

from improved health, and (2) decreased substance abuse. We use a self-reported rating of health 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.16

(excellent, very good, good, fair, or poor) as our main measure of health.27

1. Quality of life. CBAs of interventions that may improve health typically focus on changes in 

the quality of life index, which assumes a one percentage point increase in the index is 

valued at $1,000 per year (Cutler and Richardson 1998). We use work by Nyman et al. 

(2007) to translate our self-reported health measure into the quality of life index, which 

ranges from 0.941 (excellent health status) to 0.498 (poor health status). We assume that any 

changes in health occurred six months after the average individual began SE employment. 

 We further used 

answers to questions about being in counseling for substance abuse to measure substance abuse. 

2. Substance abuse. Work by Groot (2000), implies that substance abuse issues are associated 

with losses of approximately $18,000 per person per year.28

27 There may be further benefits in this domains associated with increases in insurance coverage over the 

period of interest but we cannot capture these in our analysis. Although we asked about health insurance in both our 

baseline and follow-up surveys, individuals had some difficulty in reporting both their coverage and the source of 

their insurance. Thus, concerns about data quality led us to omit this variable from our analysis.

28 Groot (2000) produced this estimate based the self-reported overall health of individuals with and without 

substance abuse problems, which may lead to a concern that changes in substance abuse are “double-counted” by 

our direct measure of substance abuse and our measure of overall health. To mitigate this concern, we omitted 

changes in substance abuse from our benefit estimates. Section D provides details. 

 We therefore assume an 

additional $18,000 annual benefit for every individual who stopped requiring counseling for 

substance abuse after SE employment. We assume that any changes in substance abuse 

occurred six months after the average individual began SE employment. 

Revenue. We include two types of SE revenue from the Cost Capture Project in our CBA: 

revenues received by the SE for selling goods and services in the market (business mission) and 

grant money provided by the government and other foundations supporting the SEs’ mission to 

employ individuals with substantial barriers (social mission). Estimates were discounted and 

calculated at the per-employee month level in the same manner as cost estimates (Section B).

While business revenues represent the value of the goods and services produced by the SE and 

represent a benefit accruing to the SE, grant money must be treated differently because it 

represents a transfer to the SE from organizations classified in our CBA as taxpayers not directly 

involved with the SE. Because this CBA is conducted from the perspective of the SE, social 

revenues are added to our measure of benefits to the SE, subtracted from our measure of benefits 

to taxpayers not directly involved with the SE, and do not affect the benefits to society as a 

whole. 

2. Benefits summary

Per-employee benefits were estimated using the sum of all dollar-denominated benefits of 

SE employment accruing to society as a whole, the SE worker, the SE, and taxpayers not directly 

involved with the SE. We calculated the benefits in three ways, using (1) changes in outcomes

across all SEs contributing to the CBA population, estimated in the outcomes analysis (see 

Appendix A); (2) changes in outcomes specific to each organization, estimated in the outcomes 

analysis; and (3) impacts of SE employment, estimated in the impact analysis. Table B.4

 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.17

provides an overview of estimated per-employee benefits for each organization. 29 On average, 

the outcomes study CBA suggests each worker generated a benefit of $22,632 to society as a 

whole with each SE worker losing $165 and the SE and taxpayers not directly involved with the 

SE gaining $9,822 and $13,250 per employee respectively. The impact study CBA suggests that 

each worker generated a benefit of $8,745 to society as a whole with workers losing $443 from 

SE employment and the SE and taxpayers gaining $6,593 and $2,751 per employee respectively. 

Table B.4. Per employee benefits of social enterprise employment (dollars)

To society as a 

whole To SE worker To SE

To taxpayers 

not directly 

involved with 

SE

Aggregate

Outcomes analysis average 22,632 -165 9,822 13,250

Organization

CEO 18,195 1,684 8,052 9,171

Chrysalis 21,871 -206 6,593 15,473

CHP 36,378 11,449 7,465 17,577

CRC 20,883 -4,823 18,801 7,111

Taller 65,780 6,994 73,947 -15,578

Weingart 46,042 27,293 41,245 -10,751

Impact analysis 8,745 -443 6,593 2,751

Income

Outcomes analysis average 6,254 3,774 0 2,755

Organization

CEO 3,806 2,120 0 2,399

Chrysalis 5,701 3,891 0 1,800

CHP 17,630 12,218 0 5,525

CRC 3,102 -5,095 0 8,403

Taller 9,748 6,646 0 2,685

Weingart 18,136 27,161 0 2,720

Impact analysis 1,257 -235 0 1,647

Housing

Outcomes analysis average 612 -3,070 0 10,126

Organization

CEO 746 -1,770 0 7,677

Chrysalis 513 -3,119 0 10,849

CHP 234 234 0 13,731

CRC 285 285 0 11,231

Taller 360 360 0 3,789

Weingart 111 111 0 12,421

Impact analysis 75 -1,332 0 503

 

29 Table B.4 includes the estimates of quality of life associated with using each housing situation. These are 

created for housing status i as max{1,1 + − b c / ( c )} , where b and c are defined in Table B.3. For individuals using i 6 1

multiple housing categories, the coefficients are summed (for example, if i and j were used as housing, the estimated 

quality of life index would be max{1,1− + (b b i j)/(c6 1 −c )} . 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.18

Table B.4 (continued)

To taxpayers 

not directly 

To society as a involved with 

whole To SE worker To SE SE

Criminal activity

Outcomes analysis average 10,126 0 0 `

Organization

CEO 7,677 0 0 7,677

Chrysalis 10,849 0 0 10,849

CHP 13,731 0 0 13,731

CRC 11,231 0 0 11,231

Taller 3,789 0 0 3,789

Weingart 12,421 0 0 12,421

Impact analysis 503 0 0 503

Health

Outcomes analysis average -869 -869 0 0

Organization

CEO 1,334 1,334 0 0

Chrysalis -978 -978 0 0

CHP -1,003 -1,003 0 0

CRC -13 -13 0 0

Taller -12 -12 0 0

Weingart 22 22 0 0

Impact analysis 1,125 1,125 0 0

Revenue

Outcomes analysis average 6,509 0 9,822 -3,313

Organization

CEO 4,631 0 8,052 -3,421

Chrysalis 5,786 0 6,593 -807

CHP 5,786 0 7,465 -1,679

CRC 6,278 0 18,801 -12,523

Taller 51,894 0 73,947 -22,053

Weingart 15,352 0 41,245 -25,893

Impact analysis 5,786 0 6,593 -807

Source: MJS database. 

Note: In the impact study, propensity-score weighted analysis is used for all benefits except revenues and 

unweighted analysis is used for revenues. Benefits of SE employment to the friends and relatives of SE 

workers are excluded from this table, but are included in benefits to society as a whole.

D. Putting it together

Descriptions of costs faced by SEs bring us insight into how organizations fund their SEs 

and the resources it takes to provide employment opportunities to individuals with employment 

barriers. Monetized and aggregated benefits allow us to understand the benefits that SE 

employment provides to society ads a whole, SE workers, the SE, and taxpayers not directly 

involved with the SE. We can use the discounted, monetized per-employee costs and benefits 

described in Sections B and C to create a measure of benefits per dollar spent by the SE to SE 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.19

workers, taxpayers, the SE, and society as a whole, as well as the ROI of SE employment to 

society: 

(3) SE worker

Per-employee benefit to worker Benefits per dollar spent Per-employee cost

  =    , 

(4) SE

Per-employee benefit to taxpayer not directly involved with SE Benefits per dollar spent = Per-employee cost

     , 

(5) taxpayer

Per-employee benefit to taxpayer not directly involved with SE Benefits per dollar spent Per-employee cost

  =    , 

(6) society

Per-employee benefit to society as a whole Benefits per dollar spent Per-employee cost

  =    , 

(7) 1 ROI Benefits per dollar spent = society − . 

We calculated the benefits per dollar spent in three ways, consistent with the three different 

ways that we captured benefits: (1) changes in outcomes across all SEs contributing to the CBA 

population (outcomes study CBA); (2) changes in outcomes specific to each organization 

(outcomes study CBA); and (3) impacts of SE employment (impact study CBA). Table B.5 

shows these estimates. The outcomes study CBA suggests that each dollar spent by the SE 

created $2.23 in value for society as a whole. Positive returns accrued to both the SE and 

taxpayers not directly involved with the SE. Each dollar spent by the SE produced $0.97 of 

revenue for SE itself and benefits worth $1.31 to taxpayers not directly involved with the SE. SE 

workers lost $0.02 for each dollar spent by the SE. In the impact study CBA, each dollar spent 

by the SE produced $1.01 for the SE, $0.42 for taxpayers not directly involved with the SE, and 

$1.34 for society as a whole. SE employees also lost $0.07 of value for each dollar spent by the 

SE.
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.20

Table B.5. ROI and benefits per dollar spent

Benefits per 

dollar spent 

Benefits per Benefits per to taxpayers 

dollar spent dollar spent Benefits per not directly 

ROI to society to SE dollar spent involved 

(percent) as a whole worker to SE with SE 

Average for outcomes 

analysis 123 2.23 -0.02 0.97 1.31

Organization

CEO 126 2.26 0.21 1.00 1.14

Chrysalis 387 4.87 1.53 1.00 2.35

CHP 5 1.05 -0.24 0.94 0.36

CRC 236 3.36 -0.03 1.01 2.38

Taller -19 0.81 0.09 0.91 -0.19

Weingart -6 0.94 0.56 0.85 -0.22

Impact analysis 34 1.34 -0.07 1.01 0.42

Source: MJS database and cost-capture project, and employee counts. See Tables B.1 (costs) and B.4 (benefits).

Notes: See equations (2)-(5) for definitions. Benefits of SE employment to the friends and relatives of SE workers 

are excluded from this table, but are included in benefits to society as a whole.

We further explored an alternative measure of the ROI in SEs, based on a purely social 

perspective. This calculation ignores all costs the SE faces related to running their business (such 

as paying for labor and capital), as well as the revenues the SE receives from selling their goods 

and/or services. We refer to this measure as the social ROI and calculate:

(8) 1 social

Per-employee benefit of SE social mission ROI

Per-employee costs associated with SE mission = − . 

We calculate the per-employee benefit of the SE’s social mission as the total per-employee 

benefits of the SE to society as a whole minus business revenues received by the SE from the 

market. Costs include any outlays associated with training and employing the target population 

(over and above the standard costs an employer would face). Essentially, by dropping the costs 

and benefits associated with the SE as a business, this measure allows us to evaluate the ROI of 

adding a social mission to an existing business. Additionally, the measure allows us to 

understand the ratio of social benefits to social costs, which could be potentially useful for 

individuals most interested in the pro-social aspects of the SE. 

Table B.6 examines the various quantities entering equation (8) and the implied government 

ROI. On average, SEs faced costs associated with their social mission of $2,398 and produced a 

social benefit worth $16,123. The outcomes study CBA suggests that each dollar invested in the 

SE’s social mission produces a return of 572 percent of value to the government. The ROI from 

the impact study is lower but still large: Chrysalis spent $1,080 per employee on their social 

mission and produced a non-business value of $2,959 per worker. On net, each dollar given to 

the SE to fund the SE’s social mission generates a social return of 174 percent. 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.21

Table B.6. ROI from the government perspective

Variable 

Social costs Social benefits Social ROI 

(dollars) (dollars) (percentage) 

Average for outcomes analysis 2,398 16,123 572

Organization

CEO 3,417 13,564 297

Chrysalis 1,080 16,085 1,389

CHP 621 30,592 4,826

CRC 9,990 14,605 46

Taller 7,177 13,886 93

Weingart 13,726 30,690 124

Impact analysis 1,080 2,959 174

Source: MJS database and cost-capture project, and employee counts. See Tables B.1 (costs) and B.4 (benefits).

Note: In the impact study, propensity-score weighted analysis is used for all benefits except revenues in impact 

study and unweighted analysis is used for revenues and costs. Benefits of SE employment to the friends 

and relatives of SE workers are excluded from this table, but are included in benefits to society as a whole.

Our ROI calculations required many assumptions. To explore how sensitive our ROI 

estimates are to these assumptions, we performed the following sensitivity analyses: 

1. Discount rates. Our analysis assumed a discount rate of eight percent per year and we 

assessed alternate ROIs assuming rates of 4 and 12 percent. 

2. Housing quality of life regression specification. Our analysis of quality of life associated 

with housing (equation 1) used an ordinal probit regression and did not include controls for 

other determinants of happiness. We assessed alternate ROIs associated with two changes to 

this specification: (1) using an ordinal logit regression instead of the ordinal probit and (2) 

including controls for employment status and health at baseline. 

3. Persistence of gains. We truncated the estimated benefits of SE employment about one year 

after SE employment started and implicitly assumed that all benefits ended after the followup year, which is very conservative. Therefore, we estimated an alternate ROI under the 

assumption that benefits persisted though shrunk by 10 percent each year after our last 

contact with the individual. For example, if monthly income rose by $600, we assumed that 

SE workers had monthly income that was $540 (600*0.90) higher the following year, $486 

(540*0.90) higher in the year after that, and so on. 

4. Path of earnings growth. We assumed all estimated changes in earnings occurred 

immediately after an individual began SE employment. We calculated an alternate ROI 

assuming that earnings grew linearly over time between the beginning of SE employment 

and one year later.

5. Accounting for substance abuse. We were concerned about double-counting because our 

analysis considers both overall health and changes in substance abuse as benefits. If an 

individual ceases drug use and reports better health, accounting for both changes might 

overestimate the benefits of SE employment. We therefore assessed ROI excluding reduced 

substance abuse as a benefit. 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.22

6. Alternative measure of housing costs. In light of the availability of public and subsidized 

housing, we were concerned that assuming that all individuals in stable housing paid fair 

markets rents would lead to an overestimation of housing outlays. We therefore alternatively 

assessed the ROI generated by assuming individuals are not asked to pay more than 30 

percent of their income for housing (as suggested by HUD policies).30

Table B.7 shows the results of these analyses. Overall, it appears that our CBA is relatively 

robust to most of the assumptions assessed. Changing the discount rate did little to impact our 

qualitative results. Different specifications of the regression relating housing to quality of life 

yielded no discernible differences in benefits per dollar spent. Omitting benefits related to 

reduced substance abuse changed the benefits per dollar spent slightly but did not impact our 

qualitative conclusions. Some changes occurred in the ROI when we assumed gradual changes in 

income over time (the ROI shrinks to 95 percent in the outcomes study CBA and 18 percent in 

the impact study CBA). But still our main conclusions remain intact. Capping the costs that 

individuals pay for housing to 30 percent of their income does not impact the overall ROI; 

however, this change makes the benefits of SE employment per dollar spent positive for SE 

workers in both the impact and outcomes study CBAs. The change also results in a reduction in 

the benefits per dollar spent accruing to taxpayers. 

Our results may be sensitive to our assumption that benefits stop accruing one year after SE 

employment begins. Assuming that benefits persist but shrink at a rate of 10 percent per year 

leads to highly inflated estimates of benefits per dollar spent. The overall ROI increases more 

than four-fold in the outcomes study CBA and more than double in the impact study CBA. Thus, 

it appears that our estimates of the ROI in SE employment would be higher if benefits persist for 

more than one year after individuals began an SE job. 

30 See Schwartz and Wilson (2008) for details on this standard.

 
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.23

Table B.7. Benefits per dollar spent sensitivity analyses

Benefits per dollar spent 

Taxpayers 

not directly 

Society as a involved with 

whole SE worker SE SE

Outcomes study

Original analysis 2.23 -0.02 0.97 1.31

Discount rates (originally 8 percent)

4 percent 2.30 -0.02 0.97 1.38

12 percent 2.17 -0.02 0.97 1.24

Housing quality of life specification 

Ordinal logit regression 2.23 -0.02 0.97 1.31

Additional controls for life stability 2.23 -0.02 0.97 1.31

Persistence of gains 10.19 -0.10 0.97 9.48

Path of earnings growth 1.95 -0.19 0.97 1.18

Exclude the benefit of reduced 2.31 0.07 0.97 1.31

substance abuse (instead of 

combination)

Assume housing payments do not 

exceed 30 percent of income

Original analysis

2.23 0.21

Impact study

1.35 -0.07

0.97

1.01

1.08

0.42

Discount rates (originally 8 percent)

4 percent 1.36 -0.07 1.01 0.44

12 percent 1.33 -0.07 1.01 0.40

Housing quality of life specification 

Ordinal logit regression 1.34 -0.07 1.01 0.42

Additional controls for life stability 1.34 -0.07 1.01 0.42

Persistence of gains 3.62 -0.41 1.01 3.16

Path of earnings growth 1.25 -0.05 1.01 0.31

Exclude the benefit of reduced 1.18 -0.23 1.01 0.42

substance abuse (instead of 

combination)

Assume housing payments do not 

exceed 30 percent of income

1.34 0.07 1.01 0.28

Source: MJS database and cost-capture project, and employee counts. See Tables B.1 (costs) and B.4 (benefits).

Note: In the impact study, propensity-score weighted analysis is used for all benefits except revenues in impact 

study and unweighted analysis is used for revenues and costs. Benefits of SE employment to the friends 

and relatives of SE workers are excluded from this table, but are included in benefits to society as a whole.
APPENDIX B. COST-BENEFIT ANALYSIS MATHEMATICA POLICY RESEARCH

B.24

E. Limitations of CBA

Although the methods used for the CBA met the highest possible standards for rigor given 

the limited sample size and design constraints, limitations from at least six sources should be 

recognized: 

1. Our CBA includes the benefits of SE employment in only five domains. If SE employment 

produced positive changes in outcomes outside these areas, the study will underestimate the 

benefits of SE employment. For example, we do not capture benefits gained from SE 

employment associated with increased confidence, interpersonal skills, or engagement with 

families or communities (Maxwell et al. 2013). 

2. Within each of the five domains, benefits might not be fully captured, which would 

understate the benefits of SE employment. For example, we capture the monetary benefit of 

an averted arrest as a reduction in the costs of incarceration but do not capture the benefits 

victims enjoy from not being victimized by a crime or those participants enjoy from not 

being arrested and imprisoned. 

3. Because costs include only those incurred by SEs between April 2012 and September 2013, 

they likely omit important fixed costs of SE employment, including the time staff spend 

launching and developing the SE before the business began (for example, to recruit and hire 

workers or book revenue). 

4. Cost data were retrieved from organizations and may be subject to reporting errors, 

misclassification, or other accounting issues. Maxwell et al. (2013) suggested that not all 

organizations use the most meticulous accounting practices, which implies our financial data 

may be imprecise.

5. Our CBA estimates are necessarily based on many assumptions, which we have detailed 

throughout this appendix. Where possible, we have tested the sensitivity of our results to 

assumptions made but it is impossible to explore all potential combinations of assertions. It is 

therefore feasible that some alternative combination of choices could lead to different results 

and conclusions. 

6. Our CBA estimates benefits are based on analyses from the outcomes and impacts study, 

which subjects them to all the caveats associated with those studies (Appendix A, Section D). 
APPENDIX C

COLLECTING AND ANALYZING DATA ON OUTCOMES 
This page has been left blank for double-sided copying.
APPENDIX C. VARIABLE DEFINITIONS MATHEMATICA POLICY RESEARCH

C.3 

This appendix provides a detailed description of variables used in the analysis presented in 

Chapters III to V. Each of the tables in the appendix provides the name of the variable, its 

definition and, for outcome variables, an indicator whether it was used in the outcomes study, 

impact study or CBA. Appendix A provides a description of how the MJS database, which 

provided data for the variable construction, was constructed. 

We use three types of variables in the analyses.

1. Outcome variables capture employment, life stability, and self-sufficiency at one year and 

are taken from information provided in the follow-up survey. They are defined in Table C.1. 

2. Descriptor variables describe the social enterprise experience, based on information 

provided in the follow-up and exit surveys. They are defined in Table C.2. 

3. Control variables are used in regression analysis to hold individual characteristics and 

external context constant. They include outcome variables that are captured before being 

hired into the SE or entry into the Chrysalis labor pool; supportive factors like education; 

demographics; and environmental factors like unemployment rate. They are constructed from 

information obtained at intake and in the baseline survey and are defined in Table C.3. 

Variables may be listed in multiple categories to make it easier for a reader to quickly reference 

needed information. 

Variables in each category can be either continuous or binary. All binary variables are 

indicator variables with one indicating that the condition listed in the table is met, and zero 

otherwise. In the regression analyses, we imputed the values of variables for which information 

was not available in order to include survey respondents with missing information in our 

analysis.31 In regression analyses using the full and noninstitutional samples, we used the hostorganization specific mean for any independent variables with missing values. In regression 

analyses using the full propensity score and noninstitutional propensity score samples, we 

imputed any independent variables included in the regression as the host-organization specific 

mean by treatment status. That is, missing values for observations in the treatment group are set 

to the treatment group mean, and missing values for observations in the comparison group are set 

to the comparison group mean. 

 

31 All regressions also include indicators for missing values. 
APPENDIX C. VARIABLE DEFINITIONS MATHEMATICA POLICY RESEARCH

C.4 

Table C.1. Outcome variables

Variable Definition 

Used in confirmatory analysis

Worked last week Binary variable equal to one if worked in a job for pay, or was selfemployed, in the week before the follow-up survey and equal to zero X X otherwise. In the full sample, this variable was set to zero for individuals 

who were incarcerated.

Stable housing in past Binary variable equal to one if an individual reported using only their own 

year owned or rented home as housing since baseline. For individuals who did 

not complete the exit survey, this variable used information from the 

follow-up survey on housing used since baseline. For individuals who X X X completed the exit survey, this variable is equal to one if an individual 

reports only using their own housing since the exit survey, and used only 

their own housing in the week of the exit survey. In the full sample, this 

variable was set to zero for individuals who were incarcerated.

Used in exploratory analysis

Employment (Constructed based on the detailed job history provided in the follow-up survey.) 

Worked last month Binary variable equal to one if worked in a job for pay, or was selfemployed, in the month before follow-up interview and equal to zero X X 

otherwise. 

Worked continuously Binary variable equal to one if individual was employed for six consecutive 

for six months in past months in the year before follow-up X X 

year 

Share of past year Continuous variable equal to the number of months recorded any X X employed employment in the past year divided by 12. 

Housing 

Homeless in past year Binary variable equal to one if an individual reported using an emergency 

shelter, emergency voucher, transitional housing, or permanent housing 

for the previously homeless as housing or reported sleeping outside or in

public since baseline. For individuals who did not provide exit survey X X information, this variable uses information from the follow-up survey on Outcomes 

housing used since baseline. For individuals who completed the exit 

survey, this variable is equal to one if an individual reports using any of the 

Impact 

above methods since the exit survey or in the week of the exit survey.

Quality of life housing Continuous variable based on different housing method individuals 

CBA

index reported. An individual is said to use a housing method if they used it since 

the last time surveyed. Each housing method (including missing housing) 

was assigned a quality of life index value based on an ordinal probit X 

regression (detailed in Appendix B). Individuals using multiple forms of 

housing were assigned the average index value across all forms of 

housing reported.

Income 

Total income in past Key measure of income. Continuous variable equal to the sum of income X X X month from all sources in month before follow-up.

Wage and salary Continuous variable equal to the sum of income from social enterprise and 

income in last month other employment in month before follow-up survey. In the full sample, this X X X 

variable was set to zero for individuals who were incarcerated.

Government transfers Continuous variable equal to the sum of all government transfers received X in past month in the month before follow-up. Includes both cash and in-kind transfers.
APPENDIX C. VARIABLE DEFINITIONS MATHEMATICA POLICY RESEARCH

C.5 

Table C.1 (continued)

Variable Definition 

Outcomes Impact CBA

Transfers from others 

in past month

Continuous variable equal to the sum of all transfers from friends and 

family received in the month before follow-up. Includes both cash and inkind transfers.X 

Share of income from 

work

Continuous variable equal to 100 * wage and salary income in past month

/ total income in past month. Set to missing for individuals with total 

income of zero.

X X 

Share of income from 

government

Continuous variable equal to 100 * income from government in past month

/ total income in past month. Set to missing for individuals with total 

income of zero.

X X 

Criminal activity

Arrested in past year Key measure of criminal activity. Binary variable equal to one if 

individual was arrested since baseline data collection. Arrests may be 

reported during the follow-up or exit surveys (if applicable). In the full 

sample, this variable has been set to one for individuals who were found to 

be incarcerated.

X X 

Arrests averted Based on the expected number of arrests since hire considering the 

employee’s age, number of arrests prior to hire, and any sentences for jail 

or prison prior to hire (divided by two to consider types sentences served 

in California):

= (years since hire * times arrested before hire) / (age at hire – 18 – total 

years of jail or prison sentences/2) – actual arrests since hire. 

X X 

Health

Depression index Key measure of mental health. A continuous variable measuring the 

prevalence of symptoms of depression based on individuals’ self-reports of 

having the following feelings in the past seven days: feeling no interest in 

things, feeling lonely, feeling blue, feeling worthless, feeling hopeless 

about the future, and thoughts of ending your life. Respondents indicated 

the prevalence of these emotions on a 1 to 5 scale: never, a little bit, 

moderately, quite a bit, and extremely often. The individual variables are 

combined into an index by subtracting the mean values at baseline and 

dividing by the standard deviation at baseline, summing the standardized 

scores, and again normalizing using the mean and standard deviation of 

the sum of standardized scores from the baseline data. As a result, the 

variable has mean zero and standard deviation one in the baseline data 

but may not have this distribution in the follow-up data. 

X X 

Excellent physical 

health

Key measure of physical health. A binary variable equal to one if an 

individual reports being in excellent physical health. X X 

At least good physical 

health

A binary variable equal to one if an individual reports being in excellent, 

very good, or good physical health. X 

Poor or fair physical 

health

A binary variable equal to one if an individual reports being in either poor 

or fair physical health. X 

Quality of life health 

index 

A continuous variable created from self-reported health (rated as excellent, 

very good, good, fair, or poor). We use work by Nyman et al. (2007) to 

translate the self-reported health measure into this quality of life index, 

which ranges from 0.941 (excellent health status) to 0.498 (poor health 

status).

X 

In substance abuse 

counseling in past 

year

Binary variable equal to one if an individual was in counseling for drug- or 

alcohol-related issues since baseline data collection. Counseling may be 

reported during the follow-up or exit surveys (if applicable).

X X X 
APPENDIX C. VARIABLE DEFINITIONS MATHEMATICA POLICY RESEARCH

C.6 

Table C.1 (continued)

Variable Definition 

Outcomes Impact CBA

Attitudes 

Believe a job is just a 

way of earning money

A binary variable equal to one if an individual agrees or strongly agrees 

with: “A job is just a way of earning money – no more” X 

Would like a job even 

if did not need money

A binary variable equal to one if an individual agrees or strongly agrees 

with: “I would enjoy having a paid job even if I did not need the money” X 

Plan to be established 

in a career in 5 years

A binary variable equal to one if an individual reports that she thinks she 

will be established in a career in 5 years. X 

Plan to have own 

home or apartment in 

5 years

A binary variable equal to one if an individual reports that she thinks she 

will own or rent her own home in the 5years. X 

Plan to be removed 

from illegal activity in 

5 years

A binary variable equal to one if an individual reports that she thinks she 

will be removed from illegal activity in 5 years. X 

Plan to be 

economically selfsufficient in 5 years

A binary variable equal to one if an individual reports that she thinks she 

will be economically self-sufficient in 5 years. X 

Plan to rarely drink 

alcohol or use drugs 

in 5 years

A binary variable equal to one if an individual reports that she thinks she 

will rarely drink alcohol or use drugs in 5 years. X 

CBA = cost benefit analysis. 
APPENDIX C. VARIABLE DEFINITIONS MATHEMATICA POLICY RESEARCH

C.7 

Table C.2. Descriptor variables 

Variable Definition 

Organization A set of binary variables equal to one for an individual’s SE work assignment organization. 

Categories are Buckelew, CEO, CHP, Chrysalis, CRC, Taller, and Weingart. 

Hours worked per 

week

The number of hours the individual worked per week, on average, in the SE.

Length of 

employment

The number of weeks the individual worked in total in the SE.

Hired by SE A binary variable equal to one for an individual that was hired in to an SE.

Reason left SE A set of binary variables equal to one to capture the individual’s status at SE exit. Categories 

are “Success” (started another job or training), “Termination” (including individuals who were 

fired or timed out of the program), “Problematic” (dissatisfied, incarcerated, or drug use), “Still 

Employed” (those still working at SE), or “Other” (including family and personal reasons).

The still employed designation reflects the individual’s status as of follow-up. All other 

statuses reflect the reason an individual gave for employment during the exit survey (if they 

completed this survey themselves) or follow-up survey (otherwise). 

Soft skills training A binary variable equal to one if an individual received soft skills training through their SE or 

host organization experience. 

ABE/GED 

preparation

A binary variable equal to one if an individual received adult basic education or GED 

preparation through their SE or host organization.

Vocational training A binary variable equal to one if an individual received vocational or job specific training 

through their SE or host organization.

Technical training A binary variable equal to one if an individual received computer literacy or technology 

training through their SE or host organization experience.

Work supports A binary variable equal to one if an individual received any work supports (housing 

assistance, work clothing, transportation) through their SE or the host organization.

Life-stability supports A binary variable equal to one if an individual received any life-stability supports (health 

services, education, counseling, food security, and public benefits assistance) through their 

SE or host organization.

Transition supports A binary variable equal to one if an individual received any employment transition supports 

(job readiness, career counseling, or job search assistance) through their SE or the host 

organization.

Any supports after 

transition

A binary variable equal to one if an individual received any supports after leaving the SE 

(continuing employment counseling, life-stability services, or other services) through their SE 

or host organization.
APPENDIX C. VARIABLE DEFINITIONS MATHEMATICA POLICY RESEARCH

C.8 

Table C.3. Control variables

Variable Definition 

Lagged confirmatory outcome measures

Worked last week Binary variable equal to one if worked in a job for pay, or was self-employed, in the week 

before intake and equal to zero otherwise.

Stable housing in 

past year

Binary variable equal to one if an individual reported using only their own owned or rented 

home as housing in the year before the baseline survey was administered. 

Other lagged outcome and related measures

Alternative measures of employment

Worked last month Binary variable equal to one if worked in a job for pay, or was self-employed, in the month 

before intake. 

Worked continuously 

for six months in past 

year 

Binary variable equal to one if the individual was employed for six consecutive months in the 

year before intake. Constructed based on the date of last continuous employment reported at 

intake.

Alternative measures of housing

Homeless in past 

year (week)

Binary variable equal to one if an individual reported using an emergency shelter, emergency 

voucher, transitional housing, or permanent housing for the previously homeless as housing 

or reported sleeping outside or in public in the year (week) before the baseline survey.

Quality of life 

housing index

Continuous variable created based on housing individuals report using in the year before the 

baseline survey. Each housing method (including missing housing) was assigned a quality of 

life index value based on an ordinal probit regression (detailed in Appendix B). Individuals 

using multiple forms of housing were assigned the average index value across all forms of 

housing reported.

Income

Total income in past 

month

Continuous variable equal to the sum of income from all sources in month before the 

baseline survey.

Wage and salary 

income in last month

Continuous variable equal to the sum of income from the social enterprise and other 

employment in month before baseline.

Government 

transfers received in 

past month

Continuous variable equal to the sum of all government transfers received in the month 

before baseline. Includes both cash and in-kind transfers.

Transfers from 

others received in 

past month

Continuous variable equal to the sum of all transfers from friends and family received in the 

month before baseline. Includes both cash and in-kind transfers.

Share of income 

from work

Continuous variable equal to 100 * wage and salary income in past month / total income in 

past month. Set to missing for individuals with total income of zero

Share of income 

from government

Continuous variable equal to 100 * income from government in past month / total income in 

past month. Set to missing for individuals with total income of zero

Criminal activity

Ever arrested (at 

entry/hire)

Binary variable equal to one if an individual reported ever being arrested in the baseline 

survey.

Number of times 

arrested

Count variable equal to the number of times an individual reports having been arrested in the 

baseline survey.

Arrested 1-9 times Binary variable equal to one if an individual reported having been arrested 1 to 9 times in the 

baseline survey. This variable is zero for individuals arrested either 0 or 10 or more times. 

Arrested 10 or more 

times

Binary variable equal to one if an individual reported having been arrested 10 or more times 

in the baseline survey.

Ever convicted (at 

entry/hire)

Binary variable equal to one if an individual reports having ever been convicted of a crime 

and sentenced to time in jail or prison in the baseline survey.
APPENDIX C. VARIABLE DEFINITIONS MATHEMATICA POLICY RESEARCH

C.9 

Table C.3 (continued)

Variable Definition 

Health

Depression index A continuous variable indicating the prevalence of symptoms of depression based on 

individuals’ self-reports of having the following feelings in the past seven days: feeling no 

interest in things, feeling lonely, feeling blue, feeling worthless, feeling hopeless about the 

future, and thoughts of ending your life. Respondents indicated the prevalence of these 

emotions on a 1 to 5 scale: never, a little bit, moderately, quite a bit, and extremely often.

The individual variables are combined into an index by subtracting the mean values at 

baseline and dividing by the standard deviation, summing the standardized scores, and again 

normalizing using the mean and standard deviation of the sum. This index is designed to 

have mean zero and standard deviation one in the baseline data.

Excellent physical 

health

A binary variable equal to one if an individual reports being in excellent physical health.

Quality of life health 

index 

A continuous variable created from self-reported health (rated as excellent, very good, good, 

fair, or poor). We use work by Nyman et al. (2007) to translate the self-reported health 

measure into this quality of life index, which ranges from 0.941 (excellent health status) to 

0.498 (poor health status).

In substance abuse 

counseling in past 

year

Binary variable equal to one if an individual was in counseling for drug- or alcohol-related 

issues in the year before the baseline survey.

Attitudes

Believe a job is just a 

way of earning 

money

A binary variable equal to one if an individual agrees or strongly agrees with the statement: 

“A job is just a way of earning money – no more”

Would like a job even 

if did not need 

money

A binary variable equal to one if an individual agrees or strongly agrees with the statement: “I 

would enjoy having a paid job even if I did not need the money”

Be established in a 

career in 5 years

A binary variable equal to one if an individual believes he will be established in a career in 5 

years.

Have own home or 

apartment in 5 years

A binary variable equal to one if an individual believes he will own or rent own home in the 5 

years.

Be removed from 

illegal activity in 5 

years

A binary variable equal to one if an individual believes he will be removed from illegal activity 

in 5 years.

Be in good mental 

health in 5 years

A binary variable equal to one if an individual believes he will be in good mental health in 5 

years.

Rarely drink alcohol 

or use drugs in 5 

years

A binary variable equal to one if an individual believes he will rarely drink alcohol or use 

drugs in 5 years.

Other supportive factors

Education A set of binary variables equal to one if an individual reports educational attainment and zero 

otherwise. Categories are less than high school, high school diploma/graduate/GED, and any 

post-secondary education. Reported at intake.

Worked last year Binary variable equal to one if worked in a job for pay or was self-employed in the year 

before intake. 

Did not work last 

year

Binary variable equal to one if did not work in a job for pay or was self-employed in the year 

before intake.

Temporary housing 

in past (year) week 

A binary variable equal to one if an individual reports using a temporary housing situation in 

the year (week) before intake.

Ever arrested 

(intake) 

Binary variable equal to one if an individual reported having been ever arrested at intake. 

Note that the version of this variable collected at baseline is typically used for analysis unless 

the target population (and not full or survey sample) is being analyzed. 
APPENDIX C. VARIABLE DEFINITIONS MATHEMATICA POLICY RESEARCH

C.10

Table C.3 (continued)

Variable Definition 

Ever convicted 

(intake)

Binary variable equal to one if an individual reports having ever been convicted of a crime 

and sentenced to time in jail or prison at intake.

Note that the version of this variable collected at baseline is typically used for analysis unless 

the target population (and not full or survey sample) is being analyzed.

Counselor-assessed 

level of support 

needed

A mutually exclusive set of binary variables for a counselor’s assessment of support likely 

needed to succeed in the labor force at intake. 1 = needs a lot of support and 5 = needs no 

support. A lot of support is 1 or 2, needs some support is 3 or 4, and needs no support is 5.

Income under 200 

percent of federal 

poverty level

A binary variable equal to one if a counselor determined at intake that an individual’s income 

was below 200 percent of the federal poverty line (based on annual income and household 

size).

Health insurance A binary variable equal to one if an individual had (public or private) health insurance at 

baseline.

Share of income 

from family and 

friends

Continuous variable equal to 100 * income from family and friends in past month / total 

income in past month. Set to missing for individuals with total income of zero

Demographic characteristics 

Male A binary variable equal to one if the individual is male (or gender-identifies as male). From 

intake data.

Age A continuous variable for age in years at intake.

Hispanic A binary variable equal to one if an individual reports Hispanic origin. Independent of race. 

From intake data.

Race A set of binary variables equal to one if an individual reports that race and zero otherwise, 

independent of Hispanic ethnicity. Categories are white, black, or other race/refused to 

disclose. From intake data.

Married or in a 

domestic partnership

A binary variable equal to one if an individual was married or in a domestic partnership at 

intake.

No dependents A binary variable equal to one if an individual reported having no financial or physical 

dependents at intake.

Veteran A binary variable equal to one if an individual served in active duty. From intake data.

Native English 

speaker

A binary variable equal to one if an individual spoke English natively. From intake data.

Other control variables

Unemployment rate A continuous variable for the prevailing unemployment rate at baseline or follow-up in the 

MSA in which the host organization is located. 

Cohort A set of binary variables equal to one if an individual was administered the baseline survey 

during a given calendar quarter. 

Time between 

baseline and followup surveys

A continuous variable for the number of months elapsed between when the baseline and

follow-up surveys were administered.

Completed exit 

survey

Binary variable equal to one if the individual completed the exit survey themselves. 

MSA = metropolitan statistical area; SE = social enterprise.
APPENDIX D

TABLES
This page has been left blank for double-sided copying.
APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

D.3 

This appendix contains the data tables that show the results of the study’s main analyses. 

Appendix A describes the construction of the Mathematica Jobs Study (MJS) database and the 

analytic methods used to produce the results presented in these tables. Appendix C provides a 

description of the variables presented in these tables. All analysis presented is weighted with 

adjustments for differential nonresponse (Appendix A describes the weights used). The glossary 

and acronyms lists at the beginning of the report define terms and acronyms used in all tables. 

We applied the following rules to the descriptive tables in this appendix (D.1 to D.14): 

• Item-specific nonresponse reduces the number of customers in some cells. Appendix Table 

A.7 shows cells affected by missing data.

• Tables show percentages, except where noted. 

• The following statistical tests compared differences between the analysis for treatment and 

comparison groups in descriptive tables: 

- A two-tailed t-test tests for difference in means in continuous variables (for example, 

female) or categories not part of a distribution (for example, females). An asterisk (*) 

designates statistically significant (p ≤ 0.05) comparisons. 

- A chi-squared test tests for differences in distributions for categorical variables (for 

example, race). If the chi-squared test is significant, a two-tailed t-test tests for 

significant differences between each category in the distribution and an asterisk (*) 

designates statistically significant differences.

We applied the following rules to the multivariate tables in this appendix (D.15 to D.19): 

• We used ordinary least squares in all estimations because probit specifications with binary 

outcome variables did not converge or failed to provide standard errors. Numbers capture 

linear probabilities for binary variables and impacts measured in units of the dependent 

variable for continuous variables with robust standard errors reported in brackets. 

• An asterisk (*) designates a coefficient whose difference from zero is statistically 

significantly (p ≤ 0.05).

• Numbers reported are coefficients with robust standard errors in brackets. 

• Regressions include control variables beyond the coefficients shown in tables, including 

time between baseline and follow-up surveys, unemployment rate at follow-up, cohort, and 

whether the respondent completed the exit survey. We also include variables with missing 

values that take the value of the mean of the variable. Indicator variables (1 = missing for a 

given variable and 0 = not missing value) are constructed for variables that contained 

missing data and are included in the analysis. Appendix Table A.7 shows variables affected 

by missing data.

• Core measures of life stability, included in some models, include worked in last week, stable 

housing in last year, total monthly income, the depression index, excellent physical health, 

and in substance abuse treatment in past year.
APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

D.4 

Table D.1. Satisfaction with and employment in SE 

Response 

in exit 

surveying 

Response in exit survey 

(if completed) or followup (if did not) 

Response in 

follow-up 

surveying 

Sample size 242 242 242

Employed at SE for more than one period of time NA NA 28.0

Average hours worked per week 23.6 23.7 24.1

Average length of employment in weeks 18.4 18.5 24.3

Median length of employment in weeks 21.4 21.4 19.0

Hours worked at SE

Fewer than 8 hours 4.3 4.4 4.7

8 to 20 hours 5.2 5.3 5.5

21 to 32 hours 3.3 3.3 3.1

33 to 80 hours 6.1 5.9 6.7

81 to 160 hours 9.0 9.1 9.1

161 to 320 hours 9.0 9.1 9.6

321 to 640 hours 27.1 27.1 17.4

641 to 960 hours 27.8 26.5 16.7

961 or more hours 8.0 9.4 27.1

Status with SE

Currently working at SE 40.5 40.8 23.4

Never worked at SE n.a. n.a. 3.2

Left SE: found outside employment or started 

other training

39.4 38.6 27.8

Left SE: terminated for any reason (including 

program ending)

28.3 23.5 28.0

Left SE: dissatisfied or decided did not want 

job 

9.7 14.9 21.5

Left SE: incarcerated 4.2 5.8 1.4

Left SE: drug use 4.4 2.2 1.8

Left SE: family or personal reasons (including 

illness, disability, or logistical limitations)

14.0 12.0 10.4

Left SE: other reason (write-in) NA NA 9.1

Satisfied with ... at SE

Salary 74.6 74.3 67.6

Benefits 45.7 51.2 50.3

Type of work 88.9 89.8 85.2

Number of hours 73.2 74.7 75.2

Job location 91.7 92.5 86.8

Opportunities for advancement 66.9 65.2 54.9

Challenge of job 79.6 77.8 68.2

Level of responsibility 74.6 74.3 67.6

Independence on the job 95.0 94.0 86.7

Contribution of job to society 97.1 97.0 96.0

Job security 82.0 81.5 73.3

Feedback received 90.8 92.3 90.6

Support received 94.2 93.6 81.5

Source: MJS database, noninstitutional sample.
APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

D.5 

Table D.2. Services received at SE or host organization

Outcomes 

study 

Impact study 

Treatment 

group 

Comparison 

group Difference 

Sample size 242 138 32 n.a.

Any skills training 91.4 93.1 91.0 2.1

Soft-skills training 82.2 86.9 84.0 2.9

ABE or GED preparation 23.3 26.5 10.5 16.0*

Vocational or job-specific skills training 64.0 57.7 49.5 8.1

Computer or technology skills training 53.2 67.2 54.2 13.0

Any work supports 80.4 81.1 65.2 15.8

Housing or rental assistance 12.4 7.1 5.3 1.8

Transportation assistance 47.9 58.9 58.9 0.1

Work clothing assistance 68.0 68.2 40.6 27.6*

Any life-stability supports 64.4 64.6 38.0 26.6*

Physical health services 15.2 14.6 2.3 12.3*

Assistance with physical or learning 

disability 11.3 9.3 5.3 4.0

Substance abuse counseling or treatment 11.7 8.7 2.2 6.5

Domestic abuse protection, counseling, or 

related services

15.8 13.5 2.2 11.3*

Financial education or asset building 

education, including help setting up a bank 

account

51.7 54.0 36.8 17.3

Assistance with food security 27.8 25.9 8.2 17.7*

Access to public benefits 21.0 16.8 11.3 5.5

Help with tax preparation 20.7 13.4 5.2 8.3

Help plan to avoid relapse of behavior 24.6 21.1 10.9 10.3

Any employment transition supports 96.8 98.3 100.0 -1.7

Job-readiness skills training 91.2 94.9 89.4 5.5

Career counseling or job coaching 72.6 70.1 60.3 9.7

Job search assistance 86.4 87.7 89.2 -1.4

Any services after leaving host organization 67.9 64.2 60.3 3.9

Access to employment counselor or 

associated services

64.2 63.3 60.3 3.0

Access to life support counselor or 

associated services

43.3 38.9 11.1 27.9*

Any other services 11.4 13.4 17.0 -3.6

Source: MJS database, noninstitutional sample.

Notes: Supports are omitted from this table if less than five percent of individuals in the outcomes study report 

receiving a given service. 
D.6 

APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

Table D.3. Employment status 

Outcomes study Impact study 

Baseline Follow-up Difference 

DifferenceTreatment: Treatment: Comparison: Comparison: inbaseline follow-up baseline follow-up differences 

Sample size

Worked in the past week for pay

Worked at 2 or more jobs last week

Worked in past month for pay

Worked in past six months for pay

Worked continuously for six months 

in past year

Share of time in past year spent 

employed

Not currently employed

Sample size

Reason not working (all that apply)

Discouraged

Lack of qualifications

Cannot find a desirable job

Criminal history/record

Health limitations or substance 

abuse

Lack of transportation

Other (family responsibilities and 

write-in)

242

18.8

2.4

31.0

49.4

39.2

NA

192

45.4

28.8

26.3

22.7

11.8

10.2

10.0

242

61.5

5.4

62.4

79.0

65.5

67.2

88

61.2

46.7

40.9

1.5

32.6

26.9

9.4

n.a.

42.7*

3.0

31.4*

29.6*

26.3*

NA

n.a.

15.7*

17.9*

14.6*

-21.2*

20.8*

16.7*

-0.6

138

16.0

1.7

37.4

57.6

53.1

NA

115

53.5

27.7

27.1

28.5

6.4

11.0

8.8

138

58.0

6.5

59.7

76.0

66.1

66.1

54

58.4

40.5

37.4

1.4

35.9

27.3

8.9

32

5.4

0.0

9.7

41.3

36.1

NA

31

54.1

30.8

29.2

11.1

9.3

12.2

2.8

32

47.0

0.0

53.8

67.5

40.7

49.1

17

66.6

25.2

29.9

5.8

65.9

34.3

0.0

n.a.

0.3

4.8

-21.8

-7.9

8.4

NA

n.a.

-7.6

18.4

9.6

-21.8*

-27.1

-5.8

2.8

Source: MJS database, noninstitutional sample.
D.7 

APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

Table D.4. Characteristics of current or most recent job 

Outcomes study Impact study

Baseline Follow-up Difference

Treatment: Treatment: Comparison: Comparison: Difference-inbaseline follow-up baseline follow-up differences

Sample size

Current or most recent job at SE

Current or most recent job not at SE

Sample size

Know that job is subsidized

Don’t know if subsidized

Job provided support services

Average hours worked per week 

Worked part-time (<30 hours/week)

Worked full-time (30+ hours/week)

Know that job offered health insurance

How found last or current job (all that apply)

Former or current employer

State or private employment agency

Friends, relative, or colleagues

Media

SE or host organization

Other (union, self-employed, school or write-in)

Satisfied with (if job not SE)

Salary

Benefits

Type of work

Number of hours worked

Job location

Job security

Opportunities for advancement

Intellectual challenge of job

Responsibility on the job

Independence on the job

Job’s contribution to society

Feedback received

Support received

Difficult to leave work to handle personal issues

242

n.a.

135

NA

NA

NA

NA

NA

NA

NA

0.7

11.9

42.7

3.2

n.a.

45.3

77.9

39.8

93.5

76.3

86.9

78.9

55.0

74.5

92.1

93.5

89.1

88.8

88.7

23.5

242

43.9

135

14.6

41.0

54.3

30.2

41.8

58.2

34.7

27.2

34.3

49.8

6.7

53.3

5.7

70.9

60.0

85.7

81.3

90.8

83.8

59.3

77.2

91.8

92.1

86.8

83.7

82.4

36.3

n.a.

n.a.

n.a.

NA

NA

NA

NA

NA

NA

NA

26.5*

22.4*

7.0

3.5

n.a.

-39.6*

-7.0

20.1*

-7.8

5.0

3.9

4.8

4.3

2.7

-0.3

-1.4

-2.2

-5.1

-6.3

12.8*

138

n.a.

67

NA

NA

NA

NA

NA

NA

NA

0.0

17.6

58.9

2.5

n.a.

28.6

78.8

35.4

90.3

73.7

83.8

78.2

50.6

76.4

90.2

97.9

86.8

91.5

89.5

24.5

138

53.0

67

9.6

43.9

57.9

30.0

38.5

61.5

27.5

27.5

30.9

52.9

4.2

55.3

5.2

65.9

49.1

84.9

85.1

94.2

88.4

59.7

79.5

91.6

94.4

92.7

83.9

84.4

39.4

32

n.a.

31

NA

NA

NA

NA

NA

NA

NA

0.0

47.6

20.0

32.4

n.a.

0.0

100.0

67.6

67.6

100.0

100.0

67.6

20.0

100.0

67.6

100.0

67.6

67.6

100.0

32.4

32

2.5

31

3.6

18.9

17.4

40.5

21.0

79.0

39.7

16.2

10.2

57.7

3.1

12.7

12.3

66.5

61.3

88.8

75.3

85.0

78.1

66.0

79.3

85.3

83.1

100.0

91.7

86.3

46.5

n.a.

n.a.

n.a.

NA

NA

NA

NA

NA

NA

NA

11.3

50.7

-43.6

31.0

n.a.

-35.7*

20.6

20.0

-26.7

36.2*

25.4*

-0.3

-36.9

23.8*

-16.3

13.3

-26.5

-31.7

8.6

0.8

Source: MJS database, noninstitutional sample.
D.8 

APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

Table D.5. Attitudes toward employment 

Outcomes study Impact study

Treatment: Treatment: Comparison: Comparison: Difference-inBaseline Follow-up Difference baseline follow-up baseline follow-up differences

Sample size 242 242 n.a. 138 138 32 32 n.a.

Believe that will be established in a 86.8 70.2 -16.6* 90.0 63.7 77.7 47.4 4.0

career in five years

Believe job Is only a way to earn 20.9 28.5 7.6 21.9 34.2 11.5 33.2 -9.4

money—nothing more

Would enjoy having a job even If did 83.1 71.9 -11.2* 82.9 68.5 87.5 61.4 11.7

not need money

Believe the following are important 

or very important for job quality

Job security 98.7 99.7 0.9 99.5 99.5 92.2 95.6 -3.4

Opportunities for advancement 98.4 96.6 -1.7 98.4 95.6 92.6 92.5 -2.7

Interesting 98.0 97.6 -0.3 99.4 98.3 97.0 92.5 3.5

Useful to society 94.0 96.8 2.8 97.2 97.0 100.0 96.9 2.9

Allows worker to help others 96.9 94.3 -2.5 99.4 96.5 100.0 94.9 2.2

Independence on the job 82.7 87.4 4.7 86.6 90.3 82.7 94.8 -8.3

High income 81.0 89.6 8.6* 81.6 89.8 77.0 89.5 -4.3

Flexible work days or times 59.8 69.4 9.6* 68.3 70.6 64.0 61.4 4.9

Source: MJS database, noninstitutional sample.
D.9 

APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

Table D.6. Housing 

Outcomes study Impact study

Treatment: Comparison: DifferenceBaseline baseline Treatment: baseline Comparison: in-

(estimate) Follow-up Difference (estimate) follow-up (estimate) follow-up differences

Sample size 242 242 n.a. 138 138 32 32 n.a.

Believe that in five years will own 90.1 82.6 -7.4* 91.6 77.8 82.7 66.1 2.7

or rent his/her own home or 

apartment

Used as housing since baseline

Owned or rented room, 48.7 80.9 32.2* 45.1 82.3 39.5 76.7 0.1

apartment, or home

Home of family member or 39.6 36.4 -3.2 35.3 38.4 26.5 28.7 0.8

friend

Jail, prison, or juvenile 24.8 6.0 -18.7* 16.8 4.0 11.4 3.0 -4.4

detention

Transitional housing 26.5 24.5 -2.0 32.7 29.1 29.3 18.8 6.8

Psychiatric hospital, 15.5 6.7 -8.8* 17.8 6.2 28.4 12.3 4.6

substance abuse treatment 

facility, or other related facility

Emergency shelter or voucher 13.8 8.7 -5.1 15.7 9.4 22.1 9.8 5.9

Outside or in public 10.1 12.9 2.9 9.7 11.7 5.2 7.3 -0.2

Hotel or motel 6.2 6.8 0.6 4.1 9.3 10.3 3.3 12.2

Halfway home for those with 6.0 4.9 -1.0 7.9 4.3 0.0 0.0 -3.6

criminal history or similar 

facility

Permanent housing for 9.4 14.3 4.9 8.6 15.8 0.0 10.7 -3.5

previously homeless people

Other (group home, 5.1 13.7 8.7* 5.6 11.6 11.7 19.8 -2.0

nonpsychiatric hospital, and 

write-in options)

Stable housing since baseline 16.1 50.1 34.0* 19.1 57.3 17.2 44.1 11.3

Homeless at any point since 37.3 35.4 -2.0 40.6 36.3 39.6 29.9 5.5

baseline

Source: MJS database, noninstitutional sample.

Notes: Estimate from baseline data is use of housing method in past 12 months at baseline. 
D.10

APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

Table D.7. Criminal activity 

Outcomes study Impact study

Treatment: Treatment: Comparison: Comparison: Difference-inBaseline Follow-up Difference baseline follow-up baseline follow-up differences

Sample size 242 242 n.a. 138 138 32 32 n.a.

Believe that in five years will be 94.1 80.6 -13.5* 97.0 77.5 97.1 74.3 3.3

removed from illegal activity

Ever arrested 84.1 84.6 0.4 83.9 83.9 73.1 73.1 0.0

Number of times arrested 7.5 7.5 0.1 7.3 7.4 4.2 4.3 0.0

Number of arrests since baseline 0.0 0.2 0.2 0.3 0.1 0.2 0.1 -0.1

(at baseline, expected values 

based on arrest history)

Ever convicted and sentenced to 68.9 70.0 1.1 68.4 70.5 55.8 58.7 -0.8

time in jail or prison

Convicted and sentenced to time n.a. 3.2 n.a. n.a. 2.7 n.a. 2.9 n.a.

in jail or prison since baseline

Average total time sentenced 103.2 102.9 -0.3 107.5 104.8 85.3 81.1 1.6

(months, if any)

Sentenced to less than 1 year 11.7 11.8 0.1 11.7 12.5 23.6 27.5 -3.1

Sentenced to 1 to 2 years 17.9 17.4 -0.5 16.9 16.4 23.4 22.2 0.7

Sentenced to more than 2 years 70.4 70.8 0.3 71.4 71.1 53.0 50.3 2.5

Source: MJS database, noninstitutional sample.
D.11

APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

Table D.8. Benefit receipt 

Outcomes study Impact study

Baseline Follow-up Difference

Treatment: 

baseline

Treatment: 

follow-up

Comparison: 

baseline

Comparison: 

follow-up

Difference-indifferences

Sample size 242 242 n.a. 138 138 32 32 n.a.

Believe that in five years will be 

economically self-sufficient

95.8 86.0 -9.8* 97.5 83.5 100.0 84.1 1.9

Receiving any income from...last month

Food stamps; Supplemental Nutrition 

Assistance Program; or Women, Infants 

and Children Program

63.0 31.7 -31.4* 69.5 34.2 64.9 43.5 -13.9

Welfare programs (for example, TANF 

or CalWORKs)

44.5 10.1 -34.4* 55.6 13.8 44.1 19.9 -17.7

Disability or worker’s compensation 9.1 9.8 0.7 4.6 6.5 5.5 4.7 2.6

Unemployment insurance 7.4 2.6 -4.8* 4.1 2.5 2.5 6.3 -5.4

Other government transfers 0.9 4.2 3.3 0.8 3.3 0.0 8.8 -6.2

Transfers from others 14.1 20.4 6.3 9.5 14.3 8.3 2.0 11.1

Other sources 1.4 0.4 -1.0 0.9 1.3 7.0 0.0 7.4

Moved off of any form of public 

assistance since baseline

n.a. 47.5 n.a. n.a. 52.8 n.a. 14.6 n.a.

Began receiving any form of public 

assistance since baseline

n.a. 6.5 n.a. n.a. 4.0 n.a. 20.8 n.a.

Stopped receiving transfers from others 

since baseline

n.a. 13.6 n.a. n.a. 10.9 n.a. 14.6 n.a.

Began receiving transfers from others 

since baseline

n.a. 22.7 n.a. n.a. 16.9 n.a. 16.6 n.a.

Source: MJS database, noninstitutional sample.
D.12

APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

Table D.9. Income 

Outcomes study Impact study

Baseline Follow-up Difference

Treatment: 

baseline

Treatment: 

follow-up

Comparison: 

baseline

Comparison: 

follow-up

Difference-indifferences

Sample size 242 242 n.a. 138 138 32 32 n.a.

Any wage or salary income 31.0 75.4 44.4* 24.0 74.7 20.6 73.3 -2.0

Wage or salary income (dollars) 237.0 950.5 713.5* 167.2 812.2 110.1 853.6 -98.5

Any earned income 32.5 76.2 43.7* 25.0 75.9 22.0 73.3 -0.4

Earned income (dollars) 253.6 962.3 708.7* 176.6 824.9 150.9 809.8 -10.6

Total income below 200 percent of 

federal poverty level 96.4 90.8 -5.6* 98.8 94.9 100.0 91.4 4.7

Opened bank account since baseline n.a. 69.8 n.a. n.a. 62.6 n.a. 66.1 n.a.

Average monthly income from ... last 

month (dollars)

All sources 677.0 1,240.3 563.3* 530.1 1,019.4 481.5 1,118.7 -147.9

Salary or wage income from work 237.0 950.5 713.5* 167.2 812.2 110.1 853.6 -98.5

Food stamps; Supplemental 

Nutritional Assistance Program; or 

Women, Infants and Children 

Program 128.3 67.0 -61.3* 144.3 74.8 127.0 137.9 -80.3

Welfare programs (for example, 

TANF or CalWORKs) 107.6 25.5 -82.1* 125.7 32.9 106.6 76.7 -62.9

Disability or worker’s compensation 70.9 63.4 -7.6 31.6 48.4 47.8 20.1 44.5

Unemployment insurance 56.5 0.0 -56.5* 33.0 0.0 13.0 5.2 -25.2

Other government transfers 3.8 40.9 37.2 1.2 28.0 0.0 14.5 12.3

Transfers from others 58.0 56.7 -1.3 17.6 16.4 23.7 0.0 22.5

Other sources 14.1 4.1 -10.0 8.5 12.8 54.5 0.0 58.8

Share of Income from ... last month

Work 22.5 69.0 46.5* 17.9 71.3 15.6 64.1 5.0

Government transfers 71.3 23.8 -47.5* 79.5 24.8 75.3 35.9 -15.3

Transfers from others 5.0 7.1 2.1 1.6 3.6 4.8 0.0 6.8

Other sources 1.2 0.1 -1.1 1.0 0.4 4.2 0.0 3.6

Source: MJS database, noninstitutional sample.

Notes: Baseline estimate for total monthly income has been adjusted downward by monthly income from the earned income tax credit.
D.13

APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

Table D.10. Health 

Outcomes study Impact study

Baseline Follow-up Difference

Treatment: 

baseline

Treatment: 

follow-up

Comparison: 

baseline

Comparison: 

follow-up

Difference-

in-

differences

Sample size 242 242 n.a. 138 138 32 32 n.a.

Physical health

Believe will be in good physical 

health in 5 years 

96.3 86.1-10.3* 98.1 85.0 88.8 87.6-11.9

Have physical health condition 

limiting work 6.6 8.7 2.1 5.3 10.8 0.0 18.5-13.0

Self-reported physical health

Excellent 31.2 21.9-9.3* 34.1 25.0 23.8 10.2 4.5

Very good 34.6 31.0-3.6 34.0 31.2 29.4 31.2-4.6

Good 23.3 24.1 0.8 20.0 20.4 37.0 10.0 27.5*

Fair 9.8 16.0 6.2 11.0 16.1 9.8 27.0-12.1

Poor 1.1 7.0 5.9* 0.8 7.2 0.0 21.6-15.3*

Physical health improved since 

baseline

n.a. 17.0 n.a. n.a. 18.3 n.a. 16.7 n.a.

Physical health worsened since 

baseline

n.a. 41.3 n.a. n.a. 42.0 n.a. 61.7 n.a.

Mental health

Believe will be in good mental 

health five years from now

96.0 89.2-6.9* 99.4 86.8 86.5 93.2-19.3*

Have mental health condition 

limiting work

5.3 7.4 2.2 4.4 6.8 12.0 14.1 0.4

Persistently bothered by ... in 

past week

Lack of interest 11.0 24.9 13.9* 10.9 22.5 12.2 14.9 8.9

Loneliness 19.1 23.0 3.9 19.8 21.3 30.9 22.5 9.9

Feeling blue 17.1 22.6 5.5 16.1 21.7 23.1 29.1-0.3

Feeling worthless 11.9 16.7 4.8 11.1 16.0 13.4 13.5 4.9

Feeling hopeless about future 13.5 17.1 3.6 11.3 19.2 17.4 16.7 8.6

Suicidal thoughts 1.0 3.8 2.8* 1.3 3.4 1.7 3.1 0.7

Depression index (standard 

deviations)

-0.0 0.3 0.3*-0.0 0.3 0.1 0.4 0.0

Any suicidal thoughts 3.1 6.2 3.2 3.6 6.0 1.7 3.1 1.0

Health nsurance

Have health insurance 43.8 63.9 20.1* 34.8 59.8 41.9 75.7-8.8

Public insurance 38.7 50.0 11.3* 31.1 51.3 35.6 66.7-10.8

Private insurance 4.0 13.9 9.9* 3.3 8.5 6.2 7.1 4.3

Source: MJS database, noninstitutional sample.
D.14

APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

Table D.11. Substance abuse since baseline 

Outcomes study Impact study

Baseline 

(estimate) Follow-up Difference

Treatment: 

baseline 

(estimate)

Treatment: 

follow-up

Comparison: 

baseline 

(estimate)

Comparison: 

follow-up

Differenceindifferences

Sample size 242 242 n.a. 138 138 32 32 n.a.

Believe that in five years will 

rarely drink alcohol or use drugs 

(actual, not estimated, baseline 

value)

82.7 62.9 -19.8* 90.4 57.8 86.2 54.3 -0.7

Alcohol use

Drank 4 or more drinks 22.5 26.9 4.4 14.7 27.3 17.8 20.2 10.2

Average times drank 4 or more 

drinks (if any)

20.3 20.1 -0.2 17.9 17.6 9.4 7.1 2.0

Average times drank 4 or more 

drinks (unconditional)

4.6 5.0 0.5 2.6 4.5 1.7 1.4 2.1

Had alcohol use interfere with life 2.6 3.5 0.9 1.3 2.0 2.9 2.8 0.8

Marijuana use

Used marijuana 17.2 17.9 0.7 13.3 17.3 5.5 5.9 3.7

Average times used marijuana 

(if any)

98.5 79.0 -19.4 29.9 41.8 12.6 62.0 -37.6

Average times used marijuana 

(unconditional)

16.9 13.2 -3.7 4.0 6.7 0.7 3.6 -0.2

Hard drug use

Used hard drugs 4.9 3.4 -1.5 4.5 2.7 8.3 8.9 -2.4

Average times used hard drugs 

(if any)

132.9 31.9 -101.1 98.9 61.2 18.1 3.7 -23.2

Average times used hard drugs 

(unconditional)

6.5 1.1 -5.4 4.5 1.6 1.5 0.3 -1.6

Entered counseling program for 

alcohol or drug dependency

20.8 16.2 -4.6 22.0 14.6 28.4 20.8 0.2

Source: MJS database, noninstitutional sample.

Notes: For indicator variables (for example, used hard drugs), baseline estimate is based on activity in the 12 months before baseline. For count variables (for 

example, times used hard drugs), baseline estimate is the number of times the event occurred in 12 months prior to baseline multiplied by the days since 

baseline (which has been divided by 365.25). 
D.15

APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

Table D.12. Time-varying demographic characteristics 

Outcomes study Impact study

Baseline Follow-up Difference 

Treatment: 

baseline 

Treatment: 

follow-up 

Comparison: 

baseline 

Comparison: 

follow-up 

Difference-indifferences 

Sample size 242 242 n.a. 138 138 32 32 n.a.

Marital status

Single 74.3 65.0 -9.3* 72.4 65.9 67.0 50.1 10.3

Married or in a domestic partnership 12.0 23.0 11.0* 11.6 20.3 6.2 22.5 -7.6

Divorced or widowed 13.7 12.0 -1.7 16.0 13.8 26.8 27.4 -2.8

Dependents

Average number of financial and 

physical dependents

0.4 0.5 0.2 0.4 0.4 0.2 0.4 -0.1

No financial and physical dependents 81.8 77.0 -4.7 81.6 79.7 88.5 82.4 4.3

Average number of financial dependents 0.6 0.7 0.2 0.6 0.7 0.3 0.4 0.0

No financial dependents 70.4 68.7 -1.7 69.7 69.0 83.0 78.7 3.6

Average number of physical dependents 0.4 0.6 0.1 0.4 0.5 0.2 0.4 -0.1

No physical dependents 78.5 76.7 -1.9 80.6 79.0 86.0 77.4 7.1

Source: MJS database, noninstitutional sample.
D.16

APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

Table D.13. SE training provided 

Outcomes study Impact study

Baseline

Followup DifferenceTreatment: 

baseline

Treatment: 

follow-up

Comparison: 

baseline

Comparison: 

follow-up

Difference-indifferences

Sample size 242 242 n.a. 138 138 32 32 n.a.

Any training toward degree, 

certificate, or license

77.2 80.2 2.9 78.6 81.6 66.9 74.5 -4.5

Average number of training 

programs participated in

1.8 2.4 0.5* 1.8 2.3 1.8 2.3 -0.0

Participated in 3 or more training 

programs

28.1 39.6 11.6* 28.4 39.7 25.6 35.4 1.5

Completed any training program 52.1 59.0 6.8 53.4 59.2 57.6 63.8 -0.4

Average number of training 

programs completed

0.9 1.0 0.2 0.9 1.0 0.9 0.9 0.1

Completed 3 or more training 

programs

9.6 14.1 4.5 12.3 15.4 7.4 7.4 3.1

Currently in training program 11.5 9.6 -2.0 6.4 6.2 13.0 11.0 1.9

In 5 years, believe he or she will 

have continued education or 

undertaken additional job 

training

83.5 70.2 -13.3* 87.3 63.7 81.3 47.4 10.4

Source: MJS database, noninstitutional sample.

Table D.14. Outcomes in full sample 

Outcomes study Impact study

Baseline

Followup DifferenceTreatment: 

baseline

Treatment: 

follow-up

Comparison: 

baseline

Comparison: 

follow-up

Difference-indifferences

Sample size 282 282 n.a. 154 154 37 37 n.a.

Employed last week 17.9 51.2 33.3* 15.9 53.1 6.3 41.0 2.5

Stable housing in past year 15.4 53.2 37.8* 18.5 58.1 15.3 46.2 8.7

Arrested since baseline n.a. 24.9 n.a. n.a. 16.6 n.a. 21.1 n.a.

Wage and salary income (dollars) 215.7 777.3 561.6* 166.9 733.3 98.6 704.7 -39.8

Source: MJS database, full sample.
APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

D.17

Table D.15. Outcomes one year after the SE job began: multivariate analysis

Stable Total income 

Worked last housing in in past Depression Arrested in 

week past year montha Outcome index past year 

Sample full full noninstitutional noninstitutional full

Dependent variable binary binary continuous continuous binary

Sample size 281 244 195 242 282

Demographic characteristics before SE employment

High school 0.126* 0.014 95.08 0.094 -0.055

diploma/GEDb [0.045] [0.040] [164.0] [0.129] [0.028]

Any post-secondary -0.034 0.146* -92.77 0.102 0.017 b education [0.055] [0.060] [283.5] [0.215] [0.047]

0.011 -0.086 154.6 -0.574* -0.0139 Male [0.042] [0.065] [340.8] [0.122] [0.022]

-0.001 -0.000 9.573 -0.004 -0.002* Age [0.004] [0.001] [6.008] [0.006] [0.001]

0.117 -0.022 275.5 -0.151 -0.122 Hispanic [0.138] [0.033] [291.9] [0.338] [0.072]

-0.017 -0.113 101.7 -0.097 -0.193 Black [0.034] [0.062] [373.4] [0.121] [0.103]

-0.061 0.024 -314.9 -0.207 -0.035 Other race [0.065] [0.121] [243.5] [0.252] [0.033]

Native English 0.371* 0.050 -412.0 -0.139 -0.051

speaker [0.147] [0.185] [423.9] [0.932] [0.055]

-0.111 -0.062 707.0 0.438 0.330* Veteran [0.086] [0.048] [499.6] [0.263] [0.062]

Married/dom. 0.048 0.279* -285.8 -0.252 -0.081

partner. [0.071] [0.071] [197.7] [0.144] [0.046]

-0.026 -0.055 -63.18 0.201 -0.028 No dependents [0.037] [0.062] [35.67] [0.162] [0.029]

Barriers to work before SE employment

Did not work last -0.131 -0.117* -92.65 0.102 0.071

year [0.085] [0.030] [195.5] [0.157] [0.030]

Stable housing in 0.089 0.352* 264.9 0.082 -0.002

past year [0.048] [0.099] [201.0] [0.249] [0.049]

Excellent physical 0.055 0.092* 309.1 -0.097 -0.028

health [0.023] [0.028] [147.7] [0.128] [0.029]

0.021 -0.005 -47.30 0.642* 0.027* Depression index [0.018] [0.028] [63.97] [0.064] [0.011]

Substance abuse -0.129 -0.067 15.52 0.614* -0.003

counseling in past 

year [0.072] [0.104] [138.6] [0.121] [0.039]

-0.001 0.149 -221.9 0.008 0.097 Arrested 1-9 times [0.106] [0.075] [243.1] [0.182] [0.048]

Arrested 10 or more 0.128* 0.168* 153.9 0.011 0.059

times [0.053] [0.029] [239.2] [0.145] [0.039]
APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

D.18

Table D.15 (continued)

Outcome 

Worked last 

week 

Stable 

housing in 

past year 

Total income 

in past 

montha 

Depression 

index 

Arrested in 

past year 

SE employment experience

Hours worked per 

week

0.005 0.001 22.93 -0.018* -0.003

[0.002] [0.002] [11.96] [0.004] [0.002]

Length of 

employment

0.001 0.003* 5.013 -0.007* 0.001

[0.001] [0.001] [2.399] [0.002] [0.001]

Soft skills training -0.003 0.032 144.6 0.199 0.180*

[0.093] [0.034] [187.8] [0.256] [0.047]

ABE/GED 

preparation

-0.199 0.035 -185.2 -0.267* 0.062*

[0.103] [0.055] [226.1] [0.107] [0.024]

Vocational training 0.191* -0.080 -79.06 -0.001 -0.053

[0.050] [0.040] [131.7] [0.277] [0.044]

Technical training 0.064 -0.212* 64.97 0.052 -0.035

[0.071] [0.040] [303.1] [0.316] [0.035]

SE employment supports

Work supports -0.221* -0.142 -428.9 -0.043 0.021

[0.046] [0.074] [296.8] [0.131] [0.056]

Life supports -0.029 -0.166* 3.277 -0.158 0.003

[0.176] [0.063] [192.6] [0.084] [0.024]

Transition supports -0.191 0.298 -399.7 0.309 -0.331

[0.294] [0.176] [503.1] [0.427] [0.239]

Any supports after 

transition

0.149 0.213* 428.2* -0.683* -0.052

[0.067] [0.050] [89.97] [0.157] [0.075]

Additional controls

Lagged dependent 

variablec Yes Yes Yes Yes Yes

Organization Yes Yes Yes Yes Yes

Constant -1.192* 1.830* 65.31 6.556* 1.119*

[0.289] [0.652] [2,666] [1.641] [0.257]

Source: MJS database, full and noninstitutional samples. a

We use the level measure of income due to sensitivity to the treatment of zeros in the log transformation.

b

As compared to those with no high school diploma or GED.

c

All models include a lagged version of the dependent variable before SE employment began. For worked last week, 

the lagged dependent variable is worked last week (at intake). For stable housing in past year, stable housing in past 

year (before SE employment began) is included. For depression index, depression index (at hire) is included. For 

arrested in past year, ever convicted (at hire) is included.
APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

D.19

Table D.16. Employment outcomes one year after entering Chrysalis labor 

pool: multivariate analysis

Outcome 

Worked last 

week 

Worked last 

week 

Worked 

continuously 

for six months 

in last year 

Share of last 

year employed 

Propensity-score sample full full noninstitutional noninstitutional

Dependent variable binary binary binary binary

Sample size 91 91 81 81

Treatment effect

Worked at an SE hosted by 

Chrysalis 

0.220 0.194 0.244* 0.192

[0.113] [0.116] [0.119] [0.107]

Demographic characteristics before labor pool entrance

High school diploma/GED 0.330* 0.287 0.366 0.127

[0.149] [0.173] [0.185] [0.176]

Any post-secondary 

education

0.279 0.190 0.183 0.090

[0.152] [0.173] [0.192] [0.164]

Male -0.262 -0.274* -0.238 -0.083

[0.135] [0.124] [0.142] [0.106]

Age 0.001 0.001 -0.007 -0.006

[0.005] [0.005] [0.006] [0.004]

Hispanic 0.114 0.122 0.068 0.003

[0.158] [0.193] [0.184] [0.140]

Black -0.171 -0.228 -0.118 -0.146

[0.164] [0.171] [0.179] [0.126]

Other race -0.003 -0.0697 -0.066 -0.054

[0.148] [0.161] [0.173] [0.139]

Married/domestic partner 0.411* 0.382* 0.098 0.078

[0.139] [0.165] [0.196] [0.154]

No dependents -0.021 0.020 -0.0473 -0.036

[0.118] [0.132] [0.138] [0.111]

Employment measures before labor pool entrance

Worked last week 0.491* 0.556 0.474 0.283

[0.236] [0.348] [0.262] [0.242]

Worked last month -0.194 -0.078 -0.050 0.135

[0.245] [0.308] [0.241] [0.192]

Worked continuously for six 

months in last year

0.071 0.077 0.159 0.036

[0.116] [0.129] [0.128] [0.110]

Additional controls

Core measures of life 

stability at entry

No Yes Yes Yes

Constant 0.451 0.372 0.750 0.798*

[0.316] [0.341] [0.385] [0.296]

Source: MJS database, full and noninstitutional propensity-score samples.
APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

D.20

Table D.17. Housing outcomes one year after entering Chrysalis labor pool: 

multivariate analysis

Outcome 

Stable housing in 

last year 

Stable housing in 

last year 

Homeless in last 

year 

Propensity-score sample full full noninstitutional

Dependent variable binary binary binary

Sample size 83 83 76

Treatment effect

Worked at an SE hosted by 

Chrysalis 

0.138 0.124 0.160

[0.115] [0.116] [0.107]

Demographic characteristics before labor pool entrance

High school diploma/GED 0.253* 0.181 0.154

[0.122] [0.141] [0.149]

Any post-secondary education

0.190 0.126 0.110

[0.140] [0.162] [0.178]

Male -0.137 -0.096 0.0455

[0.145] [0.157] [0.144]

Age -0.001 -0.001 -0.000

[0.006] [0.006] [0.004]

Hispanic 0.039 0.079 -0.054

[0.191] [0.193] [0.142]

Black -0.004 -0.004 0.0797

[0.189] [0.203] [0.178]

Other race -0.117 -0.045 0.159

[0.154] [0.174] [0.124]

Married/domestic partner 0.298 0.269 -0.339*

[0.151] [0.163] [0.151]

No dependents -0.115 -0.131 -0.080

[0.130] [0.149] [0.130]

Housing measures before labor pool entrance

Stable housing in last year 0.514* 0.474* 0.082

[0.118] [0.147] [0.143]

Homeless in last year -0.022 0.013 0.425*

[0.152] [0.163] [0.153]

Additional controls

Core measures of life stability 

at entry

No Yes Yes

Constant 0.353 0.201 0.170

[0.328] [0.405] [0.369]

Source: MJS database, full and noninstitutional propensity-score samples.
APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

D.21

Table D.18. Monthly income outcomes one year after entering Chrysalis labor 

pool: multivariate analysis

Percent of Percent of 

Wage and salary Total monthly income from income from 

Outcome income income work government 

Propensity-score 

sample full noninstitutional noninstitutional noninstitutional

Dependent variable continuous continuous continuous continuous

Sample size 85 66 60 60

Treatment effect

Worked at an SE hosted 111.3 -140.7 5.050 -15.93

by Chrysalis [187.1] [206.4] [13.18] [10.84]

Demographic characteristics before labor pool entrance

High school 513.9* 242.0 26.33 -33.83*

diploma/GED [247.6] [320.8] [18.44] [13.99]

Any post-secondary 324.6 466.4 6.652 1.066

education [328.6] [305.2] [18.68] [14.86]

-286.1 -106.7 -0.557 -3.155 Male [305.0] [317.3] [14.26] [14.46]

6.936 -3.328 -1.284 0.460 Age [9.952] [12.33] [0.824] [0.513]

146.1 -266.8 29.59 -33.35* Hispanic [394.9] [358.5] [18.56] [14.92]

-176.9 -383.4 21.86 -14.77 Black [272.6] [322.8] [17.45] [15.27]

121.8 -353.8 12.52 -27.89* Other race [308.1] [282.9] [20.72] [13.17]

Married/ 434.6 141.0 2.709 4.853

Domestic partner [366.4] [427.4] [17.71] [15.87]

-108.5 -538.1 3.429 -4.128 No dependents [245.4] [311.2] [16.89] [14.11]

Income measures before labor pool entrance

-0.487 0.225 -0.021 0.020 Total monthly income [0.514] (0.600) (0.035) (0.034)

Monthly wage and 1.099 -0.017 -0.009 -0.0131

salary income [0.934] [1.013] [0.063] [0.059]

Share of income from -2.153 -2.935 1.638* -1.363*

work [9.232] [11.38] [0.652] [0.597]

Share of income from -0.413 -11.33 0.842 -0.866*

government [5.162] [8.849] [0.451] [0.417]

Additional controls

Core measures of life Yes Yes Yes Yes

stability at entry

1,621 858.4 173.0* 1,621 Constant [1,550] [1,235] [69.32] [1,550]

Source: MJS database, full and noninstitutional propensity-score samples.
APPENDIX D. DATA TABLES MATHEMATICA POLICY RESEARCH

D.22

Table D.19. Criminal activity and health outcomes one year after entering 

Chrysalis labor pool: multivariate analysis

Outcome 

Arrested since 

baseline 

Depression 

index 

Excellent 

physical health 

Substance 

abuse 

counseling 

since baseline 

Propensity-score sample full noninstitutional noninsitutional noninstitutional

Dependent variable binary continuous Binary binary

Sample size 91 83 83 83

Treatment effect

Worked at an SE hosted 

by Chrysalis 

-0.0296 0.055 0.201* 0.067

[0.099] [0.341] [0.074] [0.081]

Demographic characteristics before labor pool entrance

High school -0.184 -0.283 -0.107 0.076

diploma/GED [0.148] [0.355] [0.127] [0.158]

Any post-secondary -0.142 0.327 -0.162 0.176

education [0.168] [0.566] [0.129] [0.162]

Male 0.0480 0.220 0.115 0.006

[0.119] [0.584] [0.089] [0.081]

Age -0.001 0.024 -0.010* 0.000

[0.004] [0.020] [0.004] [0.002]

Hispanic -0.093 0.028 -0.023 0.123

[0.140] [0.368] [0.130] [0.089]

Black -0.098 0.598 -0.091 -0.037

[0.158] [0.355] [0.108] [0.104]

Other race 0.164 0.100 -0.230 0.041

[0.131] [0.371] [0.120] [0.077]

Married/

Domestic partner

-0.201 -1.047* -0.006 -0.230*

[0.126] [0.507] [0.137] [0.107]

No dependents -0.007 -0.600 -0.242* -0.189*

[0.102] [0.576] [0.091] [0.092]

Domain-specific outcome measures before labor pool entrance

Ever arrested 0.268* n.a. n.a. n.a.

[0.112]

Arrested 10 or more 

times

-0.178 n.a. n.a. n.a.

[0.160]

Depression index n.a. 0.767* 0.032 0.062*

[0.262] [0.036] [0.031]

In excellent physical n.a. -0.115 0.376* -0.065

health [0.355] [0.086] [0.089] 

Substance abuse 

counseling in past year

n.a. 0.549 0.144 0.636*

[0.468] [0.103] [0.099]

Additional controls

Core measures of life 

stability at entry

Yes Yes Yes Yes

Constant 0.261 -1.033 0.581* -0.040

[0.255] [1.662] [0.276] [0.192]

Source: MJS database, full and noninstitutional propensity-score samples.
APPENDIX E

FOLLOW-UP SURVEY
This page has been left blank for double-sided copying.
Reference No.: 40004

Mathematica Job Study

Follow-Up Survey

September 20, 2013

Prepared by:

Mathematica Policy Research
Prepared by Mathematica Policy Research 1

PRELIMINARIES

Before we begin the survey, I have two questions for you.

P1. Are you currently working at [SOCIAL ENTERPRISE]?

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

P2. Now, I’d like to ask you a general question. If you were to consider your life in 

general these days, how happy or unhappy would you say you are, on the whole?

Please use a scale of 1 to 7 to rate your happiness with 1 being very happy and 7

being not at all happy. 

INTERVIEWER: CIRCLE ONLY ONE RESPONSE.

Very Happy 1 2 3 4 5 6 7 Not At All Happy

CAN’T CHOOSE ................................................................................ d 

REFUSED .......................................................................................... r 
Prepared by Mathematica Policy Research 2

SECTION 1. EMPLOYMENT

The first set of questions asks about your current employment.

A1. In the last week, did you work at a job for pay? 

INTERVIEWER: READ THIS ONLY

(1) FOR BLUE CONTACT SHEETS AND 

(2) IF CURRENTLY WORKING AT SOCIAL ENTERPRISE: 

“Please include your job at [SOCIAL ENTERPRISE].”

Include both part-time and full-time jobs, as well as any self-employment jobs held 

for pay or profit.

YES.................................................................................................... 1 GO TO A3

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

GO TO A5

ASK A2 ONLY IF THE RESPONDENT DID NOT WORK DURING THE 

PAST WEEK

A2. People say that they are not working for a number of reasons. The following are 

some of the reasons people sometimes give for not working. Please tell me all of 

the reasons why you are not currently working.

INTERVIEWER: READ EACH POSSIBLE RESPONSE ALOUD AND ALLOW THEM TO 

INDICATE IF IT IS APPLICABLE TO THEIR SITUATION. 

CIRCLE ALL THAT APPLY

A physical or mental condition prevents you from working ......... 1 

You cannot find a job that you are qualified for............................. 2 

You do not have reliable transportation to and from work............ 3 

You are caring for someone else .................................................... 4 

You cannot find a job you want....................................................... 5 

You are waiting to finish school or a training program ................. 6 

Workplaces are not accessible to people with your disability...... 7 

You do not want to lose benefits such as disability, 

workers’ compensation, or Medicaid.............................................. 8 

Previous attempts to work have been discouraging ..................... 9 

Others do not think you can work................................................... 10

Employers will not give you a chance to show you can work....... 11

You lack skills................................................................................... 12

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

There are other reasons why you are not working ........................ 13 GO TO A2a

GO TO A5
Prepared by Mathematica Policy Research 3

A2a. Please specify the other reasons why you are not currently working. 

RECORD VERBATIM

 

 

 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

AFTER ANSWERING A2a SKIP TO A5

A3. In the last week, did you have more than one job, including part-time, evening, or 

weekend work? Please count work for an employment agency or as a consultant 

as one job.

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

A4. Altogether, how many jobs have you had IN THE LAST WEEK? Please count work 

for an employment agency or as a consultant as one job.

| | | NUMBER OF JOBS

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

GO TO A5
Prepared by Mathematica Policy Research 4

A5. INTERVIEWER: ASK APPROPRIATE QUESTION:

IF WORKED LAST WEEK: For the job at which you worked the most hours LAST 

WEEK, what was the name of the employer where you 

worked? 

IF NOT CURRENTLY EMPLOYED: What was the name of the employer where you 

worked most recently?

[SOCIAL ENTERPRISE]..................................................................... 1 GO TO A7

OR

 

NAME OF EMPLOYER

PARTICIPANT NEVER WORKED...................................................... 99 GO TO A23

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

IF A CLIENT DOES NOT KNOW THE NAME OF THEIR 

PREVIOUS EMPLOYER OR REFUSES TO ANSWER A5, 

PLEASE CONTINUE ON TO A6. IF THE CLIENT 

REFUSES TO ANSWER THESE QUESTIONS, PLEASE 

WRITE IN “REFUSED”

A6. What kind of business or industry is this company? What kinds of things do they 

make, do, or sell?

 

BUSINESS OR INDUSTRY

A7. What kind of work do (did) you do, that is what is or was your occupation?

 

OCCUPATION

A8. What are (were) your usual activities or duties at this job?

 

ACTIVITIES OR DUTIES
Prepared by Mathematica Policy Research 5

A9. How did you find this job? How did you hear about it?

INTERVIEWER: READ EACH POSSIBLE RESPONSE ALOUD AND ALLOW THEM TO 

INDICATE IF IT IS APPLICABLE TO THEIR SITUATION. 

CIRCLE ALL THAT APPLY

You were recalled by a former employer........................................ 1 

You heard/found it through a state employer agency/state job 

service............................................................................................... 2 

You heard/found it through a private employment agency ........... 3 

You heard/found it through friends/relatives/colleagues .............. 4 

You found it through the want ads/newspaper/local paper........... 5 

You heard/found it directly through your employer....................... 6 

You heard/found it through your union .......................................... 7 

You are self-employed ..................................................................... 8 

You heard/found it through school ................................................. 9 

You heard/found it through the Internet/internet job 

service/TV/ Craig’s list ..................................................................... 10

You heard/found it through [SOCIAL ENTERPRISE] or 

[ORGANIZATION] ............................................................................. 11

You heard/found it some other way (SPECIFY).............................. 12

 ______________________________________________________ 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

A10. How difficult is (was) it for you to take an hour or two off during work hours to 

take care of personal or family matters?

Not difficult at all, ............................................................................. 1 

Not too difficult,................................................................................ 2 

Somewhat difficult, or...................................................................... 3 

Very difficult? ................................................................................... 4 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 
Prepared by Mathematica Policy Research 6

INTERVIEWER CHECK: SKIP A11 IF JOB BEING DISCUSSED IS AT [SOCIAL ENTERPRISE].

A11. I’m going to read you a list of characteristics about the job at [FILL A5]. Please rate 

how satisfied you are (were) with respect to each of the following. Are (were) you very 

satisfied, somewhat satisfied, somewhat dissatisfied, or very dissatisfied?

CIRCLE ONE FOR EACH ROW

VERY 

SATISFIED

SOMEWHAT 

SATISFIED

SOMEWHAT 

DISSATISFIED

VERY 

DISSATISFIED

DON’T 

KNOW REFUSED

a. Your salary (the amount of 

money you made)................. 1 2 3 4 d r 

b. The benefits you 

receive/received.................... 1 2 3 4 d r 

c. The type of work you do/did . 1 2 3 4 d r 

d. The number of hours you 

work/worked.......................... 1 2 3 4 d r 

e. Where the job is/was located 1 2 3 4 d r 

f. The opportunities for you to 

move up in the company ...... 1 2 3 4 d r 

Now I’d like to talk to you about some more characteristics of this job. Please use 

the same scale and rate how satisfied you are (were) with respect to each of the 

following. Are (were) you very satisfied, somewhat satisfied, somewhat 

dissatisfied, or very dissatisfied?

CIRCLE ONE FOR EACH ROW

VERY 

SATISFIED

SOMEWHAT 

SATISFIED

SOMEWHAT 

DISSATISFIED

VERY 

DISSATISFIED

DON’T 

KNOW REFUSED

g. How much it challenges/ 

challenged you intellectually... 1 2 3 4 d r 

h. The level of responsibility 

you have/were given............... 1 2 3 4 d r 

i. How much independence 

you have/had in your work...... 1 2 3 4 d r 

j. How much the job 

contributes/contributed to 

society..................................... 1 2 3 4 d r 
Prepared by Mathematica Policy Research 7

Now I’d like to talk to you about the support you receive/received at this job. 

Please use the same scale and rate how satisfied you are (were) with respect to 

each of the following. Are (were) you very satisfied, somewhat satisfied, 

somewhat dissatisfied, or very dissatisfied?

CIRCLE ONE FOR EACH ROW

VERY 

SATISFIED

SOMEWHAT 

SATISFIED

SOMEWHAT 

DISSATISFIED

VERY 

DISSATISFIED

DON’T 

KNOW REFUSED

k. How secure you feel/felt in 

keeping the job ..................... 1 2 3 4 d r 

l. Getting feedback about how 

well you are/were doing the 

job ......................................... 1 2 3 4 d r 

m. Getting the support you 

need/needed......................... 1 2 3 4 d r 

A12. Other than the job you just told me about, how many other jobs did you have IN THE

PAST TWELVE MONTHS? Please include other jobs in which you are currently 

working and count work for an employment agency like Manpower or as a consultan

as one job.

| | | NUMBER OF JOBS

IF 0..............................................................................

IF 1 OR MORE............................................................

t

....................... 1 GO TO A14

....................... 2 GO TO A13
Prepared by Mathematica Policy Research 8

CURRENT JOB/MOST RECENT JOB 2

A13. Including the job you have 

just told me about, where 

have you worked in the past 

year? Include work at 

[SOCIAL ENTERPRISE] if we 

have not already talked about 

it and any self-employment 

and any other current jobs.

Please tell me the most recent 

job first. ..................................... 

INTERVIEWER: IF THE CLIENT 

HELD MORE THAN FIVE 

ADDITIONAL JOBS, ONLY 

ENTER THE FIVE MOST 

RECENT JOBS IN THIS GRID.

____________________________ ____________________________

A14. How many hours per week, 

including regular overtime 

hours do/did you usually

| | | | HOURS PER WEEK

work 

on this job?............................... DON’T KNOW................................... d 

REFUSED......................................... r

| | | |HOURS PER WEEK

DON’T KNOW ................................... d 

REFUSED ......................................... r

A15. When do/did you start

PROBE: Your best guess is 

fine.

working at this job? ................. 

START DATE:

| | | / | | | | | 

MONTH YEAR

DON’T KNOW................................... d 

REFUSED......................................... r

START DATE:

| | | / | | | | | 

MONTH YEAR

DON’T KNOW ................................... d 

REFUSED ......................................... r

A16. Does/Did your employer 

receive funding from an 

outside source (for example 

grant funding or federal, state, 

or local government funding) 

to help pay for your 

employment?............................ 

YES................................................... 1 

NO..................................................... 0 

DON’T KNOW................................... d 

REFUSED......................................... r

YES ................................................... 1 

NO..................................................... 0 

DON’T KNOW ................................... d 

REFUSED ......................................... r

A17. Does/Did this job provide you 

with support services like a 

case manager or employment 

counselor while you were 

working there?.......................... 

 

YES................................................... 1 

NO..................................................... 0 

DON’T KNOW................................... d 

REFUSED......................................... r

YES ................................................... 1 

NO..................................................... 0 

DON’T KNOW ................................... d 

REFUSED ......................................... r

A18. Is/Was health care coverage 

available to you at this job? .... 

YES................................................... 1 

NO..................................................... 0 

DON’T KNOW................................... d 

REFUSED......................................... r 

YES ................................................... 1 

NO..................................................... 0 

DON’T KNOW ................................... d 

REFUSED ......................................... r 

FIRST ANSWER A13 FOR ALL JOBS (UP TO 5)

THEN ANSWER A14 - A19

FOR THE FIRST JOB, 

THEN ANSWER A14 – A19 

FOR THE SECOND JOB 

AND CONTINUE UNTIL 

THERE ARE NO MORE.
Prepared by Mathematica Policy Research 9

CURRENT JOB/MOST RECENT JOB 2

A19. If you are not currently 

working at this job, when did 

you stop working at this job? . 

PROBE: Your best guess is 

fine.

STOP DATE:

STILL WORKING ...................................... 1 

| | | / | | | | | 

MONTH YEAR

DON’T KNOW................................... d 

REFUSED......................................... r

STOP DATE:

STILL WORKING ...................................... 1 

| | | / | | | | | 

MONTH YEAR

DON’T KNOW ................................... d 

REFUSED ......................................... r

INTERVIEWER: IF THERE IS 

ANOTHER JOB GO BACK TO A13 

AND LIST THE NEXT JOB 

IF YOU ARE ON THE LAST JOB, GO 

TO A20.

NO MORE JOBS .............................. 1 NO MORE JOBS............................... 1 
Prepared by Mathematica Policy Research 10

JOB 3 JOB 4

A13. Including the job you have 

just told me about, where 

have you worked in the past 

year? Include work at 

[SOCIAL ENTERPRISE] if we 

have not already talked about 

it and any self-employment 

and any other current jobs.

Please tell me the most recent 

job first. ..................................... 

INTERVIEWER: IF THE CLIENT 

HELD MORE THAN FIVE 

ADDITIONAL JOBS, ONLY 

ENTER THE FIVE MOST 

RECENT JOBS IN THIS GRID.

____________________________ ____________________________

A14. How many hours per week, 

including regular overtime 

hours do/did you usually

| | | | HOURS PER WEEK

work 

on this job?............................... DON’T KNOW................................... d 

REFUSED......................................... r

| | | | HOURS PER WEEK

DON’T KNOW ................................... d 

REFUSED ......................................... r

A15. When do/did you start

PROBE: Your best guess is 

fine.

working at this job? ................. 

START DATE:

| | | / | | | | | 

MONTH YEAR

DON’T KNOW................................... d 

REFUSED......................................... r

START DATE:

| | | / | | | | | 

MONTH YEAR

DON’T KNOW ................................... d 

REFUSED ......................................... r

A16. Does/Did your employer 

receive funding from an 

outside source (for example 

grant funding or federal, state, 

or local government funding) 

to help pay for your 

employment?............................ 

YES................................................... 1 

NO..................................................... 0 

DON’T KNOW................................... d 

REFUSED......................................... r

YES ................................................... 1 

NO..................................................... 0 

DON’T KNOW ................................... d 

REFUSED ......................................... r

A17. Does/Did this job provide you 

with support services like a 

case manager or employment 

counselor while you were 

working there?.......................... 

CHECK YES AND DO NOT 

ASK IF JOB WAS AT [SOCIAL 

ENTERPRISE].

YES................................................... 1 

NO..................................................... 0 

DON’T KNOW................................... d 

REFUSED......................................... r

YES ................................................... 1 

NO..................................................... 0 

DON’T KNOW ................................... d 

REFUSED ......................................... r

A18. Is/Was health care coverage 

available to you at this job? .... 

YES................................................... 1 

NO..................................................... 0 

DON’T KNOW................................... d 

REFUSED......................................... r 

YES ................................................... 1 

NO..................................................... 0 

DON’T KNOW ................................... d 

REFUSED ......................................... r 
Prepared by Mathematica Policy Research 11

JOB 3 JOB 4

A19. If you are not currently 

working at this job, when did 

you stop working at this job? . 

PROBE: Your best guess is 

fine.

STOP DATE:

STILL WORKING ...................................... 1 

| | | / | | | | | 

MONTH YEAR

DON’T KNOW................................... d 

REFUSED......................................... r

STOP DATE:

STILL WORKING ...................................... 1 

| | | / | | | | | 

MONTH YEAR

DON’T KNOW ................................... d 

REFUSED ......................................... r

INTERVIEWER: IF THERE IS 

ANOTHER JOB GO BACK TO 

A13 AND LIST THE NEXT JOB 

IF YOU ARE ON THE LAST 

JOB, GO TO A20.

NO MORE JOBS .............................. 1 NO MORE JOBS............................... 1 
Prepared by Mathematica Policy Research 12

JOB 5

A13. Including the job you have 

just told me about, where 

have you worked in the past 

year? Include work at 

[SOCIAL ENTERPRISE] if we 

have not already talked about 

it and any self-employment 

and any other current jobs...... 

Please tell me the most recent 

job first.

INTERVIEWER: IF THE CLIENT 

HELD MORE THAN FIVE 

ADDITIONAL JOBS, ONLY 

ENTER THE FIVE MOST 

RECENT JOBS IN THIS GRID.

____________________________

A14. How many hours per week, 

including regular overtime 

hours do/did you usually

| | | | HOURS PER WEEK

work 

on this job?............................... DON’T KNOW .................................. d 

REFUSED ........................................ r

A15. When do/did you start

PROBE: Your best guess is 

fine.

working at this job? ................. 

START DATE:

| | | / | | | | | 

MONTH YEAR

DON’T KNOW .................................. d 

REFUSED ........................................ r

A16. Does/Did your employer 

receive funding from an 

outside source (for example 

grant funding or federal, state, 

or local government funding) 

to help pay for your 

employment?............................ 

YES .................................................. 1 

NO .................................................... 0 

DON’T KNOW .................................. d 

REFUSED ........................................ r

A17. Does/Did this job provide you 

with support services like a 

case manager or employment 

counselor while you were 

working there? ......................... 

CHECK YES AND DO NOT 

ASK IF JOB WAS AT [SOCIAL 

ENTERPRISE].

YES .................................................. 1 

NO .................................................... 0 

DON’T KNOW .................................. d 

REFUSED ........................................ r

A18. Is/Was health care coverage 

available to you at this job?.... 

YES .................................................. 1 

NO .................................................... 0 

DON’T KNOW .................................. d 

REFUSED ........................................ r 
Prepared by Mathematica Policy Research 13

JOB 5

A19. If you are not currently 

working at this job, when did 

you stop working at this job?.. 

PROBE: Your best guess is 

fine.

STOP DATE:

STILL WORKING ...................................... 1 

| | | / | | | | | 

MONTH YEAR

DON’T KNOW ................................... d 

REFUSED ......................................... r

INTERVIEWER: IF THERE IS 

ANOTHER JOB GO BACK TO 

A13 AND LIST THE NEXT JOB 

IF YOU ARE ON THE LAST 

JOB, GO TO A20.

NO MORE JOBS............................... 1 
Prepared by Mathematica Policy Research 14

INTERVIEWER CHECK: 

YELLOW CONTACT SHEET, DID NOT WORK AT SOCIAL 

ENTERPRISE. 

BLUE CONTACT SHEET, RESPONDENT WORKED AT

[SOCIAL ENTERPRISE] IN THE PAST.

BLUE CONTACT SHEET, RESPONDENT IS STILL WORKING 

AT [SOCIAL ENTERPRISE].

RESPONDENT REFUSED TO ANSWER IF THEY WORKED 

AT A SOCIAL ENTERPRISE (P1 = D OR R) READ:

“I want to verify I have the correct answer to this question. Did you work at 

[SOCIAL ENTERPRISE]?” 

YES.................................................................................................... 1 GO TO A20

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

DO NOT ASK A20, SELECT 20 

AND GO TO A23

GO TO A20

DO NOT ASK A20, SELECT 

19 AND GO TO A21

DO NOT ASK A20, 

SELECT 20 AND 

GO TO A23

GO TO A23
Prepared by Mathematica Policy Research 15

Now I’d like to talk to you about your experience at [SOCIAL ENTERPRISE]. 

A20. What was the main reason

INTERVIEWER: ASK THE QUESTION AND CIRCLE THE APPROPRIATE RESPONSE. DO 

NOT READ ANSWERS. 

that you left [SOCIAL ENTERPRISE]?

CIRCLE ONE ONLY

FOUND A JOB/EMPLOYED OUTSIDE OF [SOCIAL 

ENTERPRISE] ................................................................................... 1 

MOVED.............................................................................................. 2 

STARTED OTHER SCHOOL/TRAINING ........................................... 3 

NOT INTERESTED ............................................................................ 4 

DIDN’T LIKE WORKING AT [SOCIAL ENTERPRISE]........................ 5 

COULD NOT WORK AS MANY HOURS AS DESIRED ..................... 6 

PAY WAS TOO LOW ......................................................................... 7 

ILLNESS ............................................................................................ 8 

PREGNANCY OR CHILDCARE ISSUES........................................... 9 

OTHER FAMILY REASONS............................................................... 10

TRANSPORTATION/LOGISTICAL PROBLEMS................................ 11

PERSONAL PROBLEMS ................................................................... 12

[SOCIAL ENTERPRISE] POORLY RUN ............................................ 13

DIDN’T THINK WORKING AT [SOCIAL ENTERPRISE] WOULD 

HELP ME FIND ANOTHER JOB ........................................................ 14

DECIDED I DIDN’T WANT A JOB...................................................... 15

INCARCERATED/JAIL ....................................................................... 16

DRUG USE ........................................................................................ 17

TERMINATED BY [SOCIAL ENTERPRISE] ...................................... 18

STILL WORKING AT [SOCIAL ENTERPRISE] .................................. 19 GO TO A21

DID NOT WORK AT [SOCIAL ENTERPRISE].................................... 20 GO TO A23

LEFT FOR SOME OTHER REASON (SPECIFY)............................... 21

 ______________________________________________________ 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 
Prepared by Mathematica Policy Research 16

Now I’d like to talk to you about your experience at the Social Enterprise.

A21. Did you work at [SOCIAL ENTERPRISE] for more than one period of time? That is, 

was there a period of time in which you did not work at [SOCIAL ENTERPRISE] 

between the time you first started there and the time you stopped working there?

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

A22. Please rate how satisfied you are/were with the job you held at [SOCIAL 

ENTERPRISE] with respect to each of the following. Were you very satisfied, 

somewhat satisfied, somewhat dissatisfied, or very dissatisfied?

CIRCLE ONE FOR EACH ROW

VERY 

SATISFIED

SOMEWHAT 

SATISFIED

SOMEWHAT 

DISSATISFIED

VERY 

DISSATISFIED

DON’T 

KNOW REFUSED

a. Your salary (the amount 

of money you 

make/made).................. 1 2 3 4 d r 

b. The benefits you 

receive/received............ 1 2 3 4 d r 

c. The type of work you 

do/did ............................ 1 2 3 4 d r 

d. The number of hours 

you work/worked........... 1 2 3 4 d r 

e. Where the job is/was 

located .......................... 1 2 3 4 d r 

f. The opportunities for 

you to move up in the 

company ....................... 1 2 3 4 d r 
Prepared by Mathematica Policy Research 17

Now I’d like to talk to you about some more characteristics of this job. Please use 

the same scale and rate how satisfied you are (were) with respect to each of the 

following. Are (were) you very satisfied, somewhat satisfied, somewhat 

dissatisfied, or very dissatisfied?

CIRCLE ONE FOR EACH ROW

VERY 

SATISFIED

SOMEWHAT 

SATISFIED

SOMEWHAT 

DISSATISFIED

VERY 

DISSATISFIED

DON’T 

KNOW REFUSED

g. How much it 

challenges/challenged 

you intellectually ........... 1 2 3 4 d r 

h. The level of 

responsibility you 

are/were given .............. 1 2 3 4 d r 

i. How much 

independence you 

have/had in your work .. 1 2 3 4 d r 

j. How much the job 

contributes/contributed 

to society....................... 1 2 3 4 d r 

Now I’d like to talk to you about the support you receive/received at this job. 

Please use the same scale and rate how satisfied you are (were) with respect to 

each of the following. Are (were) you very satisfied, somewhat satisfied, 

somewhat dissatisfied, or very dissatisfied?

CIRCLE ONE FOR EACH ROW

VERY 

SATISFIED

SOMEWHAT 

SATISFIED

SOMEWHAT 

DISSATISFIED

VERY 

DISSATISFIED

DON’T 

KNOW REFUSED

k. How secure you feel/felt 

in keeping the job.......... 1 2 3 4 d r 

l. Getting feedback about 

how well you are/were 

doing the job ................. 1 2 3 4 d r 

m. Getting the support you 

need/needed................. 1 2 3 4 d r 
Prepared by Mathematica Policy Research 18

A23. Some organizations provide various services to clients. I’d like to ask you about 

services you may have received from [ORGANIZATION]. First, let’s talk about 

education and training. Which of the following services did you receive from 

[ORGANIZATION]?

INTERVIEWER: READ IF NECESSARY, “Did you receive . . .”

CIRCLE ONE FOR EACH ROW

RECEIVED

DID NOT 

RECEIVE

DON’T 

KNOW REFUSED

a. Soft skills training (for example time management, working 

in a team, conflict resolution, attitudes toward work, anger 

management, stress management)........................................ 1 2 d r 

b. Adult Basic Education or GED preparation ............................ 1 2 d r 

c. Vocational or job specific skills training .................................. 1 2 d r 

d. Computer literacy or skills and technology training................ 1 2 d r 

Next, let’s talk about work supports. Which of the following services did you 

receive from [ORGANIZATION]?

CIRCLE ONE FOR EACH ROW

RECEIVED

DID NOT 

RECEIVE

DON’T 

KNOW REFUSED

e. Housing or rental assistance ................................................... 1 2 d r 

f. Transportation assistance ....................................................... 1 2 d r 

g. Work clothing assistance (for example, access to clothes 

closets, work uniforms, protective or supportive shoes).......... 1 2 d r 
Prepared by Mathematica Policy Research 19

Next, let’s talk about life stability supports. Which of the following services did 

you receive from [ORGANIZATION]?

CIRCLE ONE FOR EACH ROW

RECEIVED

DID NOT 

RECEIVE

DON’T 

KNOW REFUSED

h. Physical health services ......................................................... 1 2 d r 

i. Assistance with a physical or learning disability..................... 1 2 d r 

j. Substance abuse counseling or treatment ............................. 1 2 d r 

k. Domestic abuse protection, counseling or other services...... 1 2 d r 

l. Financial education and asset building (for example, 

budgeting, EITC, savings assistance, financial literacy, IDAs, 

(re)building credit), including setting up a bank account ........ 1 2 d r 

m. Assistance with food stability or food security (food pantries, 

reduced price meals, nutritional education)............................ 1 2 d r 

n. Access to public benefits (for example, food stamps, 

Medicaid, SSI) ........................................................................ 1 2 d r 

o. Help with tax preparation........................................................ 1 2 d r 

p. Help plan to avoid relapse of behavior................................... 1 2 d r 

Next, let’s talk about employment transition supports. Which of the following 

services did you receive from [ORGANIZATION]?

PROBE: Employment transition supports are any type of help an organization 

may give you to find a job or employment outside of the organization.

CIRCLE ONE FOR EACH ROW

RECEIVED

DID NOT 

RECEIVE

DON’T 

KNOW REFUSED

q. Job readiness skills training (for example, resume 

preparation, interview skills, goal setting)................................ 1 2 d r 

r. Career counseling or job coaching (for example, career 

pathways, advancement)......................................................... 1 2 d r 

s. Job search assistance ............................................................. 1 2 d r 
Prepared by Mathematica Policy Research 20

INTERVIEWER: 

IF RESPONDENT IS STILL WORKING AT SOCIAL ENTERPRISE

Finally, let’s talk about services you received after you left [ORGANIZATION]. Which of 

the following services did you receive from [ORGANIZATION]?

CIRCLE ONE FOR EACH ROW

RECEIVED

DID NOT 

RECEIVE

DON’T 

KNOW REFUSED

t. Access to employment counselor or other services 

related to employment (for example, to address workrelated challenges, support job retention and 

advancement, identify new job opportunities) ........................ 1 2 d r 

u. Access to staff or services that support life stability (for 

example, to address challenges with substance abuse, 

mental health, housing, childcare, financial stability, or 

other services) ........................................................................ 1 2 d r 

v. Other (SPECIFY):................................................................... 1 2 d r 

 _____________________________________________________ 

DO NOT ASK A23 items t –v

GO TO SECTION 2
Prepared by Mathematica Policy Research 21

SECTION 2. RECIDIVISM

Now I have some questions about your interactions with the criminal justice system. Your 

answers will be kept strictly confidential.

B1. Were you ever in jail or prison prior to [MONTH AND YEAR OF LAST SURVEY?

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

B1a. Prior to [MONTH AND YEAR OF LAST SURVEY], when were you most recently 

released from jail or prison? 

PROBE: Your best guess is fine.

 | | | / | | | | | 

MONTH YEAR

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

B2. Have you been arrested since [MONTH AND YEAR OF LAST SURVEY]?

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

B2a. How many times have you been arrested since [MONTH AND YEAR OF LAST 

SURVEY]?

 | | | NUMBER OF ARRESTS

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

B3. Since [MONTH AND YEAR OF LAST SURVEY], have you been in jail or prison?

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

GO TO B2

GO TO 

SECTION 3

C1

GO TO 

SECTION 3

C1
Prepared by Mathematica Policy Research 22

B4. Since [MONTH AND YEAR OF LAST SURVEY], have you been convicted and 

sentenced to jail or prison?

Please include any sentence you received, even if you did not serve any time.

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

B4a. When was your most recent conviction?

PROBE: Your best guess is fine.

 | | | / | | | | | 

MONTH YEAR

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

B5. Please think about all the sentences you’ve received since [MONTH AND YEAR OF 

LAST SURVEY]. Please tell me the total length of those prison sentences, even if 

you did not serve all of them.

PROBE: Your best guess is fine.

 | | | | NUMBER OF

DAYS ................................................................................................. 1 

WEEKS .............................................................................................. 2 

MONTHS............................................................................................ 3 

YEARS............................................................................................... 4 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

B6. When were you most recently released from prison?

PROBE: Your best guess is fine.

 | | | / | | | | | 

MONTH YEAR

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

B7. Since [MONTH AND YEAR OF LAST SURVEY], did you start a probation or parole?

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

GO TO 

SECTION 3

C1
Prepared by Mathematica Policy Research 23

SECTION 3. LIFE STABILITY

READ: Now I’d like to ask you some questions about how things are going in your life.

C1. Did you open any bank accounts, including a checking account, since [MONTH 

AND YEAR OF LAST SURVEY]?

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

C2. What is your best guess of your total earnings (money from work) in the past 

month? We would like to know how much you made before taxes and other 

deductions. Please include tips, commissions, and overtime pay.

If you held more than one job, include your total earnings from all your jobs 

during the past month.

PROBE: Your best estimate is fine.

$ | || | , | | | | . | | | TOTAL MONTHLY EARNINGS

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 
Prepared by Mathematica Policy Research 24

C3. We are interested in learning about the benefits and income you received on [MONTH 

AND YEAR OF LAST SURVEY] and those that you received in the past month. Can you 

tell me if you received each of the following on [MONTH AND YEAR OF LAST SURVEY] 

and if you received them last month? If you received them last month, please tell me 

the amount you received per month.

INTERVIEWER: ASK RESPONDENT IF THEY RECEIVED THE BENEFIT OR INCOME ON [MONTH 

AND YEAR OF LAST SURVEY]. THEN ASK IF THEY RECEIVED IT LAST MONTH. 

PLEASE CIRCLE ‘RECEIVED IN EACH PERIOD. IF THEY RECEIVED THE

BENEFIT OR INCOME IN THE LAST MONTH, ASK THEM HOW MUCH THEY 

RECEIVE PER MONTH. 

CIRCLE ONE FOR EACH ROW COMPLETE ONE FOR EACH ROW

RECEIVED [MONTH AND YEAR OF 

LAST SURVEY] LAST MONTH

YES NO 

DON’T 

KNOW REFUSED YES NO 

DON’T 

KNOW REFUSED 

TOTAL AMOUNT PER 

MONTH

a. Food Stamp or SNAP 

benefits? ............................... 1 0 d r 1 0 d r $__________

b. Welfare programs such as 

TANF, General Assistance or 

GA, CAL Works, or Safety 

Net? ...................................... 1 0 d r 1 0 d r $__________

c. SSI, SSDI, or other disability 

benefits? ............................... 1 0 d r 1 0 d r $__________

d. Social Security or pension 

benefits? ............................... 1 0 d r 1 0 d r $__________

e. Unemployment insurance 

benefits or UI?....................... 1 0 d r 1 0 d r $__________

f. WIC benefits? ....................... 1 0 d r 1 0 d r $__________

g. Workers’ compensation

benefits? ............................... 1 0 d r 1 0 d r $__________

h. Alimony, child support, or 

rent payments? ..................... 1 0 d r 1 0 d r $__________

i. Interest and/or dividends? .... 1 0 d r 1 0 d r $__________

j. Money from friends or 

relatives?............................... 1 0 d r 1 0 d r $__________

k. Non-monetary support from 

friends or relatives (for 

example food, rent support, 

help paying bills)? ................. 1 0 d r 1 0 d r $__________

l. Any other income sources? 

(SPECIFY) ............................ 1 0 d r 1 0 d r $__________

 
Prepared by Mathematica Policy Research 25

C4. In general would you say your physical health is...

Excellent, .......................................................................................... 1 

CIRCLE ONE ONLY

Very good,......................................................................................... 2 

Good,................................................................................................. 3 

Fair, or............................................................................................... 4 

Poor?................................................................................................. 5 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

C5. Has your physical health changed since [MONTH AND YEAR OF LAST SURVEY]?

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

C6. Did your physical health...

Improve or......................................................................................... 1 

Get worse?........................................................................................ 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

C7. During the past 7 days, how often did you feel distressed or bothered by the 

following feelings: never, a little bit, moderately, quite a bit, or extremely often?

CIRCLE ONE FOR EACH ROW

NEVER 

A LITTLE 

BIT MODERATELY

QUITE 

A BIT

EXTREMELY 

OFTEN

DON’T 

KNOW REFUSED 

a. feeling no interest in things?........ 1 2 3 4 5 d r 

b. feeling lonely?.............................. 1 2 3 4 5 d r 

c. feeling blue? ................................ 1 2 3 4 5 d r 

d. feelings of worthlessness? .......... 1 2 3 4 5 d r 

e. feeling hopeless about the 

future?.......................................... 1 2 3 4 5 d r 

f. thoughts of ending your life? ....... 1 2 3 4 5 d r 

INTERVIEWER: IF THE RESPONDENT ANSWERS 2-5 FOR C7F, PLEASE MAKE A NOTE OF THAT AND 

PROVIDE THEM WITH THE GEOGRAPHICALLY APPROPRIATE HELP LINE 

INFORMATION AFTER THE SURVEY IS COMPLETE. 

GO TO C7
Prepared by Mathematica Policy Research 26

C8. Do you now have an emotional or other health condition that limits the amount or 

type of work you could do?

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

C9. What condition is the main

 

reason you are limited? By what name do doctors call 

your health condition?

NAME OF CONDITION

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

C10. Since [MONTH AND YEAR OF LAST SURVEY], how many times did you have four 

or more drinks in one day?

 | | | | NUMBER OF TIMES

NEVER............................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

C11. Since [MONTH AND YEAR OF LAST SURVEY], has there been a time when your 

drinking or being hung over interfered with your job, school, or home life?

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

C12. Since [MONTH AND YEAR OF LAST SURVEY], have you been in counseling or 

therapy for alcohol problems?

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

GO TO C10

GO TO C12
Prepared by Mathematica Policy Research 27

C13. Since [MONTH AND YEAR OF LAST SURVEY], how many times did you smoke 

marijuana or hashish (pot, grass, hash)?

 | | | | NUMBER OF TIMES

NEVER............................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

C14. Since [MONTH AND YEAR OF LAST SURVEY], how many times did you use any 

hard drugs such as heroin, cocaine, or LSD?

 | | | | NUMBER OF TIMES

NEVER............................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

C15. Since [MONTH AND YEAR OF LAST SURVEY], have you been in counseling or 

therapy for drug problems?

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 
Prepared by Mathematica Policy Research 28

C16. Since [MONTH AND YEAR OF LAST SURVEY], have you spent the night at any of 

the following places? 

We are interested if any of these places were your primary residence. 

INTERVIEWER: STRESS PRIMARY RESIDENCE FOR STREET, CAR, PARK, OR OTHER 

PLACE OUTSIDE; HOSPITAL; OR IN A FRIEND’S OR FAMILY MEMBER’S ROOM, 

APARTMENT, OR HOUSE. WE ARE ONLY INTERESTED IF THE PARTICIPANTS SLEPT 

THERE BECAUSE THEY WERE UNABLE TO SLEEP SOMEWHERE ELSE. 

CIRCLE ONE FOR EACH ROW

YES NO

DON’T 

KNOW REFUSED

a. Emergency shelter, including hotel or motel voucher paid 

for by a social service or charitable organization .................... 1 0 d r 

b. Transitional housing for homeless persons............................. 1 0 d r 

c. Permanent supportive housing for formerly homeless 

persons ..................................................................................... 1 0 d r 

d. Psychiatric hospital or other psychiatric facility ....................... 1 0 d r 

e. Substance abuse treatment facility, rehabilitation center,

or other detox facility ............................................................... 1 0 d r 

f. Hospital (non-psychiatric) (because you did not have a 

fixed, regular nighttime residence) .......................................... 1 0 d r 

g. Jail, prison or juvenile detention facility ................................... 1 0 d r 

h. Half-way house or three-quarter-way home for persons 

with criminal offenses .............................................................. 1 0 d r 

i. Room, apartment or house that you rent................................. 1 0 d r 

j. Apartment or house that you own............................................ 1 0 d r 

k. In a friends or family member’s room, apartment or house 

(because you did not have a fixed, regular nighttime 

residence)................................................................................ 1 0 d r 

l. Hotel or motel paid for without emergency shelter voucher 

(because you did not have a fixed, regular nighttime 

residence)................................................................................ 1 0 d r 

m. Group home or other supervised residential care facility 

(because you did not have a fixed, regular nighttime 

residence)................................................................................ 1 0 d r 

o. Street, car, park, other place outside (because you did not 

have a fixed, regular nighttime residence) .............................. 1 0 d r 

p. Other (SPECIFY)..................................................................... 1 0 d r 

 
Prepared by Mathematica Policy Research 29

C17. Please tell me if the following statements are very much like you, mostly like you, 

somewhat like you, not much like you or not like you at all.

CIRCLE ONE FOR EACH ROW

VERY 

MUCH 

LIKE YOU

MOSTLY 

LIKE YOU

SOMEWHAT 

LIKE YOU

NOT MUCH 

LIKE YOU

NOT LIKE 

YOU AT ALL

DON’T 

KNOW REFUSED

a. I have overcome setbacks to 

conquer an important challenge... 1 2 3 4 5 d r 

b. New ideas and projects 

sometimes distract me from 

previous ones ............................... 1 2 3 4 5 d r 

c. My interests change from year to 

year............................................... 1 2 3 4 5 d r 

d. Setbacks don’t discourage me..... 1 2 3 4 5 d r 

e. I have been obsessed with a 

certain idea or project for a short 

time but later lost interest ............. 1 2 3 4 5 d r 

f. I am a hard worker ....................... 1 2 3 4 5 d r 

g. I often set a goal but later choose 

to pursue a different one .............. 1 2 3 4 5 d r 

h. I have difficulty maintaining my 

focus on projects that take more 

than a few months to complete .... 1 2 3 4 5 d r 

i. I finish whatever I begin................ 1 2 3 4 5 d r 

j. I have achieved a goal that took 

years of work ................................ 1 2 3 4 5 d r 

k. I become interested in new 

pursuits every few months............ 1 2 3 4 5 d r 

l. I am diligent .................................. 1 2 3 4 5 d r 
Prepared by Mathematica Policy Research 30

SECTION 4. FACILITATING FACTORS

Now I’m going to ask you about other programs you may have participated in and your 

thoughts about them and work life in general.

F1. Since [MONTH AND YEAR OF LAST SURVEY], have you participated in any 

education and training programs and courses that were supposed to lead to a 

degree, license or certificate?

PROBE: Please include training programs that helped you learn job skills or 

prepare for an occupation, as well as general educational programs, 

such as college, regular high school, or GED courses.

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

F2. Since [MONTH AND YEAR OF LAST SURVEY], how many different education and 

training programs have you participated in?

 | | | NUMBER OF EDUCATION AND TRAINING PROGRAMS

 0 ......................................................................................................... 0 GO TO F8

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

F3. In how many training and education programs are you currently participating?

 | | | NUMBER OF EDUCATION AND TRAINING PROGRAMS

 0 ......................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

GO TO F8
Prepared by Mathematica Policy Research 31

I would like to ask you about up to 3 of these training programs. If you participated in more 

than 3 programs, can you tell me about the 3 most recent ones. 

PROGRAM 1 PROGRAM 2 PROGRAM 3

F4. What is the name 

of each program?.. ________________________ ________________________ ________________________

F5. Did you complete 

this program? .........

YES ................................................ 1 

NO.................................................. 0 

CURRENTLY PARTICIPATING .... 2 

DON’T KNOW................................ d 

REFUSED ...................................... r 

YES ................................................ 1 

NO.................................................. 0 

CURRENTLY PARTICIPATING..... 2 

DON’T KNOW................................ d 

REFUSED ...................................... r 

YES ............................................... 1 

NO ................................................. 0 

CURRENTLY PARTICIPATING.... 2 

DON’T KNOW ............................... d 

REFUSED ..................................... r 

F6. What is the name 

of the certificate, 

degree, or 

license you 

received (will 

receive) for 

completion of 

this program? ......... 

________________________

NAME OF CERTIFICATE

DON’T KNOW................................ d 

REFUSED ...................................... r 

________________________

NAME OF CERTIFICATE

DON’T KNOW................................ d 

REFUSED ...................................... r 

________________________

NAME OF CERTIFICATE

DON’T KNOW ............................... d 

REFUSED ..................................... r 

F7. Is that a 

certificate, a 

license, or 

degree? .................. 

CIRCLE ONE ONLY

CERTIFICATE ............................... 1 

LICENSE........................................ 2 

DEGREE........................................ 3 

OTHER........................................... 4 

DON’T KNOW................................ d 

REFUSED ...................................... r 

CIRCLE ONE ONLY

CERTIFICATE................................ 1 

LICENSE........................................ 2 

DEGREE ........................................ 3 

OTHER........................................... 4 

DON’T KNOW................................ d 

REFUSED ...................................... r 

CIRCLE ONE ONLY

CERTIFICATE............................... 1 

LICENSE ....................................... 2 

DEGREE ....................................... 3 

OTHER.......................................... 4 

DON’T KNOW ............................... d 

REFUSED ..................................... r 

F7a. What kind of 

work is this 

certificate, 

degree, or 

license for? That 

is, what kind of 

job would you be 

prepared to do?..... 

________________________

KIND OF WORK

DON’T KNOW................................ d 

REFUSED ...................................... r 

________________________

KIND OF WORK

DON’T KNOW................................ d 

REFUSED ...................................... r 

________________________

KIND OF WORK

DON’T KNOW ............................... d 

REFUSED ..................................... r 

INTERVIEWER: IF 

THERE IS ANOTHER 

PROGRAM GO BACK 

TO F5 

IF YOU ARE ON THE 

LAST PROGRAM, GO 

TO F8.

NO MORE PROGRAMS................ 1 NO MORE PROGRAMS................ 1 NO MORE PROGRAMS ............... 1
Prepared by Mathematica Policy Research 32

F8. Think about work in general, please tell me how much you agree or disagree with the 

following statements. Do you strongly agree, agree, neither agree nor disagree, 

disagree, or strongly disagree?

CIRCLE ONE FOR EACH ROW

STRONGLY 

AGREE AGREE

NEITHER 

AGREE 

NOR 

DISAGREE DISAGREE

STRONGLY 

DISAGREE

DON’T 

KNOW REFUSED 

a. A job is just a way of earning 

money – no more....................... 1 2 3 4 5 d r 

b. I would enjoy having a paid job 

even if I did not need the 

money ........................................ 1 2 3 4 5 d r 

F9. How important do you think the following qualities are in a job: very important, 

important, neither important nor unimportant, not important, not important at all.

How important is...

CIRCLE ONE FOR EACH ROW

VERY 

IMPORTANT IMPORTANT

NEITHER 

IMPORTANT 

NOR

UNIMPORTANT

NOT 

IMPORTANT

NOT 

IMPORTANT 

AT ALL

DON’T 

KNOW REFUSED

a. ...job security?.................... 1 2 3 4 5 d r 

b. ...high income?................... 1 2 3 4 5 d r 

c. ...good opportunities for 

advancement? .................... 1 2 3 4 5 d r 

d. ...an interesting job?........... 1 2 3 4 5 d r 

e. ...a job that allows 

someone to work 

independently? ................... 1 2 3 4 5 d r 

f. ...a job that allows 

someone to help other 

people? ............................... 1 2 3 4 5 d r 

g. ...a job that is useful to

society?............................... 1 2 3 4 5 d r 

h. ...a job that allows 

someone to decide their 

times or days of work?........ 1 2 3 4 5 d r 
Prepared by Mathematica Policy Research 33

F10. We are interested in what you think you might be doing in 5 years. Please answer yes 

if you think you might be doing the activity, no if you don’t think you will, and maybe 

if you think you might possibly be doing it. Do you think you will...

CIRCLE ONE FOR EACH ROW

YES NO MAYBE

DON’T 

KNOW REFUSED

a. Have continued your education or 

undertaken additional job training? ................ 1 0 2 d r 

b. Own or rent your own home or apartment? ... 1 0 2 d r 

c. Be established in a career?............................ 1 0 2 d r 

d. Be in good mental health?.............................. 1 0 2 d r 

e. Be in good physical health? ........................... 1 0 2 d r 

f. Be economically self sufficient? ..................... 1 0 2 d r 

g. Rarely drink alcohol and use drugs?.............. 1 0 2 d r 

h. Be removed from illegal activity?.................... 1 0 2 d r 

i. Other (SPECIFY)............................................ 1 0 2 d r 

 
Prepared by Mathematica Policy Research 34

SECTION 5. DEMOGRAPHICS

This is the final section of the survey. 

D1. What is your current

CIRCLE ONE ONLY

marital status—are you now...

Married, ............................................................................................. 1 

In a domestic partnership,............................................................... 2 

Separated,......................................................................................... 3 

Divorced,........................................................................................... 4 

Widow, or.......................................................................................... 5 

Single? .............................................................................................. 6 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

D2. What kind of health insurance plans are you

CIRCLE ALL THAT APPLY 

currently covered by?

NOT CURRENTLY COVERED BY HEALTH INSURANCE ................ 1 

MEDICAID/MEDICARE...................................................................... 2 

AN EMPLOYER OR UNION SPONSORED HEALTH PLAN .............. 3 

A GOVERNMENT PROGRAM OTHER THAN MEDICAID OR 

MEDICARE ........................................................................................ 4 

MILITARY HEALTH CARE................................................................. 5 

INSURANCE PURCHASED DIRECTLY FROM AN INSURER........... 6 

SOME OTHER TYPE OF HEALTH INSURANCE (SPECIFY)............ 7 

 ______________________________________________________ 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

D3. We are interested in learning about any changes in the individuals who are dependent 

on you since [MONTH AND YEAR OF LAST SURVEY]. By dependents we mean 

individuals that are financially dependent on you and those that are dependent on you 

for day-to-day care, or both. The person or people do not necessarily need to live with 

you. Has the number of individuals who are dependent on you changed since [MONTH 

AND YEAR OF LAST SURVEY]? Please do NOT include yourself in your count.

YES.................................................................................................... 1 

NO...................................................................................................... 0 

DON’T KNOW .................................................................................... d 

REFUSED .......................................................................................... r 

GO TO D6
Prepared by Mathematica Policy Research 35

D4. Please tell me the... 

INTERVIEWER: ENTER “0” IN EACH BOX IF RESPONDENT HAS NOT GAINED OR LOST 

DEPENDENTS. 

COMPLETE ONLY ONE COLUMN PER 

RESPONSE

FILL COLUMN FOR EACH AGE GROUP GAINED LOST

a. Number of dependents that you GAINED since [MONTH 

AND YEAR OF LAST SURVEY] that you support 

financially AND have responsibility for their day-to-day 

activities. 

_____________________

NUMBER 

d □ DON’T KNOW

r □ REFUSED

Number of dependents that you LOST since [MONTH 

AND YEAR OF LAST SURVEY] that you supported 

financially AND had responsibility for their day-to-day 

activities but no longer do.

_____________________

NUMBER 

d □ DON’T KNOW

r □ REFUSED

b. Number of dependents that you GAINED since [MONTH 

AND YEAR OF LAST SURVEY] that you support 

financially only but do NOT have responsibility for their 

day-to-day activities.

_____________________

NUMBER 

d □ DON’T KNOW

r □ REFUSED

Number of dependents that you LOST since [MONTH 

AND YEAR OF LAST SURVEY] that you supported 

financially only but did NOT have responsibility for their 

day-to-day activities and no longer support financially?

_____________________

NUMBER 

d □ DON’T KNOW

r □ REFUSED

c. Number of dependents that you GAINED since [MONTH 

AND YEAR OF LAST SURVEY] that you have 

responsibility for their day-to-day activities but do NOT 

support financially.

_____________________

NUMBER 

d □ DON’T KNOW

r □ REFUSED

Number of dependents that you LOST since [MONTH 

AND YEAR OF LAST SURVEY] that you had 

responsibility for their day-to-day activities but did NOT 

support financially and you are no longer responsible for 

their day-to-day care.

_____________________

NUMBER 

d □ DON’T KNOW

r □ REFUSED

SUM THE SIX RESPONSES FROM D4A – D4C: _______

IF 0 GO TO D6

IF 1 OR 2 READ: “I am now going to ask you some specific questions about 

these dependents.” THEN GO TO D5 

IF 3 OR MORE READ: “Please think about the two dependents you have 

gained or lost since [MONTH AND YEAR OF LAST SURVEY] for whom you 

have the greatest financial responsibility. The next questions will be about 

them.” THEN GO TO D5
Prepared by Mathematica Policy Research 36

DEPENDENT 1 DEPENDENT 2

D5a.Did you gain or lose 

responsibility for this 

dependent? ...........................

GAIN.......................................................................... 1 

LOSE ......................................................................... 2 

DON’T KNOW............................................................ d 

REFUSED..................................................................r

GAIN.......................................................................... 1 

LOSE ......................................................................... 2 

DON’T KNOW............................................................ d 

REFUSED..................................................................r

D5b. What is their relationship 

to you? ................................. 

CIRCLE ONE ONLY

Spouse/partner.......................................................... 1 

Boyfriend/girlfriend..................................................... 2 

Child (natural step, or custodial)................................ 3 

Parent or stepparent.................................................. 4 

Grandparent, aunt, or uncle ...................................... 5 

Sibling (brother or sister) ........................................... 6 

Nephew or niece, cousin ........................................... 7 

Grandchild ................................................................. 8 

Other relative or in-law .............................................. 9 

Non-relative (including roomer or boarder) ............... 10

Other.......................................................................... 11

DON’T KNOW............................................................ d 

REFUSED..................................................................r

CIRCLE ONE ONLY

Spouse/partner .......................................................... 1 

Boyfriend/girlfriend..................................................... 2 

Child (natural step, or custodial)................................ 3 

Parent or stepparent.................................................. 4 

Grandparent, aunt, or uncle....................................... 5 

Sibling (brother or sister) ........................................... 6 

Nephew or niece, cousin ........................................... 7 

Grandchild ................................................................. 8 

Other relative or in-law............................................... 9 

Non-relative (including roomer or boarder) ...............10

Other.......................................................................... 11

DON’T KNOW............................................................ d 

REFUSED..................................................................r

D5c. Are they male or female? ... MALE......................................................................... 1 

FEMALE .................................................................... 2

MALE......................................................................... 1 

FEMALE.....................................................................2

D5d. Approximately how old are 

they? .................................... | | | YEARS OLD

Less than one year old .............................................. 0 

DON’T KNOW............................................................ d 

REFUSED..................................................................r

| | | YEARS OLD

Less than one year old .............................................. 0 

DON’T KNOW............................................................ d 

REFUSED..................................................................r

D5e. Are you financially 

responsible for them, 

responsible for their dayto day activities, or both? ..

FINANCIALLY RESPONSIBLE................................. 1 

RESPONSIBLE FOR DAY-TO-DAY ACTIVITIES .... 2 

BOTH......................................................................... 3 

DON’T KNOW............................................................ d 

REFUSED .................................................................. r

FINANCIALLY RESPONSIBLE................................. 1 

RESPONSIBLE FOR DAY-TO-DAY ACTIVITIES..... 2 

BOTH......................................................................... 3 

DON’T KNOW............................................................ d 

REFUSED .................................................................. r

D5f. What type of health 

insurance plan are they 

currently covered by? ........

Currently not covered by any health insurance......... 1 

CIRCLE ALL THAT APPLY

Medicaid/Medicare .................................................... 2 

An employer or union sponsored health plan............ 3 

Military health care .................................................... 4 

Other government health plan (SPECIFY)................. 5 

_________________________________________

Insurance purchased directly from an insurers ......... 6 

Some other type of health insurance.... 7-GO TO D4g

DON’T KNOW............................................................ d 

REFUSED..................................................................r

Currently not covered by any health insurance......... 1 

CIRCLE ALL THAT APPLY

Medicaid/Medicare..................................................... 2 

An employer or union sponsored health plan............ 3 

Military health care..................................................... 4 

Other government health plan (SPECIFY)................. 5 

_________________________________________

Insurance purchased directly from an insurers ......... 6 

Some other type of health insurance.... 7-GO TO D4g

DON’T KNOW............................................................ d 

REFUSED..................................................................r

D5g. If necessary, please 

specify the type of health 

insurance. ............................

____________________________

HEALTH INSURANCE

DON’T KNOW............................................................ d 

REFUSED .................................................................. r

____________________________

HEALTH INSURANCE

DON’T KNOW........................................................... d 

REFUSED ................................................................. r

INTERVIEWER: IF THERE IS A SECOND DEPENDENT GAINED OR LOST GO BACK TO D4A AND COMPLETE 

FOR THE SECOND DEPENDENT.
Prepared by Mathematica Policy Research 37

D6a. Thank you for completing our survey. We would like to send you a Target gift 

card.

D6b. Now, please tell me the correct spelling of your name and your current mailing 

address so that we can mail your $20 gift card.

 

NAME (VERIFY SPELLING) 

 

ADDRESS LINE 1

 

ADDRESS LINE 2

 

APT. #

 

CITY/TOWN

 

STATE

 

ZIP CODE

REFUSED .......................................................................................... r 

IF PARTICIPANT REFUSES TO PROVIDE ADDRESS READ: “You do not have to tell us 

your address, but you should know that if we do not have your address, we will not 

be able to send you a gift card for participating in our survey. Do you understand 

this?”

STILL REFUSES ............................................................................................. r __________

(INTERVIEWER: initial here to 

indicate you read statement)

AGREES TO PROVIDE ADDRESS ................................................................ 1 GO TO D6b 
Prepared by Mathematica Policy Research 38

INTERVIEWER: IF THE RESPONDENT REPORTS THOUGHTS ABOUT ENDING LIFE (FOR 

EXAMPLE, QUESTION C7.F IS A 3, 4, OR 5) OR ASKS ABOUT WHETHER WE 

COULD PROVIDE THEM WITH SOME HELP, STATE, “At Mathematica we 

conduct interviews and do research. but we wanted participants to know of a 

place to call in case they want to speak with a mental health professional in 

their area. In [location] the name and number of a place to call is [SEE 

BELOW]:”

Location Mental Health Hotline Information

Los Angeles Didi Hirsch Mental Health Services

http://www.didihirsch.org/spc

(877)727-4747

Marin County Psychiatric Emergency Services

http://www.co.marin.ca.us/depts/HH/main/mh/pes_faq.cfm

(415) 473-6666 (no toll free available)

San Diego 800-SUICIDE (784.2433)

National Domestic Violence Hotline at 1-800-799-7233

Non-emergency referral service: Dial 211

Bay Area (San Francisco) Mobile Crisis Treatment Team Phone: (415) 355-8300 

OR

Westside Community Services (415) 355-0311.

Thanks again and best wishes to you.
This page has been left blank for double-sided copying. 
REFERENCES MATHEMATICA POLICY RESEARCH

R.1 

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