First Steps on the Road To

Financial Well-Being:

Final Report from the Evaluation of LISC’s 

Financial Opportunity Centers 

Anne Roder

Economic Mobility Corporation

September 2016
This report is based upon work supported by the Social Innovation Fund (SIF), 

which unites public and private resources to evaluate and grow innovative 

community-based solutions with evidence of results. The Social Innovation Fund is a 

program of the Corporation for National and Community Service, a federal agency 

that engages more than 5 million Americans in service through its AmeriCorps, 

Senior Corps, Social Innovation Fund, and Volunteer Generation Fund programs, 

and leads the President’s national call to service initiative, United We Serve.

LISC also acknowledges the philanthropic contributions that, together with SIF 

resources, helped bring this evaluation report to fruition. Thank you to the following 

funders for their generous support of this report: The Annie E. Casey Foundation, 

Citi Foundation, JPMorgan Chase, John D. and Catherine T. MacArthur Foundation, 

MetLife Foundation, and Walmart Foundation.

Board of Directors

Cynthia Shoss Chair

Plinio Ayala

Harry J. Holzer

Mary Pena

Russ Pomeranz

Mark Elliott

The Economic Mobility 

Corporation (Mobility) 

identifies, develops and 

evaluates programs 

and policies that enable 

disadvantaged individuals

to acquire the education, 

skills and networks 

needed to succeed in the 

labor market so that they 

can support themselves 

and their families.

Mail

Economic Mobility Corporation, Inc.

50 Broadway, Suite 1604

New York, NY 10004

Telephone

212.280.6975

Email

info@economicmobilitycorp.org

2 First Steps on the Road To Financial Well-Being: Final Report from the Evaluation of LISC’s Financial Opportunity Centers
Acknowledgments

Mobility would like to thank the individuals who participated in 

the study for completing lengthy surveys about their finances and 

allowing us to access their credit reports so that others can learn 

from their experiences.

We thank the Financial Opportunity Center program directors 

and staff members at Association House, Instituto Del Progreso 

Latino, Metropolitan Family Services, North Lawndale Employment 

Network, and The Cara Program for helping us with enrollment, 

allowing us to observe the programs, taking part in interviews, and 

arranging focus groups with participants. We also thank staff at 

the Chicago Department of Family and Support Services and at the 

Garfield, Mid-South, Northside, Pilsen, and Southwest Workforce 

Centers for allowing us to recruit job seekers at the centers to be 

part of the study.

We are grateful to the funders who have supported the evaluation. 

We thank Craig Howard, director of community and economic development at the MacArthur Foundation, for introducing us to LISC’s 

work in this area. We thank LISC for the opportunity to work on the 

project. We also thank the Corporation for National and Community 

Service, Annie E. Casey Foundation, Citi Foundation, JPMorgan 

Chase, MetLife Foundation, and Walmart Foundation for making 

grants to LISC that have supported Mobility’s work.

We would like to thank Laura D'Alessandro, Kevin Jordan, Katrin 

Kark, Seung Kim, Jennifer McClain, Sarah Rankin, and Chris Walker 

at LISC for their valuable insights and feedback on early drafts of 

this report. We are grateful to Ricki Lowitz, formerly at LISC and 

now with Working Credit, for her help in developing the project early 

on. Several other people contributed to this report. Audrey Waysse 

pulled and prepared the credit report data for analysis. Caitlin Van 

Dusen edited the report. Penelope Malish designed the publication.

 3 First Steps on the Road To Financial Well-Being: Final Report from the Evaluation of LISC’s Financial Opportunity Centers
Table of Contents

8 Executive Summary

13 Introduction

22 Characteristics of the Study Participants

27 Implementation

37 Program Impacts

55 Conclusions

57 Endnotes

58 References

59 Appendix A — Methodology: Matched Comparison Group Design

73 Appendix B — Implementation Analysis

79 Appendix C — Impact Analysis

Figures

15 Figure 1.1: Financial Opportunity Centers Logic Model

18 Figure 1.2: The Five Organizations in the FOC Study

20 Figure 1.3: Credit Report Terms

23 Figure 2.1: Target Populations at the Five FOC Study Sites

24 Figure 2.2: Residential Location of Study Participants at the Time of Program Entry

25 Figure 2.3: Key Baseline Characteristics of the FOC Participant Group and Comparison Group after Matching

29 Figure 3.1: Receipt of Assistance from the FOC Counselors during the Two Years after Program Entry 

Among All Enrolled Study Participants

29 Figure 3.2: Receipt of Assistance from the FOC Counselors during the Two Years after Program Entry Among 

Participants Who Received Any Counseling

32 Figure 3.3: Services Offered at the Chicago Workforce Centers

33 Figure 3.4: Study Participants’ Self-Reported Receipt of Services From Any Agency

34 Figure 3.5: Study Participants’ Reported Satisfaction with Services

38 Figure 4.1: Primary Outcome Measures

40 Figure 4.2: FOC Program Impacts on Participants’ Employment and Earnings

41 Figure 4.3: FOC Program Impacts on Participants’ Education and Training

42 Figure 4.4: FOC Program Impacts on Participants’ Income, Expenses, and Net Income

44 Figure 4.5: FOC Program Impacts on Participants’ Credit History and Credit Scores

45 Figure 4.6: FOC Program Impacts on Participants’ Credit Scores by Their Credit Status at the 

Time of Program Entry

46 Figure 4.7: FOC Program Impacts on Participants’ Assets, Debts, and Net Worth

49 Figure 4.8: Program Impacts for FOC Group Members Who Received Services (Differences in Outcomes between 

FOC Group Members and Comparison Group Members)

52 Figure 4.9: FOC Program Impacts across the Five Study Sites

4 First Steps on the Road To Financial Well-Being: Final Report from the Evaluation of LISC’s Financial Opportunity Centers
61 Figure A1: Timeline for the Evaluation of the Financial Opportunity Centers

67 Figure A2: Baseline Characteristics of the Study Participants Before and After Matching

68 Figure A3: Box Plot of Propensity Scores by Treatment Status (n=1,206)

69 Figure A4: Diagnostics of Covariate Balancing Before and After Matching Across Methods

71 Figure A5: Analysis of the Impact of Attrition from the Baseline Sample of 810 FOC Group Members

75 Figure B1: Differences in Receipt of FOC Services During the Two Years After Program Entry between 

FOC Study Participants in the Final Sample and Those Not in the Final Sample

76 Figure B2: Regression Analysis of the Likelihood of Receiving Any FOC Counseling Among All Enrolled 

FOC Group Members (n=810)

77 Figure B3: Regression Analysis of the Likelihood of Receiving FOC Counseling in All Three 

Core Service Areas Among All Enrolled FOC Group Members (n=810)

78 Figure B4: Regression Analysis of the Likelihood of Receiving FOC Counseling Services 19 to 24 Months After 

Program Entry Among All Enrolled FOC Group Members (n=604)

84 Figure C1: Descriptive Statistics for the Outcome Measures Included in the Impact Analysis For the 

FOC Group and Weighted Comparison Group-Not Regression-Adjusted for Baseline 

Differences in Participant Characteristics

85 Figure C2: Regression-Adjusted Statistics for the Outcome Measures Included in the Impact Analysis For the FOC 

Group and Weighted Comparison Group

87 Figure C3: Intraclass Correlation Coefficients (ICCs) for the ITT and TOT Samples

88 Figure C4: Results After Applying the Benjamini-Hochberg Family-Wise Adjustment to the 

Overall ITT Impact Estimates

89 Figure C5: Logistic Regression Analysis of Treatment Effects on the Likelihood of Being Employed At Any Time in 

the Second Year After Program Entry Among All Enrollees (Figure 4.2)

90 Figure C6: Logistic Regression Analysis of Treatment Effects on the Likelihood of Being Employed Year Round in 

the Second Year After Program Entry Among All Enrollees (Figure 4.2)

91 Figure C7: Linear Regression Analysis of Treatment Effects on Logged Annual Earnings in the Second Year After 

Program Entry Among All Enrollees (Figure 4.2)

92 Figure C8: Linear Regression Analysis of Treatment Effects on Logged Annual Hours Worked in the Second Year 

After Program Entry Among All Enrollees (Figure 4.2)

92 Figure C9: Linear Regression Analysis of Treatment Effects on Logged Hourly Wages at Current or Most Recent 

Job Two Years After Program Entry Among All Enrollees (Figure 4.2)

94 Figure C10: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a High School Diploma 

or GED Two Years After Program Entry Among All Enrollees (Figure 4.3)

95 Figure C11: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a College Degree 

Two Years After Program Entry Among All Enrollees (Figure 4.3)

96 Figure C12: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any College Credits 

Two Years After Program Entry Among Enrollees Without a College Degree (Figure 4.3)

97 Figure C13: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having an Occupational 

Certificate/License Two Years After Program Entry Among All Enrollees (Figure 4.3)

98 Figure C14: Linear Regression Analysis of Treatment Effects on Logged Monthly Income Two Years After Program 

Entry Among All Enrollees (Figure 4.4)

99 Figure C15: Linear Regression Analysis of Treatment Effects on Logged Monthly Expenses Two Years After Program 

Entry Among All Enrollees (Figure 4.4)

100 Figure C16: Linear Regression Analysis of Treatment Effects on Monthly Net Income Two Years After Program 

Entry Among All Enrollees (Figure 4.4)

101 Figure C17: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Net Income Greater 

Than Zero Two Years After Program Entry Among All Enrollees (Figure 4.4)

102 Figure C18: Logistic Regression Analysis of Treatment Effects on the Likelihood of Receiving Any Income Supports 

Two Years After Program Entry Among All Enrollees (Figure 4.4)

103 Figure C19: Negative Binomial Regression Analysis of Treatment Effects on the Number of Open Trade Accounts 

Two Years After Program Entry Among All Enrollees (Figure 4.5)

104 Figure C20: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Trade Accounts Paid 

as Agreed Two Years After Program Entry Among All Enrollees (Figure 4.5)

105 Figure C21: Negative Binomial Regression Analysis of Treatment Effects on the Number of Trade Accounts Paid as 

Agreed Two Years After Program Entry Among All Enrollees (Figure 4.5)

106 Figure C22: Linear Regression Analysis of Treatment Effects on the Logged Number of On-Time Payments Made 

on Trade Accounts in the Second Year After Program Entry Among All Enrollees (Figure 4.5)

 5 First Steps on the Road To Financial Well-Being: Final Report from the Evaluation of LISC’s Financial Opportunity Centers
107 Figure C23: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Credit Score Two Years 

After Program Entry Among All Enrollees (Figure 4.5)

108 Figure C24: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Prime Credit Score Two 

Years After Program Entry Among All Enrollees (Figure 4.5)

109 Figure C25: Linear Regression Analysis of Treatment Effects on Credit Scores Two Years After Program Entry 

Among Enrollees Who Had Scores at Program Entry and Two Years Later (Figure 4.5)

110 Figure C26: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having an Increase in Credit 

Score Two Years After Program Entry Among Enrollees Who Had Scores at Program Entry and 

Two Years Later (Figure 4.5)

111 Figure C27: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Credit Score Two Years 

After Program Entry by Credit Status at Program Entry Among All Enrollees (Figure 4.6)

112 Figure C28: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Prime Credit Score Two 

Years After Program Entry by Credit Status at Program Entry Among All Enrollees (Figure 4.6)

113 Figure C29: Linear Regression Analysis of Treatment Effects on Credit Scores Two Years After Program Entry by 

Credit Status at Program Entry, Among Enrollees Who Had Scores at Program Entry and Two Years 

Later (Figure 4.6)

114 Figure C30: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Increase in Net 

Worth Two Years After Program Entry Among All Enrollees (Figure 4.7)

115 Figure C31: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Net Worth Greater Than 

Zero Two Years After Program Entry Among All Enrollees (Figure 4.7)

116 Figure C32: Linear Regression Analysis of Treatment Effects on Net Worth Two Years After Program Entry Among 

All Enrollees (Figure 4.7)

117 Figure C33: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Assets Two Years 

After Program Entry Among All Enrollees (Figure 4.7)

118 Figure C34: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Money in Savings or 

Checking Accounts Two Years After Program Entry Among All Enrollees (Figure 4.7)

119 Figure C35: Linear Regression Analysis of Treatment Effects on Logged Dollar Amount in Savings and Checking 

Accounts Two Years After Program Entry Among All Enrollees (Figure 4.7)

120 Figure C36: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Asset-Related Debts 

Two Years After Program Entry Among All Enrollees (Figure 4.7)

121 Figure C37: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Non-Asset-Related 

Debts Two Years After Program Entry Among All Enrollees (Figure 4.7)

122 Figure C38: Logistic Regression Analysis of Treatment Effects on the Likelihood of Being Employed Year Round in 

the Second Year After Program Entry (Figure 4.8)

122 Figure C39: Linear Regression Analysis of Treatment Effects on Logged Annual Earnings in the Second Year After 

Program Entry (Figure 4.8)

123 Figure C40: Linear Regression Analysis of Treatment Effects on Logged Annual Hours Worked in the Second Year 

After Program Entry (Figure 4.8)

123 Figure C41: Linear Regression Analysis of Treatment Effects on Logged Hourly Wages at Current or Most Recent 

Job in the Two Years After Program Entry (Figure 4.8)

124 Figure C42: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having an Occupational 

Certificate/License Two Years After Program Entry (Figure 4.8)

124 Figure C43: Linear Regression Analysis of Treatment Effects on Logged Monthly Income Two Years After Program 

Entry (Figure 4.8)

125 Figure C44: Linear Regression Analysis of Treatment Effects on Logged Monthly Expenses Two Years After Program 

Entry (Figure 4.8)

125 Figure C45: Linear Regression Analysis of Treatment Effects on Logged Monthly Net Income Two Years After 

Program Entry (Figure 4.8)

126 Figure C46: Figure C46. Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Net Income 

Greater Than Zero Two Years After Program Entry (Figure 4.8)

125 Figure C47: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Credit Score Two Years 

After Program Entry (Figure 4.8)

127 Figure C48: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Prime Credit Score Two 

Years After Program Entry (Figure 4.8)

127 Figure C49: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Trade Accounts Paid 

as Agreed Two Years After Program Entry (Figure 4.8)

128 Figure C50: Linear Regression Analysis of Treatment Effects on the Logged Number of On-Time Payments Made 

on Trade Accounts in the Second Year After Program Entry (Figure 4.8)

6 First Steps on the Road To Financial Well-Being: Final Report from the Evaluation of LISC’s Financial Opportunity Centers
128 Figure C51: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Net Worth Greater Than 

Zero Two Years After Program Entry (Figure 4.8)

129 Figure C52: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Assets Two Years 

After Program Entry (Figure 4.8)

129 Figure C53: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Asset-Related Debts 

Two Years After Program Entry (Figure 4.8)

130 Figure C54: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Non-Asset-Related 

Debts Two Years After Program Entry (Figure 4.8)

130 Figure C55: Standardized Effect Sizes for the TOT Estimates of Program Impacts

131 Figure C56: Logistic Regression Analysis of Treatment Effects on the Likelihood of Being Employed Year Round in 

the Second Year After Program Entry for Key Subgroups

132 Figure C57: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Net Income Greater 

Than Zero Two Years After Program Entry for Key Subgroups

132 Figure C58: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Credit Score Two Years 

After Program Entry for Key Subgroups

133 Figure C59: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Prime Credit Score Two 

Years After Program Entry for Key Subgroups

133 Figure C60: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Net Worth Greater Than 

Zero Two Years After Program Entry for Key Subgroups

134 Figure C61: Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Non-Asset-Related Debts 

Two Years After Program Entry for Key Subgroups

134 Figure C62: Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Being Employed Year 

Round in the Second Year After Program Entry (Figure 4.9)

135 Figure C63: Linear Regression Analysis of Site-Level Treatment Effects on Logged Annual Earnings in the Second 

Year After Program Entry (Figure 4.9)

135 Figure C64: Linear Regression Analysis of Site-Level Treatment Effects on Logged Annual Hours Worked in the 

Second Year After Program Entry (Figure 4.9)

135 Figure C65: Linear Regression Analysis of Site-Level Treatment Effects on Logged Hourly Wages at Current or Most 

Recent Job in the Two Years After Program Entry (Figure 4.9)

136 Figure C66: Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having an 

Occupational Certificate/License Two Years After Program Entry (Figure 4.9)

136 Figure C67: Linear Regression Analysis of Site-Level Treatment Effects on Logged Monthly Income Two Years After 

Program Entry (Figure 4.9)

136 Figure C68: Linear Regression Analysis of Site-Level Treatment Effects on Logged Monthly Expenses Two Years 

After Program Entry (Figure 4.9)

137 Figure C69: Linear Regression Analysis of Site-Level Treatment Effects on Logged Monthly Net Income Two Years 

After Program Entry (Figure 4.9)

137 Figure C70: Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having Net Income 

Greater Than Zero Two Years After Program Entry (Figure 4.9)

137 Figure C71: Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having a Credit Score 

Two Years After Program Entry (Figure 4.9)

138 Figure C72: Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having a Prime Credit 

Score Two Years After Program Entry (Figure 4.9)

138 Figure C73: Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having Any Trade 

Accounts Paid as Agreed Two Years After Program Entry (Figure 4.9)

138 Figure C74: Linear Regression Analysis of Site-Level Treatment Effects on the Logged Number of On-Time 

Payments Made on Trade Accounts in the Second Year After Program Entry (Figure 4.9)

139 Figure C75: Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having Net Worth 

Greater Than Zero Two Years After Program Entry (Figure 4.9)

139 Figure C76: Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having Any Assets 

Two Years After Program Entry (Figure 4.9)

139 Figure C77: Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having Any 

Asset-Related Debts Two Years After Program Entry (Figure 4.9)

140 Figure C78: Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having Any 

Non-Asset-Related Debts Two Years After Program Entry (Figure 4.9)

 7 First Steps on the Road To Financial Well-Being: Final Report from the Evaluation of LISC’s Financial Opportunity Centers
Executive Summary

Low-income families face substantial challenges to achieving financial security and 

upward mobility. Since the end of the recession, in 2009, the real median wages 

of workers in the lowest-wage quintile have declined by 5.7 percent.1 The number 

of all workers involuntarily employed part-time remains unusually high.2 Periods 

of unemployment, low wages, and part-time work make it difficult for families to 

cover basic expenses and to save. In 2011, 78 percent of low-income households 

were liquid-asset poor, meaning they did not have enough savings or other financial 

assets to cover basic living expenses for three months at the federal poverty level.3

These families need to borrow to weather crises such as job loss, illness, or unexpected expenses. However, they often lack access to mainstream forms of credit 

due to their limited credit histories or low credit scores. About 30 percent of consumers in low-income neighborhoods are “credit invisibles”—they have no credit 

report with the three major credit reporting agencies; another 15 percent have 

credit reports but insufficient credit histories to generate a score.4 Low-income families’ lack of financial assets and lack of access to affordable forms of credit hinder 

their ability to accumulate assets, such as homes, vehicles, and retirement savings, as well as afford quality education, further limiting their potential for increasing their net worth and achieving economic mobility.

In an effort to improve low-income families’ financial well-being, the Local Initiatives 

Support Corporation (LISC) provides community organizations financial support and 

technical assistance to operate Financial Opportunity Centers (FOCs). Based on 

the Center for Working Families model developed by the Annie E. Casey Foundation, 

FOCs seek to increase low-income families’ financial prospects by providing integrated services in three core areas: financial counseling, employment assistance, 

and assistance accessing public benefits to supplement income from work. FOCs 

strive to help individuals become consistently employed, improve their credit rating, 

and increase their net income and net worth. Employment services include basic 

job readiness training and job placement as well as connections to training in basic 

skills, computer skills, and occupational skills. Financial services include education 

and individual financial coaching focused on solving specific problems, planning for 

financial stability, and connecting individuals to financial service providers, financial 

vehicles for saving and building credit, and free tax preparation services. Income support counselors help families navigate public benefit systems’ complex eligibility and 

enrollment processes in order to access benefits to supplement income. The FOC 

model maintains that the three core services work best when they are integrated.

In 2010, the Corporation for National and Community Service (CNCS) awarded LISC 

a Social Innovation Fund grant to expand and evaluate the FOC model. Since then, 

the model has expanded from 24 centers in 6 cities to over 75 centers in 30 cities 

around the country. FOCs offer an important model for the workforce development 

and asset-building fields. They seek to address a number of barriers to financial 

8 First Steps on the Road To Financial Well-Being: Executive Summary 
stability that low-income families face, including periods of unemployment, low financial literacy, barriers to accessing financial services, and the need for income supports to supplement wages that are not sufficient to support a family.

LISC contracted with the Economic Mobility Corporation (Mobility) to conduct an 

independent evaluation of the effectiveness of five FOCs in Chicago, where the 

network had been operating for several years and, therefore, we expected the programs would provide a fair test of the fully implemented model. The individuals in 

the study were seeking assistance with employment and training, and the FOCs 

sought to engage them in financial and income support counseling as well. The 

organizations in the study were:

• Association House

• Instituto Del Progreso Latino

• Metropolitan Family Services

• North Lawndale Employment Network

• The Cara Program

To determine the FOCs’ effectiveness, the study used a quasi-experimental design 

that compared program participants’ outcomes to those of a similar group of job 

seekers who sought assistance with employment and training from the city’s workforce centers. After collecting data for the FOC and comparison group members, 

we used propensity score matching to select the final study sample; that is, we 

matched comparison group members to FOC participants at the individual level 

based on their likelihood of being in the FOC program group given their demographics, recent employment experience, and financial situation. Only FOC and comparison group members who were sufficiently close matches were included in the final 

sample. This approach produced a strong comparison group of individuals who 

were similar to FOC participants in their financial situations and motivation to find 

employment and in the labor market they faced. The primary research questions 

the study addressed follow.

• Did participants receive services across the three core areas of employment, 

financial counseling, and income support counseling as expected?

• Two years after program entry, did the FOCs have a positive impact on:

• The likelihood that individuals were employed year-round?

• The likelihood that individuals had net income greater than zero?

• The likelihood that individuals who lacked credit scores at program entry had 

scores?

• The likelihood that individuals who had credit scores at program entry had an 

increase in scores?

• The likelihood that individuals had net income greater than zero?

Past studies of the integrated service model on which the FOCs are based have 

analyzed program implementation and participant outcomes.6 The FOC evaluation 

9 First Steps on the Road To Financial Well-Being: Executive Summary
seeks to increase the existing evidence base, targeting a moderate level of evidence according to the CNCS guidelines. That is, the study uses a quasi-experimental design that demonstrates equivalence between the treatment and comparison 

groups and, therefore, supports causal conclusions. However, the study was conducted with five FOC programs in one city, limiting its generalizability.

To answer the study’s research questions we analyzed data from phone surveys 

and credit reports for FOC and comparison group members and from FOC program 

records, program observations, and staff interviews. The surveys gathered detailed 

information about study participants’ employment, education and training, family income, expenses, assets, and debts, which we used to create the outcome 

measures related to employment, net income, and net worth. Measures of credit 

outcomes were based on participants’ TransUnion credit reports, which contained 

credit scores and information about use of credit products, such as mortgages, 

loans, credit cards, and other lines of credit, including payments made, delinquencies, and current status of each account.

We used multivariate regression analysis to examine program impacts, that is, 

differences between the outcomes of FOC and comparison group members. The 

outcomes presented account for any differences in demographic and financial characteristics between the FOC and comparison groups that remained after matching. 

Our primary analysis used an intent-to-treat (ITT) framework to assess program 

impacts; that is, we examined the impacts for all participants who sought employment assistance from the programs, regardless of whether or not they actually 

ended up receiving services. We also conduct exploratory analyses of the effect of 

the FOCs on subgroups of individuals who received program services.

Study enrollment took place from October 2011 to August 2012. We conducted 

baseline surveys and collected credit report data at the time of program entry for 

810 FOC participants and 1,030 comparison group members. We attempted the twoyear follow-up survey with the 810 FOC participants and with 850 comparison group 

members who best matched the FOC participants on demographic and financial characteristics. We completed two-year follow-up surveys with 553 FOC participants and 

653 comparison group members. We then conducted the propensity score matching 

with the individuals who completed the follow-up survey, resulting in a final analysis 

sample of 500 FOC participants and 649 comparison group members.

Our final report presents the findings on the FOCs’ impacts on low-income job seekers’ employment, net income, credit, and net worth two years after entering the 

programs. Our primary findings regarding program impacts include the following.

• The percent of FOC group members who were employed year-round increased 

almost 21-percentage points from the year before to the second year after program entry—a change that was significantly greater than that among comparison group members. Both FOC and comparison group members experienced 

10 First Steps on the Road To Financial Well-Being: Executive Summary 
about a 15 percentage-point increase in the likelihood of being employed at 

all during the year and a $2,000 increase, on average, in annual earnings. The 

small differences between the groups in employment rates and earnings were 

not statistically significant.

• The increased employment did not translate into positive impacts on participants’ net income two years after program entry. While participants’ earnings 

from work increased, monetary support from family and friends decreased, as 

did reliance on unemployment insurance benefits. At the same time, participants’ expenditures on basic living expenses, including rent, utilities, and food, 

increased.

• The FOCs had significant positive impacts on participants’ credit. FOC participants 

were more likely to have positive activity on their credit reports in the form of ontime payments on loans, credit cards, and other lines of credit, as well as trade 

accounts with positive ratings. Among individuals who lacked a credit score at 

program entry, FOC group members were significantly more likely than those in 

the comparison group to have a score after two years—a 9.3 percentage-point difference. Among those who had more-recent credit activity at program entry, FOC 

group members were significantly more likely than those in the comparison group 

to have prime credit scores after two years—a 13.8 percentage-point difference.

• There were no significant impacts on participants’ net worth (the total value of 

assets minus the total value of debts). However, two years after program entry, 

FOC participants were less likely than comparison group members to have any 

debts unrelated to asset accumulation, such as medical or legal debts, child 

support arrears, or back taxes.

• Engaging individuals in ongoing integrated services was important but challenging. Thirty-seven percent of all study participants who sought assistance from 

the FOCs had at least two meetings with both the FOC financial and employment 

counselors. Among these participants, the FOC programs produced more consistently significant positive impacts. On average, in the second year after program 

entry, FOC participants who had two or more meetings with the financial and 

employment counselors earned $436 more and worked 132 hours more than 

comparison group members. These FOC participants were also significantly more 

likely than comparison group members to have a prime credit score after two 

years—a 6.4 percentage-point difference.

In sum, in the two years after program entry, the FOCs helped individuals take 

some initial steps to improve their financial stability. Relative to the comparison group, FOC participants were more likely to be employed year-round, to have 

reduced certain types of debts, and to have built more positive credit histories 

as reported on their credit reports. These advances had not translated into 

11 First Steps on the Road To Financial Well-Being: Executive Summary
improvements in net income or net worth by the time data collection concluded, 

which perhaps was not surprising given the FOC participants' limited recent attachment to the labor market, lack of assets, and level of debt when they entered the 

programs.

The findings indicate that integrating financial coaching and employment services 

can be an effective strategy for helping low-income individuals improve their financial situations. In particular, educating individuals about credit and their own credit 

situations is a powerful tool for helping them take steps to build positive credit 

histories. Achieving financial stability and mobility is a long-term process, and programs need to structure services to promote long-term engagement. Policies that 

support integrated service strategies need to recognize the time needed to achieve 

financial goals and support efforts to establish lasting relationships between participants and counselors. Throughout the report, we discuss the implications of the 

findings for policies and programs that seek to engage individuals in integrated services and help them achieve financial well-being.

12 First Steps on the Road To Financial Well-Being: Executive Summary 
Chapter 1

Introduction

Low-income families face substantial obstacles to achieving financial well-being; 

that is, having financial security and the ability to achieve their financial goals. 

Populations that were already vulnerable in the labor market fared worse during the Great Recession. Blacks, Hispanics, high school dropouts, and unskilled 

workers experienced the highest increases in unemployment rates from 2007 to 

2009 (Hout et al. 2011). Since the end of the recession, in 2009, the real median 

wages of workers in the lowest-wage quintile have declined by 5.7 percent (National 

Employment Law Project 2015). The number of all workers involuntarily employed 

part-time remains unusually high (Cajner et al. 2014), and the rate of involuntary 

part-time work for those in low-wage occupations is more than double the rate for 

all workers (Watson et al. 2014).

Periods of unemployment, low wages, and part-time work make it difficult for 

families to cover basic expenses and to save. In 2011, 78 percent of low-income 

households were liquid-asset poor, meaning they did not have enough savings or 

other financial assets to cover basic living expenses for three months at the federal poverty level (Brooks et al. 2014). These families need to borrow to weather 

crises, such as job loss, illness, or unexpected expenses. However, they often lack 

access to mainstream forms of credit due to their limited credit histories or low 

credit scores. About 30 percent of consumers in low-income neighborhoods are 

“credit invisibles”—they have no credit report with the three major credit reporting 

agencies; another 15 percent have credit reports but insufficient credit histories 

to generate a score (Brevoort et al. 2015). Mainstream lenders are reluctant to 

extend loans to individuals without credit scores because they are seen as risky 

and inexperienced in managing credit.

Families without savings or access to affordable credit face higher rates, fees, and 

down payments on basic financial transactions, making it even more difficult to 

save. When these families experience sudden changes in income, they are more 

likely to face food shortages, utility cutoffs, and eviction (Barr and Blank 2009; 

Stegman and Faris 2005). At the same time, families’ low wages, lack of financial 

assets, and lack of access to low-cost forms of credit hinder their ability to accumulate assets, such as homes, vehicles, retirement savings, and quality education 

or training, further limiting their potential for increasing their net worth and achieving economic mobility.

In an effort to improve low-income families’ financial well-being, the Local Initiatives 

Support Corporation (LISC) provides community organizations financial support and 

technical assistance to operate Financial Opportunity Centers (FOCs). Based on 

the Center for Working Families model developed by the Annie E. Casey Foundation, 

FOCs seek to increase low-income families’ financial stability by providing 

13 First Steps on the Road To Financial Well-Being: Introduction
integrated services in three core areas: financial counseling, employment assistance, and income support counseling. FOCs strive to help individuals become consistently employed, improve their credit rating, and increase their net income and 

net worth.

Figure 1.1 illustrates the logic model underlying the FOC theory of change. 

Employment services often provide an entry point through which individuals participate in financial coaching and income supports access. FOC employment services include basic job readiness training and placement as well as connection 

to training in basic skills, computer skills, and occupational skills. High levels of 

debt, back child support, poor credit history, and expenses undermine the value 

of work. FOCs offer financial education and individual financial coaching focused 

on solving specific problems, planning for financial stability, and connecting individuals to financial service providers, financial vehicles for saving and building 

credit, and free tax preparation services. Finally, public benefits play a key role in 

helping families pay for everyday expenses, but complex eligibility and enrollment 

processes can be difficult for individuals to navigate. FOC counselors help individuals access needed benefits.

The FOC model maintains that the three core services work best when they are 

integrated. The expectation was that participants would work with the programs 

anywhere from six months to three years, depending on their needs and goals, 

although there was no limit on how long individuals could receive services. The 

program seeks to help participants achieve positive net income by removing barriers to employment, obtaining public benefits, and reducing expenses. The program 

also seeks to engage participants in credit-building activities; that is, making regular payments on existing or newly obtained loans or credit cards. Improved credit 

scores are expected to help participants further reduce expenses, access credit 

under better terms, increase savings, and build assets. The FOC model also posits 

that community-based organizations are better able than other agencies to provide 

an individualized level of assistance in a trusted, familiar, and accessible environment—factors that can be important in reaching underserved populations that may 

be alienated from mainstream financial and labor markets.

In 2010, LISC received a Social Innovation Fund (SIF) grant from the Corporation 

for National and Community Service (CNCS) to expand and evaluate the FOC 

model as part of CNCS’s efforts to support innovative community-based solutions 

for improving the lives of people in low-income communities. Since then, the FOC 

model has expanded from 24 centers in 6 cities to over 75 centers in 30 cities 

around the country. FOCs offer an important model for the workforce development and asset-building fields. The model seeks to address a number of barriers 

to financial stability that low-income families face, including periods of unemployment, low financial literacy, institutional barriers to accessing financial services 

and products, and the need for income supports to supplement wages that are 

not sufficient to support a family.

14 First Steps on the Road To Financial Well-Being: Introduction
Figure 1.1 Financial Opportunity Centers Logic Model

15 First Steps on the Road To Financial Well-Being: Introduction

 INPUTS

Low-to-moderate income 

families

Trusted community-based 

agencies

Staff with expertise in 

employment, training, 

financial services, income 

supports

 ACTIVITIES

Tailored bundled services, 

including:

Employment/Training

• Job readiness training

• Computer/web skills

• Occupational training

• Basic education

• Transitional jobs

• Job placement

• Retention support

Income Supports

• Benefits screening

• Benefit application

• Tax prep services/EITC

Financial Services/Tools

• 1:1 coaching

• Financial education 

workshops

• Credit report review

• Budget and balance sheet 

creation/review

• Credit building loans

• Savings vehicles

• Secure credit cards

 OUTPUTS

Number receiving:

• Services in 2 of 3 areas

• Services in 3 of 3 areas

• Enroll in education/ 

training/college

• Job readiness training

• Job counseling

• Financial coaching

• Financial education

• Complete budget and 

balance sheet

• Credit report review

•Work on building credit

• Enroll in Twin Accounts

•Work on saving using 

savings vehicles

• Credit union membership

• Develop plan to resolve 

medical debt

• Income support counseling

• Screen for supports

• Apply for supports

 OUTCOMES

Career Advancement 

(6 to 18 months)

• Complete education or 

training

• Obtain employment

• Retain employment

• Increase wages and hours

Improve Credit Rating 

(6 to 18 months)

• Correct credit report errors 

and show positive activity 

on report

•Move from un-scored to 

scored

• Develop relationships 

with mainstream financial 

institutions

• Increase credit scores

•Maintain strong credit Improved 

profile Financial Well-Being 

and Ability to Weather 

Financial Hardships Increase Net Income 

(6 to 18 months) (18 to 36 months)

• Obtain income supports

• Increase income from work 

and supports

• Get income to equal 

expenses

• Use improved credit scores 

to get better terms and 

rates on expenses

•Move to positive net 

income

Increase Net Worth 

(6 to 18 months)

• Increase savings 

• Decrease liabilities

• Resolve long-term debt, 

e.g., medical 

• Use improved credit scores 

to build assets, e.g., home, 

car, retirement, education

PLANNED WORK INTENDED RESULTS
This report presents our findings on the impacts of five FOCs in Chicago on lowincome job seekers’ employment, net income, credit, and net worth two years after 

entering the programs. We found that the FOCs were effective in helping individuals 

take initial steps to improve their financial stability two years after program entry. 

Relative to a comparison group, FOC participants experienced greater employment 

stability and reductions in certain types of debts, and built more positive credit histories, as reflected on their credit reports. These advances had not translated into 

improvements in net income or net worth by the time data collection concluded two 

years after program entry. Given the FOC participants’ limited recent attachment to 

the labor market, lack of assets, and level of debt when they entered the programs, 

it is perhaps not surprising that helping them achieve financial stability and mobility 

requires a long-term strategy. Throughout the report, we offer lessons learned from 

the FOC experience about engaging individuals in integrated services and helping 

them achieve financial stability.

The FOC Evaluation

LISC contracted with the Economic Mobility Corporation (Mobility) to conduct an 

independent study of the effectiveness of five FOCs in Chicago. Given that the FOC 

network in Chicago had been operating for several years and other sites were fairly 

new, LISC and Mobility felt that a study of the Chicago programs would provide a fair 

test of the fully implemented FOC model. The five organizations in the study were 

selected from the 11 programs operating in Chicago at the time because (1) they 

built the FOC services into employment programs, which was the model we were 

interested in testing; (2) they served a diverse group of low-income job seekers; and 

(3) they represented a mix of agency types and service offerings. Figure 1.2 provides 

a brief description of the five organizations in the FOC study.

The FOC evaluation addressed the following research questions.

Implementation Questions

• What employment, financial counseling, and income support services did individuals receive from the FOCs?

• How frequent was participants’ contact with the programs and over what duration of time did their contact take place?

• Did participants receive services across the three core areas as expected and 

for the duration expected?

• Were certain subgroups more likely than others to receive the intended 

services?

• In what important ways did the implemented model differ from the planned 

model?

• How much variation in implementation fidelity was there across the five 

organizations?

• Were FOC participants more likely than comparison group members to receive 

integrated services in the three core areas?

16 First Steps on the Road To Financial Well-Being: Introduction
Impact Questions

• Did the FOCs have a positive impact on individuals’ outcomes two years after 

entering the programs across the domains of career advancement, net income, 

credit, and net worth? We present all of the indicators we examined within each 

of these domains in Figure 4.1 in Chapter 4. The primary impact questions were:

• Did the FOCs increase the likelihood that individuals were employed yearround in the second year after program entry?

• Did the FOCs increase the likelihood that individuals had net income greater 

than zero two years after program entry?

• Did the FOCs increase the likelihood that individuals who lacked credit 

scores at program entry had scores two years later?

• Did the FOCs increase the likelihood that individuals who had credit scores 

at program entry had an increase in scores two years later?

• Did the FOCs increase the likelihood that individuals had net income greater 

than zero two years after program entry?

• Did program impacts vary for subgroups of job seekers?

• Did program impacts vary across the five study sites?

To assess program impacts, the study used a quasi-experimental design that compared FOC participants’ outcomes to those of a similar group of individuals who 

sought assistance with employment and training from the city’s workforce centers. 

The design addressed a primary concern with using quasi-experimental methods 

to evaluate voluntary programs; that is, the potential selection bias that results 

from differences in motivation between program participants and nonparticipants. 

The study focused on individuals who were seeking employment and training 

assistance from the FOC programs to help them get a job—the same motivation 

expressed by the comparison group members, who were seeking assistance from 

the workforce centers. The benefits of this approach were that at the time of study 

enrollment the comparison group members and FOC participants were likely to be 

in similar employment situations, similarly motivated to find employment, and navigating the same or similar labor, housing, and financial markets.

Despite the advantages of this approach, the characteristics of members of the 

FOC program and comparison groups were unlikely to be identical at the time of 

study enrollment. Therefore, we utilized a propensity score matching approach to 

select the final sample; that is, we matched comparison group members to FOC 

participants at the individual level based on their likelihood of being in the FOC 

program group given their demographics, recent employment experience, and financial situation. Only FOC participants and comparison group members who were 

sufficiently close matches were included in the final sample. Researchers have 

17 First Steps on the Road To Financial Well-Being: Introduction
found that propensity score matching has been effective in replicating experimental 

results from evaluations of employment and training programs when three criteria 

are met: (1) the data for the intervention and comparison groups are collected 

using the same data source; (2) the participants and nonparticipants reside in 

the same local labor market; and (3) the data contain variables relevant to modeling the program participation decision (Smith and Todd 2005). The FOC study met 

these criteria. We provide details about how we constructed the comparison group 

in Appendix A.

Figure 1.2 The Five Organizations in the FOC Study

Association House (AH)

Founded in 1899 as a settlement house, AH is a nonprofit multiservice organization that provides child welfare, behavioral health, education, and 

employment services to help adults and youth become self-sufficient. AH’s traditional focus is on emergency services, and it serves thousands 

through its food pantry and intensive case-management services. In 2006, AH opened a career center to provide adult education and employment services and began providing FOC services to individuals in these programs. In January 2013, AH decided to end its FOC program. A case 

manager and a financial counselor continued to provide limited services to existing participants through May 2013, about nine months after the 

study enrollment period ended.

Instituto Del Progreso Latino (IDPL)

IDPL is a nonprofit workforce development organization founded in 1977 with a mission of contributing to the “development of Latino immigrants 

and their families through education, training, and employment that fosters full participation in the changing U.S. society while preserving cultural 

identity and dignity.” Its programs include occupational training, classes in GED preparation, citizenship, and English as a second language, youth 

development programs, a charter high school, and an alternative high school. IDPL was one of the first organizations to implement the FOC model 

in Chicago in 2005.

Metropolitan Family Services (MFS)

MFS is a nonprofit multiservice agency founded in 1857 with a mission to strengthen families and communities by providing services in economic 

stability, education, emotional wellness, and empowerment. Its traditional programmatic focus is providing counseling to families involved with 

the state’s family services department and the city’s housing authority. The MFS FOC was the newest program among the five study sites. It began 

operating in June 2011 out of Kennedy King College (KKC). MFS took over management of the FOC in January 2012, when the original parent 

organization, Jane Addams Hull House, ceased operations. The entire staff remained on board, and program operations were largely uninterrupted 

by the change.

North Lawndale Employment Network (NLEN)

NLEN is a nonprofit workforce development organization founded in 1999 to address the employment needs of community residents. Its mission is to improve residents’ earning potential through employment initiatives that lead to economic advancement and improved quality of life, 

including skills training and subsidized employment opportunities. Like IDPL, NLEN was one of the first organizations to implement an FOC in 

Chicago in 2005.

The Cara Program (TCP)

TCP is a nonprofit workforce development organization founded in 1991 to help adults affected by homelessness and poverty to find employment. The organization provides training in life skills, job readiness, and career development, as well as subsidized employment opportunities, 

job placement, and job retention assistance. The TCP FOC was originally operated by another multiservice community agency, and TCP assumed 

management of the program in 2008.

18 First Steps on the Road To Financial Well-Being: Introduction
Past studies of the integrated service model on which the FOCs are based have 

analyzed program implementation and participant outcomes.5 The FOC evaluation seeks to increase the existing evidence base, targeting a moderate level of 

evidence according to the guidelines issued by CNCS. The study uses a quasiexperimental design that demonstrates equivalence between the treatment and 

comparison groups and, therefore, supports causal conclusions. However, the 

study was conducted with five FOC programs in one city out of the more than 75 

programs operating in 30 cities around the country, limiting its generalizability.

To answer the research questions, we collected data using the following methods:

• Baseline and follow-up surveys of study participants. We conducted phone surveys of FOC participants and comparison group members at the time they sought

assistance from their respective agencies and again two years later. The surveys

gathered information about study participants’ education, employment history, 

family income, expenses, assets, and debts, as well as demographic data, such 

as age, race and ethnicity, gender, criminal-record status, housing status, and 

family structure.

• Participants’ credit reports. We accessed participants’ credit reports from 

TransUnion, one of the three major credit bureaus, at the time of program entry 

as well as both one and two years later. The credit reports included credit scores 

and information about use of credit-based products, such as mortgages, installment loans (e.g., automobile loans, student loans), credit cards, and other lines 

of credit, including payments made during the previous two years, history of delinquencies, and current status of each account (positive or negative). Figure 1.3 

provides definitions of the credit report terms used in this report.

• FOC program data. We collected data from the performance management 

system that LISC maintains and that all FOC organizations use to track program participation. The data included information about the types of counseling the participants received as well as the duration of their participation in the 

program.

• Site visits. To learn about differences in the five organizations’ program structure and content, we conducted interviews with FOC staff members, observed 

program activities, and conducted focus groups with participants. The staff 

interviews included the program directors; employment, financial, and income 

support counselors; career coaches; and job readiness instructors. Activities we 

observed included FOC orientations, job readiness and life skills workshops, and

financial workshops (where provided).

Study enrollment took place from October 2011 to August 2012 for the FOC participant group and from October to December 2011 for the comparison group. We 

conducted baseline surveys and collected credit report data at the time of program

entry for 810 FOC participants and 1,030 comparison group members. We used 

propensity score matching to select a sample of 850 comparison group members 

19 First Steps on the Road To Financial Well-Being: Introduction
Figure 1.3 Credit Report Terms

Credit scores

The credit scores analyzed in this study are FICO scores, a universal scoring system that uses data from the three major credit bureaus. FICO scores 

play a critical role in individuals’ access to financial services and products. Credit scores are a function of payment history on trade accounts (e.g., 

loans and credit cards), debt-to-credit ratio, length of credit history, types of extended credit, and variables related to recent transactions. FICO 

scores range between 350 and 850.

Subprime scores

Subprime scores signify high financial risk. While different lenders use different thresholds for determining subprime scores, a score below 620 is 

generally considered subprime.

Unscored

Credit reports may indicate that an individual is unscored due to insufficient credit history. To have a credit score, individuals generally must have 

at least one trade account that has been open for six months or more and have had activity on an account in the past six months.

Trade accounts

Credit reports include information about three types of trade accounts that remain on the report for as long as they are active or, if they are no 

longer active, for seven to ten years from the date of last activity. Installment accounts—most commonly mortgages, car loans, and student loans—

have fixed terms and require regular payments. Revolving accounts include credit cards, charge cards, and home equity lines of credit, which have 

open terms and minimum payments that vary with the balance. “Open” accounts have no credit limit and must be paid in full at the end of each 

month. Examples include utility, telecommunications, and child support accounts. For each trade account, credit reports include the credit limit, 

balance, late payments, amount past due, date opened, payment history over the previous two years, and date closed, if applicable.

Thin files

Individuals are said to have thin credit files if they have few active trade accounts or only new accounts on their credit reports. Many lenders will 

not offer their best terms to applicants with thin files because without sufficient information to review, they assume the applicants are at high risk 

of default. In this report, we define thin files as credit reports with fewer than three open trade accounts.7

Positive versus negative rating

Trade accounts have a positive rating if the account is paid as agreed as of the most recent payment or when it was closed. Trade accounts are 

given negative ratings if the most recent payment was made late or if the account is in collections, repossession, or has been charged off to bad 

debt—that is, if the creditor has declared that the debt is unlikely to be collected.

20 First Steps on the Road To Financial Well-Being: Introduction
from this pool who best matched the FOC participants. We attempted the twoyear follow-up survey with this sample of 810 FOC participants and 850 comparison group members and completed the surveys with 553 FOC participants and 

653 comparison group members—response rates of 68 percent and 77 percent, 

respectively. We then conducted the propensity score matching with the individuals 

who completed the two-year follow-up survey, resulting in a final analysis sample of 

500 FOC participants and 649 comparison group members. Appendix A provides 

additional details about the data, the matching process, and differences between 

the FOC participants who are in the final sample and those not in the final sample 

due to attrition from the survey or the matching process.

This Report

In Chapter 2, we present the demographic, employment, and financial situations 

of the study participants at the time they sought assistance from the programs. 

Chapter 3 examines the implementation of the FOC model, including the extent 

to which individuals received the intended services and whether FOC participants 

were more likely than comparison group members to receive the targeted services. 

In Chapter 4 we present the findings on the FOC programs’ impacts on participants’ outcomes in the domains of career advancement, net income, credit, and 

net worth. We conclude with the implications of the findings for policy and practice.

21 First Steps on the Road To Financial Well-Being: Introduction
Chapter 2

Characteristics of the Study Participants

The FOCs broadly sought to serve low-income individuals living in or near the Chicago 

communities in which the programs were located. While some organizations had eligibility criteria for participating in certain employment and training services, only one 

conducted an extensive assessment process for admitting participants. Figure 2.1

summarizes the populations recruited at each of the FOC study sites. All of the FOCs 

served participants who lived throughout the city, but a large portion of the participants in each program resided in or near the communities the organizations sought 

to serve. While the FOCs generally enrolled individuals seeking a range of services, 

including supportive services, the study included only individuals who were seeking 

employment and training services to help them get a job.

We recruited comparison group members from the five workforce centers operating 

in Chicago at the time the study began. The locations and percentages of comparison group members recruited from each location follow. The differences in the percentages recruited from each center reflect the level of activity and flow of eligible 

job seekers at the time of study enrollment.

• Garfield Workforce Center (8 percent), in the East Garfield Park community, on 

the West Side

• Mid-South Workforce Center (31 percent), in the Kenwood community, on the 

South Side

• Pilsen Workforce Center (28 percent), in the Lower West Side community area

• Sheridan Workforce Center (9 percent), in the Uptown community, on the North Side

• Southwest Workforce Center (24 percent), in the Ashburn community, on the 

Southwest Side

Given the broad population the FOCs aimed to serve, we used limited eligibility 

criteria when recruiting individuals from the workforce centers to make up the comparison group pool. To be eligible, individuals recruited at the workforce centers 

had to meet the following criteria:

• Be age 18 or older.

• Have household income at or below 200 percent of the federal poverty level during the previous 12 months (which was $37,060 for a family of three in 2011).

• Be seeking employment and training services in order to get a job.

The individuals seeking assistance from the FOC organizations and workforce centers came from many of the same communities in Chicago. The maps in Figure 2.2

depict the residential location of the study participants at the time they sought assistance from their respective programs. The blue dots indicate the location of the FOC 

organizations (one FOC organization had two locations) and workforce centers. The 

largest concentrations of participants in both groups lived on the West, South, and 

Southwest Sides of Chicago. The main difference between the groups was the cluster of comparison group members who lived on the North Side near one of the city’s 

workforce centers.

22 First Steps on the Road To Financial Well-Being: Characteristics of the Study Participants
Figure 2.1 Target Populations at the Five FOC Study Sites

Association House (AH)

AH primarily served individuals in or near the Humboldt Park community in which it is located, a more racially diverse area than those served by 

the other FOC sites. Individuals at any skill level could participate in its job readiness training—the program in which most FOC participants took 

part. Those interested in customer service skills training had to test at least at the eighth-grade level in reading and math, and AH’s subsidized 

employment program was open only to individuals with criminal records who passed a drug test and were deemed suitable for one of the positions.

Instituto del Progreso Latino (IDPL)

IDPL primarily served individuals in the predominantly Latino communities of Pilsen, Little Village, and Back of the Yards. Individuals at any skill 

level could participate in its FOC program. Those interested in receiving assistance with employment (true of all study participants) were required 

to provide documentation of their legal status.

Metropolitan Family Services (MFS)

MFS served residents of the predominantly African American communities of Englewood, Washington Park, and Woodlawn, on Chicago’s South 

Side, as well as students at Kennedy King College—the community college where the program was located. The only eligibility criterion was that 

individuals were age 18 or older.

North Lawndale Employment Network (NLEN)

NLEN served individuals in the predominantly African American community of North Lawndale, on Chicago’s West Side. Its flagship program, 

U-TURN Permitted, was open only to individuals with felony convictions who passed a drug test and a multiday assessment of their willingness and 

ability to participate in and benefit from the program. NLEN piloted a shorter version of U-TURN Permitted for community residents without felony 

convictions, but few people enrolled during the study period. Individuals interested in its urban weatherization training program were required to 

have at least a high school diploma, to pass a drug test, and to test at the tenth-grade level or better in reading and math.

The Cara Program (TCP)

TCP primarily served individuals in the predominantly African American Quad Communities, on the South Side of Chicago. To participate, individuals were required to be 18 or older and to have been drug-free for at least four months. Individuals interested in receiving employment assistance 

from TCP also could not have convictions for certain types of violent crimes.

Our interim report from the FOC evaluation provides extensive information about 

the characteristics of the individuals who sought assistance from the FOCs.8 In 

Figure 2.3, we present key characteristics of the FOC and comparison group members in the final matched sample at the time of program entry. None of the differences in the characteristics of the two groups are statistically significant. (Appendix 

A presents descriptive statistics for all variables used in the matching process for 

the FOC and comparison groups before and after matching.)

The data show that participants faced substantial financial hardship when they 

came to the FOC programs.

• While nearly all had worked in the past, most were struggling after the recession 

when they came to the programs. Half had not worked at all during the past year, 

and 40 percent had not worked during the previous two years.

• Most had only a high school diploma or GED (63.4 percent), while 23 percent 

had no diploma or degree.

23 First Steps on the Road To Financial Well-Being: Characteristics of the Study Participants
• More than three-quarters were living below the poverty level in the month prior to 

program entry.

• Most did not have sufficient enough income to cover their expenses; only 32 

percent had positive net income (income minus expenses). In the month prior to 

program entry, more than 60 percent received Supplemental Nutrition Assistance 

Program (SNAP) benefits, formerly known as food stamps, to help make ends 

meet.

• Most had debts that exceeded the value of their assets; only 32.4 percent had 

net worth (the value of assets minus the value of debts) greater than zero.

• Nearly all participants were liquid-asset poor; that is, they did not have enough 

savings to cover basic expenses for three months.

• Forty-two percent had no linkages to mainstream financial institutions in the 

form of bank accounts, retirement accounts, credit cards, mortgages, or loans.

• More than 40 percent did not have a sufficient credit history to have a credit 

score, and another third had scores but thin credit files with fewer than three 

open accounts.

24 First Steps on the Road To Financial Well-Being: Characteristics of the Study Participants

Figure 2.2 Residential Location of Study Participants at the Time of Program Entry

Notes: The blue dots indicate the location of the FOC programs and workforce centers. The gray dots indicate where study 

participants resided. Each gray dot represents one person.
Figure 2.3 Key Baseline Characteristics of the FOC Participant Group and 

Comparison Group after Matching

FOC Comparison 

group group

(N=500) (N=649)

Average age 38.3 38.6

Age 18 to 24 18.0% 17.2%

Female 54.6% 54.0%

Race

Black 71.6% 70.2%

Latino 24.0% 25.6%

White 1.6% 1.5%

Other non-White 2.8% 2.7%

Highest Degree

No degree or diploma 23.0% 23.3%

GED 16.0% 15.6%

High school diploma 47.4% 47.4%

Associate’s degree 6.4% 5.9%

Bachelor degree 6.0% 6.0%

Graduate degree 1.2% 1.8%

Employment

Employed at any time during the past year 49.6% 52.3%

Employed during all 12 months of the past year 14.7% 16.6%

Had a criminal conviction (misdemeanor or felony) 41.4% 40.2%

Marital Status

Never married 62.0% 64.1%

Separated, divorced, or widowed 20.2% 19.3%

Married or living in marriage-like relationship 17.8% 16.6%

Had any children under age 18 48.6% 51.2%

Housing Status

Owned home 15.0% 13.0%

Rented home 54.0% 55.9%

Lived rent-free 25.8% 25.9%

Homeless 5.2% 5.2%

Had monthly income below the poverty level 77.3% 79.2%

Received SNAP in the previous month 62.2% 64.6%

Had net income greater than zero 32.0% 31.1%

Had net worth greater than zero 32.4% 34.1%

Were liquid-asset poor 92.7% 92.9%

Credit Status

Did not have a credit score 41.6% 40.2%

Had a credit score but a thin credit file 33.4% 35.8%

Had a credit score and a thick credit file 25.0% 24.0%

Had any linkages to mainstream financial institutions 58.0% 61.7%

Reported at least one form of financial distress 56.4% 59.8%

25 First Steps on the Road To Financial Well-Being: Characteristics of the Study Participants
• More than half (56.4 percent) reported at least one form of financial distress at 

the time of program entry, such as being behind on rent, mortgage, or utility payments; being contacted by collection agencies; or bouncing checks or not paying 

the minimum balance on credit cards in the past three months.

• Many individuals relied on family and friends to get by while they were unemployed. 

About a quarter reported living in a house or apartment without paying rent (not 

including individuals living in fully subsidized housing). Nearly a third (31.9 percent) 

received financial help from family or friends in the month prior to program entry.

Differences between the Final Sample and the 

Baseline Sample

As noted in the introduction, the original sample of participants who enrolled 

in the study and had both baseline survey data and baseline credit report data 

included 810 individuals seeking assistance from the FOC programs. There was 

attrition from this sample due to non-response to the two-year follow-up survey, 

either because individuals could not be located or because they chose not to 

complete it. There was some additional attrition due to sample members not 

having close enough matches in the comparison group to be included in the final 

analysis. In Appendix A, we present details about the differences in the characteristics of the 500 FOC participants who were in the final sample and the 310 

who were not. Overall, the individuals in the final sample were somewhat more 

likely than those not in the final sample to be Black than Latino, to be older, and 

to have at least a high school diploma or GED. They were also somewhat more 

likely to have a credit score, to have any connections to mainstream financial 

institutions, and to have worked at any time during the two years prior to entering 

the program. On the other hand, they were also somewhat more likely to have a 

criminal conviction. The results suggest that the findings in this report may not 

represent the outcomes that would be found for the full range of individuals the 

five FOCs served. Particularly underrepresented in the final sample were young 

adult Latino job seekers with no high school diploma or connections to mainstream financial institutions.

Discussion

The FOC participants and comparison group members faced substantial barriers 

to achieving financial stability when they entered the programs. About half had not 

worked at all during the previous year and two-thirds did not have enough income 

to cover their expenses. About a third relied on financial help from family or friends 

to get by, and more than a third reported that they were late in making payments on 

rent, mortgages, utilities, or credit card bills. About 40 percent had no connections 

to mainstream financial institutions that could help facilitate savings or access to 

credit, which could help them cope with periods of unemployment. The findings indicated that helping these individuals become financially stable—that is, achieving 

consistent employment and positive net income and building enough savings or other 

assets to weather crises—within a two-year period posed a significant challenge.

26 First Steps on the Road To Financial Well-Being: Characteristics of the Study Participants
Chapter 3

Implementation

At the core of the FOC model is a team of counselors, including an employment 

counselor, financial counselor, and income support counselor. The expectation was 

that all participants would meet at least once with each counselor and then work 

with the programs anywhere from six months to three years, depending on their 

needs and goals. FOC financial counselors were required to complete a financial 

assessment of each participant during the initial meeting using a template that 

LISC designed. This assessment gathered information about participants’ income, 

expenses, assets, and debts so that counselors could generate a budget and a 

balance sheet that showed participants their net income and net worth. The financial counselors also pulled participants’ credit reports and FICO credit scores and 

reviewed the information with them. The counselors were then expected to provide 

coaching focused on budgeting, saving, and credit building, and to help individuals 

with specific issues as needed, such as high debt, credit report errors, or building 

assets. LISC did not prescribe what the income support and employment counselors should do beyond working with participants to achieve the program’s goals. 

That is, income support counselors were expected to screen participants for public benefits eligibility and help them access the benefits for which they qualified. 

Employment counselors were expected to help participants find jobs.

The individuals in the study were seeking assistance with employment and job 

training when they came to the FOCs, and the programs sought to engage them in 

financial coaching and assistance with accessing income supports as well. The five 

organizations in the study were expected to offer job readiness workshops to participants who needed this assistance. Other types of employment services, such 

as occupational skills training, basic education classes, and subsidized employment opportunities could either be provided in-house or participants could be 

referred to other agencies for these services. As detailed in our interim report on 

the FOCs,4 the five organizations in the study offered different types of employment 

services and took different approaches to organizing the FOC services within their 

programs. All five sites offered basic job readiness training that lasted between 

one and five weeks across the sites, and most participants were required to complete this training prior to meeting with the employment counselor.

In this chapter, we first examine to what extent participants received the intended 

services during the two years after program entry, using data from the FOC program 

staff on the types of counseling participants received. We then examine whether 

FOC participants were more likely than comparison group members to receive 

employment, financial, and income support assistance during the two years after 

program entry, using data from the follow-up survey. This analysis focuses on the 

500 FOC participants and 649 comparison group members in the final analysis 

sample; that is, those who completed the follow-up survey and were matched. 

27 First Steps on the Road To Financial Well-Being: Implementation
Program participation rates for the FOC participants in the final sample were 

slightly higher than those for all individuals who enrolled in the study. Appendix B 

describes the implementation measures used and presents how program participation rates for the final sample compared to the rates for the full sample of individuals who enrolled.

Participant Engagement in FOC Services

As we explain in more detail in Chapter 4, our analysis used an intent-to-treat (ITT) 

framework, and all individuals who attended an orientation about the FOC program, 

decided to participate in the program, consented to being included in the research, 

and completed the baseline survey were included in the study sample, regardless 

of whether or not they ever received services. To assess program implementation, 

we analyzed data from the performance management system that the FOC organizations used to track program participation and outcomes. This system included 

information about the assistance participants received from the FOC counselors 

but did not include data about attendance and completion of job readiness training. Therefore, participants who attended job readiness training but dropped out 

and never received assistance from an FOC counselor are not captured in the participation rates reported below.

All five organizations in the study employed counselors in the three core service 

areas—employment, financial coaching, and income supports—as expected. As 

shown in Figure 3.1, 65.8 percent of the 500 FOC study participants received 

assistance from an FOC counselor in at least one of the three core areas during 

the two years after program entry. Thirty-five percent received assistance from 

the counselors in all three areas. The highest percentage of participants (58.8 

percent) received assistance from the financial counselor, and the next highest 

percentage from the income support counselor (51.6 percent). About half (49.6 

percent) of study participants received assistance from the employment counselor. Participants were required to complete other program components prior to 

meeting with the employment counselor—either job readiness training, meeting 

with the financial counselor, meeting with the income support counselor, or all of 

these. A portion of study participants at each site dropped out of the program 

prior to meeting the requirements for working with the employment counselor, 

which explains why the percent who received assistance from the employment 

counselor is lower than the percent who received other types of counseling, even 

though study participants were seeking assistance with finding a job when they 

entered the FOC programs.

28 First Steps on the Road To Financial Well-Being: Implementation
Figure 3.1 Receipt of Assistance from the FOC Counselors during the Two Years after Program Entry 

Among All Enrolled Study Participants

AH IDPL MFS NLEN TCP All

(N=108) (N=96) (N=113) (N=48) (N=135) (N=500)

Percent of all study participants who received 

assistance from:

 Any FOC counselors 31.5% 80.2% 78.8% 91.7% 63.0% 65.8%

 Counselor in one service area 7.4% 5.2% 8.0% 8.3% 5.9% 6.8%

 Counselors in two service areas 13.9% 11.5% 37.2% 20.8% 30.4% 23.8%

 Counselors in all three service areas 10.2% 63.5% 33.6% 62.5% 26.7% 35.2%

Percent of all study participants who received 

assistance from the:

 Financial counselor 25.0% 66.7% 74.3% 83.3% 58.5% 58.8%

 Income support counselor 20.4% 77.1% 70.8% 81.3% 31.9% 51.6%

 Employment counselor

Source: FOC administrative program records

20.4% 75.0% 38.1% 72.9% 56.3% 49.6%

Figure 3.2 presents participation data just for the FOC study participants who 

received assistance from any of the FOC counselors. Among participants who 

received any counseling, 53.5 percent received assistance from the FOC counselors in all three core service areas and another 36.2 percent received assistance 

from counselors in two of the three core areas. About 61 percent received counseling for more than six months after entering the programs. A third received counseling 19 to 24 months after program entry. On average, participants who received 

counseling did so over an 11-month period. The median number of contacts with 

the FOC counselors was nine.

Figure 3.2 Receipt of Assistance from the FOC Counselors during the Two Years after Program Entry 

Among Participants Who Received Any Counseling

AH IDPL MFS NLEN TCP All

(N=34) (N=77) (N=89) (N=44) (N=85) (N=329)

Percent who received counseling in two of three 

service areas 44.1% 14.3% 47.2% 22.7% 48.2% 36.2%

Percent who received counseling in all three service 32.4% 79.2% 42.7% 68.2% 42.4% 53.5% areas 

Duration of contact with the FOC counselors 

 Less than 1 month 23.5% 13.0% 9.1% 9.5% 44.4% 20.5%

 1 to 6 months 55.9% 9.1% 8.0% 19.1% 23.5% 18.6%

 7 to 12 months 14.7% 16.9% 3.4% 35.7% 12.4% 14.3%

 13 to 18 months 5.9% 31.2% 9.1% 11.9% 6.2% 13.7%

 19 to 24 months 0.0% 29.9% 70.5% 23.8% 13.6% 32.9%

Median number of contacts with the FOC counselors 

Source: FOC administrative program records

8 10 9 15 5 9

29 First Steps on the Road To Financial Well-Being: Implementation
As Figures 3.1 and 3.2 show, participation rates varied considerably across the five 

organizations. At three sites—IDPL, MFS, and NLEN—more than three-quarters of 

study participants received assistance from at least one of the FOC counselors. 

At two sites, IDPL and NLEN, more than 60 percent of study participants received 

assistance from the FOC counselors in all three core service areas. Among study 

participants who received any FOC counseling, more than 70 percent received 

counseling six months or more after program entry at IDPL, MFS, and NLEN. Many 

factors related to program content and quality would have influenced participation 

rates in integrated services across the sites, but the findings suggest that basic 

program structure played a role.

• Two sites, IDPL and MFS, required that participants meet one-on-one with the 

financial counselor and income support counselor prior to participating in any 

employment services, including the job readiness training, and at least two-thirds 

of participants at these sites received financial and/or income support counseling. These two sites also established longer relationships with participants, providing counseling a year or more after program entry to the highest percentages 

of participants among the five sites.

• NLEN required that participants meet one-on-one with the financial counselor 

and encouraged but did not require a meeting with the income support counselor 

while participants attended job readiness or occupational skills training. NLEN 

also conducted extensive screening prior to accepting job seekers into the program (see Figure 2.1), unlike the other programs, which likely contributed to its 

higher participation rates.

• At TCP, participants were first required to attend job readiness training, including 

a workshop with the financial counselor that took place during the first week of 

this training. TCP did not require a one-on-one meeting with the financial counselor. It also did not require participants to meet with the income support counselor, and only 31.9 percent of participants did so. Only a third of participants at 

TCP received assistance from the FOC counselors for more than six months.

• AH did not require meetings with either the financial counselor or the income 

support counselor in order to receive employment services during most of the 

study enrollment period. The first step was to attend job readiness training, 

and nearly 70 percent of study participants dropped out of the program without 

meeting with any of the FOC counselors. As noted earlier, AH stopped providing 

FOC services about 20 months after the study began—nine months after study 

enrollment ended—limiting the length of time that study participants could have 

received FOC counseling.

30 First Steps on the Road To Financial Well-Being: Implementation
Who Was More Likely to Participate in the FOC Services

As noted earlier, the FOC programs broadly sought to assist low-income individuals 

in the communities they served. Given the variation in study participants’ receipt of 

FOC services, we examined whether certain demographic subgroups of individuals 

who sought assistance from the FOCs were more likely than others to participate 

in the services. To do so, we used multivariate regression analysis that controlled 

for differences in program participation rates across the five sites and included 

the demographic, employment, and financial characteristics used in the matching 

process. We examined whether certain subgroups were more likely than others to 

obtain assistance from any of the FOC counselors, assistance from the counselors in all three core service areas, and assistance from the counselors 19 to 24 

months after program entry. The analysis is based on the full sample of 810 FOC 

study participants who enrolled and had baseline survey and credit report data, 

in order to examine differences in program participation not influenced by attrition 

from the survey or matching process. The full results of the models tested appear 

in Appendix B. The main findings follow.

Two findings stand out in terms of overall participation in FOC counseling. First, 

FOC study participants who had at least a high school diploma or GED were significantly more likely than those who did not have a diploma or degree to participate in 

the intended FOC services, using the three measures of participation noted above. 

Second, FOC study participants who were ages 25 and older were more likely than 

those ages 18 to 24 to obtain assistance from any of the FOC counselors and 

from the counselors in all three core service areas. We caution that the analysis 

demonstrates only that there were associations between participants’ demographic 

characteristics and participation in FOC counseling. We cannot definitively say why 

the associations occurred. However, the sites sought to serve low-income job seekers of different ages and education levels, and these individuals could be expected 

to benefit from integrated employment, financial, and income support assistance 

regardless of their level of education or age. The differential participation rates 

by education level and age suggest that different strategies may be needed to 

increase participation among young adults and those who lack a high school 

diploma or degree.

We also found that certain subgroups of job seekers who may have faced more 

substantial barriers to achieving financial stability than others were more likely to 

obtain more-intensive services. Individuals with criminal convictions, immigrants, 

and individuals who lacked sufficient credit history to have a credit score were 

more likely to get assistance in all three core service areas. Those who had a 

health condition that limited their ability to work, those who had greater expenses, 

and those with a greater number of derogatory public records on their credit 

reports, such as tax liens, bankruptcies, and civil judgments, were more likely to 

continue to receive counseling 19 to 24 months after program entry.

31 First Steps on the Road To Financial Well-Being: Implementation
FOC Impacts on Service Receipt

We next examined the differences in the services received by members of the FOC 

and comparison groups, based on self-reported data from the two-year follow-up 

survey. As noted earlier, members of the comparison group were seeking employment and training services in order to find a job from one of five workforce centers 

in Chicago. Figure 3.3 summarizes the services the workforce centers offered job 

seekers during the study enrollment period.

Figure 3.4 presents the differences in FOC participants’ and comparison group 

members’ self-reported receipt of assistance in the three core areas of the FOC 

model. We used chi-square tests to assess whether the receipt of services differe

significantly between the FOC and comparison groups. These figures include assis

tance received from any agency, not just the FOC programs and workforce centers.

The main findings were that participants in the FOC group were significantly:

d 

-

32 First Steps on the Road To Financial Well-Being: Implementation

Figure 3.3 Services Offered at the Chicago Workforce Centers 

The Chicago workforce centers provide employment and training services funded under the federal Workforce Investment Act (WIA). In general,

there are three levels of services:

• Core services include the use of a resource room and computers to look for a job, job search workshops, and job clubs, where individuals can

get advice on their job search.

• Intensive services include comprehensive assessments of skills and aptitudes, development of individual employment plans, counseling, and

career planning.

• Training services include links to occupational training and basic skills training in the community. Eligible individuals use an Individual Training

Account (ITA) to select a program from a qualified training provider.

At the time of study enrollment, the Chicago workforce centers offered seminars on resume writing, job search strategies, and interviewing skills;

access to computerized listings of job openings; and free use of copiers, fax machines, telephones, and the internet for searching and applying

for jobs. The centers also provided information and referrals for basic education classes, such as GED preparation and English as a second language, and orientations for the Job Corps program. Job seekers could complete assessments of their reading and math skills and meet with a case

manager to determine their eligibility for intensive services. Job seekers were required to participate in at least one core and one intensive service

before they could seek training services. However, during the study enrollment period, city officials and workforce center directors indicated that

the city’s intensive services slots were at capacity. Therefore, newly registered job seekers were unlikely to receive intensive employment or training

services through the workforce centers. 

While many of the employment services the workforce centers offered were similar to those that FOCs offered, the workforce centers did not offer

financial education or counseling. Regarding income supports, individuals could obtain information about unemployment insurance at all of the

workforce centers. One center had staff on site from multiple agencies who could help people access public benefits and other community services. Three other centers were at the same location as the city’s Community Service Centers, where staff offered assistance with accessing a range 

of resources, including shelter, food, and clothing, as well as information about rental, utility, and other financial assistance programs.
• More likely than comparison group members to report having received assistance with looking for a job, with reviewing their credit report, and with financial 

issues, such as budgeting, opening a bank account, obtaining a loan, reducing 

their debts, improving their credit score, or other types of financial issues.

• Less likely than comparison group members to report receiving assistance with 

applying for benefits to increase their income or cover their expenses, such 

as food stamps, unemployment insurance, utility assistance, or other types of 

benefits.

• More likely than comparison group members to report receiving integrated 

services, that is, services in at least two of three core areas that the FOC 

model targets.

Figure 3.4 Study Participants’ Self-Reported Receipt of Services From Any Agency

FOC

group

(N=500)

Comparison group

(N=649) Difference

Percent of all study participants who received:

Assistance with looking for a job 79.8% 35.2% 44.6% ***

Credit report review 55.6% 6.7% 48.9% ***

Assistance with financial issues 44.0% 12.7% 31.3% ***

Assistance with applying for benefits to 

increase income or cover expenses 41.2% 48.5% -7.3% **

Any assistance in one of the three FOC core 

service areas 87.0% 68.7% 18.3% ***

One core service 32.8% 44.5% -11.7% ***

Two core services 30.4% 20.6% 9.8% ***

All three core services 23.8% 3.6% 20.2% ***

Source: 2-year follow-up survey of study participants

***p<.01 **p<.05 *p<.10

It is worth noting that a higher percentage of FOC group members reported receiving help with finding a job from the FOCs in the survey than the percent who met 

with the employment counselor according to the FOC program records (74.2 percent versus 49.6 percent). As noted earlier, the FOCs offered job readiness training 

classes but were not able to provide data on participation in this training. The job 

readiness training included help with developing or refining resumes and improving 

interviewing skills, as well as tips on conducting a job search. The survey data indicate that FOC participants viewed this assistance as help with finding a job even if 

they did not eventually meet one-on-one with the FOC employment counselor.

Figure 3.5 presents the study participants’ reporting of their satisfaction with the 

services they received. For participants in the FOC group, the figures represent 

33 First Steps on the Road To Financial Well-Being: Implementation
their satisfaction with the services they received from the FOC agencies. For the 

comparison group members, the figures represent their satisfaction with the services they received from any agency. The figures reveal that:

• While FOC participants were more likely than comparison group members to 

report receiving help looking for a job (79.8 versus 35.2 percent), among those 

who received this assistance, satisfaction rates were similar; just over 55 percent of participants in both groups said they were very satisfied with the assistance they received.

• Satisfaction rates were also similar among those who reported receiving assistance with financial issues. Again, the FOC impact was in increasing the percentage who received assistance with financial issues.

• While FOC participants were somewhat less likely than comparison group members to say they received assistance with applying for benefits, among those who 

received this assistance, participants in the FOC group were more likely than 

those in the comparison group to say they were very satisfied with the assistance they received.

Figure 3.5 Study Participants’ Reported Satisfaction with Services

FOC

group from the FOC 

agency

Comparison group 

from any agency Difference

Percent who said they were very satisfied with the 

assistance (among those who received it):

Assistance with looking for a job 55.7% 55.6% 0.1%

Assistance with financial issues 72.9% 69.5% 3.4%

Assistance with applying for benefits to 

increase income or cover expenses 73.3% 58.8% 14.5% ***

Source: 2-year follow-up survey of study participants

***p<.01 **p<.05 *p<.10

Discussion

The findings indicate that the FOC model was successful in increasing receipt of 

integrated services among low-income job seekers, as FOC participants were significantly more likely than comparison group members to receive integrated services, 

particularly the combination of employment and financial counseling. However, 

implementation of the FOC model varied substantially across the five programs, 

with three of the five sites engaging more than three-quarters of participants in any 

FOC counseling and two engaging more than 60 percent of participants in the full 

bundle of financial, income support, and employment counseling. Our interim report 

includes details on differences in program content and structure across the sites. 

34 First Steps on the Road To Financial Well-Being: Implementation
Based on the findings, our interviews with staff, and observations of the programs, 

the following factors appeared to play an important role in influencing organizations’ ability to implement the FOC model.

• Program structure. Given that individuals coming to the FOC programs primarily 

sought assistance with finding a job, LISC expected the FOCs to require participants to meet with the financial counselor before receiving assistance from the 

employment counselor in order to increase receipt of financial counseling. LISC 

had found that once individuals obtained jobs, it was difficult to get them to 

return to meet with the financial counselors. AH did not implement this requirement, and TCP required only attendance at a group workshop rather than a oneon-one meeting. These two sites and NLEN did not require meetings with the 

income support counselor. The sites that required one-on-one meetings with the 

financial and/or income support counselors prior to meeting with the employment counselor engaged higher percentages of participants in these services.

 Additionally, the program requirements focused on ensuring that at least one 

meeting with each counselor took place. While participants were encouraged to 

come back to the program whenever they needed assistance, most sites did not 

have a formal strategy or structure for promoting continued engagement in the 

services. The two sites that did have such a strategy engaged a higher percentage of participants in counseling for at least a year after program entry. IDPL 

used the initial meetings with each counselor to develop one-year career, financial, and technology plans for each participant. At MFS the financial counselor 

actively reached out to participants at least once every six months.

• Staff commitment. Program staff—from managers to front-line staff—must be 

committed to all of the FOC goals, not just the employment and training goals. 

When managers viewed the model’s financial goals as secondary or unachievable, decisions made about program structure did not promote engagement in 

integrated services.

• Staff expertise. Shortly after the study enrollment period ended, LISC tested 

the financial counselors and found that many needed training on reading credit 

reports to identify how best to help participants build their credit histories and 

improve their scores. LISC subsequently increased its efforts to gain counselors’ 

buy-in to the credit-building approach and to train them on how to implement it. 

Counselors’ lack of expertise about credit issues and credit building may have 

reduced the programs’ ability to engage some individuals in financial services 

over the longer term.

• Program quality. As noted, the sites required that most participants attend job 

readiness training prior to meeting with the employment counselor. While information about completion of the job readiness training was not available, data 

about the percent of participants that received any services and the percent who 

eventually met with the employment counselor indicate that there was significant 

35 First Steps on the Road To Financial Well-Being: Implementation
attrition from this training at some sites. The content and quality of the job readiness training we observed varied across the sites. While not at the core of the 

FOC model, these services, as well as the basic education, occupational training, and subsidized employment programs the organizations offered, formed the 

foundation upon which they built the FOC model. Offering strong employment programs that are relevant to participants’ needs is important to engaging individuals in the full array of FOC counseling services.

• Characteristics of participants. Our analysis indicates that participation rates were 

higher among individuals with at least a high school diploma or GED and among 

individuals ages 25 and older. Organizations seeking to engage individuals who 

lack a high school diploma or GED or young adults ages 18 to 24 in integrated services may need to adapt program strategies to meet their needs and interests.

36 First Steps on the Road To Financial Well-Being: Implementation
Chapter 4

Program Impacts

FOCs strive to help individuals achieve a range of goals, including consistent employment, improved credit ratings, and increased net income and net worth. In this chapter, we present the findings on the FOC programs’ impacts on participants’ outcomes 

across these domains two years after program entry. As noted in Chapter 3, we used 

an intent-to-treat (ITT) analysis framework to assess program impacts; that is, we 

examined the impacts for all participants who sought employment assistance from 

the programs, regardless of whether or not they actually ended up receiving services. 

There are two primary reasons for using an ITT framework. First, it helps to address 

the potential selection bias that results from differences in motivation between 

individuals who participate in the program and those who do not, which is a primary 

concern with using quasi-experimental methods to evaluate voluntary programs. By 

including everyone who sought assistance, regardless of whether or not they eventually received the assistance, we compare two groups who were not only demographically similar but were also similarly motivated at the time of study enrollment. 

Second, an ITT framework addresses the relevant policy question of whether the FOC 

program model is effective based on its ability to both engage people in the intended 

services and achieve the targeted outcomes.

Figure 4.1 presents the primary outcomes the FOCs expected to influence in the 

domains of career advancement, net income, credit, and net worth. We used 

regression analysis to examine program impacts with models that included participants’ pre-program demographic and financial characteristics, the baseline value 

of the outcome of interest, and whether participants were members of the FOC or 

comparison group. The outcomes presented in Figures 4.2 through 4.7 are regression-adjusted; that is, they account for any differences in demographic and financial characteristics between the FOC and comparison groups that remained after 

matching. These figures also include the baseline values of the outcomes of interest to show the changes that occurred over time. Because the two-year outcomes 

are adjusted to account for any baseline differences between the groups, we present the averages at baseline for the FOC and comparison groups combined. Finally, 

the figures include standardized effect sizes for each outcome, which provide a 

measure of the magnitude of program impacts and can be used to compare results 

across studies.10 A description of the regression models, the dependent and independent variables included in the models, and our analysis of correlation between 

participants within FOC program and workforce centers are included in Appendix C. 

The full results of the regression models are presented in Figures C5 through C37 

in Appendix C.

37 First Steps on the Road To Financial Well-Being: Program Impacts
Figure 4.1 Primary Outcome Measures* 

A. Career Advancement

• Percent employed year-round in the second year after program entry

• Percent employed at any time during the two years

• Annual earnings from work during the second year after program entry

• Annual hours worked during the second year after program entry

• Average hourly wage in current or most recent job

• Percent who had at least a high school diploma or GED 

• Percent who had a college degree (associate’s or higher)

• Percent who earned college credits but not a degree

• Percent who earned an occupational certificate or license

B. Net Income

• Percent who had net income greater than zero in the previous month 

• Average monthly income from all sources

• Average monthly net income from all sources (income minus expenses)

• Percent who received income supports in the previous month 

• Average fees paid for check or money-order cashing and bank or credit union accounts in the 

previous month

C. Credit Rating

• Percent who had a credit score (among participants who did not have scores at program entry)

• Percent who had an increase in credit score (among participants who had scores at 

program entry)

• Percent who had a prime credit score (620 or greater)

• Average credit scores (among those who had scores at both points in time)

• Average number of open trade accounts (as a measure of use of mainstream financial institutions)

• Percent who had any trade accounts paid on time and number of trade accounts paid on time 

(as a measure of positive activity reported on credit reports) 

• Number of on-time payments made on trade accounts in the past year 

D. Net Worth

• Percent who had an increase in net worth 

• Percent who had net worth greater than zero

• Average and median net worth

• Percent who had any assets

• Percent who had any money in savings or checking accounts

• Average dollar amount in savings and checking accounts

• Percent who had any non-asset-related debts

*Confirmatory research questions for each outcome domain appear in bold.

Given the four domains the FOCs expected to influence, our analysis included 

multiple tests for program effects. This raises a concern about obtaining falsepositive results, that is, the more statistical tests one conducts, the greater the 

probability of finding a statistically significant impact estimate purely by chance. To 

address this concern, we designated a confirmatory outcome in each domain, that 

is, the one that LISC felt was the most critical effect the FOCs sought to produce 

38 First Steps on the Road To Financial Well-Being: Program Impacts
and, therefore, should carry the most weight in assessing program effectiveness. 

The confirmatory research questions are bolded in Figure 4.1.11 We also present 

program impacts on secondary measures that represent progress in achieving 

the primary goals in each domain.12 Later in this chapter, we present findings from 

exploratory analyses of impacts for subgroups of participants who received FOC 

services as well as differences in impacts for demographic subgroups and for the 

five FOC study sites. Given the large number of tests we conducted, some of the 

findings may reflect chance variation and not true impacts. However, the findings 

can be useful to program administrators for identifying the level of participation 

that may be required to achieve impact and the subgroups of individuals who benefitted more from the FOC model as implemented by the study sites.

Among all individuals who sought assistance from the programs, we found that 

the FOCs had a positive impact on the likelihood that individuals were steadily 

employed during the second year after program entry, meaning they worked during all 12 months of the year. This increased employment did not translate into 

positive impacts on net income as other sources of monetary support decreased, 

including financial help from family and friends and unemployment insurance benefits, and participants’ expenditures on basic living expenses, such as rent, utilities, 

and food, increased after two years. However, the FOCs increased the likelihood 

that participants had positive activity on their credit reports in the form of on-time 

payments and trade accounts with positive ratings. FOC participants who did not 

have sufficient recent credit activity to have a credit score at program entry were 

significantly more likely than their counterparts in the comparison group to have a 

score after two years. FOC participants were also less likely to have any non-assetrelated debts, such as overdue rent or utility payments, medical or legal debts, or 

back taxes, two years after program entry. For FOC participants who took part in 

both financial and employment counseling, the programs had additional positive 

impacts on annual earnings, hours worked, and the likelihood of having a prime 

credit score. The magnitude of the impact on the likelihood of working during all 12 

months of the second year after program entry was also greater for this group.

FOC Program Impacts among All Study Participants

Career Advancement

As noted earlier, the FOC programs and workforce centers offered similar types of 

employment services. Therefore, the question the study addresses is whether the 

provision of integrated services, including employment, financial, and income support 

counseling, leads to improved outcomes for low-income job seekers. FOC program 

administrators expected that the financial counseling and assistance accessing 

income supports would improve participants’ ability to retain their jobs. Greater savings, access to credit, and monetary or nonmonetary sources of support, such as 

SNAP and subsidies that reduced participants’ child care, housing, or utility costs, 

would increase individuals’ capacity to handle emergencies that might otherwise 

39 First Steps on the Road To Financial Well-Being: Program Impacts
result in job loss. Greater job retention would result in increased earnings. Similarly, 

the integrated services would help participants attending education or training to persist in and complete their programs by increasing their ability to cover expenses and 

handle emergencies that might otherwise lead to them dropping out.

As shown in Figure 4.2, we found that members of both the FOC and comparison groups experienced increases in employment and earnings two years after 

program entry, and most differences between the two groups were not statistically significant. In the second year after program entry, study participants in both 

groups experienced about a 15-percentage-point increase in employment rates 

and a $2,000 increase in annual earnings, on average, from the year prior to program entry. The FOCs did have a significant positive impact on employment stability: the percent of FOC participants who were employed year-round increased by 

21-percentage points from the year before program entry to the second year after. 

This change was significantly greater than that found among comparison group 

members.

Members of the comparison group were more likely than FOC study participants to 

attend education or training during the two years after program entry (38.2 percent 

versus 33.1 percent), including GED classes, college classes, and occupational 

training, with the greatest difference in the percent who attended occupational training (17.7 percent of comparison group members versus 11.5 percent of FOC group 

members). As shown in Figure 4.3, two years after program entry there were no 

significant differences between the groups in the percent who had at least a high 

school diploma or GED or the percent who had a college degree. Comparison group 

members were somewhat more likely to have earned college credits, among those 

who did not have a college degree, and to have earned an occupational certificate or 

license, but the differences with the FOC group were not statistically significant.

Figure 4.2 FOC Program Impacts on Participants’ Employment and Earnings

Baseline Value Second Year After Program Entry

for the Matched 

Sample (Year Before 

Program Entry)

FOC group

(N=500)

Comparison group

(N=649)

Impact Effect Size

Percent employed at any time 50.9% 66.3% 66.0% 0.3% .01

Percent employed year-round 15.6% 36.5% 31.1% 5.4% * .15

Average annual earnings (including zero earners) $7,967 $9,957 $9,951 $6 .00

Average annual hours worked (including zero 732 hours) 903 862 41 .04

Average hourly wage in current or most recent $10.58 job during the two years after program entry^ $11.14 $11.85 -$0.70 -.11

Source: baseline and 2-year follow-up surveys of study participants

***p<.01 **p<.05 *p<.10

^Average hourly wages are for those who worked at any time during the two years after program entry, including 323 FOC group members and 410 

comparison group members. 

40 First Steps on the Road To Financial Well-Being: Program Impacts
Figure 4.3 FOC Program Impacts on Participants’ Education and Training

Two Years After

Baseline Value for Program Entry

the Matched Sample Impact Effect Size

(At Program Entry) FOC group Comparison group

(N=500) (N=649)

Percent who had at least a high school diploma 

or GED 76.9% 79.6% 78.8% 0.8% .03

Percent who had a college degree (associate’s 

or higher) 13.7% 16.4% 15.2% 1.2% .05

Percent who had earned any college credits 

among those without a college degree^ 31.8% 36.2% 41.2% -5.0% -.13

Percent who had an occupational certificate/

license 31.4% 33.5% 41.1% -7.6% -.20

Source: baseline and 2-year follow-up surveys of study participants 

***p<.01 **p<.05 *p<.10

^The percent who earned college credits is out of those who did not have a college degree two years after program entry, including 417 FOC group members and 502 comparison group members.

Net Income

LISC expected the FOCs to help participants increase their net income in several 

ways. The integrated services would help individuals increase their employment 

and earnings from work as well as monetary and nonmonetary forms of income 

support that would help them cover expenses, such as food, transportation, child 

care, and utility costs. Financial counselors would help participants identify ways to 

reduce expenses, encourage them to set up bank accounts to avoid check-cashing 

fees, and help them improve their credit histories and credit scores so that they 

could access lower-cost forms of credit and avoid the security deposits that utility 

companies and landlords often charge individuals with poor credit.

As shown in Figure 4.4, we found that average monthly income, including income 

from work, public benefits, and other sources, increased two years after program 

entry by about $300 among study participants in both groups. However, monthly 

expenses also increased and remained higher than income, on average. Average 

net income, that is, total income minus total expenses, decreased among FOC 

group members and increased slightly among comparison group members. The differences between the groups in average income, expenses, and net income were 

not statistically significant. Comparison group members were significantly more 

likely to have net income greater than zero two years after program entry.

41 First Steps on the Road To Financial Well-Being: Program Impacts
Figure 4.4 FOC Program Impacts on Participants’ Income, Expenses, and Net Income

Two Years After

Baseline Value for Program Entry

the Matched Sample Impact Effect Size

(At Program Entry) FOC group Comparison group

(N=500) (N=649)

Average monthly income $1,159 $1,431 $1,510 -$79 -.05

Average monthly expenses $1,453 $1,811 $1,764 $47 .04

Average monthly net income -$294 -$381 -$254 -$127 -.09

Percent who had monthly net income greater 

than zero 31.6% 29.4% 35.9% -6.5% ** -.18

Percent who received any income supports 

last month 87.2% 79.4% 83.7% -4.3% -.17

Source: baseline and 2-year follow-up surveys of study participants 

***p<.01 **p<.05 *p<.10

We examined the details of study participants’ income sources and expenses to 

better understand the outcomes. While both groups experienced an increase in 

income from work, they also experienced substantial decreases in financial help 

from family and friends and in unemployment insurance benefits. The percent of 

FOC participants who received any financial help from family or friends decreased 

from 29.4 percent to 15.8 percent, while the percent who received any unemployment benefits decreased from 20.3 percent to 5.2 percent. These changes 

were not significantly different from those the comparison group experienced. 

Comparison group members were slightly more likely than members of the FOC 

group to receive any income supports two years after program entry, such as SNAP, 

TANF/cash assistance, unemployment insurance, Supplemental Security Income, 

veterans’ benefits, social security, Medicaid, Medicare, and assistance with home 

heating and cooling, transportation, child care, clothing, or housing (Figure 4.4). 

While differences between the groups for most individual items were not statistically significant, comparison group members were significantly more likely than 

members of the FOC group to receive SNAP (68.5 versus 63.3 percent) and 

heating/cooling assistance (15.3 versus 11.2 percent).13

Both groups experienced substantial increases in the percent of participants paying 

basic expenses, such as rent, utilities, and food not covered by SNAP. The findings 

suggest that some individuals relied on support from family or friends while they 

were unemployed but started paying for these basic expenses once they obtained 

employment. FOC study participants were significantly more likely than comparison 

group members to make car payments and to make payments on credit cards. 

While FOC study participants were significantly less likely to pay fees for cashing 

checks or money orders, they were more likely to pay fees related to bank or credit 

union accounts.

42 First Steps on the Road To Financial Well-Being: Program Impacts
Credit

The FOC model anticipated that counselors would help participants learn about 

credit and the content of their credit reports and encourage them to use their 

increased income to build a positive credit history and improve their credit scores. 

As noted earlier, the FOC model called for a credit-building approach in which financial counselors were expected to help those who lacked a credit score due to insufficient credit history and those with thin credit files to access credit-building loans 

and secured credit cards. For those who had credit scores and a substantial credit 

history, financial counselors were expected to advise them on how to manage their 

accounts to maximize their scores, that is, by using credit but keeping their balances low and making on-time payments. Counselors also advised participants on 

negotiating repayment terms and disputing errors on their credit reports. Through 

these efforts, the FOCs sought to help participants establish linkages to mainstream financial institutions and to show positive activity on their credit reports in 

the form of on-time payments on loans, credit cards, and other lines of credit. This 

positive activity, in turn, was expected to help participants who were unscored at 

program entry to become scored and to help those who already had credit scores 

to improve their scores.

As shown in Figure 4.5, the FOCs were successful in helping participants make 

progress in building positive credit histories. Two years after program entry, members of the FOC group had a greater number of open trade accounts, on average, 

than comparison group members, indicating greater use of mainstream forms of 

credit that are reported to the credit bureaus. The percent of FOC participants who 

had any trade accounts with positive ratings, that is, loan or credit accounts that 

were paid as agreed, increased by 11-percentage points—a significantly greater 

change than that found among comparison group members. Members of the FOC 

group also had a significantly greater number of accounts with positive ratings and 

made a greater number of on-time payments on trade accounts, on average, than 

comparison group members.

Despite the progress FOC participants made in building positive credit histories, as 

a group their outcomes related to credit scores were not significantly different than 

those experienced by the comparison group. The percent of FOC study participants 

who had a credit score increased by 5-percentage points and the percent who had a 

prime credit score increased by 6.5-percentage points two years after program entry. 

While the changes were greater for the FOC group than for the comparison group, the 

differences between the groups were not statistically significant. Among participants 

who had credit scores both at program entry and two years later, average scores 

were nearly identical for the two groups and a similar percentage of FOC and comparison group members had any increase in credit scores after two years.

43 First Steps on the Road To Financial Well-Being: Program Impacts
Figure 4.5 FOC Program Impacts on Participants’ Credit History and Credit Scores

Two Years After

Baseline Value for Program Entry

the Matched Sample Impact Effect Size

(At Program Entry) FOC group Comparison group

(N=500) (N=649)

Average number of open trade accounts 1.68 2.49 2.14 .36 ** .11

Percent who had any trade accounts (open or 59.2% closed) paid as agreed 70.7% 61.7% 9.0% *** .24

Average number of trade accounts (open or 4.4 closed) paid as agreed 5.8 4.6 1.2 *** .16

Average number of on-time payments made on 17.4 trade accounts in the past year 23.0 19.1 3.9 *** .10

Percent who had a credit score 61.5% 66.5% 62.4% 4.1% .11

Percent who had a prime score 15.9% 22.4% 19.9% 2.5% .09

Average credit score among those with scores at 581 both points in time^ 598 597 1 .01

Percent who had any increase in credit score — among those scored at both points in time^ 61.7% 62.6% -1.0% -.02

Source: baseline and 2-year follow-up surveys of study participants

***p<.01 **p<.05 *p<.10

^The percent who had an increase in credit score and average credit scores are for those who had credit scores both at program entry and two years later, including 247 FOC 

group members and 360 comparison group members.

Given the widely varying credit situations of the study participants when they came 

to the programs, we examined the primary credit outcomes for three subgroups of 

participants based on their credit status at program entry: 1. those who did not 

have a credit score, 2. those who had a credit score but a thin credit file, that is, 

fewer than three open trade accounts, and 3. those who had a credit score and 

a thick credit file. As shown in Figure 4.6, we found that FOC impacts related to 

credit scores differed across these three groups. Among study participants who 

were unscored at program entry, members of the FOC group were significantly more 

likely than comparison group members to have a credit score after two years—a 

9.3-percentage point difference. About a quarter of both the FOC and comparison 

group members who had thin credit files at program entry were unscored after two 

years, while nearly all of those who had thick files remained scored. Among individuals who had scores and thick credit files at program entry, members of the FOC 

group were significantly more likely to have a prime credit score after two years—a 

13.8-percentage point difference. Among individuals who had credit scores both at 

program entry and two years later, average credit scores at the time of the followup did not differ significantly between the groups.

44 First Steps on the Road To Financial Well-Being: Program Impacts
Figure 4.6 FOC Program Impacts on Participants’ Credit Scores by Their Credit Status at the Time of 

Program Entry

Baseline Value for 

the Matched Sample 

Two Years After

Program Entry Impact Effect Size

(At Program Entry) FOC group Comparison group

Unscored at program entry (N=199) (N=217)

Percent who had a credit score 0.0% 36.3% 26.9% 9.3% * .27

Percent who had a prime score 0.0% 10.0% 10.8% -0.8% -.05

Scored but had a thin credit file (N=164) (N=235)

Percent who had a credit score 100.0% 75.1% 74.5% 0.6% .02

Percent who had a prime score 15.7% 13.9% 15.3% -1.4% -.07

Average credit score among those with scores at 

both points in time^ 561 574 582 -8 -.13

Scored and had a thick credit file (N=123) (N=153)

Percent who had a credit score 100.0% 100.0% 98.7% 1.3% -—

Percent who had a prime score 42.3% 53.4% 39.6% 13.8% *** .34

Average credit score among those with scores at 

both points in time^ 608 623 612 11 .13

Source: TransUnion credit reports pulled at the time of program entry and two years later 

***p<.01 **p<.05 *p<.10

^ The percent who had an increase in credit score and average credit scores are for those who had credit scores both at program entry and two years later. Among individuals 

with thin credit files, this includes 124 FOC group members and 192 comparison group members. Among individuals with thick credit files, this includes 123 FOC group members 

and 168 comparison group members.

Net Worth

The FOCs expected that the integrated services would help participants to increase 

savings, reduce debt, and access mainstream forms of credit, which would enable 

them to start building assets and net worth. Given the two-year study period, program administrators recognized that debt associated with accumulating assets, 

such as homes, vehicles, or education, would likely increase due to individuals’ need to obtain loans for these purposes. However, non-asset-related debts, 

such as unpaid utility, rent, medical, or legal bills, back taxes owed, child support 

arrears, or credit card balances were expected to decrease as participants paid off 

past-due amounts and made on-time payments going forward.

For two reasons, we urge caution in interpreting the change in net worth over time. 

First, our analysis of the baseline and follow-up survey data indicates that study 

participants in both groups underreported assets in the baseline survey, particularly home and car ownership and retirement accounts. Even though the programs 

did not require asset tests and participants’ responses to the survey would not 

be shared with the programs, participants may have assumed that both were true 

and that reporting certain assets might disqualify them from receiving services. We 

discuss the details of the underreporting and the steps taken to address the issue, 

45 First Steps on the Road To Financial Well-Being: Program Impacts
where possible, in Appendix A. Second, among homeowners, reporting of home 

values was highly unreliable, and the reported home values heavily influenced the 

results, even though only 14 to 15 percent of participants were homeowners. While 

the results regarding change over time are problematic, the issues occurred with 

both FOC and comparison group members and did not necessarily affect the estimates of the differences between the groups two years after program entry.

As shown in Figure 4.7, we found that there were no statistically significant differences between the FOC and comparison group members in net worth or in the 

percent who reported increases in net worth two years after program entry. Similar 

percentages reported having any assets and any debts. The percent of study participants who had any money in savings or checking accounts and the average 

amount in savings or checking accounts did not differ significantly between the FOC 

and comparison groups.

Figure 4.7 FOC Program Impacts on Participants’ Assets, Debts, and Net Worth

Two Years After

Baseline Value for Program Entry

the Matched Sample Impact Effect Size

(At Program Entry) FOC group Comparison group

(N=500) (N=649)

Percent who had any increase in net worth after 

two years — 55.1% 50.6% 4.5% .11

Percent who had net worth greater than zero 33.2% 41.6% 40.9% 0.7% .02

Average net worth $5,901 $10,721 $8,790 $1,931 .03

Median net worth -$500 $0 $0 $0 —-

Percent who had any assets 62.9% 67.7% 71.9% -4.2% -.12

Percent with any money in savings or checking 

accounts 34.9% 42.4% 44.1% -1.7% -.04

Average dollar amount in savings or checking 

accounts $342 $538 $785 -$247 -.08

Percent who had any asset-related debts 36.7% 39.6% 39.2% 0.4% .01

Percent who had any non-asset-related debts 66.8% 53.0% 58.7% -5.7% * -.14

Source: TransUnion credit reports pulled at the time of program entry and two years later

 ***p<.01 **p<.05 *p<.10

As expected, the percent of participants who reported any asset-related debts 

increased slightly after two years, while the percent who reported any non-assetrelated debts decreased. Two years after program entry, FOC participants were 

significantly less likely than comparison group members to report non-asset-related 

debts—a 5.7 percentage point difference. The largest declines were in the percent 

who had unpaid utility, medical, and legal bills. FOC group members also owed 

significantly less in back taxes, on average, than comparison group members two 

years after program entry.

46 First Steps on the Road To Financial Well-Being: Program Impacts
FOC Program Impacts for Subsets of Participants Who 

Received Services

In addition to examining program impacts for everyone who sought assistance 

from the FOC programs, we were interested in examining whether the FOCs had an 

impact on the outcomes of the individuals who were engaged in the FOC services; 

that is, the effect of the treatment on the treated (TOT). We considered a number 

of ways of defining which FOC study participants received the treatment. While the 

FOC model emphasizes the provision of integrated services, program administrators expected that participants might experience positive outcomes in certain 

domains as a result of receiving assistance from at least one of the FOC counselors. For example, individuals who received assistance only from the financial counselor might have positive credit-related outcomes even if they did not also receive 

assistance from the income support or employment counselors. There were also 

issues to consider related to the measurement of who received program services. 

While the program model focused on assistance provided by the three FOC counselors, the five programs provided other employment services, including job readiness workshops, for which attendance and completion data were not available. As 

noted in Chapter 3, a higher percentage of participants reported receiving help with 

finding a job from the FOC programs in the follow-up survey than the percent who 

met with the FOC employment counselor as reported by the FOCs.

In addition to these issues, one concern with a TOT analysis is that it is not possible to know which members of the comparison group would have participated in 

the FOC services if they had been made available to them. While we used propensity score matching to match FOC participants and comparison group members on 

factors that we expected would influence whether they received services, there is 

a greater chance with the TOT than with the ITT analysis that unmeasured differences in motivation remained that could have influenced participants’ outcomes 

and biased the impact estimates.

Given these considerations, we tested three methods for conducting the TOT analysis and present the impact estimates from each. The methods follow:

1. A comparison of FOC study participants who received counseling in any of 

the three core service areas according to the FOC administrative records 

and comparison group members who matched them on pre-program demographic, employment, and financial characteristics, regardless of what 

services they received.

2. A comparison of FOC study participants who had at least two meetings each 

with the FOC financial counselor and the FOC employment counselor according to the FOC administrative records and comparison group members who 

matched them on pre-program characteristics, regardless of what services 

they received.

47 First Steps on the Road To Financial Well-Being: Program Impacts
3. A comparison of FOC participants who reported in the follow-up survey that 

they received help with finding a job from the FOC programs and the comparison group members who said they received help with finding a job from the 

workforce centers or other agencies and who matched the FOC group on preprogram characteristics.

The first method uses a low-intensity definition of participation that includes having 

at least one meeting with any of the FOC counselors. Program administrators felt 

the second method captured individuals who participated more fully in the intended 

FOC services by having more than one meeting with both the financial and employment counselors. The third method enabled us to address some of the selectionbias concerns by comparing two groups that sought assistance with employment 

and followed through and received this assistance. This method also enabled 

us to examine outcomes for all FOC study participants who received any type of 

assistance with employment from the FOCs and not just those who met with the 

FOC employment counselor. The method addresses an important policy question 

of whether individuals who received employment assistance from the FOCs had 

better outcomes than individuals who received employment assistance elsewhere. 

We repeated the matching process described in Appendix A to create a comparison 

group for each of the three groups of FOC participants who received services. The 

estimated impacts in this section represent only the effect of the treatment on the 

treated; that is, the estimates do not apply to all participants who sought assistance from the FOC programs.

Figure 4.8 presents the estimated program impacts—the difference between the 

FOC group and comparison group outcomes—using each method of service receipt 

as well as the ITT results reported earlier, to allow for easier comparisons. The 

results of the regression models and standardized effect sizes for the TOT impact 

estimates and are presented in Figures C38 to C55 in Appendix C. The results 

largely confirm the findings for the overall sample. Individuals who received any FOC 

counseling were more likely than comparison group members to work year-round 

and to have positive activity on their credit reports, and were less likely to have 

non-asset-related debts two years after program entry.

Comparing the FOC and comparison group members who reported in the survey 

that they received help with finding a job (method 3), the findings regarding program impacts were similar to those for the full group who sought assistance from 

the FOCs, though generally the differences in outcomes were larger. The difference 

between FOC participants who received any counseling and comparison group 

members in the likelihood of working year-round increased to 10 percentage points. 

Although the estimated differences in average annual earnings and hours worked 

were greater using this method, the differences were not statistically significant. FOC 

group members who reported receiving help with finding a job were significantly more 

likely than their counterparts in the comparison group to have net worth greater than 

zero two years after program entry—a 7.3 percentage point difference.

48 First Steps on the Road To Financial Well-Being: Program Impacts
Figure 4.8 Program Impacts for FOC Group Members Who Received Services (Differences in Outcomes between 

FOC Group Members and Comparison Group Members)

TOT Method 2

ITT

TOT Method 1

(FOC group members who received 

(FOC group members who received 

assistance from the TOT Method 3

Results (All FOC 

group members versus the comparison 

group)

assistance from any 

of the FOC counselors versus the 

comparison group)

FOC financial and 

employment counselors versus the 

comparison group)

(FOC versus comparison group members 

who received any 

help finding a job)

Employment and Training in the Second Year After Program Entry

Percent employed year round 5.4% * 6.8% ** 7.9% * 10.0% **

Average annual earnings (including zero earners) $6 $234 $436 * $1,599

Average hours worked (including zero hours) 41 118 132 * 157

Average hourly wage in current or most recent job -$0.70 -$0.93 * -$0.25 -$1.30 *

Percent who had an occupational certificate/license -7.6% -7.1% -4.8% -16.0% ***

Net Income Two Years After Program Entry

Average monthly income -$79 -$92 -$38 $8

Average monthly expenses $47 $0 $46 $121

Average monthly net income -$127 -$94 -$88 -$106

Percent who had monthly net income greater than zero -6.5% ** -5.0% -1.9% -7.2% *

Credit Two Years After Program Entry

Percent who had a credit score 4.1% 2.4% 2.1% 3.2%

Percent who had a prime credit score 2.5% 3.4% 6.4% * 2.9%

Percent who had any trade accounts paid as agreed 9.0% *** 6.6% ** 9.4% *** 9.1% ***

Average number of on-time payments made on trade accounts 

in the past year 3.9 *** 3.0 * 2.6 4.5

Net Worth Two Years After Program Entry

Percent who had net worth greater than zero 0.7% 2.2% 5.8% 7.3% *

Percent who had any assets -4.2% -4.3% -1.3% -3.8%

Percent who had any asset-related debts 0.4% -3.0% 0.4% -6.3% *

Percent who had any non-asset-related debts -5.7% * -6.4% * -3.5% -12.2% ***

FOC Group N=500 N=322 N=181 N=334

Comparison Group N=649 N=649 N=636 N=249

Source: baseline and 2-year follow-up surveys of study participants and credit reports from TransUnion

 ***p<.01 **p<.05 *p<.10

Among FOC group members who participated in the services more intensely—that 

is, they had two or more meetings with the FOC financial and employment counselors (method 2)—impacts on employment were more positive than for the entire 

group who sought assistance. FOC group members who had two or more meetings 

with the financial and employment counselors earned $436 more and worked 132 

hours more, on average, than comparison group members in the second year after 

program entry. Both differences are statistically significant. The magnitude of the 

impact on the likelihood of working year-round was also greater for this group. At 

49 First Steps on the Road To Financial Well-Being: Program Impacts
the same time, the differences in average hourly wages and the percent who had 

occupational certificates were smaller for this group and not statistically significant. 

The more-intensely engaged FOC participants were more likely than comparison 

group members to have a prime credit score after two years—a 6.4 percentage 

point difference. However, the difference in the percent who reported any non-assetrelated debts was not statistically significant.

Differences in FOC Program Impacts for Demographic 

Subgroups

We explored differences in program impacts for subgroups of participants on the 

primary outcomes across the four domains, using multivariate regression analysis 

with interaction terms between treatment status and the subgroups. We examined 

whether treatment effects differed for demographic subgroups of interest for policy 

and programming, including race, gender, age, education level, criminal history, 

marital status, and whether participants had children under age 18, had worked in 

the past year, and had monthly net income greater than zero at the time of program 

entry. This analysis uses the ITT framework; that is, it includes all individuals who 

sought assistance from the programs. The results of the regression models are 

included in Appendix C.

We found that the FOCs had positive impacts across multiple outcome domains for 

certain subgroups, including the following.

Male participants. Increased the likelihood of having a credit score and 

decreased the likelihood of having non-asset-related debts.

Had a high school diploma or GED. Increased the likelihood of having a credit 

score and decreased the likelihood of having non-asset-related debts.

Had never been married. Increased the likelihood of being employed year-round 

and of having a prime credit score and decreased the likelihood of having nonasset-related debts.

Did not have children under age 18. Increased the likelihood of being employed 

year-round and of having a prime credit score.

Had zero or negative net income at program entry. Increased he likelihood of 

being employed year-round and of having a credit score and decreased the likelihood of having non-asset-related debts.

In addition, the FOCs had positive impacts on the likelihood of being employed 

year-round for young adults ages 18 to 24 and individuals who had a college 

degree (Associate’s degree or higher). The programs had positive impacts on the 

likelihood of having a credit score and of having a prime score among individuals 

•

•

•

•

•

50 First Steps on the Road To Financial Well-Being: Program Impacts
who were ages 25 or older and those who had not worked in the previous year 

while increasing the likelihood of having a credit score among those who did not 

have a criminal record.

Differences in FOC Program Impacts across the 

Five Study Sites

To identify lessons about program implementation, we explored differences in program impacts across the five FOC organizations in the study. To do so, we used 

multivariate regression analysis in which the models included dummy variables for 

each of the FOC program sites as well as participants’ pre-program demographic and 

financial characteristics and the baseline value of the outcome of interest in order to 

control for any differences in the characteristics of participants at the individual FOC 

sites and the comparison group. As with the subgroup analysis, this analysis uses 

the ITT framework—it included all individuals who sought assistance from the programs. The results of the regression models are included in Appendix C.

The small sample sizes at the individual FOC sites substantially reduce our ability to 

detect statistically significant program impacts at this level. Therefore, we discuss 

the substantive differences in impacts across the sites and their implications. Figure 

4.9 presents program impacts (the difference between the FOC group and comparison group outcomes) for each of the five study sites. The primary findings follow.

• The three sites that engaged more than 80 percent of participants in any FOC 

counseling services—IDPL, MFS, and NLEN—had greater impacts on year-round 

employment than the two sites that engaged a smaller percentage as well as 

larger impacts on annual earnings and hours worked. NLEN in particular, which 

provided more than a third of its participants with occupational skills training or 

subsidized employment opportunities, had substantively larger impacts on earnings and hours worked than the other sites.

• As with the overall sample, gains in employment and earnings did not translate 

into positive impacts on net income two years after program entry across the 

five sites.

• There were statistically significant impacts across all five sites on the likelihood 

that participants had trade accounts with positive ratings on their credit reports. 

Regarding the likelihood of having a credit score or of having a prime score, two 

sites had substantively larger impacts than the others—one of which was a strong 

implementer of the FOC model (IDPL) and one of which was not (AH). These two 

sites served either predominantly Latino or mixed-race communities while the 

other three served predominantly African-American communities. While the regression models controlled for differences in the racial composition of participants 

across the groups, the findings suggest that community characteristics interact 

with the efforts of local programs in influencing individuals’ access to credit.

51 First Steps on the Road To Financial Well-Being: Program Impacts
Figure 4.9 FOC Program Impacts across the Five Study Sites

AH IDPL MFS NLEN TCP

N=108 N=96 N=113 N=48 N=135

Employment and Training in the Second Year After 

Program Entry

Percent employed year round 2.2% 9.2% 7.3% 10.6% 1.8%

Average annual earnings 

(including zero earners) -$1,219 $181 $543 $2,560 -$433

Average hours worked (including zero hours) -34 89 28 256 3

Average hourly wage in current or most 

recent job -$1.53 * -$0.58 -$0.43 $0.20 -$0.69

Percent who had an occupational 

certificate/license -14.4% *** -7.3% 3.0% -0.7% -13.0% ***

Net Income Two Years After Program Entry

Average monthly gross income -$119 $47 -$60 -$88 -$146

Average monthly expenses $46 $95 $34 -$40 $55

Average monthly net income -$171 -$58 -$89 -$44 -$198

Percent who had monthly net income 

greater than zero -7.7% -1.4% -4.5% -5.4% -11.0% **

Credit Two Years After Program Entry

Percent who had a credit score 

(among all participants) 9.3% ** 8.2% 1.4% -0.1% 1.5%

Percent who had a prime credit score 6.5% 11.2% ** 0.7% -1.2% -3.1%

Percent who had any trade accounts 

(open or closed) paid as agreed 11.1% *** 14.4% *** 5.6% 10.4% ** 6.6% **

Number of on-time payments made on trade 

accounts in the past year 2.8 ** -0.3 6.1 * 6.4 4.8

Net Worth Two Years After Program Entry

Percent who had net worth greater than zero -6.6% -0.3% -1.0% 4.5% 7.9% *

Percent who had any assets -2.1% 3.8% -9.9% ** -1.9% -4.7%

Percent who had any asset-related debts 2.8% 7.0% 1.0% -4.5% -4.5%

Percent who had any non-asset-related debts 

Figures are the estimated regression-adjusted differences in ou

-6.0%

tcomes between the F

5.4%

OC participants at each 

-9.4% *

site and comparison gr

-11.8% *

oup members.

-8.2% *

Source: baseline and 2-year follow-up surveys of study participants and credit reports from TransUnion

***p<.01 **p<.05 *p<.10

52 First Steps on the Road To Financial Well-Being: Program Impacts
• The results regarding participants’ assets, debts, and net worth across the sites 

were mixed and do not support firm conclusions about how differences in implementation influenced these outcomes.

Discussion

LISC and the FOCs set ambitious goals—particularly over a two-year period for a 

population with limited recent attachment to the labor market, low education levels, 

and, in most cases, a lack of savings or connections to mainstream financial institutions that might have helped them weather an extended period of unemployment. 

While the FOCs did not achieve positive impacts in all of the domains they sought 

to influence, the impacts they produced indicate that integrating employment services and financial services has the potential to improve low-income individuals’ 

financial situations by helping them take necessary steps to achieving greater 

financial stability and economic mobility.

Among all study participants who sought assistance from the FOCs, the program 

increased year-round employment in the second year after program entry. On the 

other hand, comparison group members were more likely to complete training and 

obtain an occupational license or certificate. We do not know when during the 

two-year period individuals obtained the certificates or licenses or to what extent 

participation in training influenced the percent who were employed year-round in 

the second year. There are a number of possible explanations for the differences 

in attainment of occupational licenses or certificates. FOCs may have focused 

more than the workforce centers did on immediate employment over occupational 

training for all groups, including young adults ages 18 to 24. However, city officials 

indicated that job seekers were unlikely to be able to enroll in WIA-funded training during the study period due to the lack of available intensive services slots. 

Comparison group members may have been more inclined to pursue occupational 

training than individuals who sought assistance from the FOCs, or they may have 

sought training after initially being unsuccessful in finding employment.

While the FOCs did not produce significant impacts on annual earnings and hours 

worked among all individuals who sought assistance, they did so among individuals 

who were engaged in both financial and employment counseling. While the sitelevel analysis is not conclusive given the small sample sizes at the individual sites, 

the findings also suggest that the organizations that implemented the program well 

had greater impacts on annual earnings and hours worked.

Whether examining the full group of individuals who sought assistance or those 

who actually received assistance, the gains in employment did not translate into 

positive impacts on net income for FOC participants. Program administrators 

expected the FOCs would help individuals identify ways to reduce their expenses in 

addition to increasing their income. However, the results reveal that expenditures 

on basic items, such as rent and utilities, as well as car payments and credit card 

payments, increased over time as earnings from work increased. Participants’ 

53 First Steps on the Road To Financial Well-Being: Program Impacts
households were complex, including varying combinations of immediate and 

extended family members and non-relatives, and changed over time for some participants. We could not capture through the phone surveys the details needed to 

understand how participants had dealt with basic living expenses when they were 

unemployed and how this changed as their earnings from work increased. But the 

data suggest that while they were unemployed, some participants relied on support 

from extended family and friends to get by, either through monetary support or by 

not being asked to contribute to household expenses. Some allowed payments on 

basic living expenses to go past due. When their earnings increased, they resumed 

payments on these expenses. The surveys indicate that the FOCs significantly 

reduced the likelihood that individuals had non-asset-related debts, such as overdue utility payments, medical or legal debts, or back taxes, after two years. While 

expenditures increased, outstanding debts decreased. Although this had not produced statistically significant improvements in net worth by the end of the two-year 

study period, it is an important step in participants achieving financial stability.

Finally, the FOCs increased the likelihood that participants had positive activity on 

their credit reports in the form of trade accounts with positive ratings and on-time 

payments made on accounts. The FOCs increased the likelihood that individuals 

who lacked a credit score at the time of program entry had a score after two years. 

The FOCs also increased the likelihood that those who had scores and thick credit 

files at program entry had prime credit scores after two years. While the FOCs did 

not have an impact on participants’ accumulation of assets after two years, building positive credit histories is an important step in accessing mainstream forms of 

credit with lower interest rates and better terms, which can help individuals build 

assets. Demonstrating positive credit histories on a credit report can also influence insurance premiums, security deposits required on utilities, the ability to rent 

a home, and the ability to obtain or advance in a job in certain fields.

54 First Steps on the Road To Financial Well-Being: Program Impacts
Chapter 5

Conclusions

The FOC evaluation sought to determine whether offering integrated employment, 

financial, and income support counseling to individuals seeking assistance with 

finding a job is an effective strategy for improving low-income individuals’ financial 

stability and economic mobility. The primary implications of the findings for policy 

and programming follow.

• The FOC model was effective in helping individuals take initial steps to improve 

their financial stability.7KHUHVXOWVSURYLGHHYLGHQFHWKDWWKH)2&VKDGVLJQL¿FDQW

impacts on increasing employment stability, reducing non-asset-related debts, and building 

positive credit histories as reported on credit reports two years after entering the programs.

• Achieving financial stability and mobility is a long-term process requiring longterm interventions, particularly for the individuals the FOCs served. Helping 

low-income individuals with limited education or recent attachment to the labor market 

DFKLHYH¿QDQFLDOVWDELOLW\LVDORQJWHUPSURFHVVWKDWPD\SOD\RXWRYHUVHYHUDO\HDUV

Programs need to help individuals lay out realistic short- and long-term goals and plans 

for achieving them and structure services to promote long-term engagement. Policies 

that support integrated service strategies need to recognize the time needed to achieve 

¿QDQFLDOJRDOVDQGVXSSRUWHIIRUWVWRHVWDEOLVKORQJWHUPUHODWLRQVKLSVEHWZHHQSDUWLFLpants and counselors.

• Reviewing credit reports, budgets, and balance sheets is an important tool 

in helping individuals improve their financial situations. The core component of 

WKH)2&¿QDQFLDOFRXQVHOLQJZDVFRPSOHWLRQRID¿QDQFLDODVVHVVPHQWGXULQJZKLFK

counselors reviewed participants’ income, expenses, assets, debts, and credit reports. 

Counselors reported that most participants were unaware of the contents of their credit 

UHSRUWVRUODFNWKHUHRIRUKRZWKHLQIRUPDWLRQDIIHFWHGWKHLU¿QDQFLDOVLWXDWLRQV7KH

¿QGLQJVVXJJHVWWKDWH[SRVXUHWRWKLVLQIRUPDWLRQDVSDUWRID¿QDQFLDOFRDFKLQJVWUDWegy, is a powerful tool for helping low-income individuals take steps to build positive 

credit histories.

• Engaging individuals in integrated services is challenging, and program structure can promote greater engagement and benefits. Programs need to structure 

services to promote participation in integrated services, particularly among low-income 

MREVHHNHUVZKRPD\QRWVHH¿QDQFLDOVHUYLFHVDVUHOHYDQW3DUWLFLSDQWVZKRZHUHPRUH

LQWHQVHO\HQJDJHGLQWKH)2&¿QDQFLDODQGHPSOR\PHQWFRXQVHOLQJH[SHULHQFHGODUJHU

DQGPRUHVLJQL¿FDQWLPSDFWVRQHDUQLQJVKRXUVZRUNHGDQGWKHOLNHOLKRRGRIKDYLQJD

prime credit score.

55 First Steps on the Road To Financial Well-Being: Conclusions
• The FOC model was more effective for certain subgroups of participants, at 

least within the two-year study time frame. Individuals with a high school diploma 

or GED were more likely than those who lacked a diploma or GED to participate in the 

FOC counseling, and they experienced positive impacts across multiple domains. The 

FOCs also produced positive impacts for individuals who were male, did not have children under 18, and were never married. This indicates that individuals who lack a high 

school diploma, have children under 18, or are currently or formerly married may have 

needs that require different approaches in program content and structure, or they may 

need more than two years to achieve the gains that others achieved. On the other hand, 

the FOCs produced positive impacts across multiple outcome domains for individuals 

who had zero or negative net income at the time of program entry, a group that faces 

VXEVWDQWLDOEDUULHUVWRDFKLHYLQJ¿QDQFLDOVWDELOLW\DQGPRELOLW\

56 First Steps on the Road To Financial Well-Being: Conclusions
Endnotes

1. National Employment Law Project. September 2015. “Occupational 

Wage Declines Since the Great Recession.” Data Brief. New York: 

National Employment Law Project.

2. Cajner et al. 2014. “Why Is Involuntary Part-Time Work Elevated?” 

FEDS Notes April 14, 2014.

3. Brooks et al. 2014. Treading Water in the Deep End: Findings from the 

2014 Assets and Opportunity Scorecard. Washington, DC: CFED.

4. Brevoort et al. 2015. Data Point: Credit Invisibles. Washington, DC: 

Consumer Financial Protection Bureau Office of Research.

5. See, for example, Kaul, Bulbul et al. 2011. Pathways to Success: 

Service Pathways Analysis for the Center for Working Families 

Participants. Abt Associates Inc., and Liston, Cynthia and Robert 

Donnan. 2012. Center for Working Families at Community Colleges: 

Clearing the Financial Barriers to Student Success. MDC. 

6. See, for example, Kaul, Bulbul et al. 2011. Pathways to Success: 

Service Pathways Analysis for the Center for Working Families 

Participants. Abt Associates Inc., and Liston, Cynthia and Robert 

Donnan. 2012. Center for Working Families at Community Colleges: 

Clearing the Financial Barriers to Student Success. MDC. 

7. Individuals with thin credit files may or may not have a credit score. 

In our analysis of program impacts on credit, in Chapter 4, we divide 

individuals into three categories based on their credit status at program entry: unscored, had a credit score but a thin file, and had a 

credit score and a thick file.

8. See Roder, Anne. 2015. Building Stronger Financial Futures: Interim 

Findings from the Evaluation of LISC’s Financial Opportunity Centers. 

New York: Economic Mobility Corporation.

9. See Roder, Anne. 2015. Building Stronger Financial Futures. New 

York: Economic Mobility Corporation.

10. For continuous variables, effect sizes were calculated using Hedges 

g, the difference between the mean outcomes for the treatment 

group and the comparison group divided by the pooled within group 

standard deviation. For binary variables, effect sizes were calculated 

using the Cox index, which is the difference in the log odds for the 

treatment group minus the log odds for the comparison group divided 

by 1.65. While researchers caution that effect sizes should be interpreted using relevant empirical benchmarks (see Hill et al. 2007), the 

What Works Clearinghouse Procedures and Standards Handbook considers effect sizes of .25 or greater to be substantively important.

11. In the domain of credit, we originally proposed a confirmatory 

research question that combined two outcomes depending on 

participants’ credit status at program entry, that is, the percent of 

participants who experienced a positive credit outcome in the form 

of unscored participants becoming scored or of scored participants 

having an increase in score. Combining these concepts masked 

significant findings for the subgroups. Therefore, we divided the confirmatory question into two separate questions—one that applies to the 

unscored and one to the scored.

12. Another strategy for accounting for the effects of multiple-hypothesis 

testing is to use the Benjamini-Hochberg family-wise adjustment. 

We discuss this procedure and present the results in Figure C4 in 

Appendix C.

13. We examined whether the FOC and comparison group members met 

the income requirements for both SNAP and heating/cooling assistance and found no differences in potential eligibility rates between 

the groups at the time of the follow-up survey.

 57 First Steps on the Road To Financial Well-Being: Endnotes
Endnotes References

Austin, Peter. 2011. “An Introduction to Propensity Score Methods 

for Reducing the Effects of Confounding in Observational 

Studies.” Multivariate Behavioral Research 46:399–424.

Austin, Peter. 2008. “Assessing Balance in Measured Baseline 

Covariates When Using Many-to-One Matching on the 

Propensity-Score.” Pharmacoepidemiology and Drug Safety 

17:1218–1225.

Barr, Michael and Rebecca Blank. 2009. “Savings, Assets, Credit 

and Banking Among Low-Income Households: Introduction 

and Overview.” Pp. 1–22 in Insufficient Funds: Savings, Assets, 

Credit and Banking Among Low-Income Households, edited 

by Rebecca Blank and Michael Barr. New York: Russell Sage 

Foundation.

Brevoort, Kenneth P., Philipp Grimm, and Michelle Kambara.

2015. Data Point: Credit Invisibles. Washington, DC: 

Consumer Financial Protection Bureau Office of Research.

Brooks, Jennifer, Kasey Wiedrich, Lebaron Sims Jr., and Jennifer 

Medina. 2014. Treading Water in the Deep End: Findings from 

the 2014 Assets and Opportunity Scorecard. Washington, DC: 

CFED.

Cajner, Tomaz, Dennis Mawhirter, Christopher Nekarda, and 

David Ratner. 2014. “Why Is Involuntary Part-Time Work 

Elevated?” FEDS Notes April 14, 2014. Accessed May 8, 

2014. http://www.federalreserve.gov/econresdata/notes/

feds-notes/2014/why-is-involuntary-part-time-work-elevated-20140414.html.

Dehejia, Rajeev and Sadek Wahba. 2002. “Propensity-Score 

Matching Methods for Non-Experimental Causal Studies.” The 

Review of Economics and Statistics 84(1):151–161.

Elliehausen, G., Christopher Lundquist and M.E. Staten. 2007. 

“The Impact of Credit Counseling on Subsequent Borrower 

Behavior.” Journal of Consumer Affairs 41:1–28.

Heckman, James, Hidehiko Ichimura, and Petra Todd. 1997. 

“Matching as an Econometric Evaluation Estimator: Evidence 

from Evaluating a Job Training Programme.” Review of 

Economic Studies 64(4):605–654.

Heinrich, Carolyn, Peter Meuser, and Kenneth Troske. 2008. 

Workforce Investment Act Non-Experimental Net Impact 

Evaluation Final Report. Columbia, MD: IMPAQ International.

Hout, Michael, Asaf Levanon, and Erin Cumberworth. 2011. “Job 

Loss and Unemployment.” Pp.59-81 in The Great Recession

edited by David Grusky, Bruce Western, Christopher Wimer. 

New York: Russell Sage Foundation.

Leuven, E. and B. Sianesi. 2003. “PSMATCH2: Stata Module to 

Perform Full Mahalanobis and Propensity Score Matching, 

Common Support Graphing, and Covariate Imbalance Testing.” 

Version 4.0.10. Accessed February 10, 2014. http://ideas.

repec.org/c/boc/bocode/s432001.html.

Mueser, Peter R., Kenneth R. Troske, and Alexey Gorislavsk.

2007. “Using State Administrative Data to Measure Program 

Performance.” The Review of Economics and Statistics

89(4):761–783.

National Employment Law Project (NELP). 2015. “Occupational 

Wage Declines Since the Great Recession.” Data Brief. 

September 2015. New York: National Employment Law 

Project.

Smith, Geoff and Sarah Duda. 2010. Bridging the Gap: Credit 

Scores and Economic Opportunity in Illinois Communities of 

Color. Chicago, IL: Woodstock Institute.

Smith, Jeffrey and Petra Todd. 2005. “Does Matching Overcome 

LaLonde’s Critique of Nonexperimental Estimators?” Journal 

of Econometrics 125:305–353.

Stegman, Michael and Robert Faris. 2005. “Welfare, Work and 

Banking: The Use of Consumer Credit by Current and Former 

TANF Recipients in Charlotte, North Carolina.” Journal of 

Urban Affairs 27(4):379–402.

Watson, Liz, Lauren Frohlich, and Elizabeth Johnston. 2014. 

“Collateral Damage: Scheduling Challenges for Workers in 

Low-Wage Jobs and Their Consequences.” Fact Sheet. April 

2014. Washington, DC: National Women’s Law Center.

58 First Steps on the Road To Financial Well-Being: References
59 First Steps on the Road To Financial Well-Being: Appendix

Appendix A 

Methodology: Matched Comparison Group Design

To assess the impact of a program, it is necessary 

to understand how program participants’ outcomes 

compare to the outcomes they would have experienced without the intervention. The evaluation of the 

FOC program uses a quasi-experimental design that 

compares FOC participants’ outcomes with a matched 

comparison group of individuals who were equivalent 

across a range of demographic, labor market, and 

financial characteristics but who did not participate 

in the FOC programs. As we describe in more detail 

below, to identify an appropriate comparison group, 

we first recruited individuals for the study who were 

seeking services similar to those sought by individuals from the FOC programs. We then used propensity 

score matching techniques to identify individuals in 

this group who were similar to those in the FOC group.

The sample of FOC participants includes individuals 

who sought employment and training assistance from 

the five FOC programs in order to obtain a job and who

consented to take part in the study. To be eligible for 

the study, individuals also had to be at least 18 years 

old. All FOC study participants attended a program orientation at one of the five sites during which FOC staff 

members explained the study. Staff members then 

collected signed consent forms and contact information from those who agreed to take part in the study, 

so that they could be called to complete the baseline 

survey. The study sample includes only individuals 

who were seeking assistance with employment and 

training in order to find a job and who were expected 

to participate in the three core FOC services; that is, 

employment, financial, and income support counseling. Though the programs also served people who 

were primarily seeking financial or income support 

assistance, these individuals were not part of the 

study. Enrollment of the FOC participant sample took 

place from October 2011 through August 2012.

The comparison group sample consists of lowincome adults who sought employment and training 

assistance in order to find a job from five City of 

Chicago workforce centers. Mobility hired recruiters 

who presented the study to individuals attending orientations about the workforce centers’ services. We 

established basic eligibility criteria in order to increase 

the likelihood of recruiting individuals who would be 

similar to those in the FOC participant sample. To be 

eligible for the study, individuals seeking assistance at 

the workforce centers had:

1. To be at least 18 years old,

2. To have earned annual income over the past year 

within 200 percent of the federal poverty guidelines for their family size, and

3. To be seeking assistance with employment and/

or job training in order to find a job. Individuals 

who were only filing for unemployment insurance or 

other benefits and were not seeking employment 

or job training assistance in order to find a job 

were not eligible for the study.

4. The recruiters collected signed consent forms 

and contact information from those who were eligible and who agreed to be part of the study so 

that individuals could be called to complete the 

baseline survey. Enrollment of the comparison 

group sample took place from October through 

December 2011.

The study design addresses the potential selection bias that results from differences in motivation 

between program participants and nonparticipants, 

which is a primary concern with using quasi-experimental methods to evaluate voluntary programs. Study participants in both the FOC and comparison groups were 

motivated to seek assistance with employment and 

training from community agencies. They also faced 

the same or similar labor, housing, and financial markets. The study meets a moderate level of evidence 

according to the guidelines issued by the Corporation 
60 First Steps on the Road To Financial Well-Being: Appendix

for National and Community Service. The study uses a 

quasi-experimental design that demonstrates equivalence between the treatment and comparison groups 

(as reported below) and, therefore, supports causal 

conclusions. However, the study was conducted with 

five FOC programs in one city out of the more than 75 

programs operating in 30 cities around the country, 

limiting its generalizability.

Cross-Contamination

One concern given the study design was the possibility 

of “cross-contamination”—particularly that comparison group members would receive assistance from 

an FOC organization or that treatment group members 

would receive assistance from more than one FOC 

organization—complicating the interpretation of the 

findings. This was of particular concern given that the 

individuals seeking assistance from the FOC organizations and workforce centers came from many of the 

same communities in Chicago. Study participants may 

have learned about other programs through individuals in their neighborhoods or through other community 

organizations. Individuals may have sought assistance 

from multiple agencies if they did not receive the services they were seeking from the agency they attended 

when they enrolled in the study or if they did not find a 

job after receiving the services. Individuals may have 

also sought assistance from another agency if they 

moved to another community.

However, the survey data suggest that few comparison 

group members received services from an FOC organization and few FOC participants received services 

from more than one FOC organization. We asked study 

participants a series of questions about the employment and financial counseling services they received 

during the two years after enrolling in the study, including where they received these services. Only 2.6 percent of comparison group members indicated that they 

had received assistance with employment from one of 

the organizations that operated an FOC program and 

1.4 percent had received financial counseling from 

an FOC agency (less than one percent received both). 

Among the FOC participants, 1.6 percent said they 

had received employment services from an FOC organization other than the original agency at which they 

enrolled, and less than one percent had received financial counseling services from another FOC agency.

Data Collection Activities

Figure A1 provides a timeline of the study’s data collection activities, analysis, and reporting. We originally 

anticipated that that the FOC sites would need six 

months to enroll the targeted sample size. However, 

enrollment was lower than expected when the study 

began, and the study enrollment and baseline-interviewing period was extended by five months. This 

extended the follow-up data collection period as well 

because study participants were contacted two years 

after their baseline survey date for the follow-up surveys and credit reports were pulled one year and two 

years after the baseline pull. For both the interim and 

final reports, the process of drafting, revising, gaining final approval of, and publishing the reports took 

longer than the original study timeline allowed. There 

were no changes to the Mobility research team or to 

the survey firm with which we subcontracted during the 

study period.
61 First Steps on the Road To Financial Well-Being: Appendix

Figure A1 Timeline for the Evaluation of the Financial Opportunity Centers

Dates Activities

October 2011 to August 2012

October 2011 to August 2014

Study enrollment. Survey firm conducts baseline surveys with FOC and comparison groups.

FOC program staff record data about participants’ meetings with the FOC counselors in the FOC client tracking 

database.

January 2012 to September 2012 Mobility research team collects baseline credit reports from TransUnion.

January 2012 to July 2013 Mobility research team conducts interviews with program staff, observations of program activities, and focus 

groups with participants.

February 2012 to August 2014 Survey firm conducts interim tracking tasks to remain in touch with the study sample.

January 2013 to September 2013 Mobility research team collects 12-month post-enrollment credit report data for use in interim report

October 2013 to August 2014 Mobility research team analyzes implementation data and interim credit outcome data, writes interim report, and 

makes revisions based on feedback from LISC.

August 2014 Draft of interim report submitted for CNCS review.

September 2014 to January 2015 Finalize and publish interim report.

October 2013 to December 2014 Survey firm conducts the two-year follow-up survey.

January 2014 to September 2014 Mobility research team collects two-year post-enrollment credit report data.

January 2015 to October 2015 Mobility research team conducts final analysis of program impacts and implementation, drafts final report, and 

makes revisions based on feedback from LISC.

October 2015 Draft of final report submitted for CNCS review.

November 2015 to September 2016 Finalize and publish final report.

Baseline and Follow-Up Surveys with Study 

Participants

Mobility contracted with a survey firm, Research 

Support Services (RSS) and its partner IMPAQ 

International, to conduct the baseline and two-year 

follow-up surveys by telephone with members of both 

the FOC and comparison groups. To be included in 

the study sample, individuals in the FOC group had 

to complete the baseline survey within two weeks of 

intake and members of the comparison group within 

three weeks of intake. The time frame for the FOC 

group was slightly shorter so that the baseline surveys were completed prior to the receipt of the FOC 

counseling services, and the participants’ responses 

reflected their knowledge prior to reviewing their credit 

reports and finances with the financial counselor. Both 

FOC and comparison group members who completed 

the baseline survey received a $30 money order. RSS 

completed baseline surveys with 829 FOC participants 

and 1,071 comparison group members. The baseline 

surveys were completed between October 2011 and 

August 2012.

The surveys included detailed questions about study 

participants’ income, expenses, assets and debts, 

employment history during the previous two years, 

education and training, demographic characteristics, 

financial practices, and indicators of financial hardship. The outcome measures were based on measures developed for the Centers for Working Families 

model by a team led by the Annie E. Casey Foundation 

that included LISC’s FOC program director. These 

outcomes have been updated to reflect adaptations 

to the original model by the FOCs. We based the 

question wording on models for collecting extensive 

income, expense, asset, and liability data in existing 

national surveys. Where similar questions were asked 

in the Census Bureau’s Survey of Income and Program 

Participation (SIPP), we based question wording on 

those questions. For questions not included in the 

SIPP, we used question wording from the Panel Study 

of Income Dynamics and the Survey of Consumer 

Finances, when possible. Questions about participants’ financial practices and indicators of financial 
62 First Steps on the Road To Financial Well-Being: Appendix

hardship were based on measures developed by LISC 

as part of the financial assessment that FOC financial 

coaches complete.

Mobility anticipated that the characteristics of some 

comparison group members would not be similar 

enough to the FOC group members to be included 

in the analysis and contracted with RSS to attempt 

the two-year follow-up survey with 1,850 individuals. 

Prior to the start of the follow-up survey, Mobility used 

propensity score matching to identify the comparison 

group members who were the closest matches to 

the FOC group. We first removed individuals from the 

sample for whom we were unable to obtain a baseline credit report (described below), including 19 FOC 

group members and 31 comparison group members. 

The sample of individuals with both a baseline survey 

and credit report included 810 FOC group members 

and 1,030 comparison group members. We then 

used propensity score matching to select a sample 

of comparison group members from this pool who 

best matched the FOC group members. Because 

we needed to reduce the size of the comparison 

group sample, we used a nearest neighbor matching 

approach with replacement, selecting the five comparison group members with the closet scores to each 

FOC group member as long as the scores were within 

.2 of the pooled standard deviation of the log odds 

of the propensity score (we discuss the selection of 

the caliper in the propensity score matching section 

below). This resulted in a sample of 850 comparison 

group members.

RSS attempted the two-year follow-up survey with 

the final sample of 810 FOC group members and 

850 comparison group members. Sample members 

became eligible for the follow-up survey two years 

after the date of their baseline survey. We took a number of steps to reduce attrition. At intake, we collected 

extensive contact information for the study participant 

and up to four individuals who would know how to 

contact them in the future. IMPAQ interviewers verified the contact information at the end of the baseline 

survey. Between the baseline and follow-up surveys, 

RSS took steps to stay in touch with participants and 

obtain new contact information in order to reduce attrition from the sample. Once a quarter, RSS contacted 

study participants by telephone to verify and update 

their contact information and to identify individuals 

whose contact information was no longer valid and 

who would require additional tracking efforts. Just 

prior to the two-year anniversary of individuals’ baseline survey, RSS sent letters to study participants 

reminding them of the survey and encouraging them to 

complete it when the interviewer called. For individuals 

who had not completed the survey within a few weeks, 

RSS employed field locators to visit the individuals’ 

homes to verify their location and phone numbers 

and encourage them to participate. During the data 

collection period, RSS sent additional postcards and 

letters reminding study participants that they had 

agreed to take part in the survey. Both FOC and comparison group members who completed the follow-up 

survey received a $40 money order. The follow-up 

surveys were conducted between November 2013 and 

December 2014. RSS conducted the follow-up surveys 

with 553 FOC group members and 653 comparison 

group members, for response rates of 68 percent and 

77 percent, respectively.

Both the baseline and follow-up surveys gathered 

detailed information about participants’ employment, 

education, income, expenses, assets, and debts so 

that we could measure change in the outcomes of 

interest over time and assess whether there were 

significant differences between the changes the FOC 

and comparison groups experienced. The baseline 

survey included questions about individuals’ demographic and other background characteristics, such as 

age, race and ethnicity, gender, criminal record status, 

housing status, and family structure. The follow-up 

survey included questions about FOC group members’ 

receipt of employment, financial, and income support services from the FOC programs and about FOC 

and comparison group members’ receipt of similar 

services from other agencies. We used data from the 

baseline survey in the propensity score matching process and as independent variables in the regression 

models estimating program impacts, as described in 

later sections. We used data from the follow-up survey 
63 First Steps on the Road To Financial Well-Being: Appendix

to create the outcome variables used to estimate program impacts in the domains of career advancement, 

net income, and net worth. We also used the follow-up 

survey data to examine differences in the receipt of 

employment, financial, and income support services 

between the FOC and comparison group members.

Credit Reports

Mobility secured an agreement to access study participants’ credit reports from TransUnion, one of the 

three major credit bureaus. Mobility research team 

members accessed the TransUnion database and 

attempted to pull the reports for all study participants 

who completed the baseline survey. There is a typically 

a 4- to 6-week delay between when credit-related activity takes place and when it appears on a credit report. 

Therefore, we pulled the baseline credit reports about 

4 to 6 weeks after participants enrolled so that the 

reports captured activity that may have taken place 

just prior to enrollment that may have influenced the 

participants’ decision to seek assistance. We then 

pulled the credit reports again one year and two years 

later to capture change over time.

The reports included participants’ FICO credit scores, 

a universal scoring system that uses data from all 

three major credit bureaus and that plays a critical 

role in individuals’ access to financial services and 

products. Three-quarters of all mortgage originations 

use FICO scores (Smith and Duda 2010). The credit 

reports also indicated if an individual was unscored 

due to insufficient credit history. In addition to the 

credit scores, the credit reports included information about individuals’ use of credit-based products, 

including loans, mortgages, credit cards, home equity 

lines of credit, and open credit accounts, such as 

accounts with utility companies. For each account, 

the reports included the credit limit, balance, late 

payments, amount past due, current status of the 

account, date opened, payment history over the 

previous two years, and date closed, if applicable. 

The credit reports also included information about 

accounts referred to collections, derogatory public 

record information, such as bankruptcies, tax liens, 

and civil judgments during the previous 7 to 10 

years, and a history of creditors’ inquiries into the 

consumer’s credit resulting from individuals’ applications for credit during the previous two years.

We used the data from the baseline credit reports as 

independent variables in the propensity score matching process and in the regression models estimating 

program impacts. We used data from the follow-up 

credit reports to create the outcome variables used 

to estimate program impacts in the domain of credit. 

Our interim evaluation report included the one-year 

post-program entry credit data while this final report 

focuses on the two-year follow-up credit data. Out of 

the 829 FOC group members and 1,071 comparison 

group members who completed the baseline survey, 

we were able to access baseline and two-year followup credit reports for 791 FOC group members and 

974 comparison group members. Individuals removed 

from the sample included those for whom we could 

not find a match using the identifying information 

collected at intake and those whom the TransUnion 

system indicated were deceased. The final sample 

of individuals who completed both the baseline and 

follow-up surveys and had baseline and follow-up 

credit report data included 544 FOC group members 

and 619 comparison group members. We examined 

the credit impacts for both the larger sample of study 

participants with credit report data and the smaller 

sample who had both credit and survey data and 

obtained similar results. Therefore, we present the 

results for the survey sample to be consistent with the 

sample included in the other outcome domains (career 

advancement, net income, and net worth).

FOC Administrative Program Data

We collected data from the performance management 

ystem that LISC maintained and that all FOC organiations used to track program participation. The data 

ncluded information about the types of counseling 

he participants received, including financial, income 

upport, and employment counseling, as well as the 

uration of their participation in the program. FOC 

taff members collected the data for all individuals 

s

z

i

t

s

d

s
64 First Steps on the Road To Financial Well-Being: Appendix

who completed the intake process and enrolled in the 

program. LISC staff members extracted the individuallevel data from the tracking system for all five FOC 

study sites and provided the data to Mobility. The data 

in this report include all contacts FOC staff members 

recorded during the two years following study participants’ program entry. We used the FOC program data 

to assess program implementation in Chapter 3 and 

to identify the sample for the TOT analysis in Chapter 

4 using methods one and two.

Site Visits

Members of the Mobility research team conducted 

one-on-one interviews with FOC staff members, 

observed program activities, and conducted focus 

groups with program participants. Staff interviews 

included the program directors, counselors in the 

three core service areas, intake staff, career coaches, 

and job readiness instructors. Activities we observed 

included FOC orientations, job readiness and life skills 

workshops, and financial workshops (where provided). 

We used semi-structured protocols for all interviews 

and focus groups so that consistent information was 

gathered across sites. We first interviewed FOC staff 

members prior to the start of the study and conducted 

additional interviews, observations, and focus groups 

during the study enrollment period and the year following enrollment. We followed up by telephone with program directors as needed to obtain updates. We used 

the information collected to understand how the FOC 

program operated at each site and to identify differences in the five organizations’ program structure, content, and staffing. We also interviewed the workforce 

center directors to learn about the services available 

to job seekers during the study enrollment period.

IRB Approval

After developing the study procedures, data collection protocols, and consent forms, Mobility obtained 

Institutional Review Board (IRB) approval of the study 

from the IRB at the Vera Institute of Justice, with which 

Mobility has an agreement for IRB services. The IRB 

reviewed the project on annual basis for the duration 

of the data collection and reporting period. There were 

no difficulties securing IRB approval.

Propensity Score Matching

As expected, there were some differences between 

the characteristics of the individuals recruited from the 

FOC programs and workforce centers. Therefore, we 

used propensity score matching techniques to select 

the final analysis sample. The propensity score is the 

probability of treatment assignment conditional upon 

individuals’ observed characteristics. In this case, 

the propensity score is the probability of being in the 

FOC program group, conditional upon individuals’ 

observed demographic characteristics, recent employment experience, and financial situation at the time 

of enrollment. We matched individuals in the FOC and 

comparison groups based on these estimated probabilities. Only FOC participants and comparison group 

members who were sufficiently close matches were 

included in the final analysis sample. Researchers 

have found that propensity score matching performs 

well in replicating experimental results when three 

criteria are met: (1) the data for the intervention and 

comparison groups are collected using the same data 

source, (2) the participants and nonparticipants reside 

in the same local labor market, and (3) the data contain variables relevant to modeling the program participation decision (Smith and Todd 2005). The FOC study 

meets these criteria.

Previous research has confirmed that the critical variables for modeling participation in employment and 

training programs are employment and earnings during the two years prior to program entry. Looking two 

years back is important because research indicates 

that people who volunteer for employment and job 

training programs have typically experienced a drop in 

earnings just prior to entering the program (Dehejia 

and Wahba 2002; Heckman et al. 1997). As a result, 

their earnings in the year prior to program entry may 

not be indicative of their earnings capacity prior to 

their loss of employment. Therefore, we included variables for earnings during the previous two years in 
65 First Steps on the Road To Financial Well-Being: Appendix

the propensity score model. While individuals in the 

study were seeking employment and/or job training 

assistance, some individuals who applied to the FOC 

programs might have been motivated to enroll by the 

additional financial counseling services the programs 

offered. Therefore, we also included factors expected 

to influence individuals’ decision to seek financial 

counseling services, including their level of financial 

distress, willingness to change their financial situation, 

and ability to manage credit (Elliehausen et al. 2007). 

Following is a description of the variables included in 

the model to estimate the propensity scores.

Earnings from work during the past two years (continuous, includes the sum of amounts earned from 

each job held based on questions about wages, hours 

worked per week, weeks worked per month, and dates 

of employment)

• Total value of assets at the time of program 

entry (continuous, includes the sum of amounts 

reported for vehicles, home, other property, business, savings or checking accounts, cash not in 

a bank account, retirement accounts, and other 

financial assets)

• Total value of debts at the time of program 

entry (continuous, includes the sum of amounts 

reported for mortgages, vehicle loans, student 

loans, late rent and utility payments, child support arrears, taxes owed, medical debt, credit card 

debt, legal debt, family loans, pay day loans, business loans, and other loans)

• Total gross income in the month prior to program 

entry (continuous, includes the sum of amounts 

reported for SNAP, earnings from work for the 

participant and other family members, cash from 

friends/family, unemployment insurance, cash 

assistance-TANF/ GA, interest on savings or checking accounts, child support, Social Security, and 

SSI)

• Total expenses in the month prior to program 

entry (continuous, includes the sum of amounts 

reported for food, phone, mortgage/rent, utilities, 

vehicles, gas, car insurance, repair and maintenance, public transit, eating out, medical bills 

and copays, health insurance premiums, credit/

store card payments, and fees for cashing checks, 

money orders, or bank transactions)

Net income greater than zero in the month prior to 

program entry (a binary variable equal to one if the 

individual had positive net income—total income 

minus total expenses—with negative or zero net 

income serving as the base category)

Net worth greater than zero at program entry (a 

binary variable equal to one if the individual had 

positive net worth—the total value of assets 

minus the total value of debts—with negative or 

zero net worth serving as the base category)

Gender (a binary variable equal to one if the 

person was male with female serving as the 

base category)

Age at program entry (continuous)

Race/ethnicity (a categorical variable including 

Black, White, Hispanic, and other/missing race 

with other/missing race serving as the base 

category)

Highest degree at program entry (a categorical 

variable including no diploma, GED, high school 

diploma, and college degree with high school 

diploma serving as the base category)

Employed during the year pre-program (a binary 

variable equal to one if the individual worked at 

any time during the year prior to program entry)

Criminal record at program entry (a binary variable equal to one if the individual had ever been 

convicted of a crime, including misdemeanors and 

felonies)

Disability at program entry (a binary variable equal 

to one if the individual had a health condition that 

limited her/his ability to work)

Born in the US (a binary variable equal to one if 

the individual was born in the US)

Marital status (a categorical variable including formerly married (separated, divorced, or widowed), 

currently married or living with a partner in a 

marriage-like relationship, and never married with 

formerly married serving as the base category)

Children (a binary variable equal to one if the individual had any children under age 18)

•

•

•

•

•

•

•

•

•

•

•

•
66 First Steps on the Road To Financial Well-Being: Appendix

• Family size (count variable of the number of family members living in the participants’ household, including the participant, other adults, and 

children)

• Housing status (a categorical variable including owns hone, rents home, homeless, and lives 

rent-free with lives rent-free serving as the base 

category)

• Received SNAP in the month prior to program entry

(a binary variable equal to one if the individual 

received SNAP)

• Bankruptcy (a binary variable equal to one if the 

individual had filed for bankruptcy in the past year 

or was in the process of doing so)

• Collections (a binary variable equal to one if the 

individual said collection agencies were calling 

him/her about unsettled claims)

• Had a prime credit score at program entry (a 

binary variable equal to one if the individual had a 

prime credit score of 620 or greater)

• Credit score status (a categorical variable including had a credit score and thick credit file (three or

more open trade accounts), had a credit score but 

thin credit file (two or fewer open accounts), and 

unscored with had score and thick file serving as 

the base category)

• Number of derogatory public records (count variable

of the number of bankruptcies, tax liens, and civil 

judgments reported on individuals’ credit reports)

• Number of trade accounts with balances (count variable of the number of trade accounts with positive 

balances reported on individuals’ credit reports)

• Number of inquiries made into credit (count variable

of the number of creditors that had inquired about 

individuals’ credit profiles during the previous two 

years as reported on individuals’ credit reports)

• Had late payments on trade accounts in the 

past year (a binary variable equal to one if the 

individual had made any late payments on trade 

accounts in the past year as reported on individuals’ credit reports)

• Number of trade accounts with no late payments 

(count variable of the number of trade accounts 

on individuals’ credit reports that never had a 

late payment)

To achieve balanced groups, the final model also 

includes the following interaction terms:

• Male*Hispanic

• Male*Born in the United States

• Black*Had a criminal record

• Black*Had a child under age 18

• Hispanic*Married/living with partner in marriagelike relationship

• Black*Does not have a high school diploma or 

degree

• Black*Had a college degree

• Hispanic*Does not have a high school diploma or 

degree

• Hispanic*Had a college degree

• Black*Received SNAP in the month prior to program entry

• Hispanic*Had a credit score but thin credit file

• Hispanic*Had positive net income in the month 

prior to program entry

We conducted the matching at the individual level with 

the 553 FOC group members and 653 comparison 

group members who completed the two-year followup survey. Figure A2 presents the characteristics of 

these FOC and comparison group members prior to 

matching. The FOC group members were more likely 

than comparison group members to be Hispanic, to 

be younger, to live rent free, to have a criminal record, 

to be married, to have a larger family size, to have 

greater assets, and to have positive net worth. The 

FOC group members were less likely than comparison group members to have a high school diploma or 

degree, to have a credit score, to have worked in the 

past year, to be born in the United States, and to have 

received SNAP in the past month.
67 First Steps on the Road To Financial Well-Being: Appendix

Figure A2 Baseline Characteristics of the Study Participants Before and After Matching

Prior to Matching After Matching

FOC Group Comparison 

Group

Standardized

% bias FOC Group Comparison Group

Standardized

% bias

Earnings during past two years

Total value of assets

$13,833

$30,159

$15,657

$20,585

-9.1

13.9

$13,975

$25,802

$14,847

$22,348

-4.3

5.0

Total value of debts $20,202 $23,321 -6.5 $19,632 $16,716 6.1

Total income last month $1,175 $1,189 -1.0 $1,152 $1,166 -1.0

Total expenses last month

Had positive net income last month

Had positive net worth

Male

$1,490

32.2%

34.4%

43.2%

$1,488

28.6%

25.9%

43.5%

0.2

7.7

18.5

-0.5

$1,451

32.0%

32.4%

45.4%

$1,456

31.1%

34.1%

46.0%

-0.4

2.0

-3.7

-1.1

Age

Black

37.9

64.7%

40.4

82.2%

-19.7

-40.4

38.3

71.6%

38.6

70.2%

-2.1

3.2

Hispanic

White

31.3%

1.4%

11.2%

3.2%

50.7

-11.7

24.0%

1.6%

25.6%

1.5%

-4.1

0.9

No diploma or degree

GED

29.3%

14.5%

11.9%

14.5%

43.9

-0.2

23.0%

16.0%

23.3%

15.6%

-0.7

1.1

Any college degree

Employed during the past year

Had a criminal record

12.8%

47.9%

38.9%

21.3%

62.0%

30.5%

-22.6

-28.6

17.7

13.6%

49.6%

41.4%

13.7%

52.3%

40.2%

-0.3

-5.4

2.5

Had a disability

Born in the United States

13.2%

82.6%

15.8%

91.3%

-7.3

-25.8

14.0%

86.2%

14.1%

88.5%

-0.3

-6.9

Married/living with partner

Never married

20.4%

60.2%

14.2%

64.6%

16.4

-9.1

17.8%

62.0%

16.6%

64.1%

3.1

-4.4

Had a child under age 18

Family size

Owns home

48.5%

3.0

16.6%

43.5%

2.4

12.7%

10.0

34.0

11.1

48.6%

2.9

15.0%

51.2%

2.9

13.0%

-5.2

-2.4

5.5

Homeless 4.9% 5.2% -1.5 5.2% 5.2% 0.2

Rents home 52.4% 63.2% -22.0 54.0% 55.9% -3.8

Received SNAP last month 59.7% 66.3% -13.8 62.2% 64.6% -5.0

Had filed for bankruptcy

Had collection agencies calling

Had a prime credit score

Did not have a credit score

6.9%

31.6%

17.7%

43.0%

6.4%

33.7%

14.5%

33.7%

1.8

-4.4

8.6

19.3

6.6%

32.6%

17.2%

41.6%

6.5%

34.3%

14.5%

40.2%

0.3

-3.5

7.2

3.0

Had a credit score but thin file 31.5% 39.5% -16.9 33.4% 35.8% -5.0

Number of derogatory public records

Number of trade accounts with balances

.26

2.1

.39

2.7

-19.2

-19.9

.27

2.1

.27

2.1

0.5

1.9

Number of inquiries made into credit

Had late payments on trade accounts past year

Number of trades with no late payments

1.5

9.8%

3.5

1.7

10.9%

4.2

-9.3

-3.6

-9.9

1.5

10.2%

3.5

1.5

11.1%

3.6

1.1

-3.1

-1.0
68 First Steps on the Road To Financial Well-Being: Appendix

To produce the propensity scores, we fit a logit model 

predicting the likelihood of being in the FOC group 

using the control variables listed above. As researchers have suggested, we matched the samples by the 

log odds of the propensity score, which is more likely 

to be normally distributed (Austin 2011). Matching on 

the log odds of the score is appropriate for choicebased sampling where the treatment and control 

group pools are obtained from different sources and 

the number of people in each group does not reflect 

the likelihood that an individual with given characteristics will participate in the program in the full universe 

(Heinrich et al. 2008). Figure

A3 presents the distribution of the propensity scores 

in the treatment and comparison groups, demonstrating substantial overlap between the two groups. The 

log odds of the propensity scores ranged from -2.3 

to 5.2 for the FOC group and from -3.7 to 3.2 for the 

comparison group.

Figure A3 Box Plot of Propensity Scores by 

Treatment Status (n=1,206)

6

4

2

0

-2

-4

PROPENSITY SCORE (LOG ODDS)

Comparison Group FOC Group

To conduct the propensity score matching, we 

used the psmatch2 module in Stata, developed by 

Leuven and Sianesi (2003). There are many ways 

to implement propensity score matching. Recent 

research suggests that with sufficient sample overlap 

and well-balanced covariate distributions, impact 

estimates should be relatively insensitive to the 

details of how the matching is undertaken (Heinrich et 

al. 2008; Mueser et al. 2007).

The analysis of program impacts in this report uses 

radius matching within a specified caliper distance 

with replacement. Radius matching does not limit the 

number of cases that are matched with a given participant as long as the cases are close enough (that is, 

within the specified caliper distance). Research has 

found that estimates are more stable and make better 

use of all available data if they consider all comparison cases that are sufficiently close to a treatment 

case, rather than making a one-to-one match (Heinrich 

et al. 2008). The mean outcome for cases matched 

with a given treated case is the estimate of the outcome that would occur if the treatment group member 

had not received the service. There is no uniformly 

agreed-upon definition of the maximum acceptable distance between scores. As suggested by Austin (2011) 

and others, we used a caliper of width equal to .2 of 

the pooled standard deviation of the log odds of the 

propensity score. Radius matching allows replacement 

of comparison group members, that is, a comparison 

group member may be matched with multiple treatment group members.

Following the matching, we tested for statistically significant differences in the sample means between the 

treatment group and the weighted comparison group 

to assure that the propensity score balanced the independent variables. As Austin (2008) suggests when 

using many-to-one matching methods, we examined 

the weighted standardized percentage difference in 

each variable between the groups and considered the 

groups balanced if the standardized percentage bias 

was less than 10 percent. None of the differences 

were equal to or greater than 10 percent. Figure A2 

presents descriptive baseline statistics for the treatment group and weighted comparison group after 

matching as well as the standardized percentage bias 

for each variable.

We tested other strategies for matching, including oneto-one matching, matching with and without replacement, and nearest-neighbor matching, and tested 
69 First Steps on the Road To Financial Well-Being: Appendix

Figure A4 Diagnostics of Covariate Balancing Before and After Matching Across Methods

Before Matching One-to-one no 

replacement

One-to-one with 

replacement

Five nearest 

neighbors with 

replacement

Radius matching 

(selected method)

Treatment N 553 377 500 500 500

Comparison N

Pseudo R2

653

.160

377

.015

254

.042

557

.016

649

.014

Likelihood Ratio (LR) chi-square

P-value of LR chi-square

Mean standardized bias

266.15

.000

16.7

15.96

1.000

2.4

58.17

.173

5.5

22.46

1.000

3.1

19.58

1.000

3.0

Median standardized bias 13.8 2.1 4.2 2.5 2.4

each to determine whether the method balanced the 

treatment and comparison groups on the covariates. 

Figure A4 presents the results of diagnostic tests 

of covariate balancing across the primary methods 

tested. Our goal was to balance the groups while 

maximizing the sample sizes and, therefore, the statistical power of the impact analysis. We considered 

one-to-one matching without replacement (that is, a 

comparison group member could only be matched to 

one treatment group member). This method produced 

well-balanced groups but resulted in a significant loss 

of sample, which would reduce the generalizability of 

the impact analysis to the full sample of study participants and reduce our ability to detect significant 

impacts. We tested one-to-one matching with replacement. While this method resulted in a sample of 500 

treatment group members, it reduced the comparison 

group sample to 254, again unacceptably reducing 

the statistical power of the analysis. We tested using 

nearest neighbor methods including up to five nearest 

neighbors—that is, the comparison group members 

with the closest scores were matched to treatment 

group members. This method produced similar results 

to the radius matching method we selected but with a 

substantially smaller comparison group sample. The 

radius matching method we used produced groups 

that were well balanced on the covariates while retaining a large portion of the sample.

Among the 553 FOC group members who completed 

the follow-up survey, 53 or 9.6 percent were unmatched, 

while 4 comparison group members (0.6 percent of the 

653 who completed the survey) were unmatched. All of 

the 53 FOC group members who were unmatched were 

Hispanic. Most (38) were IDPL participants, while 14 

were from AH, and one from MFS.

Figure A5 presents data on differences in the characteristics of the 500 FOC group members in the final 

sample and the 310 who were not in the final sample 

(out of the 810 who had both a baseline survey and 

baseline credit report). The attrition was due either to 

nonresponse to the follow-up survey (257) or to not 

being matched (53). We break down the differences 

between FOC group members who completed the survey and those who did not, as well as between FOC 

group members who were matched and those who 

were not. FOC group members in the final analysis 

sample differed from those not in the final sample in 

the following ways. They were:

• More likely to be Black and less likely to be 

Hispanic.

• More likely to have at least a high school diploma 

or GED at program entry.

• More likely to have worked at some time during 

the year prior to program entry and had greater 

earnings, on average, during the two years prior to 

program entry.

• More likely to have a criminal record.

• More likely to be born in the United States.

• Less likely to be married and had a smaller family 

size, on average.

• More likely to be receiving calls from collection 

agencies about unsettled claims
70 First Steps on the Road To Financial Well-Being: Appendix

• Less likely to lack a credit score and more likely to 

have a score but thin credit file.

• More likely to have any late payments on trade 

accounts in the past year.

Data Cleaning and Treatment of 

Missing Data

Baseline and Follow-Up Survey Data

The baseline and follow-up surveys asked detailed 

questions about participants’ employment and 

whether they had various sources of income, 

expenses, assets, and debts. For those who were 

employed, we asked a series of questions about each 

job held during the previous two years at baseline 

and about each job held between the baseline and 

follow-up survey at follow-up. For sources of income, 

expenses, assets and debts, we asked about the dollar value of each item for those who indicated they 

had the item. As noted in the variable descriptions, 

we used these items to calculate total earnings from 

work, total income, total expenses, net income, total 

assets, total debts, and net worth. While the amount 

of missing data for any one variable was low (less than

10 percent for 97 percent of the items), once the hundreds of variables were used to calculate the totals, 

the number of missing cases was unacceptable. In 

order to retain as much of the study sample as possible, we employed a multiple imputations approach to 

deal with missing values.

While some cases were missing on the yes/no questions about whether the participant had the item, more

often the dollar amount was missing for individuals 

who indicated that they had the item. In those cases, 

we knew that the person had the item and that the 

amount was greater than zero. For those missing on 

the yes/no question, an amount of zero was a plausible value if the person did not have the item. In 

order to address both scenarios appropriately, we performed the imputations separately for those who said 

they had the item but the amount was missing and for 

those who were missing on the yes/no question for 

the item. The first imputation, for those who said they 

had the item, was limited to values greater than zero 

and based on the values only for those who had the 

item. The second imputation, for those missing on the 

yes/no question, was not restricted in this way and 

was based on the values for all respondents, including 

those who reported a value of zero.

We performed the multiple imputations using predictive mean matching, which is more appropriate than 

a regression-based method when the variables being 

imputed are highly skewed, which was the case with 

the dollar amounts we needed to impute. For each 

missing value, predictive-mean matching fills in values 

randomly from among the observed values for observations whose regression-predicted values are closest to the regression-predicted value for the missing 

observation from a simulated regression model. We 

followed the common practice of using the five cases 

with observed data that had the closest predicted values to each case with missing data. By using existing 

values in the data, the method ensures that imputed 

values are plausible.

Underreporting of assets. In chapter 4, we noted that 

our analysis of the baseline and follow-up survey data 

indicated that study participants underreported certain 

assets in the baseline survey, perhaps out of concern 

that they would not qualify for assistance, but then 

reported the assets in the follow-up survey. We suspected under-reporting due to the changes we found 

over time. For example, the percent of study participants who said they owned a home increased from 

11 to 15 percent while the percent who said they had 

a mortgage remained nearly the same. The percent 

who reported having one or more cars increased by 10 

percent while the percent with auto loans increased 

by less than two percent—although it is possible that 

some individuals purchased used vehicles with cash. 

Some individuals reported new retirement savings at 

follow-up that could not have realistically been earned 

during the two-year follow-up period, particularly given 

the generally low wages that participants earned and 

the zero or negative net income that two-thirds had at 

the time of the follow-up survey. We examined whether 

changes in participants’ marital status explained the 
71 First Steps on the Road To Financial Well-Being: Appendix

Figure A5 Analysis of the Impact of Attrition from the Baseline Sample of 810 FOC Group Members

Attrition from Two-Year Follow-Up Survey Attrition from Propensity Score Matching Overall Attrition1

Did Not Not In Final Not in Final 

Completed Complete Matched Matched Sample Sample

(n=553) (n=257) (n=500) (n=53) (n=500) (n=310)

Earnings during past two years $13,833 $10,735 ** $13,975 $12,499 $13,975 $11,037 **

Total value of assets $26,012 $12,418 *** $25,802 $71,255 *** $23,141 $19,371

Total value of debts $18,958 $21,036 $19,632 $25,575 $18,648 $21,182

Total income last month $1,268 $1,112 $1,152 $1,390 $1,173 $1,292

Total expenses last month $1,619 $1,523 $1,451 $1,861 ** $1,580 $1,603

Had positive net income last month 29.7% 26.9% 32.0% 34.0% 28.8% 28.7%

Had positive net worth 32.9% 29.2% 32.4% 52.8% *** 31.2% 32.6%

Male 67.1% 70.8% * 45.4% 22.6% *** 45.4% 45.8%

Age 37.9 33.3 *** 38.3 34.4 ** 38.3 33.5

Black 65.5% 60.2% 71.6% 0.0% *** 71.6% 49.7% ***

Hispanic 31.6% 36.3% 24.0% 100.0% *** 24.0% 47.1% ***

White 1.5% 2.0% 1.6% 0.0% 1.6% 1.6%

No diploma or degree 29.3% 38.5% *** 23.0% 88.7% *** 23.0% 47.1% ***

GED 14.5% 16.3% 16.0% 0.0% *** 16.0% 13.6%

Any college degree 12.8% 11.3% 13.6% 5.7% 13.6% 10.3%

Employed during the past year 49.9% 38.1% *** 49.6% 32.1% ** 49.6% 40.3% ***

Had a criminal record 38.7% 36.6% 41.4% 15.1% *** 41.4% 32.9% **

Had a disability 13.2% 12.5% 14.0% 5.7% * 14.0% 11.4%

Born in the United States 82.6% 82.1% 86.2% 49.1% *** 86.2% 76.5% ***

Married/living with partner 20.4% 19.8% 17.8% 45.3% *** 17.8% 24.2% **

Never married 60.2% 66.9% * 62.0% 43.4% *** 62.0% 62.9%

Had a child under age 18 48.5% 56.4% ** 48.6% 47.2% 48.6% 54.8%

Family size 3.0 3.2 2.9 3.9 *** 2.9 3.3 ***

Owns home 13.2% 8.6% * 15.0% 32.1% *** 12.2% 11.0%

Homeless 4.9% 9.7% *** 5.2% 1.9% 5.2% 8.4% *

Rents home 52.4% 55.3% 54.0% 37.7% ** 55.2% 52.9%

Received SNAP last month 59.3% 62.3% 62.2% 35.9% *** 62.2% 57.7%

Had filed for bankruptcy 6.9% 8.2% 6.6% 9.4% 6.6% 8.4%

Had collection agencies calling 31.7% 22.2% * 32.6% 22.6% 32.6% 22.3% ***

Had a prime credit score 17.9% 11.3% ** 17.2% 22.6% 17.2% 13.2%

Did not have a credit score 42.5% 54.1% *** 41.6% 56.6% 41.6% 54.5% ***

Had a credit score but thin file 32.0% 28.4% 33.4% 13.2% *** 33.4% 25.8% **

Number of derogatory public records 0.26 0.33 0.27 0.09 ** 0.27 0.29

Number of trade accounts with 

balances 2.1 1.8 2.1 1.6 2.1 1.8 *

Number of inquiries made into credit 1.5 1.3 1.5 1.3 1.5 1.3

Had late payments on trade 

accounts past year 9.8% 5.4% ** 10.2% 5.7% 10.2% 5.5% **

Number of trades with no late 

payments 3.5 2.5 ** 3.5 4.2 3.5 2.8

1 The comparison of individuals in the final sample and those not in the final sample is based on the baseline data prior to knowledge gained from the follow-up survey (because 

257 of those not in final sample did not complete the follow-up survey). Therefore, some figures in the “In the final sample” column do not match figures in the “Matched” 

column for which information from the follow-up survey was used to impute missing values or otherwise clean the data, as described below.
72 First Steps on the Road To Financial Well-Being: Appendix

inconsistencies, but only a small portion of individuals 

with inconsistent data experienced changes in whether

they were married or living with a partner.

We did not have a means of estimating the amount 

of under-reporting for most of the asset variables and 

did not make adjustments to the figures reported. The 

one exception was the data on home ownership. In 

the follow-up survey, we asked participants how many 

years they had lived in their current home. If individuals had lived in their home for three years or less, 

then we did not make any adjustments as changes 

in their living situation after completing the baseline 

survey may have explained the differences in reporting. However, if individuals reported living in the same 

home for more than three years and reported owning 

a home at follow-up but not at baseline, then we used 

information from the follow-up survey to impute values 

for owning a home, the value of the home, and the 

value of the mortgage, if applicable, in the baseline 

data. We made this adjustment for two percent of the 

final study sample.

Credit Report Data

Mobility research team members converted the credit 

reports into an analyzable format, coded the data, and 

created the variables used in matching process and 

analysis of program impacts. For the study participants 

for whom we were able to access credit reports, there 

was no missing data in terms of the credit scores, that 

is, each study participant either had a credit score 

or there was an indication on the report that the individual was “not scored due to insufficient credit.” In 

terms of measures related to trade account activity, we 

treated the data as either being reported on the credit 

report or not reported. If trade accounts were listed on 

the report, we counted each account. If no accounts 

were listed, we coded the person as having no trade 

accounts. If the fields for monthly payments on a trade 

account included codes indicating a payment was 

made, we coded it appropriately as either an on-time 

or late payment. If a field for a monthly payment was 

blank, we coded it as no payment made.

FOC Administrative Program Data

Mobility provided LISC a list that included each FOC 

study participants’ name, date of birth, and the FOC 

site at which she/he enrolled. LISC identified the 

study participants who were in the FOC participant 

tracking system and provided Mobility individual-level 

data on program participation for those who received 

any FOC counseling. The matching was done by hand 

so that minor discrepancies in the spelling of participant names or in the numbers in dates of birth could 

be assessed to determine whether it was a close 

enough match to include the data in the analysis. If 

individuals did not have any FOC counseling contact 

records or if they did not have any records in any one 

of the three core service areas, we coded them as not 

receiving the service(s). Individuals who had records 

indicating that they had received counseling in the 

FOC core service areas were coded as receiving the 

services as appropriate. Individuals who enrolled early 

in the enrollment period may have had more than two 

years of post-enrollment service data while those who 

enrolled at the end only had two years of data. The 

flow of enrollment varied somewhat across the five 

sites, meaning some sites may have had more contacts than others due to the length of time individuals 

were in the program. Therefore, we removed contacts 

that occurred more than two years after enrollment 

from the analysis.
73 First Steps on the Road To Financial Well-Being: Appendix

Appendix B 

Implementation Analysis

As noted in Appendix A, the implementation analysis 

uses data from two sources: FOC administrative program records for FOC group members and the two-year 

follow-up survey for both FOC and comparison group 

members. Below is a description of the variables used 

to assess implementation. Descriptive statistics for 

each are reported in Chapter 3.

Implementation Variables from the 

FOC Administrative Program Records

The FOC administrative data contained a record of 

each contact made with each participant, including 

the date the contact took place, the type of counseling (financial, income support, or employment), and a 

description of the topic discussed. We used this information to construct the following variables.

• Received any FOC counseling (a binary variable 

equal to one if the individual received counseling in 

any one of the three FOC core service areas—financial, income support, or employment counseling; 

variable included in the regression analysis of subgroup differences in receipt of program services)

• Received FOC counseling in one core service area 

(a binary variable equal to one if the individual 

received counseling in only one FOC core service 

area—either financial, income support, or employment counseling)

• Received FOC counseling in two core service areas 

(a binary variable equal to one if the individual 

received counseling in any combination of two 

FOC service areas—financial, income support, or 

employment counseling)

• Received FOC counseling in all three core service 

areas (a binary variable equal to one if the individual received counseling in all three FOC core 

service areas—financial, income support, and 

employment counseling; variable included in the 

regression analysis of subgroup differences in 

receipt of program services)

Received financial counseling (a binary variable 

equal to one if the individual received any FOC 

financial counseling)

Received income support counseling (a binary variable equal to one if the individual received any FOC 

income support counseling)

Received employment counseling (a binary variable equal to one if the individual received any FOC 

employment counseling)

Duration of FOC counseling (a categorical variable 

based on the first and last dates of FOC counseling recorded within the two-year study follow-up 

period, including categories for less than one 

month, 1 to 6 months, 7 to 12 months, 13 to 18 

months, and 19 to 24 months; the regression 

analysis of subgroup differences in the receipt of 

program services included a binary variable equal 

to one if the individual received counseling in any 

one of the three FOC core service areas 19 to 24 

months after program entry)

•

•

•

•

Implementation Variables from the 

Two-Year Follow-Up Survey

The follow-up survey included a series of questions 

about what assistance with employment, financial 

issues, and public benefits access the FOC and 

comparison group members received from the FOC 

agencies and other service providers, including the 

workforce centers. The survey also asked about participants’ satisfaction with the services they received. 

We used this information to construct the following 

variables.
74 First Steps on the Road To Financial Well-Being: Appendix

• Received assistance with looking for a job (a binary 

variable equal to one if the individual received assistance with looking for a job from any agency)

• Received assistance reviewing credit report (a 

binary variable equal to one if the individual 

received assistance reviewing their credit report 

from any agency)

• Received assistance with financial issues (a binary 

variable equal to one if the individual received 

assistance with financial issues from any agency)

• Received assistance with applying for benefits to 

increase income or cover expenses (a binary variable equal to one if the individual received assistance with applying for benefits to increase income 

or cover expenses from any agency)

• Received any assistance in at least one of the FOC 

core service areas (a binary variable equal to one 

if the individual received assistance in any one 

of the three FOC core service areas—financial, 

income support, or employment—from any agency)

• Received assistance in one core service area 

(a binary variable equal to one if the individual 

received assistance in only one core service 

area—either financial, income support, or employment—from any agency)

• Received assistance in two core service areas 

(a binary variable equal to one if the individual 

received assistance in any combination of two FOC 

core service areas—financial, income support, or 

employment—from any agency)

• Received assistance in all three core service areas 

(a binary variable equal to one if the individual 

received assistance in all three FOC core service 

areas—financial, income support, and employment—from any agency)

• Satisfaction with assistance looking for a job (a 

binary variable equal to one if the individual indicated she/he was very satisfied with the assistance received with looking for a job)

• Satisfaction with assistance with financial issues 

(a binary variable equal to one if the individual 

indicated she/he was very satisfied with the assistance received with financial issues)

• Satisfaction with assistance with applying for benefits to increase income or cover expenses (a binary 

variable equal to one if the individual indicated 

she/he was very satisfied with the assistance 

received with applying for benefits to increase 

income or cover expenses)

As noted in Chapter 3, program participation rates for 

the FOC participants in the final sample were slightly 

higher than those for all individuals who enrolled in the 

study. Figure B1 presents participation rates in FOC 

counseling services for the full sample of FOC participants in study as well as differences between those in 

the final sample and those not in the final sample.

To assess whether the receipt of services differed 

significantly among the five FOC sites and between 

the FOC and comparison groups, we used chi-square 

tests. To assess whether certain demographic subgroups of FOC participants were more likely than others to receive the intended services, we used logistic 

regression analysis. The models included the independent variables used in the propensity score matching 

analysis described in Appendix A as well as controls 

for differences in service receipt across the five FOC 

sites. The models took the following form:

Y2 = α + β2S + β3X + ε

Where:

Y2 is the log odds of observing the outcome of 

 interest

α; β are coefficients

S is a categorical variable for the site at which 

 the person participated

X is a vector of explanatory variables

ε is the individual-specific error term

For the models estimating the likelihood of receiving 

any FOC counseling and of receiving counseling in all 

three FOC core service areas, AH served as the base 

category. Because AH terminated its FOC program, 

no participants received services between 19 and 24 

months after program entry. Therefore, AH is omitted from the analysis of the likelihood of receiving 
75 First Steps on the Road To Financial Well-Being: Appendix

services 19 to 24 months after program entry and TCP 

serves as the base category. The results of the regression analysis are presented in Figures B2 through B4.

Figure B1 Differences in Receipt of FOC Services During the Two Years After Program Entry between FOC Study 

Participants in the Final Sample and Those Not in the Final Sample

All In the Final Sample Not in the Final Sample Difference

(n=810) (n=500) (n=310)

Percent of all study participants who received 

assistance from:

Any FOC counselors 61.9% 65.8% 55.5% 10.3%***

Counselor in one service area 7.7% 6.8% 9.0% -2.2%

Counselors in two service areas 20.6% 23.8% 15.5% 8.3%***

Counselors in all three service areas 33.6% 35.2% 31.0% 4.2%

Percent of all study participants who received 

assistance from the:

Financial counselor 54.4% 58.8% 47.4% 11.4%***

Income support counselor 48.9% 51.6% 44.5% 7.1%***

Employment counselor 46.3% 49.6% 41.0% 8.6%**

(n=501) (n=329) (n=172)

Duration of contact with the FOC counselors 

among those who received any counseling

Less than 1 month 20.7% 20.5% 21.2% -0.7%

1 to 6 months 18.3% 18.6% 17.7% 0.9%

7 to 12 months 16.7% 14.3% 21.2% -6.9%*

13 to 18 months 15.5% 13.7% 18.8% -5.1%

19 to 24 months 28.9% 32.9% 21.2% 11.7%***

Median number of contacts with the FOC 

counselors among those who received any 

counseling

9 9 9

Source: FOC administrative program records

0
76 First Steps on the Road To Financial Well-Being: Appendix

Figure B2 Regression Analysis of the Likelihood of Receiving Any FOC Counseling Among All Enrolled FOC Group 

Members (n=810)

b Robust SE (b) P-value Odds Ratio

IDPL 1.762 0.279 0.000 5.823

MFS 1.869 0.267 0.000 6.485

NLEN 3.075 0.510 0.000 21.648

TCP 1.157 0.261 0.000 3.181

Earnings during past two years (logged) 0.018 0.034 0.602 1.018

Total value of assets (logged) -0.021 0.037 0.573 0.979

Total value of debts (logged) 0.023 0.027 0.400 1.023

Total income last month (logged) -0.037 0.049 0.454 0.964

Total expenses last month (logged) -0.061 0.108 0.572 0.941

Had positive net income last month 0.192 0.211 0.364 1.211

Had positive net worth 0.265 0.282 0.348 1.303

Male 0.347 0.182 0.056 1.415

Age 25 to 77 0.399 0.228 0.080 1.490

Black -0.221 0.279 0.428 0.801

White 0.060 0.664 0.928 1.062

Other or unknown race -0.896 0.558 0.108 0.408

Had at least a high school diploma or GED 0.419 0.189 0.027 1.520

Employed during the year pre-program -0.338 0.331 0.307 0.713

Had a criminal record -0.082 0.198 0.680 0.922

Had a disability -0.045 0.236 0.848 0.956

Born in the United States -0.020 0.296 0.947 0.980

Had a child under age 18 -0.131 0.178 0.462 0.877

Separated, divorced, or widowed 0.186 0.227 0.411 1.205

Married/living with partner -0.023 0.244 0.925 0.977

Family size -0.015 0.055 0.789 0.986

Homeless -0.139 0.400 0.729 0.870

Rents home 0.258 0.230 0.261 1.295

Owns home 0.026 0.395 0.947 1.027

Received SNAP last month 0.208 0.196 0.288 1.231

Had filed for bankruptcy -0.383 0.320 0.231 0.682

Had collection agencies calling 0.256 0.198 0.196 1.292

Had a prime credit score 0.002 0.303 0.995 1.002

Had a credit score but thin file 0.006 0.226 0.980 1.006

Had a credit score and thick file 0.071 0.395 0.857 1.074

Number of derogatory public records 0.000 0.125 0.998 1.000

Number of trade accounts with balances -0.036 0.051 0.474 0.964

Number of inquiries made into credit -0.003 0.045 0.948 0.997

Had late payments on trade accounts past year -0.147 0.309 0.635 0.863

Number of trades with no late payments 0.044 0.027 0.099 1.045

Intercept -1.063 0.732 0.146 0.345

Pseudo R-square (.153)

Wald chi-square statistic (129.42)

P-value of Wald chi-square statistic (.000)
77 First Steps on the Road To Financial Well-Being: Appendix

Figure B3 Regression Analysis of the Likelihood of Receiving FOC Counseling in All Three Core Service Areas 

Among All Enrolled FOC Group Members (n=810)

b Robust SE (b) P-value Odds Ratio

IDPL 2.474 0.337 0.000 11.867

MFS 1.519 0.358 0.000 4.567

NLEN 2.714 0.409 0.000 15.086

TCP 1.125 0.367 0.002 3.079

Earnings during past two years (logged) 0.071 0.035 0.045 1.073

Total value of assets (logged) 0.033 0.035 0.352 1.033

Total value of debts (logged) -0.004 0.029 0.881 0.996

Total income last month (logged) 0.019 0.050 0.712 1.019

Total expenses last month (logged) -0.070 0.104 0.498 0.932

Had positive net income last month -0.138 0.224 0.537 0.871

Had positive net worth 0.030 0.289 0.916 1.031

Male -0.208 0.203 0.305 0.812

Age 25 to 77 0.464 0.245 0.058 1.591

Black -0.392 0.348 0.259 0.675

White 0.297 0.528 0.574 1.345

Other or unknown race -0.241 0.617 0.696 0.786

Had at least a high school diploma or GED 0.491 0.209 0.019 1.633

Employed during the year pre-program -0.427 0.336 0.205 0.653

Had a criminal record 0.394 0.217 0.069 1.484

Had a disability 0.045 0.263 0.864 1.046

Born in the United States -0.533 0.312 0.087 0.587

Had a child under age 18 -0.289 0.189 0.126 0.749

Separated, divorced, or widowed -0.056 0.245 0.818 0.945

Married/living with partner 0.139 0.253 0.582 1.149

Family size 0.019 0.059 0.751 1.019

Homeless -0.547 0.445 0.219 0.579

Rents home 0.121 0.245 0.622 1.128

Owns home -0.520 0.406 0.201 0.594

Received SNAP last month 0.159 0.202 0.432 1.172

Had filed for bankruptcy -0.537 0.375 0.152 0.585

Had collection agencies calling 0.038 0.213 0.859 1.039

Had a prime credit score -0.141 0.324 0.664 0.869

Had a credit score but thin file -0.835 0.269 0.002 0.434

Had a credit score and thick file -0.387 0.380 0.309 0.679

Number of derogatory public records 0.052 0.120 0.667 1.053

Number of trade accounts with balances 0.010 0.046 0.831 1.010

Number of inquiries made into credit 0.013 0.050 0.800 1.013

Had late payments on trade accounts past year 0.569 0.334 0.089 1.766

Number of trades with no late payments 0.025 0.023 0.290 1.025

Intercept -2.020 0.708 0.004 0.133

Pseudo R-square (.197)

Wald chi-square statistic (150.63)

P-value of Wald chi-square statistic (.000)
78 First Steps on the Road To Financial Well-Being: Appendix

Figure B4 Regression Analysis of the Likelihood of Receiving FOC Counseling Services 19 to 24 Months After 

Program Entry Among All Enrolled FOC Group Members (n=604)1

b Robust SE (b) P-value Odds Ratio

IDPL 0.006 0.680 0.993 1.006

MFS 2.740 0.382 0.000 15.490

NLEN 0.993 0.478 0.038 2.700

Earnings during past two years (logged) 0.049 0.050 0.329 1.050

Total value of assets (logged) -0.018 0.046 0.703 0.983

Total value of debts (logged) -0.027 0.039 0.494 0.974

Total income last month (logged) -0.096 0.063 0.131 0.909

Total expenses last month (logged) 0.619 0.213 0.004 1.858

Had positive net income last month 0.399 0.296 0.178 1.490

Had positive net worth 0.134 0.362 0.711 1.143

Male -0.310 0.268 0.247 0.733

Age 25 to 77 0.293 0.332 0.376 1.341

Black -1.189 0.546 0.029 0.305

White 0.971 0.902 0.282 2.640

Other or unknown race 0.204 0.755 0.787 1.226

Had at least a high school diploma or GED 0.505 0.282 0.074 1.657

Employed during the year pre-program -0.271 0.499 0.587 0.763

Had a criminal record 0.290 0.297 0.329 1.336

Had a disability 0.639 0.336 0.057 1.895

Born in the United States -0.231 0.425 0.587 0.794

Had a child under age 18 -0.225 0.257 0.382 0.799

Separated, divorced, or widowed 0.085 0.331 0.797 1.089

Married/living with partner -0.169 0.370 0.648 0.844

Family size -0.087 0.088 0.323 0.917

Homeless 0.107 0.638 0.866 1.113

Rents home -0.357 0.309 0.249 0.700

Owns home -0.240 0.514 0.640 0.786

Received SNAP last month 0.010 0.280 0.970 1.010

Had filed for bankruptcy 0.046 0.397 0.908 1.047

Had collection agencies calling 0.163 0.279 0.561 1.177

Had a prime credit score -0.110 0.432 0.798 0.895

Had a credit score but thin file -0.556 0.364 0.127 0.574

Had a credit score and thick file -0.308 0.539 0.567 0.735

Number of derogatory public records 0.284 0.155 0.068 1.328

Number of trade accounts with balances -0.027 0.058 0.646 0.974

Number of inquiries made into credit 0.022 0.049 0.653 1.022

Had late payments on trade accounts past year -0.466 0.488 0.340 0.627

Number of trades with no late payments 0.019 0.027 0.466 1.020

Intercept -5.159 1.431 0.000 0.006

Pseudo R-square (.216)

Wald chi-square statistic (123.77)

P-value of Wald chi-square statistic (.000)

1 This analysis excludes AH participants because the program ceased operations and no AH participants received services 19 to 24 months after program entry.
79 First Steps on the Road To Financial Well-Being: Appendix

Appendix C 

Impact Analysis

To assess program impacts on FOC participants, we 

used multivariate regression analysis including both 

FOC participants and comparison group members in 

the models. We used linear regression to estimate 

program impacts for the continuous outcome variables, negative binomial regression for the count outcome variables, and logistic regression for the binary 

outcome variables. As indicated in the variable list 

below, when the continuous outcome variables were 

highly skewed, we included the natural log of the values in the models. The models estimating the overall 

ITT and TOT impacts included a binary variable equal 

to one if the individual was in the FOC group as well as 

the baseline value of the outcome variable to estimate 

the change associated with being in the FOC group. 

The models took the following form:

Y2 = α + β1Y1 + β2T + β3X + ε

Where:

Y2 is the follow-up value of the outcome variable 

 of interest (or the log odds of observing the 

 outcome of interest for binary variables)

α; β are coefficients

Y1 is the baseline value of the outcome variable 

 of interest

T is a binary variable equal to 1 if the individual 

 is in the FOC group

X is a vector of explanatory variables

ε is the individual-specific error term

The models included the vector of explanatory variables described below to control for any differences 

that remained between the FOC and comparison 

groups after matching. As described earlier, we used 

a many-to-one matching method to form the two 

groups. Therefore, we applied the probability weights 

generated by the propensity score matching process 

to each of the models. In addition to estimating program impacts for the FOC participants overall, we 

examined how impacts varied across the five FOC 

sites by substituting the binary treatment variable 

with a categorical variable that included each site with 

the comparison group serving as the base category. 

We also examined program impacts for subgroups of 

participants using a series of interaction terms to estimate the relationship between being in the FOC group 

and being part of a particular subgroup. For the subgroup analysis, we selected a subset of all the independent variables included in the models that were of 

primary interest for policy and programming. The list of 

independent variables below indicates which variables 

were tested in the subgroup analysis and which were 

used only as explanatory variables.

Outcome Measures

Career Advancement

Confirmatory Measure

• Employed year-round during the second year postprogram entry (a binary variable equal to one if the 

individual was employed during all 12 months of 

the year)

Secondary Measures

• Employed during the second year post-program 

entry (a binary variable equal to one if the individual worked at any time during the year)

• Annual earnings during the second year post-program entry (continuous, logged, sum of amounts 

earned from each job held based on questions 

about wages, hours worked per week, weeks 

worked per month, and dates of employment; is 

zero for those with no employment)
80 First Steps on the Road To Financial Well-Being: Appendix

from friends or family, unemployment insurance, 

cash assistance-TANF/GA, interest on savings or 

checking accounts, child support, Social Security, 

and SSI)

Total monthly expenses two years after program entry (continuous, logged, sum of amounts 

reported for the last month for food, phone, mortgage/rent, utilities, vehicles, gas, car insurance, 

repair and maintenance, public transit, eating out, 

medical bills and copays, health insurance premiums, credit or store card payments, and fees 

for cashing checks or money orders or for bank 

transactions)

Monthly net income two years after program 

entry (continuous, total gross income minus total 

expenses in the last month)

Received any income supports two years after 

program entry (a binary variable equal to one if the 

individual had received any monetary or non-monetary assistance in the last month including SNAP, 

TANF/cash assistance, unemployment insurance, 

Supplemental Security Income, veteran’s benefits, 

social security, Medicaid, Medicare, or assistance 

with heating/cooling, transportation, child care, 

clothing, or housing)

•

•

•

Credit

Confirmatory Measure

• Among those without a credit score at program 

entry, had a credit score two years after program 

entry (a binary variable equal to one if the individual had a credit score)

• Among those with a credit score at program entry, 

had any increase in credit score two years after 

program entry (a binary variable equal to one if the 

individual had an increase in credit score from program entry to two years later)

Secondary Measures

• Number of open trade accounts two years after 

program entry (count variable of the number of 

open trade accounts on individuals’ credit reports)

• Annual hours worked during the second year postprogram entry (continuous, logged, sum of hours 

worked at each job reported based on hours 

worked per week, weeks worked per month, and 

dates of employment; is zero for those with no 

employment)

• Hourly wage in current or most recent job (continuous, logged, hourly wage at current job two years 

after program entry or, if not currently working, 

hourly wage at most recent job during the two 

years after program entry)

• Had a high school diploma or GED two years after 

program entry (a binary variable equal to one if 

the individual had at least a high school diploma 

or GED)

• Had a college degree two years after program 

entry (a binary variable equal to one if the individual had a college degree, including an Associate’s, 

Bachelor’s, or graduate degree)

• Had earned any college credits two years after program entry among those without college degrees 

(a binary variable equal to one if the individual had 

earned any college credits)

• Had an occupational certificate or license two 

years after program entry (a binary variable equal 

to one if the individual had an occupational certificate or license)

Net Income

Confirmatory Measure

• Had monthly net income greater than zero two 

years after program entry (a binary variable equal 

to one if the individual had positive net income—

total income minus total expenses—in the last 

month with negative or zero net income serving as 

the base category)

Secondary Measures

• Total monthly gross income two years after program entry (continuous, logged, sum of amounts 

reported for the last month for SNAP, earnings from 

work for the participant and family members, cash 
81 First Steps on the Road To Financial Well-Being: Appendix

• Had any trade accounts paid as agreed two years 

after program entry (a binary variable equal to 

one if the individual had any open or closed trade 

accounts paid as agreed on the credit report)

• Number of trade accounts paid as agreed (count 

variable of the number of open or closed trade 

accounts on individuals’ credit reports that were 

paid as agreed)

• Number of on-time payments made on trade 

accounts during the second year after program 

entry (continuous, logged, sum of on-time payments made on all trade accounts on individuals’ 

credit reports)

• Had a credit score two years after program entry 

among all participants (a binary variable equal to 

one if the individual had a credit score)

• Had a prime credit score two years after program 

entry (a binary variable equal to one if the individual had a prime credit score of 620 or greater)

• Credit score two years after program entry among 

those with scores at program entry and two years 

later (continuous)

Net Worth

Confirmatory Measure

• Had any increase in net worth two years after program entry (a binary variable equal to one if the 

individual had an increase in net worth, defined 

above, from program entry to two years later)

Secondary Measures

• Net worth two years after program entry (continuous, total value of assets—sum of amounts 

reported for vehicles, home, other property, business, savings or checking accounts, cash not in 

a bank account, retirement accounts, and other 

financial assets—minus total value of debts—sum 

of amounts reported for late rent, late utility payments, child support arrears, taxes owed, medical debt, credit card debt, legal debt, mortgages, 

vehicle loans, student loans, family loans, payday 

loans, business loans, and other loans)

• Had net worth greater than zero two years after 

program entry (a binary variable equal to one if the 

individual had positive net worth with negative or 

zero net worth serving as the base category)

• Had any assets two years after program entry (a 

binary variable equal to one if the individual had 

any assets)

• Had any money in savings and/or checking 

accounts two years after program entry (a binary 

variable equal to one if the individual had any 

money in savings and/or checking accounts)

• Amount in savings and/or checking accounts two 

years after program entry (continuous, logged, sum 

of amounts reported in savings and/or checking 

accounts)

• Had any asset-related debts two years after program entry (a binary variable equal to one if the 

individual had any asset-related debts, including 

mortgages, vehicle loans, other property loans, 

business loans, and student loans)

• Had any non-asset-related debts two years after 

program entry (a binary variable equal to one if 

the individual had any non-asset-related debts, 

including unpaid utility, rent, medical, or legal 

bills, back taxes owed, child support arrears, and 

credit card balances)

Independent Variables in the Impact Analysis

The following variables were included in the models 

estimating overall ITT and TOT program impacts as 

well as impacts across the five FOC sites and were 

included in interaction terms with being in the FOC 

participant group in the models estimating program 

impacts for demographic subgroups of participants.

• Gender (a binary variable equal to one if the person 

was male with female serving as the base category)

• Age at program entry (continuous variable in all 

models; in subgroup analysis models included a 

binary variable equal to one if the person was 

age 25 to 77 with age 18 to 24 serving as the 

base category)
82 First Steps on the Road To Financial Well-Being: Appendix

loans, late rent and utility payments, child support arrears, taxes owed, medical debt, credit card 

debt, legal debt, family loans, pay day loans, business loans, and other loans)

• Total gross income in the month prior to program entry (continuous, logged, includes sum of 

amounts reported for SNAP, earnings from work 

for the individual and other family members, cash 

from friends or family, unemployment insurance, 

cash assistance-TANF/GA, interest on savings or 

checking accounts, child support, Social Security, 

and SSI)

• Total expenses in the month prior to program entry 

(continuous, logged, includes sum of amounts 

reported for food, phone, mortgage/rent, utilities, 

vehicles, gas, car insurance, repair and maintenance, public transit, eating out, medical bills and 

copays, health insurance premiums, credit or store 

card payments, and fees for cashing checks or 

money orders or for bank transactions)

• Net worth greater than zero at program entry (a 

binary variable equal to one if the individual had 

positive net worth—the total value of assets minus 

the total value of debts—with negative or zero net 

worth serving as the base category)

• Had an occupational certificate at program entry (a 

binary variable equal to one if the individual had an 

occupational certificate or license)

• Currently attending education/training at program 

entry (a binary variable equal to one if the individual 

was attending college, training, or GED classes)

• Disability at program entry (a binary variable equal 

to one if the individual had a health condition that 

limited her/his ability to work)

• Born in the United States (a binary variable equal to 

one if the individual was born in the United States)

• Family size (count variable of the number of family members living in the participants’ household, 

including the participant, other adults, and children)

• Housing status (a categorical variable including owns hone, rents home, homeless, and lives 

rent-free with living rent-free serving as the base 

category)

• Race/ethnicity (a categorical variable including 

Black, White, other/missing race, and Hispanic 

with Hispanic serving as the base category)

• Highest degree at program entry (a categorical variable including GED, high school diploma, college 

degree, and no diploma/degree with no diploma/

degree serving as the base category)

• Criminal record at program entry (a binary variable equal to one if the individual had ever been 

convicted of a crime, including misdemeanors and 

felonies)

• Marital status (a categorical variable including formerly married (separated, divorced, or widowed), 

currently married or living with a partner in a 

marriage-like relationship, and never married with 

never married serving as the base category)

• Children (a binary variable equal to one if the individual had any children under age 18)

• Employed during the year pre-program (a binary 

variable equal to one if the individual worked at 

any time during the year prior to program entry)

• Net income greater than zero in the month prior to 

program entry (a binary variable equal to one if the 

individual had positive net income—total income 

minus total expenses—with negative or zero net 

income serving as the base category)

The models estimating overall ITT and TOT program 

impacts and those estimating impacts across the five 

FOC sites also included the following variables.

• Earnings from work during the past two years 

(continuous, logged, includes the sum of amounts 

earned from each job held based on questions about wages, hours worked, and dates of 

employment)

• Total value of assets at the time of program entry 

(continuous, logged, includes sum of amounts 

reported for vehicles, home, other property, business, savings or checking accounts, cash not in 

a bank account, retirement accounts, and other 

financial assets)

• Total value of debts at the time of program entry 

(continuous, logged, includes sum of amounts 

reported for mortgages, vehicle loans, student 
83 First Steps on the Road To Financial Well-Being: Appendix

• Received SNAP in the month prior to program entry 

(a binary variable equal to one if the individual 

received SNAP)

• Bankruptcy (a binary variable equal to one if the 

individual had filed for bankruptcy in the past year 

or was in the process of doing so)

• Collections (a binary variable equal to one if the 

individual said collection agencies were calling 

him/her about unsettled claims)

• Had a prime credit score at program entry (a 

binary variable equal to one if the individual had a 

prime credit score of 620 or greater)

• Credit score status (a categorical variable including had a credit score and thick credit file (three 

or more open trade accounts), had a credit score 

but thin credit file (two or fewer open accounts), 

and unscored with unscored serving as the base 

category)

• Number of derogatory public records (count variable 

of the number of bankruptcies, tax liens, and civil 

judgments reported on individuals’ credit reports)

• Number of trade accounts with balances (count variable of the number of trade accounts with positive 

balances reported on individuals’ credit reports)

• Number of inquiries (count variable of the number 

of creditors that had inquired about individuals’ 

credit profiles during the previous two years as 

reported on individuals’ credit reports)

• Had late payments on trade accounts in the 

past year (a binary variable equal to one if the 

individual had made any late payments on trade 

accounts in the past year as reported on individuals’ credit reports)

• Number of trade accounts with no late payments 

(count variable of the number of trade accounts 

on individuals’ credit reports that never had a 

late payment)

The impact data presented in Chapter 4 are the 

regression-adjusted probabilities and means for the 

FOC and comparison groups after controlling for any 

differences that remained in the independent variables 

listed above after conducting the propensity score 

matching. Figure C1 presents the unadjusted proportions, means, and standard deviations for the FOC and 

comparison groups for each outcome measure analyzed. Figure C2 summarizes the regression-adjusted 

proportions, means, and standard errors for the FOC 

and comparison group members.
84 First Steps on the Road To Financial Well-Being: Appendix

Figure C1 Descriptive Statistics for the Outcome Measures Included in the Impact Analysis For the FOC 

Group and Weighted Comparison Group-Not Regression-Adjusted for Baseline Differences in 

Participant Characteristics

FOC Group Comparison Group

% or Mean Std. Dev. % or Mean Std. Dev.

Percent employed at any time 66.4% 0.47 65.9% 0.47

Percent employed year-round 36.6% 0.48 31.3% 0.46

Average annual earnings (including zero earnings) $9,951 $12,471 $9,957 $14,442

Average annual hours worked (including zero hours) 904 989 862 975

Average hourly wage in current or most recent job $11.16 $5.91 $11.83 $6.91

Percent who had at least a high school diploma or 

GED 80.2% 0.40 78.1% 0.41

Percent who had a college degree (Associate’s or 

higher) 16.6% 0.37 15.1% 0.36

Percent who had earned any college credits among 

those without college degrees 15.2% 0.36 17.1% 0.38

Percent who had an occupational certificate/license 33.6% 0.47 40.9% 0.49

Average monthly gross income $1,433 $1,473 $1,508 $1,561

Average monthly expenses $1,811 $1,246 $1,764 $1,120

Average monthly net income -$379 $1,426 -$256 $1,471

Percent who have monthly net income greater than 

zero 29.6% 0.46 35.7% 0.48

Percent who received any income supports last 

month 78.6% 0.41 84.4% 0.36

Average number of open trade accounts 2.50 3.76 2.01 3.05

Percent who had any trade accounts (open or 

closed) paid as agreed 69.8% 0.46 63.3% 0.48

Average number of trade accounts (open or closed) 

paid as agreed 5.25 7.80 4.49 6.86

Average number of on-time payments made on trade 

accounts in the past year 23.20 39.88 18.86 34.92

Percent who had a credit score 66.3% 0.47 62.8% 0.48

Percent who had a prime score 23.1% 0.42 19.1% 0.39

Average credit score among those with scores at 

both points in time 598 82 596 70

Percent who had any increase in credit score among 

those scored at both points in time 59.1% 0.49 65.2% 0.48

Percent who had any increase in net worth after two 

years 55.6% 0.50 50.2% 0.50

Percent who had net worth greater than zero 41.2% 0.49 41.3% 0.49

Average net worth $10,954 $75,746 $8,556 $74,308

Percent who had any assets 67.4% 0.47 72.3% 0.45

Percent with any money in savings or checking 

accounts 41.6% 0.49 44.8% 0.50

Average dollar amount in savings or checking 

accounts $603 $2,575 $721 $3,574

Percent who had any asset-related debts 39.4% 0.49 39.3% 0.49

Percent who had any non-asset-related debts 53.6% 0.50 58.2% 0.49
85 First Steps on the Road To Financial Well-Being: Appendix

Figure C2 Regression-Adjusted Statistics for the Outcome Measures Included in the Impact Analysis For the 

FOC Group and Weighted Comparison Group

FOC Group Comparison Group

% or Mean Std. Error % or Mean Std. Error

Percent employed at any time 66.3% 0.021 66.0% 0.027

Percent employed year-round 36.5% 0.022 31.1% 0.025

Average annual earnings (including zero earnings) $9,957 $559 $9,951 $769

Average annual hours worked (including zero hours) 903 44 862 52

Average hourly wage in current or most recent job $11.14 $0.34 $11.85 $0.42

Percent who had at least a high school diploma or 

GED 79.6% 0.020 78.8% 0.028

Percent who had a college degree (Associate’s or 

higher) 16.4% 0.016 15.2% 0.017

Percent who had earned any college credits among 

those without college degrees 36.2% 0.023 41.2% 0.028

Percent who had an occupational certificate/license 33.5% 0.021 41.1% 0.025

Average monthly gross income $1,431 66 $1,510 91

Average monthly expenses $1,811 54 $1,764 51

Average monthly net income -$381 65 -$254 83

Percent who have monthly net income greater than 29.4% 0.021 35.9% 0.029 zero 

Percent who received any income supports last 

month 79.4% 0.018 83.7% 0.017

Average number of open trade accounts 2.49 0.15 2.14 0.14

Percent who had any trade accounts (open or 

closed) paid as agreed 70.7% 0.020 61.7% 0.026

Average number of trade accounts (open or closed) 

paid as agreed 5.8 0.34 4.6 0.29

Average number of on-time payments made on trade 

accounts in the past year 23.0 1.58 19.1 1.63

Percent who had a credit score 66.5% 0.021 62.4% 0.027

Percent who had a prime score 22.4% 0.018 19.9% 0.020

Average credit score among those with scores at 

both points in time 598 4.9 597 4.3

Percent who had any increase in credit score among 

those scored at both points in time 61.7% 0.032 62.6% 0.035

Percent who had any increase in net worth after two 55.1% 0.022 50.6% 0.028

years

Percent who had net worth greater than zero 41.6% 0.023 40.9% 0.028

Average net worth $10,721 $3,415 $8,790 $3,965

Percent who had any assets 67.7% 0.020 71.9% 0.022

Percent with any money in savings or checking 

accounts 42.4% 0.021 44.1% 0.027

Average dollar amount in savings or checking 

accounts $538 $103 $785 $212

Percent who had any asset-related debts 39.6% 0.021 39.2% 0.024

Percent who had any non-asset-related debts 53.0% 0.023 58.7% 0.027
86 First Steps on the Road To Financial Well-Being: Appendix

Analysis of Intraclass Correlation

The analysis of FOC impacts combines data for individuals from five FOC program sites and five Chicago 

workforce centers. Individuals within an FOC program 

or workforce center may be more similar to each other 

than to individuals in another center. Therefore, in 

regression models the errors for individuals in the 

same center may be correlated. Not controlling for 

within center error correlation can lead to biased standard errors and inaccurate statistical inferences. The 

intraclass correlation coefficient (ICC) indicates the 

proportion of the variance in the outcome (dependent) 

variable that is accounted for by the groups. Non-zero 

ICCs indicate that some correction may be needed to 

account for clustering. Figure C3 presents the ICCs 

for each outcome examined in chapter 4 for the ITT 

sample and the three TOT samples examined.

While most of the ICCs were small, even a small 

amount of correlation can bias the standard errors. 

Two methods for correcting for clustering include using 

multi-level models and using clustered robust standard errors. The study does not have enough centers 

to use multi-level modeling. Research has found that 

clustered standard errors can also be problematic 

with a small number of clusters (fewer than 50) and 

unbalanced groups and can produce misleadingly 

small standard errors. We initially tested the significance of the coefficients using robust standard errors 

because the data do not meet the assumption of 

random samples required by conventional standard 

errors. We then conducted sensitivity tests comparing 

the results using robust and clustered robust standard 

errors. For most of the ITT and TOT impact estimates, 

using robust or clustered robust standard errors produced the same findings regarding the significance of 

the coefficients. When the clustered robust standard 

errors were more conservative and changed the findings regarding statistical significance, we use the more 

conservative estimate. This affected five outcomes:

ITT: the -7.6 percentage point difference between FOC 

and comparison group members in the likelihood of 

having an occupational certificate or license changed 

from being significant at the p<.05 level to not significant (p=.116).

• ITT: the -4.3 percentage point difference between 

FOC and comparison group members in the likelihood of having received any income supports in 

the last month changed from being significant at 

the p<.10 level to not significant (p=.146).

• TOT Method 1: the -7.1 percentage point difference between FOC and comparison group members in the likelihood of having an occupational 

certificate or license changed from being significant at the p<.05 level to not significant (p=.227).

• TOT Method 2: the 6.4 percentage point difference

between FOC and comparison group members 

in the likelihood of having a prime credit score 

changed from being significant at the p<.05 level 

to significant at the p<.10 level.

• TOT Method 3: the difference between FOC and 

comparison group members in the average numbe

of on-time payments made on trade accounts in 

the past year (4.5) changed from being significant 

at the p<.10 level to not significant (p=.140).

r 

Corrections for Multiple Hypothesis 

Testing

As noted in Chapter 4, the more statistical tests 

one conducts, the greater the probability of finding 

a statistically significant impact estimate purely by 

chance. To address this issue in this evaluation, we 

chose to denote one confirmatory research question within each domain that LISC felt should carry 

the most weight in assessing program effectiveness. 

Another method for addressing this issue is to adjust 

the significance levels using the Benjamini-Hochberg 

family-wise adjustment. This involves comparing each 

estimated p-value with an adjusted p-value criterion 

based on the formula, pi

 = i * (α/M) where α is the 

target level of statistical significance (.10 in this 

case), M is the total number of p-values estimated 

within the domain of outcomes, and i is the rank of 

the p-value, with i = 1 through m. Estimated p-values 

that are less than the adjusted p-values are judged to 
87 First Steps on the Road To Financial Well-Being: Appendix

be statistically significant. Figure C4 presents how the 

findings would change using the Benjamini-Hochberg 

family-wise adjustment. After making this adjustment, 

four positive credit-related impacts remain statistically 

significant: the percent who had any trade accounts 

paid as agreed, the average number of trade accounts 

paid as agreed, the average number of open trade 

accounts, and the average number of on-time payments made on trade accounts during the past year.

Figure C3 Intraclass Correlation Coefficients (ICCs) for the ITT and TOT Samples

ITT TOT 1 TOT 2 TOT 3

Percent employed at any time 0.016

Percent employed year-round 0.010 0.011 0.057 0.019

Average annual earnings (including zero earnings) 0.015 0.022 0.046 0.006

Average annual hours worked (including zero hours) 0.017 0.025 0.052 0.009

Average hourly wage in current or most recent job 0.008 0.026 0.087 0.018

Percent who had at least a high school diploma or 

GED 0.055

Percent who had a college degree (Associate’s or 

higher) 0.010

Percent who had earned any college credits among 

those without college degrees 0.047

Percent who had an occupational certificate/license 0.038 0.037 0.056 0.041

Average monthly gross income (logged) 0.005 0.008 0.017 0.039

Average monthly expenses (logged) 0.050 0.057 0.111 0.027

Average monthly net income 0.000 0.006 0.040 0.000

Percent who have monthly net income greater than 0.010 0.018 0.057 0.000 zero 

Percent who received any income supports last 

month 0.054

Average number of open trade accounts 0.034

Percent who had any trade accounts (open or 

closed) paid as agreed 0.016 0.023 0.098 0.047

Average number of trade accounts (open or closed) 

paid as agreed 0.040

Average number of on-time payments made on trade 

accounts in the past year 0.052 0.050 0.147 0.074

Percent who had a credit score 0.017 0.022 0.100 0.040

Percent who had a prime score 0.103 0.104 0.211 0.118

Average credit score among those with scores at 

both points in time 0.152

Percent who had any increase in credit score among 

those scored at both points in time 0.001

Percent who had any increase in net worth after two 0.003

years

Percent who had net worth greater than zero 0.039 0.058 0.127 0.026

Average net worth 0.005

Percent who had any assets 0.061 0.068 0.076 0.044

Percent with any money in savings or checking 

accounts 0.092

Average dollar amount in savings or checking 

accounts 0.087

Percent who had any asset-related debts 0.008 0.010 0.023 0.014

Percent who had any non-asset-related debts 0.000 0.007 0.029 0.017
88 First Steps on the Road To Financial Well-Being: Appendix

Figure C4 Results After Applying the Benjamini-Hochberg Family-Wise Adjustment to the Overall ITT 

Impact Estimates

Career Advancement Estimated p-value Rank Adjusted target p-value 

Percent employed year-round 0.081 1 0.011

Percent who had an occupational certificate/license 0.116 2 0.022

Average hourly wage in current or most recent job 0.149 3 0.033

Percent who had earned any college credits among those without college 

degrees 0.177 4 0.044

Percent who had a college degree (Associate’s or higher) 0.577 5 0.056

Average annual earnings (including zero earnings) 0.726 6 0.067

Average annual hours worked (including zero hours) 0.747 7 0.078

Percent who had at least a high school diploma or GED 0.767 8 0.089

Percent employed at any time 0.867 9 0.100

Net Income Estimated p-value Rank Adjusted target p-value

Percent who have monthly net income greater than zero 0.041 1 0.020

Percent who received any income supports last month 0.146 2 0.040

Average monthly gross income 0.208 3 0.060

Average monthly net income 0.212 4 0.080

Average monthly expenses 0.568 5 0.100

Credit Estimated p-value Rank Adjusted target p-value

Percent who had any trade accounts (open or closed) paid as agreed 0.000 1 0.013

Average number of trade accounts (open or closed) paid as agreed 0.000 2 0.025

Average number of open trade accounts 0.030 3 0.038

Average number of on-time payments made on trade accounts in the 

past year 0.039 4 0.050

Percent who had a credit score 0.131 5 0.063

Percent who had a prime score 0.257 6 0.075

Percent who had any increase in credit score among those scored at both 

points in time 0.811 7 0.088

Average credit score among those with scores at both points in time 0.848 8 0.100

Net Worth Estimated p-value Rank Adjusted target p-value

Percent who had any non-asset-related debts 0.069 1 0.013

Percent who had any assets 0.114 2 0.025

Percent who had any increase in net worth after two years 0.175 3 0.038

Average dollar amount in savings or checking accounts 0.247 4 0.050

Percent with any money in savings or checking accounts 0.560 5 0.063

Average net worth 0.599 6 0.075

Percent who had net worth greater than zero 0.807 7 0.088

Percent who had any asset-related debts 0.881 8 0.100

Regression Results

Figures C5 through C37 present the results of the 

regression models used to assess program impacts 

using the ITT framework. The figures include the 

unstandardized coefficients, robust standard errors, 

p-values, and the following measures of effect size: 

odds ratios for the logistic regression models, squared 

partial correlation coefficients for the linear regression 

models, and incident rate ratios (IRR) for the negative 

binomial models.
89 First Steps on the Road To Financial Well-Being: Appendix

Figure C5 Logistic Regression Analysis of Treatment Effects on the Likelihood of Being Employed At Any Time in 

the Second Year After Program Entry Among All Enrollees (Figure 4.2)

b Robust SE (b) P-value Odds Ratio

Employed in the second year pre-program -0.464 0.881 0.598 0.629

Treatment 0.019 0.163 0.908 1.019

Earnings during past two years (logged) 0.065 0.034 0.057 1.067

Total value of assets (logged) 0.073 0.033 0.026 1.076

Total value of debts (logged) 0.058 0.025 0.022 1.059

Total income last month (logged) 0.148 0.051 0.004 1.160

Total expenses last month (logged) -0.055 0.114 0.632 0.947

Had positive net income last month -0.161 0.215 0.455 0.852

Had positive net worth 0.348 0.272 0.200 1.417

Male -0.133 0.172 0.438 0.875

Age -0.019 0.008 0.021 0.981

Black -0.097 0.324 0.765 0.908

White 0.199 0.563 0.724 1.220

Other or unknown race 1.391 0.660 0.035 4.021

GED 0.564 0.273 0.039 1.758

High school diploma 0.395 0.219 0.071 1.484

Any college degree 0.562 0.308 0.067 1.755

Had an occupational certificate/license -0.060 0.170 0.724 0.942

Currently attending education/training 0.151 0.267 0.572 1.163

Employed during the year pre-program 0.583 0.899 0.517 1.791

Had a criminal record 0.289 0.183 0.115 1.335

Had a disability -0.560 0.226 0.013 0.571

Born in the United States -0.065 0.339 0.849 0.937

Had a child under age 18 0.291 0.191 0.128 1.338

Separated, divorced, or widowed -0.052 0.212 0.805 0.949

Married/living with partner 0.020 0.252 0.937 1.020

Family size 0.025 0.051 0.621 1.026

Homeless 0.208 0.322 0.518 1.231

Rents home -0.044 0.238 0.855 0.957

Owns home -0.552 0.412 0.180 0.576

Received SNAP last month -0.415 0.199 0.037 0.661

Had filed for bankruptcy -0.713 0.323 0.027 0.490

Had collection agencies calling 0.342 0.189 0.071 1.407

Had a prime credit score 0.086 0.301 0.775 1.090

Had a credit score but thin file -0.326 0.234 0.164 0.722

Had a credit score and thick file -0.149 0.349 0.669 0.861

Number of derogatory public records 0.190 0.128 0.139 1.209

Number of trade accounts with balances 0.013 0.045 0.773 1.013

Number of inquiries made into credit -0.013 0.048 0.786 0.987

Had late payments on trade accounts past year -0.124 0.269 0.645 0.883

Number of trades with no late payments -0.007 0.023 0.773 0.993

Intercept -0.310 0.758 0.683 0.733

Pseudo R-square (.136)

Wald chi-square statistic (122.75)

P-value of Wald chi-square statistic (.000)
90 First Steps on the Road To Financial Well-Being: Appendix

Figure C6 Logistic Regression Analysis of Treatment Effects on the Likelihood of Being Employed Year Round in 

the Second Year After Program Entry Among All Enrollees (Figure 4.2)

b Robust SE (b) P-value

0.050

Odds Ratio

Employed year round in the year pre-program 0.455 0.232 1.575

Treatment 0.278 0.159 0.081 1.321

Earnings during past two years (logged) 0.055 0.033 0.092 1.057

Total value of assets (logged) 0.075 0.032 0.018 1.078

Total value of debts (logged) 0.033 0.026 0.207 1.034

Total income last month (logged) -0.045 0.051 0.374 0.956

Total expenses last month (logged) 0.215 0.152 0.157 1.239

Had positive net income last month 0.368 0.210 0.080 1.444

Had positive net worth -0.002 0.264 0.995 0.998

Male -0.139 0.188 0.460 0.870

Age -0.003 0.008 0.698 0.997

Black 0.082 0.261 0.752 1.086

White 0.973 0.544 0.074 2.645

Other or unknown race 0.460 0.444 0.299 1.585

GED 0.474 0.293 0.106 1.606

High school diploma 0.809 0.248 0.001 2.246

Any college degree 0.665 0.299 0.026 1.944

Had an occupational certificate/license 0.085 0.170 0.619 1.088

Currently attending education/training -0.007 0.235 0.976 0.993

Employed during the year pre-program -0.071 0.292 0.807 0.931

Had a criminal record 0.120 0.192 0.531 1.128

Had a disability -0.732 0.245 0.003 0.481

Born in the United States -0.511 0.312 0.101 0.600

Had a child under age 18 0.067 0.172 0.699 1.069

Separated, divorced, or widowed 0.045 0.216 0.834 1.046

Married/living with partner 0.025 0.241 0.917 1.025

Family size -0.063 0.054 0.242 0.939

Homeless 0.353 0.358 0.324 1.423

Rents home -0.185 0.237 0.435 0.831

Owns home -0.734 0.374 0.050 0.480

Received SNAP last month 0.039 0.190 0.838 1.040

Had filed for bankruptcy 0.200 0.375 0.593 1.221

Had collection agencies calling -0.033 0.180 0.855 0.968

Had a prime credit score 0.245 0.267 0.359 1.277

Had a credit score but thin file -0.188 0.221 0.396 0.829

Had a credit score and thick file -0.010 0.310 0.974 0.990

Number of derogatory public records 0.265 0.123 0.031 1.304

Number of trade accounts with balances 0.034 0.038 0.375 1.034

Number of inquiries made into credit 0.018 0.034 0.602 1.018

Had late payments on trade accounts past year 0.057 0.262 0.828 1.059

Number of trades with no late payments -0.019 0.020 0.345 0.981

Intercept -2.938 1.003 0.003 0.053

Pseudo R-square (.106)

Wald chi-square statistic (110.46)

P-value of Wald chi-square statistic (.000)
91 First Steps on the Road To Financial Well-Being: Appendix

Figure C7 Linear Regression Analysis of Treatment Effects on Logged Annual Earnings in the Second Year After 

Program Entry Among All Enrollees (Figure 4.2)

b Robust SE (b) P-value Partial Corr Coef2

Earnings during the year pre-program (logged) -0.329 0.199 0.099 0.0026

Treatment 0.103 0.294 0.726 0.0002

Earnings during past two years (logged) 0.166 0.064 0.010 0.0082

Total value of assets (logged) 0.164 0.059 0.005 0.0082

Total value of debts (logged) 0.108 0.047 0.023 0.0060

Total income last month (logged) 0.260 0.093 0.005 0.0090

Total expenses last month (logged) -0.013 0.215 0.951 0.0000

Had positive net income last month -0.176 0.396 0.657 0.0003

Had positive net worth 0.581 0.462 0.209 0.0018

Male -0.155 0.323 0.633 0.0003

Age -0.036 0.015 0.015 0.0069

Black -0.311 0.531 0.558 0.0007

White 0.797 0.958 0.406 0.0005

Other or unknown race 1.417 0.766 0.065 0.0027

GED 1.017 0.490 0.038 0.0053

High school diploma 0.860 0.420 0.041 0.0059

Any college degree 1.219 0.556 0.028 0.0059

Had an occupational certificate/license -0.144 0.301 0.632 0.0002

Currently attending education/training 0.151 0.457 0.741 0.0001

Employed during the year pre-program 2.877 1.772 0.105 0.0024

Had a criminal record 0.503 0.334 0.133 0.0028

Had a disability -1.260 0.434 0.004 0.0104

Born in the United States -0.341 0.562 0.544 0.0005

Had a child under age 18 0.566 0.358 0.114 0.0038

Separated, divorced, or widowed -0.073 0.396 0.855 0.0000

Married/living with partner 0.074 0.426 0.863 0.0000

Family size -0.010 0.084 0.906 0.0000

Homeless 0.438 0.645 0.498 0.0005

Rents home -0.077 0.439 0.861 0.0000

Owns home -1.150 0.690 0.096 0.0035

Received SNAP last month -0.768 0.341 0.025 0.0058

Had filed for bankruptcy -1.145 0.646 0.076 0.0044

Had collection agencies calling 0.479 0.326 0.143 0.0024

Had a prime credit score 0.041 0.529 0.938 0.0000

Had a credit score but thin file -0.547 0.437 0.211 0.0023

Had a credit score and thick file -0.260 0.586 0.657 0.0002

Number of derogatory public records 0.412 0.222 0.063 0.0036

Number of trade accounts with balances 0.050 0.073 0.498 0.0005

Number of inquiries made into credit -0.003 0.080 0.966 0.0000

Had late payments on trade accounts past year -0.096 0.465 0.837 0.0000

Number of trades with no late payments -0.012 0.040 0.761 0.0001

Intercept 3.563 1.450 0.014

R-square (.185)

F-statistic (5.97)

P-value of F-statistic (.000)
92 First Steps on the Road To Financial Well-Being: Appendix

Figure C8 Linear Regression Analysis of Treatment Effects on Logged Annual Hours Worked in the Second Year 

After Program Entry Among All Enrollees (Figure 4.2)

b Robust SE (b) P-value Partial 

Corr Coef2

Hours worked during the year pre-program (logged) -0.225 0.161 0.161 0.0017

Treatment 0.071 0.219 0.747 0.0001

Earnings during past two years (logged) 0.121 0.048 0.011 0.0078

Total value of assets (logged) 0.115 0.044 0.009 0.0072

Total value of debts (logged) 0.088 0.035 0.012 0.0070

Total income last month (logged) 0.197 0.069 0.004 0.0091

Total expenses last month (logged) -0.015 0.159 0.924 0.0000

Had positive net income last month -0.089 0.294 0.763 0.0001

Had positive net worth 0.559 0.342 0.102 0.0029

Male -0.138 0.241 0.568 0.0004

Age -0.024 0.011 0.030 0.0053

Black -0.265 0.401 0.509 0.0009

White 0.465 0.718 0.518 0.0003

Other or unknown race 0.783 0.548 0.153 0.0015

GED 0.731 0.364 0.045 0.0048

High school diploma 0.621 0.313 0.048 0.0055

Any college degree 0.808 0.414 0.051 0.0046

Had an occupational certificate/license -0.165 0.224 0.462 0.0005

Currently attending education/training 0.121 0.333 0.716 0.0001

Employed during the year pre-program 1.479 1.094 0.177 0.0015

Had a criminal record 0.356 0.251 0.157 0.0025

Had a disability -0.958 0.326 0.003 0.0106

Born in the United States -0.216 0.428 0.615 0.0003

Had a child under age 18 0.513 0.266 0.054 0.0056

Separated, divorced, or widowed -0.086 0.293 0.770 0.0001

Married/living with partner 0.055 0.319 0.863 0.0000

Family size -0.018 0.063 0.777 0.0001

Homeless 0.500 0.481 0.299 0.0011

Rents home -0.061 0.325 0.850 0.0000

Owns home -0.884 0.516 0.087 0.0037

Received SNAP last month -0.662 0.254 0.009 0.0077

Had filed for bankruptcy -0.818 0.494 0.098 0.0040

Had collection agencies calling 0.302 0.241 0.212 0.0017

Had a prime credit score 0.111 0.392 0.777 0.0001

Had a credit score but thin file -0.341 0.328 0.298 0.0016

Had a credit score and thick file -0.185 0.436 0.672 0.0002

Number of derogatory public records 0.283 0.167 0.090 0.0030

Number of trade accounts with balances 0.048 0.054 0.372 0.0008

Number of inquiries made into credit -0.016 0.059 0.784 0.0001

Had late payments on trade accounts past year -0.099 0.351 0.777 0.0001

Number of trades with no late payments -0.017 0.030 0.577 0.0004

Intercept 2.619 1.079 0.015

R-square (.184)

F-statistic (5.84)

P-value of F-statistic (.000)
93 First Steps on the Road To Financial Well-Being: Appendix

Figure C9 Linear Regression Analysis of Treatment Effects on Logged Hourly Wages at Current or Most Recent Job 

Two Years After Program Entry Among All Enrollees (Figure 4.2)

b Robust SE (b) P-value Partial Corr Coef2

Earnings during the year pre-program (logged) 0.062 0.023 0.007 0.0118

Treatment -0.046 0.032 0.149 0.0035

Earnings during past two years (logged) 0.002 0.007 0.741 0.0002

Total value of assets (logged) -0.001 0.006 0.851 0.0000

Total value of debts (logged) -0.008 0.005 0.128 0.0034

Total income last month (logged) 0.021 0.008 0.009 0.0065

Total expenses last month (logged) 0.020 0.021 0.345 0.0012

Had positive net income last month 0.010 0.042 0.819 0.0001

Had positive net worth -0.080 0.054 0.141 0.0041

Male 0.019 0.036 0.590 0.0005

Age -0.004 0.002 0.026 0.0092

Black 0.040 0.049 0.422 0.0012

White 0.055 0.112 0.621 0.0003

Other or unknown race 0.155 0.121 0.201 0.0044

GED 0.045 0.057 0.427 0.0011

High school diploma 0.110 0.055 0.046 0.0095

Any college degree 0.203 0.063 0.001 0.0182

Had an occupational certificate/license 0.054 0.033 0.097 0.0038

Currently attending education/training 0.018 0.048 0.702 0.0002

Employed during the year pre-program -0.664 0.209 0.002 0.0165

Had a criminal record -0.047 0.038 0.219 0.0028

Had a disability -0.008 0.056 0.879 0.0000

Born in the United States -0.056 0.050 0.260 0.0016

Had a child under age 18 -0.006 0.035 0.857 0.0001

Separated, divorced, or widowed 0.078 0.047 0.100 0.0046

Married/living with partner 0.135 0.046 0.003 0.0131

Family size -0.017 0.011 0.138 0.0045

Homeless 0.081 0.087 0.352 0.0016

Rents home 0.047 0.045 0.293 0.0017

Owns home 0.131 0.068 0.053 0.0052

Received SNAP last month -0.044 0.036 0.224 0.0022

Had filed for bankruptcy -0.097 0.068 0.154 0.0032

Had collection agencies calling 0.042 0.038 0.269 0.0022

Had a prime credit score -0.053 0.047 0.259 0.0014

Had a credit score but thin file -0.028 0.045 0.527 0.0007

Had a credit score and thick file -0.065 0.059 0.275 0.0017

Number of derogatory public records 0.027 0.022 0.220 0.0018

Number of trade accounts with balances -0.013 0.008 0.084 0.0039

Number of inquiries made into credit -0.005 0.006 0.359 0.0010

Had late payments on trade accounts past year 0.005 0.064 0.934 0.0000

Number of trades with no late payments 0.009 0.003 0.008 0.0083

Intercept 2.301 0.146 0.000

R-square (.129)

F-statistic (2.30)

P-value of F-statistic (.000)
94 First Steps on the Road To Financial Well-Being: Appendix

Figure C10 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a High School Diploma or 

GED Two Years After Program Entry Among All Enrollees (Figure 4.3)

b Robust SE (b) P-value Odds Ratio

Treatment 0.061 0.206 0.767 1.063

Earnings during past two years (logged) 0.003 0.045 0.949 1.003

Total value of assets (logged) 0.082 0.046 0.077 1.086

Total value of debts (logged) 0.060 0.030 0.047 1.061

Total income last month (logged) -0.040 0.069 0.568 0.961

Total expenses last month (logged) 0.079 0.145 0.587 1.082

Had positive net income last month 0.503 0.291 0.084 1.654

Had positive net worth -0.736 0.376 0.050 0.479

Male 0.007 0.233 0.977 1.007

Age -0.009 0.011 0.444 0.991

Black 0.345 0.359 0.337 1.412

White 1.501 0.840 0.074 4.485

Other or unknown race 0.298 0.689 0.665 1.347

Had an occupational certificate/license 1.546 0.284 0.000 4.694

Currently attending education/training 1.071 0.391 0.006 2.919

Employed during the year pre-program 0.357 0.426 0.402 1.429

Had a criminal record -0.327 0.261 0.210 0.721

Had a disability -0.764 0.278 0.006 0.466

Born in the United States 0.417 0.414 0.314 1.518

Had a child under age 18 -0.548 0.239 0.022 0.578

Separated, divorced, or widowed 0.052 0.277 0.850 1.054

Married/living with partner -0.139 0.331 0.675 0.870

Family size -0.083 0.070 0.241 0.921

Homeless -0.549 0.438 0.210 0.578

Rents home -0.348 0.299 0.244 0.706

Owns home 0.137 0.496 0.783 1.147

Received SNAP last month -0.304 0.263 0.248 0.738

Had filed for bankruptcy -0.673 0.384 0.080 0.510

Had collection agencies calling -0.572 0.241 0.018 0.564

Had a prime credit score -0.241 0.408 0.555 0.786

Had a credit score but thin file 0.785 0.274 0.004 2.193

Had a credit score and thick file 1.547 0.587 0.008 4.698

Number of derogatory public records 0.250 0.189 0.185 1.284

Number of trade accounts with balances -0.008 0.073 0.913 0.992

Number of inquiries made into credit 0.069 0.047 0.141 1.072

Had late payments on trade accounts past year -0.489 0.386 0.205 0.613

Number of trades with no late payments -0.011 0.030 0.720 0.989

Intercept 0.465 0.982 0.636 1.592

Pseudo R-square (.215)

Wald chi-square statistic (132.28)

P-value of Wald chi-square statistic (.000)
95 First Steps on the Road To Financial Well-Being: Appendix

Figure C11 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a College Degree Two 

Years After Program Entry Among All Enrollees (Figure 4.3)

b Robust SE (b) P-value Odds Ratio

Treatment 0.115 0.206 0.577 1.122

Earnings during past two years (logged) -0.051 0.045 0.262 0.950

Total value of assets (logged) 0.050 0.038 0.194 1.051

Total value of debts (logged) 0.078 0.037 0.032 1.081

Total income last month (logged) 0.017 0.064 0.794 1.017

Total expenses last month (logged) 0.527 0.202 0.009 1.695

Had positive net income last month 0.266 0.257 0.300 1.305

Had positive net worth 0.127 0.323 0.694 1.136

Male -0.221 0.225 0.327 0.802

Age 0.040 0.011 0.000 1.041

Black 0.838 0.431 0.052 2.312

White 1.416 0.817 0.083 4.120

Other or unknown race 0.576 0.580 0.321 1.778

Had an occupational certificate/license 0.186 0.211 0.377 1.205

Currently attending education/training 0.604 0.286 0.035 1.829

Employed during the year pre-program 0.513 0.432 0.235 1.671

Had a criminal record -0.044 0.249 0.859 0.957

Had a disability -0.926 0.318 0.004 0.396

Born in the United States -0.516 0.385 0.181 0.597

Had a child under age 18 -0.060 0.213 0.779 0.942

Separated, divorced, or widowed 0.139 0.281 0.621 1.149

Married/living with partner -0.573 0.369 0.120 0.564

Family size -0.106 0.083 0.201 0.899

Homeless -0.088 0.720 0.902 0.915

Rents home -0.018 0.284 0.950 0.982

Owns home -0.414 0.421 0.326 0.661

Received SNAP last month -0.509 0.227 0.025 0.601

Had filed for bankruptcy -0.281 0.410 0.494 0.755

Had collection agencies calling -0.069 0.219 0.754 0.934

Had a prime credit score 0.416 0.299 0.165 1.516

Had a credit score but thin file 0.080 0.299 0.790 1.083

Had a credit score and thick file 0.278 0.404 0.491 1.321

Number of derogatory public records -0.003 0.132 0.982 0.997

Number of trade accounts with balances 0.137 0.049 0.005 1.147

Number of inquiries made into credit -0.037 0.053 0.477 0.963

Had late payments on trade accounts past year -0.496 0.293 0.091 0.609

Number of trades with no late payments -0.011 0.020 0.592 0.989

Intercept -7.907 1.435 0.000 0.000

Pseudo R-square (.212)

Wald chi-square statistic (182.09)

P-value of Wald chi-square statistic (.000)
96 First Steps on the Road To Financial Well-Being: Appendix

Figure C12 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any College Credits Two 

Years After Program Entry Among Enrollees Without a College Degree (Figure 4.3)

b Robust SE (b) P-value Odds Ratio

Treatment -0.275 0.169 0.102 0.759

Earnings during past two years (logged) 0.020 0.036 0.580 1.020

Total value of assets (logged) 0.017 0.038 0.658 1.017

Total value of debts (logged) 0.086 0.028 0.002 1.090

Total income last month (logged) -0.112 0.056 0.047 0.894

Total expenses last month (logged) 0.167 0.120 0.165 1.182

Had positive net income last month 0.160 0.222 0.470 1.174

Had positive net worth 0.363 0.295 0.219 1.438

Male -0.063 0.188 0.738 0.939

Age -0.016 0.009 0.072 0.984

Black 1.090 0.298 0.000 2.975

White -0.149 0.760 0.844 0.861

Other or unknown race 0.905 0.504 0.073 2.472

GED 1.214 0.299 0.000 3.368

High school diploma 1.423 0.244 0.000 4.151

Had an occupational certificate/license 0.433 0.188 0.021 1.541

Currently attending education/training 1.183 0.298 0.000 3.265

Employed during the year pre-program -0.188 0.336 0.576 0.828

Had a criminal record -0.273 0.199 0.170 0.761

Had a disability 0.138 0.241 0.568 1.148

Born in the United States 0.263 0.345 0.446 1.301

Had a child under age 18 -0.391 0.184 0.033 0.676

Separated, divorced, or widowed -0.139 0.247 0.575 0.871

Married/living with partner 0.099 0.256 0.697 1.104

Family size -0.105 0.059 0.075 0.900

Homeless 0.105 0.389 0.787 1.111

Rents home -0.213 0.238 0.372 0.808

Owns home -0.028 0.409 0.946 0.973

Received SNAP last month 0.028 0.212 0.894 1.029

Had filed for bankruptcy 0.293 0.339 0.388 1.340

Had collection agencies calling 0.096 0.195 0.622 1.101

Had a prime credit score 0.334 0.304 0.273 1.396

Had a credit score but thin file -0.460 0.229 0.044 0.631

Had a credit score and thick file -0.044 0.380 0.908 0.957

Number of derogatory public records -0.178 0.138 0.196 0.837

Number of trade accounts with balances 0.074 0.052 0.156 1.077

Number of inquiries made into credit 0.112 0.045 0.012 1.118

Had late payments on trade accounts past year -0.094 0.281 0.737 0.910

Number of trades with no late payments 0.005 0.026 0.853 1.005

Intercept -2.815 0.898 0.002 0.060

Pseudo R-square (.188)

Wald chi-square statistic (156.91)

P-value of Wald chi-square statistic (.000)
97 First Steps on the Road To Financial Well-Being: Appendix

Figure C13 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having an Occupational 

Certificate/License Two Years After Program Entry Among All Enrollees (Figure 4.3)

b Clustered SE (b) P-value Odds Ratio

Treatment -0.384 0.244 0.116 0.681

Earnings during past two years (logged) -0.014 0.029 0.623 0.986

Total value of assets (logged) 0.047 0.037 0.199 1.048

Total value of debts (logged) 0.028 0.023 0.225 1.028

Total income last month (logged) 0.022 0.046 0.628 1.023

Total expenses last month (logged) -0.013 0.102 0.898 0.987

Had positive net income last month 0.326 0.157 0.038 1.386

Had positive net worth -0.293 0.338 0.387 0.746

Male -0.232 0.165 0.159 0.793

Age 0.026 0.008 0.001 1.027

Black -0.015 0.127 0.907 0.985

White -0.780 0.478 0.103 0.459

Other or unknown race 1.033 0.395 0.009 2.811

GED 1.529 0.222 0.000 4.616

High school diploma 1.268 0.243 0.000 3.553

Any college degree 1.328 0.287 0.000 3.773

Currently attending education/training 0.371 0.231 0.109 1.449

Employed during the year pre-program 0.217 0.305 0.478 1.242

Had a criminal record 0.527 0.142 0.000 1.695

Had a disability 0.427 0.148 0.004 1.533

Born in the United States -0.028 0.237 0.905 0.972

Had a child under age 18 0.161 0.151 0.285 1.175

Separated, divorced, or widowed -0.010 0.184 0.956 0.990

Married/living with partner 0.087 0.301 0.772 1.091

Family size -0.086 0.057 0.128 0.917

Homeless 0.001 0.311 0.997 1.001

Rents home -0.079 0.128 0.537 0.924

Owns home -0.015 0.398 0.970 0.985

Received SNAP last month -0.266 0.181 0.141 0.766

Had filed for bankruptcy -0.281 0.281 0.317 0.755

Had collection agencies calling 0.049 0.143 0.734 1.050

Had a prime credit score -0.063 0.142 0.659 0.939

Had a credit score but thin file 0.104 0.126 0.407 1.110

Had a credit score and thick file 0.426 0.144 0.003 1.532

Number of derogatory public records 0.232 0.115 0.043 1.261

Number of trade accounts with balances 0.008 0.029 0.792 1.008

Number of inquiries made into credit -0.022 0.029 0.439 0.978

Had late payments on trade accounts past year 0.135 0.326 0.678 1.145

Number of trades with no late payments -0.018 0.016 0.266 0.982

Intercept -2.908 0.761 0.000 0.055

Pseudo R-square (.130)

Wald chi-square statistic 

P-value of Wald chi-square statistic
98 First Steps on the Road To Financial Well-Being: Appendix

Figure C14 Linear Regression Analysis of Treatment Effects on Logged Monthly Income Two Years After Program 

Entry Among All Enrollees (Figure 4.4)

b Robust SE (b) P-value Partial Corr Coef2

Treatment -0.15 0.12 0.208 0.0019

Earnings during past two years (logged) 0.03 0.03 0.323 0.0012

Total value of assets (logged) 0.01 0.02 0.665 0.0001

Total value of debts (logged) 0.05 0.02 0.023 0.0066

Total income last month (logged) 0.10 0.05 0.026 0.0079

Total expenses last month (logged) 0.10 0.12 0.370 0.0017

Had positive net income last month -0.01 0.16 0.963 0.0000

Had positive net worth 0.18 0.22 0.431 0.0009

Male -0.22 0.13 0.090 0.0029

Age 0.00 0.01 0.916 0.0000

Black -0.15 0.20 0.450 0.0009

White 0.42 0.27 0.116 0.0008

Other or unknown race -0.12 0.42 0.780 0.0001

GED -0.04 0.21 0.866 0.0000

High school diploma 0.11 0.17 0.512 0.0006

Any college degree 0.32 0.22 0.144 0.0023

Had an occupational certificate/license 0.29 0.11 0.009 0.0054

Currently attending education/training 0.34 0.13 0.012 0.0033

Employed during the year pre-program 0.07 0.24 0.780 0.0001

Had a criminal record 0.12 0.13 0.368 0.0009

Had a disability -0.06 0.14 0.687 0.0001

Born in the United States -0.09 0.22 0.673 0.0002

Had a child under age 18 0.28 0.15 0.074 0.0052

Separated, divorced, or widowed 0.05 0.14 0.721 0.0001

Married/living with partner 0.13 0.19 0.474 0.0006

Family size 0.07 0.04 0.144 0.0032

Homeless 0.18 0.24 0.448 0.0005

Rents home 0.02 0.18 0.903 0.0000

Owns home 0.12 0.31 0.691 0.0002

Received SNAP last month -0.21 0.14 0.134 0.0024

Had filed for bankruptcy -0.27 0.23 0.241 0.0014

Had collection agencies calling 0.11 0.12 0.361 0.0007

Had a prime credit score -0.03 0.23 0.886 0.0000

Had a credit score but thin file 0.06 0.14 0.638 0.0002

Had a credit score and thick file -0.12 0.21 0.550 0.0003

Number of derogatory public records 0.06 0.08 0.421 0.0005

Number of trade accounts with balances -0.01 0.03 0.769 0.0001

Number of inquiries made into credit 0.01 0.04 0.886 0.0000

Had late payments on trade accounts past year -0.05 0.21 0.805 0.0001

Number of trades with no late payments 0.00 0.01 0.878 0.0000

Intercept 4.37 0.81 0.000

R-square (.129)

F-statistic (3.36)

P-value of F-statistic (.000)
99 First Steps on the Road To Financial Well-Being: Appendix

Figure C15 Linear Regression Analysis of Treatment Effects on Logged Monthly Expenses Two Years After Program 

Entry Among All Enrollees (Figure 4.4)

b Robust SE (b) P-value Partial Corr Coef2

Treatment -0.02 0.04 0.568 0.0003

Earnings during past two years (logged) 0.02 0.01 0.047 0.0040

Total value of assets (logged) 0.02 0.01 0.039 0.0037

Total value of debts (logged) -0.01 0.01 0.406 0.0008

Total income last month (logged) 0.02 0.01 0.086 0.0026

Total expenses last month (logged) 0.15 0.05 0.002 0.0234

Had positive net income last month 0.00 0.05 0.936 0.0000

Had positive net worth -0.07 0.06 0.269 0.0010

Male 0.03 0.05 0.507 0.0005

Age -0.01 0.00 0.000 0.0409

Black -0.12 0.06 0.061 0.0035

White -0.20 0.16 0.211 0.0012

Other or unknown race -0.09 0.12 0.440 0.0004

GED 0.01 0.08 0.929 0.0000

High school diploma -0.02 0.07 0.738 0.0001

Any college degree 0.09 0.07 0.233 0.0011

Had an occupational certificate/license 0.10 0.05 0.039 0.0039

Currently attending education/training 0.04 0.06 0.533 0.0003

Employed during the year pre-program -0.09 0.08 0.269 0.0011

Had a criminal record 0.10 0.05 0.049 0.0041

Had a disability -0.14 0.07 0.064 0.0046

Born in the United States -0.06 0.07 0.421 0.0005

Had a child under age 18 -0.01 0.05 0.792 0.0001

Separated, divorced, or widowed 0.12 0.06 0.058 0.0037

Married/living with partner 0.10 0.06 0.082 0.0023

Family size 0.06 0.01 0.000 0.0196

Homeless -0.21 0.15 0.146 0.0042

Rents home -0.10 0.07 0.153 0.0026

Owns home 0.08 0.09 0.403 0.0006

Received SNAP last month -0.17 0.05 0.000 0.0110

Had filed for bankruptcy 0.02 0.10 0.843 0.0000

Had collection agencies calling 0.12 0.05 0.023 0.0054

Had a prime credit score 0.07 0.07 0.291 0.0008

Had a credit score but thin file 0.12 0.06 0.039 0.0042

Had a credit score and thick file 0.05 0.08 0.522 0.0003

Number of derogatory public records 0.05 0.03 0.094 0.0023

Number of trade accounts with balances 0.00 0.01 0.912 0.0000

Number of inquiries made into credit 0.00 0.01 0.927 0.0000

Had late payments on trade accounts past year -0.13 0.09 0.173 0.0029

Number of trades with no late payments 0.00 0.01 0.582 0.0003

Intercept 6.43 0.33 0.000

R-square (.286)

F-statistic (9.78)

P-value of F-statistic (.000)
100 First Steps on the Road To Financial Well-Being: Appendix

Figure C16 Linear Regression Analysis of Treatment Effects on Monthly Net Income Two Years After Program Entry 

Among All Enrollees (Figure 4.4)

b Robust SE (b) P-value Partial Corr Coef2

Net income last month 0.28 0.08 0.000 0.0230

Treatment -126.54 101.34 0.212 0.0020

Earnings during past two years (logged) 20.50 27.25 0.452 0.0011

Total value of assets (logged) -2.96 19.14 0.877 0.0000

Total value of debts (logged) 31.45 16.27 0.053 0.0043

Total income last month (logged) -51.57 34.61 0.136 0.0025

Total expenses last month (logged) -14.66 75.29 0.846 0.0000

Had positive net income last month -93.06 150.36 0.536 0.0005

Had positive net worth 159.27 159.45 0.318 0.0011

Male -119.85 114.28 0.295 0.0014

Age 9.37 4.52 0.038 0.0040

Black -139.52 198.48 0.482 0.0011

White 89.20 375.56 0.812 0.0001

Other or unknown race -85.48 323.16 0.791 0.0001

GED 58.52 174.88 0.738 0.0001

High school diploma 54.66 140.15 0.697 0.0002

Any college degree 558.95 198.77 0.005 0.0105

Had an occupational certificate/license -73.18 105.17 0.487 0.0005

Currently attending education/training 14.58 138.79 0.916 0.0000

Employed during the year pre-program 90.29 249.65 0.718 0.0003

Had a criminal record 132.56 115.26 0.250 0.0016

Had a disability 30.08 117.31 0.798 0.0001

Born in the United States -116.79 199.84 0.559 0.0005

Had a child under age 18 121.84 116.99 0.298 0.0015

Separated, divorced, or widowed 67.88 129.85 0.601 0.0003

Married/living with partner 95.24 145.95 0.514 0.0005

Family size -18.09 29.88 0.545 0.0004

Homeless 57.99 195.21 0.766 0.0001

Rents home 166.13 137.61 0.228 0.0017

Owns home 337.35 234.60 0.151 0.0026

Received SNAP last month 75.50 122.33 0.537 0.0005

Had filed for bankruptcy -403.55 177.75 0.023 0.0046

Had collection agencies calling -169.07 109.61 0.123 0.0026

Had a prime credit score 32.01 164.67 0.846 0.0000

Had a credit score but thin file -142.71 138.93 0.305 0.0014

Had a credit score and thick file -259.42 201.77 0.199 0.0018

Number of derogatory public records -5.50 94.50 0.954 0.0000

Number of trade accounts with balances 11.90 24.49 0.627 0.0002

Number of inquiries made into credit 42.36 27.50 0.124 0.0044

Had late payments on trade accounts past year 407.26 150.26 0.007 0.0067

Number of trades with no late payments -6.64 13.18 0.614 0.0003

Intercept -527.66 558.64 0.345

R-square (.090)

F-statistic (2.27)

P-value of F-statistic (.000)
101 First Steps on the Road To Financial Well-Being: Appendix

Figure C17 Figure C17. Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Net Income 

Greater Than Zero Two Years After Program Entry Among All Enrollees (Figure 4.4)

b Robust SE (b) P-value Odds Ratio

Treatment -0.32 0.16 0.041 0.724

Earnings during past two years (logged) 0.02 0.03 0.641 1.015

Total value of assets (logged) 0.00 0.03 0.921 0.997

Total value of debts (logged) 0.06 0.02 0.020 1.060

Total income last month (logged) -0.02 0.05 0.751 0.984

Total expenses last month (logged) -0.07 0.11 0.533 0.934

Had positive net income last month 0.55 0.20 0.005 1.730

Had positive net worth 0.57 0.27 0.034 1.770

Male -0.27 0.18 0.131 0.763

Age 0.01 0.01 0.215 1.010

Black -0.05 0.29 0.870 0.953

White 0.70 0.50 0.164 2.016

Other or unknown race -0.08 0.47 0.864 0.923

GED -0.03 0.28 0.924 0.973

High school diploma 0.06 0.24 0.788 1.066

Any college degree 0.78 0.29 0.007 2.174

Had an occupational certificate/license -0.07 0.16 0.671 0.934

Currently attending education/training -0.05 0.23 0.819 0.948

Employed during the year pre-program -0.07 0.29 0.809 0.932

Had a criminal record 0.46 0.20 0.024 1.578

Had a disability -0.04 0.22 0.853 0.960

Born in the United States -0.55 0.30 0.064 0.575

Had a child under age 18 0.12 0.17 0.472 1.128

Separated, divorced, or widowed -0.21 0.20 0.313 0.814

Married/living with partner -0.34 0.24 0.154 0.709

Family size 0.00 0.06 0.974 1.002

Homeless -0.09 0.37 0.813 0.916

Rents home 0.21 0.22 0.345 1.230

Owns home 0.03 0.34 0.941 1.025

Received SNAP last month -0.11 0.18 0.532 0.896

Had filed for bankruptcy -0.28 0.33 0.409 0.759

Had collection agencies calling -0.13 0.18 0.447 0.875

Had a prime credit score 0.35 0.25 0.160 1.426

Had a credit score but thin file -0.19 0.21 0.383 0.830

Had a credit score and thick file -0.30 0.32 0.356 0.742

Number of derogatory public records 0.13 0.11 0.232 1.137

Number of trade accounts with balances 0.05 0.04 0.215 1.049

Number of inquiries made into credit 0.05 0.03 0.161 1.047

Had late payments on trade accounts past year 0.64 0.30 0.030 1.899

Number of trades with no late payments -0.04 0.02 0.076 0.965

Intercept -0.86 0.80 0.282 0.422

Pseudo R-square (.063)

Wald chi-square statistic (63.91)

P-value of Wald chi-square statistic (.010)
102 First Steps on the Road To Financial Well-Being: Appendix

Figure C18 Logistic Regression Analysis of Treatment Effects on the Likelihood of Receiving Any Income Supports 

Two Years After Program Entry Among All Enrollees (Figure 4.4)

b Clustered SE (b) P-value Odds Ratio

Received any income supports last month 0.817 0.381 0.032 2.263

Treatment -0.371 0.255 0.146 0.690

Earnings during past two years (logged) -0.046 0.023 0.045 0.955

Total value of assets (logged) -0.006 0.034 0.864 0.994

Total value of debts (logged) -0.004 0.040 0.929 0.996

Total income last month (logged) -0.107 0.062 0.084 0.899

Total expenses last month (logged) -0.551 0.123 0.000 0.576

Had positive net income last month -0.390 0.267 0.144 0.677

Had positive net worth 0.066 0.337 0.845 1.068

Male -0.756 0.151 0.000 0.469

Age 0.034 0.005 0.000 1.034

Black 0.053 0.213 0.804 1.054

White -0.359 0.603 0.552 0.698

Other or unknown race 0.362 0.249 0.146 1.436

GED -1.014 0.447 0.023 0.363

High school diploma -0.765 0.442 0.083 0.465

Any college degree -0.894 0.420 0.033 0.409

Had an occupational certificate/license 0.209 0.166 0.207 1.232

Currently attending education/training -0.180 0.222 0.417 0.835

Employed during the year pre-program 0.381 0.321 0.236 1.463

Had a criminal record 0.214 0.351 0.543 1.238

Had a disability 1.254 0.227 0.000 3.503

Born in the United States 0.491 0.131 0.000 1.634

Had a child under age 18 0.777 0.168 0.000 2.175

Separated, divorced, or widowed -0.400 0.197 0.043 0.670

Married/living with partner -0.507 0.320 0.113 0.602

Family size 0.256 0.052 0.000 1.292

Homeless -0.348 0.443 0.432 0.706

Rents home 0.105 0.285 0.712 1.111

Owns home -0.176 0.526 0.738 0.839

Received SNAP last month 0.697 0.146 0.000 2.008

Had filed for bankruptcy -0.357 0.597 0.550 0.700

Had collection agencies calling 0.464 0.215 0.031 1.590

Had a prime credit score -0.185 0.236 0.435 0.831

Had a credit score but thin file -0.031 0.341 0.928 0.970

Had a credit score and thick file 0.046 0.599 0.939 1.047

Number of derogatory public records -0.172 0.169 0.309 0.842

Number of trade accounts with balances -0.056 0.038 0.142 0.946

Number of inquiries made into credit -0.043 0.033 0.192 0.958

Had late payments on trade accounts past year -0.163 0.273 0.550 0.849

Number of trades with no late payments 0.014 0.017 0.407 1.014

Intercept 3.797 0.722 0.000 44.589

Pseudo R-square (.230)

Wald chi-square statistic 

P-value of Wald chi-square statistic 
103 First Steps on the Road To Financial Well-Being: Appendix

Figure C19 Negative Binomial Regression Analysis of Treatment Effects on the Number of Open Trade Accounts 

Two Years After Program Entry Among All Enrollees (Figure 4.5)

b Robust SE (b) P-value IRR

Number of open trade accounts at program entry 0.089 0.018 0.000 1.093

Treatment 0.154 0.071 0.030 1.166

Earnings during past two years (logged) 0.016 0.015 0.285 1.016

Total value of assets (logged) 0.004 0.014 0.780 1.004

Total value of debts (logged) 0.026 0.013 0.046 1.026

Total income last month (logged) -0.007 0.025 0.788 0.993

Total expenses last month (logged) 0.046 0.071 0.522 1.047

Had positive net income last month 0.071 0.088 0.417 1.074

Had positive net worth 0.162 0.103 0.116 1.176

Male -0.255 0.081 0.002 0.775

Age -0.005 0.004 0.183 0.995

Black 0.007 0.132 0.960 1.007

White 0.513 0.279 0.066 1.670

Other or unknown race 0.128 0.312 0.682 1.136

GED 0.410 0.144 0.004 1.507

High school diploma 0.301 0.128 0.018 1.351

Any college degree 0.349 0.130 0.007 1.418

Had an occupational certificate/license -0.041 0.074 0.577 0.959

Currently attending education/training 0.217 0.089 0.015 1.242

Employed during the year pre-program -0.157 0.132 0.234 0.855

Had a criminal record 0.070 0.084 0.401 1.073

Had a disability 0.025 0.090 0.779 1.026

Born in the United States -0.197 0.119 0.098 0.821

Had a child under age 18 0.012 0.081 0.880 1.012

Separated, divorced, or widowed -0.033 0.086 0.705 0.968

Married/living with partner -0.004 0.105 0.973 0.996

Family size 0.026 0.021 0.221 1.026

Homeless -0.085 0.272 0.756 0.919

Rents home 0.047 0.116 0.686 1.048

Owns home -0.013 0.148 0.928 0.987

Received SNAP last month -0.042 0.083 0.616 0.959

Had filed for bankruptcy -0.029 0.135 0.827 0.971

Had collection agencies calling 0.202 0.078 0.010 1.224

Had a prime credit score 0.366 0.103 0.000 1.441

Had a credit score but thin file 0.390 0.141 0.006 1.477

Had a credit score and thick file 1.280 0.161 0.000 3.595

Number of derogatory public records 0.056 0.050 0.260 1.058

Number of trade accounts with balances 0.021 0.016 0.199 1.021

Number of inquiries made into credit 0.014 0.011 0.208 1.014

Had late payments on trade accounts past year 0.017 0.099 0.862 1.017

Number of trades with no late payments -0.007 0.006 0.288 0.993

Intercept -1.152 0.443 0.009 0.316

Pseudo R-square (.205)

Wald chi-square statistic (1361.84)

P-value of Wald chi-square statistic (.000)
104 First Steps on the Road To Financial Well-Being: Appendix

Figure C20 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Trade Accounts Paid 

as Agreed Two Years After Program Entry Among All Enrollees (Figure 4.5)

b Robust SE (b) P-value Odds Ratio

Had any trades paid as agreed at program entry 1.996 0.336 0.000 7.357

Treatment 0.882 0.244 0.000 2.415

Earnings during past two years (logged) 0.075 0.044 0.086 1.078

Total value of assets (logged) -0.072 0.051 0.157 0.931

Total value of debts (logged) 0.024 0.035 0.485 1.025

Total income last month (logged) -0.031 0.068 0.654 0.970

Total expenses last month (logged) 0.050 0.179 0.780 1.051

Had positive net income last month -0.627 0.282 0.026 0.534

Had positive net worth 0.947 0.418 0.024 2.577

Male -0.515 0.252 0.041 0.598

Age -0.007 0.011 0.536 0.993

Black 0.321 0.367 0.382 1.378

White 1.593 0.616 0.010 4.921

Other or unknown race 0.892 0.685 0.193 2.440

GED 0.497 0.369 0.178 1.643

High school diploma 0.499 0.273 0.068 1.647

Any college degree 0.636 0.428 0.138 1.888

Had an occupational certificate/license 0.185 0.245 0.450 1.204

Currently attending education/training -0.387 0.371 0.297 0.679

Employed during the year pre-program -0.222 0.388 0.566 0.801

Had a criminal record 0.014 0.242 0.954 1.014

Had a disability -0.264 0.275 0.337 0.768

Born in the United States -0.906 0.457 0.047 0.404

Had a child under age 18 0.541 0.250 0.030 1.718

Separated, divorced, or widowed -0.381 0.302 0.207 0.683

Married/living with partner 0.264 0.319 0.407 1.302

Family size -0.059 0.078 0.446 0.942

Homeless 0.261 0.446 0.558 1.298

Rents home 0.237 0.321 0.460 1.267

Owns home 0.531 0.549 0.333 1.701

Received SNAP last month -0.015 0.301 0.959 0.985

Had filed for bankruptcy 0.102 0.460 0.824 1.108

Had collection agencies calling -0.079 0.281 0.779 0.924

Had a prime credit score 0.491 0.614 0.424 1.635

Had a credit score but thin file -0.209 0.356 0.557 0.811

Had a credit score and thick file 2.003 1.664 0.229 7.413

Number of derogatory public records 0.191 0.218 0.380 1.211

Number of trade accounts with balances 0.303 0.159 0.056 1.354

Number of inquiries made into credit 0.183 0.110 0.095 1.201

Had late payments on trade accounts past year 0.401 0.430 0.352 1.493

Number of trades with no late payments 0.543 0.141 0.000 1.721

Intercept -1.799 1.195 0.132 0.165

Pseudo R-square (.503)

Wald chi-square statistic (220.42)

P-value of Wald chi-square statistic (.000)
105 First Steps on the Road To Financial Well-Being: Appendix

Figure C21 Negative Binomial Regression Analysis of Treatment Effects on the Number of Trade Accounts Paid as 

Agreed Two Years After Program Entry Among All Enrollees (Figure 4.5)

b Robust SE (b) P-value IRR

Number of trades paid as agreed at program entry 0.113 0.011 0.000 1.119

Treatment 0.226 0.057 0.000 1.253

Earnings during past two years (logged) 0.031 0.011 0.006 1.032

Total value of assets (logged) -0.005 0.011 0.618 0.995

Total value of debts (logged) 0.029 0.010 0.003 1.030

Total income last month (logged) -0.029 0.020 0.154 0.972

Total expenses last month (logged) 0.006 0.060 0.924 1.006

Had positive net income last month 0.014 0.075 0.847 1.015

Had positive net worth 0.208 0.071 0.004 1.231

Male -0.134 0.062 0.031 0.874

Age -0.002 0.003 0.554 0.998

Black -0.008 0.108 0.939 0.992

White 0.495 0.201 0.014 1.641

Other or unknown race -0.034 0.255 0.894 0.967

GED 0.289 0.110 0.009 1.335

High school diploma 0.245 0.096 0.011 1.278

Any college degree 0.238 0.106 0.025 1.269

Had an occupational certificate/license 0.045 0.055 0.412 1.046

Currently attending education/training 0.037 0.076 0.628 1.038

Employed during the year pre-program -0.216 0.095 0.023 0.805

Had a criminal record -0.003 0.068 0.965 0.997

Had a disability -0.055 0.072 0.440 0.946

Born in the United States -0.218 0.093 0.018 0.804

Had a child under age 18 0.044 0.070 0.531 1.045

Separated, divorced, or widowed -0.063 0.067 0.350 0.939

Married/living with partner 0.062 0.077 0.426 1.063

Family size 0.023 0.019 0.212 1.024

Homeless 0.154 0.175 0.380 1.167

Rents home 0.162 0.097 0.095 1.176

Owns home 0.049 0.111 0.656 1.051

Received SNAP last month -0.031 0.061 0.615 0.970

Had filed for bankruptcy 0.142 0.091 0.121 1.152

Had collection agencies calling 0.139 0.059 0.017 1.150

Had a prime credit score 0.186 0.082 0.024 1.204

Had a credit score but thin file 0.822 0.119 0.000 2.275

Had a credit score and thick file 1.420 0.133 0.000 4.138

Number of derogatory public records 0.074 0.036 0.040 1.077

Number of trade accounts with balances 0.031 0.010 0.002 1.031

Number of inquiries made into credit 0.022 0.010 0.031 1.023

Had late payments on trade accounts past year -0.032 0.073 0.655 0.968

Number of trades with no late payments -0.055 0.011 0.000 0.947

Intercept -0.684 0.349 0.050 0.504

Pseudo R-square (.241)

Wald chi-square statistic (2188.5)

P-value of Wald chi-square statistic (.000)

 
106 First Steps on the Road To Financial Well-Being: Appendix

Figure C22 Linear Regression Analysis of Treatment Effects on the Logged Number of On-Time Payments Made on 

Trade Accounts in the Second Year After Program Entry Among All Enrollees (Figure 4.5)

b Robust SE (b) P-value Partial Corr Coef2

Number of on-time payments in the year pre-program 

(logged) 0.220 0.044 0.000 0.0428

Treatment 0.161 0.078 0.039 0.0057

Earnings during past two years (logged) 0.017 0.016 0.294 0.0014

Total value of assets (logged) 0.003 0.016 0.844 0.0000

Total value of debts (logged) 0.008 0.012 0.494 0.0005

Total income last month (logged) -0.005 0.026 0.857 0.0000

Total expenses last month (logged) 0.032 0.061 0.600 0.0003

Had positive net income last month 0.008 0.102 0.940 0.0000

Had positive net worth 0.191 0.130 0.144 0.0028

Male -0.244 0.084 0.004 0.0096

Age -0.001 0.004 0.879 0.0000

Black -0.027 0.160 0.867 0.0001

White 0.729 0.298 0.015 0.0068

Other or unknown race 0.256 0.344 0.457 0.0008

GED 0.093 0.134 0.486 0.0007

High school diploma 0.132 0.104 0.205 0.0021

Any college degree 0.180 0.135 0.182 0.0019

Had an occupational certificate/license -0.005 0.083 0.954 0.0000

Currently attending education/training 0.251 0.126 0.046 0.0049

Employed during the year pre-program -0.137 0.144 0.340 0.0010

Had a criminal record 0.027 0.092 0.770 0.0001

Had a disability 0.037 0.101 0.712 0.0001

Born in the United States -0.293 0.148 0.048 0.0053

Had a child under age 18 0.115 0.087 0.186 0.0024

Separated, divorced, or widowed 0.003 0.097 0.972 0.0000

Married/living with partner 0.042 0.123 0.731 0.0002

Family size 0.035 0.028 0.201 0.0024

Homeless 0.172 0.180 0.342 0.0011

Rents home 0.172 0.118 0.147 0.0030

Owns home 0.177 0.169 0.297 0.0012

Received SNAP last month -0.081 0.090 0.367 0.0010

Had filed for bankruptcy 0.035 0.145 0.810 0.0001

Had collection agencies calling 0.200 0.086 0.020 0.0062

Had a prime credit score 0.564 0.139 0.000 0.0213

Had a credit score but thin file 0.095 0.115 0.410 0.0009

Had a credit score and thick file 1.261 0.178 0.000 0.0584

Number of derogatory public records 0.095 0.052 0.068 0.0028

Number of trade accounts with balances 0.051 0.019 0.007 0.0075

Number of inquiries made into credit 0.039 0.017 0.020 0.0059

Had late payments on trade accounts past year -0.082 0.156 0.600 0.0005

Number of trades with no late payments 0.027 0.008 0.001 0.0092

Intercept 0.153 0.354 0.666

R-square (.645)

F-statistic (70.37)

P-value of F-statistic (.000)
107 First Steps on the Road To Financial Well-Being: Appendix

Figure C23 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Credit Score Two Years 

After Program Entry Among All Enrollees (Figure 4.5)

b Robust SE (b) P-value Odds Ratio

Had a credit score at program entry 3.136 0.923 0.001 23.012

Treatment 0.314 0.208 0.131 1.369

Earnings during past two years (logged) -0.003 0.040 0.931 0.997

Total value of assets (logged) 0.007 0.043 0.865 1.007

Total value of debts (logged) 0.045 0.031 0.147 1.046

Total income last month (logged) -0.006 0.062 0.917 0.994

Total expenses last month (logged) 0.068 0.173 0.696 1.070

Had positive net income last month -0.267 0.251 0.288 0.766

Had positive net worth 0.297 0.365 0.416 1.346

Male -0.480 0.213 0.024 0.619

Age -0.006 0.010 0.570 0.994

Black 0.152 0.345 0.658 1.165

White 1.849 0.715 0.010 6.354

Other or unknown race -0.238 0.901 0.792 0.789

GED 0.186 0.315 0.556 1.204

High school diploma -0.039 0.251 0.877 0.962

Any college degree 0.217 0.436 0.618 1.243

Had an occupational certificate/license -0.048 0.219 0.827 0.953

Currently attending education/training 0.128 0.322 0.692 1.136

Employed during the year pre-program 0.085 0.363 0.815 1.089

Had a criminal record 0.007 0.224 0.975 1.007

Had a disability -0.074 0.267 0.781 0.929

Born in the United States -0.469 0.402 0.244 0.626

Had a child under age 18 0.108 0.225 0.633 1.114

Separated, divorced, or widowed -0.789 0.286 0.006 0.454

Married/living with partner -0.286 0.293 0.329 0.751

Family size -0.011 0.070 0.876 0.989

Homeless -0.438 0.439 0.319 0.646

Rents home 0.391 0.286 0.172 1.478

Owns home 0.459 0.465 0.324 1.582

Received SNAP last month -0.243 0.236 0.304 0.784

Had filed for bankruptcy -0.133 0.390 0.732 0.875

Had collection agencies calling 0.015 0.234 0.951 1.015

Had a prime credit score 0.315 0.395 0.425 1.370

Had a credit score but thin file -2.270 0.827 0.006 0.103

Number of derogatory public records 0.205 0.175 0.241 1.228

Number of trade accounts with balances 0.404 0.111 0.000 1.497

Number of inquiries made into credit 0.191 0.068 0.005 1.211

Had late payments on trade accounts past year 0.154 0.408 0.705 1.167

Number of trades with no late payments 0.163 0.089 0.067 1.178

Intercept -1.163 1.182 0.325 0.312

Pseudo R-square (.388)

Wald chi-square statistic (209.77)

P-value of Wald chi-square statistic (.000)
108 First Steps on the Road To Financial Well-Being: Appendix

Figure C24 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Prime Credit Score Two 

Years After Program Entry Among All Enrollees (Figure 4.5)

b Robust SE (b) P-value Odds Ratio

Treatment 0.261 0.230 0.257 1.298

Earnings during past two years (logged) -0.034 0.048 0.474 0.966

Total value of assets (logged) -0.037 0.048 0.435 0.963

Total value of debts (logged) 0.003 0.038 0.939 1.003

Total income last month (logged) 0.042 0.072 0.559 1.043

Total expenses last month (logged) -0.010 0.175 0.955 0.990

Had positive net income last month 0.032 0.288 0.912 1.032

Had positive net worth 0.762 0.364 0.036 2.142

Male 0.098 0.282 0.729 1.103

Age 0.007 0.012 0.522 1.007

Black -0.081 0.358 0.821 0.922

White 0.486 0.955 0.611 1.626

Other or unknown race 0.378 0.607 0.534 1.459

GED 0.329 0.379 0.385 1.389

High school diploma 0.115 0.312 0.712 1.122

Any college degree 0.711 0.383 0.064 2.036

Had an occupational certificate/license -0.277 0.233 0.235 0.758

Currently attending education/training -0.324 0.381 0.396 0.723

Employed during the year pre-program 0.769 0.438 0.079 2.157

Had a criminal record 0.128 0.266 0.631 1.136

Had a disability 0.329 0.303 0.277 1.390

Born in the United States -1.338 0.366 0.000 0.262

Had a child under age 18 -0.232 0.241 0.337 0.793

Separated, divorced, or widowed 0.080 0.281 0.776 1.083

Married/living with partner 0.318 0.358 0.374 1.374

Family size -0.003 0.076 0.963 0.997

Homeless -0.368 0.649 0.571 0.692

Rents home 0.066 0.319 0.835 1.069

Owns home 0.555 0.472 0.240 1.742

Received SNAP last month 0.063 0.255 0.804 1.065

Had filed for bankruptcy -0.070 0.582 0.905 0.933

Had collection agencies calling 0.329 0.269 0.222 1.389

Had a prime credit score 1.814 0.288 0.000 6.137

Had a credit score but thin file 0.335 0.308 0.277 1.398

Had a credit score and thick file 1.687 0.459 0.000 5.402

Number of derogatory public records 0.015 0.176 0.934 1.015

Number of trade accounts with balances -0.226 0.060 0.000 0.797

Number of inquiries made into credit -0.121 0.064 0.058 0.886

Had late payments on trade accounts past year -1.117 0.452 0.013 0.327

Number of trades with no late payments 0.075 0.023 0.001 1.077

Intercept -2.233 1.179 0.058 0.107

Pseudo R-square (.377)

Wald chi-square statistic (233.06)

P-value of Wald chi-square statistic (.000)
109 First Steps on the Road To Financial Well-Being: Appendix

Figure C25 Linear Regression Analysis of Treatment Effects on Credit Scores Two Years After Program Entry Among 

Enrollees Who Had Scores at Program Entry and Two Years Later (Figure 4.5)

b Robust SE (b) P-value Partial Corr Coef2

Credit score at program entry 0.387 0.061 0.000 0.0871

Treatment 0.996 5.192 0.848 0.0001

Earnings during past two years (logged) 0.139 1.102 0.900 0.0000

Total value of assets (logged) -0.080 0.967 0.934 0.0000

Total value of debts (logged) -0.997 0.907 0.272 0.0024

Total income last month (logged) -0.988 1.624 0.543 0.0007

Total expenses last month (logged) 2.712 4.228 0.521 0.0008

Had positive net income last month -2.205 6.798 0.746 0.0003

Had positive net worth 6.914 8.564 0.420 0.0016

Male 5.098 5.712 0.372 0.0017

Age 0.372 0.277 0.180 0.0040

Black -15.881 8.560 0.064 0.0092

White -7.474 24.735 0.763 0.0003

Other or unknown race 41.704 22.090 0.060 0.0038

GED -10.373 9.617 0.281 0.0025

High school diploma -15.092 7.411 0.042 0.0086

Any college degree 4.000 9.530 0.675 0.0004

Had an occupational certificate/license 2.105 5.408 0.697 0.0003

Currently attending education/training -9.221 8.315 0.268 0.0033

Employed during the year pre-program 7.198 9.757 0.461 0.0011

Had a criminal record -3.226 5.947 0.588 0.0007

Had a disability 0.784 6.800 0.908 0.0000

Born in the United States -15.490 9.545 0.105 0.0070

Had a child under age 18 -1.716 5.405 0.751 0.0002

Separated, divorced, or widowed -3.777 6.976 0.588 0.0006

Married/living with partner -4.410 8.993 0.624 0.0007

Family size -0.579 2.040 0.777 0.0003

Homeless -34.133 13.936 0.015 0.0103

Rents home -7.284 7.510 0.333 0.0019

Owns home 5.466 11.913 0.647 0.0005

Received SNAP last month 7.666 5.527 0.166 0.0039

Had filed for bankruptcy 3.701 9.002 0.681 0.0004

Had collection agencies calling 4.473 5.245 0.394 0.0015

Had a prime credit score 11.178 9.853 0.257 0.0031

Had a credit score and thick file 11.865 6.633 0.074 0.0072

Number of derogatory public records -3.096 3.279 0.345 0.0017

Number of trade accounts with balances -3.284 1.045 0.002 0.0212

Number of inquiries made into credit -2.123 0.941 0.024 0.0107

Had late payments on trade accounts past year -4.170 5.857 0.477 0.0008

Number of trades with no late payments 0.925 0.482 0.055 0.0082

Intercept 380.836 42.209 0.000

R-square (.578)

F-statistic (15.52)

P-value of F-statistic (.000)
110 First Steps on the Road To Financial Well-Being: Appendix

Figure C26 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having an Increase in Credit 

Score Two Years After Program Entry Among Enrollees Who Had Scores at Program Entry and Two 

Years Later (Figure 4.5)

b Robust SE (b) P-value Odds Ratio

Score at program entry -0.019 0.003 0.000 0.981

Treatment -0.054 0.226 0.811 0.947

Earnings during past two years (logged) 0.008 0.046 0.859 1.008

Total value of assets (logged) 0.075 0.045 0.092 1.078

Total value of debts (logged) -0.016 0.041 0.706 0.984

Total income last month (logged) -0.028 0.081 0.731 0.972

Total expenses last month (logged) -0.243 0.201 0.225 0.784

Had positive net income last month -0.248 0.284 0.382 0.780

Had positive net worth 0.027 0.369 0.942 1.027

Male 0.079 0.257 0.758 1.082

Age 0.018 0.011 0.106 1.018

Black -0.584 0.410 0.155 0.558

White -0.155 0.686 0.822 0.857

Other or unknown race 1.152 1.308 0.379 3.163

GED 0.018 0.434 0.968 1.018

High school diploma -0.256 0.333 0.442 0.774

Any college degree 0.170 0.431 0.692 1.186

Had an occupational certificate/license 0.170 0.244 0.487 1.185

Currently attending education/training -0.076 0.318 0.812 0.927

Employed during the year pre-program 0.195 0.419 0.642 1.215

Had a criminal record -0.386 0.246 0.117 0.680

Had a disability 0.482 0.337 0.153 1.619

Born in the United States -0.998 0.496 0.044 0.369

Had a child under age 18 -0.104 0.248 0.674 0.901

Separated, divorced, or widowed -0.532 0.323 0.100 0.588

Married/living with partner -0.307 0.379 0.418 0.736

Family size -0.041 0.080 0.613 0.960

Homeless -0.451 0.526 0.391 0.637

Rents home 0.081 0.353 0.819 1.084

Owns home -0.288 0.547 0.598 0.749

Received SNAP last month 0.209 0.262 0.426 1.232

Had filed for bankruptcy 0.379 0.519 0.465 1.461

Had collection agencies calling -0.144 0.250 0.566 0.866

Had a prime credit score 0.398 0.405 0.326 1.488

Had a credit score and thick file 0.307 0.289 0.289 1.359

Number of derogatory public records -0.200 0.155 0.198 0.819

Number of trade accounts with balances -0.073 0.044 0.102 0.930

Number of inquiries made into credit -0.085 0.039 0.030 0.918

Had late payments on trade accounts past year 0.078 0.306 0.800 1.081

Number of trades with no late payments 0.018 0.024 0.439 1.019

Intercept 14.104 2.289 0.000 1334942

Pseudo R-square (.184)

Wald chi-square statistic (87.27)

P-value of Wald chi-square statistic (.000)
111 First Steps on the Road To Financial Well-Being: Appendix

Figure C27 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Credit Score Two Years 

After Program Entry by Credit Status at Program Entry Among All Enrollees (Figure 4.6)

b Robust SE (b) P-value Odds Ratio

Treatment 0.487 0.285 0.087 1.628

Had a credit score but thin file 1.094 0.407 0.007 2.987

Had a credit score and thick file 2.527 0.938 0.007 12.512

Treatment*Had a credit score but thin file -0.447 0.405 0.270 0.640

Treatment*Had a credit score and thick file (omitted due to collinearity)

Earnings during past two years (logged) -0.001 0.041 0.972 0.999

Total value of assets (logged) 0.007 0.044 0.876 1.007

Total value of debts (logged) 0.044 0.031 0.154 1.045

Total income last month (logged) -0.007 0.061 0.914 0.993

Total expenses last month (logged) 0.071 0.173 0.681 1.074

Had positive net income last month -0.278 0.251 0.268 0.757

Had positive net worth 0.294 0.365 0.421 1.341

Male -0.485 0.215 0.024 0.616

Age -0.007 0.010 0.519 0.993

Black 0.180 0.342 0.599 1.197

White 1.847 0.707 0.009 6.340

Other or unknown race -0.232 0.887 0.793 0.793

GED 0.187 0.316 0.555 1.205

High school diploma -0.040 0.251 0.874 0.961

Any college degree 0.213 0.439 0.628 1.237

Had an occupational certificate/license -0.046 0.219 0.832 0.955

Currently attending education/training 0.096 0.321 0.765 1.101

Employed during the year pre-program 0.073 0.370 0.845 1.075

Had a criminal record 0.003 0.222 0.989 1.003

Had a disability -0.051 0.266 0.848 0.950

Born in the United States -0.460 0.399 0.249 0.631

Had a child under age 18 0.120 0.225 0.594 1.128

Separated, divorced, or widowed -0.761 0.282 0.007 0.467

Married/living with partner -0.273 0.289 0.347 0.761

Family size -0.015 0.069 0.830 0.985

Homeless -0.422 0.428 0.324 0.656

Rents home 0.390 0.287 0.174 1.477

Owns home 0.455 0.465 0.328 1.577

Received SNAP last month -0.261 0.235 0.267 0.770

Had filed for bankruptcy -0.139 0.386 0.719 0.870

Had collection agencies calling 0.025 0.235 0.915 1.025

Had a prime credit score 0.284 0.392 0.468 1.329

Number of derogatory public records 0.206 0.175 0.241 1.228

Number of trade accounts with balances 0.425 0.114 0.000 1.529

Number of inquiries made into credit 0.183 0.069 0.008 1.200

Had late payments on trade accounts past year 0.136 0.402 0.736 1.145

Number of trades with no late payments 0.157 0.085 0.066 1.170

Intercept -1.267 1.201 0.292 0.282

Pseudo R-square (.326)

Wald chi-square statistic (198.9)

P-value of Wald chi-square statistic (.000)
112 First Steps on the Road To Financial Well-Being: Appendix

Figure C28 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Prime Credit Score Two 

Years After Program Entry by Credit Status at Program Entry Among All Enrollees (Figure 4.6)

b Robust SE (b) P-value Odds Ratio

Treatment -0.099 0.392 0.801 0.906

Had a credit score but thin file 0.332 0.399 0.405 1.394

Had a credit score and thick file 1.002 0.569 0.078 2.724

Treatment*Had a credit score but thin file -0.065 0.552 0.906 0.937

Treatment*Had a credit score and thick file 1.291 0.593 0.029 3.635

Earnings during past two years (logged) -0.054 0.052 0.296 0.948

Total value of assets (logged) -0.038 0.049 0.435 0.962

Total value of debts (logged) 0.010 0.038 0.800 1.010

Total income last month (logged) 0.044 0.074 0.546 1.045

Total expenses last month (logged) 0.009 0.184 0.963 1.009

Had positive net income last month 0.012 0.289 0.968 1.012

Had positive net worth 0.835 0.376 0.027 2.305

Male 0.064 0.282 0.820 1.066

Age 0.006 0.011 0.588 1.006

Black -0.059 0.364 0.871 0.942

White 0.660 0.965 0.494 1.935

Other or unknown race 0.512 0.578 0.375 1.669

GED 0.340 0.384 0.376 1.405

High school diploma 0.137 0.320 0.667 1.147

Any college degree 0.686 0.386 0.076 1.985

Had an occupational certificate/license -0.260 0.232 0.263 0.771

Currently attending education/training -0.420 0.400 0.294 0.657

Employed during the year pre-program 0.955 0.476 0.045 2.598

Had a criminal record 0.152 0.271 0.574 1.164

Had a disability 0.359 0.311 0.249 1.432

Born in the United States -1.391 0.375 0.000 0.249

Had a child under age 18 -0.230 0.244 0.346 0.794

Separated, divorced, or widowed 0.112 0.282 0.691 1.119

Married/living with partner 0.303 0.355 0.394 1.354

Family size -0.006 0.079 0.943 0.994

Homeless -0.442 0.684 0.518 0.643

Rents home 0.025 0.323 0.940 1.025

Owns home 0.500 0.478 0.295 1.648

Received SNAP last month 0.048 0.250 0.848 1.049

Had filed for bankruptcy -0.033 0.574 0.954 0.967

Had collection agencies calling 0.323 0.271 0.234 1.381

Had a prime credit score 1.780 0.288 0.000 5.929

Number of derogatory public records 0.079 0.171 0.645 1.082

Number of trade accounts with balances -0.227 0.059 0.000 0.797

Number of inquiries made into credit -0.122 0.066 0.067 0.885

Had late payments on trade accounts past year -1.312 0.488 0.007 0.269

Number of trades with no late payments 0.073 0.023 0.002 1.076

Intercept -2.102 1.250 0.093 0.122

Pseudo R-square (.385)

Wald chi-square statistic (241.36)

P-value of Wald chi-square statistic (.000)

 
113 First Steps on the Road To Financial Well-Being: Appendix

Figure C29 Linear Regression Analysis of Treatment Effects on Credit Scores Two Years After Program Entry by 

Credit Status at Program Entry, Among Enrollees Who Had Scores at Program Entry and Two Years 

Later (Figure 4.6)

b Robust SE (b) P-value Partial Corr Coef2

Credit score at program entry 0.377 0.060 0.000 0.0832

Treatment -7.354 6.306 0.244 0.0027

Had a credit score and thick file 3.524 8.304 0.671 0.0004

Treatment*Had a credit score and thick file 17.763 10.298 0.085 0.0072

Earnings during past two years (logged) -0.006 1.096 0.996 0.0000

Total value of assets (logged) -0.100 0.962 0.917 0.0000

Total value of debts (logged) -0.892 0.892 0.318 0.0019

Total income last month (logged) -0.914 1.626 0.574 0.0006

Total expenses last month (logged) 3.062 4.297 0.476 0.0010

Had positive net income last month -2.867 6.762 0.672 0.0005

Had positive net worth 7.608 8.521 0.372 0.0020

Male 5.352 5.692 0.347 0.0019

Age 0.373 0.274 0.175 0.0040

Black -14.632 8.444 0.084 0.0078

White -4.751 26.148 0.856 0.0001

Other or unknown race 44.146 21.361 0.039 0.0043

GED -9.840 9.535 0.302 0.0023

High school diploma -14.482 7.260 0.047 0.0080

Any college degree 3.924 9.310 0.674 0.0004

Had an occupational certificate/license 2.020 5.325 0.705 0.0003

Currently attending education/training -10.536 8.375 0.209 0.0042

Employed during the year pre-program 8.292 9.781 0.397 0.0015

Had a criminal record -3.101 5.907 0.600 0.0006

Had a disability 1.199 6.791 0.860 0.0001

Born in the United States -16.454 9.368 0.080 0.0079

Had a child under age 18 -1.716 5.366 0.749 0.0002

Separated, divorced, or widowed -3.185 6.939 0.646 0.0004

Married/living with partner -5.150 8.758 0.557 0.0010

Family size -0.458 1.978 0.817 0.0002

Homeless -35.139 13.862 0.012 0.0110

Rents home -8.079 7.527 0.284 0.0023

Owns home 4.629 11.848 0.696 0.0004

Received SNAP last month 7.505 5.460 0.170 0.0038

Had filed for bankruptcy 4.232 8.757 0.629 0.0005

Had collection agencies calling 4.437 5.200 0.394 0.0015

Had a prime credit score 11.231 9.761 0.250 0.0032

Number of derogatory public records -2.675 3.257 0.412 0.0013

Number of trade accounts with balances -3.429 1.045 0.001 0.0231

Number of inquiries made into credit -2.278 0.948 0.017 0.0123

Had late payments on trade accounts past year -6.077 5.724 0.289 0.0017

Number of trades with no late payments 0.954 0.482 0.048 0.0087

Intercept 387.345 42.059 0.000

R-square (.581)

F-statistic (16.39)

P-value of F-statistic (.000)
114 First Steps on the Road To Financial Well-Being: Appendix

Figure C30 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Increase in Net 

Worth Two Years After Program Entry Among All Enrollees (Figure 4.7)

b Robust SE (b) P-value Odds Ratio

Treatment 0.205 0.151 0.175 1.228

Earnings during past two years (logged) 0.003 0.031 0.918 1.003

Total value of assets (logged) -0.113 0.032 0.000 0.893

Total value of debts (logged) 0.129 0.025 0.000 1.138

Total income last month (logged) 0.032 0.051 0.530 1.033

Total expenses last month (logged) 0.011 0.141 0.936 1.011

Had positive net income last month 0.477 0.192 0.013 1.612

Had positive net worth -0.452 0.257 0.078 0.636

Male 0.155 0.175 0.373 1.168

Age -0.003 0.008 0.668 0.997

Black -0.808 0.283 0.004 0.446

White -0.415 0.526 0.430 0.661

Other Race -0.389 0.465 0.402 0.678

GED -0.197 0.272 0.469 0.821

High school diploma -0.223 0.222 0.315 0.800

Any college degree -0.315 0.292 0.282 0.730

Had an occupational certificate/license -0.168 0.159 0.292 0.845

Currently attending education/training 0.037 0.241 0.877 1.038

Employed during the year pre-program -0.092 0.283 0.745 0.912

Had a criminal record -0.269 0.188 0.151 0.764

Had a disability 0.099 0.215 0.646 1.104

Born in the United States -0.120 0.330 0.716 0.887

Had a child under age 18 -0.149 0.167 0.375 0.862

Separated, divorced, or widowed 0.382 0.207 0.064 1.466

Married/living with partner -0.137 0.242 0.573 0.872

Family size 0.036 0.059 0.539 1.037

Homeless -0.126 0.340 0.712 0.882

Rents home 0.064 0.215 0.765 1.066

Owns home 0.506 0.345 0.142 1.659

Received SNAP last month -0.124 0.183 0.499 0.884

Had filed for bankruptcy -0.505 0.338 0.134 0.603

Had collection agencies calling -0.182 0.175 0.297 0.833

Had a prime credit score -0.103 0.247 0.677 0.902

Had a credit score but thin file 0.543 0.202 0.007 1.720

Had a credit score and thick file 0.331 0.302 0.274 1.392

Number of derogatory public records -0.123 0.109 0.258 0.884

Number of trade accounts with balances -0.058 0.038 0.128 0.944

Number of inquiries made into credit 0.045 0.034 0.189 1.046

Had late payments on trade accounts past year 0.445 0.296 0.133 1.560

Number of trades with no late payments -0.006 0.019 0.770 0.994

Intercept 0.311 0.913 0.733 1.365

Pseudo R-square (.105)

Wald chi-square statistic (111.0)

P-value of Wald chi-square statistic (.000)

 
115 First Steps on the Road To Financial Well-Being: Appendix

Figure C31 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Net Worth Greater Than 

Zero Two Years After Program Entry Among All Enrollees (Figure 4.7)

b Robust SE (b) P-value Odds Ratio

Treatment 0.042 0.170 0.807 1.042

Earnings during past two years (logged) 0.036 0.034 0.289 1.037

Total value of assets (logged) 0.078 0.037 0.035 1.081

Total value of debts (logged) -0.077 0.027 0.005 0.926

Total income last month (logged) -0.007 0.056 0.898 0.993

Total expenses last month (logged) 0.109 0.117 0.349 1.115

Had positive net income last month 0.618 0.222 0.005 1.855

Had positive net worth 0.626 0.271 0.021 1.871

Male 0.472 0.193 0.015 1.604

Age -0.023 0.008 0.005 0.977

Black -0.948 0.307 0.002 0.387

White -1.111 0.584 0.057 0.329

Other Race -0.346 0.466 0.458 0.708

GED -0.639 0.268 0.017 0.528

High school diploma -0.663 0.234 0.005 0.515

Any college degree -0.683 0.306 0.026 0.505

Had an occupational certificate/license 0.221 0.180 0.220 1.247

Currently attending education/training 0.080 0.259 0.757 1.083

Employed during the year pre-program -0.188 0.306 0.539 0.829

Had a criminal record -0.136 0.200 0.497 0.873

Had a disability -0.190 0.261 0.467 0.827

Born in the United States 0.451 0.332 0.175 1.570

Had a child under age 18 -0.515 0.200 0.010 0.597

Separated, divorced, or widowed 0.303 0.218 0.164 1.354

Married/living with partner -0.089 0.263 0.736 0.915

Family size 0.002 0.059 0.969 1.002

Homeless -0.221 0.400 0.581 0.802

Rents home -0.157 0.233 0.500 0.854

Owns home 1.137 0.375 0.002 3.117

Received SNAP last month -0.304 0.193 0.115 0.738

Had filed for bankruptcy -0.690 0.414 0.096 0.501

Had collection agencies calling -0.248 0.191 0.194 0.780

Had a prime credit score 0.318 0.306 0.299 1.374

Had a credit score but thin file 0.378 0.237 0.110 1.460

Had a credit score and thick file -0.392 0.371 0.291 0.676

Number of derogatory public records -0.171 0.160 0.284 0.843

Number of trade accounts with balances -0.097 0.045 0.033 0.908

Number of inquiries made into credit 0.004 0.040 0.921 1.004

Had late payments on trade accounts past year 0.315 0.307 0.304 1.370

Number of trades with no late payments 0.035 0.021 0.100 1.036

Intercept 0.386 0.812 0.634 1.472

Pseudo R-square (.230)

Wald chi-square statistic (205.99)

P-value of Wald chi-square statistic (.000)
116 First Steps on the Road To Financial Well-Being: Appendix

Figure C32 Linear Regression Analysis of Treatment Effects on Net Worth Two Years After Program Entry Among 

All Enrollees (Figure 4.7)

b Robust SE (b) P-value Partial Corr Coef2

Net worth at program entry 0.902 0.072 0.000 0.3569

Treatment 1931.152 3666.788 0.599 0.0003

Earnings during past two years (logged) 377.827 870.157 0.664 0.0003

Total value of assets (logged) 1232.515 844.372 0.145 0.0026

Total value of debts (logged) -387.936 515.767 0.452 0.0004

Total income last month (logged) 769.506 1297.963 0.553 0.0004

Total expenses last month (logged) 931.500 1724.864 0.589 0.0001

Had positive net income last month 3168.211 4880.157 0.516 0.0005

Had positive net worth -22262.310 6840.084 0.001 0.0141

Male -224.290 4882.028 0.963 0.0000

Age 471.783 180.493 0.009 0.0067

Black -9339.958 7217.394 0.196 0.0033

White 666.789 13233.180 0.960 0.0000

Other Race -2201.131 11729.460 0.851 0.0000

GED 2765.581 5590.064 0.621 0.0002

High school diploma 582.684 4165.510 0.889 0.0000

Any college degree -5773.769 7221.659 0.424 0.0008

Had an occupational certificate/license -143.366 4650.391 0.975 0.0000

Currently attending education/training 4234.040 5164.536 0.412 0.0005

Employed during the year pre-program -6425.108 8134.575 0.430 0.0008

Had a criminal record -1431.171 4125.738 0.729 0.0001

Had a disability -5053.641 5247.394 0.336 0.0010

Born in the United States -6893.568 9143.046 0.451 0.0011

Had a child under age 18 2867.644 4473.968 0.522 0.0006

Separated, divorced, or widowed -675.680 5247.044 0.898 0.0000

Married/living with partner -4673.414 5673.121 0.410 0.0008

Family size 818.949 1221.286 0.503 0.0005

Homeless 1041.981 11681.420 0.929 0.0000

Rents home -11184.880 4460.016 0.012 0.0051

Owns home -22285.490 10977.840 0.043 0.0063

Received SNAP last month -2791.199 4704.578 0.553 0.0004

Had filed for bankruptcy -19923.790 10821.090 0.066 0.0074

Had collection agencies calling -1329.733 4811.901 0.782 0.0001

Had a prime credit score 11086.210 6718.725 0.099 0.0033

Had a credit score but thin file 1023.771 4276.225 0.811 0.0000

Had a credit score and thick file -4827.174 8254.428 0.559 0.0004

Number of derogatory public records -358.704 4690.620 0.939 0.0000

Number of trade accounts with balances -1169.861 1137.354 0.304 0.0015

Number of inquiries made into credit -803.649 815.436 0.325 0.0011

Had late payments on trade accounts past year 9210.524 4990.793 0.065 0.0023

Number of trades with no late payments 236.033 429.208 0.582 0.0003

Intercept 3039.067 13956.310 0.828

R-square (.490)

F-statistic (12.76)

P-value of F-statistic (.000)
117 First Steps on the Road To Financial Well-Being: Appendix

Figure C33 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Assets Two Years 

After Program Entry Among All Enrollees (Figure 4.7)

b Robust SE (b) P-value Odds Ratio

Had any assets at program entry 0.394 0.315 0.211 1.483

Treatment -0.278 0.176 0.114 0.758

Earnings during past two years (logged) 0.035 0.040 0.382 1.036

Total value of assets (logged) 0.127 0.054 0.019 1.135

Total value of debts (logged) 0.024 0.028 0.384 1.024

Total income last month (logged) 0.119 0.063 0.060 1.126

Total expenses last month (logged) 0.103 0.114 0.368 1.108

Had positive net income last month 0.077 0.227 0.733 1.081

Had positive net worth 0.252 0.340 0.460 1.286

Male 0.699 0.204 0.001 2.012

Age -0.042 0.010 0.000 0.959

Black -0.713 0.366 0.051 0.490

White -0.981 0.553 0.076 0.375

Other Race -0.818 0.581 0.159 0.441

GED -0.125 0.277 0.653 0.883

High school diploma -0.089 0.247 0.718 0.914

Any college degree 0.358 0.385 0.353 1.430

Had an occupational certificate/license 0.324 0.196 0.099 1.383

Currently attending education/training 0.217 0.254 0.394 1.242

Employed during the year pre-program -0.018 0.381 0.963 0.983

Had a criminal record -0.135 0.212 0.524 0.874

Had a disability -0.196 0.239 0.412 0.822

Born in the United States -0.734 0.420 0.080 0.480

Had a child under age 18 -0.453 0.207 0.029 0.636

Separated, divorced, or widowed 0.390 0.239 0.103 1.477

Married/living with partner -0.076 0.285 0.791 0.927

Family size -0.112 0.058 0.052 0.894

Homeless -0.367 0.362 0.311 0.693

Rents home -0.236 0.249 0.343 0.790

Owns home 1.018 0.627 0.104 2.769

Received SNAP last month -0.500 0.232 0.031 0.606

Had filed for bankruptcy 0.102 0.368 0.781 1.108

Had collection agencies calling 0.128 0.196 0.512 1.137

Had a prime credit score 0.248 0.314 0.430 1.282

Had a credit score but thin file 0.225 0.234 0.335 1.253

Had a credit score and thick file -0.039 0.384 0.918 0.961

Number of derogatory public records -0.088 0.130 0.499 0.916

Number of trade accounts with balances -0.004 0.049 0.943 0.996

Number of inquiries made into credit -0.018 0.040 0.654 0.982

Had late payments on trade accounts past year -0.077 0.283 0.784 0.926

Number of trades with no late payments -0.009 0.029 0.741 0.991

Intercept 1.885 0.951 0.047 6.588

Pseudo R-square (.249)

Wald chi-square statistic (187.36)

P-value of Wald chi-square statistic (.000)
118 First Steps on the Road To Financial Well-Being: Appendix

Figure C34 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Money in Savings or 

Checking Accounts Two Years After Program Entry Among All Enrollees (Figure 4.7)

b Robust SE (b) P-value Odds Ratio

Had any money in a bank account at program entry 1.278 0.214 0.000 3.589

Treatment -0.103 0.178 0.560 0.902

Earnings during past two years (logged) 0.008 0.041 0.847 1.008

Total value of assets (logged) 0.108 0.038 0.004 1.114

Total value of debts (logged) 0.014 0.030 0.633 1.014

Total income last month (logged) 0.151 0.051 0.003 1.163

Total expenses last month (logged) -0.009 0.126 0.944 0.991

Had positive net income last month 0.418 0.232 0.071 1.519

Had positive net worth 0.067 0.292 0.820 1.069

Male 0.178 0.197 0.365 1.195

Age -0.031 0.009 0.001 0.970

Black -0.533 0.322 0.098 0.587

White -0.170 0.530 0.749 0.844

Other Race -0.954 0.508 0.061 0.385

GED 0.334 0.310 0.282 1.396

High school diploma 0.727 0.266 0.006 2.068

Any college degree 1.061 0.354 0.003 2.888

Had an occupational certificate/license 0.241 0.185 0.191 1.273

Currently attending education/training 0.385 0.236 0.103 1.470

Employed during the year pre-program 0.186 0.374 0.619 1.204

Had a criminal record -0.245 0.218 0.263 0.783

Had a disability 0.110 0.247 0.656 1.116

Born in the United States -0.760 0.362 0.036 0.468

Had a child under age 18 -0.539 0.212 0.011 0.583

Separated, divorced, or widowed 0.193 0.239 0.419 1.213

Married/living with partner 0.242 0.281 0.390 1.274

Family size -0.100 0.059 0.092 0.905

Homeless -0.332 0.383 0.387 0.718

Rents home -0.466 0.252 0.064 0.628

Owns home -0.612 0.404 0.130 0.542

Received SNAP last month -0.575 0.194 0.003 0.563

Had filed for bankruptcy 0.157 0.405 0.698 1.170

Had collection agencies calling 0.012 0.196 0.951 1.012

Had a prime credit score 0.283 0.313 0.366 1.326

Had a credit score but thin file 0.034 0.251 0.892 1.035

Had a credit score and thick file -0.429 0.359 0.233 0.651

Number of derogatory public records 0.115 0.125 0.357 1.122

Number of trade accounts with balances 0.018 0.041 0.654 1.019

Number of inquiries made into credit -0.084 0.043 0.048 0.919

Had late payments on trade accounts past year -0.639 0.320 0.046 0.528

Number of trades with no late payments 0.009 0.022 0.686 1.009

Intercept 0.428 0.911 0.639 1.534

Pseudo R-square (.271)

Wald chi-square statistic (249.55)

P-value of Wald chi-square statistic (.000)
119 First Steps on the Road To Financial Well-Being: Appendix

Figure C35 Linear Regression Analysis of Treatment Effects on Logged Dollar Amount in Savings and Checking 

Accounts Two Years After Program Entry Among All Enrollees (Figure 4.7)

b Robust SE (b) P-value Partial Corr Coef2

Amount in bank account at program entry (logged) 0.286 0.048 0.000 0.0490

Treatment -0.047 0.178 0.793 0.0001

Earnings during past two years (logged) 0.046 0.043 0.276 0.0018

Total value of assets (logged) 0.103 0.040 0.010 0.0079

Total value of debts (logged) 0.006 0.027 0.823 0.0001

Total income last month (logged) 0.110 0.058 0.059 0.0042

Total expenses last month (logged) 0.027 0.135 0.843 0.0001

Had positive net income last month 0.665 0.244 0.006 0.0105

Had positive net worth 0.095 0.307 0.756 0.0001

Male 0.141 0.196 0.471 0.0006

Age -0.027 0.009 0.003 0.0100

Black -0.513 0.356 0.150 0.0048

White -0.495 0.489 0.312 0.0005

Other Race -0.973 0.578 0.092 0.0035

GED 0.601 0.291 0.039 0.0049

High school diploma 0.779 0.235 0.001 0.0129

Any college degree 1.377 0.353 0.000 0.0197

Had an occupational certificate/license 0.114 0.193 0.557 0.0004

Currently attending education/training 0.468 0.264 0.077 0.0030

Employed during the year pre-program -0.236 0.391 0.546 0.0005

Had a criminal record -0.365 0.216 0.091 0.0039

Had a disability -0.001 0.252 0.997 0.0000

Born in the United States -0.841 0.377 0.026 0.0079

Had a child under age 18 -0.462 0.211 0.029 0.0069

Separated, divorced, or widowed 0.313 0.238 0.188 0.0018

Married/living with partner 0.548 0.274 0.046 0.0050

Family size -0.030 0.056 0.590 0.0003

Homeless -0.022 0.336 0.949 0.0000

Rents home -0.353 0.249 0.157 0.0024

Owns home -0.142 0.416 0.733 0.0001

Received SNAP last month -0.688 0.227 0.002 0.0124

Had filed for bankruptcy 0.066 0.397 0.868 0.0000

Had collection agencies calling 0.109 0.193 0.572 0.0003

Had a prime credit score 0.361 0.319 0.258 0.0016

Had a credit score but thin file 0.171 0.256 0.504 0.0006

Had a credit score and thick file -0.267 0.350 0.445 0.0006

Number of derogatory public records -0.082 0.132 0.536 0.0004

Number of trade accounts with balances 0.010 0.041 0.805 0.0001

Number of inquiries made into credit -0.102 0.041 0.012 0.0081

Had late payments on trade accounts past year -0.701 0.298 0.019 0.0063

Number of trades with no late payments -0.003 0.022 0.894 0.0000

Intercept 2.675 0.949 0.005

R-square (.368)

F-statistic (18.83)

P-value of F-statistic (.000)
120 First Steps on the Road To Financial Well-Being: Appendix

Figure C36 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Asset-Related Debts 

Two Years After Program Entry Among All Enrollees (Figure 4.7)

b Robust SE (b) P-value Odds Ratio

Had any asset-related debts at program entry 2.111 0.223 0.000 8.253

Treatment 0.027 0.178 0.881 1.027

Earnings during past two years (logged) 0.010 0.042 0.803 1.011

Total value of assets (logged) 0.003 0.036 0.944 1.003

Total value of debts (logged) 0.028 0.035 0.434 1.028

Total income last month (logged) 0.051 0.061 0.407 1.052

Total expenses last month (logged) 0.080 0.117 0.495 1.083

Had positive net income last month -0.024 0.218 0.911 0.976

Had positive net worth -0.028 0.296 0.924 0.972

Male -0.148 0.185 0.425 0.863

Age -0.017 0.010 0.086 0.983

Black -0.134 0.281 0.633 0.875

White -0.196 0.482 0.684 0.822

Other Race -0.182 0.511 0.721 0.833

GED 0.659 0.315 0.036 1.933

High school diploma 0.498 0.256 0.052 1.646

Any college degree 1.156 0.365 0.002 3.176

Had an occupational certificate/license 0.123 0.187 0.511 1.131

Currently attending education/training 0.248 0.262 0.345 1.281

Employed during the year pre-program -0.089 0.389 0.819 0.915

Had a criminal record -0.137 0.199 0.490 0.872

Had a disability 0.125 0.270 0.643 1.134

Born in the United States 0.058 0.333 0.861 1.060

Had a child under age 18 -0.313 0.203 0.123 0.731

Separated, divorced, or widowed 0.025 0.247 0.920 1.025

Married/living with partner 0.016 0.270 0.952 1.016

Family size -0.106 0.060 0.077 0.899

Homeless -0.461 0.385 0.231 0.630

Rents home -0.106 0.240 0.659 0.899

Owns home -0.036 0.414 0.930 0.964

Received SNAP last month -0.148 0.216 0.492 0.862

Had filed for bankruptcy 0.547 0.355 0.123 1.727

Had collection agencies calling 0.391 0.207 0.059 1.479

Had a prime credit score -0.012 0.285 0.967 0.988

Had a credit score but thin file -0.022 0.244 0.929 0.978

Had a credit score and thick file -0.026 0.403 0.949 0.975

Number of derogatory public records -0.052 0.134 0.698 0.949

Number of trade accounts with balances 0.137 0.059 0.021 1.146

Number of inquiries made into credit -0.006 0.033 0.869 0.995

Had late payments on trade accounts past year -0.631 0.313 0.044 0.532

Number of trades with no late payments -0.007 0.027 0.803 0.993

Intercept -1.846 0.872 0.034 0.158

Pseudo R-square (.302)

Wald chi-square statistic (265.41)

P-value of Wald chi-square statistic (.000)
121 First Steps on the Road To Financial Well-Being: Appendix

Figure C37 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Non-Asset-Related 

Debts Two Years After Program Entry Among All Enrollees (Figure 4.7)

b Robust SE (b) P-value Odds Ratio

Had any non-asset-related debts at program entry 0.957 0.235 0.000 2.605

Treatment -0.291 0.160 0.069 0.747

Earnings during past two years (logged) 0.033 0.035 0.338 1.034

Total value of assets (logged) -0.019 0.032 0.556 0.981

Total value of debts (logged) 0.060 0.032 0.063 1.061

Total income last month (logged) -0.010 0.051 0.847 0.990

Total expenses last month (logged) 0.039 0.132 0.770 1.039

Had positive net income last month -0.032 0.209 0.877 0.968

Had positive net worth 0.036 0.261 0.889 1.037

Male -0.155 0.177 0.382 0.856

Age 0.008 0.008 0.313 1.008

Black 0.361 0.305 0.237 1.435

White -0.170 0.499 0.734 0.844

Other Race 0.309 0.480 0.519 1.362

GED 0.127 0.277 0.646 1.136

High school diploma -0.063 0.237 0.791 0.939

Any college degree 0.009 0.326 0.978 1.009

Had an occupational certificate/license -0.021 0.171 0.903 0.979

Currently attending education/training 0.439 0.246 0.075 1.551

Employed during the year pre-program -0.171 0.320 0.594 0.843

Had a criminal record 0.447 0.196 0.023 1.564

Had a disability 0.289 0.242 0.233 1.335

Born in the United States -0.028 0.323 0.930 0.972

Had a child under age 18 0.334 0.182 0.067 1.396

Separated, divorced, or widowed 0.556 0.215 0.010 1.744

Married/living with partner 0.109 0.257 0.672 1.115

Family size -0.006 0.055 0.916 0.994

Homeless -0.310 0.375 0.408 0.734

Rents home -0.181 0.230 0.431 0.835

Owns home 0.009 0.358 0.980 1.009

Received SNAP last month -0.155 0.199 0.435 0.856

Had filed for bankruptcy 0.129 0.334 0.698 1.138

Had collection agencies calling 0.719 0.186 0.000 2.053

Had a prime credit score -0.106 0.261 0.685 0.900

Had a credit score but thin file -0.238 0.222 0.284 0.788

Had a credit score and thick file -0.005 0.321 0.986 0.995

Number of derogatory public records 0.422 0.125 0.001 1.524

Number of trade accounts with balances 0.010 0.045 0.817 1.010

Number of inquiries made into credit 0.048 0.037 0.193 1.049

Had late payments on trade accounts past year 0.070 0.299 0.815 1.073

Number of trades with no late payments -0.002 0.021 0.927 0.998

Intercept -1.995 0.901 0.027 0.136

Pseudo R-square (.166)

Wald chi-square statistic (179.08)

P-value of Wald chi-square statistic (.000)
122 First Steps on the Road To Financial Well-Being: Appendix

TOT Regression Analysis Results

Figures C38 through C54 present truncated results of 

the regression analysis of the impact of treatment on 

the treated (TOT) using the three methods described in 

chapter 4. The TOT regression models included all of the 

predictor variables that were included in the ITT analysis 

and presented in Figures C5 through C37. For the TOT 

models, we show only the results for the treatment variable, the baseline value of the outcome of interest, and 

the intercept as well as model fit statistics. Figure C55 

provides standardized effect sizes for each outcome and 

each method of estimating the TOT results.

Figure C38 Logistic Regression Analysis of Treatment Effects on the Likelihood of Being Employed Year Round in 

the Second Year After Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Odds Ratio

Employed year round in the year pre-program 0.409 0.253 0.105 1.506

Treatment 0.350 0.174 0.045 1.419

Intercept -3.198 1.110 0.004 0.041

Pseudo R-square (.118)

TOT Method 2 b Robust SE (b) P-value Odds Ratio

Employed year round in the year pre-program 0.376 0.332 0.258 1.456

Treatment 0.414 0.230 0.072 1.514

Intercept -2.736 1.317 0.038 0.065

Pseudo R-square (.161)

TOT Method 3 b Robust SE (b) P-value Odds Ratio

Employed year round in the year pre-program 0.308 0.319 0.334 1.361

Treatment 0.557 0.220 0.011 1.745

Intercept -3.372 1.240 0.007 0.034

Pseudo R-square (.132)

Figure C39 Linear Regression Analysis of Treatment Effects on Logged Annual Earnings in the Second Year After 

Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Partial Corr Coef2

Earnings during the year pre-program (logged) -0.330 0.232 0.156 0.0026

Treatment 0.342 0.316 0.280 0.0018

Intercept 3.799 1.595 0.017

R-square (.227)

TOT Method 2 b Robust SE (b) P-value Partial Corr Coef2

Earnings during the year pre-program (logged) -0.447 0.289 0.123 0.0057

Treatment 0.689 0.366 0.060 0.0073

Intercept 5.303 1.918 0.006

R-square (.262)

TOT Method 3 b Robust SE (b) P-value Partial Corr Coef2

Earnings during the year pre-program (logged) -0.238 0.280 0.397 0.0013

Treatment 0.121 0.398 0.762 0.0002

Intercept 3.309 1.938 0.088

R-square (.220)
123 First Steps on the Road To Financial Well-Being: Appendix

Figure C40 Linear Regression Analysis of Treatment Effects on Logged Annual Hours Worked in the Second Year 

After Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Partial Corr Coef2

Hours worked during the year pre-program (logged) -0.244 0.188 0.195 0.0021

Treatment 0.292 0.237 0.218 0.0023

Intercept 2.666 1.187 0.025

R-square (.226)

TOT Method 2 b Robust SE (b) P-value Partial Corr Coef2

Hours worked during the year pre-program (logged) -0.267 0.231 0.248 0.0031

Treatment 0.536 0.275 0.052 0.0078

Intercept 3.893 1.453 0.008

R-square (.257)

TOT Method 3 b Robust SE (b) P-value Partial Corr Coef2

Hours worked during the year pre-program (logged) -0.226 0.225 0.317 0.0017

Treatment 0.154 0.293 0.600 0.0007

Intercept 2.166 1.408 0.124

R-square (.215)

Figure C41 Linear Regression Analysis of Treatment Effects on Logged Hourly Wages at Current or Most Recent Job 

in the Two Years After Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Partial Corr Coef2

Earnings during the year pre-program (logged) 0.061 0.024 0.011 0.0122

Treatment -0.065 0.035 0.065 0.0068

Intercept 2.314 0.143 0.000

R-square (.158)

TOT Method 2 b Robust SE (b) P-value Partial Corr Coef2

Earnings during the year pre-program (logged) 0.051 0.032 0.115 0.0085

Treatment -0.042 0.047 0.373 0.0027

Intercept 2.428 0.166 0.000

R-square (.197)

TOT Method 3 b Robust SE (b) P-value Partial Corr Coef2

Earnings during the year pre-program (logged) 0.088 0.036 0.015 0.0213

Treatment -0.082 0.045 0.071 0.0113

Intercept 2.621 0.156 0.000

R-square (.161)
124 First Steps on the Road To Financial Well-Being: Appendix

Figure C42 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having an Occupational 

Certificate/License Two Years After Program Entry (Figure 4.8)

TOT Method 1 b Clustered SE (b) P-value Odds Ratio

GED 1.615 0.153 0.000 5.028

High school diploma 1.230 0.229 0.000 3.420

Any college degree 1.265 0.258 0.000 3.543

Treatment -0.349 0.289 0.227 0.705

Intercept -2.133 0.779 0.006 0.119

Pseudo R-square (.128)

TOT Method 2 b Robust SE (b) P-value Odds Ratio

GED 1.702 0.402 0.000 5.486

High school diploma 1.263 0.356 0.000 3.536

Any college degree 1.320 0.441 0.003 3.743

Treatment -0.243 0.213 0.254 0.784

Intercept -2.952 1.071 0.006 0.052

Pseudo R-square (.166)

TOT Method 3 b Robust SE (b) P-value Odds Ratio

GED 1.297 0.399 0.001 3.660

High school diploma 0.754 0.368 0.040 2.126

Any college degree 1.140 0.422 0.007 3.127

Treatment -0.786 0.208 0.000 0.456

Intercept -2.272 1.266 0.073 0.103

Pseudo R-square (.132)

Figure C43 Linear Regression Analysis of Treatment Effects on Logged Monthly Income Two Years After Program 

Entry (Figure 4.8)

TOT Method 1

b

Robust SE (b) P-value Partial Corr Coef2

Total income last month (logged) 0.121 0.055 0.028 0.0106

Treatment -0.223 0.138 0.106 0.0041

Intercept 4.153 0.902 0.000

R-square (.162)

TOT Method 2 b Robust SE (b) P-value Partial Corr Coef2

Total income last month (logged) 0.143 0.073 0.051 0.0143

Treatment -0.171 0.168 0.307 0.0027

Intercept 2.787 1.076 0.010

R-square (.212)

TOT Method 3 b Robust SE (b) P-value Partial Corr Coef2

Total income last month (logged) 0.215 0.082 0.009 0.0338

Treatment -0.046 0.171 0.790 0.0002

Intercept 3.608 1.164 0.002

R-square (.208)
125 First Steps on the Road To Financial Well-Being: Appendix

Figure C44 Linear Regression Analysis of Treatment Effects on Logged Monthly Expenses Two Years After Program 

Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Partial Corr Coef2

Total expenses last month (logged) 0.109 0.058 0.062 0.0118

Treatment -0.043 0.051 0.397 0.0009

Intercept 6.618 0.371 0.000

R-square (.270)

TOT Method 2 b Robust SE (b) P-value Partial Corr Coef2

Total expenses last month (logged) 0.075 0.054 0.170 0.0076

Treatment 0.002 0.057 0.969 0.0000

Intercept 6.686 0.361 0.000

R-square (.356)

TOT Method 3 b Robust SE (b) P-value Partial Corr Coef2

Total expenses last month (logged) 0.133 0.044 0.002 0.0256

Treatment -0.011 0.064 0.860 0.0001

Intercept 6.576 0.335 0.000

R-square (.240)

Figure C45 Linear Regression Analysis of Treatment Effects on Logged Monthly Net Income Two Years After 

Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Partial Corr Coef2

Net income last month 0.272 0.070 0.000 0.0262

Treatment -94.028 117.361 0.423 0.0011

Intercept -721.638 638.964 0.259

R-square (.114)

TOT Method 2 b Robust SE (b) P-value Partial Corr Coef2

Net income last month 0.278 0.095 0.004 0.0236

Treatment -87.972 151.609 0.562 0.0010

Intercept -1036.380 599.069 0.084

R-square (.163)

TOT Method 3 b Robust SE (b) P-value Partial Corr Coef2

Net income last month 0.352 0.109 0.001 0.0324

Treatment -106.351 134.496 0.429 0.0014

Intercept -956.234 543.684 0.079

R-square (.123)
126 First Steps on the Road To Financial Well-Being: Appendix

Figure C46 Figure C46. Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Net Income 

Greater Than Zero Two Years After Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Odds Ratio

Had positive net income last month 0.551 0.218 0.012 1.734

Treatment -0.249 0.170 0.144 0.780

Intercept -0.778 0.861 0.366 0.459

Pseudo R-square (.075)

TOT Method 2 b Robust SE (b) P-value Odds Ratio

Had positive net income last month 0.808 0.280 0.004 2.243

Treatment -0.103 0.221 0.640 0.902

Intercept -1.805 0.979 0.065 0.164

Pseudo R-square (.153)

TOT Method 3 b Robust SE (b) P-value Odds Ratio

Had positive net income last month 0.157 0.282 0.579 1.170

Treatment -0.366 0.216 0.090 0.694

Intercept -1.033 1.113 0.354 0.356

Pseudo R-square (.071)

Figure C47 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Credit Score Two Years 

After Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Odds Ratio

Had a credit score at program entry 3.355 0.910 0.000 28.632

Treatment 0.196 0.238 0.410 1.216

Intercept -1.269 1.362 0.351 0.281

Pseudo R-square (.423)

TOT Method 2 b Robust SE (b) P-value Odds Ratio

Had a credit score at program entry 2.401 1.128 0.033 11.031

Treatment 0.192 0.300 0.522 1.212

Intercept -1.365 1.406 0.332 0.255

Pseudo R-square (.472)

TOT Method 3 b Robust SE (b) P-value Odds Ratio

Had a credit score at program entry 1.468 0.358 0.000 4.340

Treatment 0.273 0.283 0.335 1.314

Intercept -2.220 1.400 0.113 0.109

Pseudo R-square (.476)
127 First Steps on the Road To Financial Well-Being: Appendix

Figure C48 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Prime Credit Score Two 

Years After Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Odds Ratio

Had a prime credit score at program entry 1.690 0.315 0.000 5.420

Treatment 0.333 0.246 0.176 1.395

Intercept -1.917 1.143 0.094 0.147

Pseudo R-square (.374)

TOT Method 2 b Clustered SE (b) P-value Odds Ratio

Had a prime credit score at program entry 1.863 0.387 0.000 6.442

Treatment 0.654 0.344 0.057 1.923

Intercept -2.388 1.239 0.054 0.092

Pseudo R-square (.445)

TOT Method 3 b Robust SE (b) P-value Odds Ratio

Had a prime credit score at program entry 1.693 0.417 0.000 5.437

Treatment 0.347 0.328 0.290 1.415

Intercept -4.130 1.704 0.015 0.016

Pseudo R-square (.378)

Figure C49 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Trade Accounts Paid 

as Agreed Two Years After Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Odds Ratio

Had any trade accounts paid as agreed at program 

entry 1.581 0.415 0.000 4.859

Treatment 0.677 0.278 0.015 1.968

Intercept -0.441 1.291 0.733 0.643

Pseudo R-square (.526)

TOT Method 2 b Robust SE (b) P-value Odds Ratio

Had any trade accounts paid as agreed at program 

entry 2.029 0.601 0.001 7.608

Treatment 1.165 0.368 0.002 3.205

Intercept -0.555 1.454 0.703 0.574

Pseudo R-square (.598)

TOT Method 3 b Robust SE (b) P-value Odds Ratio

Had any trade accounts paid as agreed at program 

entry 2.527 0.447 0.000 12.516

Treatment 0.967 0.322 0.003 2.630

Intercept -4.120 1.466 0.005 0.016

Pseudo R-square (.545)
128 First Steps on the Road To Financial Well-Being: Appendix

Figure C50 Linear Regression Analysis of Treatment Effects on the Logged Number of On-Time Payments Made on 

Trade Accounts in the Second Year After Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Partial Corr Coef2

Number of on-time payments in the year pre-program 

(logged) 0.211 0.047 0.000 0.0449

Treatment 0.106 0.086 0.220 0.0026

Intercept 0.290 0.407 0.476

R-square (.663)

TOT Method 2 b Robust SE (b) P-value Partial Corr Coef2

Number of on-time payments in the year pre-program 

(logged) 0.139 0.056 0.013 0.0191

Treatment 0.128 0.107 0.231 0.0039

Intercept -0.051 0.444 0.909

R-square (.696)

TOT Method 3 b Clustered SE (b) P-value Partial Corr Coef2

Number of on-time payments in the year pre-program 

(logged) 0.296 0.052 0.000 0.0741

Treatment 0.182 0.113 0.140 0.0080

Intercept 0.092 0.475 0.851

R-square (.651)

Figure C51 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Net Worth Greater Than 

Zero Two Years After Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Odds Ratio

Had positive net worth at program entry 0.716 0.306 0.019 2.046

Treatment 0.122 0.186 0.512 1.130

Intercept -0.130 0.903 0.885 0.878

Pseudo R-square (.217)

TOT Method 2 b Robust SE (b) P-value Odds Ratio

Had positive net worth at program entry 0.384 0.372 0.302 1.468

Treatment 0.348 0.236 0.140 1.416

Intercept 0.300 1.121 0.789 1.349

Pseudo R-square (.266)

TOT Method 3 b Robust SE (b) P-value Odds Ratio

Had positive net worth at program entry 0.340 0.377 0.367 1.405

Treatment 0.434 0.234 0.064 1.543

Intercept 1.160 1.090 0.287 3.188

Pseudo R-square (.214)
129 First Steps on the Road To Financial Well-Being: Appendix

Figure C52 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Assets Two Years 

After Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Odds Ratio

Had any assets at program entry 0.379 0.358 0.290 1.460

Treatment -0.279 0.193 0.149 0.757

Intercept 1.054 0.980 0.282 2.870

Pseudo R-square (.232)

TOT Method 2 b Robust SE (b) P-value Odds Ratio

Had any assets at program entry 0.253 0.478 0.596 1.288

Treatment -0.093 0.255 0.715 0.911

Intercept 2.233 1.205 0.064 9.325

Pseudo R-square (.257)

TOT Method 3 b Robust SE (b) P-value Odds Ratio

Had any assets at program entry 0.782 0.461 0.090 2.186

Treatment -0.244 0.238 0.305 0.783

Intercept 3.202 1.195 0.007 24.584

Pseudo R-square (.246)

Figure C53 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Asset-Related Debts 

Two Years After Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Odds Ratio

Had any asset-related debts at program entry 2.166 0.252 0.000 8.721

Treatment -0.208 0.196 0.290 0.813

Intercept -1.448 0.938 0.123 0.235

Pseudo R-square (.324)

TOT Method 2 b Robust SE (b) P-value Odds Ratio

Had any asset-related debts at program entry 2.011 0.345 0.000 7.472

Treatment 0.026 0.250 0.917 1.026

Intercept -1.355 1.006 0.178 0.258

Pseudo R-square (.358)

TOT Method 3 b Robust SE (b) P-value Odds Ratio

Had any asset-related debts at program entry 2.684 0.341 0.000 14.645

Treatment -0.442 0.265 0.095 0.643

Intercept -1.890 1.165 0.105 0.151

Pseudo R-square (.345)
130 First Steps on the Road To Financial Well-Being: Appendix

Figure C54 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Any Non-Asset-Related 

Debts Two Years After Program Entry (Figure 4.8)

TOT Method 1 b Robust SE (b) P-value Odds Ratio

Had any non-asset-related debts at program entry 1.195 0.266 0.000 3.304

Treatment -0.338 0.178 0.058 0.713

Intercept -3.082 1.052 0.003 0.046

Pseudo R-square (.176)

TOT Method 2 b Robust SE (b) P-value Odds Ratio

Had any non-asset-related debts at program entry 1.349 0.343 0.000 1.349

Treatment -0.203 0.233 0.385 -0.203

Intercept -4.148 1.209 0.001 -4.148

Pseudo R-square (.236)

TOT Method 3 b Robust SE (b) P-value Odds Ratio

Had any non-asset-related debts at program entry 1.112 0.307 0.000 3.040

Treatment -0.653 0.217 0.003 0.521

Intercept -2.980 1.194 0.013 0.051

Pseudo R-square (.190)

Figure C55 Standardized Effect Sizes for the TOT Estimates of Program Impacts

TOT Method 1 TOT Method 2 TOT Method 3 

Percent employed year-round 0.18 0.20 0.29

Average annual earnings (including zero earnings) 0.02 0.03 0.13

Average annual hours worked (including zero hours) 0.12 0.13 0.17

Average hourly wage in current or most recent job -0.14 -0.04 -0.20

Percent who had an occupational certificate/license -0.18 -0.12 -0.41

Average monthly gross income -0.06 -0.02 0.01

Average monthly expenses 0.00 0.04 0.11

Average monthly net income -0.06 -0.06 -0.08

Percent who have monthly net income greater than zero -0.14 -0.05 -0.20

Percent who had any trade accounts (open or closed) paid as agreed 0.18 0.27 0.24

Average number of on-time payments made on trade accounts in the 0.08 past year 0.07 0.13

Percent who had a credit score 0.06 0.06 0.08

Percent who had a prime score 0.12 0.20 0.13

Percent who had net worth greater than zero 0.05 0.14 0.20

Average net worth

Percent who had any assets -0.12 -0.04 -0.10

Percent who had any asset-related debts -0.08 0.01 -0.16

Percent who had any non-asset-related debts -0.16 -0.09 -0.31
131 First Steps on the Road To Financial Well-Being: Appendix

Subgroup Regression Analysis Results

Figures C56 through C61 present truncated results 

of the regression analysis of program impacts for key 

demographic subgroups of participants presented in 

chapter 4. The regression models included all of the 

predictor variables that were included in the ITT analysis and presented in Figures C5 through C37. For the 

subgroup models, we show only the results for the interaction terms of treatment status by each demographic 

subgroup, the baseline value of the outcome of interest, 

and the intercept as well as model fit statistics. The 

p-values in the figures below tell us if the difference 

between FOC and comparison group members in one 

subgroup was significantly different than the difference 

between FOC and comparison group members in the 

reference subgroup. We analyzed the margins for all 

subgroups to understand whether any of the differences 

between FOC and comparison group members within a 

subgroup were significant.

Figure C56 Logistic Regression Analysis of Treatment Effects on the Likelihood of Being Employed Year Round in 

the Second Year After Program Entry for Key Subgroups

b Robust SE (b) P-value Odds Ratio

Employed year round in the year pre-program 0.473 0.234 0.043 1.604

Treatment status*Black -0.572 0.417 0.170 0.564

Treatment status*White -1.554 1.135 0.171 0.211

Treatment status*Other race -0.960 0.854 0.261 0.383

Treatment status*Age 25 or older -0.912 0.427 0.032 0.402

Treatment status*Male 0.182 0.340 0.593 1.199

Treatment status*High school/GED -0.475 0.483 0.326 0.622

Treatment status*College degree 0.141 0.588 0.810 1.152

Treatment status*Had a criminal conviction 0.047 0.358 0.896 1.048

Treatment status*Separated, divorced, widowed -0.369 0.403 0.360 0.692

Treatment status*Married or living with partner -0.724 0.465 0.120 0.485

Treatment status*Had child under age 18 -0.277 0.314 0.378 0.758

Treatment status*Employed during the year pre- -0.043 0.320 0.893 0.958 program

Treatment status*Had positive net income last 

month -0.360 0.350 0.305 0.698

Intercept -4.056 1.085 0.000 0.017

Pseudo R-square (.118)
132 First Steps on the Road To Financial Well-Being: Appendix

Figure C57 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Net Income Greater Than 

Zero Two Years After Program Entry for Key Subgroups

b Robust SE (b) P-value Odds Ratio

Had positive net income last month 0.590 0.258 0.022 1.804

Treatment status*Black -0.003 0.427 0.994 0.997

Treatment status*White 0.263 1.008 0.794 1.301

Treatment status*Other race -0.258 0.940 0.784 0.773

Treatment status*Age 25 or older -0.675 0.437 0.123 0.509

Treatment status*Male 0.294 0.350 0.401 1.341

Treatment status*High school/GED 0.376 0.546 0.491 1.457

Treatment status*College degree 0.185 0.435 0.670 1.204

Treatment status*Had a criminal conviction -0.164 0.532 0.757 0.848

Treatment status*Separated, divorced, widowed 0.043 0.402 0.914 1.044

Treatment status*Married or living with partner 0.306 0.448 0.495 1.357

Treatment status*Had child under age 18 -0.069 0.309 0.824 0.933

Treatment status*Employed during the year pre- 0.149 0.311 0.631 1.161 program

Treatment status*Had positive net income last 

month -0.169 0.329 0.608 0.845

Intercept -1.239 0.888 0.163 0.290

Pseudo R-square (.080)

Figure C58 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Credit Score Two Years 

After Program Entry for Key Subgroups

b Robust SE (b) P-value Odds Ratio

Had a credit score at program entry 3.257 0.854 0.000 25.978

Treatment status*Black -0.418 0.589 0.478 0.658

Treatment status*White 0.405 1.363 0.766 1.499

Treatment status*Other race -0.110 1.976 0.956 0.896

Treatment status*Age 25 or older 0.846 0.534 0.113 2.329

Treatment status*Male 0.498 0.430 0.247 1.646

Treatment status*High school/GED 0.448 0.480 0.351 1.565

Treatment status*College degree 0.242 0.903 0.788 1.274

Treatment status*Had a criminal conviction -0.747 0.429 0.082 0.474

Treatment status*Separated, divorced, widowed -0.462 0.537 0.390 0.630

Treatment status*Married or living with partner 0.174 0.591 0.768 1.190

Treatment status*Had child under age 18 0.168 0.382 0.659 1.183

Treatment status*Employed during the year preprogram -0.574 0.394 0.145 0.563

Treatment status*Had positive net income last 

month -0.431 0.418 0.303 0.650

Intercept -1.155 1.297 0.373 0.315

Pseudo R-square (.401)
133 First Steps on the Road To Financial Well-Being: Appendix

Figure C59 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having a Prime Credit Score Two 

Years After Program Entry for Key Subgroups

b Robust SE (b) P-value Odds Ratio

Had a prime credit score at program entry 1.734 0.290 0.000 5.661

Treatment status*Black -1.436 0.578 0.013 0.238

Treatment status*White -1.717 2.061 0.405 0.180

Treatment status*Other race -15.178 1.028 0.000 0.000

Treatment status*Age 25 or older 1.131 0.652 0.083 3.100

Treatment status*Male -0.169 0.488 0.728 0.844

Treatment status*GED -0.137 0.754 0.856 0.872

Treatment status*High school 0.669 0.588 0.255 1.953

Treatment status*College degree 0.658 0.739 0.373 1.931

Treatment status*Had a criminal conviction 0.035 0.534 0.947 1.036

Treatment status*Separated, divorced, widowed -1.157 0.565 0.041 0.314

Treatment status*Married or living with partner -0.244 0.667 0.715 0.784

Treatment status*Had child under age 18 -0.638 0.468 0.172 0.528

Treatment status*Employed during the year -0.742 0.460 0.107 0.476 pre-program

Treatment status*Had positive net income last 

month -0.138 0.495 0.780 0.871

Intercept 1.734 0.290 0.000 5.661

Pseudo R-square (.398)

Figure C60 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Net Worth Greater Than 

Zero Two Years After Program Entry for Key Subgroups

b Robust SE (b) P-value Odds Ratio

Had positive net worth at program entry 0.476 0.268 0.075 1.609

Treatment status*Black 0.820 0.456 0.072 2.271

Treatment status*White -0.099 1.100 0.928 0.906

Treatment status*Other race 1.963 0.976 0.044 7.124

Treatment status*Age 25 or older -0.133 0.452 0.768 0.875

Treatment status*Male 0.084 0.364 0.817 1.088

Treatment status*High school/GED 1.179 0.529 0.026 3.250

Treatment status*College degree 0.844 0.426 0.047 2.326

Treatment status*Had a criminal conviction 0.258 0.589 0.662 1.294

Treatment status*Separated, divorced, widowed 0.320 0.384 0.404 1.377

Treatment status*Married or living with partner 0.653 0.432 0.131 1.921

Treatment status*Had child under age 18 0.504 0.522 0.335 1.655

Treatment status*Employed during the year 0.506 0.354 0.153 1.659 pre-program

Treatment status*Had positive net income last 

month 0.220 0.335 0.512 1.246

Intercept 0.190 0.372 0.609 1.210

Pseudo R-square (.247)
134 First Steps on the Road To Financial Well-Being: Appendix

Figure C61 Logistic Regression Analysis of Treatment Effects on the Likelihood of Having Non-Asset-Related Debts 

Two Years After Program Entry for Key Subgroups

b Robust SE (b) P-value Odds Ratio

Had non-asset-related debts at program entry 1.049 0.227 0.000 2.855

Treatment status*Black 0.484 0.393 0.218 0.740

Treatment status*White 1.274 0.787 0.105 0.050

Treatment status*Other race 0.923 0.676 0.172 0.310

Treatment status*Age 25 or older 0.166 0.438 0.705 1.180

Treatment status*Male -0.290 0.347 0.403 0.748

Treatment status*High school/GED -0.088 0.439 0.841 0.916

Treatment status*College degree 0.823 0.601 0.171 2.278

Treatment status*Had a criminal conviction -0.189 0.381 0.620 0.828

Treatment status*Separated, divorced, widowed 0.205 0.418 0.624 1.228

Treatment status*Married or living with partner 0.574 0.467 0.219 1.776

Treatment status*Had child under age 18 -0.279 0.317 0.378 0.756

Treatment status*Employed during the year -0.124 0.328 0.705 0.883 pre-program

Treatment status*Had positive net income last 

month 0.305 0.355 0.390 1.357

Intercept -2.067 0.994 0.038 0.127

Pseudo R-square (.177)

Site-Level Regression Analysis Results

Figures C62 through C78 present truncated results of 

the regression analysis of site-level program impacts 

presented in chapter 4. The site-level regression models included all of the predictor variables that were 

included in the ITT analysis and presented in Figures 

C5 through C37. For the site-level models, we show 

only the results for the site variable, the baseline 

value of the outcome of interest, and the intercept as 

well as model fit statistics.

Figure C62 Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Being Employed Year 

Round in the Second Year After Program Entry (Figure 4.9)

b Robust SE (b) P-value Odds Ratio

Employed year round in the year pre-program 0.478 0.235 0.042 1.612

AH 0.113 0.270 0.676 1.120

IDPL 0.460 0.348 0.186 1.584

MFS 0.370 0.251 0.140 1.447

NLEN 0.529 0.338 0.118 1.698

TCP 0.093 0.230 0.685 1.098

Intercept -3.036 1.041 0.004 0.048

Pseudo R-square (.108)
135 First Steps on the Road To Financial Well-Being: Appendix

Figure C63 Linear Regression Analysis of Site-Level Treatment Effects on Logged Annual Earnings in the Second 

Year After Program Entry (Figure 4.9)

b Robust SE (b) P-value Partial Corr Coef2

Earnings during the year pre-program (logged) -0.340 0.199 0.089 0.0027

AH 0.249 0.495 0.615 0.0003

IDPL 0.146 0.646 0.821 0.0001

MFS 0.213 0.462 0.645 0.0002

NLEN 0.612 0.599 0.308 0.0009

TCP -0.304 0.426 0.475 0.0005

Intercept 3.459 1.518 0.023

R-square (.187)

Figure C64 Linear Regression Analysis of Site-Level Treatment Effects on Logged Annual Hours Worked in the 

Second Year After Program Entry (Figure 4.9)

b Robust SE (b) P-value Partial Corr Coef2

Hours worked during the year pre-program (logged) -0.232 0.161 0.150 0.0018

AH 0.173 0.370 0.641 0.0003

IDPL 0.129 0.471 0.784 0.0001

MFS 0.083 0.341 0.807 0.0001

NLEN 0.476 0.444 0.285 0.0010

TCP -0.203 0.320 0.526 0.0004

Intercept 2.524 1.132 0.026

R-square (.186)

Figure C65 Linear Regression Analysis of Site-Level Treatment Effects on Logged Hourly Wages at Current or Most 

Recent Job in the Two Years After Program Entry (Figure 4.9)

b Robust SE (b) P-value Partial Corr Coef2

Earnings during the year pre-program (logged) 0.062 0.023 0.008 0.0118

AH -0.097 0.053 0.067 0.0054

IDPL -0.017 0.055 0.761 0.0001

MFS -0.041 0.053 0.443 0.0009

NLEN -0.011 0.071 0.881 0.0000

TCP -0.044 0.055 0.419 0.0012

Intercept 2.296 0.147 0.000

R-square (.132)
136 First Steps on the Road To Financial Well-Being: Appendix

Figure C66 Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having an 

Occupational Certificate/License Two Years After Program Entry (Figure 4.9)

b Robust SE (b) P-value Odds Ratio

GED 1.592 0.286 0.000 4.913

High school diploma 1.295 0.262 0.000 3.651

Any college degree 1.344 0.311 0.000 3.834

AH -0.768 0.286 0.007 0.464

IDPL -0.372 0.333 0.264 0.689

MFS 0.147 0.247 0.552 1.158

NLEN -0.034 0.356 0.924 0.967

TCP -0.684 0.225 0.002 0.504

Intercept -2.822 0.883 0.001 0.059

Pseudo R-square (.138)

Figure C67 Linear Regression Analysis of Site-Level Treatment Effects on Logged Monthly Income Two Years After 

Program Entry (Figure 4.9)

b Robust SE (b) P-value Partial Corr Coef2

Total income last month (logged) 0.104 0.046 0.025 0.0080

AH -0.057 0.191 0.767 0.0001

IDPL -0.141 0.257 0.585 0.0004

MFS -0.179 0.186 0.334 0.0009

NLEN -0.224 0.312 0.472 0.0007

TCP -0.184 0.184 0.316 0.0011

Intercept 4.337 0.815 0.000

R-square (.129)

Figure C68 Linear Regression Analysis of Site-Level Treatment Effects on Logged Monthly Expenses Two Years 

After Program Entry (Figure 4.9)

b Robust SE (b) P-value Partial Corr Coef2

Total expenses last month (logged) 0.155 0.050 0.002 0.0235

AH -0.008 0.091 0.927 0.0000

IDPL 0.005 0.074 0.951 0.0000

MFS -0.065 0.069 0.345 0.0008

NLEN -0.054 0.105 0.609 0.0003

TCP -0.014 0.085 0.867 0.0000

Intercept 6.412 0.328 0.000

R-square (.287)
137 First Steps on the Road To Financial Well-Being: Appendix

Figure C69 Linear Regression Analysis of Site-Level Treatment Effects on Logged Monthly Net Income Two Years 

After Program Entry (Figure 4.9)

b Robust SE (b) P-value Partial Corr Coef2

Net income last month 0.283 0.077 0.000 0.0231

AH -170.727 165.724 0.303 0.0013

IDPL -57.774 239.859 0.810 0.0001

MFS -89.223 173.190 0.607 0.0003

NLEN -44.433 172.552 0.797 0.0000

TCP -197.836 122.282 0.106 0.0020

Intercept -561.607 570.836 0.325

R-square (.091)

Figure C70 Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having Net Income 

Greater Than Zero Two Years After Program Entry (Figure 4.9)

b Robust SE (b) P-value Odds Ratio

Had positive net income last month 0.539 0.197 0.006 1.715

AH -0.384 0.275 0.163 0.681

IDPL -0.067 0.328 0.839 0.935

MFS -0.220 0.258 0.393 0.802

NLEN -0.265 0.355 0.456 0.767

TCP -0.567 0.237 0.017 0.567

Intercept -1.001 0.818 0.221 0.368

Pseudo R-square (.065)

Figure C71 Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having a Credit Score 

Two Years After Program Entry (Figure 4.9)

b Robust SE (b) P-value Odds Ratio

Had a credit score at program entry 3.196 0.928 0.001 24.425

AH 0.709 0.316 0.025 2.032

IDPL 0.624 0.490 0.203 1.866

MFS 0.110 0.332 0.741 1.116

NLEN -0.004 0.471 0.993 0.996

TCP 0.115 0.284 0.686 1.122

Intercept -1.468 1.205 0.223 0.230

Pseudo R-square (.391)
138 First Steps on the Road To Financial Well-Being: Appendix

Figure C72 Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having a Prime Credit 

Score Two Years After Program Entry (Figure 4.9)

b Robust SE (b) P-value Odds Ratio

Had a prime credit score at program entry 1.803 0.286 0.000 6.066

AH 0.611 0.391 0.118 1.843

IDPL 0.980 0.426 0.021 2.665

MFS 0.070 0.353 0.843 1.072

NLEN -0.130 0.577 0.821 0.878

TCP -0.363 0.374 0.331 0.696

Intercept -2.802 1.239 0.024 0.061

Pseudo R-square (.386)

Figure C73 Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having Any Trade 

Accounts Paid as Agreed Two Years After Program Entry (Figure 4.9)

b Robust SE (b) P-value Odds Ratio

Had any trade accounts paid as agreed at program 

entry 2.023 0.335 0.000 7.562

AH 1.088 0.346 0.002 2.968

IDPL 1.404 0.478 0.003 4.072

MFS 0.556 0.377 0.141 1.743

NLEN 1.018 0.498 0.041 2.768

TCP 0.651 0.330 0.049 1.917

Intercept -2.172 1.193 0.069 0.114

Pseudo R-square (.506)

Figure C74 Linear Regression Analysis of Site-Level Treatment Effects on the Logged Number of On-Time Payments 

Made on Trade Accounts in the Second Year After Program Entry (Figure 4.9)

b Robust SE (b) P-value Partial Corr Coef2

Number of on-time payments in the year pre-program 

(logged) 0.219 0.043 0.000 0.0426

AH 0.305 0.133 0.022 0.0071

IDPL 0.178 0.165 0.281 0.0017

MFS 0.208 0.122 0.089 0.0034

NLEN 0.197 0.193 0.307 0.0014

TCP -0.013 0.113 0.909 0.0000

Intercept 0.088 0.356 0.804

R-square (.646)
139 First Steps on the Road To Financial Well-Being: Appendix

Figure C75 Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having Net Worth 

Greater Than Zero Two Years After Program Entry (Figure 4.9)

b Robust SE (b) P-value Odds Ratio

Had positive net worth at program entry 0.614 0.269 0.023 1.847

AH -0.398 0.296 0.179 0.671

IDPL -0.018 0.369 0.962 0.982

MFS -0.061 0.283 0.828 0.940

NLEN 0.259 0.378 0.494 1.295

TCP 0.458 0.238 0.055 1.580

Intercept 0.556 0.830 0.503 1.744

Pseudo R-square (.235)

Figure C76 Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having Any Assets 

Two Years After Program Entry (Figure 4.9)

b Robust SE (b) P-value Odds Ratio

Had any assets at program entry 0.393 0.313 0.209 1.482

AH -0.139 0.276 0.615 0.870

IDPL 0.266 0.522 0.610 1.305

MFS -0.631 0.276 0.022 0.532

NLEN -0.124 0.433 0.775 0.883

TCP -0.307 0.234 0.190 0.736

Intercept 1.550 0.987 0.116 4.713

Pseudo R-square (.252)

Figure C77 Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having Any 

Asset-Related Debts Two Years After Program Entry (Figure 4.9)

b Robust SE (b) P-value Odds Ratio

Had any asset-related debts at program entry 2.098 0.224 0.000 8.153

AH 0.182 0.298 0.542 1.199

IDPL 0.454 0.366 0.214 1.575

MFS 0.065 0.289 0.823 1.067

NLEN -0.310 0.504 0.538 0.734

TCP -0.309 0.268 0.249 0.734

Intercept -2.189 0.888 0.014 0.112

Pseudo R-square (.305)
140 First Steps on the Road To Financial Well-Being: Appendix

Figure C78 Logistic Regression Analysis of Site-Level Treatment Effects on the Likelihood of Having Any 

Non-Asset-Related Debts Two Years After Program Entry (Figure 4.9)

b Robust SE (b) P-value Odds Ratio

Had any non-asset-related debts at program entry 0.980 0.234 0.000 2.663

AH -0.312 0.267 0.242 0.732

IDPL 0.291 0.341 0.393 1.338

MFS -0.487 0.271 0.072 0.614

NLEN -0.612 0.368 0.096 0.542

TCP -0.427 0.231 0.065 0.652

Intercept -2.382 0.931 0.011 0.092

Pseudo R-square (.170)
141 First Steps on the Road To Financial Well-Being: Executive Summary

Economic Mobility Corporation, Inc.

50 Broadway, Suite 1604

New York, NY 10004

212.280.6975

www.economicmobilitycorp.org