ENCOURAGING 

NONRETIREMENT 

SAVINGS AT 

TAX TIME

Final Impact 

Findings 

from the 

SaveUSA 

Evaluation

Gilda Azurdia

Stephen Freedman

January 2016

Encouraging Nonretirement Savings at Tax Time

Final Impact Findings 

from the SaveUSA Evaluation

Gilda Azurdia

Stephen Freedman

January 2016 
SaveUSA is one of five evidence-based programs that were implemented as part of the Social 

Innovation Fund (SIF) grant to the Mayor’s Fund to Advance New York City and the New York 

City Center for Economic Opportunity. The SIF is a federal program administered by the Corporation for National and Community Service (CNCS). It catalyzes a unique public-private funding 

model in which each federal dollar must be matched by private and local contributions. Matching 

funds required by the SIF for SaveUSA were provided by several foundations and organizations: 

Bloomberg Philanthropies, Open Society Foundations, The Rockefeller Foundation, The Annie E. 

Casey Foundation, The Ford Foundation, MetLife Foundation, George Kaiser Family Foundation, 

Tulsa Community Foundation, and United Way of San Antonio and Bexar County.

Dissemination of MDRC publications is supported by the following funders that help finance 

MDRC’s public policy outreach and expanding efforts to communicate the results and implications of our work to policymakers, practitioners, and others: The Annie E. Casey Foundation, 

Charles and Lynn Schusterman Family Foundation, The Edna McConnell Clark Foundation, Ford 

Foundation, The George Gund Foundation, Daniel and Corinne Goldman, The Harry and Jeanette 

Weinberg Foundation, Inc., The JBP Foundation, The Joyce Foundation, The Kresge Foundation, 

Laura and John Arnold Foundation, Sandler Foundation, and The Starr Foundation.

In addition, earnings from the MDRC Endowment help sustain our dissemination efforts. Contributors to the MDRC Endowment include Alcoa Foundation, The Ambrose Monell Foundation, 

Anheuser-Busch Foundation, Bristol-Myers Squibb Foundation, Charles Stewart Mott Foundation, Ford Foundation, The George Gund Foundation, The Grable Foundation, The Lizabeth and 

Frank Newman Charitable Foundation, The New York Times Company Foundation, Jan Nicholson, Paul H. O’Neill Charitable Foundation, John S. Reed, Sandler Foundation, and The Stupski 

Family Fund, as well as other individual contributors.

This material is based upon work supported by CNCS’s Social Innovation Fund under Grant 

No. 10SIHNY002. Opinions or points of view expressed in this document are those of the 

authors and do not necessarily reflect the official position of, or a position that is endorsed by, 

CNCS or the Social Innovation Fund. 

The findings and conclusions in this report do not necessarily represent the official positions or 

policies of the funders, the Mayor’s Fund to Advance New York City, or the New York City Center 

for Economic Opportunity.

For information about MDRC and copies of our publications, see our website: www.mdrc.org. 

Copyright © 2016 by MDRC®. All rights reserved.
iii

Overview

Launched in 2011, SaveUSA encourages low- and moderate-income individuals to set aside money 

from their tax refund for savings. Tax filers at participating Volunteer Income Tax Assistance 

(VITA) sites can directly deposit all or a portion of their tax refund into a special savings account, 

set up by a bank or credit union, and pledge to save between $200 and $1,000 of their deposit for 

about a year. Money can be withdrawn from the accounts at any time and for any purpose, but only 

those who maintain their initially pledged savings amount throughout a full year receive a 50 percent 

match on that amount. Account holders, irrespective of match receipt, can deposit tax refund dollars 

in subsequent years and become eligible to receive additional savings matches on their new tax refund deposits.

This report presents findings on SaveUSA’s implementation in four cities — New York City, Tulsa, 

Newark, and San Antonio — and on its longer-term effects on savings and other financial outcomes 

in two cities, New York City and Tulsa. In these latter cities, tax filers interested in SaveUSA in 

2011 were randomly selected either to a group whose members were offered the opportunity to open 

a special savings account (the “SaveUSA group”) or to a group that could not do so (the “Regular 

Tax Filers” group). The report compares the savings and other financial behaviors of these two 

groups over time to estimate SaveUSA’s effects. Its findings thus suggest the effects that savings 

policies structured similarly to SaveUSA’s might have.

SaveUSA’s operation and evaluation were supported by the Social Innovation Fund (SIF), a program of the Corporation for National and Community Service (CNCS). This particular SIF project 

has been led by the Mayor’s Fund to Advance New York City and the New York City Center for 

Economic Opportunity (CEO) in collaboration with MDRC. CEO and the New York City Department of Consumer Affairs Office of Financial Empowerment led SaveUSA program operations, and 

MDRC conducted the program’s evaluation.

Key Findings

• SaveUSA was successfully implemented in all four cities. About two-thirds of the SaveUSA 

group received at least one savings match during the three program years. Across the whole 

SaveUSA group, total match dollars averaged $365 over the three program years. 

• As of the 42-month follow-up point, SaveUSA had increased the percentage of individuals with 

any nonretirement savings by almost 8 percentage points and had increased the average total 

amount of savings held by $522, or 30 percent, above the average for the group that did not have 

access to a SaveUSA account. These effects were present even after most of the SaveUSA 

group no longer had access to a 50 percent match on savings. 

• The program led to improvements in some measures of financial security, such as having more 

cash available to pay for normal household expenses or for emergency or unexpected expenses, 

that were directly related to (and reflected) the program’s savings increases. SaveUSA had no 

positive or negative effects on general indicators of financial security, including debt, financial 

net worth, and incidence of financial hardship.

v

Contents

Overview iii

List of Exhibits vii

Preface xi 

Acknowledgments xiii

Executive Summary ES-1 

Chapter

1 Introduction 1 

 Policy Context and Background 4 

Past Programs and Research 5 

SaveUSA Model 8 

Research Design 10 

Research Questions 13 

Data Sources 14 

Sample Intake 16 

Sample Characteristics 18 

SaveUSA Study Take-Up Rate 23 

 Evaluation Follow-Up Periods 24 

Structure of This Report 24 

2 SaveUSA Implementation and Participation 27 

Main Findings 28 

Program Implementation 28 

 Program Implementation in the Final Year of the Study 29 

Opening SaveUSA Accounts and Rates of Savings Match Receipt 32 

Differences in Characteristics of Savings Match Recipients and Nonrecipients 35 

SaveUSA Account Savings Patterns 38 

 How Did Individuals Use the Savings Match? 42 

SaveUSA Participation by City 42 

 Conclusion 44 

3 Impacts on Savings and Liquid Assets 47 

 Introduction 47 

Main Findings 48 

Data Sources and Estimation Procedures 48 

Impacts on Nonretirement Savings as Measured at the Time of the

 42-Month Survey 49 

 Impacts on Deposits of Tax Refund Dollars to Savings 54 

Impacts on Measures of Nonretirement Savings Since the First

 Interview at 18 Months or Measured as Change over Time 56 

Impacts on Retirement Savings 62 
vi

Impacts on Liquid Assets as of the 42-Month Interview 63 

Impacts on Attitudes Toward Saving 63 

 Conclusion 65 

4 Impacts on Household Expenditures, Debt, and Material Well-Being 67 

 Introduction 67 

Main Findings 68 

 Background: Respondents’ Financial Situation as of the 42-Month Interview 69 

 Impacts on Managing Expenses with Savings and Current Income 72 

 Impacts on Connection to Traditional Financial Institutions and Sources 

 of Credit 77 

 Impacts on Non-Housing-Related Debt 78 

Impacts on Measures of Financial Security and Material Well-Being 79 

 Conclusion 84 

5 Impacts on Savings and Financial Stability for Selected Subgroups 87 

Main Findings 88 

Participation Results for All Subgroups 88 

 Impacts by Random Assignment City 90 

 Impacts on Selected Survey Outcomes for Other Subgroups 94 

 Conclusion 98 

6 Conclusion 99 

Appendix

A Selected Characteristics of Study Sample Members and 

SaveUSA-Eligible Nonparticipants 103 

B SaveUSA 42-Month Survey Response Analysis 115 

C Additional Impact Comparisons 133 

D Supplementary Tables to Chapter 2 139 

E Background Information on Conducting Random Assignment, 

Collecting Data, and Estimating Program Impacts 147 

References 167
vii

List of Exhibits

Table

ES.1 SaveUSA Account Activity, by Program Year, in All Four Cities ES-5

ES.2 Impacts on Selected Outcomes as of 42-Month Interview ES-7

1.1 Selected Baseline Characteristics of Sample Members Enrolled in 2011 20

2.1 SaveUSA Sites in 2011 30

2.2 SaveUSA Account Activity, by Program Year, in All Four Cities 33

2.3 Selected Baseline Characteristics of SaveUSA Group Members, by Savings 

Match Receipt Status, in All Four Cities 36 

2.4 SaveUSA Year 1 Pledge and Initial Deposit, and Savings Match Outcomes

over Three Program Years, by City 44 

3.1 Impacts on Savings as of the 42-Month Interview 51

3.2 Impacts on Tax Refund Receipt and Use During Year 4 (2014) 55

3.3 Impacts on Savings Between the 18-Month and 42-Month Interviews 58

3.4 Impacts on Changes in Nonretirement Savings, Retirement Savings, and 

Liquid Assets Between the 18-Month and 42-Month Interviews 60 

3.5 Impacts on Savings Attitudes and Behaviors 64

4.1 Impacts on Employment, Income, Health Coverage, and Housing Status 70

4.2 Impacts on Managing Monthly Household Expenses and Emergency 

Expenses 73 

4.3 Impacts on Use of Financial Services 78

4.4 Impacts on Non-Housing-Related Debt 80

4.5 Impacts on Indicators of Financial Security 82

4.6 Impacts on Changes in Selected Indicators of Financial Security Between 

the 18-Month and 42-Month Follow-Up Interviews 85 

5.1 Baseline Characteristics and Savings Match Outcomes Across All Program 

Years, for Selected Subgroups 89 

5.2 Impacts on Selected Outcomes, by City 91
viii

Table

5.3 Impacts on Selected Outcomes, by Selected Baseline Characteristics 95

A.1 Selected Baseline Characteristics of Sample Members Enrolled in 2011,

by City 105 

A.2 Selected Baseline Characteristics, by Year of Program Enrollment 107

A.3 Selected Baseline Characteristics, by Age Group, New York City and

Tulsa Only 109 

A.4 Selected Baseline Characteristics of 2011 Sample Members, by Research 

Group, New York City and Tulsa Only 111 

A.5 Selected Characteristics of SaveUSA-Eligible Individuals Who Declined to 

Participate in the Study and of Sample Members Who Enrolled in the Study 113 

B.1 SaveUSA 18- and 42-Month Follow-Up Survey Response Rates, by City 

and Research Group 119 

B.2 Estimated Regression Coefficients for the Probability of Being a 

Respondent to the SaveUSA 42-Month Survey 121 

B.3 Selected Baseline Characteristics of 42-Month Survey Respondents, by 

Research Group 123 

B.4 Estimated Regression Coefficients for the Probability of Being a SaveUSA 

Group Member, Among 42-Month Survey Respondents 125 

B.5 Comparison of Participation Outcomes Across All Program Years for the 

42-Month Fielded Sample, 42-Month Survey Respondent Sample, and 

18- and 42-Month Survey Respondent Sample 126 

B.6 Comparison of Impacts on Allocation of 2010 Federal Tax Refund for the 

42-Month Fielded Sample, 42-Month Survey Respondent Sample, and 

18- and 42-Month Survey Respondent Sample 127 

B.7 Comparison of 18-Month Impacts Between the 18-Month Survey

Respondent Sample and the 18- and 42-Month Respondent Sample 129 

B.8 Impacts for the Respondent Sample and Fielded Sample, Including 

Predicted Outcomes for Nonrespondents 131 

C.1 Comparison of SaveUSA Account Activity, by Data Source 135

C.2 Comparison of Impacts on Total Nonretirement Savings, by Wording of 

Survey Question 136 
ix

Table

C.3 Comparison of Indicators of Savings and Debt, by Level of Exclusion of 

Outlier Values 137 

C.4 Comparison of Impacts on Savings and Debt, by Level of Exclusion of 

Outlier Values 138 

D.1 SaveUSA Account Activity (Self-Reported from Survey Responses) 141

D.2 Additional Findings on Use of SaveUSA Account (Self-Reported from 

Survey Responses) 142 

D.3 Factors Associated with Receiving the Savings Match in Only One Year

or in Two or More Years 143 

D.4 SaveUSA Account Activity, by Program Year 144

E.1 Impacts on Allocation of 2010 Federal Tax Refund 155

E.2 SaveUSA 42-Month Follow-Up Survey Response Rates for Selected 

Outcomes 157 

E.3 Regression Coefficients for Estimated Impacts on Nonretirement Savings 

After 42 Months of Follow-Up 160 

E.4 Adjusted and Unadjusted Impacts on Selected 42-Month Survey Outcomes 161

E.5 Effects of Imputing Missing Values on Selected 42-Month Survey 

Outcomes and Impacts 163 

E.6 Statistical Data on Savings-Related Outcomes and Impacts 164 

Figure

1.1 Recruitment and Random Assignment Process 17

1.2 SaveUSA Study Samples Enrolled in 2011 19

1.3 Random Assignment and Follow-Up Periods 25

2.1 Percentage of SaveUSA Accounts with a Balance Greater Than $10, by 

Month of Follow-Up, in All Four Cities 39 

2.2 Average Balance of SaveUSA Accounts, by Month of Follow-Up, in All 

Four Cities 41 

2.3 Average Balance of SaveUSA Accounts, by Month of Follow-Up, in

New York City and Tulsa 45 
x

Box

1.1 SaveUSA Partners 3

3.1 How to Read the Impact Tables in the SaveUSA Report 50
xi

Preface 

In recent years, many policymakers and providers of social services have advocated for programs that encourage low- and moderate-income families to increase their savings. Having savings on hand has been linked with better financial outcomes, such as the ability to weather sudden losses of income or sudden increases in expenses without resorting to “payday” loans, credit cards, or other high-cost sources of credit. 

The SaveUSA initiative helps low- and moderate-income tax filers build up short-term 

nonretirement savings by encouraging filers to pledge to save a portion of their tax refund in a 

special account. Tax filers who maintain their pledged savings amount for a year earn a 50 percent match on that amount, which they can use for any purpose. In the period covered by this 

study, individuals could pledge to save annually at least three times and be eligible to receive 

savings matches. 

Although other asset-building programs have been studied, the effects of SaveUSA are 

noteworthy because they were measured using a randomized controlled trial. The SaveUSA

evaluation also examines effects beyond the short term. A previous report from this evaluation 

found that in the first 18 months of follow-up, SaveUSA increased the percentage of tax filers 

with short-term nonretirement savings, compared with what would have occurred without the 

program. Longer-term effects, measured about four years after study entry, are presented in this 

final report. The results show that SaveUSA continued to produce gains in nonretirement savings and increased the likelihood that low- and moderate-income families would have cash 

available to pay for normal household expenses or for emergency or unexpected expenses. No 

positive or negative effects were found on study participants’ debt, material hardship, or other 

aspects of financial security, such as use of high-cost sources of credit. 

The SaveUSA evaluation provides strong evidence that low- and moderate-income 

families can accumulate short-term savings and that using tax refund dollars is a viable strategy 

to accomplish this aim. We are confident that the results of the SaveUSA evaluation will inform 

future policymaking and research on asset-building strategies for low- and moderate-income 

families. 

Gordon L. Berlin

President, MDRC

xiii

Acknowledgments

This report reflects the generous contributions and support of many people. We are especially 

grateful to the individuals participating in the SaveUSA evaluation, who have allowed us to 

learn from their experiences. The report also would not have been possible without the work 

and dedication of the participating partners. One group of partners consists of the nonprofit 

agencies that operated SaveUSA in New York City, Tulsa, Newark, and San Antonio. Past and 

present staff at these agencies helped us understand their efforts in implementing the program 

and supplied us with essential data. In particular, for assistance with this report, we would like 

to thank Irene Baldwin at Ariva in New York City; Janae Bradford and Brandy Holleyman at 

CAP Tulsa; German Tejeda, Arlene Sabdull, and Andy Nieto at Food Bank For New York 

City; Victor Silva at Newark Now; and Anna Weaver and Toni Van Buren at United Way of 

San Antonio and Bexar County. Another essential group of partners is made up of the financial 

institutions that volunteered to provide SaveUSA accounts. Individuals in these institutions facilitated the opening and ongoing maintenance of the accounts and shared periodic data files 

with MDRC. We thus express gratitude to Derek Martin at the Bank of Oklahoma; Mariadelle 

del Priest and Jamie Lutton at Capital One Bank; Doug Lindsey at Carver Federal Savings 

Bank; Juan Solis III and Patricio Lobos, formerly at Citibank; Tammie Zacharie and Priscilla 

Mendez at Select Federal Credit Union; and Brian Blake and Angel Fuentes at Spring Bank. 

We owe special gratitude to our colleagues at the New York City Center for Economic 

Opportunity (CEO) and the New York City Department of Consumer Affairs Office of Financial Empowerment (OFE), who led the operations of SaveUSA. In particular, for help with this 

report, we thank Sinead Keegan, Emily Firgens, Emily Apple, Brigit Beyea, and Monica 

Copeland. 

In addition, we appreciate the continuing support of the funders of the SaveUSA evaluation. The report is based upon work supported by the Corporation for National and Community 

Service’s Social Innovation Fund under Grant No. 10SIHNY002. Additional funders included 

Bloomberg Philanthropies, Open Society Foundations, The Rockefeller Foundation, The Annie 

E. Casey Foundation, The Ford Foundation, MetLife Foundation, George Kaiser Family Foundation, Tulsa Community Foundation, and United Way of San Antonio and Bexar County. 

Special thanks are also due to external reviewers who carefully reviewed an early draft 

of the report and offered valuable comments: In addition to the above-named CEO and OFE 

staff members, Jonathan Mintz and Tamara Lindsay at the Cities for Financial Empowerment 

Fund, Inc., Amy Brown at The Ford Foundation, and Cathie Mahon at the National Federation 

of Community Development Credit Unions were very generous with their time and provided 

insights on the findings. Appreciated comments and reactions were also provided by a group of
xiv

asset-building researchers, including Rachel Black, Ray Boshara, Michael Collins, Jessica Dorrance, Lucy Gorham, Justin King, Signe-Mary McKernan, and Ida Rademacher. 

At MDRC, Gayle Hamilton provided general oversight of SaveUSA evaluation activities, and Caroline Schultz oversaw SaveUSA program operations. Joshua Vermette and Paul 

Veldman assisted with the recent acquisition, processing, and analysis of all quantitative data. 

Jo Anna Hunter helped manage the survey of sample members, which was administered by Decision Information Resources, Inc. (DIR); Ron Bass helped design the survey instrument and 

monitored its fielding.

For this report, Gordon Berlin, James Riccio, Dan Bloom, Cynthia Miller, Joshua Malbin, and Jennie Kaufman provided valuable internal feedback on drafts. Crystal Ganges-Reid 

coordinated the production of the report, and Diane Singer helped with the production of the 

exhibits. Jennie Kaufman edited the report and Stephanie Cowell and Carolyn Thomas prepared 

it for publication.

The Authors
ES-1 

Executive Summary

Launched in 2011, SaveUSA is a voluntary tax-time savings program that offers low- and moderate-income families the opportunity to directly deposit all or a portion of their tax refund into 

a special savings account and pledge to save a specific amount for about a year. As an incentive 

to continue saving, account holders who maintain their pledged savings amount throughout the 

year earn a 50 percent match on that amount. SaveUSA replicated an earlier program, called 

$aveNYC, that was designed and managed by the New York City Department of Consumer 

Affairs Office of Financial Empowerment (OFE). SaveUSA’s goal is to encourage low- and 

moderate-income households to accumulate unrestricted savings to meet financial emergencies, 

pay bills or debts, or make necessary purchases, in order to strengthen their overall financial 

well-being.

This is the final report in a multiyear evaluation of SaveUSA. The evaluation broke new 

ground by using a randomized controlled trial to examine the effects (or impacts) of SaveUSA 

in two cities — New York City and Tulsa — on a wide range of outcome measures. Most outcomes analyzed in this report were measured using responses to a survey administered about 42 

months (or three and a half years) after study entry. The report also analyzes the implementation 

of SaveUSA in four cities — New York City, Tulsa, San Antonio, and Newark — over three 

years of operation. 

The evaluation sought to answer a range of primary questions: Can this type of program 

be implemented in different settings, and to whom and to what extent does it appeal? Does access to a SaveUSA account increase short-term nonretirement savings beyond what low- and 

moderate-income tax filers would have accumulated on their own? And, if so, are these increases large enough to improve households’ overall financial security? 

SaveUSA’s implementation and its evaluation were supported by the Social Innovation 

Fund (SIF), a program of the Corporation for National and Community Service (CNCS). This 

particular SIF project has been led by the Mayor’s Fund to Advance New York City and the 

New York City Center for Economic Opportunity (CEO) in collaboration with MDRC. A number of foundations and organizations provided matching funds for the effort, as required by the 

SIF. CEO and OFE led (and continue to lead) SaveUSA program operations, and MDRC conducted the program’s evaluation.

The results show that a majority (68 percent) of the “SaveUSA group” — sample 

members who had access to a SaveUSA savings account — received at least one savings 

match during the three program years, and about 40 percent of SaveUSA group members deposited tax refund dollars into their SaveUSA accounts more than once across the three program years. They accumulated an average of $365 in match dollars ($540 per match recipient). 
ES-2 

As of the 42-month follow-up point, the program had increased the percentage of SaveUSA 

group members with nonretirement savings (of any type) by 8 percentage points and had increased the total average amount of savings held by $522, compared with savings levels for the 

“Regular Tax Filers” group, who did not have access to a SaveUSA account. The program led 

to improvements on some measures of financial security, such as having more cash available 

to pay for normal household expenses or for emergency or unexpected expenses, that were directly related to the program’s savings increases. SaveUSA had no positive or negative effects 

on more general indicators of financial security, including debt, financial net worth, and incidence of financial hardship. 

The SaveUSA Program Model

SaveUSA, offered during the tax return preparation seasons of 2011 through 2013, built on the 

free tax preparation services provided by a total of 18 Volunteer Income Tax Assistance 

(VITA) sites across the four cities. To be eligible for SaveUSA, tax filers had to be at least 18 

years old and meet certain annual income requirements ($50,000 or less for filers with dependents, and generally $25,000 or less for filers without dependents). When preparing their tax 

returns, both single filers and couples filing jointly could open a SaveUSA account with a local 

financial institution participating in the program or redeposit into a SaveUSA account they established in a prior year. On their tax returns, SaveUSA participants instructed the Internal 

Revenue Service (IRS) or state taxing agency to deposit at least $200 from their tax refunds 

directly into their SaveUSA account. Participants also pledged to keep a certain amount of 

their initial deposit, from $200 to $1,000, in the account until January 31 of the following year. 

On February 1, participants who fulfilled this pledge received a 50 percent savings match, up 

to $500, on their pledged savings amount. Account holders whose balance fell below their 

pledged amount at an earlier date lost their eligibility for the savings match but did not incur 

any further penalty for withdrawing funds. Regardless of whether they received a match, individuals could pledge to save part or all of their tax refunds in subsequent years and again be 

eligible for eventual matches.

SaveUSA focused on tax-time saving because tax refunds, supported by the Earned Income Tax Credit (EITC) and other credits, typically constitute the largest source of cash that 

low- and moderate-income individuals receive at any one time. In addition, SaveUSA’s design 

was intended to influence federal tax policy, possibly leading to the creation of a federal program enabling low- and moderate-income households to directly deposit tax refund dollars in 

unrestricted savings accounts and receive tax credits.
ES-3 

The SaveUSA Evaluation

In New York City and Tulsa, eligible tax filers who were interested in opening a SaveUSA account and willing to enter the study were randomly assigned in 2011 to either a SaveUSA group

(eligible to open a SaveUSA account) or a Regular Tax Filers group (not eligible to do so but 

with access to other savings products normally offered at tax sites). Random assignment ensured that, on average, the characteristics of the tax filers — such as income, refund amounts, 

and motivation to save — were similar for the SaveUSA and Regular Tax Filers groups at the 

start of the study. By tracking both groups over time and comparing their outcomes, MDRC can 

determine the impact or “value added” of the SaveUSA program. Random assignment was not 

conducted in Newark and San Antonio, where all eligible tax filers interested in SaveUSA could 

open SaveUSA accounts. Program implementation was studied by MDRC in all four cities.

Study Sample

During the 2011 tax return preparation season, about 28,000 tax filers in participating VITA 

sites were determined to be eligible for SaveUSA, and nearly 2,500 were interested in and enrolled in the study. The SaveUSA take-up rates, which ranged between 6 percent and 13 percent 

across the four cities, were comparable to take-up rates found for other tax-time asset-building 

initiatives, including the predecessor $aveNYC program.

Study enrollees on average received larger refunds than nonenrollees, some supported 

by the EITC, and had slightly higher adjusted gross incomes. (An informal survey of a subsample of nonenrollees suggested that many had already earmarked all of their anticipated refund to 

pay bills or reduce debt.) Even so, SaveUSA study enrollees faced significant barriers to initiating or increasing savings: About half of all enrollees were single filers with at least one dependent child; their average income during 2010 was about $18,000, with a quarter having an income of less than $10,000; and a significant share of enrollees reported that they did not have 

enough money to make ends meet and/or had sizable debt.

SaveUSA Implementation 

• SaveUSA was consistently marketed and successfully integrated into the 

normal VITA tax return preparation process. 

In all four cities where SaveUSA was offered, VITA program operators marketed 

SaveUSA before and during the tax filing season. The VITA staff also conducted SaveUSA 

eligibility screening and study enrollment, coordinated the opening of SaveUSA accounts, and 

assisted tax return preparers in arranging for direct deposit of tax refund dollars into the ac-
ES-4 

counts. As Table ES.1 shows, about 98 percent of all SaveUSA group members were successful 

in opening a SaveUSA account during the study enrollment period in 2011.

In 2013, the last year MDRC tracked direct deposits to SaveUSA accounts, SaveUSA 

operations remained strong, except in Newark, where program operators ceased opening new 

SaveUSA accounts but allowed individuals with existing accounts to deposit tax dollars through 

a new process. According to survey responses from New York City and Tulsa, the vast majority 

of SaveUSA group members were aware that they could participate in SaveUSA in 2013, the 

final program year for the study period. 

SaveUSA Pledge Amounts, Match Eligibility, and Use of 

Account Monies

• Over three years of program operation, SaveUSA group members pursued different savings strategies. About two-thirds received at least one 

savings match, and about 40 percent pledged to save tax refund dollars 

in their SaveUSA accounts in more than one year.

Despite their modest income, individuals pledged to save a significant portion of their 

refunds — on average 14 percent of their refund in Year 1. During the first year, when almost 

all sample members participated in SaveUSA, 30 percent pledged to save $1,000, the maximum 

amount allowed to be matched, and 37 percent pledged to save $200, the minimum amount

(Table ES.1). 

Participation declined after the first program year, and just 23 percent of SaveUSA 

group members pledged to save during all three years (not shown). According to survey responses in New York City and Tulsa, among SaveUSA group members who did not pledge 

again in the second year, over half reported that they were not able to do so because they received no refund or their refund was not big enough, or they needed to use their refund to pay 

debts or bills or make expenditures. Similar reasons for not pledging to save again were provided regarding the final program year for the study period. 

A majority of SaveUSA group members — 68 percent — received at least one savings 

match over the three program years (Table ES.1). Among all members, the SaveUSA group 

averaged a total of $365 in match dollars over this period (a $540 average among only those 

who received a match). Recipients of two or more savings matches were more likely to have

pledged the maximum of $1,000 in any given program year. They also tended to be older and 

have higher adjusted gross incomes (that is, between $20,000 and $50,000), compared with other SaveUSA group members. Conversely, those who received only one or no savings match 

during the three program years had very low incomes (that is, less than $10,000); were more
ES-5 

The SaveUSA Evaluation

Table ES.1

SaveUSA Account Activity, by Program Year, in All Four Cities

Outcome Year 1 Year 2 Year 3 All Years

SaveUSA account opened or pledged

Distribution of pledged deposita

 (%)

$0

$1 - $200

$201 - $999

$1,000

Average initial deposit amountb

 ($) 

Distribution of initial depositb

 (%) 

$0

$1 - $200

$201 - $999

$1,000 or more

Received savings match (%)

Average amount of savings match ($)

Average savings match, among those 

who received the savings match ($) 

Distribution of savings match (%)

$0

$1 - $100

$101 - $499

$500

(%) 97.5

0.0

36.7

33.3

30.0

506

10.0

32.0

29.8

28.3

65.5

191

291

34.5

20.1

22.1

23.3

39.1

60.8

8.4

15.1

15.7

293

62.6

7.4

14.2

15.9

27.5

96

348

72.5

4.3

10.0

13.2

28.8

71.2

5.0

9.7

14.2

244

72.2

4.0

9.1

14.7

20.5

78

383

79.5

1.7

7.1

11.6

97.9

0.0

25.0

31.3

43.8

1,042

8.7

21.4

27.7

42.2

67.5

365

540

32.5

14.0

19.8

33.7

Sample size 1,554

SOURCES: MDRC calculations from MDRC baseline data and financial institution data.

NOTES: The sample includes SaveUSA group members who were ages 18 to 64 at their time of study entry.

Rounding may cause slight discrepancies in calculating sums.

aThe pledged deposit refers to the amount of tax refund dollars that individuals committed to savings at 

the time of study entry.

bThe initial deposit refers to the tax refund amount directly deposited into the SaveUSA account by the 

Internal Revenue Service.
ES-6 

likely to pledge to save smaller amounts and thus received a smaller match if they did receive 

one; and were less likely to pledge to save again in subsequent years. By city, savings match 

rates over the three program years ranged from 52 percent to almost 80 percent.

• Most recipients of savings matches withdrew their pledged savings 

amount plus their savings match within several weeks of receiving the 

match. 

When surveyed, most match recipients reported that they used their savings match either for expenditures — such as a big purchase, usual household expenses, travel or a family 

event, or education — or to pay bills or debts. Typically, SaveUSA group members who did not 

receive a savings match withdrew some or all of their pledged savings within three months after 

pledging to save. During survey interviews, they reported most often that they used the funds to 

cover an emergency expense, to pay bills or debts, or to buy necessities. 

SaveUSA’s Effects on Savings

SaveUSA’s effects (or impacts) are defined as the differences between the SaveUSA group and 

the Regular Tax Filers group in financial outcomes or in attitudes toward saving. Impacts in this 

report are estimated primarily from surveys administered to both research groups at about 18 

months and 42 months after random assignment.

• At the 42-month follow-up point, SaveUSA group members had more 

nonretirement savings than members of the Regular Tax Filers group.

When interviewed at 42 months, 80 percent of SaveUSA group members reported having at least $1 in nonretirement savings, an increase of 8 percentage points above the level reported by members of the Regular Tax Filers group. As Table ES.2 shows, on average, 

SaveUSA group respondents reported having a total of $2,281 in nonretirement savings, an increase of $522, or 30 percent, above the average for the Regular Tax Filers group. SaveUSA’s 

impact on total nonretirement savings resulted in part from the program’s turning some nonsavers into savers (as represented by the impact on the proportion with any nonretirement savings).

In addition, when only respondents with nonretirement savings are considered, the SaveUSA 

group savers averaged about $400 more in nonretirement savings than their counterparts in the 

Regular Tax Filers group. As expected, SaveUSA did not affect sample members’ accumulation 

of longer-term retirement savings. 

• On average, SaveUSA group members saved more consistently during 

the follow-up period than members of the Regular Tax Filers group — 

even as their savings patterns changed over time.
ES-7 

The SaveUSA Evaluation

Table ES.2 

Impacts on Selected Outcomes as of 42-Month Interview 

Outcome 

SaveUSA

Group

Regular Tax

Filers

Difference

(Impact) P-Value

Nonretirement savings

Has savings (%) 80.0 72.4 7.6 *** 0.002

Total savings ($) 2,281 1,758 522 ** 0.024

Total savings (%)

$0

$1 - $500

$501 - $1,000

$1,001 - $2,000

$2,001 - $5,000

$5,001 - $10,000

More than $10,000

20.0

31.2

9.1

12.1

15.1

9.1

3.4

27.6

28.7

11.5

12.3

12.0

5.1

2.8

-7.6 ***

2.6

-2.4

-0.2

3.1

4.0 ***

0.5

0.002

0.340

0.175

0.929

0.117

0.008

0.603

Savings goals and attitudes

Has a current savings goal (%) 78.2 71.6 6.6 *** 0.007

Thinks it very important to have money in a savings 

account (%) 78.6 76.1 2.5 0.304

Financial security

Used high-interest credit since the 18-month interview (%) 24.1 27.5 -3.3 0.186

Total non-housing-related debt ($) 9,733 10,309 -576 0.542

Has liquid net worth greater than zero (%) 36.2 34.7 1.4 0.620

Had financial hardship since the 18-month interview (%) 60.7 60.1 0.6 0.827

Sample size (total = 1,236) 626 610

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City and Tulsa who were ages 18 to 64 at their time of 

random assignment.

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.
ES-8 

SaveUSA’s impacts on nonretirement savings as of 42 months of follow-up closely resemble the program’s effects measured two years previously, when most SaveUSA group 

members had recently received a savings match and many were saving for another match the 

following year. Nonetheless, SaveUSA group members’ savings patterns changed over time, 

which affected the program’s impacts on nonretirement savings in other ways. At 18 months, 

SaveUSA led to an extremely large impact on the incidence of directly depositing tax refund 

dollars into nonretirement savings and also to smaller but consistent increases above the Regular Tax Filers group in responses to questions about their views on the importance of savings. In 

contrast, two years later, the SaveUSA group’s incidence of directly depositing tax refund dollars into savings, and the program’s impact on this measure, had decreased substantially. Moreover, at their 42-month interview, respondents from both research groups voiced similar support 

for saving. The main exception to this finding was that SaveUSA group members were more 

likely to report that they had a specific savings goal.

These trends in program impacts suggest that SaveUSA group members were using a 

greater variety of savings strategies. They were relying less on tax-time savings and more on 

other savings products, most likely ones paying relatively low interest. This transition to a 

broader set of strategies may be interpreted as a notable achievement of the program. 

• SaveUSA group members attained some gains in financial security compared with members of the Regular Tax Filers group — in outcomes 

that were directly related to (and reflected) the program’s savings increases. 

Proponents of programs that encourage nonretirement savings among low- and moderate-income households have often posited that even modest increases in savings can have the 

positive effect of helping households avoid financial hardship and increase their financial wellbeing. The evidence from the SaveUSA evaluation is mixed on this perspective.

A primary reason why low- and moderate-income households with a certain level of 

savings can experience greater financial security is that household members can use savings or 

current income to pay for normal or unexpected expenses. In fact, at 42 months of follow-up, 

SaveUSA group members were more likely than members of the Regular Tax Filers group to 

report having the equivalent of cash on hand to pay expenses for at least one month. SaveUSA 

also led to an increase in the proportion of respondents who reported a preference for relying on 

savings or current income to pay for emergency or unexpected expenses, as opposed to increasing their debt. Furthermore, as a result of SaveUSA’s impact on nonretirement savings, the program led to a reduction of 6 percentage points in the incidence of “liquid-asset poverty” (defined as having insufficient liquid assets to subsist at the poverty level for three months in the 
ES-9 

absence of income). These findings suggest that SaveUSA produced some modest gains in financial security.

• At 42 months, SaveUSA had no positive or negative effects on more general indicators of financial security, including debt, financial net worth, 

and incidence of financial hardship. 

Findings indicate that SaveUSA did not positively or negatively affect other measures 

of financial security: Similar proportions of SaveUSA and Regular Tax Filers group members 

reported that they had used a high-cost source of credit, such as a cash advance with a credit 

card or overdrawing a checking account, in the previous two years. SaveUSA also did not affect 

non-housing-related debt levels, which averaged about $10,000 for both research groups, and 

did not increase the incidence of having positive “liquid net worth” (liquid assets that exceed 

non-housing-related debt). Similarly, about the same proportion of both research groups reported that their debt levels had decreased in the previous two years. 

SaveUSA also did not affect the incidence of financial hardship among survey respondents. About 60 percent of both research groups reported that they had experienced at least one 

type of financial hardship in the past two years, such as an inability to pay housing or utility 

costs, food insecurity, or forgone use of medical care or prescription drugs. Finally, SaveUSA 

and Regular Tax Filers group members expressed similar sentiments about having made progress financially since their 18-month interview and in their views of their future financial 

prospects. 

The above findings are similar to the results reported as of 18 months of follow-up.1

• In general, SaveUSA’s effects were consistent across subgroups. 

SaveUSA could have worked differently for different types of people or depending on 

the enrollment city. For example, the effects of SaveUSA could have differed depending on 

tax filers’ circumstances when they entered the study. Certain characteristics, such as having a 

very low income or having children, could make it harder for individuals to save. The findings, 

however, show that SaveUSA led to savings increases that were consistent across subgroups 

defined by city of residence, age, income level, educational attainment, and tax filing status.

Similar to the results for the full sample, no effects were found on general indicators of financial security for any of these subgroups.

 1

Gilda Azurdia, Stephen Freedman, Gayle Hamilton, and Caroline Schultz, Encouraging Low- and Moderate-Income Tax Filers to Save: Implementation and Interim Impact Findings from the SaveUSA Evaluation

(New York: MDRC, 2014).
ES-10

Discussion 

SaveUSA was designed as a possible precursor to a federal tax-time savings incentive for lowand moderate-income households. The program embodies one of several strategies that policymakers think might increase nonretirement savings, and it is best evaluated as one candidate 

for inclusion among a potential “toolkit” of types of savings programs — each varying in appeal and possibly leading to benefits for different segments of the low- and moderate-income 

population. 

Results from this evaluation show that SaveUSA was successfully implemented over 

several years at VITA sites in a variety of settings and that the program maintained the active 

support of a number of financial institutions. Two-thirds of SaveUSA group members received 

at least one savings match, but the program experienced sustained involvement by less than a 

quarter of SaveUSA group members, disproportionately those with higher incomes (between 

$20,000 and $50,000) at the start of the study. Different types of savings interventions, however, can be expected to engage different people. Programs that use lotteries, prizes, or carefully 

targeted marketing as savings incentives, for example, likely appeal to certain types of savers as 

well. From the standpoint of assembling a toolkit of savings options for low- and moderateincome households, the SaveUSA model should be considered as a viable option.

The impact results show that opening a SaveUSA account had positive behavioral effects. SaveUSA increased nonretirement savings without increasing debt and engendered greater longer-term support for having a savings goal. On average, SaveUSA group members 

demonstrated a stronger commitment to save, using a variety of savings strategies. Tax refund 

dollars and the SaveUSA match provided opportunities to save, but SaveUSA group members 

made use of other savings products, such as personal savings and checking accounts, as well.

Moreover, the impacts on nonretirement savings at 42 months after random assignment showed 

that SaveUSA could sustain savings increases above the level for members of the Regular Tax 

Filers group even after most SaveUSA group members no longer had access to a 50 percent 

match on savings. 

Although generally positive, the longer-term findings from the SaveUSA evaluation also demonstrate the limitations of programs that focus solely on helping low- and moderateincome households increase their nonretirement savings. Three and a half years after random 

assignment, members of the SaveUSA group did not report better outcomes than Regular Tax 

Filers on a series of general indicators of financial security, including accumulated levels of 

debt, liquid net worth, and incidence of experiencing financial hardship. It could be that 

SaveUSA’s savings increase of $522, while of a magnitude associated in past studies with increased financial security, was not enough to markedly improve the financial situations of a significant portion of study sample members who already had some savings at study entry. Possibly, SaveUSA could have had greater positive effects on financial security if it could have been 
ES-11

targeted only to individuals who did not have any savings. However, individuals with very low 

incomes, who likely had little or no savings, were the least likely to participate in SaveUSA and 

receive savings matches.

It should also be noted that the financial situations of sample members as of the 42-

month follow-up point were very precarious — with average non-housing-related debt of 

around $10,000, well in excess of their accumulated savings, and an average household income 

of less than $2,000 per month (including public assistance). In order to improve their financial 

security, savings increases may need to be much larger, or other interventions may need to be 

tried — alone or coupled with SaveUSA-like programs. For example, additional rigorous research could be conducted on programs that give incentives to save and also seek to increase 

income through skills training or other means, or that combine savings incentives with other 

services, such as financial coaching on debt reduction or financial management strategies. Research on savings interventions is still at an early stage, and the findings from the SaveUSA 

evaluation represent just a single piece of the puzzle.

1 

Chapter 1

Introduction

Many U.S. households do not have enough savings to help them manage temporary losses of 

income or increased expenditures from unexpected events.

1 Particularly for low- and moderateincome families, who may resort to high-cost loans or miss monthly housing payments in the 

face of financial shocks, increased savings might strengthen financial stability.

2 Although many 

low- and moderate-income individuals recognize the importance of saving, they may have barriers to achieving it; they may not have enough income to save or may lack access to financial 

products that would make saving easier.

3

 Some experts have proposed encouraging low- and 

moderate-income individuals to save part of their tax refund, capitalizing on the large, annual

influx of cash.4 Some past research suggests that this approach may be promising; other research indicates that many low- and moderate-income individuals need their refunds to pay bills 

or reduce debt.

In 2008, the New York City (NYC) Department of Consumer Affairs Office of Financial Empowerment (OFE) developed $aveNYC, a tax-time matched savings program that encouraged emergency savings for low- and moderate-income tax filers. From 2011 through 

2014, the program was replicated by the NYC Center for Economic Opportunity (CEO), OFE, 

and nonprofit partners under the name of “SaveUSA” in four cities: New York City, Tulsa, 

Newark, and San Antonio.

5 SaveUSA encouraged low- and moderate-income tax filers to directly deposit a portion of their tax refund into a special matched savings account that they 

could later use to pay for unexpected or emergency expenses or for any other purpose. Individuals who left their money untouched for about a year were eligible to earn a 50 percent matching incentive (hereafter referred to as the “savings match”). The program thus sought to increase 

individuals’ unrestricted-use savings, capitalizing on the moment when individuals learn the 

size of their tax refunds.

MDRC conducted a randomized controlled trial to measure the effects of SaveUSA in 

New York City and in Tulsa. Individuals who met SaveUSA eligibility criteria were assigned, 

at random, to a “SaveUSA group” or to a control group (called the “Regular Tax Filers” group). 

Those in the SaveUSA group were eligible to open a special savings account (or “SaveUSA 

 1

Pew Charitable Trusts (2015). 2

Cramer, King, and Schreur (2015); Hannagan and Morduch (2015); Abbi (2012). 3

Edin and Lein (1997); Collins and Gjertson (2013); Abbi (2012); Brobeck (2008a). 4

Edin, Greene, Halpern-Meekin, and Levin (2015); Rothstein and Black (2013). 5

The last year during which individuals in all four cities were eligible to receive the SaveUSA savings 

match was 2014. With some modifications, the program continued in New York City and San Antonio through 

the 2015 tax season, with final matching funds delivered in 2016. 
2 

account”) and receive the savings match, while those in the Regular Tax Filers group were not 

eligible to open the special savings account but could open other savings accounts offered at the 

tax sites. The evaluation shows whether short-term incentivized tax-time savings can encourage 

participants to save more than they normally would have without the program and lead to

longer-term savings habits, reduce material hardships, result in accrued assets, and improve the 

overall financial well-being of participants. 

The SaveUSA program and its evaluation were funded through the federal Social Innovation Fund (SIF), a program of the Corporation for National and Community Service (CNCS). 

The Social Innovation Fund combines public and private resources to increase the impact of 

innovative, community-based solutions that have compelling evidence of improving the lives of 

people in low-income communities throughout the United States. This particular SIF project has 

been led by the Mayor’s Fund to Advance New York City and the NYC Center for Economic 

Opportunity in collaboration with MDRC and OFE. (Box 1.1 lists the local nonprofit organizations, financial institutions, and funding partners that have been involved in the project.)

This report presents the final findings from the SaveUSA evaluation. It builds on an interim report that showed that SaveUSA was successfully implemented in all four cities.6 At the 

interim point — about a year and a half after enrollment — the majority of SaveUSA group 

members had received a first savings match, and 39 percent deposited part of their tax refund 

again in the program’s second year. The 18-month effectiveness, or “impact,” findings presented in that report showed that the SaveUSA program increased the percentage of individuals 

with short-term, nonretirement savings and their total amount of savings beyond what would 

have occurred in the absence of the program. As of the 18-month follow-up period, the program 

had also increased the percentage of individuals with pro-savings attitudes. These were the program designers’ primary short-term goals for the program. 

This report discusses implementation findings for the third, and final, program evaluation year and presents impact findings covering a much longer follow-up period — 42 months 

after individuals entered the study. The report provides a complete assessment of whether 

SaveUSA was able to sustain the savings increases observed at the 18-month point and examines whether the program was able to improve various aspects of individuals’ overall financial 

well-being. 

The final results show that SaveUSA was marketed and implemented as intended during each program year. The majority (68 percent) of the SaveUSA group received at least one 

savings match during at least one of the three program years during the study period. On average, SaveUSA group members received a total of $365 in savings matches over the three

 6

Azurdia, Freedman, Hamilton, and Schultz (2014).
3 

Box 1.1

SaveUSA Partners

Nonprofit Agencies

 Ariva

 CAP Tulsa (formerly known as Community Action Project of 

Tulsa County)

 Food Bank For New York City

 Newark Now

 United Way of San Antonio and Bexar County

Financial Institutions

 Bank of Oklahoma

 Capital One Bank

 Carver Federal Savings Bank

 Citibank

 Select Federal Credit Union

 Spring Bank (formerly known as CheckSpring Bank)

SaveUSA Funders

 Corporation for National and Community Service

 Bloomberg Philanthropies

 Open Society Foundations

 The Rockefeller Foundation

 The Annie E. Casey Foundation

 Ford Foundation

 George Kaiser Family Foundation

 MetLife Foundation

 Tulsa Community Foundation

 United Way of San Antonio and Bexar County

program years (including zeros for those who did not get a match). About 40 percent of 

SaveUSA group members deposited part of their tax refund into their SaveUSA accounts more 

than once. As of the 42-month follow-up point, SaveUSA had increased the percentage of 

SaveUSA group members with nonretirement savings, by 8 percentage points, and had increased the total amount of savings they held, by $522, compared with savings levels for the 

Regular Tax Filers group. The program led to improvements in some measures of financial security, such as having more cash available to pay for normal household expenses or for emergency or unexpected expenses, that were directly related to (and reflected) the program’s sav-
4 

ings increases. SaveUSA had no positive or negative effects on more general indicators of financial security, including debt, financial net worth, and incidence of financial hardship. 

Policy Context and Background

Many U.S. households do not save enough money to smooth consumption throughout their lifetime and to weather economic storms.

7 Before the Great Recession of 2007-2009, Americans 

saved, on average, less than 2 percent of their disposable income.

8 While savings rates increased

during the recession, and generally have hovered near 5 percent since early 2013, saving has not 

translated into greater household wealth during the recovery.9 Lower-income and middleincome families alike failed to recover from the roughly 40 percent decline in wealth that they 

experienced during the recession. Meanwhile, racial and ethnic wealth disparities have persisted, and young households have seen only small increases in net worth.10 

Consumer survey data indicate that among households earning less than $50,000 per 

year, holding even small amounts of unrestricted “emergency savings” — as little as $500 — is 

associated with a lower incidence of unfavorable financial events, such as overdrawing a checking account, initiating high-cost loans, or failing to meet monthly bills, such as rent and minimum credit card payments.11 Thus it is possible that building a cushion of accessible savings 

may help families manage temporary losses of income or increased expenditures from unexpected events — such as illness, job loss, or death.12 Some research has also found that the accumulation of savings can help individuals in the lowest income brackets move up the income 

ladder. For example, in one study, people with $10,000 in liquid savings were 6.5 times more 

likely to have moved up in income and 5.5 times more likely to have made it to at least a middle 

income bracket over at least a 25-year period, compared with people who started with only 

$1,000 in liquid savings.13 

 7

Pew Charitable Trusts (2015); McKernan and Ratcliffe (2008); Brobeck (2008b). 8

McCully (2011). 9

U.S. Bureau of Economic Analysis, National Income and Product Accounts tables, December 2007 

through June 2015. The personal savings rate is expressed in terms of savings as a percentage of disposable 

personal income. 10Wolff (2014); Fry and Kochhar (2014).

11Brobeck (2008b); Lopez-Fernandini (2010). This association does not necessarily mean that holding 

unrestricted savings of at least $500 was the cause of this lower incidence of unfavorable financial events. The 

overall financial circumstances of people who have and do not have emergency savings are likely to be quite 

different. 12In addition, research suggests that asset accumulation leads to benefits ranging from improved financial 

stability to better health and education outcomes for families and children. Asset accumulation may also have a 

positive effect on children’s wage-earning potential and the likelihood of their building savings into adulthood 

(Lopez-Fernandini, 2010; McKernan, Ratcliffe, and Vinopal, 2009; Rank, 2008; Lerman and McKernan, 2008; 

Chase, Gjertson, and Collins, 2011).

13Pew Charitable Trusts (2013).
5 

Although many low- and moderate-income individuals recognize the importance of 

saving, many have barriers to building or increasing their savings, such as not having enough 

income to save and a lack of access to financial products that would make it easy to save.14 Furthermore, even among individuals who save, many underestimate how much they need to save. 

One study found that among households in the lowest income quintile, people perceived their 

annual emergency savings needs at about $1,500, even though their typical annual unexpected 

expense was around $2,000.

15 

Current federal policies support families in building certain assets, such as retirement 

savings and home equity, but policies that help families build unrestricted savings do not exist.

Given the growing policy interest in encouraging individuals to increase their savings, there 

have been efforts by some to introduce federal legislation to Congress that would modify the tax 

code to enable the creation of matched savings accounts funded with tax refund dollars, with

disbursement of a savings match as a tax credit.

16

Past Programs and Research 

Various types of interventions designed to increase saving by low- and moderate-income families have been tried. These interventions have varied on several dimensions, including (1) the 

savings goal (for a specific purpose or any purpose); (2) how deposits were made; (3) when 

savers could access their accounts; and (4) whether additional savings incentives were offered.

One type of savings intervention focuses on saving for specific assets. For example, the 

American Dream Demonstration (ADD) and select Temporary Assistance to Needy Families 

(TANF)-funded initiatives have offered Individual Development Account (IDA) programs, 

which pair financial education with matched savings incentives for making home purchases, 

educational payments, or business investments.17 As of 2010, about 80,000 people participated 

in IDA programs, of which 34,000 made expenditures from their IDA accounts that triggered a 

savings match.18 The ADD evaluation found that access to an IDA program with incentives increased asset holdings, particularly among subgroups of low- and moderate-income participants, including African-Americans, those not on public assistance, and those who did not initially have a checking or savings account.19 However, IDAs have been costly to administer and 

seem to require considerable commitment and long-term planning by low-income households.

An analysis of an IDA program previously implemented showed that cumulative administrative 

 14Edin and Lein (1997); Collins and Gjertson (2013); Abbi (2012); Brobeck (2008a). 15Brobeck (2008b).

16Black and Cramer (2011); Weller and Unger (2013). 

17Authorized withdrawals were matched at 2:1 for home purchase and at 1:1 for all other allowable uses. 18U.S. Department of Health and Human Services (2012).

19Mills, Patterson, Orr, and DeMarco (2004). 
6 

costs exceeded the total amount of savings match dollars paid to program participants.20 In 

short, IDA programs may be too expensive to bring to a large scale and, moreover, they are focused on saving for specific assets — not on saving for any use, including emergency use. 

Other programs focus on increasing retirement savings. For example, Save More Tomorrow (or the SMarT program) encouraged people to commit in advance to allocating a portion of their future salary increases toward retirement savings. A nonexperimental study found 

that 78 percent of those offered the program joined and the average savings rates for program 

participants increased from 3.5 percent to 13.6 percent over the course of 40 months.21 A study 

by HelloWallet suggests, however, that some households may be better off postponing saving 

toward retirement until an emergency fund has been established. The study found that one out 

of four households withdrew early from their retirement accounts to pay day-to-day expenses, 

many because they lacked access to liquid savings.22

Other interventions attempt to increase unrestricted savings. One strategy encourages 

individuals to regularly set aside small amounts. An example of this strategy was the AutoSave

model, piloted beginning in 2009, which automatically diverted, through payroll deduction, a 

small portion of low- and moderate-income workers’ wages into a savings account. These savings were fully liquid and available both to cover short-term needs and, potentially, to increase 

attachment to mainstream financial services or to serve as building blocks to longer-term asset 

accumulation.23

Some savings interventions are embedded within programs that offer multiple services 

to enhance the financial security of low- and moderate-income families. An example of this 

strategy is the U.S. Department of Housing and Urban Development’s Family Self-Sufficiency 

(FSS) program, which provides case management services to Housing Choice (Section 8) 

Voucher recipients to increase employment and has an asset-building component. Voucher recipients generally pay 30 percent of their income in rent (with the government making up the 

balance); as their earnings rise, so does their rent. FSS allows families to build savings by diverting these rent increases into interest-bearing “escrow accounts” maintained by their local 

housing authority and paid to participants after several years when they complete the program.24

A study showed that 18 months after enrollment in FSS, about 29 percent of the households had 

accumulated some savings in FSS escrow savings accounts. In addition, among households 

with any escrow savings, those in a group subject to normal FSS procedures saved an average 

 20Schreiner (2005).

21Thaler and Benartzi (2004).

22Fellowes and Willemin (2013).

23Lopez-Fernandini and Schultz (2010). 24U.S. Department of Housing and Urban Development (2014). 
7 

of $1,112, while those in a group that received special incentives, which paid to encourage sustained full-time employment or educational training, accrued $1,312.25 

Among the savings interventions previously tried is a group of programs that leverage 

the tax-time moment to encourage savings, because tax refunds, supported by the Earned Income Tax Credit (EITC) and other credits, typically constitute the largest source of cash that 

low- and-moderate income individuals receive at any one time. Some theories of tax refund use 

suggest that individuals are more willing to save when receiving money in a lump sum.26 In fact, 

one study found that over a quarter of EITC recipients were planning to save part of their 

EITC.27 At the same time, one study of post-filing and post-refund household consumption 

found that spending corresponded strongly to the moment of receiving refunds rather than the 

anticipation of refunds, suggesting that diverting a portion of an anticipated refund at the time 

of filing may be an effective way to avoid spending down the entire refund once it is received.28

Some tax-time interventions have focused on increasing the use of tax refund dollars to purchase U.S. savings bonds or invest in Individual Retirement Accounts (IRAs). The take-up rates 

for these interventions have ranged between 3 percent and 14 percent.29 Other interventions, like 

the $aveNYC program in New York City (described below), encourage low- and moderateincome tax filers to deposit tax refund dollars into an unrestricted savings account. 

Many of the interventions that encourage savings have focused on financial incentives. 

As noted earlier, the ADD evaluation found that providing incentives increased asset holdings. 

Similarly, a small tax-time pilot study found significant increases in participation rates and initial savings amounts among tax filers who were offered the opportunity to open an IRA with a 

savings match, compared with those who were not offered a match.30 Another strategy to help 

motivate individuals to save involves prize-linked savings (PLS) products. For example, in 

Michigan the D2D (Doorways to Dreams) Fund ran a pilot nonexperimental study called “Save 

to Win” whereby credit unions offered a prize-linked account that gave individuals the opportunity to win a $10,000 annual jackpot and a range of regularly awarded smaller prizes. Results 

from the pilot show that 80 percent of Save to Win accounts that were opened in December 

2011 were still open a year later.31 Finally, a study of the $aveNYC program found that offering 

 25Verma et al. (2012).

26Shefrin and Thaler (1988); Mendenhall et al. (2012).

27Smeeding (2000).

28Baugh, Ben-David, and Park (2014). 29Bronchetti, Dee, Huffman, and Magenheim (2011); Duflo et al. (2006); Doorways to Dreams Fund 

(2012). 30Duflo et al. (2006). 

31Duch (2013).
8 

the savings match incentive increased participation and the likelihood of maintaining savings 

for about a year.32 

Even without monetary incentives, the application of behavioral economics concepts 

has illustrated the power of certain lower-touch interventions to influence saving behavior. For 

example, participation by low-wage workers in employer-sponsored retirement plans can be 

doubled or tripled by structuring enrollment and periodic contribution increases as automatic —

these are referred to as “opt-out” plans.33 Similar interventions have been tested that suggest that 

saving might be increased via techniques such as simplifying the array of savings options that 

individuals must choose from; presenting “default” choices for individuals who are indecisive 

about selecting from among saving options; and streamlining the amount of time and effort required to initiate a savings habit. A recent large random assignment study tested electronic messages embedded in Turbo Tax software, informed by behavioral economics, to encourage economically vulnerable households to save part of their refund.34 The study found that the messages significantly increased the percentage of individuals who saved at the time of tax filing, 

but the increases were modest. Average savings increased from $197 to $224. The likelihood of 

having some amount of savings six months later increased from 25 percent to 30 percent. 

Among the asset-building programs for lower-income individuals that have been previously tried, only a limited number have sought to help individuals save for emergencies or for 

uses that are not restricted — and random assignment studies of the effects of such unrestricted, 

short-term savings programs on savings and financial stability are rare. The SaveUSA program 

and evaluation thus broke new ground in determining the extent to which such programs can be 

implemented at tax time in different settings and can appeal to low- and moderate-income 

households. The study is particularly important in showing whether such programs can result in 

people saving more than they normally would save and can improve participants’ overall financial situation over the short term and the longer term.

SaveUSA Model 

SaveUSA replicated the $aveNYC program, which had shown promising results in recent studies of the program’s implementation and outcomes.35 $aveNYC was developed by the New 

York City Department of Consumer Affairs Office of Financial Empowerment (OFE). Piloted 

 32The study evaluated $aveNYC by using a quasi-experimental design. The study identified a comparison 

group meant to be similar, in terms of demographic and financial characteristics, to those who took up the offer 

to open $aveNYC accounts (Key, Grinstein-Weiss, Tucker, and Holub, 2013). $aveNYC study participants 

were not randomly assigned to program and control groups, as is being done in the SaveUSA evaluation, and 

the two $aveNYC study groups’ comparative baseline levels of motivation to save are unknown. 33Thaler and Benartzi (2004).

34Grinstein-Weiss et al. (2014). 35Manturuk, Gorham, and Dorrance (2013).
9 

in selected Volunteer Income Tax Assistance (VITA) organizations in New York City between 

2008 and 2010, $aveNYC offered tax filers the opportunity to open a savings account with their 

tax refund at a participating financial institution and receive a 50 percent match on their initial 

deposit if they kept their initial deposit for about a year.36 $aveNYC participants could use their 

savings and match for any purpose. During 2009 and 2010, $aveNYC’s primary years of operation, the program enrolled an average of 1,255 tax filers per year.37 Over 90 percent of enrollees 

deposited tax refund dollars in their $aveNYC savings account and nearly three-quarters of enrollees (or 80 percent of depositors) maintained their deposits for about a year and received the 

savings match. The $aveNYC study conducted by the Center for Community Capital at the 

University of North Carolina found that 31 percent of $aveNYC participants did not have a 

bank account (that is, were “unbanked”) and 36 percent reported having no savings when they 

entered the program. 

$aveNYC’s administrators and funders, and the researchers who studied the program, 

concluded that $aveNYC met several requirements for making the program a potential model 

for implementation on a larger scale. These included: (1) successful integration with other tax 

preparation services offered by VITA organizations; (2) enrollment of large numbers of lowand moderate-income families; (3) ongoing participation of financial institutions; and (4) relatively high rates of receipt of the savings match. Creation of the SaveUSA program and running 

a randomized controlled trial (RCT) to test its effects represented logical next steps. 

SaveUSA builds on the free tax preparation services provided by participating VITA 

organizations in four cities: New York City, Tulsa, Newark, and San Antonio.38 During the 

2011 tax season, SaveUSA offered both single filers and couples who filed jointly the opportunity to open a SaveUSA account at a local financial institution by directly depositing a portion 

of their tax refund into it and to earn a matching incentive by leaving their savings untouched 

for about one year.39 The SaveUSA account was designed to minimize risks and to facilitate the 

maintenance of small savings by account holders. Thus, by design, the accounts were intended 

 36In 2011, $aveNYC was offered to a limited number of former participants. 37Manturuk, Gorham, and Dorrance (2013).

38VITA sites offer free tax return preparation to low- and moderate-income individuals. Internal Revenue 

Service-certified volunteers provide basic income tax return preparation with electronic filing to qualified individuals in local communities and inform taxpayers about special tax credits for which they may qualify, such 

as the EITC. VITA sites are typically located at community and neighborhood centers, libraries, schools, and 

other convenient locations. Across the United States in 2012, about 1.6 million returns were completed at more 

than 6,000 VITA sites, according to the National Community Tax Coalition. Other types of tax-time savings 

strategies, currently in use or proposed, rely on other providers or would be available to individuals who file 

their taxes on their own. 39“Single filers” include any tax filer who was unmarried or who did not file jointly with his or her spouse 

at study entry. 
10

to have no automated teller machine (ATM) card, no minimum deposit requirement, no fees for 

withdrawals of funds, and no dormant account fees.40

When preparing their tax returns, SaveUSA participants instructed the VITA tax preparer to fill out forms that would result in the Internal Revenue Service (IRS) or state taxing 

agency directly depositing at least $200 from their tax refund into the SaveUSA account, described above. In each of the three years the program was offered, participants could pledge to 

keep a certain amount of their initial deposit, from $200 to $1,000, in their account for approximately one year. Participants who fulfilled this pledge would receive a 50 percent savings 

match, up to $500, about a year later. After receipt of the savings match, participants could use 

their accumulated savings and savings match dollars for any purpose or could continue to save 

at the market rate of interest (typically about 1 percent or less).

Account holders who withdrew any funds below their pledge amount at any time during 

the follow-up year would lose their eligibility for a savings match, even if they subsequently 

replaced the funds. However, the program design specified that they would incur no further 

penalty for withdrawing the funds. Regardless of whether they withdrew funds or received a 

savings match, account holders could pledge to save again the following year if they had their 

tax returns prepared at a SaveUSA-participating VITA site. 

The model applied design principles suggested by prior behavioral economics research 

— simplified options to save, a separate account for savings, electronic deposit into the account, 

incentives (high match rate) to maintain savings and disincentives (ineligibility for the match) to 

removing even small amounts of savings — to a tax-linked savings program for low- and moderate-income families.

Finally, some features of the SaveUSA model were designed with federal policy reform 

proposals in mind. These features were meant to mimic tax-time savings credit proposals being 

developed at the time for low- and moderate-income tax filers. The results of the SaveUSA research were intended to inform possible changes in federal tax policy.

Research Design

The SaveUSA evaluation measured the program’s effects, or “impacts,” through a randomized 

controlled trial (RCT) conducted by MDRC in New York City and Tulsa.41 In these two cities, 

 40See Azurdia, Freedman, Hamilton, and Schultz (2014) for details about the intended account features 

and how, in the course of implementation, the account features varied from what was intended and across the 

SaveUSA-involved financial institutions. 41In Newark and San Antonio, random assignment was not conducted because the enrollment numbers 

were expected to be small.
11

eligible tax filers who were interested in opening a SaveUSA account and who volunteered to 

enter the study in 2011 were randomly assigned into one of two groups:

• The SaveUSA group. Members of the SaveUSA group were offered a 50 

percent match, up to $500, if they deposited at least $200 of their tax refund 

into a SaveUSA account and maintained their initial deposit for approximately one year. In 2012 and 2013, SaveUSA group members were again eligible 

to deposit money from their tax refunds into their SaveUSA account and be 

eligible to receive the 50 percent match. Their savings matches could be received in 2012, 2013, and 2014.42

• The Regular Tax Filers group. Regular Tax Filers were offered the opportunity to deposit money in any other savings product that the VITA organization made available to tax filers who receive a tax refund. However, Regular 

Tax Filers were not offered the special matched-refund SaveUSA account in 

2011, 2012, or 2013.43 

The random assignment design produced very reliable estimates of the effectiveness of 

the SaveUSA program. It ensured that there were no systematic differences in the characteristics, both measured and unmeasured, of sample members in the two research groups. Thus, any 

differences between the two groups that emerged over time — for example, in savings or financial stability — can be attributed to the SaveUSA program. These differences in outcomes are 

known as impacts.

Random assignment was not conducted in Newark and San Antonio, where all eligible 

tax filers who volunteered to enter the study could open a SaveUSA account. 

Expected Effects and Timing

At the start of the study, SaveUSA was hypothesized to produce a number of different 

intended effects at different points in time — all of which are being investigated in the 

SaveUSA evaluation. The intended longer-term effects are discussed in this section to provide 

background on the types of outcomes examined in the evaluation. 

Over the three years following account opening, SaveUSA account holders, especially 

those who made yearly repeated deposits of tax refund dollars into their SaveUSA accounts, 

might receive additional benefits. (Account holders who deposited the maximum match-

 42SaveUSA participants in New York City and San Antonio were still eligible to receive savings matches 

in early 2015 and 2016. 43Members of the Regular Tax Filers group were eligible to pledge to save in SaveUSA accounts in New 

York City during the 2014 tax season. Only a small number of them (21) did so.
12

eligible amount each year from 2011 to 2013 theoretically could have ended up accumulating 

up to $4,500 in individual savings — $3,000 in direct deposits and $1,500 in possible match 

payments.) Account holders who repeatedly received a match and then used their funds soon 

after might be able to lower their debt by paying it down at set yearly intervals and by making

purchases and payments while minimizing the use of high-interest loans or credit cards. Other 

account holders who maintained their savings longer might have been able to afford larger 

purchases with less debt three to four years after random assignment. Moreover, it was hypothesized that account holders might become accustomed to saving, in part encouraged by 

their accumulation of savings and match dollars. These individuals would perhaps contribute 

additional dollars to savings for several years — beyond those they pledged to save from their 

tax refunds — and thereby realize additional purchasing power, lower debt, and, perhaps, 

higher credit scores. 

Over time, SaveUSA account holders might also develop a greater sense of financial 

security and greater confidence in their ability to manage their family’s finances and, if necessary, be better able to weather financial crises.

Under some circumstances, having a SaveUSA account was expected to have additional related effects. For example, having an account might contribute to greater employment stability for account holders, if they used their accumulated savings and match dollars to support 

more reliable transportation or child care or to deal with family issues that might otherwise have 

required time off from work to address.

At the study’s outset, there also were several hypotheses suggesting reasons why 

SaveUSA might not lead to positive effects. First, there was the possibility that the SaveUSA 

account and savings match would simply provide a “windfall” for people who were already inclined to save. If so, the program would not encourage more people to save or cause people to 

increase the amount they would otherwise have saved.44

Second, while it is generally assumed that increased savings are beneficial, SaveUSA’s 

requirement that people forgo use of their savings for about a year in order to qualify for an 

eventual match could have motivated some people to incur more debt or incur penalties from 

postponing paying off debt. 

Third, while there is a growing consensus among researchers and policymakers that 

even small savings amounts can help low-income families weather short-term financial emergencies or prevent small debts from spiraling out of control, it was not clear whether SaveUSA 

 44A “windfall” effect from the SaveUSA match may be interpreted by some policymakers as a reasonable 

way to increase the income of low- and moderate-income tax filers by several hundred dollars annually. However, it could be argued that other strategies, such as increasing the Earned Income Tax Credit, could achieve 

the same goal more efficiently and at lower cost. 
13

would boost savings enough to produce these effects, and whether these effects would continue 

in the longer term. 

Research Questions

The SaveUSA evaluation included three major components: (1) an implementation analysis, 

which studied how the program was operated in four cities; (2) a participation analysis, which 

examined the savings patterns of study participants with access to a SaveUSA account; and 

(3) an impact analysis, which examined a series of financial outcomes and assessed what difference the SaveUSA program made relative to what would have happened in the absence of 

the program.

This final report focuses on the following questions about SaveUSA’s implementation, 

participation, and impacts:45

Implementation and Participation

• How successful were the VITA organizations in adding the SaveUSA program to their other services for low- and moderate-income individuals? 

• Was it possible to engage financial institutions in marketing, opening, and 

administering SaveUSA accounts? 

• What proportion of tax filers eligible for SaveUSA were interested in opening such accounts? How do their characteristics differ from those of tax filers 

who were eligible but were not interested in opening a SaveUSA account? 

• What are the patterns of saving over the three years that SaveUSA was offered in the study period, for tax filers who opened SaveUSA accounts in 

2011?

• How much on average did filers initially deposit? 

• What proportion of SaveUSA account holders received a savings match during the three years following their program entry? 

• How often, and when, did SaveUSA account holders withdraw the savings 

and the match, if they received one, in their accounts? 

 45See Azurdia, Freedman, Hamilton, and Schultz (2014) for a thorough discussion of how the program 

was implemented during the first year of program operations, including detailed answers to some of the implementation questions listed below. 
14

• What were the characteristics of repeat savers and repeat match recipients, 

and how did their characteristics differ from those of SaveUSA account 

holders who did not attain these outcomes?

Impacts

• Did SaveUSA increase eligible tax filers’ accumulated savings and other financial assets, over a 42-month follow-up period, relative to what would 

have happened in the absence of the program (represented by the experiences 

of Regular Tax Filers)?

• Did SaveUSA increase eligible tax filers’ financial well-being, ability to 

maintain control over family finances, and/or ability to weather financial 

emergencies, over a 42-month follow-up period, relative to what would have 

happened in the absence of the program (represented by the experiences of 

Regular Tax Filers)?

• Did SaveUSA decrease eligible tax filers’ total debt, reliance on high-interest 

credits and loans, and material hardship over a 42-month follow-up period?

• Did any effects vary by city or for certain subgroups of tax filers?

Data Sources 

The data sources examined for each type of analysis are described below:

Baseline Data

At the time of random assignment, the program staff recorded some tax return data and 

baseline data in an MDRC database used to enroll individuals into the study. MDRC also collected additional demographic characteristics from the VITA sites’ intake surveys, which individuals completed on the day of study enrollment. 

Financial Institution Data

SaveUSA account activity and balance information were collected from the six financial institutions participating in the study (see Box 1.1). These data were used by MDRC to determine who was eligible for the savings match and the savings match amount.

46 

 46See Appendix E for more information on how the financial institution data were collected, checked, organized, and analyzed. 
15

SaveUSA 18-Month and 42-Month Survey Data

MDRC conducted two waves of survey interviews of SaveUSA and Regular Tax Filers 

group members in New York City and Tulsa. The first wave was about 18 months after each 

sample member’s date of random assignment; the second wave was about 42 months after study 

entry. The first survey wave explored what members of both research groups did with their tax 

refunds after the 2011 tax season. It also measured the amount of savings, income, assets, and 

debt these individuals had at the time of their follow-up interview, as well as their financial stability and material hardship throughout the follow-up period. Among SaveUSA group members, the survey also examined their experience with the SaveUSA program, and among those 

who received the first savings match, it examined what individuals did with their savings match 

amount and individuals’ reasons for participating or not participating in subsequent years. The 

42-month survey was nearly identical to the previous wave, except that the reference periods 

were different. For example, the second survey collected data for the 2013 tax season, and sample members who had also completed the 18-month survey were asked about their experiences 

since that time. High response rates (80 percent) were achieved for both survey waves.47 

Tax Return Data

Collected from tax year 2010 returns for study sample members in all cities except San 

Antonio, these data included information on tax filers’ adjusted gross income, total refund 

amount, and how individuals allocated their refunds (to paper checks, checking accounts, and 

savings accounts). These data were analyzed in the interim SaveUSA report, but they are also 

used in this report to check for possible bias in survey response results and are shown in Appendix E. 

Aggregate Data

Where available, VITA partners provided aggregate reports of the demographic characteristics of tax filers at VITA organizations who were eligible for SaveUSA. These reports were

used to compare the characteristics of SaveUSA study participants with those of eligible tax

filers who declined to participate.

Nonparticipant Survey Data

These data include responses from a survey conducted on a few selected days during

the tax return preparation season in 2011, which asked eligible tax filers at the VITA sites the

main reasons why they did not enroll in the study.

 47See Appendix B and Appendix E for additional information on the survey efforts and analysis of possible bias in the response results. 
16

Sample Intake 

In January 2011, enrollment in the SaveUSA study began in select Volunteer Income Tax Assistance (VITA) sites in New York City, Tulsa, Newark, and San Antonio. In addition to 

providing tax return preparation services to hundreds of low- to moderate-income clients per 

week, VITA staff members marketed the SaveUSA account, enrolled individuals in the study, 

and helped SaveUSA group members open their SaveUSA accounts. The program was voluntary, but anyone meeting the program’s eligibility requirements and interested in opening a 

SaveUSA account had to agree to participate in the study to be eligible to open an account. 

To be eligible for the SaveUSA program, tax filers had to be at least 18 years old and 

meet income eligibility requirements ($50,000 or less for filers with dependent children and 

$25,000 or less for filers without dependents). In most cities, the study’s income requirement 

followed the VITA income requirement to receive free tax preparation services.48 Prospective 

study participants also had to anticipate at least a $200 tax refund to meet the minimum deposit 

requirement for opening a SaveUSA account. This rule added a level of complexity to the enrollment process, since neither VITA staff nor prospective participants knew the exact refund 

amount that would be recorded on the tax return, and thus who was eligible for SaveUSA, until 

the last stages of the tax preparation process.

Figure 1.1 illustrates the enrollment flow and research design of the SaveUSA program. 

SaveUSA was marketed, using a combination of publicity and direct outreach, to existing customers before and during the tax season, but most prospective participants learned about the 

SaveUSA program at the tax sites. SaveUSA flyers and handouts listing “frequently asked questions” were distributed to each filer as part of the VITA intake process. On-site recruitment was 

then conducted primarily by dedicated SaveUSA staff — called “asset specialists” — who 

worked in the VITA sites. While filers waited for their taxes to be prepared, these staff members 

made announcements about SaveUSA in customer waiting areas and then approached individual filers to explain the program in more detail and to ask whether the filers were interested in it.

VITA staff members had to take several steps before study enrollment occurred. As a 

first step, tax preparers began filling out tax returns through the regular VITA process. Once it 

was determined that a tax filer was eligible for SaveUSA, the VITA staff members explained 

the SaveUSA program, the features of the SaveUSA account, and the SaveUSA study. In New 

York City and Tulsa, the 50 percent chance of being randomly assigned to the Regular Tax Filers (control) group was also described. Tax filers interested in opening a SaveUSA account and 

in taking part in the SaveUSA study subsequently signed a series of informed consent forms

 48In New York City, filers without dependent children (filing as single or married/jointly) needed to have 

an Adjusted Gross Income (AGI) of $18,000 or less to qualify for VITA services.
17

The SaveUSA Evaluation

Figure 1.1

Recruitment and Random Assignment Process

Intake Process

• Signs in, meets with intake specialist, takes VITA survey

• Initial SaveUSA pitch (waiting period)

• Receives information about SaveUSA

Tax Preparation

• Taxes are prepared except for completion of Form 8888

• Income and refund eligibility are determined

Enrollment

• SaveUSA program and study are explained further

• Commits to deposit at least $200 in SaveUSA account

• Agrees to participate in study and signs consent forms

Random Assignment

SaveUSA Group

• Opens a SaveUSA account and decides on 

amount to deposit

• Can open other accounts or make other 

deposits

• Completes Form 8888

• Quality review of tax return is conducted

• Taxes are completed and submitted 

electronically

Regular Tax Filers Group

• Other savings options are presented

• Can open other accounts or make other 

deposits

• May complete Form 8888

• Quality review of tax return is conducted

• Taxes are completed and submitted 

electronically
18

that authorized MDRC to collect data on filers from tax returns, financial institution records, 

and survey responses. 

VITA staff members then entered information on study participants into a database designed by MDRC to enroll individuals in the study. Immediately following data entry, tax filers 

in the two RCT cities, New York City and Tulsa, were randomly assigned to the SaveUSA 

group or to the Regular Tax Filers group. Tax filers in the two non-RCT cities, Newark and San 

Antonio, enrolled in the study in a similar way, except that MDRC’s random assignment application assigned every eligible and interested tax filer to the SaveUSA group. Those assigned to 

the SaveUSA group subsequently continued with the account opening procedures. The random 

assignment process worked as expected.49 As Figure 1.2 shows, about 2,500 individuals were 

enrolled in the study in 2011 across all cities.50

In New York City and Tulsa, individuals assigned to the Regular Tax Filers group were 

not allowed to open a SaveUSA account but were offered other savings products normally offered by the VITA site. It is important to note that all Regular Tax Filers (as well as all those in 

the SaveUSA group) were intending to save before random assignment, either because that was 

their original goal for their tax refund or because the VITA site staff convinced them that saving 

some of their tax refund and participating in the program was a good idea. 

Sample Characteristics

Table 1.1 presents selected demographic and tax return characteristics of sample members between ages 18 and 64 at the point they entered the study, for the 2011 sample pooled across cities.51 At their time of study entry, the average age of enrollees was 39 years old, about threequarters were female, and 62 percent had children. Except in Tulsa, the majority of sample 

members were either black or Hispanic. The majority had at least a high school diploma or

 49A total of 11 of these individuals (8 SaveUSA group members and 3 Regular Tax Filers group members) 

later withdrew from the study. For additional information on the random assignment process, see Appendix E. 50SaveUSA account opening occurred in all four cities from 2011 through 2013, and accounts continued to 

be offered past 2013 in some cities. From 2011 to 2013, about 7,500 individuals enrolled in SaveUSA, and 

MDRC tracked account information for all of these individuals over the three years to determine savings match 

eligibility. MDRC tracked only the 2011 cohort, however, for in-depth research purposes. 51As shown in Figure 1.2, almost 2,500 tax filers were enrolled in the study in 2011 across all four cities. 

However, this report focuses on the 2,337 tax filers who were between the ages of 18 and 64 at the time of 

study enrollment. This decision was made because the characteristics (and expected savings behavior) of those 

65 years and older are very different from the rest of the enrollees (see Appendix Table A.3). For example, 

most of the older sample members were filers without tax dependents. Therefore, most did not receive the 

Earned Income Tax Credit (only 17 percent did, compared with 70 percent for those between 18 and 64). In 

addition, the average anticipated tax refund amount and average adjusted gross income for the older adults are 

much lower than for the younger adults. 
19

The SaveUSA Evaluation

Figure 1.2

SaveUSA Study Samples Enrolled in 2011

2011 Enrollees

2,489

Report Sample (Ages 18-64)

2,337

San Antonio

418

Newark

342

New York City

922

SaveUSA 

Group 

463

Regular Tax 

Filers Group

459

Tulsa

655

SaveUSA 

Group 

331

Regular Tax 

Filers Group

324

Tulsa

511

SaveUSA 

Group 

259

Regular Tax 

Filers Group

252

New York City

725

SaveUSA 

Group 

367

Regular Tax 

Filers Group 

358

Non-RCT Cities RCT Cities

42-Month Survey Sample
20

(continued)

The SaveUSA Evaluation

Table 1.1

Selected Baseline Characteristics of Sample Members Enrolled in 2011

Characteristic All Cities

Demographic characteristic

Average age (years) 39

Age (%)

18-24 14.2

25-34 25.5

35-44 22.6

45-59 33.0

60-64 4.7

Gendera (%)

Male 26.9

Female 73.1

Number of childrena,b (%)

0 37.8

1 32.6

2 21.0

3 or more 8.7

Race/ethnicitya

 (%)

Hispanic/Latino 27.8

White 17.7

Black/African-American 46.3

Other 8.2

Highest educational credentiala (%)

GED certificate 3.7

High school diploma 59.6

Technical credential or associate's degree 14.3

4-year college degree or higher 10.8

None of the above 11.6

Tax filing information

Number of tax filers (%)

1 88.0

2 12.0

Tax filing status (%)

Single filer without children 32.0

Single filer with children 56.0

Joint filer without children 1.9

Joint filer with children 10.1

Average adjusted gross income ($) 18,029

Adjusted gross income amount (%)

$0 - $9,999 26.0

$10,000 - $19,999 35.8

$20,000 or more 38.3
21

 

Table 1.1 (continued)

Characteristic All Cities

Average total tax refund amount ($) 3,894

Average federal tax refund ($) 3,288

Average state and city tax refundc

 ($) 606

Received federal Earned Income Tax Credit (EITC) (%) 69.3

Among those who received the

EITC, average amountd

 ($) 2,245

Month of study entry (%)

January 2011 10.7

February 2011 38.7

March 2011 32.6

April 2011 18.0

Sample size 2,337

SOURCES: MDRC calculations from MDRC baseline data, VITA survey data, 2010 tax 

return records, and responses to the SaveUSA 18-Month and 42-Month Follow-Up Surveys. 

NOTES: Indicators of respondent's gender, number of children, race/ethnicity, and highest 

educational credential were recorded from responses to the 18-Month and 42-Month FollowUp Survey for respondents with missing data at baseline. The survey responses for these 

indicators are available only for participants from New York City and Tulsa. 

The sample includes SaveUSA group members who were ages 18 to 64 at their time of 

study entry.

Demographic characteristics data were collected for one tax filer when couples filed 

jointly.

Rounding may cause slight discrepancies in calculating sums.

Sample sizes for specific measures may vary because of missing values.

aInformation on gender, number of children, and ethnicity was not available for San 

Antonio. The highest educational credential indicator is missing for both San Antonio and 

Newark.

bThis refers to the number of dependents claimed in 2010 tax return records.

cOnly New York City has a city income tax. Texas does not have a state income tax. 

dThe maximum possible Earned Income Tax Credit was $5,666 in tax year 2010.

GED certificate; only 12 percent did not have any educational credential, and an almost equal 

percentage had a four-year college degree or higher.

52 

52Data for educational attainment are available only for New York City and Tulsa.
22

The vast majority (88 percent) of enrollees were single tax filers. Over half the enrollees 

were single tax filers with at least one tax dependent (56 percent), and the majority (69 percent) 

received the Earned Income Tax Credit (EITC).

As expected, all study participants reported low or moderate levels of income. The average enrollee reported an adjusted gross income of $18,029 on his or her federal tax return.53

About a quarter of the sample (26 percent) had an income of less than $10,000, and 38 percent 

had an income of $20,000 or more.54 The average tax refund anticipated in 2011 was $3,894.

The majority of enrollees were “banked,” meaning they already had a checking or savings account. Evidence for this finding derives from tax return data collected in New York City 

and Tulsa that show that, among Regular Tax Filers, 85 percent in Tulsa and 69 percent in New 

York City directed their refunds into a checking or savings account.55 These percentages are 

higher than what has been found among the general population at similar income levels.56

As noted earlier, all individuals enrolled in the study had volunteered, which indicates 

that they were all interested in saving. Therefore, it is not surprising that many of the individuals 

in the sample appeared to have been saving prior to study enrollment. According to VITA survey data, 61 percent of the Newark sample had savings at the time of study enrollment, and 47 

percent of the Tulsa sample were planning to use part of their tax refund for savings (not 

shown).57 These results also suggest that SaveUSA may have induced some individuals who 

were not planning to save their tax refund to open a SaveUSA account.

Although many may have been predisposed to save, SaveUSA study sample members 

faced significant barriers to doing so, given their low average income and that many were single 

parents. Furthermore, as indicated by data available in only certain cities, 27 percent of the Tulsa and Newark samples reported not having enough money to make ends meet, and about half 

of the New York City sample and the majority of the Tulsa and Newark samples reported hav-

 53“Adjusted gross income” is defined as gross income minus adjustments to income. Adjustments include 

but are not limited to deductions for student loans and contributions to individual retirement accounts (IRAs). If 

there are no deductions, adjusted gross income is the same as total income. 54According to the Census Bureau in 2011, the average household income received by the lowest quintile 

(where households are ranked by income) was $11,239. 55See Azurdia, Freedman, Hamilton, and Schultz (2014), Table 7.2 and Chapter 7, for the precise figures 

and for more discussion of this point. 56For example, the proportion of households “fully banked” ranges from about 48 percent for households 

with annual incomes under $15,000 to 60 percent for households with annual incomes between $15,000 and 

$30,000 and to about 69 percent for households with annual incomes between $30,000 and $50,000. Fully 

banked households are those that have a bank account of any kind and have not recently relied on any “alternative financial services,” including nonbank check-cashing places, payday loans, and nonbank money orders 

(Federal Deposit Insurance Corporation, 2012). 57Comparable data for enrollees in the other two cities are not available.
23

ing debt (not shown). Among the sample from Tulsa that reported having debt, over half noted 

having a debt amount of $3,000 or more (not shown). 

The baseline characteristics for individuals enrolled in New York City and Tulsa were 

examined by research group (see Appendix Table A.4) and, as expected given random assignment, only small differences were found between the research groups.58 

SaveUSA Study Take-Up Rate 

SaveUSA was a voluntary program, so anyone eligible and interested in opening a SaveUSA 

account could enroll. Across all four cities, between 6 percent and 13 percent of tax filers eligible for SaveUSA expressed interest in opening a SaveUSA account and enrolled in the study in 

2011. These take-up rates are comparable to (and in some cases, higher than) the take-up rates 

in other similar tax-time asset-building studies. For example, the $aveNYC take-up rate ranged 

between 6 percent and 10 percent during program operating years 2008, 2009, and 2010. Savings bond participation rates reported in several studies done by the D2D Fund ranged from 5

percent to 9 percent, following extensive marketing campaigns.59

To shed some light on why the majority of individuals eligible for SaveUSA did not 

take up the program, MDRC conducted two analyses. The first analysis compared study enrollees’ characteristics with the characteristics of individuals who were eligible for the SaveUSA 

study but declined to enroll. The second analysis examined the responses to a nonparticipant 

survey given to individuals who declined to participate in SaveUSA on the day that they completed their tax return at the VITA sites. 

The first analysis, a comparison of available characteristics data for study participants 

and nonenrollees, suggests that enrollees were more likely to be female and to have dependent 

children, and therefore more likely to receive the EITC than nonenrollees. (See Appendix Table 

A.5.) Although both groups had low or moderate incomes from employment or other sources, 

enrollees were better positioned to save than nonenrollees because they had more resources 

available for savings: They reported somewhat higher average adjusted gross incomes and had 

larger tax refunds. 

The second analysis examined data collected over several days mid-tax season. On 

these days, MDRC asked SaveUSA staff to administer an informal survey to customers who 

were eligible to sign up for SaveUSA but declined to do so.60 This helped the project team to 

 58Appendix Table A.1 shows the characteristics of the 2011 sample broken down by city.

59New York City Department of Consumer Affairs, Office of Financial Empowerment (2010); Doorways 

to Dreams Fund (2009, 2011, 2012); Bronchetti, Dee, Huffman, and Magenheim (2011). 60Across the four cities, about 700 VITA tax filers were asked in this informal survey why they were not 

interested in SaveUSA.
24

better understand the factors that influenced customers’ decisions about enrolling in SaveUSA, 

and helped the VITA sites to refine their marketing and recruitment efforts. Among the filers 

who appeared to be eligible and gave reasons for why they were not interested in SaveUSA, 

most indicated that they had already earmarked all of their anticipated refund to spend on bills 

or pay off debts. Others reported that they were not interested in saving or that they already had 

savings accounts and did not want to open an additional account. A small number reported that 

they did not trust banks in general, or they did not like the particular financial institution that 

was offering the SaveUSA account.61 These findings are consistent with past studies.62

Evaluation Follow-Up Periods

For this final report, follow-up periods vary in length for the different parts of the analysis that 

use different data sources. Figure 1.3 shows the random assignment and report follow-up periods. As noted earlier, random assignment took place during the tax year 2010 filing season, 

from January to April 2011. The follow-up period is then divided into “program years.” A program year is defined as the months between the start of the tax filing season and the date when 

SaveUSA account holders who maintained their pledge amount qualified to receive the savings 

match. For example, the first program year starts in January 2011 and ends on January 31, 2012. 

Account holders who qualified for a savings match received the money on February 1, 2012. 

This report examines program enrollment data and account activity data through the 

end of June 2014 for enrollees in all four cities. Therefore, results from all three savings match 

distributions are included. The report also presents final findings on the program’s effects, or 

“impacts,” over the 42 months after study enrollment in two cities (New York City and Tulsa). 

Hence, the impact findings show the effects of SaveUSA about 6 months after SaveUSA group 

members could have received their final savings match. 

Structure of This Report

Chapter 2 focuses on the implementation of SaveUSA in the final program year: the percentage 

of individuals who received the savings match in 2012, 2013, and/or 2014, the savings match 

amounts individuals received at the end of all three program years, the factors associated with 

receiving a savings match, and SaveUSA account savings patterns. Chapter 3 reports on

 61Very few individuals mentioned the study enrollment procedures or random assignment process as a reason they did not want to sign up for SaveUSA. 62Mendenhall et al. (2012); Smeeding (2000); Bronchetti, Dee, Huffman, and Magenheim (2011).
The SaveUSA Evaluation

Figure 1.3

Random Assignment and Follow-Up Periods

Year 1 

savings 

match

Year 2 

savings 

match

Year 3 

savings 

match

2011 2012 2013 2014

J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D

7/1/2012 - 12/1/2012

18-Month Survey

11/19/2015 - 11/26/2015

Random

assignment

period

Report follow-up period: January 2011 – October 2014

7/1/2012 - 12/1/2012

42-Month Survey

 Overlap between program years

Program Year 1 Program Year 2 Program Year 3

25
26

SaveUSA’s impacts on tax refund allocations and on the levels of nonretirement savings, retirement savings, and liquid assets that SaveUSA group members and Regular Tax Filers accumulated over a 42-month follow-up period. Chapter 4 examines SaveUSA’s effects over a 42-

month period on measures of financial well-being. Chapter 5 examines SaveUSA’s effects for 

several key subgroups, and Chapter 6 conveys some conclusions.
27

Chapter 2

SaveUSA Implementation and Participation

The SaveUSA program enrolled about 2,500 tax filers during the 2011 tax season. Enrollees 

during this year who were randomly assigned to the SaveUSA group were encouraged to set 

aside tax refund dollars and have them directly deposited into SaveUSA accounts, and to do so 

for three consecutive years, from 2011 to 2013. This chapter discusses the implementation of 

SaveUSA during this three-year evaluation period, focusing on any changes to the program in 

Year 3, the last year of program operations for the evaluation. The chapter also focuses on the 

extent to which SaveUSA group members took advantage of the program, by examining the 

percentage of sample members who deposited money into SaveUSA accounts and received savings matches, as well as the amount of savings accumulated in SaveUSA accounts, over a 39-

month follow-up period, through June 2014. 

Many questions about the program’s implementation were answered in the interim 

SaveUSA report.1 This report recaps the answers to some of these questions and extends them

into the third program year. The interim report, for example, discussed how SaveUSA was integrated into the Volunteer Income Tax Assistance (VITA) tax return preparation process and the 

roles various SaveUSA partners played. The interim report showed that most SaveUSA group 

members opened a SaveUSA account and nearly two-thirds received the savings match in the 

first program year. More than a third (39 percent) of sample members deposited into accounts 

again in the second year, and 27 percent of sample members received the savings match. The 

savings patterns from the SaveUSA accounts during the first two program years showed that 

individuals withdrew their savings from their accounts soon after receiving a savings match, 

suggesting that the SaveUSA accounts were mainly used to obtain the match.

Data used in this chapter come from field observations, 42-month survey responses, and 

financial institution records.

2 Reference is also made, where appropriate, to data that were collected from the 18-month survey.

 1

Azurdia, Freedman, Hamilton, and Schultz (2014), Chapter 3.

2

An analysis of survey response bias was conducted for the 42-month survey. The results show that the 

survey is reliable and the results for the survey respondent sample can be generalized to the report sample. (See 

Appendix B.)
28

Main Findings

• SaveUSA was consistently marketed and successfully implemented for three 

years during the study period. Program operations during the last program 

year remained strong in most sites.

• About a third of SaveUSA group members pledged to save tax refund dollars 

again in Year 3, and about one in five individuals received a savings match at 

the end of Year 3.

• A majority of SaveUSA group members — 68 percent — received at least 

one savings match at some point over the three program years. Including 

those who never received a match, the SaveUSA group received, on average, 

a total of $365 in matches over this period.

• Overall, those who received the savings match during two or more years appear to have been in a better position to save. In contrast, SaveUSA group 

members with especially low incomes or who pledged the minimum amount 

of $200 were the least likely to ever receive a savings match.

• Similar to patterns found in previous years, SaveUSA group members mainly used the SaveUSA account to obtain the savings match, not for general 

saving.

Program Implementation

SaveUSA implementation was led by organizations that operate Volunteer Income Tax Assistance programs, which provide free tax preparation to income-tax filers. These organizations 

managed SaveUSA with assistance from participating financial institutions, MDRC, the New 

York City Center for Economic Opportunity (CEO), and the New York City Office of Financial 

Empowerment (OFE). 

• The SaveUSA program was implemented as designed. VITA operators 

were able to integrate the program into the normal VITA tax return 

preparation process. 

VITA operators were responsible for marketing SaveUSA; screening and enrollment 

into the study; coordinating the opening of SaveUSA savings accounts; and designating, on 

each SaveUSA enrollee’s tax return form, the tax refund amount to be deposited into the 

SaveUSA account. Participating financial institutions offered the SaveUSA accounts, which in 

most cases were financial products with features designed specifically for the SaveUSA program. Financial institutions also provided quarterly electronic account activity data to MDRC, 
29

which analyzed and used these data to determine savings match eligibility and payment information. Table 2.1 shows the VITA site locations, lead agencies, and local partner organizations 

in each city. 

During the first year, most SaveUSA group members in each city were successful in 

opening a SaveUSA account. VITA operators were able to conduct enrollment into the study, 

open the accounts, and complete tax returns for the SaveUSA group members. Some bottlenecks were identified in the first year during the account-opening procedures, which mostly reflected start-up issues and which occurred less frequently during subsequent years. The main 

problems at this stage involved errors made by VITA or financial institution staff members in 

opening or correctly designating SaveUSA accounts on tax day, or a financial institution rejecting an individual’s account application after random assignment.3 Overall, the issues affected a 

small percentage of the sample, and they did not have a considerable influence on the SaveUSA 

account-opening or savings match rates in most cities. These issues did add to the administrative costs in running the program during the first year. (In 2012 and 2013, those who successfully opened an account during the first year could continue using the same account.) Later in this 

chapter, SaveUSA account activity is discussed in detail.4

Program Implementation in the Final Year of the Study 

A key component of the SaveUSA intervention was to encourage participation in multiple years 

so that participants could accumulate a substantial amount of combined savings and match dollars and develop a habit of saving, which could result in additional benefits to participants over 

the follow-up period. This section examines the efforts that VITA staff made to encourage participation in Year 3, which was the last year MDRC tracked financial institution data for the 

study. SaveUSA program group members had the opportunity to deposit tax refund dollars into 

a SaveUSA account in 2013 and receive a savings match in February 2014.5

 3

Most financial institutions report negative account activity (including overdrawing an account, bouncing 

checks, and fraud) to industrywide databases, the most common of which is called ChexSystems. When processing applications for SaveUSA accounts, most financial institutions ran the applicants’ names through one 

of these databases. Some financial institutions may deny everyone whose name appears in one of these databases, while others may deny only people with certain types of reported activity. SaveUSA group members 

were expected to receive less-stringent-than-normal eligibility screening from ChexSystems or similar banking 

history reviews, but some participants nevertheless were rejected through this process. 4

See Azurdia, Freedman, Hamilton, and Schultz (2014), Chapter 3, for further discussion of implementing 

SaveUSA; see also Appendix E of the present report for additional information on financial institution data 

processing and collection. 5

As discussed in this report, SaveUSA continued to operate beyond the evaluation period in New York 

City and San Antonio, allowing savings pledges in 2014 and onward, but MDRC did not track participation for 

these subsequent program years. 
The SaveUSA Evaluation

Table 2.1

SaveUSA Sites in 2011

City Lead Agency VITA Site Hosts and Locations Financial Institution Partners

New York City Food Bank For New York City Ariva, South Bronx Spring Bank (formerly CheckSpring)

BronxWorks Morris Senior Center, South Bronx Spring Bank

St. Mark's AME Church, Jackson Heights, Queens Spring Bank

Capital One Bank branch, Downtown Brooklyn Capital One

Carver Financial Literacy Center, Harlem Carver Federal Savings Bank

Tulsa CAP Tulsa CAP headquarters, Southeast Tulsa Bank of Oklahoma

(formerly Community Action Bank of Oklahoma branch, North Tulsa Bank of Oklahoma

Project of Tulsa) McClure Early Childhood Center, South Tulsa Bank of Oklahoma

San Antonio United Way of San Antonio ACTN @ Claude Black Community Center, East Side Select Federal Credit Union

and Bexar County Guadalupe Community Center, West Side Select Federal Credit Union

The Neighborhood Place, West Side Citibank

YWCA, West Side Citibank

Newark Newark Now Essex County College, Central Newark Spring Bank

Financial Empowerment Center, North Central Newark Spring Bank

Newark City Hall Spring Bank

South Ward Spring Bank

NOTES: In Newark, the South Ward site was replaced with the Essex County College site in late February 2011.

In New York City, the BronxWorks site was added in March 2011. 

In 2012 and 2013, additional sites were added or dropped. In total, 18 VITA sites offered SaveUSA in 2011, 2012, and/or 2013. 

Not included in this table are Transfiguration of Christ Church (New York City, 2012 and 2013) and St. Philips College (San Antonio, 2013).

30
31

• During the last program year, SaveUSA continued to be consistently 

marketed and offered in all cities except Newark.

Two or three rounds of outreach were conducted in each city before the Year 3 tax return preparation season, January through mid-April of 2013, to all SaveUSA program group 

members to encourage participation. In addition, SaveUSA was marketed, with posters and flyers, during the tax season at participating VITA sites. Finally, for everyone who showed up at a 

SaveUSA VITA site, staff members attempted to look up personal identification information on 

a study sample list to determine whether the individual was a SaveUSA group member; if so, 

the staff encouraged him or her to participate in SaveUSA again.

Program operations for Tulsa, New York City, and San Antonio remained mostly unchanged in the final year relative to the previous year, with a few exceptions. San Antonio added an additional site (St. Philips College). The Community Action Project (CAP) of Tulsa continued to offer SaveUSA in 2013 but began to scale back its community-wide tax preparation 

services as part of a strategic shift to limit tax services to certain targeted groups of clients. This 

may have resulted in some confusion to participants about whether they could have their tax 

returns prepared at CAP.

In 2013, Newark Now ceased providing VITA tax services. Therefore, Newark stopped 

assisting people in opening new SaveUSA accounts. However, SaveUSA program group members in Newark were given the opportunity to participate if they still had a SaveUSA account.

They were required to submit a form to Newark Now or MDRC specifying the amount they 

wanted to save over the year, and the deposit still had to derive from their tax refund. Only a 

small number of Newark program group members took advantage of this offer. 

The SaveUSA study stopped tracking savings pledges, matches, and account balances 

in mid-2014. As part of the study, program group members were eligible to directly deposit tax 

refund dollars into SaveUSA accounts in only three years (2011, 2012, and 2013) and to receive 

the matches in the three corresponding years (2012, 2013, and 2014). However, New York City

and San Antonio continued offering SaveUSA to study participants and others past these dates 

— including, in New York, members of the “Regular Tax Filers” group, who had not been allowed to open SaveUSA accounts during the evaluation. A data match between the records of 

Food Bank For New York City (which continued to offer SaveUSA in 2014 and 2015) and financial institutions indicated that the percentage of 2011 SaveUSA and Regular Tax Filers 

group members who made a pledge to save in 2014 was very small. (A total of 21 Regular Tax 

Filers and 27 SaveUSA group members, representing 3 percent of the SaveUSA evaluation 

sample, pledged to save tax refund dollars in 2014.) Given that only 48 people pledged to save 

and the fact that not all of them would have earned the savings match in early 2015, the study 

was not compromised by this. 
32

Opening SaveUSA Accounts and Rates of Savings Match Receipt

During the first year of program implementation in 2011, everyone in the SaveUSA group was 

required to open a SaveUSA account in order to participate in the program. In the subsequent 

program years, VITA site staff members continued to encourage individuals to deposit again, 

regardless of whether they earned the match during their first year. 

• Most SaveUSA group members opened a SaveUSA account during the 

first program year. Individuals who didn’t pledge to save tax refund dollars in the second program year were very unlikely to do so in the third 

year. 

Table 2.2 shows the SaveUSA account activity for each program year in all cities. As 

discussed earlier in this chapter, although there were some issues when opening a bank account, 

most individuals successfully opened an account during the study period. As shown, rates of 

depositing tax refund money into the accounts after the first year declined: 97.5 percent designated money to go into their accounts in Year 1, 39.1 percent did so in Year 2, and only 28.8 

percent did so in Year 3.

Declines in participation from Year 2 to Year 3 were particularly striking in Newark: 

Fewer than half of Newark SaveUSA account holders who participated in Year 2 participated 

again in Year 3 (not shown). Some of this decline may have reflected the reduction in program 

operations in Newark. The process that Newark SaveUSA account holders had to follow in order to take advantage of the program in Year 3, described above, may have been too onerous for 

some people. 

Responses to the 42-month survey, given in New York City and Tulsa as part of the 

randomized controlled trial, offer reasons why individuals did not pledge to save as part of 

SaveUSA during subsequent years. In order for individuals to participate in SaveUSA, (1) they

needed to be aware that they could participate, (2) they had to return to a SaveUSA VITA site to 

have their taxes prepared, and (3) they had to be receiving a tax refund of at least $200. Even if 

their income increased, SaveUSA group members were still eligible to participate in the program if they met these other three criteria.

According to the survey, about 90 percent of all SaveUSA group members in New 

York City and Tulsa were aware that they could deposit tax refund dollars into a SaveUSA 

account in Year 3 (or 2013) (not shown). The 18-month survey showed that 79 percent of 

SaveUSA group members in 2012 returned to the same VITA site where they enrolled in 

2011 (not shown). In 2013, the number of sample members returning to their 2011 VITA site

declined. Among the New York City sample, only 55 percent returned to the same VITA
33

The SaveUSA Evaluation

Table 2.2

SaveUSA Account Activity, by Program Year, in All Four Cities

Outcome Year 1 Year 2 Year 3 All Years

SaveUSA account opened or pledged

Distribution of pledged deposita

 (%)

$0

$1 - $200

$201 - $999

$1,000

Average initial deposit amountb

 ($) 

Distribution of initial depositb

 (%) 

$0

$1 - $200

$201 - $999

$1,000 or more

Received savings match (%)

Average amount of savings match ($)

Average savings match, among those 

who received the savings match ($) 

Distribution of savings match (%)

$0

$1 - $100

$101 - $499

$500

(%) 97.5

0.0

36.7

33.3

30.0

506

10.0

32.0

29.8

28.3

65.5

191

291

34.5

20.1

22.1

23.3

39.1

60.8

8.4

15.1

15.7

293

62.6

7.4

14.2

15.9

27.5

96

348

72.5

4.3

10.0

13.2

28.8

71.2

5.0

9.7

14.2

244

72.2

4.0

9.1

14.7

20.5

78

383

79.5

1.7

7.1

11.6

97.9

0.0

25.0

31.3

43.8

1,042

8.7

21.4

27.7

42.2

67.5

365

540

32.5

14.0

19.8

33.7

Sample size 1,554

SOURCES: MDRC calculations from MDRC baseline data and financial institution data.

NOTES: The sample includes SaveUSA group members who were ages 18 to 64 at their time of study entry.

Rounding may cause slight discrepancies in calculating sums.

aThe pledged deposit refers to the amount of tax refund dollars that individuals committed to savings at 

the time of study entry.

bThe initial deposit refers to the tax refund amount directly deposited into the SaveUSA account by the 

Internal Revenue Service.
34

site.6 In Tulsa, about three-quarters of the sample did their taxes on their own or used a paid tax 

preparer like H&R Block or an accountant in 2013. 

The 42-month follow-up survey asked respondents who did not pledge to save as part 

of SaveUSA in Year 3 to list the main reason they did not (see Appendix Table D.1). Among 

the 74 percent of the sample members who did not participate in Year 3, about a third did not 

receive a refund or felt that their refund was not big enough to participate,7 and 23 percent felt 

that they needed their refund to pay expenses. Fourteen percent reported being unaware that 

they were eligible to participate again in Year 3 — suggesting that additional marketing would 

have been beneficial. A further 13 percent encountered problems when filing their taxes, which 

included filing taxes late or getting wrong information from the VITA site staff on the availability of SaveUSA. Finally, about 17 percent did not participate because they were unprepared 

when filing taxes or just had no interest in the program. Taking into account all these reasons, 

the results suggest that participation could not have been much higher in Year 3. Among those 

who did not participate in Year 3, all but 14 percent were aware that they could pledge again, 

but many decided not to.

• The majority of SaveUSA group members received at least one savings 

match over the three program years. Across all SaveUSA group members, including those who did not get matches, an average of $365 in savings matches was received. Those who pledged to save $1,000 were more 

likely to receive a savings match.

Table 2.2 shows that the pledged deposits varied by program year. In Year 1, 37 percent

pledged the minimum amount of $200 and 30 percent pledged the maximum amount of $1,000.

In subsequent years, those who participated again were more likely to pledge the maximum 

amount of $1,000. Those who pledged $200 in Year 1 were less likely to pledge again in subsequent years (not shown). 

The 42-month survey asked all New York City and Tulsa SaveUSA group respondents 

whether they had any savings goals and, if so, what their main savings goals were (see Appendix Table D.2). Most SaveUSA group members had a savings goal (78.4 percent): About 21 

percent of group members were saving for a big purchase, 13 percent were saving for education, 

and 20 percent were saving for a variety of reasons, including investments, travel, and household expenses. Only about 11 percent were saving for emergencies.

 6

Note that even if sample members did not return to the same VITA site, they still could have had their tax 

return prepared at a different SaveUSA-participating VITA location. 7

According to the 18-month survey, about 10 percent of the SaveUSA group members did not receive a 

tax refund in 2012 (see Azurdia, Freedman, Hamilton, and Schultz, 2014, Table 5.2, pp. 64-65). According to 

the 42-month survey, 18 percent of SaveUSA group members did not receive a tax refund in 2014 (see Table 

3.2). It is likely that the percentage of individuals without a refund in 2013 is between these two statistics.
35

As Table 2.2 indicates, the majority of sample members (67.5 percent) received at least 

one savings match over the three program years. On average, SaveUSA group members received a total of $365 in savings matches over the three program years (including zeros for 

those who did not get a match). Among those who obtained at least one savings match, the average amount of match dollars received over the three years was $540. The amount of money 

received varied widely; about a third of the sample received no savings match, a third received 

between $1 and $499, and a third received $500 or more in savings matches over the three-year 

period.8

Not shown, those who initially pledged $200 in Year 1 were less likely to receive the 

savings match. Conversely, those who pledged the maximum amount were more likely to receive a savings match. Receiving the savings match in Year 1 predicted whether someone 

would participate in subsequent years. Only 6 percent of the sample members who did not receive a savings match in Year 1 received a savings match in subsequent years (not shown).

Over half the SaveUSA group members who did not receive a savings match withdrew 

some or all of their pledged savings within three months after pledging to save (not shown).

During the 18-month survey interviews, they reported most often that they used the funds to 

cover an emergency expense, to pay bills or debts, or to buy necessities.

Differences in Characteristics of Savings Match Recipients and

Nonrecipients

This section examines the characteristics of repeat match recipients, and how their characteristics differed from those who never received a savings match or received only one savings match 

during the evaluation period.

• About a third of the SaveUSA group members received two or more 

savings matches. These individuals appeared to have been in a better 

position to save, in terms of their age, income, and high savings pledge 

amounts. Individuals who had especially low incomes (including those 

who received an Earned Income Tax Credit at study entry) or who 

pledged the minimum amount of $200 were the least likely to ever receive a savings match.

Table 2.3 shows the baseline characteristics of SaveUSA group members in all four cities who never received the savings match (33 percent), who received the savings match once 

(37 percent), and who received the savings match in two years or more (31 percent).

 8

Although the minimum SaveUSA pledge was $200, initial deposits were sometimes less when the IRS 

adjusted or withheld a portion of the anticipated refund amount. 
36

The SaveUSA Evaluation

Table 2.3

Selected Baseline Characteristics of SaveUSA Group Members,

 by Savings Match Receipt Status, in All Four Cities

Characteristic 

Received Savings Match in

2 or More 

0 Years 1 Year Years P-Value

Average age (years)

Age (%)

18-24

25-34

35-44

45-59

60-64

Savings pledge amount in 2011 (%)

$200

$201 - $999

$1,000

Tax filing statusa (%)

With children

Without children

Adjusted gross income amount (%)

$1 - $9,999

$10,000 - $19,999

$20,000 or more

Average total tax refund amount ($)

Received federal Earned Income Tax Creditb

 (%)

Month of random assignment (%)

January 2011

February 2011

March 2011

April 2011

City (%)

New York City

Tulsa

Newark

San Antonio

38

18.2

27.9

21.2

29.5

3.2

49.9

31.3

18.8

62.8

37.2

34.0

37.8

28.2

3,603

74.7

13.3

40.6

30.7

15.4

31.1

31.3

13.7

24.0

38

16.3

29.9

20.1

28.8

4.9

37.6

34.6

27.8

63.8

36.2

24.0

33.7

42.3

3,700

64.2

9.6

36.0

34.8

19.6

26.7

16.4

32.9

24.0

43 ***

***

10.1

16.1

26.0

40.7

7.1

***

21.8

33.8

44.4

**

70.2

29.8

***

17.6

33.3

49.1

3,907

65.6 ***

**

8.2

41.1

31.4

19.3

***

32.1

16.6

17.8

33.5

0.000

0.000

0.000

0.029

0.000

0.208

0.000

0.037

0.000

Sample size (total = 1,554) 505 572 477

(continued)
37

Table 2.3 (continued)

SOURCES: MDRC calculations from MDRC baseline data and financial institution data. 

NOTES: The sample includes SaveUSA group members who were ages 18 to 64 at their time of study entry. 

Tax filing information and refund amounts refer to 2010, the tax year prior to random assignment. 

Demographic characteristics data were collected for one tax filer when couples filed jointly.

Rounding may cause slight discrepancies in calculating sums.

Sample sizes for specific measures may vary because of missing values.

A chi-square test for categorical variables and an F-test for continuous variables were run to determine 

whether there is a difference in the distribution of the characteristics across the years that the savings match was 

received. Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent. a

This includes single and joint filers.

bThe maximum possible Earned Income Tax Credit was $5,666 in tax year 2010.

Overall, those who received a savings match in two or more years appeared to have 

been in a better position to save. They tended to be older, had more income, and were more 

likely to have pledged the maximum allowed amount of $1,000, compared with other SaveUSA 

group members. SaveUSA group members with especially low incomes or who pledged the 

minimum amount of $200 were the least likely to ever receive a savings match. Those who received the savings match for two or more years were also more likely to have dependent children, compared with those who never received a savings match. Differences were also found 

based on the city in which individuals enrolled. Refund amount, however, was not found to be 

related to receiving the savings match. 

A separate analysis (Appendix Table D.3) was conducted to examine the associations 

between the characteristics of study sample members and the likelihood of receiving a savings 

match. Using a logistic regression model, the effect of each characteristic was regressed on the 

likelihood of receiving a savings match, while holding the value of all other characteristics constant. The findings confirm the results from Table 2.3, with some differences: Filing status (for 

example, having dependent children) does not significantly predict receiving a savings match, 

but whether one received the Earned Income Tax Credit (EITC) does. Those who received the 

EITC at study enrollment were less likely to get the savings match than those who did not receive the EITC. In a separate analysis (not shown), it was found that those who received the 

EITC at study entry had the lowest incomes and relied more on their refund as a source of income than did other study sample members, which may have made it harder for them to keep 

their pledged deposit untouched for a full year. 

Among those who received the savings match in two or more years, about half received 

the savings match in all three years (not shown). The average total dollars they received in savings matches over the three years was $881 (not shown). This group of individuals took ad-
38

vantage of the program the most. These repeat savers likely would have continued to participate 

in SaveUSA if the program had still been available to them. In fact, the majority of New York 

City program group members who participated again in Year 4, when the program was offered 

outside of the evaluation period, came from this group of people who had already received at 

least two matches (18 out of the 27). 

Note that the above discussion should not be interpreted as implying that SaveUSA’s 

effects were larger for some groups of individuals than for others. In order to determine the 

impact of SaveUSA on different groups of individuals, the behavior of individuals in the Regular Tax Filers group needs to be taken into account. SaveUSA may not have had large effects 

for the subgroups that had high savings match rates if their Regular Tax Filers group counterparts were saving at the same level. Conversely, subgroups with low match rates may show 

large benefits from SaveUSA if their Regular Tax Filers group counterparts saved little or not 

at all. SaveUSA’s effects for different subgroups of study sample members are examined in 

Chapter 5. 

SaveUSA Account Savings Patterns

Although individuals could withdraw their initial deposit at any time, for any reason, without 

penalties or fees, the program was designed to encourage participants to keep their initial deposit in the SaveUSA account long enough to receive the savings match. Individuals were also encouraged to participate in multiple years so that they could build up savings. 

• The vast majority of SaveUSA group members placed some tax refund 

dollars in the SaveUSA accounts at the start of the first program year.

At the end of the follow-up period, only 29 percent of the SaveUSA accounts had a balance. In general, SaveUSA group members did not use 

their SaveUSA accounts for general saving. 

Figure 2.1 shows the percentage of SaveUSA accounts with a balance greater than $10 

for all four cities during the follow-up period. It also shows the results for those who pledged 

during two or more program years and for those who pledged only once.9

 

As shown, the percentage of SaveUSA group members with a balance in their 

SaveUSA account declined steadily over 39 months in all four cities. It peaked at 79 percent in

Month 5 and gradually declined to 29 percent at the end of the follow-up period. Note that the 

percentage of individuals with a balance above zero was never expected to reach 100 percent,

 9

This latter group also includes a small percentage of individuals that never pledged (2 percent) during the 

follow-up period.
The SaveUSA Evaluation

Figure 2.1

Percentage of SaveUSA Accounts with a Balance Greater Than $10, 

by Month of Follow-Up, in All Four Cities

SOURCES: MDRC calculations from MDRC baseline data and financial institution data.

0

10

20

30

40

50

60

70

80

90

100

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

Percentage (%)

Month relative to study enrollment

Pledged in 2 or more years

All SaveUSA group members

Pledged in just 1 year

Year 2

savings

match

Year 1

savings

match

Year 3

savings

match

39
40

since about 10 percent of the SaveUSA accounts never received an initial deposit from the IRS, 

either because of the account-opening issues described earlier or because the IRS withheld the 

refund from participants. 

Repeat savers maintained their account balances most consistently. As shown, those 

who pledged to save in two or more years maintained some savings in their accounts throughout the follow-up period. Among this group, the percentage with a balance in their accounts 

peaked at 91 percent in Month 5. After the second savings match, a total of 72 percent still had 

a balance in the account. A large decline is observed for this group after the third savings 

match, as the study ended. Only 48 percent had a balance in their account at the end of the follow-up period.

Among those who pledged in only one year, the percentage with a balance peaked at 69 

percent in Month 5 and gradually declined as many individuals withdrew all their money from 

their accounts. Only about 14 percent had a balance at the end of the 39-month follow-up period. As mentioned earlier, those who did not pledge to save tax refund dollars in the second program year were not likely to participate in subsequent years.

• Most individuals withdrew their initial deposit and savings match from 

their SaveUSA accounts immediately after the savings match was placed 

into their accounts. This pattern was observed after the match distribution in each program year.

Over the follow-up period, individuals’ average balance in their SaveUSA accounts 

ranged from $179 to about $600 in any given month. As Figure 2.2 indicates, the average balance went up after individuals initially enrolled in SaveUSA and as deposits from the IRS were 

put into the accounts. The average balance peaked after the savings match was deposited in 

February 2012 (between Months 11 and 14) and another match was deposited in February 2013 

(between Months 23 and 26). As shown, the savings balances increased slightly after the third 

savings match was deposited but not thereafter; individuals did not make subsequent deposits 

into the accounts, because the program ended for most participants. The balance decreased for 

most sample members after the final savings match was given, as individuals withdrew money 

from their accounts.

The patterns of average account balances are different for those who participated in 

multiple years. As shown in the graph, the average balances increased as the Year 1 savings 

matches were deposited in 2012 and as the accounts received the IRS deposits from the new 

Year 2 pledges. The average balances gradually decreased over the course of Year 2 as individuals withdrew their Year 1 savings and as some withdrew all or part of their Year 2 deposits as

well. The balances peaked again as the accounts received the Year 2 savings matches in 2013. 

In Year 3, the balances increased, but not at the same level as in the previous year. This reflects
41

The SaveUSA Evaluation

Figure 2.2

Average Balance of SaveUSA Accounts, by Month of Follow-Up, in All Four Cities

0

200

400

600

800

1,000

1,200

1,400

1,600

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

Average account balance ($)

Month relative to study enrollment

Pledged in 2 or more years

All SaveUSA group members

Pledged in just 1 year

Year 2

savings

match

Year 1

savings

match

SOURCES: MDRC calculations from MDRC baseline data and financial institution data.

Year 3

savings

match
42

the decline in Year 3 participation. Among those who pledged only once, the account balances 

diminished as individuals withdrew their savings from their accounts. 

As Figure 2.2 illustrates, after receiving a savings match, most individuals withdrew the 

savings (initial deposit and savings match) from their SaveUSA accounts. This pattern is observed in each program year after each savings match distribution and for those who frequently 

pledged as well as for those who pledged only once. Again, this suggests that SaveUSA group 

members mainly used their SaveUSA accounts to obtain the savings match and did not continue 

to use the accounts for general saving.

How Did Individuals Use the Savings Match?

• The savings match money was mainly used to pay debts and bills, and 

for other expenditures. Very few individuals used the money for emergency expenses or big purchases.

Unlike in the Individual Development Account (IDA) model discussed in Chapter 1, 

SaveUSA group members could use their initial deposit and savings match for any purpose.

Data from the 18-month survey showed that among those who received the first savings match 

in 2012 and reported withdrawing money from the account, 40 percent used their money to pay 

debts or bills. Over half (53 percent) used their money for some type of expenditure (other than 

paying debts and bills), and only about 7 percent used it for further savings.10 

Since a sizable percentage of SaveUSA group members received at least two savings 

matches, the ways in which match money was used could have changed from what they reported at the 18-month follow-up point to what they reported at the 42-month follow-up point, as 

some individuals accumulated more savings over time. However, results from the 42-month 

survey are very similar to the ones from the 18-month survey. As reported by respondents to the 

later survey who received the savings match, about 44 percent used the match to pay debts or 

bills, and over half used their savings match for some other type of expenditure. (See Appendix 

Table D.2.) Only a small percentage of SaveUSA group members used the match for a big purchase or for emergency expenses.

SaveUSA Participation by City

This section examines the variation in participation rates for the four cities in the SaveUSA 

study. As noted in previous chapters, the characteristics of the sample members and account 

features varied for each city, which may have resulted in different experiences. 

 10Azurdia, Freedman, Hamilton, and Schultz (2014), Appendix Table D.4, p. 157.
43

• Savings match rates ranged from 52 percent to almost 80 percent across 

the four cities. 

Table 2.4 shows the average initial savings pledge in Year 1 and participation statistics 

over the three program years for each of the four cities. As shown, the three-year savings match 

rates varied by city, ranging from 52 percent in Tulsa to almost 80 percent in Newark. These 

results are interesting given that Newark ceased most of its SaveUSA operation in Year 3. This 

suggests that three-year participation results are mostly driven by the Year 1 results, when almost all of the SaveUSA group members opened SaveUSA accounts.

Several factors could have resulted in the participation differences by city, and it appears that multiple factors did contribute. First, as noted earlier in this chapter, some individual 

characteristics were associated with the likelihood of receiving a savings match: age, income, 

and pledged deposit amount. If the individuals who enrolled in a particular city differed significantly on these characteristics from those who enrolled in other cities, the participation results 

by city may reflect these differences rather than other city factors. But, as shown in Table 2.4, 

the savings pledge amount in Year 1 was very similar across the cities. And although the baseline characteristics of individuals in each city are somewhat different (see Appendix Table A.1), 

the differences are not large. The most notable difference is that, on average, San Antonio’s 

sample members were older and had slightly higher incomes compared with sample members 

from other cities.

Second, the organizational structures in each city varied. In Tulsa and Newark, 

SaveUSA was staffed by a single agency in each city; in New York and San Antonio, a lead 

agency collaborated with one or more community partners. Furthermore, as discussed earlier in 

this chapter, the lead agency in Newark partly dismantled its program in Year 3, affecting the 

percentage of individuals who were able to deposit money into the accounts.

Third, the financial institutions providing the SaveUSA accounts were different in each 

city, which resulted in differences in account-opening procedures and features. For example, 

withdrawing money before the savings match from the financial institution in the Newark program was difficult, because the bank was located in New York City and no ATM cards were 

provided. In contrast, Tulsa’s and New York City’s financial partners included large banks with 

many branches. Furthermore, two financial institutions, in Tulsa and in New York City, had 

more stringent background checks, which resulted in a higher percentage of individuals unable 

to open SaveUSA accounts during the enrollment period.

Finally, a factor that contributed to city differences was that tax filers in Newark and 

San Antonio, which were not part of the randomized controlled trial (RCT), typically opened 

accounts before study enrollment, while tax filers in New York City and Tulsa opened the accounts after study entry. Therefore, individuals in Newark and San Antonio who changed their
44

The SaveUSA Evaluation

Table 2.4

SaveUSA Year 1 Pledge and Initial Deposit, and Savings Match Outcomes

over Three Program Years, by City

Year 1 Years 1 to 3

Average Pledge Average Initial Received Savings Average Savings Sample

City Amount ($) Deposit ($)a Match (%) Match ($) Size

New York City 523 1,181 66.1 370 463

Tulsa 498 973 52.3 294 331

Newark 508 791 79.8 320 342

San Antonio 590 1,147 71.1 452 418

Sample size 1,554

mind or failed the financial institution’s background check were not enrolled in the study, resulting in a larger percentage of individuals in those two cities being eligible for the savings 

match from the beginning of the study. When combined, the savings match rate for San Antonio 

and Newark is 75 percent for the full follow-up period, compared with 60 percent for New York 

City and Tulsa (Appendix Table D.4). However, as noted earlier, the vast majority of SaveUSA 

group members in all cities were able to open a SaveUSA account. Therefore, although the timing of account opening was a contributing factor to differences in match rates by city, it was not 

the main one.

Comparing the two random assignment cities, New York City sample members were 

more likely than Tulsa sample members to receive a savings match (Table 2.4). This difference 

can be mainly attributed to the differences in account eligibility and account-opening procedures between the cities. As Figure 2.3 shows, however, overall savings patterns were similar 

between the two cities.

SOURCES: MDRC calculations from MDRC baseline data and financial institution data.

NOTES: The sample includes SaveUSA group members who were ages 18 to 64 at their time of study 

entry. aThe initial deposit refers to the tax refund amount directly deposited into the SaveUSA account by the 

Internal Revenue Service.

Conclusion

Results in this chapter show that the majority of SaveUSA group members participated in 

SaveUSA by successfully opening an account, and the majority of SaveUSA group members
The SaveUSA Evaluation

Figure 2.3

Average Balance of SaveUSA Accounts, by Month of Follow-Up, in New York City and Tulsa

0

100

200

300

400

500

600

700

800

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

Average account balance ($)

Month relative to study enrollment

Tulsa New York City

Year 2

savings

match

Year 1

savings

match

SOURCES: MDRC calculations from MDRC baseline data and financial institution data.

Year 3

savings

match

45
46

received at least one savings match during the three program years. Those who received the 

savings match in the first year were more likely to participate in subsequent years. 

Participation was the highest during the first year, when almost all SaveUSA group 

members opened SaveUSA accounts. During subsequent years, the majority did not participate, 

due to various factors. Some sample members did not receive a tax refund, and thus were unable to participate. The majority, however, did receive a refund but chose not to save it, for a 

variety of reasons. 

Repeat participation was concentrated among a third of the sample who appeared to be 

in a better position to save. This group was more likely to be older, to have pledged to save the 

maximum amount, and to have a higher income. Those who never received a savings match 

were more likely to have lower incomes and were more likely to have pledged to save the minimum amount of $200. 

SaveUSA account activity suggests that program group members mainly used their accounts to get the savings matches and not as regular savings accounts. Most SaveUSA group 

members who did not pledge to save part of their tax refund in the second program year ceased 

using their accounts. Similarly, the majority of SaveUSA group members who received a savings match withdrew their initial deposit and savings match from their accounts shortly after 

receiving the match. 

In the next chapter of this report, the SaveUSA group’s overall savings activities (not 

just the activity in their SaveUSA accounts) will be compared with those of the Regular Tax 

Filers group. This analysis will indicate whether the SaveUSA program had an effect on individuals’ propensity to save and on how much they saved. 
47

Chapter 3 

Impacts on Savings and Liquid Assets

Introduction

This chapter begins the analysis of the estimated effects (or impacts) of SaveUSA for study participants in New York City and Tulsa. As discussed in previous chapters, SaveUSA encourages 

accumulation of short-term nonretirement savings that can be used to protect households from 

financial hardship due to unforeseen expenses or sudden decreases in income or for other purposes. Accordingly, the analysis focuses primarily on whether the program increased SaveUSA 

group members’ nonretirement savings above the levels (as represented by the Regular Tax Filers group) that low- and moderate-income tax filers with a strong interest in saving would be 

expected to accumulate without access to a SaveUSA account and savings match. The chapter 

then explores SaveUSA’s possible effects on longer-term retirement savings (which were not 

expected from the program model) and total savings, also referred to as liquid assets.1

The chapter analyzes SaveUSA’s impacts on nonretirement savings cumulatively and at 

different points during the 42-month follow-up period. (The analysis of trends in SaveUSA’s

effects is based on findings for a large subgroup of respondents who were interviewed at 18 

months and again at 42 months.) As discussed in the previous SaveUSA report, at 18 months of 

follow-up, SaveUSA led to a 7 percentage point increase above the level for the Regular Tax 

Filers group in the incidence of having nonretirement savings and an average increase of $512 

in total dollars saved. In addition, a larger proportion of SaveUSA group members reported savings-oriented responses on questions concerning attitudes toward savings.2 Over the next 24 

months of follow-up — the time period included in the follow-up for this report — impacts on 

savings could have changed, for several reasons. Direct effects of SaveUSA could be expected 

to diminish because of the year-by-year decreases in deposits to SaveUSA accounts and in receipt of a savings match. (See Chapter 2.) Alternatively, the program could still realize impacts 

on nonretirement savings from a combination of the continued use of SaveUSA accounts by 

some SaveUSA group members and from SaveUSA group members making greater use of other savings products than members of the Regular Tax Filers group. 

 1

While there are different definitions of “liquid assets,” this analysis adopts the one used by the Corporation for Enterprise Development (CFED): “Liquid assets are those that are held in cash or can be liquidated 

quickly: bank accounts and other interest-earning assets; and equity in stocks, mutual funds and retirement 

accounts (IRAs, 401(k)s and KEOGH accounts). Liquid assets exclude equity in businesses, vehicles, homes 

and other real estate” (Corporation for Enterprise Development, 2013). 2

Azurdia, Freedman, Hamilton, and Schultz (2014), Chapter 5, pp. 59-79. SaveUSA did not affect levels 

of retirement savings at the 18-month follow-up point.
48

Gauging the possibility of longer-term effects on savings, the chapter also explores 

whether SaveUSA group members, when surveyed, expressed a greater confidence in their ability to save, compared with members of the Regular Tax Filers group. Finally, the chapter considers whether SaveUSA may have encouraged some low- and moderate-income households to 

shift the target of their savings from longer-term retirement savings to nonretirement savings 

without affecting the total amount that they saved.

Main Findings

• As of the 42-month follow-up point, SaveUSA had increased short-term, 

nonretirement savings above levels for the Regular Tax Filers group. 

SaveUSA led to impacts on the incidence of having nonretirement savings 

and on total dollars saved.

• As expected, relatively few study participants in either research group invested in longer-term retirement savings. SaveUSA did not increase or decrease 

retirement savings compared with the Regular Tax Filers group. 

• SaveUSA led to impacts on “consistent saving,” defined as having nonretirement savings at both 18 months and 42 months of follow-up. 

• Unlike at 18 months, SaveUSA did not lead to consistent increases in expressions of confidence in saving.

The impacts on nonretirement savings at 42 months after random assignment are important, because they show that SaveUSA could sustain savings increases above the level for 

the Regular Tax Filers group even after SaveUSA group members no longer had access to a 50 

percent match on savings. 

Data Sources and Estimation Procedures

All outcomes included in the impact analyses for this report were calculated from responses to 

the SaveUSA 18-month and 42-month surveys. Survey response rates were high for all 

measures discussed below, which increases confidence that findings for respondents can be 

generalized to the full research sample. As is common with survey data, some respondents’ recall error, exaggeration, or reluctance to divulge sensitive information decreases the precision of 

specific estimates for each research group. Nevertheless, these issues affected both research 

groups equally and response bias analyses showed that the findings on program effects presented below can be considered reliable and unbiased. (See Appendixes B, C, and E for further details.) To improve precision, when estimating program effects on dollar-value measures, such as 
49

total nonretirement savings, values above the 99th percentile were identified as outliers and excluded from the calculations.

The analysis uses ordinary least squares (OLS) regression to estimate the values for 

each research group. The regression model controls for randomly occurring differences in study 

participant characteristics recorded at baseline (such as highest educational credential, adjusted 

gross income, and total refund amount) that could affect a participant’s savings, debt, or other 

financial outcomes during the follow-up. Averages for the Regular Tax Filers group represent 

expected levels of savings and other financial outcomes for low- and moderate-income tax filers 

with a strong interest in accumulating savings (as evidenced by their enrollment in the 

SaveUSA study). These averages serve as benchmarks for estimating program effects, and for 

that reason, the impact analyses presented below start with a summary of financial outcomes for 

the Regular Tax Filers group. The impact of SaveUSA is estimated by calculating the difference 

in average outcomes for the SaveUSA group and the Regular Tax Filers group. Differences 

with a statistical significance of 10 percent or lower are considered to be impacts of SaveUSA 

and not the result of chance. (See Box 3.1 for further details.) Estimates for SaveUSA group 

members and members of the Regular Tax Filers group combine results for the research samples in New York City and Tulsa, the two RCT sites. (See Chapter 5 for a discussion of impact 

results for each city.) No special weighting was used to control for possible differences in program effects by city (and, as discussed in Chapter 5, such differences were few and small). 

Impacts on Nonretirement Savings as Measured at the Time of 

the 42-Month Survey 

• At 42 months, SaveUSA continued to lead to impacts on measures of 

current accumulation of nonretirement savings that were of similar 

magnitude to the impacts recorded at 18 months. 

As noted above, results for the Regular Tax Filers group on savings and other outcomes represent the benchmarks by which to estimate the effects, or impacts, of the SaveUSA 

program. After 42 months of follow-up, most members of the Regular Tax Filers group had 

accumulated nonretirement savings but, on average, had only a small amount of savings on 

hand. As Table 3.1 shows, about 72 percent of respondents in the Regular Tax Filers group 

reported having some type of short-term, nonretirement savings. Most often, members of the

Regular Tax Filers group maintained a savings account or kept a minimum balance in their
50

Box 3.1

How to Read the Impact Tables in the SaveUSA Report

Most impact tables in this report use a similar format, illustrated below. The data 

show survey responses by SaveUSA group members and members of the Regular 

Tax Filers group concerning how they received their 2013 federal tax refund. For 

example, the table shows that about 47 (46.5) percent of the SaveUSA group and 

about 41 (41.1) percent of Regular Tax Filers group members had the IRS directly 

deposit all or part of their refund into a savings account.

 

Because individuals were assigned randomly either to the SaveUSA group or to the 

Regular Tax Filers group, the effects of the program can be estimated by the difference in outcomes between the two groups. The “Difference (Impact)” column in the 

table shows the differences between the two research groups’ rates — that is, the 

program’s impacts on a particular way of directly depositing tax refund dollars. For 

example, the impact on the incidence of directly depositing tax refund dollars into a 

savings account can be calculated by subtracting 41.1 percent from 46.5 percent, 

yielding 5.4 percentage points. 

Differences marked with asterisks are described as “statistically significant” and are 

considered to be true program effects. For each measure, the number of asterisks indicates whether the impact is statistically significant at the 1 percent (***), 5 percent 

(**), or 10 percent (*) level, meaning that there is only a 1, 5, or 10 percent probability, respectively, that the impact arose by chance. The p-value shows the exact level 

of statistical significance of the difference in decimal form, as a number that ranges 

from 0.000 (near zero percent probability of having occurred by chance) to 0.999 

(near 100 percent probability). By convention, one asterisk is used for any p-value 

less than 0.1 but greater than 0.05, and the difference is described as being statistically significant at the 10 percent level. For example, as shown below, the SaveUSA 

group had a statistically significant impact of 5.4 percentage points at the 10 percent 

level on the outcome of directly depositing tax refund dollars into a savings account. 

 Impacts on Allocation of 2013 Federal Tax Refund

Outcome

SaveUSA

Group

Regular Tax

Filers

Difference

(Impact) P-Value

How refund was received (%)

Direct deposit into savings account 46.5 41.1 5.4 * 0.054

Purchase U.S. savings bonds 2.7 1.1 1.6 ** 0.042

Direct deposit into checking account 62.0 64.4 -2.5 0.361
51

(continued)

The SaveUSA Evaluation

Table 3.1

Impacts on Savings as of the 42-Month Survey

SaveUSA Regular Tax Difference

Outcome Group Filers (Impact) P-Value

Nonretirement savings

Has nonretirement savings (%) 80.0 72.4 7.6 *** 0.002

Nonretirement savings type (%)

Savings account 67.1 58.8 8.3 *** 0.002

Certificate of deposit 7.2 5.1 2.1 0.126

U.S. savings bond 6.3 3.9 2.3 * 0.067

Stocks, bonds, or mutual funds 10.0 6.3 3.7 ** 0.017

Education savings plan 3.2 2.7 0.5 0.580

Whole-life insurance policy 28.8 29.7 -0.9 0.725

Individual Development Account 1.4 1.1 0.3 0.672

Cash, jewelry, or gold 36.1 29.8 6.3 ** 0.018

Money loaned with expectation of repayment 15.7 14.5 1.2 0.573

Money in some other place 9.7 9.6 0.1 0.936

Maintain minimum balance in checking account 55.7 51.3 4.4 0.127

Total nonretirement savings ($) 2,281 1,758 522 ** 0.024

Total nonretirement savings (%)

$0 20.0 27.6 -7.6 *** 0.002

$1 - $500 31.2 28.7 2.6 0.340

$501 - $1,000 9.1 11.5 -2.4 0.175

$1,001 - $2,000 12.1 12.3 -0.2 0.929

$2,001 - $5,000 15.1 12.0 3.1 0.117

$5,001 - $10,000 9.1 5.1 4.0 *** 0.008

More than $10,000 3.4 2.8 0.5 0.603

Retirement savings

Has retirement savings (%) 31.1 30.3 0.8 0.760

Total retirement savings ($) 3,326 3,512 -186 0.731

Total retirement savings (%)

$0 68.9 69.7 -0.8 0.760

$1 - $2,000 10.4 8.7 1.7 0.337

$2,001 - $5,000 5.0 6.2 -1.2 0.389

$5,001 - $10,000 7.6 6.6 1.0 0.495

$10,001 - $30,000 5.7 6.2 -0.6 0.669

More than $30,000 2.4 2.5 -0.2 0.855

Liquid assets: Nonretirement + retirement savings

Has liquid assets (%) 83.1 75.6 7.6 *** 0.001

Total liquid assets ($) 5,030 4,458 572 0.272
52

 

Table 3.1 (continued)

SaveUSA Regular Tax Difference

Outcome Group Filers (Impact) P-Value

Total liquid assets (%)

$0 16.9 24.4 -7.6 *** 0.001

$1 - $2,000 43.5 41.0 2.5 0.397

$2,001 - $5,000 14.0 12.9 1.2 0.560

$5,001 - $10,000 11.7 8.2 3.5 ** 0.049

$10,001 - $25,000 9.2 9.0 0.3 0.875

More than $25,000 4.7 4.5 0.1 0.908

Sample size (total = 1,236) 626 610

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

checking account. The Regular Tax Filers group averaged $1,758 in nonretirement savings, 

although more than half had less than $500 or none at all.

3

SaveUSA led to increases over levels for the Regular Tax Filers group on several 

measures of nonretirement savings as of 42 months of follow-up. About 80 percent of SaveUSA 

group members reported that they currently had nonretirement savings, a gain of 8 percentage 

points over the Regular Tax Filers group. Having access to the SaveUSA account (for at least 

part of the follow-up period) led to greater use of savings accounts overall by SaveUSA group 

respondents (an impact of 8 percentage points) and to smaller positive, statistically significant

3

About 4 percent of respondents in the Regular Tax Filers group reported that they had exactly $500 in 

nonretirement savings (not shown but included in the “$1-$500” category in Table 3.1). Results at 18 months 

were similar for the Regular Tax Filers group (Azurdia, Freedman, Hamilton, and Schultz, 2014, Table 5.3, pp. 

67-68). The savings levels reported by the Regular Tax Filers group may be considered “only a small amount” 

compared with $2,000, the level of savings reported by low- and moderate-income respondents in a national 

survey as most likely to provide protection against sudden increases in expenses or losses of income (Bricker, 

Kennickell, Moore, and Sabelhaus, 2012, Table 3.1, p.16). 
53

impacts in having cash, jewelry, or gold on hand; having stocks, bonds, or mutual funds; and 

having U.S. savings bonds.

4

The typical SaveUSA group respondent reported having a total of $2,281 in nonretirement savings, an increase of $522, or 30 percent, above the average for the Regular Tax Filers

group. SaveUSA’s impact on total nonretirement savings resulted in part from the program 

turning some nonsavers into savers, as represented by the impact on the proportion with any 

nonretirement savings. In addition, when only respondents with nonretirement savings are considered, the SaveUSA group savers averaged about $400 more in nonretirement savings than 

their counterparts in the Regular Tax Filers group.5

When estimating SaveUSA’s effect on nonretirement savings, it is also important to 

consider whether the increase in dollars saved included study participants at different savings 

levels or was concentrated among particular types of savers. As shown in Table 3.1, a larger 

proportion of SaveUSA group members reported having more than $2,000 in nonretirement 

savings.6 This finding is important because $2,000 is a level of savings reported by low- and 

moderate-level income respondents in a national survey as most likely to provide protection 

against sudden increases in expenses or losses of income.7

These impact findings demonstrate that SaveUSA continued to achieve its primary goal 

of increasing nonretirement savings beyond expected levels for low- and moderate-income 

households that expressed a strong interest in saving (as evidenced by their enrollment in the 

SaveUSA study). Nonetheless, like their counterparts among the Regular Tax Filers group, 

most SaveUSA group members reported maintaining a relatively small amount of nonretirement savings at the time of their 42-month interview. At least two-thirds of both groups had 

savings below $2,000.

8

• Different savings strategies contributed to SaveUSA’s overall impact on 

nonretirement savings at 42 months.

In several respects, the longer-term findings on nonretirement savings closely resemble 

results measured at 18 months of follow-up. During both interviews, SaveUSA group members 

reported having an average of about $2,250 in nonretirement savings, which exceeded the aver-

 4

As shown in Table 3.1, increases in the proportion of respondents who reported owning a certificate of 

deposit and or maintaining a minimum balance in a checking account were slightly above the 10 percent level 

of statistical significance. 5

Based on data displayed in Table 3.1, the average savings for SaveUSA group savers is $2,850 

($2,281/0.800), whereas the average savings for Regular Tax Filers group savers is $2,428 ($1,758/0.724). 6

The difference is statistically significant at the 1 percent level (p-value = 0.002). 7

Bricker, Kennickell, Moore, and Sabelhaus (2012), Table 3.1, p. 16. 8

About 5 percent of respondents in each research group reported that they had exactly $2,000 in nonretirement savings (not shown but included in the “$1,001-$2,000” category in Table 3.1).
54

age for the Regular Tax Filers group by a little more than $500.9 Other findings from financial 

records (for SaveUSA group members) and from survey responses suggest that savings patterns 

for both research groups changed over time. By Month 42 of follow-up, SaveUSA’s impacts on 

nonretirement savings appear to have resulted from SaveUSA group members’ greater use of 

different kinds of savings strategies (such as keeping money in a low-interest savings account or 

keeping cash at home), rather than from SaveUSA group members’ access to a SaveUSA account and 50 percent savings match. Put differently, having access to a SaveUSA account appears to have encouraged low- and moderate-income households to develop a stronger commitment to accumulate short-term nonretirement savings, even at very low interest rates. This 

transition to a broader set of savings strategies may be seen as a positive effect of SaveUSA.

Impacts on Deposits of Tax Refund Dollars to Savings

• The direct effect of the SaveUSA savings account on nonretirement savings diminished over time. 

As noted in Chapter 2, the typical SaveUSA group member received about $365 in savings match dollars during the three years in which he or she was eligible to participate in the 

program (Table 2.2). This average represents about 70 percent of the impact of SaveUSA on 

nonretirement savings as measured at 42 months. Most SaveUSA group members experienced a

“direct effect” of adding the savings match to their nonretirement savings during the first two 

years of follow-up and little or no effect thereafter. Most often, SaveUSA group members who 

received a savings match withdrew their match dollars and a large portion of their SaveUSA 

deposits within six months of receiving the match. Moreover, relatively few SaveUSA group 

members directly deposited tax refund dollars in a SaveUSA account after Year 1. For the 

SaveUSA group to have reported a similar average in nonretirement savings at 42 months as 

they had reported two years previously (and recorded a similar impact), many group members 

must have replenished their nonretirement savings over time using funds from other sources.

• During the 2014 tax season (Year 4 of follow-up), SaveUSA increased 

the incidence of the direct deposit of tax refund dollars into savings, but 

the impact was smaller at 42 months after random assignment than 

when recorded at 18 months.

Tax refund dollars continued to be an important source of savings for both groups during the last year of the follow-up period. As Table 3.2 indicates, according to survey data, in 

Year 4 (2014), the first year after SaveUSA ended in Tulsa, about 47 percent of SaveUSA

 9

See Table 3.1 above and Azurdia, Freedman, Hamilton, and Schultz (2014), Table 5.3, pp. 67-68. 
55

The SaveUSA Evaluation

Table 3.2

Impacts on Tax Refund Receipt and Use During Year 4 (2014) 

Outcome 

SaveUSA

Group

Regular Tax

Filers

Difference

(Impact) P-Value

Filed a tax return (%)

Received a tax refund (%)

Total tax refund ($)

Total tax refund (%)

$0

$1 - $500

$501 - $1,000

$1,001 - $2,000

$2,001 - $5,000

$5,001 - $10,000

More than $10,000

How refund was received (%)

Direct deposit into savings account

Purchase U.S. savings bonds

Direct deposit into checking account

Prepaid debit card

Refund check in the mail

Had plan to save all or part of tax refund (%)

Amount of tax refund currently saved (%)

All

More than half

About half

Less than half

None

Primary use of tax refund (%)

No refund

Savings

Pay bills, debts, or taxes owed 

Expenditures

Pay for emergency expenses 

Pay for housing costs and household expenses

Pay for clothing and other items for family members

Pay for work- or business-related expenses 

Pay for education

Pay for a major purchase 

Give to a family member 

Pay for dining out, entertainment, vacation, or travel

Other uses

88.7

82.4

2,786

18.0

10.3

9.7

15.8

26.2

19.4

0.6

46.5

2.7

62.0

11.1

15.2

47.8

6.3

5.8

9.1

22.8

56.1

18.1

21.5

35.2

25.2

1.3

6.5

1.9

0.1

2.8

5.6

1.8

1.8

3.2

88.5

81.5

2,558

19.3

11.6

13.0

13.9

24.8

16.7

0.7

41.1

1.1

64.4

9.0

12.9

39.1

3.2

3.5

7.0

18.2

68.0

19.0

14.0

36.9

30.1

1.3

9.0

1.4

0.8

2.4

6.6

3.3

2.5

2.7

0.3

0.9

228 *

-1.3

-1.3

-3.3 *

1.8

1.4

2.8

-0.2

5.4 *

1.6 **

-2.5

2.1

2.3

8.7 ***

3.1 **

2.2 *

2.0

4.6 **

-11.9 ***

-0.9

7.6 ***

-1.7

-4.9 *

0.0

-2.5

0.5

-0.7 *

0.4

-0.9

-1.5

-0.6

0.5

0.882

0.666

0.084

0.568

0.455

0.067

0.367

0.571

0.167

0.713

0.054

0.042

0.361

0.237

0.247

0.002

0.013

0.063

0.199

0.047

0.000

0.685

0.001

0.536

0.056

0.972

0.100

0.515

0.087

0.677

0.503

0.105

0.455

0.628

Sample size (total = 1,236) 626 610

(continued)
56

Table 3.2 (continued)

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

group members directly deposited tax refund dollars into savings accounts at a bank or credit 

union, an increase of about 5 percentage points above the level for the Regular Tax Filers group. 

SaveUSA also led to somewhat larger gains above the Regular Tax Filers group in the likelihood of having had a plan to save tax refund dollars and in saving at least part of the tax refund 

until the time of their 42-month interview.

Nonetheless, both the SaveUSA group’s incidence of directly depositing tax refund dollars into savings and the impact on this measure decreased substantially between Years 2 and 

4.10 This decrease was partly the result of fewer SaveUSA group members receiving a tax refund in Year 4 and partly because fewer SaveUSA group members had planned to use some of 

their tax refund for savings.11 Thus, SaveUSA group members’ use of tax refund dollars contributed to the program’s overall impact on nonretirement savings at 42 months, but to a lesser 

extent than when measured at 18 months.

Impacts on Measures of Nonretirement Savings 

Since the First Interview at 18 Months or Measured as Change

over Time 

• SaveUSA led to impacts on longer-term trends in nonretirement savings, 

when savings patterns between Months 18 and 42 are considered. 

 10Azurdia, Freedman, Hamilton, and Schultz (2014), Table 5.2, pp. 64-65. In 2012 (Year 2) about 61 percent of SaveUSA group members reported that they had directly deposited tax refund dollars into savings, 

about 26 percentage points above the level for the Regular Tax Filers group. 11Azurdia, Freedman, Hamilton, and Schultz (2014), Table 5.2, pp. 64-65. In 2012 (Year 2) about 90 percent of SaveUSA group members reported receiving a tax refund and 57 percent reported having had a plan to 

save all or part of their tax refund.
57

Survey respondents were asked a series of questions about changes in their savings 

behavior over time. Responses for the large majority of study participants who answered both 

surveys cover the two years following their 18-month interview, whereas respondents to only

the 42-month survey reported on their savings behavior during the entire 42-month follow-up 

period.12 

When interviewed at 42 months, relatively large majorities of survey respondents in 

both research groups reported that they had maintained savings for at least part of the time during the previous two years. As Table 3.3 shows, about 78 percent of Regular Tax Filers group 

members reported that they had kept money in nonretirement savings at some point since their

18-month interview, and a slightly smaller proportion (71 percent) had maintained a savings 

account for at least part of the time. Most members of the Regular Tax Filers group recalled 

having deposited money into nonretirement savings at least once or twice since Month 18. 

Between Months 18 and 42 of follow-up, SaveUSA led to an 8 percentage point increase above the level for the Regular Tax Filers group in ever having a savings account and a 7 

percentage point gain in ever having nonretirement savings. By a margin of 7 percentage points, 

a larger proportion of SaveUSA group members reported having deposited money in their nonretirement savings at least once since Month 18.

It is also possible to assess SaveUSA’s effects on changes in nonretirement savings by 

comparing each respondent’s reported savings at the time of the 42-month interview with his or 

her reported savings at the time of the 18-month interview. By necessity, this analysis is limited 

to the large subsample of respondents who answered both surveys. Using these responses, the 

analysis identifies groups of respondents who demonstrated more positive (from the standpoint 

of accumulating nonretirement savings) changes in savings behavior. The groups include respondents with savings at both interviews; respondents who reported having more savings at the 

42-month interview than previously at the 18-month interview, and especially those whose increase exceeded $1,000;

13 and “most committed savers,” who reported having nonretirement 

savings at both interviews and increased their nonretirement savings over time. The analysis 

also identifies respondents who reported a decrease in nonretirement savings, based on a comparison of their reported current savings at their 18-month and 42-month interviews. Assessing 

further SaveUSA’s longer-term impacts on nonretirement savings, the analysis next considers 

whether a larger proportion of SaveUSA group members reported positive changes in their nonretirement savings behavior and a smaller proportion reported negative changes compared with

 12To simplify the description of changes over time, in this section and elsewhere in the report, the analysis 

will refer to the 18-month interview as the initial point in time for all survey respondents. 13This group includes survey respondents who reported having nonretirement savings at the 42-month interview but no savings at the 18-month interview.
58

The SaveUSA Evaluation

Table 3.3

Impacts on Savings Between the 18-Month and 42-Month Interviews

Outcome (%)

SaveUSA

Group

Regular Tax

Filers

Difference

(Impact) P-Value

Nonretirement savings

Ever had savings account

Had at least $1 in nonretirement savings

How often added money to savings

At least once per month 

A few times per year

Once or twice

Never

Have automatic system for adding money to savings

Direct deposit from employer

Automatic transfer from checking account

Automatic transfer from other account

How often withdraw money from savings 

Every week

Once or twice per month

A few times per year

Once or twice

Never

No savings at any time

Wanted to withdraw money from savings but did not

Change in total nonretirement savings 

Increased

No change

Decreased

Retirement savings

Change in total retirement savings 

Increased

No change

Decreased

79.1

85.1

36.7

13.8

12.7

36.7

23.7

16.7

10.8

1.3

9.6

21.0

17.7

16.4

20.7

14.6

40.3

27.0

33.8

39.3

21.8

66.2

12.0

71.2

78.0

34.9

10.1

11.0

44.1

19.3

13.7

9.8

1.4

8.9

20.2

13.7

12.4

23.2

21.7

34.6

26.7

41.5

31.8

22.0

69.1

8.9

7.9 ***

7.1 ***

1.8

3.7 **

1.8

-7.4 ***

4.4 *

3.0

1.1

-0.1

0.7

0.8

4.1 *

4.0 **

-2.5

-7.1 ***

5.7 **

0.3

-7.7 ***

7.4 ***

-0.2

-2.9

3.1 *

0.001

0.002

0.501

0.048

0.343

0.008

0.058

0.150

0.541

0.841

0.658

0.747

0.054

0.047

0.296

0.001

0.045

0.914

0.006

0.007

0.932

0.261

0.080

Sample size (total = 1,236) 626 610

(continued)
59

Table 3.3 (continued)

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

Regular Tax Filers group members. Finally, a nonexperimental analysis identifies subgroups of 

survey respondents who reported the same type of change in their nonretirement savings and 

then examines whether SaveUSA group members in each subgroup averaged greater nonretirement savings at 42 months than their counterparts in the Regular Tax Filers group.

As shown in Table 3.4, study participants in both research groups reported different patterns of change in their nonretirement savings. Most Regular Tax Filers group members (60 

percent) reported having nonretirement savings at both interviews. A somewhat smaller proportion (46 percent) increased their nonretirement savings, and nearly one-third of the Regular Tax 

Filers group met the criteria for being “most committed savers.” 

Overall, SaveUSA group members demonstrated a greater commitment to saving 

over time than members of the Regular Tax Filers group. SaveUSA led to a relatively large 

(11 percentage point) increase in having savings at both interviews. Despite this impact, 

SaveUSA did not produce a statistically significant difference in the proportion of respondents who increased their savings over time or a statistically significant difference in the likelihood of becoming “most committed savers” (Table 3.4). Nonetheless, it appears that 

SaveUSA did have some effect on indicators of positive changes in nonretirement savings.

By a margin of nearly 6 percentage points, a larger proportion of SaveUSA group members 

increased their nonretirement savings by more than $1,000 between the 18-month and

42-month interviews. In addition (not shown), when only “most committed savers” are

considered, SaveUSA group members on average reported having nearly $500 more in nonretirement savings at 42 months than their counterparts among the Regular Tax Filers
60

The SaveUSA Evaluation

Table 3.4

Impacts on Changes in Nonretirement Savings, Retirement Savings, and

Liquid Assets Between the 18-Month and 42-Month Interviews

Outcome (%)

SaveUSA

Group

Regular Tax

Filers

Difference

(Impact) P-Value

Nonretirement savings

Had nonretirement savings

At 18-month and 42-month interviews

At 42-month interview only

At 18-month interview only

At neither interview

Change in total savings between interviews

Increased

By more than $1,000

No change

Decreased

By more than $1,000

Retirement savings

Had retirement savings

At 18-month and 42-month interviews

At 42-month interview only

At 18-month interview only

At neither interview

Change in total savings between interviews

Increased

By more than $1,000

No change

Decreased

By more than $1,000

a Liquid assets

Had liquid assets (%)

At 18-month and 42-month interviews

At 42-month interview only

At 18-month interview only

At neither interview

Change in liquid assets between interviews

Increased

By more than $1,000

No change

Decreased

By more than $1,000

 71.1 

 10.7 

 8.4 

 9.8 

 47.7 

 23.5 

 17.2 

 35.1 

 14.6 

 22.7 

 9.2 

 10.5 

 57.6 

 24.6 

 18.4 

 58.5 

 16.9 

 12.3 

 76.3 

 8.6 

 5.8 

 9.3 

 53.1 

 31.8 

 13.4 

 33.5 

 19.3 

60.0

13.5

11.6

14.9

46.2

17.6

19.6

34.2

13.3

22.0

8.0

5.7

64.3

22.8

15.8

65.9

11.3

8.8

64.4 

12.1

10.2

13.4

50.4

26.3

16.7

33.0

15.9

11.0 ***

-2.8

-3.2 *

-5.1 **

1.5

5.8 **

-2.3

0.8

1.3

0.7

1.2

4.8 ***

-6.7 **

1.8

2.6

-7.5 ***

5.6 ***

3.5 *

11.9 ***

-3.4 *

-4.4 **

-4.0 **

2.7

5.5 *

-3.2

0.6

3.5

0.000

0.173

0.092

0.013

0.634

0.020

0.338

0.780

0.539

0.786

0.485

0.005

0.017

0.477

0.256

0.008

0.008

0.064

0.000

0.074

0.011

0.044

0.398

0.051

0.152

0.854

0.154

Sample size (total = 1,108) 552 556

(continued)
61

 

Table 3.4 (continued)

SOURCE: MDRC calculations from responses to the SaveUSA 18-Month and 42-Month Follow-Up 

Surveys. 

NOTES: The sample includes respondents from New York City (N = 650) and Tulsa (N = 458).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences 

in sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular 

Tax Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax 

Filers arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

a"Liquid assets" are calculated as total nonretirement savings + total retirement savings.

group.

14 These findings imply that SaveUSA increased nonretirement savings at 42 months in 

large part by increasing savings among the most savings-oriented study participants.

To a lesser extent, SaveUSA also appears to have increased nonretirement savings 

among study participants with less positive savings experiences. These are respondents who 

also reported having nonretirement savings at both interviews, but whose savings were lower at 

42 months than at 18 months. This outcome group includes about 27 percent of SaveUSA group 

respondents and 23 percent of the Regular Tax Filers group. (The difference is not statistically 

significant.) Within this outcome group, SaveUSA group members reported about $270 more in 

savings than their counterparts among the Regular Tax Filers group.

15 This difference supports 

the finding that SaveUSA increased consistency of savings commitment over time.

14See Table 3.4. To obtain the percentage of “most committed savers” for each research group, calculate 

the difference between two measures, those who increased nonretirement savings between interviews (second 

panel) and those who had nonretirement savings at the 42-month interview only (first panel). Thus, the percentage of “most committed savers” for SaveUSA group members equals 47.7 percent ­ 10.7 percent = 37.0 

percent and for members of the Regular Tax Filers group equals 46.2 percent ­ 13.5 percent = 32.7 percent.

The difference between the research groups of 4.2 percentage points (with rounding) is not statistically significant (p-value = 0.149). Not shown, SaveUSA group members in the “most committed savers” group reported 

an average of $4,465 in nonretirement savings at their 42-month interview, compared with an average of 

$3,979 for their counterparts in the Regular Tax Filers group—a difference of $485 (with rounding). 15See Table 3.4. To obtain the percentage of survey respondents with nonretirement savings that were 

lower at their 42-month interview than at their 18-month interview, calculate the difference between two 

measures, those who “decreased nonretirement savings between interviews” (second panel) and those who 

“had nonretirement savings at the 18-month interview only” (first panel). Among these survey respondents 

with less nonretirement savings, members of the SaveUSA group averaged $1,575 in nonretirement savings, 

compared with $1,305 for the Regular Tax Filers group.
62

Impacts on Retirement Savings

SaveUSA was not expected to affect the amount of money that study participants deposited into 

longer-term retirement savings. Nonetheless, it is important to determine whether either research group accumulated more retirement savings than the other group during the follow-up 

period. Given that the only difference between the SaveUSA group and the Regular Tax Filers 

group was access to a SaveUSA account, a higher average for SaveUSA group members would 

suggest that SaveUSA inspired a more general commitment to save than anticipated. Alternatively, if retirement savings decreased for SaveUSA group members more than for Regular Tax 

Filers group members, it would suggest that SaveUSA led to a shifting of savings from longterm to short-term accounts among low- and moderate-income households that would have 

saved anyway.16

• As expected, SaveUSA led to no positive or negative impacts on retirement savings. 

Similar to the findings at 18 months, only about 30 percent of the Regular Tax Filers 

group reported having retirement savings at their 42-month interview.17 As Table 3.1 shows, 

members of the Regular Tax Filers group averaged $3,512 in retirement savings, including zeros for nonsavers. (The average was $11,596 among those with retirement savings.) Most Regular Tax Filers group members with retirement savings at 42 months also reported savings at 18 

months (Table 3.4). There was no recent trend toward starting to accumulate retirement savings 

among members of the Regular Tax Filers group. 

As expected, SaveUSA had no impact on the incidence of having retirement savings at 

the 42-month interview, nor on total retirement savings (Table 3.1). Analysis of trends in retirement savings suggests that the persistence of SaveUSA’s impacts on nonretirement savings 

did not come about because SaveUSA group members were more likely to shift savings from 

retirement savings to nonretirement savings. Most SaveUSA group members who increased 

their nonretirement savings between the 18- and 42-month interviews either increased their retirement savings as well or reported about the same amount of retirement savings in both interviews (mostly, no retirement savings at both interviews). In addition, only 6 percent of 

SaveUSA group members increased their nonretirement savings between Months 18 and 42 of 

follow-up and also decreased their retirement savings (not shown). 

 16Retirement savings typically carry penalties for withdrawals made before age 591⁄2, making these savings 

less desirable than nonretirement savings as a source of emergency cash. 17Among members of the Regular Tax Filers group, the incidence of having retirement savings at 18 

months was 28 percent. See Azurdia, Freedman, Hamilton, and Schultz (2014), Table 5.3, pp. 67-68. Some 

Regular Tax Filers group members began (or resumed) investing in retirement savings after Month 18 and 

others no longer maintained retirement savings by Month 42 (Table 3.4).

 
63

Impacts on Liquid Assets as of the 42-Month Interview

To respond to a large emergency or unexpected expense or to a sustained loss of income, 

households may need to draw upon financial resources beyond their nonretirement savings. For 

this study, respondents’ total liquid assets, defined as the sum of nonretirement and retirement 

savings, represent the maximum financial resources that they have on hand.18 

• At 42 months, SaveUSA increased the incidence of having at least $1 in 

financial assets above the level for the Regular Tax Filers group, which 

was entirely driven by the increase in nonretirement savings discussed 

above. 

As Table 3.1 indicates, at their 42-month interview, about three-quarters of Regular Tax 

Filers group survey respondents reported having some type of liquid asset. The typical member 

of the Regular Tax Filers group owned $4,458 in liquid assets (including zeros for Regular Tax 

Filers group members without liquid assets). SaveUSA led to an increase of 8 percentage points 

in the proportion of respondents with liquid assets — another indication that SaveUSA turned 

some nonsavers into savers. SaveUSA group members averaged more in liquid assets, but the 

difference was not statistically significant.19

Impacts on Attitudes Toward Saving 

Respondents to the 42-month survey answered a series of questions that gauged their level of 

confidence in their ability to save at present and in the future. When previously surveyed at 18 

months, SaveUSA respondents expressed stronger and more consistent support for savings 

compared with respondents in the Regular Tax Filers group. These results were cited in the previous report as important reasons for expecting that impacts on savings and financial assets 

would continue in future years. 

• Over time, the two research groups became more similar in their level of 

support for savings. 

When interviewed at 42 months, as shown in Table 3.5, a somewhat larger proportion 

of Regular Tax Filers group members reported statements supportive of savings than when previously surveyed at 18 months. For example, by 6 percentage points, a larger proportion of 

members of the Regular Tax Filers group (29 percent) reported that they had increased the

 18Home equity would be another potential source of cash to meet emergencies, but, as discussed in Chapter 4 (Table 4.1), a large majority of study participants rented their place of residence. 19SaveUSA led to a statistically significant increase in the proportion of respondents who reported having 

more than $2,000 in liquid assets (not shown).
64

The SaveUSA Evaluation

Table 3.5

Impacts on Savings Attitudes and Behaviors

Outcome (%)

SaveUSA

Group

Regular Tax

Filers

Difference

(Impact) P-Value

Has a current savings goal 

Importance of having money in a savings account

Very important

Somewhat important

Not that important

Changes in financial decisions since

the 18-month interview

Amount of money in savings or investments

More

No change

Less

Amount of tax refund used for savings or investments

More

No change

Less

Length of time that money is saved or invested

Keep longer

No change

Withdraw sooner

Amount of planning for the future

More

No change

Less

Likelihood of keeping money in a bank 

More likely

No change

Less likely

How closely bank account balances are checked

More closely

No change

Less closely

78.2

78.6

15.8

5.6

33.2

37.1

29.7

21.6

46.5

31.9

32.8

46.8

20.4

54.5

35.2

10.3

30.0

57.8

12.2

42.5

53.2

4.3

71.6

76.1

17.4

6.5

29.2

45.1

25.7

19.4

56.3

24.3

28.8

51.4

19.7

50.8

39.6

9.6

26.4

62.1

11.5

36.4

57.1

6.5

6.6 ***

2.5

-1.6

-0.9

4.1

-8.0 ***

4.0

2.2

-9.8 ***

7.6 ***

3.9

-4.6

0.7

3.7

-4.5

0.7

3.6

-4.3

0.7

6.2 **

-4.0

-2.2 *

0.007

0.304

0.462

0.511

0.118

0.004

0.127

0.333

0.001

0.004

0.138

0.110

0.771

0.184

0.105

0.674

0.159

0.121

0.709

0.028

0.165

0.091

Sample size (total = 1,236) 626 610

(continued)
65

 

Table 3.5 (continued)

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

amount of money that they were keeping in savings or investments than had been reported two 

years previously. In addition, Regular Tax Filers group members were more likely to assess that 

they were using more money from their tax refund for savings and investments, were keeping 

money in savings longer, and were more likely to keep money in a bank than when previously 

surveyed.20 As a result, at 42 months, savings attitudes expressed by members of the Regular 

Tax Filers group more closely resembled attitudes reported by SaveUSA group members. The 

primary exception to this finding concerns one question about having a savings goal. About 78 

percent of SaveUSA group members stated that they had a savings goal, an impact of 7 percentage points above the level for the Regular Tax Filers group. 

Possibly, the absence of consistent impacts on attitudes toward savings means that 

SaveUSA’s impacts on nonretirement savings will decrease in future years. Yet SaveUSA’s

ongoing increases in nonretirement savings provide stronger evidence that these impacts will 

persist. 

Conclusion

At the 42-month follow-up point, SaveUSA continued to increase nonretirement savings above 

the level for the Regular Tax Filers group, even after the immediate incentive of attaining a savings match was no longer available to many SaveUSA group members. With the use of 

SaveUSA accounts having decreased over time, SaveUSA group members’ savings patterns 

became more varied, and impacts on nonretirement savings reflected their greater use of other 

savings products. These changes may be viewed as positive effects of the program. The experience of saving tax refund dollars had strengthened some low- and moderate-income tax filers’ 

20Azurdia, Freedman, Hamilton, and Schultz (2014), Table 5.6, pp. 78-79.
66

overall commitment to savings. Tempering this finding, there is some evidence that study participants with the least propensity to save did not experience positive effects on accumulating 

nonretirement savings. This issue will be explored further in the subgroup analysis of 

SaveUSA’s effects presented in Chapter 5.
67

Chapter 4 

Impacts on Household Expenditures, Debt,

and Material Well-Being

Introduction

This chapter analyzes whether SaveUSA led to additional, longer-term positive effects on 

household finances beyond the greater accumulation of nonretirement savings reported in the 

previous chapter. Based on study participants’ responses to the 42-month survey, it considers 

whether SaveUSA group members were better able than members of the Regular Tax Filers 

group to (1) pay household expenses and meet financial emergencies with savings or current 

income; (2) forgo reliance on high-cost nontraditional lending sources; (3) manage debt; and (4) 

avoid financial hardship. The chapter also analyzes whether SaveUSA group members, on average, reported a greater sense of control over life decisions and more optimism for the future.1

As noted in Chapter 3, at the 42-month follow-up point SaveUSA group members had 

accumulated an average of $522 more in nonretirement savings than members of the Regular 

Tax Filers group. Some proponents of programs that encourage nonretirement savings among 

low- and moderate-income households have posited that even modest increases in savings (of 

similar magnitude to the impact for SaveUSA) can help households avoid financial hardship 

and attain other positive outcomes.2 Other proponents have posited more limited effects of having additional savings and advocate for combining savings initiatives with financial counseling 

and programs that increase income from employment, tax credits, and benefits.3 For the 

SaveUSA evaluation, each hypothesis will be tested for the SaveUSA sample: low- and moderate-income tax filers with a strong savings orientation. The analysis also considers other possible results. For example, under some circumstances, effects on financial well-being could be 

negative if a large proportion of SaveUSA group members maintained their savings by increasing their debt or delaying payment on existing bills or debts. 

The analysis presented below is similar in content to the findings presented in the previous report that covered the first 18 months of follow-up. The additional two years of followup covered in this report provide a fairer test of any possible broader effects of offering matched 

 1

In this chapter, the terms “material well-being,” “financial well-being,” and “financial security” are used 

interchangeably. 2

See, for example, Collins and Gjertson (2013); Abbi (2012); Lopez-Fernandini (2010); McKernan, 

Ratcliffe, and Vinopal (2009). 3

New York City Department of Consumer Affairs, Office of Financial Empowerment (2013), p.17. The 

report describes OFE’s efforts to integrate asset-building initiatives like SaveUSA with expanding low- and 

moderate-income families’ access to financial education and counseling, enhanced consumer protection, and 

social services.
68

savings accounts to low- and moderate-income tax filers with a strong interest in saving. At 42 

months, the second interview took place after SaveUSA group members could have received up 

to three possible savings matches and after the SaveUSA program had ended in Tulsa.

Main Findings 

• At 42 months, most SaveUSA group members and members of the Regular 

Tax Filers group reported being in relatively precarious economic situations, 

characterized by their high incidence of having experienced either a sudden 

loss of income or an emergency or unexpected expense and by accumulating 

more total non-housing-related debt than savings.

• Some SaveUSA group members’ higher average nonretirement savings provided them with a modest “financial cushion.” They were more likely than 

Regular Tax Filers group members to report having savings equivalent to 

cash on hand to pay expenses for at least one month. SaveUSA group members were also more likely to report using savings or current income to pay 

for emergency or unexpected expenses, rather than accumulate debt.

• SaveUSA led to a slightly higher incidence of having a savings or checking 

account at a bank or credit union as of the 42-month interview. These institutions are generally credited with providing greater security and lower fees for 

conducting financial transactions than nontraditional financial institutions 

like check-cashing or payday loan establishments or pawn shops.

• SaveUSA did not affect, positively or negatively, survey respondents’ accumulation of debt from unpaid medical bills, credit or store cards, car loans, 

student loans, or other types of non-housing-related debt. 

• SaveUSA did not increase or decrease the likelihood that survey respondents 

would report having “positive liquid net worth,” meaning that their total nonretirement and retirement savings exceeded their total debts. 

• As of 42 months, SaveUSA had not shown positive effects on other indicators of financial security, such as having a good credit rating or avoiding experiences of serious financial hardship (for example, being unable to pay for 

food or to pay rent, mortgage, or utility bills, or postponing medical care or 

prescription drug purchases because of cost). 
69

Background: Survey Respondents’ Financial Situation 

as of the 42-Month Interview

Although the impact analysis focuses on the possible “spillover” effects of increasing nonretirement savings, other factors could affect study participants’ financial situation, such as their 

employment, household income, access to health coverage, and housing status.

4

When surveyed, members of the Regular Tax Filers group often described themselves 

as encountering difficulties maintaining a steady income. Table 4.1 shows that, as of their 42-

month interview, about two-thirds of them were working for pay, but fewer than half were 

working full-time hours, defined as 35 or more hours of work per week.5 About half of the 

Regular Tax Filers group indicated that they had experienced at least one month of joblessness 

since the 18-month interview, and 36 percent reported having no employment for six months or 

more. Regular Tax Filers group members earned $293 per week on average (including zeros for 

survey respondents without employment), equivalent to a little more than $15,000 per year or a 

little less than $23,000 for those currently employed.

The typical member of the Regular Tax Filers group reported that his or her household 

had received $1,990 per month, equivalent to about $24,000 per year, in income from employment or other sources during the month before the interview. Most Regular Tax Filers group 

members received some type of publicly funded benefit, most often SNAP (food stamps, not 

shown). A little less than half of the Regular Tax Filers group reported having experienced at 

least one month of zero or unusually low household income since the 18-month interview, with 

more than 30 percent of the group reporting three or more months of “income shock.”6

Table 4.1 also indicates that at their 42-month interview, most members of the Regular 

Tax Filers group had health coverage, either privately or publicly funded; about 22 percent 

(with rounding) did not. Coverage levels were higher for the children of Regular Tax Filers 

group members (85 percent among respondents with dependent children) — similar to coverage

 4

In theory, impacts on savings could lead to longer-term gains in employment or income, if, for example, a 

larger proportion of SaveUSA group members than Regular Tax Filers group members could use their savings 

to pay for reliable transportation to jobs, reliable child care, or health insurance premiums. Additionally, if having access to a matched savings account enhances households’ ability to manage debt, SaveUSA group members could eventually receive better credit ratings than members of the Regular Tax Filers group. These, in 

turn, could lower the cost of credit and improve job applicants’ prospects of finding a job among the substantial 

number of employers who check applicants’ credit reports. 5

Azurdia, Freedman, Hamilton, and Schultz (2014), Table 6.1, pp. 84-85. Employment levels and average 

earnings were slightly lower than those reported at 18 months. 6

Azurdia, Freedman, Hamilton, and Schultz (2014), Table 6.1, pp. 84-85. Average monthly income for 

members of the Regular Tax Filers group was slightly higher at 42 months than at 18 months ($1,836). 

 
70

The SaveUSA Evaluation

Table 4.1

Impacts on Employment, Income, Health Coverage, and Housing Status 

SaveUSA Regular Tax Difference

Outcome Group Filers (Impact) P-Value

Employment

Employed since the 18-month interview (%) 75.8 78.5 -2.6 0.274

Currently employed (%) 63.7 66.6 -2.9 0.284

Works full-time hours 44.6 45.6 -1.0 0.707

Weekly earnings at current job ($) 290 293 -3 0.865

Receives employee benefits (%)

Sick days with full pay 39.0 43.0 -4.1 0.153

Vacation days with full pay 43.0 44.6 -1.6 0.583

Enrolled in retirement plan 26.5 25.5 1.0 0.699

Enrolled in health insurance plan 30.8 32.3 -1.6 0.559

Number of months without employment

since the 18-month interview (%)

0 46.5 47.1 -0.6 0.829

Less than 1 3.4 2.1 1.3 0.174

1-2 3.9 5.2 -1.3 0.281

3-5 6.7 9.2 -2.5 0.107

6 or more 39.5 36.4 3.1 0.262

Income

Income sources in prior month (%)

Respondent employment 73.9 75.9 -2.0 0.414

Employment of other household members 33.2 39.0 -5.8 ** 0.034

Public assistance and other government benefits 58.0 59.0 -1.0 0.701

Child support or alimony 10.3 10.8 -0.5 0.779

Pension or retirement plan 6.4 4.3 2.1 * 0.091

Total household income in prior month ($) 1,923 1,990 -67 0.395

Number of months with unusually low or no income

since the 18-month interview (%)

0 51.2 53.2 -1.9 0.508

1-2 15.6 15.8 -0.2 0.937

3 or more 33.1 31.1 2.1 0.444

Health coverage

Currently has coverage (%)

Respondent 77.5 77.5 -0.1 0.982 a All of respondent's children 85.7 85.4 0.3 0.911

(continued)
71

 

Table 4.1 (continued)

SaveUSA Regular Tax Difference

Outcome Group Filers (Impact) P-Value

Housing

Current housing situation (%)

Own home or apartment 20.0 18.6 1.4 0.472

Rent home or apartment 62.0 62.8 -0.9 0.738

Live with family or friends and pay part

of rent or mortgage 10.6 11.1 -0.6 0.746

Live with family or friends and

do not pay rent or mortgage 4.0 4.4 -0.4 0.705

Live in a group shelter 1.1 0.4 0.7 0.144

Other 2.4 2.7 -0.2 0.800

Sample size (total = 1,236) 626 610

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax 

Filers arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

aPercentages shown are for respondents with dependent children.

levels at 18 months. Fewer than one in five members of the Regular Tax Filers group owned 

their own home.7

As expected, as of their 42-month interview, SaveUSA group members reported levels 

similar to those of Regular Tax Filers group members on most outcomes discussed above, including current employment, weekly earnings, monthly household income, health coverage, and 

likelihood of owning their own home. In addition, respondents in the two research groups reported a similar incidence of unemployment for at least six months between Months 18 and 42 

of follow-up.

7

Azurdia, Freedman, Hamilton, and Schultz (2014), Table 6.1, pp. 84-85. Therefore, most members of the 

Regular Tax Filers group lacked home equity as an asset and mortgage debt as a liability. The same is true for 

SaveUSA group members (Table 4.1). These findings are important when analyzing possible effects of 

SaveUSA on respondents’ overall net worth, discussed later in the chapter. 
72

Impacts on Managing Expenses with Savings and 

Current Income

• SaveUSA led to positive effects on some measures of using current income or savings to pay for routine and emergency expenses.

As discussed previously, SaveUSA was intended to boost low- and moderate-income 

households’ nonretirement short-term savings to help them pay for emergency or unexpected 

expenses or to meet basic household expenses during times of unusually low income. Because 

SaveUSA led to an increase in nonretirement savings above the level for the Regular Tax Filers 

group, it would be expected that SaveUSA group members would demonstrate greater capacity 

to manage expenses with savings or current income and without increasing debt. To explore this 

issue, the analysis begins by comparing (as a ratio) the reported value of each household’s nonretirement savings with the value of its total monthly household expenses. The ratio of savings 

to expenses shows the number of months a study participant could pay his or her regular expenses from nonretirement savings alone should household income cease. This measure may be 

calculated in different ways, and for that reason the impact estimates discussed below, and 

shown in Table 4.2, are presented as a range of possible effects.

According to survey data, members of the Regular Tax Filers group incurred an average 

of about $1,632 in household expenses per month.8 Using their nonretirement savings alone, 

Regular Tax Filers group members could cover their household expenses for about 1.5 months

(with rounding). A large majority of Regular Tax Filers group members (70 percent) reported 

having nonretirement savings that would cover less than one month of household expenses.9

About 63 percent of the Regular Tax Filers group reported using a spending plan or budget to 

manage their household expenses, but only about a quarter of group members stated that they 

usually had money left over at the end of the month — a potential source of savings.

On average, SaveUSA group members reported having a level of monthly household 

expenses similar to that of their counterparts in the Regular Tax Filers group. Averaged across 

the entire group, their greater nonretirement savings would pay expenses for less than one week

 8

Interviewers asked respondents to include expenses for housing, food, clothing, transportation, child care, 

phone, utilities, medical care, medical insurance premiums, and prescription drugs when estimating their typical household expenses. This monthly household expense amount was similar to the average reported by a 

national sample of low-income households. See U.S. Department of Labor, Bureau of Labor Statistics (2013),

Table 1101. Households in the lowest income quintile averaged about $1,526 in total monthly expenditures 

(not counting expenditures for cash contributions, education, entertainment, insurance or pension contributions, 

or vehicle purchases). 9

Respondents were also asked to estimate how long they could pay for bills and living expenses with 

money saved, if their income suddenly stopped (not shown). About 38 percent of the Regular Tax Filers group 

reported having savings that would pay for expenses for less than one month.
73

The SaveUSA Evaluation

Table 4.2

Impacts on Managing Monthly Household Expenses and Emergency Expenses

SaveUSA Regular Tax Difference

Outcome Group Filers (Impact) P-Value

Monthly household expenses

Total household expenses in prior month ($) 1,638 1,632 6 0.918

Ratio of nonretirement savings to monthly household

expenses 1.59 1.45 0.14 0.514

Ratio of nonretirement savings to monthly household

expenses (%)

0.00 - 0.99 61.3 69.9 -8.6 *** 0.002

1.00 - 1.99 13.2 13.6 -0.4 0.847

2.00 - 2.99 9.4 4.3 5.1 *** 0.001

3.00 or more 16.1 12.2 3.9 * 0.059

Uses a spending plan or budget to manage expenses (%) 66.1 62.8 3.2 0.244

Usually has money left over at the end of the month (%) 26.2 25.3 0.9 0.709

Emergency or unexpected expenses

Had emergency or unexpected expense since 

the 18-month interview (%) 48.4 46.8 1.6 0.576

Total emergency or unexpected expenses since

the 18-month interview ($) 1,255 1,320 -66 0.634

Total emergency or unexpected expenses since

the 18-month interview (%)

$0 53.0 54.3 -1.3 0.657

$1 - $500 6.8 6.8 0.0 0.980

$501 - $1,000 9.6 9.4 0.3 0.879

$1,001 - $2,000 10.2 10.1 0.1 0.947

$2,001 - $5,000 16.2 13.8 2.4 0.247

$5,001 - $10,000 3.1 4.3 -1.1 0.302

$10,001 - $20,000 1.0 1.4 -0.4 0.510

Primary method of paying for emergency or

unexpected expensesa (%)

Use money on hand or current income 54.9 47.9 7.0 ** 0.021

To pay for previously incurred expenses 28.2 21.5 6.7 ** 0.011

To pay for future expenses 26.6 26.4 0.3 0.917

Increase debt 37.0 44.0 -7.0 ** 0.019

To pay for previously incurred expenses 19.9 23.8 -4.0 0.114

To pay for future expenses 17.2 20.2 -3.0 0.196

(continued)
74

 

Table 4.2 (continued)

SaveUSA Regular Tax Difference

Outcome Group Filers (Impact) P-Value

Primary method of paying for emergency or

unexpected expensesa

 (%) (continued)

Get help from family, friends, government agency, 

or organization 5.9 7.1 -1.2 0.406

To pay for previously incurred expenses 3.2 3.8 -0.5 0.627

To pay for future expenses 2.6 3.3 -0.7 0.501

Does not know how he or she would pay for

future expenses 2.2 1.0 1.3 0.101

b Is liquid-asset poor (%) 71.8 77.6 -5.8 ** 0.018

Sample size (total = 1,236) 626 610

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

a

Methods of paying for emergency or unexpected expenses are mutually exclusive. Respondents who reported 

having incurred at least one emergency or unexpected expense since the 18-month interview answered follow-up 

questions on how they had paid for their previously incurred expenses. Other respondents who reported having 

incurred no emergency or unexpected expense since the 18-month interview answered alternative questions on 

how they would pay for a future expense of $500 or more.

b"Liquid-asset poor" is defined as having insufficient liquid assets (total nonretirement savings + total 

retirement savings) to subsist at the poverty level for three months in the absence of income.

more, compared with the Regular Tax Filers group.10 More positive effects were found when 

comparing the groups’ distribution of values for the ratio of nonretirement savings to monthly 

10The savings-to-expense ratios displayed in Table 4.2 were calculated in multiple steps. First, a ratio of 

nonretirement savings to monthly household expenditures was calculated for each respondent with nonmissing data on both measures. Next, a regression-adjusted mean of these ratios was calculated for each research group. Finally, the difference in the research group means was calculated and presented as the impact of 

SaveUSA. Although the adjusted means for each research group are relatively low, values may be inflated in 

either of two ways. First, some respondents may report unusually high savings. Second, other respondents may 

report unusually low expenses. As a sensitivity test, two alternative ways of estimating respondents’ ability to 

use nonretirement savings to cover household expenses were tried. Similar to results displayed in Table 4.2, 
75

expenses. SaveUSA increased by 9 percentage points the proportion of respondents who reported an amount of nonretirement savings that could cover their household expenses for at least 

one month. Although this effect is positive, it should be kept in mind that only about 40 percent 

of SaveUSA group members reported having this level of savings. As also occurred for members of the Regular Tax Filers group, the more typical experience for SaveUSA group members 

was to have savings on hand that covered less than one month of household expenses.11 

SaveUSA did not increase the likelihood of using a budget or spending plan to manage 

household expenses; nor did the program change the proportion of low- and moderate-income 

respondents who reported having money left over at the end of each month. These last two 

measures may be seen as more general indicators of financial stability (Table 4.2). 

Respondents to the 42-month survey answered a series of questions concerning their 

preferred method of paying for emergency or unexpected expenses. Those who reported having 

incurred such expenses since their 18-month interview were then asked how they had dealt with 

them. Examples included paying the expense out of savings or out of current income, using a 

credit card or another type of loan, getting help from others, or forgoing payment. Respondents 

who reported having incurred no emergency or unexpected expense since Month 18 were asked 

how they would likely pay for a sudden expense of $500 or more, if it should occur. 

A little less than half of survey respondents in the Regular Tax Filers group (47 percent) 

reported that they had incurred an emergency or unexpected expense since their 18-month interview (Table 4.2). Including all respondents, Regular Tax Filers group members averaged 

about $1,320 in emergency or unexpected expenses since that time — or about $2,820 among 

only those respondents who reported that they had incurred such an expense.12 Among all sur-

 

each method yields a small difference between the research groups. One way uses research group averages 

only. It constructs a ratio of the SaveUSA group’s adjusted mean for nonretirement savings shown in Table 3.1 

to the group’s adjusted mean for household expenses, shown in Table 4.2 ($2,281 / $1,638 = 1.39), and a similar ratio for the Regular Tax Filers group ($1,758 / $1,632 = 1.08). The resulting difference between the research groups is 0.32 month (with rounding; statistical significance is not applicable). Another calculation 

method creates a ratio for each respondent, similar to the method used to calculate results shown in Table 4.2, 

but it drops from the calculations respondents with the highest 1 percent of ratio values. This calculation method yields a difference in adjusted means of 0.22 month (not statistically significant). 11Impacts on this ratio are consistent with respondents’ assessments of the number of months they could 

pay bills and household expenses without income (not shown). A larger proportion of SaveUSA group members than Regular Tax Filers group members reported that they could continue to pay bills and expenses for at 

least two months in the absence of income. 12In comparison, see Abbi (2012), p. 3. Abbi analyzed responses to a national survey of households with 

annual incomes of between $20,000 and $60,000 and found that 62 percent of respondents reported having 

experienced at least one financial shock in the previous 12 months. As another comparison, see Brobeck 

(2008b), p. 4. Brobeck found that low-income respondents to a national survey reported incurring an average 

of $2,000 in emergency or unexpected expenses; moderate-income respondents reported a similar average. For 

a final comparison, see Bricker, Kennickell, Moore, and Sabelhaus (2012), Table 3.1, p. 16. Based on calculations from the Federal Reserve 2010 Survey of Consumer Finances, the authors found that respondents in the 
76

vey respondents, about 48 percent of the Regular Tax Filers group expressed a preference to use 

savings or current income to pay for emergency or unexpected expenses, either actual or hypothetical, whereas 44 percent indicated that they would borrow the money to pay for the expense 

or allow the expense to go unpaid.

A similar proportion of SaveUSA group respondents reported that they had incurred an 

emergency or unexpected expense since Month 18, compared with the Regular Tax Filers 

group. The research groups also averaged about the same amount in total expenses of this type. 

But unlike their counterparts in the Regular Tax Filers group, SaveUSA group members expressed a clear preference for paying for emergency or unexpected expenses, either actual or 

hypothetical, with savings or current income, rather than by borrowing the money or delaying 

payment. The program led to a 7 percentage point increase above the level for the Regular Tax 

Filers group in reported actual use of money on hand or current income to pay for emergency or 

unexpected expenses.13 

• SaveUSA reduced the incidence of “liquid-asset poverty.”

To respond to a large emergency or unexpected expense or to a sustained loss of income, households may need to draw upon financial resources beyond their nonretirement savings. A useful measure for estimating the potential of each low- and moderate-income household to respond to a financial emergency (loss of income or unexpected expense) is called “liquid-asset poverty.” Households are considered to be liquid-asset poor if they lack sufficient liquid assets, either nonretirement savings or retirement savings, to subsist at the poverty level for 

three months in the absence of income.14 For example, using Census Bureau poverty thresholds 

for 2014, a household with one parent and two children and with liquid assets that totaled less 

than $4,768 would experience liquid-asset poverty.15

As of their 42-month interview, 78 percent of respondents in the Regular Tax Filers 

group had total liquid assets below this poverty threshold. As a result of SaveUSA’s impact on 

nonretirement savings, the program also led to a reduction of 6 percentage points in the incidence of liquid-asset poverty (Table 4.2). 

 

lowest income quintile reported a median desired savings level to meet emergencies (“precautionary savings”) 

of $2,000. 13The impact was realized for the group that had reported incurring emergency or unexpected expenses 

since the 18-month interview, not for the group that answered the question hypothetically. 14Corporation for Enterprise Development (2013) estimates that nearly 44 percent of households in the 

United States are liquid-asset poor. 15Calculated from DeNavas-Walt and Proctor (2015), data displayed in unnumbered table, “Poverty 

Thresholds for 2014 by Size of Family and Number of Related Children Under 18 Years,” p. 43. 
77

Impacts on Connection to Traditional Financial Institutions and

Sources of Credit

• At 42 months, SaveUSA increased the likelihood of owning a savings account but did not affect use of nontraditional financial institutions. 

Having a savings, money market, or checking account at a bank or credit union and using these institutions to cash checks or as a source of credit can often protect households from 

paying high fees and high interest rates. It would be expected that having access to the 

SaveUSA account would lead to increases above levels for the Regular Tax Filers group in the 

use of financial products offered by traditional financial institutions. The effect on this outcome 

would likely be small because members of the two research groups entered the study having a 

relatively strong orientation to saving and a high incidence of already owning a savings or 

checking account. 

As of their 42-month interview, as Table 4.3 shows, an overwhelming majority of the 

Regular Tax Filers group (84 percent) reported that they currently had a checking or savings 

account, or both.

16 Although four out of five Regular Tax Filers group members had a checking 

account, a substantial proportion of the group reported using a nontraditional financial institution, such as a payday loan establishment, check casher, or pawn shop, either to cash checks or 

pay bills (32 percent) or to take out a loan (27 percent, with rounding).

SaveUSA led to a small increase (of 4 percentage points) above the level for the Regular Tax Filers group in the proportion of study participants who reported currently having a savings or checking account. As expected, the program produced gains (of 8 percentage points) in 

the likelihood of owning a savings account, and a small and statistically insignificant difference 

in the likelihood of owning a checking account. The two research groups reported a similar incidence of using check-cashing services to cash checks or pay bills at least once per month.17

SaveUSA also had no effect on the incidence of using nontraditional lending sources.18 

 16Based on responses to the Federal Reserve 2010 Survey of Consumer Finances, 76 percent of households in the lowest income quintile reported having a transaction (savings or checking) account (Bricker, Kennickell, Moore, and Sabelhaus, 2012, Table 6.B, p. 28). 17Some respondents in both research groups may also have used check-cashing services to send money to 

relatives or friends in other countries. The 42-month survey did not ask about use of these services. 18Neither survey asked about use of prepaid debit cards, except as a way to receive a tax refund. As shown 

in Table 4.4, in response to questions about their current sources of debt, a slightly smaller proportion of 

SaveUSA group members reported that they currently had debt from a payday loan. Use of payday lenders was 

extremely rare for both groups.
78

The SaveUSA Evaluation

Table 4.3

Impacts on Use of Financial Services 

Outcome (%)

SaveUSA

Group

Regular Tax

Filers

Difference

(Impact) P-Value

Has checking or savings account at interview

Checking account

Savings account

Uses check-cashing service at least once per month

To cash check

To pay bills

Used high-interest credit since the 18-month interview

Got a cash advance on a credit card

Got a payday loan

Wrote a check for more money than was in account

Borrowed or withdrew money from retirement 

or insurance plan

Got a loan from a pawn shop

87.9

82.7

67.1

31.7

20.0

20.9

24.1

9.9

4.5

8.2

5.3

2.2

84.3

79.3

58.8

31.8

17.1

22.9

27.5

8.4

6.0

9.7

6.6

2.5

3.7 *

3.4

8.3 ***

-0.1

2.9

-2.0

-3.3

1.6

-1.5

-1.5

-1.3

-0.4

0.059

0.117

0.002

0.961

0.194

0.399

0.186

0.334

0.229

0.374

0.330

0.675

Sample size (total = 1,236) 626 610

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

Impacts on Non-Housing-Related Debt

• After 42 months of follow-up, SaveUSA did not affect study participants’ average levels of non-housing-related debt.

Although some use of credit can help individuals establish good credit scores, minimizing debt — especially high-interest credit card debt — usually increases households’ financial 
79

security. By encouraging low- and moderate-income households to accumulate nonretirement 

savings, SaveUSA was expected to lead to reductions in debt when individuals used some of 

their savings to pay down expensive debt.19

As Table 4.4 indicates, nearly all respondents in the Regular Tax Filers group (83 percent) reported that they were currently carrying some type of non-housing-related debt—most 

often from credit cards (47 percent), but also from student loans (38 percent) or unpaid medical 

bills (35 percent). On average, Regular Tax Filers group members owed a substantial amount of 

debt, about $10,300 (including zeros), with nearly one-quarter reporting non-housing-related

debt levels that exceeded $10,000.20 Average debt levels for the Regular Tax Filers group members appear to have increased since their 18-month interview.21 Moreover, a greater proportion 

of group members reported that they had increased their amount of debt since Month 18 of follow-up (41 percent) than reported that their level had decreased (34 percent).

As of 42 months of follow-up, it does not appear that SaveUSA group members had 

become better able than members of the Regular Tax Filers group to use accumulated savings to 

finance both present consumption and debt reduction. SaveUSA group members reported having incurred a slightly lower level of debt ($9,700) than members of the Regular Tax Filers 

group, but the difference was not statistically significant. SaveUSA had almost no effect on the 

proportion of respondents with relatively high or low levels of debt, except for a small reduction 

in the proportion of respondents with the highest level of debt (more than $50,000). SaveUSA 

group members were just as likely as their counterparts in the Regular Tax Filers group to report 

that their debt levels had decreased or increased since their 18-month interview (Table 4.4). 

Impacts on Measures of Financial Security and 

Material Well-Being

• As of 42 months of follow-up, SaveUSA had not realized positive effects 

on several general indicators of financial security. 

 19The analysis of debt excludes mortgage or home-equity loans. As noted above, relatively few study participants owned their home. 20About 57 percent of survey respondents in the Regular Tax Filers group reported having total debt in excess of three times the value of their total nonretirement savings (not shown). Bricker, Kennickell, Moore, and 

Sabelhaus (2012), Table 13.B, p. 63, shows similar levels of non-housing-related debt for respondents in the 

lowest quintile, with median values of debt, by source, as follows: from installment loans ($7,600); credit cards 

($1,000); lines of credit, not secured by real estate ($1,000); and other, not secured by real estate ($2,000). 21Members of the Regular Tax Filers group reported an average of about $9,300 in non-housing-related 

debt when interviewed at 18 months (Azurdia, Freedman, Hamilton, and Schultz, 2014, Table 6.4, p. 93).
80

The SaveUSA Evaluation

Table 4.4

Impacts on Non-Housing-Related Debt

Outcome 

SaveUSA

Group

Regular Tax

Filers

Difference

(Impact) P-Value

Has debt at interview (%)

Car loan

Student loan

Hospital or medical bill

Credit card bill

Store bill

Unpaid child support

Unpaid rent

Payday loan

Loan from a pawn shop

Loan from family members or friends

Other loan

Total debt at interview ($)

Total debt at interview (%)

$0

$1 - $500

$501 - $1,000

$1,001 - $2,000

$2,001 - $5,000

$5,001 - $10,000

$10,001 - $50,000

More than $50,000

Change in total debt since the 18-month interview (%)

Increased

No change

Decreased

82.2

21.9

38.0

35.4

48.6

21.6

3.1

10.8

3.2

2.2

16.4

4.7

9,733

17.0

10.4

10.6

9.3

15.1

12.1

22.7

3.0

41.8

22.7

35.5

82.9

23.3

38.4

34.8

47.4

19.9

2.7

8.8

6.4

2.5

16.5

4.4

10,309

19.5

10.2

8.7

9.9

15.6

11.7

19.2

5.1

41.1

24.7

34.2

-0.7

-1.4

-0.4

0.6

1.2

1.7

0.4

2.0

-3.2 ***

-0.4

-0.1

0.2

-576

-2.5

0.2

1.8

-0.6

-0.5

0.3

3.4

-2.1 *

0.6

-2.0

1.4

0.738

0.535

0.888

0.827

0.672

0.460

0.686

0.244

0.009

0.675

0.962

0.839

0.542

0.260

0.908

0.298

0.723

0.799

0.866

0.144

0.067

0.833

0.427

0.623

Sample size (total = 1,236) 626 610

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax 

Filers arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.
81

Ultimately, savings programs and other types of financial interventions aim to increase 

the financial security of low- and moderate-income households by helping them avoid financial 

hardships and exercise greater control over their financial decisions.22

As Table 4.5 shows, when surveyed at 42 months, a relatively large proportion of the 

Regular Tax Filers group reported experiencing some type of financial insecurity or material 

hardship since their 18-month interview. Nearly two-thirds of group members reported having 

negative “liquid net worth” as of their 42-month interview, owing, on average, nearly $6,000 

more in non-housing-related debt than they could cover with the liquid assets that they owned. 

A larger proportion (70 percent) of the Regular Tax Filers group reported that they had experienced a financial shock since their 18-month interview — either a sudden loss of income or an 

emergency or unexpected expenditure. About 60 percent of respondents in the group reported 

having experienced at least one type of financial hardship, such as an inability to pay housing or 

utilities costs, food insecurity, or forgone use of medical care or prescription drugs. Finally, only 

one-third of Regular Tax Filers group members reported that their credit rating was “good.”23

As of 42 months of follow-up, SaveUSA had not produced positive effects on these 

measures of financial security. A similar proportion of respondents in both research groups reported having experienced a financial shock since their 18-month interview. On average, 

SaveUSA group members reported higher liquid net worth than members of the Regular Tax 

Filers group, but the difference, although substantial, was not statistically significant. In addition, SaveUSA group members were about equally as likely as their counterparts in the Regular 

Tax Filers group to have liquid net worth greater than zero. Finally, SaveUSA did not affect the 

incidence of experiencing any financial hardship since the 18-month interview.

• SaveUSA did not affect survey respondents’ assessments of their level of 

financial security. 

In addition to providing quantitative data on their household finances, respondents to 

the 42-month survey assessed their financial situation in more personal terms. They described 

their sense of control over their financial situation and related whether their household was doing better financially at the time of their 42-month interview than when interviewed at 18 

months. These outcomes could reflect possible secondary effects of savings gains, such as reduced anxiety and stress, and offer clues about respondents’ possible future financial behavior. 

 22Collins and Gjertson (2013), p.13. As one example of longer-term research on financial well-being, the 

authors cite studies that conclude that accumulating savings can help low- and moderate-income households 

experience upward mobility within two decades or in the next generation. 23Only one-third of respondents in the Regular Tax Filers group reported working full-time (35 hours or 

more) per week at both their 18-month and 42-month interviews (not shown). Furthermore, 42 percent of respondents in the Regular Tax Filers group reported lower monthly income at 42 months than at 18 months. 
82

The SaveUSA Evaluation

Table 4.5

Impacts on Indicators of Financial Security 

SaveUSA Regular Tax Difference

Outcome Group Filers (Impact) P-Value

Managing credit and debt

Average liquid net wortha ($) -4,756 -5,940 1,184 0.294

Has liquid net worth greater than zero (%) 36.2 34.7 1.4 0.620

Current credit rating (self-reported) (%)

Good 34.8 33.5 1.3 0.622

Average 37.6 37.6 0.0 0.992

Bad 27.6 28.9 -1.3 0.621

Experiencing financial shocks and hardships

Experienced financial shockb (%) 72.1 70.4 1.7 0.514

Had financial hardship since the 18-month interview (%) 60.7 60.1 0.6 0.827

Unable to pay rent, mortgage, or utility bills 36.1 33.0 3.1 0.255

Had phone service disconnected due to late payment 27.8 25.3 2.5 0.330

Did not have enough money to buy food 24.6 26.9 -2.3 0.359

Postponed seeing a doctor because of cost 33.5 37.2 -3.7 0.168

Used fewer prescription medications because of cost 29.0 29.8 -0.8 0.757

Lives in at-risk housingc (%) 7.5 7.4 0.1 0.971

Respondent's assessment of level of financial security

Agrees with the following statement (%)

My financial situation is better than it was at

the 18-month interview 62.8 59.1 3.7 0.189

I don't worry about having enough money in the future 24.4 22.2 2.1 0.389

These days I can generally afford the things I need 72.8 68.0 4.8 * 0.069

The way I manage money today will affect my future 90.1 90.4 -0.4 0.820

There sometimes is enough money to buy something

or go somewhere just for fun 28.6 33.5 -4.8 * 0.066

I feel confident making decisions about money 86.5 87.3 -0.8 0.688

How often felt unable to control important things in life

since the 18-month interview (%)

Very often or often 26.2 24.0 2.3 0.368

Sometimes 32.8 36.4 -3.5 0.202

Never or rarely 41.0 39.7 1.3 0.652

Sample size (total = 1,236) 626 610

(continued)
83

Table 4.5 (continued)

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

a

"Liquid net worth" is calculated as total nonretirement savings + total retirement savings − total non-housingrelated debt. 

b"Financial shock" includes experiencing one or more months of unusually low or no income or incurring an 

emergency or unexpected expense since the 18-month interview. c"At-risk housing" includes living alone or with family or friends and not paying rent or mortgage; living in a 

shelter, institutional setting, or other temporary housing situation; or being homeless.

For example, it would be expected that savers who feel confident about their financial decisions 

will continue to save.

Despite experiencing financial challenges, respondents in the Regular Tax Filers group 

often described their current financial situation in fairly upbeat ways (Table 4.5). About 59 percent of group members reported that their financial situation had improved since their 18-month 

interview, and larger majorities described themselves as being generally able to afford needed 

items and feeling confident in their ability to make financial decisions. But members of the 

Regular Tax Filers group also expressed anxiety about their household finances. About 60 percent reported that they felt unable to control important things in life at least part of the time, and 

a larger proportion of the group indicated concern about having enough money in the future.

For the most part, SaveUSA group members expressed similar sentiments about having 

made progress financially since their 18-month interview but feeling uncertain about their 

household’s future financial security. They did not assess their present financial situation or future prospects more positively than the Regular Tax Filers group. By about 5 percentage points, 

SaveUSA group members were more likely to report that they could generally afford to buy the 

things that they need, but by the same margin they were less likely to report that they had 

enough money to buy something or go somewhere just for fun. 
84

Finally, Table 4.6 shows that SaveUSA did not lead to positive effects for the SaveUSA 

group on measures of change over time in financial security among study participants who were 

interviewed at 18 months and again at 42 months. More than 70 percent of each research group 

reported having non-housing-related debt at each interview, and only about 20 percent of both 

groups reported having positive liquid net worth at both 18 and 42 months. In addition, for both 

research groups, the largest subgroup of survey respondents (about 50 percent of SaveUSA 

group members and 46 percent of members of the Regular Tax Filers group) reported higher 

debt levels at their 42-month interview than at 18 months. Finally, only about 20 percent of both 

groups assessed their credit rating more positively at their 42-month interview than at their 18-

month interview.

Conclusion

On average, as of 42 months of follow-up, SaveUSA group members had attained some gains 

in financial security compared with members of the Regular Tax Filers group on measures that 

were a direct outgrowth of the savings impacts of the program. Specifically, the program led to

the following impacts: 

• An increase in the proportion of respondents with nonretirement savings who 

could pay for household expenses for at least one month

• An increase in the proportion of respondents who reported a preference for 

relying on money on hand or current income to pay for emergency or unexpected expenses, as opposed to increasing their debt

• A decrease in liquid-asset poverty

The longer-term impact findings also demonstrate the limitations of programs aimed 

solely at increasing nonretirement savings for alleviating poverty and helping low- and moderate-income households attain greater financial security. 

• SaveUSA produced no positive effects on broader quantitative measures of 

financial security, such as reduction in debt or higher liquid net worth.

• The program did not reduce the incidence of self-reported financial hardship.

• The program did not improve study participants’ self-assessed levels of financial security. 
85

The SaveUSA Evaluation

Table 4.6

Impacts on Changes in Selected Indicators of Financial Security 

 Between the 18-Month and 42-Month Follow-Up Interviews

SaveUSA Regular Tax Difference

Outcome (%) Group Filers (Impact) P-Value

Non-housing-related debt

Had non-housing-related debt 

At 18-month and 42-month interviews 75.1 71.5 3.6 0.181

At 42-month interview only 9.6 10.0 -0.3 0.853

At 18-month interview only 9.0 10.6 -1.5 0.409

At neither interview 6.3 8.0 -1.7 0.281

Change in total non-housing-related debt between interviews

Increased 50.4 45.8 4.5 0.150

By more than $1,000 37.3 32.8 4.5 0.133

No change 9.5 12.8 -3.3 * 0.093

Decreased 40.1 41.4 -1.2 0.696

By more than $1,000 27.9 31.5 -3.6 0.218

Liquid net wortha

Had liquid net worth greater than zero 

At 18-month and 42-month interviews 21.2 21.0 0.2 0.937

At 42-month interview only 14.4 14.5 -0.1 0.974

At 18-month interview only 12.9 10.9 1.9 0.369

At neither interview 51.5 53.6 -2.1 0.530

Change in total liquid net worth between interviews

Increased 49.8 51.0 -1.2 0.720

By more than $1,000 40.4 40.7 -0.3 0.923

No change 1.7 1.7 0.0 0.969

Decreased 48.5 47.3 1.2 0.712

By more than $1,000 39.7 37.9 1.8 0.564

b Credit rating

Change in self-reported credit rating between interviews

Improved 21.6 21.9 -0.3 0.911

No change 60.1 59.0 1.1 0.727

Worsened 18.2 19.0 -0.8 0.749

Sample size (total = 1,108) 552 556

(continued)
86

Table 4.6 (continued)

SOURCE: MDRC calculations from responses to the SaveUSA 18-Month and 42-Month Follow-Up Surveys.

NOTES: The sample includes respondents from New York City (N = 650) and Tulsa (N = 458).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

a"Liquid net worth" is calculated as total nonretirement savings + total retirement savings − total nonhousing-related debt. About 15 percent of the sample is excluded from this measure because of missing values.

bAn improvement in credit rating would involve a self-reported change from a "bad" or lower credit rating at 

the 18-month interview to an "about average" or higher credit rating at the 42-month interview; or from an 

"about average" credit rating at the 18-month interview to a "good" or higher credit rating at the 42-month 

interview. A worsening in credit rating would involve the opposite changes in self-reported credit ratings.

 Thus, increasing savings can contribute to some aspects of financial security, but other 

positive financial developments, such as increases in steady or higher-paying employment, increases in income, and reductions in debt, may matter more for the financial security of lowand moderate-income households. The broader implications of the findings outlined in this 

chapter will be discussed in the report’s conclusion. 
87

Chapter 5

Impacts on Savings and Financial Stability for

Selected Subgroups

The results presented so far show that the SaveUSA program continued to increase nonretirement savings from the 18-month follow-up point to the 42-month point. They also show that the 

SaveUSA program did not produce effects on measures of financial security, such as debt and 

material well-being. However, the presence or absence of impacts for the full report sample 

may mask effects for certain groups that may have had different exposure and/or responses to 

the SaveUSA program. This chapter presents the effects of SaveUSA for key subgroups, defined by city, income, age, education, and tax filing status.1

 

The earlier SaveUSA report found that SaveUSA’s effects were consistent across all 

subgroups, with only one notable exception: SaveUSA increased the percentage of sample 

members with nonretirement savings among those age 35 or older, but it did not have this effect 

for those younger than 35.2 This chapter updates the earlier report’s effects for key subgroups 

using data from the 42-month survey.

It is easy to imagine why the effects of SaveUSA might have varied across subgroups

— for example, the effects for the New York City and Tulsa samples. First, as discussed earlier 

in this report, New York City had experience running the program for several years under 

$aveNYC (the previous version of the program being evaluated). Second, the characteristics of 

the sample members in each city varied, and differences between the cities in labor and housing 

markets could also affect the results. Finally, as shown in Chapter 2, the proportion of participants receiving the savings match differed by city.

The effects of SaveUSA also may have differed depending on tax filers’ circumstances when they entered the study. Certain characteristics, such as having a very low income or 

having children, may make it harder for individuals to save. Furthermore, past research has 

found that certain characteristics are associated with being more financially stable. For example, higher income, education, and number of children have been found to be associated with 

being banked.3 In addition, one study found asset differences by age, race, and family struc-

 1

In randomized controlled trials, it is reasonable to estimate impacts for any subgroup, as long as the subgroups are defined according to characteristics measured before random assignment. The outcomes for 

SaveUSA members in each subgroup are compared with the outcomes for Regular Tax Filers group members 

in that same subgroup, applying the same regression-adjustment procedures and tests of statistical significance 

that were used for the full report sample. 2

Azurdia, Freedman, Hamilton, and Schultz (2014).

3

Berry (2004); Chan (2011). 
88

ture.4 Therefore, this chapter also considers variation in impacts by age, adjusted gross income, highest educational attainment, and tax filing status. In theory, SaveUSA’s effects 

could be stronger for individuals facing greater challenges to save or for individuals with 

greater ability to save, or the program may have the same effects for both groups.

Main Findings

• As of 42 months of follow-up, SaveUSA continued to increase nonretirement 

savings across all key subgroups. 

• On measures of financial security, including debt and material well-being, 

SaveUSA did not produce effects for any of the subgroups.

The key focus of subgroup analysis is not on the impacts for a given subgroup, but on 

whether the differences in impacts across the subgroups are statistically significant (as indicated by the daggers in the tables).5 However, the sample sizes for some of these subgroups are 

fairly small, meaning that differences in impacts between subgroups are less likely to be statistically significant. The limitation of small sample sizes should be kept in mind when interpreting the results. 

Participation Results for All Subgroups

• Easy access to the SaveUSA account deposits and reliance on tax refunds as a source of income seem to have been the main factors contributing to high match rates for different subgroups.

Different groups of individuals may have had an easier or harder time keeping their 

pledged savings amounts in the SaveUSA account for the full year. Among the SaveUSA group 

members enrolled in 2011, this section examines the savings match rates for the entire study 

period. As a reminder, during the study period, individuals had three chances of getting a savings match, in 2012, 2013, and 2014, as long as they deposited tax refund dollars in their 

SaveUSA account and kept the pledged savings amount in the account for about a year. The 

goal of this analysis is to provide context, before examining the subgroup differences in impacts 

on savings and other outcomes shown later in this chapter.

Table 5.1 shows selected participation outcomes for selected subgroups. With the exception of the subgroup defined by city, the results pool all cities together, including the San

 4

McKernan and Ratcliffe (2008). 5

For each measure, a separate statistical test was performed on the difference in impacts for related subgroups.
89

The SaveUSA Evaluation

Table 5.1

Baseline Characteristics and Savings Match Outcomes Across All Program Years, 

for Selected Subgroups

At Study Entry Years 1 to 3

Sample

Outcome Size

Average Average

Adjusted Gross Refund 

Income ($) Amount ($)

Average

Initial

Deposit ($)a

Received Average

Savings Savings

Match (%) Match ($)

City

New York City

Tulsa

Newark

San Antonio

Age 

18-34

35-64

Adjusted gross income

$1 - $9,999

$10,000 - $19,999

$20,000 - $50,000

b Filing status

Single filer with dependents

Single filer without

 dependents

Educational attainmentc

High school diploma

 or GED certificate

At least some college

16,509

17,790

18,659

20,504

15,554

20,182

6,327

14,831

29,071

19,938

12,598

16,382

20,033

3,915

3,464

3,859

3,606

3,591

3,813

2,174

4,000

4,472

4,975

1,247

3,640

3,768

1,181

973

791

1,147

832

1,182

745

1,080

1,195

1,189

715

983

1,411

66.1

52.3

79.8

71.1

62.5

70.8

56.4

64.9

77.0

67.8

65.2

57.8

68.1

370

294

320

452

265

432

235

356

456

397

271

295

443

463

331

342

418

622

932

392

542

618

859

508

462

216

Sample size 1,554

SOURCES: MDRC calculations from MDRC baseline data, VITA survey data, responses to the SaveUSA 18-

Month and 42-Month Follow-Up Surveys, and financial institution data.

NOTES: Tax filing information and refund amounts refer to 2010, the tax year prior to random assignment. 

Demographic characteristics data were collected for one tax filer when couples filed jointly.

The sample includes SaveUSA group members who were ages 18 to 64 at their time of study entry.

Indicators of respondent's highest educational credential were recorded from responses to the 18-Month or 42-

Month Follow-Up Survey for respondents with missing data at baseline. The survey responses for these indicators 

are available only for participants from New York City and Tulsa. Educational credential information was not 

available for sample members in Newark and San Antonio.

Sample sizes for specific measures may vary because of missing values.

aThe initial deposit refers to the tax refund amount directly deposited into the SaveUSA account by the Internal 

Revenue Service.

bThis excludes joint tax filers because of small sample sizes.

c

This excludes study participants with no high school diploma or GED certificate because of small sample sizes.
90

Antonio and Newark samples. It shows that at least half the sample members in each subgroup 

received at least one savings match during the follow-up period. The savings match rates vary 

from 52 percent for SaveUSA group members in Tulsa to almost 80 percent for SaveUSA 

group members in Newark. 

The results are consistent with the interim report findings and the results shown in 

Chapter 2 of this report. The Newark sample had the highest savings match rate across all the 

cities, followed by San Antonio. As discussed earlier, withdrawing money from the financial 

institution used in the Newark site was difficult because the bank was located in New York City 

and individuals were not given ATM cards. 

Older sample members were also more likely than younger people to receive the savings match. In addition, the subgroup with adjusted gross incomes between $20,000 and 

$50,000 was more likely than the subgroup with lower incomes to receive the savings match.

Finally, those with at least some college were also more likely to receive the savings match, 

compared with those with only a high school diploma or General Educational Development 

(GED) certificate. 

As shown, many of the subgroups with the highest savings match rates were ones with 

larger incomes and larger refund amounts, compared with their subgroup counterparts. This 

suggests that individuals in the subgroups with higher savings match rates were less reliant on 

their tax refunds as a source of income, which may have made it easier for them to keep their 

pledged savings amounts in the SaveUSA accounts. The next section will examine whether 

having higher savings match rates translates into greater effects on savings, material hardship, 

or financial security.

Impacts by Random Assignment City

• SaveUSA effects were consistent between New York City and Tulsa.

SaveUSA increased nonretirement savings in the short and longer terms 

in both cities, but there were no effects on overall finances or financial 

security in either city. 

Based on responses to the 42-month survey, outcomes for the Regular Tax Filers 

groups in the two cities were generally similar, and only a few city differences were found. As 

Table 5.2 shows, almost three-quarters (74 percent) of the Regular Tax Filers group in Tulsa no 

longer had any of their 2014 tax refund saved at the time of the survey interview, compared

with 63 percent of the Regular Tax Filers group in New York City. But Tulsa had a larger percentage of Regular Tax Filers group members with retirement savings (39 percent in Tulsa
The SaveUSA Evaluation

Table 5.2

Impacts on Selected Outcomes, by City

New York City Tulsa

Regular Regular

SaveUSA Tax Difference SaveUSA Tax Difference

Outcome Group Filers (Impact) P-Value Group Filers (Impact) P-Value

Year 4 (2014) (%)

How refund was received 

Direct deposit into savings account 51.7 43.8 7.9 ** 0.034 38.7 37.8 0.9 0.831

Purchased U.S. savings bonds 3.3 1.4 2.0 * 0.084 1.6 0.7 0.9 0.337

Direct deposit into checking account 61.6 64.5 -2.9 0.425 62.0 64.8 -2.9 0.492

Prepaid debit card 11.6 9.4 2.2 0.340 10.3 8.5 1.8 0.486

Refund check in the mail 20.6 16.6 3.9 0.182 7.6 7.7 -0.2 0.946

Had plan to save all or part of tax refund 51.3 41.7 9.5 ** 0.010 42.5 35.8 6.7 0.114

Amount of tax refund currently saved 

All 6.3 3.4 2.9 * 0.074 6.3 3.0 3.3 * 0.079

More than half 7.0 4.7 2.3 0.192 4.1 1.8 2.3 0.135

About half 12.2 7.3 4.9 ** 0.028 4.4 7.0 -2.6 0.222 ††

Less than half 24.1 21.6 2.6 0.413 20.0 14.4 5.6 * 0.096

None 50.4 63.1 -12.7 *** 0.001 65.2 73.8 -8.6 ** 0.037

At 42-month interview

Has liquid assets (%) 82.3 76.0 6.3 ** 0.040 83.6 75.8 7.8 ** 0.032

Has nonretirement savings 79.1 73.4 5.7 * 0.073 80.4 71.9 8.5 ** 0.026

Has retirement savings 26.4 24.5 2.0 0.534 36.9 39.2 -2.3 0.586

Total liquid assets ($) 4,406 3,971 435 0.474 5,753 5,326 427 0.639

Total nonretirement savings 2,243 1,850 393 0.165 2,274 1,691 583 0.126

Total retirement savings 2,708 2,351 358 0.542 4,138 5,220 -1,081 0.287

Has checking or savings account (%) 86.1 82.8 3.3 0.215 90.1 86.7 3.4 0.215

Usually has money left over at the

end of the month (%) 21.9 21.8 0.1 0.974 32.0 30.3 1.8 0.672

(continued)

91
Table 5.2 (continued)

New York City Tulsa

Outcome 

SaveUSA

Group

Regular

Tax Difference

Filers (Impact) P-Value

SaveUSA

Group

Regular

Tax

Filers

Difference

(Impact) P-Value

Since the 18-month interview (%)

Change in total nonretirement savings

Increased

No change

Decreased

Change in total retirement savings 

Increased

No change

Decreased

Change in total non-housing-related debt

Increased

No change

Decreased

Experienced financial shocka

Had financial hardship

26.6

34.3

39.1

18.0

70.7

11.2

41.2

21.3

37.5

69.9

57.2

25.5

41.6

32.9

17.2

74.6

8.2

37.8

26.7

35.5

68.9

61.0

1.2

-7.3 **

6.2 *

0.8

-3.8

3.0

3.4

-5.4 *

2.0

1.0

-3.7

0.724

0.043

0.086

0.760

0.236

0.182

0.365

0.097

0.582

0.778

0.308

26.8

33.5

39.7

26.7

60.5

12.8

43.4

24.3

32.3

75.4

64.8

29.0

40.8

30.2

29.3

60.6

10.2

44.8

22.4

32.9

72.4

59.4

-2.3

-7.3 *

9.5 **

-2.6

-0.1

2.7

-1.4

1.9

-0.5

3.0

5.4

0.568

0.094

0.026

0.509

0.980

0.354

0.763

0.619

0.897

0.454

0.216

Sample size (total = 1,236) 367 358 259 252

(continued)

92
93

Table 5.2 (continued)

SOURCES: MDRC calculations from MDRC baseline data, VITA survey data, and responses to the SaveUSA 18-Month and 42-Month Follow-Up 

Surveys. 

NOTES: Indicators of respondent's highest educational credential were recorded from responses to the 18-Month and 42-Month Follow-Up Surveys 

for respondents with missing data at baseline.

The table excludes study participants with no high school diploma or GED certificate from comparisons of impacts by level of education and 

excludes joint tax filers from comparisons of impact by tax filing status because of insufficient sample sizes for these subgroups.

The sample includes respondents from New York City (N = 725) and Tulsa (N = 511). Estimates were regression-adjusted using ordinary least 

squares, controlling for pre-random assignment characteristics of sample members. No special weights were applied to responses to adjust for 

differences in sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences.

Sample sizes for specific outcomes may vary because of missing values.

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

The H-statistic is used to assess whether the difference in impacts between sites or subgroups is statistically significant. Statistical significance 

levels are indicated as follows: † = 10 percent; †† = 5 percent; ††† = 1 percent. a

"Financial shock" includes experiencing one or more months of unusually low or no income or incurring an emergency or unexpected expense 

since random assignment.
94

compared with 24 percent in New York City, with rounding). Outcomes related to financial 

hardship, such as debt or financial shocks, were similar between cities. For example, about 60 

percent of the Regular Tax Filers group members in both cities had experienced some financial 

hardship since the 18-month interview.

SaveUSA’s effects were consistent between the cities. In New York City, the SaveUSA 

program increased the percentage of SaveUSA group members with nonretirement savings by 

5.7 percentage points above the Regular Tax Filers group level; in Tulsa, the increase was 8.5 

percentage points. SaveUSA did not produce effects on other outcomes for either city. For example, the program had no effect on the percentage of individuals who experienced a financial 

shock or had a financial hardship in either city.

Impacts on Selected Survey Outcomes for Other Subgroups

• SaveUSA’s effects on nonretirement savings were consistent across subgroups defined by age, adjusted gross income, educational attainment, 

and tax filing status. SaveUSA increased nonretirement savings for 

many different groups of people but did not produce effects for any subgroups on any measures of financial security, including debt and material well-being.

The financial situations of Regular Tax Filers group members at the 42-month followup point varied by subgroup. As Table 5.3 indicates, the majority of Regular Tax Filers group 

members had nonretirement savings at the time of the 42-month survey interview. Savings 

amounts for the Regular Tax Filers subgroups at that point averaged between $1,006 and 

$2,792. The percentage with nonretirement savings was similar across subgroups, except for 

those defined by educational status and adjusted gross income. Among those with a postsecondary degree or some college, almost 81 percent had nonretirement savings, compared with only 

71 percent of those with a high school diploma or GED certificate. Among the subgroups defined by adjusted gross income, the lowest income bracket had a lower percentage of individuals with nonretirement savings (65 percent) compared with the higher income brackets (73 percent and 77 percent). As would be expected, subgroups with the highest adjusted incomes at the 

time of random assignment also had, on average, larger amounts of nonretirement savings.

Overall, SaveUSA’s effects on nonretirement savings were consistent across subgroups 

based on age, adjusted gross income, education, and tax filing status (Table 5.3). SaveUSA’s 

impacts on nonretirement savings for all subgroups were all in a positive direction, although 

some were not statistically significant. No large differences in the impacts between subgroups

were found. This suggests that SaveUSA increased nonretirement savings for many different 

groups of people.
95

The SaveUSA Evaluation

Table 5.3

Impacts on Selected Outcomes, by Selected Baseline Characteristics

Outcome 

SaveUSA

Group

Regular Tax

Filers

Difference

(Impact) P-Value

Has nonretirement savings (%)

City

New York City

Tulsa

Age

18-34

35-64

Adjusted gross income

$1 - $9,999

$10,000 - $19,999

$20,000 - $50,000

Highest educational attainment

High school diploma or GED certificate

Postsecondary degree or some college

Tax filing status

Single filer, no dependent children

Single filer with dependent children

Total nonretirement savings ($)

City

New York City

Tulsa

Age

18-34

35-64

Adjusted gross income

$1 - $9,999

$10,000 - $19,999

$20,000 - $50,000

Highest educational attainment

High school diploma or GED certificate

Postsecondary degree or some college

Tax filing status

Single filer, no dependent children

Single filer with dependent children

79.1

80.4

81.8

78.8

68.3

85.7

84.0

78.2

86.3

78.6

80.5

2,243

2,274

2,178

2,348

1,859

2,163

2,736

2,215

2,997

2,275

2,316

73.4

71.9

74.9

70.5

65.1

73.0

77.3

71.2

80.7

73.5

70.8

1,850

1,691

1,808

1,744

1,006

1,577

2,537

1,407

2,792

1,925

1,484

5.7 *

8.5 **

6.9 *

8.3 **

3.2

12.7 ***

6.7 *

7.0 **

5.6

5.2

9.7 ***

393

583

370

605 *

853 **

586 *

199

807 ***

205

350

832 ***

0.073

0.026

0.059

0.012

0.537

0.002

0.084

0.033

0.191

0.227

0.004

0.165

0.126

0.277

0.056

0.041

0.062

0.676

0.004

0.718

0.449

0.002

(continued)
96

Table 5.3 (continued)

SaveUSA Regular Tax Difference

Outcome Group Filers (Impact) P-Value

Has retirement savings (%)

City

New York City 26.4 24.5 2.0 0.534

Tulsa 36.9 39.2 -2.3 0.586

Age

18-34 26.3 30.2 -3.9 0.312

35-64 34.7 30.6 4.1 0.232

Adjusted gross income

$1 - $9,999 13.9 14.2 -0.3 0.939

$10,000 - $19,999 25.3 27.0 -1.7 0.689

$20,000 - $50,000 51.0 47.5 3.5 0.476

Highest educational attainment

High school diploma or GED certificate 28.8 26.6 2.2 0.507

Postsecondary degree or some college 44.9 45.5 -0.6 0.916

Tax filing status

Single filer, no dependent children 28.1 26.3 1.8 0.683

Single filer with dependent children 33.7 31.8 1.9 0.582

Total retirement savings ($)

City

New York City 2,708 2,351 358 0.542

Tulsa 4,138 5,220 -1,081 0.287

Age

18-34 1,923 1,997 -74 0.889

35-64 4,352 4,648 -296 0.731

Adjusted gross income

$1 - $9,999 904 942 -38 0.948

$10,000 - $19,999 1,708 1,442 266 0.570

$20,000 - $50,000 7,069 7,865 -796 0.576

Highest educational attainment

High school diploma or GED certificate 2,712 3,145 -433 0.530

Postsecondary degree or some college 6,217 5,270 947 0.473

Tax filing status

Single filer, no dependent children 3,074 1,735 1,339 0.122

Single filer with dependent children 3,541 3,716 -175 0.800

(continued)
97

Table 5.3 (continued)

SaveUSA Regular Tax Difference

Outcome Group Filers (Impact) P-Value

Had financial hardship since the 18-month interview (%)

City

New York City 57.2 61.0 -3.7 0.308

Tulsa 64.8 59.4 5.4 0.216

Age

18-34 57.8 53.1 4.7 0.291

35-64 62.7 64.9 -2.2 0.541

Adjusted gross income

$1 - $9,999 64.6 65.8 -1.2 0.819

$10,000 - $19,999 56.2 57.2 -1.0 0.831

$20,000 - $50,000 60.4 59.9 0.5 0.916

Highest educational attainment

High school diploma or GED certificate 60.4 59.6 0.8 0.817

Postsecondary degree or some college 56.5 58.2 -1.7 0.764

Tax filing status

Single filer, no dependent children 57.5 59.3 -1.9 0.712

Single filer with dependent children 61.7 60.4 1.3 0.718

SOURCES: MDRC calculations from MDRC baseline data, VITA survey data, and responses to the SaveUSA

18-Month and 42-Month Follow-Up Surveys. 

NOTES: Indicators of respondent's highest educational credential were recorded from responses to the 18-Month 

and 42-Month Follow-Up Surveys for respondents with missing data at baseline.

The table excludes study participants with no high school diploma or GED certificate from comparisons of 

impacts by level of education and excludes joint tax filers from comparisons of impact by tax filing status 

because of insufficient sample sizes for these subgroups.

Sample sizes for subgroups are as follows: New York City = 725; Tulsa = 511; age 18-34 = 521; age 35-64 = 

715; AGI $1 - $9,999 = 349; AGI $10,000 - $19,000 = 447; AGI $20,000 or higher = 439; high school diploma 

or GED certificate = 736; postsecondary degree or some college = 341; single filer, no dependent children = 

395; single filer with dependent children = 713.

The sample includes respondents from New York City and Tulsa who were ages 18 to 64 at their time of 

random assignment.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

Sample sizes for specific outcomes may vary because of missing values.

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

The H-statistic is used to assess whether the difference in impacts between cities or subgroups is statistically 

significant. Statistical significance levels are indicated as follows: † = 10 percent; †† = 5 percent; ††† = 1 

percent.
98

The interim (18-month) results had shown differences in impacts for the subgroups 

based on age: SaveUSA significantly increased the percentage of sample members with nonretirement savings among those age 35 or older, but it did not have this effect for those younger 

than 35. This result is no longer apparent in the longer-term follow-up. As shown in Table 5.3, 

SaveUSA had similar effects on nonretirement savings for those in different age brackets. 

Similar to previous results, SaveUSA did not produce positive or negative effects for 

any subgroups on any measures of financial security, including debt and material well-being.

Conclusion

This chapter examined SaveUSA’s participation results and longer-term effects for key subgroups defined by city, age, income, educational attainment, and tax filing status at the time of 

study entry. The results show that subgroups with the highest income, who appeared less reliant 

on their tax refund as a source of income, were more likely to receive a savings match. However, subgroups with the highest rates of receiving the savings match did not experience greater 

impacts on savings amounts or financial security than did other subgroups. In other words, even 

for subgroups with higher savings match rates, statistically significant impacts on financial security did not materialize. Overall, SaveUSA’s effects on nonretirement savings as of the 42-

month follow-up point continued (relative to the 18-month follow-up point) to be consistent 

across a range of subgroups. Furthermore, SaveUSA did not produce effects on measures of 

financial security or financial hardship for any subgroups. 
99

Chapter 6

Conclusion

The SaveUSA program encouraged low- and moderate-income tax filers to deposit a portion 

of their tax refund into a matched savings account. SaveUSA was designed to take advantage 

of once-a-year, relatively large tax refunds as a source for savings. Since low- and moderateincome families often lack access to financial products, the SaveUSA program provided access 

to a special account with features that encouraged saving, such as no minimum balance fees or 

ATM cards. The main goal of SaveUSA was to increase families’ short-term unrestricted savings, which could then potentially be used to manage emergencies and strengthen their overall 

finances.1

 

SaveUSA was designed as a possible precursor to a federal tax-time savings incentive 

for low- and moderate-income households. The program embodies one of several strategies that 

policymakers think might increase nonretirement savings, and it is best evaluated as one candidate for inclusion among a potential “toolkit” of types of savings programs — each with its own

appeal and possibly leading to benefits for different segments of the low- and moderate-income 

population. 

Results from this evaluation show that SaveUSA was successfully implemented over 

several years at Volunteer Income Tax Assistance (VITA) sites in a variety of settings, and also 

that the program maintained the active support of a number of financial institutions. During 

2011, the interest in SaveUSA on the part of eligible tax filers (about 1 in 10 enrolled in the 

study) was comparable to interest shown in other tax-time savings programs. Enrollment patterns demonstrated that many low- and moderate-income individuals and families are willing to 

try different savings strategies given the right opportunities. SaveUSA mostly appealed to the 

tax filers who were in a somewhat better position to save or to put aside tax dollars for an extended period of time. Many were already using tax refunds as a source of short-term savings.

Those with especially low incomes were least able to take advantage of SaveUSA — they were 

least likely to enroll in the study and, if enrolled, to receive a savings match or to take advantage 

of the program for more than one year. As other studies found, individuals had already made 

plans for spending their refund.2 According to the SaveUSA survey responses, many individuals who were eligible to take advantage of the program, but did not, reported that they could not 

afford to set aside money — even as little as $200 in savings for a year — because they needed 

their refunds to pay bills or pay off debts. 

 1

City of New York (2012). 2

Beverly, Schneider, and Tufano (2006).
100

About two-thirds of SaveUSA group members received at least one savings match, a 

notable achievement. Yet the program experienced sustained involvement by less than a quarter 

of SaveUSA group members, disproportionately those with higher incomes (between $20,000 

and $50,000) at the start of the study. Different types of savings interventions likely engage different groups of people, reflecting the fact that people save in a variety of ways. Programs that 

use lotteries, prizes, or carefully targeted marketing as savings incentives, for example, likely 

appeal to certain types of savers as well. From the standpoint of assembling a toolkit of savings 

choices for low- and moderate-income households, the SaveUSA model should be considered 

as a viable option.

The impact results show that opening a SaveUSA account had positive behavioral effects. SaveUSA increased nonretirement savings without increasing debt, and it engendered

greater longer-term support for having a savings goal. On average, SaveUSA group members 

demonstrated a stronger commitment to save, using a variety of savings strategies. Tax refund 

dollars and the SaveUSA savings match provided opportunities to save, but SaveUSA group 

members made use of other savings products, such as personal savings and checking accounts, 

as well. Moreover, the impacts on nonretirement savings at 42 months after random assignment 

showed that SaveUSA could sustain savings increases above the level for members of the 

Regular Tax Filers group even after SaveUSA group members no longer had access to a 50 percent match. Finally, given that SaveUSA provided an average of $365 in match money over the 

42-month follow-up period, the program’s impact on total nonretirement savings of $522 per 

study participant shows that the program produced a savings effect that went beyond the worth 

of the matches. 

A primary reason why low- and moderate-income households with a certain level of 

savings can experience greater financial security is that household members can use savings or 

current income to pay for normal or unexpected expenses. And in fact, at 42 months of followup, SaveUSA group members were more likely than members of the Regular Tax Filers group 

to report having the equivalent of cash on hand to pay expenses for at least one month. 

SaveUSA also led to an increase in the proportion of survey respondents who reported a preference for relying on savings or current income to pay for emergency or unexpected expenses, as 

opposed to increasing their debt. Furthermore, as a result of SaveUSA’s impact on nonretirement savings, the program led to a reduction of 6 percentage points in the incidence of liquidasset poverty (defined as having insufficient liquid assets to subsist at the poverty level for three 

months in the absence of income). These findings suggest that SaveUSA produced some modest gains in financial security.

The longer-term findings from the SaveUSA evaluation, while showing that the program did increase savings, also demonstrate the limitations of programs that focus solely on 

increasing savings. SaveUSA did not lead to better outcomes on other measures of financial 
101

security. Three and a half years after random assignment, members of the SaveUSA group did 

not report better outcomes than Regular Tax Filers group members on a series of indicators of 

financial security, including accumulated levels of debt, liquid net worth, use of high-cost credit, and incidence of experiencing financial hardship, such as being unable to pay rent or a utility 

bill. Thus the program did not help individuals avoid financial situations that can lead to greater 

financial hardships. It could be that the SaveUSA-produced savings increase of $522, while of a 

magnitude associated in past studies with increased financial security, was not enough to markedly improve the financial situation of the significant portion of study sample members who 

already had some savings at study entry. Possibly, SaveUSA could have had greater positive 

effects on financial security if it could have been exclusively targeted to individuals who did not 

have any savings when they were offered SaveUSA. However, individuals with very low incomes, who probably had little or no savings, were the least likely to participate in SaveUSA 

and receive savings matches.

It should also be noted that the financial situations of sample members as of the 42-

month follow-up point were very precarious — with average non-housing-related debt of 

around $10,000, well in excess of their accumulated savings, and an average household income 

of less than $2,000 per month (including public assistance). In order to improve their financial 

security, savings increases may need to be much larger, or other interventions may need to be 

tried — alone or coupled with SaveUSA-like programs. For example, additional rigorous research could be conducted on programs that not only provide savings incentives but also seek to 

increase income through skills training or other means, or provide services such as financial 

coaching on debt reduction or financial management strategies. Research on savings interventions is still at an early stage, and the findings from the SaveUSA evaluation represent just a 

single piece of the puzzle.

Appendix A 

Selected Characteristics of Study Sample Members 

and SaveUSA-Eligible Nonparticipants

105

(continued)

The SaveUSA Evaluation

Appendix Table A.1

Selected Baseline Characteristics of Sample Members Enrolled in 2011, by City

Characteristic New York City Tulsa Newark San Antonio All Cities

Demographic characteristic

Average age (years) 39 39 38 43 39

Age (%)

18-24 15.0 15.3 17.8 8.1 14.2

25-34 26.9 26.1 28.1 19.1 25.5

35-44 22.5 20.9 21.6 26.1 22.6

45-59 32.5 32.2 29.2 38.5 33.0

60-64 3.1 5.5 3.2 8.1 4.7

Gender (%)

Male 24.7 29.0 29.2 NA 26.9

Female 75.3 71.0 70.8 NA 73.1

Number of childrena

 (%)

0 39.0 35.4 39.1 NA 37.8

1 33.4 32.5 30.3 NA 32.6

2 19.9 21.9 22.2 NA 21.0

3 or more 7.7 10.1 8.4 NA 8.7

Race/ethnicity (%)

Hispanic/Latino 39.8 9.2 31.6 NA 27.8

White 3.2 44.5 3.9 NA 17.7

Black/African-American 49.9 37.1 54.9 NA 46.3

Other 7.0 9.2 9.5 NA 8.2

Highest educational credential (%)

GED certificate 5.1 3.4 NA NA 3.7

High school diploma 51.3 62.4 NA NA 59.6

Technical credential or associate's degree 10.7 26.1 NA NA 14.3

4-year college degree or higher 15.8 2.2 NA NA 10.8

None of the above 17.2 5.9 NA NA 11.6

Tax filing information

Number of tax filers (%)

1 92.4 84.1 92.4 80.6 88.0

2 7.6 15.9 7.6 19.4 12.0

Tax filing status (%)

Single filer without children 36.6 30.2 32.7 24.2 32.0

Single filer with children 55.9 53.9 59.6 56.5 56.0

Joint filer without children 0.8 3.2 1.2 3.1 1.9

Joint filer with children 6.8 12.7 6.4 16.3 10.1
106

Appendix Table A.1 (continued)

Characteristic New York City Tulsa Newark San Antonio All Cities

Average adjusted gross income ($)

Adjusted gross income amount (%)

$0 - $9,999

$10,000 - $19,999

$20,000 or more

Average total tax refund amount ($)

Average federal tax refund ($)

Average state and city tax refundb

 ($)

Received federal Earned Income

Tax Credit (EITC) (%)

Among those who received the

EITC, average amountc

 ($)

Month of study entry (%)

January 2011

February 2011

March 2011

April 2011

16,353

30.6

39.2

30.3

4,198

3,076

1,122

68.9

2,162

7.3

40.5

31.5

20.8

18,480

26.3

32.5

41.2

3,648

3,291

357

72.2

2,182

16.9

35.7

33.4

13.9

18,659

22.2

36.5

41.2

3,862

3,432

429

67.3

2,231

12.3

37.7

36.3

13.7

20,504

18.4

32.8

48.8

3,637

3,637

0

67.5

2,548

7.2

40.4

30.9

21.5

18,029

26.0

35.8

38.3

3,894

3,288

606

69.3

2,245

10.7

38.7

32.6

18.0

Sample size 922 655 342 418 2,337

SOURCES: MDRC calculations from MDRC baseline data, VITA survey data, 2010 tax return records, and 

responses to the SaveUSA 18-Month and 42-Month Follow-Up Surveys. 

NOTES: Indicators of respondent's gender, number of children, race/ethnicity, and highest educational 

credential were recorded from responses to the 18-Month and 42-Month Follow-Up Surveys for respondents 

with missing data at baseline. The survey responses for these indicators are available only for participants 

from New York City and Tulsa. 

The sample includes SaveUSA group members who were ages 18 to 64 at their time of study entry.

Demographic characteristics data were collected for one tax filer when couples filed jointly.

Rounding may cause slight discrepancies in calculating sums.

Sample sizes for specific measures may vary because of missing values.

No statistical significance tests were performed on differences across the cities.

"NA" refers to data that are not available.

aThis refers to the number of dependents claimed in 2010 tax return records.

bOnly New York City has a city income tax. Texas does not have a state income tax. 

cThe maximum possible Earned Income Tax Credit was $5,666 in tax year 2010.
107

The SaveUSA Evaluation

Appendix Table A.2

Selected Baseline Characteristics, by Year of Program Enrollment

Characteristic 2011 2012 2013 All Years

Demographic characteristic

Average age (years) 41 41 41 41

Age (%)

18-24 13.4 12.7 14.3 13.4

25-34 23.9 23.5 22.8 23.4

35-44 21.2 23.0 21.3 21.9

45-59 31.0 30.9 30.7 30.9

60-64 10.5 9.9 10.9 10.4

Tax filing information

Number of tax filers (%)

1 87.1 87.4 86.5 87.0

2 12.9 12.6 13.5 13.0

Tax filing status (%)

Single filer without children 33.6 22.9 29.2 28.4

Single filer with children 53.5 64.5 57.3 58.6

Joint filer without children 2.8 2.1 1.7 2.2

Joint filer with children 10.2 10.5 11.8 10.8

Average adjusted gross income ($) 17,929 18,364 18,258 18,186

Adjusted gross income amount (%)

$0 - $4,999 7.5 5.5 7.4 6.7

$5,000 - $9,999 18.3 18.0 17.4 17.9

$10,000 - $14,999 19.1 19.3 19.5 19.3

$15,000 - $19,999 17.3 18.9 17.6 18.0

$20,000 - $24,999 15.0 15.3 14.0 14.8

$25,000 - $29,999 9.5 8.6 8.8 9.0

$30,000 - $39,999 9.7 10.6 11.2 10.5

$40,000 - $49,999 3.5 3.7 4.1 3.7

$50,000 or higher 0.2 0.2 0.0 0.1

Average total tax refund amount ($) 3,763 4,354 3,950 4,031

Total tax refund (%)

$1 - $499 4.2 4.2 5.5 4.6

$500 - $999 14.3 13.2 13.8 13.8

$1,000 - $1,499 17.3 11.7 15.1 14.7

$1,500 - $1,999 7.8 7.2 7.8 7.6

$2,000 - $2,999 11.1 9.6 9.9 10.2

$3,000 - $3,999 9.1 10.2 7.8 9.0

$4,000 - $4,999 11.8 12.0 10.6 11.5

$5,000 - $7,499 21.2 27.6 25.6 24.8

$7,500 or more 3.2 4.4 3.8 3.8

(continued)
108

Appendix Table A.2 (continued)

Characteristic 2011 2012 2013 All Years

Average federal tax refund ($)

Average state and city tax refunda

 ($)

Received federal Earned Income

Tax Credit (EITC) (%)

Among those who received the EITC, average

amountb ($)

Federal Earned Income Tax Credit amount (%)

$0

$1 - $499

$500 - $999

$1,000 - $1,499

$1,500 - $1,999

$2,000 - $2,999

$3,000 - $3,999

$4,000 - $4,999

b $5,000 or more

3,181

582

66.3

1,485

33.7

15.4

3.8

4.6

6.2

13.3

12.8

5.3

4.8

3,589

766

71.8

1,891

28.2

10.7

3.7

4.6

5.5

14.9

16.6

9.3

6.5

3,253

697

68.6

1,694

31.4

12.6

3.5

5.2

6.0

12.9

16.0

6.2

6.3

3,347

683

69.0

1,694

31.0

12.9

3.7

4.8

5.9

13.7

15.1

7.0

5.9

Sample size 2,489 2,644 2,365 7,498

SOURCE: MDRC calculations from MDRC baseline data, tax data, and VITA survey data. 

NOTES: All four cities' combined tax filing information and refund amounts refer to 2010, 2011, 

and 2012, the tax years prior to study enrollment.

Demographic characteristics data were collected for one tax filer when couples filed jointly.

Rounding may cause slight discrepancies in calculating sums.

Sample sizes for specific measures may vary because of missing values.

No statistical significance tests were performed on differences across the years.

a

Only New York City has a city income tax. Texas does not have a state income tax. 

bThe maximum possible federal Earned Income Tax Credit was $5,666 in tax year 2010, $5,751 

in tax year 2011, and $5,891 in tax year 2012.
109

(continued)

The SaveUSA Evaluation

Appendix Table A.3

Selected Baseline Characteristics, by Age Group,

New York City and Tulsa Only

Characteristic Ages 18-64 Age 65+ All Ages

Demographic characteristic

Gender (%)

Male 26.5 30.1 26.7

Female 73.5 69.9 73.3

Number of childrena

 (%) ***

0 37.5 88.2 40.1

1 33.1 7.1 31.7

2 20.7 2.4 19.8

3 or more 8.7 2.4 8.4

Race/ethnicity (%) ***

Hispanic/Latino 27.1 19.5 26.7

White 20.4 45.1 21.6

Black/African-American 44.6 26.8 43.7

Other 7.9 8.5 7.9

Highest educational credential (%)

GED certificate 4.4 4.9 4.4

High school diploma 55.9 57.3 56.0

Technical credential or associate's degree 17.1 20.7 17.3

4-year college degree or higher 10.1 3.7 9.8

None of the above 12.5 13.4 12.5

Tax filing information

Number of tax filers (%) **

1 89.0 81.4 88.6

2 11.0 18.6 11.4

Tax filing status (%) ***

Single filer without children 33.9 68.6 35.7

Single filer with children 55.0 12.8 52.9

Joint filer without children 1.8 14.0 2.4

Joint filer with children 9.3 4.7 9.0

Average adjusted gross income ($) 17,237 14,382 17,089 ***

Adjusted gross income amount (%)

$0 - $9,999 28.8 31.4 28.9

$10,000 - $19,999 36.4 44.2 36.8

$20,000 or more 34.8 24.4 34.3
110

Appendix Table A.3 (continued)

Characteristic Ages 18-64 Age 65+ All Ages

Average total tax refund amount ($) 3,970 1,601 3,847 ***

Average federal tax refund ($) 3,165 1,271 3,067 ***

Average state and city tax refundb ($) 804 330 780 ***

Received federal Earned Income Tax

Credit (EITC) (%) 70.3 17.4 67.5 ***

Among those who received the EITC, average

amountc

($) 2,170 1,622 2,163

Month of random assignment (%) ***

January 2011 11.3 1.2 10.8

February 2011 38.5 18.6 37.5

March 2011 32.3 64.0 33.9

April 2011 17.9 16.3 17.9

Sample size 1,577 86 1,663

SOURCES: MDRC calculations from MDRC baseline data, VITA survey data, 2010 tax return 

records, and responses to the SaveUSA 18-Month and 42-Month Follow-Up Surveys. 

NOTES: Indicators of respondent's gender, number of children, race/ethnicity, and highest 

educational credential were recorded from responses to the 18-Month and 42-Month Follow-Up 

Surveys for respondents with missing data at baseline.

Demographic characteristics data were collected for one tax filer when couples filed jointly.

Rounding may cause slight discrepancies in calculating sums.

Sample sizes for specific measures may vary because of missing values.

A chi-square test for categorical variables and a t-test for continuous variables were run to 

determine whether there is a difference in the distribution of the characteristics by age group. 

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 

percent. a

This refers to the number of dependents claimed in 2010 tax return records.

bOnly New York City has a city income tax.

cThe maximum possible Earned Income Tax Credit was $5,666 in tax year 2010.
111

(continued)

The SaveUSA Evaluation

Appendix Table A.4

Selected Baseline Characteristics of 2011 Sample Members,

by Research Group, New York City and Tulsa Only 

SaveUSA Regular

Characteristic Group Tax Filers All

Demographic characteristic

Average age (years) 40 41 41

Age (%) **

18-24 16.5 12.1 14.3

25-34 25.4 25.0 25.2

35-44 19.5 21.9 20.7

45-59 29.5 32.0 30.7

60 or older 9.2 9.0 9.1

Gender (%)

Male 26.4 26.9 26.7

Female 73.6 73.1 73.3

Number of childrena

 (%) ***

0 42.6 37.6 40.1

1 32.6 30.9 31.7

2 18.3 21.3 19.8

3 or more 6.6 10.2 8.4

Race/ethnicity (%)

Hispanic/Latino 28.2 25.2 26.7

White 22.2 21.0 21.6

Black/African-American 42.2 45.2 43.7

Other 7.4 8.5 7.9

Highest educational credential (%)

GED certificate 4.3 4.5 4.4

High school diploma 55.9 56.2 56.0

Technical credential or associate's degree 17.5 17.1 17.3

4-year college degree or higher 10.5 9.1 9.8

None of the above 11.8 13.2 12.5

Tax filing information

Number of tax filers (%)

1 89.3 87.9 88.6

2 10.7 12.1 11.4
112

Appendix Table A.4 (continued)

Characteristic

SaveUSA

Group

Regular

Tax Filers All

Tax filing status (%)

Single filer without children

Single filer with children

Joint filer without children

Joint filer with children

Average adjusted gross income ($)

Adjusted gross income amount (%)

$0 - $9,999

$10,000 - $19,999

$20,000 or more

Average total tax refund amount ($)

Average federal tax refund ($)

Average state and city tax refundb ($)

Received federal Earned Income Tax

Credit (EITC) (%)

Among those who received the EITC, average

amountc($)

Month of random assignment (%)

January 2011

February 2011

March 2011

April 2011

38.5

50.7

2.4

8.4

16,911

30.2

36.0

33.8

3,620

2,899

721

66.0

2,050

10.6

37.7

33.8

17.9

32.8

55.0

2.4

9.7

17,270

27.6

37.6

34.8

4,077

3,237

840

69.1

2,272

10.9

37.2

34.1

17.8

35.7

52.9

2.4

9.0

17,089

28.9

36.8

34.3

3,847 ***

3,067 ***

780 ***

67.5

2,163

10.8

37.5

33.9

17.9

Sample size 838 825 1,663

SOURCES: MDRC calculations from MDRC baseline data, VITA survey data, 2010 tax 

return records, and responses to the SaveUSA18-Month and 42-Month Follow-Up Surveys. 

NOTES: Indicators of respondent's gender, number of children, race/ethnicity, and highest 

educational credential were recorded from responses to the 18-Month and 42-Month FollowUp Surveys for respondents with missing data at baseline.

Demographic characteristics data were collected for one tax filer when couples filed 

jointly.

Rounding may cause slight discrepancies in calculating sums.

Sample sizes for specific measures may vary because of missing values.

A chi-square test for categorical variables and a t-test for continuous variables were run to 

determine whether there is a difference in the distribution of the characteristics by research 

group. Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** 

= 1 percent. a

This refers to the number of dependents claimed in 2010 tax return records.

bOnly New York City has a city income tax. 

c

The maximum possible Earned Income Tax Credit was $5,666 in tax year 2010.
113

(continued)

The SaveUSA Evaluation

Appendix Table A.5

Selected Characteristics of SaveUSA-Eligible Individuals Who Declined to

Participate in the Study and of Sample Members Who Enrolled in the Study

SaveUSA-Eligible

and Declined to SaveUSA Study

Characteristic Participate Sample P-Value

a Demographic characteristic (%)

b Gender *** 0.000

Male 39.8 27.4

Female 60.2 72.6

Age *** 0.000

18-24 14.3 14.8

25-44c 40.6 46.2

45 or older 45.0 39.0

Highest educational attainmentb *** 0.000

Less than 2-year college degree 78.4 72.4

2-year college degree or higher 21.6 27.6

Tax filing information

Number of tax filers in 2010 (%) *** 0.003

1 85.3 87.8

2 14.7 12.2

Tax filing status (%) *** 0.000

Single filer without children 47.3 35.9

Single filer with children 38.0 52.1

Joint filer without children 4.7 3.2

Joint filer with children 10.0 8.9

Average adjusted gross income ($) 16,784 17,903 *** 0.001

Adjusted gross income amount (%) *** 0.000

$1 - $4,999 13.7 7.9

$5,000 - $9,999 19.4 20.1

$10,000 - $14,999 19.8 20.2

$15,000 - $19,999 16.3 17.2

$20,000 - $24,999 12.7 14.3

$25,000 - $29,999 6.5 8.7

$30,000 - $39,999 8.3 8.9

$40,000 - $49,999 3.4 2.8
114

Appendix Table A.5 (continued)

Characteristic

SaveUSA-Eligible

and Declined to

Participate

SaveUSA Study

Sample P-Value

Total federal refund amount (%)

$1 - $499

$500 - $999

$1,000 - $1,499

$1,500 - $1,999

$2,000 - $2,999

$3,000 - $3,999

$4,000 - $4,999

$5,000 - $7,499

$7,500 - $9,999

$10,000 or more

Received federal Earned Income Tax

Credit (EITC) (%)

Federal Earned Income Tax Credit amountd (%)

$1 - $499

$500 - $999

$1,000 - $1,499

$1,500 - $1,999

$2,000 - $2,999

$3,000 - $3,999

$4,000 - $4,999

$5,000 - $7,499

14.9

22.6

15.9

6.3

8.3

6.8

7.4

12.0

4.6

1.3

54.6

32.3

6.9

7.9

8.4

17.1

15.0

7.2

5.1

***

6.6

16.3

15.1

6.0

11.3

10.5

11.3

17.5

5.4

0.1

68.3 ***

***

23.3

6.9

6.2

9.9

22.5

17.9

7.7

5.7

0.000

0.000

0.000

Sample size 19,094 2,021

SOURCES: MDRC calculations from MDRC baseline data, VITA survey data, and TaxWise data. 

NOTES: Data were collected for New York City, Tulsa, and Newark but were unavailable for San Antonio.

Rounding may cause slight discrepancies in calculating sums.

Sample sizes for specific measures may vary because of missing values.

A chi-square test for categorical variables and a t-test for continuous variables were run to determine 

whether there is a difference in the distribution of the characteristics by study participation. Statistical 

significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent. aDemographic characteristics data were collected for one tax filer when couples filed jointly. bGender and educational attainment are not available for Newark.

c

The age category includes 24-year-olds from Tulsa.

dEarned Income Tax Credit information is not available for New York City study-eligible individuals.
Appendix B 

SaveUSA 42-Month Survey Response Analysis

117

Most estimates of SaveUSA’s effects on financial outcomes were calculated using responses to 

the SaveUSA 42-Month Follow-Up Survey. The report also shows impact estimates for a group 

of sample members who completed both the 18-month and 42-month surveys. When only a 

subset of the sample completes a survey, potential issues can arise about the reliability of results 

estimated for survey respondents and, also, whether results for respondents can be generalized 

to all study participants. 

This appendix summarizes the results of tests of the reliability and generalizability of 

impact estimates calculated with survey responses. First, the appendix assesses whether research group differences in financial outcomes are unbiased (and therefore reliable) indicators 

of SaveUSA’s effects. Survey results are considered to be unbiased if a large proportion of each 

research group responded to the survey and if respondents in both research groups closely 

resemble each other in characteristics, such as educational attainment or adjusted gross income, 

that would be likely to affect members’ ability to save, forgo debt, or attain financial security 

after study entry. 

Second, this appendix considers whether impact results estimated for survey respondents may be generalized to all study participants. Survey results are considered to be generalizable if it can be inferred with confidence that the analysis would have reached similar conclusions about SaveUSA’s effects on financial outcomes had every study participant completed a 

42-month survey interview. 

Overall, the results show that the survey is reliable and that results for the survey respondent samples (42-month survey respondents and 18- and 42-month survey respondent 

samples) can be generalized to the larger report sample, which includes nonrespondents. 

Main Findings 

• A high response rate was achieved: About 80 percent of sample members of 

each research group responded to the 42-month survey. 

• Among 42-month survey respondents, characteristics at baseline were similar 

for the two research groups. No systematic differences between the groups 

were found.

• A comparison of survey respondents and nonrespondents shows few statistically significant differences in pre-random assignment characteristics.

• SaveUSA’s impacts on tax return and survey outcomes among respondents 

are similar to the impacts for the report sample and the survey-eligible samples, which include nonrespondents. 
118

Survey Sample Selection

As noted in Chapter 1, the research sample includes about 2,500 sample members who were 

enrolled in 2011 in all four cities. The report sample in this appendix comprises 1,577 sample 

members who were randomly assigned in New York City and Tulsa in 2011 who were 18 to 64 

years of age at the time of random assignment. (Sample members in Newark and San Antonio 

were not included in the survey efforts since those cities did not conduct random assignment.) 

Nearly all study participants from New York City and Tulsa were eligible to respond to the 

SaveUSA 42-Month Follow-Up Survey. A few study participants were excluded because of 

death, incarceration, or lack of fluency in English or Spanish. The remaining study participants 

are referred to as the fielded sample, which totaled 1,547 sample members.1 From May 12, 

2014, through November 2, 2014, the survey firm for the study, Decision Information Resources (DIR), attempted to interview everyone in the fielded sample. 

Survey Response Rates

Sample members who were interviewed for the 42-month survey are referred to as “survey 

respondents” or the respondent sample, while sample members who were not interviewed are 

known as “nonrespondents” or the nonrespondent sample. As Figure 1.2 and Appendix Table 

B.1 show, a total of 1,236 sample members, or almost 80 percent of the fielded sample, completed the survey. The majority of the nonrespondent sample either refused to be interviewed or 

could not be located. The response rates by city were very similar. The response rates by 

research group were also similar: About 80 percent of both the SaveUSA group and the Regular 

Tax Filers group completed the 42-month survey. 

As shown in Appendix Table B.1, the response rate among the 18-month survey sample 

was also 80 percent. About 72 percent of the 42-month fielded sample completed both an 18-

month and a 42-month survey interview. Rates by research group and by city are very similar.

Although the overall response rates are high, whenever the response rate is lower than 

100 percent, nonresponse bias may occur. Differences may exist between the respondent 

sample and the larger, fielded sample, owing to differences between the sample members who 

completed a survey and those who did not. Furthermore, the estimates may be biased if background characteristics differ between the research groups in the respondent sample.

 1

During the fielding periods for the two surveys, Decision Information Resources (DIR) discovered that

30 members of the report sample were ineligible to be interviewed — 11 during the fielding period for the 18-

month survey and an additional 19 during the fielding period for the 42-month survey. Per DIR’s recommendation, MDRC dropped all 30 of these sample members from the fielded sample for the 42-month survey. In 

addition, one sample member withdrew from the study four months after the start of the fielding period for the 

42-month survey. This former sample member has been excluded from all samples discussed in this report.
119

The SaveUSA Evaluation

Appendix Table B.1

SaveUSA 18- and 42-Month Follow-Up Survey Response Rates, 

by City and Research Group

Survey Respondent (%)

SaveUSA

Group

Regular Tax

Filers Total

18-month survey

New York City

Tulsa

80.1

80.6

79.3

80.5

79.5

81.9

80.3

80.0

80.6

Sample size 788 779 1,567

42-month survey

New York City

Tulsa

80.7

80.8

80.4

79.1

78.9

79.5

79.9

79.8

80.0

Sample size 776 771 1,547

18-month and 42-month surveys

New York City

Tulsa

71.1

71.4

70.8

72.1

71.8

72.6

71.6

71.6

71.7

Sample size 776 771 1,547

SOURCES: MDRC calculations from MDRC baseline data and responses to the SaveUSA 

18-Month and 42-Month Follow-Up Surveys. 

NOTES: Rounding may cause slight discrepancies in calculating average response rates.

Chi-square tests were run to determine whether there are differences in the response rates 

by research groups. Statistical significance levels are indicated as: * = 10 percent; ** = 5 

percent; and *** = 1 percent. 

No asterisks are shown for any variable because all differences by research group were 

determined to be above the 10 percent level of statistical significance.

Comparison of Respondents and Nonrespondents Within

the Fielded Survey Sample

In order to examine whether there are systematic differences between those who responded to 

the survey and those who did not, a (0/1) indicator of survey respondent status was created (in 

which survey respondents receive a 1 and nonrespondents receive a 0), and then logistic 
120

regression analysis was used to identify whether any pre-random assignment characteristics 

were significantly related to the indicator.

Appendix Table B.2 shows the estimated regression coefficients for the probability of 

being a respondent. As can be noted from this table, besides background characteristics such as 

race, age, and number of children, a (0/1) indicator of membership in the SaveUSA group was 

included in the model. This procedure tests for differences in characteristics likely to affect 

financial outcomes. The second column of the table provides the parameter estimates that 

indicate the effect of each variable on the probability of completing the survey. The p-values 

show the level of statistical significance of this relationship.

The results show that there were a few characteristics that were statistically significant 

in predicting whether or not someone would complete a 42-month interview. Members of the 

fielded sample with larger tax refunds were less likely to respond than those who received 

smaller refunds.2 Younger adults were more likely to respond to the survey.3 Fielded sample 

members who are black or female or who received the Earned Income Tax Credit (EITC) also 

had a greater likelihood of responding to the survey. Finally, fielded sample members randomly 

assigned earlier in 2011 were more likely to respond than those from the later months of 

random assignment. 

The p-values for the entire model displayed at the bottom of Table B.2 show that the 

differences in sample member characteristics between the survey respondents and the survey 

nonrespondents are statistically significant. Nonetheless, the R-square value (a summary 

indicator of the predictive power of the effects) of 0.029 is very low, which suggests that sample 

member characteristics have a very small effect on the likelihood of responding to the 42-month 

survey. Furthermore, results from this test show that membership in the SaveUSA group did not 

predict whether someone would complete the survey. This finding implies that the survey 

results are unbiased.

The same analysis described above was performed to examine whether there were systematic differences between those who responded to both survey waves and those who did not 

(not shown). Except for gender and month of random assignment, the results show no statistically significant characteristics in predicting whether someone would complete both the 18- and 

42-month surveys. 

 2

Not shown because of rounding in Appendix Table B.2, the value of the coefficient associated with each 

additional dollar of tax refund is -0.00007. 3

The reference group for comparisons by age includes sample members ages 18 to 24.
121

The SaveUSA Evaluation

Appendix Table B.2

Estimated Regression Coefficients for the Probability of Being a 

Respondent to the SaveUSA 42-Month Survey

Variable

Baseline measure

Month of random assignment

Capital One Bank

Ariva Tax Assistance Center

Carver Financial Literacy Center

St. Mark's AME Church

Fordham Road

Pine and Lewis

Exchange Center

Assigned to SaveUSA Group

Female

Hispanic

Black

Age 25-34

Age 35-44

Age 45-59

Age 60 or older

No educational degree

AA/BA/BS/Grad school degree

Number of children

Adjusted gross income

Adjusted gross income, squared

Received federal Earned Income Tax Credit

Total tax refund amount (sum of federal, state, and city)

Likelihood ratio

Wald statistic

R-square (.0294)

Sample size

Fielded Sample

Parameter

Estimate P-Value

-0.142 0.065

-0.104 0.548

-0.011 0.947

-0.073 0.651

0.182 0.412

0.207 0.468

-0.130 0.414

-0.158 0.353

0.096 0.464

0.551 0.000

-0.161 0.429

0.322 0.073

-0.573 0.013

-0.559 0.026

-0.487 0.034

-0.317 0.396

-0.094 0.651

0.070 0.652

0.164 0.142

0.000 0.782

0.000 0.907

0.420 0.016

0.000 0.080

46.2 0.003

44.5 0.005

1,547

SOURCES: MDRC calculations from MDRC baseline data, VITA survey data, 2010 

tax return records, and responses to the SaveUSA 18-Month and 42-Month Follow-Up 

Surveys. 

NOTE: Capital One Bank, Ariva Tax Assistance Center, Carver Financial Literacy 

Center, St. Mark's AME Church, Fordham Road, Pine and Lewis, and Exchange 

Center are SaveUSA sites.
122

Comparison of the Research Groups in the Survey

Respondent Sample

Random assignment designs minimize the possibility of potential biases in the results. Although 

the response rates were similarly high in both research groups, there is still the possibility that 

different types of sample members within each research group responded to the survey. If so, 

the impact estimates for the respondent sample may be biased.

Appendix Table B.3 shows baseline characteristics of the SaveUSA and Regular Tax 

Filers group members among the respondent sample. The differences between the groups are 

relatively small and few are statistically significant. In addition, a logistic regression analysis 

was performed to further test for associations between sample member characteristics and 

research group membership. A (0/1) indicator of membership in the SaveUSA group was 

regressed on pre-random assignment characteristics. As shown in Appendix Table B.4, only 

one baseline characteristic, random assignment at the St. Mark’s AME Church Volunteer 

Income Tax Assistance (VITA) site in Queens, New York, was found to be significantly 

related to research group membership. These results support the finding that survey results are 

unbiased.

Comparison of Survey Respondents with the Report Sample

Using administrative records data from tax year 2010 federal tax returns, SaveUSA account 

participation data, 18-month survey data, and 42-month survey data, this section discusses 

whether the survey respondents’ impacts can be generalized to the report sample.

Appendix Table B.5 shows SaveUSA account participation results for SaveUSA group 

members in the fielded sample and in two different respondent samples: the 42-month respondent sample and the 18- and 42-month survey respondent sample. As shown, the participation 

results are very similar across the samples on different measures. 

A second test of generalizability uses data on tax returns that sample members completed immediately following random assignment. For this test, estimates of SaveUSA’s impacts on 

allocation of tax refund dollars to savings and other financial options were compared across the 

42-month fielded sample and the respondent samples. Appendix Table B.6 shows that the 

impacts were very similar across samples. The direction, significance, and impact levels are 

almost identical. 

For a third test of generalizability, Appendix Table B.7 compares impacts on a series of 

financial outcomes measured at 18 months for all members of the 18-month survey respondent 

sample and for the sample members who completed both the 18-month and the 42-month
123

The SaveUSA Evaluation

Appendix Table B.3

Selected Baseline Characteristics of 42-Month Survey Respondents,

by Research Group

SaveUSA Regular

Characteristic Group Tax Filers All

Demographic characteristic

Average age (years) 38 39 39

Age (%) *

18-24 17.7 13.9 15.9

25-34 27.0 25.6 26.3

35-44 20.1 23.4 21.8

45-59 31.0 33.4 32.2

60-64 4.2 3.6 3.9

Gender (%)

Male 23.5 24.3 23.9

Female 76.5 75.7 76.1

Number of childrena

 (%) **

0 38.0 32.8 35.4

1 34.8 32.8 33.8

2 19.6 23.9 21.8

3 or more 7.5 10.5 9.0

Race/ethnicity (%)

Hispanic/Latino 27.5 24.5 26.0

White 20.7 19.7 20.2

Black/African-American 45.7 47.5 46.6

Other 6.1 8.2 7.2

Highest educational credential (%)

GED certificate 4.5 5.3 4.9

High school diploma 54.7 55.2 54.9

Technical credential or associate's degree 17.8 16.1 17.0

4-year college degree or higher 11.1 10.3 10.7

None of the above 11.9 13.1 12.5

(continued)
124

Appendix Table B.3 (continued)

Characteristic

SaveUSA

Group

Regular

Tax Filers All

Tax filing information

Number of tax filers (%)

1

2

Tax filing status (%)

Single filer without children

Single filer with children

Joint filer without children

Joint filer with children

Average adjusted gross income ($)

Adjusted gross income amount (%)

$1 - $9,999

$10,000 - $19,999

$20,000 or more

Average total tax refund amount ($)

Average federal tax refund ($)

Average state and city tax refundb ($)

Received federal Earned Income Tax

Credit (EITC) (%)

Among those who received the EITC, average

amountc($)

Month of random assignment (%)

January 2011

February 2011

March 2011

April 2011

90.6

9.4

35.1

55.4

1.4

8.0

17,373

28.6

35.5

35.8

3,837

3,075

762

69.3

2,078

12.1

39.6

30.7

17.6

88.7

11.3

28.7

60.0

1.6

9.7

17,422

27.9

36.9

35.2

4,235

3,378

857

73.9

2,264

11.8

38.5

32.6

17.0

89.6

10.4

32.0

57.7

1.5

8.8

17,397

28.3

36.2

35.5

4,033 **

3,224 **

809 **

71.6 *

2,173

12.0

39.1

31.6

17.3

Sample size 626 610 1,236

SOURCES: MDRC calculations from MDRC baseline data, VITA survey data, 2010 tax 

return records, and responses to the SaveUSA 18-Month and 42-Month Follow-Up Surveys. 

NOTES: Indicators of respondent's gender, number of children, race/ethnicity, and highest 

educational credential were recorded from responses to the 18-Month and 42-Month FollowUp Surveys for respondents with missing data at baseline.

Demographic characteristics data were collected for one tax filer when couples filed 

jointly.

Rounding may cause slight discrepancies in calculating sums.

Sample sizes for specific measures may vary because of missing values.

A chi-square test for categorical variables and a t-test for continuous variables were run to 

determine whether there is a difference in the distribution of the characteristics by research 

group. Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** 

= 1 percent. aThis refers to the number of dependents claimed in 2010 tax return records.

bOnly New York City has a city income tax.

c

The maximum possible Earned Income Tax Credit was $5,666 in tax year 2010.
125

The SaveUSA Evaluation

Appendix Table B.4

 Estimated Regression Coefficients for the Probability of Being a

SaveUSA Group Member, Among 42-Month Survey Respondents

Variable

Baseline measure

Month of random assignment

Capital One Bank

Ariva Tax Assistance Center

Carver Financial Literacy Center

St. Mark's AME Church

Fordham Road

Pine and Lewis

Exchange Center

Female

Hispanic

Black

Age 25-34

Age 35-44

Age 45-59

Age 60 or older

No educational degree

AA/BA/BS/Grad school degree

Number of children

Adjusted gross income

Adjusted gross income, squared

Received federal Earned Income Tax Credit

Total tax refund amount (sum of federal, state, and city)

Likelihood ratio

Wald statistic

R-square (.0262)

Sample size

Respondent Sample

Parameter

Estimate P-Value

-0.053 0.441

-0.137 0.368

-0.011 0.945

-0.087 0.542

-0.505 0.016

0.257 0.308

-0.189 0.185

-0.059 0.698

0.092 0.512

0.292 0.130

0.038 0.808

-0.135 0.494

-0.294 0.167

-0.249 0.197

-0.107 0.750

0.006 0.976

0.146 0.286

-0.141 0.151

0.000 0.758

0.000 0.987

-0.045 0.776

0.000 0.577

32.9 0.064

31.2 0.092

1,236

SOURCES: MDRC calculations from MDRC baseline data, VITA survey data, 2010 

tax return records, and responses to the SaveUSA 18-Month and 42-Month Follow-Up 

Surveys. 

NOTE: Capital One Bank, Ariva Tax Assistance Center, Carver Financial Literacy 

Center, St. Mark's AME Church, Fordham Road, Pine and Lewis, and Exchange Center 

are SaveUSA sites.
126

The SaveUSA Evaluation

Appendix Table B.5 

Comparison of Participation Outcomes Across All Program Years for

the 42-Month Fielded Sample, 42-Month Survey Respondent Sample, 

and 18- and 42-Month Survey Respondent Sample

Outcome All Years

SaveUSA account opened or pledged (%)

42-month fielded sample

42-month survey respondent sample

18- and 42-month survey respondent sample

Received savings match (%)

42-month fielded sample

42-month survey respondent sample

18- and 42-month survey respondent sample

Average amount of savings match ($)

42-month fielded sample

42-month survey respondent sample

18- and 42-month survey respondent sample

96.0

96.0

96.2

60.4

61.7

63.4

338

356

372

Sample size

42-month fielded sample

42-month survey respondent sample

18- and 42-month survey respondent sample

776

626

552

SOURCES: MDRC calculations from financial institution data, and responses to the SaveUSA 

18-Month and 42-Month Follow-Up Surveys.

NOTE: The samples include SaveUSA group members who were ages 18 to 64 at their time of 

random assignment and who were eligible to participate in the 42-Month Follow-Up Survey. 

The fielded sample includes nonrespondents.
127

(continued)

The SaveUSA Evaluation

Appendix Table B.6

Comparison of Impacts on Allocation of 2010 Federal Tax Refund for the

42-Month Fielded Sample, 42-Month Survey Respondent Sample, 

 and 18- and 42-Month Survey Respondent Sample 

SaveUSA Regular Tax Difference

Outcome Group Filers (Impact) P-Value

Deposited money in any bank account (%)

42-month fielded sample 98.3 75.7 22.5 *** 0.000

42-month survey respondent sample 98.4 77.1 21.3 *** 0.000

18- and 42-month survey respondent sample 98.1 78.0 20.1 *** 0.000

Deposited money in savings account (%)

42-month fielded sample 93.2 14.5 78.6 *** 0.000

42-month survey respondent sample 92.9 15.2 77.7 *** 0.000

18- and 42-month survey respondent sample 92.4 14.8 77.6 *** 0.000

Deposited money in checking account (%)

42-month fielded sample 70.8 68.5 2.3 0.300

42-month survey respondent sample 70.6 69.9 0.7 0.782

18- and 42-month survey respondent sample 70.9 70.6 0.4 0.889

Received a tax refund check (%)

42-month fielded sample 18.6 25.1 -6.5 *** 0.001

42-month survey respondent sample 17.9 23.7 -5.8 ** 0.011

18- and 42-month survey respondent sample 17.4 22.8 -5.4 ** 0.021

Total amount deposited in any bank account ($)

42-month fielded sample 3,322 3,062 259 *** 0.004

42-month survey respondent sample 3,378 3,174 204 ** 0.032

18- and 42-month survey respondent sample 3,405 3,223 181 * 0.067

Total amount deposited in savings account ($)

42-month fielded sample 693 227 466 *** 0.000

42-month survey respondent sample 728 258 470 *** 0.000

18- and 42-month survey respondent sample 719 269 449 *** 0.000

Total amount deposited in checking account ($)

42-month fielded sample 2,628 2,835 -207 ** 0.039

42-month survey respondent sample 2,650 2,917 -266 ** 0.014

18- and 42-month survey respondent sample 2,686 2,954 -268 ** 0.019

Total amount received in tax refund check ($)

42-month fielded sample 521 758 -237 *** 0.007

42-month survey respondent sample 490 684 -194 ** 0.035

18- and 42-month survey respondent sample 479 644 -165 * 0.082
128

Appendix Table B.6 (continued)

Outcome

SaveUSA

Group

Regular Tax

Filers

Difference

(Impact) P-Value

Percentage of tax refund deposited in any bank account

42-month fielded sample

42-month survey respondent sample

18- and 42-month survey respondent sample

Percentage of tax refund deposited in savings account

42-month fielded sample

42-month survey respondent sample

18- and 42-month survey respondent sample

Percentage of tax refund deposited in checking account

42-month fielded sample

42-month survey respondent sample

18- and 42-month survey respondent sample

Percentage of tax refund received in refund check

42-month fielded sample

42-month survey respondent sample

18- and 42-month survey respondent sample

85.3

85.7

85.8

28.7

29.0

28.9

56.6

56.7

57.0

14.3

13.8

13.6

75.1

76.4

77.3

8.3

8.4

8.4

66.8

68.0

68.9

24.7

23.3

22.4

10.2 ***

9.3 ***

8.6 ***

20.4 ***

20.6 ***

20.5 ***

-10.1 ***

-11.3 ***

-11.9 ***

-10.4 ***

-9.5 ***

-8.8 ***

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

Sample sizes

42-month fielded sample

42-month survey respondent sample

18- and 42-month survey respondent sample

776

626

552

771

610

556

SOURCES: MDRC calculations from MDRC baseline data, 2010 tax return records, and responses to the 

SaveUSA 18-Month and 42-Month Follow-Up Surveys.

NOTES: The samples include tax filers in New York City and Tulsa who were ages 18 to 64 at their time of 

random assignment and who were eligible to participate in the 42-Month Follow-Up Survey. The fielded sample 

includes nonrespondents.

 A small number of sample members allocated tax refund dollars for the purchase of U.S. savings bonds (not 

shown). Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

ch aracteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.
129

(continued)

The SaveUSA Evaluation

Appendix Table B.7

Comparison of 18-Month Impacts Between the 18-Month Survey Respondent

Sample and the 18- and 42-Month Survey Respondent Sample 

SaveUSA Regular Tax Difference

Outcome Group Filers (Impact) P-Value

Has nonretirement savings (%)

18-month survey respondent sample 79.4 71.9 7.5 *** 0.002

18- and 42-month survey respondent sample 79.8 71.8 8.1 *** 0.002

Total nonretirement savings ($)

18-month survey respondent sample 2,241 1,730 512 * 0.052

18- and 42-month survey respondent sample 2,216 1,753 463 * 0.090

Has retirement savings (%)

18-month survey respondent sample 32.5 28.4 4.0 0.101

18- and 42-month survey respondent sample 33.9 28.6 5.3 ** 0.043

Total retirement savings ($)

18-month survey respondent sample 2,582 3,279 -697 0.146

18- and 42-month survey respondent sample 2,644 3,131 -487 0.317

Has liquid assets (%)

18-month survey respondent sample 81.8 75.3 6.5 *** 0.006

18- and 42-month survey respondent sample 82.5 75.3 7.2 *** 0.003

Total liquid assets ($)

18-month survey respondent sample 4,114 4,265 -151 0.750

18- and 42-month survey respondent sample 4,206 4,331 -126 0.804

Has non-housing-related debt (%)

18-month survey respondent sample 84.2 81.8 2.4 0.249

18- and 42-month survey respondent sample 84.1 82.3 1.8 0.429

Total non-housing-related debt ($)

18-month survey respondent sample 9,695 9,276 419 0.632

18- and 42-month survey respondent sample 9,794 9,363 431 0.649
130

Appendix Table B.7 (continued)

Outcome

SaveUSA

Group

Regular Tax

 Filers

Difference

(Impact) P-Value

Ratio of nonretirement savings to monthly household

expenses

18-month survey respondent sample

18- and 42-month survey respondent sample

2.4

2.5

1.3

1.3

1.1 ***

1.2 **

0.009

0.011

Sample sizes

18-month survey respondent sample

18- and 42-month survey respondent sample

631

552

627

556

SOURCES: MDRC calculations from responses to the SaveUSA 18-Month and 42-Month Follow-Up Surveys.

NOTES: Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to response to adjust for differences in sample 

size by site.

 Rounding may cause slight discrepancies in calculating sums and differences.

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax Filers.

 The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

surveys. As shown, the impact results for the two samples are very similar in magnitude and 

level of statistical significance, which would be expected given the relatively high response rate 

for both surveys. 

MDRC conducted a final test of generalizability for five key outcomes from the 42-

month survey. For this test, MDRC compared the magnitude, direction, and statistical significance of impacts that were estimated from survey responses with a second series of estimates 

that included predicted values for the more than 300 nonrespondents in the 42-month fielded 

sample. Appendix Table B.8 displays the results of these tests. As shown, the magnitude and 

direction of impact estimates are similar in the two panels. The impacts for the fielded sample 

(with predicted values for nonrespondents included) are statistically significant for two additional outcomes, probably because of its larger sample size. Overall, these results provide 

additional evidence that the impacts estimated for survey respondents are generalizable to the 

report sample.
131

The SaveUSA Evaluation

Appendix Table B.8

Impacts for the Respondent Sample and Fielded Sample, Including Predicted Outcomes for Nonrespondents

Respondent Sample Fielded Sample

SaveUSA Regular Tax Difference SaveUSA Regular Tax Difference

Outcome ($) Group Filers (Impact) P-Value Group Filers (Impact) P-Value

Total nonretirement savings 2,296 1,742 554 ** 0.017 2,307 1,759 548 *** 0.000

Total retirement savings 3,347 3,490 -143 0.802 3,425 3,586 -161 0.405

Total liquid assets 5,095 4,391 704 0.198 5,161 4,515 645 *** 0.000

Total non-housing-related debt 10,009 10,023 -14 0.988 9,984 10,091 -106 0.674

Liquid net wortha -4,995 -5,687 692 0.542 -4,923 -5,677 754 *** 0.001

Sample size 626 610 1,236 776 771 1,547

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey.

NOTES: The fielded sample includes sample members ages 18 to 64 from New York City (N = 908) and Tulsa (N = 639) who were eligible to 

participate in the 42-Month Follow-Up Survey. The respondent sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment characteristics of sample members. No 

special weights were applied to responses to adjust for differences in sample size by site.

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

a

"Liquid net worth" is calculated as total nonretirement savings + total retirement savings − total non-housing-related debt. 

Appendix C 

Additional Impact Comparisons

135

The SaveUSA Evaluation

Appendix Table C.1

Comparison of SaveUSA Account Activity, by Data Source

Outcome (%)

SaveUSA

Group

Pledged to participate in 2013

According to neither survey nor financial institution records

According to both survey and financial institution records

According to survey only 

According to financial institution records only

Received savings match in 2014a

According to neither survey nor financial institution records

According to both survey and financial institution records

According to the survey only 

According to financial institution records only

 Savings match amount

$1 - $100

$101 - $499

$500 or more

b Had SaveUSA account balance at the time of interview

According to neither survey nor financial institution records

According to both survey and financial institution records

According to survey only 

According to financial institution data only

Account balance amount

$10 - $100

$101 - $300

$301 - $500

$501 - $999

$1,000 or more

59.1

20.8

4.9

15.2

74.9

12.1

4.0

9.1

0.8

3.4

4.9

72.8

8.8

8.2

10.3

3.4

1.1

1.1

1.3

3.2

Sample size 605

SOURCES: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up 

Survey and financial institution data.

NOTES: aIncludes respondents whose SaveUSA account was at a bank that provided 

MDRC with transaction-level data. 

bSaveUSA accounts were deemed to have a balance if the financial institution data 

showed an amount of $10 or more at the end of the month of the survey interview.
136

The SaveUSA Evaluation

Appendix Table C.2

Comparison of Impacts on Total Nonretirement Savings, 

by Wording of Survey Question

SaveUSA Regular Tax Difference

Survey Question ($) Group Filers (Impact) P-Value

All respondents who were asked to report the total amount

of their nonretirement savings 2,281 1,758 522 ** 0.024

SaveUSA group respondents who later were asked if their

reported total had included the current balance of their

SaveUSA account. The SaveUSA account balance was

added to the total if previously excluded. 2,312 1,758 554 ** 0.016

All respondents who were asked to report their total monthly

household expenses and the number of months that expenses

could be covered by current savings alone 3,423 3,002 421 * 0.082

Sample size (total = 1,236) 626 610

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511) who were ages 

18 to 64 at their time of random assignment.

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.
The SaveUSA Evaluation

Appendix Table C.3

Comparison of Indicators of Savings and Debt, by Level of Exclusion of Outlier Values

137

Number Standard 95th 99th 

Outcome at Interview ($) (N) Mean Deviation Minimum Median Percentile Percentile Maximum

Total nonretirement savings

Including all responses 1,204 2,749 8,591 0 500 10,000 35,000 99,997

Excluding top 1 percent 1,192 2,023 4,016 0 500 9,000 20,000 35,000

Excluding top 5 percent 1,155 1,466 2,239 0 400 7,000 10,000 10,000

Total retirement savings

Including all responses 1,204 4,261 13,205 0 0 25,000 80,000 99,997

Excluding top 1 percent 1,193 3,417 9,890 0 0 20,000 56,301 80,000

Excluding top 5 percent 1,150 1,798 4,413 0 0 10,000 20,000 25,000

Total non-housing-related debt

Including all responses 1,193 11,920 20,922 0 3,000 60,000 99,997 122,767

Excluding top 1 percent 1,168 10,016 16,535 0 2,500 50,000 80,000 98,000

Excluding top 5 percent 1,138 8,292 12,779 0 2,500 40,000 60,000 60,000

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes all respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of exclusion of outliers and missing values.
138

The SaveUSA Evaluation

Appendix Table C.4

Comparison of Impacts on Savings and Debt,

by Level of Exclusion of Outlier Values 

Outcome at Interview ($)

SaveUSA

Group

Regular Tax

Filers

Difference

(Impact) P-Value

Total nonretirement savings

Including all responses

Excluding top 1 percent

Excluding top 5 percent

Total retirement savings

Including all responses

Excluding top 1 percent

Excluding top 5 percent

Total non-housing-related debt

Including all responses

Excluding top 1 percent

Excluding top 5 percent

3,006

2,281

1,692

3,852

3,326

1,785

11,463

9,733

8,117

2,487

1,758

1,236

4,679

3,512

1,812

12,392

10,309

8,473

519

522 **

455 ***

-827

-186

-27

-929

-576

-356

0.297

0.024

0.001

0.253

0.731

0.913

0.432

0.542

0.632

Sample size (total = 1,236) 626 610

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up 

Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 

511) who were ages 18 to 64 at their time of random assignment.

Sample sizes for specific outcomes may vary because of exclusion of outliers and 

missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for prerandom assignment characteristics of sample members. No special weights were applied 

to responses to adjust for differences in sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA 

group and Regular Tax Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group 

and Regular Tax Filers arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** 

= 1 percent.
Appendix D 

Supplementary Tables to Chapter 2

141

The SaveUSA Evaluation

Appendix Table D.1

SaveUSA Account Activity

(Self-Reported from Survey Responses)

Outcome (%) Total

Year 3 (2013)

Received savings match 16.6

Deposited tax refund dollars in SaveUSA account 25.9

Did not deposit tax refund dollars in SaveUSA account 74.1

Among those who did not deposit tax refund dollars

No refund or refund too small 32.5

Used refund to pay debts or bills or for expenditures 23.0

No interest in SaveUSA program 8.6

Did not know about Year 3 eligibility to deposit 14.1

Encountered problems when filing taxes 13.0

Forgot or was unprepared to deposit 8.9

Sample size 698

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up 

Survey. 

NOTES: The sample includes SaveUSA group respondents from New York City (N = 410) 

and Tulsa (N = 288) who were ages 18 to 64 at their time of random assignment.

Sample sizes for specific outcomes may vary because of missing values.

Rounding may cause slight discrepancies in calculating sums.
142

The SaveUSA Evaluation

Appendix Table D.2

Additional Findings on Use of SaveUSA Account

 (Self-Reported from Survey Responses)

Outcome (%) Total

Has savings goal

Main savings goal

Emergency expenses

Retirement

Education

Big purchase

Pay debts/bills a Other

Main use of savings

No match

Never withdrew

Received savings match and withdrew money

Among those who received any savings match and withdrew money

Other savings

Pay debts/bills

Expenditures

Emergency expenses

Usual household expenses

Other household needs

Education

Big purchase

Spend on family member(s)

Travel, entertainment, family event

Miscellaneous

Work-related expenses

78.4

10.6

9.1

12.7

20.9

5.5

19.7

84.1

5.7

10.2

1.6

44.4

54.0

3.2

9.5

1.6

4.8

9.5

6.4

6.4

11.1

1.6

Sample size 626

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up 

Survey. 

NOTES: The sample includes SaveUSA group respondents from New York City (N = 367) 

and Tulsa (N = 259) who were ages 18 to 64 at their time of random assignment.

Sample sizes for specific outcomes may vary because of missing values.

Rounding may cause slight discrepancies in calculating sums.

aOther savings goals include investments, spending on family members, travel, 

entertainment, family event, miscellaneous, usual household expenses, other household 

needs, and work-related expenses.
143

The SaveUSA Evaluation

Appendix Table D.3

Factors Associated with Receiving the Savings Match in Only One Year

or in Two or More Years

Odds Ratios and Statistical Significance

For Receiving a Match For Receiving a Match

in Only 1 Year vs. in 2 or More Years vs.

Characteristic Never Receiving a Match Never Receiving a Match 

Age

18-24 (omitted group) 1.000 1.000

25-34 1.189 0.940

35-44 0.961 1.642 *

45-59 1.039 2.234 *

60-64 1.809 3.249 *

Savings pledge amount in 2011

$200 0.679 * 0.351 *

$201 - $999 (omitted group) 1.000 1.000

$1,000 1.283 2.081 *

Adjusted gross income amount in 2010

$1 - $9,999 0.553 * 0.556 *

$10,000 - $19,999 0.560 * 0.633 *

$20,000 or more (omitted group) 1.000 1.000

Tax filing status

With children 0.997 1.205

Without children (omitted group) 1.000 1.000

Average total tax refund amount 1.000 1.000

Received federal Earned Income Tax Credit 0.589 * 0.499 *

Month of study enrollment 1.178 * 1.139

City

New York City 0.360 * 0.831

Tulsa 0.223 * 0.433 *

Newark (omitted group) 1.000 1.000

San Antonio 0.379 * 0.820

Sample size (total = 1,554) 1,077 982

SOURCES: MDRC calculations from MDRC baseline data, tax data, and financial institution data.

NOTES: Demographic characteristics data were collected for one tax filer when couples filed jointly.

An asterisk (*) indicates that the difference in values is statistically significant at the 10 percent level or less.
144

(continued)

The SaveUSA Evaluation

Appendix Table D.4

SaveUSA Account Activity, by Program Year 

Outcome Year 1 Year 2 Year 3 All Years

RCT cities (New York City and Tulsa)

SaveUSA account opened or pledged (%) 95.3 43.3 32.4 96.1

Distribution of pledged deposit (%) 

$0 0.0 56.7 67.6 0.0

$1 - $200 38.5 11.0 7.3 24.3

$201 - $999 33.8 16.6 11.5 33.3

$1,000 27.7 15.7 13.6 42.4

Average initial deposit amount ($) 490 329 275 1,095

Distribution of initial deposit (%) 

$0 13.0 58.8 69.4 10.8

$1 - $200 32.0 9.3 5.4 20.0

$201 - $999 29.5 15.5 11.0 28.6

$1,000 or more 25.6 16.4 14.2 40.6

Received savings match (%) 57.6 27.1 19.4 60.3

Average amount of savings match ($) 169 95 74 338

Average savings match, among those who received

the savings match ($) 294 351 383 561

Distribution of savings match (%)

$0 42.4 72.9 80.6 39.7

$1 - $100 17.1 4.0 1.6 11.5

$101 - $499 19.8 9.7 7.1 18.0

$500 20.7 13.4 10.7 30.9

Non-RCT cities (Newark and San Antonio)

SaveUSA account opened or pledged (%) 99.7 34.7 25.0 99.7

Distribution of pledged deposit (%) 

$0 0.0 65.1 75.0 0.0

$1 - $200 34.9 5.8 2.5 25.7

$201 - $999 32.8 13.4 7.8 29.2

$1,000 32.4 15.7 14.7 45.1

Average initial deposit amount ($) 522 254 211 986
145

Appendix Table D.4 (continued)

Year 1 Year 2 Year 3 All Years

Distribution of initial deposit (%) 

$0

$1 - $200

$201 - $999

$1,000 or more

Received savings match (%)

Average amount of savings match ($)

Average savings match, among those who received

the savings match ($) 

Distribution of savings match (%)

$0

$1 - $100

$101 - $499

$500

6.8

32.0

30.1

31.1

73.8

214

289

26.2

23.2

24.6

26.1

66.5

5.4

12.8

15.4

27.9

96

345

72.1

4.5

10.4

13.0

75.1

2.5

7.2

15.1

21.6

83

384

78.4

1.8

7.2

12.5

6.5

22.9

26.8

43.8

75.0

392

523

25.0

16.7

21.7

36.6

Sample size 1,554 1,554 1,554 1,554

SOURCES: MDRC calculations from MDRC baseline data and financial institution data.

NOTES: RCT = randomized controlled trial. The sample includes SaveUSA group members who 

were ages 18 to 64 at their time of study entry.

Rounding may cause slight discrepancies in calculating sums.

The pledged deposit refers to the amount of tax refund dollars that individuals committed to 

savings at the time of study entry.

The initial deposit refers to the tax refund amount directly deposited into the SaveUSA account 

by the Internal Revenue Service.

Appendix E 

Background Information on Conducting

Random Assignment, Collecting Data,

and Estimating Program Impacts 

149

Random Assignment Process

This section describes the procedures that Volunteer Income Tax Assistance (VITA) organization staff members performed when enrolling tax filers into the SaveUSA study and helping 

MDRC conduct random assignment to the SaveUSA group or Regular Tax Filers group.

Random assignment occurred after each tax filer had nearly completed his or her tax return, 

except for Internal Revenue Service (IRS) Form 8888, which records how a filer chooses to 

allocate his or her tax refund.

The first step in the process of study enrollment and random assignment was to market 

SaveUSA and encourage individuals to participate. If a tax filer showed interest in SaveUSA, 

an assigned asset specialist described the SaveUSA program, using scripts and talking points 

prepared by MDRC and the VITA organization. Additionally, in New York City, VITA staff 

members checked whether prospective study participants had participated (as members of the 

program group) in the evaluation of $aveNYC, the predecessor to SaveUSA. VITA staff 

members used a database provided by Food Bank For New York City, which had previously 

served as the $aveNYC VITA organization, to perform this check. Staff members excluded any 

$aveNYC participants from the SaveUSA study. 

When interested tax filers were deemed eligible for SaveUSA, VITA staff members explained to them in detail the goals of the study, the random assignment process, the data that the 

research team would collect, and the benefits and risks of participation. Next, prospective study 

participants received a SaveUSA research document packet that contained the research-related 

forms: (1) SaveUSA Evaluation Informed Consent Form; (2) IRS Consent to Use Form; and (3)

IRS Consent to Disclose Form. Prospective study participants also received a verbal explanation 

from VITA staff members about the contents of each form. According to evaluation requirements, study participants needed to sign all three consent forms and undergo random assignment to have a chance at opening a SaveUSA account.

Once the consent forms were signed, a VITA staff member logged into MDRC’s online 

random assignment application and entered the study participant’s identifying information and 

selected measures about the participant’s tax return. Key identifiers (like name and Social 

Security number) were entered twice into the database. The application recognized discrepancies in data entry of these fields and required the VITA staff member to correct one or both 

versions before submitting the record. For study participants in New York City and Tulsa, the 

application then randomly assigned the study participant to either the SaveUSA group or the 

Regular Tax Filers group. All study participants in Newark and San Antonio were automatically 

assigned to the SaveUSA group.
150

Immediately following random assignment, SaveUSA group members met with VITA 

staff members, who explained to them the features and conditions of the SaveUSA savings 

account, including how depositors could earn a savings match if they maintained their initial 

deposit for about 12 months. VITA staff members then helped each SaveUSA group member 

open a SaveUSA account (involving a financial institution representative, if needed). Next, 

SaveUSA group members completed IRS Form 8888 to direct part or all of their refund to the 

SaveUSA account. They also filled out a form documenting the amount that they pledged to 

save for one year.1

 

Regular Tax Filers group members also completed their tax returns after random assignment but could not open a SaveUSA account. However, other savings opportunities were 

explained to them. For example, Regular Tax Filers group members could directly deposit tax 

refund dollars in other savings accounts or use them to purchase U.S. savings bonds.

Thus, the random assignment process worked as expected in 2011, when the study 

sample was enrolled. The treatment (access to a SaveUSA account) and counterfactual (no 

access) conditions were implemented as originally intended by the designers of the evaluation.

As discussed in this report, during subsequent years, SaveUSA continued enrolling new 

participants and offering existing SaveUSA group members the chance to make subsequent 

deposits into their SaveUSA account. In 2012 and 2013, VITA organizations in New York City

and Tulsa continued to use MDRC’s random assignment application to determine which 

eligible tax filers could open a SaveUSA account. During these years, MDRC made available to 

VITA organizations in these cities a searchable online database with identifying information on 

all tax filers who had previously been randomly assigned to the SaveUSA or Regular Tax Filers 

groups. VITA staff members used these data to verify that each prospective study participant 

had not previously enrolled in the SaveUSA evaluation. This procedure helped the research 

team to limit access to the SaveUSA account until 2014, when the embargo on opening a 

SaveUSA account ended for all members of the Regular Tax Filers group and for participants in 

the previous $aveNYC study.2 As noted in previous chapters, only 21 members of the Regular 

Tax Filers group in New York City who were randomly assigned in 2011 opened a SaveUSA 

account in 2014.

 1

A small percentage of SaveUSA group members were unable to open a SaveUSA account after random 

assignment. SaveUSA group members were expected to receive less-stringent-than-normal eligibility screening from ChexSystems or similar banking systems, but some participants were rejected through this process.

Rejection rates varied by financial institution (ranging from zero to 3 percent in 2011). 2

The program model did not change from the original design during the first year of enrollment or subsequent years.
151

Data Collection

Baseline Data 

As noted above, at the time of random assignment, MDRC collected a limited amount 

of demographic and tax-related information on each study participant. No prospective study 

participant could proceed with random assignment without providing this information. Baseline 

data collection and random assignment both ended on the last day of the 2011 tax season in 

mid-April, as expected. 

Except for study participants in San Antonio, MDRC collected additional demographic 

characteristics from the VITA agencies’ intake surveys, which tax filers completed on site while 

waiting to prepare their tax return.

3 The VITA agencies created their own intake surveys and 

included questions on demographic characteristics, such as race/ethnicity and gender, as well as 

information on participant needs for additional services (such as child care assistance) that the 

agency provided. Completion of these VITA agency surveys was voluntary, and the quality and 

completeness of these data varied by site. Questions on study participants’ current employment 

and earnings, household income, savings, and debt had relatively high rates of nonresponse and 

were therefore not used in the research. (See below for additional discussion of missing values 

for data on demographic characteristics.) 

Financial Institution Data

Five banks and one credit union offered SaveUSA matched savings accounts, but other organizations and agencies — MDRC, the VITA organizations, the Mayor’s Fund to 

Advance New York City, the New York City Center for Economic Opportunity (CEO), and 

the New York City Department of Consumer Affairs Office of Financial Empowerment 

(OFE) — helped administer these accounts. MDRC assumed primary responsibility for 

monitoring each account’s deposits and withdrawals, determining which accounts became 

eligible to receive the SaveUSA savings match, and addressing account holders’ inquiries and 

complaints. VITA organizations assisted financial institutions in opening SaveUSA accounts, 

assisted MDRC in troubleshooting account holders’ problems, and worked with financial 

institutions to distribute savings match funds. 

As a third-party administrator of SaveUSA accounts, MDRC needed to negotiate separate data-sharing agreements with each financial institution. MDRC also needed to work with 

financial institutions’ database administrators to develop protocols for extracting account data 

 3

In Newark, MDRC also created a short questionnaire that was used to collect additional baseline 

measures. 
152

and transmitting them to MDRC. Given the complexity of the project, MDRC designed several 

databases to ensure the smooth implementation of the program, and, most important, to prevent 

individuals assigned to the Regular Tax Filers group from opening a SaveUSA account. To 

facilitate the tracking of SaveUSA accounts, MDRC worked with the VITA organizations to 

collect each SaveUSA group member’s financial institution name, account number, and pledge 

amount at enrollment. MDRC maintained this information in a separate database and used these 

data to check the completeness and accuracy of account data extracted from financial institutions’ systems. 

Creating the account data files frequently required considerable work on the part of financial institution staff members, in excess of their normal day-to-day account management for 

customers. After gathering the account data for SaveUSA participants, financial institutions sent 

quarterly data files in one of two formats: at the transaction level or as “snapshot balances.” 

In tracking SaveUSA account activity, MDRC encountered several difficulties. While 

most issues were fixed before the match distribution, some data issues were not resolved until 

after the savings match had been distributed, at which time some SaveUSA participants noticed 

that they had received either an incorrect match amount or no match at all. These specific errors 

in determining the savings match were remedied by the VITA sites, financial institutions, and 

MDRC as soon as possible. Listed below are examples of these issues and how they were 

corrected.4

Account-Opening Issues at the Time of Study Entry

• VITA or financial institution staff did not open or designate some SaveUSA 

accounts correctly on tax day. Participants who intended to open an account, but 

for whom an error prevented it from being opened, were contacted to resolve the 

situation. These participants were usually given another chance to open a SaveUSA 

account. Other account opening or identification problems were resolved by working with the financial institution. For example, the SaveUSA deposit was occasionally directed to an existing account with the financial institution, instead of a newly

opened SaveUSA account.

 4

For additional information on program implementation and a complete description of the financial institution processing and challenges, see Chapter 3 and Appendix E from the SaveUSA interim report (Azurdia, 

Freedman, Hamilton, and Schultz, 2014).
153

Initial-Deposit Issues

A critical task in the early stages of data processing was identifying the correct initial 

tax refund deposit amount. This amount was important, as it was used as the benchmark balance 

for determining savings match eligibility, in conjunction with the specified intended pledge 

amount. MDRC assumed the benchmark balance to be the pledged amount or the actual initial 

deposit — whichever was lower. 

• In some cases, the pledged deposit did not match the initial deposit in the 

SaveUSA account. For example, federal and state refund amounts could be deposited at separate times. Some SaveUSA group members, who did not realize that only part of their refund had been deposited, withdrew funds from their account.

These individuals would lose their eligibility to receive a savings match if the organization monitoring the accounts did not make allowances for the delay in receipt 

of the entire tax refund. Where necessary, MDRC “backdated” all tax refund deposits to a date before the account holder’s first withdrawal to maintain account holders’ eligibility for the match.

Fees and Erroneous Transactions

When processing SaveUSA account transaction data, MDRC tracked eligibility for the 

savings match by keeping a running account balance based on transactions. If the account 

balance fell below the pledge amount (or the actual initial SaveUSA deposit, if lower than the 

pledge amount), the participant was deemed ineligible for the savings match. 

• Transaction fees were levied on SaveUSA accounts that should not have been 

applied. The SaveUSA accounts were not intended to have any fees charged to 

them. Some of the financial institutions, however, did charge fees, which appeared 

as deductions from the account and could be mistaken as withdrawals by the account holder. In the calculation of the match, MDRC needed to identify these fees, 

notify financial institutions, and then disregard the deductions. Some of the fees 

were later reimbursed by the financial institutions.

Tax Return Data

MDRC used data from tax returns to analyze how study participants allocated their 

2010 federal tax refund. MDRC received access to tax data for one year only, based on provisions of the data-sharing agreements between the IRS and participating VITA organizations.

MDRC followed security standards required by the IRS when working with tax records. The 

IRS consent forms described above followed the IRS protocols for requesting consent and 

included standard language provided by the IRS. MDRC also followed other IRS security 
154

standards when managing data, such as keeping the tax return data encrypted while stored on 

MDRC’s network server.

After the end of the 2011 tax season, or when study enrollment ended, MDRC received 

electronic tax data files from Food Bank For New York City, Ariva, Community Action Project 

of Tulsa, and Newark Now. Since the data reflected information from before study entry, these 

data were used as baseline indicators, to compare characteristics between those who enrolled in 

the study and those who did not enroll in the study.

Data on allocation of tax refunds were also used to confirm that members of the Regular Tax Filers group did not open a SaveUSA account. MDRC was able to collect account 

numbers from the tax records to confirm that only SaveUSA group members opened a 

SaveUSA account. The 2011 (Year 1) results, shown in Appendix Table E.1, indicate that 

nearly all SaveUSA group members asked the IRS to directly deposit all or part of their tax 

refund into a savings account, compared with only 15 percent for the Regular Tax Filers group.

Although not eligible to open a SaveUSA account, members of the Regular Tax Filers group, 

when filing their taxes, could also directly deposit tax refund dollars into one or more savings 

products. As shown, the majority of Regular Tax Filers group members did not.

18- and 42-Month Surveys

MDRC contracted with Decision Information Resources (DIR) to design and administer the SaveUSA 18- and 42-Month Follow-Up Surveys. MDRC has worked with DIR on 

surveys for other studies, including evaluations of asset-building initiatives for low- and 

moderate-income individuals and households. DIR used its Computer-Assisted Telephone 

Interviewing (CATI) call center and database system to conduct all interviews. Per agreement 

with MDRC, DIR sought to interview at least 80 percent of members of the SaveUSA and 

Regular Tax Filers groups in New York City and Tulsa and to attain this response rate for both 

research groups in each city. DIR successfully met these goals for both surveys. (See Appendix 

B for further details.)

Efforts to attain consistently high response rates included:

• Respondent location efforts. MDRC worked with administrators of the VITA organizations to collect and transmit to DIR updated contact information for study 

participants in both research groups. This effort was relatively successful because 

study participants typically returned to the same VITA organization each year to 

file their taxes. DIR also used standard locator databases to find additional contact 

information.
155

The SaveUSA Evaluation

Appendix Table E.1

Impacts on Allocation of 2010 Federal Tax Refund

Outcome 

SaveUSA

Group

Regular Tax

Filers

Difference

(Impact) P-Value

Allocation of tax refund (%)

To any bank account

Savings account

Checking account

To tax refund check

Amount allocated ($)

To any bank account

Savings account

Checking account 

To tax refund check 

Percentage of total refund allocated (%)

To any bank account

Savings account

Checking account 

To tax refund check 

98.2

93.1

71.0

18.6

3,297

686

2,611

511

85.4

28.7

56.7

14.2

75.6

14.6

68.3

25.2

3,029

226

2,803

758

75.0

8.4

66.5

24.8

22.6 ***

78.5 ***

2.7

-6.6 ***

269 ***

460 ***

-191 *

-246 ***

10.4 ***

20.3 ***

-9.8 ***

-10.6 ***

0.000

0.000

0.234

0.001

0.003

0.000

0.052

0.004

0.000

0.000

0.000

0.000

Sample size (total = 1,577) 794 783

SOURCE: MDRC calculations from 2010 tax return records.

NOTES: Calculations include sample members from New York City (N = 922) and Tulsa (N = 655) who were 

ages 18 to 64 at their time of random assignment.

A small number of sample members allocated tax refund dollars for the purchase of U.S. savings bonds (not 

shown).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.
156

• Marketing efforts. MDRC worked with administrators of the VITA organizations and with members of the DIR survey team to create marketing materials (including letters, postcards, email messages, website postings, and refrigerator 

magnets) and scripts for marketing phone calls to encourage participants to complete survey interviews.

• Financial incentives. Respondents received a gift card after completing an interview. Notice of the gift cards was included in marketing materials. During each 

fielding period, MDRC and DIR team members monitored survey response rates, 

and at strategic points in time (when response rates had dropped for several weeks 

in a row) increased the value of the gift card. For the 42-month survey, about 74 

percent of respondents received a $30 gift card, 16 percent received a $60 gift card, 

and 10 percent received a $100 gift card.

• Field locators. DIR employed and trained a group of field locators and assigned 

them to personally contact study participants who had not yet completed an interview. Field locators set up appointments for interviews with DIR’s call center but 

did not interview respondents on site. DIR monitored the success rate of each field 

locator weekly. In some instances locators were reassigned to different VITA organization catchment areas to boost response rates. DIR also replaced locators who 

were not successful in contacting study participants.

• Monitoring responses. During the fielding period, DIR prepared and shared with 

MDRC weekly reports on survey response rates, organized by city, VITA organization catchment area, and research group. Members of the DIR and MDRC teams 

reviewed these reports weekly and made adjustments to fielding efforts within 

VITA organization catchment areas (for example, allocation of field locators) in response to identification of low response rates or relatively large differences in response rates by research group.

Efforts to increase item response rates included:

• Survey design. Questions concerning dollar amounts followed one or more lead-in 

yes/no questions concerning the outcome. In addition, respondents who expressed 

difficulty answering or reluctance to report a specific dollar amount were then 

asked a series of follow-up questions by which they could approximate the dollar 

amount within a specific range.

• Monitoring responses. During the fielding period, DIR circulated weekly reports 

on item response rates of key dollar amount outcomes. Reports were reviewed during weekly meetings.
157

• MDRC programming. When creating continuous outcome measures with dollar 

amounts, MDRC transformed the range values of dollar amounts into specific values for respondents who chose to report a range value. For each of these respondents, MDRC randomly selected a value within the range. MDRC included these 

imputed values in checks for outliers and dropped respondents from calculations of 

outcomes and impacts if their imputed value exceeded the 99 percent cutoff level.

As a result of these strategies, item response rates were high for both research groups, 

as shown in Appendix Table E.2.

The SaveUSA Evaluation

Appendix Table E.2

SaveUSA 42-Month Follow-Up Survey 

Response Rates for Selected Outcomes

SaveUSA Regular Tax

Outcome Group Filers

Total nonretirement savings 96.3 96.6

Total retirement savings 96.8 96.2

Total liquid assets 93.8 92.6

Total household expenses in prior month 94.9 95.1

Total non-housing-related debt 95.0 93.9

Average liquid net worth 90.3 87.5

Sample size (total = 1,236) 626 610

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTE: The sample includes respondents from New York City (N = 725) and Tulsa (N = 

511).

Efforts to increase the completeness and accuracy of responses included:

• Survey design. Survey respondents answered nearly identical questionnaires at 18 

and 42 months. Most questions included in the survey had been used in previous 

surveys on household finances, including surveys administered by DIR for previous 

MDRC studies. The survey questionnaire was reviewed by a team that included experts in survey design and researchers in asset-building initiatives. DIR prepared a 

Spanish-language version of the survey instrument, which was reviewed by a 
158 

member of the MDRC research team and by an administrator from a VITA organization, both of whom are fluent in Spanish.

• CATI system testing. Before fielding the surveys, members of MDRC’s research 

team and corporate Survey Unit and the DIR survey team tested the programming 

of DIR’s CATI system, using 1,000 randomly generated responses and additional 

mock interviews. Team members reviewed technical issues in the CATI system 

weekly and modified the questionnaire and CATI system as needed before fielding 

the surveys. 

• Monitoring interviewer training. Before fielding the surveys, members of 

MDRC’s research team and corporate Survey Unit reviewed DIR’s interviewer 

training materials and scripts and attended a training session for interviewers.

• Identifying survey respondents. MDRC shared with DIR each respondent’s date 

of birth and the last four digits of his or her Social Security number. At the start of 

each interview, respondents were asked to provide this information to the interviewer to verify their identity. No proxy interviews by third parties were allowed.

• Interviewing respondents. All interviews were conducted by phone with interviewers at DIR’s call center. Field locators did not interview respondents.

• DIR review of survey responses. DIR recorded all survey interviews and made the 

recordings available for review. For a sample of respondents, DIR supervisors monitored interviews in real time. In addition, DIR conducted verification calls on 10 

percent of each CATI interviewer’s completed interviews and on 10 percent of each 

field locator’s completed contacts that led to an interview. Finally, for a sample of 

respondents, DIR supervisors reviewed every recorded response and compared the 

response with the value recorded in the CATI system.

• MDRC review of survey responses. During the first months of survey fielding, 

DIR transmitted to MDRC two test files of survey responses. Members of MDRC’s 

research team processed the data in these test files and ran quality control checks on 

the data. Team members checked item response rates and skip patterns and identified missing and outlier responses and responses that appeared to be inconsistent 

with responses to related questions. MDRC performed quality control checks on 

individual items and composite measures (for example, total weekly earnings at interview). MDRC shared examples of problem responses with DIR. DIR team 

members then reviewed each issue and, where necessary, reviewed the recorded 

verbal response to determine whether the interviewer had entered the response incorrectly in DIR’s CATI system. MDRC and DIR repeated these procedures fol-
159

lowing transmission to MDRC of the final version of the survey file. DIR corrected 

responses as needed based on these reviews.

Estimating Effects of SaveUSA

Appendix Table E.3 presents full regression impact results for two primary outcomes as of the 

42-month survey interview: “Has nonretirement savings” (binary, 0/1) and “Total nonretirement 

savings” (continuous). MDRC estimated adjusted means and differences (impacts) for binary 

(0/1) and continuous outcomes using ordinary least squares (OLS) regression.5 Regression 

adjustment can increase the statistical precision of the estimated effects, helping to distinguish 

normal variation in outcomes from the effects of SaveUSA group members’ exclusive access to 

the SaveUSA account and savings match. Following is a list of the baseline characteristics that 

MDRC used as covariates in the regression model: 

Random assignment month 

SaveUSA site

Gender 

Race/ethnicity 

Age

Educational attainment

Number of children

Adjusted gross income

Received the federal Earned Income Tax Credit (EITC)

Total tax refund

To test the sensitivity of the regression adjustment, MDRC compared the adjusted and 

unadjusted research group means and differences (impacts) for key outcome measures. As

Appendix Table E.4 shows, the adjusted and unadjusted estimates are very similar. These 

findings help confirm that the random assignment process resulted in creation of research 

groups with similar characteristics and that the effort to field the 42-month survey did not bias 

the results.

 5

As sensitivity tests for several previous studies, MDRC estimated outcomes and impacts on binary (0/1) 

measures using OLS regression and again using logistic regression. MDRC found that the results were nearly 

identical for each regression procedure.
160

The SaveUSA Evaluation

Appendix Table E.3

 Regression Coefficients for Estimated Impacts on Nonretirement Savings

After 42 Months of Follow-Up

Has Nonretirement 

Savings (%)

Total Nonretirement 

Savings ($)

Variable

Parameter

Estimate P-Value

Parameter

Estimate P-Value

Intercept

Assigned to SaveUSA Group (impact)

Covariates

Month of random assignment

Capital One Bank

Ariva Tax Assistance Center

Carver Financial Literacy Center

St. Mark's AME Church

Fordham Road

Pine and Lewis

Exchange Center

Female

Hispanic

Black

Age 25-34

Age 35-44

Age 45-59

Age 60 or older

No educational degree

AA/BA/BS/Grad school degree

Number of dependent children

Adjusted gross income

Adjusted gross income, squared

Received federal Earned Income Tax Credit

Total tax refund amount

(sum of federal, state, and city)

R-square

0.554

0.076

0.037

0.096

-0.039

0.041

0.102

-0.024

-0.009

0.064

0.014

-0.037

-0.020

-0.081

-0.088

-0.136

-0.146

-0.028

0.077

-0.005

0.000

0.000

-0.037

0.000

0.066

<.0001

0.002

0.010

0.153

0.563

0.536

0.180

0.777

0.888

0.314

0.622

0.353

0.539

0.049

0.048

0.001

0.040

0.472

0.007

0.791

0.004

0.037

0.267

0.709

813

522

185

719

658

529

945

1,098

311

284

-170

-662

-799

-67

-461

-153

-235

-497

943

-231

0

0

-95

0

0.057

0.321

0.024

0.176

0.257

0.303

0.397

0.191

0.165

0.605

0.635

0.542

0.081

0.011

0.863

0.272

0.690

0.727

0.181

0.001

0.238

0.782

0.311

0.766

0.311

Sample size 1,192 1,192

SOURCES: MDRC calculations from MDRC baseline data, VITA survey data, 2010 tax return records, and 

responses to the SaveUSA 18-Month and 42-Month Follow-Up Surveys.

NOTES: The sample includes respondents to the 42-Month Follow-up Survey in New York City (N = 696) and 

Tulsa (N = 496). 

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

Capital One Bank, Ariva Tax Assistance Center, Carver Financial Literacy Center, St. Mark's AME Church, 

Fordham Road, Pine and Lewis, and Exchange Center are SaveUSA sites.
The SaveUSA Evaluation

Appendix Table E.4

Adjusted and Unadjusted Impacts on Selected 42-Month Survey Outcomes

Adjusted Impacts Unadjusted Impacts

SaveUSA Regular Tax Difference SaveUSA Regular Tax Difference

Outcome Group Filers (Impact) P-Value Group Filers (Impact) P-Value

Has nonretirement savings (%) 80.0 72.4 7.6 *** 0.002 80.1 72.3 7.8 *** 0.002

Total nonretirement savings ($) 2,281 1,758 522 ** 0.024 2,296 1,742 554 ** 0.017

Total retirement savings ($) 3,326 3,512 -186 0.731 3,347 3,490 -143 0.802

Total liquid assets ($) 5,030 4,458 572 0.272 5,095 4,391 704 0.198

Total non-housing-related debt ($) 9,733 10,309 -576 0.542 10,009 10,023 -14 0.988

Had financial hardship since the 60.7 60.1 0.6 0.827 59.9 60.8 -0.9 0.750

18-month interview (%)

Sample size (total = 1,236) 626 610 626 610

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of missing values.

Regression-adjusted estimates used ordinary least squares, controlling for pre-random assignment characteristics of sample members. Unadjusted 

estimates used ordinary least squares with no covariates. No special weights were applied to responses to adjust for differences in sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

161
162

Missing Data

Covariates

Sixteen of the 22 covariates in the regression model for estimating program impacts 

(see Appendix Table E.3) had nonmissing values for all study participants. The other six 

measures were collected primarily from responses to VITA organizations’ intake surveys, 

which, as noted above, had some issues with completeness and accuracy of responses. These 

measures concern study participants’ gender, race/ethnicity, educational attainment, and number 

of dependent children. Among respondents to the 42-month survey, the proportion of missing 

responses for these measures ranged from about 5 percent (for number of dependent children) to 

about 18 percent (for educational attainment). 

In response, MDRC recorded values for these measures in two ways. Where available, 

MDRC copied values of related measures from the study participant’s responses to the 18-

month survey or, if unavailable, from the 42-month survey.6 This procedure reduced the number 

of study participants with missing values for these measures to near zero. The remaining 

missing values for covariates were imputed using the research group’s mean. 

Outcomes

Sample members with missing values for dependent variables (outcomes) were excluded from the impact estimates. As discussed above in this appendix, for a few outcome measures, 

such as total current nonretirement savings, where respondents did not report a specific dollar 

amount, but instead provided a range, MDRC randomly assigned a value to the outcome 

variable that fell between the minimum and maximum value of the range provided. As a 

sensitivity check, Appendix Table E.5 shows the estimates for the main outcomes that used 

these imputations. The table shows that the impact estimates are similar for outcomes with and 

without imputed values. These findings may be interpreted to mean that the imputation procedure did not bias the results.

Effect Sizes of Program Impacts and Assessment of Possible Effects

of Multiple Comparisons

Appendix Table E.6 displays statistical data on MDRC’s impact estimates of savingsrelated outcomes. As discussed in Chapters 3 and 4, MDRC determined that these impacts are

 6

MDRC assumed that values would be more accurate when collected from post-random assignment survey responses for the study participant than from an imputation procedure based on averages for the research 

group — although more so for gender and race/ethnicity than for educational attainment and number of 

children.
163

The SaveUSA Evaluation

Appendix Table E.5

Effects of Imputing Missing Values on Selected 42-Month Survey Outcomes and Impacts

With Imputed Values

SaveUSA Regular Tax Difference

Without Imputed Values

SaveUSA Regular Tax Difference

Outcome ($)

Total nonretirement savings

Group

2,281

Filers

1,758

(Impact)

522 **

P-Value

0.024

Group

2,310

Filers

1,712

(Impact)

598 **

P-Value

0.014

Total retirement savings 3,326 3,512 -186 0.731 3,116 3,302 -186 0.723

Total non-housing-related debt 9,733 10,309 -576 0.542 9,869 10,082 -213 0.823

Total emergency expenditures

Sample size (varies)a

1,255 1,320 -66 0.634 1,168 1,279 -112 0.392

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment characteristics of sample members. 

No special weights were applied to responses to adjust for differences in sample size by site.

Rounding may cause slight discrepancies in calculating sums and differences. 

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers arose by chance.

Statistical significance levels are indicated as: * = 10 percent; ** = 5 percent; and *** = 1 percent.

aSample sizes for specific outcomes vary because of missing values. Imputations increase sample sizes by 14 to 36 respondents, depending on the 

outcome measure and research group. 
164

The SaveUSA Evaluation

Appendix Table E.6

Statistical Data on Savings-Related Outcomes and Impacts 

(continued)

Outcome 

Difference

(Impact) P-Value

Adjusted

a P-Value R2

Standard

b Deviation

Effect

c Size

Primary outcomes

As of 42-month interview

Has nonretirement savings (%)

Total nonretirement savings ($)

Has liquid assets (%)

Has savings or checking account (%)

Has a current savings goal (%)

Never deposited money in savings since

18-month interview (%)

Additional outcomes

As of 42-month interview (%)

Has more than $2,000 in nonretirement savings

Has more than $2,000 in liquid assets

Nonretirement savings would cover at least

1 month of household expenses

Is liquid-asset poor

Can generally afford the things needed

There is sometimes enough money for fun

Between date of filing taxes during 2014 and

42-month interview (%)

Directly deposited tax refund into savings account

Primarily used tax refund for savings

Had plan to save all or part of tax refund

None of tax refund currently saved

Between 18-month and 42-month interview (%)

Ever had savings account

Had at least $1 in nonretirement savings

Had savings at 18- and 42-month interviews

Had liquid assets at 18- and 42-month interviews

Used savings or current income to pay for

emergency expenses

7.6

522

7.6

3.7

6.6

-7.4

7.6

5.1

8.6

-5.8

4.8

-4.8

5.4

7.6

8.7

-11.9

7.9

7.1

11.0

11.9

6.7

0.002

0.024

0.001

0.059

0.007

0.008

0.002

0.061

0.002

0.018

0.069

0.066

0.054

0.001

0.002

0.000

0.001

0.002

0.000

0.000

0.011

0.004

0.029

0.004

0.063

0.014

0.012

0.004

0.061

0.005

0.024

0.069

0.088

0.061

0.003

0.008

0.000

0.003

0.003

0.000

0.000

0.016

0.066

0.057

0.082

0.078

0.075

0.077

0.061

0.129

0.064

0.130

0.051

0.052

0.058

0.053

0.057

0.065

0.070

0.060

0.088

0.128

0.038

42.6

4,016

40.4

34.6

43.3

49.1

42.6

48.3

47.5

43.5

45.6

46.3

49.6

38.3

49.6

48.6

43.2

38.8

47.6

45.7

43.3

0.178

0.130

0.187

0.106

0.152

-0.150

0.179

0.106

0.182

-0.134

0.104

-0.105

0.109

0.198

0.176

-0.245

0.184

0.182

0.232

0.260

0.155

Sample size (total = 1,236) 626 610
165

 

Appendix Table E.6 (continued)

SOURCE: MDRC calculations from responses to the SaveUSA 42-Month Follow-Up Survey. 

NOTES: The sample includes respondents from New York City (N = 725) and Tulsa (N = 511).

Sample sizes for specific outcomes may vary because of missing values.

Estimates were regression-adjusted using ordinary least squares, controlling for pre-random assignment 

characteristics of sample members. No special weights were applied to responses to adjust for differences in 

sample size by site.

A two-tailed t-test was applied to differences between outcomes for the SaveUSA group and Regular Tax 

Filers. 

The p-value indicates the likelihood that the difference between the SaveUSA group and Regular Tax Filers 

arose by chance. The table displays statistical data for differences with p-values of 0.1 or below. aAdjusted p-values were calculated based on the Benjamini-Hochberg procedure. For the SaveUSA 

evaluation, this procedure tests for possible erroneous findings of statistically significant impacts at the 10 

percent level or below due to multiple comparisons of related outcomes. Adjusted p-values of 0.1 or below 

confirm impact estimates as true effects of SaveUSA.

bStandard deviations were calculated for the combined sample of respondents from both research groups.

c

Effect sizes were calculated by dividing the difference by the standard deviation.

likely to represent true effects of the SaveUSA program at the 10 percent level of statistical 

significance or below. For each measure, the first two columns of the table show the value of 

the program impact and level of statistical significance (p-value), using the results shown in 

tables in Chapters 3 and 4. The rightmost two columns display the “pooled” standard deviations 

(calculated for all respondents from both research groups) for each outcome and the “effect 

sizes” of each impact. For any given outcome, the effect size is calculated by dividing the 

impact by the pooled standard deviation. Effect sizes standardize impact estimates for comparison with impacts estimated by other studies. As shown in Appendix Table E.6, the absolute 

values of effect sizes vary from 0.104 to 0.260 standard deviations. In the statistical literature on 

effect sizes, these values are often considered to denote small to moderate-sized impacts, but 

standards vary based on domains. Placing effect sizes in their true context requires the comparison of impacts for SaveUSA with effects from other randomized controlled trials of assetbuilding initiatives for low- and moderate-income adults and households.7 At present, there are 

few such studies available for comparison.

For savings-related outcomes, MDRC also tested for possible errors in inference that 

could have resulted from conducting multiple comparisons of related outcomes. For the outcome measures displayed in Appendix Table E.6, MDRC ran two Benjamini-Hochberg tests to 

7

See Hill, Bloom, Black, and Lipsey (2007) for further discussion on placing effect sizes in the context of 

related studies.
166

estimate the likelihood that MDRC erroneously rejected the null hypothesis (of no difference or 

impact), when the impact was calculated to have a 10 percent level of statistical significance or 

below. The first test included 17 savings-related outcomes with statistically significant differences, and the second test included 4 measures of attitudes about saving that have statistically 

significant differences. For each test, MDRC sorted the measures in ascending order based on 

the p-value of the impact. Once the measures are sorted in this way, the Benjamini-Hochberg 

test uses the position or rank of each measure to calculate an adjusted p-value, which is then 

used to determine whether the researcher can continue to consider the estimated impact to be a 

true effect of the intervention. 

Appendix Table E.6 displays the results of the Benjamini-Hochberg tests, using a variation of the formula for calculating adjusted p-values: (original p-value × N / j), where N represents the total number of measures included in the test (17 and 4, respectively) and j represents 

the position or rank of the measure (1, 2, 3, ..., N) after sorting.8 Thus for the first savings 

measure the adjusted p-value equals the original p-value multiplied by (17 / 1 = 17); for the 

second savings measure, the adjusted p-value equals the original p-value multiplied by 

(17 / 2 = 8.5); and so on. For each measure, if the adjusted p-value is below 0.1, the maximum 

level of statistical significance used in this report, the impact may continue to be viewed as a 

true effect of SaveUSA.

As shown in Appendix Table E.6, all impact estimates have adjusted p-values below 

0.1. These results affirm that SaveUSA’s impacts on nonretirement savings and on attitudes 

toward savings, where found, are real effects of the program.9

 8

In Appendix Table E.6, the measures are displayed in an order similar to how they appear in report tables 

and text. The adjusted p-values associated with each measure were calculated when the measures were sorted 

in ascending order based on p-value. 9

Another way to run the Benjamini-Hochberg test is to compare each original p-value with a “threshold 

value” that is calculated as (j / N) × α, where α is the maximum level of statistical significance (here, 0.1). For 

both tests, each original p-value is below its corresponding threshold value, which again indicates that the 

impacts on nonretirement savings and attitudes toward saving may be considered as true effects of SaveUSA.
167

References

Abbi, Sarika. 2012. “A Need for Product Innovation to Help LMI Consumers Manage Financial

Emergencies.” Allston, MA: Doorways to Dreams Fund. 

Azurdia, Gilda, Stephen Freedman, Gayle Hamilton, and Caroline Schultz. 2014. Encouraging 

Low- and Moderate-Income Tax Filers to Save: Implementation and Interim Impact 

Findings from the SaveUSA Evaluation. New York: MDRC.

Baugh, Brian, Itzhak Ben-David, and Hoonsuk Park. 2014. “Disentangling Financial 

Constraints, Precautionary Savings, and Myopia: Household Behavior Surrounding Federal 

Tax Returns.” National Bureau of Economic Research Working Paper No. 19783.

Berry, Christopher. 2004. “To Bank or Not to Bank: A Survey of Low-Income Households.” 

Working Paper BABC 04-3. Cambridge, MA: Harvard University, Joint Center for 

Housing Studies. 

Beverly, Sondra, Daniel Schneider, and Peter Tufano. 2006. “Splitting Tax Refunds and 

Building Savings: An Empirical Test.” Pages 111-162 in James Poterba (ed.), Tax Policy 

and the Economy, vol. 20. Cambridge, MA: National Bureau of Economic Research and 

MIT Press.

Black, Rachel, and Reid Cramer. 2011. “Incentivizing Savings at Tax Time: $aveNYC and the 

Saver’s Bonus.” Washington, DC: New America Foundation.

Bricker, Jesse, Arthur B. Kennickell, Kevin B. Moore, and John Sabelhaus. 2012. “Changes in 

U.S. Family Finances from 2007 to 2010: Evidence from the Survey of Consumer 

Finances.” Federal Reserve Bulletin 98, 2: 1-80. 

Brobeck, Stephen. 2008a. “The Essential Role of Banks and Credit Unions in Facilitating 

Lower-Income Household Saving for Emergencies.” Washington, DC: Consumer 

Federation of America. Website: www.consumerfed.org. 

Brobeck, Stephen. 2008b. “Understanding the Emergency Savings Needs of Low- and 

Moderate-Income Households: A Survey-Based Analysis of Impacts, Causes, and 

Remedies.” Washington, DC: Consumer Federation of America. Website: 

www.consumerfed.org. 

Bronchetti, Erin Todd, Thomas S. Dee, David B. Huffman, and Ellen Magenheim. 2011. “When 

a Nudge Isn’t Enough: Defaults and Savings Among Low-Income Tax Filers.” Working 

Paper 16887. Cambridge, MA: National Bureau of Economic Research. 

Chan, Pamela. 2011. “Beyond Barriers: Designing Attractive Savings Accounts for LowerIncome Consumers.” Working paper. Washington, DC: New America Foundation. 

Chase, Stephanie, Leah Gjertson, and J. Michael Collins. 2011. “Coming Up with Cash in a 

Pinch: Emergency Savings and Its Alternatives.” CFS Issue Brief 6.1. Madison: University 

of Wisconsin-Madison, Center for Financial Security.
168

City of New York. 2012. SaveUSA: Program Implementation: Insights from the Field. New 

York: New York City Department of Consumer Affairs, Office of Financial 

Empowerment, and Center for Economic Opportunity. 

Collins, J. Michael, and Leah Gjertson. 2013. “Emergency Savings for Low-Income 

Consumers.” Focus 30, 1: 12-17. University of Wisconsin-Madison, Institute for Research 

on Poverty. 

Corporation for Enterprise Development (CFED). 2013. “Assets and Opportunity Scorecard: 

Liquid Asset Poverty Rate.” Washington, DC: Corporation for Enterprise Development. 

Website: http://scorecard.assetsandopportunity.org/2013/measure/liquid-asset-poverty-rate.

Cramer, Reid, Justin King, and Elliot Schreur. 2015. Flexible Savings: The Missing Foundation 

for Financial Security and Economic Mobility. Washington, DC: New America 

Foundation. 

DeNavas-Walt, Carmen, and Bernadette D. Proctor. 2015. Income and Poverty in the United 

States: 2014. U.S. Census Bureau, Current Population Reports, P60-252. Washington, DC: 

U.S. Government Printing Office. 

Doorways to Dreams Fund. 2009. Yes We Can: Inclusive Saving at Tax Time. Allston, MA:

Doorways to Dreams Fund. Website: www.d2dfund.org. 

Doorways to Dreams Fund. 2011. 2011 Savings Bond Report: Saving Is Impossible Complex 

Confusing Hard. Bonds Make It Easy. Allston, MA: Doorways to Dreams Fund. Website: 

www.d2dfund.org. 

Doorways to Dreams Fund. 2012. 2012 Savings Bond Report: Keep Bonds Easy. Preserving 

Universal Access Through Advocacy, Awareness and Action. Allston, MA: Doorways to 

Dreams Fund. Website: www.d2dfund.org. 

Duch, Melanie Kwon. 2013. “Save to Win: Highlights from Michigan 2012.” Allston, MA: 

Doorways to Dreams Fund. Website: www.d2dfund.org. 

Duflo, Esther, William Gale, Jeffrey Liebman, Peter Orszag, and Emmanuel Saez. 2006. 

“Saving Incentives for Low- and Middle-Income Families: Evidence from a Field 

Experiment with H&R Block.” Quarterly Journal of Economics 121, 4: 1311-1346. 

Edin, Kathryn, Sara S. Greene, Sarah Halpern-Meekin, and Ezra Levin. 2015. “The Rainy Day 

EITC: A Reform to Boost Financial Security by Helping Low-Wage Workers Build 

Emergency Savings.” Federal policy proposal. Washington, DC: Corporation for 

Enterprise Development.

Edin, Kathryn, and Laura Lein. 1997. “Work, Welfare, and Single Mothers’ Economic Survival

Strategies.” American Sociological Review 62, 2: 253-266. 

Federal Deposit Insurance Corporation (FDIC). 2012. 2011 FDIC National Survey of Unbanked

and Underbanked Households. Washington, DC: Federal Deposit Insurance Corporation. 
169

Fellowes, Matt, and Katy Willemin. 2013. The Retirement Breach in Defined Contribution 

Plans: Size, Causes, and Solutions. Washington, DC: Hello Wallet. 

Fry, Richard, and Rakesh Kochhar. 2014. “America’s Wealth Gap Between Middle-Income and 

Upper-Income Families Is Widest on Record.” Pew Research Center. Website: 

http://www.pewresearch.org. 

Grinstein-Weiss, Michal, Krista Comer, Blair Russell, Clinton Key, Dana C. Perantie, and Dan 

Ariely. 2014. Refund to Savings: 2013 Evidence of Tax-Time Saving in a National 

Randomized Control Trial (CSD Research Report 14-03). St. Louis: Washington 

University, Center for Social Development. 

Hannagan, Anthony, and Jonathan Morduch. 2015. “Income Gains and Month-to-Month 

Income Volatility: Household Evidence from the U.S. Financial Diaries.” NYU Wagner 

Research Paper 2659883. 

Hill, Carolyn J., Howard S. Bloom, Alison Rebeck Black, and Mark W. Lipsey. 2007. 

Empirical Benchmarks for Interpreting Effect Sizes in Research. New York: MDRC.

Key, Clinton C., Michal Grinstein-Weiss, Jenna N. Tucker, and Krista Holub. 2013. “Savings at

Tax Time: The Effects of $aveNYC on Savings in Low-Income Households.” Working 

paper. Chapel Hill: University of North Carolina, UNC Center for Community Capital. 

Lerman, Robert, and Signe-Mary McKernan. 2008. The Effects of Holding Assets on Social and

Economic Outcomes of Families: A Review of Theory and Evidence. Washington, DC: 

Urban Institute. 

Lopez-Fernandini, Alejandra. 2010. “Unrestricted Savings: Their Role in Household Economic 

Security and the Case for Policy Action.” Working paper. Washington, DC: New America 

Foundation. 

Lopez-Fernandini, Alejandra, and Caroline Schultz. 2010. “Automating Savings in the

Workplace: Insights from the AutoSave Pilot.” Washington, DC: New America 

Foundation. 

Manturuk, Kim, Lucy S. Gorham, and Jessica Dorrance. 2013. “The Importance of Tax Time 

for Building Emergency Savings: Major Findings from $aveNYC.” Chapel Hill: University 

of North Carolina, UNC Center for Community Capital. Website: www.ccc.unc.edu. 

McCully, Clinton P. 2011. “Trends in Consumer Spending and Personal Saving, 1959-2009.” 

Survey of Current Business 91, 6: 14-23. Website: 

https://www.bea.gov/scb/pdf/2011/06%20June/0611_pce.pdf. 

McKernan, Signe-Mary, and Caroline Ratcliffe. 2008. “Enabling Families to Weather

Emergencies and Develop: The Role of Assets.” New Safety Net Paper 7. Washington, 

DC: Urban Institute. 

McKernan, Signe-Mary, Caroline Ratcliffe, and Katie Vinopal. 2009. “Do Assets Help Families

Cope with Adverse Events?” Brief 10. Washington, DC: Urban Institute. 
170

Mendenhall, Ruby, Kathryn Edin, Susan Crowley, Jennifer Sykes, Laura Tach, Katrin Kriz, and

Jeffrey R. Kling. 2012. “The Role of Earned Income Tax Credit in the Budgets of LowIncome Households.” Social Service Review 86, 3: 367-400. 

Mills, Gregory, Rhiannon Patterson, Larry Orr, and Donna DeMarco. 2004. Evaluation of the

American Dream Demonstration: Final Evaluation Report. Cambridge, MA: Abt 

Associates Inc.

New York City Department of Consumer Affairs, Office of Financial Empowerment (OFE).

2010. “The $aveNYC Account: Innovation in Asset Building — Research update.” New 

York: New York City Department of Consumer Affairs, Office of Financial 

Empowerment. 

New York City Department of Consumer Affairs, Office of Financial Empowerment (OFE). 

2013. Municipal Financial Empowerment: A Supervitamin for Public Programs. Strategy 

#5: Integrating Asset Building. New York: New York City Department of Consumer 

Affairs, Office of Financial Empowerment. 

Pew Charitable Trusts. 2013. Moving On Up: Why Do Some Americans Leave the Bottom of the 

Economic Ladder, but Not Others? Washington, DC: Pew Charitable Trusts.

Pew Charitable Trusts. 2015. The Precarious State of Family Balance Sheets. Washington, DC: 

Pew Charitable Trusts.

Rank, Mark R. 2008. Asset Building Over the Life Course: Final Report. Washington, DC: U.S.

Department of Health and Human Services, Office of the Assistant Secretary for Planning 

and Evaluation (DHHS/ASPE).

Rothstein, David, and Rachel Black. 2013. “Making Tax Time the Financial Management 

Moment.” Pages 227-241 in Reid Cramer and Trina R. Williams Shanks (eds.), The Asset 

Perspective: The Rise of Asset Building and its Impact on Social Policy. New York: St. 

Martin’s Press.

Schreiner, Mark. 2005. What Does an IDA Cost? Some Measures from ADD. Center for Social

Development Research Report 05-38. St Louis: Washington University, George Warren 

Brown School of Social Work, Center for Social Development. 

Shefrin, Hersh M., and Richard H. Thaler. 1988. “The Behavioral Life-Cycle Hypothesis.” 

Economic Inquiry 26: 609-643. 

Smeeding, Timothy M. 2000. “The EITC and USAs/IDAs: Maybe a Marriage Made in 

Heaven?” Working paper commissioned for the Inclusion in Asset Building: Research and 

Policy Symposium. St Louis: Washington University, George Warren Brown School of 

Social Work, Center for Social Development. 

Thaler, Richard H., and Shlomo Benartzi. 2004. “Save More TomorrowTM: Using Behavioral

Economics to Increase Employee Saving.” Journal of Political Economy 112, 1: S164-

S187. 
171

U.S. Department of Health and Human Services, Administration for Children and Families,

Office of Community Services. 2012. Assets for Independence Program: Status at the 

Conclusion of the Eleventh Year, Results Through September 30, 2010. Report to 

Congress. Washington, DC: U.S. Department of Health and Human Services. 

U.S. Department of Housing and Urban Development, Office of Public Housing and Voucher

Programs and Office of Public Housing Investments. 2014. “Fact Sheet: Family SelfSufficiency (FSS) Program.” Website: http://portal.hud.gov/huddoc/fssfactsheet.pdf. 

U.S. Department of Labor, Bureau of Labor Statistics. 2013. “Table 1101: Quintiles of Income 

Before Taxes: Annual Expenditure Means, Shares, Standard Errors, and Coefficient of 

Variation, Consumer Expenditure Survey, 2012.” Consumer Expenditure Survey data 

tables. Website: http://www.bls.gov/cex/#tables.

Verma, Nandita, Betsy Tessler, Cynthia Miller, James A. Riccio, Zawadi Rucks, and Edith 

Yang. 2012. Working Toward Self-Sufficiency: Early Findings from a Program for 

Housing Voucher Recipients in New York City. New York: MDRC.

Weller, Christian, and Sam Unger. 2013. The Universal Savings Credit. Washington, DC: 

Center for American Progress.

Wolff, Edward N. 2014. “Household Wealth Trends in the United States, 1962-2013: What 

Happened over the Great Recession?” Working Paper 20733. Cambridge, MA: National 

Bureau of Economic Research. 

About MDRC

MDRC is a nonprofit, nonpartisan social and education policy research organization 

dedicated to learning what works to improve the well-being of low-income people. Through 

its research and the active communication of its findings, MDRC seeks to enhance the 

effectiveness of social and education policies and programs.

Founded in 1974 and located in New York City and Oakland, California, MDRC is best 

known for mounting rigorous, large-scale, real-world tests of new and existing policies and 

programs. Its projects are a mix of demonstrations (field tests of promising new program 

approaches) and evaluations of ongoing government and community initiatives. MDRC’s 

staff bring an unusual combination of research and organizational experience to their work, 

providing expertise on the latest in qualitative and quantitative methods and on program 

design, development, implementation, and management. MDRC seeks to learn not just 

whether a program is effective but also how and why the program’s effects occur. In addition, 

it tries to place each project’s findings in the broader context of related research — in order 

to build knowledge about what works across the social and education policy fields. MDRC’s 

findings, lessons, and best practices are proactively shared with a broad audience in the 

policy and practitioner community as well as with the general public and the media.

Over the years, MDRC has brought its unique approach to an ever-growing range of policy 

areas and target populations. Once known primarily for evaluations of state welfare-to-work 

programs, today MDRC is also studying public school reforms, employment programs 

for ex-offenders and people with disabilities, and programs to help low-income students 

succeed in college. MDRC’s projects are organized into five areas:

•	 Promoting Family Well-Being and Children’s Development

•	 Improving Public Education

•	 Raising Academic Achievement and Persistence in College

•	 Supporting Low-Wage Workers and Communities

•	 Overcoming Barriers to Employment

Working in almost every state, all of the nation’s largest cities, and Canada and the United 

Kingdom, MDRC conducts its projects in partnership with national, state, and local 

governments, public school systems, community organizations, and numerous private 

philanthropies.