Individual Development Accounts and Homeownership among Low-income Adults with Disabilities

Abstract

We examined the long-term effects of Individual Development Accounts (IDAs), savings accounts that match funds deposited by participants for qualified purposes, on homeownership rates among study participants with disabilities in a randomized experiment. Results from a 10-year follow-up of the IDAs indicate that rates of homeownership were nearly 10 percentage points higher for treatment participants with disabilities than for control-group members with disabilities (p < .10). The impacts of IDAs seem to vary with the baseline socioeconomic characteristics of participants—particularly with homeownership, bank account ownership, and public housing assistance. We conclude by discussing policy implications of using asset-building programs to support people with disabilities.

Keywords

asset building disability experiment individual development accounts homeownership

Introduction

This study evaluates whether Individual Development Accounts (IDAs), a policy initiative combing individual savings with matching funds for long-term asset accumulation, are effective tools for low-income individuals with disabilities to build homeownership. Over the past decade, asset-building policies, such as IDAs, have gained recognition as an institutional strategy to enable people with disabilities to accumulate financial assets for the purchase of goods and services that improve their economic security (e.g., automobiles, educational tuition, home down payments). Interest has grown in asset-building programs as a complement to traditional income-support programs to enable individuals with disabilities to achieve greater financial stability and reach important life goals (Sherraden 1991). Despite increased availability of asset-building programs for individuals with disabilities, few studies assess whether such programs have improved financial stability among this population.

It is important to answer this question because the level of accumulated assets among people with disabilities has been very low. For example, in 2003, only 10 percent of people with disabilities owned homes, but the rate was 71 percent among those without disabilities. The mean net worth was 15 percent less among married-couple households that included a member with a disability than among married-couple households without a disabled member; a comparison of households headed by single women indicated that net worth was 31 percent lower among households with a disabled member than among those without a disabled member (Parish et al. 2010).

To fill this knowledge gap, the present study generates empirical evidence based on a randomized experiment of IDAs in Tulsa, Oklahoma, and provides important insights for policymakers and applied social science researchers. In particular, the work presented here examines the long-term impact of IDAs on homeownership among low-income individuals with disabilities.

Background

IDAs and Impacts on Asset Building

The product of an innovative and progressive effort to help low-income people accumulate assets, IDAs are savings vehicles that provide matching funds if deposits are used for qualified purposes. Such purposes typically include homeownership, education, and small-business start-up. The rates at which IDAs match participants’ savings vary by funders and saving purposes. They are typically 1:1, 2:1, or 3:1 (i.e., a match of $1, $2, or $3 is provided for every dollar saved). Many IDA programs have other features, such as automatic-deposit capabilities (to simplify saving) and financial education (to improve participants’ financial literacy). The design of these features has been grounded in an institutional theory of saving, which suggests that asset accumulation is determined by institutional support (Sherraden 2009). In the last two decades, more than 50,000 IDAs have been opened at 544 project sites through the federal Assets for Independence Program, which has provided grants to community-based organizations and local governments (U.S. Department of Health and Human Services 2010a).

Studies on IDAs have demonstrated that low-income participants are capable of saving but that the total amount of IDA savings may be limited (Richards and Thyer 2011). The American Dream Demonstration (ADD), the first large-scale test of IDAs in the United States, includes 2,350 participants in 14 local IDA programs hosted by 13 community organizations around the country. Participants who receive IDAs through ADD do indeed accumulate assets in their IDA accounts: On average, they deposit $16.60 each month for an annual savings of about $200 over a four-year participation period (Schreiner and Sherraden 2007). The IDA research also shows that some low-income IDA participants achieve the IDA program goal (e.g., homeownership, education, and microenterprise). For example, the Canadian Learn$ave study, the largest experimental demonstration of IDAs, finds that IDA participation positively affects participants’ chances of enrolling in postsecondary education and starting a small business—two of the purposes that qualify for matching funds (Leckie et al. 2010).

Asset Accumulation and People with Disabilities

The idea of helping individuals with disabilities and their families to accumulate assets has gained popularity in the disability community and among applied social science scholars in the field of disability studies. In 2001, the World Institute on Disability initiated an IDA pilot program for this population (Leydorf and Kaplan 2001). In its annual progress reports on disability policy, the National Council on Disability (e.g., 2009) has proposed asset-based initiatives, such as IDAs, to expand the opportunities of employment and homeownership for people with disabilities. Cosponsored by federal and state agencies, the Florida Freedom Initiative is an employment demonstration for young Supplemental Security Income (SSI) recipients with disabilities, and the demonstration provided participants with an IDA (Livermore and Goodman 2009). The state of Washington’s IDA legislation allows people with disabilities to use IDA assets for assistive technology and automobiles (National Council on Disability 2008). Recently passed by the Congress, the ABLE Act proposed to expand the Section 529 college savings plan to allow accounts designed to accumulate savings for the qualified expenses of people with disabilities: education, a primary residence, transportation, obtaining and maintaining employment, health and wellness, and other personal support expenses.

The disability community’s enthusiasm for asset-building programs may be partially explained by the general lack of institutional support for efforts to enlarge the assets of people with disabilities. An analysis by the Internal Revenue Service, Wage and Investment Research (2007) indicates that only 37 percent of people with disabilities use financial institutions for savings and asset investments compared with 51 percent of their nondisabled peers. Nearly one-third (30 percent) of people with disabilities do not make any asset investments at all. This is more than twice the percentage of people without disabilities who have no asset investments (12 percent). In terms of access to financial institutions and services, the percentage of people with disabilities who use online banking is about half of that of people who report no disability (18 percent vs. 37 percent), and only 16 percent of people with disabilities use financial advisors compared with 29 percent of people without disabilities (Internal Revenue Service, Wage and Investment Research 2007). The 2004 National Organization on Disability/Harris Survey of Americans with Disabilities demonstrates that people with disabilities are more likely to be asset poor and less likely to own savings accounts than nondisabled adults. They have less access to financial institutions and lower rates of ownership of stocks and bonds. They make less use of the home mortgage tax deduction, and the rate at which they hold IDAs is lower (Schmeling et al. 2006).

Despite the disability community’s growing interest in asset-building efforts, few studies have examined the saving performance of people with disabilities in these programs. This limited research has shown that people with disabilities have successful saving outcomes in IDAs but tend to save less than their nondisabled counterparts and are less likely to participate in asset-specific education (Lombe et al. 2010). An evaluation of a small-scale IDA program for low-income individuals with developmental disabilities (N = 20) also found that most participants were able to save a maximum matchable deposit of $47 each month (Rosato 2005).

Method

The Tulsa Experiment

The Tulsa IDA effort was an ADD program with a randomized experimental design. The program was administered by the Community Action Project of Tulsa County (CAPTC), a local antipoverty community organization that recruited participants for the Tulsa experiment between October 1998 and December 1999. These participants were CAPTC clients who received other services, such as tax-preparation assistance and homeownership-preparation classes. Eligibility rules required applicants to be employed at the time of application and to have household income amounts below 150 percent of the federal poverty line. The study randomly assigned 1,103 applicants who completed the baseline survey into the treatment (n = 537) or control (n = 566) group.

During the experimental period (1998 through 2003), treatment-group members had access to multiple services, including an IDA matched-savings account held at the Bank of Oklahoma, financial education, and case management (see Figure 1). After opening the IDA, participants in the treatment group could make matchable deposits into it for 36 months. Deposits in the account earned a 2 to 3 percent interest rate and were matched by the IDA program up to $750 per year; deposits in excess of $750 in a given year were eligible for a match in the subsequent year. Unmatched withdrawals were permitted at any time. The program only allowed matched withdrawals six or more months after account opening. It provided a 2:1 match for home purchase and a 1:1 match for home repair, small-business investment, postsecondary education, or retirement saving. A treatment group member with the maximum matchable deposit in all three years could have accumulated $6,750 (plus interest) for a home purchase or $4,500 (plus interest) for other qualified uses. After the experimental period (by the end of 2003), any remaining balance in the IDA program was to be transferred into a Roth IRA account. The Tulsa program’s financial education component involved general money-management training and asset-specific training. Participation in both forms of training was voluntary, and the program only offered training to treatment participants.

Figure 1.

Study design, enrollment, and retention for IDA participants in Tulsa, Oklahoma.

Participants in both the treatment and control groups agreed not to use other matched-savings programs or other homeownership assistance offered by CAPTC during the study period. After 2003, treatment- and control-group members became eligible again for all CAPTC services. In sum, this experimental design exposed treatment participants to an IDA program, but control-group members received none of the services discussed above. Control-group members had no access to the IDA, received no financial incentive to make deposits, and obtained no financial education from the program.

Data and Sample

Four surveys were administered to participants in each group (treatment and control): (1) Face-to-face baseline interviews (Wave 1 baseline survey) were conducted between October 1998 and December 1999, before group assignment; (2) the Wave 2 follow-up survey was administered between May 2000 and August 2001 (18 months after enrollment); (3) the Wave 3 follow-up survey was administered between January and September 2003 (48 months after enrollment); and (4) the Wave 4 follow-up survey (the 10-year follow-up) was administered between August 2008 and March 2009. To examine the impacts of the IDA participation on homeownership among participants with disabilities, we used data mainly from the baseline and the 10-year follow-up surveys (i.e., Wave 1 and Wave 4). We created a subsample of individuals who reported a disability based on a retrospective measure. Among the 855 participants who completed the Wave 4 survey, 336 respond affirmatively to the disability question: 174 in the treatment group and 162 in the control group.

Measures

Homeownership, the outcome variable, indicates whether a participant owned his or her home at the time of the Wave 4 interview. The survey asked participants whether they owned or rented the home they lived in. In coding, the research team assigned a value of 1 to homeowners and a value of 0 to all others. The reader should note that the Wave 4 survey also asked retrospective questions about homeownership between 1998 and 2009. For example, it asked respondents to indicate their homeownership status at a specific time. It also asked when they bought a home and when they sold it. We used this information to build a homeownership history for each program participant and to calculate yearly homeownership rates for both groups.

The major independent variable is participants’ treatment status in the Tulsa IDA program (coded 1 for treatment-group members and 0 for control-group members). Other variables used in this study include demographic and socioeconomic characteristics measured at baseline: participants’ age, gender (1 = female, 0 = male), race (1 = Caucasian, 0 = others), marital status (1 = married, 0 = others), and educational attainment (0 = high school graduate or less, 1 = some college, 2 = college degree or more). Control variables also include the number of persons living in the household, presence of children in household (1 = yes, 0 = no), Wave 1 homeownership, public housing assistance (1 if receiving Section 8 housing assistance and 0 if not), household income per month, bank account ownership (1 = yes, 0 = no), car ownership (1 = yes, 0 = no), and ownership of a small business (1 = yes, 0 = no). All control variables capture information on characteristics at the time of the baseline survey.

Statistical Analyses

We first compared baseline characteristics of the treatment and control groups, and then used bivariate analyses (t test) to examine the long-term impacts of the IDA intervention on homeownership. Because the groups differed significantly on several socioeconomic variables (see “Results” section and Table 1), we also examined whether the intervention’s effect on Wave 4 homeownership varied by the three baseline variables: homeownership, public housing assistance, and bank account ownership. In addition, we estimated regression-adjusted treatment effects by including Wave 1 homeownership, demographic characteristics, and socioeconomic characteristics in ordinary least squares models (variables are listed in Table 1). The criterion for statistical significance in all of these analyses is a two-sided p value of less than or equal to .10 due to a small sample size in the study. The sample of participants with disabilities was relatively small and breaks down into even smaller groups by demographic characteristics.

Table 1.

Baseline Characteristics of Participants with Disabilities in the Tulsa IDA Program (N = 336).

Variables	Treatment	Control	Difference
Variables	(n = 174)	(n = 162)	Difference
Age (M)	37.83	38.06	−0.23
Female	80.46	81.48	−1.02
Race (Caucasian)	43.68	47.80	−4.12
Married	21.26	22.84	−1.58
Education
High school graduate or less	29.89	29.63	0.26
Some college	45.98	46.30	−0.32
College degree or more	24.14	24.07	0.07
Household size (mean)	3.07	3.08	−0.01
Presence of children in household	75.86	74.07	1.79
Homeownership	17.92	27.16	−9.24**
Section 8 housing assistance	23.12	14.81	8.31**
Monthly household income (mean)	1,375.34	1,435.06	−59.72
Bank account ownership	88.37	79.38	8.99**
Own car	83.33	87.65	−4.32
Own a business	8.05	8.64	−0.59

Note. IDA = Individual Development Account.

p < .05.

Results

Sample Characteristics

Table 1 presents the baseline demographic and socioeconomic characteristics for all participants with disabilities (N = 336), distinguishing results by treatment status. The mean age was about 38 years. About 80 percent of the participants were female, less than half were Caucasian, and slightly more than 20 percent were married. More than two-thirds of participants had some college or more, and, on average, participants’ monthly household income was about $1,400. Three-fourths of participants reported having children in the household. The sample is balanced with respect to most of the variables except homeownership, public housing assistance, and ownership of a bank account. Compared with the control-group members, treatment participants were 8 percentage points more likely to receive Section 8 public housing assistance (23 percent vs. 15 % p < .05), 9 percentage points more likely to own a bank account (88 percent vs. 79 % p < .05), and 9 percentage points less likely to own a home at baseline (18 percent vs. 27 % p < .05). Overall, the treatment group seems more socioeconomically disadvantaged than the control group. As previous ADD studies noted (e.g., Mills et al. 2008), the Tulsa IDA program’s sample was not representative of low-income households eligible for the program.

Bivariate Analyses

Table 2 reports results from bivariate analyses concerning the IDA intervention’s impacts on homeownership for participants with disabilities and for several subsamples. In the sample, nearly half of treatment participants (48 percent) and 40 percent of control-group members owned a home in 2009 (at the Wave 4 survey); the difference between the groups is nonsignificant (p = .11). This is noteworthy because the two groups differed significantly on rates of homeownership at the time of the baseline survey (18 percent vs. 27 percent). The result seems to suggest that IDA participation had a positive impact on homeownership among participants with disabilities.

Table 2.

Bivariate Analyses: The IDA Treatment Effects on Homeownership at Wave 4.

	Treatment	Control	Difference	p value
Homeownership rate for full sample (N = 336)	48.27	39.51	8.76	.11
Homeownership rate for Wave 1 homeowners (n = 75)	83.87	63.64	20.23*	.06
Homeownership rate for Wave 1 renters (n = 260)	40.14	30.51	9.63	.11
Homeownership rate for Wave 1 Section 8 recipients (n = 64)	66.43	70.34	−3.91	.51
Homeownership rate for Wave 1 Section 8 nonrecipients (n = 270)	51.52	41.30	10.22*	.09
Homeownership rate for Wave 1 participants with bank accounts (n = 279)	47.37	46.46	0.91	.88
Homeownership rate for Wave 1 participants without bank accounts (n = 53)	50.00	15.15	34.85***	.01

Note. IDA = Individual Development Account.

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

As Table 1 shows, randomization did not achieve a balance between treatment and control groups on rates of homeownership, receipt of public housing assistance, and ownership of a bank account at Wave 1; the observed difference in homeownership at Wave 4 might be associated with these unbalanced baseline variables. Thus, we examined the impacts of the IDA intervention by considering differences in the levels of these three variables.

Among participants who owned a home at baseline (n = 75), rates of homeownership fall by Wave 4: to 84 percent among treatment participants and to 64 percent among control-group members. The resulting 20-percentage-point difference is significant (p < .10). Because this difference is a conditional estimate, it is not likely to have resulted from the unbalance observed between the groups in rates of Wave 1 homeownership. Rates of homeownership rose over the 10-year period among those who rented their dwelling at baseline (n = 260); 40 percent of baseline renters in the treatment group owned their home at Wave 4, and 31 percent of those in the control group owned their home by then. However, this difference is not significant (p = .11).

Among participants who received public housing assistance at baseline, homeownership rates were quite high at Wave 4. The treatment group was less likely to include homeowners (66 percent vs. 70 percent) at the 10-year follow-up, but the difference (−3.91 percentage points, p = .51) is not statistically significant. Among those who did not receive public housing assistance at baseline, treatment participants were significantly more likely to own a home than are counterparts in the control group (52 percent vs. 41 percent, p < .10). Wave 4 homeownership rates were similar for treatment- and control-group members who owned bank accounts at the time of the baseline (47 percent vs. 46 percent). However, Wave 4 homeownership rates were three times higher for treatment participants who had no bank account at baseline than for control-group counterparts (50 percent vs. 15 percent, p < .01). This suggests that obtaining a bank account through the IDA program had some effect on homeownership. Overall, the bivariate analyses suggest that effects of the intervention may vary by homeownership, public housing assistance, and ownership of a bank account.

Regression Analysis

We evaluated the regression-adjusted treatment effects on homeownership controlling for demographic characteristics listed in Table 1. Information on some control variables in Table 1 is missing for 26 participants. These variables include household income, bank account ownership, and public housing assistance at baseline. Because the Wave 2 survey occurred only 18 months after baseline, we used information from the Wave 2 survey in place of missing values for those three variables in the analysis. This increased the valid sample size by 15 (N = 328). The analysis indicated that the intervention (IDA participation) increased the treatment group’s Wave 4 homeownership rate by 9 percentage points (p = .08).

Overall, both bivariate and regression analyses suggest that the intervention (IDA participation) had positive impacts on rates of Wave 4 homeownership among low-income participants with disabilities. This finding is also confirmed by the comparison of year-by-year homeownership rates for the treatment and control groups. Figure 2 shows results from a comparison of the homeownership rates for the treatment and control groups: the retrospective information indicates that the treatment group had a lower homeownership rate at baseline, a greater increase in homeownership rate between 1999 and 2002 (during the IDA participation period), and a higher homeownership rate since then.

Figure 2.

Homeownership rate among people with disabilities over time by treatment status.

Discussion

This study provides empirical evidence of the long-term impacts of IDAs on homeownership for low-income individuals with disabilities. The first lesson from this evaluation is that, overall, the IDA intervention had positive impacts on homeownership for this group. Ten years after implementation of the IDA intervention, and six years after the IDA program ended, the homeownership rate was nearly 10 percentage points higher among treatment participants with disabilities than among control-group members with disabilities. For social science practitioners, in particular, for those working with the disability community, the Tulsa IDA provides a concrete example of a program that promotes homeownership among individuals with disabilities using asset-building strategies. Several questions on how to implement asset-building programs for the disability community should be further examined in future applied social science research. For example, can the program be expanded to an even broader disability community or to other asset-building purposes (e.g., postsecondary education or health care needs) for individuals with disabilities? The programs designed and implemented by the disability organizations may provide better data to evaluate these questions.

Another noteworthy lesson from this study is that the impacts of IDAs seemed to vary with the socioeconomic characteristics of participants. For example, results showed that the IDA treatment had statistically significant and positive impacts (about 20 percentage points) for participants who were homeowners but not for those who were renters at baseline. This indicates that the IDA program may have protected baseline homeowners from the loss of their home during the period of the study. A high proportion of homeowners lost their homes in the latest economic recession, and the risk of losing a home is even higher for individuals with disabilities than for their nondisabled peers (Huang 2012). As Figure 2 shows, the homeownership rate for the control group decreased after 2007, but that for treatment participants continues to grow.

Similarly, findings suggest that the IDA program had positive and significant impacts (35 percentage points) for participants who owned no bank account at baseline. In contrast, the treatment had positive but nonsignificant impacts on homeownership for those who owned a bank account at baseline. This result should be interpreted with caution because few participants (n = 53) did not own a bank account at baseline. Nonetheless, the finding is interesting because it suggests that the IDA program had positive impacts on homeownership rates among individuals who were previously unbanked. Access to basic financial services, such as bank accounts, is critical for financial security among individuals with disabilities. The latest report on banking status and financial behaviors of individuals with disabilities (McDonald et al. 2015) suggests that nearly one-fifth of households headed by an individual with a disability are unbanked and another one-fourth are under-banked. To improve financial inclusion (i.e., access to mainstream financial and banking services) among the disability community, the Consumer Financial Protection Bureau (CFPB) and the National Disability Institute recently launched a financial well-being initiative in six communities, which delivered information, tools, and support through financial counselors and removed obstacles faced by individuals with disabilities to access basic financial services (CFPB 2015).

Future research should test the explanations discussed above to gain further understanding of how IDAs affect financial inclusion, homeownership, and other asset holding among individuals with disabilities. When designing and implementing asset-building programs for this population, practitioners need to take into consideration heterogeneous impacts of IDAs by socioeconomic status. For example, if the program generates more benefits for those without a bank account, some components of the program could be providing basic financial services for individuals with disabilities.

Study Limitations

Several limitations should be noted. First, the Tulsa IDA study was designed to assess the impact of IDAs on low-income households but not specifically for people with disabilities. The retrospective measure of disability was included in the Wave 4 survey and is subject to recall bias. Second, the sample of participants with disabilities is relatively small and breaks down into even smaller groups if participants are categorized by levels of socioeconomic characteristics (e.g., homeownership and bank account ownership at baseline). Third, to evaluate the impacts of IDAs, program participants were restricted from receiving some potential services provided by the CAPTC. This arrangement may raise concerns related to research ethics. One different strategy could be to provide alternative services to control group members. Finally, the present study’s findings may not be generalizable to the overall low-income disability population. The sample is not representative of the low-income disability population or of IDA program participants. In this regard, the qualitative interviews with some participants with disabilities in the experiment could be explored to provide further important information on the impacts of IDAs among this population. Future research should address these limitations, use data from IDA programs specifically designed to assess outcomes for people with disabilities, and examine other impacts of the IDA program (e.g., its effects on postsecondary education and small-business start-up).

Implications

The analysis indicates that IDA programs can help low-income people with disabilities become homeowners. The findings suggest that IDA programs may be a potentially powerful intervention for helping people with disabilities to secure long-term housing in the community. Most federal supports for people with disabilities focus on community living as an outcome goal, and housing is a critical component of this (U.S. Department of Health and Human Services 2010b). Moreover, the findings suggest that IDA program features, such as access to bank accounts, may facilitate saving and homeownership of low-income people with disabilities. This supports findings from related research on financial empowerment for people with disabilities (Lombe et al. 2010). That said, evidence emerges from this study’s findings that asset-building programs may be a new tool for achieving goals across multiple policy domains: financial inclusion and empowerment, housing, economic security, and community living. More research is needed in this area. Given the struggle of existing policy interventions to arrest negative financial conditions associated with disability, it is encouraging to see this small but growing body of evidence that people with disabilities can save, even in small amounts, and can attain saving goals consistent with stated program aims.

Finally, it is notable that women of color and single parents comprise a high proportion of the subsample of IDA and control participants with disabilities in the Tulsa experiment. Although the participation of these subgroups in IDA programs is not unusual, they have received little attention in previous disability-related policy research. This study demonstrates that IDAs have positive effects on homeownership among women of color and single parents who are disabled, and this result should encourage further exploration of asset-building strategies among these important subgroups.

Footnotes

Acknowledgements

For financial support, we thank Annie E. Casey Foundation, F. B. Heron Foundation, John D. and Catherine T. MacArthur Foundation, Charles Stewart Mott Foundation, the National Poverty Center at the University of Michigan, Rockefeller Foundation, the Smith Richardson Foundation, and the University of North Carolina. We greatly appreciate Brian Burke, Susan Triplet, and Melissa Hobbs at RTI International for their diligence in tracing the IDA participants 10 years after the experiment began and six years after it ended, and Clinton Key and Mark Schreiner for preparing the data. We give special thanks to Lissa Johnson, Administrative Director at the Center for Social Development at Washington University in St. Louis, for playing a key role in implementing and overseeing the study. We also are grateful to Chris Leiker for providing editorial assistance.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Annie E. Casey Foundation, F. B. Heron Foundation, John D. and Catherine T. MacArthur Foundation, Charles Stewart Mott Foundation, the National Poverty Center at the University of Michigan, Rockefeller Foundation, the Smith Richardson Foundation, and the University of North Carolina.

Author Biographies

Jin Huang, PhD, is an assistant professor at Saint Louis University School of Social Work. He is interested in social policy that supports family and child well-being, with a particular focus on asset building and financial capability.

Margaret Lombe, PhD, is an associate professor at the Boston College School of Social Work. She is also a faculty associate at the Center for Social Development at Washington University in St. Louis. Her area of expertise is international social development with an emphasis on social inclusion/exclusion and capacity building. Lombe also specializes in methods for food security and nutrition assessment. Her current research portfolio includes understanding social effects of foreclosure, food security and livelihood mechanism in vulnerable households, exploration of the intersectionality between poverty and disability, empowerment of OVC as well as capacity building strategies.

Michelle Putnam is Professor and Associate Dean for Research at the School of Social Work, Simmons College. She is a leading expert in the area of aging with disability and social welfare policy.

Michal Grinstein-Weiss, PhD, is the associate director of the Center for Social Development and an associate professor with the George Warren Brown School of Social Work at Washington University in St. Louis.

Michael Sherraden, PhD, is the George Warren Brown Distinguished University Professor, founding director of the Center for Social Development (CSD) and the founding director of Next Age Institute at Washington University in St. Louis. He is the author or editor of several books in asset building: Assets and the Poor (1991), Inclusion in the American Dream (2005), Can the Poor Save? (2007), Saving in Low-Income Families (2008), and Asset Building Innovations and Strategies in Asia (2014). In 2010, he was listed by Time magazine as one of the 100 most influential people in the world.

References

Consumer Financial Protection Bureau. 2015. CFPB Launches Financial Well-being Initiative Targeted to People with Disabilities. Washington, DC: Consumer Financial Protection Bureau.

Huang

Jin

. 2012. “Work Disability, Mortgage Default, and Life Satisfaction in the Economic Downturn: Evidence from the Panel Study of Income Dynamics.” Journal of Disability Policy Studies 22(4):237–46.

Internal Revenue Service, Wage and Investment Research. 2007. “Characteristics of Disabled Taxpayers Ages 18 to 59: Study of Filing Patterns and Preferences for Receiving Tax Information and Services.” Disabilities Research Report, Publication No. 4640, Internal Revenue Service, Washington, DC.

Leckie

Norm

Hui

Taylor Shek-Wai

Tattrie

Doug

Robson

Jennifer

Voyer

Jean-Pierre

. 2010. Final Report: Learn$ave Individual Development Accounts Project. Ottawa, Ontario: Social Research and Demonstration Corporation. Retrieved July 3, 2015 (http://www.srdc.org/uploads/learnSave_final_EN.pdf).

Leydorf

Dede

Kaplan

Deborah

. 2001. Use of Individual Development Accounts by People with Disabilities: Barriers and Solutions. Oakland, CA: World Institute on Disability.

Livermore

Gina A.

Goodman.

Nanette

2009. A Review of Recent Evaluation Efforts Associated with Programs and Policies Designed to Promote the Employment of Adults with Disabilities. Ithaca, NY: Employment & Disability Institute, Cornell University.

Lombe

Huang

Jin

Putnam

Michelle

Cooney

Kate

. 2010. “Exploring Saving Performance in an IDA Program: Findings for People with Disabilities.” Social Work Research 34(2):83–93.

McDonald

Katherine

Conroy

Nicole E.

Michael Morris

M.S.

Elizabeth Jennings

J.D.

2015. Banking Status and Financial Behaviors of Adults with Disabilities: Findings from the FDIC National Survey of Unbanked and Underbanked Households. Washington, DC: National Disability Institute.

Mills

Gregory

Gale

William G.

Patterson

Rhiannon

Engelhardt

Gary V.

Eriksen

Michael D.

Apostolov

Emil

. 2008. “Effects of Individual Development Accounts on Asset Purchases and Saving Behavior: Evidence from a Controlled Experiment.” Journal of Public Economics 92(5–6):1509–30.

10.

National Council on Disability. 2008. The State of 21st Century Financial Incentives for Americans with Disabilities. Washington, DC: National Council on Disability.

11.

Parish

Susan L.

Grinstein-Weiss

Michal

Yeo

Yeong Hun

Rose

Roderick A.

Rimmerman.

Arie

2010. “Assets and Income: Disability-based Disparities in the United States.” Social Work Research 34(2):71–82.

12.

Richards

Kristin V.

Thyer

Bruce A.

2011. “Does Individual Development Account Participation Help the Poor? A Review.” Research on Social Work Practice 21(3):348–62.

13.

Rosato

Nancy Scotto

. 2005. An Evaluation of the Success of Saving-growing Personal Assets Project: Individual Development Accounts for People with Developmental Disabilities. New Brunswick, NJ: Rutgers Center for State Health Policy.

14.

Schmeling

James

Schartz

Helen A.

Morris

Michael

Blanck.

Peter

2006. “Tax Credits and Asset Accumulation: Findings from the 2004 N.O.D./Harris Survey of Americans with Disabilities.” Disability Studies Quarterly 26(1). Retrieved (http://dsq-sds.org/article/view/654/831).

15.

Schreiner

Mark

Sherraden

Michael

. 2007. Can the Poor Save?: Saving and Asset Building in Individual Development Accounts. New Brunswick, NJ: Transaction Publishers.

16.

Sherraden

Michael

. 1991. Assets and the Poor: A New American Welfare Policy. Armonk, NY: M. E. Sharpe.

17.

Sherraden

Michael

. 2009. “Individual Development Accounts and Asset-building Policy: Lessons and Directions.” Pp. 191–217 in Insufficient funds: Savings, Assets, Credit, and Banking among Low-income Households, edited by Blank

R. M.

Barr

M. S.

New York, NY: Russell Sage.

18.

U.S. Department of Health and Human Services. 2010a. Assets for Independence Program: Status at the Conclusion of the Ninth Year. Washington, DC: U.S. Department of Health and Human Services.

19.

U.S. Department of Health and Human Services 2010b. Community Living Initiative. Washington, DC: U.S. Department of Health and Human Services.