Individual Development Accounts: Participant and Program Characteristics Related to Savings Performance Center on Poverty, Work and Opportunity Policy Brief Series Michal Grinstein-Weiss, PhD, MSW, MA and Kate Irish, MSW Candidate School of Social Work University of North Carolina at Chapel Hill 301 Pittsboro Street - CB 3550 Chapel Hill, NC 27599-3550 (V) 919.962.6446 (F) 919.843.8715 (E) michalgw@email.unc.edu The authors wish to thank the foundation funders of ADD, especially the Ford Foundation, Charles Stewart Mott Foundation, FB Heron Foundation, and Metropolitan Life Foundation for funding ADD research; the IDA program staff at the 13 research sites of the American Dream Demonstration (ADD); the Corporation for Enterprise Development for implementing ADD; Lissa Johnson, ADD research project manager; Margaret Clancy, for ensuring quality of the monitoring data; Mark Schreiner for data management and preparation, and Diane Wyant for her review of the manuscript.
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 2 Introduction Social policy in the United States aimed at poverty alleviation shifted during the midtwentieth century to a greater focus on income support programs such as income transfers, rental assistance, or other types of consumption assistance. Although these income support programs may succeed in easing hardships and supporting a certain level of consumption, the pathway out of poverty is not through consumption but through saving and accumulation (Sherraden, 2001). Assets, defined as the total amount of an individual s accumulated wealth held at a given time, offer resources that create opportunities for investment in long-term economic and social wellbeing (Sherraden, 2005). Asset building or wealth accumulation is supported in the United States through a number of asset-based policies; however, most of these policies do not benefit the poor. The United States spends approximately $400 billion a year in tax expenditures in the form of tax deductions, tax credits, preferential tax rates, tax deferrals, or income exclusions, with over 90% of these tax benefits going to households earning more than $50,000 a year (Boshara & Reid, 2005). In the late 1980s, Michael Sherraden, director of the Center for Social Development at Washington University, proposed that U.S. social policy take a more progressive and inclusive approach by including asset-building opportunities for all people. In this approach to social and economic development, individuals can achieve long-term economic well-being through the accumulation of assets such as a home, a business, or an education. Although saving is a key pathway to asset ownership and wealth accumulation for many families, it can be particularly difficult for low-income households that have limited resources and face uncertainties regarding their future income. Low-income households face a number of barriers to wealth accumulation including consumption needs, the low-income employment
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 3 market, lack of access to mainstream financial services, individual characteristics, government policies, and a lack of institutional mechanisms to save. Despite these considerable challenges, evidence suggests that low-income households can and do save and accumulate assets when provided with support and incentives. One assetbuilding approach, Individual Development Accounts, addresses the barriers families face, particularly the institutional variables, to foster savings and asset accumulation among lowincome households. Individual Development Accounts (IDAs) are matched savings accounts that provide support, financial education, and incentives for low-income families. IDA programs promote savings by offering participants 1:1, 2:1, or higher ratio matches for their deposits. IDA participants save toward asset-building purposes such as home purchase, postsecondary education, and business development; important assets that promote long-term well-being and financial self-sufficiency (Sherraden, 1988, 1991). This study sought to answer the following research questions: (a) What are the individual characteristics (demographic and financial) associated with saving outcomes among IDA participants? (b) What program characteristics were associated with positive saving outcomes among IDA participants? (c) What are the program and policy implications for supporting assetbuilding? Findings from this study indicate that several individual characteristics and all of the program characteristics studied were important in explaining the saving performance of IDA participants. The individual characteristics of residency, race, household composition, education, income, and asset ownership were found to influence savings rates. All four program characteristics examined direct deposit, match rate, financial education, and monthly savings
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 4 targets were associated with greater saving performance. These findings have important implications for both policy and program development, and are included in the discussion. Asset building: An Innovative Strategy for Long-Term Social and Economic Development For many years, income has been the primary measure of poverty in America (Sherraden, 2005). However, income-based policies, such as income transfers, rental assistance, and other types of consumption assistance, have failed to provide the poor with the asset-building tools that are necessary to break the cycle of poverty and secure long-term social and economic development (Boshara, 2001). In recent years, there has been a growing recognition that income measures alone are insufficient, and that asset measures need to be considered to adequately measure and define poverty (Boshara, 2006). Statistics on asset inequality in the United States reported by Wolff (2001) revealed that the bottom 40% of households controlled less than 1% of all wealth, while the top 20% of households possessed more than 83% of all wealth. The implication of this concentrated disparity is that millions of households in the United Sates have accumulated little or no savings and have few or no assets. Furthermore, disparities in wealth have been increasing in recent decades. From 1989 to 2004, while the average net worth of the top 25% of households increased by 65%, the net worth of the bottom 25% of households remained stagnant, hovering just below zero (Johnson, Mensah, & Steuerle, 2006). Asset building or wealth accumulation is supported in the United States through a number of asset-based policies; however, most of these policies do not benefit the poor. Many asset-based policies operate primarily through the tax system by providing tax benefits on assets such as homeownership, investments, and retirement accounts (Sherraden, 2005). However, in most instances, the poor do not benefit from these policies because they have little or no tax
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 5 liability, are less likely to own investment assets, and have little incentive for asset accumulation (Sherraden, 2005). In the late 1980s, Michael Sherraden, director of Center for Social Development at Washington University, proposed that U.S. social policy take a more progressive approach by including asset-building opportunities for all. Research indicates that asset holding yields many long-term positive outcomes for individuals, families, and their communities. Examples of potential outcomes include improved household economic stability, fostering long-term thinking and planning, encouraging greater educational attainment, creating a basis for risk-taking, lowering rates of intergenerational poverty, and increasing rates of civic involvement (Boshara, 2006; Sherraden, Schreiner, & Beverly, 2002; Carney & Gale, 2001). Barriers to Saving and Accumulating Assets Faced by Low-Income Households Saving is a key pathway to asset ownership and wealth accumulation for many families. Although saving is not easy for anyone, it can be particularly difficult for low-income households that usually earn low wages, have limited resources, and face uncertainties regarding their future income. Without support, low-income households accumulate little if any wealth or assets. There are a number of factors related to the lack of wealth accumulation among lowincome households including consumption needs, the low-wage employment market, limited access or no access to mainstream financial services, individual characteristics, government policies, and a lack of institutional mechanisms to save. Consumption Needs Low-income families often face difficulties in meeting day-to-day needs and, therefore it is a struggle for them to defer or reduce consumption (Birdsall, Pinckney, & Sabot, 1996). In
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 6 addition, low-income households may face unstable job situations, unexpected expenses, or emergencies that may require available income or savings to meet these needs. Low-wage employment market Low-wage workers face a number of obstacles that make saving difficult. First, according to 1996 data from the Survey of Income and Program Participation (SIPP), low-wage workers earned an average of $5.58 per hour, compared to $13.62 for all workers (Schochet & Rangarajan, 2004). The characteristics of low-wage employment, such as fewer benefits and limited opportunities for job advancement, also make saving a greater challenge for low-income families (Meyers & Lee, 2003). For example, low-wage workers are less likely to be covered by health insurance through their employers than higher wage workers. Based on 1996 SIPP data, 50% of low-wage workers 1 had employer-based health insurance coverage compared with about 90% of medium-wage workers and 96% of high-wage workers (Schochet & Rangarajan, 2004). This lack of insurance creates a challenge to saving when medical expenses must be paid out of pocket. Furthermore, much of the low-income employment market consists of shift-work positions that involve changing schedules and require working at night and on weekends. Frequently, these work hours create a significant challenge for low-income families with children in finding childcare; a challenge that often results in dramatically increased child care expenses (Lowe & Weisner, 2004). Families whose incomes fall just above the federal poverty line may also experience a number of hardships that undermine saving. Many working-poor families with children have 1 This study defined low-wage workers using the hourly wage at which a full-time worker would have annual earnings below federal poverty guidelines for a family of four. Low-wage cutoff values were calculated for each calendar year the SIPP panel covered. A worker was classified as "low-wage" if the worker's wage rate was less than the poverty level in the calendar year when the wage rate was reported. Medium-wage workers are those with wage rates between one and two times the low-wage cutoff value and high-wage workers as those with wages more than twice the low-wage cutoff value.
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 7 earnings that are just high enough to make them ineligible for government assistance (e.g., child care assistance, housing assistance, and health care) but their earnings are not sufficiently high to benefit from tax incentives. This gap makes it very difficult for the working-poor to save and accumulate assets because much of their income may be going towards child care, housing, and medical care. Lack of access to mainstream financial services Many low-income families do not utilize mainstream financial services. In fact, about 22% of low-income families do not have a bank account (Barr, 2004). Without access to financial services, particularly a bank account, it is difficult for families to save. Many lowincome families utilize alternative financial service providers including check cashers, payday lenders, and title lenders. Low-income families without bank accounts and those who rely on these types of fee-for-service entities are not establishing the credit history required for loans and bank accounts. In addition, these alternative financial services are often very costly. Government policies and programs Although most asset-based policies do not benefit poor households, strict asset limits in means-tested programs, such as Temporary Aid to Needy Families (TANF); Supplemental Security Income; Medicaid; and food stamps, are disincentives for the poor to accumulate assets (Powers, 1998; Sherraden, 2005; Ziliak, 2003). As participants in these programs reach or exceed asset limits, their benefits are either reduced or eliminated altogether (Lowe & Weisner, 2004). Thus, even the most modest asset accumulation can jeopardize a low-income family s eligibility for assistance with meeting basic needs. Interestingly, several studies have reported that when means-tested programs have increased asset limits, the savings activity among families with children also increased (Hubbard, Skinner, & Zeldes, 1995; Hurst & Ziliak, 2001).
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 8 Individual characteristics A number of individual characteristics may influence the ability of low-income households to save, including age, race, education, employment status, and family structure. The heads of low-income households tend to be younger as compared to those of higher-income households (Acs & Loprest, 2005). Life cycle saving theory states that a person s age and stage in the life cycle affect saving behavior. Young people generally acquire little or even negative savings, people in middle age acquire positive savings, and individuals at retirement age begin to draw down on their savings to support their retirement (Modigliani & Ando, 1957). In addition, race is a key factor affecting saving and assets accumulation. Specifically, African Americans have significantly lower levels of savings and assets (Gittleman & Wolff, 2004). In addition, education and savings rates are positively correlated (Bernheim & Garrett, 1996). The heads of low-income working households report fewer years of education than those of higher-income households (Acs & Loprest, 2005). Low-income households have also been found more likely to be headed by single parents who are less likely to be employed full-time (Acs & Loprest, 2005). Employment status is an important factor in savings, because full-time employees are more likely to have access to saving mechanisms like employer sponsored pension plans, financial education and payroll deduction (Beverly, 1997). Institutional variables The institutional theory of saving suggests that access to and knowledge of savings institutions greatly influence an individual s ability to save (Sherraden, 1991). This theory may be particularly relevant for low-income households because they often lack access to these institutions. Sherraden and his colleagues (2003) identified five variables that appeared to
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 9 contribute to an individual s saving and asset accumulation: access, information, incentives, facilitation, and expectations. Access to basic banking services (i.e., checking or savings accounts) is often limited for low-income persons because of the scarcity of bank branches in their neighborhood, required minimum monthly balances, and high punitive fees for falling below such limits or overdrafts (Barr, 2004; Beverly & Sherraden, 1999). Moreover, low-income individuals often lack access to other institutionalized mechanisms such as 401(k) accounts that are typically provided through employers. Information refers to the extent to which people understand the process and rewards of saving. Although some evidence has suggested that most Americans have limited financial knowledge, low-income individuals may have substantially less access to financial information (Beverly & Sherraden, 1999). Incentives increase the likelihood that people will save; however, low-income individuals have less access to saving and asset building incentives such as tax deductions for mortgage interest, and employer-matched pension programs. In addition, the employment settings for most low-income individuals offer few options for mechanisms that facilitate savings such as automatic payroll deduction savings plans and over-withholding taxes. Last, people who have specific savings expectations are more likely to save more than those who do not, but low-income households may not have the expectation of saving and accumulating assets if they have no experience with seeing others save. IDAs: Tools that Foster Asset Accumulation Individual Development Accounts (IDAs) address the barriers to savings faced by lowincome households, particularly the institutional factors, to foster savings and asset accumulation among low-income households. IDAs are matched savings accounts that promote savings by offering participants 1:1, 2:1, or higher ratio matches for their deposits. IDA programs help
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 10 participants save toward the purchase of assets that may promote long-term well-being and financial self-sufficiency such as home ownership, additional education, or business development (Sherraden, 1988, 1991). IDA programs are typically implemented at the local level through community-based organizations working in collaboration with financial institutions that manage the savings accounts. Program participation includes mandatory financial education that focuses on general money management as well as asset-specific financial education, such as homeownership counseling. Matched contributions are often funded through public and private sources (e.g., nonprofit organizations, foundations, and faith-based organizations). IDA Programs in the United States: Prevalence and Empirical Evidence IDA programs have been implemented in every state and their prevalence is growing rapidly. The Corporation for Enterprise Development (CFED) estimates that there are between 500 and 1,000 IDA programs across the country with more than 15,000 participants (CFED, 2005). However, IDA programs are not widely available throughout the United States. To address this shortage, state and local coalitions are currently working to expand IDA programs to more counties (Center for Social Development, n.d.). The inclusion of IDAs in federal and state policy represents a fundamental paradigm shift to include asset-based policy as a complement to income-based policies. Currently, 34 states, Washington D.C., and Puerto Rico have passed some form of IDA legislation, and 30 states include IDAs in their state welfare plans (i.e., TANF). Funding for IDAs comes through three main avenues: federal, state, and private sources. Federal grants are the largest source of funding for IDAs, followed by financial institutions and private foundations. Annually, public funding for IDAs totals approximately $225 million; roughly $185 million is provided by the federal government and the remainder by the states
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 11 (Boshara, 2005). The Assets for Independence Act (AFIA), passed by Congress in 1998, authorized a five-year, $125 million IDA demonstration project. States provide funding for IDAs in a number of ways including direct expenditure of federal funds under state control (e.g., TANF), direct expenditure of general state revenue, or through tax credit programs (Miller & Gruenstein, 2002). Private funders for IDAs have included philanthropic organizations, corporations, and financial institutions. Compared with public funding sources, private grants are often less restrictive and have fewer reporting requirements (Miller & Gruenstein, 2002). Additional federal legislation that would expand the availability of IDA programs is before Congress. IDA tax credit legislation, The Savings for Working Families Act, was introduced in the Senate (S.922) in 2005 and in the House (H.R.4571) in 2006. The American Savings for Personal Investment, Retirement, and Education Act (ASPIRE) was introduced in the Senate (S.868) and House (H.R.1767) in 2005. ASPIRE would create savings accounts at birth for all children, and include added incentives for lower-income children. Empirical Evidence Findings from the American Dream Demonstration (ADD) 2, the first large-scale test of IDAs, showed that low-income people and even the very poor did save and accumulate assets when provided with institutional support and structured saving mechanisms. Participants in the ADD accumulated an average of $700 per year (Schreiner, Clancy, & Sheraden, 2002). Moreover, research shows that these households were using IDA funds towards asset purchases. Data from ADD indicated that matched withdrawals were used for home purchase, 2 For additional information on the ADD, see Method section of this paper.
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 12 microenterprise, higher education, job training, and retirement (Schreiner et al., 2002; Sherraden, 2002). When compared with the ADD control group (which did not use IDAs), IDA participants reported greater economic benefits including an increased ability to save, more savings and investment in assets, and greater determination to continue saving in the future. IDA participants reported that they modified their behavior in a number of ways, including reducing consumption, monitoring resources, and increasing their spending efficiency, in order to meet their saving goals. Furthermore, IDA participants indicated a greater likelihood to stay employed, as well as a greater likelihood to make educational plans for themselves and their children. Participation in IDAs is associated with many positive effects beyond financial outcomes (Sherraden et al., 2005). Evidence from in-depth interviews and cross-sectional surveys of IDA participants indicated numerous psychological, cognitive, behavioral, and economic effects (Moore McBride, Lombe, & Beverly, 2003; Sherraden et al., 2005). IDA participants reported increased feelings of both short- and long-term security, greater self-confidence, enhanced hope for the future, increased ability to set and achieve goals, greater sense of responsibility, heightened civic attitudes, and reduced levels of stress. Some IDA participants with children reported feeling reassured that their savings would benefit their children by paying for their children s education, improving their living environment, or generally providing for their children s future. Recent research explored the characteristics of the participants that are associated with program success. Based on interviews with staff and participants in 21 IDA programs in North Carolina, factors that contributed to either program completion or program dropout were identified. Findings suggested that the ability to complete the program was influenced by four
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 13 factors: intensive case management, the personal motivation of participants, effective economic literacy training, and the incentive of matching funds. According to IDA staff, job loss and financial emergencies were the most frequently cited negative factors (Rohe, Gorham, & Quercia, 2005). The present study further explored both the individual and program factors related to saving performance in IDA participants. Study Purpose This study sought to answer the following research questions: (a) What were the individual characteristics (demographic and financial) associated with saving outcomes among IDA participants? (b) What were the program characteristics associated with saving outcomes among IDA participants? (c) What are the program and policy implications for supporting asset building? Program and Individual Characteristics Previous research and theory has suggested that both institutional and individual factors are associated with saving behaviors. As previously discussed, the institutional model of saving posits that institutional factors other than income flow and personal preferences affect saving behavior (Sherraden et al., 2003). This model suggests that without institutional incentives and opportunities, poor households will save less (Sherraden, 1991). As such, we hypothesized that five institutional features, measured in terms of program characteristics, would be associated with saving level and the participants frequency of making deposits. These program characteristics included direct deposit (i.e., as a measure of facilitation), the match rate (i.e., as a measure of incentive), monthly saving target (i.e., as a measure of expectation), and financial education and peer group meetings (i.e., as a measure of information). In addition, individual
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 14 factors such as age, gender, marital status, race, education, employment, and household composition have been found to be associated with saving performance. Method The data in this study were obtained from the American Dream Policy Demonstration (ADD), a systematic test designed to evaluate the merits of IDAs as a community development and public policy tool (Sherraden et al., 2002). The ADD evaluation followed 2,364 participants in 14 community-based program sites across the United States for 4 years (1997-2001), with an additional 4 years of postprogram evaluation. ADD used an extensive multimethod research design to gather data on the effectiveness of the programs in terms of the communities, participants, design, and administration in order to inform IDA policy and program development (Sherraden et al., 2000). IDA programs in the ADD were implemented through community-based organizations working in conjunction with financial institutions. In most cases, participants in ADD were at or below 200% of the federal poverty guideline, with a median value of 100% of the poverty guideline. As part of their program participation, IDA participants were required to attend free general financial education and asset-specific financial education classes. The financial education classes covered general saving strategies and financial management topics including how to make a budget, how to manage money, and how to repair or establish a credit history. The asset-specific classes provided information on the particular desired asset. For example, participants who were saving for a home purchase participated in classes that addressed how to shop in the real estate market or how to work with real estate agents and loan officers. Typically, the participants also received help in establishing creditworthiness, and in showing potential future income to repay their debt (Schreiner et al., 2001).
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 15 Participant s deposits in IDAs were matched when they were ready to invest in their asset (i.e., homeownership, education, or microenterprise). The IDAs were similar to other defined contribution plans such as 401(k) retirement plans as they offered a monetary incentive for participation. Every dollar saved in an IDA account was matched with funds from a private source (e.g., charitable organization or foundation); in the ADD, only private sources of funding were used for the IDA matched funds. The organizations implementing the IDA programs in the ADD represented a diverse group of community development corporations, social service agencies, and for-profit and notfor-profit organizations selected through a competitive process to participate in the demonstration (see Table 1). Although all the programs offered matched funds as an incentive to save, each program offered slightly different opportunities, services, and rules based on individual program discretion. For example, some programs offered a 1:1 match for the microenterprise saving goal, whereas other programs may have offered a 2:1 or 3:1 match for microenterprise savings. The match rate across programs or uses ranged from 1:1 to 7:1. Similarly, programs varied in the hours of financial education offered to participants. In addition, programs differed on several other measurable variables including whether a program encouraged direct deposit for IDA savings, and whether a program supplemented general financial education with a peer-group mentoring system. The variation in measurable program characteristics, including the match rate, was based both on the individual program requirements and on the requirements set by funding sources. Data The quantitative data used in this study included the monitoring data set obtained from the evaluation of ADD. Program staff collected sociodemographic, financial, and savings
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 16 transactions for all ADD participants (N=2,364) using the Management Information System for Individual Development Accounts (MIS IDA; Johnson, Hinterlong, & Sherraden, 2001). MIS IDA was also used to track program characteristics of the 14 ADD sites. Savings data came from monthly passbook savings account records from depository institutions and thus were highly accurate. Dependent Variables The savings outcomes were the main outcome variables used to measure the performance participants in the ADD program. Two dependent variables were used to measure the savings performance of IDA participants: (a) average monthly net deposit and (b) deposit frequency. These variables captured the two principal aspects of savings: savings amount and saving regularity. These variables were constructed for use in previous reports on ADD programs (Schreiner et al., 2002). Average monthly net deposit (AMND) was defined as net deposits per month, and was calculated using the following formula: deposit plus interest minus unmatched withdrawals, divided by the number of months of participation. Thus, AMND controls for the length of participation in the program. The variable net deposits used to calculate AMND was defined as deposits plus interest (net of fees) minus unmatched withdrawals. Net deposits included matched withdrawals, but excluded deposits in excess of the match cap (maximum amount eligible for matched funds) or after the time cap. Although excess deposits, late deposits, and unmatched withdrawals were savings in IDA accounts, they were not eligible for matched funds and therefore, were not considered net deposits. AMND functioned as a key measure of savings outcomes in this study because greater AMND may lead to greater savings and assets accumulation.
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 17 Deposit frequency was defined as the number of months with a deposit divided by the number of months of participation. This variable showed with what regularity a participant saved over time, which served as an important indicator of whether IDA participants were acquiring the habit of saving. Deposit frequency may also be useful as an indicator of whether participants continue to save after graduating from the program. Independent Variables Participant characteristics were measured through demographic (i.e., gender, age, residency, marital status, number of children, number of adults in household, race, education and employment status) and financial variables (i.e., household income, car ownership, home ownership, and checking or savings account ownership). Participant demographics included gender (1 = female, 0 = male); age (in years) and age squared; residency (1 = rural, 0 = urban); a set of dummies that measured marital status (single, divorced/separated/widowed, and married [the reference group]); number of children (under 18 years); and number of adults (18 years and older) in the household. A set of dummy variables measured whether the participants identified their race as African American, Latino, Other category, or Caucasian (the reference category). Another set of dummies measured the educational attainment of participants (do not have a high school diploma [reference group], has a high-school diploma, some college/ no degree, and graduated from college). Employment status was measured as full-time (more than 35 hours per week); part-time (less than 35 hours per week); unemployed (reference group); or a student. Financial characteristics included a dummy variable for whether a participant had ever received income support (e.g., TANF or its predecessor, Aid to Families with Dependent Children [AFDC]); monthly household income; car ownership (1 = yes, 0 = no); home ownership (1 = yes, 0 = no); and having either a checking or savings account (1 = yes, 0 = no).
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 18 For the purpose of interpretation, we divided the household income by 100 for the regression analyses. Program Characteristics. Four program characteristics were measured in the analyses: direct deposit, match rate, financial education, and monthly savings target. All of the variables were measured at the individual level because, although all of these variables captured program characteristics, they were subject to rules and restrictions as determined by the different programs. For example, although the programs had differing rules for financial education (i.e., offered different amount of hours or established different requirements), the number of financial education classes taken by a participant was measured at the individual level. The direct deposit variable measured whether an individual used direct deposit (1 = yes, 0 = no). The match rate measure included four dummy variables that measured the different match rates that participants received: 1:1 (reference group), 2:1, 3:1, and 4:1 to 7:1. The financial education variable was measured by the total number of hours of financial education classes taken by participants. The monthly savings target variable was the total match cap (i.e., the limit on the amount of deposits eligible for matched funds) divided by the time cap (i.e., the number of months after opening an account in which a participant could make deposits that qualified for matched funds). Additional program dummies were included in the hierarchical regression analysis. A dummy variable that indicated which of the 14 programs the participant belonged to represents the program dummies. Results The statistical techniques used in the analysis phase included univariate analyses of the variables of interest to examine data for missing values, outliers, and normality; the multivariate analyses included hierarchical Ordinary Least Squares regressions.
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 19 Table 2 presents descriptive statistics for the IDA participants in ADD. The majority of the sample was female (80%) and lived in an urban area (87%). Participant ages ranged from 13 to 72 years, with a mean age of 36 years. There were 53 participants that were younger than 18 but were allowed to participate in the program. Nearly half (49%) of the participants were single/ never married, 29% were divorced, separated, or widowed, and 22% were married. Households had an average of two children, and the average number of adults per household was 1.5. The majority of the participants were African American (47%), followed by Caucasian (37%), Latino (9%) and other ethnicity (7%). Approximately 16% of the participants had not completed high school, 26% had a high school degree, 37% had attended college but did not graduate, and 22% had a college degree (either 2 year or 4 year). The majority (59%) of participants was employed full-time (35 hours per week or more); 23% worked part-time; 10% were unemployed or not working (retired); and 8% were students. The mean monthly household income was $1,379. About 62% reported that they had never received AFDC or TANF. The majority (77%) of the participants in ADD had either a checking or savings account in addition to their IDA. Homeowners constituted a small segment (16%) of participants, but 65% of participants owned a car (see Table 2). Table 2 compares IDA participants in ADD to a general low-income population sample, obtained through the 2000 National Longitudinal Survey of Youth (NLSY; U.S. Department of Labor). The NLSY sample included respondents whose household income was at or below 200% of the federal poverty threshold. Compared to the NLSY sample, ADD participants were more likely to be female, African American, and single. In addition, ADD participants were more highly educated, more likely to be employed full-time, and more likely to have either a checking
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 20 or savings account. Although it appears that the participants of the ADD may be more advantaged in some areas, it also appears that they face greater challenges in others. Program Characteristics Table 3 presents the program characteristics. Only 6% of ADD participants used direct deposit into IDA account. As previously mentioned, match rates varied between and within programs: 25% had a match rate of 1:1; 49% had a match rate of 2:1; 14% had a 3:1 match rate; and 6% had a match rate from 4:1 to 6:1. ADD participants received an average of 10.1 hours of general financial education. The average monthly savings target (i.e., defined as the amount that qualified for matched funds) for ADD participants was $41.58. Dependent Variables The AMND was defined as an individual s net deposits per month of participation. The AMND was $19.07 (median =$9.83). Deposit frequency was defined as the number of months with a deposit divided by the number of months of participation. The mean deposit frequency for this group was 48% (median = 44%). Typical IDA participants made a deposit in 6 out of 12 months. The missing cases in this study ranged from 0% to 5%, with the majority of the cases having no missing cases. An examination of the variables with the missing cases in this study revealed no obvious pattern in the missing data. Regression Diagnostics The assumptions of regression, i.e., linearity, homoscedasticity, normality of the error terms, and lack of multicolinearity were tested for the regression analysis with AMND and depostit frequency as the dependent variables. The first three assumptions (linearity, homoscedasticity, and normality) were tested via the examination of the residual
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 21 scatterplots: It appears that the assumptions of linearity, homoscedasticity, and normality are met. However, a few outliers were noticeable, but an earlier examination of these outliers suggested that they were real values because it is suggested that moderate violations of the normality assumption may often be ignored, especially with larger sample sizes (Mertler & Vannatta, 2002, p. 174), these outliers were included in the analyses as is. Further analysis to assess multicollinearity was used by obtaining tolerance values for each of the independent variables. The results indicate that there is a multicollinearity problem when the program dummies are included in the regression analysis. Therefore, it was decided to include program dummies only in the third step of the hierarchical regression assessing the increment in the variance explained. Finally, the correlation between the two dependent variables was examined. The results suggested that although the two dependent variables are correlated, the correlation is within a normal range (r=.48; p=.000) and does not represent a problem. Individual and Program Characteristics Associated with Saving Two hierarchical Ordinary Least Squares (OLS) regression analyses were conducted to examine the individual and program characteristics associated with saving among IDA participants. The first regression used AMND as the dependent variable, and the second regression used deposit frequency as the dependent variable. The first step of the hierarchical regression was to answer the question, What individual characteristics were associated with saving? Two additional questions were addressed by the second and third steps of the hierarchical regression: (a) Controlling for the effects of individual characteristics, what program characteristics were associated with saving; and (b) controlling for the effects of individual
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 22 characteristics, did program characteristics (measured [step 2] and unmeasured [step 3]) as a block affect the saving performances of IDA participants? When AMND was regressed on the individual characteristics and measured institutional characteristics, the results of the OLS regression model showed significance [F(28, 2,039) = 18.75, p =.000] and explained approximately 21% of the variance in AMND (R 2 =.21, Adjusted R 2 =.20; see Table 4). Similarly, when deposit frequency was regressed on the individual characteristics and measured program characteristics, the results of the OLS regression analysis were also significant [F(28, 2,039) = 25.61, p =.000] and explained approximately 26% of the variance in AMND (R 2 =.26, Adjusted R 2 =.25; see Table 4). The regression results indicated that several individual variables and all of the program variables were associated with savings performance for the homeownership group. Several demographic variables were associated with savings for IDA participants. First, rural residency was associated with a $5.61 lower AMND and 11-percentage points lower deposit frequency as compared to urban residency. Second, the number of adults (18 years or older) in a participant s household was associated with higher AMND. Specifically, an additional adult in the household increased AMND by $2.32. Third, race was also found as significantly related to AMND and deposit frequency. When compared to Caucasians, being African American was associated with a $6.93 decrease in AMND, and 5-percentage points lower deposit frequency. Education was also found to be significantly related to AMND and deposit frequency. Specifically, having graduated from college was associated with $6.19 higher AMND and 4-percentage point higher deposit frequency than IDA participants who did not complete high school. In addition, being a working student is associated with $4.98 higher AMND when compared with being unemployed.
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 23 Financial variables were also associated with savings outcomes. As might be expected, higher income was associated with higher AMND; however, the higher income had only a small effect on savings amounts. A $100 increase in total income was shown to be associated with only a $0.27 increase in AMND. Asset ownership (e.g., home, car, and bank account) was associated with saving performance for IDA participants. Specifically, participants who were homeowners were associated with a $7.59 higher AMND and 7-percentage point higher deposit frequency than participants who were not homeowners. Participants who were car owners were associated with a 3-percentage point higher deposit frequency than participants who did not own a car. Furthermore, participants who had either a checking or savings account prior to their IDA account were associated with a $4.94 higher AMND and 5-percentage point higher deposit frequency than participants without prior accounts. All program characteristics were associated with savings outcomes. Direct deposit was found to be associated with deposit frequency. Compared to participants who did not have the option of direct deposit, having direct deposit was associated with a 22-percentage point higher deposit frequency. Match rate was also associated with AMND and deposit frequency. Specifically, a match rate of 3:1 was associated with a $5.54 increase in AMND, and a 14- percentage point higher deposit frequency compared with a 1:1 match rate. Hours of financial education attended by IDA participants was also statistically related to AMND and deposit frequency. Each additional hour of financial education was associated with a $0.80 increase in AMND and a 1-percentage point increase in deposit frequency. In addition, the monthly saving target was also found to be significantly related to the two saving outcomes of AMND and deposit frequency. Specifically, each additional dollar in the monthly saving target was associated with a $0.25 increase in AMND and a.003 increase in deposit frequency.
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 24 Effect of Program Characteristics as a Block Hierarchical OLS regressions were conducted to determine the specific amount of variance attributable to the program variables (measured and unmeasured) beyond what had been explained by the individual variables when predicting AMND and deposit frequency. Table 5 illustrates that, controlling for individual characteristics, the measured program characteristics as a block significantly (p<.001) increased the variance explained in AMND for this group. Furthermore, Table 5 also shows that individual characteristics alone accounted for 14% of the variance explained in AMND (R 2 =.14). Adding the measured program characteristics to the model as a block increased the variance explained in AMND by 6% (R 2 =.21), and adding the program dummies (unmeasured factors linked with programs) as a block accounted for an additional 6% increase in the variance of AMND (R 2 =.26). Similar results were obtained when adding measured and unmeasured program characteristics to the model with deposit frequency as the dependent variable (see Table 6). Controlling for individual characteristics, the measured program characteristics as a block significantly (p<.001) increased the variance explained in deposit frequency for the IDA participants. Individual characteristics alone accounted for 12% of the variance explained in deposit frequency (R 2 =.12). Adding the measured program characteristics to the model as a block increased the variance explained in deposit frequency by 14% (R 2 =.26), and adding the program dummies (unmeasured factors linked with programs) as a block accounted for an additional 7% increase in deposit frequency of the variance (R 2 =.33). A few limitations of this study are important to note. First, participants in IDA programs in ADD were both program-selected because of eligibility criteria and self-selected because they volunteered to participate in the program (Schreiner et al., 2001). Therefore, ADD participants
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 25 differed in some aspects as compared with the general U.S. low-income population. Therefore, results in this study may not represent how the low-income population outside ADD would perform in IDAs. Second, we could not test for the possibility of reshuffling savings and other assets. Although results suggested that participation in IDAs increased savings, it is likely that some of the savings did not represent new savings, but we cannot determine how much. These savings could have been transfers of other assets to IDAs. However, such reshuffling is less likely for low-income households because they have fewer assets. Third, because the data used in this study were not collected using randomized assignment techniques, there is lack of control in the data, which means that it is hard to attribute the effects of participating in IDAs on the saving outcomes. It is hard to determine how the participants would have saved if they were not participating in IDAs. The final limitation was that this study included a relatively small number of IDA programs. Discussion IDAs are a new tool to help low-income people to save and accumulate assets with the goal of long-term social and economic development. The creation of IDA programs was based on the concept that all people should have access to structured opportunities that encourage saving and asset accumulation. Contrary to the belief that the poor are not concerned with saving or cannot save, earlier analyses from the ADD have shown that low-income participants are willing and able to save when provided structured opportunities (Schreiner et al., 2002). This study has contributed important information to the field by identifying and examining the individual and program characteristics associated with the saving performance of IDA participants.
Grinstein-Weiss & Irish, IDAs: Path to the American Dream 26 The results indicate that several individual characteristics and all of the program characteristics studied are important in explaining the saving performance of IDA participants. First, IDA participants who were living in rural areas saved smaller amounts and less frequently than those living in urban areas. Rural areas have fewer resources and offer fewer benefits and supports for rural participants (Grinstein-Weiss & Curley, 2002). Because of the distance and lack of public transportation, many rural participants may find it difficult to attend financial education classes and to make deposits. In addition, the lack of economic infrastructure in rural areas means that many of the funding organizations that assist urban IDA programs are not available to assist rural programs. Rural communities also have fewer economic opportunities, lower earnings, fewer high quality jobs, and fewer educational and training opportunities than urban communities (Zimmerman & Hirsch, 2003). Second, African American participants saved smaller amounts and less frequently as compared with Caucasians. These findings are consistent not only with those of other researchers who examined racial differences in savings in IDAs (Grinstein-Weiss & Sherraden, 2006; Schreiner et al., 2002), but also with the existing statistics on income and wealth inequalities between Blacks and Whites (i.e., Altonji & Doraszelski, 2001 Gabriel & Rosenthal, 2005; Retsinas & Belsky, 2002). Further research is needed to determine how best to tailor policies and programs to narrow this saving gap in IDAs. Third, participants who had a college degree saved more than participants without a high school degree. These results are consistent with research that has suggested that higher levels of education are associated with increased savings and wealth (Bernheim & Garrett, 1996; Zhan & Pandey, 2004), and that education is typically a proxy for general economic awareness and exposure (Hogarth & Anguelov, 2003).