Introduction

Policies and programs designed to help low-income families join and save for developmental uses such as higher education, homeownership and entrepreneurship are emerging and being tested in a number of countries (Boshara and Sherraden 2004; Loke and Sherraden 2006; Pearce et al. 2006) including the U.S. (CFED 2006; Loke and Sherraden 2006). Such policies and programs are based on Sherraden’s (1991) proposed system for universal accounts opened at birth with progressive funding aimed at building assets for all children. It is assumed that assets have positive long-term effects for households and children that go beyond income alone (Sherraden 1991). It is expected that families with the same levels of income but differing assets typically have different levels of well-being and outcomes for their children (Wolff 2002). An interesting study that voiced support for asset building programs through findings that showed that households’ assets were associated with food security among lower income households. In fact, in the presence of assets, the effect of income on food security decreased (Guo 2010). Also, it is assumed that most assets are built through institutional mechanisms (Howard 1997; Oliver and Shapiro 2006; Seidman 2001; Sherraden 1991, 2005) normally unavailable to low-income households. Similar structures, if provided to low-income families, might help reduce asset gaps that currently exist (Beverly and Sherraden 1999; Sherraden 1991).

Asset building accounts for children and youth are frequently known as child development accounts or child savings accounts, and are either universal or targeted to children from low-income families. Child development accounts are established for children to provide structure and support for building assets, and their most common intended use for them is higher education (Curley and Sherraden 2000). The term child development account is used in this paper even though the term child savings accounts has been used in similar fashion. Such accounts have features that include: (1) start-up with an initial deposit from a private or public funding source, (2) eligibility for matching funds, (3) management by a financial institution, and (4) are tax benefitted. Youth are typically able to access their accounts at 18 years of age (Loke and Sherraden 2006).

Researchers have begun to look at factors deemed important in helping low-income families join and save in these programs. Beverly (2006) analyzed the demographic characteristics that are associated with joining a child development accounts program, while Schreiner et al. (2005) included demographic and program-level factors to analyze what explained saving among low-income households in subsidized savings accounts programs designed to help them meet household development goals, also known as individual development accounts (IDAs). In Schreiner’s et al. (2005) study, structures such as matched savings, financial education, and facilitation of automatic deposits into IDAs had positive effects on savings outcomes. Stegman and Faris (2005) used demographic variables and program features (intended use of IDAs, months in program, hours of financial education, match rate, and total contribution limit) to estimate the probability of saving if the participants had not joined the IDAs programs.

While Beverly’s (2006) study was informative for the present study, it was limited to bivariate analyses and did not include attention to the outreach services aimed at helping the parents make decisions about enrolling in the program. Both Schreiner et al. (2005) and Stegman and Faris (2005), although also relevant, used a different outcome variable of asset building from the one used herein. In addition, the program features examined in these latter studies were different from the direct social services provided to parents in the study reported here.

Some general policy and program features that have been identified by scholars in helping low-income households save in a child development accounts program include: (1) A universal system achieved by starting at birth for all new-born children and is facilitated by a centralized administrative system (Curley and Sherraden 2000; Gregory and Drakeford 2006; Sherraden 2003). (2) A progressive element that provides greater public or private subsidies for poorer households to reduce long-term inequalities is seen as very crucial (Maxwell 2005; Sherraden 2003). (3) Integrating various asset accounts into one simple and portable tool that is multi-purpose and can be transferred to other personal development uses like retirement or to other siblings (Sherraden 2003). (4) Well-designed financial education classes to encourage savings rates (Schreiner and Sherraden 2007), rates of participation in a savings program, and future saving habits of children and youth when they are adults (Sherraden et al. 2005). (5) Since welfare participation can restrict saving (Hubbard et al. 1995), a good policy should be excluded from assets-based means-tested welfare programs and be tax-exempt (Loke and Sherraden 2006). (6) Since families that save for college reduce their eligibility for financial aid through an implicit tax imposed on household assets and income (Yilmazer 2008), a good policy must therefore, minimize the negative consequences of account ownership on college financial aid eligibility for asset-poor households (Rist et al. 2005).

The research reported here is one part of a large multi-year, multi-method research project on the first large scale child development account initiative in the U.S. known as Saving for Education, Entrepreneurship, and Downpayment (SEED). Parents who enrolled in SEED accounts for their pre-school children were compared with their peers who decided not to enroll in the largest community-based SEED program. An institutional framework for saving and asset building was used in this study.

Conceptual Framework

The behavioral economic theory developed by Shefrin and Thaler (1988, 1992) is among the best-developed frameworks in conceptualizing how families save and build assets. Families save through strategies that include saving goals for a particular purpose or precommitment constraints like automatic monthly deductions (Maital and Maital 1994; Shefrin and Thaler 1988). Some individual characteristics that shape one’s financial behavior may include lack of self-control, the tendency to interpret default options as advice, and the tendency to use mental accounting techniques (Beverly et al. 2008). These tendencies may result in behaviors that defeat saving goals. For example, a lack of discipline or limited financial knowledge may impede one’s ability to save for a goal, e.g., a washing machine and dryer or car. While individual strategies to save are vital to saving, it is acknowledged that external factors also affect saving behavior. For example, it could have been that the parents who decided not to enroll in the SEED program be automatically enrolled in the program, unless they opted out. This action would at least ensure that the children from these families received the private and public funding, which would grow over time with compound interest. In a paper that synthesized previous empirical research on five automatic savings and investing strategies for employees, it was suggested that one way to achieve high participation rates in the programs is to enroll employees with an option of “opt-out” instead of the standard default option of not enrolling workers unless they actively elected to enroll (O’Neill 2007).

The institutional theory on saving has shaped the design of most contemporary saving programs and experiments for low-income households in the United States and beyond. In reference to a speech offered by President Bush in 2004, Sanders and Portefield (2010) questioned the underlying assumption in the U.S. that most people could simply save, invest and own assets. The institutional theory on saving suggests that external factors other than income and individual preferences may influence saving behavior. This is in contrast to standard neo-classical economic explanations of saving that rest on the notion that people simply have different “propensities” to consume and save. From an institutional perspective, it is theorized that low savings and asset accumulation in poor households is partially explained by limited institutional saving and asset-building opportunities (Beverly and Sherraden 1999; Sherraden 1991). Noting that non-poor people do most of their asset building using structures that have been built by institutions and established through public policies and employment policies that are tax benefited, Sherraden notes that “many people accumulate assets and do so in a manner that cannot accurately be described as ‘saving’ Rather, for most Americans, the largest parts of assets accumulate in structured systems that are defined, regulated, and subsidized by public policy (Sherraden 2005, p. 9).” Poor people have often been excluded or ignored from structured savings and asset-building policies (Seidman 2001).

Beverly et al. (2008) suggested that institutional factors are largely responsible for savings and asset building and used the term “institutions” to refer to purposefully created policies, programs, products and services that shape opportunities, constraints, and consequences. This institutional perspective posits that saving and asset accumulation are not only shaped by individual knowledge, choices and propensities, but also result in large part from social structures that facilitate asset building. Sherraden (1991) earlier described these social structures as incorporating “connections, rules, incentives, and subsidies” (p. 116). Beverly et al. (2008) noted that, for non-poor households, asset building mechanisms include income tax benefits for home mortgage interest and retirement savings, as well as employer contributions to the pension plans of workers. Further, low-income households without existing savings lacked similar access to or benefits from structured mechanisms that help people build assets.

From this emerging institutional perspective, seven aspects of institutions that promote saving and asset accumulation are: access, information, incentives, facilitation, expectations, restrictions, and security. These aspects often exist in concert rather than in isolation. For example, a defined contribution retirement plan with a match from an employer provides a number of aspects of institutional support for saving including access, information, incentives, facilitation, and restrictions (Beverly et al. 2008). Han and Sherraden (2009) found that IDAs participants generally had positive views of institutional features such as security, incentives, expectations, restrictions and information.

In a study of the saving performance and program participation of IDAs participants, Grinstein-Weiss et al. (2010) found that program characteristics such as financial education, monthly saving targets, peer group meetings, and direct deposit were important predictors of program performance. In addition, race/ethnicity, homeownership, and household income were also associated with program performance. In the study reported here, parents received program information to help them decide about enrolling in the program through in-person, telephone and mail contacts, incentives to enroll in the program were in the form of matched savings plus the initial foundation and public deposits. Facilitation was in the form of the ease of enrollment and only basic information was needed to enroll in the program. Expectations were built in the form of match caps ($1,200) and the social pressure and encouragement from the SEED staff for the parents to save. Since accounts were managed under 529 college savings plans, there were restrictions to access and their use. Finally, the institutional feature of security in this program was the perception from parents that their savings were safe from unreasonable risks. Thus it was anticipated that these institutional and programmatic supports would facilitate and encourage a greater majority of the parents to enroll in the program.

The purpose of this exploratory study was to examine the extent to which socio-demographic characteristics, as well as program social services affected enrollment decisions in a child development accounts program. The study sought to answer the following primary research question: What individual, household, and social service factors are associated with a parental decision to enroll in a children’s college savings account program for their pre-school aged child? The implications for this study are directed toward an understanding of program features and institutional supports that may be useful in helping more low-income households join and participate in savings and asset building programs, both now and in the future. The study is unique because there has been limited work on enrollment decisions in such programs.

Method

Setting and Sample

SEED research team members from the University of Kansas, University of Michigan, and the Center for Social Development (CSD) at Washington University designed the SEED impact assessment and developed an extensive survey instrument so that outcomes such as school readiness, parenting practices, and family social and economic well-being for the treatment group could be measured against comparison group outcomes at the end of the initiative (Beverly and Williams Shanks 2004). Of particular interest were parenting behaviors such as reading with their children and monitoring television time, children’s school readiness and early academic achievement, parental and child expectations and plans regarding college, as well as a number of other age-appropriate measures of social and economic well-being for participants and their families. The SEED research design involves multiple methods, one of which is a quasi-experimental pre-school demonstration and impact assessment site at a large community-based agency in Michigan (Adams 2008; Adams et al. 2008).

SEED research team members developed matched pairs of pre-school centers in Oakland and Livingston counties in Michigan with similar enrollment and demographic characteristics including poverty rates, racial and ethnic composition, and proportion of one-parent families. One pre-school center from each of the seven matched pairs was randomly assigned to a treatment condition and the other to a comparison condition. Using this research design and survey instrument, parents of children in the treatment and comparison groups were interviewed by RTI International in fall the 2004 (Marks and Rhodes 2005). These baseline survey data were used in the analysis reported here. Before the baseline survey, human subject protection protocols were approved by the Institutional Review Boards at the University of Kansas University of Michigan, and RTI International.

A total of 790 study participants were interviewed through a computer assisted telephone interviewing for the baseline study from the RTI International call center. Of these, 381 (48.2%) were assigned to the treatment condition and were offered the opportunity to enroll in the SEED accounts for their pre-school children, and 409 (51.8%) were the comparison group (Marks and Rhodes 2005) and had no opportunities to enroll in the program. Parents in the treatment group received program social services aimed at helping them enroll and join SEED by opening the accounts. The analyses for this study included only the sub-group of 381 parents in the treatment group who had opportunity to enroll in the program.

When parents decided to enroll, an initial $800 through the funders was deposited into their accounts. An additional progressive incentive included $200 deposit from the State of Michigan for families with household incomes less than $80,000 a year. Further, personal deposits made by parents and others on behalf of children with SEED accounts were matched dollar-for-dollar during the four years of the initiative. At the end of the 4-year demonstration, personal deposits could still be made but will no longer be matched by the SEED initiative. If the maximum amount that the initiative could match was saved on behalf of a child with a SEED account, there could be up to $3,400 in her account at the end of the four-year period.

Social service data were collected by the author from agency files and records, and include only the services provided prior to the decision to enroll by the parents. Working in collaboration with two graduate students from the University of Michigan, and under the direction of faculty members on the SEED research team from the University of Kansas and University of Michigan. A list of social service variables were developed to document and assesses the outreach and recruitment services that families received. The two graduate students provided higher inter-rater reliability for the social services data. Social service items were designed to measure frequency, rather than the quality or intensity of the services.

Measures

The decision by parents to enroll or not enroll in the program was the dependent variable in this study. Similar to opening a regular savings account through a bank or a financial institution, the decision by parents to enroll was the first step toward saving and building assets for their children. Like staff in SEED programs across the country, the staff in this study found the recruitment phase of the parents to enroll in the program to be very challenging. Some parents expressed interest about enrolling in the program, completed the necessary administrative documents, but still failed to enroll. A decision to enroll in the program for this study was a simple “yes/no” question. Decision to enroll was coded 1 while decision not to enroll was coded 0. Clearly, this dichotomy does not capture the complexity of the enrollment decision making process.

Socio-Demographic Characteristics

Individual and household characteristics were derived from the impact assessment baseline data and selected on the basis of existing hypothesis and prior empirical findings on asset building. These included: household income, household economic strain, number of children in household, receipt of means-tested public assistance, having household savings, race, and participant’s level of education. Income categories included: (a) 1 ≤$4,999 (b) $5,000–$9,999 (c) $10,000–$14,999 (d) $15,000–$19.999 (e) $20,000–$24,999 (f) 25,000–$34,999 (g) $35,000–$49,999 and (h) ≥$50,000. Household economic strain index (Conger and Conger 2002; Conger et al. 2002; Conger et al. 1994) included seven indicators for parents to rate whether “My family has enough money to afford the kind of home, clothing, furniture or household equipment, car, food, medical care, and leisure we need.” A composite score was computed ranging from 1 (very low) to 6 (very high) for household economic strain. Number of children ranged from 1 to 4 or more, and age was a continuous variable.

Receipt of means-tested assistance was a composite index assessing whether parents participated in a food stamps program, Temporary Assistance to Needy Families, and/or public subsidized housing. Having household savings at the time of the baseline survey was a yes/no dichotomous variable. Race was dichotomized into whites and non-whites. Parental level of education was grouped into four: (a) less than high school (b) high school or GED (c) some college, vocational training, or trade school and (d) two-year college degree or more. For the logistic regression model, the first category in each variable was set as the reference group.

Program Social Service Variables

Like staff in other SEED programs across the country, agency staff found the outreach and efforts to recruit parents to enroll in SEED to be much more challenging than expected. The decision by SEED program administrators to devote resources to heavily staffing the outreach and recruitment aspects of the program was based on the belief that services during the initial phases of the program would have a positive impact on enrollment decision, and that services from staff throughout the SEED initiative would positively impact saving in SEED accounts. Program social services in the form of outreach efforts provided through the agency that were measured in this study included: program orientation, telephone contacts, and in-person contacts.

Program Orientation

As part of the process of joining SEED and opening accounts for their children, parents were expected to attend a SEED program orientation session at the agency. Orientation sessions offered parents an opportunity to learn about the details of the program and to ask questions. Parents could attend one-on-one orientations or attend one of the various group orientations organized by program staff. The information regarding date of enrollment was found in the SEED Participant Form, an administrative document that showed when parents attended an orientation session and enrolled. In addition to client log files, administrative information was kept in a database that showed the date and type of orientation that the parents attended. This qualitative and quantitative information was used to create a variable indicating what type of orientation a parent attended. The type of orientation attended by parents was seen as important because it could help identify the best approach to reach parents and suggest programming and policy implications of using such approaches.

Telephone Contacts with Parents

It was the expectation of the agency administrators and those providing program supports that prompting telephone contacts would lead parents to enroll in the program. Challenges to those efforts included non-working numbers, disconnected phone service, increasingly heavy reliance on cell phones with frequent number changes, residential moves with related new phone numbers, increasing use of voice mail to screen calls, parents working several part-time jobs to make ends meet and having little time to return calls.

In-person Contacts with Parents

Parents received home visits, classroom contacts, and paid office visits with SEED staff in efforts to enroll in the program. In-person contacts were recorded by staff in client logs with dates, type of in-person contact, and the subject of the contacts. During in-person contacts, staff answered questions about the program, helped parents fill out forms, and held discussion on ways and strategies of joining SEED and saving. Staff typically made appointments before embarking on home visits. Home visits were considered complete if the client log records showed that the participant was at home and spoke with the staff or, an adult who understands SEED was at home and was able to receive the message, promising to relay it to the child’s caretaker. Classroom contacts were another type of in-person contact where staff tried to meet the parents during drop-off or pick-up hours. Lastly, staff met parents at the agency offices on program-related issues. Mostly, parents dropped off forms or documents and held discussions with staff. The first category in each variable was set as the reference group.

Analyses

Zero order correlations were first examined between all the independent variables, as well as between the independent and dependent variables using Pearson’s correlation coefficient for continuous variables and Spearman’s rho for categorical variables. As would be expected, most of the variables in this study were highly correlated. Since logistic regression is sensitive to highly correlated independent variables (Hair et al. 2006; Hosmer and Lemeshow 2000), tolerance tests were conducted to ensure that multicolinearity would not pose a problem. Values from tolerance tests need to be at least .1 to establish that multicollinearity would not make the logistic regression model unstable (Mertler and Vannatta 2002; Tabachnick and Fidell 2007). The colinearity statistics showed that the tolerance for all variables in this study was greater than .1. Bivariate relationships were then conducted between the independent variables and the dependent variable. Finally, variables that were significant (p < .05) at the bivariate level were included in a logistic regression model to test which were the strongest contributors to the decision by parents to enroll in SEED.

Results

Of the study sample, 235 (62%) parents decided to enroll and 146 (38%) decided not to enroll in the program. One noteworthy finding between those who decided to enroll and those who did not was that they shared similar demographic characteristics based on: gender, ethnicity, relationship to focal child, and gender of SEED focal child. Table 1 presents the bivariate results for all the independent variables by decision to enroll in the program.

Table 1 Differences by enrollment status––SEED baseline impact assessment survey (N = 381)
Full size table

Interestingly, Table 1 revealed that household income [r (381) = .105, p < .05], and participant’s level of education [χ2 (3, n = 381) = 15.19, p < .01, Cramer’s V = .191] were the only demographic factors significantly associated with the decision to enroll. Results showed that participants with higher levels of education were more likely to enroll than those with low education levels. Approximately 46% of those who enrolled had some college education or higher compared with only 25.6% of parents who did not enroll. In terms of income, 70.5% of households who did not enroll had incomes less than $25,000 compared with 62.5% of enrollees with similar incomes.

In regard to social services reviewed, in-person contacts [χ2 (2, n = 381) = 40.80, p < .001, Cramer’s V = .33] and the type of program orientation that parents attended [χ2 (2, n = 381) = 116.91, p < .001; Cramer’s V = .55] were significantly associated with enrollment decision. Program service variables measuring outreach and recruitment efforts sent by mail and telephone contacts from staff members to parents did not have a significant relationship with enrollment decisions. In terms of in-person contacts, 63.9 and 18.5% of the account holders and non-account holders respectively received more than one in-person contact. In terms of program orientation, 84.7% of those who enrolled attended some form of orientation compared to only 30.2% of participants who did not enroll.

Since this was an exploratory study, a forward stepwise logistic regression model was used to determine which of the individual, household, and social service variables explained the variations in odds of enrollment decisions controlling for other factors. Forward stepwise logistic regression enters variables one-at-a-time, using likelihood ratio estimates to determine which variables will add the most accuracy in determining odds ratios in the regression equation (Mertler and Vannatta 2002). The asset-building literature does not yet have sufficient empirical findings to suggest that either household and individual characteristics or program and institutional factors are more important in predicting asset building. The variables included in the logistic regression were those significant in the bivariate analysis and included (1) household income, (2) participant’s level of education, (3) in-person contacts, and (4) the type of SEED orientation that parents attended. In this study, a p-value of .05 was used for the variable entry in the regression model.

Overall, the model correctly classified 79.0% of the total sample, 86.4% of parents who decided to enroll and 67.1% of those who decided not to enroll in the program. The unexplained variance indicates that there were some unmeasured variables not in the equation that may have contributed to a more complete explanation of why a number of parents chose not to enroll in the program. Table 2 presents the regression coefficients, Z statistics, and odds ratios from the multivariate analysis.

Table 2 Logistic regression results of parents’ decisions to enroll in SEED––SEED baseline impact
Full size table

Parental level of education and the type of program orientation were retained in the final model as the only significant variables. Having more education was associated with higher likelihood of enrolling in the program than having less than high school education for parents. However, there was no significant difference in the likelihood of enrollment decision between those with less than high school diplomas and those with high school diplomas or GED. Parents with some college education, vocational or trade school diplomas and those with 2-year college degrees or more were nearly three times as likely to enroll in the program as compared to those with less than high school diplomas. Generally, parents who attended some form of orientation were more likely to enroll than those who did not, with those who attended face-to-face orientations sixteen times more likely to enroll than those who did not. Parents who attended group program orientations were at least 8 times more likely to enroll that those who did not attend any program orientation.

Discussion and Implications for Policy and Practice

This study sought to assess whether individual and household-level factors as well as services provided to parents were important in their decision to enroll in a program to save for their children’s future higher education. Final results showed that very few variables were useful in explaining decision about enrollment in the program. Controlling for other factors, only the parent’s level of education and the program orientation provided to parents prior to enrollment were significantly related with eventual enrollment decision. Interestingly, many individual and household-level factors such as income and having some savings at the time of the survey that were hypothesized to be important did not turn out to be statistically significant. The setting of the study was in a human service agency in the mid-western region of the United States and should, therefore, not be generalized to the many ongoing initiatives to establish child development accounts for children worldwide. Still, it offers those interested in child development accounts with some important implications that may be relevant to policy, practice, theory, and research.

While it would be easy to focus on individual and household-level characteristics, along with social service variables that are significant in their relationships with the asset building outcome of enrolling in a child development accounts program, the most central finding from this study is that relatively few individual or household characteristics were consistently associated with enrollment decision. Upon finding few demographic differences between the groups in a descriptive study using a smaller sample, Beverly (2006) suggested that understanding asset building among low-income families in community-based savings programs may involve looking more at program and institutional features rather than focusing on individual-level differences between participants. For example, a more effective way to include more parents in these programs may involve automatic enrollment where parents play minimal roles in the process of enrollment.

The services provided by the agency staff members were expected to make it easier for families to enroll in the program. While only one social service variable in this study had a positive association with the decision to enroll when controlling for other factors, findings from this study lend some limited support to the need for easier facilitation of saving among low-income families and communities. From a practice perspective, even more time and money may need to be devoted to social services that help those with particularly difficult economic challenges to save and build assets for their children’s futures. Particularly, strategies that build the trust of low-income families with financial institutions aimed at their welfare and that of their children may be critical. Since higher education levels were associated with higher likelihood of program enrollment, it may also mean using different outreach mechanisms for parents with varying socio-economic status. This is an interesting finding with implications for both groups of parents; for parents with less education, this may point to the need for specialized program and institutional features. In addition, issues of trust and confidence in the financial products being offered may need to be designed in a manner that attracts theses parents. For parents with more education, the results may point to their financial capability, i.e., their ability to understand and participate in various savings programs.

Specifically, responsive program orientations may prove critical in providing stable foundations for future fruitful engagements between the families and agencies that are involved. The program orientation result is rather biased since most parents who attended orientation may be presumed to be those interested in enrolling in the program. Still, it points to the key roles that agency personnel and outreach strategies can play in including such families in savings programs. A study by Fry et al. (2008) with low-income households in the Australian Saver Plus, a matched savings program, similar to IDAs, identified relationship managers that helped and encouraged parents to save; however, the study did not include the characteristics nor the effects of the relationship managers on the savings outcomes for the parents. Future research should clarify the latent programmatic factors that may be useful in successfully including parents in these programs and in helping them to save. Clearly though, it is possible that program orientations provided an opportunity for familiarity with the program and a comfort that some parents may not have had at the beginning of the program.

There are unmeasured effects that may have influenced enrollment decisions. These may include the local economy at the time, mistrust of financial institutions, language barriers between agency staff and the parents, the different skills and attributes of the SEED staff, and parent’s sophistication in financial products. These and other factors may be important to control for in future studies. In concluding their article on the strategies that low-income households use to save and accumulate assets, Beverly et al. (2003) stated that “asset accumulation is a much more complex process than conventional economic models assume. Individuals are not perfectly rational and omniscient economic agents. Instead, they sometimes have trouble choosing behaviors that are in their best interests” (p. 154). Also, peer group effects could have had some effect on the enrollment decision; parents who made either decision may have been influenced to do so by peers whom they trusted, more than the program staff.

While child development accounts initiatives originated in the USA, the United Kingdom recently established universal accounts for all children with public funding initiated at birth, known as the Child Trust Fund (CTF). Its goal is to provide start-up monies when children become adults. The CTF is a progressive savings and investment account for children born on or after September 1, 2002. To ensure that funds will be used for personal development, the government is incorporating financial education into the national education curriculum (Gregory and Drakeford 2006). Ideally, this may provide youth with knowledge and skills that may equip them with sustainable saving strategies and enable them to make better investment options. However, fully one-quarter of the certificates provided to parents of newborns in May, 2005 had not been used to open accounts as of May, 2006 (HM Revenue and Customs 2006). Most of these accounts, which by law were opened by the government as the one-year mark occurred, are thought to be for children from minority and low-income households (Gregory and Drakeford 2006; Sodha 2006).

As discussed earlier, agency personnel can increase the inclusion of vulnerable families in asset building programs by using their professional helping, communication, advocacy, and community practice skills. In addition, there have been some concerns that children in the UK child welfare system may be left behind in asset building since their personal contributions in the CTF are expected to be minimal, which could result in further inequality in the future. To curb this foreseeable scenario, there are campaigns to have local authorities, as guardians, make regular contributions into these children’s accounts (Maxwell 2005).

A recent CTF program report indicated that 24 percent of all the accounts had received personal contributions averaging €279 or $413 as at the 2007/2008 reporting period. However, accounts opened directly by parents had higher contribution rates of 31 percent averaging €280 or $415, compared to only 2 percent and averaging €232 or $343 for Revenue Allocated Accounts, accounts allocated to approved CTF providers if parents don’t open an account within 12 months of a check receipt from the government (HM Revenue and Customs 2006). Clearly, parental involvement and interest in child development accounts programs are critical for such policies to succeed.

However, in a rather somewhat disturbing development and since most policies are driven by politics, the recent election of David Cameron in partnership with Nick Klegg has led to the complete scrapping of the CTF. Reid Cramer of the New America Foundation argued that: (1) This decision is mostly a result of politics and prevailing economic decisions rather than the perceived failure of the program, (2) evidence showed that the program was indeed working with accounts being established with deposits, including instances where 30% of low-income households saving an additional $30 a month for their children, and (3) the program was a long-term strategy that needed time to determine its successes or weaknesses. Therefore, the policy was not offered an opportunity to run its course even for its first cohort (Cramer 2010). The decision by the new Tory and Liberal Democrats coalition is indeed a setback for asset building initiatives around the world. Earlier, Sherraden (2000) acknowledged that asset based initiatives are long-term strategies; therefore, policy innovators should “stay the course and roll with the inevitable high seas. In policy innovation, one will be tossed and on occasion nearly drowned (p. 178).” Clearly, it is an interesting time to study and advocate for assets for the poor in our societies.

The findings reported here answered the study question posed earlier by identifying individual and household characteristics as well as social services associated with a decision to enroll in child development accounts program. It found that program social services, specifically program orientation, provided by case managers may play a significant role in influencing parental decisions to enroll. However, as a result of the small number of either individual or household characteristics or social services that were found to be associated with enrollment decision, it is clear that further research is needed on both community-based and universal child development accounts programs. One model under study in another part of the SEED initiative is a universal approach whereby accounts are opened automatically for children at birth, with initial deposits from public, and/or private funders, and deposits that are more generous for children in low-income families (Sherraden and Clancy 2005).

In the meantime, studies of community-based asset building programs for children like this one provide some understanding of how challenging it is for low-income parents living in economically distressed communities to participate in savings programs. For many parents at all income levels, saving for college when children are three- or four-years old may not be an urgent priority. Further, the major priority for low-income parents is to meet their children’s basic daily household needs. Therefore, the fact that more than half of the parents enrolled in the child development accounts program is a positive finding, in and of itself. There is still need for further studies of alternative approaches to children’s development savings accounts with ways of improving participation in such accounts.