Economic mobility of single mothers: the role of assets and human capital development.

Zhan, Min

This study examines the economic mobility of single mothers. It highlights the relationships between single mothers' financial assets and human capital development (educational advancement, job training, and work hours) with their economic mobility. Analysis of data from the National Longitudinal Survey of Youth (NLSY79) indicates that assets may help improve upward economic mobility. Assets, however, have differential impact on single mothers with different income levels. In addition, human capital development mediates the positive link between assets and the economic mobility for mothers living between the 100% and 200% federal poverty. These results support asset building as an investment strategy to enhance the long-term economic well-being of single mothers. The findings also underscore the importance of examining within-group variations among single mothers in designing effective asset-building policies and programs.

Key words: economic mobility, human capital, single mothers

**********

The rapid increase of single-mother families in the past decades and the higher poverty rates among these families have been widely recognized (Fields & Casper, 2001; McLanahan & Booth, 1989; McLanahan & Kelly, 1999; McLanahan & Sandefur, 1994; Nichols-Casebolt & Krysik, 1997). Studies also found that compared with other groups, female-headed households have experienced lower upward economic mobility (Caputo, 1999; Weinstein, 2000). These studies indicate that contributing factors to the economic hardship of single mothers include their low earning capacity, low job opportunities in economically depressed areas, and meager public benefits.

This research, however, has not paid adequate attention to the impact of assets on the economic mobility of single mothers. Interest in asset accumulation for low-income families has increased in recent years in both policy and academic discussions. Studies show that increasing asset inequality has become much more prominent than that of income (Oliver & Shapiro, 1995; Wolff, 2001). Single mothers accumulate fewer assets compared to the general population (Bernheim & Scholz, 1993; Carney & Gale, 1999; Schmidt, 2004; Yamokoski & Keister, 2004). Lack of asset accumulation may not only contribute to the lower economic status of single mothers, but, perhaps more important, restrict their economic mobility (Sherraden, 1991).

Furthermore, while theory suggests different potential pathways through which assets may enhance economic status (Sherraden, 1991; Shobe & Page-Adams, 2001), empirical research has not examined possible mechanisms by which asset holding may impact the economic success (Scanlon & Page-Adams, 2001). Studies also indicate that the impact of assets on the economic well-being of single mothers may vary by their specific life circumstances (Edin, 2001). Existing research has sparsely examined these possible differences yet.

To address these issues, this study explores the associations between financial assets and human capital development with economic mobility between 1994 and 2000. Specifically, this study seeks to answer the following research questions. First, what is the relationship between single mothers' assets and their upward economic mobility? Second, do assets impact the economic mobility of single mothers through its influence on their human capital development? Third, does the impact of assets on the economic mobility vary by the income levels of single mothers?

Understanding the dynamic relationships between assets, human capital development, and the economic status of single mothers is particularly important in the context of welfare policy. The implementation of the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) in 1996 has focused on individual responsibility for long-term economic well-being. While welfare caseload has largely decreased since the welfare reform, many welfare leavers face precarious financial circumstances (Anderson & Gryzlak, 2002; Cancian, 2001; Loprest, 2001). These have led to increasing interest in investment approaches for assisting welfare recipients, and the low-income single parents in general, to enhance their economic well-being. Thus, it is necessary to understand how asset building, a promising investment strategy, impacts the economic mobility of single mothers.

Background: Theory and Past Research

Theoretical Framework

Within economic perspectives, some scholars make a distinction between income and assets as economic resources (Oliver & Shapiro, 1995; Sherraden, 1991; Wolff, 1995). These scholars indicate that the importance of assets is more than a flow of income for current or deferred consumption. Assets, as the stock of wealth in a household, can provide economic security for many families. Supporting this argument, a number of studies have found positive associations of assets with economic well-being (Page-Adams & Sherraden, 1997; Scanlon & Page-Adams, 2001).

Furthermore, assets may indirectly affect people's economic status by helping them invest in themselves and enhance their human capital development. Assets can provide security and resources for investments to improve long-term development. Assets also may enhance self-sufficiency and future orientation (Sherraden, 1991; Yadama & Sherraden, 1996; Zhan & Sherraden, 2003). For example, Yadama and Sherraden (1996) found that savings and house values had links with positive attitudes and behaviors. Some positive attitudes such as personal efficacy and future orientation may be important determinants of performance in a wide range of life events, including active engagement in long-term planning and productive activities (Bandura, 1997; Shobe & Page-Adams, 2001). A person with these qualities may want to further invest in education or skill training and pose positive work attitudes or efforts (Cho, 2001) Finally, in order to protect their existing assets, people may be more motivated to work and to improve their skills. Due to all these reasons, assets may stimulate people to engage investment and productive activities.

Based on these arguments, this study explores the direct impact of assets on the economic mobility of single mothers as well as assets' possible indirect impact through its influence on human capital development.

Assets and Economic Well-Being

In the last decade, as more attention has been given to assets as an indicator of household economic status, some studies have explored how assets are associated with the economic well-being of single-mother families. Cho (1999) found that financial assets had positive effects on the economic well-being of women after their marital disruption; financial assets were associated with increased income and reduced welfare dependency of divorced women. Rocha (1997) found that single mothers with assets (home ownership and savings) were more likely to live above the 100 percent poverty level compared with their counterparts without such assets. Raheim and Alter (1995) noted that assets appeared to increase the economic security of families on public assistance. Cheng (1995) further indicated that assets could help reduce the intergenerational transmission of poverty in female-headed households.

Assets and Human Capital Development

A few studies also have examined the impact of assets on labor force participation and educational improvement. Yadama and Sherraden (1996) found that among general population, both house values and savings were positively related to future planning activities, such as finding a new job. However, they found that assets were not related to productive money saving or human capital accumulation activities. Cho (2001) found that asset holding (both financial assets and having a vehicle) before and one-year after marital disruption was related to increased work hours of divorced women, especially for non-remarried women. Self-report surveys of the participants of structured savings programs for the poor (McBride, Lombe, & Beverly, 2003) further indicated that participants were more likely to plan for their and their children's education after joining the programs.

Human Capital Development and Economic Well-Being

Human capital theory argues that investment in human capital can raise future returns in the labor market even though one may forgo short-term earnings for long-term gains (Becker, 1993; Mincer, 1979, 1989; Schultz, 1993). Human capital usually refers to education, work experience, and job-related training.

Empirical studies indicate that educational attainment, especially post-secondary education, positively affects the economic standing of single mothers (e.g., Cho, 1999; Mauldin, 1990; McKeever & Wolfinger, 2001; Rocha, 1997; Smock, 1993). Most of these studies have examined the economic status of divorced women after a couple of years of their marital disruption. The longitudinal study of Sandfort and Hill (1996) further showed that young single mothers' education predicted their self-sufficiency and increased the possibility to get married in later years. Studies that examine the economic status of welfare leavers also indicate that a majority of former welfare recipients with postsecondary education worked at jobs with better pay and benefits, and were less likely to return to welfare (Cancian, 2001; Harris, 1996; Loprest, 2002; Meyer & Cancian, 1998; Smith, Deprez, & Butler, 2002; Strawn, 2004).

In terms of the impact of employment and job training, studies found that employed single mothers and those with more work hours had higher incomes (Dixon & Rettig, 1994; Mauldin, 1990; Smock, 1993, 1994). The findings on the impact of single mothers' prior work history are mixed (Bianchi, Subaiya, & Kahn, 1999; Smock, 1993, 1994). Findings on the effects of job training are also mixed (Cho, 1999; Hamilton, 2002; Mauldin, 1990; Mauldin & Koonce, 1990).

This Study

As seen from the above discussion, this previous research has several limitations. First, the potential association between assets and the long-term economic well-being of single mothers has not been adequately studied. Second, the possible mediating effect of human capital development in the link between assets and economic mobility, which is highlighted by theoretical arguments, has not been examined. Third, it is also important to investigate whether the impact of assets varies by the income levels of single mothers. Through the analysis of a nationally longitudinal representative sample, this study examines how the asset accumulation of single mothers (measured in 1994) and their human capital development (measured between 1995 and 1999) are related to their economic mobility (changes of income-to-needs ratio in 2000 compared to that in 1994). This study investigates how these relationships differ by single mothers' income levels.

Methods

Data and Sample

This study uses data from the National Longitudinal Survey of Youth (1979 cohort, NLSY79), a household survey of a representative sample of 12,868 young men and women who were 14 to 22 years when first interviewed in 1979 (Center for Human Resource Research, 2001). Respondents were interviewed annually between 1979 and 1994, and then biannualy between 1994 and 2002. The NLSY79 is well-suited for the purpose of this study because it oversamples the economically disadvantaged population, and it includes a variety of asset measures.

The sample for this study includes the respondents who were single mothers in 1994, remained in the sample, and have relevant information during the study period (1994-2000). Single mothers were defined as female respondents who were not married and had at least one child under 18 living in households in 1994. After listwise deletion of cases with missing data for all variables used in the analysis, the final sample include 704 single mothers (N = 856 before deletion). Further analysis indicates that there is no systematic difference in the demographic and socioeconomic characteristics between the missing data sample and the study sample. Thus bias as a result of missing data is likely to be minimal.

In order to examine how assets and other factors influence the economic mobility of single mothers with different economic status, the sample is divided into three groups for analyses according to their income-to-needs ratio in 1994: mothers who lived below the 100% federal poverty ("poor single mothers"), mothers who lived between the 100% and 200% federal poverty ("middle-income single mothers"), and those who lived above the 200% federal poverty ("high-income single mothers").

Measures

Assets. The assets of a mother includes her net worth and three types of ownership in 1994. Net worth in 1994 was calculated by subtracting the total value of debts (debts of home, business, credit card and others) from the total value of assets (assets of home, business, bank accounts, real estate, stocks, and all other assets). Because the distribution of this variable was quite skewed, the natural log of this measure was used in regression models.

Dichotomous measures of assets ownership include home ownership (yes = 1, no = 0), savings or checking account ownership (yes = 1, no = 0), and automobile ownership (yes = 1, no = 0). Dichotomous measures instead of actual amounts of these assets are included because the values of these types of assets are correlated with net worth. Other types of assets ownership (e.g., IRAs, CDs, stocks, business) were not included in the analyses because a small percentage of single mothers had these assets.

Human Capital Development. The human capital development of a mother includes her educational advancement, work experience, and job-related training between 1995 and 1999. Educational advancement is measured as whether women had any increased educational years during this period (yes = 1, no = 0). Work experience is measured as the average annual work hours, and job training indicates whether women had received any forms of job-related training (yes = 1, no = 0).

Economic Mobility. The dependent variable in this study, the economic mobility of a mother, is measured as the change of her income-to-needs ratio in 2000 compared to that in 1994. A family's income-to-needs ratio is defined as family income divided by the family-size-adjusted poverty guideline. Family income in NLSY79 is measured as the sum of income of all sources from all family members.

Control Variables. Because of their potential influence on the economic mobility indicated by previous studies (see a review by Caputo, 2003), the following demographic, social and economic variables are included in the analysis as control variables. The inclusion of these variables will help eliminate omitted variable bias and possible alternative explanations of variance in the dependent variables.

Variables that were measured in 1994 include women's age, race/ethnicity, marital status, educational status, number of children in households, health status, and income-to-needs ratio. Race/ethnicity was dummy coded (White, African American, and others), and White is the reference group in regression analyses. Marital status also was dummy coded: those who were never married are the reference group and coded as 0, and those who were previously married (divorced, separated, or widowed) were coded as 1. Mother's education in 1994 was coded as a nominal variable with three categories: less than high school degree (<12 years of education), high school degree (12 years of education), some college education or above education (>12 years of education). This variable was dummy-coded in multiple regressions, with less than a high school degree being the reference group. Health status is measured as whether mothers had any health problems that limited types or amount of work that they could do (yes=1, no=0). The age of a mother at the birth of her first child is also controlled.

In order to control for environmental factors, whether women lived in rural areas and the unemployment rates of their residence in 1994 are included. In addition, due to their potential influence on the economic mobility, three cumulative variables between 1995 to 1999 are also included: whether women got married (yes = 1, no = 0), whether they had new child(ren) (yes = 1, no = 0), and years they had received AFDC/TANE

Analysis

Descriptive information was first presented on the characteristics of poor, middle-income, and high-income single mothers. In order to examine the independent impact of assets and human capital development on the economic mobility after controlling for other demographic and socioeconomic factors, and to examine possible mediating effects of human capital development, hierarchical regression models were used in which economic mobility was first regressed on control variables, and then assets and human capital development were added sequentially to the models. Results of regression analyses are presented separately for poor, middle-income and high-income mothers.

Results

The characteristics of the sample are presented in Table 1. Of the 704 single mothers in 1994, 36% (n=257) lived below the 100% federal poverty, 33% (n=229) lived between the 100% and 200% federal poverty, and 31% (n=218) lived above the 200% federal poverty. Lower-income single mothers were more likely to be African Americans, to be never married, and to have health problems, and they were less educated and had more children living in households. Lower-income mothers also were less likely to get married and more likely to have additional children between 1995 and 1999. Some characteristics of the middle-income mothers, such as race/ethnicity, marital status, whether having health problems, and percentages of having new-born children, were similar to those of high-income mothers.

Single mothers were also diverse in their assets accumulation and human capital development. While three groups of mothers all made progress in their asset accumulation between 1994 and 2000, especially in home ownership and bank account ownership, middle-income mothers had lower, and poor mothers had much lower asset ownership and net worth in both 1994 and 2000 than high-income mothers. Poor single mothers were much less likely to receive job training and to continue their education compared to other two groups. Middle-income single mothers on average had the most increase in their upward economic mobility (0.81), followed by poor single mothers (0.64) and high-income single mothers (0.55).

As mentioned, in order to examine how assets and human capital development are related to the economic mobility of single mothers, three regression analyses were conducted for the poor, middle-income, and high-income single mothers, with economic mobility regressed on control variables and then on assets and human capital development variables. Results are presented in Tables 2, 3, and 4. To further examine whether the impact of assets on the economic mobility differ by mothers' income levels, a regression analysis on the economic mobility was conducted for the full sample which included interactions of asset variables with mothers' economic levels (middle-income mothers was the reference group) (Table 5).

Poor Single Mothers. Table 2 shows that the regression model was statistically significant and the control variables together explained about 21% of the variance in economic mobility. Among the control variables, women who were previously married in 1994 and those who got married between 1995 and 2000 had more increase in their income-to-needs ratio. Single mothers who received more years of welfare had less upward economic mobility. Income status in 1994 was negatively related to the upward economic mobility, i.e., the poorest poor had lower economic mobility.

After assets variables entered, the R2 increased by about 24% (from 21% to 26%). Results show that bank account ownership and automobile ownership of poor single mothers were positively related to their economic mobility; home ownership and net worth, however, were not related their economic mobility (the correlation coefficient for home ownership was negative). Furthermore, after assets variables were entered, the relationships between marital status in 1994 and years of receiving welfare with economic mobility disappeared, indicating that assets may account for the links between these variables and economic mobility.

Table 2 also shows the full model with human capital variables added. Poor single mothers who continued their education experienced higher level of economic mobility. Work hours and receiving training, however, were not related to economic mobility. In addition, after these three variables were entered, bank account ownership and automobile ownership were still related to economic mobility; the coefficients for bank account ownership, however, dropped by about 15% (from 0.69 to 0.59).

Middle-Income Single Mothers. Table 3 shows that the regression model for single mothers who lived above poverty but below 200% poverty line. The model was statistically significant and the control variables together explained about 26% of the variance in economic mobility. Among the control variables, women who had more children and those had new child between 1995 and 1999 had less economic mobility. Those who got married during this period had more increase in income-to-needs ratio.

After assets variables were entered, the [R.sup.2] increased by about 31% (from 26% to 34%). Bank account ownership of single mothers was positively related to their economic mobility; home ownership, automobile ownership, and net worth, however, were not related to their economic mobility. Results also show that women who had educational improvement after 1994 also had higher increase in income-to-needs ratio. Furthermore, after human capital variables were entered, the relationship between bank account and economic mobility Full Sample with Interactions of Women's Income Status and Assets disappeared, indicating educational advancement may mediate the links between bank account ownership and economic mobility for these mothers.

High-Income Single Mothers. Table 4 shows that the regression model for high-income mothers was statistically significant, and the control variables together explained about 11% of the variance in economic mobility. Among control variables, only health status was negatively related to the economic mobility, i.e., women who had health problems were less likely to improve their economic status.

After assets variables were entered, the R2 increased by about 18% (from 11% to 13%). Bank account ownership and net worth were positively related to their economic mobility. After human capital development variables were further added, results show that women who had educational improvement, receiving training, and those who worked more hours had higher increase in income-to-needs ratio. Furthermore, after human capital variables were entered, bank account and net worth were still positively related to with economic mobility, but their coefficients moderately dropped (about 40% drop for bank account ownership, and 25% drop for net worth).

What factors might explain why the middle-income mothers made the most progress in their upward economic mobility? First, the high-income mothers were probably not changing much in their economic status because they were already in good shape in 1994. Second, the above results indicate that marital status, educational advancement, and asset accumulation might help explain the differences in the economic mobility between poor mothers and middle-income mothers. Getting married between 1994 and 2000 was positively related to the economic mobility for both poor and middle-income mothers. However, a much higher proportion of middle-income mothers (28%) got married than poor mothers (19%). Similarly, educational advancement was related to the economic mobility of both groups of mothers, and middle-income mothers were much more likely to continue their education (40%) than poor mothers (28%).

The results presented in Tables 2-4 also suggest that asset accumulation might have stronger association with the economic mobility for middle-income single mothers. For example, after assets variables were added to the model for middle-income mothers, the variance explained in the economic mobility increased by 31%, compared to 24% increase in the model for poor mothers and 18% increase in the model for high-income mothers. Furthermore, for middle-income mothers, the impact of bank account ownership on the economic mobility operated mainly through its influence on educational advancement (Table 3). This indicates that bank account ownership may have stronger impact on the educational improvement of these mothers. In order to further determine whether the impact of assets on the economic mobility varies by the three income levels of single mothers, interaction terms between mothers' income levels and asset variables were constructed and added into the regression model on the economic mobility for the full sample (Table 5). Results show that compared to poor mothers, bank account ownership had stronger impact on the economic mobility for middle-income mothers. Net worth had stronger impact on the economic mobility for high-income mothers.

Discussion and Implications

Consistent with previous studies, this study found positive associations between assets and the economic mobility of single mothers, after controlling for household income and a variety of other respondent characteristics. The links between assets and economic mobility, however, were different for poor, middle-income, and high-income mothers. Net worth was only linked to the economic mobility for high-income mothers. It is possibly because net worth was much lower for mothers living below the 200% federal poverty. Automobile ownership was only related to the economic mobility of poor mothers, perhaps because the automobile was the only important asset for most of these mothers. Furthermore, bank account ownership had stronger influence on the economic mobility of middle-income mothers than its impact on poor mothers, which helps explain the higher levels in the economic mobility of middle-income mothers.

Home ownership was not related to the economic mobility of single mothers in this study (for poor single mothers, the coefficient was negative). This is not consistent with findings from some previous research (Scanlon & Page-Adams, 2001). The possible poor quality of housing owned by single mothers, especially by poor mothers, may contribute to this inconsistency. Previous studies have suggested that the location of a home and neighborhood conditions may be as important as ownership (Coulton, 2003; Denton, 2001; Finn, Zorita, & Coulton, 1994). This issue is very important for the consideration of asset-based policies, and more studies are needed.

Furthermore, the results show that after human capital variables were added into the model, the relationships between mothers' bank account ownership and upward economic mobility disappeared for middle-income mothers. This result provides somewhat tentative evidence that mothers' human capital development may mediate the relationship between bank account ownership with the economic mobility of these mothers. In other words, owning bank accounts may provide some economic security for middle-income single mothers to pursue further education or job-related training. These findings may be able to provide some insight into possible mechanisms that transmit the economic effects of assets. Again, these mechanisms could be different for single mothers with different economic status and need to be further elaborated.

Mother's education advancement increased their upward economic mobility, irrespective of their income levels. Work hours, however, were related to the economic mobility of higher-income mothers only. It is possibly because low-income single mothers are more likely to have low-wage jobs that offer little opportunities for advancement. Similarly, job-related training was only positively linked to the economic mobility of high-income mothers, perhaps due to the fact that this group of single mothers is more likely to receive high quality job training with potentials for career advancement. These findings may indicate that the quality of employment or job-related training of single mothers is important for their economic upward mobility. Somewhat surprisingly, educational status in 1994 of mothers was not related to their economic mobility. Further analysis indicates that for the full sample, education was positively linked with economic mobility. Limited variations in educational status within each group of mothers may contribute to the insignificant findings.

It is worth mentioning that different demographic factors were related to the economic mobility of poor, middle-income and high-income mothers. For example, marriage helped improve the economic status of poor and middle-income single mothers, but not for the mothers living above the 200% federal poverty. This is possibly because high-income mothers were better educated and were more likely be employed, thus depending less on marriage to improve their economic status. Also, number of children and having additional children were negatively related to the economic mobility of middle-income mothers only. This may be due to the fact that these mothers were more likely to be employed than poor mothers; on the other hand, they had less financial ability to pay quality childcare compared to high-income mothers (Hofferth, 1995). Thus, reliable child care maybe a more important factor that prevents these single mothers from participating in the labor force or skill-building activities, thus reducing their earnings potential.

When interpreting the above results, it should be noted that while causal ordering was established between assets (measured in 1994), human capital development (measured between 1995 and 1999), and economic mobility (measured in 2000), possible alternative explanations exist. A wide range of personal, family, and community characteristics could affect assets accumulation, human capital development, and economic mobility of single mothers. In other words, single mothers who own assets may have unobserved characteristics that also lead to human capital development and economic mobility. It could be that these characteristics are causing both assets and development. Although important factors that were indicated by previous studies have been controlled in this study, it is not possible to control for all relevant variables.

The results from this study suggest that promoting asset accumulation of single mothers could be a useful strategy to improve their economic status. Asset building strategy could be particularly potential to help the middle-income single mothers (i.e., mothers living between the 100% and 200% of federal poverty) improve their educational status and economic well-being. While bank account and automobile ownership were positively related to the economic mobility of poor mothers, these mothers benefited less from their assets compared to higher-income mothers. Thus, asset-building programs may need to be adjusted to accommodate specific needs of poor single mothers.

Home ownership of single mothers was not related to economic mobility, indicating that poor neighborhood conditions may be an obstacle to asset accumulation and compromise the positive impact of assets. Asset-building programs that incorporate community services and that are tailored to specific life circumstances of single mothers may have better potential to promote their economic well-being.

Among human capital variables, this study shows that education advancement helped singe mothers improve their economic status, irrespective their poverty status. Obtaining continued education, however, is often difficult for single mothers, especially for low-income single mothers with small children who are trying to juggle through multiple responsibilities. For example, this study found only a small percentage of women had advanced their education. Thus, special designed policies or programs are perhaps needed to promote their education. The results of this study also underscore the importance of high-quality employment or job-related training for low-income mothers.

In sum, the findings from this study support strategies of assets building and human capital development to help enhance single mothers' economic status. It is equally important to note that single mothers are a diverse group and assets may have different impact on the economic mobility of its subgroups. Asset-building policies and programs may need to take into particular consideration of the specific life context of poor single mothers.

References

Anderson, S. G., & Gryzlak, B. (2002). Social work advocacy in the post-TANF environment: Lessons from early TANF research studies. Social Work, 47(3), 301-314.

Bandura, A. (1997). Self-Efficacy: The exercise of control. New York: Freeman.

Becker, G. S. (1993). Human capital: A theoretical and empirical analysis, with special reference to education. Chicago: University of Chicago Press.

Bernheim, B., & Scholz (1993). Private saving and public policy. Tax policy and Economy, 7, 73-110.

Bianchi, S. M., Subaiya, L., & Kahn, J. R. (1999). The gender gap in the economic well-being of nonresident fathers and custodial mothers. Demography, 36(2), 195-203.

Cancian, M. (2001). Rhetoric and reality of work-based welfare reform. Social Work, 46, 309-314.

Caputo, R. K. (1999). Advantage White and male, disadvantage Black and female: Income inequality, economic well-being, and economic mobility among families in a youth cohort, 1979-1991. Danbury, CT: Rutledge Books.

Caputo, R. K. (2003). Assets and economic mobility in a youth cohort, 1985-1997. Families in Society: The Journal of Contemporary Human Services, 84(1), 51-62.

Carney, S. & Gale, W. (1999). Asset accumulation among low-income households. Brookings Discussion Paper in Domestic Studies. Washington, DC: The Brookings Institution.

Center for Human Resource Research (2001). NLSY79 User's guide: A guide to the 1979-2000 National Longitudinal Survey of Youth data. Columbus, OH: The Ohio State University.

Cheng, L. (1995). Assets holding and intergenerational poverty vulnerability in female-headed families. Paper presented at the Seventh International Conference of the Society for the Advancement of Socio-Economics, Washington, DC.

Cho, E. Y. (2001). The effects of tangible assets and human capital on the economic well-being of women after marital disruption. Doctoral Dissertation. St. Louis, MO: Center for Social Development, Washington University.

Cho, E. Y. (1999). The effects of assets on the women. Working Paper No. 99-6. St. Louis, MO: Center for Social Development, Washington University.

Coulton, C. J. (2003). Metropolitan inequalities and the ecology of work: Implications for welfare reform. Social Service Review, 77(2), 159-190.

Denton, N. A. (2001). Housing as a means of asset accumulation: A good strategy for the poor?. In T. Shapiro & E.N. Wolff, (Eds.), Assets for the poor: Benefits and mechanisms of spreading asset ownership, (pp. 232-266). New York: Russell Sage Foundation.

Fields, J., & Casper, L. C. (2001). American's families and living arrangements. Washington, DC: U. S. Census Bureau.

Finn, C. M., Zorita, P., & Coulton, C. (1994). Assets and financial management among poor households in extreme poverty neighborhoods. Journal of Sociology and Social Welfare, 21(4), 75-94.

Hamilton, G. (2002, July). Moving people from welfare to work: Lessons from the National Evaluation of Welfare-to-Work Strategies. Manpower Demonstration

Harris, K. M. (1996). Life after welfare: Women, work, and repeat dependency. American Sociological Review, 61, 407-426.

Hofferth, S. L. (1995). Caring for children at the poverty line. Children and Youth Service Review, 17, 61-90.

Loprest, P. (2001). How are families that left welfare doing? A comparison of early and recent welfare leavers. New Federalism: National survey of America's families, Series B. No. B-36. Washington, DC: The Urban Institute.

Loprest, P. (2002). Who returns to welfare? New Federalism: National Survey of American's Families. Series B, No. B-49. Washington, DC: Urban Institute.

Mauldin, T. A. (1990). Women who remain above the poverty level in divorce: Implications for family policy. Family Relations, 39, 141-146.

Mauldin, T. A., & Koonce, J. (1990 Spring). The effect of human capital on the economic status of divorced and separated women: Differences by races. The Review of Black Political Economy, 55-68.

McBride, A.M., Lombe, M., & Beverly, S.G. (2003). The effects of Individual Development Account programs: Perceptions of participants. Social Development Issues, 25(1&2), 59-73.

McKeever, M., & Wolfinger, N. (2001). Reexamining the economic costs of marital disruption for women. Social Science Quarterly, 82(1), 202-217.

McLanahan, S. S., & Booth, K. (1989). Mother-only families: Problems, prospects, and politics. Journal of Marriage and the Family, 51, 557-580.

McLanahan, S. S., & Kelly, E. (1999). The feminization of poverty. In J. Chafetz (Ed.), Handbook of the Sociology of Gender, (pp. 127-146). New York: Plenum Press.

McLanahan, S. S., & Sandefur, G. (1994). Growing up with a single parent: What hurts, what helps. Boston, MA: Harvard University Press.

Meyer, D. R., & Cancian, M. (1998). Economic well-being following an exit from aid to families with dependent children. Journal of Marriage and the Family, 60, 479-492.

Mincer, J. (1979). Human capital and earnings. In D. M. Windham (Ed.) Economic dimensions of education (pp. 1-31). Washington, DC: The Academy.

Mincer, J. (1989). Human capital and the labor market: A review of current research. Educational Researcher, 18, 27-34.

Nichols-Casebolt, A., & Krysik, J. (1997). The economic well-being of never-and ever-married single mother families: A cross-national comparison. Journal of Social Service Review, 23(1), 19-40.

Oliver, M. L., & Shapiro, T. M. (1995). Black Wealth/White Wealth: A new Perspective on Racial Inequality. New York: Routledge.

Raheim, S. & Alter, C. (1995, March). Self-Employment investment demonstration final evaluation report. Iowa City, IA: University of Iowa School of Social Work.

Raphael, S., & Rice, L. (2000). Car ownership, employment, and earnings. Berkeley: University of California, Berkeley, Program on Housing and Urban Policy.

Rocha, C. J. (1997). Factors that contribute to the economic well-being in female-headed households. Journal of Social Service Research, 23(1), 1-17.

Sandfort, J. D., & Hill, M. S. (1996). Assisting young, unmarried mothers to become self-sufficient: The effects of different types of early economic support. Journal of Marriage and the Family, 58, 311-326.

Scanlon, E., & Page-Adams, D. (2001). Effects of asset holding on neighborhoods, families, and children: A review of research. In R. Boshara (Ed.) Building Assets: A Report on the Asset-Development and IDA Field, (pp. 25-49). Washington, D.C.: Corporation for Enterprise Development.

Schmidt, L. (2004). Gender, marriage, and asset accumulation in the United States. Paper presented at the Workshop on Women and The Distribution of Wealth, Yale Center for International and Area Studies, November 12-13, 2004.

Schultz, P. T. (1993). Returns to women's education. In E. M. King & M. A.Hill (Eds.), Women's education in developing countries (pp. 51-99). Baltimore: Johns Hopkins University Press.

Sherraden, M. (1991). Assets and the poor: A new American welfare policy. M.E. Sharpe. Armonk, New York.

Shobe, M., & Page-Adams, D. (2001). Assets, future orientation and well-being: Exploring and extending Sherraden's framework. Journal of Sociology and Social Welfare, 28(3): 109-127.

Smith, R. J., Deprez L., & Butler, S. S. (2002). Parents as scholars: Education works. Augusta, ME: Alliance for Family Success.

Smock, P. (1993). The economic costs of marital disruption for young women over the past two decades. Demography, 30(3), 353-371.

Smock, P. (1994). Gender and the short-run economic consequences of marital disruption. Social Forces, 73(1), 243-262.

Strawn, J. (2004). Why congress should expand, not cut, access to long-term training in TANF. Washington, DC: Center for Law and Social Policy.

Weinstein, M. M. (2000, February 18). America's rags-to-riches myth. The New York Times, p. A28.

Wolff, E. N. (1995). Top Heavy: A study of the Increasing Inequality of Wealth in America. New York: Twentieth Century Fund Press.

Wolff, E. (2001). Recent trends in wealth ownership. In T. Shapiro & E. Wolff (Eds.), Benefits and mechanisms for spreading asset ownership in the United States, (pp. 34-73). NY: Russell Sage Foundation.

Yadama, G. N., & Sherraden, M. (1996). Effects of assets on attitudes and behaviors: Advance test of a social policy proposal. Social Work Research, 20(1): 3-11.

Yamokoski, A., & Keister, L. A. (2004). The wealth of single females: Marital status and fertility in the asset accumulation of young baby boomers in the United States. Paper presented at the Workshop on Women and The Distribution of Wealth, Yale Center for International and Area Studies, November 12-13, 2004.

Zhan, M., & Sherraden, M. (2003). Assets, expectations, and children's educational achievement in female-headed households. Social Service Review, 77(2), 191-211

MIN ZHAN

School of Social Work

University of Illinois at Urbana-Champaign

Table 1: Sample Characteristics

Variable Women between
 Women below 100% and 200% Women above
 100% poverty poverty 200% poverty
 (N=257) (N=229) (N=218)

 Mean or Mean or Mean or
 Percentage Percentage Percentage

Control Variables
Age 33 33 33
Race /ethnicity
 White 32% 48% 51%
 African American 59% 45% 43%
 Others 9% 7% 6%
Marital status
 Never married 47% 33% 30%
 Previously married 53% 69% 70%
Educational Status
 Less than high school 33% 15% 6%
 HS graduate 50% 48% 44%
 Some college 15% 32% 33%
 Bachelor's degree 2% 5% 16%
Number of children 2.5 2.0 1.6
Age at birth of first 19 20 21
 child
Having health 23% 8% 6%
 limitations
Living in rural areas 20% 18% 16%
Unemployment rate of
residence 2.9 2.9 2.8
Having newborn
child(ren) (1995-1999) 19% 13% 11%
Having been married
(1995-1999) 19% 28% 38%
Years of receiving
welfare (1995-1999) 2.4 0.8 0.2
Assets in 1994
Home ownership 11% 23% 39%
Bank account ownership 18% 49% 70%
Automobile ownership 49% 77% 85%
Net worth ($) 4,276 10,524 18,864
Assets in 2000
Home ownership 21% 43% 58%
Bank account ownership 28% 57% 72%
Automobile ownership 56% 79% 87%
Net worth ($) 4,498 10,873 20,475
Human Capital
Development
Having educational
advancement 10% 14% 15%
Having received job
training 28% 40% 44%
Average annual work
hours 1,091 1,771 2,035
Dependent Variable
Changes in income-to-
needs ratio 0.64 0.81 0.55

Table 2: Regression Analysis of Women's Economic Mobility:
Women below 100% Poverty

Variables Coefficients Coefficient Coefficient

Control Variables
Age in 1994 0.07 0.08 0.09
(White)
 African American -0.04 0.08 0.11
 Others -0.49 -0.40 -0.50
(Never married)
Previously married 0.34 * 0.19 0.12
(Less than high school)
High school graduate 0.30 0.14 0.18
Some college or above 0.38 0.14 0.03
 education
Number of children in -0.11 -0.11 -0.13
 households
Age at the birth of first -0.01 -0.02 -0.02
 child
Health limitations -0.25 -0.34 -0.13
Income-to-needs ratio -0.83 ** -0.69 * -0.68 *
 in 1994
Rural residents -0.12 -0.09 -0.08
Unemployment rate of -0.03 -0.03 -0.02
 residence
Having additional children -0.23 0.16 -0.07
Having been married 0.68 ** 0.62 ** 0.47 *
Years of receiving welfare -0.09 * -0.07 -0.04
Assets Variables
Home ownership -0.43 -0.43
Bank account ownership 0.69 ** 0.59 *
Automobile ownership 0.45 * 0.45 *
Log net worth 0.81 0.21
Human Capital Development
1995-2000
Educational advancement 0.73 *
Receiving training -0.19
Work hours 0.0002
[R.sup.2] 0.21 0.26 0.30
N 257 257 257

Note--Categories in parentheses are reference groups.

* p < .05. ** p < .01. *** p < .001.

Table 3: Regression Analysis of Women's Economic Mobility:
Women above 100% and below 200% Poverty

Variables Coefficients Coefficient Coefficient

Control Variables
Age at 1994 -0.01 0.002 0.003
(White)
 African American -0.08 0.14 -0.002
 Others 0.06 0.17 0.12
(Never married)
 Previously married -0.02 0.05 -0.09
(Less than high school)
 High school graduate 0.32 0.20 0.23
 Some college or above 0.37 0.21 0.31
 education
Number of children in -0.22 * -0.20 * -0.19 *
 households
Age at the birth of first -0.05 -0.05 -0.06
 child
Health limitations 0.35 0.41 0.80 *
Income-to-needs ratio -0.31 -0.35 -0.36
 in 1994
Rural residents 0.46 0.61 * 0.55 *
Unemployment rate of 0.04 0.04 0.08
 residence
Having additional children -0.85 * -0.75 * -0.81 *
Having been married 1.42 *** 1.54 *** 1.64 ***
Years of receiving welfare -0.11 -0.05 -0.07
Assets Variables
Home ownership 0.14 0.14
Bank account ownership 0.78 * 0.27
Automobile ownership 0.26 0.04
Log net worth 0.61 0.60
Human Capital Development
1995-2000
Educational advancement 0.67 *
Receiving training 0.03
Work hours 0.0002
[R.sup.2] 0.26 0.34 0.41
N 229 229 229

Note--Categories in parentheses are reference groups.

* p < .05. ** p < .01. *** p < .001.

Table 4: Regression Analysis of Women's Economic Mobility:
Women above 200% Poverty

Variables Coefficients Coefficient Coefficient

Control Variables
Age in 1994 0.09 0.09 0.09
(White)
 African American 0.28 0.28 0.21
 Others 0.72 0.65 0.55
(Never married)
 Previously married 0.25 0.21 0.23
(Less than high school)
 High school graduate 0.41 0.38 0.26
 Some college or above 0.49 0.36 0.41
 education
Number of children in 0.03 0.05 0.05
 households
Age at the birth of first 0.02 0.02 0.03
 child
Health limitations -1.31 * -1.19 -1.07
Income-to-needs ratio -0.21 -0.17 -0.20
 in 1994
Rural residents 0.14 0.32 0.25
Unemployment rate of 0.17 0.21 0.13
 residence
Having additional children -0.16 -0.16 -0.07
Having been married 0.31 0.29 0.29
Years of receiving welfare -0.39 -0.38 -0.38
Assets Variables
Home ownership 0.11 0.11
Bank account ownership 0.15 * 0.09 *
Automobile ownership 0.11 0.10
Log net worth 0.25 * 0.19 *
Human Capital Development
1995-2000
Educational advancement 0.41 *
Receiving training 0.56 *
Work hours 0.008 **
[R.sup.2] 0.11 0.13 0.20
N 218 218 218

Note--Categories in parentheses are reference groups.

* p < .05. ** p < .01. *** p < .001.

Table 5: Regression Analysis of Women's Economic Mobility:
Full Sample with Interactions of Women's Income Statue and Assets

Variables Coefficients

Control Variables
Age in 1994 0.06
(White)
 African American 0.29
 Others 0.16
(Never married)
 Previously married 0.15
(Less than high school)
 High school graduate 0.23
 Some college or above education 0.48 *
Number of children in households -0.11 *
Age at the birth of first child 0.05
Health limitations -0.15
Rural residents -0.01
Unemployment rate of residence -0.09
Having additional children -0.23 *
Having been married 0.65 **
Years of receiving welfare -0.07
(Middle-income mothers)
 Poor mothers -1.49 *
 High-income mothers -0.69
Assets Variables
Home ownership 0.07
Bank account ownership 0.38 *
Automobile ownership 0.08
Log net worth 0.17 *
Human Capital Development 1995-2000
Educational advancement 0.19 *
Receiving training 0.08
Work hours 0.0003 *
Interactions of assets with mothers'
 income status
Home ownership * poor mothers -0.16
Bank account ownership * poor mothers -0.27 *
Automobile ownership * poor mothers 0.21
Log net worth * poor mothers -0.22
Home ownership * high-income mothers 0.29
Bank account ownership * high-income mothers 0.06
Automobile ownership * high-income mothers -0.28
Log net worth * high-income mothers 0.41 *
[R.sup.2] 0.13
N 704

Note--Categories in parentheses are reference groups.

* p < .05. ** p < .01. *** p < .001.