Families on TANF in Illinois:  Employment Assets and Liabilities

Chapter IV.
The Determinants of Labor Market Success or Failure

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Contents

• The Prevalence of Multiple Employment Liabilities
• Influence of Liabilities on Substantial Employment: Multivariate Analysis
• Effects of Strategies to Address Employment Liabilities: Simulations

Endotes

The previous chapter examined the specific assets and liabilities that may foster or impede TANF recipients' success in the labor market. Information on assets and liabilities is useful because it reflects the prevalence of certain characteristics among TANF recipients and provides a framework for thinking about how these characteristics can influence the ability to transition from welfare to work or meet work requirements while receiving TANF benefits. In this chapter, we expand the analysis by (1) examining the prevalence of multiple employment liabilities among case heads, (2) using a multivariate model to estimate how the number of liabilities or the presence of specific liabilities influences the likelihood that a recipient is substantially employed (i.e., working at least 30 hours per week), and (3) simulating the changes in employment rates that could result from various strategies designed to address the employment liabilities of TANF recipients.

The Prevalence of Multiple Employment Liabilities

Previous studies on the characteristics of welfare recipients have found that they often have multiple employment liabilities and that the likelihood of employment decreases as the number of liabilities increases (Olson and Pavetti 1996; Danziger et al. 2000; Loprest and Zedlewski 1999). For example, a recipient with limited education but substantial work experience is likely to experience more success in finding employment than a recipient with limited education and no work experience. A recipient with limited education and poor health may have less success in finding a job than a recipient with only one of these liabilities; the first individual may need to find a job that does not require a high school diploma and that provides a work schedule that is flexible enough to accommodate the employee's medical needs. A recipient experiencing major depression who faces child care and transportation problems may be overwhelmed by the prospect of finding a job in the face of these obstacles, whereas a recipient who experiences major depression but no other liabilities may be able to manage her depression well enough to find and maintain employment.

In our analysis, we examine 16 employment liabilities. Broadly speaking, they fall into three categories: human capital liabilities, personal challenges, and logistical and situational challenges (Table IV.1). The category of human capital liabilities, newly introduced in this chapter, was established by identifying the employment assets lacking in TANF case heads.⁽¹⁾

• Multiple liabilities for employment are extremely common among TANF case heads and are significantly more common among those who are not substantially employed.

The majority of TANF case heads have 3 or more liabilities for employment (Figure IV.1).⁽²⁾ Only 4 percent do not have any liabilities, and 12 percent have only 1. Ten percent have 7 or more liabilities. The most liabilities for any one TANF recipient is 11.

TANF case heads who are not employed at least 30 hours per week have an average of 3.9 liabilities for employment, which is significantly higher than the average of 2.8 liabilities characteristic of substantially employed case heads (Table IV.2). On average, TANF recipients who are not substantially employed have significantly more liabilities--whether human capital, personal, or logistical and situational--than their counterparts who work at least 30 hours per week. Regardless of employment status, the presence of multiple human capital and logistical and situational liabilities is more pronounced than the presence of multiple personal liabilities. For instance, among TANF case heads not substantially employed, more than 40 percent have multiple human capital liabilities (46 percent) or multiple logistical and situational challenges (44 percent). In contrast, only 29 percent have multiple personal challenges (results not shown, see Appendix D, Table D-26).

• Multiple liabilities are especially common among TANF case heads facing personal challenges and transportation issues.

TANF case heads who have a mental health problem, are chemically dependent, have experienced severe domestic violence in the past year, show signs of a learning disability, have difficulty with English, or face a transportation problem always have other liabilities as well (Table IV.3). Among TANF case heads with at least one employment liability, only 10 percent have seven or more liabilities (Table IV.3). However, the likelihood of having seven or more liabilities is substantially higher for recipients with certain liabilities. For example, about 40 percent of TANF case heads who have difficulty with English, show signs of a learning disability, or are chemically dependent have a total of seven or more liabilities. About 30 percent of TANF case heads with a physical health problem, a mental health problem, a recent history of severe domestic violence, or a transportation problem have a total of seven or more employment liabilities. TANF case heads who have a child under age one in the household are the least likely to have a high number of liabilities; specifically, only 9 percent have seven or more liabilities.

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Influence of Liabilities on Substantial Employment: Multivariate Analysis

This section presents the results of our multivariate analysis of the influence of various liabilities on the likelihood that a TANF recipient is working more than 30 hours per week. We initially investigated how the number of liabilities affects a recipient's employment status. We then investigated how the presence of specific liabilities affects a recipient's employment status. In these analyses, we considered background characteristics such as demographic traits, neighborhood and local labor market conditions, and the amount of time on TANF in the past 25 months.

As shown in Table IV.4, and consistent with previous studies, the greater the number of liabilities, the less likely a case head is to work 30 or more hours per week. The multivariate logit model predicts that a TANF recipient with no liabilities has a 58 percent probability of working 30 hours or more per week. Recipients with one or more liabilities have significantly lower probabilities of working. Specifically, the probability that a recipient with one liability works 30 or more hours per week is only 35 percent, nearly 23 percentage points lower than the probability for a recipient with no employment liabilities. Recipients with two or three liabilities have a slightly lower likelihood (33 percent) of working 30 or more hours per week than those with just one liability. For recipients with four to six liabilities, the likelihood of working 30 and more hours per week drops by an additional 10 percentage points to just 23 percent. And, for TANF recipients with seven or more liabilities, the probability of working 30 hours or more hours per week is extremely low, at just 7 percent.

Table IV.5 presents predicted probabilities based on a model that estimates the relative influence of each liability on the likelihood that a recipient works 30 or more hours per week, assuming that a TANF case head has "average" characteristics and only the liability under consideration. The model predicts that a TANF recipient with no liabilities has a 50 percent chance of working 30 hours or more per week.⁽³⁾

Only 4 of the 16 liabilities in the model are significantly related to a recipient's employment status: fewer than four quarters of recent work experience, a health problem, two or more arrests in the past six years, and a child care problem. Recipients with a child care problem have only a 30 percent chance of working 30 or more hours per week, which is 20 percentage points less than recipients with no employment liabilities. Similarly, recipients with a health problem, fewer than four quarters of recent work experience, or two or more arrests have a 32, 36, or 34 percent chance, respectively, of being employed 30 or more hours per week. Note that unobserved variables not included in this model (such as personal motivation or family support) could be significantly related to a recipient's employment status. While some of these unobserved variables could directly influence a recipient's employment status, they also might influence employment through other variables in the model. For example, personal motivation might influence whether a recipient has worked in the past, and it might be personal motivation rather than recent work experience that is exerting the observed influence on employment status.

Four liabilities that have a significant bivariate relationship with employment (Table III.7) did not show a significant relationship with employment in the multivariate analysis: mental health problems, transportation problems, pregnancy, and caring for a child under the age of one. Mental health and transportation problems are liabilities that occur in combination with many other liabilities, reducing their independent influence. Pregnancy and caring for a child under the age of one do not occur in combination as often as many other liabilities, although they do tend to occur more often among younger rather than older women.

The background characteristics in these models that significantly influence a recipient's employment status include age, race, county unemployment rate, and number of children. Consistent with the results of other studies, older recipients are significantly more likely to be working than are younger recipients. Recipients who are neither black nor white also are significantly more likely to be substantially employed than are white recipients. Possibly because of Illinois's generous earned-income disregard, which makes it easier for larger families to continue to receive assistance, families with three or more children are also significantly more likely to be substantially employed than are families with just one child. As expected, a higher county unemployment rate is associated with a significantly lower probability of being employed 30 or more hours per week.

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Effects of Strategies to Address Employment Liabilities: Simulations

The model used to estimate the influence of employment liabilities can be used not only to examine the relative effect of each liability on employment status but also to simulate various strategies designed to increase work among TANF recipients. For example, the model can be used to examine how much the share of TANF recipients working 30 or more hours per week would increase if all child care problems were eliminated. The answer to this question depends on the proportion of recipients with a child care problem and the extent to which child care reduces the chance of working. The answer may also be influenced by the presence of other liabilities that may have independent effect on the likelihood of employment. In Table IV.6, we present the results of policy simulations that address one liability at a time. We then present simulations of strategies that address multiple liabilities, as in a policy or program that emphasizes development of human capital.

Under the current constellation of liabilities, about 28 percent of TANF case heads work 30 hours a week or more. Our simulation results suggest that no strategy that addresses just one specific liability would go far in increasing this rate. Eliminating the negative influence of limited work experience, possibly by placing recipients in transitional jobs or work experience programs, would increase the proportion of TANF recipients working 30 or more hours per week by 6 percentage points to 34 percent. Eliminating all child care problems would raise the proportion working by 4 percentage points to 32 percent.

Strategies that address multiple liabilities that may be related to one another would further increase the proportion of recipients working 30 or more hours per week. Still, these comprehensive strategies, even if successful, would raise the level of substantial employment to no more than two-fifths of the caseload. The comprehensive strategies that would go the furthest toward increasing work participation are those that address human capital liabilities and/or logistical challenges. A strategy that increases the level of work experience and eliminates the logistical challenges of child care and transportation would increase the proportion of the caseload working 30 or more hours per week by nearly 14 percentage points, to 41 percent. A strategy that eliminates all human capital liabilities (e.g., education and work experience) and addresses special learning needs (e.g., learning disabilities and language barriers) would increase the proportion to 36 percent. A strategy that eliminates all child care and transportation problems would raise the proportion of workers by about 6 percentage points. A strategy that focuses only on physical health and mental health problems and chemical dependence would raise the proportion of workers by only 3 percentage points. However, if these personal challenges contribute to the logistical and situational challenges faced by recipients, such a strategy could increase considerably the proportion of the caseload that is substantially employed.

The simulation results should be interpreted with caution. First, if the influence of unobserved variables (such as personal motivation) is reflected in observed variables (such as work experience), then the simulations overestimate the effects of a particular policy change, such as increasing work experience among TANF recipients. Second, because the results are based on cases that were on TANF at a point in time (and consequently may oversample long-term cases, as noted earlier), they do not capture the effects of strategies that are already successful at moving cases both into substantial employment and off of TANF.

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Endnotes

1. The liabilities analyzed in this chapter differ slightly from those presented in Chapter III. For purposes of the multivariate analysis, the measure of work experience omits the study quarter, the fourth quarter of 2001. The liability is, therefore, defined as employed in less than four of the seven quarters preceding selection into the study. Three liabilities--perceived discrimination, a criminal conviction, and perceived problems within one's neighborhood--are excluded from the analysis presented in this chapter. We did not include convictions because, based on our initial analysis, arrests are the more important determinant of employment. Perceived discrimination was not included because it was not asked of recipients who were not working, and perceived problems in one's neighborhood were not included because many respondents did not respond to all the components of the question that were necessary in developing the summary measure.

2. For purposes of counting liabilities we grouped child care problems, pregnancy, and having a child under one year old in the household together as one.

3. This predicted probability is different than that for the model that includes the number of liabilities. The difference in the model specification results in a different set of coefficients used to produce the predicted probabilities.

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