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Welfare reform and the subjective well-being of single mothers

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Abstract

Although a large body of research examines the impact of welfare reform, there remains considerable uncertainty as to whether single mothers’ well-being improved in the wake of these policy changes. Using unique data from the DDB Worldwide Communications Life StyleTM survey, this paper exploits a large battery of survey questions on self-reported life satisfaction and physical and mental health to study the impact of welfare reform on the subjective well-being of single mothers. The identification strategy relies on a difference-in-differences framework to estimate intent-to-treat effects for the welfare waiver and TANF periods. Results indicate that the bundle of TANF reforms had mostly positive effects on single mothers’ subjective well-being. These women experienced an increase in life satisfaction, greater optimism about the future, and more financial satisfaction. Furthermore, these improvements did not come at a cost of reducing mental and physical health. Welfare waivers, in contrast, had largely neutral effects on well-being. I provide indirect evidence that the increase in single mothers’ employment after welfare reform can plausibly explain the gains in subjective well-being.

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Notes

  1. Kahneman and Krueger (2006) argue that subjective well-being does not contain a “single, unifying concept that motivates all human choices and registers all relevant feelings and experiences” (p. 4). Consistent with the multi-dimensional nature of subjective well-being, Diener (2006) suggests that it “refers to all of the various types of evaluations, both positive and negative, that people make of their lives. It includes reflective cognitive evaluations, such as life satisfaction and work satisfaction, interest and engagement, and affective reactions to life events, such as joy and sadness. Thus, subjective well-being is an umbrella term for the different valuations people make regarding their lives, the events happening to them, their bodies and minds, and the circumstances in which they live” (pp. 399–400).

  2. See, for example, Frey and Stutzer (2002), Gruber and Mullainathan (2005), and Kahneman and Krueger (2006).

  3. Measures of subjective well-being are also shown to be highly correlated with many objective measures, including income (Stevenson and Wolfers 2008) and macro-economic conditions (e.g., GDP, inflation, and the unemployment rate) (Di Tella et al. 2003).

  4. These measures are, however, not without their criticisms (e.g., Bertrand and Mullainathan 2001). For example, a sizeable body of evidence indicates that subjective well-being measures are prone to reporting error stemming from questions order-effects and, more generally, the relative placement of these questions in the survey. It has been shown using the GSS that preceding the global happiness question with one on marital happiness has non-trivial effects on self-reported happiness. In addition, contemporaneous mood (at the time the survey is administered) is found to influence on how people respond to subjective well-being questions.

  5. To my knowledge, Robert Putnam was the first individual to use these data for the purpose of academic research. Specifically, the Life Style survey was a key dataset in his book Bowling Alone. Please refer to Putnam’s (2000) data appendix for an extensive introduction to these data, as well as a detailed discussion of his reliability tests. See also Putnam and Yonish (1999) and Groeneman (1994) for further information about the survey. This is a proprietary data archive, although the 1975–1998 surveys are freely available on Putnam’s (2000) Bowling Alone website.

  6. Ifcher (2011), who uses the GSS in his welfare reform study, defines the pre-reform period as 1990, 1991, 1993, 1994, and 1996. The post-reform period includes 1998, 2000, 2002, and 2004. As a result, he does not take advantage of the differential timing in states’ implementation of welfare waivers or PRWORA. In addition, the definition of the pre-reform period includes several years in which states implemented welfare waiver reforms. In particular, 3 of the 5 years in the pre-reform period (1993, 1994, and 1996) are marked by state experimentation with federal AFDC statutes, leaving 1990 and 1991 as the only years without any welfare-related policy reforms.

  7. In an early version of the paper, Ifcher’s (2009) analysis sample includes 158 low-skilled single mothers in the pre-reform period and 198 in the post-reform period. In addition, Ifcher (2009) is forced to define the treatment and comparison groups differently in order to maintain a sufficient sample size. The treatment group includes single mothers with less than a high school degree, while the comparison group members (single, childless women) are allowed to have such a degree. Ifcher’s (2009) analysis sample has just 94 single, childless women without a high school degree, which increases to 359 women when using the higher education cut-off. An implication of the educational imbalance between these groups is that it reduces the comparability of treatment and comparison women, a potential problem in a difference-in-differences framework. In a more recent version of the paper, Ifcher (2011) reports the overall analysis sample size, which includes 2,699 when all unmarried women are retained and only 1,212 when low-skilled unmarried women are retained.

  8. The survey underwent a redesign in 2006. Therefore, I end the observation period in 2005.

  9. All Appendix Tables in this paper are available online as Electronic Supplementary Material.

  10. A key motivation for conducting the analysis using multiple education criteria is that the broader welfare reform literature is unsettled as to what the most appropriate education cut-off should be. For example, some studies (e.g., Grogger 2003; Herbst 2008; Meyer and Rosenbaum 2001) do not use any education cut-off, while others examine women with a high school degree or less (e.g., Kaushal and Kaestner 2001). Still other studies experiment with multiple education cut-offs (e.g., Bitler et al. 2005; Bitler and Hoynes 2010).

  11. It is important to reiterate that the measure of life satisfaction used here is fairly close to other standard measures used in the happiness literature. For example, the Eurobarometer survey asks respondents: “On the whole, are you very satisfied, fairly satisfied, not very satisfied, and not at all satisfied with the life you lead?”

  12. The full set of responses is the following: 1 (definitely disagree), 2 (generally disagree), 3 (moderately disagree), 4 (moderately agree), 5 (generally agree), and 6 (definitely agree).

  13. The mean for the continuous life satisfaction index is 3.20 (SD = 1.57) for single mothers and 3.44 (SD = 1.56) for single childless women.

  14. Crouse (1999) and Grogger and Karoly (2005) provide lists of implementation dates for various welfare waivers. It should be noted that not all states implemented a welfare waiver, and that the implementation dates vary dramatically across these states. Twenty-eight states in the Life Style survey analysis sample had a wavier “turned on” at some point between 1992 and 1997. The first states to implement welfare waivers—in 1992—were California, Michigan, and New Jersey. The welfare waiver period ended in 1997, when California implemented its TANF plan (the last state to do so) in January of 1998. The pre-reform period in states with a waiver extends to the year of the waiver’s implementation. In the states that did not implement a welfare waiver, the pre-reform period extends until the on-set of TANF.

  15. TANF implementation dates are taken from Grogger and Karoly (2005). Unlike the waiver period, all states implemented TANF reforms, but like the waiver period, there is temporal variation in the timing of implementation. States implemented TANF over the period 1996 to 1998.

  16. I implement a robustness check that continues to “turn on” these policy dummies in the year of implementation, but allows the waiver and TANF dummies to be “turned on” at the same time (i.e., both dummies take a value of one) during the implementation overlap years. In addition, I implement a check in which the waiver and TANF coding is based on the fraction of the year in which each reform is in effect. Results from these additional analyses are quite similar to those discussed in the paper.

  17. A recent paper by Athey and Imbens (2006), which discusses a “changes-in-changes” model for non-linear outcomes, may have implications for (1), given that it is estimated using an ordered probit model. Under some conditions (e.g., assuming strict monotonicity in the relationship between the unobservables and treatment statuses well as the common assumption of stability in the differences between treatment and comparison group characteristics), estimates from the changes-in-changes model will mirror those from the standard D-in-D model.

  18. With the exception of the presence and number of children in the home and educational attainment, all variables necessary to create the analysis sample (gender, age, and marital status) are measured in precisely the same manner throughout the study period. Between 1986 and 2000, eight age-specific categories capture the presence of children ages 0 to 17. Starting in 2001, the survey was changed to incorporate seven categories. The measure of educational attainment changed three times (1986–1998; 1999, 2000, and 2002–2005; and 2001) throughout the study period. I carefully standardize these measures.

  19. Note that (1) omits controls for employment status and family income, as these are likely to be endogenous. Inclusion of these variables also complicates the interpretation of the estimated effect of Waiver and TANF, given that welfare reform is expected to work through the employment and income channels.

  20. In robustness checks, I replace the vector of year-specific indicator variables with two dummy variables capturing the entire post-Waiver and post-TANF periods (in this case, the pre-reform period is the omitted category). The results are very similar to those reported here.

  21. To explore life satisfaction trends in more detail, I estimate a regression of life satisfaction (“generally agree” or “definitely agree”) on a dummy variable for single mothers and separate linear time trends for single mothers and single women without children (using the less disadvantaged sample). The time trend coefficients reveal an upward trend in single mothers’ life satisfaction over the period 1986 to 2005 and a statistically significant downward trend for single women without children. In addition, I conduct an analogous analysis using the more disadvantaged sample of women. I find that single mothers’ life satisfaction trended upward (more so than in the less disadvantaged sample), while single women without children continued to experience a statistically significant downward trend in well-being.

  22. I conduct a similar time trend analysis for this variable as well. Although the trend coefficient for single mothers is positively signed and the trend coefficient for single women without children is negatively signed, both coefficients are small in magnitude and neither is statistically significant (using the less disadvantaged sample). Similar findings emerge when the analysis is conducted on the more disadvantaged sample.

  23. The Life Style survey includes weight, but there is insufficient documentation on how the weight is constructed. Therefore, I conduct the analyses using unweighted data. However, applying the weight does not change any of the results discussed in the text.

  24. As an additional check, I estimate the model on women with less than a high school degree, which represents an even more disadvantaged sample. Although the estimates are imprecisely estimated (due to a very small sample size), the results suggest that both welfare waivers and TANF are associated with reductions in life satisfaction. For example, the D-in-D TANF estimate is −0.06 (standard error, 0.13).

  25. To conserve space, all robustness checks presented and discussed here come from the less disadvantaged sample of unmarried women (i.e., those ages 18 to 60 with less than a bachelor’s degree). However, all of these analyses are estimated on the more disadvantaged sample (i.e., those ages 18 to 45 with no more than a high school degree) as well, and the results are comparable.

  26. The controls for per capita income and population density further guard against differences across states and over time in the underlying stocks of wealth and human capital that may drive both welfare reform and subjective well-being. The dummy variable for Republican governor accounts for politically induced differences in social policy reforms that affect subjective well-being, while the percentage voting Republican controls for unobserved attitudes, norms, and preferences within the electorate regarding certain types of social policies.

  27. In addition, I estimate the trends model on the pre-waiver and pre-TANF period, 1986 to 1991, conditional on the covariates. The results continue to provide strong evidence of common time trends in the well-being outcomes for single mothers and single childless women. Interestingly, results from an unconditional trends model suggests that single mothers’ life satisfaction remained flat, while single childless women experienced a downward shift in well-being. Such results highlight the importance of conditioning on the observable characteristics.

  28. In a further check, I include the placebo wavier and TANF D-in-Ds as well as a comparable set of unrestricted placebo waiver and TANF dummy variables in the model. In this case, coefficients on eight of the 20 placebo dummy variables are statistically significant.

  29. To perform the F test, I first run a regression of each characteristic on a set of interactions between a single mother dummy variable and the three period dummy variables, the remaining demographic controls, state fixed effects, and year dummy variables. I then test the equality of the single-mother-by-period interactions. Results from the test are shown in column (4).

  30. In a related specification check, I examine only the impact of TANF (i.e., I omit the D-in-D estimator of welfare waivers) and begin the analysis period in 1992. Results are once again robust to this sample definition.

  31. See, for example, Fletcher (2011) and Brewer et al. (2011).

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Acknowledgements

I would like to thank participants at the 2010 meeting of the Association for Public Policy Analysis and Management, John Ifcher, Lawrence Mead, and an anonymous referee for helpful comments and suggestions. I am also grateful to Chris Callahan, at DDB Worldwide Communications, who provided the Life Style™ survey data between 1999 and 2005 and answered many questions along the way.

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    Chris M. Herbst

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Herbst, C.M. Welfare reform and the subjective well-being of single mothers. J Popul Econ 26, 203–238 (2013). https://doi.org/10.1007/s00148-012-0406-z

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