Welfare Reform and Labor Force Exit by Young, Low-Skilled Single Males

Abstract

While the labor market woes of low-skilled male workers in the United States over the past several decades have been well documented, the academic literature identifying causal factors leading to declines in labor force participation (LFP) by young, low-skilled males remains scant. To address this gap, I use the timing and characteristics of welfare-reform policies implemented during the 1990s and fixed-effects, instrumental variable regression modeling to show that policies seeking to increase LFP rates for low-skilled single mothers inadvertently led to labor force exit by young, low-skilled single males. Using data from the Current Population Survey and a bundle of work inducements enacted by states throughout the 1990s as exogenous variation in a quasi-experimental design, I find that the roughly 10 percentage point increase in LFP for low-skilled single mothers facilitated by welfare reform resulted in a statistically significant 2.8 percentage point decline in LFP for young, low-skilled single males. After conducting a series of robustness checks, I conclude that this result is driven entirely by white males, who responded to welfare-reform policies with a 3.7 percentage point decline in labor supply. Young black males, as well as other groups of potentially affected workers, appear to be uninfluenced by the labor supply response of less-educated single mothers to welfare reform. Impacts on young, single white males are large and economically significant, suggesting that nearly 150,000 males departed the formal labor market in response to directed welfare-reform policies.

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Notes

  1. 1.

    In particular, the decline of married, two-biological-parent households has been tied to a host of other issues, including the reduced role of low-skilled men in the family life of their biological children, the increase in the number of complex families, and the various challenges and impacts associated with child support payments.

  2. 2.

    In this analysis, low-skilled workers are defined as those individuals with an educational level of high school diploma or less. Furthermore, the terms “low-skilled” and “less-educated” will be used interchangeably and “high school diplomas” include both traditional and general equivalency diplomas.

  3. 3.

    As is common in the literature, in this article, I concentrate on LFP rather than employment. LFP is arguably a more accurate depiction of labor supply because it captures the intent to provide labor. Employment, on the other hand, can be based on a number of factors outside the individual’s control, especially the demand for labor.

  4. 4.

    The general decline in LFP rates for low-skilled males since the early 1980s has been documented very thoroughly by Holzer and various colleagues. In Holzer and Offner (2006), they report declines in labor supply for young, less-educated white males from approximately 92 % in 1979 to roughly 87 % in 2000. Correspondingly, rates for black males have dropped from roughly 82 % to 70 % over the same time period. In Holzer et al. (2005), the authors report that at least half of the decline in employment among less-educated black males can be attributed to increases in incarceration rates and stronger child support enforcement laws.

  5. 5.

    Recent estimates of the scale of the illicit drug trade in the United States indicate that it is a highly lucrative industry which is estimated to produce up to $150 billion in revenue each year (Bagley 2012; United Nations Office on Drugs and Crime 2012). Given this scope, it does not seem unreasonable to contend that low-skilled males are more likely than other groups to enter these illegal professions given that they have fewer employment options.

  6. 6.

    Scholars have noted their skepticism regarding whether low-skilled men and women compete in the same labor markets (Blank 2002; Blank and Gelbach 2006). However, there is seemingly enough overlap in some low-skilled sectors, such as fast-food services, custodial services, and security and retail jobs, for this supposition. For example, Card and Krueger (1994) claimed that fast-food franchises are a leading employer of low-wage workers, and low-skilled workers of either gender seem equally qualified for these entry-level positions.

  7. 7.

    A positive relationship could indicate peer effects, whereby the welfare-reform work inducements create positive spillovers in the form of increased LFP for applicable males residing within the household or the community.

  8. 8.

    The state dummy variables account for time-invariant unobserved factors that influence historical LFP rates in a particular state. The year, quarter, and year-quarter fixed effects control for omitted factors that impact labor supply rates in all states during a particular period. In addition, the quarterly variables account for seasonality in the LFP rates for young males. The CPS considers university-bound males on summer break (i.e., those between their last year of high school and first year of college) as potential labor force participants. During the summer months, this influx of short-term labor drives down the LFP rate for single males aged 16–29.

  9. 9.

    Other instruments were considered in this analysis but were excluded because of their weak predictive power.

  10. 10.

    In some states this ratio changed well before the implementation of either an AFDC waiver or their state-level TANF program. For example, New York implemented its TANF program in November 1997, but the LFP rates for single mothers in that state increased markedly before this point—presumably because of the large increases in the federal EITC beginning in 1994.

  11. 11.

    The vast majority of CPS data used in this analysis come from the IPUMS-CPS database (Flood et al. 2015).

  12. 12.

    These periods are identified with vertical lines in the forthcoming figures.

  13. 13.

    Upcoming modeling is not sensitive to the choice of using single mothers aged 16–44. Models using single mothers aged 16–30 produce very similar estimates.

  14. 14.

    As previously mentioned, it is important to control for the seasonality of LFP for young, single males given the influx of university-bound males during the summer months. Thus, the graph is seasonally adjusted.

  15. 15.

    I also analyzed a number of other characteristics of welfare reform, but did not include them in the final first-stage regression models because of their weak predicative power. Including them violates the relevance criterion of an instrumental variable. Examined in this analysis but not included in the final modeling were TANF attributes regarding the strictness of sanctions and time limits (Pavetti and Bloom 2001), state diversion policies under TANF (Urban Institute, Welfare Rules Database; anfdata.urban.org/wrd/wrdwelcome.cfm), and states with childcare fee waivers available through the Child Care Development Fund (Blau 2003).

  16. 16.

    The first three are rather self-explanatory. The personal responsibility clauses include restrictions on benefits for increasing the family size (i.e., family caps), as well as the children’s regular school attendance and health check-ups.

  17. 17.

    Wolfers et al. (2015) reported the number of 25- to 54-year-old black males who are “missing” based on deviations from the biological sex ratio at birth. Using Census Bureau data and a ratio of 1 male to every female, they found almost 20 % (or 1.5 million) fewer black men living in the U.S. general population. As the authors noted, the primary factors contributing to this gap are differential incarceration and mortality rates—issues that disproportionately impact low-skilled males. The authors also found that this gap exists among whites, although the impact is not as pronounced.

  18. 18.

    This ratio is derived from the same underlying CPS data. To obtain a more precise estimate, I construct this ratio at the annual level. In addition, I estimate it for all less-educated males and females within the group examined. This latter decision further increases the precision of the estimate and reduces bias from contemporaneous family structure decisions based on sex ratios.

  19. 19.

    This test reveals that the instruments in this analysis do not directly influence male labor supply but that the impact is moderated through the instrument’s influence on female LFP. In other words, they meet the exclusion restriction of a valid IV. This finding is critical to establish a valid IV research design and, as will be shown, is not typically found in the modeling for other groups.

  20. 20.

    Holzer et al. (2005) examined the 1979–2000 period as well as a different set of age categories.

  21. 21.

    Recall that DI and SSI beneficiaries, by definition, are not part of the labor market. This is also the rationale behind not including variables such as the unemployment rate in the models. Other modeling (not shown but available upon request) includes state and federal SSI generosity in both the first and second stages, but results remain substantively unaffected.

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Acknowledgments

PhD program funding from Syracuse University greatly supported this research. The author gratefully acknowledges Leonard M. Lopoo for his patience, helpful comments and suggestions, and encouragement throughout the various iterations of this work. Invaluable input was received from a number of other faculty at the Maxwell School, including Sarah Hamersma, Jeffery D. Kubik, Douglas Wolf, Robert Bifulco, David Popp, and Leonard Burman. Finally, the author would like to thank the editorial staff at Demography, as well as a number of anonymous referees, for their valuable feedback during the review process.

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Correspondence to Lincoln H. Groves.

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Groves, L.H. Welfare Reform and Labor Force Exit by Young, Low-Skilled Single Males. Demography 53, 393–418 (2016). https://doi.org/10.1007/s13524-016-0460-3

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Keywords:

  • Labor supply
  • Instrumental variables
  • Welfare reform
  • Young, low-skilled males
  • Disconnected men