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Minimum wages, poverty, and material hardship: new evidence from the SIPP


While a number of policymakers have argued that raising the minimum wage will reduce material hardship, empirical evidence to support or refute this claim is scant. Using data drawn from the Survey of Income and Program Participation, we examine the effect of minimum wage increases on poverty, material hardship, and government program participation. Difference-in-difference estimates provide little evidence that state and federal minimum wage increases between 1996 and 2007 reduced poverty, material hardship, or receipt of public program benefits among all individuals, workers, younger individuals without high school degrees, or younger black individuals. Our findings are robust across several measures of hardship, including poverty, financial hardship, housing stress, food insecurity, durable goods deprivation, and health insecurity. We find some evidence of modest redistribution effects of the minimum wage among low-skilled individuals.

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  1. Target efficiency in the context of the minimum wage refers to how well targeted minimum wage increases are to poor workers.

  2. Sen. Kennedy (1999) argued in support of minimum wage increases by saying that “no one who works for a living should live in poverty.” In later years, Senators Kennedy and Obama made similar claims, referring to full time workers. Senator Kennedy (2005) argued that: “It is shameful that in America today…that nearly a fifth of all children go to bed hungry at night because their parents, many of whom are working full time at the minimum wage, still can't make ends meet.” Then Sen. Obama (2008) supported a minimum wage increase so “full-time workers can earn a living wage that allows them to raise their families out of poverty and pay for basic needs such as food, transportation, and housing—things so many people take for granted.”

  3. Other explanations include, but are not limited to: (i) larger average wage gains for low-skilled workers in hardship relative to low-skilled workers in poverty, and (ii) larger (in absolute value) negative employment elasticities with respect to the minimum wage for those in poverty relative to those in hardship.

  4. However, there is little empirical evidence in support of wage spillover effects for those low-wage workers earning wages slightly above newly set minimum wages or for more highly-skilled workers (see, for example, Sabia et al. 2012).

  5. Card and Krueger (1995) found that among workers, minimum wage increases “may have led to a modest reduction in the rate of poverty among workers.” Addison and Blackburn (1999) found that minimum wage increases reduce poverty among junior high school dropouts. However, as Neumark and Wascher (2008) note, junior high school dropouts are older and unlikely to have small children, whereas most anti-poverty efforts focus on families with younger children.

  6. However, a recent study by Dube et al. (2010) finds that minimum wages were associated with no changes in low-skilled industry employment when using variation in minimum wages in contiguous counties across state borders for identification. Addison et al. (2009) also find no evidence that minimum wage increases are associated with reductions in retail employment. While each of these studies raises the important concern that unmeasured state-specific time trends may lead to biased estimates of minimum wage effects, these authors are not the first to raise this criticism (see Deere et al. 1995). One limitation of studies that explore industry-wide employment is that estimates may mask important labor–labor substitution. Moreover, the conclusions are less policy-relevant given that they do not focus on populations of low-skilled workers that policymakers wish to help. Other studies that have examined more policy-relevant low-skilled workers across sectors have found evidence of adverse employment and welfare take-up effects even after controlling for unmeasured state trends (Page et al. 2005; Sabia 2008) and a new study by Sabia et al. (2012) uses a synthetic cohort control group approach to address concerns about unmeasured time trends and find evidence that New York State’s 2005–2006 minimum wage increase had large adverse labor demand effects for 16-to-29 year-olds without a high school diploma.

  7. Another study has examined the effect of minimum wage increases on consumer spending and debt. Using data from the Consumer Expenditure Survey, Aaronson et al. (2011) find that among households with minimum wage workers, minimum wage increases are associated with increases in automobile purchases in the short-run, financed with consumer debt.

  8. The Census Bureau collects SIPP data in four staggered rotation groups, with one rotation group beginning each month. Therefore, estimating calendar-year values requires that the data be aligned by calendar month. Unfortunately, the four-year 1996 panel and the three-year 2001 panel rotation groups do not provide adequate overlap in the 2000 calendar year to make 2000 calendar year estimates. Therefore, the calendar years used in here include 1996–1999 and 2001–2007.

  9. The standard poverty measure is the ratio of the family’s income and the family’s poverty threshold. Income sources include earnings, unemployment compensation, workers' compensation, Social Security, Supplemental Security Income, public assistance, veterans' payments, survivor benefits, pension or retirement income, interest, dividends, rents, royalties, income from estates, trusts, educational assistance, alimony, child support, assistance from outside the household, and other miscellaneous sources (all pre-tax) while excluding capital gains or losses and the receipt of noncash benefits such as food stamps and housing subsidies. Our alternative measure of poverty includes the same sources of income but adds a Census Bureau-constructed variable that is the sum total of noncash benefits received through AFDC or TANF, food stamps, and energy assistance.

  10. The Census Bureau began including the variables used to calculate housing cost burden in wave 8 of the 1996 panel (1998 calendar year). Annual housing cost burden estimates are made from 1998 to 2007.

  11. These housing hardship indicators are included in wave 8 of the 1996 and 2001 panels, and wave 5 of the 2004 panel. This corresponds to calendar years 1998, 2003, and 2005, respectively. Note that these data do not cover the period covering the financial and housing crisis. Thus, none of our estimates of minimum wage effects are confounded by the timing of state-specific financial or housing shocks that might have been incidentally correlated with minimum wage increases.

  12. The question about forgoing a doctor’s visit is included in wave 8 of the 1996 and 2001 panels, and wave 5 of the 2004 panel. This corresponds to calendar years 1998, 2003, and 2005, respectively.

  13. There are differences between food sufficiency and food insecurity that were considered when deciding which measure we used. Reports of food insufficiency track somewhat closely with reports of food insecurity with hunger. Using the 1996 panel of the SIPP, Gundersen et al. (2003) found that approximately 2.5 % of the population are food insufficient, whereas Nord et al. (2003), using the 2000 Current Population Survey, estimated that 3.7 % of households are food-insecure with hunger. Of particular importance to our investigation is the category of the food insecurity measure labeled food insecure without hunger. Nord and colleagues found that approximately 12 % of households are food insecure without hunger, suggesting that many households face threats to their economic well-being that do not result in actual hunger. We use food insecurity here because the language in the food insecurity questions more explicitly capture economic hardship than the language in the food insufficiency questions and there is evidence that reduced consumption, as measured by food expenditures, is associated with declining economic well-being (Jensen 2002).

  14. The food insecurity measure is included in wave 8 of the 1996 and 2001 panels, and wave 5 of the 2004 panel. This corresponds to calendar years 1998, 2003, and 2005, respectively.

  15. These questions are: “My child was/our child was/the children were) not eating enough because (I/WE) couldn't afford enough food,” “In the past four months did you or the other adults in the household ever cut the size of your meals or skip meals because there wasn't enough money for food?” and “In the past four months did you or the other adults in the household ever eat less than you felt you should because there wasn't enough money to buy food?”.

  16. There is no universally-accepted definition of public and private health insurance coverage, and federal surveys vary with respect to public health insurance definitions. Here, public health insurance includes Medicaid, SCHIP or similar state insurance program, and Medicare. This excludes CHAMPUS/TRICARE.

  17. aIn SIPP public-release data respondents in Maine and Vermont are grouped together and respondents in North Dakota, South Dakota, and Wyoming are grouped together in the 1996 and 2001 panels, prohibiting assignment of state-specific policies and economic data. Therefore, respondents in these states are excluded from all analyses.

  18. Age, educational attainment, and marital status may vary over time for an individual over the sample period, whereas race is time-invariant.

  19. Because SIPP respondents are interviewed every four months, reports of change between interviews tend to be higher than within interviews (months 1–4). We reduce the potential for seam bias by aligning the four staggered rotation groups by calendar months, thus distributing any potential bias more evenly across all months while allowing us to make calendar-year estimates.

  20. By 2009, the Federal minimum wage had risen to $7.25 per hour. While much of the federal variation in the minimum wage will be captured by the year effects in equation, to the extent that hikes in the federal minimum wage change state minimum wages at different rates due to differences in each state’s minimum wage at the time of the federal increase, this variation will also be used to identify β.

  21. For instance, if states are less likely to implement minimum wage increases during tight labor markets, then estimated economic effects for low-skilled workers may be biased (see Allegretto et al. 2011 for a discussion of this issue).

  22. One concern with examining more highly-educated and experienced individuals is the possibility that these workers are indirectly affected by the minimum wage. If the minimum wage increases, the demand for higher-skilled workers may be affected if low- and high-skilled workers are gross substitutes or complements. If the substitution effect dominates the scale effect, then the demand for high-skilled workers may increase, leading to reduced material hardship among this population. If the scale effect dominates, the opposite will be true.

  23. Because of incomplete data on weeks per month and months per year worked, the imputation of hourly wage rates in the SIPP for those paid hourly is prone to substantial measurement error. In appendix tables available upon request, we repeat the analysis presented in Tables 2 and 3 using the full sample of workers, with wages imputed from information on hours per week and part-time work status in the previous month. The results are similar.

  24. Following Burkhauser and Sabia (2007), we include those earning between $5.00 and $5.14 as affected workers because these individuals’ hourly wages may be reported with modest error; we also assume that those earning less than $5.00 are tipped employees who would not gain from the federal minimum wage increase to $7.25 per hour.

  25. In Appendix Table 2 we also provide evidence that many individuals in hardship are not working; thus many of these individuals will also not benefit from minimum wage increases.

  26. Using a SIPP-provided monthly employment status recode variable we define workers as those who satisfied any of the following three requirements each month of the calendar year: the respondent was with a job the entire month and worked all weeks; was with a job all month and was absent from work without pay 1+ weeks, where the absence was not due to a layoff; was with a job at least 1 but not all weeks, no time on layoff and no time looking for work. In appendices available upon request, we experiment with alternate poverty threshold cutoffs of 75 % and 200 % and with other policy relevant groups, including single female-headed households with children and older junior high school dropouts (Addison and Blackburn 1999). We find little evidence of poverty- or hardship-alleviating effects in these alternate models.

  27. Of course, this does not rule out the possibility of a state-specific time-varying unobservable related to the minimum wage and poverty rates specifically of less-skilled (but not higher-skilled) individuals.

  28. Recent work by Neumark and Wascher (Forthcoming) suggests that the minimum wage and the EITC interact to produce higher earnings for young minority women, but lower wages for young minority men. We explored whether interacting the minimum wage with the EITC produced any poverty alleviating effects in our full sample or sub-populations, but found no evidence of an interaction effect.

  29. In appendix tables available upon request, we examine the sensitivity of our material hardship findings to controlling for state-specific linear time trends and using a lagged minimum wage measure. The results continue to show little evidence of hardship-alleviating effects of the minimum wage.

  30. Moreover, in an appendix available upon request, we find the inclusion of state-specific linear time trend renders the estimated food stamp participation effect positive and statistically indistinguishable from zero. The results in Table 6 are robust to the inclusion of a lagged minimum wage variable to the right hand-side of Eq. (1).

  31. See also: Eissa and Liebman 1996; Eissa and Hoynes 2005; Hotz et al. 2002, 2003; Ellwood 2000; Meyer et al. 2000, 2001.


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The authors thank Al Gottschalck, Carolyn Hronis, and Sharon O’Donnell for providing valuable information about constructing our files. Thanks also to Michael Saltsman and an EPI reviewer for useful comments and suggestions on an earlier draft of this paper and to Alex Chesney, Andrew Barone, and Melissa Wilmarth for editorial assistance. This research was funded, in part, by the Employment Policies Institute. Work on an earlier draft of this paper was completed when Sabia was an assistant professor at the United States Military Academy. Any errors that appear are the authors.

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Correspondence to Joseph J. Sabia.



See Tables 10 and 11.

Table 10 State and federal variation in the minimum wage, 1996−2007
Table 11 Percent of individuals ages 16–64 in hardship who are not working (2005)

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Sabia, J.J., Nielsen, R.B. Minimum wages, poverty, and material hardship: new evidence from the SIPP. Rev Econ Household 13, 95–134 (2015).

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  • Minimum wage
  • Material hardship
  • Poverty

JEL Classification

  • J38
  • I32
  • I38