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How Living Wage Legislation Affects U.S. Poverty Rates

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Abstract

I investigate how living wage legislation affects poverty. I find evidence that living wage ordinances modestly reduce poverty rates where such ordinances are enacted. However, there is no evidence that state minimum wage laws do so. The difference in the impacts of the two types of legislation conceivably stems from a difference in the party responsible for bearing the burden of the cost.

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Notes

  1. An exception is the living wage legislation in Santa Fe, NM, and San Francisco, CA, where the legislation was extended to the entire private sector in 2003.

  2. In Adams and Neumark (2005a, b), these analyses are refined and updated through 2002, and these results are confirmed.

  3. The definition of poverty level used by the government in the calculation of regional poverty rates is the subject of some criticism, inasmuch as there is no adjustment for regional differences in the cost of living. The measured poverty rate therefore understates the needy in locations with high costs of living and overstates the needy in locations with low costs of living. Madden (1996: 1583) suggests that this problem is less relevant when comparing changes in regional poverty rates over time. For additional discussion of the criticism surrounding the measured poverty rate, see Schiller (2001).

  4. By contrast, Neumark and Adams (2003a) and Adams and Neumark (2005a) estimated a linear probability model for a dependent variable that indicates whether a family’s total income is below the poverty threshold. Among the independent variables is the natural logarithm of the living wage, where legislated.

  5. The economic theory of public choice guides the precise specification for Eq. 2. The Appendix contains a brief discussion of the model behind this equation.

  6. The statistical package that I used (LIMDEP) has Probit and Logit procedures for binary data and a two- stage estimation procedure corresponding to two-stage least squares for fixed-effect panel data models of this type.

  7. The second step of the two-step LIMDEP procedure used to estimate this equation includes a calculation of a corrected covariance matrix, as suggested by Murphy and Topel (1985).

  8. There are 232 U.S. counties in the ACS. t-tests are performed to compare the means of the variables of interest across ACS and non-ACS counties. The ACS counties are significantly different from the non-ACS counties for all variables considered. Consequently, one must use caution in interpreting the results of the analysis in the “Empirical Results” section.

  9. These sources also provide measurements of the percent of the population with at least a college degree. This exogenous variable is expected to be a factor in Eq. 3.

  10. Note that these calculations do not follow the convention of measuring civilian labor force relative to the population age 16 and over.

  11. The 1994 County and City Data Book also provides the percent of voters supporting the Democratic candidate in the 1992 presidential election; this exogenous variable is expected to be a factor in Eq. 2.

  12. The data are available at http://www.trinity.edu/bhirsch/unionstats. For a description of the database and the use of the Current Population Survey to derive these data, see Hirsch and Macpherson (2003).

  13. This source also provides a measurement of the percent of public-sector workers who are unionized; this exogenous variable is expected to be a factor in Eq. 2.

  14. The 2005 information for all 50 states came from http://www.dol.gov/esa/minwage/america.htm, accessed on 9/5/2005.

  15. This information was taken from Employment Policy Foundation (2004), accessed on 10/3/2005, and confirmed information from ACORN Living Wage Resources Center (2005), accessed on 10/27/2005.

  16. In a small number of these cases (New Orleans, LA; Omaha, NE; Salem, OR; and Pittsburgh, PA), living wage legislation was enacted and then repealed or overturned, prior to 2003. The counties that include these municipalities are therefore not identified as having living wage legislation in any meaningful way for my analysis. Note that counties in which living wage legislation has been adopted at the county level only are similarly not identified as having living wage legislation for the purposes of my analysis. Neumark and Adams (2003a) also ignored living wage legislation adopted by counties, arguing that these counties tended to be small. Noting that county living wage laws have not attracted as much attention as city living wage laws, they speculated that the number of workers covered by county wage laws might be quite low in comparison.

  17. Comparisons of 1990 measurements of the data yield the same conclusions. Moreover, they suggest a smaller percent of high school graduates in counties where living wage legislation would later be adopted.

  18. Counties with living wage legislation also tend to be more populated, on average. However, not all highly populated counties have local governments that have adopted living wage legislation. It is hoped that the variation is sufficient to identify the effects of adopting living wage legislation, in the analysis that follows.

  19. A comparison of poverty rates in 1989 shows the same pattern.

  20. One might argue that migration decisions of the poor are influenced by the legislation. An argument along these lines could conceivably support migration in either direction. The prospects of better job opportunities where the legislation has been enacted could encourage in-migration of the poor. Should the legislation have disemployment effects, however, there may be incentives for the poor to migrate out of the area where living wage legislation has been adopted. Addressing these considerations is beyond the scope of the current investigation.

  21. The first-stage estimation results for the other equations are in the Appendix. Interestingly, the effects of state wage policies on unemployment rates are positive but not significant. Note that missing data for one of the exogenous variables in the specification of Eq. 2 result in the omission of one of the ACS counties from the estimation.

  22. A test for the exogeneity of this variable (Gujarati 2003: 756) suggests that it should indeed be viewed as endogenous. Estimation of Eq. 1 ignoring this endogeneity yields a coefficient of −0.49 (with a P value of 0.12) for this variable.

  23. In order to explore the possibility that this result is associated with the larger-than-average size of counties with living wage legislation, rather than the living wage legislation itself, the model is re-estimated excluding the 23 counties without living wage legislation that are smaller than the smallest county with living wage legislation. The estimation results are largely unchanged by this re-estimation; specifically, the estimated coefficient for the living wage variable increases by at most 0.1 and remains significant at its previous level.

  24. Both Neumark and Adams (2003a) and Adams and Neumark (2005a) estimated that a 1% increase in the level of the living wage above the otherwise applicable state or federal minimum wage decreases the probability of a family being below the poverty threshold by 0.19%. Neumark and Adams (2003b) reported that in 2000, all living wage levels but one exceeded the federal minimum wage by at least 30%; the median living wage was 59% higher. Together, their findings suggest that, in states without specific state minimum wage laws, the presence of living wage laws reduces the poverty rate by at least 5.7% (30 times 0.19%) or more typically by 11.2% (59 times 0.19%). Evaluated at the mean poverty rate (18.61%) in the data used by Adams and Neumark (2005a), the reduction is equivalent to at least 1.06 percentage points (0.057 times 18.61) or more typically 2.09 percentage points (0.112 times 18.61). These estimates bracket those found here.

  25. This finding runs counter to the racial pattern shown by Schiller (2001) in his analysis of the poverty rates of different demographic groups, but it is similar to the results of researchers who used multiple regression analysis to explore poverty rates (Levernier 2003; Levernier et al. 2000; Madden 1996). Levernier (2003) suggested that this pattern could be partly due to an inverse relationship between the percentage of minorities in the community and the degree of labor market discrimination faced by these minority individuals.

  26. Levernier (2003) found the percent of the population under the age of 16 to affect the poverty rate of families in a positive and significant manner; Levernier et al. (2000) found a similar effect for the average number of children per family. In a study of metropolitan areas of the southwestern United States, Murdock et al. (1999) found that the greater the increase in the dependency ratio, the greater the increase in the number of persons in poverty; this effect was significant for the population as a whole, but only for one of the three ethnic-specific subpopulations for which the model was also estimated.

  27. This result is consistent with the findings of earlier studies. Madden (1996) and Murdock et al. (1999) found that the variable’s effect loses significance as additional explanatory variables are added to the model and as the analysis is applied to certain ethnic subpopulations, respectively.

  28. This finding duplicates those of Levernier (2003) and Levernier et al. (2000), who used similar measurements for educational attainment. Attempts to use an additional variable measuring the percent of college graduates in the population were not useful, supporting earlier evidence that “high school attainment is more effective in lifting poor families out of poverty” (Levernier et al. 2000: 487).

  29. Levernier (2003) and Levernier et al. (2000) used variables that measure labor force participation rates and found similar but significant effects. Variables measuring employment rates had negative and significant effects in studies by Levernier and White (1998) and Levernier et al. (2000). In the work of Murdock et al. (1999), a variable measuring percent change in the number of unemployed had a positive and significant effect on the percent change in the number of persons in poverty in a model estimated for the total population in metropolitan areas in the southwestern United States.

  30. In other studies, the percent of employment in manufacturing (where unionization rates are higher) was found to be negatively and significantly related to poverty rates (Levernier and White 1998; Levernier et al. 2000; Levernier 2003).

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  1. Villanova University, Villanova, PA, 19085, USA

    Suzanne Heller Clain

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Correspondence to Suzanne Heller Clain.

Appendix

Appendix

The specification of Eq. 2 is based on a model to explain why the civic leaders in some municipalities have adopted living wage legislation, while the civic leaders in others have not. In this model, it is assumed that civic leaders support living wage legislation when the perceived net political benefits of the legislation (benefits in excess of costs) are positive.

A measurement of the perceived net political benefits (y*) is itself not directly observable. However, suppose that it is a stochastic function of a vector (X) of observable characteristics of the local community. That is,

$$y^{ * } = X\beta + \varepsilon $$

where β is a vector of unknown slope coefficients and ɛ is a stochastic error term. Then civic leaders support living wage legislation when y* > 0; they do not support living wage legislation when y* < 0. Factors that could reasonably be included in X are measurements capturing the local economic conditions (such as poverty rate and unemployment rate), the strength of certain pre-existing local interest groups, the local political attitudes, and the pre-existence of state wage policies.

Though the perceived net political benefits are not directly observable, the decision of community leaders to enact living wage legislation (y) is observable. That is, y = 1 (living wage legislation has been enacted) where y* > 0 (the net political benefits are positive), while y = 0 (living wage legislation has not been enacted) where y* < 0 (the net political benefits are not positive.) Estimates of the parameters (β) in the specification for y* can be obtained by applying Probit or Logit analysis, if ɛ is assumed to have a normal or logistic probability distribution. The latter is assumed for the current application.

Table 5 First-stage reduced-form estimation results
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Table 6 First-stage reduced-form estimation results
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Clain, S.H. How Living Wage Legislation Affects U.S. Poverty Rates. J Labor Res 29, 205–218 (2008). https://doi.org/10.1007/s12122-007-9028-8

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