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Effects of Minimum Wages on Youth Employment: the Importance of Accounting for Spatial Correlation

Abstract

The relationship between minimum wage increases and youth employment is investigated using county-level data and spatial econometric techniques. Results that account for spatial correlation indicate that a 10% increase in the effective minimum wage is associated with a 3.2% decrease in youth employment, a result that is 28% higher than the corresponding estimate that does not control for spatial correlation. Thus, estimates that do not take into account spatial correlation may significantly underestimate the negative effect of the minimum wage on teenage employment. Improperly controlling for factors that vary systematically over space can lead to incorrect inferences and misinform policy.

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Notes

  1. A recent review of the literature by Neumark and Wascher (2006) shows that the weight of the evidence supports a negative effect of minimum wages on employment.

  2. The analysis is a cross-sectional rather than a panel one due to limitations in the availability of data at the county level as well as changing county definitions over time.

  3. http://factfinder.census.gov/home/saff/main.html? _lang=en.

  4. Washington, D.C. and cities in Virginia, which technically do not reside in counties, were also excluded.

  5. http://www.dol.gov/esa/programs/whd/state/MWHistoryTable.pdf.

  6. The county-level adult male unemployment rate and the percent rural are not logged because a small number of zero values were reported for each.

  7. http://www.bls.gov/cew/home.htm.

  8. For a very small number of counties that were missing a value for the average county wage, the missing value was replaced with the average over all counties in the state and a missing value dummy variable was created and included in all specifications.

  9. Anselin (1988) provides an example of measurement error.

  10. Given maximum likelihood estimation’s dependence on the assumption of normality, we also estimated our models using the Generalized Method of Moments (GMM) technique and found that our results were not sensitive to the method used.

  11. Please see Anselin and Bera (1998).

  12. Anselin et al. (1996) provide Monte Carlo evidence that the Lagrange Multiplier tests utilized in this study may be superior to Wald and Likelihood Ratio tests for spatial dependence.

  13. Kim et al. (2003) derive the procedure for this marginal effect calculation. The (1/(1 − ρ)) term is referred to as a spatial multiplier and is the effect if a unit change were induced at every location.

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Acknowledgements

The authors would like to thank Diana Castillo-Trejo and Adam Lucchesi for their excellent research assistance.

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Correspondence to Charlene M. Kalenkoski.

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Kalenkoski, C.M., Lacombe, D.J. Effects of Minimum Wages on Youth Employment: the Importance of Accounting for Spatial Correlation. J Labor Res 29, 303–317 (2008). https://doi.org/10.1007/s12122-007-9038-6

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  • DOI: https://doi.org/10.1007/s12122-007-9038-6

Keywords

  • Spatial econometrics
  • Spatial autoregressive model
  • Minimum wage
  • Youth employment