Abstract
This study uses employment data on California county-industry pairs (CIPs) between 1990 and 2016 to test whether minimum wage increases caused employment growth to slow most in the CIPs with a large share of low wage workers. Evidence supports the hypothesis, and we use the estimates to simulate the effect of a 10% increase in the minimum wage. The simulations suggest that a 10% increase could cause a 3.4% employment loss in the average CIP in California. The job loss is projected to be concentrated in two industries: accommodation and food services, and retail. While the most populated counties of California are expected to incur the largest employment loss in terms of the number of workers, the smaller counties generally experience a larger percentage point loss in employment due to the lower wages and the greater number of workers that would be affected by the minimum wage hike. Moreover, there is substantial variation across counties in terms of the percentage of jobs lost within a given industry.
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Notes
This estimate does not include the job loss in rural areas not included in our analysis.
This statement is based on analysis of historical federal and state minimum wages provided by the Department of Labor at https://www.dol.gov/whd/state/stateMinWageHis.htm. The data goes back to 1968 and there is no point at which a state had a minimum wage that exceeded the federal minimum by $7.75 or more. While data prior to 1968 wasn’t available, we are confident that the no state had a minimum wage that exceeded the minimum by that amount in earlier years. In fact, Alaska had the highest state minimum wage in 1968 ($2.10) when the federal minimum wage was $1.60.
Several cities will have a $15 minimum wage by 2022, including Los Angeles, Minneapolis, New York City, San Francisco, Seattle, and Washington D.C.
We also had to map census codes for industry to match NAICS codes in the QCEW and account for the fact that NAICS codes changed in the QCEW over time.
In the early stages of this project, we attempted to do the analysis using employment measured at the three-digit level. We abandoned that approach and switched to two-digit analysis because the narrower definition of industries results in missing data for a large share of the CIPs since the QCEW masks employment counts when there are a small number of employers due to concerns with confidentiality of firm level data. Also, the sample sizes for the wage estimates drawn from the Census become too small to be reliable for many of the CIPs.
This restriction results in Alameda, Contra Costa, and Santa Clara counties being dropped from the sample. The largest cities in these counties are Oakland, Concord, and San Jose, respectively.
We also experimented with using the average weekly wage measure available in the QCEW for each CIP. The advantage of this measure is that it is available for all CIPs. The disadvantage is that it is a noisier measure of how much the minimum wage would bind since it is a measure of weekly (not hourly) earnings and does not directly translate into the percentage of workers who are close to the minimum wage. While the qualitative effects of a minimum wage hike were similar with either measure of earnings, the share of workers with low wages fit the employment data better (i.e., adjusted R-squared was higher).
The Bureau of Labor Statistics points out that, in the QCEW, large month-to-month changes in employment could reflect changes in employer reporting practices at the beginning of a new calendar year. For example, an employer with multiple locations in the state may report as a single corporation. In a subsequent reporting period, the company may change their method of reporting leading to a large change in employment. This issue is discussed on the BLS website at https://www.bls.gov/cew/cewfaq.htm#Q11
In the context of the analysis here, pre-trends exist when leading values of the minimum wage have explanatory power for the current level of employment.
The results for these models are available in appendix table A3.
We also tested the robustness of our results to reducing the minimum sample size in the Census for a CIP wage estimate from 200 to 50. This added 63 additional CIPs to the data set, but increased total employment in the sample by only 2.6%. The estimated effects change only slightly.
The coefficient on dlmin_low_wage is negative and statistically significant at the .01 level for the first 3 specifications for differences between 1 and 10 years. For the fourth specification, the effect is negative and statistically significant at the .10 level for all differences of 5 or more years, but statistically insignificant for differences of four or fewer years.
While the range of elasticities is −0.23 to −0.28 for all workers, this translates into an elasticity −2.7 to −3.5 for workers who are directly affected by the minimum wage.
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Acknowledgements
We thank Michael Saltsman and conference participants at the Association of Private Enterprise Education and Southern Economic Association meetings. This research was partially funded by the Employment Policies Institute.
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Even, W.E., Macpherson, D.A. Where Does the Minimum Wage Bite Hardest in California?. J Labor Res 40, 1–23 (2019). https://doi.org/10.1007/s12122-018-9281-z
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DOI: https://doi.org/10.1007/s12122-018-9281-z