Over the last 20 years, local municipalities have been implementing minimum wage ordinances at an accelerated rate. These local changes, along with state and federal minimum wage increases, are included in the examination of the impact of minimum wage hikes on employment growth of teenagers in the food services and drinking places subsector. While most minimum wage research focuses on employment levels, recent contributions highlight the importance of analyzing employment growth. Following this trend, this study focuses on teenagers within the restaurant industry to test for the impact of minimum wages on inexperienced workers. Using a distributed-lag model, the results show that an increase in a minimum wage reduces employment growth for teenagers within this subsector. The effects of minimum wages within this demographic were most strongly felt in the first three years following an increase in minimum wage. Specifically, the results show that a 10% increase in the minimum wage decreases the employment growth rate by approximately 2.27% over a period of three years.
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See Neumark and Wascher (2007) for a detailed list of studies and their sample demographics.
Totty (2017) emphasized that larger minimum wage increases may lead to larger negative employment effects.
For further detail of these criticisms, see Neumark et al. (2014).
For benefits and costs for these models (and alternative models), see Meer and West (2016).
Further in the paper, these results were compared to the employment effects on teenagers and the restaurant industry. The weight used was the total country population, as dividing by teenage population would lead to inaccurate results.
See Meer and West (2016) for further clarification.
The local jurisdictions in our dataset are: Albuquerque, NM; Bangor, ME; Berkley, CA; Bernalillo, NM; Chicago, IL; Cook County, IL; Cupertino, CA; El Cerrito, CA; Emeryville, CA; Flagstaff, AZ; Johnson County, IA; Las Cruces, NM; Los Altos, CA; Los Angeles, CA; Los Angeles County, CA; Louisville, KY; Malibu, CA; Milpitas, CA; Montgomery County, MD; Mountain View, CA; Oakland, CA; Palo Alto, CA; Pasadena, CA; Portland, CA; Prince George County, MD; Richmond, CA; San Diego, CA; San Francisco, CA; San Jose, CA; San Leandro, CA; San Mateo, CA; Santa Clara, CA; Santa Fe, NM; Santa Fe County, NM; Santa Monica, CA; Seattle, WA; Sunnyvale, CA; Tacoma, WA; Washington D.C.
A similar method was used in assigning a minimum wage value for counties (without a minimum wage increase) with state and federal minimum wage increases. The largest value between state and federal minimum wage was used as each jurisdiction’s value.
As there were no data available for gross county product, GSP was used instead. However, as GSP was used to control for productivity and business cycle variations, it should still fulfill the same role even at the county level. For more details on the benefits and cost of these control variables, see Meer and West (2013, 2016) and Dube (2013).
As an example, from the dataset which includes 3141 counties, only 44 counties had more than one million in population total for the first quarter of 2017. The dataset contains 3112 counties for quarter one of 2017, because 29 county equivalences were dropped from Alaska which did not contain 2017 data.
This is consistent with the findings in Meer and West (2016).
For all industries and age groups, employment was weighted by total population multiplied by the share of population 15 through 59. This term was logged.
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Gilyard, S., Podemska-Mikluch, M. Effects of Local, State, and Federal Minimum Wage on Employment Growth among Teenagers in the Restaurant Industry. Int Adv Econ Res 26, 89–101 (2020). https://doi.org/10.1007/s11294-020-09770-8
- Minimum wage
- Employment growth
- City ordinances