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<br /> <br />The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 61 <br /> <br />2. We exclude the following workers from our sample who would not be eligible for a <br />municipal or county minimum wage law: <br />a. Federal and state government workers would not be eligible for the minimum wage <br />increases in Scenario A and Scenario B because local governments do not have <br />jurisdiction over federal or state employees. <br />b. Public education employees are excluded from our sample because local school <br />districts are state entities and are exempt from local minimum wage laws. <br />c. In-Home Supportive Service (IHSS) workers are also excluded because IHSS <br />programs are administered at the county level and are exempt from local minimum <br />wage laws. <br />Wage measure <br />Because the ACS only records workers’ annual earnings, it is necessary to estimate an hourly <br />wage variable in order to perform simulations of the effects of minimum wage increases. The <br />hourly wage is estimated for all workers in the sample using their reported annual earnings, usual <br />hours of work per week, and weeks worked in the previous year. The annual earnings measure <br />includes wages, salaries, commissions, cash bonuses, and tips from all jobs, before deductions <br />for taxes. The “number of weeks worked in the previous year” variable is a categorical variable of <br />intervals of weeks worked (such as 14–26 weeks or 50–52 weeks). This variable is converted to <br />a discrete variable using the mid-point of each interval. The hourly wage variable is then <br />estimated as annual earnings divided by the product of the number of weeks worked in the <br />previous year and usual hours worked per week. Workers in occupations that receive tips as the <br />majority of their earnings are coded with hourly wage values equal to state minimum wage, since <br />we only want to measure wages paid by their employer in this study. <br />Geography <br />The smallest geographic unit for the ACS place-of-work variable is the county. In order to <br />estimate the impact of the minimum wage scenarios for cities within Santa Clara County, we <br />conduct our simulation as described above using county-level data, and then estimate the <br />number of affected workers in the city by applying the percentage of affected workers to city-level <br />employment estimates from the Quarterly Census of Employment and Wages (QCEW). This step <br />introduces additional assumptions; namely, that the wage distribution of those who work in the <br />city (not all of whom live in the city) is the same as the wage distribution of those who work in the <br />county, and that future wage and employment growth trends in the city will mirror those at the <br />county level. We therefore make two adjustments to our county-level ACS data to better <br />approximate the city-level wage distribution: <br />8.A. - Page 75