AgdaPkt 2017-09-25 Closed and Joint SA PFA

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The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 66 Santa Clara County GDP so that it is consistent with the underlying value of the GDP in IMPLAN in 2019 (see section A2.2), and we estimate that the share of consumer spending in GDP is 58.8 percent (see section A2.2). We estimate that the annual aggregate consumer spending is $57.9 billion in 2019 in San Jose and 146.5 billion in Santa Clara County. • The annual reduction in jobs resulting from price increases is estimated using the 2014 IMPLAN model (see (Day 2013) for documentation on this software). We adjust those estimates by working age population growth from 2014 to 2019, estimated to be 6.79 percent for the overall period in both San Jose and Santa Clara County (see section A2.2). Part C: Income effects Part C of Table A1 estimates the income effects resulting from pay increases for low-wage workers, the resultant increase in consumer demand, and its impact on employment. Our estimates are calculated as follows: • The net change in compensation for affected workers ([19]) is calculated as the total wage bill increase for affected workers ([20]) minus the wage bill reduction from a reduction in the Supplemental Nutrition Assistance Program (SNAP) and in premium tax credits under the Affordable Care Act benefit reduction ([21]). • The offset from SNAP and premium tax credits ([21]) under the ACA is estimated to be 14.75 percent of the total wage increase (see Appendix A2) and is applied to the total wage bill increase for all households, as there is no easy way to separate this out by income brackets. • The annual increase in jobs resulting from higher consumer demand is estimated using the 2014 IMPLAN model. We adjust those estimates by the working age population growth from 2014 to 2019, estimated to be 6.79 percent for the overall period in both San Jose and Santa Clara County (see section A2.2 for the source). Part D: Net effects Part D of Table A1 estimates the cumulative net effect on employment ([24]), simply by subtracting the reduction in employment due to substitution effects, productivity gains ([4]), and scale effects ([17]) from ([ the employment gains due to income effects 22]). We compute the annual estimates by dividing the cumulative effects on employment by five, to account for the number of years needed for the policy to be fully phased in. These numbers are therefore approximate annual averages. A2.2 Key parameters and assumptions used in the model Our key parameters are drawn from the best available evidence. We vary some of them in our robustness tests. We explain and document below the range of those parameters and the 8.A. - Page 80