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Title: Data and Methods for Estimating the Impact of Proposed Local Minimum Wage Laws
Citation Type: Miscellaneous
Publication Year: 2016
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Abstract: In this technical report we document a methodology developed by the UC Berkeley Center for Labor Research and Education to estimate the number of workers impacted by proposed local minimum wage laws, as well as the expected increase in wages. This methodology is similar to that used by researchers to generate impact estimates for national and state minimum wage proposals, but differs in several respects because of significant data limitations for city-or county-based analyses. In Section A we describe the data source, sample definition, and wage variable creation and cleaning. In Section B we describe the process for estimating the number of workers affected and the expected increase in wages. A. Data and Wage Variable Creation 1. Data source We use the 2012-2013 IPUMS American Community Survey (ACS) (https://usa.ipums.org/usa/). We use the ACS rather than the Current Population Survey (CPS) because the ACS (a) has much larger sample sizes, which is critical for local analyses; (b) is representative at the city or county level, which the CPS is not; and (c) allows us to construct a sample based on place of work, which the CPS does not. That said, the ACS does not have a respondent-reported measure of hourly wages; we address this issue below. 2. Sample definition The sample consists of U.S. civilians aged 16 to 64, who had non-zero income in the previous 12 months, who worked last week, and who were not self-employed, unpaid family workers, or federal or state government employees (these groups of workers are not covered by city or county minimum wage laws).
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Authors: Perry, Ian; Thomason, Sarah; Bernhardt, Annette
Publisher: CWED
Data Collections: IPUMS USA
Topics: Labor Force and Occupational Structure
Countries: United States