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Marriott Marquis, Solana
Hosted By:
American Economic Association
Firms and Wage-Setting
Paper Session
Sunday, Jan. 5, 2020 8:00 AM - 10:00 AM (PDT)
- Chair: Sydnee Caldwell, Massachusetts Institute of Technology
Temporary Work Agencies, Outsourcing, and Wage Inequality: Evidence from Administrative Data
Abstract
We paint a comprehensive picture of the prevalence and nature of temp agency work, its underlying drivers as well as the distributional consequences of this phenomenon. We draw on unique administrative data from Argentina that allow us to observe the universe of workers in temporary work arrangements both with their temp agency as well as the client firms that rely on their services. This unique feature – “dual registration” – combined with matched employer-employee data permits us to tackle three long-standing, interrelated questions empirically: First, we characterize the prevalence of temp agency work and outsourcing across the economy. Starting on the firm side, we describe and analyze which type of firms draw on temp agencies to outsource labor. Second, we estimate temporary work pay penalties. Here, our data permits us to hold fixed both the characteristics of the worker as well as the workplace, using data across all industries and occupations. In addition, we estimate how rent-sharing differs for workers within the same workplace that are only separated by the contractual arrangement at the same workplace and in the same occupation. Third, we investigate the economic and social mechanisms leading firms to contract out work to temp agencies, testing several core theoretical hypotheses in the literature.Do wage setting shocks propagate across firms? Evidence from employer minimum wage increases
Abstract
Low unionization rates, a falling real federal minimum wage, and prevalent non-competes characterize the low-wage sector in the United States and contribute to growing inequality. In recent years, a number of private employers in the US have opted to institute or raise company-wide minimum wages for their employees, sometimes in response to public pressure. To what extent do these policy changes at major employers spill over to other employers in a local labor market? This paper examines spillover effects of recent company minimum wage increases, including Amazon's recent increase to $15 an hour in 2019 and Walmart to $9 an hour in 2015. We estimate the impact of these policies on other low-wage employers in the same county using data on minimum posted wages from online job ads. We find large spillover effects from both Amazon's 2019 and Walmart's 2015 increases. We discuss potential mechanisms and plans to extend the analysis to over 100 recent employer minimum wage increases across the US.Outside Options in the Labor Market
Abstract
This paper develops a method to estimate the employment opportunities available to each worker, and to assess the impact of these outside options on the gender wage gap. We outline a matching model with two-sided heterogeneity, from which we derive a sufficient statistic, the “outside options index” (OOI), that captures the effect of outside options on wages, holding productivity constant. This OOI uses the cross-sectional concentration of similar workers across job types to quantify the availability of outside options as a function of workers’ commuting or moving costs, preferences, and skills. Higher concentration in a narrower range of job types implies lower OOI and higher dispersion across a wide variety of job types means higher OOI. We use administrative data to estimate the OOI for every worker in a representative sample of the German workforce. We estimate the elasticity between the OOI and wages using two sources of quasi-random variation in the OOI, that holds workers’ productivity constant: the introduction of high-speed commuter rail stations, and a shift-share (“Bartik”) instrument. Using this elasticity and the observed distribution of options, we find that differences in options explain 30% of the gender wage gap. The differences in (similarly skilled) men and women’s option sets are driven primarily by differences in the implicit costs of commuting and moving.JEL Classifications
- J3 - Wages, Compensation, and Labor Costs
- J2 - Demand and Supply of Labor