Discrimination, Mechanization and Precarity: New Challenges for Older Workers
Paper Session
Monday, Jan. 4, 2021 12:15 PM - 2:15 PM (EST)
- Chair: Richard McGahey, New School
Age Discrimination, HR Managers, and Eye-Tracking: Evidence from a Lab-in-the-Field Experiment
Abstract
Age discrimination can have negative effects on both individuals being discriminated against and on government programs and the economy, as potentially productive workers are unable to find work. This paper explores age discrimination at the hiring level in a lab-in-the-field experiment in which we go to Human Resources (HR) fairs and conferences and ask HR managers to rate resumes for an administrative assistant I position. While they rate the resumes, we track their eyes using a Tobii X2-60. After they have rated resumes, we ask them a series of questions to elicit explicit and implicit discrimination against older workers. We find evidence of quadratic age discrimination against older workers. Similarly, participants spend less time looking at resumes of older workers. Participants who hold stereotypes that older workers are less enterprising, less able to handle physically taxing jobs, and less likely to undergo training are more likely to exhibit these discriminatory behaviors. Although there is suggestive evidence that participants who explicitly prefer working with 45 year olds to 65 year olds using a Bogardus social distance task also rate resumes differently by age, this evidence is not robust to specification choice. Finally, there is no evidence that the Implicit Association Test (IAT) for age has any relation to how HR managers rate resumes by age.The Impact of Robot Intensity on Employment and Wages of Older Workers
Abstract
This paper studies the effects of exposure to robots on the employment and wages of older workers (those between the ages of 50 and 65) in the U.S. between 2004 and 2017. I argue theoretically and document findings that the adoption of robots in the manufacturing industry since the Great Recession has had a negative impact on the less-educated older workers' employment and wages, while some other groups of workers have seen an increase in wages. Using the "robot intensity" model as constructed by Freeman and Rodgers III (2019) to estimate patterns of robot exposure across the metropolitan areas in the U.S., I find that older workers in Right-to-Work states have higher robot intensities than in highly unionized states. I focus on the Midwest region since it has the greatest concentration of robotization and show that robots have considerably decreased employment and wages for older workers in the manufacturing industry. Lastly, I estimate the impact of robot intensity on this group of workers absent the economic expansion that began in 2009.Training and Technical Change
Abstract
Despite the widely acknowledged importance of employer-provided job training in the U.S., we know little about the key predictors of this training (Lerman 2010). Of particular interest is the relationship between the amount of technical change in an industry and the level of job training provided by industry business establishments. Understanding this relationship is key to both preparing workers for future jobs and facilitating economic growth. In theory, greater technical change could lead to either decreased training (via substitution effects) or increased training (via complementarity effects). Early employee-side survey evidence indicated that industries with higher rates of technical change may provide more training (Bartel and Sicherman 1998). However, more recent studies have found varied effects at the individual level (Ahituv and Zeira 2011, Burlon and Vialta-Bufi 2016). None of the recent studies are able to connect technical change to training outcomes at the establishment level as the last nationally representative surveys measuring training from the employer side (as opposed to individual/employee surveys) were conducted in the 1990s. In this research, we use unique, nationally representative survey data on U.S. manufacturing establishments to explore the relationship between technical change and the provision of training at both the extensive and intensive margins. We construct multiple indicators of industry-level technical change in order to test the robustness of the results to various measures. The findings imply heterogeneous effects that provide useful guidance for policy.Discussant(s)
John Schmitt
,
Economic Policy Institute
William M. Rodgers III
,
Rutgers University
Siavash Radpour
,
New School
JEL Classifications
- J8 - Labor Standards: National and International
- J0 - General