Automatability of Skills and Unretirement Occupational Sorting
Thursday, November 7, 2019
Plaza Building: Concourse Level, Plaza Exhibits (Sheraton Denver Downtown)
*Names in bold indicate Presenter
We live in a world whereby human workers and machines are competing against each other for jobs. In the process, machines are replacing—‘automating’—human workers. Interpreting the effect of automation in the context of the growing phenomena of population aging and unretirement, I explore the impacts of technology-induced automation on retirees’ job sorting behaviors as they return to the labor force. Drawing data from the HRS and O*NET between 1992 and 2014, I examine whether the ‘automatability’ of individuals plays a role in dictating their unretirement inclinations, and if so, what types of jobs the unretirees choose, and whether there exist financial and psychological returns to working in old age and being un-automatable. Applied in this study are the theoretical models of skill-biased and task-based technological changes to reflect the nature of automation: I use the former to categorize workers based on their varying abilities to resist the pressures of automation as dictated by their primary skills, while the latter is used to rank occupations based on their heterogeneous levels of automatability determined by task contents. Various panel estimation strategies are employed, including the panel fixed-effect regressions for the unretirement probabilities as well as the Heckman two-stage correction for the job-sorting behaviors. In the end, the findings of this study reveal new information about the nature and motives behind unretirement. The outcomes of this study are expected to assist policy-makers of many developed nations in further promoting longer working lives of the aging workers—a policy intervention set to account for the nations’ rapid population aging and shrinking tax bases unable to financially sustain the current level of public pension and health benefits for retirees.