October 08, 2021
Keynote Speakers
Laura Gee (Tufts University and IZA)
Lisa Kahn (University of Rochester and IZA)
Danielle Li (MIT Sloan School of Management)
Full program
About the Workshop
Like many forms of economic exchange, the process of matching workers to jobs has rapidly migrated online in the last two decades. Thus, understanding how online labor matching mechanisms work; how they affect economic outcomes like employment, wages, and inequality; and learning how to take advantages of the ‘big data’ that are generated by online markets all have important implications for the future of labor. To address these issues we invite paper submissions on the following topics:
Laura Gee (Tufts University and IZA)
Lisa Kahn (University of Rochester and IZA)
Danielle Li (MIT Sloan School of Management)
Full program
About the Workshop
Like many forms of economic exchange, the process of matching workers to jobs has rapidly migrated online in the last two decades. Thus, understanding how online labor matching mechanisms work; how they affect economic outcomes like employment, wages, and inequality; and learning how to take advantages of the ‘big data’ that are generated by online markets all have important implications for the future of labor. To address these issues we invite paper submissions on the following topics:
- The effectiveness of the new internet-based job search methods, both relative to more traditional forms of job search and to each other.
- Descriptive studies of how workers and firms look for each other online: How do firms craft job ads? How do workers search for jobs over time? To what extent are recruiting and job search ‘batch’ or sequential processes? How effectively do job ads direct applications? What is the role of informal networks and referrals, both on- and off-line, in the labor search and matching process? What does all this descriptive information mean for theoretical models of labor market search and matching?
- Experimental and other studies of online search with the potential to improve the efficiency of online matching. Examples include improving job and worker categorizations, better algorithmic recommendations, certification of worker qualifications, online reviews of employers and workers, and increasing the information content of postings (for example adding information on the number of competing applicants).
- Using data derived from online labor market matching processes to study key research questions in labor economics, including minimum wages, labor market dynamics, discrimination, the effects of unemployment insurance, and trends in the types of skills demanded.
- The potential for data from online labor market matching process to supplement existing publicly-available data on labor market trends and conditions. Examples include measuring and forecasting local, national and occupation-level unemployment and vacancy rates, forecasting labor market shortages and ‘bottlenecks’, and informing educational and training institutions about emerging skill needs.
- Other topics related to the online matching of workers and firms.