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      講座:Matching in Labor Marketplaces: The Role of Experiential Information

      發布者:人力資源辦公室    發布時間:2020-11-04

      題    目:Matching in Labor Marketplaces: The Role of Experiential Information

      演講人:張吉玎   講師 上海紐約大學

      主持人:李成璋   助理教授 上海交通大學安泰經濟與管理學院

      時 間:2020年11月4日(周三)14:00-15:30

      地 點:上海交通大學徐匯校區 安泰樓A305室 


      Online labor marketplaces assign workers to short-term jobs. For some jobs, the choice of the best worker is based on ex-ante observable information (e.g., driver assignment based on location in ride-hailing). In others, the assignment is driven by experiential information, that is information obtained privately only through the worker performing the job (e.g., the fit of a childcare provider with a family). This study develops an empirical framework to impute the relative importance of each kind of information from participants' past hiring choices. Our moment inequality approach accommodates high worker turnover, varying choice sets, and limited observations of a very large number of market participants -- all key characteristics of online labor markets. We apply our framework to two markets, exploiting a natural experiment that changed marketplace commissions. Based on over 1.2M hiring decisions, we estimate that experiential information is a key driver of hiring choices, while ex-ante observable fit is relevant only for the simplest jobs. Using our estimates, we propose and evaluate alternate assignment policies. The best-performing policies prioritize repeat work and, surprisingly, ignore ex-ante observable information to instead experiment with new workers and generate experiential information. Such policies can increase buyer welfare by as much as 45.3% (47.1%) of gross revenue in the Data Entry (Web Development) market compared to the current practice of skills-based matching. Policies exploiting buyers' past revealed preferences (in repeat work) without incorporating exploration still under-perform by 18.9% in Data Entry and 8.7% in Web Development. 


      Jiding Zhang is an Instructor (Assistant Professor from 2021) of Operations Management at NYU Shanghai. Prior to joining NYU Shanghai, she was a PhD candidate at The Wharton School, University of Pennsylvania. Jiding’s research interests lie in the field of marketplace analytics. In her recent work, she analyzes the operations and economics of various digital platforms using both data analytic and modeling tools. Her research has been recognized with several awards, including Finalist for the 2019 MSOM Student Paper Competition, Finalist for the 2019 Revenue Management and Pricing Section Student Paper Prize, and Finalist for the 2019 IBM Best Student Paper Award.