Online Labor Markets
The rise of online labor markets presents a novel set of opportunities to increase efficiency and reduce frictions. At the same time, these advances come with a number of challenges, many of which are not yet fully understood. The MD4SG Online Labor Markets working group consists of researchers from a variety of backgrounds who are interested in recognizing and tackling these challenges. We draw upon our perspectives from disciplines such as computer science, economics, game theory, and operations research to address social issues related to and perpetuated by online labor markets.
Spring 2019 Presentations #
| Presenter | Title | Abstract |
|---|---|---|
| Irene Lo | Theory of Online Labor Markets | In this talk, I will be presenting a summary of the following papers: * Agrawal, A., Horton, J., Lacetera, N. & E. Lyons. “Digitization and the contract labor market: A research agenda.” Economic analysis of the digital economy. University of Chicago Press, 2015. 219-250. * Autor, David H. “Wiring the labor market.” The Journal of Economic Perspectives 15.1 (2001): 25-40. * Horton, John J. “Online labor markets.” International workshop on internet and network economics. Springer, Berlin, Heidelberg, 2010. |
| Sara Kingsley | Online Labor Market Research: Case Studies, Methods & Questions | Relevant Links: * NDWA Labs: https://www .ndwalabs.org/ * Fair Care pledge: http://faircarepledge.com/ * AirBnb pledge: [https://www.airbnb.com/help/article/1975/what-s-airbnb-s-living-wage-pledge](https://www.airbnb.com/help/article/1975 /what-s-airbnb-s-living-wage-pledge) * Turkopticon: https://turkopticon.ucsd.edu/ |
| Shreyas Sekar | The Effect of (advance) Subscription Pricing in Ride-sharing Systems |
Fall 2018 Presentations #
| Presenter | Title | Abstract |
|---|---|---|
| Anson Kahng | Incentivizing Effort with Bids | Consider a setting in which n agents each answer a set of m pairwise comparison questions, and further assume that there exists a ground truth answer to each question. For each question, an agent can choose to not put in effort, which costs the agent nothing and means that they get the question right with some probability, or put in effort at a cost, which means they get the question right with higher probability. We study the problem of incentivizing agents to put in effort when evaluating questions by giving each agent a budget B with which to bid on questions, where each bid gets mapped to a weight via some subadditive function f and each agent receives payoff equal to the sum of the weights on questions she got correct minus the cost of effort. In particular, what kind of function f best incentivizes effort? |
| Faidra Monachou | Social bias and discrimination in online markets |
Working Group Organizers #
| Manish Raghavan | Ph.D. Student in Computer Science | Cornell University |
| Sara Kingsley | Researcher |
Working Group Members #
| Kashish Arora | Ph.D. Student in Operations Management | Cornell University |
| Nikhil Garg | Ph.D. Student in Electrical Engineering | Stanford University |
| Kira Goldner | Ph.D. Student in Computer Science | University of Washington |
| Nicole Immorlica | Senior Researcher | Microsoft Research New England |
| Anson Kahng | Ph.D. Student in Computer Science | Carnegie Mellon University |
| Irene Lo | Postdoctoral Scholar in Economics | Stanford University |
| Nick Mattei | Assistant Professor | Tulane University |
| Faidra Monachou | Ph.D. Student in Management Science & Engineering | Stanford University |
| Matthew Olckers | Ph.D. Student in Economics | Paris School of Economics |
| Shreyas Sekar | Postdoctoral Fellow | Laboratory for Innovation Science at Harvard |
| Eva Tardos | Professor of Computer Science | Cornell University |