Ga Young Ko, a 3rd year PhD student, won a Tipton Snavely Award for Outstanding Summer Research (an honor shared with Joaquin Saldain). She summarizes her paper, titled The Role of Rating System and Hiring Biases in Freelance Auction Platforms, as follows: "A freelance auction platform is a website that matches buyers of services with sellers using an auction mechanism. Sellers differ in terms of their observed demographics (e.g., gender, race, etc.) and unobserved quality. Buyers on the platform may use demographic information as cues to evaluate quality. If these characteristics are correlated, then observing demographics allows buyers to make informed task-specific hiring decisions. But these characteristics may also induce intrinsic racial or gender-based hiring biases. I develop and estimate a model of a sequential first-price auction with beauty contest and endogenous entry. I use an extensive new data-set from a freelance platform. The data-set includes price offers, assignments, and seller characteristics, where machine learning techniques are employed to infer race and gender. Through various counterfactual analyses I will determine the role of information about sellers on the market outcome and also quantify the efficiency of the freelance system."