About

I am an Associate Professor of Economics at Rice University and a Korean-American economist. My research interests are in Empirical Industrial Organization, Applied Microeconomics, and Applied Microeconometrics. My latest research focuses on the empirical analysis of auctions, arbitration, and exchanges.

Research topics

• Computer Science
• Economics
• Microeconomics
• Finance
• Machine Learning
• Econometrics
• Mathematical economics
• Financial economics

Selected publications

• Identification of English auctions when losing entrants are not observed

  International Journal of Industrial Organization · 2022-08-28 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Multidimensional Auctions of Contracts: An Empirical Analysis

  American Economic Review · 2022 · 25 citations

  1st authorCorresponding
  • Computer Science
  • Economics
  • Microeconomics

  In this paper, we conduct a structural analysis of multi-attribute auctions of contracts with a general allocation rule when private information is multidimensional. Upon modeling bidders’ contract value that accounts for their endogenous ex post actions, we nonparametrically identify bidders’ private information from their bids and estimate their joint distribution. Analyzing cash-royalty auctions of Louisiana oil leases, we find government revenue worse and development rates no better than in a cash auction with a fixed royalty in view of adverse selection and moral hazard. Our findings revise conventional wisdom on the optimality of multi-attribute auctions. (JEL D44, D82, D86, H82, Q35)

  PublisherDOI

• Not knowing the competition: evidence and implications for auction design

  The RAND Journal of Economics · 2020 · 28 citations

  1st authorCorresponding
  • Microeconomics
  • Economics
  • Financial economics

  Abstract In a government auction program where first‐price auctions generate significantly higher revenue than English auctions, I document evidence that bidders are uncertain about the number of auction entrants. Motivated by additional data evidence, I estimate a structural model of auctions in which rivals' participation is stochastic, allowing for bidders' risk aversion and asymmetry. Counterfactual simulations reveal that bidders' uncertainty about the number of entrants, combined with risk aversion, substantially softens the revenue impact of low competition in first‐price auctions. This explains the observed revenue patterns and uncovers an empirically important reason for sellers to favor first‐price auctions over English auctions.

  PublisherDOI

• Sequential Auctions with Synergy and Affiliation across Auctions

  Journal of Political Economy · 2020 · 50 citations

  1st authorCorresponding
  • Computer Science
  • Microeconomics
  • Machine Learning

  This paper performs a structural analysis of sequential auctions with both synergy and affiliation across auctions. I propose a flexible yet tractable sequential auction model under the private value paradigm and establish its nonparametric identification, demonstrating an intuitive and general method for disentangling synergy from affiliation. After developing an estimation procedure closely tied to the identification steps, I apply it to data on adjacent oil and gas leases that are auctioned sequentially. I assess the role played by affiliation versus synergy in the observed allocation patterns and evaluate the counterfactual policy of bundled auctions.

  PublisherDOI

• Discrimination and identification of azimuth using spectral shape

  The Journal of the Acoustical Society of America · 2008-11-01 · 37 citations

  articleOpen access

  Monaural measurements of minimum audible angle (MAA) (discrimination between two locations) and absolute identification (AI) of azimuthal locations in the frontal horizontal plane are reported. All experiments used roving-level fixed-spectral-shape stimuli processed with nonindividualized head-related transfer functions (HRTFs) to simulate the source locations. Listeners were instructed to maximize percent correct, and correct-answer feedback was provided after every trial. Measurements are reported for normal-hearing subjects, who listened with only one ear, and effectively monaural subjects, who had substantial unilateral hearing impairments (i.e., hearing losses greater than 60 dB) and listened with their normal ears. Both populations behaved similarly; the monaural experience of the unilaterally impaired listeners was not beneficial for these monaural localization tasks. Performance in the AI experiments was similar with both 7 and 13 source locations. The average root-mean-squared deviation between the virtual source location and the reported location was 35 degrees, the average slopes of the best fitting line was 0.82, and the average bias was 2 degrees. The best monaural MAAs were less than 5 degrees. The MAAs were consistent with a theoretical analysis of the HRTFs, which suggests that monaural azimuthal discrimination is related to spectral-shape discrimination.

  PublisherOA PDFDOI

Frequent coauthors

• Daniel E. Shub

  Walter Reed National Military Medical Center

  4 shared

• H. Steven Colburn

  Boston University

  4 shared

• Suzanne P. Carr

  Boston University

  2 shared

• Quang Vuong

  1 shared

• Isabelle Perrigne

  1 shared