About

Isabelle Perrigne is a Full Professor of Economics at Rice University and serves as the Director of Ph.D. Admissions. She holds the Reginald Henry Hargrove Chair in Economics. Before joining Rice University, she held appointments at the University of Southern California and Pennsylvania State University. Her area of expertise is empirical industrial organization, with a particular focus on the development of the structural analysis of auction data. Her current research interests include nonlinear pricing and insurance, and she combines models of incomplete information with data and microeconometrics in her work. Professor Perrigne has received grants from the National Science Foundation and has published in leading economic journals such as Econometrica and the Review of Economic Studies.

Research topics

• Microeconomics
• Economics
• Sociology
• Computer Science
• Business
• Finance
• Industrial organization
• Econometrics
• Marketing

Selected publications

• Discrete Games: A Historical Perspective

  Advanced studies in theoretical and applied econometrics · 2025-01-01

  book-chapter
  PublisherDOI

• Econometrics of insurance with multidimensional types

  Quantitative Economics · 2025-01-01 · 1 citations

  articleOpen access

  In this paper, we address the identification and estimation of insurance models where insurees have private information about their risk and risk aversion. The model includes random damages and allows for several claims, while insurees choose from a finite number of coverages. We show that the joint distribution of risk and risk aversion is nonparametrically identified despite bunching due to multidimensional types and a finite number of coverages. Our identification strategy exploits the observed number of claims as well as an exclusion restriction, and a full support assumption. Furthermore, our results apply to any form of competition. We propose a novel estimation procedure combining nonparametric estimators and GMM estimation that we illustrate in a Monte Carlo study.

  PublisherDOI

• Econometrics of Insurance with Multidimensional Types

  arXiv (Cornell University) · 2024-10-10

  preprintOpen access

  In this paper, we address the identification and estimation of insurance models where insurees have private information about their risk and risk aversion. The model includes random damages and allows for several claims, while insurers choose from a finite number of coverages. We show that the joint distribution of risk and risk aversion is nonparametrically identified despite bunching due to multidimensional types and a finite number of coverages. Our identification strategy exploits the observed number of claims as well as an exclusion restriction, and a full support assumption. Furthermore, our results apply to any form of competition. We propose a novel estimation procedure combining nonparametric estimators and GMM estimation that we illustrate in a Monte Carlo study.

  PublisherOA PDFDOI

• Multidimensional Auctions of Contracts: An Empirical Analysis

  American Economic Review · 2022 · 25 citations

  • 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

• Empirical Perspectives on Auctions

  SSRN Electronic Journal · 2021 · 3 citations

  Senior authorCorresponding
  • Business
  • Economics
  • Microeconomics
  PublisherDOI

• Semiparametric Estimation of First-Price Auctions with Risk-Averse Bidders

  UNC Libraries · 2021-10-09

  articleOpen access1st authorCorresponding

  This paper proposes a semiparametric estimation procedure of the first-price auction model with risk averse bidders within the independent private value paradigm. We show that the model is nonidentified in general from observed bids. We then exploit heterogeneity across auctioned objects to establish semiparametric identification under a conditional quantile restriction and parameterization of the bidders’ von Neuman Morgenstern utility function. Next we propose a semiparametric method for estimating the corresponding auction model. This method involves several steps and allows to recover the parameters of the utility function as well as the bidders’ private values and their density. We show that our semiparametric estimator of the utility function parameters converges at the optimal rate, which is slower than the parametric one. An illustration of the method on U.S. Forest Service timber sales is presented and a test of bidders’ risk neutrality is performed.

  PublisherDOI

• Empirical Perspectives on Auctions

  National Bureau of Economic Research · 2021-11-01 · 3 citations

  reportOpen accessSenior author

  The empirical analysis of auction data has become a thriving field of research over the past thirty years. Relying on sophisticated models and advanced econometric methods, it addresses a wide range of policy questions for both public and private institutions. This chapter offers a guide to the literature by stressing how data features and policy questions have shaped research in the field. The chapter is organized by types of goods for sale and covers auctions of timber, construction and services procurement, oil and gas leases, online auctions, internet advertising, electricity, financial securities, spectrum, as well as used goods. It discusses the idiosyncrasies of each applied setting and the respective empirical findings.

  PublisherDOI

• Empirical Perspectives on Auctions

  SSRN Electronic Journal · 2021-01-01

  articleOpen accessSenior author
  PublisherDOI

• Empirical perspectives on auctions

  Handbook of Industrial Organization · 2021 · 9 citations

  Senior authorCorresponding
  • Sociology
  • Industrial organization
  • Business
  PublisherDOI

• Econometrics of Scoring Auctions

  2020-03-27 · 2 citations

  book-chapter

  Abstract This chapter develops a structural framework for the analysis of scoring procurement auctions where bidder’s quality and bid are taken into account. With exogenous quality, the authors characterize the optimal mechanism whether the buyer is private or public and show that the optimal scoring rule need not be linear in the bid. The model primitives include the buyer benefit function, the bidders’ cost inefficiencies distribution and cost function, and potentially the cost of public funds. We show that the model primitives are nonparametrically identified under mild functional assumptions from the buyer’s choice, firms’ bids and qualities. The authors then develop a multistep kernel-based procedure to estimate the model primitives and provide their convergence rates. Our identification and estimation results are general as they apply to other scoring rules including quasi-linear ones.

  PublisherDOI

Recent grants

• Econometrics of Asymmetric Information Models with Applications to Nonlinear Pricing, Insurance and Compensation

  NSF · $413k · 2012–2013

• Econometrics of Asymmetric Information Models with Applications to Nonlinear Pricing, Insurance and Compensation

  NSF · $413k · 2012–2017

• Econometrics of Auctions and Regulation

  NSF · $263k · 2005–2009

Frequent coauthors

• Quang Vuong

  289 shared

• Emmanuel Guerre

  Pontifical Catholic University of Rio de Janeiro

  261 shared

• Sandra Campo

  Universidad Politécnica de Madrid

  260 shared

• Gaurab Aryal

  10 shared

• Chêng Hsiao

  National Taiwan University

  9 shared

• Alı Hortaçsu

  5 shared

• Jean‐Jacques Laffont

  4 shared

• Jeffrey B. Nugent

  University of Southern California

  4 shared

Awards & honors

• Recipient of grants from the National Science Foundation