About

Francesca Molinari is the H. T. Warshow and Robert Irving Warshow Professor in the Department of Economics at Cornell University. She received her Ph.D. from the Department of Economics at Northwestern University, after obtaining a BA and Masters in Economics at the Università degli Studi di Torino in Italy. Her research interests lie in econometrics, encompassing both theoretical and applied aspects. Theoretically, she focuses on the study of identification problems and the development of new methods for statistical inference in partially identified models. In her applied work, she primarily analyzes decision making under risk and uncertainty, including the estimation of risk preferences using market level data and the analysis of individuals' probabilistic expectations using survey data. Francesca Molinari is a Fellow of the Econometric Society and of the International Association for Applied Econometrics. She has served as a Joint Managing Editor of the Review of Economic Studies and is currently a Coeditor at the Journal of Political Economy.

Research topics

• Computer Science
• Mathematics
• Econometrics
• Artificial Intelligence
• Mathematical economics
• Statistics
• Economics
• Data Mining
• Macroeconomics
• Biology
• Labour economics
• Microeconomics
• Surgery
• Geometry
• Mathematical optimization
• Actuarial science
• Medicine
• Accounting
• Demography
• Internal medicine

Selected publications

• Learning about Stability of Risk Preferences 

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Testing Sign Congruence Between Two Parameters

  Journal of Applied Econometrics · 2025-10-02

  article

  ABSTRACT We test the null hypothesis that two parameters have the same sign, assuming that (asymptotically) normal estimators are available. Examples of this problem include the analysis of heterogeneous treatment effects, causal interpretation of reduced‐form estimands, meta‐studies, and mediation analysis. A number of tests were recently proposed. We recommend a test that is simple and rejects more often than many of these recent proposals. Like all other tests in the literature, it is conservative if the truth is near and therefore also biased. To clarify whether these features are avoidable, we also provide a test that is unbiased and has exact size control on the boundary of the null hypothesis, but which has counterintuitive properties and hence we do not recommend. We show how to improve ‐values in an existing paper from information contained in that paper's main text, and we revisit an empirical analysis of the effect of trade on voter behavior.

  PublisherDOI

• Inference for an Algorithmic Fairness-Accuracy Frontier

  arXiv (Cornell University) · 2024-02-14 · 1 citations

  preprintOpen accessSenior author

  Algorithms are increasingly used to aid with high-stakes decision making. Yet, their predictive ability frequently exhibits systematic variation across population subgroups. To assess the trade-off between fairness and accuracy using finite data, we propose a debiased machine learning estimator for the fairness-accuracy frontier introduced by Liang, Lu, Mu, and Okumura (2024). We derive its asymptotic distribution and propose inference methods to test key hypotheses in the fairness literature, such as (i) whether excluding group identity from use in training the algorithm is optimal and (ii) whether there are less discriminatory alternatives to a given algorithm. In addition, we construct an estimator for the distance between a given algorithm and the fairest point on the frontier, and characterize its asymptotic distribution. Using Monte Carlo simulations, we evaluate the finite-sample performance of our inference methods. We apply our framework to re-evaluate algorithms used in hospital care management and show that our approach yields alternative algorithms that lie on the fairness-accuracy frontier, offering improvements along both dimensions.

  PublisherOA PDFDOI

• Information Based Inference in Models with Set-Valued Predictions and Misspecification

  arXiv (Cornell University) · 2024-01-19

  preprintOpen accessSenior author

  This paper proposes an information-based inference method for partially identified parameters in incomplete models that is valid both when the model is correctly specified and when it is misspecified. Key features of the method are: (i) it is based on minimizing a suitably defined Kullback-Leibler information criterion that accounts for incompleteness of the model and delivers a non-empty pseudo-true set; (ii) it is computationally tractable; (iii) its implementation is the same for both correctly and incorrectly specified models; (iv) it exploits all information provided by variation in discrete and continuous covariates; (v) it relies on Rao's score statistic, which is shown to be asymptotically pivotal.

  PublisherOA PDFDOI

• Inference for an Algorithmic Fairness-Accuracy Frontier

  2024-07-08 · 1 citations

  articleSenior author

  Decision-making processes increasingly rely on the use of algorithms. Yet, algorithms' predictive ability frequently exhibits systematic variation across subgroups of the population. While both fairness and accuracy are desirable properties of an algorithm, they often come at the cost of one another. What should a fairness-minded policymaker do then, when confronted with finite data? In this paper, we provide a consistent estimator for a theoretical fairness-accuracy (FA) frontier put forward by Liang, Lu, Mu, and Okumura [2024, https://arxiv.org/abs/2112.09975]. To do so, we recognize that the FA-frontier is a part of the boundary of a convex set---the feasible set of group-specific expected losses associated with all possible algorithms---that can be fully represented by its support function. We provide an estimator of this support function and show that it converges to a tight Gaussian process as the sample size increases.

  PublisherDOI

• Testing Sign Congruence Between Two Parameters

  arXiv (Cornell University) · 2024-05-20 · 1 citations

  preprintOpen access

  We test the null hypothesis that two parameters $(μ_1,μ_2)$ have the same sign, assuming that (asymptotically) normal estimators $(\hatμ_1,\hatμ_2)$ are available. Examples of this problem include the analysis of heterogeneous treatment effects, causal interpretation of reduced-form estimands, meta-studies, and mediation analysis. A number of tests were recently proposed. We recommend a test that is simple and rejects more often than many of these recent proposals. Like all other tests in the literature, it is conservative if the truth is near $(0,0)$ and therefore also biased. To clarify whether these features are avoidable, we also provide a test that is unbiased and has exact size control on the boundary of the null hypothesis, but which has counterintuitive properties and hence we do not recommend. We use the test to improve p-values in Kowalski (2022) from information contained in that paper's main text and to establish statistical significance of some key estimates in Dippel et al. (2021).

  PublisherOA PDFDOI

• Information based inference in models with set-valued predictions and misspecification

  2024-01-29 · 1 citations

  reportOpen accessSenior author

  This paper proposes an information-based inference method for partially identified parameters in incomplete models that is valid both when the model is correctly specified and when it is misspecified.Key features of the method are: (i) it is based on minimizing a suitably defined Kullback-Leibler information criterion that accounts for incompleteness of the model and delivers a non-empty pseudotrue set; (ii) it is computationally tractable; (iii) its implementation is the same for both correctly and incorrectly specified models; (iv) it exploits all information provided by variation in discrete and continuous covariates; (v) it relies on Rao's score statistic, which is shown to be asymptotically pivotal.

  PublisherOA PDFDOI

• Testing sign congruence between two parameters

  2024-06-19

  reportOpen access

  We test the null hypothesis that two parameters ( 1 , 2 ) have the same sign, assuming that (asymptotically) normal estimators ( 1 , 2 ) are available.Examples of this problem include the analysis of heterogeneous treatment effects, causal interpretation of reduced-form estimands, meta-studies, and mediation analysis.A number of tests were recently proposed.We recommend a test that is simple and rejects more often than many of these recent proposals.Like all other tests in the literature, it is conservative if the truth is near (0, 0) and therefore also biased.To clarify whether these features are avoidable, we also provide a test that is unbiased and has exact size control on the boundary of the null hypothesis, but which has counterintuitive properties and hence we do not recommend.We use the test to improve p-values in Kowalski (2022)

  PublisherOA PDFDOI

• Rejoinder

  Journal of Business and Economic Statistics · 2023-10-02

  articleSenior authorCorresponding

  Click to increase image sizeClick to decrease image size Notes1 Our analysis, available upon request, allows for endogenous loss probabilities via a linear function of effort, (1−e)μ. The effort level, e, is in turn associated with a (potentially heterogeneous across agents) quadratic cost function. The analysis shows that for deductible levels as in our data, the choice of $200 in collision is not rationalizable, even in the presence of endogenous loss probabilities.

  PublisherDOI

• Risk Preference Types, Limited Consideration, and Welfare

  Journal of Business and Economic Statistics · 2023-09-18 · 5 citations

  articleSenior authorCorresponding

  We provide sufficient conditions for semi-nonparametric point identification of a mixture model of decision making under risk, when agents make choices in multiple lines of insurance coverage (contexts) by purchasing a bundle. As a first departure from the related literature, the model allows for two preference types. In the first one, agents behave according to standard expected utility theory with CARA Bernoulli utility function, with an agent-specific coefficient of absolute risk aversion whose distribution is left completely unspecified. In the other, agents behave according to the dual theory of choice under risk combined with a one-parameter family distortion function, where the parameter is agent-specific and is drawn from a distribution that is left completely unspecified. Within each preference type, the model allows for unobserved heterogeneity in consideration sets, where the latter form at the bundle level—a second departure from the related literature. Our point identification result rests on observing sufficient variation in covariates across contexts, without requiring any independent variation across alternatives within a single context. We estimate the model on data on households’ deductible choices in two lines of property insurance, and use the results to assess the welfare implications of a hypothetical market intervention where the two lines of insurance are combined into a single one. We study the role of limited consideration in mediating the welfare effects of such intervention.

  PublisherDOI

Recent grants

• Collaborative Research: Robust Inference and Computational Methods for Optimal Values of Nonlinear Programs

  NSF · $177k · 2018–2021

• Collaborative Research: Asymptotic Properties for Partially Identified Models

  NSF · $175k · 2006–2010

• Collaborative Research: Identification in Incomplete Econometric Models Using Random Set Theory

  NSF · $229k · 2009–2012

Frequent coauthors

• Paul Schrimpf

  61 shared

• Arun G. Chandrasekhar

  National Bureau of Economic Research

  61 shared

• Levon Barseghyan

  Cornell University

  61 shared

• Victor Chernozhukov

  61 shared

• Joshua C. Teitelbaum

  55 shared

• Maura Coughlin

  Rice University

  26 shared

• Lin Thirkettle

  Institute for Fiscal Studies

  25 shared

• Jia Panle

  Rice University

  25 shared

Education

• Ph.D., Economics

  Northwestern University

  2003

Awards & honors

• American Academy of Arts and Sciences