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Mailing address:

Queen Mary University of London

School of Economics and Finance

Graduate Centre

Mile End Road
London E1 4NS

Office: GC5.42



Cristina Gualdani 

Associate Professor in Economics at Queen Mary University of London, School of Economics and Finance

TSE Associate Faculty

CeMMAP Research Associate


Fields: Econometrics, Empirical Industrial Organisation

Curriculum Vitae​

Google Scholar profile

News: ESRC New Investigator Grant 2024-2028 ES/X011186/1 "Econometric Analysis of Dynamic Games with Limited Information"

Past events: Workshop on "Econometrics and Models of Strategic Interactions", May 24-25 2024, jointly organised by QMUL, UCL, TSE, Vanderbilt University, and the University of Naples [Program]


An Econometric Model of Network Formation with an Application to Board Interlocks between Firms, Journal of Econometrics, 2021, 224(2), p.345-370. [Paper] [Supplement]   

Partial Identification in Matching Models for the Marriage Market, with S. Sinha, Journal of Political Economy, 2023, 131(5), p.1109-1171. [PaperairXiv]

Discussion of "Risk Preference Types, Limited Consideration, and Welfare" by Levon Barseghyan and Francesca Molinari, Journal of Business and Economic Statistics, 2023, 41(4), 1035-1038.

Identification in Discrete Choice Models with Imperfect Information, with S. Sinha. [Paper] (Accepted Journal of Econometrics)

Working papers​ 

Price Competition and Endogenous Product Choice in Networks: Evidence from the US Airline Industry, with C. Bontemps and K. Remmy. [Paper] [Supplement] (R&R Econometrica)

Work in progress


Robust Identification and Inference in Repeated Games, with N. Lomys and L. Magnolfi​​

On the Identification of Models of Uncertainty, Learning, and Human Capital Acquisition and the Determinants of Sorting, with A. de Paula, E. Pastorino, and S. Salgado.

Abstract: We consider a general class of imperfectly competitive models of the labor market with human capital acquisition, learning about worker ability, and job assignment. We prove that these models are identified based on information on workers’ jobs and wages under standard assumptions. We show that accounting for differences in the opportunities for human capital acquisition and learning across jobs and firms helps explain why the measured degree of labor market sorting can be low even in the presence of a high degree of complementarity between firms and workers. We offer a novel decomposition of the determinants of cross-sectional wage inequality and estimate them based on U.S. matched employer-employee data. [Slides]

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