M
Mireia Jofre-Bonet
Researcher at City University London
Publications - 75
Citations - 2158
Mireia Jofre-Bonet is an academic researcher from City University London. The author has contributed to research in topics: Health care & Population. The author has an hindex of 22, co-authored 68 publications receiving 1983 citations. Previous affiliations of Mireia Jofre-Bonet include London School of Economics and Political Science & Yale University.
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Estimation of a Dynamic Auction Game
TL;DR: This paper applies the estimation method to repeated highway construction procurement auctions in the state of California between May 1996 and May 1999 and quantifies the effect of intertemporal constraints on bidders' costs and on bids.
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Estimation of a dynamic auction game
TL;DR: In this article, an estimation method for a repeated auction game under the presence of capacity constraints is proposed, which is computationally simple as it does not require solving for the equilibrium of the game.
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The double-edged sword of industry collaboration: Evidence from engineering academics in the UK
Albert Banal-Estañol,Albert Banal-Estañol,Mireia Jofre-Bonet,Cornelia Lawson,Cornelia Lawson +4 more
TL;DR: In this article, the impact of university-industry collaboration on academic research output is studied. But the authors focus on the channels through which the degree of industry collaboration may affect research output.
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Cost-effectiveness analysis of addiction treatment: paradoxes of multiple outcomes
TL;DR: The challenges of conducting cost-effectiveness analysis (CEA) of addiction treatments given the multiple important outcomes of substance abuse treatment (SAT) are identified and multiple outcomes should be considered in any economic analysis of addiction treatment.
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Bidding behavior in a repeated procurement auction: A summary
TL;DR: In this article, the authors consider a dynamic bidding model that takes into account the presence of inter-temporal constraints such as capacity constraints in a repeated procurement auction setting and estimate the model non-parametrically.