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Ramón y Cajal research grants awarded to Profs. Gianmarco León, Joan Monràs, and Piotr Zwiernik

Ramón y Cajal research grants awarded to Profs. Gianmarco León, Joan Monràs, and Piotr Zwiernik

Funding from the Spanish government will support their work in Economics and Statistics research

Profs. Gianmarco León, Joan Monràs, and Piotr Zwiernik

Three UPF Department of Economics and Business researchers were awarded Ramón y Cajal research grants as part of the Spanish Ministry of Economy, Industry and Competitiveness 2017 call for applications.

Grant recipients are selected from a wide variety of scientific fields. Profs. León and Monràs were selected for their proposed projects in the field of Economics, and Prof. Zwiernik in the field of Mathematics. Recipients of Ramón y Cajal grants are selected for the strength of their of prior academic and research work.

Applied microeconomist Prof. Gianmarco León's project is titled "Development and Political Economics". His ongoing research in political economics is mainly focused in three areas: (i) the causes and consequences of voter participation, (ii) the selection and incentives of politicians and bureaucrats, and (iii) the causes and consequences of civil conflict. He will also conduct research in development economics, addressing questions related to the accumulation of human capital and the consequences of regulation on organizational decisions.

Prof. Joan Monràs was granted to carry out a project titled "Mobility in Labor and Housing markets". His research to date has focused on understanding how labor and housing markets interact across and within cities. In the future he will develop related projects, including research into skill-biased agricultural technical change and industrial specialisation, and spillovers from the public to the private sector.

Prof. Zwiernik's research is in mathematical statistics and focuses on graphical models and other structured probabilistic models. His current research is investigating total positivity and its links to graphical models including models with hidden variables. His main objectives for the coming years are to analyse the geometry of graphical models with hidden variables; use these geometric insights to study existing inference methods; and propose new inference procedures for structure learning that scale and work for a large family of distributional settings.

Visit the personal webpages of these professors to find out more about their work: