Teodora Erika Uberti graduated from the Faculty of Political Science of the Università Cattolica del Sacro Cuore and subsequently earned a Master of Science in Economics from Manchester University (U.K.), and a Ph.D. in Institutions and Organisations with a dissertation titled "Trade, technological and information flows: some elements to analyze globalization" at Università Cattolica del Sacro Cuore. Nowadays she is lecturer on Economic policies against crime activities at the Faculty of Social and Political Sciences of the same Athenaeum.
Her scientific interests are related to the analysis of "material" and "immaterial" flows of trade, people, knowledge and technology among economies (being these countries, regions, or cities); economics of science; empirical applications of spatial econometrics and network analysis; the economic analysis of crime.
She has authored several journal articles such as Space versus networks in the geography of innovation: A European analysis (with M. A. Maggioni and M. Nosvelli), Papers in Regional Science, 86, pp. 471-493, 2007; Geographical Distribution of Crime in Italian Provinces: A Spatial Econometric Analysis (with M.F. Cracolici), JahrbuchfürRegionalwissenschaft-Review of Regional Research, 29, pp.1-27, 2009; Treating patent as relational data: Knowledge transfers and spillovers across Italian provinces (with M. A. Maggioni and S. Usai), Industry & Innovation, 18, pp. 39-67, 2011; Networks and geography in the economics of knowledge flows (with M. A. Maggioni); Quality & Quantity: International Journal of Methodology, 45, pp. 1031-1051, 2011. Co-authorship and productivity among Italian economists (with G. Cainelli, M. A. Maggioni and A. De Felice), Applied Economics Letters, pp. 1-5, 2012.
personal page: http://docenti.unicatt.it/ita/teodora_erika_uberti/
Collecting, Managing and Representing Data
Main objective of this course is to deal with data: from download to the creation of correct tables and graphs. Data could be provided by official statistical sources or built for particular purposes (e.g. as in a field study). In both cases, it is necessary to identify the type of data (i.e. variables) and synthetize them in the most appropriate way, i.e. using the correct statistical indexes and graphs.