Do you have a question for Patricia Prüfer?
Feel free to contact us.
About Patricia Prüfer
Patricia Prüfer is Head of Policy Research & Analytics and a member of the Centerdata management team. In addition to advanced quantitative methods, such as econometrics and experimental research, Patricia is specialized in Analytics. Together with colleagues, she applies methods such as data science, machine learning and AI to predict and evaluate (the effects of) policy, including through Social Cost-Benefit Analyses (SCBA). Patricia also supports the design of effective and efficient interventions, for example in the Smart Start programme, which has developed a data-driven and knowledge-based method for working on the prevention of complex social problems.
In her research, Patricia focuses on questions from the fields of education and labour economics, combining data-driven decision-making with appropriate (experimental) research designs and advanced data analysis techniques. As a linking pin between the worlds of research and policy, she regularly gives workshops, masterclasses, lectures and guest lectures on digital transformation, data maturity and the application of analytics and AI literacy to support a wide range of organisations, such as ministries, municipalities and civil society organisations.
Patricia is a member of the Analytics Advisory Committee of the Ministry of Finance. She is also a member of the advisory board of the “Zicht op-Methode” (Insight Method) for the Ministry of the Interior and Kingdom Relations and the Ministry of Justice and Security, advising on risk models and algorithms.
Since obtaining her PhD in Economics at Tilburg University with a thesis in Econometrics in 2008, Patricia has been affiliated with the Economics department at Tilburg University as a research fellow. For recent publications, see below. For the whole list of publications, see: https://www.centerdata.nl/publicaties.
Publications (peer-reviewed)
Carballa-Smichowski, B., Duch-Brown, N., Höcük, S., Kumar, P., Martens, B., Mulder, J. and Prüfer, P. (2025), Economies of scope in data aggregation: Evidence from health data, Information Economics and Policy, 71, 101146.
Klein, T., Kurmangaliyeva, M., Prüfer J. and P. Prüfer (2025), How important are user-generated data for search-result quality? Experimental evidence, Journal of Law & Economics, 68(3), 499-518.
Prüfer, P. & Den Uijl, M. (2024), Can occupational switches reduce labor market shortages? A skills-based optimization for labor market mobility [article in Dutch], Tijdschrift voor Arbeidsvraagstukken, 40, 222-235.
Prüfer, P., Den Uijl, M. and P. Kumar (2022), From jobs to skills: a data science approach [article in Dutch], Tijdschrift voor Arbeidsvraagstukken, 38.2, 237-260.
Prüfer, J., & Prüfer, P. (2020). Data Science for Entrepreneurship Research: Studying Demand Dynamics for Entrepreneurial Skills in the Netherlands, Small Business Economics, 55, 651-672.
Prüfer, P. & Kolthoff, E. (2020), Using data science to predict indicators of organized crime and subversion [article in Dutch], Proces, 99, 85-101.
Gerritsen, S. & Prüfer, P. (2015), Field experiments for policy [article in Dutch], TPEdigitaal, 9, 21-31.
Magnus, J.R., Powell, O., and Prüfer, P. (2010), A comparison of two model averaging techniques with an application to growth empirics, Journal of Econometrics, 154, 139-153.
Other Publications
Prüfer, J. & Prüfer, P. (2018), Data Science for Institutional and Organizational Economics, in: A Research Agenda for New Institutional Economics, Claude Ménard and Mary M. Shirley (eds.), Edward Elgar Publishers, ISBN: 978 1 78811 250 5, (pp. 248-259).