I am an Assistant Professor in Economic Geography and Innovation Studies at the University of Hong Kong.
Before moving to Hong Kong, I was a post-doctoral scholar at the department of Management and Organizations at the Kellogg School of Management of Northwestern University and affiliated with the Northwestern Institute on Complex Systems.
I received a PhD in Economic Geography from the University of California, Los Angeles (2018) and a Research Master’s degree (2012) and Bachelor’s degree (2010) in Economic Geography from Utrecht University.
My main academic interests are:
- the uneven spatial distribution of economic and innovative activities
- how networks of collaboration connect agents and places
- how distance impacts learning
- technological change, innovation and economic development
Email: fvdw [at] hku [dot] hk
- Van der Wouden, F. (2019), “A history of Collaboration in US invention: changing Patterns of Co-invention, Complexity and Geography”, Industrial and Corporate Change LINK
- Van der Wouden, F. & Van Niftrik et al. (2019), “Machine learning algorithm identifies patients at high risk for early complications after intracranial tumor surgery: registry based cohort study”, Neurosurgery LINK
- Van der Wouden, F. & D. Rigby (2019), “Co-inventor Networks and Knowledge Production in Specialized and Diversified Cities”, Papers in Regional Science LINK
Papers Under Review
- Van der Wouden, F. & D. Rigby “Inventor Mobility and Productivity: A Long-Run Perspective”
- Van der Wouden, F. & H. Youn & G. Carnabuci, “Adjacent Possible: explaining technological evolution using long-run patent data”
- Van der Wouden, F. & H. Youn, “Impact of geographical distance on acquiring know-how through scientific collaboration”
- Cortinovis, N & F. van der Wouden, “Learning in a Creative Industry”
- Van der Wouden, F., “What Mechanisms Structure Tie-Formation among U.S. Inventors: Empirical Evidence from U.S. Patents between 1836-1975”
- Van Niftrik, C.H.B. & F. van der Wouden, “Outcome Prediction Modeling in Aneurysmal Subarachnoid Hemorrhage using Machine Learning Techniques”