About

29665801903_2661f62770_o

 

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
Twitter: @fvanderwouden

Publications

  • 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 (In Print)
  • Van der Wouden, F. & D. Rigby, “Co-inventor Networks and Knowledge Production in Specialized and Diversified Cities”, Papers in Regional Science (In Print)

Papers Under Review

  1. Van der Wouden, F. , “Co-inventors on US patents: Changing patterns in Collaboration Complexity and Geography”
  2. Van der Wouden, F. & D. Rigby “Inventor Mobility and Productivity: A Long-Run Perspective”

Working Papers

  1. Van der Wouden, F. & H. Youn & G. Carnabuci “Adjacent Possible: explaining technological evolution using long-run patent data”
  2. Van der Wouden, F. & H. Youn “Impact of geographical distance on acquiring know-how through scientific collaboration”
  3. Van der Wouden, F. “What Mechanisms Structure Tie-Formation among U.S. Inventors: Empirical Evidence from U.S. Patents between 1836-1975”
  4. Van Niftrik, C.H.B. & F. van der Wouden “Outcome Prediction Modeling in Aneurysmal Subarachnoid Hemorrhage using Machine Learning Techniques”

 

 

 

 

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