The geographic reach of knowledge spillovers: A functional regression approach with precise geo-referenced data

Konferenzbeitrag

This paper applies functional regression to precise geo-coded register data to measure productivity spillovers from high-skilled workers. We use a smoothing splines estimator to model the spatial distribution of high-skilled workers as continuous curves. Our rich panel data allows us to address spatial sorting of workers and the entanglement of spillover and supply effects with an extensive set of time-varying fixed effects. Our estimates reveal that spillovers from highskilled workers attenuate monotonously with distance. Effects disappear after approximately 20 kilometers. Furthermore, our findings illustrate the benefits of applying functional regression to modern (spatial) economic data.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2019: 30 Jahre Mauerfall - Demokratie und Marktwirtschaft - Session: Econometrics - Forecasting II ; No. F18-V3

Classification and Topics

• Classification: Wirtschaft ; Externalities ; Human Capital; Skills; Occupational Choice; Labor Productivity ; Wage Level and Structure; Wage Differentials ; General Regional Economics (includes Regional Data) ; Urban, Rural, Regional, Real Estate, and Transportation Economics: Regional Migration; Regional Labor Markets; Population; Neighborhood Characteristics
• Subject: Knowledge Spillovers ; Human Capital Externalities ; Functional Regression ; Georeferenced Data ; Education ; Wages

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Eppelsheimer, Johann ; Rust, Christoph
• Event: Veröffentlichung
• (who): ZBW - Leibniz-Informationszentrum Wirtschaft
• (where): Kiel, Hamburg
• (when): 2019

Further information

• Handle: http://hdl.handle.net/10419/203667
• Last update: 10.03.2025, 11:42 AM CET