Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper proposes to include the spatial time lag in empirical applications using spatial panel data models, and also explains why the coefficient of that term can be negative. We provide simple theoretical frameworks to justify the relevance of the spatial time lag to empirical specifications, which can be caused by either partial adjustments or inter-temporal budget constraints. Monte Carlo experiments suggest that omitting a relevant spatial time lag can result in significant biases in regression estimates, while including an irrelevant spatial time lag causes no obvious loss of efficiency.