{'fraction_non_alphanumeric': 0.011458333333333333, 'fraction_numerical': 0.015625, 'mean_word_length': 5.627586206896551, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Bayesian Analysis of Knowledge Spillovers in European Regions

This paper estimates the effects of knowledge spillovers on patent growth rates across 335 European regions over the 1989-1999 period. We propose a dynamic model based on an innovation production function. A Bayesian approach is used to take into account area-specific innovation and spatial spillovers. The estimation of the model proceeds via Markov Chain Monte Carlo simulation. The results show significant positive and negative spatial effects on innovative activity. The model allows for a rich spatial specification, which we illustrate by incorporating transport proximity measured by transportation time between regions to augment the typical spatial proximity measure of connectivity between regions. Doing this produces more pronounced spatial spillovers that exhibit a more polarized spatial pattern than a model relying on spatial proximity alone. Copyright Blackwell Publishers, 2005