Agglomeration and firm-level productivity: A Bayesian Spatial approach
This paper estimates the impact of industrial agglomeration on firm-level productivity in Chinese manufacturing sectors. To account for spatial autocorrelation across regions, we formulate a hierarchical spatial model at the firm level and develop a Bayesian estimation algorithm. A Bayesian instrumental-variables approach is used to address endogeneity bias of agglomeration. Robust to these potential biases, we find that agglomeration of the same industry (i.e. localization) has a productivity-boosting effect, but agglomeration of urban population (i.e. urbanization) has no such effects. Additionally, the localization effects increase with educational levels of employees and the share of intermediate inputs in gross output. These results may suggest that agglomeration externalities occur through knowledge spillovers and input sharing among firms producing similar manufactures.
Keywords: agglomeration economies, spatial autocorrelation, Bayes, Chinese firm-level data, GIS
JEL classification: C21, C51, R10, R15
- “Agglomeration and Firm-level Productivity: A Bayesian Spatial Approach.” (with Kiyoyasu TANAKA). 2014.
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