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.

Tariff reductions and labor demand elasticities : evidence from Chinese firm-level data

International production fragmentation has been a global trend for decades, becoming especially important in Asia where the manufacturing process is fragmented into stages and dispersed around the region. This paper examines the effects of input and output tariff reductions on labor demand elasticities at the firm level. For this purpose, we consider a simple heterogenous firm model in which firms are allowed to export their products and to use imported intermediate inputs. The model predicts that only productive firms can use imported intermediate inputs (outsourcing) and tend to have larger constant-output labor demand elasticities. Input tariff reductions would lower the factor shares of labor for these productive firms and raise conditional labor demand elasticities further. We test these empirical predictions, constructing Chinese firm-level panel data over the 2000--2006 period. Controlling for potential tariff endogeneity by instruments, our empirical studies generally support these predictions.