Publication Type

Working Paper

Version

publishedVersion

Publication Date

10-2024

Abstract

This paper proposes a three-stage efficient GMM estimation algorithm for estimating firm-level production functions given spatial dependence across firms due to supplier-customer relationships, sharing of input markets, or knowledge spillover. The procedure builds on Ackerberg, Caves and Frazer (2015) and Wooldridge (2009), but in addition, allows the productivity process to depend on the lagged output levels and lagged input usages of related firms, and spatially correlated productivity shocks across firms, where the set of related firms can differ across the three dimensions of spatial dependence. We establish the asymptotic properties of the proposed estimator, and conduct Monte Carlo simulations to validate these properties. The proposed estimator is consistent under DGPs with or without spatial dependence, and with strong/weak or positive/negative spatial dependence. In contrast, the conventional estimators lead to biased estimates of the production function parameters if the underlying DGPs have spatial dependence structure, and the magnitudes of the bias increase with the strength of spatial dependence in the underlying DGPs. We apply the proposed estimation algorithm to a Japanese firm-to-firm dataset of 14,178 firms during the period 2009–2018. We find significant and positive spatial coefficients in the Japanese firm-level productivity process via all three channels proposed above.

Keywords

Productivity estimation, spatial dependence, supplier-customer network, factor market pooling, knowledge spillover

Discipline

Econometrics

Research Areas

Econometrics

First Page

1

Last Page

54

Publisher

Singapore Management University

Embargo Period

11-5-2024

Download

Included in

Econometrics Commons

Share

COinS