Estimation of industry-level productivity with cross-sectional dependence by using spatial analysis

Jaepil Han () and Robin C. Sickles ()
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Jaepil Han: Chungnam National University
Robin C. Sickles: Rice University

Journal of Productivity Analysis, 2024, vol. 62, issue 1, No 2, 29-52

Abstract: Abstract In this paper, we incorporate spatial analysis to estimate industry-level productivity in the presence of inter-sectoral linkages. Since each industry plays a role in providing intermediate goods to other sectors, the interdependence of economic activities across industries is inevitable. We exploit the linkage patterns from the input-output relationship to define cross-industry dependencies in economic space. We propose a spatial stochastic frontier model, which extends the stochastic frontier model to a spatially dependent specification. The models are estimated using quasi-maximum likelihood methods. Applying the approach to U.S. industry-level data from 1947 to 2010, we find that sectoral dependencies are the consequences of indirect effects via the supply chain network of industries resulting in larger output elasticities as well as scale effects for the networked production processes. However, productivity growth is estimated comparably across different spatial and non-spatial model specifications.

Keywords: Cross-sectional dependence; Spatial panel model; Spatial weights matrix; Stochastic frontier analysis; Industry-level productivity (search for similar items in EconPapers)
JEL-codes: C21 C23 C51 O47 R15 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11123-023-00718-8

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