Multi-Level Panel Data Models: Estimation and Empirical Analysis

Guohua Feng (), Jiti Gao and Bin Peng ()

No 4/22, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Despite its paramount importance in the empirical growth literature, productivity convergence analysis has three issues that have yet to be addressed: (1) the hierarchical structure of industry-level datasets has little been fully explored; (2) industry-level technology heterogeneity has largely been ignored; and (3) crosssectional dependence has rarely been allowed for. This paper aims to address these three problems within a hierarchical panel data framework. We establish asymptotic properties for the proposed estimator, and apply the framework to a dataset of 23 manufacturing industries from a wide range of countries over the period 1963-2018. Our results show that both the manufacturing industry as a whole and individual manufacturing industries at the ISIC two-digit level exhibit strong conditional convergence in labour productivity, but not unconditional convergence. In addition, our results show that both global and industry-specific shocks are important in explaining the convergence behaviours of the manufacturing industries.

Keywords: Convergence in manufacturing; cross-sectional dependence; growth regression; hierarchical model (search for similar items in EconPapers)
JEL-codes: C23 L60 O10 (search for similar items in EconPapers)
Pages: 60
Date: 2022
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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