Productivity convergence in manufacturing in the Eurozone: a hierarchical panel data approach

Abstract This paper investigates both conditional and unconditional convergence in labor productivity within the manufacturing industries of the Eurozone over the period 1963 – 2018. We employ two innovative models: constant and varying-coefficient hierarchical panel data convergence regression models, each equipped with two sets of latent factor structures—one comprising global factors and the other industry-specific factors. These models offer distinct advantages, allowing for both global and industry-specific cross-sectional dependencies and permitting parameter heterogeneity across individual industries. Our findings reveal both conditional and unconditional convergence across the manufacturing industry as a whole, as well as among the majority of the 23 sub-manufacturing industries at the ISIC two-digit level. Moreover, we observe significant variation in convergence dynamics among these sub-manufacturing industries. Robustness checks, performed across different subperiods, confirm the reliability of our results. Furthermore, a comparison of our model’s outcomes with those of two alternative models provides additional support for our conclusions.

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Publication Details

Journal
Macroeconomic Dynamics
Published
2025-01-01
DOI
https://doi.org/10.1017/s1365100525000100
Primary Topic
Economic Growth and Productivity
Type
article
Field-Weighted Citation Impact
0.00
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article

Productivity convergence in manufacturing in the Eurozone: a hierarchical panel data approach

Bin Peng, Chuan Wang, Guohua Feng
Macroeconomic Dynamics
Economic Growth and Productivity
article

Productivity convergence in manufacturing in the Eurozone: a hierarchical panel data approach

Bin Peng, Chuan Wang, Guohua Feng
article en

Abstract

Abstract This paper investigates both conditional and unconditional convergence in labor productivity within the manufacturing industries of the Eurozone over the period 1963 – 2018. We employ two innovative models: constant and varying-coefficient hierarchical panel data convergence regression models, each equipped with two sets of latent factor structures—one comprising global factors and the other industry-specific factors. These models offer distinct advantages, allowing for both global and industry-specific cross-sectional dependencies and permitting parameter heterogeneity across individual industries. Our findings reveal both conditional and unconditional convergence across the manufacturing industry as a whole, as well as among the majority of the 23 sub-manufacturing industries at the ISIC two-digit level. Moreover, we observe significant variation in convergence dynamics among these sub-manufacturing industries. Robustness checks, performed across different subperiods, confirm the reliability of our results. Furthermore, a comparison of our model’s outcomes with those of two alternative models provides additional support for our conclusions.

Macroeconomic DynamicsVol. 29
University of North Texas (US), Monash University (AU), Yuan Ze University (TW)
Openalex Percentile: Top 83%
Economic Growth and Productivity
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