Hostname: page-component-5b777bbd6c-w9n4q Total loading time: 0 Render date: 2025-06-19T16:45:23.522Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  27 May 2025

Guohua Feng ,
Bin Peng  and
Chuan Wang
Guohua Feng*
Affiliation:
Department of Economics, University of North Texas, Denton, TX, USA
Bin Peng
Affiliation:
Department of Econometrics and Business Statistics, Monash University, Melbourne, Victoria, Australia
Chuan Wang
Affiliation:
College of Management, Yuan Ze University, Taoyuan, Taiwan
*
Corresponding author: Guohua Feng; Email: guohua.feng@unt.edu
Get access Rights & Permissions [Opens in a new window]

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.

Keywords

Productivity convergencehierarchical panel data modelcross-sectional dependenceparameter heterogeneityC23O47
Type
Articles
Information
Macroeconomic Dynamics , Volume 29 , 2025 , e109
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Abowd, J. M., Kramarz, F. and Margolis, D. N. (1999). High wage workers and high wage firms. Econometrica 67(2), 251–333.10.1111/1468-0262.00020CrossRefGoogle Scholar
Acemoglu, D., Naidu, S., Restrepo, P. and Robinson, J. A. (2019). Democracy does cause growth. Journal of Political Economy 127(1), 47–100.10.1086/700936CrossRefGoogle Scholar
Ahn, S. C. and Horenstein, A. R. (2013). Eigenvalue ratio test for the number of factors. Econometrica 81(3), 1203–1227.Google Scholar
Andrews, D. W. K. (2005). Cross-section regression with common shocks. Econometrica 73(4), 1551–1585.10.1111/j.1468-0262.2005.00629.xCrossRefGoogle Scholar
Attanasio, O., Picci, L. and Scorcu, A. E. (2000). Saving, growth, and investment: a macroeconomic analysis using a panel of countries. Review of Economics and Statistics 82(2), 182–211.10.1162/003465300558731CrossRefGoogle Scholar
Bai, J. (2009). Panel data models with interactive fixed effects. Econometrica 77(4), 1229–1279.Google Scholar
Bai, J. and Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica 70(1), 191–221.10.1111/1468-0262.00273CrossRefGoogle Scholar
Barro, R. J. (1991). Economic growth in a cross section of countries. The Quarterly Journal of Economics 106(2), 407–443.10.2307/2937943CrossRefGoogle Scholar
Barro, R. J. and Sala-i-Martin, X. (1992). Convergence. Journal of Political Economy 100(2), 223–251.10.1086/261816CrossRefGoogle Scholar
Baumol, W. J. (1986). Productivity growth, convergence, and welfare: what the long-run data show. American Economic Review 76(5), 1072–1085.Google Scholar
Bils, M. and Klenow, P. (2000). Does schooling cause growth? American Economic Review 90(5), 1160–1183.10.1257/aer.90.5.1160CrossRefGoogle Scholar
Coe, D. T. and Helpman, E. (1995). International R and D spillovers. European Economic Review 39(5), 859–887.10.1016/0014-2921(94)00100-ECrossRefGoogle Scholar
De Long, J. B. and Summers, L. H. (1991). Equipment investment and economic growth. Quarterly Journal of Economics 106(2), 445–502.10.2307/2937944CrossRefGoogle Scholar
De Long, J. B. and Summers, L. H. (1992). Equipment investment and economic growth: how strong is the nexus? Brookings Papers on Economic Activity 1992(2), 157–199.10.2307/2534583CrossRefGoogle Scholar
Dollar, D. and Kraay, A. (2003). Institutions, trade, and growth. Journal of Monetary Economics 50(1), 133–162.10.1016/S0304-3932(02)00206-4CrossRefGoogle Scholar
Durlauf, S., Kourtellos, A. and Minkin, A. (2001). The local Solow growth model. European Economic Review 45(4–6), 928–940.10.1016/S0014-2921(01)00120-9CrossRefGoogle Scholar
Durlauf, S. N. (2003). The Convergence Hypothesis After 10 Years. Working papers 6, Department of Economics, The University of Wisconsin-Madison.Google Scholar
Durlauf, S. N. and Johnson, P. A. (1995). Multiple regimes and cross-country growth behavior. Journal of Applied Econometrics 10(4), 365–384.10.1002/jae.3950100404CrossRefGoogle Scholar
Durlauf, S. N., Johnson, P. A. and Temple, J. R. W. (2005). Growth econometrics. In Durlauf, S. N., Johnson, P. A. and Temple, J. R. W.. (eds.), Handbook of Economic Growth, Vol. 1A, pp. 555–677.Google Scholar
Durlauf, S. N. and Quah, D. T. (1999). The new empirics of economic growth. In Durlauf, S. N. and Quah, D. T.. (eds.), Handbook of Macroeconomics, Vol. 1A. Amsterdam, Elsevier, pp. 235–308.10.1016/S1574-0048(99)01007-1CrossRefGoogle Scholar
Feng, G., Gao, J. T. and Peng, B. (2022). An integrated panel data approach to modeling economic growth. Journal of Econometrics 226(2), 279–299.Google Scholar
Frankel, J. A. and Romer, D. H. (1999). Does trade cause growth? American Economic Review 89(3), 379–399.10.1257/aer.89.3.379CrossRefGoogle Scholar
Gelman, A. and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press.Google Scholar
Henderson, D. J. and Soberon, A. (2024). Chapter 9: Nonparametric models with fixed effects, The Econometrics of Multi-dimensional Panels Theory and Applications. 2nd edn. Springer, pp. 285–324.10.1007/978-3-031-49849-7_9CrossRefGoogle Scholar
Hsiao, C. (2014). Analysis of Panel Data. 3rd edn. Cambridge University Press.10.1017/CBO9781139839327CrossRefGoogle Scholar
Islam, N. (1995). Growth empirics: a panel data approach. The Quarterly Journal of Economics 110(4), 1127–1170.10.2307/2946651CrossRefGoogle Scholar
Jones, C. I. (1994). Economic growth and the relative price of capital. Journal of Monetary Economics 34(3), 359–382.10.1016/0304-3932(94)90024-8CrossRefGoogle Scholar
Kapetanios, G., Pesaran, M. H. and Yamagata, T. (2021). Panel data models with multiple unobserved factors: a survey. Journal of Economic Surveys 35(1), 122–154.Google Scholar
Lam, C. and Yao, Q. (2012). Factor modeling for high-dimensional time series: inference for the number of factors. The Annals of Statistics 40(2), 694–726.10.1214/12-AOS970CrossRefGoogle Scholar
Mankiw, N. G., Romer, D. and Weil, D. N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics 107(2), 407–437.10.2307/2118477CrossRefGoogle Scholar
Martino, R. (2015). Convergence and growth: labour productivity dynamics in the European Union. Journal of Macroeconomics 46, 186–200.10.1016/j.jmacro.2015.09.005CrossRefGoogle Scholar
Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics No. 0435, Faculty of Economics, University of Cambridge.Google Scholar
Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74(4), 967–1012.10.1111/j.1468-0262.2006.00692.xCrossRefGoogle Scholar
Phillips, P. and Sul, D. (2007). Transition modeling and econometric convergence tests. Econometrica 75(6), 1771–1855.10.1111/j.1468-0262.2007.00811.xCrossRefGoogle Scholar
Phillips, P. and Sul, D. (2009). Economic transition and growth. Journal of Applied Econometrics 24(7), 1153–1185.10.1002/jae.1080CrossRefGoogle Scholar
Rodrik, D. (2004). Institutions and Economic Performance - Getting Institutions Right, CESifo DICE Report, Vol. 02, Iss. 2. pp. 10–15. München, ifo Institut für Wirtschaftsforschung an der Universität München. ISSN 1613-6373.Google Scholar
Rodrik, D. (2013). Unconditional convergence in manufacturing. Quarterly Journal of Economics 128(1), 165–204.10.1093/qje/qjs047CrossRefGoogle Scholar
Sala-I-Martin, X., Doppelhofer, G. and Miller, R. I. (2004). Determinants of long-term growth: a Bayesian averaging of classical estimates (BACE) approach. American Economic Review 94(4), 813–835.10.1257/0002828042002570CrossRefGoogle Scholar
Salimans, T. (2012). Variable selection and functional form uncertainty in cross-country growth regressions. Journal of Econometrics 171(2), 267–280.10.1016/j.jeconom.2012.06.007CrossRefGoogle Scholar
Sondermann, D. (2014). Productivity in the Euro area: any evidence of convergence? Empirical Economics 47(3), 999–1027.10.1007/s00181-013-0762-xCrossRefGoogle Scholar
Soto, M. (2003). Taxing capital flows: an empirical comparative analysis. Journal of Development Economics 72(1), 203–221.10.1016/S0304-3878(03)00074-9CrossRefGoogle Scholar
Sun, Y., Lin, W. and Li, Q. (2024). Chapter 8: Nonparametric models with random effects, The Econometrics of Multi-dimensional Panels Theory and Applications. 2nd edn. Springer, pp. 239–284.Google Scholar