Is infrastructure capital really productive? Nonparametric modeling and data-driven model selection in a cross-sectionally dependent panel framework

Antonio Musolesi, Giada Andrea Prete and Michel Simioni ()
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Antonio Musolesi: UniFE - Università degli Studi di Ferrara = University of Ferrara
Giada Andrea Prete: UniFE - Università degli Studi di Ferrara = University of Ferrara
Michel Simioni: UMR MoISA - Montpellier Interdisciplinary center on Sustainable Agri-food systems (Social and nutritional sciences) - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - IRD - Institut de Recherche pour le Développement - CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement, TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: This paper examines the contribution of infrastructure to aggregate productivity. We address some complex and relevant issues, namely functional form, nonstationary variables and cross-sectional dependence. We adopt the CCE framework and consider both parametric and nonparametric specifications, thus allowing for different degrees of flexibility. We also employ a data-driven model selection procedure based on moving block bootstrap to choose among alternative specifications. It is found that nonparametric specifications provide the best predictive performance and that CCE models always overperform with respect to traditional panel data methods. Furthermore, we find a lack of significance of the infrastructure index, with an estimated elasticity very close to zero for all estimates.

Date: 2025-09-19
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Published in Journal of Productivity Analysis, 2025, ⟨10.1007/s11123-025-00779-x⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05281582

DOI: 10.1007/s11123-025-00779-x

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