Heterogeneity in the productivity of French construction firms: a multilevel analysis

Abdoulaye Kané and Nadine Levratto ()
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Abdoulaye Kané: CERNA i3 - Centre d'économie industrielle i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique
Nadine Levratto: CERNA i3 - Centre d'économie industrielle i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique

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Abstract: Abstract This paper examines the determinants of productivity heterogeneity among construction firms, integrating both firm-level and local contextual factors. Using a longitudinal dataset of 78,598 firms over 2009–2019, we estimate total factor productivity (TFP) and use a multilevel model across 287 labour market areas in metropolitan France. This approach allows us to separate firm-specific from location-specific effects and explains how the productivity heterogeneity can be attributed to each level. Our results show that firm characteristics significantly affect TFP. Local conditions also play a key role: higher unemployment reduces productivity, whereas higher employment density and median income enhance it. This study contributes by applying a multilevel framework to the construction sector, using longitudinal data for precise estimation, and employing TFP rather than traditional labour productivity as the performance measure. Our findings highlight the joint influence of firm and regional factors on productivity, providing insights for policymakers and managers aiming to improve firm performance and regional economic outcomes in the construction sector. Our results remain robust across different firm size categories, alternative TFP measures, French construction sector sub-sectors and Mundlak's (1978) approach to correcting for heterogeneity bias.

Date: 2026-02-05
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Published in Annals of Regional Science, 2026, 75 (1), pp.24. ⟨10.1007/s00168-026-01457-y⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05568821

DOI: 10.1007/s00168-026-01457-y

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