Total Factor Productivity in Italian Manufacturing: Does Location Matter?

In 2013 Hassan and Ottaviano defined Italy "the sleeping beauty of Europe -- a country rich in talent and history, but suffering from a long-lasting stagnation". Stagnation, in turn, was linked to the sluggish, or falling, productivity of Italian firms which began in the mid-90s, carried on well into the Two Thousands, fell further during the Great Recession, recovered slowly in 2013-19 only to fall sharply during the pandemic. The loss of productivity in Italian manufacturing contrasts sharply with the '70s and '80s, when if largely outpaced that of other countries, Germany in the first place. While there is general consensus on the main determinants of Italian firms' sluggish performance -- failure to adopt the ITC revolution, rigidities -- less attention has been devoted to the spatial aspects of the problem. In fact, although the disparity between an efficient North and a lagging South, with the Centre lying somewhere in-between, is largely acknowledged, not enough studies address the problem from a spatial point of view. This paper aims to enrich these studies. Starting from firm-level data retrieved from Bureau van Dijck's AIDA database, it estimates total factor productivity (henceforth TFP) for over 190,000 Italian manufacturing firms during 2008-20. TFP is estimated with reference to the method suggested by Olley and Pakes (1996) that explicitly addresses firms' entry, or exit, decisions, therefore accounting for a possible selection bias. The estimated TFP is then aggregated with reference to a relatively fine territorial breakdown, that of Italian (NUTS-3) provinces, and to the ATECO manufacturing breakdown. Estimates are used to analyse spatial interdependence, spillovers and to investigate the presence of clusters among administrative units and/or manufacturing sectors. The analysis is directed at assessing whether provinces' TFP response to the Great Recession and to the pandemic differs significantly across units, if it is possible to trace common patterns of adjustment moving towards TFP convergence, which elements determine them and what is the role, if any, of geographic location. The presence of clusters is identified with reference to the dynamic, nonlinear factor model developed by Philips and Sul (2007, 2009) that allows to identify groupings endogenously, rather than super-imposing clusters from above. Finally, the relative strength of sectoral specialization against that of geographic location is tested with respect to the creation of clusters.