apprenticeships, business advisory, innovation, investment promotion, etc.) have been undertaken, but the results have differed greatly across evaluations, probably indicating that the quality of design and implementation can be as important as the category of intervention.<sup>10</sup> So local leaders in lagging regions cannot access a good quality reference base on ‘what works’ in regions such as theirs.

**We contribute to this debate by analyzing the growth pathways of EU regions at the NUTS-3 level in the period 2003-2017.** We focus on correlates of economic growth in lagging regions, such as industrial structure, innovation, and inward foreign direct investments (FDI). We rely on a regional taxonomy based on income level and long-run growth criteria, which combines the standard classification adopted under Cohesion Policy with that proposed in 2015 by the European Commission under the ‘Catching Up Initiative’. Our analysis is descriptive in its nature, but contributes to the understanding of the determinants of economic growth in lagging regions, as a prelude to investigating the causal relationships from policy and investments to growth. The aim of the paper is to provide evidence on what are realistic aspirations for economic growth in different types of regions. The paper aims to give an answer to questions from national and sub-national policy makers such as: ‘What has worked in regions like mine?’; ‘How have other regions grown?’; and ‘What is realistic for my region?’

**The choice of the NUTS-3 level as unit of analysis is made on the basis that a finer spatial scale can provide a more detailed picture of growth pathways than the NUTS-2 level.** There are 1,348 NUTS-3 regions in the EU, compared to 281 NUTS-2 regions, thus giving a higher number of observations for study, and more variation in their economic structure and performance. Some regions have been more successful than others, and we can we learn from the growth patterns that have characterized different types of regions. We focus especially on low-income and low-growth regions, as the subset of regions which are the highest priorities for Cohesion Policy.

**We structure the analysis according to three research questions: on location, economic dynamism, and correlates of growth.** Our research questions are the following: (i) where are NUTS3 level lagging regions located, and how similar are these locations to NUTS-2 lagging regions? (ii) have NUTS-3 lagging regions been growing and converging with non-lagging regions? (iii) what are the correlates of growth among NUTS-3 regions, and do these correlates differ for lagging regions?

The rest of the paper is organized as follows. Section 2 provides an overview of the empirical framework by describing the data set employed, discussing the taxonomy adopted to classify regions, and presenting the empirical modeling. Section 3 presents some stylized facts on the economic geography and dynamism of regions in the EU. Section 4 presents the empirical results. Section 5 concludes the work by discussing the main results and drawing some policy implications.

# **2. EMPIRICAL FRAMEWORK**

# **2.1. Data Set**

**We use data on NUTS-3 regions, with special focus on industrial structure, innovation, and inward FDI.** According to the 2016 NUTS classification, there are 1,348 NUTS-3 regions in the EU.<sup>11</sup> Specifically, we aim to analyze the drivers of regional economic growth – defined as yearly growth of GDP per capita – by focusing on three key economic dimensions: industrial structure, innovation, and inward FDI, while controlling also for population dynamics and agglomeration forces. The choice of these three economic dimensions is for three main reasons. First, the empirical literature has identified industrial structure, innovation, and inward FDI as key growth-engine factors (e.g. Crescenzi, 2005; Menghinello et al., 2010; Crescenzi et al., 2016). Second, these dimensions are influenced by policy makers, who could have the capacity to intervene with _ad hoc_ measures in order to stimulate a particular industrial sector, promote the innovation capability of firms, or attract

> 10 One of the best attempts at a review of robust analytic evidence has been done by the What Works Centre for Local Economic Growth (“https://whatworksgrowth.org/”).

> 11 This number includes the five French Overseas Departments, and the Spanish extra-territorial autonomous cities of Ceuta and Melilla.

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