investments by foreign companies. Third, from a practical viewpoint, data are available on these three dimensions at the NUTS-3 level, but data are missing on some other dimensions. European official statistical sources provide information on a large set of variables for NUTS-1 and NUTS-2 regions, but only a reduced amount of information is available at the NUTS-3 level.

**Unfortunately, it was not possible to include data series on firm size.** We would like to estimate the role of micro (fewer than 10 employees), small (10 to 49 employees), medium (50 to 249 employees), and large (250 employees or more) firms in regional economic growth. Such analysis could help reveal the relative importance of initiatives on SMEs in lagging regions, versus initiatives to boost competitiveness of large firms as drivers of growth. Unfortunately, these data series – available from the _Regio_ database provided by Eurostat – present two key shortcomings. First, regional data are based on a too broad classification of firms with respect to their size, as data are available for zero-employment firms, firms with 1 to 9 employees, and firms with 10 or more employees. This represents a key problem, as small, medium, and large firms are grouped together in a unique size class, such that it is impossible to capture the role of SMEs versus large firms. Second, business demography data that are available only from the year 2008 – while our analysis starts in the year 2003 – do not cover nine EU Member States, and present severe missing values or inconsistent estimates over time for the remaining countries.<sup>12</sup> Since the lack of availability is not systematic, we would risk bias in the analysis if we included only the available data.

**We use data for the 2003 to 2017 period, from Eurostat, the Organisation for Economic Co-operation and Development (OECD), and** **_fDi Markets_ (Financial Times).** Given our empirical goal, and considering data availability constraints, we have collected the largest possible set of data at the NUTS-3 level from three main sources to cover the longest possible time period, namely from 2003 to 2017. On economic growth and industrial structure, we use Eurostat data from the _Regio_ database on GDP, population, employment, land area, and sectoral Gross Value Added (GVA) – for agriculture, industry (mining; electricity; manufacturing), construction, market services,<sup>13</sup> and non-market services.<sup>14</sup> Missing values in the NUTS-3 level regional series have been filled in by linearly interpolating NUTS-2 level data. On innovation, we use microdata on patents filed under the Patent Co-operation Treaty (PCT) from the _REGPAT_ database provided by the OECD, aggregated at the NUTS-3 level by priority year and inventor’s residence using the fractional count criterion. On FDI, we use data on inward ‘greenfield’ FDI from the _fDi Markets_ database provided by the Financial Times. Of particular interest for our purposes, the _fDi Markets_ database collects information on individual investment projects in terms of year, destination region at the NUTS-3 (or city) level, and business activity run in the host economy.<sup>15</sup>

**The merging and cleaning procedure of the above-mentioned data series left us with a sample of 1,321 NUTS-3 regions, which represents 98.5% of the EU-28 territory.** We also excluded _a priori_ the French Overseas Departments, as well as Ceuta and Melilla. As shown in Table

> 12 First, there are no data at all for Belgium, Cyprus, Germany, Greece, Ireland, Luxembourg, Malta, Sweden, and the United Kingdom. Second, a large number of countries present severe missing values or inconsistent estimates. A first group of countries – namely, France, Hungary, Italy, and Portugal – are covered with inconsistent regional data according to the changes occurred over time in the NUTS classification. A second group of countries is covered only for some years: Spain is covered entirely, but for 2015; Croatia and Latvia are covered only from 2011; Denmark is covered only up to 2013; Slovenia and the Netherlands are covered only up to 2010; Czech Republic has data for the year 2010, and then from 2013 onwards; Estonia has data for the period 2008-2010, and then from 2015 onwards; Poland has data for the period 2008-2010, and then for the year 2016. Therefore, a complete a reliable data series from 2008 to 2016 is available only for Austria, Bulgaria, Finland, Romania, and Slovak Republic. This lack of data would greatly reduce the number of regions and countries on which estimates for all variables could be made – thereby diminishing the accuracy of all results.

> 13 Wholesale and retail trade; transport; accommodation and food service activities; information and communication; financial and insurance activities; real estate activities; professional, scientific and technical activities; administrative and support service activities. 14 Public administration and defence; compulsory social security; education; human health and social work activities; arts, entertainment and recreation; other service activities; activities of household and extra-territorial organisations and bodies.

> 15 It is worth noting that the _fDi Markets_ database represents the best available source to analyze FDI-related phenomena at the subnational level over a long time period. It covers ‘greenfield’ FDI, that includes new investments and enlargements of existing investments, but excludes ‘brownfield’ investments such as mergers and acquisitions. Despite the limitation of not covering FDI in the form of mergers and acquisitions, its overall validity and reliability is confirmed by the many empirical studies that have used it (e.g. Crescenzi et al., 2014; Dogaru et al., 2015; Castellani and Pieri, 2016).

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