Online Appendix. The EFIGE dataset, our main source of firm-level data, is built, maintained, and provided by

Transcription

1 Appendices Online Appendix Appendix A Data cleaning and sample description The EFIGE dataset, our main source of firm-level data, is built, maintained, and provided by Bruegel. It combines survey-based information with balance-sheet data drawn from Bureau Van Dijk s Amadeus database for a representative sample of European manufacturing firms. 1 Survey information is gathered on some 15,000 firms operating in Austria, France, Germany, Hungary, Italy, Spain, and the United Kingdom in For a detailed description of the dataset, see Altomonte and Aquilante (2012). We focus on Italy and Spain for two main reasons. These countries are particularly interesting in that their economies are more strongly manufacturing-based and are characterized by industrial districts, or spatially bounded clusters of productive firms. This offers a promising scenario for international analysis in general and also allows a sufficiently robust comparison between two countries in which the agglomeration of production and industrial districts remains relevant. In addition, from a practical perspective, Italy and Spain also provide the largest number of valid and usable data for the purposes of our empirical analysis. On these premises, we first cleaned the EFIGE dataset, restricting our sample to active manufacturing firms operating in Italy and Spain. This sub-sample was further restricted by removing firms for which the data on GVC positioning were incomplete. Because the survey does not provide specific data on GVC, firm positioning along GVCs is determined using the information on sales of produced-to-order goods an approach taken, among others, by Veugelers et al. (2013), Agostino et al. (2016), and Accetturo and Giunta (2016). In fact, the data on producedto-order goods are the best instrument available in the EFIGE dataset to capture the high-targeted, vertical market relationships between suppliers and buyers within GVCs. The survey specifically asks firms to indicate the average percentage of their sales accounted for by produced-to-order 1 For some recent contributions using the EFIGE dataset, see Accetturo and Giunta (2016), Aiello and Ricotta (2016), Pinna et al. (2016), Maietta et al. (2017), and Materia et al. (2017). 1

2 goods and the primary buyer of these goods. We classify firms as suppliers if their produced-toorder sales go exclusively to other firms and as final firms if they go to end markets. We then combined the data on year of incorporation, value added, and employment for the period that was available in EFIGE with updated data drawn from Amadeus. This allows us to fill data gaps where possible and double-check the data consistency. The Bureau Van Dijk identification number available in EFIGE was used to match the two data sources. The resulting sample was further cleaned by removing firms that lacked information at the NUTS-3 geographical level. For Italian firms, this information was obtained from Amadeus, while for Spanish firms, it was already available in EFIGE. The cleaning procedure left us with an unbalanced panel of 2,235 Italian and 1,790 Spanish firms reporting strictly positive figures for value added and employment for at least three consecutive years between 2010 and Table A1 shows the country distribution of firms in the EFIGE dataset and in the cleaned sample, which was comprised of 69.79% of all the firms (Italian and Spanish) in the EFIGE dataset. Italian firms are slightly over-represented, while Spanish firms are slightly under-represented. As Table A2 shows, the number of observations for both the Italian and Spanish sub-samples peaks in Table A3 reports the sample distribution by two-digit sector and country. All the manufacturing sectors are covered, except for NACE Rev. 2 Sectors 12 (Manufacture of tobacco products) and 19 (Manufacture of coke and refined petroleum products). Geographically, the sample covers firms in 99 of Italy s 110 provinces and 47 of Spain s 59 provinces. The missing Italian provinces are Imperia, Fermo, Barletta-Andria-Trani, Isernia, Reggio di Calabria, Vibo Valentia, Carbonia-Iglesias, Medio Campidano, Ogliastra, Olbia-Tempio, and Oristano; for Spain, we lack firms in the Balearic Islands, the Canary Islands, and the extraterritorial autonomous cities of Ceuta and Melilla. Finally, Table A4 shows that supplier firms make up 78.86% of the total sample and the majority of firms in both countries: 86.71% in Italy and 69.05% in Spain. 2

3 Table A1: Sample representativeness by country. Italy Spain Italy and Spain a. v. % a. v. % a. v. % EFIGE Dataset 2, , , Sample 2, , , Notes: Percentage values are defined based on row totals. 3

4 Table A2: Sample distribution by year of observation and country. Year Number of Observations Italy and Spain Italy Spain ,167 1,460 1, ,957 2,206 1, ,025 2,235 1, ,939 2,207 1, ,669 2,074 1,595 Total 18,757 10,182 8,575 4

5 Table A3: Sample distribution by industry and country. NACE Rev. 2 Italy and Spain Italy Spain a. v. % a. v. % a. v. % Total 4, , , Notes: Percentage values are defined based on column totals. 5

6 Table A4: Sample distribution by GVC positioning and country. Value Chain Position Italy and Spain Italy Spain a. v. % a. v. % a. v. % Final Firms Suppliers 3, , , Whole Sample 4, , , Notes: Percentage values are defined based on column totals. 6

7 References in Appendix A Accetturo, A., Giunta, A., Value chains and the Great Recession: Evidence from Italian and German firms. Questioni di Economia e Finanza, No. 304, Bank of Italy, Rome. Agostino, M., Giunta, A., Scalera, D., Trivieri, F., Italian firms in global value chains: Updating our knowledge, in: Ottaviano, G., Manzocchi, S. (Eds.), Global Value Chains, Trade Networks and Firm Performance: International Evidence and the Italian Case. Rivista di Politica Economica, VII-IX/2016, pp Aiello, F., Ricotta, F., Firm heterogeneity in productivity across Europe: Evidence from multilevel models. Economics of Innovation and New Technology 25, Altomonte, C., Aquilante, T., The EU-EFIGE/Bruegel-UniCredit dataset. Bruegel Working Paper 2012/13, Bruegel, Brussels. Maietta, O.W., Barra, C., Zotti, R., Innovation and university-firm R&D collaboration in the European food and drink industry. J. Agr. Econ. 68, Materia, V.C., Pascucci, S., Dries, L., Are in-house and outsourcing innovation strategies correlated? Evidence from the European agri-food sector. J. Agr. Econ. 68, Pinna, A.M., Brau, R., Licio, V.M., Broadening or jumping? An analysis of the first export market of European Union firms. Environ. Plann. C. Gov. Policy 34, Veugelers, R., Barbiero F., Blanga-Gubbay, M., Meeting the manufacturing firms involved in GVCs, in: Veugelers, R. (Ed.), Manufacturing Europe s Future, Bruegel Blueprint Series, XXI, pp

8 Appendix B Descriptive statistics Table B1: Descriptive statistics for dependent and explanatory variables. Mean Std. Dev. Min. Max. overall between within overall between within overall between within overall between within

9 Table B2: Correlation matrix for explanatory variables. [1] [2] [3] [1] 1 [2] [3]