Local variety and innovation performance in the EU textile and clothing industry

ABSTRACT

A large body of the literature showed that related variety at local level is more relevant than unrelated variety for explaining the innovation performance of firms. Knowledge relatedness is usually measured by considering activities within the same industry (i.e. the same two-digit code) while activities in different industries are associated with unrelated variety. This approach is challenged by the increasing relevance of transversal technologies, i.e. technologies that are developed and applied in rather different sectors. As a result, between industry variety (i.e. unrelated variety) is expected to be more important than within industry variety (i.e. related variety). We test this hypothesis by examining the innovation activities of firms in the textile and clothing industry. The innovation model of these firms is characterized by low investment in R&D, little capabilities for autonomous innovation and dependence from knowledge suppliers belonging to different sectors. The empirical analysis, carried out over the 1996–2014 period at the EU NUTS2 level, shows that between industry variety has a greater impact than within industry variety for the innovative performance of firms.

KEYWORDS:

• technological variety
• innovation performance
• patenting
• textile and clothing industry

JEL CLASSIFICATION:

• O3
• R11
• L67

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Valentina Giannini http://orcid.org/0000-0002-7027-247X

Donato Iacobucci http://orcid.org/0000-0001-8463-1106

Francesco Perugini http://orcid.org/0000-0001-7102-9829

Notes

1 In this paper, TCI is defined as those industries classified under Section C, Division 13 ‘manufacture of textiles’ and 14 ‘manufacture of wearing apparel’ on the NACE REV.2 Statistical Classification of Economic Activities in the EU.

2 In the rest of the paper, the term ‘industry’ and ‘sector’ are used interchangeably.

3 The term NACE derived from the French Nomenclature statistique des activités économiques dans la Communauté européenne is the statistical classification of economic activities in the European Community. IPC, which stand for International Patent Classification, is a patent classification system administered by the World Intellectual Property Organization (WIPO) to classify the content of patents.

4 KETs are the following: micro and nanoelectronics, nanotechnology, industrial biotechnology, advanced materials, photonics and advanced manufacturing technologies (European Commission 2011).

5 Chesbrough (2003) sees the open innovation model has ‘the use of purposive inflows and outflows of knowledge to accelerate internal innovation and expand the markets for external use of innovation, respectively’.

6 The latest edition of the IPC contains 8 sections, about 120 classes (which symbols consist of the section symbol followed by a two-digit number), about 630 subclasses (which symbols consist of the class symbol followed by a capital letter), and approximately 69,000 groups (group symbols consist of the subclass symbol followed by a one- to three-digit number) (IPC, 1999).

7 The entropy index does not have an upper bound. Its maximum level depends on n: $H_{max}={\sum }_{i=1}^n\left(1/n\right){\log }_2\left(n\right)=n\left(1/n\right){\log }_2\left(n\right)={\log }_2\left(n\right).$ Given that we consider 27 two-digit IPC technological classes, the maximum value of the index is 4.75.

8 The values of the indicator range from 0 (patents in each two-digit IPC technological class is concentrated in only one of its four-digit IPC technological subclass) to $\mathrm{l}\mathrm{o}{\mathrm{g}}_2\left(I\right)-\mathrm{l}\mathrm{o}{\mathrm{g}}_2\left(n\right)$, when all four-digit IPC technological subclass within a two-digit IPC technological class have an equal patent share. Since our empirical analysis is based on 633 four-digit IPC technological subclass $\left(I\right)$ within 27 two-digit IPC technological class $\left(n\right)$ the theoretical upper bound of the indicator is 4.55.

9 The PATSTAT database is prepared by the European Patent Office (EPO) on behalf of the OECD Taskforce on Patent Statistics. It covers patent applications in more than 80 countries. The data include: (i) applicants’ and inventors’ names and addresses; (ii) title and abstract of patent applications; (iii) priority, patent families and PCT links; (iv) bibliographical information (citation links); (v) classification of patents by technology class.

10 In the ORBIS database, it is possible to distinguish firms classified according to primary NACE codes (which represent the NACE activity a given firm gains the most revenue from) and secondary NACE codes (which represent the other NACE activities of a given firm). We selected only firms classified under the primary NACE REV.2 13 or 14 sector of economic activities, i.e. firms with their primary activities in the textile or clothing sectors.

11 According to the NACE REV.2 Statistical Classification of Economic Activities in the EU, the NACE categories are divided into Section (for instance C, ‘Manufacturing’), Division (for instance 13, ‘Manufacture of textiles’), Group (for instance 13.9, ‘Manufacture of other textiles’) and Classes (for instance 13.96, ‘Manufacture of other technical and industrial textiles’).

12 The latest edition of the IPC contains 8 sections, about 120 classes (which symbols consist of the section symbol followed by a two-digit number), about 630 subclasses (which symbols consist of the class symbol followed by a capital letter), and approximately 69,000 groups (group symbols consist of the subclass symbol followed by a one- to three-digit number) (IPC, 1999).

13 We did not average across years as data are not available for all regions.

14 To compute the UV_NACE, we average employment data for the 2008–2010 period. Data for previous year are not available from the European Structural Statistics database.

15 We also estimate the model with country dummies. Results, which are available from the authors upon request, confirm the significance of unrelated variety in determining the innovation intensity of regions.

16 More precisely, we exclude regions that belong to country with a number of patent below 2.75 per thousand of person employed. These are Bulgaria, Estonia, Greece, Croatia, Hungary, Lithuania, Latvia, Poland, Portugal, Romania and Slovakia. As a result, the number of observation reduced to 195.