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Diversity in innovation and productivity in Europe

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Abstract

The diversity in innovation patterns across manufacturing and service industries and in their outcomes in terms of hourly labor productivity are investigated in this article considering six European countries. The Schumpeterian insights into the variety of innovation are developed in this work by identifying different innovation–performance relationships for industries and countries, relying either on the dominant role of product innovation, or on the diffusion of process improvements. Moreover, the “push” effect of innovation is combined with the “pull” effect of demand, by considering the impact of the dynamics of consumption and investment at the sectoral level. The results point out a “North-South” divide across EU countries, with the three countries of Northern Europe closely associated to the model of productivity growth based on product innovation, and the three Southern countries, mainly relying on the mechanisms by which process innovation is at the root of productivity improvements.

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Notes

  1. In the third EU Community Innovation Survey, for the years 1998–2000, 41% of all EU firms were successful innovators, of which 23% were both product and process innovators, 10% innovated only in products and 7% innovated only in processes (European Commission-Eurostat 2004:18).

  2. In order to estimate labor productivity, value added per number of hours worked is a more accurate indicator than value added per employee, especially in countries where labor market flexibilization has led to a large growth of the number of part time jobs.

  3. The use of consumption and gross investment is a major improvement over the use of value added as an (ex post) proxy for the evolution of aggregate demand; other final demand components (exports and public expenditure) have been examined and excluded from the analysis as they concentrate their “pull” effects on productivity on a highly limited number of sectors, mainly in manufacturing. Moreover, export success is generally associated with high productivity performance, and therefore a two-way causality relationship may operate.

  4. When this model is applied to a subset of industries, gross investment is likely to lose its relevance, as the “pull” effect on productivity applies to a limited number of sectors producing investment goods.

  5. The diversity of sectoral systems of innovation is expected to lead to differentiated effect of innovative efforts on productivity across industries; it is therefore important to test the model by removing non-observed individual fixed effects using a full set of sectoral dummies. Since our database includes both sectoral and country data, the use of sectoral dummies is allowed.

  6. From a large literature, see the approaches and the country analyses presented in Lundvall (1992) and Nelson (1993).

  7. We have also relied on the study by Amable (2005), who has combined innovation, economic and institutional aspects and has identified five major models of capitalism. In his ranking of countries from “liberal market capitalism” to “Mediterranean capitalism,” the six countries we consider are ordered in the same way as in our two groups.

  8. The overall significance of the sets of country and sectoral dummies has been confirmed by F-tests on restrictions which lead us to reject the null hypothesis of dummy coefficients equal to zero in each specification of the three different models. A parallel test of the same model on all industries has been carried out without industry dummies and introducing dummies for countries, the manufacturing/service dichotomy, and the product or process orientation of industries. All four variables become positive and significant, with the effects of consumption higher than that of investment; the manufacturing/service dichotomy is not significant; the product/process distinction is significant, and country dummies are relevant. The results of Table 1 confirm the findings of Crespi and Pianta (2008) obtained using a definition of productivity as value added per employee.

  9. We show here the tests of models where industry dummies have not been included, as they would greatly reduce the degrees of freedom of the estimated equations. However, the results obtained by including the full set of industry dummies are very close to those reported in Table 2.

  10. In fact, France and the UK appear as borderline cases in this distinction. In order to check the stability of results, we have tested the model with country groups where France and the UK switch places, and the results show a loss of significance of the innovation variables. Ideally, the test should be run on individual countries in order to assess the relevance of the two models, but the lack of numerosity limits our exercises to two groups of three countries. When innovation data from CIS3 and CIS4 will become available, tests on individual countries will be carried out.

  11. Archibugi and Pianta (1992) have provided an investigation of the specialization of EU countries in technological activities, using patent and bibliometric indicators, exploring the impact on performance. A large literature has investigated the link between specialization and performance, with little attention, however, to the specificity of innovative efforts (see Laursen 2000; Meliciani 2001). A historical reconstruction of the role of technological competitiveness and cost competitiveness strategies in Italian growth in the 1950s and 1960s is in Gomellini and Pianta (2007).

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Acknowledgements

Research for this article was partly funded by the Italian Ministry of Research FIRB project RISC, “Ricerca e imprenditorialità nella società della conoscenza: effetti sulla competitività dell’Italia in Europa” (RBNE039XKA). We thank an anonymous referee for his comments.

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Correspondence to Francesco Crespi.

Appendix

Appendix

The database used for addressing the determinants of productivity growth merges different sources of data. First, data on hourly productivity are drawn from the 60-Industry Database developed by the Groningen Growth and Development Centre. Second, innovation data are from the SIEPI dataset developed at the University of Urbino, based on data from the second Community Innovation Survey (CIS2). Third, data for the variables on demand components are calculated combining STAN data with the Eurostat Input–Output tables. For each industry, the key components of demand have been calculated from Input–Output tables and the structure and dynamics of demand (addressed either to domestic production or to imports) have been obtained. A detailed description of the methodology we followed to decompose the total demand of industries in its components is reported in Crespi and Pianta (2007).

Data cover 22 manufacturing sectors and 10 service sectors—Nace Rev.1 subsections—for 6 European countries—Germany, France, Italy, the Netherlands, Portugal and the UK.

We have used data on the inputs and outputs of firms’ innovative activities, and on the importance of different strategies related to innovation. The list of the variables considered is the following:

  • the compound average annual rate of change of value added per hours worked (1996–2001), used as an indicator of hourly labor productivity;

  • the total innovation expenditures per employee (1994–1996), used as an indicator of the overall efforts devoted to innovative activity in European industries;

  • the expenditure per employee due to acquisition of machinery and equipment linked to innovations (1994–1996), used as an indicator of innovative efforts relying on the introduction of new machinery, based on capital deepening and process innovation;

  • the percentage patent applicants, calculated as the share of firms that have applied for a patent in 1994–1996, an indicator of the inventive success of an industry, and a proxy for product innovation;

  • the share of firms aiming at improving production flexibility (1994–1996);

  • the share of firms aiming at increasing the quality of their products (1994–1996);

  • the annual rate of change of real household consumptions (1995–2000);

  • the annual rate of change of investments (1995–2000);

All the monetary data used for the analysis have been deflated with sectoral deflators (elaborated from the OECD STAN database). Nominal figures have been transformed in constant values with base year 1995.

We follow previous studies (Crespi and Pianta 2008; Evangelista and Mastrostefano 2004) that have identified a strong heterogeneity—both in terms of innovation indicators and of productivity performances—between the group of industries oriented towards product innovation, and the group where process innovation dominates. Therefore, both manufacturing and service industries have been split into two subsets (of roughly equal size) in order to carry out more specific tests. The two groups have been identified by looking at the different abilities for the introduction of product or process innovations in each manufacturing or service industry as it emerges from the empirical literature, and from the analysis of the results of the Community Innovation Surveys, in particular those related to the percentage of firms that have introduced product or process innovations (see Crespi and Pianta 2008 for the details).

The sectors identified as product innovation oriented industries are the following: office, accounting and computing machinery; chemicals and chemical products; medical, precision and optical instruments; machinery and equipment; radio, television and communication equipment; electrical machinery and apparatus; rubber and plastics products; coke, refined petroleum products and nuclear fuel; motor vehicles, trailers and semi-trailers; manufacturing n.e.c.; other transport equipment; computer and related activities; insurance and pension funding; research and development; financial intermediation; renting of machinery and equipment.

The sectors identified as process innovation oriented industries are the followings: food products and beverages; basic metals; pulp, paper and paper products; printing and publishing; fabricated metal products; recycling; other non-metallic mineral products; wood and products of wood and cork; textiles leather, leather products and footwear; wearing apparel; dressing and dying of fur; activities related to financial intermediation; other business activities; hotels and restaurants; real estate activities; post and telecommunications.

Finally, the countries considered in our analysis have been grouped according to the characteristics of the national innovation systems, in particular in terms of the propensity to introduce product or process innovation. Countries have been divided in two groups: “Northern EU” countries (Germany, the UK and the Netherlands) and “Southern EU” countries (France, Italy and Portugal). Table 3 shows data from the Third Community Innovation Survey referring to the period 1998–2000 for the six EU countries and for the average of the identified groups.

Table 3 Innovation in six European countries (manufacturing and services)

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Crespi, F., Pianta, M. Diversity in innovation and productivity in Europe. J Evol Econ 18, 529–545 (2008). https://doi.org/10.1007/s00191-008-0101-0

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