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The Pavitt Taxonomy, revisited: patterns of innovation in manufacturing and services

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Abstract

In this article we discuss how to summarize the persistent and large heterogeneity in innovative behaviour and economic performance. A revision of the Pavitt (1984) Taxonomy—covering manufacturing and services, as well as ICT activities—is proposed as a key tool for identifying common characteristics and diversities in patterns. An extensive analysis of innovation survey data, on sources, objectives, inputs and outcomes of innovation, allows us to test alternative industry groupings, leading to an extensive assessment of a Revised Pavitt Taxonomy that is able to capture major structural differences in the relationship between innovation and performance. As industries’ classifications has changed from NACE Rev. 1 to NACE Rev. 2 in 2008, a revised Pavitt Taxonomy is also provided for the new industries.

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Notes

  1. This paper is based on the IPTS project “The impact of R&D and innovation on economic performance and employment: a quantitative analysis based on innovation survey data” (J03/32/2007) by Mario Pianta. We thank Matteo Lucchese, Giulio Perani, Valeria Cirillo, Rinaldo Evangelista, Faiz Gallouj, Xabier Goenaga, Hector Hernandez, Alfred Kleinknecht, Raquel Ortega-Argilés, Leopoldo Nascia, Maria Savona, and Marco Vivarelli for their comments. We acknowledge the comments and suggestions of two anonymous referees. All remaining errors are responsibility of the authors.

  2. Although analytically elegant, this approach is lacking empirical grounds; it assumes that all firms are forced to use the best available information, or face exit; empirically, however, this selection process appears to be ineffective (Bottazzi et al. 2010).

  3. CIS are surveys representative of the universe of firms in a country. They are based on the Oslo Manual (OECD 2005). Surveys have been carried out in a fully comparable way through the European Union. The second, third and fourth waves used in the present work were carried out every 4 years (1996, 2000, 2004), each one covering also the two preceding years. For a discussion, see Eurostat (2008). Innovation surveys allow researchers to overcome many of the limitations of traditional innovative indicators, such as patents (Archibugi and Pianta 1996a, b), normally too biased towards science based sectors, but are subject to the critique of being ‘subjective’ surveys (Smith 2005). An assessment of innovation surveys carried out in emerging countries is in Bogliacino et al. (2012).

  4. Innovation survey variables are either expressed as average intensity of particular activities (e.g. average R&D expenditure per employee within firms of a sector) or are measured as share of firms with a particular effort (e.g. share of firms within an industry carrying out product innovation) highlighting the diffusion of particular activities.

  5. One of the critiques to the use of industry level data is that firms with similar technologies may potentially end up in different industries because of product characteristics (Archibugi 2001). However, this potential error (rather limited) is likely to be corrected by the subsequent mapping of sectors into Pavitt groupings. In fact, such sectors are likely to end up in the same Pavitt class.

  6. On trade see Dosi et al. (1990) and Padoan (1998). On property rights see Dosi et al. (2006). Maggioni et al. (2011) applied Pavitt classes to patenting activity of Italian NUTS3 regions in order to investigate knowledge transfers and spillovers.

  7. Heidenreich (2009) explored innovation in low and medium technology industries (CIS wave four); Castellacci (2008, 2009) explored industry patterns and cross country variability in systems of innovation (CIS wave two). Resmini (2000) explored the determinants of FDIs in the European Union according to different Pavitt groupings; Freel (2003) studied the effects of collaboration on the innovativeness of SMEs located in Northern England and Scotland; Smith et al. (2002) addressed the R&D performance in a sample of Danish firms.

  8. See Pavitt et al. (1989) for the United Kingdom; Marsili and Verspagen (2002) for the Netherlands using data for manufacturing from the Business Register and CIS; Dutrenit and Capdevielle (1993) for Mexico using industrial census data.

  9. A detailed analysis of this large literature is beyond the scope of this article; notable examples include the following: Lall (2000) maps industries according to the technological content of export; Guerzoni (2010) according to the nature of the demand pull motive to innovate; Castellacci (2008) according to innovation and the position in the vertical chain; O'Mahony and Vecchi (2009) according to factor and skill intensities; Montobbio (2003) according to R&D intensity; Peneder (2002) according to intangibles and human capital intensity; Peneder (2008) according to cost of experimentation; Raymond et al. (2006) use a parametric approach based on an estimated model.

  10. Examples of taxonomies using different units of analysis include the following: Bardolet et al. (2010) classify business types; Fudenberg and Tirole (1984) business strategies; Di Berardino and Mauro (2010) regions; Baskaran and Muchie (2009) national systems of innovation; Dana et al. (2009) entrepreneurs; Iammarino and McCann (2006) economic clusters; Gallouj (1999) goods; Jindra (2005) multinational corporations; De Cleyn and Braet (2009) spin-offs and spin-out; De Jong and Marsili (2006) innovative small businesses; Love (2003) companies receiving FDIs.

  11. CIS data are collected by national statistical institutes in cooperation with Eurostat. The questionnaire is common to all countries and is based on the Oslo Manual (OECD 2005); the same holds for the methodology, which includes stratification of the sample by industry. Firms’ responses have been reported to the two digits industry level with the use of proper weights, derived from the sampling procedure. The construction of the SID has been carried out by the University of Urbino through cooperation agreements with national data providers with access to their country’s industry-level data—either national statistical institutes or research groups with authorisation to exchange data—in the case of CIS 2 and 3; CIS 4 data are available from Eurostat, except for the UK, whose data have been obtained from the national data provider. The assembling of the database has been carried out using a common protocol following the procedure in use at the National Statistical Offices. The latest CIS surveys—using the NACE (Rev.2) classification—have recently been included in the database using the conversion methodology developed in Perani and Cirillo (2015).

  12. When monetary values are used (as in R&D expenditure) nominal variables have been deflated using the GDP deflator from Eurostat (base year 2002) and a PPP conversion for non-euro countries (from Stapel et al. 2004).

  13. The strategies are not mutually exclusive, since they can coexist in the same firm or industry. However, they have different effects on productivity and employment; sectors tend to be dominated by either one or the other strategy; for empirical assessments see Bogliacino and Pianta (2010, 2011, 2013).

  14. Since the dependent variable is in percentage scale, we consider the possibility that the truncated nature of the range of variation could affect the estimate. We run a random effect Tobit and test for the presence of unequal variance for the random effect. The null hypothesis of equal variances is again not rejected (χ2 statistics is 0.50, p value 0.23). The coefficients from the Tobit regression are equal to the OLS one (up to the second decimal), with slightly different standard errors (mainly due to bootstrapping to correct for heteroskedasticity in the former estimation), which do not affect the test of significance for any of the regressors. Indeed, the use of industry level data already correct for the problem of data censoring since there is no probability mass on the extreme values.

  15. Tiffin (2014) refers to this work as evidence that competitiveness differs across industry groups and cannot be reduce to price factors.

  16. The literature on national and sectoral systems of innovation (Malerba 2004) has long pointed out the policy implications of the diversity of innovative dynamics across industries. The importance of active and selective innovation and industrial policy has been argued by Cimoli et al. (2009), Mazzucato (2013) and Pianta (2014).

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Authors and Affiliations

  1. Universidad Nacional de Colombia, Carrera 30, No 45-03, Bogotá, Colombia

    Francesco Bogliacino

  2. University of Urbino, Urbino, Italy

    Mario Pianta

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  1. Francesco Bogliacino
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Correspondence to Francesco Bogliacino.

Appendix

Appendix

See Tables 8, 9, 10, 11 and 12.

Table 8 The Revised Pavitt taxonomy for manufacturing and services
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Table 9 A multinomial logit analysis for the Revised Pavitt taxonomy: relative risk ratio
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Table 10 A multinomial logit analysis for the Revised Pavitt taxonomy: odds ratio
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Table 11 The determinants of the share of innovative firms
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Table 12 The determinants of innovative turnover
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Bogliacino, F., Pianta, M. The Pavitt Taxonomy, revisited: patterns of innovation in manufacturing and services. Econ Polit 33, 153–180 (2016). https://doi.org/10.1007/s40888-016-0035-1

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