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Creating Powerful Indicators for Innovation Studies with Approximate Matching Algorithms. A test based on PATSTAT and Amadeus databases

  • Grid Thoma

    (Department of Mathematics and Computer Science, University of Camerino and CESPRI - Bocconi University, Milan, Italy.)

  • Salvatore Torrisi

    (Department of Management, Univesity of Bologna and CESPRI - Bocconi University, Milan, Italy.)

The lack of firm-level data on innovative activities has always constrained the development of empirical studies on innovation. More recently, the availability of large datasets on indicators, such as R&D expenditures and patents, has relaxed these constrains and spurred the growth of a new wave of research. However, measuring innovation still remains a difficult task for reasons linked to the quality of available indicators and the difficulty of integrating innovation indicators to other firm-level data. As regards quality, data on R&D expenditures represent a measure of input but do not tell much about the ‘success’ of innovative activities. Moreover, especially in the case of European firms, data on R&D expenditures are often missing because reporting these expenditures is not required by accounting and fiscal regulations in some countries. An increasing number of studies have used patents counts as a measure of inventive output. However, crude patent counts are a biased indicator of inventive output because they do not account for differences in the value of patented inventions. This is the reason why innovation scholars have introduced various patent-related indicators as a measure of the ‘quality’ of the inventive output. Integrating these measures of inventive activity with other firm-level information, such as accounting and financial data, is another challenging task. A major problem in this field is represented by the difficulty of harmonizing information from different data sources. This is a relevant issue since inaccuracy in data merging and integration leads to measurement errors and biased results. An important source of measurement error arises from inaccuracies in matching data on innovators across different datasets. This study reports on a test of company names standardization and matching. Our test is based on two data sources: the PATSTAT patent database and the Amadeus accounting and financial dataset. Earlier studies have mostly relied on manual, ad-hoc methods. More recently scholars have started experimenting with automatic matching techniques. This paper contributes to this body of research by comparing two different approaches – the character-tocharacter match of standardized company names (perfect matching) and the approximate matching based on string similarity functions. Our results show that approximate matching yields substantial gains over perfect matching, in terms of frequency of positive matches, with a limited loss of precision – i.e., low rates of false matches and false negatives.

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Paper provided by KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy in its series KITeS Working Papers with number 211.

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Length: pages 24
Date of creation: Dec 2007
Date of revision: Dec 2007
Handle: RePEc:cri:cespri:wp211
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  1. Zvi Griliches & Bronwyn H. Hall & Ariel Pakes, 1988. "R&D, Patents, and Market Value Revisited: Is There Evidence of A SecondTechnological Opportunity Related Factor?," NBER Working Papers 2624, National Bureau of Economic Research, Inc.
  2. Hall, Bronwyn H & Jaffe, Adam B & Trajtenberg, Manuel, 2001. "The NBER Patent Citations Data File: Lessons, Insights and Methodological Tools," CEPR Discussion Papers 3094, C.E.P.R. Discussion Papers.
  3. Paola Giuri & Myriam Mariani & Stefano Brusoni & Gustavo Crespi & Dominique Francoz & Alfonso Gambardella & Walter Garcia-Fontes & Aldo Geuna & Raul Gonzales & Dietmar Harhoff & Karin Hoisl & Christia, 2005. "Everything you Always Wanted to Know about Inventors (but Never Asked): Evidence from the PatVal-EU Survey," LEM Papers Series 2005/20, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  4. Zvi Griliches, 1990. "Patent Statistics as Economic Indicators: A Survey," NBER Working Papers 3301, National Bureau of Economic Research, Inc.
  5. repec:fth:harver:1473 is not listed on IDEAS
  6. Jean O. Lanjouw & Mark Schankerman, 2004. "Patent Quality and Research Productivity: Measuring Innovation with Multiple Indicators," Economic Journal, Royal Economic Society, vol. 114(495), pages 441-465, 04.
  7. Dietmar Harhoff & Francis Narin & F. M. Scherer & Katrin Vopel, 1999. "Citation Frequency And The Value Of Patented Inventions," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 511-515, August.
  8. Richard C. Levin & Alvin K. Klevorick & Richard R. Nelson & Sidney G. Winter, 1987. "Appropriating the Returns from Industrial Research and Development," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 18(3), pages 783-832.
  9. Bronwyn H. Hall & Grid Thoma & Salvatore Torrisi, 2006. "The market value of patents and R&D: Evidence from European firms," KITeS Working Papers 186, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Nov 2006.
  10. Bronwyn H. Hall & Adam Jaffe & Manuel Trajtenberg, 2005. "Market Value and Patent Citations," RAND Journal of Economics, The RAND Corporation, vol. 36(1), pages 16-38, Spring.
  11. Zvi Griliches, 1984. "Market Value, R&D, and Patents," NBER Chapters, in: R&D, Patents, and Productivity, pages 249-252 National Bureau of Economic Research, Inc.
  12. Andrea Fosfuri & Marco S. Giarratana, 2004. "Product Strategies And Startups’ Survival In Turbulent Industries: Evidence From The Security Software Industry," Business Economics Working Papers wb044816, Universidad Carlos III, Departamento de Economía de la Empresa.
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  1. Socio-Economics of Innovation

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