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

Author

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  • 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.)

Abstract

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.

Suggested Citation

  • Grid Thoma & Salvatore Torrisi, 2007. "Creating Powerful Indicators for Innovation Studies with Approximate Matching Algorithms. A test based on PATSTAT and Amadeus databases," KITeS Working Papers 211, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Dec 2007.
  • Handle: RePEc:cri:cespri:wp211
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    References listed on IDEAS

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    Cited by:

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    2. Zi‐Lin He & Tony W. Tong & Yuchen Zhang & Wenlong He, 2018. "Constructing a Chinese Patent Database of listed firms in China: Descriptions, lessons, and insights," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 27(3), pages 579-606, September.
    3. Ernest Miguélez & Rosina Moreno & Jordi Suriñach, 2010. "Inventors on the move: Tracing inventors' mobility and its spatial distribution," Papers in Regional Science, Wiley Blackwell, vol. 89(2), pages 251-274, June.
    4. Michele Pezzoni & Francesco Lissoni & Gianluca Tarasconi, 2014. "How to kill inventors: testing the Massacrator© algorithm for inventor disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 477-504, October.
    5. Kang, Taewon & Baek, Chulwoo & Lee, Jeong-Dong, 2019. "Effects of knowledge accumulation strategies through experience and experimentation on firm growth," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 169-181.
    6. Tarasconi, Gianluca & Kang, Byeongwoo, 2015. "PATSTAT revisited," IDE Discussion Papers 527, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    7. Roberta Piergiovanni & Enrico Santarelli, 2013. "The more you spend, the more you get? The effects of R&D and capital expenditures on the patenting activities of biotechnology firms," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(2), pages 497-521, February.
    8. Criscuolo, Paola & Verspagen, Bart, 2008. "Does it matter where patent citations come from? Inventor vs. examiner citations in European patents," Research Policy, Elsevier, vol. 37(10), pages 1892-1908, December.
    9. Raffo, Julio & Lhuillery, Stéphane, 2009. "How to play the "Names Game": Patent retrieval comparing different heuristics," Research Policy, Elsevier, vol. 38(10), pages 1617-1627, December.
    10. Grid Thoma & Salvatore Torrisi & Alfonso Gambardella & Dominique Guellec & Bronwyn H. Hall & Dietmar Harhoff, 2010. "Harmonizing and Combining Large Datasets - An Application to Firm-Level Patent and Accounting Data," NBER Working Papers 15851, National Bureau of Economic Research, Inc.
    11. Dornbusch, Friedrich & Schmoch, Ulrich & Schulze, Nicole & Bethke, Nadine, 2012. "Identification of university-based patents: A new large-scale approach," Discussion Papers "Innovation Systems and Policy Analysis" 32, Fraunhofer Institute for Systems and Innovation Research (ISI).
    12. Ernest Miguélez & Ismael Gómez-Miguélez, 2011. "“Singling out individual inventors from patent data”," IREA Working Papers 201105, University of Barcelona, Research Institute of Applied Economics, revised May 2011.
    13. Kim, Seung Hwan & Jun, Bogang & Lee, Jeong-Dong, 2021. "Technological relatedness: How do firms diversify their technology?," SocArXiv 47ank, Center for Open Science.

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    More about this item

    Keywords

    innovation statistics; patents; matching company names; software.;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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    1. Socio-Economics of Innovation

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