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The effect of microaggregation on regression results: an application to Spanish innovation data

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  • López, Alberto

Abstract

Microaggregation is a technique for masking confidential data by aggregation. The aim of this paper is to analyze the extent to which microaggregated data can be used for rigorous empirical research. In doing this, I adopt an empirical perspective. I use data from the Technological Innovation Panel (PITEC) and compare regression results using both original and anonymized data. PITEC is a new firm-level panel data base for innovative activities of Spanish firms based on CIS data. I find that the microaggregation procedure used has a slight effect on the coefficient estimates and their estimated standard errors, especially when estimating linear models.

Suggested Citation

  • López, Alberto, 2011. "The effect of microaggregation on regression results: an application to Spanish innovation data," MPRA Paper 30403, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:30403
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    References listed on IDEAS

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    1. Bruno Crepon & Emmanuel Duguet & Jacques Mairesse, 1998. "Research, Innovation And Productivity: An Econometric Analysis At The Firm Level," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 7(2), pages 115-158.
    2. Schmid, Matthias & Schneeweiss, Hans, 2009. "The effect of microaggregation by individual ranking on the estimation of moments," Journal of Econometrics, Elsevier, vol. 153(2), pages 174-182, December.
    3. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
    4. Schmid Matthias & Schneeweiss Hans, 2005. "The Effect of Microaggregation Procedures on the Estimation of Linear Models: A Simulation Study," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(5), pages 529-543, October.
    5. Bernard, Andrew B. & Bradford Jensen, J., 1999. "Exceptional exporter performance: cause, effect, or both?," Journal of International Economics, Elsevier, vol. 47(1), pages 1-25, February.
    6. Jacques Mairesse & Pierre Mohnen, 2001. "To Be or Not To Be Innovative: An Exercise in Measurement," NBER Working Papers 8644, National Bureau of Economic Research, Inc.
    7. Crepon, B. & Duguet, E. & Mairesse, J., 1998. "Research Investment, Innovation and Productivity: An Econometric Analysis at the Firm Level," Papiers d'Economie Mathématique et Applications 98.15, Université Panthéon-Sorbonne (Paris 1).
    8. Ronning, Gerd, 2005. "Randomized response and the binary probit model," Economics Letters, Elsevier, vol. 86(2), pages 221-228, February.
    9. Lopez, Alberto, 2008. "Determinants of R&D cooperation: Evidence from Spanish manufacturing firms," International Journal of Industrial Organization, Elsevier, vol. 26(1), pages 113-136, January.
    10. repec:crs:wpaper:9833 is not listed on IDEAS
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    Cited by:

    1. Isabel Busom & Jorge-Andrés Vélez-Ospina, 2021. "Subsidising innovation over the business cycle," Industry and Innovation, Taylor & Francis Journals, vol. 28(6), pages 773-803, July.
    2. Pilar Bernal & Juan P. Maicas & Pilar Vargas, 2016. "Exploration, exploitation and innovation performance: Disentangling environmental dynamism," Documentos de Trabajo dt2016-03, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza.
    3. Huergo, Elena & Trenado, Mayte & Ubierna, Andrés, 2016. "The impact of public support on firm propensity to engage in R&D: Spanish experience," Technological Forecasting and Social Change, Elsevier, vol. 113(PB), pages 206-219.
    4. Loles Añón Higón & Alfonso Díez-Minguela, 2021. "Location, Location, Location: Do Universities Matter for Foreign R&D?," Working Papers 2109, Department of Applied Economics II, Universidad de Valencia.
    5. Adelheid Holl & Ruth Rama, 2014. "Foreign Subsidiaries and Technology Sourcing in Spain," Industry and Innovation, Taylor & Francis Journals, vol. 21(1), pages 43-64, January.
    6. Fernández-Olmos, Marta & Ramírez-Alesón, Marisa, 2017. "How internal and external factors influence the dynamics of SME technology collaboration networks over time," Technovation, Elsevier, vol. 64, pages 16-27.
    7. Pilar Bernal & Idana Salazar & Pilar Vargas, 2019. "Understanding the Open Innovation Trends: An Exploratory Analysis of Breadth and Depth Decisions," Administrative Sciences, MDPI, vol. 9(4), pages 1-15, September.
    8. Marisa Ramírez-Alesón & Marta Fernández-Olmos, 2018. "Unravelling the effects of Science Parks on the innovation performance of NTBFs," The Journal of Technology Transfer, Springer, vol. 43(2), pages 482-505, April.
    9. Añón Higón, Dolores, 2016. "In-house versus external basic research and first-to-market innovations," Research Policy, Elsevier, vol. 45(4), pages 816-829.

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    More about this item

    Keywords

    Microaggregation; Individual ranking; Bias; Innovation data;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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