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Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications

Author

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  • Alex Coad
  • Dominik Janzing
  • Paul Nightingale

Abstract

This paper presents a new statistical toolkit by applying three techniques for data-driven causal inference from the machine learning community that are little-known among economists and innovation scholars: a conditional independence based approach, additive noise models, and non-algorithmic inference by hand. We include three applications to CIS data to investigate public funding schemes for R&D investment, information sources for innovation, and innovation expenditures and firm growth. Preliminary results provide causal interpretations of some previously-observed correlations. Our statistical 'toolkit' could be a useful complement to existing techniques.

Suggested Citation

  • Alex Coad & Dominik Janzing & Paul Nightingale, 2018. "Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 37(75), pages 779-808, March.
    • Handle: RePEc:col:000093:017128
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    References listed on IDEAS

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    1. Heidenreich, Martin, 2009. "Innovation patterns and location of European low- and medium-technology industries," Research Policy, Elsevier, vol. 38(3), pages 483-494, April.
    2. Perez, Stephen J. & Siegler, Mark V., 2006. "Agricultural and monetary shocks before the great depression: A graph-theoretic causal investigation," Journal of Macroeconomics, Elsevier, vol. 28(4), pages 720-736, December.
    3. James J. Heckman, 2010. "Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 356-398, June.
    4. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
    5. Alessio Moneta, 2008. "Graphical causal models and VARs: an empirical assessment of the real business cycles hypothesis," Empirical Economics, Springer, vol. 35(2), pages 275-300, September.
    6. Sabrina T. Howell, 2017. "Financing Innovation: Evidence from R&D Grants," American Economic Review, American Economic Association, vol. 107(4), pages 1136-1164, April.
    7. Lanne, Markku & Meitz, Mika & Saikkonen, Pentti, 2017. "Identification and estimation of non-Gaussian structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 196(2), pages 288-304.
    8. Bruno Cassiman & Reinhilde Veugelers, 2002. "R&D Cooperation and Spillovers: Some Empirical Evidence from Belgium," American Economic Review, American Economic Association, vol. 92(4), pages 1169-1184, September.
    9. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    10. Katrin Hussinger, 2008. "R&D and subsidies at the firm level: an application of parametric and semiparametric two-step selection models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(6), pages 729-747.
    11. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
    12. Leiponen, Aija & Drejer, Ina, 2007. "What exactly are technological regimes?: Intra-industry heterogeneity in the organization of innovation activities," Research Policy, Elsevier, vol. 36(8), pages 1221-1238, October.
    13. Martin Srholec & Bart Verspagen, 2012. "The Voyage of the Beagle into innovation: explorations on heterogeneity, selection, and sectors," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 21(5), pages 1221-1253, October.
    14. Hashi, Iraj & Stojčić, Nebojša, 2013. "The impact of innovation activities on firm performance using a multi-stage model: Evidence from the Community Innovation Survey 4," Research Policy, Elsevier, vol. 42(2), pages 353-366.
    15. Jaider Vega-Jurado & Antonio Gutiérrez-Gracia & Ignacio Fernández-de-Lucio, 2009. "Does external knowledge sourcing matter for innovation? Evidence from the Spanish manufacturing industry," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 18(4), pages 637-670, August.
    16. Anna, Petrenko, 2016. "Мaркування готової продукції як складова частина інформаційного забезпечення маркетингової діяльності підприємств овочепродуктового підкомплексу," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 2(1), March.
    17. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    18. Aerts, Kris & Schmidt, Tobias, 2008. "Two for the price of one?: Additionality effects of R&D subsidies: A comparison between Flanders and Germany," Research Policy, Elsevier, vol. 37(5), pages 806-822, June.
    19. Xiaojie Xu, 2017. "Contemporaneous causal orderings of US corn cash prices through directed acyclic graphs," Empirical Economics, Springer, vol. 52(2), pages 731-758, March.
    20. Scott J. Wallsten, 2000. "The Effects of Government-Industry R&D Programs on Private R&D: The Case of the Small Business Innovation Research Program," RAND Journal of Economics, The RAND Corporation, vol. 31(1), pages 82-100, Spring.
    21. Henry L. Bryant & David A. Bessler & Michael S. Haigh, 2009. "Disproving Causal Relationships Using Observational Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 357-374, June.
    22. Alessio Moneta & Doris Entner & Patrik O. Hoyer & Alex Coad, 2013. "Causal Inference by Independent Component Analysis: Theory and Applications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(5), pages 705-730, October.
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    Cited by:

    1. Cucculelli, Marco & Peruzzi, Valentina, 2020. "Innovation over the industry life-cycle. Does ownership matter?," Research Policy, Elsevier, vol. 49(1).
    2. Diego Norena-Chavez & Ruben Guevara, 2020. "Entrepreneurial Passion and Self-Efficacy as Factors Explaining Innovative Behavior: A Mediation Model," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 352-373.

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    More about this item

    Keywords

    Causal inference; innovation surveys; machine learning; additive noisemodels; directed acyclic graphs;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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