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Determinants of public funding for innovation in Chilean firms

Author

Listed:
  • Cristian Mardones

    (Universidad de Concepción, Chile)

  • Annabella Zapata

    (Universidad de Concepción, Chile)

Abstract

In this study, different versions of the Innovation Surveys carried out in Chile are used to evaluate the factors that would explain the obtaining of public funding for innovative activities. In order to achieve this, the estimated results from binary election models are contrasted with cross-sectional and pseudo-panel data. It is concluded that with pseudo-panel data it is possible to identify some relevant factors not observed with cross-sectional data, for example those firms that invest in training their workers in R&D activities in the previous year have lower probabilities of obtaining public funding. In addition, the foreign firms have greater probabilities of achieving funding than national firms. The most striking result is that larger firms have greater probability of obtaining public funding, which is contradictory when considering that many public programs declare that they are aimed to support SMEs.

Suggested Citation

  • Cristian Mardones & Annabella Zapata, 2019. "Determinants of public funding for innovation in Chilean firms," Contaduría y Administración, Accounting and Management, vol. 64(1), pages 41-42, Enero-Mar.
    • Handle: RePEc:nax:conyad:v:64:y:2019:i:1:p:41-42
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    References listed on IDEAS

    as
    1. Roberto Álvarez & Claudio Bravo-Ortega & Andrés Zahler, 2015. "Innovation and Productivity in Services: Evidence from Chile," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(3), pages 593-611, May.
    2. repec:zbw:bofrdp:2008_007 is not listed on IDEAS
    3. Claudio Bravo-Ortega & Jose Miguel Benavente & Álvaro González, 2014. "Innovation, Exports, and Productivity: Learning and Self-Selection in Chile," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(1S), pages 68-95, January.
    4. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
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    Cited by:

    1. Mónica Espinosa-Blasco & Gabriel I. Penagos-Londoño & Felipe Ruiz-Moreno & María J. Vilaplana-Aparicio, 2024. "New Insights on the Allocation of Innovation Subsidies: A Machine Learning Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 2704-2725, March.

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