Abstract
It is estimated the relationship between the investment in innovation activities, the results of innovation and productivity in Peruvian manufacturing firms, using the multi-equation model, called the CDM model, which studies the entire innovation process. The quantile regression approach was used, using data from the II National Survey of Innovation in the Manufacturing Industry 2015 in Peru. The findings were that technological innovation is associated with a 56% increase in productivity in the company. Finally, the quantile regression approach shows that the effect of technological innovation on the firm’s productivity is heterogeneous, increasing the effect on bigger firms. If investment in technological activities increases by 1%, then the labor productivity increases by 0.22% and the returns of innovation depend on the entrepreneurial position according the distribution of productivity.
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Notes
- 1.
In this study to define technological innovation it is used the concept of the Oslo Manual; it means, technological innovation, also called product or process innovation, is structured of nine activities: (i) internal Research and Development (R&D) activities; (ii) acquisition of external R&D; (iii) acquisition of capital goods; (iv) Hardware acquisition; (v) software acquisition; (vi) technology transfer; (vii) industrial design and engineering; (viii) training for innovation activities; and (ix) market research for the introduction of innovations.
- 2.
The problem of selection is that, in each period of time, we only keep with the firms that reported investment in innovation activities. By eliminating companies with zero investments in innovation activities, the sample would be biased.
- 3.
The following independent variables were used: company experience, exports per worker in the initial period, participation of foreign capital in the initial period, ratio of skilled workers, linking the company with organizations and institutions related to science and technology, concentration and market share in the initial period and the size of the firm.
- 4.
The Innovative Effort equation includes all the variables of Eq. 3 (except the size of the company) and also the following variables: demand incentives (demand pull), supply incentives (technology push), sources of information, financial restrictions, public resources, property rights and chains.
- 5.
The following variables were used: company size (logarithm of the number of workers), participation of foreign capital in the initial period and public resources.
References
Álvarez, R., Bravo, C., Navarro, L.: Innovación, investigación y desarrollo, y productividad en Chile. Revista CEPAL 104, 141–166 (2011)
Lambardi, G., Mora, J.: Determinantes de la innovación en productos o procesos: EL caso colombiano. Revista de Economía Institucional XVI(31), 251–262 (2014)
Lee, C.: The determinants of innovation in the malaysian manufacturing sector: an econometric analysis at the firm level. s.l.: University of Malaya. Centre on Regulation and Competition (2004)
Crespi, G., Katz, J.: R&D expenditure, market structure and “technological regimes” in chilean manufacturing industries. Estudios de Economía 26(2), 163–186 (1999)
Crespi, G., Zúñiga, P.: Innovation and productivity: evidence from six Latin American countries. s.l.: IDB Working Paper Series No. IDB-WP-218 (2010)
Crespi, G., Zuñiga, P.: Innovation and productivity: evidence from six Latin American countries. IDB Working Paper Series No. IDB-WP-218, pp. 1–38 (2010)
Banco Interamericano de Desarrollo: Cómo repensar el desarrollo productivo? Políticas e instituciones sólidas para la transformación económica. s.l.: BID (2014)
World Economic Forum: Global Competitiveness Report 2015-2016. World Economic Forum, Ginebra (2015)
Ministerio de la Producción: Desempeño Exportador del Sector Manufacturero por Innovación Tecnológica: Cambios en la estructura exportadora y productiva de bienes industrializados. Lima: s.n. (2015)
Tello, M.: Firms Innovation, Constrains and Productivity: The Case of Peru. Departamento de Economía PUCP, Lima, Documento de Trabajo 382 (2014)
Crepón, B., Duguet, E., Mairesse, J.: Research, innovation and productivity: an econometric analysis at the firm level. National Bureau of Economic Research, Cambridge (1998)
Ministerio de la Producción: Encuesta Nacional de Innovación en la Industria Manufacturera 2012. PRODUCE, Lima (2013)
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Ortíz Berrú, J.C., Aldana Yarlequé, C., Verástegui Huanca, L.L. (2020). Impact of Technological Innovation on the Productivity of Manufacturing Companies in Peru. In: Ahram, T., Karwowski, W., Vergnano, A., Leali, F., Taiar, R. (eds) Intelligent Human Systems Integration 2020. IHSI 2020. Advances in Intelligent Systems and Computing, vol 1131. Springer, Cham. https://doi.org/10.1007/978-3-030-39512-4_66
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DOI: https://doi.org/10.1007/978-3-030-39512-4_66
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