Innovación en Ecuador: un enfoque espacial/Innovation in Ecuador: a spatial approach
Abstract
Este trabajo explora la distribución espacial del éxito innovador de las empresas en Ecuador entre 2012 y 2014. Los datos cuentan con una muestra de 6275 empresas con representatividad provincial. En base a esta información, los objetivos que persigue esta investigación están orientados a i) establecer si existe o no relaciones espaciales en las provincias del Ecuador y ii) resaltar políticas estatales que contribuyan a la innovación. Los resultados muestran que existe influencia espacial en el éxito innovador. Asimismo, el modelo planteado sugiere políticas orientadas a la innovación mediante apoyo del Gobierno así como financiamiento por parte de la banca privada.
This paper explores the spatial distribution of the innovative success of companies in Ecuador between 2012 and 2014. The data has a sample of 6275 companies with provincial representation. Based on this information, the objectives pursued by this research are aimed at i) establishing whether or not there are spatial relationships in the provinces of Ecuador and ii) highlighting state policies that contribute to innovation. The results show that there is a spatial influence on innovative success. Likewise, the proposed model suggests policies oriented towards innovation through government support as well as financing from private banks.
Palabras clave: Spillovers espaciales, Modelo espacial autorregresivo, Modelo de error espacial, Modelo espacial de Durbin.
Keywords: Spatial spillovers, Spatial autoregressive model, Spatial Error Model, Spatial Durbin Model.
References
[1] B. H. Baltagi, S. H. Song, and W. Koh. Testing panel data regression models with spatial error correlation. Journal of econometrics, 117(1):123–150, 2003.
[2] I. Booyens and T. G. Hart. Innovation in a changing south Africa: extant debates and critical re ections. In The Geography of South Africa, pages 269–277. Springer; 2019.
[3] INEC. Encuesta nacional de actividades de ciencia, tecnología e innovación, 2019.
[4] J. LeSage and R. K. Pace. Introduction to spatial econometrics. Nueva York: Chapman and Hall/CRC, 2009.
[5] G. Millo. Maximum likelihood estimation of spatially and serially correlated panels with random effects. Computational Statistics & Data Analysis, 71:914–933, 2014.
[6] G. Millo, G. Piras, et al. splm: Spatial panel data models in r. Journal of Statistical Software, 47(1):1–38, 2012.
[7] R. Moreno, R. Paci, and S. Usai. Spatial spillovers and innovation activity in european regions. Environment and planning A, 37(10):1793–1812; 2005.
[8] D. Peña. Análisis de datos multivariantes. España: McGraw-Hill; 2013.
[9] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2014.
[10] T. K. Roger Bivand, Jan Hauke. Computing the jacobian in gaussian spatial autoregressive models: An illustrated comparison of available methods. Geographical Analysis, 45(2):150–179, 2013.
[11] O. M. Suarez. Schumpeter, innovación y determinismo tecnológico. Scientia et technica, 2(25), 2004.
[12] C. Van Egeraat, D. Kogler, and P. Cooke. Global and Regional Dynamics in Knowledge Flows and Innovation. Nueva York: Routledge, 2015.
[13] X. Wang, H. Fang, F. Zhang, and S. Fang. The spatial analysis of regional innovation performance and industry-university-research institution collaborative innovation|an empirical study of chinese provincial data. Sustainability, 10(4):1–16, April 2018.
[14] Z. Yang and L. Qixia. Innovation pattern analysis of the industry-university-research cooperation, volume 1. 2012.