Eficiencia de las empresas manufactureras de Ecuador del 2007 al 2018: dos enfoques de análisis intraindustrial

  • Ariel Cobos-Salvador Escuela Superior Politécnica del Litoral
  • Mary Armijos Yambay Superintendencia de Compañías, Valores y Seguros

Resumen

Esta investigación analiza la eficiencia de la industria manufacturera ecuatoriana en el período 2007-2018 considerando dos diferentes clasificaciones intraindustriales. Para estudiar la eficiencia de las empresas desde dos perspectivas diferentes se utiliza la clasificación de los subsectores de la taxonomía de Pavitt (1984) y la clasificación de Diaz & Sánchez (2008). Además, se emplea el modelo Análisis Envolvente de Datos (DEA, acrónimo en inglés) no paramétrico orientado a productos con retornos variables a escala. Los datos provienen de la Superintendencia de Compañías, Valores y Seguros (SCVS). Los resultados muestran que los subsectores relacionados con la producción de maquinaria y metales son eficientes en el período del estudio según la clasificación de Pavitt y Díaz, Sánchez. También se identifica que los subsectores dominados por los proveedores, que incluyen las empresas más tradicionales de manufactura y el subsector de proveedores especializados son los más eficientes. Mientras que el sector basado en la ciencia, que comprende la producción en conjunto con la investigación y que puede provenir de la academia, es uno de los que presenta niveles de eficiencia más bajo.  Esto sugiere la necesidad de innovación a través del impulso de investigación y desarrollo en las industrias manufactureras y de establecer alianza entre la academia y el sector productivo.

Citas

Aigner, D. J., & Chu, S. F. (1968). On estimating the industry production function. The American Economic Review, 58(4), 826-839.

Al-Shammari, M. (1999). Optimization modeling for estimating and enhancing relative efficiency with application to industrial companies. European Journal of Operational Research, 115(3), 488-496.

Ali, A. I., & Lerme, C. S. (1990). Determination of comparative advantage for the economy of states in the US. The University of Massachusetts, (Mimeograph)

Ali, A. I., Lerme, C. S., & Nakosteen, R. A. (1993). Assessment of intergovernmental revenue transfers. Socio-economic planning sciences, 27(2), 109-118.

Arzubi, A., & Berbel, J. (2002). Determinación de índices de eficiencia mediante DEA en explotaciones lecheras de Buenos Aires. Investigaciones Agrárias: Producción y Sanidad Animales, 17(1-2), 103-123.

Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.

Banker, R. D., & Morey, R. C. (1986). Efficiency analysis for exogenously fixed inputs and outputs. Operations research, 34(4), 513-521.

Bwana, K., & Ally, O. J. (2019). Efficiency of listed manufacturing firms in dar es salaam stock exchange: data envelopment analysis model. Business Education Journal, 1(2).

Chang, P. L., Hwang, S. N., & Cheng, W. Y. (1995). Using data envelopment analysis to measure the achievement and change of regional development in Taiwan. Journal of Environmental Management, 43(1), 49-66.

Charnes, A., Cooper, W. W., Lewin, A. Y., & Seiford, L. M. (1994). Data Envelopment Analysis: Theory. Method, and.

Charnes, A., Cooper, W. W., & Li, S. (1989). Using data envelopment analysis to evaluate efficiency in the economic performance of Chinese cities. Socio-Economic Planning Sciences, 23(6), 325-344.

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European journal of operational research, 2(6), 429-444.

Charnes, A., Cooper, W. W., Seiford, L., & Stutz, J. (1982). A multiplicative model for efficiency analysis. Socio-Economic Planning Sciences, 16(5), 223-224.

Chen, C. F., & Soo, K. T. (2009). Some university students are more equal than others: Evidence from England.

Chen, Y. Q., Lu, H., Lu, W., & Zhang, N. (2010). Analysis of project delivery systems in Chinese construction industry with data envelopment analysis (DEA). Engineering, Construction and Architectural Management, 17(6), 598-614.

Constantin, P. D., Martin, D. L., Rivera, R. Y., & De, E. B. B. (2009). Cobb-Douglas, translog stochastic production function and data envelopment analysis in total factor productivity in Brazilian agribusiness. Journal of Operations and Supply Chain Management (JOSCM), 2(2), 20-33.

Cullinane, K., Wang, T.F., Song, D.W., & Ji, P. (2006). The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis. Transportation Research Part A: Policy and Practice, 40(4):354–374.

Daraio, C., & Simar, L. (2007). Advanced robust and nonparametric methods in efficiency analysis: Methodology and applications. Springer Science & Business Media.

Diaz, M. A., & Sánchez, R. (2008). Firm size and productivity in Spain: a stochastic frontier analysis. Small Business Economics, 30(3), 315-323.

Düzakın, E., & Düzakın, H. (2007). Measuring the performance of manufacturing firms with super slacks-based model of data envelopment analysis: An application of 500 major industrial enterprises in Turkey. European journal of operational research, 182(3), 1412-1432.

Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281.

Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237-250.

Greene, W. H. (2008). The econometric approach to efficiency analysis. The measurement of productive efficiency and productivity growth, 1(1), 92-250.

Hallward-Driemeier, M., & Nayyar, G. (2017). Trouble in the Making?: The Future of Manufacturing-led Development. World Bank Publications.

Haron, M., & ARUL, C. J. (2012). Efficiency performance of manufacturing companies in Kenya: Evaluation and policies.

Herrera, T. F., Mendoza, A. M., & Cadavid, D. V. (2015). Análisis comparativo de eficiencia financiera: Estudio de un caso sectorial en Barranquilla. Prospectiva, 13(2), 16-24.

Heshmati, A. (2003). Productivity growth, efficiency and outsourcing in manufacturing and service industries. Journal of economic surveys, 17(1), 79-112.

Ji, Y. B., & Lee, C. (2010). Data envelopment analysis. The Stata Journal, 10(2), 267-280.

Keramidou, I., Mimis, A., & Pappa, E. (2011). Identifying efficiency drivers in the Greek sausage industry: a double bootstrap DEA approach. Economics Bulletin, 31(1), 442-452.

Khoshroo, A., Mulwa, R., Emrouznejad, A., & Arabi, B. (2013). A non-parametric Data Envelopment Analysis approach for improving energy efficiency of grape production. Energy, 63, 189-194.

Li, K., & Lin, B. (2016). Impact of energy conservation policies on the green productivity in China’s manufacturing sector: Evidence from a three-stage DEA model. Applied energy, 168, 351-363.

Liu, S. T., & Wang, R. T. (2009). Efficiency measures of PCB manufacturing firms using relational two-stage data envelopment analysis. Expert Systems with Applications, 36(3), 4935-4939.

Mayes, D. G., Harris, C. M., & Lansbury, M. (1994). Inefficiency in industry. Harvester Wheatsheaf.

Mohamad, N. H., & Said, F. (2010). Measuring the performance of 100 largest listed companies in Malaysia. African Journal of Business Management, 4(14), 3178.

Moreira, V. H., & Bravo-Ureta, B. E. (2010). Technical efficiency and metatechnology ratios for dairy farms in three southern cone countries: a stochastic meta-frontier model. Journal of Productivity Analysis, 33(1), 33-45.

Mujaddad, H. G., & Ahmad, H. K. (2016). MEASURING EFFICIENCY OF MANUFACTURING INDUSTRIES IN PAKISTAN. Pakistan Economic and Social Review, 54(2), 363-384.

Mujaddad, H. G., Nawaz, S. N., & Anwar, M. (2018). Small and Medium Enterprises: Pivotal to Inclusive Growth in Punjab. Papers and Proceedings, 81–95

Norman, M., & Stoker, B. (1991). Data envelopment analysis: the assessment of performance. John Wiley & Sons, Inc.

Pavitt, K. (1984). Sectoral patterns of technical change: towards a taxonomy and a theory. Technology, Management and Systems of Innovation, 15-45.

Restrepo, M. I., & Villegas, J. G. (2007). Clasificación de grupos de investigación colombianos aplicando análisis envolvente de datos. Revista Facultad de Ingeniería Universidad de Antioquia, (42), 105-119.

Seiford, L. M., & Thrall, R. M. (1990). Recent developments in DEA: the mathematical programming approach to frontier analysis. Journal of econometrics, 46(1-2), 7-38.

Simar, L., & Wilson, P. W. (1999). Estimating and bootstrapping Malmquist indices. European journal of operational research, 115(3), 459-471.

Simar, L., & Wilson, P. W. (2002). Non-parametric tests of returns to scale. European Journal of Operational Research, 139(1), 115-132.

Simar, L., & Zelenyuk, V. (2003). Statistical inference for aggregates of Farrell-type efficiencies. Discussion Papers, 324.

Superintendencia de Compañias Valores y Seguros. (2020). Portal de Información. Guayaquil.

Superintendencia de Compañias, Valores y Seguros. (2020). La eficiencia de las empresas manufactureras en el Ecuador 2013-2018. Estudios Sectoriales , Dirección Nacional de Investigación y Estudios , Guayaquil.

Tahir, I. M., & Memon, M. A. (2011). Applying DEA in analyzing the efficiency of top manufacturing companies in Pakistan. Journal of Public Administration and Governance, 1(2), 225-239.

Thanassoulis, E. (2001). Introduction to the theory and application of data envelopment analysis. Dordrecht: Kluwer Academic Publishers.
Publicado
2020-04-24
##submission.howToCite##
COBOS-SALVADOR, Ariel; ARMIJOS YAMBAY, Mary. Eficiencia de las empresas manufactureras de Ecuador del 2007 al 2018: dos enfoques de análisis intraindustrial. X-pedientes Económicos, [S.l.], v. 4, n. 8, p. 19-37, apr. 2020. ISSN 2602-831X. Disponible en: <http://ojs.supercias.gob.ec/index.php/X-pedientes_Economicos/article/view/109>. Fecha de acceso: 07 july 2020

Warning: Invalid argument supplied for foreach() in C:\inetpub\wwwroot\ojs-jg\plugins\generic\recommendByAuthor\RecommendByAuthorPlugin.inc.php on line 114

##plugins.generic.recommendByAuthor.heading##

##plugins.generic.recommendByAuthor.noMetric##