Analysis of business closure in the manufacturing sector of Ecuador, period 1901 - 2018

Main Article Content

Iván Orellana Osorio
Luis Pinos Luzuriaga
Luis Tonon Ordóñez
Marco Reyes Clavijo
Estefanía Cevallos Rodríguez

Keywords

Manufacturing sector, Business dynamics, Closed companies, Probability of closing

Abstract

The business dynamic of the manufacturing sector in Ecuador was addressed through the study of business demographics of 118 years. For the calculation of survival probabilities, a mortality table was made. 37.74 % of the companies remain active, while 89.07 % of the closed ones are micro and small companies. The probability that a newly created company closes its activities in a period less than or equal to 3 years is 4 %, and the life expectancy of a newly created company is 14.27 years.

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References

Acosta, E. y Fernández, F. (2007). Predicción del fracaso empresarial mediante el uso de algoritmos genéticos. X Encuentro de Economía Aplicada, Logroño. Universidad de Las Palmas de Gran Canaria. Las Palmas de Gran Canaria
Agudelo, G., Franco, L. y Franco, L. (2016). Cálculo actuarial: introducción a la actuaría de vida. Fondo Editorial ITM.
Ahn, B. S., Cho, S. S. y Kim, C. Y. (2000). Integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 18(2), 65-74. https://doi.org/10.1016/S0957-4174(99)00053-6
Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23, 589-609. https://doi.org/10.2307/2978933
Alva, E. (2017). La desaparición de las microempresas en el Perú una aproximación a los factores que predisponen a su mortalidad caso del cercado de lima. Economía y Desarrollo, 158(2), 76-90.
Balcaen, S. y Ooghe, H. (2006). 35 years of studies on business failure: An overview of the classic statisticalmethodologies and their related problems. British Accounting Review, 38(1), 63-93. https://doi.org/10.1016/j.bar.2005.09.001
Bamiatzi, V. C. y Kirchmaier, T. (2014). Strategies for superior performance under adverse conditions: A focus on small and medium-sized high-growth firms. International Small Business Journal, 32(3), 259-284. https://doi.org/10.1177/0266242612459534
Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.
Berg, D. (2007). Bankruptcy prediction by generalized additive models. Applied Stochastic Models in Business and Industry, 23, 129-143. https://doi.org/10.1002/asmb.658
Bermudez, N. y Bravo, A. (2019). Modelo predictivo de los determinantes del cierre empresarial de las MIPYMES en el Ecuador período 2007-2016. X - Pedientes Económicos, 3(5), 78-93.
Bowers, N., Gerber, H., James, H., Jones, D. y Nesbitt, C. (1997). Actuarial mathematics. Edward Brothers, Inc.
Bruderl, J. y Schussler, R. (1990). Organizational mortality : The Liabilities of newness and adolescence. Sage, 35(3), 530-547. https://doi.org/10.2307/2393316
Correa, A., Acosta, M. y González, A. (2003). La insolvencia empresarial: un análisis empírico para la pequeña y mediana empresa. Revista de contabilidad, 6, 47-79.
Daepp, M. I. G., Hamilton, M. J., West, G. B. y Bettencourt, L. M. A. (2015). The mortality of companies. Journal of the Royal Society Interface, 12(106). https://doi.org/10.1098/rsif.2015.0120
FitzPatrick, P. (1932). Average ratios of twenty representative industrial failures. The certified public account, 13-18.
Fuentelsaz, L., Gómez, J. y Polo, Y. (2004). Aplicaciones del análisis de supervivencia a la investigación en economía de la empresa. Cuadernos de economía y dirección de la empresa, 19(19), 81-114.
Ghazali, R., Jaafar Hussain, A., Mohd Nawi, N. y Mohamad, B. (2009). Non-stationary and stationary prediction of financial time series using dynamic ridge polynomial neural network. Neurocomputing, 72(10-12), 2359-2367. https://doi.org/10.1016/j.neucom.2008.12.005
González, R., Arteaga, A. y Ruíz, M. (2018). Cierre empresarial en la región Laja- Bajío. Management Review, 3, 1-16. https://doi.org/http://dx.doi.org/10.18583/umr.v3i2.119
Hannan, M. T. (1998). Rethinking age dependence in organizational mortality: Logical formalizations. American Journal of Sociology, 104(1), 126-164. https://doi.org/10.1086/210004
Henderson, R. y Clark, K. (1990). Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms. Administrative Science Quarterly, 35(1), 9. https://doi.org/10.2307/2393549
Hua, Z., Wang, Y., Xu, X., Zhang, B. y Liang, L. (2007). Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications, 33(2), 434- 440. https://doi.org/10.1016/j.eswa.2006.05.006
Instituto Nacional de Estadísticas y Censos (2019). Panorama laboral y empresarial 2017. https://www.ecuadorencifras. gob.ec/documentos/webinec/Bibliotecas/Libros/Panorama Laboral 2017.pdf
León, J. G., Vásquez, J. C. y Vergara, A. L. (2018). Desempeño financiero empresarial del sector agropecuario: un análisis comparativo entre Colombia y Brasil -2011-2015-. Revista Escuela de Administración de Negocios, 0(84 SE-Artículos científicos). https://doi.org/10.21158/01208160.n84.2018.1920
Martin, R. y Sunley, P. (2015). On the notion of regional economic resilience: Conceptualization and explanation. Journal of Economic Geography, 15(1), 1-42. https://doi.org/10.1093/jeg/lbu015
McKee, T. E. y Lensberg, T. (2002). Genetic programming and rough sets: A hybrid approach to bankruptcy classification. European Journal of Operational Research, 138(2), 436-451. https://doi.org/10.1016/S0377-2217(01)00130-8
Mensah, Y. M. (1984). An examination of the stationarity of multivariate bankruptcy prediction models: A methodological study. Journal of Accounting Research, 22(1), 380. https://doi.org/10.2307/2490719
Monelos, P. de L., Sánchez, C. P. y López, M. R. (2016). Business failure prediction. A contribution to the synthesis of a theory, through comparative analysis of different prediction techniques. Estudios de Economia, 43(2), 163-198. https://doi.org/10.4067/s0718-52862016000200001
Mures, J. y García, A. (2004). Factores determinantes del fracaso empresarial en Castilla y León. Revista de economía y empresa, 21(51), 95-116.
Ng-Henao, R. (2015). Marco metodológico para la determinación de la tasa de supervivencia empresarial en el sector industrial de la ciudad de Medellín en el periodo 2000-2010. Clío América, 9(18), 112. https://doi.org/10.21676/23897848.1529
Odom, M. D. y Sharda, R. (1990). A neural network model for bankruptcy prediction. IJCNN. International Joint Conference on Neural Networks, 163-168.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395
Ortega, A. (1987). Mortalidad: tablas de mortalidad. Naciones Unidas: XX Curso Regional Intensivo de Análisis Demográfico, 309. http://archivo.cepal.org/pdfs/1997/S9700588.pdf
Pereira, J., Crespo, M. y Sáez, J. (2019). La predicción del fracaso empresarial. Propuesta de un modelo secuencial basado en el análisis de supervivencia. Journal of Chemical Information and Modeling, 53(9), 1689-1699. https://doi.org/10.1017/CBO9781107415324.004
Promislow, D. (2011). Fundamentals of Actuarial Mathematics (2.a ed.). Wiley.
Promislow, D. (2015). Fundamentals of Actuarial Mathematics (3.a ed.). John Wiley & Sons, Inc.
Puebla, D., Tamayo, D. y Feijoó, E. (2015). Supervivencia empresarial: factores asociados al cierre de empresas del sector productivo ecuatoriano en el periodo 2009-2015. Instituto Nacional de Estadísticas y Censos, 26.
Puebla, D., Tamayo, D. y Feijoó, E. (2018). Factores relacionados a la supervivencia empresarial: evidencia para Ecuador. Analítika, 16.
Schumpeter, J. (1942). Capitalismo, socialismo y democracia. Harper & Brothers.
Stinchcombe, A. (1965). Social structure and organizations. Routledge.
Supercias (2016). Resoluciones SCVS-INC-DNCDN-2016-010. Registro Oficial - 868 --Primer Suplemento.
Supercias (2019). Portal de información. https://appscvsmovil.supercias.gob.ec/portalInformacion/index.zul
Williams, N. y Vorley, T. (2014). Economic resilience and entrepreneurship: Lessons from the Sheffield City Region. Entrepreneurship and Regional Development, 26(3-4), 257-281. https://doi.org/10.1080/08985626.2014.894129
Zhang, G., Hu, M. Y., Patuwo, B. E. y Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research, 116(1), 16-32. https://doi.org/10.1016/S0377-2217(98)00051-4