La proximidad geográfica en el contagio del fracaso empresarial en la pymeUna aplicación empírica con el modelo probit espacial

  1. CHRISTIAN C. RODRÍGUEZ FUENTES 1
  2. MARILUZ MATÉ SÁNCHEZ-VAL 1
  3. FERNANDO A.LÓPEZ HERNÁNDEZ 1
  1. 1 Universidad Politécnica de Cartagena
    info

    Universidad Politécnica de Cartagena

    Cartagena, España

    ROR https://ror.org/02k5kx966

Revista:
Estudios de economía aplicada

ISSN: 1133-3197,  1697-5731

Año de publicación: 2016

Título del ejemplar: Datos, información y conocimiento en Economía

Volumen: 34

Número: 3

Páginas: 629-648

Tipo: Artículo

DOI: 10.25115/EAE.V34I3.3063 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Estudios de economía aplicada

Objetivos de desarrollo sostenible

Resumen

This paper tests the role of spillover effects derived from the geographic proximity among reduced size firms in business failure. To get this purpose, we develop an empirical application on a sample of 2.710 Spanish Small, Medium size Enterprises (SMEs) located in the region of Murcia. With this information, we estimate a spatial probit regression model to contrast the significance of business spillover effects in business failure models. Our results show that the probability of business failure in SMEs depends not only on its own characteristics but also on the probability of failure of geographically close firms. Factors associated with social and/or economic interactions among the agents linked to the different firms in the same region would be behind these results.

Referencias bibliográficas

  • ADDOUM, J.; KUMAR, A. y LE, N. (2014). “Contagious Negative Sentiment and Corporate Policies: Evidence from Local Bankruptcy Filings”. Finance Meeting EUROFI-DAI-AFFI Paper.
  • ALTMAN, E. (1968). “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy”. The Journal of Finance, 23(4), 589-609.
  • AUDRETSCH, D. y FELDMAN, M. (1996). “R&D spillovers and the geography of innovation and production”. The American Economic Review, 630-640.
  • BACK, P. (2005). “Explaining financial difficulties based on previous payment behavior, management background variables and financial ratios”. European Accounting Review, 14(4), 839-868.
  • BATTISTON, S.; GATTI, D.; GALLEGATI, M.; GREENWALD, B. y STIGLITZ, J. (2007). “Credit chains and bankruptcy propagation in production networks”. Journal of Economic Dynamics and Control, 31(6), 2061-2084.
  • BEAVER, W. (1966). “Financial ratios as predictors of failure”. Journal of Accounting Research, 71-111.
  • BERGER, A. N. Y UDELL, G. F. (1998). “The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle”. Journal of Banking & Finance 22 (6-8): 613-673.
  • CALVO-FLORES, A.; GARCÍA, D. y GUIJARRO, A. (2006). “Tamaño, antigüedad y fracaso empresarial”. Working paper nº1. Grupo Interuniversitario de Investigación. Análisis Estratégico para el Desarrollo de la Pyme.
  • CINCA, C.; MOLINERO, C. y LARRAZ, J. (2005). “Country and size effects in financial ratios: A European perspective”. Global Finance Journal, 16(1), 26-47.
  • CONTABILIDAD REGIONAL DE ESPAÑA (CRE). Base 2010, Serie 2010-2014, del Instituto Nacional de Estadística (INE). http://www.ine.es/daco/daco42/cre00/b2010/dacocre_base2010.htm [Último acceso: septiembre de 2015].
  • DEAKIN, E. (1972). “A discriminant analysis of predictors of business failure”. Journal of accounting Research, 167-179.
  • DEGRYSE, H. y ONGENA, S. (2005). “Distance, lending relationships, and competition”. The Journal of Finance, 60(1), 231-266.
  • DITTMAR, A. y DUCHIN, R. (2014). “Looking in the rear view mirror: the effect of managers’ professional experience on corporate financial policy”. Ross School of Business Paper, (1221).
  • EDMISTER, R. (1972). “An empirical test of financial ratio analysis for small business failure prediction”. Journal of Financial and Quantitative analysis, 7(2), 1477-1493.
  • FERNÁNDEZ, M. y GUTIERREZ, F. (2012). “Variables y modelos para la identificación y predicción del fracaso empresarial: revisión de la investigación empírica reciente”. Revista de Contabilidad, 15(1).
  • FRANZESE JR, R. y HAYS, J. (2009). “Empirical modeling of spatial interdependence in time-series cross-sections”. Methoden der vergleichenden Politik-und Sozialwissenschaft (pp. 233-261). VS Verlag für Sozialwissenschaften.
  • GATTI, D.; DI GUILMI, C.; GAFFEO, E.; GIULIONI, G.; GALLEGATI, M. y PALESTRINI, A (2005). “A new approach to business fluctuations: heterogeneous interacting agents, scaling laws and financial fragility”. Journal of Economic Behavior and Organization, 56(4), 489-512.
  • GEPP, A. y KUMAR, K. (2012). “Business failure prediction using statistical techniques: A review”. Some Recent Developments in Statistical Theory and Applications, 1-25.
  • GRANOVETTER, M. (1985). “Economic action and social structure: the problem of embeddedness”. American Journal of Sociology, 481-510.
  • HAUNSCHILD, P. y BECKMAN, C. (1998). “When Do Interlocks Matter?: Alternate Sources of Information and Interlock Influence”. Administrative Science Quarterly, 815-844.
  • HERTZEL, M. y OFFICER, M. (2012). “Industry contagion in loan spreads”. Journal of Financial Economics, 103(3), 493-506.
  • HERTZEL, M.; LI, Z.; OFFICER, M. y RODGERS, K. (2008). “Inter-firm linkages and the wealth effects of financial distress along the supply chain”. Journal of Financial Economics, 87(2), 374-387.
  • KOLAY, M.; LEMMON, M. y TASHJIAN, E. (2015). Spreading the Misery? Sources of Bankruptcy Spillover in the Supply Chain. San Diego Meetings Paper.
  • LANG, L. y STULZ, R. (1992). “Contagion and competitive intra-industry effects of bankruptcy announcements”. Journal of Financial Economics, 32(1), 45-60.
  • LE, N. (2012). Spillover effects of intra-industry bankruptcy filings on firms’ cash holding policy. Northern Finance Association Meeting.
  • LESAGE, J. y PACE, R. (2009). Introduction to Spatial Econometrics. Boca Raton, FL: CRC Press.
  • LI, H. y SUN, J. (2011). “Principal component case-based reasoning ensemble for business failure Prediction”. Information and Management, 48(6), 220-227.
  • LI, H. y XIONG, T. (2012). Predicting business risk using combined case-based reasoning in Euclidean space. World Automation Congress (WAC), 1-6.
  • MATÉ, M.; HERNÁNDEZ, G.; SÁNCHEZ, J. y MÍNGUEZ, A. (2013). “Are there spill-over effects into financial behaviour of SMEs?”. Trimestre Económico, 80(320), 841-867.
  • MATÉ, M.; GARCÍA D. y LÓPEZ F. (2009). “Spatial effects in the productivity convergence of Spanish industrial SME's”. Spanish Journal of Finance and Accounting, 38(141), 13-36.
  • MATEOS, A. y LÓPEZ A. (2011). “Developing a business failure prediction model for cooperatives: Results of an empirical study in Spain”. African Journal of Business Management, 5(26), 10565-10576.
  • MCCULLOCH, W. y PITTS, W. (1943). “A logical calculus of the ideas immanent in nervous activity”. The bulletin of mathematical biophysics, 5(4), 115-133.
  • MCMILLEN, D. (1992). “Probit with spatial autocorrelation”. Journal of Regional Science, 32(3), 335-348.
  • MISAS, M. (2008). “Análisis del fracaso empresarial en Andalucía. Especial referencia a la edad de la empresa”. Cuadernos de Ciencias Económicas y Empresariales, 1(54), 35-56.
  • MURDOCH, J.; SANDLER, T. y VIJVERBERG, W. (2003). “The participation decision versus the level of participation in an environmental treaty: A spatial probit analysis”. Journal of Public Economics, 87 (2), 337-362.
  • OHLSON, J. (1980). “Financial ratios and the probabilistic prediction of bankruptcy”. Journal of Accounting Research, 109-131.
  • PARSONS, A.; SULAEMAN, J. y TITMAN, S. (2014). “The geography of financial misconduct”. (No. w20347) National Bureau of Economic Research.
  • PINKSE, J. y SLADE, M. (1998). “Contracting in space: An application of spatial statistics to discrete-choice models”. Journal of Econometrics, 85(1), 125-154.
  • PIRINSKY, C. y WANG, Q. (2010). Geographic location and corporate finance: A review. Handbook of Emerging Issues in Corporate Governance. London, UK, World Scien-tific Publishing.
  • QUINTANA, M.; GALLEGO, A. y PASCUAL, M. (2012). “Análisis del fracaso empresarial por sectores: factores diferenciadores”. Pecunia: Revista de la Facultad de Ciencias Económicas y Empresariales, (1), 53-83.
  • RODRÍGUEZ, M.; PIÑEIRO, C. y DE LLANO, P. (2015). Predicción de insolvencia y fracaso financiero: medio siglo después de Beaver (1966). Avances y nuevos resul-tados. XVIII Congreso AECA, Cartagena, Octubre 2015.
  • ROKACH, L. y MAIMON, O. (2008). Data Mining with Decision Trees: Theory and Appli-cations. World Scientific Publishing.
  • SHUMWAY, T. (2001). “Forecasting bankruptcy more accurately: A simple hazard model”. The Journal of Business, 74(1), 101-124.
  • TURETSKY, H. y MCEWEN, R. (2001). “An Empirical Investigation of Firm Longevity: A Model of the Ex Ante Predictors of Financial Distress”. Review of Quantitative Finance and Accounting, 16(4). 323-343.
  • WATSON, J. y EVERETT, J. (1996). “Small Business Failure Rates: Choice of Definition and the Size Effect”. The Journal of Entrepreneurial and Small Business Finance, 5(3), 271-285.
  • WILHELM, S. y DE MATOS, M. (2013). “Estimating spatial probit models in R”. The R Journal, 5(1), 130-143.
  • XU, W.; XIAO, Z.; DANG, X.; YANG, D. y YANG, X. (2014). “Financial ratio selection for business failure prediction using soft set theory”. Knowledge-Based Systems, 63, 59-67.
  • YOUN, H. y GU, Z. (2010). “Predicting Korean lodging firm failures: An artificial neural network model along with a logistic regression model”. International Journal of Hospitality Management, 29(1), 120-127.
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