Advertisement

Economia e Politica Industriale

, Volume 45, Issue 4, pp 475–491 | Cite as

Spatial autocorrelation and clusters in modelling corporate bankruptcy of manufacturing firms

  • M. Simona Andreano
  • Roberto Benedetti
  • Andrea Mazzitelli
  • Federica Piersimoni
Article
  • 26 Downloads

Abstract

The interest in the prediction of firms’ bankruptcy is increasing in the recent recession period 2008–2012, when, in Italy, the number of distressed manufacturing firms increased sharply. The most popular model applied by bankruptcy researchers is the logit model (logistic regression model). In the present paper we extend this classical model in two different ways, to take into account the spatial effects that can highly affect bankruptcy probability. We propose to apply the spatial Autologistic model and the Logit Regression Tree, with the aim to find evidence of spatial dependence and spatial heterogeneity in bankruptcy probability, of the manufacturing firms of Prato and Florence (Italy). Our application shows that spatial contagion effects are an important issue when modelling bankruptcy probability. Moreover, the application of the regression tree analysis shows the presence of three different clusters, with heterogeneous behaviours.

Keywords

Default probability Autologistic model Heterogeneity Spatial dependence 

JEL Classification

C13 C21 C54 R12 

Notes

Funding

This study was not funded by any Institution.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Agarwal, R., & Gort, M. (2002). Firm and product life cycles and firm survival. American Economic Review, 92(2), 184–190.CrossRefGoogle Scholar
  2. Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: Wiley.CrossRefGoogle Scholar
  3. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(3), 589–609.CrossRefGoogle Scholar
  4. Altman, E. I., Haldeman, R. G., & Narayana, P. (1977). ZETA analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking & Finance, 1(1), 29–54.CrossRefGoogle Scholar
  5. Anselin, L. (1988). Spatial econometrics: Methods and models. Dordrecht: Kluwer Academic Publishers.CrossRefGoogle Scholar
  6. Bauer, J., & Agarwal, V. (2014). Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test. Journal of Banking & Finance, 40, 432–444.CrossRefGoogle Scholar
  7. Beaver, W. (1966). Financial rations as predictors of failure. Journal of Accounting Research, 3, 71–111.CrossRefGoogle Scholar
  8. Bellone, F., Musso, P., Nesta, L., & Quéré, M. (2006). Productivity and market selection of French manufacturing firms in the Nineties. Revue de L’OFCE, 97(5), 319–349.Google Scholar
  9. Bernard, A., & Jensen, J. (2007). Firm structure, multinationals, and manufacturing plant deaths. The Review of Economics and Statistics, 89(2), 193–204.CrossRefGoogle Scholar
  10. Besag, J. (1972). Nearest-neighbour systems and the auto-logistic model for binary data. Journal of the Royal Statistical Society B, 34, 75–83.Google Scholar
  11. Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society B, 36, 192–236.Google Scholar
  12. Boari, C., Odorici, V., & Zamarian, M. (2003). Clusters and rivalry: Does localization really matter? Scandinavian Journal of Management, 19(4), 467–489.CrossRefGoogle Scholar
  13. Bonaccorsi di Patti, E., D’Ignazio, A., Gallo, M., & Micucci, G. (2015). The role of leverage in firm solvency: Evidence from bank loans. Italian Economic Journal, 1, 253–286.CrossRefGoogle Scholar
  14. Box, M. (2008). The death of firms: exploring the effects of environment and birth cohorton firm survival in Sweden. Small Business Economics, 31(4), 379–393.CrossRefGoogle Scholar
  15. Brealey, R. A., Myers, S. C., & Marcus, A. J. (2011). Fundamentals of corporate finance. International edition. New York: McGraw Hill Irwin.Google Scholar
  16. Cainelli, G., Iacobucci, D., & Morganti, E. (2006). Spatial agglomeration and business groups: New evidence from Italian industrial districts. Regional Studies, 40(5), 507–518.CrossRefGoogle Scholar
  17. Cashin, P., & Dattagupta, R. (2008). The anatomy of banking crises. Washington DC: IMF WP.Google Scholar
  18. Cressie, N. (1993). Statistics for spatial data, revised edition. New York: Wiley.Google Scholar
  19. Delgado, M., Porter, M. E., & Stern, S. (2014). Clusters, convergence, and economic performance. Research Policy, 43, 1785–1799.CrossRefGoogle Scholar
  20. Di Giacinto, V., Gomellini, M., Micucci, G., & Pagnini, M. (2014). Mapping local productivity advantages in Italy: Industrial districts, cities or both? Journal of Economic Geography, 14(2), 365–394.CrossRefGoogle Scholar
  21. Disney, R., Haskel, J., & Heden, Y. (2003). Entry, exit and establishment survival in UK manufacturing. Journal of Industrial Economics, 51(1), 91–112.CrossRefGoogle Scholar
  22. Dormann, C. F. (2007). Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecological Biogeography, 16, 129–138.CrossRefGoogle Scholar
  23. Esteve-Pérez, S., Sanchis, Llopis A., & Llopis, J. A. (2004). The determinants of survival of Spanish manufacturing firms. Review of Industrial Organization, 25(3), 251–273.CrossRefGoogle Scholar
  24. Esteve-Pérez, S., Mañez-Castillejo, J. A. (2008). The resource-based theory of the firm and firm survival. Small Business Economics, 30(3), 231–249.CrossRefGoogle Scholar
  25. Fackler, D., Schnabel, C., & Wagner, J. (2013). Establishment exits in Germany: The role of size and age. Small Business Economics, 41, 683–700.CrossRefGoogle Scholar
  26. Ferretti, S., Filippone, A., & Micucci, G. (2016). Le imprese uscite dal mercato nel corso della crisi (p. 317). Rome: Banca d’Italia, Occasional Papers.Google Scholar
  27. Folta, T. B., Cooper, A. C., & Baik, Y. S. (2006). Geographic cluster size and firm performance. Journal of Business Venturing, 21, 217–242.CrossRefGoogle Scholar
  28. Fort, T. C., Haltiwanger, J., Jarmin, R. S., & Miranda, J. (2013). How firms respond to business cycles: The role of firm age and firm size. Cambridge: NBER WP.CrossRefGoogle Scholar
  29. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. Chichester: Wiley.Google Scholar
  30. Fotopoulos, G., & Louri, H. (2000). Location and survival of new entry. Small Business Economics, 14, 311–321.CrossRefGoogle Scholar
  31. Frank, E., Wang, Y., Inglis, S., Holmes, G., & Witten, I. H. (1998). Using model trees for classification. Machine Learning, 32(1), 63–76.CrossRefGoogle Scholar
  32. Giovannetti, G., Ricchiuti, G., & Velucchi, M. (2011). Size, innovation and internationalization: A survival analysis of Italian firms. Applied Economics, 43(12), 1511–1520.CrossRefGoogle Scholar
  33. Gissel, J. L., Giacomino, D., & Akers, M. D. (2007). A review of bankruptcy prediction studies: 1930–present. Milwaukee: Marquette University WP.Google Scholar
  34. Gotway, C. A., & Stroup, W. W. (1997). A generalized linear model approach to spatial data analysis and prediction. Journal of Agriculture, Biological and Environmental Statistics, 2, 157–178.CrossRefGoogle Scholar
  35. Honjo, Y. (2000). Business failure of new firms: An empirical analysis using a multiplicative hazards model. International Journal of Industrial Organization, 18, 557–574.CrossRefGoogle Scholar
  36. Hughes, J., Haran, M., & Caragea, P. C. (2011). Autologistic models for binary data on a lattice. Environmetrics, 22, 857–871.CrossRefGoogle Scholar
  37. Istat (2015) The new geography of the labor market areas. Istat, Simplicissimus Book Farm, RomeGoogle Scholar
  38. Jones, S., & Hensher, D. A. (2004). Predicting firm financial distress: A mixed logit model. The Accounting Review, 79(4), 1011–1038.CrossRefGoogle Scholar
  39. Jones, S., & Hensher, D. A. (Eds.). (2008). Advances in credit risk modelling and corporate bankruptcy prediction. Cambridge: Cambridge University Press.Google Scholar
  40. Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting corporate bankruptcy: An evaluation of alternative statistical frameworks. Journal of Business Finance & Accounting, 44(1), 3–34.CrossRefGoogle Scholar
  41. Landwehr, N., Hall, M., & Frank, E. (2003). Logistic model trees. In Proceedings of the 14th European Conference on Machine Learning (pp. 241–252).CrossRefGoogle Scholar
  42. Landwehr, N., Hall, M., & Frank, E. (2005). Logistic model trees. Machine Learning, 59, 161–205.CrossRefGoogle Scholar
  43. Lau, A. H. (1987). A five-state financial distress prediction model. Journal of Accounting Research., 25(1), 127–138.CrossRefGoogle Scholar
  44. Legendre, P. (1993). Spatial autocorrelation: Trouble or new paradigm? Ecology, 74, 1659–1673.CrossRefGoogle Scholar
  45. Loader, C. (1999). Local regression and likelihood. New York: Springer.Google Scholar
  46. Lopez-Garcia, P., Puente, S., & Gómez, Á. L. (2007). Firm productivity dynamics in Spain. Banco de España Working Paper No. 0739.Google Scholar
  47. Maine, E. M., Shapiro, D. M., & Vining, A. R. (2010). The role of clustering in the growth of new technology-based firms. Small Business Economics, 34, 127–146.CrossRefGoogle Scholar
  48. Manjón-Antolín, M., & Arauzo-Carod, J. M. (2008). Firm survival: Methods and evidence. Empirica, 35(1), 1–24.CrossRefGoogle Scholar
  49. Mariani, M., Pirani, E., & Radicchi, E. (2013). La sopravvivenza delle imprese negli anni della crisi: Prime evidenze empiriche dalla Toscana. Economia e Politica Industriale, 1(1), 25–52.CrossRefGoogle Scholar
  50. McCann, B. T., & Folta, T. B. (2011). Performance differentials within geographic clusters. Journal of Business Venturing, 26, 104–123.CrossRefGoogle Scholar
  51. McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). New York: Chapman & Hall.CrossRefGoogle Scholar
  52. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131.CrossRefGoogle Scholar
  53. Ortega-Colomer, F. J., Molina-Morales, F. X. (2016). Discussing the concepts of cluster and industrial district. Journal of Technology Management and Innovation, 11(2), 139–147.CrossRefGoogle Scholar
  54. Porter, M. (1990). The competitive advantage of nations. New York: Free Press.CrossRefGoogle Scholar
  55. Porter, M. (2003). The economic performance of regions. Regional Studies, 37(6&7), 549–578.CrossRefGoogle Scholar
  56. Postiglione, P., Benedetti, R., & Lafratta, G. (2008). A regression tree algorithm for the identification of convergence clubs. Computational Statistics & Data Analysis, 54, 2776–2785.CrossRefGoogle Scholar
  57. Ramazzotti, P. (2010). Industrial districts, social cohesion and economic decline in Italy. Cambridge Journal of Economics, 34, 955–974.CrossRefGoogle Scholar
  58. Regione Toscana (2017). I distretti industriali toscani secondo i principali indicatori di contesto, Ufficio Regionale di Statistica, May 2017.Google Scholar
  59. Rusch, T., Lee, I., Hornik, K., Jank, W., & Zeileis, A. (2013). Influencing elections with statistics: Targeting voters with logistic regression trees. The Annals of Applied Statistics, 7(3), 1612–1639.CrossRefGoogle Scholar
  60. Sherman, M., Apanasovich, T. V., & Carroll, R. J. (2006). On estimation in binary autologistic spatial models. Journal of Statistical Computation and Simulation, 76, 167–179.CrossRefGoogle Scholar
  61. Strotmann, H. (2007). Entrepreneurial survival. Small Business Economics, 28(1), 87–104.CrossRefGoogle Scholar
  62. Tan, H., & See, H. (2004). Strategic reorientation and responses to the Asian financial crisis: the case of the manufacturing industry in Singapore. Asia Pacific Journal of Management, 21, 189–211.CrossRefGoogle Scholar
  63. Tokunaga, S., Kageyama, M., Akune, Y., & Nakamura, R. (2014). Empirical analysis of agglomeration economies in the japanese assembly-type manufacturing industry for 1985–2000: Using agglomeration and coagglomeration indices. Review of Urban and Regional Development Studies, 26(1), 57–79.CrossRefGoogle Scholar
  64. Varum, C. A., & Rocha, V. C. B. (2012). Do foreign and domestic firms behave any different during economic slowdowns? International Business Review, 20(1), 48–59.CrossRefGoogle Scholar
  65. Wheeler, D., & Calder, C. (2007). An assessment of coefficient accuracy in linear regression models with spatially varying coefficients. Journal of Geographical Systems, 9, 145–166.CrossRefGoogle Scholar
  66. Zhang, H., & Singer, B. (2010). Recursive partitioning and applications. New York: Springer.CrossRefGoogle Scholar
  67. Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22(1), 59–82.CrossRefGoogle Scholar

Copyright information

© Associazione Amici di Economia e Politica Industriale 2018

Authors and Affiliations

  1. 1.Universitas MercatorumRomeItaly
  2. 2.University of Chieti-PescaraPescaraItaly
  3. 3.Italian National Statistical InstituteRomeItaly

Personalised recommendations