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Insolvency of Brazilian electricity distributors: a DEA bootstrap approach

    Rodrigo Simonassi Scalzer Affiliation
    ; Adriano Rodrigues Affiliation
    ; Marcelo Álvaro da Silva Macedo Affiliation
    ; Peter Wanke Affiliation

Abstract

This study investigates the financial and operational indicators that explain the insolvency of Brazilian electricity distributors, using a data envelopment analysis (DEA) bootstrap approach. The Wagner and Shimshak (2007) stepwise procedure was used to select the variables that had the greatest impact on average efficiency estimated by DEA in the construction of an inefficient frontier. Through a second stage analysis, the Simar and Wilson (2007) bootstrapped truncated regression analyzed contextual variables associated with inefficiency, and consequently with firm insolvency. The sample was composed of electricity distributors, whose financial information for the 2000–2015 period was available on the Brazilian Securities Exchange (CVM) website. The results indicated that the Actual Equivalent Frequency of Power Interruptions/Regulatory Equivalent Frequency of Power Interruptions and Overall Indebtedness were the most important indicators in explaining insolvency. The second-stage analysis showed that the inefficiencies calculated using the selected indicators are positively related to insolvency criteria used by the literature, state control, dollar and geographical location, and negatively related to the domestic inflation index. The results provide valuable information for the Brazilian electricity sector’s regulatory body, which recently began to hold public hearings prior to setting up procedures for monitoring financial sustainability using financial and operational indicators.

Keyword : electricity distributors, Brazil, insolvency, DEA, stepwise selection, inefficiency

How to Cite
Scalzer, R. S., Rodrigues, A., Macedo, M. Álvaro da S., & Wanke, P. (2018). Insolvency of Brazilian electricity distributors: a DEA bootstrap approach. Technological and Economic Development of Economy, 24(2), 718–738. https://doi.org/10.3846/20294913.2017.1318312
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References

ABRADEE. 2016. Setor de Distribuição [online], [cited 7 July 2016]. Associação Brasileira de Disitribuidoras de Energia Elétrica. Available from Internet: http://abradee.org.br/setor-de-distribuicao/banco-de-dados/banco-de-dados

Adler, N.; Yazhemsky, E. 2010. Improving discrimination in data envelopment analysis: PCA–DEA or variable reduction, European Journal of Operational Research 202(1): 273– 284. https://doi.org/10.1016/j.ejor.2009.03.050

Ali, A. I.; Seiford, L. M. 1990. Translation invariance in data envelopment analysis, Operations Research Letters 9(6): 403–405. https://doi.org/10.1016/0167-6377(90)90061-9

Altman, E. I. 1968. Financial Ratios, Discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance 23(4): 589–609. https://doi.org/10.2307/2978933

Altman, E. I. 1984. The success of business failure prediction models, Journal of Banking & Finance 8(2): 171–198. https://doi.org/10.1016/0378-4266(84)90003-7

Altman, E. I.; Saunders, A. 1997. Credit risk measurement: developments over the last 20 years, Journal of Banking & Finance 21(11–12): 1721–1742. https://doi.org/10.1016/S0378-4266(97)00036-8

ANEEL. 2014a. Nota Técnica n.353 [online], [cited 7 July 2016]. Agência Nacional de Energia Elétrica. Available from Internet: http://www.aneel.gov.br/aplicacoes/consulta_publica/documentos/NT_Indicadores%20de%20Sustentabilidade.pdf

ANEEL. 2014b. Consulta Pública n.15 [online], [cited 7 July 2016]. Agência Nacional de Energia Elétrica. Available from Internet: http://www2.aneel.gov.br/aplicacoes/consulta_publica/detalhes_con¬sulta.cfm?IdConsultaPublica=266

ANEEL. 2015. Metodologia de Custos Operacionais [online], [cited 7 July 2016]. Agência Nacional de Energia Elétrica. Available from Internet: http://www.aneel.gov.br/aplicacoes/audiencia/arquivo/2014/023/resultado/nota_tecnica_custos_operacionais.pdf

ANEEL. 2016. Nota Técnica n.67 [online], [cited 7 July 2016]. Agência Nacional de Energia Elétrica. Available from Internet: http://www2.aneel.gov.br/aplicacoes/consulta_publica/documentos/Nota%20Técnica%202016%20067.pdf

Atici, K. B.; Ulucan, A. 2011. A multiple criteria energy decision support system, Technological and Economic Development of Economy 17(2): 219–245. https://doi.org/10.3846/20294913.2011.580563

Aziz, M. A.; Dar, H. A. 2006. Predicting corporate bankruptcy: where we stand?, Corporate Governance: The International Journal of Business in Society 6(1): 18– 33. https://doi.org/10.1108/14720700610649436

Bagdadioglu, N.; Waddams Price, C. M.; Weyman-Jones, T. G. 1996. Efficiency and ownership in electricity distribution: a non-parametric model of the Turkish experience, Energy Economics 18(1–2): 1–23. https://doi.org/10.1016/0140-9883(95)00042-9

Balcaen, S.; Manigart, S.; Buyze, J.; Ooghe, H. 2011. Firm exit after distress: differentiating between bankruptcy, voluntary liquidation and M&A, Small Business Economics 39(4): 949–975. https://doi.org/10.1007/s11187-011-9342-7

Balcaen, S.; Manigart, S.; Ooghe, H. 2010. From distress to exit: determinants of the time to exit, Journal of Evolutionary Economics 21(3): 407–446. https://doi.org/10.1007/s00191-010-0192-2

Balcaen, S.; Ooghe, H. 2006. 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems, The British Accounting Review 38(1): 63–93. https://doi.org/10.1016/j.bar.2005.09.001

Banker, R. D. 1993. Maximum likelihood, consistency and data envelopment analysis: a statistical foundation, Management Science 39(10): 1265–1273. https://doi.org/10.1287/mnsc.39.10.1265

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. https://doi.org/10.1287/mnsc.30.9.1078

Beaver, W. H. 1966. Financial ratios as predictors of failure, Journal of Accounting Research 4(1): 71–111. https://doi.org/10.2307/2490171

Bhabra, G. S.; Yao, Y. 2011. Is bankruptcy costly? Recent evidence on the magnitude and determinants of indirect bankruptcy costs, Journal of Applied Finance and Banking 1(2): 39–68.

Bogetoft, P.; Otto, L. 2011. Benchmarking with DEA, SFA, and R. 1th ed. New York: Springer. https://doi.org/10.1007/978-1-4419-7961-2

Boguslauskas, V.; Mileris, R.; Adlytė, R. 2011. New internal rating approach for credit risk assessment, Technological and Economic Development of Economy 17(2): 369– 381. https://doi.org/10.3846/20294913.2011.583721

Brazilian Securities Exchange. 2016. [online], [cited 7 July 2016]. Comissão de Valores Mobiliários. Available from Internet: http://www.cvm.gov.br/menu/regulados/companhias/ companhias.html

Burinskiene, M.; Rudzkis, P. 2010. Feasibility of the liberal electricity market under conditions of a small and imperfect market. The case of Lithuania, Technological and Economic Development of Economy 16(3): 555–566. https://doi.org/10.3846/tede.2010.34

Charnes, A.; Cooper, W. W.; Golany, B.; Seiford, L.; Stutz, J. 1985. Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions, Journal of Econometrics 30(1–2): 91–107. https://doi.org/10.1016/0304-4076(85)90133-2

Charnes, A.; Cooper, W. W.; Rhodes, E. 1978. Measuring the efficiency of decision making units, European Journal of Operational Research 2(6): 429–444. https://doi.org/10.1016/0377-2217(78)90138-8

Cielen, A.; Peeters, L.; Vanhoof, K. 2004. Bankruptcy prediction using a data envelopment analysis, European Journal of Operational Research 154(2): 526–532. https://doi.org/10.1016/S0377-2217(03)00186-3

Comdinheiro database. 2016. [online], [cited 7 July 2016]. Available from Internet: https://www.comdinheiro.com.br/home2/

Cooper, W. W.; Park, K. S.; Pastor, J. T. 1999. RAM: a range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA, Journal of Productivity Analysis 11(1): 5–42. https://doi.org/10.1023/A:1007701304281

Costellini, C.; Hollanda, L. 2014. Setor Elétrico: da MP 579 ao Pacote Financeiro [online], [cited 7 July 2016]. Available from Internet: http://fgvenergia.fgv.br/artigos/setor-eletrico-da-mp-579-ao-pacote-financeiro

Decree 8461:2015. Regulates the Extension of Electricity Distribution Concessions. Brazilian Standard.

Dimitras, A. I.; Zanakis, S. H.; Zopounidis, C. 1996. A survey of business failures with an emphasis on prediction methods and industrial applications, European Journal of Operational Research 90(3): 487–513. https://doi.org/10.1016/0377-2217(95)00070-4

Duane, T. P. 2002. Regulation’s rationale: learning from the California energy crisis, Yale Journal on Regulation 19(2): 471–540.

ERCP. 2001. Financial Capability Standards for the Restructured Philippine Electric Power Industry [online], [cited 7 July 2016]. Energy Regulatory Commission of Philippine. Available from Internet: http://pdf.usaid.gov/pdf_docs/PNADG892.pdf

Fernandez-Castro, A.; Smith, P. 1994. Towards a general non-parametric model of corporate performance, Omega 22(3): 237–249. https://doi.org/10.1016/0305-0483(94)90037-X

Fitch. 2014. U.S. Public Power Rating Criteria [online], [cited 7 July 2016]. Fitch Ratings. Available from Internet: https://www.fitchratings.com/jsp/general/Research.faces;jsessionid=iPJpqagZff095xeRuOb9HI6?listingName=criteriaReport

Gonçalves, T. S. H.; Ferreira, F. A. F.; Jalali, M. S.; Meidutė-Kavaliauskienė, I. 2016. An idiosyncratic decision support system for credit risk analysis of small and medium-sized enterprises, Technological and Economic Development of Economy 22(4): 598–616. https://doi.org/10.3846/20294913.2015.1074125

Jackson, R. H. G.; Wood, A. 2013. The performance of insolvency prediction and credit risk models in the UK: a comparative study, The British Accounting Review 45(3): 183–202. https://doi.org/10.1016/j.bar.2013.06.009

Jenkins, L.; Anderson, M. 2003. A multivariate statistical approach to reducing the number of variables in data envelopment analysis, European Journal of Operational Research 147(1): 51–61. https://doi.org/10.1016/S0377-2217(02)00243-6

Kao, C.; Liu, S. 2004. Predicting bank performance with financial forecasts: a case of Taiwan commercial banks, Journal of Banking & Finance 28(10): 2353–2368. https://doi.org/10.1016/j.jbankfin.2003.09.008

Kassai, S. 2002. Utilização da análise por envoltória de dados (DEA) na análise de demonstrações contábeis: PhD thesis. University of São Paulo, Brazil.

Kittelsen, S. A. C. 1993. Stepwise DEA: choosing variables for measuring technical efficiency in norwegian electricity distribution. Memorandum 06/1993. Oslo University, Norway.

Kumbhakar, S. C.; Hjalmarsson, L. 1998. Relative performance of public and private ownership under yardstick competition: electricity retail distribution, European Economic Review 42(1): 97–122. https://doi.org/10.1016/S0014-2921(96)00052-9

Liu, J. S.; Lu, L. Y. Y.; Lu, W.; Lin, B. J. Y. 2013. Data envelopment analysis 1978–2010: A citation-based literature survey, Omega 41(1): 3–15. https://doi.org/10.1016/j.omega.2010.12.006

Malíková, O.; Brabec, Z. 2012. The influence of a different accounting system on informative value of selected financial ratios, Technological and Economic Development of Economy 18(1): 149–163. https://doi.org/10.3846/20294913.2012.661193

Mendes, A.; Cardoso, R. L.; Mário, P. C.; Martinez, A. L.; Ferreira, F. R. 2014. Insolvency prediction in the presence of data inconsistencies, Intelligent Systems in Accounting, Finance and Management 21(3): 155–167. https://doi.org/10.1002/isaf.1352

Moody’s. 2013. Regulated Electric and Gas Utilities [online], [cited 7 July 2016]. Moody’s Investors Service. Available from Internet: https://www.moodys.com/research/Moodys-publishes-revised-methodology-for-Regulated-Electric-and-Gas-Utilities--PR_289882

Nenide, B.; Pricer, R. W.; Camp, M. S. 2003. The use of financial ratios for research: problems associated with and recommendations for using large databases, in Fourth Annual Conference of the National Business and Economics Society, 5–8 March 2003, St. Thomas, U.S. Virgin Islands.

Nova, S. P. C. 2010. Bons em ser ruins: a utilização da análise por envoltória de dados (DEA) em modelos de análise de inadimplência/insolvência de empresas, in XXXIV Encontro da ANPAD, 25–29 Setembro 2010, Rio de Janeiro, Brasil.

Nova, S. P. C. 2013. Quanto pior, melhor: estudo da utilização da análise por envoltória de dados em modelos de análise de inadimplência/insolvência de empresas, Revista Contemporânea de Contabilidade 10(19): 71–96. https://doi.org/10.5007/2175-8069.2013v10n19p71

NYPSC. 2014. Five Year Book Index of Files – 2014 [online], [cited 7 July 2016]. New York Public Service Commission. Available from Internet: http://www3.dps.ny.gov/W/PSCWeb.nsf/All/9CBDFD5CFB8664AF85257F630061C8F1?OpenDocument

OEB. 2014. Performance Measurement for Electricity Distributors: A Scorecard Approach [online], [cited 7 July 2016]. Ontario Energy Board. Available from Internet: http://www.ontarioenergyboard.ca/oeb/_Documents/EB- 2010-0379/Report_of_the_Board_Scorecard_20140305.pdf

Oh, N. 2014. Financial distress prediction models for wind energy SMEs, International Journal of Contents 10(4): 75–82. https://doi.org/10.5392/IJoC.2014.10.4.075

Ohlson, J. A. 1980. Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research 18(1): 109–131. https://doi.org/10.2307/2490395

Onusic, L. M.; Casa Nova, S. P. C.; Almeida, F. C. 2007. Modelos de previsão de insolvência utilizando a análise por envoltória de dados: aplicação a empresas brasileiras, Revista de Administração Contemporânea 11(2): 77–97. https://doi.org/10.1590/S1415-65552007000600005

Paradi, J. C.; Asmild, M.; Simak, P. C. 2004. Using DEA and worst practice DEA in credit risk evaluation, Journal of Productivity Analysis 21(2): 153–165. https://doi.org/10.1023/B:PROD.0000016870.47060.0b

Pastor, J. T. 1997. Translation invariance in data envelopment analysis: a generalization, Annals of Operations Research 73(1–4): 93–102.

Premachandra, I. M.; Bhabra, G. S.; Sueyoshi, T. 2009. DEA as a tool for bankruptcy assessment: a comparative study with logistic regression technique, European Journal of Operational Research 193(2): 412–424. https://doi.org/10.1016/j.ejor.2007.11.036

Premachandra, I. M.; Chen, Y.; Watson, J. 2011. DEA as a tool for predicting corporate failure and success: a case of bankruptcy assessment, Omega 39(6): 620–626. https://doi.org/10.1016/j.omega.2011.01.002

Provisional Measure 579:2012. Provides for the Concessions for the Generation, Transmission and Distribution of Electric Energy, for the Reduction of Sectoral Charges and for the Tariff Modicity. Brazilian Standard.

Ravi Kumar, P.; Ravi, V. 2007. Bankruptcy prediction in banks and firms via statistical and intelligent techniques – a review, European Journal of Operational Research 180(1): 1–28. https://doi.org/10.1016/j.ejor.2006.08.043

Resende, M. 2002. Relative efficiency measurement and prospects for yardstick competition in Brazilian electricity distribution, Energy Policy 30(8): 637–647. https://doi.org/10.1016/S0301-4215(01)00132-X

SARI/EI; USAID. 2004. Performance Benchmarks for Electricity Distribution Companies in South Asia [online], [cited 7 July 2016]. South Asia Regional Initiative for Energy Integration. Available from Internet: http://pdf.usaid.gov/pdf_docs/PNADD964.pdf

S&P. 2013. Utilities: Key Credit Factors For The Regulated Utilities Industry [online], [cited 7 July 2016]. Standard and Poors Rating Services. Available from Internet: http://www.standardandpoors.com/en_US/web/guest/article//view/type/HTML/sourceAssetId/1245361551274

Scalzer, R. S.; Rodrigues, A.; Macedo, M. Á. S. 2015. Insolvência empresarial: um estudo sobre as dis¬tribuidoras de energia elétrica brasileiras, Revista Contemporânea de Contabilidade 12(27): 27–60. https://doi.org/10.5007/2175-8069.2015v12n27p27

Schmidt, P. 1985. Frontier production functions, Econometric Reviews 4(2): 289–328. https://doi.org/10.1080/07474938608800089

Shetty, U.; Pakkala, T. P. M.; Mallikarjunappa, T. 2012. A modified directional distance formulation of DEA to assess bankruptcy: an application to IT/ITES companies in India, Expert Systems with Applications 39(2): 1988–1997. https://doi.org/10.1016/j.eswa.2011.08.043

Simak, P. C. 1997. DEA based analysis of corporate failure: Master’s thesis. University of Toronto, Canada.

Simar, L.; Wilson, P. W. 2007. Estimation and inference in two-stage, semi-parametric models of production processes, Journal of Econometrics 136(1): 31–64. https://doi.org/10.1016/j.jeconom.2005.07.009

Simm, J.; Besstremyannaya, G. 2016. Robust Data Envelopment Analysis (DEA) for R [online], [cited 7 July 2016]. Available from Internet:

Smith, P. 1990. Data envelopment analysis applied to financial statements, Omega 18(2): 131–138.
https://doi.org/10.1016/0305-0483(90)90060-M

Sueyoshi, T. 2005. Financial ratio analysis of the electric power industry, Asia-Pacific Journal of Operational Research 22(3): 349–376. https://doi.org/10.1142/S0217595905000509

Sueyoshi, T. 2006. DEA-Discriminant Analysis: methodological comparison among eight discriminant analysis approaches, European Journal of Operational Research 169(1): 247–272. https://doi.org/10.1016/j.ejor.2004.05.025

Sueyoshi, T.; Goto, M. 2009. Methodological comparison between DEA (data envelopment analysis) and DEA–DA (discriminant analysis) from the perspective of bankruptcy assessment, European Journal of Operational Research 199(2): 561–575. https://doi.org/10.1016/j.ejor.2008.11.030

Thanassoulis, E. 1993. A comparison of regression analysis and data envelopment analysis as alternative methods for performance assessments, The Journal of the Operational Research Society 44(11): 1129–1144. https://doi.org/10.2307/2583874

Titko, J.; Stankevičienė, J.; Lāce, N. 2014. Measuring bank efficiency: DEA application, Technological and Economic Development of Economy 20(4): 739–757. https://doi.org/10.3846/20294913.2014.984255

Viscusi, W. K.; Harrington, J. E.; Vernon, J. M. 2005. Economics of regulation and antitrust. 4th ed. Cambridge: MIT Press.

Wagner, J. M.; Shimshak, D. G. 2007. Stepwise selection of variables in data envelopment analysis: procedures and managerial perspectives, European Journal of Operational Research 180(1): 57–67. https://doi.org/10.1016/j.ejor.2006.02.048

Wanke, P.; Barros, C. P.; Faria, J. R. 2015. Financial distress drivers in Brazilian banks: a dynamic slacks approach, European Journal of Operational Research 240(1): 258–268. https://doi.org/10.1016/j.ejor.2014.06.044

Warner, J. B. 1977. Bankruptcy costs: some evidence, The Journal of Finance 32(2): 337–347. https://doi.org/10.2307/2326766

Wruck, K. H. 1990. Financial distress, reorganization, and organizational efficiency, Journal of Financial Economics 27(2): 419–444. https://doi.org/10.1016/0304-405X(90)90063-6