Estimating and Assessment of Technical Efficiency in Listed Petrochemical Companies: Bootstrap-DEA Approach

Document Type : Original Article

Authors

1 Ph.D. Student, Department of Economics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.

2 Assistant professor, Department of Economic, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.

10.48308/jimp.14.4.121

Abstract

Introduction and purpose: The petrochemical industry in Iran, as a flagship sector, plays a pivotal role in the national economy, supplying a substantial share of its output to downstream industries. In recent decades, advancements in technology and research within the petrochemical sector have led to substantial improvements, reducing costs and enhancing efficiency. Therefore, considering the importance of the petrochemical industry and its role in economic growth, this article aims to evaluate the efficiency of petrochemical companies using the bootstrap-DEA approach. This method provides statistical inference regarding technical efficiency criteria in non-parametric frontier models.
Methods: To achieve this objective, a comprehensive two-stage approach was adopted. First, input-oriented efficiency scores were estimated under the assumptions of constant, variable, and non-increasing returns to scale for all companies, using radial (Debro-Farrell efficiency) and non-radial (Russell efficiency) methodologies to provide a robust evaluation. Then, a non-parametric independence test was conducted to identify the appropriate bootstrap implementation approach. Finally, considering the test results, the efficiency scores of petrochemical companies were estimated using the bootstrap-DEA approach. The bootstrap process allows for bias estimation and the calculation of confidence intervals for initial estimates.
Findings: The results indicate that Tondgoyan Company achieved Debro-Farrell efficiency under all three scenarios: constant, variable, and non-increasing returns to scale. Additionally, Zagros and Fanavaran companies exhibited Debro-Farrell efficiency under variable returns to scale. However, 18 other companies were inefficient, with Ghadir Petrochemical Company displaying the worst performance, achieving an efficiency score of 0.68 under constant returns to scale. Bias-corrected radial technical efficiency scores across homogeneous, heterogeneous, and subsampling bootstrap methods revealed that none of the 21 petrochemical companies exhibited full technical efficiency. Among them, Fanavaran and Tondgoyan showed better performance, while Aria, Pardis, Shiraz, and Isfahan Oil companies demonstrated the lowest input-oriented technical efficiency. Output-oriented technical efficiency findings indicated that Tondgoyan and Zagros were efficient, while Ghadir had the worst performance, with an efficiency score of 1.37, signifying 37% excess input consumption. The results further demonstrate that radial efficiency estimates are prone to overestimation, as confirmed by heterogeneous smooth bootstrap and subsampling techniques.
Conclusion: One of the primary objectives of Iran's 7th Development Plan is achieving 8% economic growth, with 2.8% attributed to productivity and efficiency improvements in production factors, including capital, human resources, technology, and management. Emphasis has been placed on bolstering the value-added chain within the petrochemical industry. Therefore, attention to the development of midstream and downstream petrochemical industries through reinvesting revenues from the export of upstream products is crucial for completing the value chain of the petrochemical sector.

Keywords

Main Subjects

References

  1. Adelinik, H. (2021). Compilation of industrial and mineral policies, value chain analysis report of petrochemical industry in Iran. Business Studies and Research Institute, Technology and Innovation Studies and Research Department (In Persian). http://trindoc.itsr.ir/ProductPlanDetail.aspx?ID=7151
  2. Allen, P.G., Fildes, R. (2001). Econometric Forecasting. In: Armstrong, J.S. (eds) Principles of Forecasting. International Series in Operations Research & Management Science, vol 30. Springer, Boston, MA.
  3. Alrashidi, A. N. (2015). Data envelopment analysis for measuring the efficiency of head trauma care in England and Wales. University of Salford (United Kingdom).
  4. Amiri, H. (2018). Evaluation the Effectiveness of Selected Banks in Iran and its Relationship with Banking Internal and Macroeconomic Variables. Journal of Applied Economics Studies in Iran, 7(26), 89-114 (In Persian)
  5. Atris, A. M. (2020). Assessment of oil refinery performance: Application of data envelopment analysis-discriminant analysis. Resources Policy, 65, 101543.
  6. Badunenko, O., Henderson, D. J., Kumbhakar, S. C. (2012). When, where and how to perform efficiency estimation. Journal of the Royal Statistical Society Series A: Statistics in Society, 175(4), 863-892.
  7. 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.
  8. Banker, R. D. (1993). Maximum likelihood, consistency and data envelopment analysis: A statistical foundation. Management Science, 39(10), 1265-1273.
  9. Bazargan, A. R., Najafi, S. E., Hosseinzadeh Lotfi, F., Fallah, M. (2023). Technical analysis of petrochemical industries of Iran using a network data envelopment analysis model. International journal of research in industrial engineering, 12(4), 337-363.
  10. (2022). Chemicals Trends Report. https://cefic.org/cefic-chemicals-trends-report/
  11. Charnes, A., Cooper, W. W., Rhodes, E. (1977). Measuring the efficiency of decision-making units with some new production functions and estimation methods. Center for Cybernetic Studies, University of Texas at Austin.
  12. Charnes, A., Cooper, W. W., Rhodes, E. (1978). Measuring the efficiency of decision-making units. European journal of operational research, 2(6), 429-444.
  13. Coelho, M. B., Lacerda, D. P., Piran, F. A. S., Silva, D. O. D., Sellitto, M. A. (2023). Project Management Efficiency Measurement with Data Envelopment Analysis: A Case in a Petrochemical Company. Applied System Innovation, 7(1), 2.
  14. Coelli, T., Lauwers, L., Van Huylenbroeck, G. (2005). Formulation of technical, economic and environmental efficiency measures that are consistent with the materials balance condition. Centre for Efficiency and Productivity Analysis, Working Paper, 6.
  15. Coelli, T., Lauwers, L., Van Huylenbroeck, G. (2007). Environmental efficiency measurement and the materials balance condition. Journal of productivity analysis, 28, 3-12.
  16. Cummins, D. J., Weiss, M. A. Zi, H. (2003). Economies of Scope in Financial Services: A DEA Bootstrapping Analysis of the US Insurance Industry. The Wharton School, University of Pennsylvania.
  17. Darvish Motevally, M., SHOJA, N., Hosseinzadeh Lotfi, F., Gholam Abri, A. (2019). Calculating the Sustainable Supply Chain Performance in the Cement Industry (Application of Network Data Envelopment Analysis Model). Economic Modelling, 13(2 (46)), 73-99 (In Persian).
  18. Debreu, G. (1951). The coefficient of resource utilization. Econometrica. Journal of the Econometric Society, 273-292.
  19. Doaei, M., Shavazipour, B., Zamani Sabzi, M. (2016). Efficiency and Productivity in Tehran Stock Exchange based on Diversification Strategy. Journal of Investment Knowledge, 5(18), 229-251 (In Persian).
  20. Ebrahimi, A; Soleymani Damaneh, R; Shoul, A. (2023). Deriving the Efficiency Frontier for Two-Stage Structures: Input – Output Oriented Approach of Radial and Non-Radial, Journal of Industrial Management Perspective, 13(3), 195-222 (In Persian).
  21. Emami Meibodi, A. Karimian, Z. Rahmani Sefati, M.H. (2011). Measuring Technical Efficiency and Productivity of Iranian Petrochemical Plants over the Period 2002 to 2008. Energy Economics Review, 8(29), 61-81 (In Persian).
  22. Fallah, M., Hosseinzadeh Lotfi, F. (2020). Sustainability assessment of Iranian petrochemical companies in stock exchange: A data envelopment analysis‐based approach. Expert systems, 37(3), e12359.
  23. Färe, R. (1988). Fundamentals of production theory(22). Berlin: Springer-Verlag.
  24. Färe, R., Grosskopf, S., Lovell, C. K. (1994). Production frontiers. Cambridge university press.
  25. Färe, R., Lovell, C. K. (1978). Measuring the technical efficiency of production. Journal of Economic theory, 19(1), 150-162.
  26. Färe, R., & Primont, D. (1994). Multi-Output Production and Duality: Theory and Applications: Theory and Applications. Springer Science & Business Media.
  27. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society Series A: Statistics in Society, 120(3), 253-281.
  28. Filippou, M., Zervopoulos, P. (2011). Developing a short-term comparative optimization forecasting model for operational units’ strategic planning. MPRA Paper 41573, University Library of Munich, Germany.
  29. Ghasmi, H. R., Zahmatkesh, A., Fayaz, A., Mortazavi, M. (2019). role of government dependence in analyzing the efficiency and transparency of the company. Governmental Accounting, 5(1), 69-84 (In Persian).
  30. Gijbels, I., Mammen, E., Park, B. U. Simar, L. (1999). On estimation of monotone and concave frontier functions. Journal of the American Statistical Association, 94(445), 220-228.
  31. Hosseini, K., Stefaniec, A. (2019). Efficiency assessment of Iran's petroleum refining industry in the presence of unprofitable output: A dynamic two-stage slacks-based measure. Energy, 189, 116112.
  32. Han, Y. M., Geng, Z. Q., Zhu, Q. X. (2016). Energy optimization and prediction of complex petrochemical industries using an improved artificial neural network approach integrating data envelopment analysis. Energy Conversion and Management, 124, 73-83.
  33. Jacobs, R. (2001). Alternative methods to examine hospital efficiency: data envelopment analysis and stochastic frontier analysis. Health Care Management Science, 4(2), 103-115.
  34. Koopmans, T. C. (1951). An analysis of production as an efficient combination of activities. Activity analysis of production and allocation., 62(247), 625-628.
  35. Kneip, A., Simar, L., Wilson, P. W. (2008). Asymptotics and consistent bootstraps for DEA estimators in nonparametric frontier models. Econometric Theory, 24(6), 1663-1697.
  36. Li, A., Zhang, A., Huang, H., Yao, X. (2018). Measuring unified efficiency of fossil fuel power plants across provinces in China: An analysis based on non-radial directional distance functions. Energy, 152, 549-561.
  37. Majidi, S., Fallah Lajimi, H., Safaei ghadikolaei, A. (2021). The Application of Data Envelopment Analysis in Evaluating the Performance of Universities and Higher Education Institutions: A Systematic Review of the Literature. Journal of Industrial Management Perspective, 11(1), 53-80 (In Persian).
  38. Maghyereh, A. I. Awartani, B. (2012). Financial integration of GCC banking markets: A non-parametric bootstrap DEA estimation approach. Research in International Business and Finance, 26(2), 181-195.
  39. Marinho, A., Araújo, C. A. S. (2021). Using data envelopment analysis and the bootstrap method to evaluate organ transplantation efficiency in Brazil. Health Care Management Science, 24(3), 569-581.
  40. Murillo‐Zamorano, L. R. (2004). Economic efficiency and frontier techniques. Journal of Economic Surveys, 18(1), 33-77.
  41. Namdari, R., Eghbali, A. R., Usefi, R. (2010). Performance Evaluation on State Banks Using DEA. Financial Knowledge of Security Analysis (Financial Studies), 3(7), 97-121 (In Persian).
  42. Olfat L, Amiri M, Roshan Meymandi Z. (2023). Performance Evaluation of the Petrochemical Companies Accepted in Tehran Stock Exchange with Integrated Data Envelopment Analysis Approach with Common Set of Weight and Principal Component Analysis. Journal of Operational Research in its Applications, 20(2),1-23 (In Persian)
  43. Osman, I. H. (Ed.). (2013). Handbook of research on strategic performance management and measurement using data envelopment analysis. IGI global.
  44. Ostadi, B., Zarinkolah, M. (2020). Evaluating the Efficiency of petrochemical companies based on human resource, operations and sales functions using Data Envelopment Analysis (DEA) approach. Journal of Quality Engineering and Management, 10(3), 259-267‌ (In Persian)
  45. Pourkazemi, M.H. (2006). Evaluating Efficiency In The Confederates Of Iranian Petrochemical Industries By Using Data Envelopment Analysis. Peyke Noor Journal, 4(2 (Economics 2)), 34-43 (In Persian).
  46. See, K. F., Coelli, T. (2012). An analysis of factors that influence the technical efficiency of Malaysian thermal power plants. Energy Economics, 34(3), 677-685.
  47. Simar, L. Wilson, P.W. (1998). Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Management Science, 44, 49–61.
  48. Simar, L. Wilson, P.W. (2000). A general methodology for bootstrapping in non-parametric frontier models. Journal of Applied Statistics, 27, 779–802.
  49. Simar, L. Wilson, P.W. (2008). Estimation and inference in two-stage, semi-parametric models of productive efficiency. Journal of Econometrics, 136, 31–64.
  50. Soleimani Damaneh, R., Momeni, M., Mostafaei, A., Rostami Malkhalife, M. (2017). Developing of a Dynamic Network Data Envelopment Analysis Model for Performance Evaluating Banking Sector. Journal of Industrial Management Perspective, 7(1), 67-89 (In Persian).
  51. Sueyoshi, T., Wang, D. (2014). Sustainability development for supply chain management in US petroleum industry by DEA environmental assessment. Energy Economics, 46, 360-374.
  52. Sueyoshi, T., Wang, D. (2018). DEA environmental assessment on US petroleum industry: Non-radial approach with translation invariance in time horizon. Energy Economics, 72, 276-289.
  53. Wang, Y., Yao, L., Cui, S., Zhu, Z. (2022). Eco-efficiency Assessment of Chinese Petrochemical Enterprises: A Data Envelopment Analysis Approach. Energy & Environment, 33(6), 1160-1180.
  54. Wilson, P.W., (2003). Testing independence in models of productive efficiency. Journal of Productivity Analysis, 20, 361–390.
  55. Xue, M. Harker, P. T. (1999). Overcoming the inherent dependency of DEA efficiency scores: A bootstrap approach. Unpublished, Working Paper, Wharton Financial Institutions Center, University of Pennsylvania.
  56. Yarifard, S., Salem, A. A., Mohammadi, T., Shakeri, A. (2023). The inter-temporal relationship between risk, capital and efficiency: ‎Evidences from Iranian Banks. Stable Economy Journal, 4(2), 1-33 (In Persian).
  57. Zhang, Y. Bartels, R. (1998). The effect of sample size on the mean efficiency in DEA with an application to electricity distribution in Australia, Sweden and New Zealand. Journal of Productivity Analysis, 9(3), 187-204.
Journal of Industrial Management Perspective
Volume 14, Issue 4 - Serial Number 56
Issue in Progress
2024
Pages 121-141

Files

Share

How to cite

Statistics