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Are CEOs judged on how cost efficient their firms are?

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  • Månsson, Kristofer
  • Qasim, Muhammad
  • Söderberg, Magnus

Abstract

This paper investigates whether executive boards consider firm-specific inefficiencies when they change CEOs in the Swedish electricity distribution sector. Firm-level inefficiencies are calculated using data from all Swedish electricity distributors from 2001 to 2022 and a data envelopment analysis (DEA) approach. DEA has advantages over standard financial key performance indicators since it controls for heterogeneity in inputs and outputs. It is also frequently employed by energy regulators to calculate relative cost inefficiencies. Our baseline approach uses a multilevel model and investigates the relationship between inefficiency and CEO between-effects. This analysis shows that 9–15 % of the variation in inefficiency can be attributed to the CEO effect. The second modeling approach quantifies the CEO effect using a synthetic difference-in-differences approach, focusing on firms that have changed CEOs. The results reveal that new CEOs reduce cost inefficiency more when they succeed CEOs who were forced to leave.

Suggested Citation

  • Månsson, Kristofer & Qasim, Muhammad & Söderberg, Magnus, 2025. "Are CEOs judged on how cost efficient their firms are?," Energy Economics, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:eneeco:v:143:y:2025:i:c:s0140988325001124
    DOI: 10.1016/j.eneco.2025.108289
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    1. Timothy J. Quigley & Scott D. Graffin, 2017. "Reaffirming the CEO effect is significant and much larger than chance: A comment on Fitza (2014)," Strategic Management Journal, Wiley Blackwell, vol. 38(3), pages 793-801, March.
    2. Craig Crossland & Donald C. Hambrick, 2007. "How national systems differ in their constraints on corporate executives: a study of CEO effects in three countries," Strategic Management Journal, Wiley Blackwell, vol. 28(8), pages 767-789, August.
    3. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    4. Dirk Jenter & Fadi Kanaan, 2015. "CEO Turnover and Relative Performance Evaluation," Journal of Finance, American Finance Association, vol. 70(5), pages 2155-2184, October.
    5. Tooraj Jamasb & Magnus Söderberg, 2010. "The Effects of Average Norm Model Regulation: The Case of Electricity Distribution in Sweden," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 36(3), pages 249-269, May.
    6. Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2019. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," European Journal of Operational Research, Elsevier, vol. 274(1), pages 240-252.
    7. Mark R. Huson & Robert Parrino & Laura T. Starks, 2001. "Internal Monitoring Mechanisms and CEO Turnover: A Long‐Term Perspective," Journal of Finance, American Finance Association, vol. 56(6), pages 2265-2297, December.
    8. Magnus Söderberg, 2011. "The Role Of Model Specification In Finding The Influence Of Ownership And Regulatory Regime On Utility Cost: The Case Of Swedish Electricity Distribution," Contemporary Economic Policy, Western Economic Association International, vol. 29(2), pages 178-190, April.
    9. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    10. Richard J. Gentry & Joseph S. Harrison & Timothy J. Quigley & Steven Boivie, 2021. "A database of CEO turnover and dismissal in S&P 1500 firms, 2000–2018," Strategic Management Journal, Wiley Blackwell, vol. 42(5), pages 968-991, May.
    11. Oleg Badunenko & Harald Tauchmann, 2019. "Simar and Wilson two-stage efficiency analysis for Stata," Stata Journal, StataCorp LLC, vol. 19(4), pages 950-988, December.
    12. Zangin Zeebari & Kristofer Månsson & Pär Sjölander & Magnus Söderberg, 2023. "Regularized conditional estimators of unit inefficiency in stochastic frontier analysis, with application to electricity distribution market," Journal of Productivity Analysis, Springer, vol. 59(1), pages 79-97, February.
    13. Filippini, M. & Greene, W. & Masiero, G., 2018. "Persistent and transient productive inefficiency in a regulated industry: electricity distribution," Energy Economics, Elsevier, vol. 69(C), pages 325-334.
    14. Banker, Rajiv & Natarajan, Ram & Zhang, Daqun, 2019. "Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using Data Envelopment Analysis: Second stage OLS versus bootstrap approaches," European Journal of Operational Research, Elsevier, vol. 278(2), pages 368-384.
    15. Hoff, Ayoe, 2007. "Second stage DEA: Comparison of approaches for modelling the DEA score," European Journal of Operational Research, Elsevier, vol. 181(1), pages 425-435, August.
    16. repec:eme:mfppss:mf-06-2017-0227 is not listed on IDEAS
    17. Huang, Jiekun & Kisgen, Darren J., 2013. "Gender and corporate finance: Are male executives overconfident relative to female executives?," Journal of Financial Economics, Elsevier, vol. 108(3), pages 822-839.
    18. Timothy J. Quigley & Donald C. Hambrick, 2015. "Has the “CEO effect” increased in recent decades? A new explanation for the great rise in America's attention to corporate leaders," Strategic Management Journal, Wiley Blackwell, vol. 36(6), pages 821-830, June.
    19. Brophy Haney, A. & Jamasb, T. & Pollitt, M.G., 2009. "Smart Metering and Electricity Demand: Technology, Economics and International Experience," Cambridge Working Papers in Economics 0905, Faculty of Economics, University of Cambridge.
    20. Stead, Alexander D. & Wheat, Phill, 2020. "The case for the use of multiple imputation missing data methods in stochastic frontier analysis with illustration using English local highway data," European Journal of Operational Research, Elsevier, vol. 280(1), pages 59-77.
    21. Vincent Intintoli & Andrew Zhang & Wallace Davidson, 2014. "The impact of CEO turnover on firm performance around interim successions," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 18(2), pages 541-587, May.
    22. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    23. Denis, David J & Denis, Diane K, 1995. "Performance Changes Following Top Management Dismissals," Journal of Finance, American Finance Association, vol. 50(4), pages 1029-1057, September.
    24. Badunenko, Oleg & Kumbhakar, Subal C., 2016. "When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models," European Journal of Operational Research, Elsevier, vol. 255(1), pages 272-287.
    25. Peter Bogetoft & Lars Otto, 2011. "Data Envelopment Analysis DEA," International Series in Operations Research & Management Science, in: Benchmarking with DEA, SFA, and R, chapter 0, pages 81-113, Springer.
    26. Rajiv D. Banker & Ram Natarajan, 2008. "Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis," Operations Research, INFORMS, vol. 56(1), pages 48-58, February.
    27. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    28. Saastamoinen, Antti & Kuosmanen, Timo, 2016. "Quality frontier of electricity distribution: Supply security, best practices, and underground cabling in Finland," Energy Economics, Elsevier, vol. 53(C), pages 281-292.
    29. Duras, Toni & Javed, Farrukh & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2023. "Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data," Energy Economics, Elsevier, vol. 120(C).
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    Keywords

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    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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