IDEAS home Printed from https://ideas.repec.org/a/oup/indcch/v28y2019i3p681-706..html
   My bibliography  Save this article

Manager remuneration, share buybacks, and firm performance

Author

Listed:
  • Herbert Dawid
  • Philipp Harting
  • Sander van der Hoog

Abstract

Using a dynamic heterogeneous agent industry model, we examine the impact of manager remuneration schemes on firms’ investment decisions and on the evolution of their competitiveness and share values. Whereas an increase in the share-based manager remuneration component is always beneficial to the manager, it is beneficial for shareholders only if such a change in the remuneration scheme is adopted by all firms in the industry. In that case, productivity growth is slowed down and workers’ real wages are reduced.

Suggested Citation

  • Herbert Dawid & Philipp Harting & Sander van der Hoog, 2019. "Manager remuneration, share buybacks, and firm performance," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 28(3), pages 681-706.
    • Handle: RePEc:oup:indcch:v:28:y:2019:i:3:p:681-706.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/icc/dty073
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. repec:hal:spmain:info:hdl:2441/4pa18fd9lf9h59m4vfavfcf61e is not listed on IDEAS
    2. Roberto Dieci & Xue-Zhong He, 2018. "Heterogeneous Agent Models in Finance," Research Paper Series 389, Quantitative Finance Research Centre, University of Technology, Sydney.
    3. Fangjian Fu & Sheng Huang, 2016. "The Persistence of Long-Run Abnormal Returns Following Stock Repurchases and Offerings," Management Science, INFORMS, vol. 62(4), pages 964-984, April.
    4. Vincent L. Barker , III & George C. Mueller, 2002. "CEO Characteristics and Firm R&D Spending," Management Science, INFORMS, vol. 48(6), pages 782-801, June.
    5. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    6. Cohen, Wesley M & Klepper, Steven, 1996. "Firm Size and the Nature of Innovation within Industries: The Case of Process and Product R&D," The Review of Economics and Statistics, MIT Press, vol. 78(2), pages 232-243, May.
    7. Malerba, Franco & Nelson, Richard & Orsenigo, Luigi & Winter, Sidney, 2008. "Public policies and changing boundaries of firms in a "history-friendly" model of the co-evolution of the computer and semiconductor industries," Journal of Economic Behavior & Organization, Elsevier, vol. 67(2), pages 355-380, August.
    8. Andriosopoulos, Dimitris & Lasfer, Meziane, 2015. "The market valuation of share repurchases in Europe," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 327-339.
    9. Alex Edmans & Xavier Gabaix & Dirk Jenter, 2017. "Executive Compensation: A Survey of Theory and Evidence," NBER Working Papers 23596, National Bureau of Economic Research, Inc.
    10. Rotemberg, Julio J & Scharfstein, David S, 1990. "Shareholder-Value Maximization and Product-Market Competition," The Review of Financial Studies, Society for Financial Studies, vol. 3(3), pages 367-391.
    11. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    12. Sylvain Barde & Sander van der Hoog, 2017. "An empirical validation protocol for large-scale agent-based models," Studies in Economics 1712, School of Economics, University of Kent.
    13. Dawid, Herbert, 2006. "Agent-based Models of Innovation and Technological Change," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 25, pages 1235-1272, Elsevier.
    14. Guerini, Mattia & Moneta, Alessio, 2017. "A method for agent-based models validation," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 125-141.
    15. Kraus, Alan & Rubin, Amir, 2010. "Reducing managers' incentives to cannibalize: Managerial stock options when shareholders are diversified," Journal of Financial Intermediation, Elsevier, vol. 19(4), pages 439-460, October.
    16. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    17. Peter Cziraki & Moqi Xu, 2014. "Ceo Job Security And Risk-Taking," FMG Discussion Papers dp729, Financial Markets Group.
    18. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    19. Grazzini, Jakob & Richiardi, Matteo G. & Tsionas, Mike, 2017. "Bayesian estimation of agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 26-47.
    20. Herbert Dawid & Marc Reimann, 2011. "Diversification: a road to inefficiency in product innovations?," Journal of Evolutionary Economics, Springer, vol. 21(2), pages 191-229, May.
    21. Urs Peyer, 2009. "The Nature and Persistence of Buyback Anomalies," The Review of Financial Studies, Society for Financial Studies, vol. 22(4), pages 1693-1745, April.
    22. repec:spo:wpmain:info:hdl:2441/4pa18fd9lf9h59m4vfavfcf61e is not listed on IDEAS
    23. Daniel A. Bens & Venky Nagar & M. H. Franco Wong, 2002. "Real Investment Implications of Employee Stock Option Exercises," Journal of Accounting Research, Wiley Blackwell, vol. 40(2), pages 359-393, May.
    24. Alok Bhargava, 2013. "Executive compensation, share repurchases and investment expenditures: econometric evidence from US firms," Review of Quantitative Finance and Accounting, Springer, vol. 40(3), pages 403-422, April.
    25. Cziraki, Peter & Xu, Moqi, 2014. "CEO job security and risk-taking," LSE Research Online Documents on Economics 55909, London School of Economics and Political Science, LSE Library.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Herbert Dawid & Michael Neugart, 2023. "Effects of technological change and automation on industry structure and (wage-)inequality: insights from a dynamic task-based model," Journal of Evolutionary Economics, Springer, vol. 33(1), pages 35-63, January.
    2. Guerini, Mattia & Harting, Philipp & Napoletano, Mauro, 2022. "Governance structure, technical change, and industry competition," Journal of Economic Dynamics and Control, Elsevier, vol. 135(C).
    3. Jlenia Di Noia, 2024. "When firms buy corporate bonds: an agent-based approach to credit within firms," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 19(4), pages 689-725, October.
    4. Luca Colombo & Herbert Dawid & Philipp Harting, 2024. "R&D location in dynamic industry environments," Journal of Economic Geography, Oxford University Press, vol. 24(1), pages 41-62.
    5. Oldham, Matthew, 2020. "Quantifying the concerns of Dimon and Buffett with data and computation," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. Kukacka, Jiri & Sacht, Stephen, 2023. "Estimation of heuristic switching in behavioral macroeconomic models," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    3. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    4. Gianluca Capone & Franco Malerba & Richard R. Nelson & Luigi Orsenigo & Sidney G. Winter, 2019. "History friendly models: retrospective and future perspectives," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 9(1), pages 1-23, March.
    5. repec:hal:spmain:info:hdl:2441/20hflp7eqn97boh50no50tv67n is not listed on IDEAS
    6. Bolt, Wilko & Demertzis, Maria & Diks, Cees & Hommes, Cars & Leij, Marco van der, 2019. "Identifying booms and busts in house prices under heterogeneous expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 103(C), pages 234-259.
    7. repec:spo:wpmain:info:hdl:2441/20hflp7eqn97boh50no50tv67n is not listed on IDEAS
    8. Donovan Platt, 2019. "A Comparison of Economic Agent-Based Model Calibration Methods," Papers 1902.05938, arXiv.org.
    9. Xue-Zhong He & Kai Li & Chuncheng Wang, 2018. "Time-varying economic dominance in financial markets: A bistable dynamics approach," Published Paper Series 2018-1, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    10. Deniz Erdemlioglu & Nikola Gradojevic, 2021. "Heterogeneous investment horizons, risk regimes, and realized jumps," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 617-643, January.
    11. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    12. Westerhoff Frank H., 2008. "The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 195-227, April.
    13. Kukacka, Jiri & Jang, Tae-Seok & Sacht, Stephen, 2018. "On the estimation of behavioral macroeconomic models via simulated maximum likelihood," Economics Working Papers 2018-11, Christian-Albrechts-University of Kiel, Department of Economics.
    14. Waters, George A., 2009. "Chaos in the cobweb model with a new learning dynamic," Journal of Economic Dynamics and Control, Elsevier, vol. 33(6), pages 1201-1216, June.
    15. Serena Brianzoni & Roy Cerqueti & Elisabetta Michetti, 2010. "A Dynamic Stochastic Model of Asset Pricing with Heterogeneous Beliefs," Computational Economics, Springer;Society for Computational Economics, vol. 35(2), pages 165-188, February.
    16. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    17. Yamamoto, Ryuichi & Hirata, Hideaki, 2013. "Strategy switching in the Japanese stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 37(10), pages 2010-2022.
    18. Pascal Seppecher & Isabelle Salle & Dany Lang, 2019. "Is the market really a good teacher?," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 299-335, March.
    19. Daniele Giachini, 2018. "Rationality and Asset Prices under Belief Heterogeneity," LEM Papers Series 2018/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    20. Fischer, Thomas & Riedler, Jesper, 2014. "Prices, debt and market structure in an agent-based model of the financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 95-120.
    21. He, Xue-Zhong & Li, Youwei, 2015. "Testing of a market fraction model and power-law behaviour in the DAX 30," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 1-17.
    22. Frijns, Bart & Lehnert, Thorsten & Zwinkels, Remco C.J., 2010. "Behavioral heterogeneity in the option market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2273-2287, November.

    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D25 - Microeconomics - - Production and Organizations - - - Intertemporal Firm Choice: Investment, Capacity, and Financing
    • M52 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Compensation and Compensation Methods and Their Effects

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:indcch:v:28:y:2019:i:3:p:681-706.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/icc .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.