IDEAS home Printed from https://ideas.repec.org/p/cte/wsrepe/6382.html
   My bibliography  Save this paper

Executive pay and corporate financial performance. An exploratiove data analysis

Author

Listed:
  • Grasshoff, Ulrike
  • Schwalbach, Joachim
  • Sperlich, Stefan

Abstract

The relationship between executive pay and corporate financial performance continues to attract wide academic, media and policy attention. The very high salaries enjoyed by senior executives in corporations in the US are often contrasted with the relatively low pay received by executives in Europe and Asia. Empirical research on executive pay has mainly concentrated on the pay-performance relationship. Although the adopted data sets were very different within and across countries, the results are very similar and show very low payfor- performance elasticities. Despite the similar results, several methodological issues are still uncovered. Almost all studies assume linear or semi-log linear pay functions without applying a test of the adequate functional form. Most models do not allow for variations across corporations, industries, countries and time. It it assumed that pay functions are homogeneous across corporations, variations are captured by the fixed effects in the constants and assumption about the errors. The purpose of the paper is to circumvent these possible misspecifications by adopting an explorative data analysis using nonparametric methods which impose rather weak restrictions on the model. We start with the most general model but use methods that allow for a stepwise closer look by specifying the various objectives of investigation or the model we deduce from the previous results. In particular, we study heterogeneity between various industry groups. The results show quite clearly that all this methodological issues matter empirically, e.g. industry effects are important, assumptions of additivity crucial and nonlinearities strong and leads to underestimations of the elasticities in a standard parametric model. In sum, the results might have far reaching implications for further empirical studies on executive pay. At least, it weakens the concern expressed by many in that field that strong pay-forperformance incentives for executives are missing.

Suggested Citation

  • Grasshoff, Ulrike & Schwalbach, Joachim & Sperlich, Stefan, 1999. "Executive pay and corporate financial performance. An exploratiove data analysis," DES - Working Papers. Statistics and Econometrics. WS 6382, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:6382
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/bitstream/handle/10016/6382/ws998433.PDF?sequence=1
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sperlich, Stefan & Tjøstheim, Dag & Yang, Lijian, 2002. "Nonparametric Estimation And Testing Of Interaction In Additive Models," Econometric Theory, Cambridge University Press, vol. 18(2), pages 197-251, April.
    2. Oliver Linton & E. Mammen & J. Nielsen, 1997. "The Existence and Asymptotic Properties of a Backfitting Projection Algorithm Under Weak Conditions," Cowles Foundation Discussion Papers 1160, Cowles Foundation for Research in Economics, Yale University.
    3. Graßhoff, Ulrike & Schwalbach, Joachim, 1997. "Corporate restructuring, downsizing and managerial compensation," SFB 373 Discussion Papers 1998,35, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    4. Stefan Sperlich & Oliver Linton & Wolfgang Härdle, 1999. "Integration and backfitting methods in additive models-finite sample properties and comparison," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(2), pages 419-458, December.
    5. Jensen, Michael C & Murphy, Kevin J, 1990. "Performance Pay and Top-Management Incentives," Journal of Political Economy, University of Chicago Press, vol. 98(2), pages 225-264, April.
    Full references (including those not matched with items on IDEAS)

    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. Hengartner, Nicolas W. & Sperlich, Stefan, 2005. "Rate optimal estimation with the integration method in the presence of many covariates," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 246-272, August.
    2. Joel L. Horowitz, 2012. "Nonparametric additive models," CeMMAP working papers CWP20/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Yang, Lijian & Park, Byeong U. & Xue, Lan & Hardle, Wolfgang, 2006. "Estimation and Testing for Varying Coefficients in Additive Models With Marginal Integration," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1212-1227, September.
    4. Gao, Jiti & Lu, Zudi & Tjostheim, Dag, 2003. "Estimation in semiparametric spatial regression," MPRA Paper 11979, University Library of Munich, Germany, revised Jul 2005.
    5. Guo, Zheng-Feng & Shintani, Mototsugu, 2011. "Nonparametric lag selection for nonlinear additive autoregressive models," Economics Letters, Elsevier, vol. 111(2), pages 131-134, May.
    6. Joel L. Horowitz, 2012. "Nonparametric additive models," CeMMAP working papers 20/12, Institute for Fiscal Studies.
    7. Berthold R. Haag, 2008. "Non‐parametric Regression Tests Using Dimension Reduction Techniques," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 719-738, December.
    8. Li, Qi & Hsiao, Cheng & Zinn, Joel, 2003. "Consistent specification tests for semiparametric/nonparametric models based on series estimation methods," Journal of Econometrics, Elsevier, vol. 112(2), pages 295-325, February.
    9. Fernández, Ana I. & Rodríguez-Póo, Juan M. & Sperlich, Stefan, 1998. "Semiparametric three step estimation methods in labor supply models," SFB 373 Discussion Papers 1998,71, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    10. Stefan Profit & Stefan Sperlich, 2004. "Non-uniformity of job-matching in a transition economy - A nonparametric analysis for the Czech Republic," Applied Economics, Taylor & Francis Journals, vol. 36(7), pages 695-714.
    11. Müller, Ursula U. & Schick, Anton & Wefelmeyer, Wolfgang, 2014. "Testing for additivity in partially linear regression with possibly missing responses," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 51-61.
    12. Schimek, Michael G. & Turlach, Berwin A., 1998. "Additive and generalized additive models: A survey," SFB 373 Discussion Papers 1998,97, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    13. Stefan Sperlich & Raoul Theler, 2015. "Modeling heterogeneity: a praise for varying-coefficient models in causal analysis," Computational Statistics, Springer, vol. 30(3), pages 693-718, September.
    14. Härdle, Wolfgang & Huet, Sylvie & Mammen, Enno & Sperlich, Stefan, 2004. "Bootstrap Inference In Semiparametric Generalized Additive Models," Econometric Theory, Cambridge University Press, vol. 20(2), pages 265-300, April.
    15. Song, Qiongxia & Yang, Lijian, 2010. "Oracally efficient spline smoothing of nonlinear additive autoregression models with simultaneous confidence band," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2008-2025, October.
    16. Jing Wang & Lijian Yang, 2009. "Efficient and fast spline-backfitted kernel smoothing of additive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(3), pages 663-690, September.
    17. Martins-Filho, Carlos & yang, ke, 2007. "Finite sample performance of kernel-based regression methods for non-parametric additive models under common bandwidth selection criterion," MPRA Paper 39295, University Library of Munich, Germany.
    18. Yang, Lijian & Sperlich, Stefan & Hardle, Wolfgang, 2000. "Derivative estimation and testing in generalized additive models," DES - Working Papers. Statistics and Econometrics. WS 10084, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Abhijit Mandal, 2020. "An optimal test for the additive model with discrete or categorical predictors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(6), pages 1397-1417, December.
    20. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.

    More about this item

    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:cte:wsrepe:6382. 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: Ana Poveda (email available below). General contact details of provider: http://portal.uc3m.es/portal/page/portal/dpto_estadistica .

    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.