IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/84133.html
   My bibliography  Save this paper

Dynamic Evaluation Design

Author

Listed:
  • Smolin, Alex

Abstract

A principal owns a firm, hires an agent of uncertain productivity, and designs a dynamic policy for evaluating his performance. The agent observes ongoing evaluations and decides when to quit. While not quitting, the agent is paid a wage proportional to his perceived productivity; the principal claims the residual performance. After quitting, the agent secures a fixed safe payoff. I show that equilibrium evaluation policies are Pareto efficient and leave no rents to the agent. In a minimally informative equilibrium, for a broad class of performance technologies, the agent’s wage deterministically grows with tenure.

Suggested Citation

  • Smolin, Alex, 2017. "Dynamic Evaluation Design," MPRA Paper 84133, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:84133
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/84133/1/MPRA_paper_84133.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Guo, Yingni & Hörner, Johannes, 2015. "Dynamic Mechanisms without Money," Economics Series 310, Institute for Advanced Studies.
    2. Florian Ederer, 2010. "Feedback and Motivation in Dynamic Tournaments," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 19(3), pages 733-769, September.
    3. Maria Goltsman & Arijit Mukherjee, 2011. "Interim Performance Feedback in Multistage Tournaments: The Optimality of Partial Disclosure," Journal of Labor Economics, University of Chicago Press, vol. 29(2), pages 229-265.
    4. Stephen E. Hansen, 2013. "Performance Feedback with Career Concerns," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 29(6), pages 1279-1316, December.
    5. Jeffrey C. Ely & Martin Szydlowski, 2020. "Moving the Goalposts," Journal of Political Economy, University of Chicago Press, vol. 128(2), pages 468-506.
    6. Marina Halac & Navin Kartik & Qingmin Liu, 2016. "Optimal Contracts for Experimentation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(3), pages 1040-1091.
    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. Zhao, Wei & Mezzetti, Claudio & Renou, Ludovic & Tomala, Tristan, 0. "Contracting over persistent information," Theoretical Economics, Econometric Society.
    2. Aleksei Smirnov & Egor Starkov, 2019. "Timing of predictions in dynamic cheap talk: experts vs. quacks," ECON - Working Papers 334, Department of Economics - University of Zurich.
    3. Jacopo Bizzotto & Adrien Vigier, 2021. "Can a better informed listener be easier to persuade?," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 72(3), pages 705-721, October.
    4. Heski Bar‐Isaac & Clare Leaver, 2022. "Training, Recruitment, and Outplacement as Endogenous Adverse Selection," Economica, London School of Economics and Political Science, vol. 89(356), pages 849-861, October.
    5. Orlov, Dmitry, 2022. "Frequent monitoring in dynamic contracts," Journal of Economic Theory, Elsevier, vol. 206(C).
    6. Benjamin Brooks & Alexander Frankel & Emir Kamenica, 2022. "Information Hierarchies," Econometrica, Econometric Society, vol. 90(5), pages 2187-2214, September.
    7. Ashkenazi-Golan, Galit & Hernández, Penélope & Neeman, Zvika & Solan, Eilon, 2023. "Markovian persuasion with two states," LSE Research Online Documents on Economics 119970, London School of Economics and Political Science, LSE Library.

    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. Chen, Bin R., 2015. "Subjective performance feedback, ability attribution, and renegotiation-proof contracts," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 155-174.
    2. Klein, Arnd Heinrich & Schmutzler, Armin, 2017. "Optimal effort incentives in dynamic tournaments," Games and Economic Behavior, Elsevier, vol. 103(C), pages 199-224.
    3. Li, Jin & Mukherjee, Arijit & Vasconcelos, Luis, 2019. "Managing performance evaluation systems: Relational incentives in the presence of learning-by-shirking," Working Papers 2018-12, Michigan State University, Department of Economics.
    4. Gwen-Jiro Clochard & Guillaume Hollard & Julia Wirtz, 2022. "More effort or better technologies? On the effect of relative performance feedback," Bristol Economics Discussion Papers 22/767, School of Economics, University of Bristol, UK.
    5. Delfgaauw, Josse & Dur, Robert & Non, Arjan & Verbeke, Willem, 2014. "Dynamic incentive effects of relative performance pay: A field experiment," Labour Economics, Elsevier, vol. 28(C), pages 1-13.
    6. Jan Zabojnik, 2011. "Subjective Evaluations With Performance Feedback," Working Paper 1283, Economics Department, Queen's University.
    7. Brendan Daley & Ruoyu Wang, 2018. "When to Release Feedback in a Dynamic Tournament," Decision Analysis, INFORMS, vol. 15(1), pages 11-26, March.
    8. Katolnik, Svetlana & Hakenes, Hendrik, 2014. "On the Incentive Effect of Job Rotation," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100574, Verein für Socialpolitik / German Economic Association.
    9. Christian Ewerhart & Julia Lareida, 2018. "Voluntary disclosure in asymmetric contests," ECON - Working Papers 279, Department of Economics - University of Zurich, revised Jul 2023.
    10. Habibi, Amir, 2020. "Motivation and information design," Journal of Economic Behavior & Organization, Elsevier, vol. 169(C), pages 1-18.
    11. Marino, Anthony M., 2014. "Transparency in agency: The constant elasticity case and extensions," International Journal of Industrial Organization, Elsevier, vol. 33(C), pages 9-21.
    12. Dmitry Ryvkin, 2022. "To Fight or to Give Up? Dynamic Contests with a Deadline," Management Science, INFORMS, vol. 68(11), pages 8144-8165, November.
    13. Josse Delfgaauw & Robert Dur & Arjan Non & Willem Verbeke, 2015. "The Effects of Prize Spread and Noise in Elimination Tournaments: A Natural Field Experiment," Journal of Labor Economics, University of Chicago Press, vol. 33(3), pages 521-569.
    14. Anastasia Antsygina & Mariya Teteryatnikova, 2023. "Optimal information disclosure in contests with stochastic prize valuations," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 75(3), pages 743-780, April.
    15. Dong, Lu & Huang, Lingbo, 2019. "Is there no ‘I’ in team? Strategic effects in multi-battle team competition," Journal of Economic Psychology, Elsevier, vol. 75(PB).
    16. Julia Nafziger & Heiner Schumacher, 2013. "Information Management and Incentives," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 22(1), pages 140-163, March.
    17. Fu, Qiang & Gürtler, Oliver & Münster, Johannes, 2013. "Communication and commitment in contests," Journal of Economic Behavior & Organization, Elsevier, vol. 95(C), pages 1-19.
    18. Tal Alon & Paul Dutting & Yingkai Li & Inbal Talgam-Cohen, 2022. "Bayesian Analysis of Linear Contracts," Papers 2211.06850, arXiv.org, revised Jul 2023.
    19. Shanglyu Deng & Hanming Fang & Qiang Fu & Zenan Wu, 2023. "Information Favoritism and Scoring Bias in Contests," PIER Working Paper Archive 23-002, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    20. Wataru Tamura, 2012. "A Theory of Multidimensional Information Disclosure," ISER Discussion Paper 0828, Institute of Social and Economic Research, Osaka University.

    More about this item

    Keywords

    evaluation; information design; career concerns; bandit experimentation; downward wage rigidity; up-or-out;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • M52 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Compensation and Compensation Methods and Their Effects

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:84133. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.