Li, A;
Yang, M;
(2020)
Optimal incentive contract with endogenous monitoring technology.
Theoretical Economics
, 15
(3)
pp. 1135-1173.
10.3982/te3130.
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Abstract
Recent technology advances have enabled firms to flexibly process and analyze sophisticated employee performance data at a reduced and yet significant cost. We develop a theory of optimal incentive contracting where the monitoring technology that governs the above procedure is part of the designer's strategic planning. In otherwise standard principal-agent models with moral hazard, we allow the principal to partition agents' performance data into any finite categories and to pay for the amount of information the output signal carries. Through analysis of the trade-off between giving incentives to agents and saving the monitoring cost, we obtain characterizations of optimal monitoring technologies such as information aggregation, strict MLRP, likelihood ratio-convex performance classification, group evaluation in response to rising monitoring costs, and assessing multiple task performances according to agents' endogenous tendencies to shirk. We examine the implications of these results for workforce management and firms' internal organizations.
| Type: | Article |
|---|---|
| Title: | Optimal incentive contract with endogenous monitoring technology |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3982/te3130 |
| Publisher version: | https://doi.org/10.3982/te3130 |
| Language: | English |
| Additional information: | © 2020 The Authors. Licensed under the Creative Commons Attribution-NonCommercial License 4.0 (https://creativecommons.org/licenses/by-nc/4.0/legalcode). |
| Keywords: | Incentive contract, endogenous monitoring technology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10104740 |
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