Optimal Contracting with Subjective Evaluation: The Effects of Timing, Malfeasance and Guile

MacLeod, W. Bentley; Tan, Teck Yong

doi:10.3386/w22156

Cited by 2 publications

(2 citation statements)

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“…Section 4 builds on and discusses his model in detail. MacLeod and Tan (2016) extend the model by considering malfeasance and more general timings and information structures. Deb et al (2016) consider multiple agents working for the principal.…”

Section: Related Literaturementioning

confidence: 99%

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Stochastic contracts and subjective evaluations

Lang

2023

The RAND J of Economics

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Subjective evaluations are widely used, but call for different contracts from classical moral‐hazard settings. Previous literature shows that contracts require payments to third parties. I show that the (implicit) assumption of deterministic contracts makes payments to third parties necessary. This article studies incentive contracts with stochastic compensation, like payments in stock options or uncertain arbitration procedures. These contracts incentivize employees without the need for payments to third parties. In addition, stochastic contracts can be more efficient and can make the principal better off compared to deterministic contracts. My results also address the puzzle about the prevalence of labor contracts with stochastic compensation.

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Section: Related Literaturementioning

confidence: 99%

“…See also Deb et al (2016) and Bester and Münster (2016). 1 MacLeod (2003), Fuchs (2007), Chan and Zheng (2011) and MacLeod and Tan (2016) provide formal proofs for these claims.…”

Section: Introductionmentioning

confidence: 99%

Stochastic contracts and subjective evaluations

Lang

2023

The RAND J of Economics

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Data-driven subjective performance evaluation: An attentive deep neural networks model based on a call centre case

et al. 2022

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Every contact centre engages in some form of Call Quality Monitoring in order to improve agent performance and customer satisfaction. Call centres have traditionally used a manual process to sort, select, and analyse a representative sample of interactions for evaluation purposes. Unfortunately, such a process is marked by subjectivity, which in turn results in a distorted picture of agent performance. To address the challenge of identifying and removing subjectivity, empirical research is required. In this paper, we introduce an evidence-based, machine learning-driven framework for the automatic detection of subjective calls. We analyse a corpus of seven hours of recorded calls from a real-estate call centre using Deep Neural Network (DNN) for a multi-classification problem. The study establishes the first baseline for subjectivity detection, with an accuracy of 75%, which is comparable to relevant speech studies in emotional recognition and performance classification. We conclude, among other things, that in order to achieve the best performance evaluation, subjective calls should be removed from the evaluation process or subjective scores deducted from the overall results.

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Optimal Contracting with Subjective Evaluation: The Effects of Timing, Malfeasance and Guile

Cited by 2 publications

References 54 publications

Stochastic contracts and subjective evaluations

Stochastic contracts and subjective evaluations

Data-driven subjective performance evaluation: An attentive deep neural networks model based on a call centre case

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