scholarly journals Incentive Schemes, Sorting, and Behavioral Biases of Employees: Experimental Evidence

2012 ◽  
Vol 4 (2) ◽  
pp. 184-214 ◽  
Author(s):  
Ian Larkin ◽  
Stephen Leider
Keyword(s):  
Experimental Evidence ◽  
Incentive Scheme ◽  
Wage Bill ◽  
Incentive Schemes ◽  
Behavioral Biases ◽  
Linear Scheme ◽  
And Performance

We investigate how the convexity of a firm's incentives interacts with worker overconfidence to affect sorting decisions and performance. We demonstrate, experimentally, that overconfident employees are more likely to sort into a nonlinear incentive scheme over a linear one, even though this reduces pay for many subjects and despite the presence of clear feedback. Additionally, the linear scheme attracts demotivated, underconfident workers who perform below their ability. Our findings suggest that firms may design incentive schemes that adapt to the behavioral biases of employees to “sort in” (“sort away”) attractive (unattractive) employees; such schemes may also reduce a firm's wage bill. (JEL D03, D83, J24, J31, M12)

Behavioral Ambidexterity: The Impact of Incentive Schemes on Productivity, Motivation, and Performance of Employees in Commercial Banks

2015 ◽  
Vol 54 (S1) ◽  
pp. s45-s62 ◽  
Author(s):  
Mohammad Faisal Ahammad ◽  
Sang Mook Lee ◽  
Miki Malul ◽  
Amir Shoham
Keyword(s):  
Commercial Banks ◽  
Incentive Schemes ◽  
And Performance ◽  
The Impact

scholarly journals The Effectiveness of Incentive Schemes in the Presence of Implicit Effort Costs

2019 ◽  
Vol 65 (9) ◽  
pp. 4063-4078 ◽  
Author(s):  
Sebastian J. Goerg ◽  
Sebastian Kube ◽  
Jonas Radbruch
Keyword(s):  
Decision Analysis ◽  
Work Performance ◽  
Laboratory Experiments ◽  
Incentive Contracts ◽  
Incentive Scheme ◽  
Incentive Schemes ◽  
Outside Options ◽  
Real Effort ◽  
The Cost

Agents’ decisions to exert effort depend on the incentives and the potential costs involved. So far, most of the attention has been on the incentive side. However, our laboratory experiments underline that both the incentive and the cost side can be used separately to shape work performance. In our experiment, subjects work on a real-effort slider task. Between treatments, we vary the incentive scheme used for compensating workers. Additionally, by varying the available outside options, we explore the role of implicit costs of effort in determining workers’ performance. We observe that incentive contracts and implicit costs interact in a nontrivial manner. In general, performance decreases as implicit costs increase. Yet the magnitude of the reaction differs across incentive schemes and across the offered outside options, which, in turn, alters estimated output elasticities. In addition, comparisons between incentive schemes crucially depend on the implicit costs. This paper was accepted by Yan Chen, decision analysis.

Model of Bias-Driven Trend Followers and Interaction with Manipulators

2017 ◽  
Vol 16 (02) ◽  
pp. 573-590
Author(s):  
Ke Liu ◽  
Kin Keung Lai ◽  
Jerome Yen ◽  
Qing Zhu
Keyword(s):  
Financial Markets ◽  
Behavioral Finance ◽  
Real Data ◽  
Investor Behavior ◽  
Individual Investors ◽  
Investment Performance ◽  
Behavioral Biases ◽  
The Common ◽  
And Performance ◽  
Manipulation Detection

Stock investors are not fully rational in trading and many behavioral biases that affect them. However, most of the literature on behavioral finance has put efforts only to explain empirical phenomena observed in financial markets; little attention has been paid to how individual investors’ trading performance is affected by behavioral biases. As against the common perception that behavioral biases are always detrimental to investment performance, we conjecture that these biases can sometimes yield better trading outcomes. Focusing on representativeness bias, conservatism and disposition effect, we construct a mathematical model in which the representative trend investor follows a Bayesian trading strategy based on an underlying Markov chain, switching beliefs between trending and mean-reversion. By this model, scenario analysis is undertaken to track investor behavior and performance under different patterns of market movements. Simulation results show the effect of biases on investor performance can sometimes be positive. Further, we investigate how manipulators could take advantage of investor biases to profit. The model’s potential for manipulation detection is demonstrated by real data of well-known manipulation cases.

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