Replication data for: Incentive Schemes, Sorting, and Behavioral Biases of Employees: Experimental Evidence

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Project Citation:

Larkin, Ian, and Leider, Stephen. Replication data for: Incentive Schemes, Sorting, and Behavioral Biases of Employees: Experimental Evidence. Nashville, TN: American Economic Association [publisher], 2012. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-12-07. https://doi.org/10.3886/E116441V1

Project Description

Summary: 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)

Scope of Project

Subject Terms: Laboratory Experiment; Incentives; Overconfidence

JEL Classification:
      D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
      J24 Human Capital; Skills; Occupational Choice; Labor Productivity
      J31 Wage Level and Structure; Wage Differentials
      M12 Personnel Management; Executives; Executive Compensation

Geographic Coverage: United States

Time Period(s): 4/2009 – 5/2009

Universe: Experimental Sessions were run at the Harvard Business School CLER lab using the standard subject pool.

Data Type(s): experimental data

Methodology

Data Source: Laboratory Experiments

Unit(s) of Observation: Individuals,

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Published Versions

V1 [2019-12-07]

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