Hidden costs of control: evidence from the field
Last registered on December 10, 2018


Trial Information
General Information
Hidden costs of control: evidence from the field
Initial registration date
October 22, 2018
Last updated
December 10, 2018 5:35 AM EST

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Primary Investigator
University of Fribourg
Other Primary Investigator(s)
PI Affiliation
University of Fribourg
Additional Trial Information
On going
Start date
End date
Secondary IDs
The purpose of this research project is to find field evidence of hidden costs of control as they were found in the lab by Falk and Kosfeld (2006). Moreover, we aim to uncover heterogeneity in the population through elicitation of agents’ social preferences in a subsequent stage; and match social preferences to the behavioral response observed in the field.
External Link(s)
Registration Citation
Herz, Holger and Christian Zihlmann. 2018. "Hidden costs of control: evidence from the field." AEA RCT Registry. December 10. https://www.socialscienceregistry.org/trials/3475/history/38613
Experimental Details
A control device is implemented in a natural work environment. We will investigate how workers react to such an implementation of control: Is there a negative behavioural reaction, i.e. do workers lower their effort once control is implemented?

Two groups:
1. Control group - incomplete contract (no control device)
2. Treatment group - contract with a minimum performance requirement (a weak control device)
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Primary outcome is how workers effort is affected by the random assignment to the control and treatment group. Hence, key outcome variable is workers effort. Effort respectively shirking is measured by the number of clicks on the opt-out button, measuring effort with regard to the incentivised effort dimension, i.e. supply.
Primary Outcomes (explanation)
Primary outcome variables are POST_OO and delta_OO:
1) POST_OO = number of clicks on the opt-out option after treatment induction (task 2), being a proxy of effort respectively shirking for each individual i, directly observed
2) PRE_OO = number of clicks on the opt-out option in the pre-treatment stage (task 1), a proxy of effort respectively shirking for each individual i, directly observed
3) delta_OO = POST_OO - PRE_OO representing the difference in shirking frequency between post-treatment and pre-treatment stage, for each individual i, constructed

For a discussion concerning the exogenous variables/regressors, please refer to the attached pre-analysis plan.
Secondary Outcomes
Secondary Outcomes (end points)
Also, we will employ alternative dependent variables (non-incentivised effort dimensions) which are valid proxies for effort respectively shirking, too: First, the number of errors (representing quality) and second, the time used to complete the task.
We also construct further outcome variables - please refer to the attached pre-analysis plan for a detailed discussion.
Secondary Outcomes (explanation)
Please refer to the pre-analysis plan.
Experimental Design
Experimental Design
The (public) description of the experimental design is kept to its minimum to avoid experimenter demand effects. The (hidden) description will become public once the study is completed and will help you to understand the design in further detail.

In short, in a real labor market, we create two groups: One group receives an incomplete contract and is not subject no any controlling or monitoring device. The other group receives a more complete contract with a minimum performance requirement as a control device. First, all workers are assigned to an incomplete contract (HIT1). In a second stage, workers are assigned to one of the two mentioned groups (HIT2) and perform again a real-effort task. Some days or weeks after the real-effort task took place, workers demographics and social preferences are elicited by employing the Global Preference Survey (Falk et al., 2018).
Experimental Design Details
Not available
Randomization Method
Randomization done by a computer software (Otree).
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
no clustered treatment.
Sample size: planned number of observations
We will recruit 506 workers by setting the number of individual assignments on AMT for HIT1 to 506, anticipating a final sample of 248 subjects (due to attrition).
Sample size (or number of clusters) by treatment arms
The resulting sample size yields 124 subjects per group who have completed all three tasks, that is HIT1, HIT2 and HIT3, so in total 248 subjects.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
A one-sided two-sample t test power calculation is computed based on the data presented in the attached pre-analysis plan. Power is set to 0.9, while alpha is set to 0.05. Our hypothesis is directional, and that is why we use a one-sided power calculation. The pilot data reveals that subjects in treatment NC click on average 3.147 times (out of 20) the opt-out button, while subjects in WC click it 3.75 times. Hence, the effect size results to 0.603. Standard deviation for NC yields 1.4798, while for WC 1.7413. The resulting sample size (one-sided) yields 124 subjects per group.
IRB Name
Internal Review Board of the Department of Psychology, University of Fribourg
IRB Approval Date
IRB Approval Number