Supervisor Predictions and Intertemporal Effort

Last registered on December 06, 2023


Trial Information

General Information

Supervisor Predictions and Intertemporal Effort
Initial registration date
November 27, 2023

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
December 06, 2023, 8:01 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.


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Primary Investigator

Heidelberg University

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
What is the right level of decentralization? In a standard principal-agent model, principal(s) may delegate decision-making authority to agents to take advantage of the agents' superior information, which is offset as the principal(s) lose(s) complete control and the agent can pursue their own objectives (to a certain extent). However, this information advantage is typically for certain easily observable elements. Does this information advantage hold for elements such as intertemporal effort allocation which has harder characteristics associated with it such as present bias and the level of sophistication on part of the agent. For the latter, a large literature has establishment the existence of naivete on part of agents themselves. Our idea is that an agent might herself be unaware of her own present-bias and thus an external actor, a principal, may be able to better ascertain this about her and thus make better choices.

Further, if an agent does still possess superior information, the question for the public sector, which has multiple decision-making layers, and multiple principals is: what advantage do agents have over different levels of authority? Are principals closer to the agent better informed and how can their involvement improve performance? We conduct a field experiment to understand the informational advantage of community health workers, their immediate supervisors and senior-most Department of Health officials. However, this question is not just relevant for the public sector, but in any organization including private sector organizations, where different levels of authority exist.
External Link(s)

Registration Citation

Callen, Michael , Zain Chaudhry and Karrar Hussain. 2023. "Supervisor Predictions and Intertemporal Effort." AEA RCT Registry. December 06.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Allocation of effort; correct prediction of effort at different levels
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design

\textbf{Elicitation for Structural Estimation:} On Day 0, we call in the CHWs to collect demographic data and elicit preferences after carefully explaining the experimental protocols. The CHWs divided $m$={15...57} tasks (household health surveys) between two dates that were one week apart, Day 7 (allocation $v_1$) and Day 14 (allocation $v_2$). The CHWs make these decisions at multiple task rates, essentially interest rates between the present and future, which were experimentally varied: $R$ = {0.4, 0.5...1.7, 1.8}. For each task allocated to Day 14, the number of tasks allocated to Day 7 was reduced by $R$. These were advance choices --- made one week before the task was to be attempted. On Day 7, all CHWs make the same choices again, but for that very day (before they had to complete the tasks for that day) and the next week. These were the immediate choices --- made on the same day the task had to be attempted. As multiple choices were made, in advance and immediately, and at multiple task rates, we chose one choice probabilistically for each CHW to implement. This is the decision-that-counts and was implemented on Day 7 and then again on Day 14. %This design was based on \cite{andreoni2012estimating}.\footnote{It has been used by and Chaudhry and Hussain (2022).} %In Figure \ref{decisionset}, we show one example of the decision sets we used to elicit these preferences. This exercise took place in September 2022.

To avoid corner solutions at 0 or $m$ households in allocation decisions, we set a minimum of 5 and maximum of 27 households in the decision sets we offered. The goal of setting a minimum was to ensure that CHWs worked on both dates and made a choice about how to allocate tasks between them. Further, when CHWs made decisions on Day 7, we did not remind them of the Day 0 allocations. Importantly, on Day 0, CHWs were making decisions involving two future work dates (one and two weeks later), whereas on Day 7, they were making decisions for the same day and the week after. From every CHW we elicit 15 advance and 15 immediate decisions over two weeks, and 1 of their 30 decisions was assigned to them as the decision-that-counts.

The purpose of this exercise was to elicit the effort allocations and structurally estimate every CHW's discount factor $\delta$, present bias $\beta$, and intertemporal effort cost $\gamma$.
Experimental Design Details
Not available
Randomization Method
Randomization Unit
Health worker
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Over 10,000
Sample size (or number of clusters) by treatment arms
Equally divided
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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