Causal Evidence on Expectation Formation and the Role of Reactive vs Proactive Learning

Last registered on October 04, 2021

Pre-Trial

Trial Information

General Information

Title
Causal Evidence on Expectation Formation and the Role of Reactive vs Proactive Learning
RCT ID
AEARCTR-0006860
Initial registration date
December 22, 2020
Last updated
October 04, 2021, 8:35 PM EDT

Locations

Primary Investigator

Affiliation
David Eccles School of Business, University of Utah

Other Primary Investigator(s)

PI Affiliation
David Eccles School of Business, University of Utah
PI Affiliation
David Eccles School of Business, University of Utah

Additional Trial Information

Status
In development
Start date
2021-03-01
End date
2022-03-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We contrast two fundamental ways to learn and form expectations for businesses. On the one hand, we will provide evidence on firms’ ability to adapt through feedback, sometimes also called “local search” or “adaptive expectations”. This approach primarily reactive in nature.
On the other hand, we will investigate the effectiveness of proactive learning and experimentation, an approach that is connected to concepts such as “hypothesis-driven search” and “anticipatory expectations”.
External Link(s)

Registration Citation

Citation
Gaulin, Maclean, Nathan Seegert and Mu-Jeung Yang. 2021. "Causal Evidence on Expectation Formation and the Role of Reactive vs Proactive Learning." AEA RCT Registry. October 04. https://doi.org/10.1257/rct.6860-1.4000000000000004
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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2021-03-01
Intervention End Date
2022-03-01

Primary Outcomes

Primary Outcomes (end points)
Revenue forecasts, realized revenues, accuracy of revenue forecasts, firm growth, firm profitability, firm growth path, hiring, intended hiring, elasticity of intended and realized hiring in response to revenue changes, firm productivity, new product development, measures of strategic initiatives from text data, measures of overconfidence (overestimation, overprecision and overplacement), measures of misattribution bias, measures of digital technology adoption, measures of different types of perceived risk, measures of operating leverage, local returns to scale and (variable and fixed) costs
Primary Outcomes (explanation)
One outcome that needs to be constructed are measures of strategic initiatives from text responses for which we will use natural language processing techniques, such as Neural Language Networks as well as simple count models such as TF-IDF.

Secondary Outcomes

Secondary Outcomes (end points)
Data on business models and how they are affected by interventions
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Treatments encourage participants to learn from past outcomes as well as provide structured ways to think about strategies to realize set goals.
Experimental Design Details
Not available
Randomization Method
Computer-generated random numbers
Randomization Unit
Firm-level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1000
Sample size: planned number of observations
1000 firms, 12 observations per firm. However, we will replenish firms to offset sample attrition. We will run the treatments in the replenished firms.
Sample size (or number of clusters) by treatment arms
333 in control group, 333 firms in treatment 1, 333 firms in treatment 2
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Institutional Review Board University of Utah
IRB Approval Date
2020-12-22
IRB Approval Number
00139342