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Causal Evidence on Expectation Formation and the Role of Reactive vs Proactive Learning
Last registered on May 09, 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
May 09, 2021 5:07 PM EDT
Location(s)
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
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. May 09. https://doi.org/10.1257/rct.6860-1.3000000000000003.
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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)
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