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

Respondents previously agreed to participate in business surveys run by the University of Utah, which ask questions about business sentiment. In this context, we will provide the following two treatments to a random subset of survey participants. Each treatment consists of a set of additional questions in the survey that participants are free to either answer or ignore. These additional questions will appear every month for the upcoming month and every month for the past month. We will randomize treatments initially and then persistently bring up the treatment questions in ongoing surveys.

TREATMENT 1: Reactive Learning Treatment
PRE-PERIOD TREATMENT 1
Are your targeted revenues for the next month different from your best guess of you expected revenues? If so, what are your targeted revenues over the next month last year?
POST-PERIOD TREATMENT 1
Last month you predicted that your revenues would be [X] over the coming month. You reported a revenue of [Y] for the last month. This implies a forecast error of [X/Y]*100 % (Positive errors mean you overestimated growth, negative errors mean you underestimated growth). If your forecast error was more negative than -10% or more positive than +10%, what is the most likely reason for this deviation from your goal?

TREATMENT 2: Proactive Learning Treatment
Are your targeted revenues for the next month different from your best guess of you expected revenues? If so, what are your targeted revenues over the next month last year?
This section will ask you to specify how exactly you intend to achieve your set revenue growth goal in the previous question. We will obtain your permission in advance if we are interested in using anonymized quotes in scientific publications. As a reminder, your data will remain completely confidential and will not be released in any way that can be linked to you. Data from this study will be kept locked or password-protected, and will be destroyed when no longer needed for research purposes.

PRE-PERIOD TREATMENT 2
(1) How does your revenue growth goal for next month help you achieve your long-run business goals?
(2) What if anything is your “unfair advantage,” which distinguishes you from your competitors and helps achieve your growth goal for the next month?
(3) Please list two possible plans for what you can do to achieve your revenue goals as defined in question Q.
Ideally, these two plans would be two mutually exclusive, happy stories about how you achieve your goal. We recommend that these two plans include
• What advantage you intend to use or create to achieve your goal,
• What customer or market segment you will target
• A list the activities that you will use to deliver the intended results
Two questions other business executives have found helpful to come up with these two possible plans are the following:
• What does this company do especially well? How could that strength help to increase value for new potential customers or reduce costs to you?
• What are the underserved needs or needs that customers find hard to express, and what gaps have competitors left?

(4) What would have to be true for each of the two plans you listed in the last question, to achieve your growth goal for the next month?
For each of the two plans, please make up a list of conditions, which you could observe, and that could either assure you that your plan worked or make you confident that the plan did not work.

(5) Suppose you miss your growth goal for the next month. What is the most likely reason for this miss?

POST-TREATMENT 2
(6) You stated the following about conditions that have to be true for you to achieve your goal: [Z]
What have you learned about these conditions in the past month?
(7) Last month you predicted that your revenues would be [X] over the coming month. You reported a revenue of [Y] for the last month. This implies a forecast error of [X/Y]*100 % (Positive errors mean you overestimated growth, negative errors mean you underestimated growth). If your forecast error was more negative than -10% or more positive than +10%, what is the most likely reason for this deviation from your goal and how do you know that this is the most likely reason?

2021-03-01

2022-03-01

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

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.

Treatments encourage participants to learn from past outcomes as well as provide structured ways to think about strategies to realize set goals.

We will use a cumulative treatment design. Treatment 1 includes information on past forecast errors, while treatment 2 will include treatment 1 and add encouragements to think about strategic initiatives in a structured way.

Computer-generated random numbers

Firm-level

No

1000

1000 firms, 12 observations per firm. However, we will replenish firms to offset sample attrition. We will run the treatments in the replenished firms.

333 in control group, 333 firms in treatment 1, 333 firms in treatment 2