Consuming Values: Estimating Consumer Demand for Corporate Social Responsibility

Last registered on November 05, 2021


Trial Information

General Information

Consuming Values: Estimating Consumer Demand for Corporate Social Responsibility
Initial registration date
November 03, 2021
Last updated
November 05, 2021, 8:13 PM EDT



Primary Investigator

Stanford University

Other Primary Investigator(s)

PI Affiliation
Stanford University

Additional Trial Information

In development
Start date
End date
Secondary IDs
TESS-1415; NSF DGE-1656518
Prior work
This trial does not extend or rely on any prior RCTs.
Our project investigates the extent to which individuals’ consumption decisions are influenced by their value fit with firms on a number of corporate social responsibility dimensions (political, environmental, diversity).
External Link(s)

Registration Citation

Boxell, Levi and Jacob Conway. 2021. "Consuming Values: Estimating Consumer Demand for Corporate Social Responsibility." AEA RCT Registry. November 05.
Sponsors & Partners

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Experimental Details


Our intervention contains three separate treatments. The first provides accurate information on firm CSR activities. The second provides accurate information on consumer boycotting behaviors. The third provides incentives for respondents to share private beliefs and values on Facebook.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Our primary outcome is the change (posterior - prior) in an individual's willingness-to-pay for a firm's $50 gift card.
Primary Outcomes (explanation)
We elicit willingness-to-pay using the Becker–DeGroot–Marschak method (BDM), and we do so both before and after the experimental information provision in our experiment.

Secondary Outcomes

Secondary Outcomes (end points)
Favorability towards the firm; Beliefs on other Corporate Social Responsibility dimensions
Secondary Outcomes (explanation)
Favorability is measured on a 0-100 feeling thermometer, as in the American National Election Studies (ANES). We'll also analyze an index combining willingness-to-pay and favorability outcomes to reduce sampling noise. We may analyze consumer's prior beliefs about different firm dimensions to see: the general level of accuracy in consumer prior beliefs; over which CSR dimensions consumers have more accurate and less diffuse prior; which existing ESG ratings correlate most strongly with consumer beliefs; which types of respondents have more accurate prior beliefs; for which types of firms do respondents seem to have systematically miscalibrated beliefs.

Experimental Design

Experimental Design
Our experiment first elicits baseline beliefs about firms and preferences across five randomly selected firms per respondent, as well as priors about consumer behaviors. We then provide the information treatments to those randomly selected, and re-elicit beliefs and valuations (providing the Facebook sharing treatment during the valuation step).
Experimental Design Details
Not available
Randomization Method
Randomization done by a computer, embedded in the experimental program.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
2,500 individuals. Responses are clustered in that while an individual is asked about five firms and three CSR dimensions, a given individual will see true CSR information along the same information dimension for all five firms.
Sample size: planned number of observations
We plan to collect responses from 2,500 individuals. Each respondent is asked about 5 firms and 3 CSR dimensions, giving a total of 12,500 observations at the respondent-firm level and 37,500 observations at the respondent-firm-CSRdimension level.
Sample size (or number of clusters) by treatment arms
Our sample has three independent randomization dimensions with 4, 2, and 2 groups respectively (including the control/placebo group). These three dimensions are cross-randomized, resulting in 4x2x2 = 16 treatment arms. A roughly equal number of participants are randomized into each of these 16 conditions, giving about 156 individuals per treatment arm. Each individual is also randomly assigned 5 firms F (from our full set of eligible Fortune 500 firms), which they will be asked about throughout the survey.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Exact power calculations given our empirical strategy are complicated. While our treatment indicator is assigned at the respondent level, the experimental variation varies at the respondent-firm level. We also are using an instrumental variables estimation approach that includes pre- and post-intervention measurement and additional control variables. To gain some understanding of power, we will compute the required sample size to detect a 0.15 SD effect under pure, individual randomization and assume a design effect of 4. At α = .05, β = .80, we need about 700 respondent-firm pairs in each of the four treatment-control groups (placebo, political, environmental, and diversity) or 2,800 total respondent firm-pairs under pure randomization. Scaling 2,800 by the design effect of 4, this gives a requirement of 11,200 respondent-firm pairs. Since our survey provides 5 firms for each respondent, this leads to a required sample size of 2,240 respondents. Given our 2,500 respondents, we can detect a minimum effect size between 0.14 and 0.15 standard deviations.
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
Stanford University Panel on Human Subjects in Non-Medical Research
IRB Approval Date
IRB Approval Number
Analysis Plan

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