Grocery Shortages and Consumer Shopping Behavior

Last registered on October 17, 2022


Trial Information

General Information

Grocery Shortages and Consumer Shopping Behavior
Initial registration date
April 21, 2022

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
April 28, 2022, 5:50 PM EDT

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

Last updated
October 17, 2022, 4:33 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

Purdue University

Other Primary Investigator(s)

PI Affiliation
Purdue University

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
For cost-minimizing consumers, optimal shopping depends on the structure of the grocery shopping environment. Specifically, consumers need to account for various components of the shopping environment, for instance, product prices (deal hunting), shopping trip frequency, inventory holding cost, and fixed cost of shopping trips. Given such complexity of grocery shopping environments, understanding consumers’ grocery shopping behavior has received considerable attention in the industrial organization and marketing literature.

Within the consumer grocery shopping literature, the topic of consumer stockpiling has been studied in the context of price deals, i.e., consumers stockpile when price deals are available, so they can maintain their consumption at minimum cost. However, stockpiling can also happen due to fear of grocery shortages. When consumers cannot find their preferred products, shopping fixed costs (i.e., opportunity costs) can increase, and consumers would like to stockpile to avoid the high fixed costs. This study aims to assess the impact of grocery shortages, via an increase in shopping fixed costs, on consumers’ grocery purchase behavior in a lab environment. Understanding the impact of grocery shortages and fixed costs on shopping behavior, and generating scientific evidence, is relevant for food product companies and grocery stores. Food companies may need to consider product availability, especially during periods of grocery shortages. Grocery stores may need to think of store policies during grocery shortages.

Our initial proposal is to conduct the following: Implement a four-arm randomized controlled trial (RCT) among university students in a lab environment, to evaluate the causal effect of grocery shortages (via high fixed costs) on average purchase quantity (per transaction). Beside the control arm, the other three arms will have high fixed cost, low price, and purchase-limit treatments, respectively. The high fixed cost and low-price treatments are predicted to increase average purchase quantity, i.e., lead to stockpiling, in comparison to control arm. The purchase-limit treatment indirectly imposes a high fixed cost; however, it is to be explored whether it leads to stockpiling or not.

Regarding average purchase quantity as an outcome, we have one main hypothesis, and two exploratory hypotheses. The main hypothesis is that average purchase quantity will increase in the presence of grocery shortages (which is implemented via high fixed cost treatment) in comparison to the control arm. The two exploratory hypotheses are that each price decrease and purchase-limit will increase average purchase quantity in comparison to the control arm.

When we compare average purchase quantity in a treatment arm against the control arm, we are essentially estimating the magnitude of stockpiling (beta coefficient), i.e., how much did average purchase quantity increase in a treatment arm in comparison to the control arm.
External Link(s)

Registration Citation

Wahdat, Ahmad and Jayson Lusk. 2022. "Grocery Shortages and Consumer Shopping Behavior." AEA RCT Registry. October 17.
Experimental Details


In a lab environment, this study will present students (participants) with four different treatment arms, where each treatment varies the purchase cost of a standard item.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Average purchase quantity is the primary outcome measure.
Primary Outcomes (explanation)
Average purchase quantity (per transaction) is calculated by dividing total purchased units of item A by number of transactions. The average purchase quantity will be calculated for each treatment arm separately.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will implement a crossover study design where all participants are exposed once to 4 treatments in a random order. Each treatment varies the cost structure of item A, i.e., the purchase cost of item A. Our study design is also referred to as a one-way repeated-measures ANOVA design in which the cost structure is the within-subject factor. At the end of the experiment, students are presented with elicitation tasks for risk aversion and loss aversion, followed by demographic questions.
Experimental Design Details
Not available
Randomization Method
Randomization is implemented within the experiment’s code in oTree platform. Specifically, we use Python’s “random” module to implement randomization.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
121 subjects (participants). Each participant will engage in purchase behavior in 4 treatment arms.
Sample size: planned number of observations
121 x 4 = 484 observations.
Sample size (or number of clusters) by treatment arms
121 per arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We determine the sample size to detect a standardized effect size of 0.3 in average purchase quantity among the treatment arms with 80% statistical power, and 5% (two-sided) level of significance. Since each participant will go through 4 treatment arms, we assume a correlation of 0.5 in average purchase quantity outcome across the 4 arms. We postulate the means of our outcome measure to be 5 (Control arm), 10 (Fixed Cost arm), 10 (Price arm), and 20 (Purchase-Limit arm). Using Stata’s power analysis program for repeated-measure analysis of variance, we estimate a sample size of 121 participants.

Institutional Review Boards (IRBs)

IRB Name
Purdue University Human Research Protection Program / Institutional Review Board
IRB Approval Date
IRB Approval Number