Grocery Shortages and Consumer Shopping Behavior

Last registered on December 30, 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
December 30, 2022, 6:55 PM EST

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 purchase quantity as an outcome, we have one main hypothesis, and two exploratory hypotheses. The main hypothesis is that, on 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 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. December 30.
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)
Purchase quantity per transaction is the primary outcome measure.
Primary Outcomes (explanation)

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
Our experimental design follows a within-subject (repeated measures) design, where each participant (or subject) is exposed to a four-arm RCT in the lab. In each arm of the RCT, participants will buy 20 units of item A with a budget of 200 experimental tokens, where 40 tokens are exchanged for $1. Each participant’s payoff depends on minimizing the cost of buying 20 units of item A in each treatment arm.

Depending on how many units of item A does a participant buy in one purchase (i.e., one transaction), the per unit price of item A changes, so a participant needs to look for per unit prices for different quantities. Meanwhile, there is a fixed cost for each transaction, so splitting 20 units’ purchase into many transactions can increase the total fixed cost in a treatment arm. Each participant has to think for the optimal number of units to purchase in a transaction, so that the total costs paid for 20 units are minimized, and the leftover budget out of 200 tokens is maximized. The leftover budget in each treatment arm is a participant’s payoff from that specific arm. Participants are provided with the table of prices and cost in each treatment arm. The table remains available to participants when purchase decisions are made.

Participants will be students from the ORSEE database at Purdue. ORSEE will randomly select a subset of eligible participants, who will then get an invitation email. The invitation email contains eligible session times, maximum duration (1.5 hours), the location, and expected payoff range of the experiment. If a subject wants to participate in the experiment, they click the associated link in the invitation email to register for a given session using ORSEE. The location of experiment will be the Vernon Smith Experimental Economics Laboratory (VSEEL) at Purdue University.

The 4 treatment arms are:

Arm 1: The Control arm, where participants will see regular prices and regular fixed cost (per transaction).

Arm 2: The Fixed Cost arm, where participants will see regular prices but a high fixed cost, which is stochastic. There is 70% probability that a certain purchase transaction will have a high fixed cost. Using R software, we have determined the order in which high fixed cost transactions will take place in a total of 20 transactions. Participants don’t know in advance if a subsequent transaction has a high fixed cost.

Note: A participant has to purchase at least 1 unit in each transaction, and if that is what the participant prefers, then there will be a maximum of 20 possible transactions. But participants don’t have to buy 1 unit per transaction. A participant may buy all 20 units in one transaction, in which case the participant will move to the next treatment.

Arm 3: The Low Price arm, where participants will see low prices (being stochastic) but regular fixed cost. There is 70% probability that a certain purchase transaction will have a low prices. Using R software, we have determined the order in which low-price transactions will take place in a total of 20 transactions. Participants don’t know in advance if a subsequent transaction has low prices.

Arm 4: The Purchase-Limit arm, where participants will see regular prices and fixed cost, but there will be a random purchase limit of 1 unit on some purchase instances. There is 70% probability that a certain purchase transaction will have a purchase limit of 1 unit. Using R software, we have determined the order in which 1-unit transactions will take place in a total of 20 transactions. Participants don’t know in advance if a subsequent transaction has a purchase limit of 1 unit.

We randomize the order of the 4 treatment arms for each participant. Since our experiment is programmed in oTree platform, we write a code snippet that takes care of the random order of the 4 treatment arms for each participant during the actual experiment. This saves us a lot of time and manual effort!

Payoff: Each participant will have 200 tokens in each treatment arm to buy 20 units of item A. Once 20 units are bought, the leftover budget in each treatment arm is the payoff of that arm. At the end of the experiment, the risk and loss aversion elicitation tasks provide lottery choices in dollar amounts, so participants can earn there as well. One lottery is randomly picked in each risk and loss elicitation task for payment. A participant’s total payoff will be the sum of payoffs from four treatment arms, and the two elicitation tasks.

Participants will also receive $5 for participation fee.

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 (participants) x 4 (treatment arms) x 3 (observations on average by treatment arm) = 1452 observations.
Sample size (or number of clusters) by treatment arms
121 participants 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).

Institutional Review Boards (IRBs)

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


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