Gender price gaps and competition: Evidence from a correspondence study
Last registered on December 07, 2018


Trial Information
General Information
Gender price gaps and competition: Evidence from a correspondence study
Initial registration date
August 30, 2018
Last updated
December 07, 2018 10:58 AM EST
Primary Investigator
Brown University
Other Primary Investigator(s)
Additional Trial Information
On going
Start date
End date
Secondary IDs
This studies gender-based price discrimination in service markets. It explores if additional information about customers can close these gaps to shed light between statistical discrimination and taste-based discrimination motives. Importantly, it combines experimental results with detailed information about competition measures in this market.
External Link(s)
Registration Citation
Machelett, Margarita. 2018. "Gender price gaps and competition: Evidence from a correspondence study." AEA RCT Registry. December 07.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
total price estimates
Primary Outcomes (explanation)
The primary variable of interest is the price estimate received by each gender and customer type.
Prices will be defined using the following rules:
1) Use the total price provided in the estimate
2) Use the average price if price ranges are provided,
3) Use the price after discounts, whenever discounts are offered,
4) Use the first price provided by a shop, whenever the same shop provides more than one price
in separate emails
5) Drop prices when either the labor or radiator price are explicitly excluded in the estimate,
6) Drop extremely low or high prices, below $100 and above $2,000, and estimates where the
price range provided is so wide that the maximum price is more than twice as large as the minimum
and price composition is not given. These thresholds are arbitrary but conservative values. I will
add robustness exercises varying these thresholds.
7) Use price provided by the shop, whenever price matching is offered. In alternative specifications,
I will use the price picked from a random draw of lower nearby prices obtained by the same
user and customer type.
Secondary Outcomes
Secondary Outcomes (end points)
The proportion of emails replied, the proportion of estimates given, the proportion of estimates given on first email reply, price composition
Secondary Outcomes (explanation)
I will include indicator variables for receiving a shop reply, replying to a shop once before receiving a price and receiving a price estimate. I will use these variables to compare response rates and effort it takes to obtain prices. Shops often provide detail on the total estimate composition. I will keep track of included and explicitly not included parts, along with their prices and quantities. I can potentially explore if
shops tend to recommend overtreatment and how price components vary with customer characteristics.
Experimental Design
Experimental Design
The experimental design uses a correspondence-study approach.
Experimental Design Details
The experimental design uses a correspondence-study approach, in which I send emails to shops varying the perceived gender and customer information revealed to each shop.
Randomization Method
Randomization done by computer
Randomization Unit
The first experimental design is simple randomization.
The second experimental design is at the shop level, sending a second email to the same shop.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Approximately 60,000 shops
Sample size: planned number of observations
Approximately 60,000 shops.
Sample size (or number of clusters) by treatment arms
Half of the shops will be contacted using a female name and half of the shops with a male name
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
I should be able to detect a difference in prices between males and females within types of $14.7 to $21.9, given a statistical power of 80%, 1% significance level and sample size of 10,000 observed prices.
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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