Attorney-Case Matching: Evidence from Online Legal Marketplaces

Last registered on January 23, 2023
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Pre-Trial

Trial Information

General Information

Title
Attorney-Case Matching: Evidence from Online Legal Marketplaces
RCT ID
AEARCTR-0009699
Initial registration date
January 14, 2023

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
January 23, 2023, 6:36 AM EST

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

Locations

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Primary Investigator

Name
George Vojta
Affiliation
University of Chicago
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2023-01-09
End date
2023-10-01
Keywords
Crime, Violence, & Conflict, Gender, Labor, Welfare
Additional Keywords
JEL code(s)
K00, K13, K15, K40, K41
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
I will conduct a case-posting correspondence study on an online legal marketplace where people posts their cases and lawyers bid to represent them. This study will randomly assign race, gender, income, and prior arrests to a large number of fictitious case postings. I will study differences in the lawyer responses to determine whether and to what extent lawyers discriminate when it comes to deciding who to represent.
External Link(s)

Registration Citation

Citation
Vojta, George. 2023. "Attorney-Case Matching: Evidence from Online Legal Marketplaces." AEA RCT Registry. January 23. https://doi.org/10.1257/rct.9699-1.0
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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2023-01-09
Intervention End Date
2023-10-01

Primary Outcomes

Primary Outcomes (end points)
There are 5 different outcomes I will be analyzing as part of the study. Lawyers on the platform submit bids and thus I want to see how the components of the bids and how the lawyers themselves change with these variables. The outcomes I want to analyze are:
1. Contact Rate
2. Number of bids
3. Average fee (and potentially fee structure)
4. Lawyer quality (both by stars and law school ranking)
5. Speed of Responses

Primary Outcomes (explanation)
Average fee will be standardized whether it is hourly or flat fee. Lawyer quality will use both the star system LegalMatch has in place as well as taking the law school ranking of their education.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study is a correspondence-style audit study in which I will submit fake cases to online legal market places. The intervention will be varying key components of the hypothetical cases. Cases will be submitted over the course of a year to different zip codes throughout the country.
Experimental Design Details
Not available
Randomization Method
Randomization done by python random number generator.
Randomization Unit
Individual level after stratification
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
I will submit approximately 6,000 cases over the course of half of a year.
Sample size: planned number of observations
6000 cases, number of lawyer responses is unknown.
Sample size (or number of clusters) by treatment arms
750 per race-gender-racial_name block. Other characteristics will be added in structurally.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
By looking at existing means and variances on test data, I should be fine with about 300 per cell, without the linear terms from age, bail, etc. At 8 cells this would be about 2,400 observations.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Chicago SBS IRB
IRB Approval Date
2022-06-30
IRB Approval Number
IRB22-0867
Analysis Plan