Choice of law is a mess—or so it is said. According to conventional wisdom, choice-of-law doctrine does not significantly influence judges’ choice-of-law decisions. Instead, these decisions are primarily motivated by biases in favor of domestic over foreign law, domestic over foreign litigants, and plaintiffs over defendants. They are also highly unpredictable.
This Article argues that these “mess” claims do not accurately describe at least one domain of choice of law—intern ational choice of law—and it demonstrates what is at stake in this debate for global governance. This Article uses statistical analysis of an original dataset of published international choice-of-law decisions by U.S. district courts in tort cases to present evidence that choice-of-law doctrine indeed influences these decisions; that these decisions are not biased in favor of domestic law, domestic litigants, or plaintiffs; and that they are actually quite predictable. The mess claims, it turns out, may be myths—at least in transnational tort cases.
Lastly, the Article explains why these findings are encouraging from a global-governance perspective, and why they might plausibly extend to unpublished international choice-of-law decisions and domestic choice-of-law decisions. The Article’s findings suggest that the conventional wisdom exaggerates what is wrong with choice of law and implicitly underestimates its contributions to global governance.
Custom Dataset Terms
Our Community Norms as well as good scientific practices expect that proper credit is given via citation. Please use the data citation shown on the dataset page.
Custom Dataset Terms - the following Custom Dataset Terms have been defined for this dataset.
This file has already been deleted (or replaced) in the current version. It may not be edited.
Restricting limits access to published files. People who want to use the restricted files can request access by default. If you disable request access, you must add information about access to the Terms of Access field.
The selected file or files have already been published. Contact an administrator to change the embargo date or reason of the file or files.
The selected file or files have already been published. Contact an administrator to change the retention period date or reason of the file or files.
The file will be deleted after you click on the Delete button.
Files will not be removed from previously published versions of the dataset.
Please select one or more files.
Share this dataset on your favorite social media networks.
Citations for this dataset are retrieved from Crossref via DataCite using Make Data Count standards. For more information about dataset metrics, please refer to the User Guide.
The selected file(s) may not be downloaded because you have not been granted access or the file(s) have a retention period that has expired or the files can only be transferred via Globus.
The selected file(s) may not be transferred because you have not been granted access or the file(s) have a retention period that has expired or the files are not Globus accessible.
The files selected are too large to download as a ZIP.
You can select individual files that are below the 15.0 GB download limit from the files table, or use the Data Access API for programmatic access to the files.
Please select a file or files to be downloaded.
The selected file(s) may not be downloaded because you have not been granted access or the file(s) have a retention period that has expired.
Click Continue to download the files you have access to download.
Some file(s) cannot be transferred. (They are restricted, embargoed, with an expired retention period, or not Globus accessible.)
Click Continue to transfer the elligible files.
Are you sure you want to delete this dataset and all of its files? You cannot undelete this dataset.
Are you sure you want to delete this draft version? Files will be reverted to the most recently published version. You cannot undelete this draft.
Preview URL can only be used with unpublished versions of datasets.
Are you sure you want to disable the Preview URL? If you have shared the Preview URL with others they will no longer be able to use it to access your unpublished dataset.
The file(s) will be deleted after you click on the Delete button.
This dataset contains restricted files you may not compute on because you have not been granted access.
Are you sure you want to deaccession? This is permanent and the selected version(s) will no longer be viewable by the public.
Are you sure you want to deaccession this dataset? This is permanent an it will no longer be viewable by the public.
Please select two versions to view the differences.
Please select a file or files for access request.
Embargoed files cannot be accessed. Please select an unembargoed file or files for your access request.
Select existing file tags or create new tags to describe your files. Each file can have more than one tag.
You need to Sign Up or Log In to request access.
Please confirm and/or complete the information needed below in order to request access to files in this dataset.
This dataset is made available under the following terms. Please confirm and/or complete the information needed below in order to continue.
Upon downloading files the guestbook asks for the following information.
Account Information
Use the Download URL in a Wget command or a download manager to download this package file. Download via web browser is not recommended. User Guide - Downloading a Dataverse Package via URL
https://qa.dataverse.org/api/access/datafile/
You will not be able to make changes to this dataset while it is in review.
Are you sure you want to republish this dataset?
Select if this is a minor or major version update.
This dataset cannot be published until NYU Law Review Dataverse is published by its administrator.
This dataset cannot be published until NYU Law Review Dataverse and Harvard Dataverse are published.
Return this dataset to contributor for modification. The reason for return entered below will be sent by email to the author.
Harvard Dataverse Support
Please fill this out to prove you are not a robot.