FAQs

Author: Date: 06.05.20

Just how current is the information on the CCLT website?

We identify federal cases using Lex Machina’s amazing legal analytics for insurance litigation platform.  They generally pick up cases within a few hours of filing, but our coding process runs one to two weeks behind that.

We are only about one week behind for the state court cases we can find, but we know that we’re not finding them all.  See FAQ below about “my case isn’t included.”

What information does the CCLT dataset contain?

Insurance Law Analytics is tracking property casualty insurance coverage litigation related to the Covid 19 pandemic.  For each case, we collect the name and industry code of the policyholder(s) seeking coverage, the name and AM Best number of the insurer(s) involved, the court in which the case is being litigated, the docket number, the law firms involved, the nature of the coverage sought, the type of insurance policy at issue, the specifics of any class action allegations, and information related to case events like answers, motions, and major orders.  When we can obtain a copy of the insurance policy (or when we can identify this information from the complaint), we also collect the specific standard insurance policy forms that the parties allege are relevant to the dispute, the producer listed on the policy, and the state of issue.  In addition, we categorize the insurance forms based on relevant provisions (e.g. business income causal requirements, exclusions, affirmative coverage).  Most of this information is not yet displayed on this website.  We are working on developing ways to display it.

Why are the weekly and cumulative filing charts so far behind the CCLT Case List?

Everything on the CCLT website updates dynamically and continually with the database EXCEPT the weekly and cumulative filing charts.  We have deliberately delayed the updating of those filing charts, because we do not want to give the false impression that there have been a large number of new cases filed in recent weeks.  During those weeks there were in fact a large number of new docket numbers assigned to existing cases, mostly because of the three new MDLs.  A case with a new docket numbers gets automatically added to our database as if it were a new case. Also, slightly more than half of the newly removed cases already exist in our database as state cases, and it takes a while for us to de-duplicate those as well.  It takes us some time to manually work through those duplicate cases.  In the meantime you can always get a total case count by consulting the CCLT Case List, but please recognized that this count can be inflated by as much as 5%.  

My case isn’t included in the CCLT Case List.  Why?

If your case was filed in federal court more than two weeks ago, your case may be one of the very few not picked up by Lex Machina.  If your case was filed in state court, the situation is very different. There isn’t any database with comparable nationwide coverage even of state court dockets, and there aren’t any partial databases of state court dockets that allow the kind of complaint-based searches that are possible in Lex Machina, so we expect to miss lots of state cases.

Either way, if your case is missing, please send us an email: CCLT@law.upenn.edu. Please include the case name, court, and docket number and, if you have it, a copy of the complaint. 

How do you identify state court cases?

We start with lists of cases compiled by Westlaw’s CourtWire and by Courthouse News and supplement those lists using Bloomberg Law, Google searches, and networking to identify state court cases.  Because of the fragmented and incomplete nature of state court electronic filing and data sharing, this system is imperfect.  So we need you to tell us about cases we are missing.  Email us: CCLT@law.upenn.edu.

Who is coding the cases in the CCLT dataset?

University of Pennsylvania and University of Connecticut law students trained by Professor Tom Baker are coding the cases.  The ILA team includes Sean Bender, Sean Bissey, JJ Dunn, Robert Eaton, Jordan Einstein, Lauren Elia, Kamryn Jackson, Victoria James, Madison Kirton, Joseph Ledereich, William Lyoo, Sager Moritzky, Maham Usman, and Adam Zwick.