Mapping Immigration Court

1 Summary
2 References
3 Further reading
4 Appendix
- 4.1 Data/Methods
5 Acknowledgements

Summary

The American Bar Association (ABA) says that the U.S. immigration court system is “on the brink of collapse.”^[1] It lacks due process, up-to-date technology, and staff. The pending case backlog, as of January 2022, is almost 1.6 million.^[2] Also, the attorney representation rate is much lower than in courts that have a universal public defender system, such as in U.S. criminal court. That makes universal representation in immigration court more important now than ever before.^[3]

Below, maps display geographic differences in U.S. immigration courts and related metrics, all using publicly-available data from Transactional Records Access Clearinghouse (TRAC),^[4] the American Immigration Lawyers Association (AILA),^[5] and IPUMS NGHIS.^[6] To learn more about these data sources, see the Data/Methods subsection in the appendix.

Why do this?

This project leans into the idea that the public should be better able to participate and educate themselves about the U.S. immigration courts system.

The “Mapping Immigration Courts” project helps inform the public about key immigration court statistics using interactive maps. Readers can click through the stats they are interested in, and see how they are geographically represented. What do you see in the court closest to you?

Immigration court backlog by EOIR jurisdiction: Pending cases per 1,000 foreign-born

Sources: Transactional Records Access Clearinghouse (TRAC), IPUMS ACS 5-year estimates (2016-2020), last updated January 2022.

The U.S. immigration system functions at the federal level, meaning that any immigration court is able to adjudicate and make determinations for any non-citizen’s case, no matter where they reside in the U.S. With that said, there are still defined geographic jurisdictions the Executive Office for Immigration Review (EOIR) uses in assigning court cases.^[7] The above map is separated into these jurisdictions, displaying the pending case backlog for each area while adjusting for immigrant population size. As of January 2022, immigration courts in Louisiana had the highest number of pending cases (382.73 per 1,000 immigrants), followed by the Nebraska-Iowa jurisdiction (144.85 per 1,000 immigrants). Generally, immigration court jurisdictions in the U.S. South had higher numbers of pending cases when adjusting for foreign-born population size.

Removal defense attorney access by hearing location

Source: American Immigration Lawyers Association (AILA), last updated January 2022.

U.S. immigration courts differ not only in how many cases remain on each docket, but also in immigrants’ access to attorney representation. The above map shows how many American Immigration Lawyers’ Association (AILA) attorneys specializing in removal defense are publicly listed as contactable referrals within 100 miles of each EOIR hearing location. The number of AILA-listed attorneys is high in more traditional immigrant destinations, such as New York and Los Angeles, and lower in the U.S. Midwest and South. Clicking on each hearing location will give you an idea of how many removal defense attorneys there are in a hearing location’s surrounding area.

References

1. ^ American Bar Association Commission on Immigration. 2019. “Reforming the Immigration System: Proposals to Promote Independence, Fairness, Efficiency, and Professionalism in the Adjudication of Removal Cases (Update Report).”

2. ^ Transactional Records Access Clearinghouse. 2022. “Immigration Court Backlog Now Growing Faster Than Ever, Burying Judges in an Avalanche of Cases.”

3. ^ Vera Institute. 2022. “The Case for Universal Representation.”

4. ^ Transactional Records Access Clearinghouse. 2022. “Immigration Court Backlog Tool.”

5. ^ American Immigration Lawyers Association. 2022. “AILA’s Immigration Lawyer Search.”

6. ^ Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 16.0 [dataset]. Minneapolis, MN: IPUMS. 2021. http://doi.org/10.18128/D050.V16.0

7. ^ Transactional Records Access Clearinghouse. 2021. “Hearing Locations With Addresses and Immigration Court Each Is Under.”

Appendix

Data/Methods

All data I use is publicly-available online. U.S. immigration court statistics are provided by Transactional Records Access Clearinghouse (TRAC) at Syracuse University. While I rely on their backlog tool, TRAC also provides important data beyond the backlog (e.g., attorney representation, asylum decisions, and bond release). Backlog data are current as of January 2022, containing the number of pending immigration cases for all charges. To learn more about TRAC’s calculation methods, click here.

Immigration attorney information is available through the American Immigration Lawyers Association’s (AILA) Immigration Lawyer Search. Using a public list of immigration hearing ZIP codes (once again provided by TRAC), I used web-scraping software to compile a list of AILA attorneys within 100 miles of each hearing location, as of January 2022). Disclaimer: AILA’s repository is not a complete representation of how many immigration attorneys currently work in the U.S. First, not all attorneys are AILA-affiliated. Second, those who are choose to publicly listed on AILA’s website, meaning that some may opt out. Also, AILA’s repository contains lots of duplicates, which are accounted for in the maps. Despite AILA’s own disclaimers re: their data, their search tool provides a good first glance at understanding spatial differences in immigration attorney access in the U.S.

Lastly, credit goes to IPUMS for providing free U.S. Census data through their IPUMS NHGIS repository. With IPUMS data, I am able to weight metrics using 5-year 2016-2020 American Community Survey (ACS) state estimates of the foreign-born population.

Another disclaimer: I am not directly affiliated with these data providers, and this webpage’s analyses of these publicly-available data are my own and I take responsibility for any reporting errors or mistakes.

Acknowledgements

I wish to thank the American Sociological Association’s Doctoral Dissertation Research Improvement Grant (ASA DDRIG) for project funding support.