1328f.org

1. The Headline Numbers

Source: FJC Integrated Database, public release files (civil + bankruptcy). Downloaded March 2026. Loaded into SQLite, 94 districts.

The 391,951 figure is the cross-reference of two FJC fields that had never been joined before:

• PRFILE -"Prior filing" flag. Set to Y when the debtor has a prior bankruptcy on record.
• DIESSION (disposition) -Coded D (discharge), DC (discharge & closed), or DD (discharge & dismissed).

Result: 27.4% of prior filers with known dispositions received a discharge. Section 1328(f) requires a time-eligibility check before discharge. No federal database records whether this check occurred.

2. Data Architecture

The critical asymmetry: the data showing the problem is free; the data confirming actual violations costs money. The FJC publishes the question. PACER paywalls the answer at $0.10/page.

3. Methodology: How 391,951 Was Derived

All queries run against SQLite. Schema and SQL available on request. Reproducible by anyone who downloads the same FJC files.

4. Known Limitations

These are openly acknowledged -not hidden. Several are the direct motivation for the research collaboration.

5. Geographic Variation

The per-district prior-filer discharge rate varies dramatically, suggesting either enforcement variation, local practice differences, or both:

Source: FJC IDB, same methodology as Section 3. Full 94-district table available.

6. Attorney Outcome Analysis (13,615 cases)

Independent of the FJC national analysis, a separate dataset was built from PACER API queries and RSS monitoring across 6 federal districts. This dataset tracks individual attorney outcomes over time.

Control methodology

• Same-district controls: Other Chapter 13 attorneys in the same judicial district, same filing years. National Chapter 13 completion rate: ~33%. Subject firm: 11.2%.
• Tiered controls: Attorneys grouped by caseload volume to control for practice scale. Subject firm is an outlier at every tier.
• Cross-era tracking: The same attorney names appear across three separately sanctioned firms in two cities over 17 years (2007–2024). Dismissal rates remain stable across firm changes: 50.7% (Chicago, 2007–2014) → 51.7% (Kansas City, 2019–present).

What the attorney dataset is not

This dataset is not the basis for the 391,951 finding. The two analyses are independent. The FJC analysis uses only free government data. The attorney analysis uses paid PACER data. They converge on the same structural failure -no systematic verification of discharge eligibility -from completely different starting points.

7. The Screening Tool

An open-source tool that automates Section 1328(f) eligibility verification. Given a debtor name and district, it:

1. Queries PACER for prior bankruptcy filings
2. Extracts filing dates and chapter types from each case
3. Computes the statutory bar window (4 years for Ch. 7→13, 2 years for Ch. 13→13)
4. Compares against the current case filing date
5. Returns: eligible, barred, or insufficient data

Live at 1328f.com. Source code on GitHub. Client-side only - no server, no database, no user data collected. Runs entirely in the browser using sql.js (SQLite compiled to WebAssembly).

The screener has been stress-tested publicly across multiple online communities, with practicing bankruptcy attorneys engaging directly on methodology. Independent users have downloaded the code and begun running it against their own districts, creating the basis for the convergence study described in Section 8.

8. Convergence Study Design (Proposed)

The most novel methodological opportunity:

Research design

1. Ground truth: 264 manually verified cases (private, held by dataset builder)
2. Independent collection: GitHub cloners running the screener against new districts
3. Convergence test: Do independently collected results match the ground truth where districts overlap?
4. Expansion: With institutional PACER access, scale verification from 264 verified multi-district cases to thousands -stratified by district, attorney volume, and filing year

This design separates the person who built the tool from the people validating it. If the crowd finds the same patterns independently, that is a stronger result than any single-author study.

Full convergence study design document →

9. The PACER Access Bottleneck

With fee-exempt access through a university affiliation:

• National verification becomes feasible across all 94 districts at zero marginal cost
• The 391,951-case universe can be sampled systematically (stratified random sample by district and year)
• Attorney-level outcome tracking can scale from 6 districts to 94
• Real-time RSS monitoring already covers every federal bankruptcy court -enrichment via PACER is the only paid step

10. Policy Impact Already Underway

A federal rules committee is now acting on findings that required paid PACER access to produce. The aggregate data (FJC) is free. The verification data (PACER dockets) is paywalled. This is the access-to-justice argument reduced to a concrete, active example.

The suggestion is posted on the Advisory Committee on Bankruptcy Rules suggestions page as 26-BK-3.

11. Open Research Questions

These are the questions this dataset can answer that no one has answered yet:

1. What is the actual 1328(f) violation rate? The 391,951 is the verification universe. How many are actual violations vs. cases where the bar window had expired? Requires PACER docket-level date checking at scale.
2. What explains the 39-point geographic spread? Is D. Kansas (57.4%) genuinely less enforcement-intensive than N.D. Georgia (18.1%), or do demographic and filing-pattern differences account for the gap?
3. Does attorney volume predict dismissal rate? The 13,615-case dataset suggests yes (Z = 4.5). Can this hold nationally across all 94 districts?
4. What is the dollar cost of non-verification? Each ineligible discharge that proceeds represents fees paid to an attorney, trustee payments made, and court resources consumed. Can a per-case harm estimate be built?
5. Does the PRFILE flag have a measurable false-positive rate? Cross-validation against PACER case histories in the 264-case set suggests high accuracy, but systematic measurement has never been done.
6. Can crowdsourced PACER verification converge with institutional verification? The convergence study design (Section 8) tests this directly.
7. What is the optimal screening rule for Rule 4004? If the Advisory Committee adopts a verification requirement, what should the standard be? The screener provides a working prototype.

12. Reproducibility

Every finding on this page can be independently verified:

SQL schemas, Python scripts, and the full FJC query logic are available to research collaborators. The screener source code is already public on GitHub.

13. Path to Nonprofit

This project is incorporating as a 501(c)(3) nonprofit. The mission: unlock the entire PACER database and make comprehensive discharge eligibility screening available to every debtor, every trustee, and every court in the United States, free of charge.

How it works

• RECAP partnership: The Free Law Project's RECAP archive is the largest open collection of federal court documents. Through bulk RECAP ingestion, the system has enriched over 16,000 cases at zero cost. A nonprofit institutional partnership would scale this to full national coverage across all chapters and all discharge bars (1328(f), 727(a)(8), 109(g), and cross-chapter eligibility). Every document pulled through fee-exempt access gets contributed back to RECAP, permanently freeing it for everyone.
• Fee-exempt PACER access: As a 501(c)(3) research organization, the project would qualify for the AO's institutional fee exemption. That turns the $0.10/page barrier into $0.00/page across all 94 districts.
• Permanent screening infrastructure: The RSS monitoring system already tracks every new bankruptcy filing across all chapters in real time. Adding fee-exempt PACER enrichment creates a live, national eligibility verification system that runs continuously, not as a one-time study.
• Open data: Every dataset, tool, and methodology produced by the nonprofit would be open-source and publicly available. The goal is not to own the data. It is to make sure no one else has to pay for it.

The technology works. The tools are built. The only missing piece is institutional standing. A university research partnership is the fastest path to PACER access and accelerates nonprofit formation.