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ICE jails nearly everyone it arrests and deports most of them
New data shows ICE now jails 90% of the people it arrests — and that deportation is the most common outcome.
July 15, 2026 · David Eads and PromptQL
Leer en español →Newly linked data show that under Trump, ICE booked about 90% of the almost 390,000 people it arrested in the interior into detention. That near-universal rate isn’t new, it had already climbed to nearly 90% by the end of the Biden years. But now ICE makes roughly triple the arrests, making detention both high-likelihood and high-volume. Removal is now the single most common outcome, with more than 261,000 people — about two-thirds of those arrested — ending in deportation or departure.
This analysis and writeup was generated by PromptQL, an AI-powered collaborative platform that makes sense of sprawling data sources, documents, and tribal knowledge. I wrote significant parts of it, edited it, reached out to sources, and reviewed the data analysis.
I’m consulting with the company and have found the platform is powerful and trustworthy enough to rely on for experiments like this and our 287(g) Watch state news summaries.
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On July 13, 2026, an ICE Enforcement and Removal Operations officer in Biddeford, Maine, fatally shot a 26-year-old Colombian man who was authorized to work in the United States and was not the target of the arrest warrant — the second ICE deadly-force death in a week. It’s just the most recent example of an enforcement regime running full tilt.
On July 6, 2026, the Deportation Data Project released a new dataset linking each ICE arrest to what happened next. That matters because for the first time, a single anonymized, individual-level record follows a person from interior arrest through detention to removal. What it shows: almost everyone ICE arrests in the interior is jailed, and the most common outcome is deportation. Arrests roughly tripled after January 20, 2025, while the detention rate — already climbing through the Biden years — now sits around 90%.
DHS has issued more than 40 ‘worst of the worst’ press releases in the Trump era, touted a milestone of 10,000 gang members arrested, and celebrated 13 straight months of zero releases at the border. But the near-universal detention rate applies across the board, not just to the violent offenders their headlines feature.
In an email, a DHS spokesperson said, “The Deportation Data Project relies on information releases that have not been reviewed, audited or given context. DHS nor ICE have verified the accuracy, methodology or the analysis of the project and its results.” Despite saying they have not reviewed the data, the message goes on to claim: “The bottom line is that the Deportation Data Project is not accurate.”
Having not reviewed the information or methodology, they can’t know if it is accurate. The full response made further claims without providing evidence. It is available at the bottom of this post along with our methodology.
The Deportation Data Project does not systematically review third-party material pre-publication, but said they found no obvious inaccuracies in our analysis.
Almost everyone ICE arrests is now jailed
Of the almost 390,000 people ICE arrested since Trump’s inauguration, about 351,000 (90%) were booked into detention. That near-universal rate isn’t new — it had already climbed to nearly 90% by the end of the Biden years, up from a 61.3% period average that masks the rise. What changed under Trump is the volume: arrests roughly tripled. Combined, detention is now both high-likelihood and high-volume — ICE is arresting far more people and jailing almost all of them.
View the data
| Month | arrests | detained | released | detention rate |
|---|---|---|---|---|
| Oct '22 | 16,653 | 6,024 | 10,629 | 36.2% |
| Nov '22 | 16,447 | 5,769 | 10,678 | 35.1% |
| Dec '22 | 15,265 | 5,496 | 9,769 | 36% |
| Jan '23 | 18,019 | 5,995 | 12,024 | 33.3% |
| Feb '23 | 16,147 | 6,448 | 9,699 | 39.9% |
| Mar '23 | 15,716 | 7,415 | 8,301 | 47.2% |
| Apr '23 | 13,321 | 6,746 | 6,575 | 50.6% |
| May '23 | 13,661 | 7,386 | 6,275 | 54.1% |
| Jun '23 | 12,562 | 7,279 | 5,283 | 57.9% |
| Jul '23 | 11,628 | 7,272 | 4,356 | 62.5% |
| Aug '23 | 11,777 | 7,432 | 4,345 | 63.1% |
| Sep '23 | 10,538 | 7,290 | 3,248 | 69.2% |
| Oct '23 | 10,053 | 6,916 | 3,137 | 68.8% |
| Nov '23 | 8,939 | 6,371 | 2,568 | 71.3% |
| Dec '23 | 9,860 | 7,451 | 2,409 | 75.6% |
| Jan '24 | 8,523 | 6,205 | 2,318 | 72.8% |
| Feb '24 | 9,606 | 7,171 | 2,435 | 74.7% |
| Mar '24 | 9,640 | 7,192 | 2,448 | 74.6% |
| Apr '24 | 10,214 | 7,844 | 2,370 | 76.8% |
| May '24 | 10,267 | 8,033 | 2,234 | 78.2% |
| Jun '24 | 8,575 | 6,945 | 1,630 | 81% |
| Jul '24 | 9,489 | 7,711 | 1,778 | 81.3% |
| Aug '24 | 9,191 | 7,598 | 1,593 | 82.7% |
| Sep '24 | 8,397 | 6,906 | 1,491 | 82.2% |
| Oct '24 | 9,513 | 8,000 | 1,513 | 84.1% |
| Nov '24 | 8,181 | 7,043 | 1,138 | 86.1% |
| Dec '24 | 8,512 | 7,352 | 1,160 | 86.4% |
| Jan '25 | 12,194 | 10,861 | 1,333 | 89.1% |
| Feb '25 | 17,173 | 15,673 | 1,500 | 91.3% |
| Mar '25 | 18,640 | 17,213 | 1,427 | 92.3% |
| Apr '25 | 17,682 | 16,408 | 1,274 | 92.8% |
| May '25 | 22,374 | 21,005 | 1,369 | 93.9% |
| Jun '25 | 29,851 | 27,306 | 2,545 | 91.5% |
| Jul '25 | 26,877 | 24,266 | 2,611 | 90.3% |
| Aug '25 | 27,881 | 25,242 | 2,639 | 90.5% |
| Sep '25 | 32,747 | 29,393 | 3,354 | 89.8% |
| Oct '25 | 37,139 | 33,617 | 3,522 | 90.5% |
| Nov '25 | 34,645 | 31,238 | 3,407 | 90.2% |
| Dec '25 | 39,829 | 34,324 | 5,505 | 86.2% |
| Jan '26 | 37,612 | 33,306 | 4,306 | 88.6% |
| Feb '26 | 29,661 | 26,368 | 3,293 | 88.9% |
Open-ended custody is growing fast, too; almost 50,000 people arrested in this window remained in ICE custody, 14.2% of everyone detained. And people now cycle through more stints — a median of three detention stints per person, up from two.
What changed is not the speed or length of detention. Book-in still happens within a day, and the median completed stay is about 22 days in both eras. What changed is whether anyone is let go at all, and how many people are being arrested.
The most common outcome is removal
Earlier ICE arrest data published by the Deportation Data Project could not tell you whether a detention ended in deportation. This release links each arrest to its detention stay’s release reason and to the arrest record’s information about departed date, destination country, and final order. That makes the arrest-to-deportation path traceable per person.
These are outcomes as of the latest data, not a final tally. About one in eight Trump-era arrests — 12.8% — hadn’t resolved: the person was still in ICE custody when the records were pulled. And because the data lands in batches, the most recent months are incomplete. So the share below is a floor that will climb as open cases close — and some people counted as still in custody may already have been deported in ways the data hasn’t caught up to.
Even so, removal or departure is already the single largest Trump-era outcome, in a little more than two-thirds of all arrests — about 261,000 people. Mexico is the top destination, with almost 129,000 departures, followed by Guatemala at 42,000 and Honduras at 32,500; Venezuela, El Salvador, Ecuador, Nicaragua, and Colombia round out the leading countries.
View the data
| Country | departures |
|---|---|
| Mexico | 128,741 |
| Guatemala | 42,305 |
| Honduras | 32,496 |
| Venezuela | 13,756 |
| El Salvador | 11,936 |
| Ecuador | 8,200 |
| Nicaragua | 8,017 |
| Colombia | 7,494 |
Two-thirds of those arrested have no criminal conviction in the US
When DHS suggests that some of these people have criminal records in other countries, it may be right — but that is not what this data measures. ICE’s own classification counts only convictions in the United States, and by that measure two-thirds of the people it has arrested since January 2025 have none.
The largest single group ICE arrested since January 2025 is people the agency itself classifies as non-criminal “other immigration violators.” These people constituted 148,782 arrests, more than either the convicted or the charged. These are people with no criminal charge at all, meaning their only offense is a civil immigration matter, a pattern documented in depth in Marshall Project reporting on a Georgia traffic-stop arrest. Yet about 83% of them were detained, up from about 23% under Biden.
The outcomes for people with a criminal conviction and people with pending charges (but not conviction) was practically indistinguishable, with both hovering a little above 94%.
The composition has shifted month by month. In February 2025, non-criminal “other immigration violators” were 22.8% of arrests; by December 2025 they were 47.6%.
Median completed stay is similar in both eras (about 22 days).
View the data
| Stay length | Biden era | Trump era |
|---|---|---|
| <1 day | 44,779 (23.2%) | 26,405 (8.8%) |
| 1–3 days | 11,759 (6.1%) | 21,482 (7.1%) |
| 3–7 days | 8,128 (4.2%) | 30,842 (10.2%) |
| 1–2 weeks | 12,737 (6.6%) | 40,405 (13.4%) |
| 2–4 weeks | 33,949 (17.6%) | 58,421 (19.4%) |
| 1–2 months | 34,937 (18.1%) | 62,456 (20.7%) |
| 2–3 months | 16,786 (8.7%) | 29,108 (9.7%) |
| 3+ months | 29,685 (15.4%) | 31,897 (10.6%) |
Local police partners extend immigration enforcement’s reach
The buildout runs partly through local law enforcement, which DHS has publicly courted. More than 1,720 state and local agencies now hold 287(g) agreements, led by Texas and Florida.
Those agreements surface directly in the arrest data, if modestly: about 20,000 arrests since Trump’s inauguration (roughly 5%) carry ICE’s 287(g) apprehension code, and 93% of them ended in detention, slightly above the overall 90% plateau.
However, that share is a floor, since arrests made inside local jails are frequently logged under other codes. Detention rates vary sharply by region, from 99.0% in the Newark, New Jersey area to 53.1% in Harlingen, Texas. DHS has repeatedly targeted the governors of holdout states, lodging detainers and demanding non-release in cases like an Illinois assault case and a Fairfax County, Virginia rape charge, and touting the removal of a man pardoned by Minnesota’s governor.
What these numbers do and don’t say
Everything here concerns ICE arrests and their consequences, not ICE detention as a whole. Many detention stays begin without an ICE arrest — most often at the border, following a CBP arrest — and are not included; a reader focused on detention itself should turn to the detention-stays datasets. A detention stay is attached only when book-in falls within five days before to ten days after the arrest, a 77% match rate, comfortably inside the range flagged as reliable. Months after February 2026 were incomplete and excluded.
And the criminality labels are ICE’s own administrative classifications, not court adjudications, a caveat that cuts against using them either to validate or to rebut the ‘worst of the worst’ framing.
How we did this
This document accompanies the editorial piece. It lists every quantity that appears in (or backs) the piece and, for each one, gives a short plain-language note on exactly how it was computed. It is meant to stand on its own — you should be able to read it without any prior knowledge of the tools or data behind the piece.
Where the data comes from. Most figures trace to the Deportation Data Project (DDP), a public-interest effort hosted at UC Berkeley Law that obtains internal U.S. government immigration-enforcement datasets through the Freedom of Information Act and publishes them — mostly as anonymized, individual-level records — under a CC-0 public-domain dedication, each accompanied by DDP’s own documentation and codebook. This piece uses DDP’s published dataset that joins ICE interior-arrest records to ICE detention-stay records. The field names and coded values referenced below (e.g. apprehension_date, final_order_yes_no, the 1/2/3 criminality codes) are DDP’s own — consult the DDP codebook for their authoritative definitions.
Two families of figures come from outside DDP. The 287(g) counts (see “Local enforcement”) come from a daily scrape of ICE’s own public list of participating state and local agencies, not from DDP. And the rhetoric counts (see “Rhetoric corpus”) come from DHS’s public press-release archive. Each of those sections says so again where its numbers appear.
How these numbers are produced. The raw data is run once through a single analysis routine that computes every figure and writes them to one machine-readable file; this document is then produced purely by reading and formatting that file. It runs no database query and calls no AI or statistical model — it is a plain, deterministic transform, so the same inputs always yield exactly these numbers. (Verified: regenerating this document repeatedly from the same inputs yields a byte-for-byte identical result.)
Base dataset. The analysis works over DDP’s joined arrests x detention-stays dataset — one row per deduplicated ICE interior arrest, with a detention stay attached when book-in falls within 5 days before to 10 days after the arrest.
Administration split (apprehension_date vs. the Trump inauguration boundary): BIDEN = apprehension_date < TIMESTAMP '2025-01-20', TRUMP = apprehension_date >= TIMESTAMP '2025-01-20'. The two administrations are read separately, never as one continuous curve.
Headline (all arrests, both administrations)
Each row is one deduplicated ICE arrest, with detention meaning a detention stay booked within five days before to ten days after the arrest.
| Figure | Value | How it’s computed |
|---|---|---|
| Total arrests | 705,286 | COUNT(1) over the whole joined table |
| Total detained | 544,300 | SUM(CASE WHEN has_detention_stay THEN 1 ELSE 0 END) |
| Overall detention rate | 77.2% | detained / arrests × 100 |
| Coverage window | 2022-10 → 2026-02 | monthly series after trimming lagged/incomplete trailing months (a trailing month is dropped while its arrests < 0.6 × median of the prior 6) |
| Median book-in lag | 0.42 hours | APPROX_PERCENTILE_CONT(0.5) of (stay_book_in_date_time − apprehension_date_time)/3600 over detained arrests with both timestamps |
Detention rate by administration
These are two distinct enforcement administrations, so the full series should not be read as one continuous trend.
| Figure | Value | How it’s computed |
|---|---|---|
| Biden arrests / detained / rate | 315,461 / 193,474 / 61.3% | same headline formulas, filtered to BIDEN window |
| Trump arrests / detained / rate | 389,825 / 350,826 / 90.0% | same headline formulas, filtered to TRUMP window |
Detention consequences per arrest
This is the capability the July arrests x detention-stays join newly unlocks, with each percentage taken over arrests unless it is marked ‘of detained’.
| Figure | Value | How it’s computed |
|---|---|---|
| Trump: swift release (no book-in) | 38,999 (10.0% of arrests) | SUM(NOT has_detention_stay); pct / arrests |
| Trump: still in custody | 49,810 (14.2% of detained) | SUM(has_detention_stay AND stay_book_out_date_time IS NULL); pct / detained |
| Trump: bond posted | 28,465 (8.1% of detained) | SUM(bond_posted_amount_lowest_seen > 0); pct / detained |
| Trump: median completed stay | 21.9 days | APPROX_PERCENTILE_CONT(0.5) of (book_out − book_in)/86400 over closed stays (book_out not null) |
| Trump: median stints per detained person | 3 | APPROX_PERCENTILE_CONT(0.5) of n_stints over detained people. NB: n_stints = facility booking segments (transfers create multiple stints within one stay); it is NOT distinct detention stays (n_stays, whose median is 1 in both eras) |
| Biden: swift release (no book-in) | 121,987 (38.7% of arrests) | SUM(NOT has_detention_stay); pct / arrests |
| Biden: still in custody | 714 (0.4% of detained) | SUM(has_detention_stay AND stay_book_out_date_time IS NULL); pct / detained |
| Biden: bond posted | 21,010 (10.9% of detained) | SUM(bond_posted_amount_lowest_seen > 0); pct / detained |
| Biden: median completed stay | 22.8 days | APPROX_PERCENTILE_CONT(0.5) of (book_out − book_in)/86400 over closed stays (book_out not null) |
| Biden: median stints per detained person | 2 | APPROX_PERCENTILE_CONT(0.5) of n_stints over detained people. NB: n_stints = facility booking segments (transfers create multiple stints within one stay); it is NOT distinct detention stays (n_stays, whose median is 1 in both eras) |
Outcome mix (3-way, share of all arrests)
Released after detention equals detained minus those still in custody, and each percentage is taken over all arrests.
| Figure | Value | How it’s computed |
|---|---|---|
| Trump: swift release | 38,999 (10.0%) | SUM(NOT has_detention_stay) / arrests |
| Trump: released after detention | 301,016 (77.2%) | (detained − still_in_custody) / arrests |
| Trump: still in custody | 49,810 (12.8%) | SUM(has_detention_stay AND book_out IS NULL) / arrests |
| Biden: swift release | 121,987 (38.7%) | SUM(NOT has_detention_stay) / arrests |
| Biden: released after detention | 192,760 (61.1%) | (detained − still_in_custody) / arrests |
| Biden: still in custody | 714 (0.2%) | SUM(has_detention_stay AND book_out IS NULL) / arrests |
Outcome taxonomy (5-way, share of all arrests)
Each person is placed in a single bucket based on their release reason and arrest-side departure fields.
| Figure | Value | How it’s computed |
|---|---|---|
| Trump: released no detention | 38,999 (10.0%) | NOT has_detention_stay |
| Trump: removed departed | 261,302 (67.0%) | release reason IN (‘Removed’,‘Voluntary departure’,‘Voluntary Return’) |
| Trump: released into us | 26,913 (6.9%) | release reason IN the bonded-out / order-of-supervision / paroled / relief set |
| Trump: transferred other | 12,793 (3.3%) | any other non-null release reason (transfer/handoff/other) |
| Trump: still in custody | 49,818 (12.8%) | release reason IS NULL (booked in, not yet released) |
| Biden: released no detention | 121,987 (38.7%) | NOT has_detention_stay |
| Biden: removed departed | 130,147 (41.3%) | release reason IN (‘Removed’,‘Voluntary departure’,‘Voluntary Return’) |
| Biden: released into us | 51,725 (16.4%) | release reason IN the bonded-out / order-of-supervision / paroled / relief set |
| Biden: transferred other | 10,353 (3.3%) | any other non-null release reason (transfer/handoff/other) |
| Biden: still in custody | 1,249 (0.4%) | release reason IS NULL (booked in, not yet released) |
Removal signal (Trump era)
These signals corroborate how often a Trump-era arrest ended in an actual removal or departure.
| Figure | Value | How it’s computed |
|---|---|---|
| Removed / departed | 261,302 (67.0% of arrests) | release reason IN the removed/departed set; pct / arrests |
| Has a departed date | 278,637 | COUNT where the arrest-side departed_date is set |
| Has a final order (YES) | 219,945 | COUNT where final_order_yes_no = 'YES' (value is uppercase) |
| Removed-reason corroborated by departed date | 97.0% | share of ‘Removed’ stays that also carry a departed_date — the two independent signals corroborate |
| Top departure countries | Mexico 128,741, Guatemala 42,305, Honduras 32,496, Venezuela 13,756, El Salvador 11,936, Ecuador 8,200, Nicaragua 8,017, Colombia 7,494 | GROUP BY departure country over removed/departed arrests, top 8 (country values are stored uppercase) |
Criminality (ICE’s 3-way classification, Trump era)
Labels carry a leading digit: ‘1 Convicted Criminal’, ‘2 Pending Criminal Charges’, ‘3 Other Immigration Violator’. These are ICE administrative classifications, not court adjudications.
| Figure | Value | How it’s computed |
|---|---|---|
| Convicted criminal | 128,000 arrests, 94.2% detained | WHERE apprehension_criminality = '1 Convicted Criminal' — has a conviction |
| Pending charges | 113,043 arrests, 94.6% detained | ‘2 Pending Criminal Charges’ — charged but NOT convicted |
| Other immigration violator | 148,782 arrests, 82.9% detained | ‘3 Other Immigration Violator’ — neither charged nor convicted (civil) |
| Convicted of nothing | 261,825 (67.2% of Trump arrests) | pending_charges + other_immigration_violator (no conviction). Say ‘convicted of nothing’, never ‘charged with nothing’ |
| Charged with nothing | 148,782 (38.2% of Trump arrests) | other_immigration_violator ONLY (no charge at all) |
| Non-criminal detention rate: Biden → Trump | 22.9% → 82.9% | detention rate of ‘3 Other Immigration Violator’, each administration window |
Local enforcement — 287(g)
These 287(g) figures do not come from DDP; they come from a daily scrape of ICE’s own public list of state and local agencies that have signed 287(g) agreements, collated across all daily snapshots and counted as distinct currently-participating agencies.
| Figure | Value | How it’s computed |
|---|---|---|
| National participating agencies | 1,720 | COUNT(DISTINCT COALESCE(ori, normalized_agency_name)) in tracking_287g_agreements WHERE is_current = true |
| Top states | Texas 350, Florida 272, Pennsylvania 104, Arkansas 98, Missouri 97, Tennessee 80 | same distinct-agency count, grouped by state |
| Sanctuary states with zero agencies | California, Connecticut, Washington, Vermont, Oregon, Illinois, New Jersey | states from the sanctuary watch-list whose distinct-agency count is 0 |
Rhetoric corpus (DHS press releases)
These counts come from the Department of Homeland Security’s own public press-release archive, covering releases issued from January 20, 2025 through July 13, 2026 (corpus span 2025-01-20..2026-07-13).
| Figure | Value | How it’s computed |
|---|---|---|
| DHS releases, Trump era | 971 | COUNT(1) of DHS releases with release_date >= 2025-01-20 |
| ‘Worst of the worst’ releases | 42 | title LIKE 'WORST OF THE WORST%' |
| Sanctuary-titled releases | 88 | title matching the sanctuary pattern |
| Detainer-titled releases | 68 | title matching the detainer pattern |
| Named-governor attack releases | Pritzker 11, Walz 7, Spanberger 10, Newsom 9, Hochul 1 | count of releases whose title names each governor |
Series (breakdowns available in full in the underlying data)
These are longer breakdowns whose full arrays live in the underlying data; only their shape is summarized here.
| Figure | Value | How it’s computed |
|---|---|---|
| Monthly arrests / detention rate | 41 months, 2022-10 → 2026-02 | GROUP BY DATE_TRUNC('month', apprehension_date); rate = detained/arrests |
| Monthly criminality mix | 14 months, within-month shares | monthly GROUP BY apprehension_criminality, share of that month’s total |
| Stay-length histogram | 8 buckets × 2 administrations | bucketed (book_out − book_in)/86400 over closed detained stays |
| By program | 10 rows | arrests and detention rate grouped by that dimension, top-N by arrests |
| By citizenship | 12 rows | arrests and detention rate grouped by that dimension, top-N by arrests |
| By state | 15 rows | arrests and detention rate grouped by that dimension, top-N by arrests |
| By area of responsibility (AOR) | 15 rows | arrests and detention rate grouped by that dimension, top-N by arrests |
Note: this document is single-sourced from the same computed figures that back the chart and the piece, recomputed from the raw DDP release. The published prose is hand-edited, so a few narrative roundings may differ from these exact figures — that difference is intentional editorial latitude, and these figures are the numeric ground truth.
Response from DHS, 7/16/2026
The Deportation Data Project relies on information releases that have not been reviewed, audited or given context. DHS nor ICE have verified the accuracy, methodology or the analysis of the project and its results. The bottom line is that the Deportation Data Project is not accurate.
Many of the individuals that are counted as ‘non-criminals’ are actually terrorists, human rights abusers, gangsters and more; they just don’t have a rap sheet in the U.S. Further, every single one of these individuals committed a crime when they came into this country illegally.
Since Day One, DHS law enforcement has been delivering on President Trump’s promise to the American people to arrest and deport criminal illegal aliens including murderers, rapists, pedophiles, gang members, and terrorists. Nearly 70% of ICE arrests are of illegal aliens charged or convicted of a crime in the U.S. More than 3 million illegal aliens are out of the country and counting. Our message is clear: if you come to our country illegally, we will find you, we will arrest you, and we will deport you.
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