Published Feb 27, 2025
While Immigration and Customs Enforcement (ICE) has been waging a media campaign on its "stepped up enforcement," details have been sketchy to non-existent for independently verifying many of these claims. Given the dearth of information, the Transactional Records Access Clearinghouse (TRAC) — now located at tracreports.org — has examined the latest case-by-case Immigration Court records.
This report focuses on new Notices to Appear (NTA) issued by the Department of Homeland Security (DHS) dated after Trump assumed office (January 20-January 31).[1] These are compared to DHS NTAs dated from January 1-January 19 while former President Biden was still in office.
NTA records are a particularly useful data source because we have a continuous series going back in time which we can keep updated on a monthly basis to monitor immigration enforcement activity in the coming months. It is also among the very few sources with geographic detail as each NTA issued is associated with the city, state, and zip code of the noncitizen’s residence recorded in Immigration Court records where hearing notices are sent.
However, DHS arrests resulting in the issuance of an NTA make up only an unknown proportion of DHS enforcement actions during these initial 12 days and can include some enforcement actions that began earlier. So we cannot reliably generalize to all immigration enforcement actions that took place.
In this report we focus on this question: did ICE targeting change in the first days of the Trump administration? No pejorative meaning is attached to the existence of targeting. It is an inherent part of the discretion that all law enforcement agencies exercise since they cannot be in all places at the same time. However, some types of targeting can indeed be discriminatory. Past reporting consistently shows that ICE specifically concentrates enforcement in certain locations and communities for a variety of reasons.[2]
Presently DHS needs a removal order from an Immigration Judge in order to deport many arrestees. The date on an NTA records the day DHS officials issued the form to the noncitizen who was apprehended. A time series day-by-day plot for the dates in January 2025 court records on these new NTAs is shown in Figure 1.[3] While the number of NTAs dropped, the Trump administration continued to send many cases to Immigration Court to obtain authorization to deport individuals it had arrested.
Court data before Trump’s inauguration indicate that DHS issued an average of 2,586 NTAs during January weekdays.[4] Upon taking office, Trump immediately canceled all appointments on CBP One, a phone app introduced under the Biden administration which immigrants had used to make appointments at ports of entry to seek entry with asylum and other claims. After closing off entry in this manner, NTAs dropped by a little over half (55%). For the January 20-31 period newly issued NTAs averaged 1,180 during weekdays but at that point were not continuing to decline.
While overall NTAs declined, different groups had different experiences. NTA issuance to some groups saw less of a drop, while other groups experienced a greater decline. We focus on identifying which groups experienced notable differences.[5] The report looks first at differential targeting by residence – county and state – of the noncitizen, and then by nationality.
To aid readers in identifying the direction and relative magnitude of differences from what would be expected in the absence of targeting, this report uses an index that measures the relative change from what was expected. This index is measured in percentage point changes and is our “relative change from expected” index.[6] What is “expected” is what the actual national drop in NTAs was under Trump. Where a group experienced a parallel drop to what was seen nationally, the group’s index would be 0. Small differences from zero (0) can simply reflect chance variation. Report tables flag a change up or down of at least 10 percent as notable.[7]
Clearly, ICE did not have the capacity to immediately send out arrest teams to every community across the country. Thus, the selection of which communities to target initially was clearly an important ingredient in who ICE arrested in these initial days. Results are, of course, also related to how these arrest teams were employed – were they knocking on doors out in the community or picking up noncitizens already being held by other law enforcement agencies? Many factors may have influenced the receptions that ICE arrest teams received in different communities.
While news headlines saw Chicago with early immigration raids once Trump took office, residents of Cook County where Chicago is located actually showed lower than expected numbers of NTAs issued during the last 12 days of January 2025. The proportion of NTAs targeting residents of Cook County was notably lower than expected by 10 percentage points on our index.
Table 1 lists targeting index scores for the counties that saw at least 1,000 NTAs issued to their residents in January. Appendix Table A provides details on smaller counties receiving at least 100 NTAs.
Similar to Cook County in Illinois, NTAs issued in Dallas County, Texas ended in January with a notably lower number of NTAs than would be expected if it had been similar to the national pattern. It registered -16 percent on the targeting index, a larger relative drop than for Cook County. Denver County, Colorado also showed a notable decline of 13 percent on the targeting index.
There were also counties with notable proportional increases under Trump. These included San Diego County, California (+21 percent on the targeting index). Pinal County, Arizona’s proportion under Trump was overrepresented by a whopping +34 percent on this same index. This meant its residents saw a much larger number of NTAs than would be expected. This contrasted with neighboring Maricopa County in Arizona where Phoenix is located which had fewer NTAs issued than the overall national pattern – down by 8 percent on the targeting index. Pima County in Arizona where Tucson is located had relatively few NTAs under Biden or Trump with only a total of 106 NTAs during all of January 2025. But during the Trump administration NTAs were proportionately down by 6 percent as measured by this index.
Harris County, Texas, where Houston is located and the county with the most NTAs in the country in January 2025, saw lower-than-expected NTAs issued once Trump assumed office – minus 6 percent on this targeting index. The same was true for Miami-Dade County, Florida. Its residents also experienced lower than expected NTAs issued, registering minus 6 percent on the index.
Other metropolitan areas showed little change. Residents in Los Angeles County in California resembled national trends with a targeting index of exactly zero (0). Queens County, New York in the New York City metropolitan area also had a targeting index score of zero (0). Its numbers thus closely resembled the national pattern. When the five counties making up New York City were combined, New York City ranked on top with the highest number of NTAs issued during both the Biden and Trump periods. Under Trump, residents in these metropolitan areas closely followed national trends. For NYC its targeting index was similar to national trends registering just plus 2 percent in its targeting index.
Counties with at least 500 NTAs | Number of NTAs | Targeting Index** | |||
---|---|---|---|---|---|
Total | Biden* | Trump* | |||
United States | 58,588 | 45,639 | 12,949 | ||
Harris County, TX | 2,590 | 2,166 | 424 | -6% | |
Miami-Dade County, FL | 2,567 | 2,153 | 414 | -6% | |
Cook County, IL | 2,217 | 1,940 | 277 | down | -10% |
Los Angeles County, CA | 1,837 | 1,440 | 397 | 0% | |
Not Known | 1,675 | 1,257 | 418 | 3% | |
Dallas County, TX | 1,162 | 1,091 | 71 | down | -16% |
San Diego County, CA | 951 | 541 | 410 | up | 21% |
Broward County, FL | 901 | 716 | 185 | -2% | |
Queens County, NY*** | 819 | 635 | 184 | 0% | |
Denver County, CO | 671 | 608 | 63 | down | -13% |
Bexar County, TX | 648 | 564 | 84 | -9% | |
Cameron County, TX | 644 | 350 | 294 | up | 24% |
Live Oak County, TX | 636 | 303 | 333 | up | 30% |
Pinal County, AZ | 622 | 273 | 349 | up | 34% |
Maricopa County, AZ | 610 | 526 | 84 | -8% | |
Hillsborough County, FL | 606 | 527 | 79 | -9% | |
Tarrant County, TX | 598 | 560 | 38 | down | -16% |
Kings County, NY*** | 582 | 443 | 139 | 2% | |
Orange County, FL | 567 | 476 | 91 | -6% | |
Palm Beach County, FL | 553 | 463 | 90 | -6% | |
Travis County, TX | 537 | 481 | 56 | down | -12% |
Table 2 provides a comparison of changes by state – zooming out a bit on the level of geographic granularity examined. To be conservative, again at least a 10-percentage point change up or down was required to be tagged in Table 2. None of the top 10 states showed that large of a change. Differences tend to average out as larger areas are compared. Here, however, smaller percentage point index changes could and did alter some state rankings.
The top three states – Texas, Florida, and California – remained the top three. Migrants in Texas and California received greater ICE enforcement activity than the overall proportion by 3 percent on this index. Over the last 12 days of January, Florida featured 4 percentage points lower enforcement than the national baseline under Trump and also dropped below California in the number of NTAs issued during the Trump presidency.
Illinois and New York were in fourth and fifth place based on the volume of NTAs issued during the Biden administration. However, expected numbers for Illinois were 8 percentage points lower than expected on our index, while New York was up 2 percent in comparison to national averages under Trump. Again, rankings changed.
Arizona also increased its ranking, and vaulted above Illinois for sheer numbers, with an index of 11 percent greater enforcement than the national average. The number of NTAs issued in New Mexico (up 20 percent) and Mississippi (up 21 percent) increased. However, each of these states had fewer NTAs issued during all of January and thus ranked lower in rankings based on the total number of NTAs.
Residents of states with proportional drops under Trump – all more than 10 percent on the index scale – included North Carolina, Tennessee, and Utah. See Table 2 for details on additional states.
States | Number of NTAs | Targeting Index** | |||
---|---|---|---|---|---|
Total | Biden* | Trump* | |||
United States | 58,588 | 45,639 | 12,949 | 0% | |
Texas | 11,930 | 8,992 | 2,938 | 3% | |
Florida | 7,714 | 6,312 | 1,402 | -4% | |
California | 6,381 | 4,799 | 1,582 | 3% | |
Illinois | 3,155 | 2,717 | 438 | -8% | |
New York | 2,899 | 2,186 | 713 | 2% | |
Georgia | 2,044 | 1,582 | 462 | 1% | |
New Jersey | 1,875 | 1,493 | 382 | -2% | |
Not Known | 1,675 | 1,257 | 418 | 3% | |
Colorado | 1,497 | 1,236 | 261 | -5% | |
Louisiana | 1,484 | 1,093 | 391 | 4% | |
Arizona | 1,447 | 975 | 472 | up | 11% |
Virginia | 1,138 | 820 | 318 | 6% | |
North Carolina | 955 | 852 | 103 | down | -11% |
Tennessee | 932 | 852 | 80 | down | -14% |
Washington | 921 | 695 | 226 | 2% | |
Pennsylvania | 919 | 639 | 280 | 8% | |
Indiana | 863 | 702 | 161 | -3% | |
Massachusetts | 770 | 617 | 153 | -2% | |
Maryland | 751 | 553 | 198 | 4% | |
Michigan | 713 | 538 | 175 | 2% | |
Ohio | 705 | 549 | 156 | 0% | |
Utah | 697 | 627 | 70 | down | -12% |
Kentucky | 593 | 517 | 76 | -9% | |
Nevada | 586 | 480 | 106 | -4% | |
South Carolina | 498 | 389 | 109 | 0% | |
New Mexico | 465 | 289 | 176 | up | 16% |
Minnesota | 462 | 353 | 109 | 1% | |
Mississippi | 455 | 256 | 199 | up | 22% |
Wisconsin | 453 | 319 | 134 | 7% | |
Kansas | 425 | 339 | 86 | -2% | |
Oregon | 419 | 346 | 73 | -5% | |
Connecticut | 374 | 314 | 60 | -6% | |
Missouri | 369 | 285 | 84 | 1% | |
Alabama | 333 | 292 | 41 | down | -10% |
Oklahoma | 312 | 282 | 30 | down | -12% |
Nebraska | 256 | 189 | 67 | 4% | |
Iowa | 229 | 197 | 32 | -8% | |
Arkansas | 178 | 163 | 15 | down | -14% |
Rhode Island | 138 | 107 | 31 | 0% | |
Idaho | 105 | 84 | 21 | -2% | |
Delaware | 82 | 65 | 17 | -1% | |
Maine | 62 | 50 | 12 | -3% | |
District of Columbia | 61 | 46 | 15 | 2% | |
New Hampshire | 53 | 32 | 21 | up | 18% |
South Dakota | 47 | 42 | 5 | down | -11% |
North Dakota | 37 | 30 | 7 | -3% | |
Wyoming | 32 | 29 | 3 | down | -13% |
Montana | 24 | 23 | 1 | down | -18% |
Hawaii | 22 | 15 | 7 | up | 10% |
Puerto Rico | 21 | 3 | 18 | up | 64% |
Vermont | 15 | 8 | 7 | up | 25% |
West Virginia | 13 | 6 | 7 | up | 32% |
Alaska | 4 | 3 | 1 | 3% |
Experiences also varied by the nationality of the noncitizen. This was likely an indirect result of many factors including where individuals and families happened to live as well as information ICE had available which made them harder or easier to locate and take into custody.
Table 3 provides index scores for nationalities who had at least 100 NTAs issued during January. There was a substantial drop in the relative proportion of immigrants from Venezuela and Cuba. Venezuelan immigrants who had topped the list of DHS-issued NTAs under Biden had an index of minus 12 percent reflecting below the share of NTAs expected given national trends. While overall DHS NTAs under Trump represented 22 percent of NTAs issued in January, immigrants from Venezuela represented just 10 percent. Individuals from Cuba had an index of minus 13 percent, experiencing much lower numbers of NTAs than the national pattern.
Other nationalities received more NTAs than expected based on national patterns. These included India and China, as well as Nicaragua. The relative proportion of Indian immigrants receiving NTAs was 17 percent above the national average on the index. Nicaraguans’ index was 15 percent more. Chinese noncitizens registered an index score of 13 percent above what would be expected based on the national pattern.
Nationalities that historically have smaller numbers receiving NTAs, also showed proportionately large jumps on the index of 20 percent or more. These included Nepal, Cameroon, Uzbekistan, and the Dominican Republic.
Little change was shown in the proportion of immigrants from Mexico, Honduras, and Columbia. See Table 3.
Nationalities with at least 100 NTAs | Number of NTAs | Targeting Index** | |||
---|---|---|---|---|---|
Total | Biden* | Trump* | |||
All Nationalities | 58,588 | 45,639 | 12,949 | ||
Venezuela | 11,747 | 10,582 | 1,165 | down | -12% |
Mexico | 8,751 | 6,546 | 2,205 | 3% | |
Cuba | 6,422 | 5,847 | 575 | down | -13% |
Honduras | 5,018 | 3,909 | 1,109 | 0% | |
Colombia | 4,365 | 3,341 | 1,024 | 1% | |
Guatemala | 3,970 | 2,791 | 1,179 | 8% | |
Ecuador | 2,838 | 2,228 | 610 | -1% | |
El Salvador | 1,874 | 1,340 | 534 | 6% | |
Haiti | 1,760 | 1,509 | 251 | -8% | |
Nicaragua | 1,369 | 855 | 514 | up | 15% |
India | 1,260 | 767 | 493 | up | 17% |
China | 1,076 | 698 | 378 | up | 13% |
Peru | 946 | 661 | 285 | 8% | |
Brazil | 869 | 601 | 268 | 9% | |
Turkey | 638 | 456 | 182 | 6% | |
Dominican Republic | 454 | 264 | 190 | up | 20% |
Russia | 431 | 320 | 111 | 4% | |
Afghanistan | 268 | 182 | 86 | up | 10% |
Vietnam | 260 | 180 | 80 | 9% | |
Nepal | 233 | 128 | 105 | up | 23% |
Chile | 218 | 188 | 30 | -8% | |
Bolivia | 201 | 141 | 60 | 8% | |
Uzbekistan | 186 | 103 | 83 | up | 23% |
Egypt | 185 | 113 | 72 | up | 17% |
Cameroon | 182 | 101 | 81 | up | 22% |
Iran | 175 | 103 | 72 | up | 19% |
Armenia | 171 | 113 | 58 | up | 12% |
Bangladesh | 142 | 85 | 57 | up | 18% |
Ethiopia | 128 | 68 | 60 | up | 25% |
Pakistan | 121 | 79 | 42 | up | 13% |
Nigeria | 121 | 69 | 52 | up | 21% |
Jamaica | 114 | 68 | 46 | up | 18% |
Angola | 112 | 73 | 39 | up | 13% |
The Trump administration’s use of shifting and incomplete indices-- often without the underlying numbers but only reported in percentage terms – cannot be relied upon to tell the whole story of what actually is taking place. It is also important when comparing numbers from the Biden administration that definitional differences in how events are counted haven’t occurred. Otherwise, comparisons will be biased and not accurately reflect what has actually happened.
Continued empirical monitoring of what changes are taking place as more months pass will be essential. TRAC will continue tracking cases arriving at Immigration Court as February and March case-by-case court records become available. These will be made available in its free public web query tool New Proceedings Filed in Immigration Court.
Counties with at least 100 NTAs | Number of NTAs | Targeting Index** | |||
---|---|---|---|---|---|
Total | Biden* | Trump* | |||
United States | 58,588 | 45,639 | 12,949 | ||
Harris County, TX | 2,590 | 2,166 | 424 | -6% | |
Miami-Dade County, FL | 2,567 | 2,153 | 414 | -6% | |
Cook County, IL | 2,217 | 1,940 | 277 | down | -10% |
Los Angeles County, CA | 1,837 | 1,440 | 397 | 0% | |
Not Known | 1,675 | 1,257 | 418 | 3% | |
Dallas County, TX | 1,162 | 1,091 | 71 | down | -16% |
San Diego County, CA | 951 | 541 | 410 | up | 21% |
Broward County, FL | 901 | 716 | 185 | -2% | |
Queens County, NY*** | 819 | 635 | 184 | 0% | |
Denver County, CO | 671 | 608 | 63 | down | -13% |
Bexar County, TX | 648 | 564 | 84 | -9% | |
Cameron County, TX | 644 | 350 | 294 | up | 24% |
Live Oak County, TX | 636 | 303 | 333 | up | 30% |
Pinal County, AZ | 622 | 273 | 349 | up | 34% |
Maricopa County, AZ | 610 | 526 | 84 | -8% | |
Hillsborough County, FL | 606 | 527 | 79 | -9% | |
Tarrant County, TX | 598 | 560 | 38 | down | -16% |
Kings County, NY*** | 582 | 443 | 139 | 2% | |
Orange County, FL | 567 | 476 | 91 | -6% | |
Palm Beach County, FL | 553 | 463 | 90 | -6% | |
Travis County, TX | 537 | 481 | 56 | down | -12% |
Montgomery County, TX | 486 | 274 | 212 | up | 22% |
Webb County, TX | 476 | 246 | 230 | up | 26% |
Salt Lake County, UT | 455 | 404 | 51 | down | -11% |
Polk County, TX | 443 | 248 | 195 | up | 22% |
Denton County, TX | 435 | 403 | 32 | down | -15% |
Gwinnett County, GA | 433 | 381 | 52 | down | -10% |
San Bernardino County, CA | 414 | 295 | 119 | 7% | |
Frio County, TX | 405 | 188 | 217 | up | 31% |
Williamson County, TX | 403 | 328 | 75 | -3% | |
Stewart County, GA | 387 | 170 | 217 | up | 34% |
Jefferson County, MS | 386 | 192 | 194 | up | 28% |
Clark County, NV | 384 | 341 | 43 | down | -11% |
Essex County, NJ | 375 | 297 | 78 | -1% | |
Arapahoe County, CO | 366 | 209 | 157 | up | 21% |
Jefferson County, KY | 364 | 335 | 29 | down | -14% |
Marion County, IN | 347 | 306 | 41 | down | -10% |
El Paso County, TX | 330 | 194 | 136 | up | 19% |
Lee County, FL | 324 | 268 | 56 | -5% | |
Union County, NJ | 318 | 257 | 61 | -3% | |
Santa Clara County, CA | 313 | 261 | 52 | -5% | |
Davidson County, TN | 312 | 280 | 32 | down | -12% |
King County, WA | 309 | 234 | 75 | 2% | |
Ouachita Parish, LA | 306 | 201 | 105 | up | 12% |
Riverside County, CA | 296 | 263 | 33 | down | -11% |
Bronx County, NY *** | 278 | 187 | 91 | up | 11% |
Duval County, FL | 277 | 215 | 62 | 0% | |
Orange County, CA | 275 | 228 | 47 | -5% | |
LaSalle Parish, LA | 264 | 186 | 78 | 7% | |
Kane County, IL | 244 | 215 | 29 | down | -10% |
Prince George's County, MD | 244 | 187 | 57 | 1% | |
Mecklenburg County, NC | 242 | 218 | 24 | down | -12% |
New York County, NY *** | 240 | 196 | 44 | -4% | |
Osceola County, FL | 239 | 207 | 32 | -9% | |
Hidalgo County, TX | 239 | 183 | 56 | 1% | |
Kern County, CA | 236 | 178 | 58 | 2% | |
Imperial County, CA | 236 | 74 | 162 | up | 47% |
Jackson Parish, LA | 230 | 171 | 59 | 4% | |
Shelby County, TN | 230 | 223 | 7 | down | -19% |
Suffolk County, NY | 226 | 166 | 60 | 4% | |
Hennepin County, MN | 225 | 154 | 71 | 9% | |
Wayne County, MI | 224 | 186 | 38 | -5% | |
Pierce County, WA | 222 | 141 | 81 | up | 14% |
Philadelphia County, PA | 217 | 166 | 51 | 1% | |
Franklin County, OH | 217 | 191 | 26 | down | -10% |
Middlesex County, MA | 216 | 185 | 31 | -8% | |
Clearfield County, PA | 211 | 78 | 133 | up | 41% |
Polk County, FL | 198 | 163 | 35 | -4% | |
Alameda County, CA | 196 | 172 | 24 | down | -10% |
Hudson County, NJ | 193 | 156 | 37 | -3% | |
Otero County, NM | 191 | 100 | 91 | up | 26% |
Fairfax County, VA | 187 | 129 | 58 | 9% | |
Johnson County, TX | 187 | 92 | 95 | up | 29% |
Jones County, TX | 175 | 37 | 138 | up | 57% |
Sacramento County, CA | 174 | 120 | 54 | 9% | |
Will County, IL | 164 | 139 | 25 | -7% | |
Fairfield County, CT | 163 | 146 | 17 | down | -12% |
Winn Parish, LA | 160 | 88 | 72 | up | 23% |
Montgomery County, MD | 157 | 103 | 54 | up | 12% |
Multnomah County, OR | 153 | 137 | 16 | down | -12% |
Suffolk County, MA | 152 | 131 | 21 | -8% | |
Passaic County, NJ | 151 | 111 | 40 | 4% | |
Middlesex County, NJ | 148 | 120 | 28 | -3% | |
Willacy County, TX | 147 | 82 | 65 | up | 22% |
Fresno County, CA | 146 | 128 | 18 | down | -10% |
DuPage County, IL | 145 | 127 | 18 | down | -10% |
Charlton County, GA | 141 | 67 | 74 | up | 30% |
Cobb County, GA | 140 | 131 | 9 | down | -16% |
Mercer County, NJ | 139 | 118 | 21 | -7% | |
San Mateo County, CA | 135 | 129 | 6 | down | -18% |
Utah County, UT | 134 | 127 | 7 | down | -17% |
Wake County, NC | 133 | 120 | 13 | down | -12% |
Collier County, FL | 129 | 104 | 25 | -3% | |
Plymouth County, MA | 129 | 87 | 42 | up | 10% |
Jefferson Parish, LA | 128 | 108 | 20 | -6% | |
Pasco County, FL | 127 | 99 | 28 | 0% | |
Providence County, RI | 125 | 94 | 31 | 3% | |
Lake County, IL | 124 | 81 | 43 | up | 13% |
Westchester County, NY | 122 | 99 | 23 | -3% | |
Sarasota County, FL | 121 | 100 | 21 | -5% | |
Fulton County, GA | 121 | 106 | 15 | down | -10% |
Adams County, CO | 117 | 109 | 8 | down | -15% |
Contra Costa County, CA | 117 | 96 | 21 | -4% | |
DeKalb County, GA | 116 | 98 | 18 | -7% | |
Oklahoma County, OK | 114 | 101 | 13 | down | -11% |
Baltimore city, MD | 114 | 87 | 27 | 2% | |
Pinellas County, FL | 113 | 83 | 30 | 4% | |
San Francisco County, CA | 112 | 81 | 31 | 6% | |
San Joaquin County, CA | 112 | 89 | 23 | -2% | |
Tulsa County, OK | 110 | 104 | 6 | down | -17% |
Bergen County, NJ | 109 | 86 | 23 | -1% | |
Nassau County, NY | 108 | 85 | 23 | -1% | |
Oakland County, MI | 106 | 87 | 19 | -4% | |
Pima County, AZ | 105 | 89 | 16 | -7% | |
Manatee County, FL | 104 | 91 | 13 | down | -10% |
Milwaukee County, WI | 104 | 91 | 13 | down | -10% |
Snohomish County, WA | 104 | 72 | 32 | 9% | |
Washington County, OR | 103 | 87 | 16 | -7% | |
Evangeline Parish, LA | 102 | 89 | 13 | -9% | |
Nye County, NV | 101 | 41 | 60 | up | 37% |