Federal Executive Branch
Here's a look at documents from the U.S. Executive Branch
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Surface Transportation Board Issues Decision Involving Quarterly Rail Cost Adjustment Factor
WASHINGTON, Feb. 7 -- The U.S. Department of Transportation Surface Transportation Board issued the following decision (Docket No. EP 290; Sub-No. 5) entitled "Quarterly Rail Cost Adjustment Factor":
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In Railroad Cost Recovery Procedures, 1 I.C.C.2d 207 (1984), the Interstate Commerce Commission (ICC) outlined the procedures for calculating the all-inclusive index of railroad input prices and the method for computing the rail cost adjustment factor (RCAF). Under the procedures, the Association of American Railroads (AAR) is required to calculate the index on a quarterly basis and submit
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WASHINGTON, Feb. 7 -- The U.S. Department of Transportation Surface Transportation Board issued the following decision (Docket No. EP 290; Sub-No. 5) entitled "Quarterly Rail Cost Adjustment Factor":
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In Railroad Cost Recovery Procedures, 1 I.C.C.2d 207 (1984), the Interstate Commerce Commission (ICC) outlined the procedures for calculating the all-inclusive index of railroad input prices and the method for computing the rail cost adjustment factor (RCAF). Under the procedures, the Association of American Railroads (AAR) is required to calculate the index on a quarterly basis and submitit to the agency on the fifth day of the last month of each calendar quarter. In Railroad Cost Recovery Procedures--Productivity Adjustment, 5 I.C.C.2d 434 (1989), aff'd sub nom. Edison Electric Institute v. ICC, 969 F.2d 1221 (D.C. Cir. 1992), the ICC adopted procedures that require the adjustment of the quarterly index for a measure of productivity.
The provisions of 49 U.S.C. Sec. 10708 direct the Surface Transportation Board (Board) to continue to publish both an unadjusted RCAF and a productivity-adjusted RCAF. In Productivity Adjustment--Implementation, 1 S.T.B. 739 (1996), the Board decided to publish a second productivity-adjusted RCAF called the RCAF-5. Consequently, three indices are now filed with the Board: the RCAF (Unadjusted); the RCAF (Adjusted); and the RCAF-5.
On December 5, 2025, AAR filed its RCAF calculations for a forecast of the first quarter of 2026. The Board served a decision on December 18, 2025, adopting AAR's RCAF figures. However, on December 23, 2025, the Western Coal Traffic League (WCTL) filed a letter with the Board alleging that AAR made two errors in its calculations. (WCTL Ltr. 1.) According to WCTL, AAR calculated the "Forecast of Depreciation Index (1982 = 100)" and "Forecast of Other Index (1982 = 100)" by incorrectly using an average of the December 2025, January 2026, and February 2026 figures instead of an average of the January 2026, February 2026, and March 2026 figures. (Id. at 1-2.) These figures are used to compute the all-inclusive index. (Id.) WCTL states that AAR's errors "do not alter the ultimate RCAF forecast." (Id. at 1.)
To assist the Board in assessing the alleged errors raised by WCTL, AAR will be directed to respond to WCTL's letter by February 13, 2026.
It is ordered:
1. AAR is directed to respond to WCTL's letter by February 13, 2026.
2. This decision is effective on its service date.
By the Board, Anika S. Cooper, Chief Counsel, Office of Chief Counsel.
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Original text here: https://dcms-external.s3.amazonaws.com/DCMS_External_PROD/1770405831095/52918.pdf
Seattle Man Sentenced to 35 Years in Prison Following September 2025 Conviction by Jury for Role in Transnational Drug Trafficking Organization
PITTSBURGH, Pennsylvania, Feb. 7 -- The office of the U.S. Attorney for the Western District of Pennsylvania posted the following news release on Feb. 6, 2026:
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Seattle Man Sentenced to 35 Years in Prison Following September 2025 Conviction by Jury for Role in Transnational Drug Trafficking Organization
A resident of Seattle, Washington, was sentenced in federal court to 420 months in prison, to be followed by five years of supervised release, on his conviction of violating federal narcotics laws in relation to a transnational criminal organization (TCO), United States Attorney Troy Rivetti
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PITTSBURGH, Pennsylvania, Feb. 7 -- The office of the U.S. Attorney for the Western District of Pennsylvania posted the following news release on Feb. 6, 2026:
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Seattle Man Sentenced to 35 Years in Prison Following September 2025 Conviction by Jury for Role in Transnational Drug Trafficking Organization
A resident of Seattle, Washington, was sentenced in federal court to 420 months in prison, to be followed by five years of supervised release, on his conviction of violating federal narcotics laws in relation to a transnational criminal organization (TCO), United States Attorney Troy Rivettiannounced today. The defendant was among 35 individuals charged through a Second Superseding Indictment unsealed in January 2024 for their participation in a domestic and international narcotics and money laundering conspiracy involving substantial quantities of fentanyl, methamphetamine, and cocaine (read the Second Superseding Indictment news release here [https://www.justice.gov/usao-wdpa/pr/thirty-five-individuals-charged-second-superseding-indictment-participating-violent]).
United States District Judge J. Nicholas Ranjan imposed the sentence on Bryce Hill, 28. Hill was convicted by a jury following a two-and-a-half-week trial in September 2025.
Evidence presented during the trial established that Hill was a member of the Phoenix-based Monarrez Drug Trafficking Organization--a transnational criminal organization responsible for the distribution of millions of fentanyl pills, hundreds of pounds of methamphetamine, and dozens of kilograms of cocaine, from August 2021 to June 2023. The Monarrez TCO provided the drugs to a network of subordinate drug distributors, who redistributed the narcotics throughout the country, including into western Pennsylvania. Hill was intercepted over a federal wiretap obtaining hundreds of thousands of fentanyl pills and kilograms of fentanyl powder for redistribution.
Additional evidence presented at trial included testimony regarding the execution of a search warrant on January 11, 2023, during which law enforcement seized 27 kilograms of fentanyl pills, multiple firearms, and $387,000 cash from Hill's apartment, and the seizure of 28 kilograms of fentanyl pills, 7.5 kilograms of fentanyl powder, three kilograms of cocaine, 48 kilograms of methamphetamine, and 20 firearms (pictured below) from a short-term rental property in Scottsdale, Arizona, on December 25, 2022.
The jury found that, in the Western District of Pennsylvania and elsewhere, Hill conspired with others to distribute and possess with intent to distribute five kilograms or more of cocaine, 400 grams or more of fentanyl, and 500 grams or more of methamphetamine.
Hill's sentencing follows those of 32 co-defendants.
Assistant United States Attorneys Arnold P. Bernard Jr. and Katherine C. Jordan prosecuted this case on behalf of the government.
United States Attorney Rivetti commended the Federal Bureau of Investigation's Laurel Highlands Resident Agency and Homeland Security Investigations for the investigation leading to the prosecution of Hill. Additional agencies participating in this investigation included the Internal Revenue Service-Criminal Investigation, United States Postal Inspection Service, and other local law enforcement agencies, including the Scottsdale, Arizona, Police Department.
This case is part of Operation Take Back America, a nationwide initiative that marshals the full resources of the Department of Justice to achieve the total elimination of cartels and transnational criminal organizations, combat illegal immigration, and protect our communities from the perpetrators of violent crime. Operation Take Back America streamlines efforts and resources from the Department's Organized Crime Drug Enforcement Task Forces (OCDETFs) and Project Safe Neighborhoods (PSN).
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Original text here: https://www.justice.gov/usao-wdpa/pr/seattle-man-sentenced-35-years-prison-following-september-2025-conviction-jury-role
SEC Charges Kentucky-Based Biopharmaceutical Company and Two Executives With Defrauding Investors
WASHINGTON, Feb. 7 -- The Securities and Exchange Commission issued the following litigation release (No. 26-cv-00042; E.D. Ky. filed Feb. 5, 2026):
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Securities and Exchange Commission v. CBA Pharma, Inc., et al., No. 26-cv-00042 (E.D. Ky. filed Feb. 5, 2026)
On February 5, 2026, the Securities and Exchange Commission filed charges against CBA Pharma, Inc., a private Kentucky-based biopharmaceutical company; Wayne Michael Putnam, its president; and Louis "Buzz" Carmichael, its vice president of capital markets, for allegedly conducting a fraudulent securities offering which raised approximately
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WASHINGTON, Feb. 7 -- The Securities and Exchange Commission issued the following litigation release (No. 26-cv-00042; E.D. Ky. filed Feb. 5, 2026):
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Securities and Exchange Commission v. CBA Pharma, Inc., et al., No. 26-cv-00042 (E.D. Ky. filed Feb. 5, 2026)
On February 5, 2026, the Securities and Exchange Commission filed charges against CBA Pharma, Inc., a private Kentucky-based biopharmaceutical company; Wayne Michael Putnam, its president; and Louis "Buzz" Carmichael, its vice president of capital markets, for allegedly conducting a fraudulent securities offering which raised approximately$4.1 million from approximately 160 investors.
The SEC's complaint alleges that from April 2023 to February 2024, CBA Pharma, through Putnam and Carmichael, misrepresented to investors that CBA Pharma's lone drug, CBT-1, was effective in treating cancer by preventing multidrug resistance to cancer treatments, such as chemotherapy, and was in the final stages of obtaining approval from the United States Food and Drug Administration ("FDA"). According to the complaint, however, CBT-1 was never close to FDA approval, the FDA had informed the company that its drug application for CBT-1 lacked evidence of efficacy, and, by April 2023, the FDA told the company that it had withdrawn CBA Pharma's drug application for CBT-1.
The SEC's complaint, filed in the District Court for the Eastern District of Kentucky, charges CBA Pharma, Putnam, and Carmichael with violating Section 17(a) of the Securities Act of 1933 and Section 10(b) of the Securities Exchange Act of 1934 and Rule 10b 5 thereunder. The SEC seeks a permanent injunction, disgorgement, pre-judgment interest, and a civil penalty as to CBA Pharma and permanent injunctions, civil penalties, and bars from participating in any issuance, purchase, offer or sale of any security, except for certain transactions within personal accounts as to Putnam and Carmichael.
The SEC's investigation was conducted by Tracy W. Lo and Nicholas Magena and was supervised by Steven L. Klawans of the SEC's Chicago Regional Office. The SEC's litigation will be led by Eric Phillips and Timothy Stockwell of the Chicago Regional Office.
The SEC appreciates the assistance of the U.S. Attorney's Office for the Eastern District of Kentucky, the Federal Bureau of Investigation's Louisville Field Office and the FDA.
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Resources
* SEC Complaint (https://www.sec.gov/files/litigation/complaints/2026/comp26479.pdf)
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Original text here: https://www.sec.gov/enforcement-litigation/litigation-releases/lr-26479
Remarks by Secretary of the Treasury Scott Bessent Before the Financial Literacy and Education Commission 2026 Public Meeting
WASHINGTON, Feb. 7 -- The U.S. Department of the Treasury issued the following remarks on Feb. 6, 2026, by Secretary Scott Bessent before the Financial Literacy and Education Commission 2026 public meeting:
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Good morning, everyone. Thank you for joining us for the first Financial Literacy and Education Commission public meeting of 2026. I appreciate your commitment to advancing financial literacy and education across America.
In my role as Treasury Secretary, I am proud to serve as chair of the Financial Literacy and Education Commission, which was created by Congress to improve financial
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WASHINGTON, Feb. 7 -- The U.S. Department of the Treasury issued the following remarks on Feb. 6, 2026, by Secretary Scott Bessent before the Financial Literacy and Education Commission 2026 public meeting:
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Good morning, everyone. Thank you for joining us for the first Financial Literacy and Education Commission public meeting of 2026. I appreciate your commitment to advancing financial literacy and education across America.
In my role as Treasury Secretary, I am proud to serve as chair of the Financial Literacy and Education Commission, which was created by Congress to improve financialliteracy and education for all Americans in partnership with private and non-profit sector partners. I have long believed that financial education is most powerful when it is lived, not just taught. When young people begin saving and investing early, they develop habits, judgment, and confidence that shape a lifetime of financial decision-making.
Seeing assets grow over time instills patience, discipline, and a sense of ownership in one's future in ways no classroom lesson alone can replicate. By giving children a foothold in the financial system, Trump Accounts will help the next generation envision and achieve new possibilities--like pursuing education, owning a home, or starting a business--while also reinforcing the behaviors required to plan for, manage, and ultimately achieve these life goals.
Trump Accounts are a generational downpayment on the American Dream. They will put Americans on an investing journey from the very start of life. Each eligible American child will receive a $1,000 federal seed contribution invested in the U.S. stock market, giving them a tangible stake in the most powerful economy in the world. For most families, claiming that investment will be as simple as checking a box.
This initial federal investment in Trump Accounts is only the beginning. We are now seeing in real time a groundswell of support for Trump Accounts from philanthropists, charitable organizations, employers, and state governments. This is a broad and growing bipartisan coalition that recognizes the simple steps we can take today to shape a lifetime of financial stability for American citizens. Early donors have set a powerful example: Michael and Susan Dell donated an astonishing $6.25 billion--the largest single private investment in American children in our nation's history. And Ray Dalio joined shortly thereafter to launch Treasury's "50 State Challenge," which will mobilize partnerships with philanthropy across the country.
States have been leaders for years in the effort to build generational wealth for America's children through their work with child savings accounts and financial education initiatives. Those efforts have shown what is possible with state involvement, and their experience will be essential as we move from policy to implementation. Treasury looks forward to working closely with governors and state agencies to build on what already works, scale best practices, and ensure Trump Accounts are integrated into existing financial education and outreach efforts.
I want to take a moment to call on businesses and employers across the country to participate in this effort. Many employers already help their workers save for retirement; now, we have a chance to extend that same culture of saving to the next generation. Employer contributions, family matches, and educational partnerships can multiply the impact of every Trump Account. Every contribution, every lesson, and every bit of encouragement matters.
The true power of Trump Accounts lies not only in the dollars saved and invested, but in the education and experience they create. Financial education and financial participation go hand in hand. These accounts are an opportunity for children to learn how to invest and grow their money through real-world experience. As children see their accounts grow, they learn how markets work, how patience pays off, and how financial stability builds independence.
As chair of the Financial Literacy and Education Commission, I intend to use this Commission as a platform for action. I am calling on every federal agency represented here to mobilize around Trump Accounts--by aligning programs, sharing expertise, and integrating these accounts into your financial education, outreach, and service efforts.
Trump Accounts embody the principle of learning by doing. By giving America's children a day-one stake in our economy, these accounts turn financial education into hands-on experience. I look forward to working with all of you to ensure that every child, every family, and every community seizes the opportunity Trump Accounts present to learn, grow, and prosper through this groundbreaking investment program.
With that, I'll hand it over to Geof Gradler, who is acting for the Vice Chair of the Financial Literacy and Education Commission.
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Original text here: https://home.treasury.gov/news/press-releases/sb0390
North Texas Man Indicted on Federal Charges for Threatening to Assault and Murder U.S. President and ICE Agents
DALLAS, Texas, Feb. 7 -- The office of the U.S. Attorney for the Northern District of Texas posted the following news release on Feb. 6, 2026:
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North Texas Man Indicted on Federal Charges for Threatening to Assault and Murder U.S. President and ICE Agents
A North Texas man who threatened to assault and murder the United States President and unnamed ICE agents was federally indicted on Tuesday this week, announced United States Attorney for the Northern District of Texas Ryan Raybould.
Francisco Jesus Mena, 36, of North Richland Hills, Texas was indicted by a federal grand jury on February
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DALLAS, Texas, Feb. 7 -- The office of the U.S. Attorney for the Northern District of Texas posted the following news release on Feb. 6, 2026:
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North Texas Man Indicted on Federal Charges for Threatening to Assault and Murder U.S. President and ICE Agents
A North Texas man who threatened to assault and murder the United States President and unnamed ICE agents was federally indicted on Tuesday this week, announced United States Attorney for the Northern District of Texas Ryan Raybould.
Francisco Jesus Mena, 36, of North Richland Hills, Texas was indicted by a federal grand jury on February3, 2026, on ten counts of threatening a federal official. Mena was previously arrested on a federal complaint for this offense and made his initial appearance before U.S. Magistrate Judge Harold R. Ray, Jr. on January 7, 2026. Mena is set to appear for an arraignment on the charges in the indictment on February 11, 2026. If convicted, he faces up to 96 years in federal prison.
"Any threat against the President, federal officials, and agents will be thoroughly investigated and swiftly prosecuted," said U.S. Attorney Ryan Raybould. "Some individuals falsely believe that they are immune from criminal penalties by posting vitriol online and not in person. The diligent work of our law enforcement partners time and again uncovers those attempting to hide behind a computer screen."
"The FBI takes online threats to federal officials and law enforcement seriously. Using online platforms to threaten the lives of others does not insulate an individual from consequences. We are committed to thoroughly investigating these crimes," said FBI Dallas Special Agent in Charge R. Joseph Rothrock. "We ask that the public continue to remain vigilant and report suspicious online activity to law enforcement."
According to the indictment, on several occasions in May 2025, Mena allegedly posted threats on a social media platform, including:
The indictment details other instances of Mena's alleged violent rhetoric.
FBI Dallas Field Office - Fort Worth Resident Agency and U.S. Secret Service investigated the case. Assistant U.S. Attorney Matthew Weybrecht is prosecuting the case.
An indictment is merely an allegation of criminal conduct, not evidence. Like all defendants, Mena is presumed innocent until proven guilty beyond a reasonable doubt in a court of law.
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Original text here: https://www.justice.gov/usao-ndtx/pr/north-texas-man-indicted-federal-charges-threatening-assault-and-murder-us-president
Leading the Transformation: USU Faculty and Students Chart the Path for AI in Military Medicine
BETHESDA, Maryland, Feb. 7 -- The Uniformed Services University issued the following research news:
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Leading the Transformation: USU Faculty and Students Chart the Path for AI in Military Medicine
USU faculty and the School of Medicine Class of 2027 co-authored a roadmap for military medical education in an era of rapid digital transformation.
By Hadiyah Brendel
As the speed of medical discovery continues to accelerate--with global medical knowledge now doubling every few months--the Uniformed Services University of the Health Sciences (USU) is ensuring its graduates are not just keeping
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BETHESDA, Maryland, Feb. 7 -- The Uniformed Services University issued the following research news:
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Leading the Transformation: USU Faculty and Students Chart the Path for AI in Military Medicine
USU faculty and the School of Medicine Class of 2027 co-authored a roadmap for military medical education in an era of rapid digital transformation.
By Hadiyah Brendel
As the speed of medical discovery continues to accelerate--with global medical knowledge now doubling every few months--the Uniformed Services University of the Health Sciences (USU) is ensuring its graduates are not just keepingpace, but leading.
A recent article in Military Medicine, titled "Transforming Military Healthcare Education and Training: AI Integration for Future Readiness," provides a definitive roadmap for this evolution. Led by Air Force Lt. Col. (Dr.) Justin Peacock, associate dean for Research, and Dr. Rebekah Cole, acting assistant dean for Academic Success, both in USU's School of Medicine, the article provides a collaborative review of how to strategically integrate artificial intelligence (AI) literacy into the core of military medical education.
USU co-authors joining Peacock and Cole include Air Force Lt. Col. (Dr.) Joshua Duncan, Department of Preventive Medicine and Biostatistics, Dr. Anita Samuel, Department of Health Professions Education, and Class of 2027 School of Medicine students Air Force 2nd Lt. Brandon Jensen, and Army 2nd Lt. Brad Snively. Peacock also serves as faculty in the Department of Radiology, and Cole in the Departments of Military and Emergency Medicine and Health Professions Education.
Preparing for the AI-Driven Battlefield
The publication arrives at a pivotal time for the Military Health System (MHS). USU leaders anticipate future conflicts are expected to take place in increasingly complex, far-forward operational environments where AI-enabled tools will be essential for casualty care and decision support.
"The goal isn't to make physicians AI experts," the authors state, "but to train them to effectively use AI's capabilities to improve patient care, diagnosis, and operational medicine."
Cole emphasizes that the strategy requires a multi-layered approach. "This paper lays out a practical roadmap for how medical education can evolve--beginning with faculty development, expanding through student engagement, and strengthened by partnerships with industry--to responsibly integrate AI throughout the curriculum," Cole says. "This approach supports competency-based medical education and ensures our students are prepared for the realities of modern healthcare delivery."
This approach aligns with USU's broader mission to produce "future-ready" clinicians. The roadmap addresses the integration of AI from the first year of medical school through graduate medical education, focusing on ethical use, technical literacy, and practical application at the point of care.
The "Student-Centered" Revolution
What sets this transformation apart is the active role of USU students in shaping the curriculum. Jensen and Snively's involvement underscores a university-wide shift toward co-creating educational tools that meet the needs of the next generation of military clinicians.
For Jensen, AI is already a daily reality in the classroom and the clinic. "I use AI every single day," Jensen says. "It can make complex topics significantly easier to understand and frame difficult concepts in ways that are easier to remember. I also use AI to generate practice questions and flashcards, which has made my studying more efficient and personalized."
Jensen's experience extends beyond the library. During an internal medicine rotation, he used AI to broaden his clinical reasoning--an approach that helped identify an important diagnosis that might have otherwise been overlooked.
While Jensen brings a focus on diagnostic precision, Snively emphasizes the "behind-the-scenes" power of AI--specifically how it acts as a high-speed clinical assistant that handles the heavy lifting of data organization, allowing medical students and residents to focus more on the patient.
Snively highlighted AI's value in generating summary tables from complex data, crucial for quickly processing patient history during clerkships. AI also serves as a rapid clinical reference, instantly bridging knowledge gaps on the hospital floor. He argues that delegating "cognitive busywork" to AI allows clinicians to focus on communication and clinical judgment, promoting a "Human-Centered" shift. Snively advises peers to proactively seek research opportunities and mentorship, citing the Radiology Interest Group as an example.
A Roadmap for Tomorrow's Clinicians
The MilMed article identifies several "force multipliers" that AI brings to the MHS:
* Reduced Cognitive Burden: Automating administrative tasks allows providers to focus on "high-touch" patient care.
* Operational Readiness: Training clinicians to use AI in austere environments ensures a high standard of care even when separated from traditional medical infrastructure.
* Personalized Learning: Tools allow students to tailor their education to their specific needs, creating a more versatile medical force.
The Path Ahead
The authors acknowledge barriers, specifically the "black box" nature of complex algorithms. This term describes systems that provide answers without revealing the underlying logic or evidence used to reach those decisions. This lack of transparency requires clinicians to maintain high AI literacy, verifying the AI's output against established medical standards and maintaining a "human-in-the loop" approach.
Despite these hurdles, the consensus is clear: integration is inevitable. Snively envisions a trusted, AI-enabled ecosystem to solve the problem of misinformation.
"One of the primary concerns with AI is its tendency to 'hallucinate' or present incorrect information as fact," Snively says. "However, if we can combine the processing power of AI with the verified data in medical journals, it becomes a powerful tool to help clinicians quickly identify the things they might have missed."
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Original text here: https://news.usuhs.edu/2026/02/leading-transformation-usu-faculty-and.html
BLS Issues Report on Metropolitan Area Employment and Unemployment December 2025
WASHINGTON, Feb. 7 (TNSLrpt) -- Metropolitan Area Employment and Unemployment December 2025 - A report from U.S. Department of Labor Bureau of Labor Statistics - Feb. 6, 2026 (24 pages)
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Unemployment rates were higher in December than a year earlier in 255 of the 387 metropolitan areas, lower in 110 areas, and unchanged in 22 areas, the U.S. Bureau of Labor Statistics reported today. A total of 61 areas had jobless rates of less than 3.0 percent and 9 areas had rates of at least 8.0 percent. Nonfarm payroll employment increased over the year in 5 metropolitan areas, decreased in 2 areas,
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WASHINGTON, Feb. 7 (TNSLrpt) -- Metropolitan Area Employment and Unemployment December 2025 - A report from U.S. Department of Labor Bureau of Labor Statistics - Feb. 6, 2026 (24 pages)
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Unemployment rates were higher in December than a year earlier in 255 of the 387 metropolitan areas, lower in 110 areas, and unchanged in 22 areas, the U.S. Bureau of Labor Statistics reported today. A total of 61 areas had jobless rates of less than 3.0 percent and 9 areas had rates of at least 8.0 percent. Nonfarm payroll employment increased over the year in 5 metropolitan areas, decreased in 2 areas,and was essentially unchanged in 380 areas. The national unemployment rate in December was 4.1 percent, not seasonally adjusted, up from 3.8 percent a year earlier.
This news release presents statistics from two monthly programs. The civilian labor force and unemployment data are based on the same concepts and definitions as those used for the national household survey estimates. These data pertain to people by where they reside. The employment data are from an establishment survey that measures nonfarm employment, hours, and earnings by industry. These data pertain to jobs on payrolls defined by where the establishments are located. For more information about the concepts and statistical methodologies used by these two programs, see the Technical Note.
Metropolitan Area Unemployment (Not Seasonally Adjusted)
In December, Decatur, AL, and Huntsville, AL, had the lowest unemployment rates, 1.9 percent each. El Centro, CA, had the highest rate, 18.6 percent. A total of 223 areas had December jobless rates below the U.S. rate of 4.1 percent, 155 areas had rates above it, and 9 areas had rates equal to that of the nation. (See table 1 and map 1.)
The largest over-the-year unemployment rate increase in December occurred in Wildwood-The Villages, FL (+2.5 percentage points). Fifty-one other areas had rate increases of at least 1.0 percentage point. Kokomo, IN, had the largest over-the-year rate decrease in December (-3.3 percentage points). Twenty-four other areas had rate declines of at least 1.0 percentage point.
Of the 56 metropolitan areas with a 2020 Census population of 1 million or more, Urban Honolulu, HI, had the lowest jobless rate in December, 2.1 percent. Fresno, CA, had the highest rate, 8.2 percent. Thirty-nine large areas had over-the-year unemployment rate increases, 13 had decreases, and 4 had no change. The largest rate increase occurred in Minneapolis-St. PaulBloomington, MN-WI (+1.6 percentage points). The largest jobless rate decline occurred in Louisville/Jefferson County, KY-IN (-1.3 percentage points).
Metropolitan Division Unemployment (Not Seasonally Adjusted)
Thirteen of the most populous metropolitan areas are made up of 37 metropolitan divisions, which are essentially separately identifiable employment centers. In December, Miami-Miami Beach-Kendall, FL, had the lowest division unemployment rate, 2.5 percent. Lake County, IL, had the highest rate among the divisions, 5.8 percent. (See table 2.)
In December, 30 metropolitan divisions had over-the-year unemployment rate increases, 2 had decreases, and 5 had no change. The largest increases occurred in Tacoma-Lakewood, WA, and Wilmington, DE-MD-NJ (+1.6 percentage points each). Eleven other divisions had rate increases of at least 1.0 percentage point. The largest unemployment rate decline from December 2024 occurred in Lake County-Porter County-Jasper County, IN (-2.1 percentage points).
Metropolitan Area Nonfarm Employment (Not Seasonally Adjusted)
In December 2025, nonfarm payroll employment increased over the year in 5 metropolitan areas, decreased in 2 areas, and was essentially unchanged in 380 areas. The largest over-the-year employment increases occurred in Charlotte-Concord-Gastonia, NC-SC (+37,600), PhiladelphiaCamden-Wilmington, PA-NJ-DE-MD (+36,400), and Salt Lake City-Murray, UT (+15,300). The largest over-the-year percentage gains in employment occurred in Rochester, MN (+4.3 percent), Fayetteville-Springdale-Rogers, AR (+3.6 percent), and Charlotte-Concord-Gastonia, NC-SC (+2.7 percent). Employment decreased over the year in Washington-Arlington-Alexandria, DCVA-MD-WV (-55,900, or -1.6 percent), and Bozeman, MT (-3,200, or -4.3 percent). (See table 3 and map 2.)
Over the year, nonfarm employment increased in 3 metropolitan areas with a 2020 Census population of 1 million or more, decreased in 1 area, and was essentially unchanged in 52 areas.
The largest over-the-year percentage increase in employment in these large metropolitan areas occurred in Charlotte-Concord-Gastonia, NC-SC (+2.7 percent), followed by Salt Lake CityMurray, UT (+1.8 percent), and Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (+1.2 percent). The over-the-year decrease in employment occurred in Washington-ArlingtonAlexandria, DC-VA-MD-WV (-1.6 percent).
Metropolitan Division Nonfarm Employment (Not Seasonally Adjusted)
In December, nonfarm payroll employment decreased over the year in 1 metropolitan division and was essentially unchanged in 36 divisions. The over-the-year decrease in employment among the metropolitan divisions occurred in Washington, DC-MD (-29,600, or -2.5 percent). (See table 4.)
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The State Employment and Unemployment news release for January 2026 will be scheduled for April. The Metropolitan Area Employment and Unemployment news release for January 2026 also is expected to be released in April.
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Upcoming Changes to Local Area Unemployment Statistics (LAUS) Data
Effective with the release of January 2026 data in April, the civilian labor force and unemployment data for the states, the District of Columbia, and the modeled substate areas presented in tables 1 and 2 of this news release will be revised to incorporate updated inputs, new population controls, reestimation of models, and adjustment to new census division and national control totals. The population controls will reflect replacement of the "blended base" method that had been in use since the start of estimation for the 2020s with data adapted from the 2020 Modified Race and Age, or MARC, file. Subsequently, civilian labor force and unemployment estimates for all other metropolitan areas and metropolitan divisions will be revised to reflect updated inputs and adjustment to the new statewide estimates. These revised estimates are expected to be released in May 2026.
Due to the lapse in federal appropriations, October 2025 data collection did not occur for the Current Population Survey (CPS), which provides the primary inputs to LAUS estimation. At the same time as the substate annual processing revisions are published in May, averages for 2025 based on the 11 months for which CPS data collection occurred will be published for all substate areas in the BLS time-series database. These 11-month averages will not be strictly comparable to annual averages for prior years.
Additional information about the impact of the shutdown on the household survey is available online at www.bls.gov/cps/methods/2025-federal-government-shutdown-impact-cps.htm.
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Upcoming Changes to Current Employment Statistics (CES) Data
Effective with the release of January 2026 data in April, all nonfarm payroll employment estimates for states and areas presented in tables 3 and 4 of this news release will be adjusted to 2025 benchmark levels. Not seasonally adjusted data beginning with April 2024 and seasonally adjusted data beginning with January 2021 are subject to revision. Some not seasonally adjusted and seasonally adjusted series may be revised as far back as 1990. Also effective with the release of January 2026 data, the establishment survey will change the birth-death model by incorporating current sample information each month. The change follows the same methodology applied to the April through October 2024 forecasts during the 2024 postbenchmark period (see question 9 in the CES Birth-Death Model Frequently Asked Questions page at www.bls.gov/web/empsit/cesbdqa.htm ).
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Technical Note
This news release presents civilian labor force and unemployment data from the Local Area Unemployment Statistics (LAUS) program (tables 1 and 2) for 387 metropolitan statistical areas, plus 6 areas in Puerto Rico. Estimates for 37 metropolitan divisions also are presented. Nonfarm payroll employment estimates from the Current Employment Statistics (CES) program (tables 3 and 4) are provided for the same areas. State estimates were previously published in the State Employment and Unemployment news release and are republished in this news release for ease of reference. The LAUS and CES programs are both federal-state cooperative endeavors.
Civilian labor force and unemployment--from the LAUS program
Definitions. The civilian labor force and unemployment data are based on the same concepts and definitions as those used for the official national estimates obtained from the Current Population Survey (CPS), a sample survey of households that is conducted for the Bureau of Labor Statistics (BLS) by the U.S. Census Bureau. The LAUS program measures employed people and unemployed people on a placeof-residence basis. The universe for each is the civilian noninstitutional population 16 years of age and older. Employed people are those who did any work at all for pay or profit in the reference week (typically the week including the 12th of the month) or worked 15 hours or more without pay in a family business or farm, plus those not working who had a job from which they were temporarily absent, whether or not paid, for such reasons as labor-management dispute, illness, or vacation. Unemployed people are those who were not employed during the reference week (based on the definition above), had actively looked for a job sometime in the 4-week period ending with the reference week, and were currently available for work; people on layoff expecting recall need not be looking for work to be counted as unemployed. The civilian labor force is the sum of employed and unemployed people. The unemployment rate is the number of unemployed as a percent of the civilian labor force.
Method of estimation. Estimates for states, the District of Columbia, the Los Angeles-Long Beach-Glendale metropolitan division, and New York City are produced using time-series models with real-time benchmarking to national CPS totals. Model-based estimates are also produced for the following areas and their respective balances: the ChicagoNaperville-Schaumburg, IL Metropolitan Division; Cleveland, OH Metropolitan Statistical Area; Detroit-Warren-Dearborn, MI Metropolitan Statistical Area; Miami-Miami BeachKendall, FL Metropolitan Division; and Seattle-TacomaBellevue, WA Metropolitan Statistical Area. Modeling improves the statistical basis of the estimation for these areas and provides important tools for analysis, such as measures of errors and seasonally adjusted series. For all other substate areas in this news release, estimates are prepared through indirect estimation procedures using a building-block approach. Estimates of employed people, which are based largely on "place of work" estimates from the CES program, are adjusted to refer to place of residence as used in the CPS. Unemployment estimates are aggregates of people previously employed in industries covered by state Unemployment Insurance (UI) laws and entrants to the labor force from the CPS. The substate estimates of employment and unemployment, which geographically exhaust the entire state, are adjusted proportionally to ensure that they add to the independently estimated model-based area totals. A detailed description of the estimation procedures is available from BLS upon request.
Annual revisions. Civilian labor force and unemployment data shown for the prior year reflect adjustments made at the beginning of each year, usually implemented with the issuance of January estimates. The adjusted model-based estimates typically reflect updated population data from the U.S. Census Bureau, any revisions in other input data sources, and model re-estimation. All substate estimates then are reestimated using updated inputs and adjusted to add to the revised model-based totals. In early 2025, implementation of synthetic intercensal population estimates for states and the 2020-based delineations for federal statistical areas necessitated the replacement of substate estimates back to their series beginnings. For more information, see www.bls.gov/lau/geography-and-data-changes-in-2025.htm.
Employment--from the CES program
Definitions. Employment data refer to people on establishment payrolls who receive pay for any part of the pay period that includesthe 12th of the month. People are counted at their place of work rather than at their place of residence; those appearing on more than one payroll are counted on each payroll. Industries are classified on the basis of their principal activity in accordance with the 2022 version of the North American Industry Classification System.
Method of estimation. CES State and Area employment data are produced using several estimation procedures. Where possible, these data are produced using a "weighted link relative" estimation technique in which a ratio of current month weighted employment to that of the previous-month weighted employment is computed from a sample of establishments reporting for both months. The estimates of employment for the current month are then obtained by multiplying these ratios by the previous month's employment estimates. The weighted link relative technique is utilized for data series where the sample size meets certain statistical criteria. For some employment series, the estimates are produced with a model that uses direct sample estimates (described above) combined with other regressors to compensate for smaller sample sizes.
Annual revisions. Employment estimates are adjusted annually to a complete count of jobs, called benchmarks, derived principally from tax reports that are submitted by employers who are covered under state unemployment insurance (UI) laws. The benchmark information is used to adjust the monthly estimates between the new benchmark and the preceding one and also to establish the level of employment for the new benchmark month. Thus, the benchmarking process establishes the level of employment, and the sample is used to measure the month-to-month changes in the level for the subsequent months. Information on recent benchmark revisions is available online at www.bls.gov/web/laus/benchmark.pdf.
Seasonal adjustment. Payroll employment data are seasonally adjusted for states, metropolitan areas, and metropolitan divisions at the total nonfarm level. For states, data are seasonally adjusted at the supersector level as well. Revisions to historical data for the most recent 5 years are made once a year, coincident with annual benchmark adjustments.
Payroll employment data are seasonally adjusted concurrently, using all available estimates, including those for the current month, to develop sample-based seasonal factors. Concurrent sample-based factors are created every month for the current month's preliminary estimate as well as the previous month's final estimate.
Reliability of the estimates
The estimates presented in this news release are based on sample surveys, administrative data, and modeling and, thus, are subject to sampling and other types of errors. Sampling error is a measure of sampling variability--that is, variation that occurs by chance because a sample rather than the entire population is surveyed. Survey data also are subject to nonsampling errors, such as those which can be introduced into the data collection and processing operations. Estimates not directly derived from sample surveys are subject to additional errors resulting from the specific estimation processes used. The sums of individual items may not always equal the totals shown in the same tables because of rounding.
Use of error measures
Civilian labor force and unemployment estimates. Measures of sampling error are not available for metropolitan areas or metropolitan divisions. Model-based error measures for states are available on the BLS website at www.bls.gov/lau/lastderr.htm. Measures of nonsampling error are not available for the areas contained in this news release.
Employment estimates. Changes in metropolitan area nonfarm payroll employment are cited in the analysis of this news release only if they have been determined to be statistically significant at the 90-percent confidence level. Measures of sampling error for the total nonfarm employment series are available for metropolitan areas and metropolitan divisions at www.bls.gov/web/laus/790stderr.htm. Measures of sampling error for more detailed series at the area and division level are available upon request. Measures of sampling error for states at the supersector level and for the private service providing, goods-producing, total private and total nonfarm levels are available on the BLS website at www.bls.gov/web/laus/790stderr.htm.
Area definitions
The substate area data published in this news release reflect the delineations issued by the U.S. Office of Management and Budget on July 21, 2023. A detailed list of the geographic definitions is available online at www.bls.gov/lau/lausmsa.htm.
Additional information
Estimates of unadjusted and seasonally adjusted civilian labor force and unemployment data for states and seven substate areas are available in the news release State Employment and Unemployment. Estimates of civilian labor force and unemployment for all states, metropolitan areas, counties, cities with a population of 25,000 or more, and other areas used in the administration of various federal economic assistance programs are available online at www.bls.gov/lau/. Employment data from the CES program for states and metropolitan areas are available on the BLS website at www.bls.gov/sae/.
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LABOR FORCE DATA NOT SEASONALLY ADJUSTED
Table 1. Civilian labor force and unemployment by state and metropolitan area
Table 1. Civilian labor force and unemployment by state and metropolitan area -- Continued
Table 1. Civilian labor force and unemployment by state and metropolitan area -- Continued
Table 1. Civilian labor force and unemployment by state and metropolitan area -- Continued
Table 1. Civilian labor force and unemployment by state and metropolitan area -- Continued
Table 1. Civilian labor force and unemployment by state and metropolitan area -- Continued
Table 1. Civilian labor force and unemployment by state and metropolitan area -- Continued
Table 2. Civilian labor force and unemployment by state, selected metropolitan area, and metropolitan division1
ESTABLISHMENT DATA NOT SEASONALLY ADJUSTED
Table 3. Employees on nonfarm payrolls by state and metropolitan area, not seasonally adjusted
Table 3. Employees on nonfarm payrolls by state and metropolitan area, not seasonally adjusted -- Continued
Table 3. Employees on nonfarm payrolls by state and metropolitan area, not seasonally adjusted -- Continued
Table 3. Employees on nonfarm payrolls by state and metropolitan area, not seasonally adjusted -- Continued
Table 3. Employees on nonfarm payrolls by state and metropolitan area, not seasonally adjusted -- Continued
Table 3. Employees on nonfarm payrolls by state and metropolitan area, not seasonally adjusted -- Continued
Table 3. Employees on nonfarm payrolls by state and metropolitan area, not seasonally adjusted -- Continued
Table 3. Employees on nonfarm payrolls by state and metropolitan area, not seasonally adjusted -- Continued
Table 3. Employees on nonfarm payrolls by state and metropolitan area, not seasonally adjusted -- Continued
Table 4. Employees on nonfarm payrolls by state, selected metropolitan area, and metropolitan division, not seasonally adjusted1
Map 1. Unemployment rates for metropolitan areas, not seasonally adjusted, December 2025
Map 2. Over-the-year percentage change in employment, by metropolitan area, not seasonally adjusted, December 2025
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View original text plus charts and tables here: https://www.bls.gov/news.release/pdf/metro.pdf
