Analysis: European Americans’ unemployment rate was unchanged from July. Asian Americans decreased 30 basis points and Latino Americans increased 30 basis points from July, respectively. African America’s unemployment rate increased by 30 basis points from July.
AFRICAN AMERICAN EMPLOYMENT REVIEW
AFRICAN AMERICAN MEN:
Unemployment Rate – 7.1%
Participation Rate – 69.8%
Employed – 9,893,000
Unemployed – 753,000
African American Men (AAM) saw a increase in their unemployment rate by 10 basis points in August. The group had an increase in their participation rate in August by 190 basis points, there highest participation rate in the past five months. African American Men gained 270,000 jobs in August and saw their number of unemployed increase by 30,000.
AFRICAN AMERICAN WOMEN:
Unemployment Rate – 6.7%
Participation Rate – 61.4%
Employed – 10,260,000
Unemployed – 739,000
African American Women saw a increase in their unemployment rate by 40 basis points in August. The group increased their participation rate in August by 30 basis points. African American Women gained 13,000 jobs in August and saw their number of unemployed increase by 45,000.
AFRICAN AMERICAN TEENAGERS:
Unemployment Rate – 24.8%
Participation Rate – 29.3%
Employed – 590,000
Unemployed – 195,000
African American Teenagers unemployment rate increased by 310 basis points. The group saw their participation rate increased by 10 basis points in August. African American Teenagers lost 24,000 jobs in August and saw their number of unemployed also increase 25,000.
African American Men-Women Job Gap: African American Women currently have 367,000 more jobs than African American Men in August. This is an decrease from 624,000 in July.
CONCLUSION: The overall economy added 22,000 jobs in August while African America added 260,000 jobs. From Reuters,”The warning bell that rang in the labor market a month ago just got louder,” Olu Sonola, head of U.S. economic research at Fitch Ratings in New York, said in reference to the U.S. labor market. “A weaker-than-expected jobs report all but seals a 25-basis-point rate cut later this month.” Fed Chair Jerome Powell had already reinforced rate cut speculation with an unexpectedly dovish speech at last month’s Fed symposium in Jackson Hole.”
Every month, the Bureau of Labor Statistics releases employment data that shapes market sentiment, economic forecasts, and policymaking. From interest rate decisions at the Federal Reserve to unemployment insurance triggers at the state level, the influence of BLS data is far-reaching. And yet, with each release, one often overlooked note quietly accompanies the data: subject to revision.
To the uninformed, this might suggest inaccuracy or even manipulation. But the reality is rooted in how data is collected, processed, and interpreted. The act of revising economic data is not a flaw but a fundamental feature of any statistical system that prioritizes accuracy over speed. This article explores why the BLS revises its data, the mechanics of seasonally adjusted vs. not seasonally adjusted numbers, and how a real-world dataset employment among African American women in 2025 illustrates the complexity of labor market measurement.
The Bureau of Labor Statistics, founded in 1884, is the principal fact-finding agency for the U.S. federal government in the field of labor economics. Its core function is to measure labor market activity, working conditions, and price changes in the economy.
Among its most closely followed outputs is the monthly Employment Situation Report, which contains data on job growth or loss, unemployment rates, participation rates, and hours worked. These figures often headline national news and affect everything from political discourse to stock market performance. But the collection and interpretation of labor data is a dynamic process. No matter how carefully designed the surveys are, initial data releases are based on incomplete information and statistical models that must later be refined.
The BLS relies on two primary surveys to produce monthly employment estimates:
Current Population Survey (CPS): Also known as the household survey, this samples about 60,000 households and is the source for data on unemployment, labor force participation, and demographic breakdowns.
Current Employment Statistics (CES): Also known as the establishment survey, this collects payroll data from roughly 122,000 businesses and government agencies, covering over 666,000 worksites.
Because of tight deadlines for monthly releases—typically the first Friday of the following month—some employer reports are late, households may be unreachable, and administrative records may not yet be available. As more responses arrive over time, the BLS incorporates the additional data, which leads to two monthly revisions: a first revision one month later and a second revision two months after the initial release.
There is also an annual benchmark revision, where the BLS aligns employment data to comprehensive counts derived from state unemployment insurance tax records, which cover nearly all employers.
These revisions are not signs of incompetence or hidden agendas. Rather, they reflect the reality that high-frequency data collection must balance timeliness with completeness. Initial estimates are snapshots; revisions bring the picture into higher resolution.
Understanding Seasonally Adjusted vs. Not Seasonally Adjusted Data
Another common source of confusion is the distinction between seasonally adjusted (SA) and not seasonally adjusted (NSA) figures.
Not Seasonally Adjusted (NSA): These are raw numbers taken directly from survey results. They reflect real, unaltered employment counts.
Seasonally Adjusted (SA): These figures are modified using statistical models that remove predictable seasonal fluctuations—such as increased hiring in December or reduced construction jobs in winter.
Seasonal adjustment allows for clearer comparisons of month-to-month changes without the noise of recurring seasonal events. For example, employment traditionally rises in retail in November and December and drops in January. Without adjustment, these fluctuations could lead to misinterpretation of actual trends.
However, seasonal models rely on historical patterns. If a new shock occurs—such as a pandemic, atypical weather events, or irregular policy shifts—these models may not capture reality perfectly, requiring future refinements and adjustments to the seasonal factors themselves.
A Practical Example: African American Women’s Employment, 2025
To illustrate how data revisions and seasonal adjustments interact, consider the seasonally adjusted number of employed African American women over five consecutive months in 2025:
March 2025: 10.300 million
April 2025: 10.260 million
May 2025: 10.332 million
June 2025: 10.248 million
July 2025: 10.247 million
At first glance, this dataset may seem inconsistent. Why the decline in April, a surge in May, and subsequent declines in June and July?
Several points are worth unpacking:
Magnitude of Monthly Change These monthly movements, ranging from about 50,000 to 80,000, may appear marginal, but in labor market terms, they represent significant shifts. These could be due to school-year employment cycles, changes in public-sector hiring, or temporary retail and service jobs.
Temporary Effects The uptick in May could represent a short-term employment increase due to localized or sector-specific conditions—perhaps related to summer hiring, public campaigns, or fiscal year-end budgeting by employers. However, this doesn’t necessarily indicate a sustained improvement, as shown by June and July numbers.
Plateau, Not Decline While there are ups and downs, the broader range—from 10.248 to 10.332 million—suggests a labor market that is relatively flat during this period. The volatility may be more reflective of sector churn than structural change.
If a future revision updates, for instance, July’s figure from 10.247 to 10.280 million, that revision would adjust interpretations about labor market strength. It may indicate more robust hiring than originally estimated. Conversely, a downward revision could reinforce a stagnation narrative. Revisions are standard practice across all major economic indicators. GDP figures are revised multiple times. Inflation statistics may be reweighted to reflect changing consumption patterns. The Census Bureau revises retail sales and trade data regularly.
In labor market statistics, revisions are particularly common because of the sheer scale and complexity of the data. Millions of businesses and households are involved, each contributing a piece of the larger puzzle. Moreover, revisions are conducted transparently. The BLS publishes revision histories, explains methodological changes, and allows the public to compare original and revised estimates. This openness is central to the integrity of the data, even if the revisions themselves can be politically or emotionally misunderstood.
A revision of 50,000 jobs may not seem impactful in an economy with over 150 million employed people. But such changes are statistically meaningful. For example, Federal Reserve interest rate decisions are often influenced by whether job growth appears to be accelerating or decelerating. A 0.1% change in employment may be the tipping point for a policy decision affecting credit costs for millions.
Revisions also matter for planning and budgeting by states, corporations, and local governments. Employment trends influence tax revenues, hiring plans, and social program allocations. A misinterpretation of the underlying data even if unintentional can have ripple effects through the economy.
While the BLS aims for statistical precision, the public conversation around the data is often shaped by headline figures and political narratives. This can result in overemphasis on preliminary numbers, even though they are explicitly marked as subject to change. It is important for observers, journalists, policymakers, and analysts to understand that early data is an estimate. Just as weather forecasts become more accurate as the date approaches, labor statistics become more reliable as more data is incorporated and models are refined.
Understanding the architecture behind the data helps prevent premature or inaccurate conclusions about the state of the economy. The BLS operates under dual pressure: provide timely data and ensure its long-term accuracy. These goals are inherently in tension, but both are critical. Without timely data, markets and policymakers would be flying blind. Without accuracy, trust in the data would erode, leading to poor decisions and broader skepticism of institutions.
Revisions are not a sign of error. They are the result of a methodical, transparent process aimed at refining the initial picture of the economy into a more complete and accurate one. For analysts and observers, the lesson is simple: understand the process, treat early numbers with caution, and always look at the data—both in the moment and over time—as a moving picture, not a still fram
Analysis: European Americans’ unemployment rate increased 10 basis points. Asian Americans increased 40 basis points and Latino Americans increased 20 basis points from June, respectively. African America’s unemployment rate increased by 40 basis points from June.
AFRICAN AMERICAN EMPLOYMENT REVIEW
AFRICAN AMERICAN MEN:
Unemployment Rate – 7.0%
Participation Rate – 67.9%
Employed – 9,623,000
Unemployed – 723,000
African American Men (AAM) saw a increase in their unemployment rate by 10 basis points in July. The group had a precipitous drop in their participation rate in July by 90 basis points. African American Men lost 129,000 jobs in July and saw their number of unemployed increase by 2,000.
AFRICAN AMERICAN WOMEN:
Unemployment Rate – 6.3%
Participation Rate – 61.1%
Employed – 10,247,000
Unemployed – 694,000
African American Women saw a increase in their unemployment rate by 50 basis points in July. The group increased their participation rate in July by 20 basis points. African American Women lost 1,000 jobs in July and saw their number of unemployed increase by 60,000.
AFRICAN AMERICAN TEENAGERS:
Unemployment Rate – 21.7%
Participation Rate – 29.2%
Employed – 614,000
Unemployed – 170,000
African American Teenagers unemployment rate increased by 250 basis points. The group saw their participation rate decreased by 80 basis points in July. African American Teenagers added 37,000 jobs in July and saw their number of unemployed also increase 15,000.
African American Men-Women Job Gap: African American Women currently have 624,000 more jobs than African American Men in July. This is an increase from 496,000 in June.
CONCLUSION: The overall economy added 73,000 jobs in July while African America lost 166,000 jobs. From CNBC, “This is a gamechanger jobs report,” said Heather Long, chief economist at Navy Federal Credit Union. “The labor market is deteriorating quickly.” The weak report, including the dramatic revisions, could provide incentive for the Federal Reserve to lower interest rates when it next meets in September. Following the report, futures traders raised the odds of a cut at the meeting to 75.5%, up from 40% on Thursday, according to CME Group data.”
“When power makes truth expendable, only the brave will keep records.” — HBCU Money Editorial Board
On August 1, 2025, the United States crossed a threshold most democracies fear but few anticipate with precision the moment a nation’s statistical agency becomes a political target not for corruption, but for accuracy.
Following a weaker-than-expected jobs report with just 73,000 jobs added in July and significant downward revisions to prior months, President Donald Trump abruptly ordered the firing of Dr. Erika McEntarfer, Commissioner of the Bureau of Labor Statistics (BLS). The justification? The data embarrassed him. The evidence? None. The implications? Profound.
For over a century, the BLS has served as the impartial scorekeeper of the American labor market. Its reports help inform everything from Federal Reserve monetary policy to wage negotiations, business expansion decisions, and university research. Most critically, the BLS is the foundation for public trust in employment data, a cornerstone of economic legitimacy.
Trump’s dismissal of Dr. McEntarfer, who was confirmed with bipartisan support and is regarded as a rigorous labor economist, did not challenge methodology, nor did it cite misconduct. Instead, it was an overt signal: when facts contradict the leader’s narrative, the facts must go.
This act is not merely executive overreach. It is an institutional decapitation. And it represents the clearest break yet from the post-WWII consensus that government data should be nonpartisan, methodologically sound, and politically untouchable. In a global economy, this is the equivalent of a currency devaluation not of the dollar, but of America’s data credibility.
When leadership no longer trusts or permits accurate data, policy becomes reactive, erratic, and performative. Investors, entrepreneurs, and institutions rely on the BLS to signal economic direction. Without it, credit markets misfire, fiscal policy lacks direction, and monetary policy becomes unmoored. For African American-owned banks, real estate firms, and HBCU endowment managers, this degrades their ability to assess employment trends in Black communities, apply for federal workforce grants, or time bond offerings based on unemployment benchmarks. Even philanthropic giving strategies may suffer if the poverty, wage, and employment data they are based on becomes manipulated or suppressed.
America’s strength lies in its institutions, not its individuals. By removing the head of a critical statistical agency on political grounds, the White House has signaled that no institution is beyond coercion. This undermines the rule of law and places civil servants especially those in technocratic roles on notice: loyalty matters more than evidence. African American civil servants, many of whom have worked tirelessly to diversify and reform these institutions from within, may see decades of credibility erased. It’s a chilling reminder that representation within agencies means little if those agencies are subject to autocratic whim.
International investors, trade partners, and credit agencies track U.S. labor data as a proxy for global economic health. If they begin to suspect that U.S. statistics are manipulated, they may hedge their investments, slow trade, or reevaluate the reliability of U.S. fiscal metrics. In the long-term, this can impact foreign direct investment in African American economic zones, HBCU research partnerships with global firms, and even diaspora remittance flows, if currency stability is affected by market anxiety.
Perhaps most dangerously, Trump’s decision follows a long trajectory of undermining truth-based systems elections, public health, the judiciary, and now economic data. This creates a vacuum in which conspiracy becomes conventional wisdom. In such an environment, fake facts become state currency. This has severe implications for African American institutions. Much of African American advocacy whether for reparations, investment, or educational equity rests on data. If national data sources are neutered or politicized, then the burden of proof shifts unfairly onto communities already under-resourced in research infrastructure.
HBCUs, Black think tanks, and African American foundations must view this firing not as a political blip, but a doctrine in action. When truth becomes negotiable, institutions that depend on it must move from passive reliance to active defense. HBCUs with strong economics, political science, or data science departments such as Howard, Spelman, and FAMU should develop Black-centered labor and socioeconomic data initiatives. These should complement, verify, or challenge federal data when necessary.
Institutions should also create safeguards digital, legal, and procedural to document how and when data manipulation may be occurring. This includes archiving historic BLS data, creating public dashboards, and writing explanatory briefs for the community. In addition, the next generation of data scientists, economists, and statisticians trained at HBCUs must be equipped not only with technical skill but a political consciousness of how truth is weaponized. Their work should be rooted not just in method, but in mission.
There is also an urgent need for civic engagement. African American policy organizations must pressure Congress to enact legal protections that insulate agencies like BLS, Census, and the Congressional Budget Office from political interference. Civil society must create watchdog coalitions that expose attempts to politicize data or intimidate public servants. Parallel to this, an emergency data defense fund backed by foundations and Black philanthropic leaders could help institutions respond rapidly to threats against data integrity.
Dr. McEntarfer’s firing is not merely about jobs data. It is about whether America will continue to govern itself by fact or by fiat. For African Americans, who have fought centuries of data invisibility, distortion, and misuse from redlining to police profiling the stakes are especially high.
The Bureau of Labor Statistics was once seen as above politics. That era is over.
African American institutions must now assume a new role not just consumers of data, but defenders of its integrity. If truth is to survive, it will not be because it was protected by tradition, but because it was guarded by those with the most to lose from its disappearance.
Despite the ongoing student loan crisis at the moment, the statistics of jobs that pay well still demand a college degree. Now, where a college degree comes from, what the economy of your geographic region leans on, and a whole host of other factors certainly play a role. Yet, despite all of this, according to the Bureau of Labor Statistics the only jobs with a median pay currently over $75,000 in 2018 and whose projected growth rate is “Much Faster Than Average” are those associated with a minimum of a bachelor’s degree or higher.
Jobs by degree classification are listed in alphabetical order:
In the end, college must be about our own personal growth towards what we believe is best for us. However, many students and families lack the information of the plethora of opportunities and career paths that are available to them. For African Americans this becomes especially true and results in a tendency towards low-paying career paths. A fundamental problem for a community that needs higher-earners desperately. The demand for these jobs is high and pays well. Who is to say one can not major in Art History and still become a veterinarian and build enough wealth that they can retire early and afford to teach art history in their community for free? Or open their own gallery? Building wealth early gives us opportunities later and in that vein, we hope this information provides a bit more for your educational arsenal.
HBCU Money 