Could the BLS Actually "Fake" the Jobs Numbers? A Reality Check on Trump's Firing

Here's what actually happened: President Trump fired Bureau of Labor Statistics Commissioner Erika McEntarfer hours after the BLS released a weak July jobs report showing only 73,000 jobs added and massive downward revisions to previous months. Trump claimed, without evidence, that McEntarfer "faked the Jobs Numbers before the Election to try and boost Kamala's chances of Victory".

But here's the thing—Trump's accusation raises a fascinating question that goes way beyond today's political theater: Could someone actually manipulate the BLS jobs data? Let's break down what would really be required.

The Numbers Behind the Numbers

Ever wonder how those monthly jobs reports actually get made? The BLS surveys approximately 560,000 worksites each month, selected to represent millions of businesses throughout the country. The sample includes approximately one-third of all nonfarm payroll jobs, making it one of the most comprehensive economic surveys on the planet.

Businesses report the total number of people who worked or received pay during the pay period that includes the 12th of the month. But here's where it gets interesting: the average collection rate at the time of the initial release was 73.1 percent, rising to 94.6 percent by the third estimate.

That's why revisions happen—and they're not mistakes or manipulation.

What Would It Actually Take to "Fake" These Numbers?

Let's think through this systematically. To meaningfully alter the jobs report, you'd need to:

Step 1: Control the Data Collection Field staff collect data using computer-assisted telephone interviews, web reporting, Electronic Data Interchange, and other methods. BLS field economists are extensively trained and given detailed instructions on data collection techniques. You'd need to coordinate across hundreds of field staff who never see the political implications of their work.

Step 2: Manipulate the Processing CES uses automated edit and screening techniques to identify potentially erroneous sample data; respondents are re-contacted as needed to validate or correct their reported information. Automated edits of the estimates are supplemented by analysts who look for errors and outliers and provide final validation of the series before publication.

Step 3: Bypass the Cross-Checks The CES-N sample-based estimates are benchmarked annually against the Quarterly Census of Employment and Wages (QCEW) program, which collects employment and wage data from each state's unemployment insurance tax records. Any systematic manipulation would show up when compared to actual UI records.

The Pattern Recognition Reality Check

William Beach, a 2017 Trump appointee and McEntarfer's immediate predecessor at BLS, sharply criticized her firing, calling it "totally groundless" and saying it "sets a dangerous precedent and undermines the statistical mission of the Bureau".

Think about that for a moment. Trump's own former appointee—someone who ran the same agency—is defending the person Trump just fired. Jed Kolko, a former Commerce Department official overseeing government statistics, said the firing "is five-alarm intentional harm to the integrity of US economic data and the entire statistical system".

Here's what the timing tells us: Trump previously praised the BLS reports when they were favorable to his administration in April, May and June. In May, the White House said that April's jobs report "proved" that Trump was "revitalizing" the economy.

Why Revisions Aren't Manipulation

Revisions aren't mistakes; they're simply updates with more complete information. The revised estimates represent a more complete and therefore more accurate picture of developments in the job market.

In 2024, initial job estimates were revised down by an average of 20,000 jobs. Revisions have been even larger so far this year, with initial estimates revised down by an average of 66,000 jobs from January through May.

The process is transparent: You can view all monthly job revisions since 1979 on the BLS website. If there was systematic manipulation, the pattern would be obvious across decades of data.

What This Actually Means

Michael Strain, an economist at the conservative American Enterprise Institute, said: "The president is risking material economic harm through his politicization of the BLS and of official government data. It is imperative that businesses, households and investors believe that official government data are accurate and do not reflect any political bias".

The politicization of economic data and potential interference with it by political appointees is something that's typically seen in nondemocratic countries like Russia, Venezuela or China.

The real issue isn't whether the BLS could fake numbers—the methodology makes systematic manipulation practically impossible without hundreds of conspirators. The issue is what happens to economic decision-making when political leaders attack data that doesn't support their narrative.

The Missing Context

What everyone's missing in today's outrage cycle: The ADP private payroll report, which utilizes actual data from the ADP employment payroll business, provided insights that aligned closely with the revised BLS figures. Over the same three-month period, the ADP report indicated job gains of +37,000 for May, a loss of -33,000 for June, and a modest increase of +104 for July.

That's important. Independent data from an entirely different methodology is showing similar results. If the BLS was manipulating data, why would private sector numbers align with the revisions?

What to Watch For

This pattern—attacking data when it's unfavorable while praising the same agency when numbers look good—tells us more about political strategy than statistical integrity.

BLS is considered the gold standard among international labor data collectors. Economists consider its revisions process key to transparency and thoroughness, even if it sometimes makes BLS look bad when preliminary numbers turn out to be wildly off.

The real question isn't whether someone could fake the BLS numbers. It's whether we want economic policy based on actual data or political narratives. Markets, businesses, and working families all depend on reliable economic information to make decisions.

Bottom line: The methodology makes large-scale manipulation virtually impossible. The firing appears to be about messaging, not data integrity. And when politicians attack independent statistical agencies for reporting inconvenient facts, that should worry everyone regardless of party.

What patterns are you seeing in how political leaders respond to unfavorable economic data? How do you distinguish between legitimate methodology questions and political theater?

Share your thoughts and analysis—this kind of institutional pressure on independent data collection affects everyone who relies on accurate economic information.