BONUS First Monday: How Did the BLS Get the Jobs Report So Wrong?

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Aug 4, 2025

Massive job revisions by the Bureau of Labor Statistics leave experts puzzled, as nearly 90% of gains for May and June were wiped out. Delving into the data collection methods reveals significant challenges, especially with a 560,000 business survey that often lacks timely responses. Inconsistencies arise when comparing BLS data with ADP's payroll insights. These discrepancies have profound implications for understanding the economy and highlight the need for more effective statistical models in rapidly changing conditions.

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INSIGHT

How BLS Jobs Data Is Compiled

The BLS jobs report relies on payroll data from 560,000 work sites, with only 60-73% responding by the initial release date.
The rest are estimated by statistical models based on historical patterns, which can cause large errors during rapid economic shifts.

INSIGHT

Why Job Report Revisions Are Massive

The BLS' use of past data models fails when economic conditions change rapidly, causing major job report revisions.
This is similar to 2009, when the Great Recession also challenged predictive accuracy and caused major revisions.

INSIGHT

ADP Data Validates BLS Revisions

Private payroll processor ADP's monthly job reports provide an independent data source covering 460,000 companies.
Comparing ADP data to BLS revisions boosts confidence in revised job numbers and reveals the magnitude of initial errors.

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Special bonus episode. The Bureau of Labor Statistics issues massive job revisions on Friday morning. The revisions wipe out nearly 90% of previously reported gains for May and June. This raises fundamental questions about how our most trusted economic data gets calculated.

In this episode, we break down how the system works. We examine why the revisions are so large. We explore what this means for understanding the real economy.

Friday arrives. The BLS delivers what appears routine: 73,000 new positions added in July. But the revisions tell a different story. May's initially reported 144,000 job gains become 19,000. June's seemingly solid 147,000 drops to just 14,000. These represent 87-90% overestimates. They fundamentally alter the economic picture for those months.

The BLS surveys 560,000 businesses each month. They use payroll data from the 12th of the month. But only 60-73% of those businesses respond by the initial release deadline. The remaining portion gets filled through statistical modeling. The models rely on historical patterns.

This approach typically produces revisions in the 20,000-50,000 range. But throughout 2025, average monthly revisions reach 66,000. That's triple the normal size. The statistical models aren't capturing current economic conditions effectively.

The problem becomes clear when economic conditions shift rapidly. Historical patterns become unreliable guides. The 2024 annual revision was the largest since 2009. What happened in 2009? The Great Recession. Another period when traditional forecasting tools struggled with rapid change.

ADP is a private payroll processor. They serve 460,000 companies. They provide useful comparison data. For May, their 37,000 private-sector job estimate aligns reasonably well with BLS's revised 19,000 total. For June, ADP reports a 33,000 job loss. BLS shows a 14,000 gain.

ADP's independent data helps validate the revised numbers while highlighting the magnitude of the initial errors.

These numbers drive real decisions. Federal Reserve officials use employment data for interest rate policy. Investors allocate capital based on these reports. Workers make career decisions based on perceived labor market strength.

When the initial data misses by 90%, everyone operates with fundamentally flawed information.

The revisions expose how fragile our economic measurement systems become when conditions change faster than models can adapt.

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