Image source: Pixabay
 

• US job growth has stalled while revisions wipe out previously reported gains.
• Inflation data now leans heavily on imputed prices as BLS staffing falls.
• Delays and gaps in surveys raise doubts, giving private data firms an edge.

The United States is facing an unusual problem. Investors do not know whether they can trust the government’s economic numbers anymore.

Jobs reports swing wildly on revision. Inflation figures are increasingly filled with guesswork. Key surveys arrive late, sometimes without explanation.

For an economy that runs on expectations, the credibility crisis in US statistics is slowly becoming a market risk.
 

Why the jobs machine is stalling

The latest payroll report showed the US added only 22,000 jobs between July and August, according to the Bureau of Labor Statistics.

Revisions cut hundreds of thousands of previously reported jobs, erasing what investors thought were real gains. To put it into perspective, the US used to add 200,000 jobs every month, while now it’s hovering around zero.

This is more than a cyclical slowdown. Entry-level jobs in software, marketing, and sales have vanished since 2022 as companies turn to artificial intelligence.

Younger workers now find opportunities in retail and health care, but even those sectors show signs of weakness. Employers remain cautious while the administration debates tariffs, taxes, and immigration.

The Federal Reserve reacted in September by cutting rates by 25 basis points.

Yet with inflation at 2.6% on its preferred PCE measure, the central bank’s scope is limited. Rate cuts stimulate demand, not supply.

They cannot reverse the structural pressures of automation or political uncertainty.

Investors who once read payroll figures as a clean signal of economic strength are now faced with noise.
 

How the data system is breaking down

The deeper issue is not the labour market itself but how it is measured. The BLS has seen its staff cut by about 20% since 2017. One third of leadership roles are vacant.

To compensate for missing data, the agency increasingly relies on imputation, which essentially means statistical guesswork based on past trends. In stable times, this is manageable. In periods of rapid change, it is misleading.

Recent reports admitted that only 55% of intended survey responses arrive in time for the initial jobs release.

Overall response rate has been trending downwards since 2015. Small businesses, which are now multiplying in the gig economy, respond at even lower rates.
 

Source: BLS
 

The result is predictable. Large employers dominate the sample. When small firms lay off staff or close, the data does not pick it up until revisions months later.

The inflation data is suffering in parallel. In June, the BLS suspended collection in three metro areas due to a lack of resources. By July, 15% of the CPI sample in other regions was suspended as well.

Bloomberg reported that the share of imputed prices has more than tripled in six months. That is significant, and it means that a meaningful portion of the CPI is no longer based on actual prices.

To fill gaps, the BLS is now advertising part-time jobs for CPI collectors, paying up to $25 an hour in major cities.

These assistants will walk into shops and hotels to note down prices, as they always have, but on a reduced scale.
 

Why delays matter more than errors

The BLS last week postponed the release of its annual consumer expenditure survey without explanation. This dataset is used to weight the CPI basket for the coming year.

In other words, it decides how much food, energy, shelter, or medical costs count in official inflation. A delay does not simply frustrate analysts. It risks mis-weighting the single most-watched indicator in macroeconomics.

Former BLS commissioner William Beach called the release “tricky” but expressed surprise that it lacked a new date.

His successor, Erika McEntarfer, was fired last month by President Trump. She has warned openly that the independence of the agency is in doubt.

An inspector general probe is now underway into how the BLS collects and reports data.

Delays like this have two consequences for investors. First, they introduce uncertainty into inflation expectations, which feed directly into bond yields and rate forecasts.

Second, they undermine confidence in the objectivity of the process itself.

Ultimately, a flawed number can be revised. But a missing number leaves a vacuum that others will fill with speculation.
 

Who benefits from the data fog

It is tempting to view this credibility crisis as a technical breakdown. It is not. It creates asymmetry. The less reliable the public data, the more valuable private data becomes.

Large investment firms are already spending more than $15 billion a year on alternative data sources.

Satellite images of car parks, credit card transaction feeds, freight flows, and scraped job postings all provide a sharper picture than official releases.

Bloomberg and other vendors package this into terminals that cost firms $30,000 a year per seat. This is a no-brainer for hedge funds. For smaller investors, however, it is out of reach.

The result is a widening information gap. Policy makers and the public are left with dated, noisy figures. Institutions with resources operate on cleaner, timelier signals.

It is not technically insider trading, but the effect is similar. Market power accrues to those who can buy the better dataset.
 

What investors should watch next

Investors cannot afford to dismiss official US statistics, but they must treat them differently.

A headline payroll print of 22,000 jobs means little without knowing how much of it is imputed and how likely revisions will be.

Inflation readings must be read with attention to the share of missing prices. Data quality itself has become a variable.

One approach is to track the “revision elasticity” of each series. When the difference between initial and final readings grows, the series is less reliable and should carry less weight in forecasts.

Another is to monitor response rates and imputation shares, which the BLS discloses deep in its methodology notes. These numbers are tedious, but they matter more now than the headline itself.

For asset allocation, this implies caution on trades that rely on crisp monthly signals. Rate-sensitive bonds, cyclical equities and dollar positions can all be whipsawed by revisions. L

long-term themes, such as the impact of AI on labour markets, or the resilience of consumer spending despite noise in the data, may prove more investable.
 

A system in need of repair

Perhaps the most important signal is not the labour market or inflation but the measurement system itself.

The United States built a statistical machine after the Great Depression because modern economies cannot function without trusted numbers.

That machine is now running on low power. Staffing cuts, political interference, and structural economic change are straining it.

Firing more commissioners will not solve the credibility crisis in the US. In fact, it widens it.

The solution requires investment in statistical infrastructure, protection of independence, and transparency about uncertainty. Until then, investors will need to treat every number as provisional.

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