As the market waits in the dark for the key January non-farm payroll report, a deeper issue has emerged—the globally watched macro data system is losing its reliability and timeliness. In the early hours of February 3, the U.S. Bureau of Labor Statistics announced that due to another "shutdown" of the federal government, the release of the January non-farm payroll report, originally scheduled for this week, will be postponed.

This is the second delay of non-farm data within three months. The U.S. government entered a "technical shutdown" due to a budget impasse starting at midnight on January 31, and resolving this issue will take at least until February 3.

However, the temporary absence of data is just the tip of the iceberg, as a more severe reality lurks behind it: recent record revisions of data and controversies over statistical methods indicate that this macro data system, relied upon by global capital, is facing systemic challenges.

1. The Macro Beacon Loses Accuracy Again

● The news of the U.S. Bureau of Labor Statistics postponing the non-farm payroll report states: "The January employment situation report will not be released as originally scheduled on February 6 and will be rescheduled after government funding is restored."

● On the surface, this is just another technical delay caused by the political deadlock in Washington. However, if we extend the timeline, we find that this is the second delay of non-farm data in recent months. In fact, this is merely the tip of a larger systemic issue.

● The real problem is that the credibility of the data itself is facing unprecedented scrutiny.

○ Preliminary revised data released by the U.S. Bureau of Labor Statistics in September 2025 shows that from April 2024 to March 2025, the U.S. added 910,000 fewer non-farm jobs than initially reported.

○ This means that the employment growth data over the past year has been overestimated by nearly half, with the average monthly job additions revised down from 147,000 to 71,000.

● Employment numbers in almost every industry have been revised down, with leisure and hospitality down by 176,000, professional and business services down by 158,000, and retail down by 126,000.

This record revision is not an isolated case. Just last August, the U.S. Bureau of Labor Statistics made significant downward revisions to the non-farm employment data for May and June, with adjustments as high as 90%, including a single revision of 258,000 jobs. These consecutive large data corrections reveal a disturbing fact.

2. Structural Defects Behind the Trust Crisis

The repeated large adjustments to U.S. non-farm employment data are not coincidental; they stem from structural defects within the statistical system itself. These defects are particularly pronounced in today's rapidly changing economy.

● The statistical methods of the U.S. Bureau of Labor Statistics suffer from significant time lags. The non-farm data released each month is actually based on preliminary estimates from a sample survey of about 121,000 businesses, covering only about 60% of the responding samples. The truly comprehensive data source—the quarterly employment and wage census data—lags by more than five months.

● The statistical models heavily rely on assumptions based on historical trends. To compensate for newly established or closed businesses that have not been timely included in the survey, the Bureau of Labor Statistics uses a "net birth/death model" to estimate employment changes in this segment.

● The problem with this model is that it predicts the future based on past trends and cannot timely capture sudden economic shifts, such as financial crises, pandemic impacts, or large-scale layoffs.

● When profound changes occur in the economic structure, models that rely on historical trends can exhibit severe biases. Analysis by the Business Times points out that this time lag and methodological mismatch have created a long-term dilemma of "initial estimate errors" and "delayed corrections." In the age of artificial intelligence, this statistical mechanism has become outdated.

Political factors have further exacerbated the data trust crisis. Last August, Trump fired the head of the Bureau of Labor Statistics on the grounds of "data falsification," accusing him of "fabricating" employment data to increase the chances of his political opponent's victory.

3. The Ripple Effect on Macroeconomic Policy

The distortion and delay of non-farm data are having profound impacts on the Federal Reserve's policy-making and market expectations. In a data-driven decision-making environment, an unreliable data foundation inevitably leads to policy deviations.

● Weak employment data has already begun to affect the Federal Reserve's policy expectations. The employment report for November 2025 showed that non-farm employment increased by only 64,000, with the unemployment rate rising to 4.6%, the highest since 2021.

● Prior to this, the non-farm employment number in August increased by only 22,000, while June actually saw a reduction of 13,000 jobs, marking the first negative growth in four and a half years.

● Data volatility is causing significant swings in market expectations. After the release of the non-farm data in July last year, the market briefly fell into a panic risk-averse state: U.S. Treasury yields plummeted, the dollar and U.S. stocks fell, and gold surged significantly.

● The uncertainty of monetary policy has also increased. The Federal Reserve maintained interest rates at its July meeting last year, but two board members cast dissenting votes, marking the first such disagreement since 1993. This divergence reflects the confusion faced by policymakers in the face of uncertain data.

As FOMC voting members rotate in 2026, there may be new changes in policy direction. There is already sufficient reason to believe that the Federal Reserve may initiate the 2026 easing cycle earlier than expected.

4. Risks and Opportunities in the Cryptocurrency Market

The distortion of macroeconomic data and the uncertainty of monetary policy are triggering complex reactions in the cryptocurrency market. The declining credibility of traditional macro indicators like non-farm data may unexpectedly enhance the appeal of crypto assets.

● Cryptocurrency prices and DeFi activities are extremely sensitive to changes in U.S. monetary policy. A shift in the Federal Reserve's policy could provide liquidity support for digital assets. If the Federal Reserve initiates the 2026 easing cycle earlier, a decline in policy rates and a slowdown in balance sheet reduction typically support risk assets.

● Starting in the fourth quarter of 2025, as macro uncertainty rises, Bitcoin spot ETF fund flows have become volatile. IBIT recorded its largest single-day outflow in November, even though Bitcoin prices remained near historical highs.

● This volatility reflects the market's response to macroeconomic uncertainty and highlights the unique nature of cryptocurrencies as an alternative asset class.

● As the issue of data distortion becomes increasingly prominent, some investors may view cryptocurrencies as a hedge against the instability of the traditional financial system. This shift in perception could bring structural inflows of capital into the crypto market.

With expectations of the Federal Reserve potentially lowering interest rates earlier, crypto investors need to pay attention to how policy changes affect on-chain yields. If the Federal Reserve lowers rates, the yields on short-term Treasury bills that support "off-chain" stablecoin reserve income and many "on-chain" dollar yield vaults will be compressed.

The market's gaze briefly lingers on the political deadlock in Washington, eagerly awaiting the resumption of data releases.

But when the January non-farm data is finally published, will market participants begin to question its accuracy? Will they wonder if these numbers will be significantly revised down in a few months? Or will they ponder how much reference value any statistical model based on past trends can have in today's rapidly changing labor market?

The flickering of traditional data beacons is forcing various industries to seek new pathways in the fog. Those who are the first to build more real-time and resilient decision-making systems will gain unusual leverage in this round of structural transformation.

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