The following guest article was submitted by Mikheil Shengelia, Research Analyst at Eagle Alpha, an alternative data aggregation platform providing supporting advisory services for data buyers and vendors.
The Trump administration has been changing how government data is collected and shared, raising concerns among economists, scientists, and policymakers about the integrity of official statistics.
Government Interference in Data Releases?
This issue came to the forefront when President Trump fired Erika McEntarfer, head of the Bureau of Labor Statistics (BLS), after the agency reported slowing job growth. While presidents often highlight favorable numbers, McEntarfer’s removal sparked alarm over potential politicization of data that has long been considered neutral and reliable. Critics, including members of Trump’s own party, argue that dismissing officials over data reporting undermines confidence in government statistics.
Beyond employment figures, the administration has scaled back or frozen several other streams of federal data. The National Weather Service reduced weather balloon launches, the National Climate Assessment lost funding and staff, and the CDC scrubbed certain HIV statistics from its website. Immigration and workforce datasets have also faced delays or long freezes. These changes have left researchers and the public with significant information gaps, impacting everything from climate forecasting to health decision-making. Experts warn that limiting or altering data could hurt public health and safety, since accurate numbers are essential for making good decisions.
Market Trends
The White House has denied accusations of data manipulation, insisting that the administration is committed to transparency and accuracy. Officials argue that staffing issues, outdated collection methods, and resource constraints explain the cutbacks, while President Trump himself has dismissed unfavorable figures as “rigged.” At times, President Trump has contradicted his own agencies’ reports, choosing to amplify positive statistics when they support his narrative. Critics argue that this selective approach damages trust in government data and could weaken institutions that are supposed to provide unbiased, reliable information to the public.
Even before political tensions escalated, the BLS was under mounting operational strain. Data collection has been reduced in several cities, forcing the agency to rely more heavily on estimates and web scraping. By June 2025, 35% of Consumer Price Index (CPI) data points were imputed rather than directly observed, compared with just 10% at the start of the year.
Falling survey response rates since the pandemic have further increased errors and revisions in flagship reports. Meanwhile, the agency faces an 8% budget cut and the loss of more than 150 staff next year, raising concerns about growing delays and declining data quality.
Similarly, the Current Employment Statistics (CES) survey, which tracks nonfarm employment, and the Job Openings and Labor Turnover Survey (JOLTS) have experienced notable drops in responses. This erosion in data collection undermines the precision of economic reports and raises concerns about the reliability of information used by policymakers, businesses, and households.
The challenge is particularly pressing for the monthly jobs report, which is released on the first Friday of every month with limited time to gather data. With fewer responses coming in from employers, the reports carry a higher margin of error, leading to larger and more frequent revisions. Economists warn that these revisions, such as those highlighted in the July 2025 report, are not evidence of manipulation but rather a reflection of the strain caused by low response rates and under-resourcing at the agency. As a result, the credibility of one of the world’s most closely watched economic indicators is increasingly at risk.
Former and current staff warn that the loss of experienced personnel has compounded the challenges. Many senior “guild masters” who once trained younger staff have opted to retire early, leaving critical expertise gaps. With funding now 18% lower in real terms than in 2009 and advisory committees eliminated, economists caution that the U.S.’s reputation for high-quality statistics is at risk.
Loss of confidence in government data would have major implications for financial markets, particularly the $2.1 trillion Treasury inflation-protected securities (TIPS) market, which relies directly on the CPI calculated by the BLS. “If there is politicization of the BLS, and somehow the data is not credible it poses an enormous risk over time to the TIPS market,” said Amar Reganti of Hartford Funds. Michael Feroli at JPMorgan added that even small changes in how inflation is calculated could make a big difference, noting that using European methods would lower U.S. inflation figures by about 0.2% a year.
Figure 1: Solid Returns for Treasury Inflation Bonds in 2025 (Source: Bloomberg)
This concern comes at a time when inflation has been running above the Federal Reserve’s 2% target, and TIPS have been performing strongly. The Bloomberg index of inflation-protected Treasuries is up 5.7% in 2025, its best run since 2021. At the same time, slowing job growth has fueled bets on an interest rate cut, even as Trump’s tariffs add pressure to prices. With TIPS making up about 7% of U.S. government debt and Treasury planning to sell more of them, investors say maintaining trust in government data is critical.
When traditional macroeconomic indicators are unreliable — whether due to delays in reporting, insufficient statistical capacity, political interference, or simply eroding trust among investors — market participants are increasingly forced to look elsewhere for signals about the health of the economy. This is where alternative data can provide valuable insights, filling in the gaps left by conventional datasets.
Alternative Data for CPI
Alternative data sources can be used to construct proxies for the CPI basket. By aggregating rental listings, retail scanner data, or online price trackers, asset managers can approximate the components of “core CPI” in near real-time. While this requires complex modeling and access to large, diverse datasets, some funds may instead turn to specialized macroeconomic data vendors who combine public and non-public sources to model inflation and other GDP-related metrics.
One such provider is Turnleaf Analytics, which gained attention for its CPI models. In recent years, one of the most widely followed economic releases has been US CPI. It has had a significant impact across a plethora of macro assets, as the market seeks to reprice assets in anticipation of the Fed’s future monetary policy. For there to be a relationship between inflation expectations and monetary policy expectations, a central bank needs to be perceived as credible when it comes to maintaining their inflation target.
If the market does not perceive a central bank as credible, then there is likely to be a breakdown between inflation expectations and monetary policy expectations. If this happens, a central bank may not hike rates, even with very high levels of inflation. Notably, as inflation expectations rise, we would expect yields to rise reflecting expectations for more hawkish policy. Conversely, falling inflation expectations would likely see falling yields to reprice more dovish expectations.
Turnleaf Analytics uses several types of data to nowcast US CPI: 1) Macroeconomic data on unemployment and growth; 2) Market data on FX rates and commodities; 3) Benchmark/consensus inflation forecasts; and 4) Time series on pollution which can be used as a proxy for industrial activity. The data is collected, pre-processed, and then fed into a machine learning model. Turnleaf’s analysis, using high-frequency trade and firm behavior data, suggests that while lower tariffs may slightly ease inflation, persistent uncertainty—especially beyond 90 days—continues to elevate long-term inflation expectations.
Figure 2: US CPI Nowcast (Source: Turnleaf Analytics)
Alternative Data for Non-Farm Payrolls
Alternative data has become an essential tool for anticipating the U.S. non-farm payrolls (NFP) report, one of the most market-sensitive economic releases. Since the Bureau of Labor Statistics publishes the data only once a month, investors and hedge funds seek earlier signals through non-traditional datasets. Private payroll processors such as ADP, workforce management systems like Kronos/UKG, and recruiting platforms including Indeed and LinkedIn provide near real-time views of hiring and labor demand that often foreshadow the official release.
LinkUp provides a job listing dataset that is sourced directly from employer websites, ensuring a high level of accuracy and authenticity. LinkUp’s job listing data is highly correlated to various job-centric macroeconomic indicators and is very predictive of overall trends in the labor market.
Figure 3 shows LinkUp 10,000, an analytic tracking job openings from the 10,000 companies with the most U.S. listings on LinkUp’s job search engine. Designed as a macroeconomic indicator, it reflects real-time changes in labor demand and serves as a predictive signal for future job growth. The daily version uses a 7-day lag to ensure data accuracy, while the monthly version aggregates unique job listings from the prior month without a lag.
Figure 3: Daily LinkUp 10,000 (Source: LinkUp)
The July jobs report highlighted a deepening problem with BLS data revisions, raising doubts about the reliability of initial releases. May and June payrolls were revised down by a combined 258,000 jobs, slashing average monthly job growth to just 85,000 this year and 35,000 since April. The gap between first estimates and final revisions has widened significantly, with the 2025 delta jumping from 21% to 47%, effectively making initial BLS reports an unreliable measure of labor market conditions.
This pattern of downward revisions has become more pronounced, leaving investors and economists increasingly reliant on alternative data sources for real-time insights into job trends. LinkUp’s data showed declines in labor demand not captured by official releases, further underscoring the growing disconnect between BLS initial prints and labor market reality.
Figure 4: Cumulative LinkUp Monthly Forecasts, ADP & Consensus Estimates v. Initial BLS Non-Farm Payrolls & Revised BLS NFP Since Jan 2022 (Source: LinkUp)
Conclusion
The erosion of trust in official government statistics, particularly from the Bureau of Labor Statistics, highlights the risks posed when data collection faces political interference, funding cuts, and falling response rates. Larger and more frequent revisions to core reports like CPI and NFP weaken the credibility of the numbers that policymakers, investors, and households depend on. With trillions of dollars in markets tied to these indicators, the stakes are high: even small doubts about accuracy or neutrality can ripple across Treasury markets, monetary policy expectations, and investor confidence.
In this environment, alternative data has emerged as a critical complement to traditional sources. From web-scraped prices used to proxy inflation to job listing datasets that anticipate payroll reports, these tools offer timelier and often more transparent signals about the economy’s direction. While not a substitute for robust public statistics, alternative data provides investors and policymakers with valuable safeguards against information gaps and credibility risks. Going forward, the balance between official statistics and innovative private datasets may shape how markets interpret—and trust—macroeconomic signals in an era of heightened uncertainty
For more information on topics relating to alternative data sourcing and compliance and to learn more about how Eagle Alpha is assisting financial institutions with their alternative data strategy, please contact us.