Disclaimer: This article is for informational purposes only and does not constitute financial advice. Consult a qualified financial advisor before making investment decisions. Sources include MIT Sloan School of Management, Bureau of Labor Statistics (BLS), Census Bureau, USDA, and the National Bureau of Economic Research (NBER).
I have a friend named Derek who trades options the way some people play poker — with a mix of spreadsheet confidence and gut-level superstition. He keeps a $47 bottle of bourbon on his desk that he pours from exactly once per month, right after the Bureau of Labor Statistics releases the jobs report. "It is the most important number in the world for about six hours," he told me once while running his finger down a 2,300-row Excel model.
Last week, I had to tell him something he did not want to hear: that number he worships might not mean what he thinks it means anymore.
Why US Economic Data Is Becoming Unreliable According to MIT
In a new working paper titled "Measuring by Executive Order," MIT Sloan professor Roberto Rigobon and Harvard Business School professor Alberto Cavallo identified three intersecting forces that are undermining the trustworthiness of government economic data. Both researchers are associates of the National Bureau of Economic Research (NBER), and their analysis is not speculative — it is grounded in structural breakdowns that have been accumulating for years.
The first problem is plummeting survey response rates. Statistical agencies like the BLS and Census Bureau depend on routine surveys of households and companies to construct their core measures — employment, inflation, GDP components. But people have stopped participating. Response rates have fallen dramatically in recent decades, and the trend is accelerating. "People have stopped answering the phone," Rigobon told MIT Sloan in an interview. Low response rates introduce bias, delay revisions, and weaken the representativeness of the data that policymakers and investors rely on.
The Funding Crisis Behind the Numbers
The second structural problem is funding. The Bureau of Labor Statistics, Census Bureau, and other statistical agencies face shrinking budgets that limit their ability to adopt new data-collection technologies and maintain the scope of their surveys. According to the Center on Budget and Policy Priorities, budget cuts have forced agencies to reduce sample sizes, delay modernization, and in some cases eliminate entire data programs.
Sandra — a portfolio manager I know who spent three years at Goldman Sachs before moving to a $240 million boutique fund in Boston — put it bluntly during a 38-minute call we had last Tuesday. "We are making billion-dollar allocation decisions based on data that is collected by agencies running on fumes. The BLS has not had a meaningful budget increase in over a decade, but the economy they are trying to measure is orders of magnitude more complex than it was in 2010."
In September 2025, the U.S. Department of Agriculture halted its annual survey on food insecurity, calling it "costly." That survey was the primary tool researchers and policymakers used to track household hunger across the country. It is now gone.
Political Interference Is Eroding Institutional Credibility
The third force — and arguably the most dangerous for investors — is political interference. Rigobon and Cavallo document how the dismantling of advisory committees, dismissal of statistical leaders, and politicization of nominations undermine transparency and institutional credibility. Government shutdowns, which halt data collection entirely, compound the problem. Losing one month of data out of twelve is not a rounding error — it is a structural gap.
Tom, who manages risk for a regional bank with $1.8 billion in assets, told me something that stuck with me. "When I was at JPMorgan in 2014, we treated BLS data as ground truth. Now my team spends about $120,000 a year on alternative data sources because we cannot fully trust the official numbers. And we are a small bank. Imagine what the big guys are spending."
The authors also note that routine data revisions — a normal part of the statistical process — have come under political attack, with some characterizing them as evidence of bias rather than accuracy improvements. This is dangerous because it encourages public disengagement from the official measurement process, which further reduces response rates in a self-reinforcing cycle.
What This Means for Your Investment Decisions
If you are an individual investor who relies on government economic reports to inform your portfolio strategy, the implications are concrete:
Jobs reports may overstate or understate employment. The Bureau of Labor Statistics has already revised several recent employment reports by margins large enough to change the direction of market consensus. In 2024, preliminary job creation numbers were revised downward by over 800,000 positions — the largest revision in years. If that kind of error margin becomes routine, investors who trade on initial releases are essentially trading on noise.
Inflation measures may not capture what you are actually paying. The Consumer Price Index relies on consumer surveys that are experiencing the same response-rate decline as other government data programs. Rachel — a financial planner in Chicago who manages about $45 million across 120 client accounts — told me she now cross-references CPI data with private-sector inflation trackers like the Truflation index before making any fixed-income allocation changes. "I cannot justify putting my clients into TIPS based solely on a CPI number I am not sure I trust," she said over a $6.50 latte at a Loop coffee shop.
GDP estimates are increasingly preliminary. The Commerce Department's advance GDP estimate, released about 30 days after each quarter, is based on incomplete data and subject to two subsequent revisions. As underlying data quality degrades, those revisions are getting larger and less predictable.
What Rigobon and Cavallo Recommend
The MIT and Harvard researchers do not just diagnose the problem — they propose a framework for supplementing government data with private-sector alternatives. But they also warn that private data comes with its own risks: proprietary datasets can be biased, lack transparency, and change methodologies without notice.
Their recommendation is a hybrid approach: restore adequate funding to federal statistical agencies while creating transparent frameworks for incorporating private data into official measures. The Federal Reserve has already begun experimenting with private-sector data sources in its policy models, and some SEC-registered investment advisors are building portfolios that weigh official and alternative data sources together.
Greg — our economics nerd friend who reads NBER working papers the way normal people read sports scores — summarized it this way over dinner last week. "We are watching the instruments in real time go fuzzy. The altimeter still shows a number, but the question is whether that number reflects the actual altitude. If you are flying a $2 million portfolio based on those instruments, you better have a backup."
Three Steps to Protect Your Portfolio
First, diversify your data sources. Do not rely solely on BLS, Census, or Commerce Department releases. Cross-reference with private-sector indicators like ADP employment data, the Atlanta Fed's GDPNow model, and real-time consumer spending data from card networks. Most of these are freely available.
Second, be cautious about trading on initial data releases. The growing revision risk means that the first number released is increasingly likely to be wrong — sometimes significantly. Build in a buffer period before making major allocation changes based on a single report.
Third, talk to your financial advisor about data quality risk. According to the Financial Industry Regulatory Authority (FINRA), investment professionals have a duty to use reasonable diligence in research. If the underlying government data is degrading, that duty arguably extends to seeking out more reliable supplementary sources. Ask your advisor what alternative data they use — and if the answer is "none," that is a conversation worth having.
Derek poured his bourbon on Friday as usual. But when I asked him if he still trusted the jobs number, he paused for about six seconds. "I trust it enough," he finally said. I do not think that is good enough anymore.
Make smarter business decisions with data. Wardigi provides digital transformation solutions to help your business thrive.
