As the government shutdown drags on, official economic data has slowed to a crawl, leaving policymakers, markets, and citizens increasingly reliant on private-sector numbers. That’s a problem.
If the 2008 financial crisis proved anything, it’s that economic stability is not a given but a fragile construct – one that can collapse at warp speed when trust, transparency, and accountability erode.
Reality has a way of reminding us if we forget.
Which brings us to a glaring red flag waving: as the government shutdown drags on (and on), the flow of official economic data has slowed to a trickle. The Fed is missing timely labor, inflation, and spending numbers. The Treasury Department is flying blind on cash flows and debt issuance. Even the smaller agencies, like the Bureau of Economic Analysis (BEA), are skipping their usual readouts on spending, income, and investment. This is basic stuff that keeps markets steady and policymakers tethered to reality.
We are becoming untethered, and it’s not just a bureaucratic problem. For everyday people, it can ripple into mortgage rates, retirement accounts, and everything in between.
In the meantime, everyone from Wall Street to Main Street is being nudged toward private-sector data for guidance. And here’s the rub: private players — often with little oversight — have every incentive to spin the numbers in their favor.
Experts are warning that this mix of missing and delayed public data, plus murky private information, is creating blind spots that could impact the whole economy. Let’s dive in.
Public vs. Private: Who’s Telling the Truth?
So how reliable are private-sector economic indicators compared to official government statistics? How do they really compare?
Ben Schiffrin, a former SEC staffer and securities policy expert at Better Markets (a financial markets watchdog), says that government-collected stats are designed to provide a baseline of trust and transparency that private-sector data don’t necessarily deliver.
“The official data is supposed to come from a neutral perspective,” Schiffrin explains. “For example, most people have long believed that data from the Bureau of Labor Statistics (BLS) is reliable. If it says something good or bad, you can trust it. That’s generally what government data is meant to be.”
On the other hand, private companies have a way of manipulating information to help their bottom line. Take 401(k)s, for example. A recent Wall Street Journal report pointed out that only about ten percent of participants were interested in private-market investment options – private equity, private real estate, and the like. But you wouldn’t know that, Schiffrin notes, from the way firms like Apollo and Blackstone have been marketing these options relentlessly, claiming data that purports to show that everyone wants in. The result is a striking gap between what investors actually want (or need) and what the industry is trying to sell.
In finance, hype often matters more than reality.
Or consider crypto. Industry surveys and advocates love to tout adoption numbers, spinning headlines as if “mass adoption” is already here. But Schiffrin points out that while crypto companies trumpet broad use, multiple surveys, including from the Fed, show that only a tiny (and even declining) fraction of Americans actually use cryptocurrency for payments or trading “The discrepancy is huge,” he says. “It can’t be both fifty percent and two percent.”
Phillip Basil, a former Federal Reserve staffer who studies banking policy, economic growth, financial stability at Better Markets, emphasizes that knowing a dataset’s limitations is just as important as the data itself. “I’d say about 90% of the data out there has some kind of limitation,” he notes. “It’s just the reality: the volume of data being collected today is so large that some compromises or shortcuts are inevitable.”
That’s why it’s important to see what those compromises might be. “Government data tends to be strong because it’s public and transparent,” he says. “You can see how it’s collected and what assumptions go into it, but private organizations often shield their sources and methods to protect proprietary processes.”
He points out that when you’re building models or making predictions, understanding those limitations is essential.
In the worst-case scenario, the limitations of private data can push the entire economy off a cliff. Exhibit A: Credit rating agencies. These profit-driven firms, paid to rate bonds, loans, and other financial products, base their assessments on a mix of public filings, issuer-provided numbers, and proprietary models, which draw on many sources of government data. They’re private, yes — but wildly influential in public markets.
And things got pretty wild in the run up to the 2008 financial crisis, when these agencies, caught in a massive conflict of interest, handed out top marks to mortgage-backed securities they had every incentive to rate favorably. Investors trusted them blindly, and the fallout was catastrophic.
Amanda Fischer, a former SEC chief of staff and now policy director at Better Markets, has seen firsthand how private credit ratings can wander far from independent reality. She recalls an episode from the Biden administration, a reminder that some lessons from 2008 never quite stick.
She observes that the National Association of Insurance Commissioners (NAIC) —the body that represents state insurance regulators — still allows insurers to lean on private credit ratings to decide how much capital they must hold. It’s a convenient system, but not always a prudent one. In one instance, Fischer recalls, the NAIC conducted a study comparing its own assessment of private credit investments with the ratings issued by a few smaller agencies — Egan-Jones and Kroll among them.
“The results showed a huge gap between the NAIC’s independent analysis and the grades these agencies were handing out,” she says. “One of the firms was so upset it publicly protested, and the NAIC quietly pulled the study from its website. The only trace left is a recent article in the Financial Times.”
Fischer adds that when she served on Biden’s Financial Stability Oversight Council (FSOC), there was a lot of discussion about how to get state insurance regulators to rely less on these private credit ratings, especially since they came from smaller shops that were often ‘friendly’ to the issuers.
But the problem persists, and inflated or overly generous private credit ratings can mask real risk, leaving insurers undercapitalized and the financial system more vulnerable in a downturn. UBS Chairman Colm Kelleher recently warned that U.S. insurers are engaging in “ratings arbitrage” similar to the subprime practices that led to the 2008 crisis. The Bank for International Settlements warns that credit ratings on private loans held by US insurers may have been systematically inflated.
Loopholes and Legal Gray Areas
The surge of private data and alternative markets exposes a glaring weakness: the rules haven’t caught up. These gaps in oversight leave both markets and ordinary Americans vulnerable.
Schiffrin emphasizes that the U.S. system for regulating securities rests entirely on accurate information. Bad data = inadequate rules of the road.
He notes that companies aren’t supposed to tell investors what to buy or sell, but they must provide truthful, complete details about themselves and their finances. Some of this information is non-financial, like a company’s strategy, leadership changes, or risk disclosures, which isn’t really raw data but still matters to investors. Other information is strictly financial, like revenues, profits, balance sheets, and other hard numbers that analysts crunch.
“Without accurate data and information, investors can’t make informed decisions, and the market breaks down,” Schiffrin warns. He stresses that we trust the SEC to enforce the laws that say companies must report earnings accurately. “If they don’t, the information isn’t useful to anyone, and the SEC will go after them. That’s the foundation of securities law.”
But when it comes to newer alternative markets, like prediction markets and certain crypto-based platforms, the SEC faces major challenges, since these markets often operate outside traditional disclosure rules, making oversight and enforcement far more difficult.
Think of prediction markets as online betting platforms, but instead of just wagering on sports, people can place bets on future events, like who will win the next presidential election, what the unemployment rate will be, or how the economy will perform next quarter.
“Debates are happening because they’re exploding,” say Fischer. “They’re opening a whole new avenue for data exploitation, and operating in a legal gray zone when it comes to securities laws.”
“What’s striking,” she continues, “is that the sports betting rules under state law are often more rigorous than the rules for these prediction markets under the Commodity Futures Trading Commission (CFTC). Right now, there’s really no one ensuring that these markets are transparent or that the information they rely on is being disclosed accurately.”
Fischer explains that many of these platforms structure their bets as “swaps” to sidestep state gambling restrictions, claiming they fall under CFTC oversight. That’s not very comforting, according to Fischer: “The problem is, the CFTC isn’t actively policing these markets, and the platforms themselves aren’t monitoring for insider trading or manipulation—at least not as far as we know.”
She points to a recent case involved a large bet placed on the Nobel Peace Prize winner just before the announcement: “The timing suggested someone may have had early information.” This is the kind of scenario where people can potentially profit from nonpublic information, blurring the line between legitimate speculation and insider trading.
Schiffrin adds, “We have a lot of concerns with certain types of event contracts. Betting on elections is its own recipe for disaster. And when it comes to sports contracts, well, FanDuel and DraftKings aren’t exactly paragons of virtue, but at least they’re regulated. They have limits, warnings, systems to deal with addiction.” He points out that the biggest players in the prediction market space, like Kalshi and Polymarket, are pushing to operate free from even the most modest oversight:
“A recent article in Rolling Stone noted that at least FanDuel tracks key metrics, like what percentage of users struggle with gambling addiction, and actually tries to address it. I think their statistics roughly line up with those from the National Council on Problem Gambling.”
By comparison, platforms like Polymarket and Kalshi face almost no accountability. “They can say whatever they want —they can pretend gambling addiction isn’t a problem in this country,” he adds, underscoring just how unregulated these markets remain.
And of course, as in the Nobel Prize case, accurate data can be weaponized by private players, depending on who sees them first. Imagine a firm tracking real-time consumer spending. If an investor glimpses those trends before the public, they can trade ahead of the market, pocketing gains off information nobody else has. Frontrunning, as the practice it known, is something Schiffrin sees it as a real threat.
“It applies to government data too,” he notes. “Sometimes questions arise: did someone know a policy announcement was coming, and trade on it? Government data is supposed to have stricter controls to prevent that kind of misuse.”
Private-sector data, he adds, is no different from any other corporate information. “Public companies have insider trading policies because they know early access to market-moving information is a problem. The government isn’t a public company, but if it becomes a source of market-moving data, the same risks apply. Just like earnings reports can move markets, early access to key government data could, too.”
Fischer stresses that the stakes are even higher in emerging arenas like prediction markets. “Having worked at the SEC, I would strongly caution against insider trading because normally, people get caught. But in prediction markets, enforcement is minimal. The usual rules about fairness and transparency don’t seem to apply.”
When Data Problems Hit Your Wallet
Inaccurate or missing economic data shapes the decisions of policymakers, and that’s especially important when the Federal Reserve sets interest rates — those rates influence everything from mortgages and credit cards to your savings. When the Fed is working with incomplete or misleading information, it can ripple through the entire economy, affecting everyone’s money.
Basil lays it out plainly: “It’s a big deal if monetary policy is not getting things right — to everybody, especially everyday workers.”
He emphasizes the extent to which rate setting really affects people’s day to day lives. “If you have a credit card or you’re taking out any kind of loan or you have some money packed away into a pension or whatever, everything moves with the rate cycle. Your retirement moves with the rate cycle. The amount of debt you owe moves with the rate cycle. How much credit a bank is willing to extend you moves with the rate cycle. So all of that, it is critically important to, to small businesses, consumers.”
Basil stresses how difficult the situation becomes for Fed officials: “If you don’t get the actual data for one of your Federal Open Market Committee (FOMC) meetings, there’s some workarounds. You project certain numbers and then put those numbers into your model. But the problem is when you’re projecting key inputs, well, those projections have uncertainty, and then you put those key inputs into your model, well, your model also has uncertainty.” He warns that the errors tend to snowball as policymakers move forward.
He point to concerning comments made by Fed Chair Jerome Powell during a recent press conference following a two-day policy meeting. As Powell explained, data lapses are making it difficult for the Fed to properly steer the economy:
“We’re going to collect every scrap of data we can find, evaluate it, and think carefully about it. And that’s our job.” He highlighted the use of private data, in-house surveys of business executives, and informal interviews with contacts across the country to make up the difference. At the same time, he acknowledged that the uncertain data environment was complicating the Fed’s work: “If you asked me could it affect … the December meeting, I’m not saying it’s going to, but yeah, you could imagine that. You know, what do you do if you’re driving in the fog? You slow down.”
“Powell is correct, but it’s still a problem,” Basil explains. “Imagine the Fed slows down to be cautious. That might seem prudent, but what if caution isn’t the right move? Real data could show they need to raise or lower rates — take actions that would actually benefit consumers — but instead they hold back. That’s the real danger: the Fed freezes when it should be acting. Meanwhile, markets and the economy keep moving on signals the Fed can’t fully see. Employment could start collapsing, or other problems could emerge. That’s the real concern.”
Basil is especially worried about gaps in data outside the banking sector. “The Fed collects plenty of information on banks, but most of it comes weekly or quarterly,” he explains. “Even then, it’s not frequent enough. But in some key non-bank markets, there’s almost no reliable data at all.”
Two of these markets have a direct impact on everyday life. The first is the repo market, where banks lend money to each other overnight. If this market falters, borrowing costs can quickly rise for everyone, from mortgages to credit cards. The second is the Treasury market, where the U.S. government borrows money by selling bonds. Treasury rates set the cost of mortgages, car loans, credit cards, basically, almost all borrowing.
Data on these markets is surprisingly thin, observes Basil. “A few years ago, the Office of Financial Research discovered that the repo market was almost twice as large as everyone thought, thanks to untracked over-the-counter activity. And those short-term rates matter because they dictate how financial institutions lend to each other. When the market falters, banks falter, and the Fed has to step in.”
Basil points to September 2019 and March 2020 as cautionary tales. In both cases, the Fed had limited visibility into these markets and responded by throwing trillions of dollars at the problem until things calmed down. “They overcorrect because they don’t know the scale of the issue,” he says.
The consequences hit everyone. Turmoil in Treasury markets can spike borrowing rates, shake businesses, and pinch consumers. “When things went haywire last April, the Fed blamed hedge funds, the Treasury blamed foreign investors — and no one really knew because there simply wasn’t enough data.”
“These gaps are invisible most of the time,” Basil says, “but when the markets blow up, they become huge problems.”
Here’s the bottom line: The shutdown isn’t just slowing the flow of information — it’s putting the economy in the hands of data we can’t fully trust. When policymakers have to rely on incomplete, delayed, or privately generated numbers, every decision gets riskier.
This isn’t just theory, because gaps in official data, murky private numbers, and unregulated alternative markets create blind spots that could trigger real problems. The fix is straightforward: get timely public data flowing again, strengthen oversight of private information that drives markets, and close the legal gray areas around emerging financial platforms. Until that happens, the whole economy is basically “driving into the fog” — and we’re all along for the ride.
