Joel Prakken

The Bureau of Labor Statistics was unable to sample prices during the government shutdown last October. Consequently, it assumed no change in shelter costs for that month. Given the particulars of how the BLS measures rents, the resulting understatement of the level of shelter costs was not corrected until April, when shelter costs jumped 0.6%, roughly double the true monthly increase.

As we approach the next report on consumer prices, bear this in mind. Going forward, monthly changes in shelter costs will be correct. However, because we often examine inflation over longer intervals, the legacy effect of the government shutdown on shelter costs is not yet fully in the rearview mirror. From October through March the level of shelter costs was low by one month’s increase. Therefore, after March, any change in shelter costs calculated over an interval starting from the months of October through March will overstate the increase over that interval by one month’s increase in rents. The overstatement is magnified if the change is expressed at an annual rate.

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The chart shows a stylized example based on recent rates of change in rents. Suppose the level of shelter costs (black dashed line) increases at a steady rate of 0.287% per month (3.6% annually) but, for the months of October 2025 through March 2026, the level is low by that same 0.287%. The green line shows the corresponding 3-month percent change annualized. The orange line shows the 12-month percent change.  At the 12-month window, we won’t be clear of this anomaly until April of 2027.

Lingering Hiccups in Shelter Cost Inflation

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Andrew Cates

The comparisons are hard to avoid. Soaring valuations, massive capital expenditure on data centres and AI infrastructure, and near-universal conviction that a transformative technology is about to reshape the economy. To many observers, today looks uncomfortably like the late 1990s.

The parallel is understandable. It is also, on the most important dimension, wrong — and Haver data help explain why.

**The critical variable: who holds the debt**

Investment booms become dangerous when they are financed by leverage. The late 1990s are a textbook case. As internet enthusiasm intensified, US corporations borrowed heavily to fund infrastructure buildout. By 2000, the non-financial corporate sector was running a financial deficit approaching 4% of GDP — spending substantially more than it earned. When expectations proved too optimistic, investment collapsed, and corporate deleveraging deepened the downturn.


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A similar mechanism operated before the Global Financial Crisis, this time concentrated in the household sector. Cheap credit, rising house prices, and aggressive lending standards left consumers dangerously overextended. When prices turned, the deleveraging that followed produced the deepest global recession since the Depression.

Leverage was not merely a symptom of those episodes. It was the mechanism that made the busts so destructive — and so protracted.

Today's picture, as measured in Haver's Flow of Funds and national accounts databases, looks fundamentally different. Despite the AI-related surge in cloud infrastructure, semiconductor, and data centre spending, the US non-financial corporate sector is currently running a **financial surplus of roughly 1.8% of GDP** (Q1 2026). Corporate America is investing aggressively — and still generating more cash than it spends. The boom is being financed through profits, not debt accumulation. Financial crises are typically balance-sheet events. That particular vulnerability is far less pronounced than it was in either 2000 or 2007.

**The borrowing hasn't disappeared — it has migrated**

If corporations are running surpluses while the US runs a sizeable current account deficit, someone must be absorbing the other side of that position. The answer, visible in federal budget data available through Haver, is the federal government. Washington has effectively become the borrower of last resort, allowing the corporate sector to invest heavily while maintaining healthy balance sheets.

This matters for the financial stability question. Governments do not face margin calls and are rarely forced into the kind of rapid deleveraging that devastates private sectors. Excessive public debt creates real pressures — higher long-term rates, rising debt-servicing costs, sustainability concerns — but these tend to unfold over years rather than in the abrupt fashion that follows private-sector deleveraging. Pockets of vulnerability remain in commercial real estate, parts of private equity, and speculative-grade borrowers — all trackable through Haver's credit and property databases — but these remain sectoral risks rather than systemic ones.

**The risks have shifted, not disappeared**

Relative financial strength should not be confused with an absence of risk. The more important challenges ahead may have little to do with leverage and much more to do with the supply side of the economy.

Energy constraints, geopolitical fragmentation, demographic pressures, and infrastructure bottlenecks are shaping the long-term growth outlook in ways that financial markets have largely chosen to ignore. This is the underappreciated dimension of the AI investment story. The productivity gains promised by AI depend not only on software and algorithms, but on electricity generation and transmission, critical minerals, and physical infrastructure — variables that Haver tracks across dozens of databases covering energy production, commodity markets, and capital expenditure by sector.

A correction in AI-related valuations remains entirely possible — current pricing may well embed unduly optimistic assumptions about the pace and breadth of productivity gains. But even a significant correction would likely play out differently from previous episodes. Without widespread private-sector leverage, falling asset prices would not trigger the deleveraging spiral that made 2001 and 2008 so economically damaging.

The last two major crises were balance-sheet events. The next one, if it comes, may have less to do with debt and more to do with whether the real economy — energy grids, supply chains, labour markets — can actually deliver what financial markets are currently pricing in.

_Haver's Flow of Funds, national accounts and energy, databases provide the granular time-series coverage needed to monitor each of these dynamics. Contact us to learn more._

This Isn't 1999 — But That Doesn't Mean There's Nothing to Watch

Joseph G. Carson

Kevin Warsh returns to the Federal Reserve as the new Fed Chair. His return occurs at a crucial time for the Federal Reserve, as its independence has been threatened multiple times over the past year. However, Mr. Warsh's greatest challenge may be preserving the Fed's credibility.

Mr. Warsh joins the Fed with controversial views on inflation measurement. Mr. Warsh advocates that policymakers move away from conventional inflation metrics and instead focus on "trimmed averages." This approach excludes the "tails," or the items with the highest and lowest price changes in a given month.

"Trimmed" inflation-like core measures are attempts to remove price outliers from the rest. However, inflation cycles are not linear; they include outliers and the composition changes over time.

But any effort by the Fed to alter the targeted measure would damage its credibility, as investors and analysts recall the Fed's actions in 2020.

In 2020, in response to criticism for consistently falling short of its 2% inflation target, the Fed introduced an "inflation-averaging targeting" framework. This approach would allow inflation to surpass the 2% mark to compensate for times when it was below target.

During the five years before inflation-averaging was implemented, the personal consumption core deflator was below target in four of five years, averaging 40% below target. Meanwhile, the consumer price index exceeded the target in three out of those five years, averaging just under 2%.

However, over the past five years, inflation trends have notably reversed, with both the personal consumption deflator and the consumer price index averaging several hundred basis points above the 2% target. This trend persists in 2026.

If the Fed opts to alter its inflation target while inflation is significantly above the target, especially after modifying the framework to promote more inflation and persistently low official interest rates when inflation was below target, it would set a bad precedent. Critics would argue that the new Fed Chair is adjusting the inflation target to squash calls for a rate hike, while opening up the possibility of an official rate cut later, which is what President Trump is seeking from his nominee.

Credibility is built by consistently following its mandate without retreating or changing targets, which can falsely suggest that you are taking the necessary actions to control inflation.

If Fed Warsh urged the FOMC to implement a new inflation target, he would risk significantly damaging both his and the Fed's credibility. Additionally, the bond market would strongly express its disapproval by substantially increasing long-term interest rates. What choice will Fed Warsh make: satisfy the individual who nominated him or prioritize the most important people, the investors?

Warsh's Biggest Challenge at the Fed: Maintaining the Fed's Credibility

Iran’s closure of the Straits of Hormuz on March 2 has sent fuel costs spiraling upwards. The Fed’s preferred measure of inflation is of the price index for core personal consumption expenditures (PCE). These exclude consumers’ direct (or “final”) purchases of gasoline & other motor fuel. However, increases in the cost of fuel used to produce and transport core consumer goods & services may pass through to core prices.  In a recent paper I present compelling empirical evidence that the pass-through of intermediate fuel costs to final core consumer prices is highly significant and could contribute as much as 0.8 extra percentage points to second-quarter annualized core inflation.

I began by constructing a price index for the intermediate consumption of the three major fuels: diesel fuel, gasoline, and jet fuel.  In doing so I assumed the domestic consumption of diesel fuel and jet fuel is all intermediate while treating as intermediate the domestic consumption of gasoline not included in PCE. The average (since 1979) shares of the three fuels in intermediate use are: diesel fuel (62%), gasoline (25%), and jet fuel (14%), but recently those shares are 66%, 13% and 21%, respectively.

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The monthly price index (2017=100, not seasonally adjusted) is shown through May in the chart. I seasonally adjusted the monthly index, quarterly averaged the seasonally adjusted series, and then included the change in the fuel price in my Phillips Curve model for core PCE inflation. The resulting highly significant estimate implies that each one percentage point of fuel cost inflation raises core PCE inflation by .00525 percentage point.

Implications? In the first quarter of 2026 the price index for intermediate fuels grew at a 44.7% annual rate, contributing 0.00525x44.7 = 0.23 percentage point to core PCE inflation. However, almost all the first-quarter increases in fuel prices occurred in March, creating unfavorable “statistical momentum” for prices heading into the second quarter.  As seems reasonable, assume that recent daily prices persist through the end of June. Then, the second-quarter rate of increase in the price index of intermediate fuels would be around 157%, contributing 0.00525x157 = 0.82 percentage point to core PCE inflation!   Lags in the model would temporarily propagate these shocks.

**A complete version** of this commentary is available [here](https://static.haver.com/Pass_Through_of_Fuel_Costs_061026_45a76561ed.pdf).

The Pass-Through of Fuel Costs to Core PCE Inflation

Mickey D. Levy

The U.S. financial headlines focus on high stock market valuations, questioning them as too high, the concentrations of capitalizations, questioning them as excessive, and big cap AI companies that are getting bigger and going public at higher capitalizations. Here’s a few economic observations on these trends and an international comparison.

**The economy**. The U.S. economy has been quite resilient to recent shocks—erratic tariffs policies and the surge in oil prices--and continues to expand. Economic growth has been driven by consumer spending and solid gains in business investment while residential investment has weakened and subtracted from growth. Currently, while the ongoing Middle East conflict and high oil prices and tariffs are weighing on consumer pocketbooks, and real disposable income has fallen in each of the last three months ((Feb-Mar-April), households have reduced their rates of personal saving to smooth real consumption. Nominal GDP, the broadest measure of current dollar spending and aggregate demand, has risen 6% in the last year and at a 5% annualized pace in the last two quarters. Sustained high energy prices are likely to adversely impact real consumption.

Real interest rates remain moderate, the economy and the probability of recession is low. Of note, history, at least going back to the 1950s, shows that the S&P500 continues to rise and does not peak until just before recession.

**Profits**. Corporate profits are rising briskly, much faster than GDP (Chart 1). That’s also typical during economic expansions. Operating profits have risen 18.7% in the last two years and are up 63.3% since 2019, just before Covid.  During those same two periods, nominal GDP rose 10.8% and 47.7%, respectively. 


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There are several reasons why profits outpace GDP during expansions and fall more during recessions.  Margins widen and volumes expand (shrink) as aggregate demand fluctuates over the business cycle; capital spending is cyclical and affects business efficiencies; and GDP measures domestic production while profits are buoyed by U.S. firms’ overseas activities not counted in GDP (for example, most of the value of Apple computers and iPhones produced overseas is not measured in GDP, although global transfer pricing makes accurate calculations very difficult, and the value of those products of U.S. firms produced overseas and purchased by U.S. consumers nets out to zero in GDP, adjusted for accounting, exclusive of leasing agreements and revenues/profits from apps). Of note, around 50% of revenues of Microsoft, Alphabet and Nvidia are generated overseas while Meta’s and Apple’s are higher, 63%, and Amazon’s are lower, 31%. Also, in recent years, the revenues of AI firms are rising dramatically, and their profit margins are very high.

Stock market valuations reflect expected profits. The continued economic expansion and upward trajectory of profits support expectations of rising future profits. The “Buffet Indicator”, which assesses whether stock market valuations are cheap or rich based on the ratio of stock market capitalization to GDP overstates concerns about excess valuations since profits have risen much more than GDP.  Market valuations relative to profits are higher than normal but much more reasonable than suggested by the Buffett Indicator (Charts 2a and 2b).

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The critical question is whether expectations of future profits are achievable. The speed and magnitude of the emergence of big cap AI—the spreading use, paid subscribers and revenues, and capital spending plans and outlays—is truly profound. Whether the leading firms continue to meet market expectations, and their ability to manage expectations, is the overwhelming question of this era.

**Concentration of capitalization**. The Magnificent 7 (Apple, Microsoft, Alphabet, Amazon, Nvidia, Meta and Tesla) account for approximately 35% of the entire S&P500 capitalization. The concentration of profits is also pronounced but not as high.

Investors tend to concentrate their portfolios in the growth companies of the day. That’s always been the case: the desire to own the leading-edge growth companies results in the concentration of stock market capitalization. The trend in technological innovations is not linear, rather leading innovations are sporadic and come in bunches (Valerie Ramey, “Secular stagnation or technological lull?” Journal of Policy Modeling, February 2020). This affects trends in economic performance, capital spending, profits and expectations. The spurt in innovations now implemented into commerce and society that is now underway is boosting productivity, economic growth, business investment and profits, and generating increases in wealth.

In 2018, the FAANG stocks (Facebook/Meta, Amazon, Apple, Netflix, Google/Alphabet) collectively accounted for 15% of the S&P500.  In the first half of 2018, they generated 98% of the S&P500’s total returns.  In the mid-1960s, the “Nifty-Fifty” represented roughly 30%-40% of the S&P500 capitalization, and the top 10 stocks dominated total market value and traded at an average P/E of 42.  During the dot.com bubble of the late-1990s, capitalization concentrations remained reasonable low, as the surge in stock values was widespread across many small firms.

What happened following these episodes? The valuations of the Nifty-fifty and the overall stock market faltered and lost value in the second half of the 1960s as inflation and interest rates rose sharply (inflation rose from 1.5% in 1965 to 5.8% in 1970 and the Fed hiked interest rates from 4.1% to 8.9% in late 1969), and then unraveled in the early 1970s as misguided economic policies (wage and price controls, et al) and the oil price shock of 1973 (along with monetary accommodation) drove up inflation and clobbered economic performance and profits. Technological innovations and new products superseded some of the firms in the Nifty-fifty, and they faded from the economic landscape.

The dot.com bubble burst following Fed rate increases (after Y2K) in the first half of 2000 and the failure of high-flying businesses to generate revenues and profits sufficient to merit their high valuations. The pattern of the FAANG’s stock valuations following their emergence is more nuanced: overall they continued to outperform the S&P500 in 2019 (as Apple and Facebook outperformed while Amazon and Netflix “faltered”—they rose but not as fast as the S&P500), fell sharply during Covid in the first half of 2020, and then rebounded dramatically to new highs in the 2020s.

The bottom line is history is marked by occasional spurts in technological innovations, and the leading-edge growth companies of the day attract concentrations of market capitalization. The subsequent and ultimate stock market behavior of the companies depends on company revenues, profits and whether they meet market expectations, and  macroeconomic fundamentals.

**Japan’s Nikkei is outpacing the U.S. S&P500**. While the S&P500 and its Magnificent 7 capture so many headlines, Japan’s Nikkei has appreciated faster since 2024, fueled by faster growth in corporate profits and a dramatic catch up in valuations (Chart 3). Since year-end 2024 through April 2026, the Nikkei has appreciated 59%, twice the S&P500’s strong appreciation of 28.9%. Since year-end 2019 just before Covid, the Nikkei has appreciated 265%, outpacing the S&P500’s 235%. Corporate profits fell dramatically more in Japan during Covid but have risen faster since mid-2020 (Chart 4).

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The U.S. economy is growing faster than Japan, benefiting from healthy gains in consumption and AI-driven business investment. While exports are a higher share of GDP in Japan (18.2%) than in the U.S. (11.2%), both nation’s corporations’ profits benefit significantly from overseas production and activities.

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**The Nikkei 225’s concentration is higher**. The Nikkei 225 index is far more concentrated in a smaller number of firms than the S&P500.  However, the Nikkei’s capitalization is based on each corporation’s price rather than market capitalization. By this method, the top 10 companies on the Nikkei account for nearly 48% of the index’s total value. Similar with the S&PP500, the Nikkei’s performance relies very heavily on hi-tech and AI firms.

**Concluding remarks**. This period of dramatic AI innovation and development that has generated higher stock market valuations and concentrations of capitalization has similarities to earlier periods of spurts in innovations. Past innovations contributed to higher standards of living and increases in wealth. Current risks are obvious, particularly if the trajectory of revenues and profits of the big cap AI firms fail to meet market expectations.  But history suggests that innovation-driven profits and concentration of higher valuations do not necessarily lead to subsequent market reversals, and the outlook for economic performance remains positive.

Lingering Hiccups in Shelter Cost Inflation

Joel Prakken

More Viewpoints

This Isn't 1999 — But That Doesn't Mean There's Nothing to Watch

Warsh's Biggest Challenge at the Fed: Maintaining the Fed's Credibility

The Pass-Through of Fuel Costs to Core PCE Inflation

Observations about Economic Performance and Stock Market Valuations