Viewpoints

On December 18 the Bureau of Labor Statistics (BLS) released the Consumer Price Index (CPI) for November. In October, when the federal government was partially shut down, BLS did not conduct its survey of prices, leaving most of them unrecorded for that month. Therefore, rather than reporting the usual 1-month percent changes in prices for November, BLS reported 2-month percent changes instead. For example, from September to November, the core CPI advanced at a 1% annualized rate – a surprisingly benign reading that, if accurate, significantly improves the current narrative on inflation and strengthens the case for easier monetary policy.

Unfortunately, the potential impact of the shutdown on both the quality and timing of the November survey raises legitimate concerns about the reliability of its results. One particularly dodgy-looking element of the report was a quite sharp deceleration in the CPI for shelter, the 2-month annualized percent change of which dropped from 3.9% in September to just 1.1% in November (gold line in chart). An erroneous reading here could be of considerable importance given that shelter costs comprise nearly 18% of “core” personal consumption expenditures.

The CPI-shelter reflects rents on tenant- and owner-occupied housing units. Imputed rents on owner-occupied units are inferred from observed rents on nearby comparable tenant-occupied units. Because shelter costs reflect average rents, they are highly inertial, lagging well behind current (i.e., marginal) market conditions for two reasons. First, rental contracts typically are for one year, requiring twelve months for all contracts to reflect a change in market conditions. Second, the BLS rotates through a panel of renters over a period of six months, adding another half year to the lag between marginal and average rents.

However, these lags allow one empirically to relate the CPI-shelter to current and past new rental contracts. I did so by estimating a model that explains the CPI-shelter with current and lagged values of Zillow indices of observed newly contracted rents. These indices are available monthly through November. I then used that model to forecast the shelter costs for the months of October and November. The resulting projection of the 2-month change in the CPI-shelter is shown in the blue line in the chart.

The model suggests that from September through November the CPI-shelter grew at an annualized rate of 2.9%, 1.8 percentage points faster than the number published by BLS. To me, the projection seems more believable than the reported figure. Replacing the latter with the former raises the 2-month annualized change in the core CPI by approximately 0.3 percentage points, to 1.3% - still a good reading, but not as good. And, of course, all this makes one wonder about the reliability of estimates of other prices in the report. So, before concluding prematurely that inflation is quiescent, it makes sense for one to await additional months of readings.

For all the talk of a weakening labor market, the wage bill for private nonfarm employees (private nonfarm employees times their average weekly earnings) has risen at annualized rates of 5.85% and 5.91% in October and November, respectively, compared to the median increase of 5.75% in the eleven moths of 2025. If these data are valid, it would seem that labor market earnings are growing relatively fast, especially in light of all the talk of a weak labor market. Why would employers be increasing the labor wage bill so rapidly if labor demand were weak? Perhaps because the labor supply is shrinking.

We have been overweight Korean equities this year—and it has paid off, handsomely. The allocation decision was anchored in the business-cycle framework: three of five key indicators pointed clearly toward expansion at the start of the year. The cost of capital was supportive, the credit cycle was in a firm upswing, and money-supply growth was accelerating. The corporate profit cycle, though still technically in downturn, was already showing improvement thanks to strong balance sheets. The major drag was the investment cycle, which continued to lag.

What has surprised us is the speed of the profit-cycle recovery, especially against the year’s backdrop. From Trump’s tariff war to domestic political turbulence following the impeachment of President Yoon Suk-yeol and the snap elections in June, one would have expected a more cautious rebound. Instead, the listed sector delivered a solid performance. Return on equity averaged 7% in the first three quarters, up from 5.6% a year earlier and nearly back to the seven-year average of 7.3%. EBITDA cash flow per share, free cash flow per share, and retained earnings all advanced. EBITDA rose almost 20% year-on-year, cash flow per share climbed 29%, and free cash flow per share swung decisively into positive territory.

Central-bank data paint a similarly encouraging picture. Profitability and interest-coverage ratios have improved markedly for large corporates, even as debt-to-capital ratios inched higher. SMEs have seen some deterioration, but the stress is neither systemic nor alarming at this stage.

And yet, despite these solid fundamentals, Korea Inc has continued to err on the side of caution (Figure 1). Companies tightened spending this year and delayed new investment plans. As a result, the investment cycle slid deeper into downturn: real spending on both facilities and construction has contracted for six consecutive quarters. Rather than expand capacity, firms have chosen to run down inventories and utilise existing manufacturing facilities more intensively. Operating rates have increased, allowing companies to meet rising shipments without committing fresh capital.

Monetary policy mistakes are often self-inflicted. Sometimes, policymakers cling to an official policy for too long, necessitating a swift and sharp reversal to mitigate economic harm. At other times, they try to address or reduce economic imbalances that aren't caused by monetary policy. That's when policy mistakes happen, and it seems that policymakers are about to make another mistake soon if they react to recent job data. Policymakers are tasked with an employment and inflation mandate, yet they are not mandated to correct or counteract the "stupidity" or "misguided" decisions of fiscal policy.

Several policymakers have publicly expressed concern that the weakness in labor markets, particularly the sharp slowdown in job growth, justifies another official rate cut at the upcoming FOMC meeting in December. However, before deciding to change its monetary policy stance, policymakers should investigate whether the weakness in the job market is connected to their official interest rate stance or to other factors beyond their control, which could result in a policy mistake.

For the week ending November 22, jobless claims fell to 216,000, the lowest level since April. The long history of jobless claims shows that this weekly indicator is a reliable leading indicator of labor market activity. This low number of claims suggests that the limited job creation is not due to weak business activity or restrictive monetary policy. If that were the case, jobless claims would be much higher, possibly double.

Small businesses, which are responsible for most of the new job creation, have experienced negative job growth throughout most of 2025. A major factor contributing to this is the uncertainty and increased costs associated with Trump's tariff policy. The increasing "cost of doing" business and the uncertainty about whether tariffs will rise or be eliminated ultimately pose substantial challenges for small businesses with thin profit margins.

The retail trade sector, one of the largest private employers and primarily consisting of small businesses, has been both directly and indirectly affected by tariffs on imported goods. This sector has seen nearly 100,000 job cuts in the first nine months of 2025. Tariff-related?

Since the tariff policy was announced in April, the unemployment rate, which is one part of the Fed's dual mandate, has risen from 4.2% to 4.4%, a relatively small increase. In response to this slight rise, the Fed has reduced overall official rates twice, totaling 50 basis points.

However, Trump's tariff policy has a more direct effect on inflation, which is the other half of the Fed's dual mandate. Since April, CPI inflation has increased to 3%, up from the 2.3% rate that existed before the tariff announcement. The Fed targets a 2% inflation rate, and despite current inflation rising considerably above this target since tariffs were introduced, policymakers have not considered changing their policy approach to tackle the increase.

If businesses are reluctant to hire due to the tariff policy under Trump's administration and inflation is moving higher due to the imposition of tariffs, and further away from the Fed's target, why should monetary policy address one part of its mandate, which is unrelated to its official rate stance, while ignoring the second part of its mandate? A dual mandate is effective only when policymakers address both sides equally.

A growing number of the current generation of Fed policymakers appear to be considerably more political in their approach to monetary policy, disregarding data and their mandates. According to public statements, this group of policymakers intends to vote for another official rate in December, despite job and price data suggesting that another rate cut is unnecessary.

The political dynamics of the Fed are likely to become more divided as President Trump is set to announce a new chair to succeed Fed Chair Powell in May 2026. A policy error in December 2025 might quickly lead to additional mistakes in 2026, making bond yields and the dollar more volatile and unpredictable factors for investors.

After forty-two days, the longest (partial) shutdown of the federal government on record, which began October 1, is finally behind us. Forecasters have scurried to gauge the shutdown’s impact on real GDP. The exercise is not simple. With savings as a buffer, delayed income is not necessarily delayed spending. With inventories as a buffer, delayed spending is not necessarily delayed production. Some delayed spending and production can be made up later, even within the current quarter. The task is further complicated because effects of the shutdown are not identified separately in the source data compiled by the Bureau of Economic Analysis (BEA). Nor does the BEA make special adjustments to GDP for the shutdown…with one important exception.

In our National Accounts services produced by government are valued at cost, the variable component of which is the compensation of government employees. The BEA treats furloughed federal workers as if their real compensation is zeroed out. This makes it straightforward to compute the real value of government services forgone during the shutdown, as well as the associated negative contribution to fourth-quarter growth of GDP. With approximately 1/3 of federal civilian workers furloughed, each extra week of the shutdown reduced the contribution of government services to fourth-quarter annualized growth of GDP by approximately 0.14 percentage point. Hence, the reduction in government services during the six-week shutdown will subtract approximately 0.8 percentage point from GDP growth in the fourth quarter.

Several years ago, when inflation had decelerated from its high peaks, I published an article in the Wall Street Journal about inflation and the Fed. It’s always an honor to get a piece in the WSJ, but one of the hazards is the WSJ holds the right to put any title on the article that it wishes. In almost all cases, the WSJ’s title is far better than the ones I have submitted. For my article on October 26, 2023, I liked my title, “Lower Inflation but Higher Prices” more than the title the WSJ used: “We’re Still Paying for the Federal Reserve’s Blunders”.

Both titles still apply to the current situation. PCE inflation is 2.7% and CPI inflation is 3.0%--which may seem modest, but the cumulative increase in prices for goods and services is the primary focus of consumers. It directly influences their standards of living, their perceptions of confidence and their assessments of government leadership. Meanwhile, the focus of financial markets and the Fed is nearly exclusively on the rate of inflation and changes in the rate, to the tenth of a percent and beyond. Presently, even amid lack of government data, the focus in financial markets is the extent to which PCE inflation is above the Fed’s 2% target, and what is the appropriate policy rate that would eventually reduce inflation to 2%.

This note considers the cumulative increase in the level of price increases in recent years and adds in an important element—how relative prices of different goods and services have changed, particularly some of the things we purchase on a regular basis—food and drink. Prices of some goods and services have gone up much more or less than others. Presumably, this is affecting household pocketbooks and our spending patterns.

The trend in inflation. As shown in Charts 1 and 2, CPI and PCE inflation have settled down but the cumulative increase in the general price level has been striking—since pre-Covid, measured as 2019Q4, the CPI is up 26.9% and the PCE is up 22.3%. That’s the biggest cumulative increase in prices over a short number of years since the 1970s. (It’s nearly unimaginable that during the Great Inflation between 1966 and 1982, the CPI rose 300%...while the S&P500 rose 150%). Nevertheless, the recent surge in the general price level comes as a rude awakening following the stable and relatively low inflation that persisted in the decade following the Great Financial Crisis.

Two quick footnotes. First, while the PCE Price Index provides a more comprehensive measure of inflation by including prices of all goods and services consumed, the CPI is a much better measure of consumers’ out-of-pocket expenses. The PCE is a price index of all consumption, whereas the CPI does not include prices of goods and services that are financed by third party payers, including health care services financed by Medicare, Medicaid and employer health insurance. Accordingly, the CPI has a significantly higher weighting of housing services (rental costs and owners’ equivalent rent of residencies, or OER) while the PCE has a much higher weighting of health services.

India remains an equity overweight, but the shine has faded from both its cyclical recovery and its structural growth story. Two issues stand out: the country is lagging badly in the AI revolution, and the corporate investment cycle—despite strong macro conditions—has failed to ignite.

AI: A Race India Is Not Winning

India has attracted several high-profile announcements. Google plans to invest US$15bn over five years to build an AI data centre. Microsoft has committed US$3bn over two years, Amazon US$12.7bn by 2030, and the government’s latest budget allocates US$1.25bn to develop the domestic AI ecosystem.

On paper, this sounds substantial. In reality, it pales next to the intensity of global AI investment. The big four US hyperscalers—Amazon, Microsoft, Google and Meta—are currently spending around US$90bn per quarter on AI. Even compared to peers like China, the gap is stark. Over the past decade, state-backed venture capital in China has channelled roughly US$912bn into strategic industries including AI. China now hosts over 4,500 AI enterprises, accounts for 45% of global computing power, filed 60% of AI patents in 2024, and is expected to invest US$85–98bn in AI capex in 2025 alone.

India’s GenAI Landscape: Growing, but Too Slowly

The domestic AI start-up ecosystem is expanding. As of 1H25, India hosts more than 890 generative AI start-ups, up from 240 a year earlier, with cumulative funding of US$900m. The funding mix, however, shows a narrow focus: 88% into AI infrastructure, 8% into services and only 4% into applications. Karnataka dominates with 39% of AI start-ups, followed by Maharashtra (14%), Delhi (9%) and Telangana (7%).

India’s AI market is forecast to grow at a 25–35% CAGR, reaching roughly US$17bn by 2027. NASSCOM, the industry body for the tech sector, estimates data and AI could contribute US$450–500bn to GDP by 2025––approximately 10% of the country’s original US$5trn target.

But scaling remains slow relative to the global frontier. India’s AI spending is small, investor appetite is weak, and the ecosystem faces structural barriers.

The Challenges

1. Lack of Regulatory Clarity Policy uncertainty has deterred funding, especially for deep-tech. Capital inflows remain limited, forcing start-ups to operate under severe funding constraints. Although 25+ new GenAI firms launched last year, both seed and late-stage investment have contracted sharply.

2. Talent Is Scarce Demand far exceeds supply. NASSCOM estimates India suffers one of the world’s highest AI talent shortages—around 50%. With insufficient advanced training capacity, scaling becomes difficult.

3. Government Response Is Reactive Thus far, policy has focused on short-term fixes and pilot initiatives. What is missing is a coordinated long-term strategy. According to NASSCOM, India needs large-scale public–private co-investment to de-risk innovation. But resistance to policy change and bureaucratic inertia remain barriers.

India is positioned to participate in the AI economy, but not to lead it. And without leadership or scale, the structural growth narrative weakens.

The Business Cycle: Why Hasn’t Investment Turned?

The second problem is more puzzling. India’s corporate investment cycle should be rising strongly, given macro conditions. Instead, it remains in a downtrend—even before pandemic disruptions and well before Trump’s 50% tariff shock.

The Good News

Growth momentum is solid. GDP has accelerated since 4Q24, powered largely by private demand—especially consumption in 2Q25 (Figure 1). The economy expanded 7.6% YoY on average in 1H25, outperforming regional peers and handily beating China.

It is generally understood that Gross Domestic Product (GDP) does not include the value of imports. It is, however, less well appreciated that in the System of National Accounts tariffs are treated as domestic value added. That is, customs duties are included in GDP.

From the first to the second quarter of this year customs duties surged $170.7 billion, from $97 billion at an annual rate to $267.7 billion -- an annualized growth rate of 5700%! -- as new tariffs imposed by the Trump Administration took effect. All else equal, if that increase in tariffs had immediately passed through to prices faced by final demanders of domestic product, the annualized rate of change in the GDP price index would have spiked nearly 3 percentage points in the second quarter. As it happened, GDP inflation in the second quarter was tame at 2.1%. Obviously, there were tariff slips twixt cup and lip.