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DLX Add-ins for all versions of Excel provide instant data updates of entire workbooks

Use DLXVG3 to locate data series codes from within any file, easily creating new spreadsheets for analysis

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HaverView

[Matlab](http://matlab.com) by The MathWorks lets you access, visualize, and analyze historic time-series data to identify patterns or uncover complex relationships. From within a single environment you can:

- Access data from multiple sources including files, spreadsheets, databases, data providers, and the Web
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MATLAB

[Stata Data Analysis and Statistical Software](https://www.stata.com) by StataCorp LP is an integrated statistical package designed for researchers of all disciplines. It provides the ability to do data analysis, data management and graphics and has features such as multiple imputation, factor variables, GMM, competing-risks regression, state-space modeling, predictive margins, and more.

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Stata

[SAS](https://www.sas.com) provides an integrated environment for predictive and descriptive modeling, data mining, text analytics, forecasting, optimization, simulation, experimental design and more. From predictive analytics to model deployment and process optimization, SAS provides a range of techniques and processes for the collection, classification, analysis and interpretation of data to reveal patterns, anomalies, key variables and relationships, leading ultimately to new insights and better answers faster.

[SASEHAVR](http://support.sas.com/documentation/cdl/en/etsug/67525/HTML/default/viewer.htm#etsug_sasehavr_gettingstarted01.htm) is a seamless interface between Haver and SAS data processing that enables SAS users to read economic and financial time series data that reside in a Haver Analytics DLX (Data Link Express) database. [SASEHAVR](http://support.sas.com/documentation/cdl/en/etsug/67525/HTML/default/viewer.htm#etsug_sasehavr_gettingstarted01.htm) requires both SAS/ETS and Base SAS Software.

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[RATS (Regression Analysis of Time Series) Econometric Software](https://estima.com) by Estima is a leading econometrics and time-series analysis software package. It provides ability to do linear and non-linear least squares, forecasting, SUR and ARIMA models and in addition, supports advanced techniques such as GMM, ARCH and GARCH models, state space models, and more.

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RATS

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A popular open source option for statistical computing and graphics that is available on many UNIX platforms, Windows and MacOS

Estimating, evaluating, and forecasting ARIMA models

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Estimating autoregressions, granger causality tests, and impulse response functions

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Estimating and forecasting with vector error correction models

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Teaches econometrics and forecasting at the University of Colorado Denver. He has over 15 years of econometric experience in both the private and public sector.

Daniel L. Jerrett, Ph.D.

Abdel M. Zellou, Ph.D.

Bootcamp and Econometric Tools 1 & 2

Econometrics Training: Applied Time Series in R

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Haver partners with SmartMacro Labs to offer a two week immersive online course introducing participants to the power of the Python programming language. This applied hands-on training develops statistical economics skills with a concentration on economics and finance.

Gain proficiency in building modern time-series models in Python through real-world applications. Designed for professionals in both the public and private sector, the course does not require prior experience, assuming only that participants have a basic understanding of regression and statistical concepts.

Proficiency working in Jupyter notebooks and the Python environment

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Remote Training: Applied Time Series in Python

Soaring AI-related investment in US underpinning strong contribution of business spending to overall growth

Michael J. Moran

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The index of consumer confidence published by the Conference Board rose 2.2 index points in February to 91.2, with the change coming from an upward revised level in January (now 89.0 rather than 84.5). The improvement is certainly welcome, but it does not meaningfully alter the picture of gloomy moods in the household sector. The preliminary index in January was below the low seen during the pandemic, and the new reading is only slightly above that trough.

The month-to month change in January was driven by the expectations component of the headline measure, which rose 4.8 index points to 72.0 (7.1%) from a revised level that was 3.2% firmer than previously believed. Individuals, though, are not especially optimistic about the future. The expectations measure still trails readings generally above 100 before the pandemic and it is light by historical norms (the measure is typically above 80 in non-recessionary periods or their aftermaths).

Assessments of the present situation naturally improved from the lows seen during the pandemic, but they never returned to pre-Covid levels and they have slipped considerably since early 2024. The latest figures did not meaningfully alter the picture. Although January results were revised 7.1% higher, the February index offset a portion of that gain by slipping 1.5%. The measure is not at a recession-like level, but it seems low for an economy that has low unemployment and is growing at a rate close to its potential.

The detail of the Conference Board report provides insight into factors that are fueling the sour moods of individuals. The loss of vigor in the labor market is perhaps the most important consideration. The monthly survey of consumers asks if jobs are plentiful or hard to get, and the difference in the in the shares responding optimistically and pessimistically has shifted markedly. After touching a record high of 47.1% in in 2022 (sharer saying jobs are plentiful less share saying Jobs are hard to get) the differential has eased to 6.8% in January before edging up to 7.4% in February. Like the assessment of the present situation, the job differential is not recession-like, as it has moved below 040% at times and the record low is 58.7% in 1982. Still, it shows that individuals very much sense a deterioration in the labor market.

Politicians have seized on affordability as a key campaign issue, and the Conference Board survey shows that individuals are sensitive to inflation. After the inflation burst in 2022, expectations of inflation one year hence cooled to 5% in late 2024. However, inflation views picked up in 2025, briefly touching 7% before easing to 5.5% in February – still a above the 2024 low and most readings in the years before the pandemic.

One factor that has not dampened the moods of individuals is the performance of the equity market. The survey asks if stock prices are likely to be higher, lower , or the same 12 month hence. Here, optimism reigns, with 53.1% of respondents in February looking for higher prices. Only 23.5% expect the stock market to lose ground.

The Consumer Confidence data are available in Haver’s **CBDB** database. The total indexes, which are indexed to 1985=100, appear in **USECON**, and market expectations are in **AS1REPNA**.

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- Firmer expectations offset dimmer views on the present situation.
- Although views on the present situation dipped, assessments of the labor market picked up.

Consumer Confidence: Improvement in February from Upward Revised Levels in January, but Individuals Are Still Unhappy

Winnie Tapasanun

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U.S. house prices edged up 0.1% m/m in December after increases of 0.7% in November (+0.6% initially) and 0.4% in October (unrevised), according to the Federal Housing Finance Agency (FHFA) House Price Index. December marked the fourth m/m increase in five months and the smallest of three consecutive m/m rises. The HPI reached a record high of 440.36 in December, up 14.4% from its August 2022 low of 384.78. The year-on-year growth rate slowed to 1.8% in December from a three-month-high 2.1% in November, remaining down from a high of 6.9% in February 2024 and well below a peak of 18.5% in February 2022. For all of 2025, FHFA house prices rose 2.9% after gains of 5.5% in 2024 and 4.7% in 2023.

The FHFA also released quarterly house price data today. In Q4'25, house prices grew 0.8% q/q, faster than a 0.3% increase in Q3'25, continuing their quarterly gains since Q4'22, with the exception of no change in Q2'25. The y/y growth rate decelerated to 1.8% in Q4'25 from 2.4% in Q3, 3.1% in Q2, and 4.3% in Q1.

House prices rose m/m in December (vs. November) in six of the nine census divisions. Gains were led by Middle Atlantic (+1.1% vs. -0.1%), followed by East North Central (+0.5% vs. +0.6%), Mountain (+0.4% vs. +0.8%), West North Central (+0.3% vs. +0.6%), South Atlantic (+0.2% vs. +0.7%), and New England (+0.1% vs. + 0.4%). In contrast, house prices fell m/m in two census divisions in December (vs. November): West South Central (-1.0% vs. +1.2%) and East South Central (-0.1% vs. +0.8%). Meanwhile, house prices in Pacific were essentially unchanged m/m in December after a 0.8% increase in November.

Year-on-year house prices continued to rise in December in six of the nine regions, except Mountain, Pacific, and South Atlantic. The y/y pace accelerated in December (vs. November) in three of the nine census divisions: East North Central (+5.2% vs. +5.1%), Middle Atlantic (+4.8% vs. +4.5%), and West North Central (+3.9% vs. +3.6%). Meanwhile, house price growth decelerated y/y in December (vs. November) in West South Central (+0.2% vs. +1.3%), East South Central (+2.1% vs. +2.9%), and New England (+2.7% vs. +3.5%). To the downside, Mountain (-0.6% vs. +0.4%), Pacific (-0.4% vs. 0.0%), and South Atlantic (-0.1% vs. +0.1%) posted y/y declines in December.

The FHFA house price index is a weighted purchase-only index that measures average price changes in repeat sales of the same property. An associated quarterly index includes refinancing the same kind of properties. The indexes are based on transactions involving conforming conventional mortgages purchased or securitized by Fannie Mae or Freddie Mac. Only mortgage transactions on single-family properties are included.

The FHFA data are available in Haver’s **USECON** database.

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- FHFA HPI +0.1% (+1.8% y/y) in Dec., the smallest of three straight m/m gains.
- House prices up m/m in six of nine census divisions, led by Middle Atlantic (+1.1%), but down in West South Central (-1.0%) and East South Central (-0.1%); prices flat m/m in Pacific.
- House prices up y/y in six of nine regions, led by East North Central (+5.2%), but down in Mountain (-0.6%), Pacific (-0.4%), and South Atlantic (-0.1%).
- House price growth accelerates to 0.8% q/q (+1.8% y/y) in Q4'25 from 0.3% q/q (+2.4% y/y) in Q3.

U.S. FHFA House Price Growth Slows in December; Q4’25 Momentum Picks Up

Robert Brusca

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Neither the service sector nor the manufacturing survey value is particularly strong. The services climate headline has a 25-percentile standing; it has been higher about 75% of the time. That is not a good result. For manufacturing and industry, the standing is just short of the 50% mark which leaves it quite close to its historic median. That is not strong, but it is not weak either; it’s a modest middle ground. But February’s reading gave up ground, falling to 102.1 from 105.4 in January, back essentially to its December 2025 level. Even so, the February value has not been exceeded persistently until we go back to August 2022—three and one-half years ago.

The rebound in manufacturing is nascent; we can question its sustainability. And it is also only a modest step up after February’s erosion. Orders and demand flared higher in January and backed down in February. Orders and demand have a 50-percentile standing, with foreign orders much stronger at a 75-percentile standing. While production has a sub-median, 42-percentile standing, responses, when aggregated, show an ‘own-personal’ response standing at 62.2%. Maybe there is more granular, industry-level confidence that is being restrained by macroeconomic pessimism. This two-tier response is recreated compatibly for prices where firms see high own price trends stronger than their whole-economy trend. The own-price trends are elevated with a ranking over their 60th percentile while the macro price expectation is at a 48-percentiel standing. Production expectations are a classical representation of this month’s survey, falling back this month after some improvement last month and sporting a 49.1 percentile standing – a near median result. The manufacturing survey does not suggest any trouble; it exhibits near normal behavior, with orders at a midstream level/ranking.

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The services survey also backs off in February, but its ranking is weak, at the 25-percentile level. The 3-month and 12-month averages, when ranked, are ‘similarly’ weak—a clear sign that this services weakness is a real phenomenon. Only one-entry among the ten in the table has a ranked percentile standing over the 50-percent mark—that is the sales-price expected over the next three months. And that is not particularly reassuring. Employment trends, as observed and expected, rank in their 6th to 12th percentiles. Observed and expected sales trends are very low-ranking, and they weaken in February. The formal outlook is a lower one-third phenomenon, and it has been weakening.

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France’s trends play out on the chart. There are issues revealed here that are only made clear by looking at the chart and the ranking, and tying-in those trends with what was reported this month. The broadest view of the manufacturing & services sectors is upsetting. The medium-term view is more constructive, but only because of the industrial rebound. Yet, looked at up close, that rebound is too nascent to trust. The short term remains expensive for industry, but unreliably so. The French economy has been struggling for a while, and France has severe budget problems that are also creating political issues. French policy is under pressure, and remember that, as an EMU member, there is no central bank to dedicate policy to resolving its issues. EMU-wide inflation is more or less under control, but rate cutting stimulus is unlikely.

France

French Manufacturing and Services Surveys: Two Sectors, Two Different Trends, and Lingering Weakness

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Total factory orders unexpectedly fell 0.7% m/m in December after a 2.7% gain in November and a 1.2% drop in October, according to data from the U.S. Census Bureau. December marked the second m/m fall in three months following m/m increases in September and August. The Action Economics Forecast Survey had expected a 0.8% m/m December increase. The year-on-year growth rate slowed to 4.3% in December from 5.4% in November (-1.8% in December 2024), remaining far below a peak of 39.6% in April 2021. Notably, orders for nondefense aircraft & parts plunged 24.8% m/m in December after a 98.2% surge in November and a 17.9% drop in October. Factory orders excluding defense fell 1.2% (+3.8% y/y) following a 3.2% November rebound. Factory orders excluding the transportation sector rose 0.4% (1.2% y/y), the third m/m rise in four months, after a 0.1% November uptick. For all of 2025, factory orders gained 4.0% after a 0.5% decline in 2024 and a 0.3% increase in 2023.

Durable goods orders fell 1.4% (+10.1% y/y) in December after a 5.4% rebound in November (matching -1.4% m/m in the advance report on February 18). The December m/m fall was led by a 5.4% orders drop (+20.2% y/y) in transportation equipment following a 15.2% November jump. Other major durable goods orders categories rose m/m, led by a 3.1% gain (7.4% y/y) in computers & electronic products, followed by increases of 2.1% (9.1% y/y) in primary metals, 0.9% (3.5% y/y) in fabricated metal products, 0.5% (10.0% y/y) in machinery, 0.3% (4.0% y/y) in electrical equipment, appliances & components, and 0.1% (4.5% y/y) in furniture & related products.

Nondurable goods orders, which equal nondurable goods shipments, were essentially unchanged (-1.4% y/y) in December after a 0.1% dip in November (the fourth successive m/m decline). The flat December reading reflected m/m increases of 2.1% (-0.7% y/y) in textile products, 1.9% (0.3% y/y) in apparel, 0.6% (3.4% y/y) in beverage & tobacco products, 0.6% (4.3% y/y) in chemical products, 0.3% (-0.1% y/y) in paper products, and 0.2% (-0.3% y/y) in food products. To the downside, the following nondurable goods shipments declined m/m in December: petroleum & coal products (-1.3%; -13.4% y/y), textile mills (-1.0%; -1.4% y/y), leather & allied products (-0.5%; -7.8% y/y), and printing (-0.4%; -1.2% y/y). Meanwhile, shipments for plastics & rubber products were virtually unchanged (+0.5% y/y) after a 0.4% November rebound.

Total shipments rose 0.5% (2.2% y/y) in December, the second m/m rise in three months, following a 0.2% decline in November. Excluding transportation, shipments increased 0.4% (1.0% y/y), the third m/m gain in four months, after a 0.1% November uptick. Shipments of durable goods industries recovered 1.0% (5.8% y/y), also up for the third time in four months, after a 0.3% November decline. This reflected m/m durable goods shipments gains of 2.5% (7.2% y/y) in primary metals, 1.4% (8.8% y/y) in transportation equipment, 1.3% (8.1% y/y) in machinery, 0.7% (4.6% y/y) in fabricated metal products, 0.6% (5.1% y/y) in computers & electronic products, and 0.5% (1.2% y/y) in nonmetallic mineral products. Durable goods shipments for furniture & related products (-0.3%; +4.0% y/y) and wood products (-0.3%; -3.9% y/y) fell m/m in December, while durable goods shipments for electrical equipment, appliances & components (+2.4% y/y) and miscellaneous durable goods (+2.3% y/y) were flat m/m.

Unfilled orders rose 0.9% (10.3% y/y) in December on top of a 1.4% advance in November, registering the fifth consecutive m/m rise and the seventh in eight months. Excluding transportation, unfilled orders grew 0.4% (1.6% y/y), the strongest of five straight m/m increases, after a 0.3% November rise. Backlogs of durable goods rose 0.9% (10.3% y/y) in December, led by m/m gains of 1.2% (16.1% y/y) in transportation equipment and 1.0% (3.7% y/y) in electrical equipment, appliances & components, partially offset by a 0.1% decline (+2.2% y/y) in primary metals.

Inventories edged up 0.1% (0.9% y/y) in December after a 0.2% rise in November and no change in October. Excluding transportation, inventories inched up 0.1% (1.2% y/y), the seventh m/m increase in eight months, after a 0.1% November uptick. Durable goods inventories grew 0.3% (1.5% y/y), the third successive m/m gain, after a 0.2% November rise, while nondurable goods inventories fell 0.2% (-0.1% y/y), the third m/m fall in four months, after a 0.1% November increase.

The factory sector data are available in Haver’s **USECON** database. The Action Economics Forecast Survey is in the **AS1REPNA** database.

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- December factory orders -0.7% m/m (+4.3% y/y); still 7.2% above the Jan. ’24 low.
- Durable goods -1.4% m/m; nondurable goods orders flat; shipments +0.5% m/m.
- Transportation orders -5.4% m/m, led by a 24.8% plunge in nondefense aircraft orders.
- Unfilled orders +0.9%, the fifth straight m/m rise.
- Inventories +0.1%, the second consecutive m/m increase.

U.S. Factory Orders Fall in December on Weak Nondefense Aircraft Orders

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The National Activity Index published by the Federal Reserve Bank of Chicago totaled 0.18 in January, a jump of .39 index points from the reading in December. The latest tally was favorable. Not only did the measure post a sharp month-to-month change, but it also signaled that the economy is growing faster than its long-run trend rate. 

(The index is constructed to equal zero when the economy is growing at its long-run trend rate. Thus, positive readings suggest brisk growth. Negative readings should not necessarily be viewed as disappointing. Researchers at the Chicago Fed note that the index needs to ease to -0.7 before recession becomes a risk. The measure would be deeper in negative territory if the economy was contracting.)

Although January brought encouraging news, an element of caution is warranted. The index often moves erratically, and thus a single monthly change could be dominated by random volatility. A three-month moving average squeezes out most of the volatility, leaving a more reliable perspective on the economy. Here, too, the news in January was favorable, as the 3-month average rose .23 index points to -.06, just a shade below a signal of trend growth and the best reading since April of last year. The eight months between April 2025 and January showed an average index value of -.24, with observations ranging from -.13 to -.38.

The National Activity Index is a composite of 85 economic indicators, grouped into four major categories: production and income; employment, unemployment, and hours worked; personal consumption and housing; sales, orders, and inventories. All four components improved month-to-month, with the production and income component providing most of the lift (up .22 index points to .19). Three of the four components were above zero in January, with only the sales, orders, and inventory in negative territory (and barely so at -0.02).

These figures are available in Haver's **SURVEYS** database.

- January results showed noticeable improvement from prior months.
- All four components contribute positively.

Chicago Fed National Activity Index: Suggesting Trend-Like Growth

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Germany's IFO index shows improvement in February compared to January. The climate index improves, the current conditions index improves, and expectations improve. All three all-sector headlines improve in February compared to January. However, all three metrics continue to register net negative readings, indicating that the sectors are showing more contraction than expansion, but less contraction than it had been the case previously. We ranked these headlines on data back to 1993; the climate index has a 22.5 percentile standing, expectations have a 22.4 standing, and current conditions have a 16.9 percentile standing. All three of the headline metrics have standings in the lower quartile of their queue of values back to the early 1990s. This is clearly not a stellar report but a report from an economy that is struggling. Even though it's widely reported that the climate index is currently on a six-month high; it may be on a six-month high, but it's not really much different than it was six months ago and only up by 1.1% from its lowest mark in the intervening five months—so not impressive. This is a grueling, crawling, dig-out from difficult circumstances. The COVID pandemic was a sucker punch to the global economy, and it was followed by the Russian invasion of Ukraine that hit Europe especially hard because of the proximity.

Since then, other geopolitical tensions have risen. Europe's relationship with the United States has deteriorated, in part because Europe—Germany in particular—was reluctant to detach itself from its mercantile relationships with Russia and was also reluctant to increase its financial participation in NATO. The U.S. was carrying the burden of the protective umbrella for Europe at a time when Europe didn't really think it needed a protective umbrella and so it was unwilling to pay for anything more substantial. When the rainy day came and when the Russian invasion of Ukraine occurred, Europe was completely unprepared and the U.S. was its protector. While Germany and other European countries quickly made it clear that they were fully on board with NATO defense, the U.S. was much less enthusiastic about carrying the burden after Europe had put its financial probity ahead of its defense spending obligations. Now Europe does not see the U.S. and its perceived greater needs for security because of improved navigation from the Arctic Circle to the North Atlantic.

These troubles continue to dog the relationship between the United States and Europe, and the European Community. And Europe’s own economic struggles continue. In the climate readings, all four of five sectors improve from January to February. Current conditions improve in three of the five sectors, with retailing and wholesaling slipping month-to-month. Expectations do improve overall, but three of five sectors take a step back in February, with expectations improving sharply for services, and substantively for wholesaling. Still manufacturing, construction, and retailing expectations stepped back.

Across the three broad areas of assessment across five sectors in each, there are only two (two of fifteen) sector metrics that rank above their 50th percentile (above their respective medians) when ranked against readings back to 1993. Both of those elevated rankings are for construction, one for Climate, and the other for current conditions, but construction expectations, at a 44.5 percentile standing, are not far below their median.

Still, the lowest environmental standings are for services, except under expectations where the services reading is second-weakest to retailing. The graphic shows some tendency for an upturn in climate and current conditions, but they are countered by an expectations path that has topped out and has been gradually leaning lower—and doing that with a current ranking in only its 22.5 percentile. So, while there is some life in the IFO survey this month, we are still looking at a patient in intensive care, not one ready to join Olympic competition—at least not yet.

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Germany

German IFO: Business Confidence Claws Its Way Out of a Hole

Tian Yong Woon

In our Letter this week, we delve deeper into AI, with a focus on Asia and, where relevant, comparisons with the US. The global economy remains firmly in the buildout phase of AI, where much of the near-term economic benefit is derived from investment in hardware and enabling infrastructure—such as AI chips, memory, and data centres. At the same time, though still at a relatively early stage, firms are beginning to adopt advanced AI capabilities and integrate them into workflows in pursuit of productivity gains. So far, labour productivity improvements have been evident in parts of Asia (chart 1), largely driven by stronger exports (chart 2) and capital deepening—similar to trends observed in the US (chart 3) and, within Asia, in economies such as Malaysia (chart 4). However, these gains have yet to translate into a clear and sustained acceleration in total factor productivity (TFP), which accounts for the combined use of labour, capital, and other inputs—though TFP is admittedly a challenging metric to estimate, as illustrated by the US case (chart 5). This raises an important question: what happens when the AI buildout phase begins to moderate, and the associated investment-led tailwinds fade? At that point, further AI-related gains will depend more heavily on the successful embedding and diffusion of AI across sectors. On this front, Asian economies remain at very different stages of readiness to adopt AI (chart 6), suggesting that the next phase of productivity gains may be uneven across the region.

**The productivity story**
Several Asian economies have already recorded substantial productivity gains in recent years, even before the narrative around AI-driven productivity boosts gained traction last year. As shown in chart 1, measured by labour productivity—simply defined as output per employed person—economies such as Taiwan and several in Southeast Asia stand out for their strong performance. However, these gains cannot be attributed solely to the promise of AI. By definition, labour productivity rises whenever output (the numerator) grows faster than employment (the denominator). Such an outcome can stem from a range of factors—cyclical recoveries, capital deepening, sectoral shifts, or efficiency improvements—and does not necessarily reflect widespread AI adoption.

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Digging deeper, however, it would be equally misleading to suggest that Asian economies have not benefited from AI-related productivity gains. As shown in chart 2, exports have been a key driver of growth across several economies in recent years. In particular, Taiwan and South Korea—both major producers of AI-related hardware—have benefited from strong global demand for advanced chips and memory used in AI infrastructure. Taiwan stands out: its Q4 real GDP growth surged to 12.7% y/y, with net exports accounting for nearly all of that expansion. Notably, integrated circuits—often comprising around a third of total exports—have played a central role in this performance. Thus, while many Asian economies may not yet be at the forefront of deploying cutting-edge AI models domestically, they are significant beneficiaries of the current AI infrastructure buildout. By supplying the semiconductors and components underpinning this expansion, parts of the region are already capturing meaningful gains from the AI cycle.

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**Capital deepening**
Another key channel through which labour productivity can be achieved—aside from reducing the denominator (employment)—is capital deepening, or an increase in capital per worker. On this front, some advanced economies, including the US, have already moved in this direction as firms ramp up investment in AI-related infrastructure and equipment. In recent years, growth in investment in information processing equipment and data centres has outpaced other demand components, such as consumption and exports, reflecting in part the ongoing AI-related buildout (chart 3).

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The growth-enhancing—and by extension productivity-supporting—effects of such AI-related capital deepening are also evident in parts of Asia. In Malaysia, for example, the rapid expansion of its data centre industry has spurred strong gains in investment and construction activity, alongside spillovers to related sectors (chart 4). Importantly, these AI-related, investment-led gains are not limited to data centre development. Capital outlays to expand production capacity for AI-related goods—such as semiconductor fabrication and advanced chip manufacturing—operate through the same channel, increasing the capital stock and, over time, lifting output per worker.

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**Beyond the AI buildout**
So far, the discussion has centred on productivity in terms of labour productivity—output per employed person. That metric can be lifted relatively easily by increasing the numerator, whether through stronger exports or higher investment, as outlined in the previous sections. But what about gains in total factor productivity (TFP), rather than just labour productivity? TFP takes into account inputs beyond labour, including capital and other factors. An increase in TFP therefore implies higher output generated from the same combination of labour, capital, and other inputs. It is arguably this type of efficiency gain that investors hope will materialise from the adoption of AI. That said, TFP is notoriously difficult to measure. In the US, for instance (see chart 5), estimates of TFP growth can vary depending on the data source and methodology used. Even so, higher-frequency measures of US TFP have yet to show an outsized acceleration, suggesting that AI-related optimism has not—at least for now—translated into a discernible surge in measured productivity.

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Another key dimension to monitor is how prepared Asian economies are to embrace AI and embed it effectively in order to capture its anticipated productivity gains. While many economies in the region are currently benefiting from labour productivity gains—driven by stronger manufacturing activity and exports of AI-related goods and infrastructure—such gains may eventually moderate, particularly once the initial buildout phase peaks. At that stage, the more durable benefits will need to come from a different channel: the total factor productivity gains associated with the actual adoption and diffusion of AI across industries. On this front, Asian economies differ markedly in their readiness. As shown in chart 6, an IMF index—incorporating factors such as digital infrastructure, innovation capacity, human capital, and regulatory frameworks—highlights this divergence. More advanced economies, including Singapore, South Korea, and Japan, rank highly, while others such as Vietnam, India, and the Philippines lag behind. That said, the concerted efforts by several developing Asian governments in recent years to strengthen digital infrastructure, upgrade skills, and refine regulatory frameworks suggest that this gap could narrow over time.

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Asia

Economic Letter from Asia: Asia and AI

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Real Gross Domestic Product rose 1.4% in the fourth quarter of 2025, slower than the recent average but no doubt influenced by the government shutdown. Federal spending often moves erratically, but the tumble of 16.6% in the final months of last year was unusually large. Indeed, one has to go back to 1972 to find a sharper decline (and to the 1950s before that), and these results were only slightly weaker than the Q4 decline.

Even allowing for the government shutdown, the latest results were a bit shy of expectations. Without the drop in federal outlays, which subtracted 1.2 percentage points from GDP growth, the economy would have expanded 2.6% in Q4, still shy of most surveys that showed consensus forecasts in the neighborhood of 3.0%. A downside surprise of sorts occurred in net exports. Monthly figures through November had suggested that international trade would provide strong support, but this sector added only 0.1 percentage point to GDP growth.

Other areas posted nondescript results. Consumer spending and business fixed investment were less vigorous than the best results of the past two years, but the latest growth rates were comfortably within recent ranges. Housing activity remained soft, making the sixth negative contribution to GDP growth in the past seven quarters. However, the constraints from housing over this span have generally been small.

The fourth quarter, and most of last year, were oddities because of the government shutdown in Q4 and the imposition of tariffs in April followed by marked adjustments in subsequent months. Such changes can lead to pronounced swings in economic activity and leave a muddled picture on underlying trends. In such environments, a different perspective on economic output can be helpful. Specifically, private domestic final demand (PDFD, GDP less government spending, net exports, and inventory investment) excludes areas that often move erratically, thus removing random shifts and reducing volatility.

PDFD was especially useful last year because tariffs generated pronounced shifts in net exports and inventory investment, and the government shutdown distorted activity in the fourth quarter. As shown in the chart below (left), quarterly variations in private domestic final demand were modest last year. The underlying trend decelerated, but the slowing was modest and growth in the fourth quarter was respectable at 2.4%.

The GDP price index rose at an annual rate of 3.6% in the fourth quarter of the year, one of the heftier advances of the past three years. A surge of 15.6 percent in the prices paid by the federal government had a strong influence on this result (shutdown related?), but readings of 3.9% for business equipment and 2.9% for consumer goods and services also were suggestive of an inflation impulse in the economy.

The increase in consumer prices was evident in the December report on personal income and consumption, which showed month-to-month increases of 0.4% for both headline and core price indexes for personal consumption expenditures. Year-over-year readings inched higher to 2.9% for all items and 3.0% for prices excluding food and energy. The latest results will no doubt leave Fed officials uncomfortable.

The GDP data can be found in Haver’s **USECON** and **USNA** databases. **USNA** contains virtually all of the Bureau of Economic Analysis detail in the national accounts. The Action Economics consensus estimates can be found in **AS1REPNA**.

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- Non-federal activity was lighter than expected, but still respectable.
- PCE inflation remains stubborn.

Q4 GDP: Constrained by Federal Spending

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The headline PPI and its components sometimes move erratically, and that was certainly the case in January. The headline index (so-called final demand PPI) jumped 0.5%, one of the firmer readings of the past few years. However, a quick review of the detail in the report shows that marked changes in frequently noisy areas accounted for most of the pressure. Without such noise, the headline measure would have increased moderately, perhaps 0.1% or 0.2%.

Prices of food and energy are well known to move erratically, and they showed noticeable downside changes in January (off 1.5% and 2.7%, respectively). However, the highly volatile trade services category provided an offset with a surge of 2.5%, the sharpest monthly increase on record (data back to 2010). The Bureau of Labor Services is well aware of the volatile nature of these areas and offers an index excluding food, energy, and trade services to provide a perspective on underlying trends. This measure rose 0.3% in January, in line with other recent month-to-month changes. The year-over-year increase of 2.9% was comfortably within the recent range of observations (2.7% to 3.2% since mid-2025).

The change in prices excluding food, energy, and trade services seems tolerable in that it is comparable to other recent readings. However, some observers will highlight the uncomfortable rate of increase (3.0% versus a benchmark of 2.0% in the minds of many). Also, the sideways nature of the year-over-year change suggests little prospect of deceleration.

These points are well taken, but additional detail in the report offered a hint of softness. Another volatile item in the service category (transportation and warehouse services) rose 1.0% in January. This measure does not match the volatility in trade services, but it often shifts erratically. For example, last year this component rose 1.4% in one month and more than 0.5% in four other months, yet the average for the year was 0.2%. Future readings are likely to be well shy of the January advance.

An increase of 1.2% in the construction component also looks suspicious. This figure is reported to be seasonally adjusted, but there is a strong tendency to post high-side increases in the first month of a quarter. Observations in the next two months are likely to be noticeably softer, and the average for the year most likely will be well shy of 1.2%. The average increase last year was 0.2%.

The PPI data are published by the Bureau of Labor Statistics and can be found in Haver’s **USECON** database. Further detail is contained in **PPI** and **PPIR**. The expectations figures are available in the **AS1REPNA** database.

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- Prices of food and energy fell sharply, while prices of trade services surged.
- Other noisy areas (transportation & warehouse services, construction) posted high-side increases.
- Excluding the (apparently) random shifts, results were tame.

January PPI: Wild Swings in Volatile Areas

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