Economy in Brief

In this week’s Letter, we examine what distinguishes Asia’s economies, how those differences have evolved, and whether the AI boom is reinforcing them. Using 13 indicators covering income, economic structure, trade, demographics and public finances, we group 16 Asian and other major economies according to their similarities (chart 1). Principal component analysis then helps identify the main patterns behind these groupings (chart 2). The results distinguish the developed economies and Singapore from the rest of Asia, while also separating the more industrialised Asian economies from the younger, more agricultural economies of India, Indonesia and the Philippines (charts 3 and 4). These structural divisions have proved persistent: every economy finished 2025 in the same cluster as in 2019, despite some movement in between (chart 5). The AI boom is now providing a stronger lift to semiconductor exporters and economies attracting data centre investment (chart 6). Without substantial structural change elsewhere, the gaps between these groups could widen even as their membership remains stable.

Asia in clusters K-means clustering offers a simple way to sort economies by how alike they are. The algorithm assigns each economy to one of k groups, placing it with the group whose average profile it most closely resembles. The number of groups, k, is a hyperparameter: the analyst chooses it, rather than the algorithm learning it from the data. We apply the method to 16 Asian and other major economies, using 13 indicators covering wealth, sectoral structure, the external sector, demographics and public finances. With k set at three, distinct groups emerge (chart 1). The developed economies form one cluster, the rest of Asia, including China and Korea, forms a second, and Singapore stands alone as a third. Raising k to four leaves the developed group and Singapore unchanged. Instead, it draws finer distinctions within emerging Asia, with India, Indonesia and the Philippines breaking away to form a cluster of their own. We explore why Singapore stands apart, and which indicators drive these splits, in the sections that follow.

The EU index for the European Monetary Union (EMU) fell to 96.9 in October from 97.5 in September. The index was last lower in June of this year, so this is a multi-month low.

The main sector indexes showed a monthly improvement in only one, the industrial sector, with a rise of one point. Weakening on the month were the construction sector, retail sector, consumer confidence, and the services sector. Each of them slipped by about one point.

Across reporters, eleven of the 16 countries that are early reporting show month-to-month declines. There were monthly increases in France and Italy as well as among the smaller economies Austria, Belgium, and Slovakia. Nine countries had shown monthly declines in September and only seven had logged weakening in August; still, that’s seven out of sixteen.

EMU-wide sectors By sector, only two of the five sectors have rankings in their queue standings of historic data above their respective medians. Those two are the industrial sector (58.0%) and construction (57.7%). Below-median rankings were turned in by retailing (43.9%), services (38.5%), and consumer confidence (13.0%). Consumer confidence globally appears much weaker than the sector you might expect to see it affect, like retailing, wholesaling, or services. Consumers keep worrying but keep spending. Still, the overall ranking for the EMU index is at its 37th percentile. That overall ranking is lower than the unweighted simple average of the five-sector ranking (at 42.2%).

Across countries, the rankings show only five of 16 reporting countries with rankings above the 50% mark, a ranking that would place them above their respective historic medians.