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Fetching primary parquet sources and computing exhibits.
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Fetching primary parquet sources and computing exhibits.
Treating the world economy as a weighted graph of intermediate-goods trade, a small set of countries anchor the network: every large flow either starts at them, passes through them, or ends at them. The question is not 'who exports most', that is already answered by trade tables, but who is most wired into the set of other well-wired countries. Two standard tools from network analysis make this precise: eigenvector centrality (Bonacich 1972; Newman 2010, chapter 7) and weighted-degree strength (Barrat, Barthélemy, Pastor-Satorras & Vespignani 2004, PNAS). When the graph is the production network, the centrality ranking is the map of whose disruption would propagate furthest into aggregate output (Acemoglu, Carvalho, Ozdaglar & Tahbaz-Salehi 2012, Econometrica).
Method and data caveat. We proxy intermediate goods by HS chapters 84 (mechanical machinery and parts) and 85 (electrical machinery and parts), which capture the bulk of BEC categories 41, 42, 51, and 53 (parts and capital goods) in the UN BEC Rev.5 - HS correspondence (UNSD, 2016). The workbench parquet build exports BACI bilateral flows only as total value, not per HS chapter, so we weight each directed bilateral flow fij by the exporter's own HS 84-85 share of its total exports in the same year. That is a coarser approximation than a true per-HS bilateral network (which would require reprocessing the raw BACI CSV), but it preserves cross-country ordering when HS 84-85 intensity varies systematically by exporter, as it does here. Eigenvector centrality is computed by power iteration on the symmetric weighted adjacency Wij = intensityi·fij + intensityj·fji.
Eigenvector centrality answers a recursive question: a country is central if it trades heavily with other central countries. The leading eigenvector of W encodes that definition uniquely under Perron-Frobenius (Newman 2010, §7.2). We rescale to [0, 1] by dividing through by the maximum; the top-ranked country scores 1.000. Countries not inside the top 50 by intermediate-proxy flow are excluded from the eigenvector computation.
-- per-exporter HS 84-85 intensity (HS96 base + HS22 extension)
WITH hs AS (
SELECT country_code, SUM(export_value) AS hs
FROM 'data/parquet/country_year_product/year=2024/*.parquet'
WHERE (product_code LIKE '84%' OR product_code LIKE '85%') AND export_value > 0
GROUP BY country_code
),
tot AS (
SELECT country_code, SUM(export_value) AS tot
FROM 'data/parquet/country_year_product/year=2024/*.parquet'
WHERE export_value > 0 GROUP BY country_code
),
bilat AS (
SELECT exporter_code, importer_code, SUM(total_value) * 1000 AS v
FROM 'data/parquet/bilateral_year/year=2024/*.parquet'
WHERE total_value > 0 AND exporter_code <> importer_code
GROUP BY exporter_code, importer_code
)
SELECT b.exporter_code, b.importer_code,
b.v * (hs.hs / NULLIF(tot.tot, 0)) AS proxy_flow
FROM bilat b
JOIN hs ON hs.country_code = b.exporter_code
JOIN tot ON tot.country_code = b.exporter_code;
-- eigenvector centrality computed in TypeScript by power iteration.A static centrality ranking is informative; the change in rank across a quarter-century is where the structural story sits. For every country in the top-50 panels of both 2000 and 2024, we take its eigenvector rank in each year and report the difference (rank2000 − rank2024). A positive value means the country has moved toward the core of the network; a negative value means it has fallen back. Carvalho (2014) emphasises that such shifts typically reflect a reorganisation of global value chains rather than a pure scale-up of trade.
The eigenvector ranking above has an obvious competing explanation: countries are central because they are big, and GDP already tracks economic mass. The production-network literature argues otherwise. Acemoglu et al. (2012) show that aggregate volatility depends on the eigenvector centrality of the input-output network, not on GDP per se, and that concentration of centrality in a few sectors or countries is what translates idiosyncratic shocks into macro fluctuations. An equivalent cross-country prediction: country-level centrality and GDP should be positively but imperfectly correlated, with identifiable outliers where centrality exceeds what GDP alone would suggest (classic trade hubs: Singapore, Netherlands, Belgium) or undershoots it (large commodity exporters).
Acemoglu et al. (2012) show formally that a negative productivity shock at a central node propagates through the input-output graph with a magnitude proportional to that node's centrality. Here we run a much simpler, first-order version: halve country X's HS 84-85-proxy exports to every partner, compute the loss each partner takes as a share of its own intermediate-goods imports, and list the ten most exposed partners. We do this for three candidate shocks, China, the United States, and Germany, chosen because they are the three most central nodes in Figure 1. This captures only the direct bilateral hit: it ignores substitution, re-exports, and higher-order Leontief propagation, and is therefore a strict lower bound on true exposure.
HS chapters 84 and 85 together have been the largest single block of world merchandise trade for decades. The time series below reports their combined share of world exports in the BACI parquet, 1996-2024, as context for the centrality numbers above: this is a large enough subuniverse that rankings on it are not a statistical artifact of a thin aggregate.
Figure 1 is a static ranking; but centrality inequality across the top-50hub set is itself a time-varying object. We recompute eigenvector centrality on the intensity-weighted adjacency for eight years across the sample and report the Gini coefficient of the centrality vector in each year. Rising Gini = a shrinking, more top-heavy core. Falling Gini = the network is becoming more evenly wired. De Benedictis & Tajoli (2011, World Economy 34(8): 1417-1454) argued on earlier data that the world trade web had been concentrating; this is the eigenvector-centrality analogue.
Figure 2 reports the long-run 2000-2024 shift; the post-global-financial-crisis decade is a distinct regime. Between 2010 and 2024the production network absorbed the China slowdown (Autor, Dorn & Hanson 2021, NBER WP 29401), the 2018-2020 tariff escalation (Fajgelbaum & Khandelwal 2022, ARE), COVID supply-chain disruptions, and the post-2020 nearshoring wave. This figure isolates that decade by reporting eigenvector-centrality rank change over the shorter window.
A stable central hub (DEU, USA, CHN) should look positionally similar across 2000, 2010, and 2024: the three snapshots span the China-shock, post-GFC, and post-COVID regimes, and a structurally-anchored hub should survive all three. We measure that by the standard deviation of rescaled eigenvector centrality across the three panels for every country present in all three with centrality above 0.10 in at least one. High sigma flags hubs whose production-network standing has oscillated regime-to-regime, typically the candidate 'switchers' whom Alfaro & Chor (2023) and Carvalho (2014) identify as GVC reshufflers.
Figures 1 to 8 collapse the network into per-country summaries. Figure 9 reads the network the other way around and lists the single largest weighted bilateral edges in 2024: each entry is an exporter's flow to one specific importer, weighted by that exporter's HS 84-85 intensity, the same construction that feeds the eigenvector ranking. Hidalgo, Klinger, Barabási & Hausmann (2007, Science 317: 482-487) make the case for inspecting the underlying edges directly: the top-of-rank hubs in Figure 1 should be those that own the heaviest pairwise links, and the spine view validates or refutes that.