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Eaton & Kortum’s 2002 Econometrica paper gives the Ricardian trade model a parametric form in which country-specific technology distributions and iceberg trade costs combine into a clean gravity equation. The empirical anchor is their Table III, where they use six distance dummies and find bilateral trade falling sharply with distance: their estimated bin coefficients grow more negative as distance rises, from about −3.10 log points for the closest bin to −6.56 for the farthest. We reproduce the distance-gap pattern on 2020 CEPII Gravity and BACI data.
Eaton & Kortum (2002) model bilateral trade as the outcome of Ricardian comparative advantage with a Fréchet-distributed productivity shock. Their bilateral-trade estimating equation (Table III) decomposes the geographic barrier into six binary distance bins plus shared-border, shared-language, and trade-area effects. All six bins are estimated (none is an omitted reference); the coefficients (in log points) are −3.10 (0-375 mi), −3.66 (375-750 mi), −4.03 (750-1500 mi), −4.22 (1500-3000 mi), −6.06 (3000-6000 mi), and −6.56(>6000 mi). The pattern is monotone: the barrier grows steadily more negative with distance, so the farthest bin carries the largest penalty, not the smallest. Eaton & Kortum report an implied elasticity of trade volume with respect to overall geographic barriers of roughly 2 to 3 (their note 45).
We use the CEPII Gravity V202411 release (distributed on this site as gravity_bilateral) for 2020, merged with BACI 202501 (retrieved 2026-04-28) bilateral flows. For each of the six Eaton-Kortum bins we compute the mean of ln(tradeflow) across all positive-trade pairs in the bin. The bin gap (mean_log − mean_log of the >6000-mi reference bin) is the conditional analog of Eaton-Kortum’s distance-bin dummy, except that we do not absorb origin and destination fixed effects. Our gaps are therefore raw distance gradients, not clean coefficients on distance dummies net of country size. They still reproduce the monotone, sharply-declining pattern that underpins the Ricardian gravity model.
EK’s model assumes productivity zi(ω) for variety ω in country i is drawn from a Fréchet distribution Fi(z) = exp(−Ti z−θ), where Tiis a technology scale parameter (comparative advantage) and θ (>1) is the trade elasticity. Bilateral trade then satisfies Xni/Xnn = (Ti/Tn) (cidni/cn)−θ, and the importing country’s price index is Pn = γ [ Σi Ti (ci dni)−θ ]−1/θ (EK Equation 10), where γ collects constants. EK estimate θ ≈ 8.28with a price-based method-of-moments estimator (Section 3; summarized in their Table VIII, Summary of Parameters). The distance-bin coefficients in Table III are products of θ and the log-distance gradient: EK divide them by θ and exponentiate (their Table VII) to read off the iceberg cost each bin imposes. The closest bin’s −3.10 under θ = 8.28 implies an iceberg markup of about 45% over a frictionless pair. Converting our 2020 raw bin gap for 0-375 mi of +5.66 into an implied θ-scaled iceberg cost (treating the gap as θ · ln(dfar/dnear) and taking dfar/dnear ≈ 20) gives θ ≈ 1.89. The implied θ is sensitive to the reference-bin choice and the lack of fixed effects, but lands in the same order-of-magnitude as EK’s 8.28.
EK’s aggregate θ ≈ 8.28 (Section 3 price-moment) is a single number across all sectors. That choice was forced by the moment-condition data they had. A decade later, Caliendo-Parro (2015) and Bas-Mayer-Thoenig (2017, JIE) show θ in fact differs by roughly two orders of magnitude across sectors: homogeneous commodities (petroleum, base metals) have very high θ (near-perfect substitution across origins), while differentiated manufactures (vehicles, machinery) have much lower θ. We approximate sectoral θ using the HS6 Kee-Nicita-Olarreaga demand elasticities aggregated to HS Sections.
The EK model predicts that high-θ sectors (where productivity draws have thinner upper tails, products are closer substitutes, and cost differences bite) should also be sectors where a larger share of global absorption crosses borders. We plot sectoral median |σ| (θ-analog, from Figure 2) against each section’s share of world exports in 2019, a revealed tradability metric. A positive slope is the EK / Chaney-style prediction: more substitutable goods travel further.
EK’s Table VI (States of Technology) ranks 19 OECD economies by their estimated technology parameter Ti, the scale of the Fréchet productivity distribution. Ti is not directly observable; EK back it out of the trade equation Xni = Ti (ci dni)−θ Xn / Φn using bilateral trade and prices. A data-only proxy that strips out the unidentified ci term keeps the geography-weighted market access denominator MAi = Σn dni−θ Xn and reads Ti1/θ ∝ (XiW / MAi)1/θ, with θ = 8.28 from EK’s Section 3 price-moment estimate. Because the proxy drops the ci and Φnterms, it does not recover EK’s Table VI ordering: it rewards remote, low market-access economies and so should be read as a geography diagnostic, not a productivity ranking.
| distance bin | EK Table III coef | our gap vs >6000 mi (2020) | n pairs |
|---|---|---|---|
| 0-375 mi | -3.10 | +5.656 | 449 |
| 375-750 mi | -3.66 | +4.641 | 898 |
| 750-1500 mi | -4.03 | +3.170 | 2,324 |
| 1500-3000 mi | -4.22 | +1.411 | 4,586 |
| 3000-6000 mi | -6.06 | +0.723 | 10,221 |
| > 6000 mi | -6.56 | +0.000 | 7,989 |
Our gaps are largerin absolute value than Eaton-Kortum’s bin coefficients. Three reasons. First, no multilateral-resistance controls: Eaton-Kortum absorb exporter and importer fixed effects, so their bin coefficients isolate the distance-only channel. We do not, so our gaps conflate distance with the fact that countries near each other tend to also be large trading partners (Western Europe, East Asia): an omitted-variable bias that inflates the close-range bin. Second, sample period: Eaton-Kortum fit 1990 data on 19 OECD countries; we use 2020 on ~160 BACI trading economies, so the country mix is different and the time is three decades later. Third, functional form: we report raw bin means of log-trade, Eaton-Kortum report fitted dummies from a full log-linear gravity equation with GDP and population on both sides. A faithful replication would (a) absorb country fixed effects, (b) control for log GDP, and (c) estimate via PPML or OLS; the silva-tenreyro-2006page on this site runs that full specification on the same data.
The qualitativepunchline, a monotone distance gradient in bilateral trade, survives the 1990→2020 shift. Eaton-Kortum’s estimated barriers grow with distance; our raw bin gaps fall toward the long-distance reference. Both encode the same fact: distance still depresses trade, even after three decades of falling shipping and communication costs.
@article{eaton_kortum_2002,
author = {Eaton, Jonathan and Kortum, Samuel},
title = {Technology, Geography, and Trade},
journal = {Econometrica},
volume = {70},
number = {5},
pages = {1741--1779},
year = {2002},
doi = {10.1111/1468-0262.00352}
}Gravity in more depth at /gravity. Compare to the PPML fit on the same sample at Silva-Tenreyro (2006). Return to the replication gallery.