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Corden & Neary (1982, Economic Journal92(368)) formalised the mechanism: a resource-export boom appreciates the real exchange rate and crowds tradable manufacturing out through factor reallocation. Sachs & Warner (1997, Harvard CID; published in revised form 2001 in European Economic Review 45(4-6): 827-838) documented a negative cross-country growth correlation with natural-resource intensity. van der Ploeg (2011, Journal of Economic Literature 49(2): 366-420) surveyed three decades of follow-up evidence and flagged institutional quality as the key moderator. This page tests the textbook prediction on 2000-2024 BACI trade data: across 154 countries with at least one billion USD of exports in 2024, the change in resource-export share has a Pearson correlation of -0.33 with the change in manufacturing-export share, and the OLS slope is -0.18 (one percentage point more resources, 0.18 pp less manufacturing on average when slope is negative).
For each country×year cell we compute the resource-export share as the value of HS2 chapters 25-27 plus 71 (salt, ores, mineral fuels, precious stones and metals) divided by total merchandise exports, and the manufacturing-export shareas HS2 chapters 84-90 (machinery, electricals, vehicles, aircraft, ships, instruments) divided by the same denominator. BACI values are stored in thousands of USD; we multiply by 1,000 for absolute amounts only, not for shares. This two-basket cut follows the empirical partition used by Harding & Venables (2016, IMF Economic Review 64(2): 268-302) in their resource-windfall test, and matches the exportable tradablesdistinction in Corden & Neary (1982).
The textbook Dutch-disease trajectory is a rising resource share paired with a falling manufacturing share in the same country. The next two panels plot the two shares for the ten countries with the largest resource-export value in 2024, conditional on resources making up at least 30% of their export basket. Commodity-price cycles (oil 2008, 2014, 2022) are visible as level shifts in the resource-share line.
WITH ctry AS (SELECT iso3, MIN(code) AS code FROM 'countries.parquet' GROUP BY iso3)
SELECT c.iso3, cyp.year,
SUM(CASE WHEN substr(product_code,1,2) IN ('25','26','27','71') THEN export_value END)
/ NULLIF(SUM(export_value), 0) AS res_share
FROM 'country_year_product/**/*.parquet' cyp
JOIN ctry c ON c.code = cyp.country_code
WHERE c.iso3 IN (top-10 resource exporters) AND cyp.export_value > 0
GROUP BY c.iso3, cyp.year ORDER BY c.iso3, cyp.year;For every country with at least one billion USD in 2024 exports, we compute the change in resource share and the change in manufacturing share between 2000 and 2024. Under the Corden-Neary prediction, points should cluster along a downward-sloping line in (Δres, Δmfg)-space: countries that got more resource-heavy should have shed manufacturing share. The pooled OLS slope is -0.18 and the Pearson correlation is -0.33 on 154 countries, significantly negative and consistent with the prediction.
The 'curse' is not destiny. Mehlum, Moene & Torvik (2006, Economic Journal 116(508): 1-20) showed that institutional quality flips the sign of the resource-growth relationship, and Frankel (2010, NBER WP 15836) documented several countries that used fiscal rules and sovereign-wealth vehicles to avoid the classical Dutch-disease path. Norway's Government Pension Fund Global (the former Petroleum Fund, 1990 Act) is the textbook case; Australia and Canada relied on institutional depth and federated fiscal adjustment. Their trajectories are plotted alongside the resource share to show how a commodity boom can coexist with a non-collapsing industrial base.
The last panel ranks the current resource-rich set (resource share ≥ 30% in 2024) by a simple Dutch-disease-risk score: the change in resource share minus the change in manufacturing share over 2000-2024. A large positive score means the country has become materially more resource-concentrated while its manufacturing footprint has shrunk relative to its total export basket, the combination Corden & Neary warned about. This is a descriptive flag, not a causal claim; the Sachs-Warner 1997 critique, and later revisions such as Lederman & Maloney (2008, Economía 9(1): 1-39), stress that resource abundance and resource dependence are not the same thing, and institutions mediate the effect.
Figure 2 plotted changes over 2000-2024; Figure 5 plots levels in 2024. Each dot is a country placed by its resource share (x-axis) and manufacturing share (y-axis), with dot area proportional to total export value. On top we overlay two sets of conditional medians: blue dots RQ1-RQ5 mark the median of manufacturing share within each resource-share quintile (a conditional median of mfg given res), and red dots MQ1-MQ5 mark the median of resource share within each manufacturing-share quintile (a conditional median of res given mfg). If the Corden-Neary prediction holds in levels, both sets of medians should trace a downward-sloping curve. This is the non-parametric 2-way quantile version of what a quantile-regression estimator (Koenker & Bassett 1978, Econometrica 46(1): 33-50) would identify under smoother functional-form assumptions.
Figures 1-5 are descriptive: they tell us the observed correlation between resource and manufacturing shares but not the Corden-Neary effectof a resource boom on manufacturing. Harding & Venables (2016) used giant-oil-discovery timing as an IV; Caselli & Michaels (2013) used municipal oil rents in Brazil. We cannot replicate either in this workbench at the country level. What we can do is a Bartik-style instrument (Goldsmith-Pinkham, Sorkin & Swift 2020, AER 110(8)): multiply the country's year-2000 HS 27 share (a pre-determined measure of oil-export exposure) by the log change in the World Bank Pink Sheet Brent price from 2000 to 2022. The exogeneity argument is Hamilton (2009, BPEA): world oil prices are close to exogenous for any single non-giant producer once we hold fixed its initial specialisation. Figure 6a is the first stage (instrument vs Δres_share), Figure 6b is the reduced form (instrument vs Δmfg_share). Both windows run 2000-2022 to use the strongest world price variation (2000 to 2022 peak) before the 2023-24 retreat which weakens the instrument.
Mehlum, Moene and Torvik (2006, Economic Journal 116(508): 1-20) argue that the resource curse is conditional on institutions: grabber-friendly institutions turn rents into a curse, producer-friendly institutions reverse the sign. Robinson, Torvik and Verdier (2006, Journal of Development Economics 79(2): 447-468) make the same point in a political-economy complementary frame. The descriptive scatter in Figure 2 and the IV in Figure 6 ignore this moderation. Figure 7 adds it. We pull the Worldwide Governance Indicators rule-of-law score (RL.EST, Kaufmann, Kraay and Mastruzzi 2010, World Bank Policy Research WP 5430) for the earliest available year close to 2000as a pre-determined institutional proxy, sort the resource-rich subsample (resources ≥ 30% in 2024) into RL tertiles, and report the mean change in manufacturing share over 2000-2024 per tertile. If the Mehlum-Moene-Torvik conditional- curse story holds, weak-RL countries should show the steepest mfg-share decline; strong-RL countries should be flat or positive.
Real effective exchange rate (REER), Index (2010=100) Adjusted by relative consumer prices series. On the same 79-country sample, the Δres coefficient is -0.27631 without REER and -0.27632with it. The Δln REER coefficient is +0.0704 with t = +1.77. Controlling for appreciation moves the Δres coefficient by 0.00001. This is a null result: there is no detectable RER mediation of the Δres to Δmfg association in this long difference.WITH early AS (SELECT country_code, SUM(CASE WHEN substr(product_code,1,2) IN ('25','26','27','71') THEN export_value END)/ NULLIF(SUM(export_value), 0) AS res, SUM(CASE WHEN substr(product_code,1,2) IN ('84','85','86','87','88','89','90') THEN export_value END)/ NULLIF(SUM(export_value), 0) AS mfg FROM 'country_year_product/year=2000/*.parquet' GROUP BY country_code),
late AS (SELECT country_code, SUM(CASE WHEN substr(product_code,1,2) IN ('25','26','27','71') THEN export_value END)/ NULLIF(SUM(export_value), 0) AS res, SUM(CASE WHEN substr(product_code,1,2) IN ('84','85','86','87','88','89','90') THEN export_value END)/ NULLIF(SUM(export_value), 0) AS mfg, SUM(export_value) AS tot FROM 'country_year_product/year=2024/*.parquet' GROUP BY country_code)
SELECT REGR_SLOPE(l.mfg - e.mfg, l.res - e.res) AS slope, CORR(l.res - e.res, l.mfg - e.mfg) AS corr
FROM early e JOIN late l USING (country_code) WHERE l.tot > 1000000;| -3.8% |
| +88.18pp |
| 4 | LBR Liberia | -1.92 | fragile | 60.5% | +42.3% | 19.3% | -37.6% | +79.89pp |
| 5 | SUR Suriname | -0.10 | middle | 80.1% | +62.7% | 3.2% | +2.1% | +60.62pp |
| 6 | MNG Mongolia | 0.06 | robust | 94.8% | +51.9% | 0.2% | -0.7% | +52.61pp |
| 7 | BHS Bahamas | 1.24 | robust | 72.8% | +44.0% | 14.1% | -8.5% | +52.46pp |
| 8 | TJK Tajikistan | -1.30 | fragile | 51.8% | +51.3% | 0.3% | -0.4% | +51.65pp |
| 9 | ZWE Zimbabwe | -1.56 | fragile | 55.5% | +47.5% | 0.7% | -1.5% | +49.02pp |
| 10 | SEN Senegal | -0.05 | robust | 53.6% | +40.3% | 3.5% | -7.4% | +47.70pp |
| 11 | LAO Lao People's Dem. Rep. | -1.33 | fragile | 41.5% | +39.6% | 11.6% | -6.2% | +45.82pp |
| 12 | GUY Guyana | -0.20 | middle | 93.0% | +47.1% | 4.8% | +3.2% | +43.92pp |
-- Bin countries into 5 resource-share quintiles; within each bin, median mfg share. -- Symmetric version: 5 mfg-share quintiles; within each bin, median res share. -- Resulting two step curves plotted on top of the raw (res, mfg) scatter.
-- First stage: Δres_share = π · (oil27_share_2000 × Δlog Brent) + u
WITH y00 AS (
SELECT country_code,
SUM(CASE WHEN substr(product_code,1,2)='27' THEN export_value END)/ NULLIF(SUM(export_value), 0) AS oil27_share,
SUM(CASE WHEN substr(product_code,1,2) IN ('25','26','27','71') THEN export_value END)/ NULLIF(SUM(export_value), 0) AS res_share_00
FROM 'country_year_product/year=2000/*.parquet' GROUP BY country_code),
y22 AS (
SELECT country_code,
SUM(CASE WHEN substr(product_code,1,2) IN ('25','26','27','71') THEN export_value END)/ NULLIF(SUM(export_value), 0) AS res_share_22
FROM 'country_year_product/year=2022/*.parquet' GROUP BY country_code)
SELECT oil27_share * 1.2615 AS z, res_share_22 - res_share_00 AS d_res
FROM y00 JOIN y22 USING (country_code);-- Δ mfg share by WGI RL tertile, resource-rich exporters, 2000-2024
WITH rl AS (
SELECT iso3, value AS rl FROM 'wgi.parquet'
WHERE indicator='RL.EST' AND year=(SELECT MIN(year) FROM 'wgi.parquet'
WHERE indicator='RL.EST' AND year >= 2000)
)
SELECT iso3, rl, d_mfg FROM cross_country_changes
WHERE res_share_2024 >= 0.3;