Fetching primary parquet sources and computing exhibits.
research note
Who bounced back from the 2020 trade shock, and who did not?
Global merchandise exports fell -7.5% from $18.57T in 2019 to $17.18Tin 2020, the sharpest single-year contraction on record outside wartime (Baldwin & Weder di Mauro 2020). By 2024 the world had recovered to 1.23x of its 2019 level. But the aggregate hides a wide cross-country dispersion. This note constructs a recovery index for each country as the ratio of its actual 2024 export value to a log-linear pre-pandemic (2010-2019) trend extrapolated forward. Values above 1 indicate over-trend recovery; below 1 indicate permanent loss relative to the pre-COVID path. Of the 160 countries with USD 1B+ of exports in 2019 and a fittable trend, 118 recovered above trend and 42 remained below.
trend window2010-2019
target year2024
countries in sample160
world 2020 dip-7.5%
world 2024 / 20191.23x
median recovery idx1.14x
The 2020 collapse and the 2021-2024 bounce in aggregate
World merchandise exports (summed across all BACI reporters) trace the textbook great trade collapsepattern first diagnosed for 2008-09 by Baldwin (2009) and re-enacted in 2020 (Bems, Johnson & Yi 2013 on trade-elasticity amplification). Commodity prices, services substitutes, and containment-driven services-to-goods rotation (Espitia et al. 2022) then pushed nominal trade to new highs by 2022 before a 2023 softening on lower energy prices.
Figure 1
Global merchandise exports, 2000-2024 (current USD)
World exports contracted from $18.57T in 2019 to $17.18T in 2020 (-7.5%). By 2024 they reached $22.83T, 1.23x the 2019 level.
Source: CEPII BACI 202501 (retrieved 2026-04-28) country-year totals summed to world. Current USD. Authors calcs.
Which countries over-recovered, and which were left behind
For each country with at least USD 1B in exports in 2019, we fit ln(exports) = a + b · year on 2010-2019, extrapolate to 2024, and compute recovery = actual_2024 / trend_2024. Log-linear extrapolation is the canonical baseline in the 'trade resilience' literature (Espitia et al. 2022, World Bank Policy Research WP 9869). The map colours deviations from 1, so blue = over-trend, red = under-trend.
Figure 2
Recovery index (2024 actual divided by 2010-2019 trend extrapolation)
Over-performers and under-performers
Ranking countries by the 2024recovery index gives a shortlist of pandemic-era winners and losers. Cross-checking the top tail against the post-2018 US-China tariff reshuffle (Fajgelbaum & Khandelwal 2022, Annual Review of Economics) and the regional near-shoring trend (Alfaro & Chor 2023, NBER WP 31755) helps distinguish countries that recovered because of structural shifts from those merely riding a commodity-price rebound.
Figure 3
Top-10 and bottom-10 recovery index vs pre-COVID trend, 2024
Is economic complexity related to recovery speed?
Hausmann & Hidalgo (2009, PNAS) argue that economic complexity ECI measures the diversity and sophistication of a country's export basket, and that complex economies enjoy more durable trade growth. If complexity also confers resilience, high-ECI economies should cluster above the recovery index value of 1. The scatter below plots the 2019 ECI (last pre-pandemic observation) against the 2024 recovery index, bubble-sized by 2024 export value.
Figure 4
Recovery index (2024) vs economic complexity ECI (2019)
What predicts recovery? OLS estimates
We regress the recovery index on (i) log-GDP in 2019 (WDI NY.GDP.MKTP.CD, current USD), (ii) ECI in 2019, and (iii) backward GVC participation, measured as the foreign value-added share of gross exports (EXGR_FVA / EXGR, OECD TiVA, 2019) per Koopman, Wang & Wei (2014, AER). Heteroskedasticity-consistent standard errors would refine the inference; the naive OLS SE below is a first pass. OxCGRT policy-stringency (Hale et al. 2021, Nature Human Behaviour) is a natural fourth regressor but is not yet ingested into the workbench and is flagged below as a known methodology gap.
Methodology limitation: OxCGRT containment-and-health index (Hale et al. 2021) is not yet ingested into data/parquet/, so the regression set omits a direct lockdown-stringency control. Peak-stringency 2020-2021 would distinguish whether harsher lockdowns permanently dented export capacity or merely delayed recovery.
regressor
coef
std. err.
t
intercept
4.3128
1.0311
4.18
log GDP 2019
-0.1013
0.0373
-2.71
ECI 2019
0.0900
0.0617
1.46
FVA share 2019
-0.0172
0.0047
-3.65
n = 75, R² = 0.178, residual SE = 0.394.
Robustness: recovery by complexity quartile
Figure 5 bins countries with observed ECI into quartiles of 2019complexity and reports mean recovery index per bin. This is a non-parametric check on the OLS slope above: if complexity were unrelated to recovery, mean recovery should be flat across bins. The pattern also serves as a robustness check on the functional form: Hausmann & Hidalgo (2009) ECI is ordinal in design (rankings of rankings), so parametric linear slopes may overstate or understate precision.
Figure 5
Mean recovery index by ECI quartile, 2024
Quartile means report percent deviation from pre-COVID trend. A monotone positive gradient from Q1 to Q4 would confirm the complexity-resilience story in Hausmann & Hidalgo (2009); a flat or noisy profile would argue that complexity captures level growth, not shock absorption.
Method: quartiles on 2019 ECI within the n = 160 countries that carry both a recovery index and an ECI observation. Sources: CEPII BACI 202501 (retrieved 2026-04-28), workbench eci_rankings (Hausmann-Hidalgo method).
Services vs goods: the asymmetric recovery
The merchandise exports used in Figures 1-5 tell only half the story. Services trade (tourism, transport, financial services, ICT, professional services) was hit harder in 2020 by border closures and the travel shutdown (Baldwin & Tomiura 2020, Economics in the Time of COVID-19; WTO 2023, World Trade Report) and then recovered more slowly than goods because travel and in-person services persisted as lagging components. Figure 6 indexes world services and goods exports to 2019 = 100 using World Bank WDI data (BX.GSR.NFSV.CD, BX.GSR.MRCH.CD), constant-sample across 152 ISO3 reporting both series in every year 2018-2024.
Figure 6
Services vs goods exports, 2018-2024 (2019 = 100, constant-sample world)
Services exports fell to 85 in 2020 (-15.4% below 2019), a deeper drop than goods at 93 (-7.1%). By 2024 services stood at 138 and goods at 126. The asymmetry is the central stylised fact in Baldwin & Tomiura (2020): travel, transport, and in-person services pulled the services line down harder and kept it down longer, while manufactured goods rebounded first on inventory-restock demand and then pandemic goods consumption. Borchert, Magdeleine, Marchetti & Mattoo (2022, World Bank Policy Research WP 9991) on GATS services schedules argue the slower services recovery is structural, not just behavioural.
V-shaped vs L-shaped recovery by sector
Baldwin & Tomiura (2020) and WTO (2023, World Trade Report 2023) argue the 2020 shock hit sectors asymmetrically: contact-intensive textiles and transport-equipment exports dropped twice as hard in 2020 as staples, and recovery paths diverged through 2022-2024 by sectoral exposure to commodity-price cycles and shifting consumer demand. Figure 7 maps nine HS2-based sectoral baskets in (dip ratio, recovery ratio)-space. The dip ratio is world exports in 2020 divided by 2019 (100 = flat, below 100 = contraction). The recovery ratio is 2024 over 2019. Sectors in the upper-right (dip >= 95, recovery >= 105) are V-shaped; those with recovery below 95 are L-shaped, they never regained their 2019 peak even four years on. The rest are U-shaped: dipped and returned roughly to trend.
Figure 7
V-shape vs L-shape recovery by HS2 sector category
Figure 8
Country-level dip (2020/2019) versus recovery (2024/2019)
Policy read and open questions
Resilience vs commodity tailwind.Much of the top decile's 'over-recovery' is dollar-denominated and tracks the 2022 energy-price shock (Baldwin & Freeman 2022, Annual Review of Economics). A quantity-based recovery index using deflated or volume indices would separate true resilience from the commodity price rotation, the required deflators are in IMF WEO but not joined in this page.
Near-shoring dividend.Bricongne, Fontagné, Gaulier, Taglioni & Vicard (2022, Journal of International Economics) and Alfaro & Chor (2023, NBER WP 31755) flag Viet Nam, Mexico, Morocco, and India as post-2018 reshuffle beneficiaries. The top tail of Figure 3 matches that shortlist, consistent with a near-shoring pull on top of the COVID rebound.
GVC intensity and the collapse-bounce asymmetry.Hayakawa & Mukunoki (2021, Journal of Asian Economics) showed that high-GVC-participation exporters fell harder in 2020 but also bounced faster. The FVA coefficient in our OLS is the cross-sectional version of that prediction; a panel with pair fixed effects would identify it more cleanly.
Policy-stringency regressor (pending). Adding Hale et al. (2021) OxCGRT containment-and-health peak-2020/2021 exposure would test whether harsher lockdowns permanently dented export capacity or merely delayed the rebound. Not yet loaded into the workbench.
References
Alfaro, L., & Chor, D. (2023). 'Global Supply Chains: The Looming Great Reallocation.' NBER Working Paper 31755.
Baldwin, R., & Freeman, R. (2022). 'Risks and Global Supply Chains: What We Know and What We Need to Know.' Annual Review of Economics 14: 153-180.
Bricongne, J.-C., Fontagné, L., Gaulier, G., Taglioni, D., & Vicard, V. (2022). 'From Macro to Micro: Heterogeneous Exporters in the Pandemic.' Journal of International Economics 135: 103571.
Hayakawa, K., & Mukunoki, H. (2021). 'Impacts of COVID-19 on Global Value Chains.' Developing Economies 59(2): 154-177.
Baldwin, R. (Ed.) (2009). The Great Trade Collapse: Causes, Consequences and Prospects. CEPR Press.
Baldwin, R., & Tomiura, E. (2020). 'Thinking Ahead about the Trade Impact of COVID-19.' In R. Baldwin & B. Weder di Mauro, eds., Economics in the Time of COVID-19, CEPR Press, pp. 59-71.
Borchert, I., Magdeleine, J., Marchetti, J. A., & Mattoo, A. (2022). 'Applied Services Trade Policy: A Guide to the Services Trade Policy Database and Services Trade Restrictions Index.' World Bank Policy Research Working Paper 9991.
World Trade Organization (2023). World Trade Report 2023: Re-globalization for a Secure, Inclusive and Sustainable Future. WTO, Geneva.
Baldwin, R., & Weder di Mauro, B. (Eds.) (2020). Economics in the Time of COVID-19. CEPR Press.
Bems, R., Johnson, R. C., & Yi, K.-M. (2013). 'The Great Trade Collapse.' Annual Review of Economics 5: 375-400.
Espitia, A., Mattoo, A., Rocha, N., Ruta, M., & Winkler, D. (2022). 'Pandemic Trade: COVID-19, Remote Work and Global Value Chains.' World Bank Policy Research Working Paper 9869. The World Economy 45(2): 561-589.
Fajgelbaum, P. D., & Khandelwal, A. K. (2022). 'The Economic Impacts of the US-China Trade War.' Annual Review of Economics 14: 205-228.
Hale, T., Angrist, N., Goldszmidt, R., Kira, B., Petherick, A., Phillips, T., et al. (2021). 'A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker).' Nature Human Behaviour 5(4): 529-538.
Hausmann, R., & Hidalgo, C. A. (2009). 'The building blocks of economic complexity.' Proceedings of the National Academy of Sciences 106(26): 10570-10575.
Koopman, R., Wang, Z., & Wei, S.-J. (2014). 'Tracing Value-Added and Double Counting in Gross Exports.' American Economic Review 104(2): 459-494.
118 of 160 countries exceed their pre-COVID trend path by 2024; 42 remain below. The median country sits at 1.14x of trend.
Method: per-country log-linear fit on 2010-2019, extrapolated to 2024. Diverging colour centred on zero deviation from trend (value = index - 1); legend shows the original ratio. Sources: CEPII BACI 202501 (retrieved 2026-04-28) country-year totals. Authors calcs.
Largest over-performer: Chad at 4.65x of trend (365% above). Largest under-performer: State of Palestine at 0.06x (-94% below). Bars show deviation from trend in percentage points.
Source: CEPII BACI 202501 (retrieved 2026-04-28). Per-country log-linear fit method as in Figure 2. Authors calcs.
The scatter shows the bivariate relationship between complexity and recovery. The multivariate regression (see below) controls for log-GDP and GVC foreign value-added share to isolate the partial effect.
Sources: CEPII BACI 202501 (retrieved 2026-04-28) for exports; Harvard Growth Lab / Hausmann-Hidalgo method ECI 2019 from workbench eci_rankings. Bubble area proportional to 2024 export value. Authors calcs.
Sources: World Bank WDI, BX.GSR.NFSV.CD (services exports, BoP, current USD) and BX.GSR.MRCH.CD (merchandise exports, BoP, current USD). Constant sample: 152 ISO3 reporting both series 2018-2024. Indexed to 2019 = 100. Authors calcs.
5 sector baskets trace a V-shape, 4 a U-shape, and 0 an L-shape over 2019-2024. Strongest V recovery: electronics (HS 85) at dip 102, recovery 130. Worst L outcome: (none). Green = V-shaped, red = L-shaped, amber = U-shaped. Bubble area is proportional to 2019 export value, so large bubbles matter for aggregate trade more than small ones regardless of their shape classification. Energy (HS 27) sits well above 100 on both axes because 2020 OPEC+ supply cuts and the 2022 invasion-driven price spike pushed nominal flows higher in dollar terms, a volume vs price separation that requires Pink Sheet or IMF WEO deflators to untangle (see Policy read below).
Each bubble is one country with USD 1B+ exports in 2019 and complete 2019/2020/2024 data (161 countries). Cross-sectional OLS of recovery on dip yields slope 2.90 with intercept -140: a positive slope means countries with shallower 2020 contractions had stronger 2024 levels, the opposite of mean-reversion. 138 of 161 countries sit on or above the 45-degree (dip = recovery) line, meaning they fully retraced their 2020 trough. Bubble area scales with 2024 export value; colour encodes ECI quartile (blue = top, green = third, amber = second, red = bottom; grey = ECI not observed). The within-country pandemic-shock mean- reversion read in Bricongne, Fontagne, Gaulier, Taglioni and Vicard (2022, JIE 135) finds heterogeneous-firm exporters that fell hardest also rebounded fastest at the firm level; at the country aggregate that mean-reversion is much weaker, consistent with the composition story (oil-price tailwind for some, services-trade persistence for others).
Sources: CEPII BACI 202501 (retrieved 2026-04-28) country_year_totals; Harvard Growth Lab ECI rankings (2019). Sample: countries with USD 1B+ in 2019 and observed 2019/2020/2024 totals. ECI quartile cut on the subsample with ECI observed. Authors calcs.