Who bounced back from the 2020 trade shock, and who did not?

Global merchandise exports fell -7.5% from $18.57T in 2019 to $17.18T in 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.

The 2020 collapse and the 2021-2024 bounce in aggregate

World merchandise exports (summed across all BACI reporters) trace the textbook great trade collapse pattern 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.

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.

Over-performers and under-performers

Ranking countries by the 2024 recovery 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.

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.

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 — flagged as TODO.

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.

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.

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.

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 (TODO). 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.