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We are very grateful to Helge Berger, Joong Shik Kan, and Martin Kaufman for many helpful discussions. We also thank Fan (Mike) Zhang for kindly sharing his lockdown stringency index for China, and Davide Furceri for very detailed comments.
Diego A. Cerdeiro: IMF, Asia and Pacific Department; Andras Komaromi: IMF, Innovation Lab Unit.
See, e.g., “World Economy Shudders as Coronavirus Threatens Global Supply Chains,” Wall Street Journal, February 23, 2020; “US supply chains and ports under strain from coronavirus,” Financial Times, March 2, 2020.
It is worth noting that some potential endogeneity issues are tackled more directly by using import growth (rather than import levels) as dependent variable. In particular, the empirical gravity model of trade tells us that country i’s import levels are determined by distance. Since distance also determines how quickly the virus could spread to country i, it also affects how consumers may adapt to news of neighboring outbreaks. The reasoning here in favor of the use of growth rates instead of levels echoes the arguments brought forward in a more general context by Goldsmith-Pinkham, Sorkin and Swift (2020; see specifically the discussion on p. 2588).
We also further investigate our results through ancillary specifications that add COVID cases and deaths in trading partners as additional controls. While not very likely, the inclusion of foreign disease variables could aid identification in cases where foreign production is affected by voluntary decisions not to report to work despite the absence of government-mandated lockdowns.
While most ships send AIS messages with a frequency of 2–10 seconds, the data we use are down-sampled to the hourly frequency. The raw AIS data were collected by MarineTraffic.
The moving-average transformation mechanically introduces autocorrelation in our error term – e.g. any ship arriving unexpectedly at time t will reverberate in our transformed data for six additional days. Econometrically, we address the resulting inference problem by clustering standard errors at the country-level which are robust to autocorrelation.
While the raw-AIS data sample starts on January 1st, 2015, the classification of a port call as imports requires knowing that the previous port is in fact located in a different country. To avoid start-point estimation problems, we censor our estimates before April 1st, 2015.
The data are available at https://covidtracker.bsg.ox.ac.uk/. For China, we use an updated version of the lockdown stringency index presented in Zhang (forthcoming). This paper broadly follows the data sources and methodology of Hale et al. (2020) but derives province-level stringency indices which are then aggregated to the national level. Since Chinese lockdowns varied substantially across provinces, this bottom-up index better captures the evolution of average lockdown intensity in China.
A stark illustration of the type of problem that could arise if these lags were not accounted for is ports in the U.S. and Europe being flooded with goods in April 2020 due to orders placed before demand conditions significantly deteriorated as the virus started spreading in those regions. See e.g. “European ports and warehouses brace for surge in containers,” Financial Times, April 12, 2020.
In the credit-supply shock literature, where e.g. firms are exposed to banks, the underlying assumption is that it is hard to switch banks. If firms can easily switch lenders and banks could easily pick up demand for loans, then a shock to a few banks should have no aggregate effect. In the more trade-related work by Autor, Dorn and Hanson (2013), the units are commuting zones and industries, and the assumption is that labor cannot easily move across commuting zones and across industries. If workers could easily switch jobs, then the China shock could not possibly have large aggregate employment effects.
See e.g. “China’s coronavirus lockdown strategy: brutal but effective,” The Guardian, March 19, 2020
See e.g. “Coronavirus Is a Wake-Up Call for Supply Chain Management,” Harvard Business Review, March 27, 2020
While input-output matrices could be used to construct higher-order weights, such weights would not necessarily correspond to input linkages embedded in trade that takes place by sea.
As we include a 14-day lag, we lose the first two weeks of observations. We checked that imputing zero indirect lockdown exposures before January 1, 2020 do not change the results in any meaningful way.