Table A1.

Impact of the Chinese lockdown on firm-level exports: Heterogeneity across treated firms

	Dep. Var: log of exports
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Treatment $\times$ Post	−0.060${}^a$	−0.024	−0.052${}^a$	−0.031${}^b$	−0.048${}^a$	−0.015	−0.063${}^a$	−0.027${}^b$
	(0.013)	(0.016)	(0.014)	(0.014)	(0.011)	(0.014)	((0.019)	(0.013)
... x 25% most exposed	0.022
	(0.016)
... x Im. Intensity		0.085${}^c$
		(0.031)
... x Ex. Intensity			−0.108${}^a$
			(0.029)
... x Im. Intensity x Ex. Intensity				−0.014
				(0.048)
... x Largest 25%					−0.054${}^a$
					(0.003)
... x Hubei						−0.074${}^a$
						(0.016)
Firm FE	Y	Y	Y	Y	Y	Y	Y	Y
Time FE	Y	Y	Y	Y	Y	Y	Y	Y
# Treated	12,086	10,973	11,516	10,973	11,709	12,086	6462	10,176
# Control	13,563	11,918	12,510	11,918	12,792	13,563	5635	11003
Export Sample	All	All	All	All	All	All	Final	Interm.
$R^2$	0.856	0.868	0.867	0.868	0.868	0.856	0.864	0.863
# Obs.	202,622	164,496	172,349	164,496	175,651	202,622	85,580	158,531

The table reports results of difference-in-difference estimations on exporting firms. "T1" means that control group are firms that import inputs from abroad outside of China whereas treated firms are those exposed to Chinese inputs in the five months before the pandemic. The date of treatment is February 2020 and the DiD compares the evolution of imports between September 2019 and January 2020 (pre-treatment period) and between February 2020 and June 2020 (post-treatment period). Estimates are on firm-level exports and “units” are firms. Column (1) augments the baseline specification with a categorical interaction between the “Treatment” dummy, the “Post” dummy and a dummy equal to 1 if the firm falls into the fourth quartile of the distribution of the share of Chinese inputs into the firm’s imports of intermediates. Columns (2)-(4) add a continuous interaction to capture the treatment intensity based on three variables, all comprised between 0 and 1: import intensity ($\frac{\text{Total intermediate imports}}{\text{Total intermediate purchases}}$), export intensity ($\frac{\text{Total exports}}{\text{Total sales}}$) and vertical specialization ($\frac{\text{Total intermediate imports}\times \text{Total exports}}{\text{Total intermediate purchases}\times \text{Total sales}}$). Purchases, sales, total imports and exports are based on 2018 data. In column (5), the interaction involves a dummy which is equal to 1 if the firm’s 2018 income is in the top quartile. In column (6), the interaction captures the likelihood that the firm imports intermediates from the Hubei region. As we do not observe the regional origin of imports in our data, we use 2014 product level export data from China, at the regional level to define as Hubei product a product whose Balassa ratio is above 1. A firm for which the triple term equals 1 would be a firm that imports a Hubei product at least once in the pre-treatment period. Finally, columns (7) and (8) reproduce the baseline regression on final goods exports (column (7)) and on intermediate goods (column (8)). Standard errors are clustered at the firm level. ${}^a$, ${}^b$ and ${}^c$ denote significance at the 1, 5 and 10% level, respectively