Table 4.

Baseline regress results.

	(1)	(2)	(3)	(4)	(5)
Variables	CS	CS	CS	CS	CS
RED	−7.7830**	−1.9938***	−1.4095***	−1.4082***	−1.5979***
	(3.5191)	(0.3109)	(0.2832)	(0.2864)	(0.3311)
Ind_A$\times$Covid	−2.1800	−1.2441	−0.5662	−0.5034	−0.6230
	(1.5750)	(1.6639)	(1.8950)	(1.9016)	(1.8458)
Ind_B$\times$Covid	−0.5644	0.8019	0.3393	0.4117	0.2167
	(1.6528)	(1.8069)	(1.7901)	(1.7951)	(1.7568)
Ind_C$\times$Covid	−2.4315	−2.1316	−2.0238	−1.9528	−2.1398
	(1.5209)	(1.4911)	(1.8077)	(1.8030)	(1.7717)
Ind_D$\times$Covid	−1.4479	−0.7492	−0.0954	−0.0334	−0.3481
	(1.4017)	(1.6357)	(1.8605)	(1.8667)	(1.8287)
Ind_E$\times$Covid	−3.0572**	−1.9132	−1.7353	−1.6551	−1.6568
	(1.2074)	(1.2004)	(1.2976)	(1.3086)	(1.3004)
Ind_F$\times$Covid	−2.0665	−1.1350	−1.0586	−1.0064	−1.1539
	(1.3949)	(1.4577)	(1.5848)	(1.5928)	(1.5660)
Ind_G$\times$Covid	−3.7803	−1.5673	−1.2991	−1.2425	−1.5399
	(2.9955)	(2.5317)	(2.5488)	(2.5275)	(2.5176)
Ind_H$\times$Covid	−4.8961*	−2.0280	−0.8999	−0.8580	−0.9823
	(2.8354)	(2.4643)	(2.0910)	(2.1084)	(2.0920)
Ind_I$\times$Covid	−4.5282**	−5.1026**	−4.6338*	−4.5393*	−4.5266*
	(2.2480)	(2.1943)	(2.4667)	(2.4674)	(2.4463)
Ind_K$\times$Covid	−1.8787	−0.9111	−0.9655	−0.9049	−1.0583
	(1.4513)	(1.3671)	(1.3227)	(1.3319)	(1.3390)
Ind_L$\times$Covid	−2.8491	−3.0237	−2.2454	−2.1676	−2.1254
	(1.9288)	(2.0737)	(2.2249)	(2.2142)	(2.2072)
Ind_M$\times$Covid	−2.3635	−1.5025	−1.8574	−1.8055	−2.0354
	(2.4290)	(2.4637)	(2.6443)	(2.6531)	(2.6251)
Ind_N$\times$Covid	−3.1355**	−3.5331**	−3.1142*	−3.0279*	−3.1109*
	(1.5084)	(1.7278)	(1.7579)	(1.7655)	(1.7425)
Ind_O$\times$Covid	−16.4940***	−19.4894***	−20.3734***	−20.5737***	−20.2920***
	(4.1370)	(4.1172)	(4.0958)	(4.2187)	(4.3025)
Ind_P$\times$Covid	−2.0188	−4.0980	−2.4746	−2.3841	−2.2320
	(2.7866)	(2.7060)	(2.5795)	(2.5575)	(2.4722)
Ind_Q$\times$Covid	0.7891	−0.5960	−0.5514	−0.4082	−0.5683
	(1.8137)	(1.6223)	(1.9452)	(1.9649)	(1.9703)
Ind_R$\times$Covid	−2.6430	−2.8260**	−2.8184**	−2.7779**	−3.0114**
	(1.6807)	(1.2011)	(1.2389)	(1.2451)	(1.2532)
Covid	0.7593	5.1412**	5.2821**	5.3969**	4.1919**
	(1.3079)	(2.0872)	(2.3058)	(2.2915)	(2.0701)
Size		−1.2134***	−1.3193***	−1.3716***	−1.3905***
		(0.3799)	(0.3936)	(0.3932)	(0.3922)
Age		−7.6571***	−7.4608***	−7.4933***	−7.4360***
		(2.0283)	(1.9961)	(1.9880)	(2.0042)
MB		3.8607***	2.8672***	2.8591***	2.8016***
		(0.5800)	(0.5908)	(0.5935)	(0.5891)
Cf			0.2746***	0.2745***	0.2750***
			(0.0094)	(0.0094)	(0.0092)
PAY			0.0050	0.0053	0.0051
			(0.0226)	(0.0227)	(0.0227)
Capx			−0.0683***	−0.0685***	−0.0679***
			(0.0153)	(0.0153)	(0.0159)
Grow			0.0001	0.0001	0.0000
			(0.0021)	(0.0021)	(0.0021)
Bsize				1.3508**	1.4676**
				(0.6217)	(0.6217)
Indep				0.0057	0.0040
				(0.0185)	(0.0184)
PGDP					0.0830***
					(0.0262)
FSIZE					0.0109***
					(0.0022)
Constant	14.6852***	46.9066***	50.3214***	48.3846***	46.8732***
	(2.1699)	(13.4984)	(13.6972)	(14.2815)	(13.9562)
Industry FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES
Observations	33,015	33,015	33,015	33,015	33,015
R-squared	0.0332	0.1454	0.2023	0.2025	0.2026
N	3613	3613	3613	3613	3613

Note: Standard deviations are in parentheses. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. According to the China Securities Regulatory Commission classification, listed Chinese companies are divided into 19 industries (see Table 9 for details). We eliminate the financial industry (Ind_J); only 17 industry dummy variables are generated for the remaining 18 industries. A comprehensive industry was used as the benchmark for Sushima.