Table 3.

Correlation of fiscal support and predicted revenue change across industries

	100 × Subsidy/Costs		100 × Subsidy /Liquidity
	Participants (1)	Full sample (2)	Participants (3)	Full sample (4)
Micro	− 0.146***	− 0.035***	− 0.018***	− 0.002***
	(0.001)	(0.000)	(0.000)	(0.000)
	[132422]	[409168]	[132422]	[409168]
Small	− 0.073***	− 0.050***	− 0.008***	− 0.009***
	(0.000)	(0.000)	(0.000)	(0.000)
	[154792]	[272966]	[154792]	[272966]
Medium	− 0.051***	− 0.041***	− 0.004***	− 0.000
	(0.000)	(0.000)	(0.000)	(0.000)
	[96047]	[135599]	[96047]	[135599]
Large	− 0.015***	− 0.013***	− 0.005***	− 0.003***
	(0.001)	(0.001)	(0.001)	(0.001)
	[7474]	[9557]	[7474]	[9557]
_cons	− 0.019**	− 0.449***	− 0.361***	− 0.293***
	(0.008)	(0.002)	(0.006)	(0.001)
All firms	− 0.040**	− 0.032**	− 0.048*	− 0.038*
	(0.019)	(0.014)	(0.026)	(0.019)
	[390735]	[827290]	[390735]	[827290]

The top panel reports coefficients from regressing the industry subsidy ratio on the predicted revenue change from Chen et al. (2020), separately for each of four size groups, weighted by the number of firms (reported in square brackets) in each industry-size bin. There are 18 industries. A bin’s subsidy ratio is calculated by dividing the total subsidy across firms in the bin by total costs or liquidity across firms in the bin. The bottom panel further aggregates up to the industry level and reports the correlation of revenue change and subsidy ratio from the analogous regression. Robust standard errors are reported in parentheses. Firm counts are reported in square brackets. * p < 0.1, **p < 0.05, ***p < 0.01