Table 6

Robustness checks

(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)
VARInnovationt+1Innovationt+1Innovationt+1Innovationt+1Innovationt+1Innovationt+1Patentt+1R&DIE_invInnovationt+2Innovationt+3
GZW  0.175***0.105*0.131***0.153***0.223***0.002*0.128**0.124**0.198***
  (3.10)(1.84)(2.76)(3.09)(3.66)(1.76)(2.27)(2.38)(3.60)
Before20.047          
(0.97)          
Before10.005          
(0.08)          
Current0.130**          
(2.03)          
After10.093          
(1.22)          
After20.223**          
(2.38)          
After30.266**          
(2.38)          
Assume_GZW 0.051         
 (1.12)         
GDP     −0.131     
     (−1.55)     
Fiscal     −0.023     
     (−1.04)     
Unemploy     −2.251     
     (−1.51)     
Cityinnov     0.192***     
     (3.09)     
ControlsYESYESYESYESYESYESYESYESYESYESYES
Firm/yearYESYESYESYESYESYESYESYESYESYESYES
City    YES      
Observations4,6304,6301,0083,3364,6304,4594,6302,3506644,6214,610
R-squared0.23560.20780.09850.26370.23840.24270.21570.15470.07380.23030.2284

Note(s): Column (1) of this table presents the results of the parallel trend test. Before2 and Before1 equal 1 if an observation is two years and one year before the establishment of its local SASACs, respectively, and 0 otherwise. After3, After2, and After1 equal 1 if an observation is three or more years, two years, and one year after the establishment of its local SASACs, respectively, and 0 otherwise. Column (2) presents the results of the placebo test. Assume_GZW is a dummy variable which equals 1 if an SOE i is controlled by a local SASAC in year t−3 and 0 if it is controlled by other government departments in year t−3. Column (3) presents the results for the innovation of SOEs around the initial establishment (from t−1 to t+1) of SASACs using the PSM-DID method. Column (4) of this table shows the result of excluding samples located in Beijing, Shanghai, Tianjin, and Chongqing. Column (5) shows the result of adding the city-fixed effect. Column (6) shows the result of controlling the city-level variables, including the gross domestic product of a city (GDP), the amount of fiscal revenue of a city (Fiscal), the unemployment rate of a city (Unemploy), and the total number of patent applications of a city (Cityinnov). Columns (7) to (9) show the results of alternative measures of SOE innovation, including the total number of patent applications (column (7), R&D expenditures (column (8), and R&D efficiency (column (9). Columns (10) and (11) show the results of innovation performance over a longer horizon. The dependent variables in columns (1) to (6) are innovation measured by the natural logarithm of 1 plus the number of invention patent applications in year t+1 (Innovationt+1). R&D is calculated as the ratio of R&D expenditures to the total assets; IE_inv is R&D efficiency, calculated as the number of total patents (invention patents) scaled by R&D expenditures. We report in parentheses t-statistics based on standard errors that are robust to heteroskedasticity. ***p < 0.01; **p < 0.05; *p < 0.10; two-tailed test. See Appendix A for other variable definitions

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