Table 10 Excluding omitted variable bias.

From: Supply chain resilience and digital transformation: perspectives from a supply chain network

 

(1)

(2)

(3)

(4)

Variables

\({{SCR}}_{i,t}\)

\({{Out}}_{i,t}\)

\({{SCR}}_{i,t}\)

\({{Out}}_{i,t}\)

\({{Treat}}_{i}\times {{Post}}_{t}\)

0.030*

0.031***

0.031**

0.030***

 

(1.851)

(3.054)

(2.021)

(3.161)

\({SOE}\)

  

−0.002

0.005

   

(−0.081)

(0.338)

\(H{igh\_tech}\)

  

0.054**

0.029*

   

(2.461)

(1.947)

\({Digital\_patent}\)

  

−0.000

0.000

   

(−0.818)

(1.533)

\({Age}\)

  

0.000

−0.000

   

(0.483)

(−0.952)

\(G{ender}\)

  

−0.020*

0.003

   

(−1.700)

(0.305)

\(A{board}\)

  

0.011

−0.002

   

(0.774)

(−0.104)

\(A{cademy}\)

  

−0.026

−0.049*

   

(−1.002)

(−1.755)

\({Constant}\)

0.173***

0.084***

0.143***

0.075**

 

(4.350)

(2.975)

(3.373)

(2.528)

Baseline Controls

Y

Y

Y

Y

Year, Industry, City FEs

Y

Y

N

N

Firm FEs

Y

Y

Y

Y

Observations

7514

7514

7514

7514

R-squared

0.744

0.811

0.734

0.797

F

1.987

2.223

2.164

2.051

  1. This table shows the robust test of Eq. 1. The variable \({{Treat}}_{i}\times {{Post}}_{i}\) is a time-varying dummy set to one if a firm is in a Supply Chain Digitization Pilot City. Columns (1) and (2) include city and industry fixed effects in the models. Supplementary Table A.1 shows the definitions of variables.
  2. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.