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Table 10 Location effect

From: Does supplier concentration matter to investors during the COVID-19 crisis: evidence from China?

Variables

\({R}^{{{\prime}}{{\prime}}}\)[− 1, 1]

Hardest hit provinces and close neighbors

\({R}^{{{\prime}}{{\prime}}}\)[− 2, 2]

Hardest hit provinces and close neighbors

\({R}^{{{\prime}}{{\prime}}}\)[− 1, 1]

Other provinces

\({R}^{{{\prime}}{{\prime}}}\)[− 2, 2]

Other provinces

SC_adjusted

− 6.875**

(− 2.12)

− 7.538**

(− 2.24)

− 2.431

(− 1.34)

− 3.894

(− 1.42)

 LnAsset

1.263***

(2.41)

1.382***

(2.89)

0.826

(1.54)

1.098

(0.34)

 BM

− 3.625***

(− 3.43)

− 4.353***

(− 2.90)

− 5.498**

(− 2.04)

− 6.764***

(− 2.78)

 Leverage

− 3.037

(− 1.45)

− 4.984**

(2.23)

− 1.101

(1.56)

− 3.010

(− 0.30)

 Constant

2.608

(0.91)

1.045

(− 1.29)

1.513**

(− 2.17)

2.728***

(− 4.93)

 Observations

1028

1028

937

937

 R-squared

0.169

0.153

0.208

0.187

  1. Cumulative abnormal return (\({R}^{{{\prime}}{{\prime}}})\) based on CAPM for short- and medium-term event windows as DV and SC_adjusted as IV for hardest hit provinces (with their close neighbors) and other provinces. All of the regressions include control variables. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. The top five hardest hit provinces are Hubei, Guangdong, Henan, Zhejiang, Hunan (Source: China Data Lab, 2020, “China COVID-19 Daily Cases with Basemap”. https://doi.org/10.7910/DVN/MR5IJN, Harvard Dataverse, V32). And the close neighbors of them include Anhui, Chongqing, Jiangxi, Shaanxi, Fujian, Guangxi. All reported t statistics are based on standard errors adjusted for clustering at the industry level