Table 3.
Sub-sample empirical result.
| Sub:COVID-19 | (1) | (2) | (3) | (4) | ||||
|---|---|---|---|---|---|---|---|---|
| lnFTHTrate | lnFTHTrate | lnFTWTrate | lnFTWTrate | |||||
| low | high | low | high | low | high | low | high | |
| lnCOVID | 0.006 | 0.107*** | 0.006 | 0.107*** | 0.040 | 0.179*** | 0.047 | 0.179*** |
| (0.42) | (5.65) | (0.44) | (5.65) | (0.67) | (2.92) | (0.78) | (2.91) | |
| CPI | −0.002 | 0.094*** | −0.002 | 0.094*** | 0.030 | 0.223*** | 0.031 | 0.223*** |
| (−0.23) | (3.66) | (−0.23) | (3.66) | (0.85) | (2.68) | (0.88) | (2.68) | |
| lnDP | −0.00003 | −0.00002 | −0.0004 | 0.00003 | ||||
| (−0.32) | (−0.07) | (−0.79) | (0.03) | |||||
| lnGP | −0.00005 | −0.00005 | −0.001 | −0.00007 | ||||
| (−0.30) | (−0.12) | (−0.99) | (−0.05) | |||||
| R square | 0.0034 | 0.4141 | 0.0044 | 0.4137 | 0.0255 | 0.4075 | 0.0268 | 0.4073 |
| N | 34 | 27 | 34 | 27 | 28 | 27 | 28 | 27 |
|
Sub: gasoline production |
low | high | low | high | low | high | low | high |
| lnCOVID | 0.076*** | 0.005 | 0.077*** | 0.005 | 0.120*** | 0.009 | 0.124*** | 0.009 |
| (4.26) | (1.29) | (4.41) | (1.27) | (2.69) | (0.92) | (2.91) | (0.94) | |
| CPI | 0.070*** | −0.002 | 0.069*** | −0.002 | 0.182*** | −0.008 | 0.177*** | −0.008 |
| (3.58) | (−0.48) | (3.54) | (−0.48) | (2.79) | (−0.80) | (2.71) | (−0.81) | |
| lnDP | −0.001 | −0.00001 | −0.001 | −0.0001 | ||||
| (−0.28) | (−0.27) | (−0.29) | (−0.82) | |||||
| lnGP | −0.001 | −0.00002 | −0.003 | −0.0003 | ||||
| (−0.57) | (−0.18) | (−0.60) | (−1.25) | |||||
| R square | 0.1542 | 0.2837 | 0.1884 | 0.2830 | 0.2791 | 0.1308 | 0.2895 | 0.1197 |
| N | 43 | 18 | 43 | 18 | 38 | 17 | 38 | 17 |
Notes: This table reports the results from the regressions under the sub-samples according to the average values of COVID-19 and gasoline production (above the mean is high and below the mean is low). Robust standard errors are calculated by the t-statistics. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively. Regressions 1 and 2 mainly study the impact of COVID-19 on road freight. Regressions 3 and 4 mainly study the impact of COVID-19 on water freight. In the sub-sample analysis, cumulative number at the end of each month by province (lnCOVID) is chosen as core independent variable. Please see the variable definitions in Table 1.