Table 4 The impacts of digital travel vouchers on tourist inflows.

From: Tourism in pandemic: the role of digital travel vouchers in China

Tour_Arrival

(1)

(2)

(3)

(4)

(5)

(6)

lnVALUE

0.090*** (0.019)

0.011** (0.005)

 

0.017** (0.008)

0.011* (0.006)

0.010* (0.005)

Issuance

  

0.078** (0.039)

   

lnPOP

 

0.790*** (0.050)

0.782*** (0.049)

0.796*** (0.050)

0.791*** (0.050)

0.746*** (0.089)

lnGDPper

 

0.584*** (0.086)

0.575*** (0.086)

0.589*** (0.086)

0.596*** (0.087)

0.574*** (0.147)

lnHSR

 

−0.022 (0.014)

−0.020 (0.014)

−0.022 (0.014)

−0.024* (0.014)

−0.038 (0.024)

lnINFRA

 

0.205*** (0.049)

0.203*** (0.049)

0.202*** (0.049)

0.205*** (0.050)

0.325*** (0.084)

lnENV

 

0.011** (0.005)

0.011** (0.005)

0.012** (0.005)

0.012** (0.005)

0.004 (0.008)

lnCOVID

 

−0.058*** (0.018)

−0.055*** (0.018)

−0.065*** (0.018)

−0.058*** (0.018)

−0.101*** (0.027)

Constant

−1.403*** (0.066)

−7.808*** (0.330)

−7.752*** (0.326)

−7.890*** (0.332)

−7.876*** (0.331)

−7.741*** (0.474)

Monthly Fixed-Effect

YES

YES

YES

YES

YES

YES

Provincial Fixed-Effect

YES

YES

YES

YES

YES

YES

N

4896

4896

4896

3672

3672

1104

Pseudo R2

0.003

0.187

0.187

0.182

0.182

0.183

  1. This table reports the baseline regression results. Models (1) and (2) are for full sample regressions. Model (3) reports the results for the staggered DID regression. Model (4) excludes extreme months, whereas model (5) excludes major holidays. The last model (6) uses samples excluding cities that never issued digital travel vouchers throughout the sample period. ***, **, and * indicate the 1, 5, and 10% levels of significance, respectively. The robust standard errors are reported in parentheses.