Figures (1)  Tables (8)

    • Figure 1. 

      Theoretical model of this paper.

    • Primary indicator Secondary indicator Tertiary indicator Code
      Digital trade infrastructure Information network infrastructure Length of long-haul fiber-optic cables (10,000 km) X1
      Number of internet broadband access ports (10,000) X2
      Population penetration rate of mobile phones (%) X3
      Logistics transport infrastructure Number of operational freight trucks on highways (10,000) X4
      Number of civil transport ships/units X5
      Express delivery volume/million items X6
      Digital technological innovation Technological innovation input Number of patents granted (1,000) X7
      R & D expenditure/10 billion RMB X8
      Digital innovation output Technology market transaction volume (billion RMB) X9
      Digital trade market Digital supply Total telecommunications services volume (100 billion RMB) X10
      Total postal services volume/billion RMB X11
      Digital demand Software services revenue (billion RMB) X12
      E-commerce sales volume/billion RMB X13
      Trade potential Economic strength GDP (100 million RMB) X14
      Trade scale Total import and export trade volume (${\text{\$}} $10,000 USD) X15
      Source: compiled by the author.

      Table 1. 

      Digital trade measurement indicators.

    • Index Variable name (Abbreviation) Calculation formula
      1 Economic development level (LNPgdp) LNPgdp Logarithm of GDP per capita
      2 Human capital level (HC) Number of students in higher education/Total population
      3 Industrialization level (IL) Industrial value added/Gross regional product
      4 Openness degree (OPEN) Total import and export trade volume/GDP
      5 Social consumption level (SCL) Total retail sales of consumer goods/Gross regional product
      Source: compiled by the author.

      Table 2. 

      Control variables.

    • VIF 1/VIF
      Dtra 3.131 0.319
      HC 1.535 0.651
      IND 4.305 0.232
      IL 2.562 0.390
      LNPgdp 4.482 0.223
      OPEN 1.893 0.528
      SCL 1.478 0.676
      Mean VIF 2.770
      Source: compiled by the author.

      Table 3. 

      Multicollinearity test.

    • (1) Expy (2) Expy (3) Expy (4) Expy (5) Expy (6) Expy
      Dtra 2.209*** 1.471*** 0.702*** 0.735*** 0.767*** 0.704***
      (39.796) (21.551) (7.745) (8.119) (8.505) (7.806)
      HC 0.663*** 0.387*** 0.381*** 0.346*** 0.405***
      (14.235) (8.210) (8.145) (7.271) (8.188)
      LNPgdp 0.790*** 0.777*** 0.810*** 0.768***
      (11.171) (11.067) (11.519) (10.974)
      SCL −0.079*** −0.058** −0.045
      (−2.799) (−2.013) (−1.600)
      IL 0.113*** 0.131***
      (2.978) (3.475)
      OPEN −0.229***
      (−3.673)
      Observations 390 390 390 390 390 390
      R2 0.815 0.882 0.913 0.914 0.917 0.920
      F 1583.7 1337.9 1242.0 951.3 779.6 674.8
      *, **, *** indicates significant at the significance level of 10%, 5%, and 1% respectively, the content inside () represents the t-statistic.

      Table 4. 

      Basic regression results.

    • (1) Expy (2) Expy (3) Expy (4) Expy
      Dtra 0.294*** 1.074*** 0.649***
      (5.011) (10.012) (6.576)
      DT 0.651***
      (6.803)
      HC 0.241*** 0.371*** 0.352*** 0.471***
      (5.623) (6.357) (7.095) (8.312)
      LNPgdp 0.660*** 0.646*** 0.704*** 0.928***
      (10.607) (7.994) (10.789) (11.928)
      SCL −0.077** −0.019 −0.050* −0.060*
      (−2.022) (−0.635) (−1.707) (−1.825)
      IL −0.098*** 0.121*** 0.094** 0.204***
      (−2.727) (2.728) (2.485) (4.518)
      OPEN −0.682*** −0.283*** −0.203*** −0.227***
      (−14.743) (−3.959) (−3.131) (−2.904)
      N 360 300 338 390
      R2 0.927 0.916 0.917
      F 558.7 559.6 648.9
      *, **, and *** respectively indicate significant at the significance level of 10%, 5%, and 1%, the content inside () represents the t-statistic.

      Table 5. 

      Results of robustness tests.

    • (1) IND (2) Expy
      Dtra 0.692*** 0.473***
      (7.300) (5.209)
      IND 0.335***
      (7.063)
      HC 0.175*** 0.346***
      (3.372) (7.352)
      LNPgdp 0.001 0.768***
      (0.007) (11.705)
      SCL 0.112*** −0.083***
      (3.769) (−3.061)
      IL −0.468*** 0.288***
      (−11.827) (6.898)
      OPEN 0.010 −0.233***
      (0.160) (−3.979)
      N 390 390
      R2 0.730 0.930
      F 159.1 665.4
      *, **, *** indicates significant at the significance level of 10%, 5%, and 1% respectively, the content inside () represents the t-statistic.

      Table 6. 

      Regression results of mediating effects.

    • RegionSpecific province
      Coastal citiesLiaoning Province, Hebei Province, Beijing Municipality, Tianjin Municipality, Shandong Province, Jiangsu Province, Shanghai Municipality, Zhejiang Province, Fujian Province, Guangdong Province, Guangxi Zhuang Autonomous Region, Hainan Province
      Inland citiesShanxi Province, Jilin Province, Heilongjiang Province, Anhui Province, Jiangxi Province, Henan Province, Hubei Province, Hunan Province, Sichuan Province, Guizhou Province, Yunnan Province, Shaanxi Province, Gansu Province, Qinghai Province, Inner Mongolia Autonomous Region, Ningxia Hui Autonomous Region, Chongqing Municipality
      Data source: Compiled based on the documents of the National Development, and Reform Commission and the National Bureau of Statistics of China.

      Table 7. 

      Regional division.

    • Coastal cities expy Inland cities expy
      Dtra 0.941*** 0.280***
      (6.353) (3.172)
      HC 0.440*** 0.389***
      (5.634) (6.365)
      LNPgdp 0.774*** 0.720***
      (6.539) (11.079)
      SCL 0.030 −0.031
      (0.672) (−0.876)
      LI 0.166* 0.080***
      (1.746) (2.767)
      OPEN −0.071 −0.056
      (−0.817) (−1.400)
      N 156 234
      R2 0.925 0.932
      F 285.7 477.2
      *, **, *** indicates significant at the significance level of 10%, 5%, and 1% respectively, the content inside () represents the t-statistic.

      Table 8. 

      Regional heterogeneity regression results.