## 5 Descriptive and Regression Findings  
Descriptive statistics, computed for the variables, include the mean, median, standard deviation, and coefficient of variation (CV). In the dependent-variable infrastructure group, the line length of optical cable, mobile phone base stations, and capacity of mobile phone exchange are low in CV, a nationwide evenness that we argue stems from governmental policies and planning for installing basic internet and mobile network structure for potential access across the nation (Table 1). The use group also has low variability, which can be explained by usage being largely individual, mobile, and inexpensive. In contrast, the CVs are high for e-commerce and very high for IT services, averaging, respectively, 101.6 and 206.1. Clearly, there are vast provincial differences in purposeful uses across the nation, which we argue represent substantial digital inequality arguably derived from their earlier stage of geographical diffusion. The percentage of enterprises with e-commerce transactions is an exception, with low CV, which suggests enterprises throughout the country have readily adopted and use e-commerce, evening out provincial differences. However, the intensity of e-commerce adoption still varies greatly. The descriptive findings for the independent variables (Table 2) reflect China's ascent as the second largest global economy but also reveal provincial variation, especially in export of goods, innovation, and knowledge production. China's advances are evident in its provincial averages, with an urban population percentage of 63.8 percent, disposable household income/capita of CNY 32,086 (USD 4650 dollars in 2020), and college education of 9.9 per 100 people, age 15+. The high CV for exports of goods stems partly from greater international and manufacturing potential for exports in the coastal or near-coastal provinces compared to those inland. The higher provincial CVs for innovation are partly \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Variable Set** & **Definition** & **N** & **Minimum** & **Maximum** & **Mean** & **Std.** & **Coeff. of** & **Variation** \\ \hline Infrastructure & Length of optical cable lines (km) per cap. & 31 & 0.02 & 0.07 & 0.04 & 0.01 & 30.81 \\  & Mobile phone base stations (10,000), per cap. & 31 & 0.05 & 0.15 & 0.07 & 0.02 & 28.04 \\  & Capacity of mobile phone exchange & 31 & 1.28 & 7.73 & 2.25 & 1.19 & 52.97 \\  & (10,000 subscribers), per cap. & 31 & 0.0044 & 0.17 & 0.03 & 0.03 & 101.14 \\ \hline Use & Mobile internet subscribers per cap. & 31 & 0.77 & 1.49 & 0.97 & 0.15 & 15.36 \\  & Broadband subscribers per cap. & 31 & 0.26 & 0.46 & 0.34 & 0.05 & 14.70 \\  & Flow of mobile internet (10,000 GB), per cap. & 31 & 0.01 & 0.02 & 0.01 & 0.003 & 22.02 \\  & Penetration rate of mobile phones per & 31 & 88.24 & 178.43 & 115.10 & 18.63 & 16.18 \\  & 100 persons & & & & & & \\ \hline ICT Services & Income from IT services (100k yuan) per & 30 & 0.0004 & 0.50 & 0.05 & 0.10 & 214.75 \\  & Income of related software businesses & & & & & & \\  & (100k yuan) per cap. & 31 & 0.0006 & 0.72 & 0.06 & 0.14 & 212.31 \\  & Income from Information Safety (100k yuan), per cap. & 30 & 0.01 & 0.06 & 0.02 & 0.01 & 74.39 \\ \hline E-commerce & Sales through E-commerce (1 million yuan) per cap. & 31 & 0.0021 & 0.12 & 0.02 & 0.03 & 167.64 \\  & Purchases through E-commerce (1 million yuan) per cap. & 31 & 0.0034 & 0.70 & 0.09 & 0.15 & 175.41 \\  & Perent of enterprises with E-commerce & & & & & & \\  & transactions & 31 & 5.50 & 22.80 & 10.26 & 3.43 & 33.46 \\  & Websites per 100 enterprises & 31 & 24.00 & 59.00 & 45.19 & 8.54 & 18.90 \\  & Computers used at the end of period per cap. & 31 & 1.12 & 22.99 & 4.23 & 4.77 & 112.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics for dependent variables, 2020.  
related to the uneven geographic distribution of government research centers and research universities. For knowledge production, there is a moderate CV for printed books but a very high CV for electronic publication volume.