## 5 Descriptive and Regression Findings
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Variable Set** & **Definition** & **N** & **Minimum** & **Maximum** & **Mean** & \begin{tabular}{l} **Std.** \\ **Deviation** \\ \end{tabular} & \begin{tabular}{l} **Coeff. of** \\ **Variation** \\ \end{tabular} \\ \hline Demographic & Sex ratio & 31 & 98.73 & 123.17 & 104.79 & 5.17 & 4.94 \\  & Urban population & 31 & 35.73 & 89.30 & 63.73 & 11.06 & 17.35 \\  & Child dependency ratio & 31 & 13.25 & 37.17 & 25.79 & 6.91 & 26.80 \\  & Han population, per cap & 31 & 0.12 & 1.00 & 0.85 & 0.21 & 24.47 \\ \hline Economic & \begin{tabular}{l} Disposable household income, in yuan, \\ per cap. \\ Unemployment of registered persons in \\ urban areas, per cap. (2018–2020 average) \\ Export of goods by producer location, \\ per cap. \\ \end{tabular} & 31 & 20,335.10 & 72,232.40 & 32,086.38 & 12,661.02 & 39.46 \\  & \begin{tabular}{l} Unemployment of registered persons in \\ urban areas, per cap. (2018–2020 average) \\ Export of goods by producer location, \\ per cap. \\ \end{tabular} & 31 & 2.30 & 3.20 & 3.03 & 0.16 & 5.21 \\  & \begin{tabular}{l} Export of goods by producer location, \\ per cap. \\ \end{tabular} & 31 & 0.0021 & 0.47 & 0.10 & 0.13 & 132.67 \\ \hline Education & \begin{tabular}{l} College education, age 15+, per cap \\ Full-time teachers, per cap \\ \end{tabular} & 31 & 0.07 & 0.15 & 0.10 & 0.02 & 19.29 \\ \hline Innovation & \begin{tabular}{l} Expenditure for science and technology, \\ per cap. \\ \end{tabular} & 31 & 0.01 & 0.19 & 0.04 & 0.04 & 99.68 \\  & \begin{tabular}{l} Expenditure for R\&D, per cap. \\ Patent applications, per cap. \\ \end{tabular} & 31 & 0.02 & 2.81 & 0.89 & 0.74 & 83.38 \\ \hline Knowledge & \
\begin{tabular}{l} Printed book copies, per cap. \\ \end{tabular} & 31 & 0.03 & 2.43 & 0.69 & 0.67 & 97.57 \\ \hline Production &
\begin{tabular}{l} Printed book copies, per cap. \\ \end{tabular} & 31 & 0.17 & 2.01 & 0.59 & 0.36 & 60.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics for independent variables, 2020. The second leading factor is disposable household income, which is supported by antecedent China studies [1,3] and by the worldwide digital divide literature outside of China [34,42,68]. The third leading inverse relationship is an association between the percentage of the Han population and infrastructure and ICT use, implying that a higher percentage of non-Han minorities is associated with an increase in ICT, including, in particular, optical cable lines, mobile phone base stations, the capacity of mobile phone exchange, mobile phone internet flow, and the number of computers. Because most of the high-minority provinces are in the west and north of the country, the strong associations between the non-Han percentage of the population and infrastructure can be explained by the need for more extensive infrastructure to cover the larger land areas and lower population densities. The inverse association of the Han population with the use factors relates to leapfrogging catch-up in use by minority populations once income and scientific-technical expenditures are controlled for. Trends towards minority catch-up are evident in studies in US states and counties; in particular, usage by ethnic minorities and White people converged during the first two decades of the 21 st century [41]. Furthermore, the percentage of use of internet apps by Black and Hispanic people has converged to become comparable to use by White people in 2019 [69]. For Chinese research, a deeper investigation is called for to understand the spatial disparities of digital usage by non-Han minorities compared to the Han population. To better investigate determinants other than the dominant one, a second set of regressions was performed, removing science and technology expenditures. In the second-round findings, per capita income and expenditure in R&D are the two dominant independent variables, which is supported by antecedent Chinese studies [3,14] and by the worldwide digital divide literature outside of China [34,42,69] (Table 4).