# A Statistical Analysis of Industrial Penetration and Internet Intensity in Taiwan +
## 5 Results and Discussion
### Regression Model with Sample Selection Correction for All Industries  
The coefficient of \(HHI_{j}\) is positive, though insignificant, in column 2 of Table 5, while the coefficient of \(CR4_{j}\) is positive and significant in column 2 of Table 6. These findings indicate that a higher degree of industrial concentration increases a firms' expenditure on internet use. The coefficient of \(export_{i}\) is positive but insignificant in column 2 of Tables 5 and 6, indicating that export intensity has no statistical impact on the expenditure of firm on internet use. The coefficient of \(GHHI_{j}\) is negative and significant in column 2 of Tables 5 and 6, indicating that the lower the level of industrial penetration, the greater the degree to which manufacturers will use the internet. The coefficient of \(city_{i}\), accordingly, has a positive and significant effect in column 2 of Tables 5 and 6. The coefficient of \(computer1_{i}\) shows a positive but insignificant effect in column 2 of Tables 5 and 6. These findings indicate that the manufacturers' expenditure on computer equipment has no statistical impact on the expenditure of firms on internet use. The coefficients of \(computer2_{i}\) show a positive but insignificant effect, with bootstrapping standard errors, in column 2 of Tables 5 and 6. These empirical findings indicate that the manufacturers' expenditure on computer equipment within the same industry and region has no statistical impact on the expenditure of firms on internet use. We calculate the marginal effects of Equations (7) and (8), and report the marginal effects in Table 7. Column 2 gives the industrial marginal effects with \(HHI_{j}\) as the proxy variable for the degree of industrial concentration. \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Variables**} & **Intensity of Internet Use (\(y_{j}\))** & **Select (\(z_{i}\))** \\ \hline \multirow{3}{*}{\(GHHI_{j}\)} & \(-1.861\) & \(0.071\) \\  & (1.064) * & (0.203) \\  & [5.246] & [0.202] \\ \hline \multirow{3}{*}{\(city_{i}\)} & \(0.904\) & \(-0.201\) \\  & (0.344) *** & (0.011) *** \\  & [0.377] ** & [0.010] *** \\ \hline \multirow{3}{*}{\(computer1_{i}\)} & \(0.240\) & \multirow{3}{*}{-} \\  & (55.104) & & \\  & [0.432] & & \\ \hline \multirow{3}{*}{\(computer2_{i}\)} & \(0.069\) & \multirow{3}{*}{-} \\  & (0.142) & & \\  & [0.019] *** & & \\ \hline \multirow{3}{*}{\(size_{i}\)} & \(0.001\) & \(0.004\) \\  & [0.002] & (0.001) *** \\  & & [0.0002] *** \\ \hline \multirow{3}{*}{\(group_{i}\)} & \multirow{3}{*}{-} & \(61.607\) \\  & & (22.335) *** \\  & & [0.007] *** \\ \hline \multirow{3}{*}{constant} & \(1.876\) & \(-60.585\) \\  & (0.763) *** & (22.243) *** \\  & [0.894] ** & \\ \hline \multirow{3}{*}{Mills lambda (\(\lambda\))} & \(-8.067\) & \multirow{3}{*}{-} \\  & (2.444) *** & \\ \cline{1-1}  & [2.172] *** & \\ \hline \# of observations & \(153,081\) & \\ \cline  
For the \(HHI_{j}\) variable, the marginal effects are \(-0.0902\) for column 2 and \(-0.007\) for column 3, in Table 7. For example, the figure \(-0.0902\) means that, when the degree of the industrial concentration rate increases by 1, the degree to which manufacturers use the internet is reduced by 0.0902%. This result indicates that the lower the degree of industrial concentration, the greater the degree to which manufacturers will use the internet. Not surprisingly, there are differences between the marginal effects of \(HHI_{j}\) and \(CR4_{j}\) on the degree to which manufacturers use the internet, as described in Section 4: \(HHI_{j}\) takes into account all the firms in an industry, use the manufacturer's market share as weights, with smaller firms being given lesser weights and larger firms being given greater weights, while \(CR4_{j}\) only considers the weighted average of the market shares of the top four firms in an industry. However, the empirical findings of industrial concentration agree with those of (18) [PERSON] and [PERSON] (2008) and (21) [PERSON] et al. (2011), who used French manufacturing industry data.