# A Statistical Analysis of Industrial Penetration and Internet Intensity in Taiwan +
## 5 Results and Discussion
### Regression Model with Sample Selection Correction for 2-Digit Industries  
This section reports the Heckman two-stage estimates with \(HHI\) as the only proxy variable for the degree of industrial concentration, and marginal effects for two-digit industries, in Tables 8 and 9, respectively. A nonzero Mill's lambda (\(\beta_{\lambda}\)) rejects the statistical null hypothesis that \(\beta_{\lambda}\) equals zero at the 1% level of significance for (08) Food, (09) Beverages, (22) Plastic Products, (28) Electrical Equipment, (29) Machinery and Equipment, (30) Motor Vehicles and Parts, and (32) Furniture. However, as the industries are different, the empirical results for individual industries that are based on the two-digit level classifications also vary. For individual two-digit industries, we first discuss the results of the sample selection-corrected equation for how much manufacturers use the internet, then present the results of the sample selection-corrected equation for the factors that determine whether manufacturers use the internet, and finally summarize the marginal effects. The effect of the degree of industrial penetration (\(GHHI_{j}\)) in terms of how much manufacturers use the internet vary across the 2-digit industries. In the case of traditional industries, such as (08) Food, (12) Wearing Apparel and Clothing Accessories, (13) Leather, Fur and Related Products, (32) Furniture, technology-intensive industries, such as (28) Electrical Equipment, (30) Motor Vehicles and Parts, (31) Other Transport Equipment, and also basic industries, such as (24) Basic Metal, all show that the lower is the level of industrial penetration, the greater is the degree to which manufacturers will use the internet. However, only two traditional industries, such as (16) Printing and Reproduction of Recorded Media, and basic industries, such as (20) Medical Goods, show that the higher the degree of industrial penetration, the greater is the degree to which manufacturers will use the internet. The effect of the degree of industrial concentration (\(HHI_{j}\)) in terms of how much manufacturers will use the internet also differs across the 2-digit industries. In the case of traditional industries, such as (08) Food, (13) Leather, Fur and Related Products, technology-intensive industries, such as (26) Electronic Parts and Components, and basic industries, such as (25) Fabricated Metal Products, show that the higher is the degree of industrial concentration, the greater is the degree to which manufacturers will use the internet. On the contrary, traditional industries, such as (32) Furniture,(33) Manufacturing Not Elsewhere Classified, and also, technology-intensive industries, such as (28) Electrical Equipment, (29) Machinery and Equipment, (30) Motor Vehicles and Parts, (31) Other Transport Equipment, show that the lower the degree of industrial concentration, the greater the degree to which manufacturers will use the internet. The variable \(export_{i}\) show a positive and significant influence on how much manufacturers use the internet for traditional industries, such as (09) Beverages, (33) Manufacturing Not Elsewhere Classified, technology-intensive industries, such as (26) Electronic Parts and Components, Machinery and Equipment, (30) Motor Vehicles and Parts, and basic industries, such as (18) Chemical Material, (19) Chemical Products, (25) Fabricated Metal Products. However, only basic industries, such as (24) Basic Metal, show a significant negative effect on the degree to which manufacturers use the internet for traditional industries. The effect of geographic location, \(city_{i}\) shows manufacturers that are located in rural areas will use the internet to a greater extent than those that are located in urban areas for traditional industries, such as (08) Food Manufacturing, (09) Beverages.