# 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
For traditional industries, such as (14) Wood and Bamboo Products, (15) Pulp, Paper and Paper Products, (16) Printing and Reproduction of Recorded Media, technology-intensive industries, such as (26) Electronic Parts and Components, (30) Motor Vehicles and Parts, and basic industries, such as (20) Medical Goods, (22) Plastic Products, when the degree of export intensity increases, manufacturers will be more likely to use the internet. On the contrary, in the case of basic industries, such as (18) Chemical Material, (19) Chemical Products, (21) Rubber Products, when the degree of export intensity increases, manufacturers will be less likely to use the internet. The coefficient of \(size_{i}\) shows a positive effect for affecting the manufacturers' decision to use the internet for most 2-digit industries. Moreover, the coefficient of \(group_{i}\) has a positive and significant effect on manufacturers' decision to use the internet for most 2-digit industries. The following discussion presents the total marginal effects of each of the explanatory variables on how manufacturers use the internet for the individual 2-digit industries in Table 9. Of these 26 industries, seven 2-digit industries significantly reject the null hypothesis that \(\beta_{\lambda}\) equal zero at the 10% level of significance, with bootstrapping standard errors, namely (08) Food, (09) Beverages, (22) Plastic Products, (28) Electrical Equipment, (29) Machinery and Equipment, (30) Motor Vehicles and Parts (32) Furniture, indicating that these industries are affected by the problem of sample selection bias, thereby making it necessary to correct for sample selection bias. In the following paragraphs, we present the marginal effects, as given by Equations (7) and (8). In terms of industrial penetration (\(GHHI_{j}\)), among traditional industries, the largest value is 2.3761 for (09) Beverages, while the smallest, is \(-\)1.4581 for (32) Furniture; for technology-intensive industries, the largest value is 5.5503 for (27) Plastic Products, while the smallest is \(-\)12.6278 for (30) Motor Vehicles and Parts; for basic industries, the largest value is 21.886 for (20) Medical Goods, while the smallest is \(-\)1.3668 for (21) Rubber Products. The marginal effects in various industries can be summarized, as follows. Industrial concentration (\(HHI_{j}\)), among traditional industries, has the largest marginal effect at 0.1812 for (13) Leather, Fur and Related Products, while the smallest is \(-\)0.1393 for (08) Food; For technology-intensive industries, the largest value is 0.2549 for (26) Electronic Parts and Components, while the smallest is \(-\)0.2781 for (29) Machinery and Equipment; for the basic industries, the largest value is 2.3671 for (22) Plastic Products, while the smallest value is \(-\)0.2068 for (24) Basic Metal. The marginal effect of export intensity (\(export_{i}\)), among traditional industries, has the largest at 0.5523 for (08) Food, while the smallest is \(-\)0.0095 for (13) Leather, Fur and Related Products; for technology-intensive industries, the largest is 0.4583 for (27) Plastic Products, while the smallest is 0.0221 for (26) Electronic Parts and Components; for basic industries, the largest is 0.5053 for (21) Rubber Products, while the smallest is 0.0393 for (19) Chemical Products. Among traditional industries, the marginal effect of geographic location (\(city_{i}\)) has the largest value at 0.0266 for (08) Food, while the smallest is \(-\)0.0018 for (11) Textiles Mills; for technology-intensive industries, the largest value is 0.0527 for (26) Electronic Parts and Components, while the smallest is \(-\)0.0249 for (27) Plastic Products; for basic industries, the largest value is 0.0578 for (21) Rubber Products, while the smallest is \(-\)0.0216 for (24) Basic Metal.