A Statistical Analysis of Industrial Penetration and Internet Intensity in Taiwan †
Abstract
:1. Introduction
2. Firm’s Internet Intensity and Geographical Concentration
3. Heckman Sample Selection Model
4. Data and Variables
5. Results and Discussion
5.1. Regression Model with Sample Selection Correction for All Industries
5.2. Regression Model with Sample Selection Correction for 2-Digit Industries
6. Conclusions
- (1)
- The manufacturer’s decision to use the internet is influenced by five factors, namely the degree of industrial concentration, export intensity, geographical location, the manufacturer’s size of operations, and the independence of operations. As Taiwan largely consists of manufacturers with independent operations, it is not surprising that the likelihood of such manufacturers using the internet is relatively high, with the manufacturers’ independence of operations having the greatest impact. The second most influential factor is the manufacturers’ export intensity, indicating that the more manufacturers rely on exports, the greater their export intensity, and the more that they need to use the internet to communicate with overseas customers. The third most influential factor is the degree of industrial concentration. The greater is the competition that manufacturers face, in order to increase their ability to compete with other manufacturers, the more likely that they be inclined to use the internet. The empirical results also show that manufacturers that are located in rural areas would be likely to use the internet for business than those that are located in urban areas, and larger firms would be more likely to use the internet for business than smaller firms. However, the impact of the degree of industrial penetration on the manufacturers’ use of the internet is not significant.
- (2)
- The degree to which manufacturers use the internet is primarily influenced by three factors, namely the degree of industrial penetration, geographical location, and the contagion effect. While the impact of the degree of industrial penetration on the manufacturers’ use of the internet is not significant, the effect on the extent to which manufacturers use the internet is significant and negative. This suggests that the extent of industrial penetration does not affect whether or not manufacturers will use the internet, but it will affect the extent to which manufacturers that already use the internet will continue to use the internet. The results suggest there exists a substitute relationship between the penetration of localization and the extent to which manufacturers use the internet, indicating that internet technology has overcome the “distance” factor, so that it is no longer especially important.
- (3)
- The variable of industrial penetration has a negative marginal effect on the extent to which the manufacturers use the internet, indicating that there exists a substitute relationship between the extent to which the manufacturers use the internet and the level of industrial penetration. Such results confirm the research by [17] Kauffman and Kumar (2007), who used US information technology-related manufacturing and service industry data, and [18] Galliano and Roux (2008), who used French manufacturing data.
- (4)
- The more competitive is the industry, manufacturers will increasingly need to use the internet to communicate and trade with other entities, and to increase their competitiveness. The empirical findings agree with those of [18] Galliano and Roux (2008) and [21] Galliano et al. (2011), who used French manufacturing industry data.
- (5)
- The export intensity has the greatest marginal effect on the extent to which manufacturers use the internet, indicating that international competition has a relatively large influence on internet intensity. The second and third largest effects are the manufacturers’ expenditure on computer equipment and the contagion effect, both of which have a positive marginal effect on the degree to which manufacturers use the internet, thought that the magnitudes for both marginal effects are quite small.
- (6)
- As the industries are different, the empirical results for the individual industries based on the two-digit level classifications are quite varied. In terms of the degree of industrial penetration, two industries, namely (09) Beverages and (32) Furniture have the largest positive (2.376) and smallest negative (−1.458) marginal effects on how manufacturers use the internet, respectively, for traditional industries; (27) Plastic Products and (30) Motor Vehicles and Parts have the largest positive (5.550) and smallest negative (−12.628) marginal effects on the ways in which manufacturers use the internet, respectively, for technology-intensive industries; (20) Medical Goods and (21) Rubber Products have the largest positive (21.886) and smallest negative (−1.367) marginal effects as to how manufacturers use the internet, respectively, for basic industries.
- (7)
- The marginal effect of localized penetration on how the manufacturers use the internet also varies widely. The largest positive and smallest negative values for the traditional industries are, respectively, 0.0266 for (08) Food and −0.0018 for (11) Textiles Mills; the largest and smallest values for technology-intensive industries are, respectively, 0.0527 for (26) Electronic Parts and Components and −0.0249 for (27) Plastic Products; the largest and smallest values for basic industries are, respectively, 0.0578 for (21) Rubber Products and −0.0216 for (24) Basic Metal.
- (8)
- Industries with a higher degree of export intensity and with a greater reliance on exports will have a higher degree of internet intensity among those manufacturers that use the internet. The results indicate that exports of export-oriented industries, such as (08) Food, (26) Electronic Parts and Components, and (22) Plastic Products, have the largest marginal effects for traditional, technology-intensive and basic industries in Taiwan, respectively.
Author Contributions
Conflicts of Interest
References
- Lin, Y.C. 2009 Industry Information Application Status and Demand Survey Report; Department of Industrial Technology, Ministry of Economic Affairs, R.O.C.: Taipei, Taiwan, 2009. (In Chinese) [Google Scholar]
- Lin, Y.C. 2010 Industry Information Application Status and Demand Survey Report; Department of Industrial Technology, Ministry of Economic Affairs, R.O.C.: Taipei, Taiwan, 2010. (In Chinese) [Google Scholar]
- Schwab, K.; Sala-i-Martin, X. The Global Competitiveness Report, 2009–2010; World Economic Forum: Geneva, Switzerland, 2009. [Google Scholar]
- Schwab, K.; Sala-i-Martin, X. The Global Competitiveness Report, 2010–2011; World Economic Forum: Geneva, Switzerland, 2010. [Google Scholar]
- Schwab, K.; Sala-i-Martin, X. The Global Competitiveness Report, 2011–2012; World Economic Forum: Geneva, Switzerland, 2011. [Google Scholar]
- Schwab, K.; Sala-i-Martin, X. The Global Competitiveness Report, 2012–2013; World Economic Forum: Geneva, Switzerland, 2012. [Google Scholar]
- Audretsch, D.B.; Feldman, M.P. R&D Spillovers and the Geography of Innovation and Production. Am. Econ. Rev. 1996, 86, 630–640. [Google Scholar]
- Bertschek, I.; Fryges, H. The Adoption of Business-to-Business E-Commerce Empirical Evidence for German Companies; ZEV Discussion Paper; Centre for European Economic Research (ZEW): Mannheim, Germany, 2002; pp. 02–05. [Google Scholar]
- Chang, C.-L.; Oxley, L. Industrial Agglomeration, Geographic Innovation and Total Factor Productivity: The Case of Taiwan. Math. Comput. Simul. 2009, 79, 2787–2796. [Google Scholar] [CrossRef]
- Forman, C.; Goldfarb, A.; Greenstein, S. Geographic Location and the Diffusion of Internet Technology. Electron. Commer. Res. Appl. 2005, 4, 1–13. [Google Scholar] [CrossRef]
- Forman, C.; Goldfarb, A.; Greenstein, S. How Do Industry Features Influence the Role of Locationon Internet Adoption? J. Assoc. Inf. Syst. 2005, 6, 383–408. [Google Scholar]
- Forman, C.; Goldfarb, A.; Greenstein, S. How Did Location Affect the Adoption of the Commercial Internet? Global Village vs. Urban Density. J. Urban Econ. 2005, 58, 389–420. [Google Scholar] [CrossRef]
- Goldfarb, A.; Prince, J. Internet Adoption and Usage Patterns are Different: Implications for the Digital Divide. Inf. Econ. Policy 2008, 20, 2–15. [Google Scholar] [CrossRef]
- Baptista, R.; Swann, P. Do firms in clusters innovate more? Res. Policy 1998, 27, 525–540. [Google Scholar] [CrossRef]
- Atrostic, B.K.; Nguyen, S.V. IT and Productivity in U.S. Manufacturing: Do Computer Networks Matter? Econ. Inq. 2005, 45, 493–506. [Google Scholar] [CrossRef]
- Kolko, J. The Death of Cities? The Death of Distance Evidence from the Geography of Commercial Internet Usage. In Mimeo; Harvard University: Cambridge, MA, USA, 1999. [Google Scholar]
- Kauffman, R.; Kumar, A. The New Economic Geography of IT Industries the Impacts of the Internet on Their Market Linkages and Agglomeration. In Proceedings of the 28th Annual International Conference on Information Systems (ICIS), Montreal, QC, Canada, 9–12 December 2007. [Google Scholar]
- Galliano, D.; Roux, P. Organisational Motives and Spatial Effects in Internet Adoption and Intensity of Use: Evidence from French Industrial Firms. Ann. Reg. Sci. 2008, 42, 425–448. [Google Scholar] [CrossRef]
- Lal, K. Determinants of the Adoption of Information Technology: A Case Study of Electrical and Electronic Goods Manufacturing Firms in India. Res. Policy 1999, 28, 667–680. [Google Scholar] [CrossRef]
- Giunta, A.; Trivieri, F. Understanding the Determinants of Information Technology Adoption. Appl. Econ. 2007, 39, 1325–1334. [Google Scholar] [CrossRef]
- Galliano, D.; Roux, P.; Soulie, N. ICT Intensity of Use and the Geography of Firms. Environ. Plan. A 2011, 43, 67–86. [Google Scholar] [CrossRef]
- Mansfield, E. The Speed of Response of Firms to New Techniques. Q. J. Econ. 1963, 77, 290–311. [Google Scholar] [CrossRef]
- Mansfield, E. Intrafirm Rates of Diffusion of an Innovation. Rev. Econ. Stat. 1963, 45, 348–359. [Google Scholar] [CrossRef]
- Saloner, R.; Sheppard, J.M. Adoption of Technologies with Network Effects: An Empirical Examination of the Adoption of Automated Teller Machines. Rand J. Econ. 1995, 26, 479–501. [Google Scholar] [CrossRef]
- Heckman, J.J. Sample Selection Bias as a Specification Error. Econometrica 1979, 47, 153–161. [Google Scholar] [CrossRef]
- Lewis, H.G. Comments on Selectivity Biases in Wage Comparisons. J. Polit. Econ. 1974, 82, 1145–1155. [Google Scholar] [CrossRef]
- Heckman, J.J. The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models. Ann. Econ. Soc. Meas. 1976, 5, 475–492. [Google Scholar]
- Greene, W. Sample Selection Bias as a Specification Error: Comment. Econometrica 1981, 49, 795–798. [Google Scholar] [CrossRef]
- Blaylock, J.R.; Blissard, W.N. Self-valuated Health Status and Smoking Behaviour. Appl. Econ. 1992, 25, 429–435. [Google Scholar] [CrossRef]
- Maddala, G.S. Limited-Dependent and Qualitative Variables in Economics; Cambridge University Press: New York, NY, USA, 1983. [Google Scholar]
- Greene, W. Econometric Analysis; Macmillan: New York, NY, USA, 2003. [Google Scholar]
Code | 2-Digit Industry | Number of Firms | |
---|---|---|---|
Traditional industries | 08 | Food | 6165 |
09 | Beverages | 644 | |
11 | Textiles Mills | 6439 | |
12 | Wearing Apparel and Clothing Accessories | 4084 | |
13 | Leather, Fur and Related Products | 1870 | |
14 | Wood and Bamboo Products | 2849 | |
15 | Pulp, Paper and Paper Products | 3605 | |
16 | Printing and Reproduction of Recorded Media | 9439 | |
23 | Non-metallic Mineral Products | 3677 | |
32 | Furniture | 2849 | |
33 | Manufacturing Not Elsewhere Classified | 5435 | |
Technology-intensive industries | 26 | Electronic Parts and Components | 6023 |
27 | Computers, Electronic and Optical Products | 3717 | |
28 | Electrical Equipment | 6198 | |
29 | Machinery and Equipment | 18,545 | |
30 | Motor Vehicles and Parts | 3580 | |
31 | Other Transport Equipment | 2905 | |
34 | Repair and Installation of Industrial Machinery and Equipment | 3907 | |
Basic industries | 17 | Petroleum and Coal Products | 229 |
18 | Chemical Material | 1549 | |
19 | Chemical Products | 2304 | |
20 | Medical Goods | 543 | |
21 | Rubber Products | 1756 | |
22 | Plastic Products | 11,012 | |
24 | Basic Metal | 4710 | |
25 | Fabricated Metal Products | 39,047 | |
Total | All manufacturing industries | 153,081 |
Variables | Description |
---|---|
Dependent variable | |
the extent to which the firm i use the internet = (online purchase amount + online sales amount)/total sales | |
= 1, if firm i use an internet equipment for business information = 0, otherwise | |
Independent variable | |
Herfindahl-Hirschman Index for industry j | |
Top Four firms Concentration Index for industry j | |
Export share for firm i = export value/total sales | |
Geographic Herfindahl-Hirschman lndex for industry j | |
Firm size: total number of employees for firm i | |
Total expenditure on computer equipment for firm i: unit NT$1000 | |
Total expenditure on computer equipment within the same industry and same area, excluding the expenditure of firm i: unit NT$1000 | |
1, if firm i is urban; 0, if firm i is rural | |
, if firm i has no subsidiary (branch); , otherwise |
Variables (Unit) | Mean | Std Dev. | Min | Max |
---|---|---|---|---|
(100%) | 1.9998 | 43.2231 | 0 | 7153.077 |
0.6069 | 0.4884 | 0 | 1 | |
0.0322 | 0.0656 | 0.0020 | 1 | |
0.2053 | 0.1683 | 0.0407 | 1 | |
0.0709 | 0.1669 | 0 | 1 | |
0.0031 | 0.0239 | 0 | 0.4752 | |
16.7994 | 113.8733 | 0 | 17,040 | |
(NT$1000) | 0.0029 | 0.2871 | 0 | 99.2 |
(NT$1000) | 0.4011 | 6.4387 | 0 | 1264.754 |
0.1845 | 0.3879 | 0 | 1 | |
0.9327 | 0.2505 | 0 | 1 |
1 | ||||||||
0.8518 | 1 | |||||||
−0.0078 | 0.0011 | 1 | ||||||
0.1558 | 0.1780 | 0.0413 | 1 | |||||
0.0261 | 0.0290 | −0.0428 | 0.0093 | 1 | ||||
0.0028 | 0.0066 | −0.0008 | −0.0032 | −0.0002 | 1 | |||
0.0077 | 0.0155 | 0.0140 | −0.0149 | 0.0010 | 0.0401 | 1 | ||
0.0803 | 0.0863 | −0.0000 | 0.1729 | 0.0072 | 0.0010 | −0.0062 | 1 |
Variables | Intensity of Internet Use () | Select () |
---|---|---|
0.148 | −1.369 | |
(3.660) | (0.065) *** | |
[2.732] | [0.067] *** | |
1.086 | 3.807 | |
(1.284) | (0.207) *** | |
[1.336] | [0.057] *** | |
−2.774 | 0.051 | |
(1.057) *** | (0.237) | |
[5.237] | [0.201] | |
0.852 | −0.201 | |
(0.523) * | (0.013) *** | |
[0.378] ** | [0.010] *** | |
0.239 | - | |
(51.880) | ||
[0.432] | ||
0.069 | - | |
(0.119) | ||
[0.019] *** | ||
0.002 [0.002] | 0.003 (0.001) *** [0.0002] *** | |
- | 58.543 | |
(16.397) *** | ||
[0.005] *** | ||
constant | 2.643 (0.755) *** [0.882] *** | −57.606 (16.400) *** |
Mills lambda () | −7.229 | |
(2.595) *** | ||
[2.193] *** | ||
# of observations | 153,081 | |
# of censored observation | 31,924 | |
Wald Chi2 (df) | 543.38 (32) |
Variables | Intensity of Internet Use () | Select () |
---|---|---|
4.137 | −0.645 | |
(1.160) *** | (0.028) *** | |
[1.244] *** | [0.025] *** | |
0.532 | 3.813 | |
(1.143) | (0.214) *** | |
[1.342] | [0.057] *** | |
−1.861 | 0.071 | |
(1.064) * | (0.203) | |
[5.246] | [0.202] | |
0.904 | −0.201 | |
(0.344) *** | (0.011) *** | |
[0.377] ** | [0.010] *** | |
0.240 | - | |
(55.104) | ||
[0.432] | ||
0.069 (0.142) [0.019] *** | - | |
0.001 [0.002] | 0.004 (0.001) *** [0.0002] *** | |
- | 61.607 | |
(22.335) *** | ||
[0.007] *** | ||
constant | 1.876 (0.763) ** [0.894] ** | −60.585 (22.243) *** |
Mills lambda () | −8.067 | |
(2.444) *** | ||
[2.172] *** | ||
# of observations | 153,081 | |
# of censored observation | 31,924 | |
Wald Chi2 (df) | 561.99 (32) |
Variables | Internet Intensity (1) | Internet Intensity (2) |
---|---|---|
−0.0243 | −0.0133 | |
−0.0897 | ||
−0.0069 | ||
0.2643 | 0.2908 | |
−0.0049 | −0.0060 | |
0.0002 | 0.0003 | |
0.0024 | 0.0024 | |
0.0007 | 0.0007 |
Variables | (8) | (9) | (11) | (12) | (13) | (14) | (15) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
−16.06 (3.53) *** | 22.91 (5.37) *** | −39.48 (16.07) ** | 206.89 (237.25) | −9.10 (10.16) | 10.74 (2.38) *** | −0.31 (0.16) * | 0.23 (0.19) | −11.52 (3.68) *** | 35.24 (19.24) * | −38.27 (67.17) | 98.98 (61.42) | −46.92 (69.32) | −193.30 (29.47) *** | |
10.06 (3.81) *** | −8.14 (0.74) *** | −0.38 (0.89) | −3.84 (60.43) | −2.95 (3.53) | 3.81 (0.98) *** | 1.70 (1.70) | −1.80 (0.61) *** | 17.76 (7.47) ** | −16.81 (6.49) *** | 13.47 (22.95) | −10.51 (7.58) | −1.46 (1.68) | 3.66 (1.34) *** | |
0.84 (1.59) | 18.50 (545.92) | 0.80 (0.39) ** | 4.26 (237.99) | 4.22 (5.22) | 21.50 (86.23) | 1.10 (1.02) | 12.96 (366.33) | −0.27 (0.17) | 17.69 (989.31) | 3.93 (3.09) | 676.48 (353.91) * | −0.14 (0.59) | 916.90 (243.48) *** | |
−0.73 (0.29) ** | 1.15 (0.22) *** | −0.21 (0.06) *** | 218.03 (139.58) | −0.09 (0.74) | −0. 21 (0.06) *** | 0.37 (0.30) | 0.46 (0.06) *** | 0.24 (0.27) | −0.12 (0.15) | 0.62 (0.36) * | −0.20 (0.11) * | −0.15 (0.21) | −0.55 (0.06) *** | |
−0.0003 (0.003) | 0.05 (0.01) *** | 0.001 (0.002) | 0.22 (0.25) | −0.01 (0.01) | 0.00005 (0.002) | 0.003 (0.002) | 0.003 (0.002) | −0.001 (0.002) | 0.01 (0.02) | 0.11 (0.07) | 0.01 (0.005) | 0.03 (0.03) | 0.01 (0.01) | |
24.42 (18.66) | −0.07 (10.86) | 1751.71 (1497.42) | 86.13 (84.32) | −0.73 (4.86) | 46.31 (114.46) | 26.20 (145.49) | ||||||||
0.01 (0.45) | −0.22 (0.31) | −5.02 (4.49) | 0.07 (0.13) | −1.94 (0.58) *** | 0.90 (1.31) | 0.60 (1.47) | ||||||||
91.68 (30.83) *** | 313.54 (193.88) | 7.09 (1.38) *** | 12.05 (2.84) *** | 25.35 (41.1) | 14.07 (5.28) *** | 16.86 (7.63) ** | ||||||||
constant | 0.74 (0.16) *** | −90.28 (30.86) *** | 0.20 (0.07) *** | −311.96 (194.32) | 0.05 (1.45) | −6.78 (1.38) *** | −0.41 (0.36) | −11.47 (2.82) *** | 0.13 (0.10) | −24.39 (41.13) | −0.63 (0.98) | −12.82 (5.30) ** | −0.31 (0.31) | −15.63 (7.68) ** |
# of observations | 6165 | 644 | 6439 | 4084 | 1870 | 2849 | 3605 | |||||||
# of censored | 1081 | 106 | 1783 | 936 | 306 | 329 | 595 | |||||||
Mills Lambda | −3.01 (1.15) *** | −1.28 (0.67) * | −2.10 (2.61) | 0.93 (0.73) | 0.14 (0.49) | 0.12 (1.85) | 1.04 (1.08) | |||||||
Wald Chi2(ddl) | 31.13 (7) | 27.53 (7) | 3.48 (7) | 15.84 (7) | 17.80 (7) | 19.65 (7) | 5.97 (7) | |||||||
(16) | (18) | (19) | (20) | (21) | (22) | |||||||||
14.21 (13.76) | −40.82 (3.26) *** | −176.22 (221.18) | 12.04 (183.62) | 86.35 (240.43) | 9.18 (107.81) | 2103.67 (1324.42) | 4.75 (162.27) | 138.09 (351.43) | 17.62 (37.44) | 321.85 (236.26) | −33.14 (12.28) *** | |||
−0.49 (2.15) | 0.08 (1.62) | −3.63 (3.02) | 0.51 (1.27) | 8.30 (5.50) | 1.13 (1.84) | 54.14 (40.35) | −0.22 (10.75) | 0.26 (6.91) | −0.97 (1.17) | 72.07 (76.82) | 25.45 (8.33) *** | |||
4.42 (3.23) | 1155.05 (573.23) ** | 5.19 (2.14) ** | −3.46 (0.30) *** | 3.13 (2.54) | −1.87 (0.31) *** | 9.68 (6.88) | 1662.65 (797.32) ** | 4.25 (3.34) | −2.96 (0.20) *** | −1.60 (1.10) | 1.32 (0.64) ** | |||
−0.02 (0.05) | −0.13 (0.03) *** | −0.52 (0.44) | 0.07 (0.32) | −0.10 (0.41) | 0.12 (0.16) | 0.80 (1.63) | −0.24 (0.28) | 2.11 (2.01) | −0.23 (0.21) | 0.77 (0.44) * | −0.31 (0.05) *** | |||
0.02 (0.02) | 0.01 (0.003) *** | −0.003 (0.01) | 0.06 (0.01) *** | −0.004 (0.01) | 0.04 (0.02) ** | −0.03 (0.02) | 0.02 (0.06) | −0.002 (0.01) | 0.11 (0.02) *** | −0.01 (0.004) *** | 0.03 (0.01) *** | |||
126.63 (32.73) *** | −22.34 (116.68) | −76.94 (166.20) | −84.18 (620.14) | −530.75 (290.49) * | 444.96 (185.26) ** | |||||||||
−0.02 (0.02) | −0.60 (2.21) | −0.11 (0.29) | 0.05 (1.43) | −0.05 (1.08) | −0.11 (0.07) | |||||||||
11.30 (1.60) *** | 218.90 (67.82) *** | 39.08 (15.73) ** | 18.50 (38.31) | 304.01 (106.46) *** | 43.54 (8.21) *** | |||||||||
constant | −0.32 (0.33) | −10.74 (1.60) *** | 1.48 (0.48) *** | −216.90 (67.79) *** | 0.76 (0.49) | −37.20 (15.76) ** | −0.16 (2.41) | −17.10 (38.74) | 0.51 (0.91) | −302.68 (106.49) *** | 2.68 (0.78) *** | −42.42 (8.24) *** | ||
# of observations | 9439 | 1549 | 2304 | 543 | 1756 | 11012 | ||||||||
# of censored observation | 2790 | 455 | 499 | 142 | 249 | 1487 | ||||||||
Mills Lambda | 0.40 (0.56) | 0.63 (6.84) | −0.72 (7.40) | −30.66 (19.23) | 15.50 (9.90) | −10.60 (2.75) *** | ||||||||
Wald Chi2(ddl) | 36.82 (7) | 10.16 (7) | 10.78 (7) | 11.61 (7) | 8.59 (7) | 24.98 (7) | ||||||||
(23) | (24) | (25) | (26) | (27) | (28) | |||||||||
−2.12 (1.63) | 0.35 (0.63) | −105.59 (60.97) * | −0.78 (4.26) | −55.07 (34.47) | 15.17 (2.74) *** | −79.22 (67.35) | −10.87 (2.87) *** | 543.29 (464.33) | 3.88 (11.80) | −42.62 (6.84) *** | 2.74 (5.40) | |||
0.58 (2.55) | 1.19 (0.57) ** | −20.61 (12.82) | −0.05 (0.25) | 13.17 (6.60) ** | −4.71 (0.32) *** | 26.67 (9.45) *** | 0.18 (0.33) | −13.53 (18.21) | −0.38 (0.28) | −3.89 (1.75) ** | −0.23 (1.30) | |||
3.83 (2.59) | 8.69 (390.12) | −7.67 (3.46) ** | 5.72 (313.81) | 3.57 (4.11) | 68.80 (65.41) | 6.63 (2.59) ** | 18.18 (1.45) *** | 8.71 (8.04) | 7.59 (266.85) | 0.51 (0.49) | 11.06 (877.09) | |||
−0.27 (0.38) | −0.25 (0.10) *** | −0.85 (1.31) | −0.37 (0.07) *** | 0.31 (0.95) | −0.09 (0.03) *** | 5.29 (6.71) | 0.09 (0.06) | −2.89 (3.29) | 0.10 (0.10) | 0.22 (0.18) | −0.05 (0.08) | |||
0.01 (0.01) | 0.03 (0.01) *** | 0.01 (0.01) | 0.01 (0.01) | 0.05 (0.02) ** | 0.003 (0.001) *** | −0.002 (0.004) | 0.0001 (0.0002) | −0.005 (0.01) | 0.003 (0.002) ** | −0.01 (0.002) *** | 0.01 (0.005) * | |||
140.06 (185.01) | 13895.31 (9161.63) | 44.14 (8.61) *** | 272.09 (303.94) | 7.24 (3224.05) | −1.10 (76.65) | |||||||||
−0.32 (0.38) | 0.08 (1.31) | 0.05 (0.06) | −0.03 (0.02) | 0.25 (5.34) | −0.04 (0.03) | |||||||||
70.24 (24.95) *** | 12.51 (8.95) | 10.52 (0.93) *** | 6.99 (2.64) *** | 18.74 (5.59) *** | 22.70 (11.12) ** | |||||||||
constant | 0.32 (0.59) | −69.12 (24.99) *** | 0.64 (1.63) | −11.41 (9.00) | 1.56 (0.51) *** | −9.93 (0.93) *** | 0.09 (0.56) | −6.59 (2.65) ** | 5.98 (6.47) | −17.80 (5.63) *** | 1.69 (0.32) *** | −21.69 (11.13) * | ||
# of observations | 3677 | 4710 | 39047 | 6023 | 3717 | 6198 | ||||||||
# of censored | 684 | 861 | 8496 | 1558 | 716 | 1065 | ||||||||
Mills Lambda | 0.06 (2.64) | −5.90 (5.90) | −0.59 (1.35) | 2.34 (4.30) | −9.37 (20.51) | −3.76 (1.16) *** | ||||||||
Wald Chi2(ddl) | 9.66 (7) | 10.64 (7) | 56.58 (7) | 30.65 (7) | 5.68 (7) | 42.45 (7) | ||||||||
(29) | (30) | (31) | (32) | (33) | ||||||||||
5.05 (5.32) | 5.31 (1.57) *** | −1270.42 (339.41) *** | −58.65 (26.40) ** | −97.88 (44.32) ** | 23.42 (9.05) *** | −142.35 (55.20) *** | −40.96 (15.36) *** | −1.70 (23.26) | −19.63 (3.91) *** | |||||
−27.89 (2.38) *** | 7.32 (1.26) *** | 0.66 (9.77) | 0.90 (0.59) | −8.66 (11.39) | −0.67 (1.33) | −75.58 (37.44) ** | 15.18 (5.73) *** | −21.36 (14.65) | 0.83 (1.44) | |||||
5.80 (0.90) *** | 99.56 (291.32) | 25.94 (5.32) *** | 2270.49 (582.85) *** | 0.73 (0.99) | 6.68 (3.12) ** | −3.08 (4.87) | 524.98 (408.23) | 0.83 (0.78) | 47.78 (534.54) | |||||
0.12 (0.20) | −0.27 (0.04) *** | 1.32 (2.05) | −0.36 (0.08) *** | 1.08 (0.58) * | −0.35 (0.10) *** | 0.49 (0.91) | −0.09 (0.12) | 0.47 (0.40) | 0.02 (0.07) | |||||
−0.01 (0.002) *** | 0.01 (0.004) *** | −0.02 (0.01) ** | 0.01 (0.005) * | 0.01 (0.01) | 0.01 (0.01) | 0.01 (0.02) | 0.01 (0.01) *** | 0.01 (0.01) | 0.004 (0.004) | |||||
60.40 (44.06) | −461.96 (295.56) | 589.45 (360.87) | 37.02 (67.99) | −0.76 (136.12) | ||||||||||
−0.15 (0.06) ** | 0.02 (0.39) | −0.08 (0.08) | −0.45 (0.63) | 0.88 (1.15) | ||||||||||
25.93 (6.44) *** | 27.24 (9.91) *** | 24.65 (15.21) | 12.47 (2.30) *** | 9.96 (3.71) *** | ||||||||||
constant | 1.61 (0.16) *** | −25.33 (6.46) *** | 3.16 (0.99) *** | −26.49 (9.94) *** | 0.96 (0.98) | −23.84 (15.26) | 4.19 (1.97) ** | −11.45 (2.32) *** | 1.61 (0.57) *** | −9.06 (3.74) ** | ||||
# of observations | 18545 | 3580 | 2905 | 2849 | 5435 | |||||||||
# of censored | 3076 | 686 | 521 | 367 | 780 | |||||||||
Mills Lambda | −0.88 (0.39) ** | 5.77 (2.53) ** | −1.74 (2.25) | −14.24 (7.45) * | −0.68 (1.60) | |||||||||
Wald Chi2(ddl) | 156.24 (7) | 47.75 (7) | 30.84 (7) | 21.73 (7) | 30.94 (7) |
Marginal Effects | ||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
(8) | (9) | (11) | (12) | (13) | (14) | (15) | (16) | (19) | (20) | (21) | (22) | |||||||||||
0.5189 | 2.2452 | −0.0516 | −0.0037 | −0.1271 | −0.3827 | −0.4692 | 0.1421 | 0.9273 | 21.0367 | −1.3420 | 0.5087 | |||||||||||
−0.1408 | −0.0529 | −0.0155 | 0.0222 | 0.1833 | 0.1347 | −0.0146 | −0.0049 | 0.0908 | 0.5414 | 0.1526 | 2.8016 | |||||||||||
0.5573 | 0.0624 | 0.1211 | −0.0261 | −0.0087 | 0.0393 | −0.0014 | 0.0442 | 0.0183 | 0.0968 | 0.5001 | 0.0921 | |||||||||||
0.0268 | - | −0.0018 | 0.0025 | 0.0024 | 0.0062 | −0.0015 | −0.0002 | −0.0002 | 0.0080 | 0.0573 | −0.0185 | |||||||||||
0.0014 | 0.0028 | 0.0001 | 0 | 0 | 0.0011 | 0.0003 | 0.0002 | −0.0002 | −0.0003 | −0.0177 | 0.0020 | |||||||||||
0.2442 | −0.0007 | 17.5171 | 0.8613 | −0.0073 | 0.4631 | 0.2620 | 1.2663 | −0.7694 | −0.8418 | −5.3075 | 4.4496 | |||||||||||
0.0001 | −0.0022 | −0.0502 | 0.0007 | −0.0194 | 0.0090 | 0.0060 | −0.0002 | −0.0011 | 0.0005 | −0.0005 | −0.0011 | |||||||||||
- | - | 0.1031 | −0.0941 | −0.0318 | 0 | 0 | 0 | 0.2633 | 0 | - | - | |||||||||||
Marginal Effects | ||||||||||||||||||||||
(23) | (24) | (25) | (26) | (27) | (28) | (29) | (30) | (31) | (32) | (33) | ||||||||||||
−0.0214 | −1.0797 | −0.5480 | −0.7682 | 5.6157 | −0.3664 | 0.0505 | −12.7042 | −0.7477 | −1.4235 | −0.0170 | ||||||||||||
0.0051 | −0.2077 | 0.1308 | 0.2663 | −0.1531 | −0.0439 | −0.2789 | 0.0066 | −0.0932 | −0.7558 | −0.2136 | ||||||||||||
0.0328 | 0.0976 | 0.0480 | 0.0261 | 0.4451 | 0.2464 | 0.0580 | 0.2594 | 0.0732 | −0.0308 | 0.0083 | ||||||||||||
−0.0025 | −0.0205 | 0.0030 | 0.0527 | −0.0241 | 0.0011 | 0.0012 | 0.0132 | 0.0072 | 0.0049 | 0.0047 | ||||||||||||
0.0001 | 0.0003 | 0.0005 | 0 | 0.0001 | 0.0001 | −0.0001 | −0.0002 | 0.0002 | 0.0001 | 0.0001 | ||||||||||||
1.4006 | 138.9531 | 0.4414 | 2.7209 | 0.0724 | −0.0110 | 0.6040 | −4.6196 | 5.8945 | 0.3702 | −0.0076 | ||||||||||||
−0.0032 | 0.0008 | 0.0005 | −0.0003 | 0.0025 | −0.0004 | −0.0015 | 0.0002 | −0.0008 | −0.0045 | 0.0088 | ||||||||||||
- | 0.6456 | 0.0451 | −0.1015 | 1.4723 | 0.7677 | 0.1331 | 0 | 0.3966 | 0 | 0.0139 |
Overall results | 1 | Manufacturers decide to use the internet for five primary reasons. | Five primary reasons:
|
2 | The extent to which manufacturers use the internet is influenced by three factors. | The three factors are:
| |
3 | Industrial penetration has a negative marginal effect on the extent to which manufacturers use the internet. | Such empirical results support the research by [17] Kauffman and Kumar (2007), who used US information on technology-related manufacturing and service industry data, and [18] Galliano and Roux (2008), who used French manufacturing data. | |
4 | The more competitive is the industry, manufacturers will need to increase their competitiveness and use the internet more to communicate and trade with other entities. | The empirical findings agree with those of [18] Galliano and Roux (2008) and [21] Galliano et al. (2011), who used French manufacturing industry data. | |
5 | Export intensity has the greatest marginal effect on the extent to which manufacturers use the internet. | This empirical finding indicates that international competition has a relatively large influence on the extent of internet intensity. | |
Decomposed industry to 2-digit industry | 6 | The empirical results for the individual industries based on the two-digit level classifications can vary substantially. | The same outcome holds for the marginal effect of localized penetration on the variable extent to which manufacturers use the internet. |
7 | Industries with a higher degree of export intensity, and with a greater reliance on exports, will have a higher degree of internet intensity among manufacturers that use the internet. | This empirical finding seems to be a novel result. |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chang, C.-L.; McAleer, M.; Wu, Y.-C. A Statistical Analysis of Industrial Penetration and Internet Intensity in Taiwan. Future Internet 2018, 10, 31. https://doi.org/10.3390/fi10030031
Chang C-L, McAleer M, Wu Y-C. A Statistical Analysis of Industrial Penetration and Internet Intensity in Taiwan. Future Internet. 2018; 10(3):31. https://doi.org/10.3390/fi10030031
Chicago/Turabian StyleChang, Chia-Lin, Michael McAleer, and Yu-Chieh Wu. 2018. "A Statistical Analysis of Industrial Penetration and Internet Intensity in Taiwan" Future Internet 10, no. 3: 31. https://doi.org/10.3390/fi10030031