Does socio-economic indicator influent ICT variable? I. Introduction This paper obtains a model of relationship between ICT indicator and macroeconomic indicator in a country. Modern economy paradigm assumes that ICT indicator reflects welfare of a country, for instance in the developed country the number of internet user tend to be higher than in developing country. II. Method of data collection, Objective and data gathered This paper performs qualitative analysis using secondary data which collected from three source such as Indonesian statistical bureau (BPS), World Bank and International Telecommunication Union (ITU). This paper aims to - To obtain whether prosperity (GDP perkapita ) of country related to number of internet usage. - To asses the strength of a cause and effect relationship between economic variable and ICT variable. - To predict the number of internet user per 100 inhabitants in a country, In order to simplify analysis two socioeconomic variables were selected (GDP perkapita and population density ) and three ICT variable ( number of telephone subscribers per 100 inhabitants, number of internet user per 100 inhabitants and number of PC per 100 inhabitants). This analysis used cross section data from 132 country and SPSS software for calculating process.
III. Analysis and evaluation of data gathered 3.a Chi square test In order to obtain whether prosperity (GDP perkapita ) of country related to number of internet usage, The chi square test were conducted. The hypothesis are Ho : There is no relationship between GDP perkapita and number of internet usage H1 : There is relationship between GDP perkapita and number of internet usage Decision rule : Using 5 % level of significance and degree of freedom (df ) = 2, critical value of X 2 distribution is 5,99 ( appendix 2 ). If the test statistic > 5,99, Ho will be rejected Table 1. Chi-Square Tests Result Value df Asymp. Sig. (2-sided) Pearson Chi-Square 41.876(a) 2.000 Likelihood Ratio 38.890 2.000 Linear-by-Linear Association 41.438 1.000 N of Valid Cases 133 a 2 cells (33.3%) have expected count less than 5. The minimum expected count is 3.79. Output Analysis The result of persons chi-square test is 41.875. This value is much higher then critical value ( 5.99), this means there were sufficient reason to reject Hypothesis null ( Ho ) consequently there is relationship between perkapita and number internet usage in a country.
3.b Coefficient correlation By using previous chi square test, statistically proved that variable internet usage is related with GDP perkapita, However the level of relationship between those variable can not be known by using chi squared test. The correlation coefficient is used to quantify the strength of the linear relationship between two quantifiable variables. The coefficient ( r ) will take value between -1 and + 1. A value of + 1 represents a perfect positive correlation, it means that two variables are precisely related and that, as values of one variable increase, values of the other variable will increase. Correlation coefficient between -1 and +1 represent weaker positive and negative correlations, a value of 0 meaning the variables are perfectly independent. This analysis uses pearson s product moment correlation coefficient (PMCC) to asses strength of relationship Table 2. Coefficient Correlations GDP Telephone internet pc density GDP Pearson Correlation 1.801(**).569(**).852(**).112 Sig. (2-tailed)..000.000.000.201 Telephone internet pc density N 133 133 133 132 133 Pearson Correlation.801(**) 1.670(**).805(**).181(*) Sig. (2-tailed).000..000.000.037 N 133 133 133 132 133 Pearson Correlation.569(**).670(**) 1.638(**).074 Sig. (2-tailed).000.000..000.399 N 133 133 133 132 133 Pearson Correlation.852(**).805(**).638(**) 1.095 Sig. (2-tailed).000.000.000..280 N 132 132 132 132 132 Pearson Correlation.112.181(*).074.095 1 Sig. (2-tailed).201.037.399.280. N 133 133 133 132 133 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Output analysis The result on table 2 shows that variable internet user has strong relationship and statistically significant between GDP perkapita ( r = 0.569, p = 0.01 ), telephone user ( r = 0.670, p = 0.01 ), number of PC ( r = 0.683, p = 0.01 ) only with variable population density ( r = 0.074, p = 0.399) the variable internet user has weak relationship and no statistically significant.
3.c To predict the number of internet user per 100 inhabitants in a country, this analysis uses multivariate regression. Internet users becomes dependent variable meanwhile GDP perkapita, number telephone, number of PC become independent variable. The regression equation for all models is shown in following equations : Internet = + 1 Perkapita + 2 Density + 3 Telephone + 4 PC Internet = number internet user per 100 inhabitants. Perkapita = GDP perkapita in USD Telp = number telephone per 100 inhabitants PC = number PC per 100 inhabitants Density = population density per 100 inhabitants The multivariate regression give the result as follow : Model Summary Adjusted R Std. Error of Model R R Square Square the Estimate 1.927(a).860.856 7.97982 a Predictors: (Constant), density, pc, Telephone, GDP Model 1 Regression Residual Total ANOVA b Sum of Squares df Mean Square F Sig. 49273.247 4 12318.312 193.448.000 a 8023.362 126 63.677 57296.609 130 a. Predictors: (Constant), density, pc, Telephone, GDP b. Dependent Variable: internet Model 1 (Constant) Telephone pc GDP density Unstandardized Coefficients a. Dependent Variable: internet Coefficients a Standardized Coefficients B Std. Error Beta t Sig. -1.404 1.197-1.173.243.210.023.555 9.040.000.475.068.488 7.033.000.000.000 -.069-1.004.317.000.000 -.031 -.904.367
Output analysis The regression equation would be: Internet = -1.404 + 0.21 Telephone + 0.475 PC This implies that the number of Internet users per 100 inhabitants would increase by 0.475 given a additional one PC per 100 inhabitants, holding the effects of number of telephone constant. The number of Internet users per 100 inhabitants would increase by 0.21 given a additional one telephone per 100 inhabitants, holding the effects of number of PC constant. Finally, the intercept (constant) value of -1.404 implies that if number of telephone and number PC were both equal to zero, the number of internet user per 100 inhabitants would be -1.404. This is realistic because without telephone and PC would be no internet service in a country. Coefficient GDP and population density is very small less than 0.001 and statistically not significant, we can excluded from model. The adjusted R 2 The value of adjusted R 2 is 0.860, it indicates that 86 percent of the variation in number internet user per 100 inhabitants is explained by or associated with number of PC per 100 inhabitants and number of telephone subscriber per inhabitants. This is a very strong relationship. F statistic test for regression model. The F statistic = 193. 448 is significant (p-value) at the.000 level, indicating that we can reject the joint null hypothesis that H 0 : 1 = 2 = 3 = 4 = 0. We can conclude that the data is consistent with some type of relationship between the dependent and independent variables. t- statistic for regression coefficient Degree of freedom (df ) = 132 ( infinite ) level of significance ( p ) = 10 % critical value = 1.28 Decision rules = If t - test statistic > 1.28, H o will be rejected.
# 1 Ho : The independent variable telephone has no statistically significance relationship with dependent variable Internet user H 1 : The independent variable telephone has statistically significance relationship with dependent variable internet user t test statistic = 9.904 Decision rule : t test statistic > 1.28, H 0 is rejected Conclusion : The independent variable telephone has statistically significance relationship with dependent variable internet user #2 Ho : The independent variable PC has no statistically significance relationship with dependent variable Internet user H 1 : The independent variable PC has statistically significance relationship with dependent variable internet user t test statistic = 9.904 Decision rule : t test statistic > 1.28, H 0 is rejected Conclusion : The independent variable number of PC has statistically significance relationship with dependent variable internet user # 3 Ho : The independent variable GDP perkapita has no statistically significance relationship with dependent variable Internet user H 1 : The independent variable GDP perkapita has statistically significance relationship with dependent variable internet user t test statistic = -1. 004 Decision rule : t test statistic < 1.28, No sufficient reason to reject H 0 Conclusion : The independent variable GDP perkapita has no statistically significance relationship with dependent variable internet user. In this case we can excluded variable GDP perkapita from model
# 4 Ho : The independent variable density has no statistically significance relationship with dependent variable Internet user H 1 : The independent variable density has statistically significance relationship with dependent variable internet user t test statistic = -0.904 Decision rule : t test statistic < 1.28, No sufficient reason to reject H 0 Conclusion : The independent variable population density has no statistically significance relationship with dependent variable internet user. In this case we can excluded variable population density from model IV. Conclusion The statistic analysis has proven that number of internet usage has relationship with GDP perkapita. In the real world this hypothesis is inline with the fact that the more prosperous country the more number of internet user will be. However the linier regression has shown the contrast think that GDP perkapita did not significantly related to number of internet user. This imply that the relationship between GDP perkapita is not in linear way, it possible logarithmic related. For further research, we suggest to explore non linear regression to find exact model of relationship between GDP perkapita and number internet user.
Appendix Key Figures Figure -1 Number of PC per 100 inhabitants in developing and developed country Number PC and Internet user per 100 inhibitants 80 70 60 50 40 30 20 10 0 Nigeria Indonesia Malaysia Japan Netherlan USA Internet PC Figure -1 Number of PC per 100 inhabitants in developing and developed country 120 100 Internet Usage High Medium Low 80 Count 60 40 20 0 Developed Country Developing Country
Figure -1 Trend of number internet usage by increasing GDP GDP Internet usage Case Number Case Number
Appendix -1 Data density GDP Telephone internet pc catcountry catinternet LOG ( GDP ) 1 Algeria 14 2079 51.24 5.83 1.06 2 3 3.32 2 Benin 67 556 2.02 5.67 0.43 2 3 2.75 3 Botswana 3 4807 54.11 3.39 4.52 2 3 3.68 4 Burkina Faso 48 378 5.06 0.49 0.24 2 3 2.58 5 Burundi 271 96 1.82 0.35 0.48 2 3 1.98 6 Cameroon 34 670 10.04 1.02 0.98 2 3 2.83 7 Cape Verde 126 1773 30.2 5.35 10.27 2 3 3.25 8 Central African Rep. 6 329 1.79 0.23 0.28 2 3 2.52 9 Chad 8 445 1.54 0.4 0.17 2 3 2.65 10 Congo 12 1114 10.41 0.94 0.45 2 3 3.05 11 Côte divoire 56 949 9.13 0.95 1.55 2 3 2.98 12 Egypt 74 1118 32.45 6.75 3.78 2 3 3.05 13 Equatorial Guinea 18 10295 21.25 0.99 1.38 2 3 4.01 14 Eritrea 47 146 1.78 1.59 0.8 2 3 2.16 15 Gabon 5 5310 49.78 4.84 3.25 2 3 3.73 16 Gambia 142 270 19.21 3.35 1.57 2 3 2.43 17 Kenya 59 490 14.29 3.22 0.95 2 3 2.69 18 Madagascar 31 336 3.07 0.5 0.5 2 3 2.53 19 Malawi 137 152 4.13 0.41 0.19 2 3 2.18 20 Mali 9 435 8.33 0.53 0.4 2 3 2.64 21 Mauritania 3 365 18.84 0.47 1.41 2 3 2.56 22 Mauritius 668 5146 86.13 14.6 16.22 2 3 3.71 23 Morocco 48 1673 43.63 14.61 2.35 2 3 3.22 24 Mozambique 25 217 4.1 0.73 0.59 2 3 2.34 25 Namibia 2 1523 20.59 3.73 10.94 2 3 3.18 26 Niger 12 244 1.39 0.19 0.07 2 3 2.39 27 Nigeria 142 506 15.07 3.8 0.68 2 3 2.7 28 Senegal 59 737 17.13 4.63 2.14 2 3 2.87 29 Seychelles 200 8912 97.21 250.28 19.84 2 1 3.95 30 South Africa 40 2239 46.21 10.75 8.36 2 3 3.35 31 Sudan 14 500 6.02 7.73 8.97 2 3 2.7 32 Swaziland 59 1871 22.75 3.32 3.32 2 3 3.27 33 Tanzania 41 268 5.56 0.89 0.74 2 3 2.43 34 Togo 108 404 5.61 4.88 3.01 2 3 2.61 35 Tunisia 61 2809 68.79 9.46 5.63 2 3 3.45 36 Uganda 122 240 5.64 1.74 0.87 2 3 2.38 37 Zambia 16 332 4.84 2.01 0.98 2 3 2.52 38 Argentina 14 4007 80.07 17.78 8.37 2 3 3.6 39 Barbados 626 9659 126.79 59.48 14.87 2 1 3.98 40 Bolivia 8 973 33.41 5.23 2.33 2 3 2.99 41 Brazil 22 3278 58.72 11.96 10.52 2 3 3.52 42 Canada 3 23213 108.08 62.36 69.82 1 1 4.37 43 Chile 21 6108 89.82 17.96 14.75 2 3 3.79 44 Colombia 40 2141 40.08 10.39 4.15 2 3 3.33 45 Costa Rica 85 4193 57.53 23.54 21.89 2 2 3.62 46 Dominica 95 3669 88.08 28.75 18.23 2 2 3.56 47 Ecuador 29 2295 60.08 4.66 3.89 2 3 3.36 48 El Salvador 322 2392 49.17 9.26 5.09 2 3 3.38 49 Grenada 298 4310 74.34 18.64 15.65 2 3 3.63 50 Guatemala 116 2117 33.97 5.97 1.82 2 3 3.33
51 Guyana 3 991 47.94 21.3 3.86 2 3 3 52 Honduras 64 1075 24.65 3.18 1.57 2 3 3.03 53 Jamaica 232 3084 114.75 39.87 6.2 2 2 3.49 54 Mexico 54 6328 62.58 17.4 13.08 2 3 3.8 55 Panama 41 4477 55.51 6.39 4.56 2 3 3.65 56 Paraguay 15 1018 35.84 3.25 7.47 2 3 3.01 57 Peru 22 2476 28.01 16.45 10.01 2 3 3.39 58 St. Vincent and the Gre 306 3162 78.26 6.61 13.22 2 3 3.5 59 Trinidad & Tobago 255 8729 86.02 12.24 7.9 2 3 3.94 60 United States 32 36273 122.71 63 76.22 1 3 4.56 61 Uruguay 17 4078 49.37 20.98 13.27 2 2 3.61 62 Venezuela 29 4164 60.19 8.84 8.19 2 3 3.62 63 Armenia 101 1175 25.97 4.96 6.61 2 3 3.07 64 Azerbaijan 97 1022 39.63 8.07 2.31 2 3 3.01 65 Bahrain 1099 15222 130.08 21.34 16.9 2 2 4.18 66 Bangladesh 985 378 2.59 0.22 1.19 2 3 2.58 67 Cambodia 78 337 3.78 0.28 0.26 2 3 2.53 68 China 137 1268 56.53 8.44 4.08 2 3 3.1 69 Georgia 64 719 33.73 3.89 4.25 2 3 2.86 70 Hong Kong, China 6630 23960 176.54 50.08 59.26 1 3 4.38 71 India 348 634 11.31 5.44 1.54 2 3 2.8 72 Indonesia 116 1156 26.79 7.18 1.36 2 3 3.06 73 Iran (I.R.) 42 2345 37.7 10.07 10.53 2 3 3.37 74 Israel 332 17040 155.97 46.63 73.4 2 2 4.23 75 Japan 339 31324 119.86 50.2 54.15 2 1 4.5 76 Jordan 59 1814 39.41 11.22 5.34 2 3 3.26 77 Kazakhstan 5 2765 35.44 2.7... 2 2 4.15 78 Korea (Rep.) 491 14136 128.56 68.35 54.49 2 1 4.35 79 Kuwait 111 22179 107.56 26.05 22.33 1 2 2.64 80 Kyrgyzstan 27 433 18.62 5.32 1.9 2 3 2.68 81 Lao P.D.R. 25 477 12.04 0.42 1.69 2 3 4.35 82 Macao, China 28750 22158 153.73 36.96 29.01 1 2 3.66 83 Malaysia 78 4604 91.97 42.37 19.16 1 2 2.68 84 Mongolia 2 483 26.95 10.14 12.84 2 3 2.43 85 Nepal 192 271 2.57 0.41 0.47 2 3 3.94 86 Oman 9 8744 62.27 9.67 4.66 1 3 2.79 87 Pakistan 192 614 11.72 6.82... 2 3 2.94 88 Palestine 615 873 39 6.56 4.59 2 3 3.02 89 Philippines 277 1046 44.01 5.32 4.46 2 3 4.46 90 Qatar 68 28920 118.56 28.16 17.88 1 2 4.02 91 Saudi Arabia 10 10462 69.59 6.62 35.39 2 3 3.01 92 Sri Lanka 316 1031 22.2 1.44 2.72 2 3 3.05 93 Syria 103 1133 30.73 5.78 4.2 2 3 4.13 94 Taiwan, China 633 13455 157.16 58.01 52.78 2 1 3.41 95 Thailand 125 2567 36.11 11.03 5.83 2 3 4.28 96 United Arab Emirates 60 18919 128.37 31.08 19.84 2 2 2.74 97 Viet Nam 256 547 29.42 12.72 1.26 2 3 2.75 98 Yemen 110 563 9.02 0.87 1.45 2 3 4.55 99 Austria 97 35660 145.14 48.93 61.12 1 2 4.53 100 Belgium 341 34048 133.38 46.07 34.72 1 2 3.49 101 Bulgaria 70 3100 112.97 20.6 5.94 2 3 3.89 102 Croatia 81 7764 107.06 31.88 19.07 2 2 4.28 103 Cyprus 90 19026 136.39 36.93 30.86 2 3 4.02 104 Czech Republic 130 10551 146.7 49.97 24 2 2 4.65 105 Denmark 126 45059 162.41 52.55 65.48 1 1 3.92 106 Estonia 29 8411 142.01 51.92 48.91 2 1 4.55 107 Finland 14 35442 140.05 63 48.22 1 1 4.53 108 France 111 33674 138.45 43.23 57.86 1 2 4.52 109 Germany 232 33156 162.35 45.35 54.54 1 2 4.27 110 Greece 84 18596 147 17.62 8.88 1 3 4 111 Hungary 109 9935 125.53 29.71 14.62 2 2 4.62 112 Iceland 3 41765 169.34 87.76 48.3 1 1 4.55 113 Ireland 60 35726 150.51 27.64 49.74 1 1 4.46 114 Italy 193 28764 166.26 48.03 31.1 1 2 3.77 115 Latvia 36 5940 108.87 44.65 21.92 2 2 3.81 116 Lithuania 53 6518 150.48 35.67 15.47 2 2 4.84 117 Luxembourg 180 69027 199.13 67.74 62.37 1 1 4.13 118 Malta 1269 13460 131.2 31.73 16.61 2 2 2.78 119 Moldova 125 609 48.02 9.52 2.63 2 3 4.57 120 Netherlands 396 37176 143.78 61.63 68.47 1 1 4.74 121 Norway 14 54390 148.98 38.97 57.2 1 3 3.73 122 Poland 123 5427 92.23 25.95 19.1 2 2 4.2 123 Portugal 115 15833 149.44 28.03 13.32 2 2 3.42 124 Romania 91 2626 81.73 20.76 11.3 2 2 3.38 125 Russia 8 2390 111.57 15.19 12.13 2 1 3.28 126 Serbia 32 1916 90.95 18.61 4.77 2 1 3.88 127 Slovak Republic 110 7630 106.23 46.29 35.72 2 2 4.21 128 Slovenia 97 16403 130.94 55.41 41.08 2 3 4.38 129 Spain 85 23930 139.72 35.41 28.11 1 1 4.59 130 Sweden 20 38850 180.02 75.46 76.14 1 1 4.57 131 Switzerland 181 36738 160.43 49.59 86.18 1 1 3.62 132 Turkey 94 4182 85.51 21.86 5.13 2 2 3.14 133 Ukraine 77 1378 55.07 9.81 3.89 2 3 4.42 134 United Kingdom 244 26369 158.51 62.88 60.02 1 1
Using data 1 ( Appendix 1 ) which has following categories : 1 = developed country ( perkapita > 20.000 USD ) 2 = developing country ( perkapita < 20.000 USD ) Internet usage category 1 = high > 51 user per 200 inhabitants 2 = medium 21 50 user per 100 inhabitants 3 = low 1 20 user per 100 inhabitants
Appendix 2 X 2 Distribution table