Does socio-economic indicator influent ICT variable? II. Method of data collection, Objective and data gathered

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Ashlie Lynch
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1 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.

2 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 (a) Likelihood Ratio Linear-by-Linear Association N of Valid Cases 133 a 2 cells (33.3%) have expected count less than 5. The minimum expected count is Output Analysis The result of persons chi-square test is 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 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) Telephone internet pc density N Pearson Correlation.801(**) 1.670(**).805(**).181(*) Sig. (2-tailed) N Pearson Correlation.569(**).670(**) 1.638(**).074 Sig. (2-tailed) N Pearson Correlation.852(**).805(**).638(**) Sig. (2-tailed) N Pearson Correlation (*) Sig. (2-tailed) N ** 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.

4 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) a Predictors: (Constant), density, pc, Telephone, GDP Model 1 Regression Residual Total ANOVA b Sum of Squares df Mean Square F Sig a 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

5 Output analysis The regression equation would be: Internet = Telephone PC This implies that the number of Internet users per 100 inhabitants would increase by 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 implies that if number of telephone and number PC were both equal to zero, the number of internet user per 100 inhabitants would be 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 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 = 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.

6 # 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 = 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 = 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 = 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

7 # 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 = 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.

8 Appendix Key Figures Figure -1 Number of PC per 100 inhabitants in developing and developed country Number PC and Internet user per 100 inhibitants Nigeria Indonesia Malaysia Japan Netherlan USA Internet PC Figure -1 Number of PC per 100 inhabitants in developing and developed country Internet Usage High Medium Low 80 Count Developed Country Developing Country

9 Figure -1 Trend of number internet usage by increasing GDP GDP Internet usage Case Number Case Number

10 Appendix -1 Data density GDP Telephone internet pc catcountry catinternet LOG ( GDP ) 1 Algeria Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Rep Chad Congo Côte divoire Egypt Equatorial Guinea Eritrea Gabon Gambia Kenya Madagascar Malawi Mali Mauritania Mauritius Morocco Mozambique Namibia Niger Nigeria Senegal Seychelles South Africa Sudan Swaziland Tanzania Togo Tunisia Uganda Zambia Argentina Barbados Bolivia Brazil Canada Chile Colombia Costa Rica Dominica Ecuador El Salvador Grenada Guatemala

11 51 Guyana Honduras Jamaica Mexico Panama Paraguay Peru St. Vincent and the Gre Trinidad & Tobago United States Uruguay Venezuela Armenia Azerbaijan Bahrain Bangladesh Cambodia China Georgia Hong Kong, China India Indonesia Iran (I.R.) Israel Japan Jordan Kazakhstan Korea (Rep.) Kuwait Kyrgyzstan Lao P.D.R Macao, China Malaysia Mongolia Nepal Oman Pakistan Palestine Philippines Qatar Saudi Arabia Sri Lanka Syria Taiwan, China Thailand United Arab Emirates Viet Nam Yemen Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Latvia Lithuania Luxembourg Malta Moldova Netherlands Norway Poland Portugal Romania Russia Serbia Slovak Republic Slovenia Spain Sweden Switzerland Turkey Ukraine United Kingdom

12 Using data 1 ( Appendix 1 ) which has following categories : 1 = developed country ( perkapita > USD ) 2 = developing country ( perkapita < USD ) Internet usage category 1 = high > 51 user per 200 inhabitants 2 = medium user per 100 inhabitants 3 = low 1 20 user per 100 inhabitants

13 Appendix 2 X 2 Distribution table

Supplementary Appendix for. Version: February 3, 2014

Supplementary Appendix for. Version: February 3, 2014 Supplementary Appendix for When Do Governments Resort to Election Violence? Version: February 3, 2014 This appendix provides supplementary information not included in the published draft. Supplementary