Economics Situation in France and Italy in 2008 - Statistics Project Example

Economics situation in France and Italy in 2008 Univeristy Department 12 April Introduction France and Italy are wealthy nations, located in Western Europe with a large sized economies. France is fifth largest economy in the world whereas Italy is ninth largest economy (Stevens, 2015). We set upon in studying the economic factors in some regions in France and Italy in regard to labour relations and education for the year 2008. Research question Does high tech jobs in France and Italy affect the participation of Kids in Early Child-hood Development in various region? Does high tech patents, income and unemployment rate have an impact on GDP(2008) of France and Italy. Hypothesis Ho = High tech jobs in various region in France and Italy has effect on participation rates of 4 years old kids in education. Ha = High tech jobs in various region in France and Italy has no effect on participation rates of 4 years old kids in education. Ho = GDP of the Western Europe countries is affected by income, high tech patents and unemployment rate. Ha = GDP of the Western Europe countries is not affected by income, high tech patents and unemployment rate. Methodology We shall use a secondary set of data, meaning we didn’t participate on any data collection and we cannot authenticate the un-biasness of the data (Jackson, 2011). The sample N = 43, with population proportion samples n = 22 regions from France and n = 21 from Italy. Data analysis We created new variable country, in order for us to be able to analyse the data, split it into regions and subject it into various statistical tests. Table 1 Descriptive Statistics Population Density 2008 (inhabitants per km²) Primary income of households Mean Range Standard Deviation Mean Range Standard Deviation Country France 143.1 938.4 196.9 18099.4 10874.7 2198.3 Italy 181.4 394.9 114.9 18216.1 12474.2 4346.2 From table 1 the mean population is 143 and 181 of France and Italy respectively with an income of 180099 to France and 18216 to Italy. Table 2 Descriptive Statistics Unemployment rates 2008 Participation rates of 4-year-olds in education Mean Range Standard Deviation Mean Range Standard Deviation Country France 7.4 5.8 1.4 100.6 9.2 1.9 Italy 6.9 11.4 3.8 98.9 10.8 2.8 The unemployment rate of France is 7.4 and Italy is 6.9 as indicated in Table 2 with an education rate 4 year old of 100.6 and 98.9 for France and Italy respectively. The graph shows that 14% Sicilia are unemployed reporting the with Provincia Autonoma Bolzano-Bozen reporting the lowest at 2% in Italy. 10% of Languedoc-Roussillon are unemployment and the rest of the regions reported 7 – 9% unemployment rate in France. Table 3 Statistics GDP (PPS per inhabitant) 2008 N Valid 22 Missing 0 Mean 23854.545 Variance 19016883.117 Skewness 3.702 Std. Error of Skewness .491 Kurtosis 15.604 Std. Error of Kurtosis .953 Range 21100.0 a. Country = France From Table 3 the mean GDP of France is equal 23855, the data normally distributed skewed to the right at 3.7 as shown in the graph above Table 4 Statistics GDP (PPS per inhabitant) 2008 N Valid 21 Missing 0 Mean 25371.429 Variance 38052142.857 Skewness -.271 Std. Error of Skewness .501 Kurtosis -1.478 Std. Error of Kurtosis .972 Range 17900.0 a. Country = Italy The mean GDP of Italy from Table 4 is 25371, the data set in sample proportion is skewed to the left at -.271 which is normally distributed. To evaluate the relationship between the high tech jobs and the education rate 4 years old we used a paired samples t-test was used to study both cases of the sample proportion in France and Italy. Table 5.1 Paired Samples Correlationsa N Correlation Sig. Pair 1 Participation rates of 4-year-olds in education & Employment in high-tech sectors 21 .387 .083 a. Country = France Table 5.2 Paired Samples Testa Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 Participation rates of 4-year-olds in education - Employment in high-tech sectors 96.51048 1.71818 .37494 95.72837 97.29258 257.404 20 .000 a. Country = France Table 5.3 Paired Samples Statisticsa Mean N Std. Deviation Std. Error Mean Pair 1 Participation rates of 4-year-olds in education 100.781 21 1.6191 .3533 Employment in high-tech sectors 4.2705 21 1.47622 .32214 a. Country = France The sample proportion of France indicated mean for participation rates (M= 100.8, SD=1.6) was significant ≥ high tech employments (M = 4.3, SD =1.4), t(20) = 257.4 p =000. Thus we reject null hypothesis as p < .05 as mean difference not equal 0 at p =.05; hence we conclude that the high tech jobs in France have no effect on the participation rates in education of 4 years old. Table 5.4 Paired Samples Statisticsa Mean N Std. Deviation Std. Error Mean Pair 1 Participation rates of 4-year-olds in education 98.779 19 2.9521 .6773 Employment in high-tech sectors 3.5689 19 1.20085 .27549 a. Country = Italy Table 5.5 Paired Samples Testa Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 Participation rates of 4-year-olds in education - Employment in high-tech sectors 95.21000 3.77002 .86490 93.39291 97.02709 110.082 18 .000 a. Country = Italy The indication from the Tables, Italy, is that, at difference of (m = 95.2, sd = 3.8), t(18) = 110 p < .05. Thus we reject the null hypothesis as p =000 and conclude that the high tech jobs have no effect of the rate of participation of 4 year old in education. We further analysed the data on multiple regression, since we the study involved more of observational data and find out whether there might be interactions between the variables (Crawley, 2015). Table 6.0 Model Summarya,c Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .988b .977 .970 779.3757 .977 156.691 4 15 .000 a. Country = France b. Predictors: (Constant), Employment in high-tech sectors, High-tech patent applications to the EPO per year, Population Density 2008 (inhabitants per km²), Primary income of households c. Dependent Variable: GDP (PPS per inhabitant) 2008 Table 6.1 ANOVAa,b Model Sum of Squares df Mean Square F Sig. 1 Regression 380714103.178 4 95178525.794 156.691 .000c Residual 9111396.822 15 607426.455 Total 389825500.000 19 a. Country = France b. Dependent Variable: GDP (PPS per inhabitant) 2008 c. Predictors: (Constant), Employment in high-tech sectors, High-tech patent applications to the EPO per year, Population Density 2008 (inhabitants per km²), Primary income of households Table 6.2 Coefficientsa,b Model Unstandardized Coefficients Standardized Coefficients t Sig. 95.0% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) -1215.771 2804.476 -.434 .671 -7193.370 4761.829 Primary income of households 1.295 .185 .645 6.993 .000 .901 1.690 Population Density 2008 (inhabitants per km²) 6.703 1.677 .302 3.997 .001 3.129 10.278 High-tech patent applications to the EPO per year 33.530 15.937 .104 2.104 .053 -.440 67.500 Employment in high-tech sectors 35.390 206.871 .011 .171 .866 -405.545 476.326 a. Country = France b. Dependent Variable: GDP (PPS per inhabitant) 2008 Table 6.3 Residuals Statisticsa,b Minimum Maximum Mean Std. Deviation N Predicted Value 20468.049 41783.578 23965.000 4476.3360 20 Residual -1541.9689 1247.0822 .0000 692.4935 20 Std. Predicted Value -.781 3.981 .000 1.000 20 Std. Residual -1.978 1.600 .000 .889 20 a. Country = France b. Dependent Variable: GDP (PPS per inhabitant) 2008 Table 6.2 indicates the strength of four variables at R2 = 97.7%, F(4, 15) = 156.7, p =.000, thus the model equation will be: GDP = 1.3 Income + 6.7 Population Density + 33.5 High tech patent + 35.3 Employment. At 97.7% variance of GDP (2008) the is accounted for the linear combination, with a positive linear coefficient correlation of .99. The bivariate coefficient indicates p < .05, showing the variables are statistically significant with a strong relation with the dependent variable GDP. And as shown in the graph below there is a strong positive correlation between the GDP and other variables Thus we can conclude that the Null hypothesis is accepted as p < .05 and at 95% CI, p = .000, GDP of France is affected by the income and the patents issued and employment in the region. Table 7.0 Model Summarya,c Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .991b .982 .977 944.9504 .982 194.992 4 14 .000 a. Country = Italy b. Predictors: (Constant), Employment in high-tech sectors, High-tech patent applications to the EPO per year, Primary income of households, Population Density 2008 (inhabitants per km²) c. Dependent Variable: GDP (PPS per inhabitant) 2008 Table 7.1 ANOVAa,b Model Sum of Squares df Mean Square F Sig. 1 Regression 696456857.984 4 174114214.496 194.992 .000c Residual 12501036.753 14 892931.197 Total 708957894.737 18 a. Country = Italy b. Dependent Variable: GDP (PPS per inhabitant) 2008 c. Predictors: (Constant), Employment in high-tech sectors, High-tech patent applications to the EPO per year, Primary income of households, Population Density 2008 (inhabitants per km²) Table 7.2 Coefficientsa,b Model Unstandardized Coefficients Standardized Coefficients t Sig. 95.0% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) -825.003 1119.114 -.737 .473 -3225.263 1575.258 Primary income of households 1.415 .071 .994 19.854 .000 1.262 1.567 Population Density 2008 (inhabitants per km²) .039 2.847 .001 .014 .989 -6.068 6.147 High-tech patent applications to the EPO per year -98.013 90.105 -.054 -1.088 .295 -291.269 95.243 Employment in high-tech sectors 214.541 272.800 .041 .786 .445 -370.557 799.639 a. Country = Italy b. Dependent Variable: GDP (PPS per inhabitant) 2008 The regression equation shows the four variables are statistically significant at .05, R2 = 98.2%, F(4, 14), = 195, p < .05 = .000, thus the model equation will be; GDP(2008) = 1.4 Income + .04 Population density -.98 High tech patents +214.5 Employment in high tech The model indicates that Italy does not innovate more thus the low high tech patents but it has a lot in employment in high tech sector. There is a highly linear correlation at R2 = 98.2 % of the variance of GDP in the linear equation. P-value < .05 for all the other independent variables in the model indicating a significance relation in the GDP variable. The graph above shows the linear regression coefficient is highly positively correlated with the dependent variable GDP. We can conclude, null hypothesis is accepted as p < .05 at 95% CI , thus the GDP in Italy is has a relationship with employment in the high tech sectors and income. Conclusion GDP of a country is market value per year of all goods and services produced in the country. The value does not include the goods and services produced in other countries (Tucker, 2013). Thus the GDP of a country can measure of the wealth of a nation. The Western Europe countries of Italy and France have a high potential in high tech sectors in that France has a high tech patent rights that contribute a lot in the GDP of the France, where as Italy has more sectors that employ more people in high tech sectors. The education plays the main part in developing the cognitive, physical and social skills. The 4 year old kid develop skills by participating in schools activities thus increasing the enjoyment in leisure, reading books, understanding jokes and playing games (O’Sullivan, 2012). Thus the high tech employments jobs in Western Europe regions don’t affect that effect the education sector and on this occasion the cofounding factor is the income that makes the parents enrol their kids to better schools. The rate of kids participation may depend on production functions depending on input and output variables (O’Sullivan, 2012), thus employment and income come hardy as the parents are able to hire better teachers for their kids or enrol kids in schools with better facilities, teachers and parents participation. References Crawley, M. J., 2015. Statistics An Introduction Using R. 2nd mhar. West Sussex: John Wiley & Sons Ltd. Jackson, S. L., 2011. Research Methods: A Modular Approach. 2nd mhar. Belmont: Cengage Learning. O’Sullivan, A., 2012. Urban Economics. 8th mhar. New York: McGraw-Hill/Irwin. Stevens, C., 2015. Statistical Data. [Web] Available at: https://www.quandl.com/c/france [Accessed 12 April 2015]. Tucker, I. B., 2013. Survey of Economics. 8th mhar. Mason: Cengage Learning. Read More