Statistical Analysis and Crowding out Effect in Economy - Assignment Example

Statistical Analysis and Crowding out effect College: Section The measure of central tendency that include median, mode, range and mean are known to be not adequate in data description. Two data sets may have same mean but they could be totally different. Hence, to describe data, it is required to be familiar with variability .It is indicated by the measure of dispersion. The mean is known to be mostly used as a measure of central tendency. Advantage of mean It is seen that mean uses every data points in the data; hence, it is the most appropriate representative of the data. The most interesting thing is that it never appears in the raw data. Repeated samples that are usually drawn from the same population seem to have means that is similar. Therefore, the mean in this case is the measure of central tendency that has the capacity of resisting fluctuation between different samples. In several measure of dispersion; it is usually closely related to standard deviation. The mean of Annual percentage Rate of Growth of real GDP is 1.9 while the standard deviation is 0.465 and they are almost close. But when we look at the maximum and minimum values, they seem to be extreme values/outliers because they are not even closer to the calculated mean. Hence they need to be removed. Disadvantage of mean The disadvantage with mean is that it is too sensitive to extreme values or outliers as seen above. For instance the mean value is smaller when compared with the maximum and minimum values (Dawson et al.,2004)In this case, it is not relevant to measure the central tendency for the distribution that is skewed (Swinscow and Campbell,2003). Another weakness of mean is that it cannot be calculated for nominal or non-nominal ordinal data. Despite it being calculated for numerical ordinal data, in several occasions, it never provide significant values Range The strength of range is that it is easy to calculate. For instance, in relation to the calculation of range of Annual percentage Rate of Growth of real GDP (maximum value –minimum value), it is seen that the data set had a higher range of 13.1 and this indicates that there is a high variability. The main weakness of range is that it never captures any variability apart from the two points which can be captured. In this case, it is very sensitive to outliers and it never uses all the observations in the data set. It is seen to be more informative to provide maximum and minimum values than the range. The standard deviation and variance that tends to be so hard to be calculated, focuses on the deviation of every point from the mean but not only the difference between the maximum and minimum (Gravetter and Wallnau,2000) Standard deviation Standard deviation is mostly applied as a measure of dispersion. It is a measure of data spread about the mean. In relation to the way it was calculated above, it is the square root of sum of squared deviation from the mean divided by the observation (Gravetter and Wallnau, 2000). The main reason why it is a very useful measure of dispersion is that it focuses on the deviation of every point from the mean but not only the difference between the maximum and minimum. Its weakness is that it tends to be so hard to be calculated (Gravetter and Wallnau, 2000) Variance The population variance provides complete information related to the way the population varies among the individuals. In this case, the variance calculated above, shows how the Annual percentage Rate of Growth of real GDP varies within the time given in years. In many occasions, this is what any given study expects to find. Through the variance, the goal of the study would have been met. Furthermore, the variance means that there would be no error in the predictions when the formulas such as standard deviation statistics are used (Gravetter and Wallnau, 2000). Advantage of variance The strength of variance is that future studies could benefit significantly from the studies done previously which analyzed the same population and found out the variance of the population simultaneously. The reason for these is that if there is a change in population since the first study, the variance in this case never changes to a large degree. This implies that the original variance is an accurate estimate of the variance that is current. The variance enables future researchers to conserve their resources (Glaser, 2000). The weakness of variance The statistical studies that are mostly applied, never find the population variance. In this case, they use standard deviation statistics as an option. Due to this, the statistical world never expects variance to have a big role in statistical analysis. For instance, most statistical computer software never considers variance as input (Glaser, 2000). Median The median is the middle score in a ranked distribution. In relation to the way the median of Annual percentage Rate of Growth of real GDP was calculated, we can see that the cases fall above as well as half below, it is seen to be equal to the 50th percentile. It was calculated by ranking the scores from the smallest to the largest. The sample size was then divided by 2 to obtain the middle score in the ranked distribution (Glaser, 2000). The strength of median is that it is useful with ratio or interval variables. It is the most appropriate statistics in cases where the score distribution is skewed. Strength is that it is insensitive to the score values in the distribution.Hence, 2 distinctly different score might have the same median (Glaser, 2000). The weakness of median is that it is sensitive to the sample size change. In this case, if there are new cases being added, the median can change drastically (Glaser, 2000). Mode It is known to be the value that is most frequently occurring in the distribution. It is very useful with variables of all measurement levels. It is insensitive to both the distribution scores and sample size. It is the least relevant measure of the 3 measures due to its narrow scope (Glaser, 2000). Question 1.2 Correlations GDP Unmployment GDP Pearson Correlation 1 -.716** Sig. (2-tailed) .000 N 29 29 Unmployment Pearson Correlation -.716** 1 Sig. (2-tailed) .000 N 29 29 **. Correlation is significant at the 0.01 level (2-tailed). The results presented above from correlation analysis show that there exists a negative relationship between the rate of growth in GDP and the rate of change in unemployment rate during the period under consideration. The negative relationship is depicted by the coefficient value of -0.716 between the two variables. Apart from this, it is also pertinent to note above that there exists a significant 2 - tailed correlation between the rate of growth in GDP and the rate of change in unemployment rate. Question 2 a) Z score is important in data standardization on one scale to enable comparison. Each Z score is known to correspond to a point in a normal distribution .In this case it is referred to as normal deviate because the Z score describes the level of deviation of points from the mean or a given specific point (Glaser, 2000). b) Z-score Z= (20000-10600)/13518=0.7 The probability between -1 and 1 is 0.5735 c) Z= (10000-10600)/13518 =-0.044 The probability between -1 and 1 is 0.2823 Section 2 . Null hypothesis: The changes in demand (G) does not influence investment (I) Alternative hypothesis: The changes in demand (G) influences the investment (I) Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Gb . Enter a. Dependent Variable: I b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .891a .793 .783 3513707546.03422 a. Predictors: (Constant), G ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 901170580367720400000.000 1 901170580367720400000.000 72.992 .000b Residual 234576673662098640000.000 19 12346140719057824000.000 Total 1135747254029819000000.000 20 a. Dependent Variable: I b. Predictors: (Constant), G Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 6262802110.072 1720279077.025 3.641 .002 G 2.813 .329 .891 8.544 .000 a. Dependent Variable: I This regression analysis shows a relatively strong model. The coefficient of Determination(R squared) shows that only 78.3% of the total variation is explained by the one factor that includes changes in demand. The standard error is 0.329, determined by the low R squared. In regards to the hypothesis testing, by considering an alpha of 0.05, the results indicate that variable changes in demand can’t be rejected because the p value is less than 0.05.This independent variable that include changes in demand influenced positively the dependent variable which is the investment. Since the calculated t value is greater than the critical value, we reject the null hypothesis and conclude that the changes in demand (G) influence the investment (I) at 0.05 level of significant. This demonstrated that improvement of changes in demand resulted to increase in the investment. Calculate estimates of β1 and β2 using the Ordinary Least Squares (OLS) regression method. The final regression equation for model 1: investment = 2.813(changes in demand) + 2 β1 is 2 and β2 is 2.813 Section 3 It is clearly evident that foreign firms in China never experience credit constraints. This shows that they do posses superior legal status than the private firms (Naughton, 2007). Optionally, foreign firms could possibly be depending less on the China’s local financial system because they have the capacity of relying on other sources for financing their development and growth. They can either rely on intra-firm financial transfers or access the capital markets that are provided by firms abroad. It is suggested that when foreign capital is stronger in the region or sector, the lower there will be the financial constraints experienced by private firms of Chinese which operates in the same sector and region Huang, Y. (2003).This clearly shows that the existence of foreign capital to some extent enables private firms of China to bypass both the legal and financial obstacles that they experience locally (Gordon and Li, 1991). According to studies, it is evident that FDI inflows correlate positively to external funding access that is restricted by private enterprises (Huang 2003). It is well known that the private enterprises in China are usually forced to search for foreign investors since they are ever constrained in terms of their activity as a result of inter alia, distortions of the financial system that is dominated by the state. In this case, evidence on crowding-out in China is not there. This finding is supported by Harrison et al. (2004) who showed that FDI inflows are related to the reduction of the firm level financial constrains. It is evident that there is a change in the situation with a rapid increase in the influx of FDI into the economies as they adopt policies that are more favorable gradually towards international trade as well as investment. It is seen that foreign firms have ended up opening up several new job search channels that include Internet job boards, job fairs, newspaper advertisements and private employment agencies (Groves et al, 1995). Such channels have allowed information exchange that is effective between potential employers and job seekers. Growth in the non-state sector is known to provide employment opportunities as a surplus to the state sector. The first growth that is outside the state sector is seen to facilitates foreign firms in the recruiting of workers of high-caliber in the SOEs.In this case; it can hinder the productivity of the state sector, as well as raise the number of SOEs that is non-profitable. Thus, creating more problems related to the reformation of state sector (Groves et al, 1995).. Hypotheses Hypothesis 1 Null hypothesis: The investment in the private companies in China is not crowded out by investment by State owned enterprise Alternative hypothesis: The investment in the private companies in China is crowded out by investment by State owned enterprise Hypothesis 2 Null hypothesis: There is no effect of investment by foreign owned enterprises on investment by State owned enterprise of China. Alternative hypothesis: There is an effect of investment by foreign owned enterprises on investment by State owned enterprise of China. Model 1. To determine if the investment in the private companies in China is crowded out by investment by State owned enterprise. In order to determine this, regression analysis was done, whereby; Gross fixed capital formation was taken to be the dependent variable while the investment in fixed assets by state owned enterprises and state owned enterprise investment were taken as the independent variables. Descriptive Statistics Mean Std. Deviation N Gross Fixed Capital Formation (100m Yuan) Source Table 2-22 p. 59 4164.1481 3049.44297 31 Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 1179.5097 556.51462 31 SOE_inv/GCF .3400 .11092 31 Correlations Gross Fixed Capital Formation (100m Yuan) Source Table 2-22 p. 59 Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 SOE_inv/GCF Pearson Correlation Gross Fixed Capital Formation (100m Yuan) Source Table 2-22 p. 59 1.000 .828 -.721 Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 .828 1.000 -.433 SOE_inv/GCF -.721 -.433 1.000 Sig. (1-tailed) Gross Fixed Capital Formation (100m Yuan) Source Table 2-22 p. 59 . .000 .000 Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 .000 . .007 SOE_inv/GCF .000 .007 . N Gross Fixed Capital Formation (100m Yuan) Source Table 2-22 p. 59 31 31 31 Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 31 31 31 SOE_inv/GCF 31 31 31 The correlation analysis above indicate that there is a significant relationship in all the variables .It is observed that the private company investment (Fixed capital) is negatively correlated to the SOE (Investment in fixed assets by SOE).This is in line with the literature whereby, the high rate of growth that is outside the state sector is seen to facilitates foreign firms in the recruiting of Workers of high-calibre in the SOEs. In this case, it can hinder the productivity of the state sector, as well as raise the number of SOEs that non-profitable Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics Durbin-Watson R Square Change F Change df1 df2 Sig. F Change 1 .920a .847 .836 1234.85506 .847 77.475 2 28 .000 1.936 a. Predictors: (Constant), SOE_inv/GCF, Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 b. Dependent Variable: Gross Fixed Capital Formation (100m Yuan) Source Table 2-22 p. 59 This regression analysis shows a relatively strong model. The coefficient of Determination(R squared) shows that only 83.6 % of the total variation is explained by the two factors that includes SOE_inv/GCF and Investment in fixed assets by SOE 2007. The standard error is 1234.85506, determined by the low R squared. ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 236276796.531 2 118138398.265 77.475 .000b Residual 42696276.804 28 1524867.029 Total 278973073.335 30 a. Dependent Variable: Gross Fixed Capital Formation (100m Yuan) Source Table 2-22 p. 59 b. Predictors: (Constant), SOE_inv/GCF, Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics B Std. Error Beta Zero-order Partial Part Tolerance VIF 1 (Constant) 4241.454 1127.289 3.763 .001 Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 3.474 .450 .634 7.728 .000 .828 .825 .571 .812 1.231 SOE_inv/GCF -12280.314 2255.507 -.447 -5.445 .000 -.721 -.717 -.403 .812 1.231 a. Dependent Variable: Gross Fixed Capital Formation (100m Yuan) Source Table 2-22 p. 59 In regards to the hypothesis testing, by considering an alpha of 0.05, the results indicate that both SOE_inv/GCF and Investment in fixed assets by SOE 2007 can’t be rejected because the p value is less than 0.05.The independent variables Investment in fixed assets by SOE 2007 and SOE inv/GCF influence the dependent variable which is private company investment positively and negatively respectively. Since the calculated t value is greater than the critical value, we reject the null hypothesis and conclude that the investment in the private companies in China is crowded out by investment by State owned enterprise. This is contrary to the study by (Harrison et al. 2004) The final regression equation for model 1: Gross Fixed Capital Formation = 3.474 (Investment in fixed assets by SOE 2007) + -12280.314(SOE_inv/GCF) +4241.454 β1 is 3.474 and β2 is 12280.314 Collinearity Diagnosticsa Model Dimension Eigenvalue Condition Index Variance Proportions (Constant) Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 SOE_inv/GCF 1 1 2.779 1.000 .00 .02 .01 2 .196 3.767 .00 .43 .15 3 .025 10.610 .99 .55 .84 a. Dependent Variable: Gross Fixed Capital Formation (100m Yuan) Source Table 2-22 p. 59 Basing on the results above, it is evident that collinearity between the independent variables is too low. Therefore, the model was relatively fit and statistically significant. Model 2 To determine if there is an effect of investment by foreign owned enterprises on investment by State owned enterprise of China. Descriptive Statistics Mean Std. Deviation N Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 1179.5097 556.51462 31 Total Investment of Foreign Funded Enterprises (100m US $) Table 17-19 p. 735 665.3871 970.10438 31 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics Durbin-Watson R Square Change F Change df1 df2 Sig. F Change 1 .499a .249 .223 490.54046 .249 9.612 1 29 .004 1.622 a. Predictors: (Constant), Total Investment of Foreign Funded Enterprises (100m US $) Table 17-19 p. 735 b. Dependent Variable: Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 This regression analysis shows a relatively weak model. The coefficient of Determination(R squared) shows that only 22.8 % of the total variation is explained by one factor that includes Total Investment of Foreign Funded Enterprises. The standard error is 490.54046, determined by the low R squared. ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 2312987.252 1 2312987.252 9.612 .004b Residual 6978268.315 29 240629.942 Total 9291255.567 30 a. Dependent Variable: Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 b. Predictors: (Constant), Total Investment of Foreign Funded Enterprises (100m US $) Table 17-19 p. 735 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics B Std. Error Beta Zero-order Partial Part Tolerance VIF 1 (Constant) 989.059 107.404 9.209 .000 Total Investment of Foreign Funded Enterprises (100m US $) Table 17-19 p. 735 .286 .092 .499 3.100 .004 .499 .499 .499 1.000 1.000 a. Dependent Variable: Investment in fixed assets by SOE 2007 (100m Yuan) Table 5-3 p. 171 By considering an alpha of 0.05, the results indicate that the Total Investment of Foreign Funded Enterprises can’t be rejected because the p value is less than 0.05.The independent variable, total Investment of Foreign Funded Enterprises influence the dependent variable positively which is the investment in fixed assets by SOE 2007. Since the calculated t value is greater than the critical value, we reject the null hypothesis and conclude that There is an effect of investment by foreign owned enterprises on investment by State owned enterprise of China.. This is supported by the study done by Huang (2003). The final regression equation for model 1: Investment in fixed assets by SOE 2007 = 286 (Total Investment of Foreign Funded Enterprises) +989.059 References Dawson B, Trapp RG.(2004). Basic and Clinical Biostatistics. 4th ed. New York: Mc-Graw Hill Economic Review 81(2), 202-206. Glaser AN. (2000). High Yield Biostatistics. 1st Ed. New Delhi, India: Lippincott Williams and Wilkins;. Gordon, R. H. and W. Li, 1991, Chinese enterprise behaviour under the reforms, American Gravetter FJ, Wallnau LB. (2000). Statistics for the behavioral sciences. 5th ed. Belmont: Wadsworth – Thomson Learning;. Groves, T., Y. Hong, J. McMillan, and B. Naughton, 1995, China’s evolving managerial labour market, Journal of Political Economy 103(4), 873-892. Harrison, A., Love, and M. McMillan (2004), “Global capital flows and financing constraints,” Journal of Development Economics, 75, pp. 269–301. Huang, Y. (2003), Selling China. Cambridge University Press, pp. 207. Naughton, B. (2007), The Chinese Economy: Transition and Growth. MIT Press, pp. Swinscow TD, Campbell MJ.(2003). Statistics at square one. 10th ed. New Delhi, India: Viva Books Private Limited. Read More