Impact of Politic on Foreign Direct Investment - Report Example

Impact of Politic on Foreign Direct Investment Student’s Name Institutional Affiliation Table of Contents Table of Contents 2 1.0.Introduction 3 2.0.Methodology 3 3.0.Source of Data 3 4.0.Results 5 5.0.Discussion and Conclusion 8 6.0.Conclusion 10 7.0.Appendices 12 1.0. Introduction In many studies, one of the most important determinants of foreign direct investment (FDI) is the GDP, political risk and labour force supply (Bekaert et al., 2014). Very few studies have been done to investigate how political risk/environment impact on the FDI. Therefore, this study intends to establish the relationship of political risk/environment and foreign direct investment in country of Kazakhstan. 2.0. Methodology Methodology gives the general approach which is used in investigating the study hypothesis. It is a framework which is well designed to help in achieving the study objectives. In this study, we have used the regression analysis and model from Statistical Package for the Social Sciences (SPSS) to find the correlation between the political risk/environment and foreign direct investment in Kazakhstan. 3.0. Source of Data The data source are provided by the World Bank database and covering 25 years from the year 1992 to 2016. The variables consist of independent variables; which is political risk (PR) and one dependent variable which is Foreign Direct Investment (FDI) in Kazakhstan. The study applied the following algebraic model as follows:- Yj = βo + β1X1 + β2X2 + Ɛj According to the model above, β0 and β1 constants, Yj and Xi are variables while Ɛ is the error term. Therefore, using similar model, the regression model which will be used in analyzing the impact will be: FDI in Kazakhstan= βo + β1 PR1 + β2 LF + Ɛj Where FDI= the foreign direct investment PR= the political risk/environment in Kazakhstan. LF=Labour force produced in a country Using a multiple linear regression techniques for the given period (1992 to 2016) the study establishes the relationship between the political riskiness/environment of Kazakhstan on foreign direct investment and the availability of labour force in Kazakhstan. The data used are shown in the table below. Table 1.0 The Kazakhstan data as listed by World Bank (1992-2016) Year FDI PR LF 1992 100000000 0.54 7819823 1993 1271400000 0.72 7871311 1994 659700000 0.50 7889229 1995 964200000 0.50 7873713 1996 1137000000 0.58 7799510 1997 1321400000 0.25 7711419 1998 1151400000 0.54 7659497 1999 1587000000 0.77 7605920 2000 1370521199 0.50 7526571 2001 2816823050 0.64 7517875 2002 2588491060 0.58 7549180 2003 2483253230 0.25 7635002 2004 5615262947 0.54 7707938 2005 2546065710 0.79 7755730 2006 7611168450 0.50 7798140 2007 11972842989 0.59 7945350 2008 16818890680 0.58 8088702 2009 14275888207 0.25 8259916 2010 7456117901 0.54 8422565 2011 13760291529 0.81 8700066 2012 13648134374 0.50 8848347 2013 10011293285 0.59 8984616 2014 7090689354 0.58 9095835 2015 6584614530 0.25 9197976 2016 6584614530 0.54 9284810 The data represent the raw data as represented by the World Bank on political risk and foreign direct investment in the Kazakhstan country (details of the data is attached in the appendix). 4.0. Results It is normal that when there is high level of political stability which is punctuated with low political risk, people would tend to invest more in such countries since there is good business climate. In the summary of the model shown in table 2 below, the correlation coefficient value R = 0.549 and the value of adjusted R-square is about 0.238 giving low figures. The value of R-square coefficient usually specifies how a given data used independent variables in the calculation of the dependent variable. One important factor is that the estimated model expresses the independent variables together and in this case, it gives only 30% of the data meaning that the model is quite adequate and reliable as a tool that assesses the connectedness between political environment and FDI within Kazakhstan. Table 2: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson 1 .549 .301 .238 4462398461.18285 .470 Another important table is the ANOVA table which is represented in table 3 below. From the table, we get that the significance = 0.019 which is less than the specify significance level of 0.05. The meaning of significance variance or difference means that we reject the null hypothesis. Accordingly, the regression is statistically significant and as a matter of our analysis the relationship between the independent variables and the independent variables can be attested that it is present and it exists from these research findings. Table 3: ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression 188801193378451130000.000 2 94400596689225560000.000 4.741 .019 Residual 438086000580075850000.000 22 19913000026367082000.000 Total 626887193958527000000.000 24 From table 3 above the concept of political risk and labour management in Kazakhstan and how it influences foreign direct investment remains predictive noting that it is the main focus of this study. From the results in table four below, the regression model, the standard errors of coefficient, 5850584587.677 and 1592.461 are within the limits. That is to mea, the labour force has the greater influence on the FDI with coefficient of 4901.533 and significance level of 0.006 0.05. Table 5: Coefficient Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -34636919886.72 13575101304.585 -2.552 .018 PR 1083338941.100 5850584587.677 .033 .185 .855 LF 4901.533 1592.461 .551 3.078 .006 The residual statistics is given in table six below. It shows that the predicted value is higher than the residual value. The standard deviation of the two is greater and the mean is also higher. Table: Residuals Statistics Minimum Maximum Mean Std. Deviation N Predicted Value 2796486144.0000 11457886208.0000 5657082521.0 2804766726.622 25 Residual -4873271808.00000 11180433408.00000 -.00001 4272421252.346 25 Std. Predicted Value -1.020 2.068 .000 1.000 25 Std. Residual -1.092 2.505 .000 .957 25 The behaviour of the FDI is further explained by the histogram shown in the figure below:- It explains the behaviour of FDI given the country political risk and the labour availability. 5.0. Discussion and Conclusion The main objective of this paper was to examine the relationship between the political environment and the foreign direct investment (FDI) in Kazakhstan. From the results and findings, it is clear that even though there is relationship between the political risk and foreign direct investment, it is not statistically significance. The interpretation of statistical significance in this case means that political environment in Kazakhstan does not affect FDI. It therefore means that the country is offering safer political environment for foreign investors. As such the equation of our model can be rewritten as:- FDI = -34636919886.72 + β2 4901.533 + Ɛj In a study by Gobinda, Goswami & Haider (2014) on the impact of political risk on FDI, they found that there is no relationship between political risk and foreign direct investment in Kazakhstan. While this study supports our finding, other elements of politics like corruption index, openness of doing business, democracy in the country were found to be impacting on the foreign direct investment. Normally, it is expected that political environment or risks negatively impact on the foreign direct investment due to uncertainty in the investment environment. In a study carried out by Kerner & Lawrence (2014) in Nigeria, the study investigated how different components of political environment impact on the foreign direct investment. The components analyzed include corruption, law and order, and democratic accountability all of which were found to impact on the investment and by extension, foreign direct investment. However, it has been found that some countries attract foreign direct investment despite inherent political risk they post (Duanmu 2014). Relating this study to our data, the impact of labor on foreign direct investment attraction in a country is a factor because they are influenced politically. Duanmu (2014) study found that cheap labour and availability of labour which are directly influenced by politics further influence foreign direct investment in a country. The study findings are in line with our study findings since there is positive relationship between labour force availability and the foreign direct investment. 6.0. Conclusion In exploring the objective of this paper, we conclude that though there is relationship between political risk and foreign direct investment, it is not statistically significant. On the other hand, labour forces which are factors of political environment have positive relationship with the foreign direct investment; a result this study finds to be statistically significance. Therefore, we can conclude that Kazhastan has positive political environment which affects labour force directly. The labour force in turn influences foreign direct investment (FDI) in a positive way. We found that the cheaper the labour force the more attractive the country and this have high level of attraction. Therefore, as far as political environment in Kazakhstan is concerned, political risks have minimal effects on foreign direct investment. References Bekaert, G., Harvey, C. R., Lundblad, C. T., & Siegel, S. (2014). Political risk spreads. Journal of International Business Studies, 45(4), 471-493. Duanmu, J. L. (2014). State-owned MNCs and host country expropriation risk: The role of home state soft power and economic gunboat diplomacy. Journal of International Business Studies, 45(8), 1044-1060. Gobinda Goswami, G., & Haider, S. (2014). Does political risk deter FDI inflow? An analytical approach using panel data and factor analysis. Journal of Economic Studies, 41(2), 233-252. Kerner, A., & Lawrence, J. (2014). What's the risk? Bilateral investment treaties, political risk and fixed capital accumulation. British Journal of Political Science, 44(01), 107-121. 7.0. Appendices Descriptive Statistics Mean Std. Deviation N FDI 5657082521.0000 5110802260.72893 25 PR .5372 .15630 25 LF 8101961.6400 574230.50363 25 Correlations FDI PR LF Pearson Correlation FDI 1.000 -.015 .548 PR -.015 1.000 -.088 LF .548 -.088 1.000 Sig. (1-tailed) FDI . .471 .002 PR .471 . .338 LF .002 .338 . N FDI 25 25 25 PR 25 25 25 LF 25 25 25 Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 LF, PRb . Enter a. Dependent Variable: FDI b. All requested variables entered. Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson 1 .549a .301 .238 4462398461.18285 .470 a. Predictors: (Constant), LF, PR b. Dependent Variable: FDI ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 188801193378451130000.000 2 94400596689225560000.000 4.741 .019b Residual 438086000580075850000.000 22 19913000026367082000.000 Total 626887193958527000000.000 24 a. Dependent Variable: FDI b. Predictors: (Constant), LF, PR Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -34636919886.722 13575101304.585 -2.552 .018 PR 1083338941.100 5850584587.677 .033 .185 .855 LF 4901.533 1592.461 .551 3.078 .006 Coefficientsa Model 95.0% Confidence Interval for B Lower Bound Upper Bound 1 (Constant) -62789956876.367 -6483882897.077 PR -11050030866.777 13216708748.976 LF 1598.971 8204.095 a. Dependent Variable: FDI Residuals Statisticsa Minimum Maximum Mean Std. Deviation N Predicted Value 2796486144.0000 11457886208.0000 5657082521.0000 2804766726.62252 25 Residual -4873271808.00000 11180433408.00000 -.00001 4272421252.34663 25 Std. Predicted Value -1.020 2.068 .000 1.000 25 Std. Residual -1.092 2.505 .000 .957 25 a. Dependent Variable: FDI Charts Country Name Kazakhstan Year FDI PR LF 1992 100000000 0.54 7819823 1993 1271400000 0.72 7871311 1994 659700000 0.50 7889229 1995 964200000 0.50 7873713 1996 1137000000 0.58 7799510 1997 1321400000 0.25 7711419 1998 1151400000 0.54 7659497 1999 1587000000 0.77 7605920 2000 1370521199 0.50 7526571 2001 2816823050 0.64 7517875 2002 2588491060 0.58 7549180 2003 2483253230 0.25 7635002 2004 5615262947 0.54 7707938 2005 2546065710 0.79 7755730 2006 7611168450 0.50 7798140 2007 11972842989 0.59 7945350 2008 16818890680 0.58 8088702 2009 14275888207 0.25 8259916 2010 7456117901 0.54 8422565 2011 13760291529 0.81 8700066 2012 13648134374 0.50 8848347 2013 10011293285 0.59 8984616 2014 7090689354 0.58 9095835 2015 6584614530 0.25 9197976 2016 6584614530 0.54 9284810 Source: Link http://data.worldbank.org/indicator/BX.KLT.DINV.CD.WD?locations=KZ Link info.worldbank.org/governance/wgi/pdf/PRS.xlsx Read More