RELATIONSHIP BETWEEN FOREIGN DIRECT INVESTMENT AND ECONOMIC DEVELOPMENT
|
|
- Jocelin Floyd
- 5 years ago
- Views:
Transcription
1 CHAPTER 7 RELATIONSHIP BETWEEN FOREIGN DIRECT INVESTMENT AND ECONOMIC DEVELOPMENT 7.0. INTRODUCTION The existing approach to the MNE theory treats the decision of a firm to go international as an extension of the firm theory. The general theory of FDI as given by Buckley and Casson (1976) works on two principles:(a) firms internalize missing or imperfect external markets until the costs of further internalization outweigh the benefits and (2) firms choose locations for their constituent activities that minimize the overall costs of their operations. Dunning s eclectic paradigm (Dunning, 1977, 1981) of ownership, location and internalization (OLI) advantages focuses on the sources of competitive advantage that allow a firm to compete abroad, the locational choices that firms make, and the mode of entry into foreign markets. In further analysis Dunning (1998) considers the developments in comparative advantage theory. Our approach is to incorporate the role economic development (socio-economic variables) into MNE theory. It arises out of a critical examination of Dunning (1998) wherein the author refers to macroeconomic aspects of the changing international allocation of economic activity. The argument contained in the above work needs to be recapitulated because it is based on a complex interplay of trade theory, MNE theory and macroeconomics. In the section on macroeconomics from Dunning s paper, he argues that even the recent developments in comparative cost advantage theory do not explain the international allocation of production. He suggests that the general understanding of literature is that the changes in geography of FDI over the last two decades have been broadly in line with the capital expenditure of all firms (sic) - MNE or otherwise. In reemphasizing the research by international business scholars, he points out that 129
2 This could mean that ownership or multinationality of firms was not a significant variable in explaining such changes: and that trade in intermediate or final product internalized and controlled by MNEs, is no differently determined than trade between independent firms, i.e., arm s length trade (Dunning (1998), pp. 55). The conclusion is that the main impact of foreignness or multinationality of firms has not been on the level of economic activity and/or trade of the countries in which they operate but on the structure of these variables. This implies that FDI will have a differential impact on the geography of economic activity. Our approach combines this fundamental understanding that MNEs are different with the implications of the new global business environment ushered in by globalization (WTO) that promotes changing international allocation of economic activity. It is this thrust that is expected to bring about a uniform development globally. FDI along with other is likely to lead to economic development (growth of socio-economic variables). In turn, economic development is likely to influence the pattern of FDI flows internationally. This leads to a two level analysis: 1. Where patterns of FDI flows (and stock) are studied in terms of concentration of inward FDI and dominance patterns of outward FDI. 2. The resultant economic development arising out of resource allocation due to FDI 1 flows in turn influence patterns of FDI through developmental variables as determinants of FDI In the light of this our study concentrates on the changes in geography of FDI over the last two decades that has let to changing international economic activity and the impact of economic development DETERMINANTS OF FDI Most studies concentrate on the effect of FDI on economic growth. Our study in the core chapter identifies and measures the determinants of international FDI patterns. 1 FDI resources imply not only capital but all the other complimentary resources which enable an international relocation of production such as technology transfer, managerial talent, organizational methods, etc. 130
3 Principal Component Analysis When we consider developmental variables like population, GDP, and so on, there is bound to be a high degree of correlation amongst independent variables. There could be three strategies that can be used for dealing with such an econometric problem: 1. If we drop all correlated variables there is a great loss of information. 2. We could use Principal Component Analysis (PCA) to determine the principal variables. 3. We could use PCA for formation of a composite index. The method of Principal Component Analysis (PCA) has two purposes. Firstly, we use PCA for data reduction, especially where the variables are interrelated. Secondly, we use PCA for compilation of a composite index. For estimating the determinants of international FDI patterns we have used a two step procedure. Firstly, components of FDI patterns are many and are correlated. Variables like GDP, human resources, trade openness, and so on which may be correlated. Under such circumstance it is not possible to use the variables directly in a regression framework on account of multicollinearity. Secondly, when there are a large number of variables we need to collapse them into a single independent variable with the help of PCA. The variable should be such that it captures all the information contained in all the individual variables. In view of these weaknesses of an ordinary regression framework, we opt for an alternative method - Principal Component Analysis (PCA). PCA is based on a linear transformation of the regressors so that they are orthogonal to each other by design. Hence, no information contained in the points in the event space is lost. Second, the normality assumption is not essential in PCA. Third, with such a dispersed set of outcomes, PCA is ideally suited because it maximizes the variance rather than minimizing the least square distance. One aim of our empirical work is to evolve a set of composite indices so as to include them as the causal variables consisting of developmental variables such as human resource, infrastructure, labour, market, openness and resources. Hence, we need to choose the essential variables by a data reduction procedure and arrive at relative weights 131
4 for the purpose of consolidating these variables into a single index. We chose Principal Components Analysis (PCA), which is popular in the literature since it has a number of desirable properties. It retains the maximum information, allows the composite of variables to remain uncorrelated amongst each other. The data reduction procedure involves selection of the most relevant variables that capture the maximum information and diverse information. Both the unrotated and rotated solutions explain exactly the same amount of variation in the variables. The choice between them hinges upon the interpretative power of each solution. The component scores (both rotated and unrotated), with respect to the first component are calculated. The most popular orthogonal rotation procedure is Kaiser s Varimax rotation. We therefore retain this procedure. The following consideration should be kept in mind while applying PCA: 1. For determining the retained component we need a criterion. 2. The PCA methodology tells us the total variance explained by each retained principal component as well as the cumulative percentage of the explained variation. This is a measure of the explanatory power of the component for the information content of the procedure. 3. There were various methods of rotation but the most popular method is the Varimax with the Kaiser normalization. The purpose of the rotation is to make the interpretation of the PCA more meaningful. Method of rotation however retains the same information and explanatory power. After doing these procedures there was a choice between retained principal components in a regression framework or selecting the principal variables that are associated with each of those components. This involves the Jolliffe procedure. In the first case regression is known as principal component regression and in the second case it is known as principal variable regression. We have chosen the latter because it is difficult to interpret the principal component regression. We have chosen to retain three components so that we finally land up with three principal variables. The reason is that using the Kaiser criterion of Eigen value less than one leaves only two components while retaining all seven variables leads to multicollinearity. 132
5 On the other hand eliminating some variables through PCA does not affect the explanatory power of the equation because the retained variables contain the information of those which are eliminated. We have used the Joliffe s procedure for selecting principal variables. We take up each rotated component and select the variable that has the highest component score. Then we move to the next component and so on. This way we get the three principal variables which represent the maximum information and eliminate the variables that are correlated to them and hence create multicollinearity. Method for Construction of the Index The method for construction of a composite index is given by Jha and Murthy (2006). Once the number of retained principal components is determined and the rotated component scores obtained, we have the choice of using the principal components as such or selecting certain sub-set of variables from the larger set of variables. Jolliffe proposes selecting one variable to represent each of the retained principal components. The variable that has the highest loading on a component is chosen to represent that component, provided that it has not already been chosen. If it has been chosen, the variable with the next largest loading is selected. The procedure starts with the largest principal component and proceeds to the smallest retained component 2. Index = 3 j wjxj Xj = retained variables Wj = component scores (weights). This procedure has been used for creating the following indices: 1. Index of Human Resources; 2. Index of Infrastructure 2 An alternative approach is to delete variables by using the discarded principal components. Starting with the smallest discarded component, the variable with the largest weight on that component is deleted. Then the variable with the largest loading on the second smallest component would be discarded. If the variable has previously been discarded, then the variable with the next highest loading would also be discarded. This procedure continues up to the largest such discarded component. 133
6 3. Index of Labour 4. Index of Market 5. Index of Trade Openness 6. Index of Resources 7.2. RESULTS OF PRINCIPAL COMPONENT ANALYSIS OF WORLD DEVELOPMENTAL VARIABLES Human Resource Human resource represents skilled manpower, which would have an impact on the FDI patterns. For measuring the variable we have identified variables- Expenditure on Education (EDUX), Primary Education, Pupils (EDU_P) and Population (POPT) and it has been explained about how we shall be developing a composite index, that summaries the information contained in all these variables. It involves three steps: First step involve the Kaiser-Meyer-Olkin (KMO) test which tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. In case of human resource, value is 0.45, which is just reasonable. Bartlett test measures sphericity which states about suitability of using of PCA. If it is statistically significant then it represents about suitability of using principal component analysis. In case of human resource, Bartlett test is highly statistically significant (Table 7.1a). Table 7.1: Results of Principal Component Analysis of Human Resource (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.452 Approx. Chi-Square Bartlett s Test of Sphericity Df 3 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we got three variables and we have imposed the condition that all three variables have to be retained. Therefore total variance explained by these three variables is 100 percent. Table 7.1b gives the total explained variation captured by three retained components. 134
7 (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of In the next step, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component EDU_P POPT EDUX Rotated component scores of EDU_P, EDUX and POPT are 0.997, and respectively (Table 7.1c). These scores are used for construction of composite index of human resource. Composite Index of Human Resources I HR = 0.997EDU_P EDUX POPT Infrastructure Infrastructure refers to the facilities through which others resources can be efficiently and optimally used. For measuring this variable we have identified following variables- Energy Production (ENP), Electricity Production (ELP), Air Transport (ATS), Air Transport-Passengers (ATP), Road Sector Energy Consumption (ROAD), Telephone Lines (TEL) and Telephone Lines (per 100 People) (TEL_P). It shall be used to develop composite index that summaries the information contained in all these variables. 135
8 KMO test tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.815, which is high and good. Bartlett test suggests infrastructure variable is highly statistically significant (Table 7.2a). Table 7.2: Results of Principal Component Analysis of Infrastructure (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.815 Approx. Chi-Square Bartlett s Test of Sphericity Df 21 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we have seven variables and we have imposed the condition that three variables have to be selected. The retained variables are ROAD, TEL, and TEL_P. variance explained by these three variables is percent. Table 7.2b gives the total explained variation captured by three retained components. (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of Now, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. 136
9 (c) Rotated Component Matrix Variable Component ROAD ATP ATS ELP TEL ENP TEL_P Rotated component scores of ROAD, TEL and TEL_P are 0.896, and respectively (Table 7.2c). These scores are used for construction of composite index of Infrastructure. Composite Index of Infrastructure I INFRA = 0.896ROAD TEL TEL_P Labour Labour represents raw human work force. Cheap raw labour may influence cost side. For measuring this variable we have identified following variables- Employment, age (EMPTEEN), Employment (EMP), GDP Per Person (GDPPC), Labour Participation Rate (LRATE), Labour Force, (LFT) and Population Working Ages (POPWA). It shall be used to develop composite index that provides the information contained in all these variables. KMO test tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.666, which is good. Bartlett test suggests labour variable is highly statistically significant (Table 7.3a). 137
10 Table 7.3: Results of Principal Component Analysis of Labour (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.666 Approx. Chi-Square Bartlett s Test of Sphericity Df 15 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we have six variables and we have imposed the condition that three variables have to be selected. The retained variables are LRATE, LFT and POPWA. variance explained by these three variables is percent (Table 7.3b). (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of Next, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component LRATE EMP EMPTEEN LFT GDPPC POPWA
11 Rotated component scores of LRATE, LFT and POPWA are 0.932, and respectively. These scores are used for construction of composite index of labour. Composites Index of Labour I LAB = 0.932LRATE LFT POPWA Market Market is a place where production is used for consumption. For measuring this variable we have identified following variables- Market Capitalization of Listed Companies (MKTCAP), Listed Domestic Companies (COS), Population Density (POPDEN), Population in Largest City (POPL), Manufacturing-Value Added (MFWA), Industry-Value Added (INVA) and Services-Value Added (SVA). It shall be used to develop composite index that summaries the information contained in all these variables. KMO test tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.824, which is high and good. Bartlett test suggests market variable is highly statistically significant (Table 7.4a). Table 7.4: Results of Principal Component Analysis of Market (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.824 Approx. Chi-Square Bartlett s Test of Sphericity Df 21 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we have seven variables and we have imposed the condition that three variables have to be selected. The retained variables are MKTCAP, POPL and POPDEN. variance explained by these variables is percent (Table 7.4b). 139
12 (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of We applied Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component MKTCAP SVA INVA MFVA COS POPL POPDEN Rotated component scores of MKTCAP, POPL and POPDEN are 0.962, and respectively. These scores are used for construction of composite index of Market (Table 7.4c). Composite Index of Market I MKT = 0.962MKTCAP POPL POPDEN 140
13 Trade Openness Trade openness refers to openness of domestic country for international trade activities. It facilitates free movement of goods and services amongst countries. For measuring the variable we have identified variables- reserves (TRES), Trade Openness (TOPEN) and Official exchange rate (EXCG) and it has been explained about how we shall be developing a composite index, that summaries the information contained in all these variables. KMO test which tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.485, which is reasonable. Bartlett test suggests trade openness variable is significant at 10 percent level of significance (Table 7.5a). Table 7.5: Results of Principal Component Analysis of Trade Openness (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.485 Approx. Chi-Square Bartlett s Test of Sphericity Df 3 Sig..073 Next step is to be found out number of principal components which are being retained. In this case, we got three variables and we have imposed the condition that all three variables have to be retained. Therefore total variance explained by these three variables is 100 percent. Table 7.5b gives the total explained variation captured by three retained components. (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of
14 In the next step, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component EXCG TRES TOPEN Rotated component scores of EXCG, TRES and TRADE are 1.00, and respectively (Table 7.5c). These scores are used for construction of composite index of trade openness. Composite Index of Trade Openness I TOPN = 1.00EXCG TRES TOPEN Resource Resource includes Gross Fixed Capital Formation (GFCF), Gross Domestic Products (GDP), GDP Per Capita (GDPPC), Gross Domestic Savings (GDS) and Natural Resources (TNRES). These resources are used to developed composite index, which gives information contained in these variables. KMO test which tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.560, which is good. Bartlett test suggests resource variable is highly significant. Table 7.6: Results of Principal Component Analysis of Resource (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.560 Approx. Chi-Square Bartlett s Test of Sphericity Df 10 Sig
15 Next step is to be found out number of principal components which are being retained. In this case, we have five variables and we have imposed the condition that three variables have to be selected. The retained variables are GFCF, GDPPC and TNRES. variance explained by these variables is percent (Table 7.6b). (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of In the next step, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component GFCF GDS GDP GDPPC TNRES Rotated component scores of GFCF, GDPPC and TNRES are 0.989, and respectively (Table 7.6c). These scores loading are used for construction of composite index of resource. Composite Index of Resources I RES = 0.989GFCF GDPPC TNRES 143
16 7.3. RESULTS OF PRINCIPAL COMPONENT ANALYSIS OF DEVELOPED COUNTRIES DEVELOPMENTAL VARIABLES Human Resource Human resource represents skilled manpower, which would have an impact on the FDI patterns. For measuring the variable we have identified variables- Expenditure on Education (EDUX), Primary Education, Pupils (EDU_P) and Population (POPT) and it has been explained about how we shall be developing a composite index, that summaries the information contained in all these variables. KMO test which tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The KMO test value is 0.766, which is high. Bartlett test is highly statistically significant. variables within human resource are only three. Hence, these three variables become principal variable. Therefore total variance explained by these variables is 100 percent. Human resource includes Expenditure on Education (EDUX), Primary Education, Pupils (EDU_P) and Population (POPT) as principal variables. Table 7.7: Results of Principal Component Analysis of Human Resource (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.766 Approx. Chi-Square Bartlett s Test of Sphericity Df 3 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we got three variables and we have imposed the condition that all three variables have to be retained. Therefore total variance explained by these three variables is 100 percent. Table 7.7b gives the total explained variation captured by three retained components. 144
17 (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of In the next step, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component EDU_P POPT EDUX Rotated component scores of EDU_P, EDUX and POPT are 0.797, and respectively (Table 7.7c). These scores loading are used for construction of composite index of human resource. Composite Index of Human Resource I HR = 0.797EDU_P EDUX POPT Infrastructure Infrastructure refers to the facilities through which others resources can be efficiently and optimally used. For measuring this variable we have identified following variables- Energy Production (ENP), Electricity Production (ELP), Air Transport (ATS), Air Transport-Passengers (ATP), Road Sector Energy Consumption (ROAD), Telephone Lines (TEL) and Telephone Lines (per 100 People) (TEL_P). It shall be used to develop composite index that summaries the information contained in all these variables. 145
18 KMO test tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.876, which is high and good. Bartlett test suggests infrastructure variable is highly statistically significant (Table 7.8a). Table 7.8: Results of Principal Component Analysis of Infrastructure (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.876 Approx. Chi-Square Bartlett s Test of Sphericity Df 21 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we have seven variables and we have imposed the condition that three variables have to be selected. The retained variables are ROAD, TEL_P and ATS. variance explained by these three variables is percent. Table 7.8b gives the total explained variation captured by three retained components. (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of Now, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. 146
19 (c) Rotated Component Matrix Variable Component ROAD ELP ATP ENP TEL ATS TEL_P Rotated component scores of ROAD, TEL_P and ATS are 0.989, and respectively (Table 7.8c). These scores loading are used for construction of composite index of Infrastructure. Composite Index of Infrastructure I INFRA = 0.989ROAD TEL_P ATS Labour Labour represents raw human work force. Cheap raw labour may influence cost side. For measuring this variable we have identified following variables- Employment, age (EMPTEEN), Employment (EMP), GDP Per Person (GDPPC), Labour Participation Rate (LRATE), Labour Force, (LFT) and Population Working Ages (POPWA). It shall be used to develop composite index that provides the information contained in all these variables. KMO test tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.667, which is good. Bartlett test suggests labour variable is highly statistically significant (Table 7.9a). 147
20 Table 7.9: Results of Principal Component Analysis of Labour (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.667 Approx. Chi-Square Bartlett s Test of Sphericity Df 15 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we have six variables and we have imposed the condition that three variables have to be selected. The retained variables are EMP, LFT and POPWA. variance explained by these three variables is percent (Table 7.9b). (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of Next, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component EMP LRATE EMPTEEN LFT GDPPC POPWA
21 Rotated component scores of EMP, LFT and POPWA are 0.961, and respectively. These scores are used for construction of composite index of labour. Composite Index of Labour I LAB = 0.961EMP LFT POPWA Market Market is a place where production is used for consumption. For measuring this variable we have identified following variables- Market Capitalization of Listed Companies (MKTCAP), Listed Domestic Companies (COS), Population Density (POPDEN), Population in Largest City (POPL), Manufacturing-Value Added (MFWA), Industry-Value Added (INVA) and Services-Value Added (SVA). It shall be used to develop composite index that summaries the information contained in all these variables. KMO test tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.824, which is high and good. Bartlett test suggests market variable is highly statistically significant (Table 7.10a). Table 7.10: Results of Principal Component Analysis of Market (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.824 Approx. Chi-Square Bartlett s Test of Sphericity Df 21 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we have seven variables and we have imposed the condition that three variables have to be selected. The retained variables are MKTCAP, POPL and POPDEN. variance explained by these variables is percent (Table 7.10b). 149
22 (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of We applied Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component MKTCAP SVA INVA MFVA COS POPL POPDEN Rotated component scores of MKTCAP, POPL and POPDEN are 0.964, and respectively (Table 7.10c). These scores are used for construction of composite index of Market. Composite Index of Market I MKT = 0.964MKTCAP POPL POPDEN 150
23 Trade Openness Trade openness refers to openness of domestic country for international trade activities. It facilitates free movement of goods and services amongst countries. For measuring the variable we have identified variables- reserves (TRES), Trade Openness (TOPEN) and Official exchange rate (EXCG) and it has been explained about how we shall be developing a composite index, that summaries the information contained in all these variables. KMO test which tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.458, which is reasonable. Bartlett test suggests trade openness variable is highly significant (Table 7.11a). Table 7.11: Results of Principal Component Analysis of Trade Openness (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.458 Approx. Chi-Square Bartlett s Test of Sphericity Df 3 Sig..001 Next step is to be found out number of principal components which are being retained. In this case, we got three variables and we have imposed the condition that all three variables have to be retained. Therefore total variance explained by these three variables is 100 percent. Table 7.5b gives the total explained variation captured by three retained components. (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of
24 In the next step, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component EXCG TRES TOPEN Rotated component scores of EXCG, TRES and TRADE are 0.998, and respectively (Table 7.11c). These scores are used for construction of composite index of trade openness. Composite Index of Trade Openness I TOPN = 0.998EXCG TRES TOPEN Resource Resource includes Gross Fixed Capital Formation (GFCF), Gross Domestic Products (GDP), GDP Per Capita (GDPPC), Gross Domestic Savings (GDS) and Natural Resources (TNRES). These resources are used to developed composite index, which gives information contained in these variables. KMO test which tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.610, which is good. Bartlett test suggests resource variable is highly significant (Table 7.12a). Table 7.12: Results of Principal Component Analysis of Resource (Developed Countries) (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.610 Approx. Chi-Square Bartlett s Test of Sphericity Df 10 Sig
25 Next step is to be found out number of principal components which are being retained. In this case, we have five variables and we have imposed the condition that three variables have to be selected. The retained variables are GFCF, GDPPC and TNRES. variance explained by these variables is percent (Table 7.12b). (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of In the next step, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component GFCF GDP GDS GDPPC TNRES Rotated component scores of GFCF, GDPPC and TNRES are 0.993, and respectively (Table 7.12c). These scores are used for construction of composite index of resource. Composite Index of Resources I RES = 0.993GFCF GDPPC TNRES 153
26 7.4. RESULTS OF PRINCIPAL COMPONENT ANALYSIS OF DEVELOPING COUNTRIES DEVELOPMENTAL VARIABLES Human Resource Human resource represents skilled manpower, which would have an impact on the FDI patterns. For measuring the variable we have identified variables- Expenditure on Education (EDUX), Primary Education, Pupils (EDU_P) and Population (POPT) and it has been explained about how we shall be developing a composite index, that summaries the information contained in all these variables. KMO test which tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The KMO test value is 0.552, which is good. Bartlett test is highly statistically significant (Table 7.13a). Table 7.13: Results of Principal Component Analysis of Human Resource (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.552 Approx. Chi-Square Bartlett s Test of Sphericity Df 3 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we got three variables and we have imposed the condition that all three variables have to be retained. Therefore total variance explained by these three variables is 100 percent. Table 7.13b gives the total explained variation captured by three retained components. (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of
27 In the next step, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component EDU_P POPT EDUX Rotated component scores of EDU_P, EDUX and POPT are 0.975, and respectively (Table 7.13c). These scores are used for construction of composite index of human resource. Composite Index of Human Resource I HR = 0.975EDU_P EDUX POPT Infrastructure Infrastructure refers to the facilities through which others resources can be efficiently and optimally used. For measuring this variable we have identified following variables- Energy Production (ENP), Electricity Production (ELP), Air Transport (ATS), Air Transport-Passengers (ATP), Road Sector Energy Consumption (ROAD), Telephone Lines (TEL) and Telephone Lines (per 100 People) (TEL_P). It shall be used to develop composite index that summaries the information contained in all these variables. KMO test tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.778, which is high and good. Bartlett test suggests infrastructure variable is highly statistically significant (Table 7.14a). variables included are seven. Out of these variables, principal component analysis selects three variables as principal infrastructure variables. These principal variables are 155
28 Electricity Production (ELP), Telephone Lines per 100 persons (TEL_P) and Road Sector Energy Consumption (ROAD). variance explained by these variables is 96.6 percent. Table 7.14: Results of Principal Component Analysis of Infrastructure (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.788 Approx. Chi-Square Bartlett s Test of Sphericity Df 21 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we have seven variables and we have imposed the condition that three variables have to be selected. The retained variables are ELP, TEL_P and ROAD. variance explained by these three variables is percent. Table 7.14 gives the total explained variation captured by three retained components. (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of Now, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. 156
29 (c) Rotated Component Matrix Variable Component ELP ENP TEL ATP ROAD TEL_P ATS Rotated component scores of ELP, TEL_P and ROAD are 0.965, and respectively (Table 7.14c). These scores are used for construction of composite index of Infrastructure. Composite Index of Infrastructure I INFRA = 0.965ELP TEL_P ROAD Labour Labour represents raw human work force. Cheap raw labour may influence cost side. For measuring this variable we have identified following variables- Employment, age (EMPTEEN), Employment (EMP), GDP Per Person (GDPPC), Labour Participation Rate (LRATE), Labour Force, (LFT) and Population Working Ages (POPWA). It shall be used to develop composite index that provides the information contained in all these variables. KMO test tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.671, which is good. Bartlett test suggests labour variable is highly statistically significant (Table 7.15a). 157
30 Table 7.15: Results of Principal Component Analysis of Labour (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.671 Approx. Chi-Square Bartlett s Test of Sphericity Df 15 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we have six variables and we have imposed the condition that three variables have to be selected. The retained variables are EMP, LFT and POPWA. variance explained by these three variables is percent (Table 7.15b). (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of Next, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component EMP LRATE EMPTEEN GDPPC LFT POPWA
31 Rotated component scores of EMP, LFT and POPWA are 0.960, and respectively (Table 7.15c). These scores are used for construction of composite index of labour. Composite Index of Labour I LAB = 0.960EMP LFT POPWA Market Market is a place where production is used for consumption. For measuring this variable we have identified following variables- Market Capitalization of Listed Companies (MKTCAP), Listed Domestic Companies (COS), Population Density (POPDEN), Population in Largest City (POPL), Manufacturing-Value Added (MFWA), Industry-Value Added (INVA) and Services-Value Added (SVA). It shall be used to develop composite index that summaries the information contained in all these variables. KMO test tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.547, which is good. Bartlett test suggests market variable is highly statistically significant (Table 7.16a). Table 7.16: Results of Principal Component Analysis of Market (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.547 Approx. Chi-Square Bartlett s Test of Sphericity Df 10 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we have seven variables and we have imposed the condition that three variables have to be selected. The retained variables are MKTCAP, POPDEN and POPL. variance explained by these variables is percent (Table 7.16b). 159
32 (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of We applied Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. (c) Rotated Component Matrix Variable Component MKTCAP MFVA POPDEN COS POPL Rotated component scores of MKTCAP, POPDEN and POPL are 0.951, and respectively (Table 7.16c). These scores loading are used for construction of composite index of Market. Composite Index of Market I MKT = 0.951MKTCAP POPDEN POPL Trade Openness Trade openness refers to openness of domestic country for international trade activities. It facilitates free movement of goods and services amongst countries. For measuring the 160
33 variable we have identified variables- reserves (TRES), Trade Openness (TOPEN) and Official exchange rate (EXCG) and it has been explained about how we shall be developing a composite index, that summaries the information contained in all these variables. KMO test which tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.493, which is reasonable. Bartlett test suggests trade openness variable is not significant (Table 7.17c). Table 7.17: Results of Principal Component Analysis of Trade Openness (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.493 Approx. Chi-Square Bartlett s Test of Sphericity Df 3 Sig..385 Next step is to be found out number of principal components which are being retained. In this case, we got three variables and we have imposed the condition that all three variables have to be retained. Therefore total variance explained by these three variables is 100 percent. Table 7.17b gives the total explained variation captured by three retained components. (b) Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings of of In the next step, we used Varimax rotation method, to arrive at rotated component score. This would enable us to have a better interpretation of components. Moreover it helps us in generating the value weights obtained from the factor loading for constructing the composite index. 161
34 (c) Rotated Component Matrix Variable Component TRES TOPEN EXCG Rotated component scores of TRES, TRADE and EXCG are 1.00, and respectively (Table 7.17c). These scores are used for construction of composite index of trade openness. Composite Index of Trade Openness I TOPN = 1.00TRES TOPEN EXCG Resource Resource includes Gross Fixed Capital Formation (GFCF), Gross Domestic Products (GDP), GDP Per Capita (GDPPC), Gross Domestic Savings (GDS) and Natural Resources (TNRES). These resources are used to developed composite index, which gives information contained in these variables. KMO test which tells us about the adequacy of sample and appropriateness of PCA as a methodology. In general, value of KMO test should be on higher side which represents good. The value of KMO test is 0.666, which is good. Bartlett test suggests resource variable is highly significant (Table 7.18a). Table 7.18: Results of Principal Component Analysis of Resource (a) KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.666 Approx. Chi-Square Bartlett s Test of Sphericity Df 10 Sig..000 Next step is to be found out number of principal components which are being retained. In this case, we have five variables and we have imposed the condition that three variables have to be selected. The retained variables are GFCF, GDPPC and TNRES. variance explained by these variables is percent (Table 7.18b). 162
Influence of Personal Factors on Health Insurance Purchase Decision
Influence of Personal Factors on Health Insurance Purchase Decision INFLUENCE OF PERSONAL FACTORS ON HEALTH INSURANCE PURCHASE DECISION The decision in health insurance purchase include decisions about
More informationPatterns of Foreign Direct Investment Flows and Economic Development- A Cross Country Analysis
Patterns of Foreign Direct Investment Flows and Economic Development- A Cross Country Analysis Abstract Submitted to the University of Delhi for the Award of the Degree of Doctor of Philosophy Research
More informationTRENDS AND DETERMINANT OF FOREIGN DIRECT INVESTMENT IN INDIA: A POST REFORM ANALYSIS
Indian Journal of Accounting (IJA) 100 ISSN : 0972-1479 (Print) 2395-6127 (Online) Vol. XLIX (1), June, 2017, pp. 100-110 TRENDS AND DETERMINANT OF FOREIGN DIRECT INVESTMENT IN INDIA: A POST REFORM ANALYSIS
More informationA STATISTICAL MODEL OF ORGANIZATIONAL PERFORMANCE USING FACTOR ANALYSIS - A CASE OF A BANK IN GHANA. P. O. Box 256. Takoradi, Western Region, Ghana
Vol.3,No.1, pp.38-46, January 015 A STATISTICAL MODEL OF ORGANIZATIONAL PERFORMANCE USING FACTOR ANALYSIS - A CASE OF A BANK IN GHANA Emmanuel M. Baah 1*, Joseph K. A. Johnson, Frank B. K. Twenefour 3
More informationEmpirical Research on the Relationship Between the Stock Option Incentive and the Performance of Listed Companies
International Business and Management Vol. 10, No. 1, 2015, pp. 66-71 DOI:10.3968/6478 ISSN 1923-841X [Print] ISSN 1923-8428 [Online] www.cscanada.net www.cscanada.org Empirical Research on the Relationship
More informationJournal of Chemical and Pharmaceutical Research, 2014, 6(6): Research Article
Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2014, 6(6):1179-1183 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 Empirical research on the bio-pharmaceutical listed
More informationImpacts of government policies and regulations on the development of international retailing and services case study of Vietnamese market
Impacts of government policies and regulations on the development of international retailing and services case study of Vietnamese market Tran Phuoc* Faculty of Accounting and Auditing, Industrial University
More informationInfluencing Dynamics of Safety in Mutual Fund Investments An Emperical Overview
ICIMP-2018 SEP- 2018 Special Issue ISSN: 2455-3085 (Online) RESEARCH REVIEW International Journal of Multidisciplinary www.rrjournals.com [UGC Listed Journal] Influencing Dynamics of Safety in Mutual Fund
More informationAnshika 1. Abstract. 1. Introduction
Micro-economic factors affecting stock returns: an empirical study of S&P BSE Bankex companies Abstract Anshika 1 1 Research Scholar, PEC University of Technology, Sector 12, Chandigarh, 160012, India
More informationEffect of Foreign Ownership on Financial Performance of Listed Firms in Nairobi Securities Exchange in Kenya
Effect of Foreign Ownership on Financial Performance of Listed Firms in Nairobi Securities Exchange in Kenya 1 Anthony Muema Musyimi, 2 Dr. Jagogo PHD STUDENT, KENYATTA UNIVERSITY Abstract: This study
More informationAN EMPIRICAL STUDY ON FACTORS INFLUENCING EMPLOYEES IN THE INVESTMENT DECISION OF PENSION FUND SCHEME IN A PUBLIC SECTOR
AN EMPIRICAL STUDY ON FACTORS INFLUENCING EMPLOYEES IN THE INVESTMENT DECISION OF PENSION FUND SCHEME IN A PUBLIC SECTOR ORGANIZATION AT TIRUCHIRAPPALLI N.Suresh (Corresponding Author) B.Com., MBA., PGDIRPM
More informationCustomer Perception on Post Purchase Services of life Insurance Companies
International Journal of Humanities and Social Science Invention (IJHSSI) ISSN (Online): 2319 7722, ISSN (Print): 2319 7714 Volume 7 Issue 01 January. 2018 PP.82-87 Customer Perception on Post Purchase
More informationSTATISTICAL MODELS FOR MONITORING THE LIKELIHOOD OF CREDIT PORTFOLIO IMPAIRMENT
Professor Nicolae DARDAC, PhD Assistant Iustina Alina BOITAN The Bucharest Academy of Economic Studies STATISTICAL MODELS FOR MONITORING THE LIKELIHOOD OF CREDIT PORTFOLIO IMPAIRMENT Abstract. Academic
More informationConstruction of Investor Sentiment Index in the Chinese Stock Market
International Journal of Service and Knowledge Management International Institute of Applied Informatics 207, Vol., No.2, P.49-6 Construction of Investor Sentiment Index in the Chinese Stock Market Yuxi
More informationJournal of Chemical and Pharmaceutical Research, 2013, 5(12): Research Article
Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2013, 5(12):1379-1383 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 Empirical research on the bio-pharmaceutical
More informationCHAPTER 6 DATA ANALYSIS: CORPORATE DIVIDEND POLICY DETERMINANTS
CHAPTER 6 DATA ANALYSIS: CORPORATE DIVIDEND POLICY DETERMINANTS The body of literature dealing with dividend determinants can be grouped into two distinct categories: (1) those based on the implicit assumption
More informationInternational Journal of Advance Research in Computer Science and Management Studies
Volume 2, Issue 11, November 2014 ISSN: 2321 7782 (Online) International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online
More informationInvestors Perception Regarding Mutual Funds in India
Dr. G.S.Batra Investors Perception Regarding Mutual Funds in India * Ms. Sukhwinder Kaur Abstract: Mutual fund industry has become a vital source of finance for the corporate sector in India. It channelizes
More informationA STUDY ON FACTORS MOTIVATING THE INVESTMENT DECISION OF MUTUAL FUND INVESTORS IN MADURAI CITY
A STUDY ON FACTORS MOTIVATING THE INVESTMENT DECISION OF MUTUAL FUND INVESTORS IN MADURAI CITY Dr. P. KUMARESAN Professor PRIST School of Business PRIST University, Vallam, Thanjavur E- Mail: pkn.commerce@gmail.com
More informationRetail Investors Responsibilities in Stock Market Imperfection in Bangladesh: An Empirical Study
Retail Investors Responsibilities in Stock Market Imperfection in Bangladesh: An Empirical Study Protap Kumar Ghosh Assistant Professor, Business Administration Discipline Khulna University, Khulna-9208,
More informationThe Dilemma of Investment Decision for Small Investors in the Hong Kong Derivatives Markets
International Journal of Humanities and Social Science Vol., No. 9; July 201 The Dilemma of Investment Decision for Small Investors in the Hong Kong Derivatives Markets Tai-Yuen Hon Department of Economics
More informationThe Role of Cash Flow in Financial Early Warning of Agricultural Enterprises Based on Logistic Model
IOP Conference Series: Earth and Environmental Science PAPER OPEN ACCESS The Role of Cash Flow in Financial Early Warning of Agricultural Enterprises Based on Logistic Model To cite this article: Fengru
More informationDETERMINANTS OF BUSINESS SENTIMENT
DETERMINANTS OF BUSINESS SENTIMENT Dan Friesner, Gonzaga University Mohammed Khayum, University of Southern Indiana ABSTRACT Sentiment surveys receive considerable attention because of their potential
More informationFactors Affecting the Liquidity Level of Commercial Banks in Bangladesh
ASA University Review, Vol. 10 No. 2, July December, 2016 Affecting the Liquidity Level of Commercial Banks in Bangladesh Afroza Parvin * Alrafa Akter Nitu ** Abstract Bank is a financial intermediary
More informationInfluential Factors of Residential Commodity Price Changes in Sanya
International Journal of Economics and Finance; Vol. 10, No. 12; 2018 ISSN 1916-971X E-ISSN 1916-9728 Published by Canadian Center of Science and Education Influential Factors of Residential Commodity
More informationCHAPTER VI RISK TOLERANCE AMONG MUTUAL FUND INVESTORS
CHAPTER VI RISK TOLERANCE AMONG MUTUAL FUND INVESTORS 6.1. Introduction Risk and return are inseparable twins 1. In generic sense, risk means the possibility of financial loss. In the investment world,
More informationInternational Journal of Business and Administration Research Review, Vol. 3, Issue.15, July - Sep, Page 193
A STUDY ON INVESTORS INVESTMENT DECISION ON DERIVATIVES MARKET IN INDIA Dr. G. Prabakaran* A. Thamotharan** *Assistant Professor, Department of Business Administration, Government Arts College, Dharmapuri
More informationThe Models of Investing Schools
Journal of Applied Mathematics and Physics, 206, 4, 090-098 Published Online June 206 in SciRes. http://www.scirp.org/journal/jamp http://dx.doi.org/0.4236/jamp.206.463 The Models of Investing Schools
More informationKAAV INTERNATIONAL JOURNAL OF LAW, FINANCE INDUSTRIAL RELATIONS THE FACTORS AFFECTING THE INTEREST RATE OF COMMERCIAL BANKS IN VIETNAM
KAAV INTERNATIONAL JOURNAL OF LAW, FINANCE & INDUSTRIAL RELATIONS THE FACTORS AFFECTING THE INTEREST RATE OF COMMERCIAL BANKS IN VIETNAM Dr. PHAN THI HANG NGA Lecturer of Ho Chi Minh City University of
More informationStudy on Debt Structure, Ownership Structure and Solvency: Based on Automobile Listed Companies Jie Liu 1, a* and Mingran Deng 2, b
6th International Conference on Electronics, Mechanics, Culture and Medicine (EMCM 2015) Study on Debt Structure, Ownership Structure and Solvency: Based on Automobile Listed Companies Jie Liu 1, a* and
More informationNAMRATA N. KHATRI. Assistant Professor, Department of Business & Industrial Management, Veer Narmad South Gujarat University, Surat
FACTORS INFLUENCING INVESTORS INVESTMENT IN INITIAL PUBLIC OFFERING NAMRATA N. KHATRI Assistant Professor, Department of Business & Industrial Management, Veer Narmad South Gujarat University, Surat Abstract
More informationCHAPTER VII PERCEPTION OF MUTUAL FUND INVESTORS
CHAPTER VII PERCEPTION OF MUTUAL FUND INVESTORS 7.1. Introduction A mutual fund collects the savings from small investors, invest them in Government and other corporate securities and earn income through
More informationFactors Influencing Individual Investor Behavior (The Case of the Karachi Stock Exchange) Athar Iqbal and Sania Usmani * ABSTRACT
South Asian Journal of Management Sciences Vol. 3, No. 1, (Spring 2009) 15-26 Factors Influencing Individual Investor Behavior (The Case of the Karachi Stock Exchange) Athar Iqbal and Sania Usmani * ABSTRACT
More informationAN ASSESSMENT OF THE CHALLENGES FACING POWER INFRASTRUCTURE FINANCING IN NIGERIA
AN ASSESSMENT OF THE CHALLENGES FACING POWER INFRASTRUCTURE FINANCING IN NIGERIA Emmanuel Oikelomen Ayorinde, Clinton Ohis Aigbavboa, Department of Civil Engineering Science, University of Johannesburg,
More informationBehavioural Analysis of Individual Investors Towards Selection of Mutual Fund Schemes: An Empirical Study
Behavioural Analysis of Individual Investors Towards Selection of Mutual Fund Schemes: An Empirical Study Neelam Jain* and Sugandh Rawal** Abstract Development of an economy necessarily depends upon its
More informationby Svetla Trifonova Marinova and Martin Alexandrov Marinov Aldershot, Ashgate Pp. 352
Book Review For oreign Direct Investment in Central and Eastern Europe by Svetla Trifonova Marinova and Martin Alexandrov Marinov Aldershot, Ashgate 2003. Pp. 352 reviewed by Dimitrios Kyrkilis* Since
More informationTHE DETERMINANTS OF FINANCIAL HEALTH IN THAILAND: A FACTOR ANALYSIS APPROACH
IJER Serials Publications 12(4), 2015: 1453-1459 ISSN: 0972-9380 THE DETERMINANTS OF FINANCIAL HEALTH IN THAILAND: A FACTOR ANALYSIS APPROACH Abstract: This aim of this research was to examine the factor
More informationFinancial Literacy and its Contributing Factors in Investment Decisions among Urban Populace
Indian Journal of Science and Technology, Vol 9(27), DOI: 10.17485/ijst/2016/v9i27/97616, July 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Financial Literacy and its Contributing Factors in
More informationCHAPTER 6 DATA ANALYSIS AND INTERPRETATION
208 CHAPTER 6 DATA ANALYSIS AND INTERPRETATION Sr. No. Content Page No. 6.1 Introduction 212 6.2 Reliability and Normality of Data 212 6.3 Descriptive Analysis 213 6.4 Cross Tabulation 218 6.5 Chi Square
More informationECONOMETRIC MODELS FOR THE ESTIMATION OF NON- PERFORMING LOANS IN ALBANIA
ECONOMETRIC MODELS FOR THE ESTIMATION OF NON- PERFORMING LOANS IN ALBANIA Phd.Gledjana Zeneli (Foto) 1, Msc Amarilda Kulli 2, Marsela Xhomaqi 3 1) Department of Applied Mathematics, Faculty of Natural
More informationIndian Private Equity Industry The Challenges Ahead
DOI: 10.7763/IPEDR. 2012. V55. 26 Indian Private Equity Industry The Challenges Ahead Sahil Sandhu 1 and Namrata Sandhu 2 1 Cisco Systems India Private Limited, Bangalore, India 2 Chitkara Business School,
More informationOnline Publication Date: 1 st July 2012 Publisher: Asian Economic and Social Society. Factors Influencing Individual Investor Behaviour in Karachi
Online Publication Date: 1 st July 2012 Publisher: Asian Economic and Social Society Factors Influencing Individual Investor Behaviour in Karachi Sania Usmani (Department of Business Administration, Iqra
More informationFactors Affecting the Stock Price Movement: A Case Study on Dhaka Stock Exchange
International Journal of Business and Management; Vol. 10, No. 10; 2015 ISSN 1833-3850 E-ISSN 1833-8119 Published by Canadian Center of Science and Education Factors Affecting the Stock Price Movement:
More informationAssessment on Credit Risk of Real Estate Based on Logistic Regression Model
Assessment on Credit Risk of Real Estate Based on Logistic Regression Model Li Hongli 1, a, Song Liwei 2,b 1 Chongqing Engineering Polytechnic College, Chongqing400037, China 2 Division of Planning and
More informationFactors Influencing Perceptions of Climate and Weather Effects on Property Ownership
Factors Influencing Perceptions of Climate and Weather Effects on Property Ownership Huili Hao¹, Patrick Long¹, and Scott Curtis 12 1. 1. Center for Sustainability: Tourism, Natural Resources, and the
More informationCHAPTER- 6 PAYMENT AND CLEARING SYSTEM IN BANKING SECTOR
CHAPTER- 6 PAYMENT AND CLEARING SYSTEM IN BANKING SECTOR 6.1 Sample Period 6.2 Computerization of Clearing and Settlement Operations 6.3 Electronic Payment System 6.4 Payment System in US 6.5 Initiatives
More informationInternational Journal of Computational Research and Development (IJCRD) Impact Factor: 4.775, ISSN (Online): (www.dvpublication.
Impact Factor: 4.775, ISSN (Online): 2456-17 (www.dvpublication.com) Volume 2, Issue 1, 217 A STUDY ON CONSUMER ATTITUDE AND PERCEPTION TOWARDS GOLD LOAN OFFERED BY SCHEDULED COMMERCIAL BANKS WITH REFERENCE
More informationINTERNATIONAL JOURNAL OF HUMANITIES AND CULTURAL STUDIES ISSN
Investigating the Factors Impacting on the Implementation of the Operating Budget Regarding Activity-Based Costing in the Ministry of Economic Affairs and Finance Saeid Azimi Financial Management, Institution
More informationX-Efficiency of Indian Commercial Banks and their Determinants of Service Quality: A Study of Post Global Financial Crisis
13 th International Conference on Data Envelopment Analysis X- of Indian Commercial Banks and their Determinants of Service Quality: A Study of Post Global Financial Crisis Gagandeep Sharma Dr. Divya Sharma
More informationFundamental Factors Influencing Individual Investors to Invest in Shares of Manufacturing Companies in the Nigerian Capital Market
Fundamental Factors Influencing Individual Investors to Invest in Shares of Manufacturing Companies in the Nigerian Capital Market Ikeobi, Nneka Rosemary 1* Jat, Rauta Bitrus 2 1. Department of Actuarial
More informationCREDIT IMPACT ON PERFORMANCE OF MICRO AND SMALL ENTERPRISES IN TELANGANA
CREDIT IMPACT ON PERFORMANCE OF MICRO AND SMALL ENTERPRISES IN TELANGANA Prabhakar Gampala, Osmania University ABSTRACT The Micro and Small Enterprise (MSEs) in India is one of the most promising sectors
More informationCredit Risk Evaluation of SMEs Based on Supply Chain Financing
Management Science and Engineering Vol. 10, No. 2, 2016, pp. 51-56 DOI:10.3968/8338 ISSN 1913-0341 [Print] ISSN 1913-035X [Online] www.cscanada.net www.cscanada.org Credit Risk Evaluation of SMEs Based
More informationLecture 14. Multinational Firms. 2. Dunning's OLI, joint inputs, firm versus plant-level scale economies
Lecture 14 Multinational Firms 1. Review of empirical evidence 2. Dunning's OLI, joint inputs, firm versus plant-level scale economies 3. A model with endogenous multinationals 4. Pattern of trade in goods
More informationShubhra Biswas (Sinha 1 ), Arindam Gupta 2
Financial Inclusion and Financial Literacy: A Comparative Study in their interrelation between selected urban and rural areas in the state of West Bengal Shubhra Biswas (Sinha 1 ), Arindam Gupta 2 Abstract:
More informationThe Effects of Economic Factors in Determining the Transition Process in Europe and Central Asia
Macalester College DigitalCommons@Macalester College Award Winning Economics Papers Economics Department 1-1-2010 The Effects of Economic Factors in Determining the Transition Process in Europe and Central
More informationCHAPTER IV COMPARATIVE ANALYSIS OF VARIOUS SERVICES OFFERED IN PUBLIC AND PRIVATE SECTOR BANKS
CHAPTER IV COMPARATIVE ANALYSIS OF VARIOUS SERVICES OFFERED IN PUBLIC AND PRIVATE SECTOR BANKS In this chapter the researcher has carried out a detailed analysis of the primary data collected for this
More informationA Comparative Study of LIC and Private Insurance Companies
A Comparative Study of and Insurance Companies Ms. Pooja Puri Associate Professor/ Research Scholar Deptt. Of Management Studies Amritsar College of Engg. & Tech,ASR Dr. Harinder Singh Gill Professor Deptt.
More informationThe Empirical Analysis of Chinese Listed Enterprises Cross-Border M&A Performance
Open Journal of Business and Management, 2016, 4, 741-750 http://www.scirp.org/journal/ojbm ISSN Online: 2329-3292 ISSN Print: 2329-3284 The Empirical Analysis of Chinese Listed Enterprises Cross-Border
More informationDeterminants of Pension Fund Investment in Nigeria: The Critical Factors
Determinants of Pension Fund Investment in Nigeria: The Critical Factors ADEOTI, Johnson Olabode (Ph.D) Department of Business Administration, University of Ilorin, Ilorin, Nigeria Email: drpastoradeoti@yahoo.com
More informationCHAPTER III RESEARCH METHODOLOGY
CHAPTER III RESEARCH METHODOLOGY RESEARCH METHODOLOGY 3.1 STATEMENT OF PROBLEM Housing loan is one of the emerging portfolio of both Private and Public sector banks. The national housing policy of the
More informationInvestment 3.1 INTRODUCTION. Fixed investment
3 Investment 3.1 INTRODUCTION Investment expenditure includes spending on a large variety of assets. The main distinction is between fixed investment, or fixed capital formation (the purchase of durable
More informationSummary of Statistical Analysis Tools EDAD 5630
Summary of Statistical Analysis Tools EDAD 5630 Test Name Program Used Purpose Steps Main Uses/Applications in Schools Principal Component Analysis SPSS Measure Underlying Constructs Reliability SPSS Measure
More informationDiscriminant Analysys of Default Risk
MPRA Munich Personal RePEc Archive Discriminant Analysys of Default Risk Aker Aragon CARIFIN 2. October 2004 Online at http://mpra.ub.uni-muenchen.de/002/ MPRA Paper No. 002, posted. December 2006 DISCRIMINANT
More informationPhysical Infrastructure Index for Govt. Schools of Odisha: An Analysis using Principal Component Analysis
S.R. No. 003 10/2015/CEFT Physical Infrastructure Index for Govt. Schools of Odisha: An Analysis using Principal Component Analysis 1. Introduction Infrastructure is an important tool for facilitating
More informationStudy on Behavioural Factors Influencing Investment Decision in Real State : A Case Study of Udham Singh Nagar (Uttrakhand)
Study on Behavioural Factors Influencing Investment Decision in Real State : A Case Study of Udham Singh Nagar (Uttrakhand) 150 Kumaun University, Nainital, Uttarakhand, India Abstract Behavioural finance
More informationREGIONAL WORKSHOP ON TRAFFIC FORECASTING AND ECONOMIC PLANNING
International Civil Aviation Organization 27/8/10 WORKING PAPER REGIONAL WORKSHOP ON TRAFFIC FORECASTING AND ECONOMIC PLANNING Cairo 2 to 4 November 2010 Agenda Item 3 a): Forecasting Methodology (Presented
More informationCreditor protection and banking system development in India
Loughborough University Institutional Repository Creditor protection and banking system development in India This item was submitted to Loughborough University's Institutional Repository by the/an author.
More informationMETHODOLOGY FOR DERIVING THE STRI *
OECD EXPERTS MEETING ON THE SERVICES TRADE RESTRICTIVENESS INDEX (STRI) Paris, 2-3 July 2009 METHODOLOGY FOR DERIVING THE STRI * * This paper summarises the methodology for the work of the OECD/TAD on
More informationA Study on Impact of Value Added Tax (VAT) Implementation in India
World Journal of Social Sciences Vol. 2. No. 5. August 2012 Special Issue. Pp. 145 160 A Study on Impact of Value Added Tax (VAT) Implementation in India A. Jayakumar* Value added tax (VAT) is a type of
More informationA MULTIDIMENSIONAL APPROACH OF POVERTY - THE CASE OF ROMANIA
610 A MULTIDIMENSIONAL APPROACH OF POVERTY - THE CASE OF ROMANIA Viorela-Ligia Văidean Lecturer, PhD, Babeș-Bolyai University of Cluj-Napoca Abstract: The purpose of this paper is to analyze the facets
More informationStudying of the Factors Affecting on the Mutual Fund by Individual Investor in Iran, Malaysia, Turkey and US
Modern Applied Science; Vol. 10, No. 9; 2016 ISSN 1913-1844 E-ISSN 1913-1852 Published by Canadian Center of Science and Education Studying of the Factors Affecting on the Mutual Fund by Individual Investor
More informationThe relationship between foreign direct investment and economic growth in Mexico,
The relationship between foreign direct investment and economic growth in Mexico, 1971-1995 Leslie Adames * Abstract The role of foreign direct investment in economic growth has been a major debatable
More informationJacek Prokop a, *, Ewa Baranowska-Prokop b
Available online at www.sciencedirect.com Procedia Economics and Finance 1 ( 2012 ) 321 329 International Conference On Applied Economics (ICOAE) 2012 The efficiency of foreign borrowing: the case of Poland
More informationThe Separate Valuation Relevance of Earnings, Book Value and their Components in Profit and Loss Making Firms: UK Evidence
MPRA Munich Personal RePEc Archive The Separate Valuation Relevance of Earnings, Book Value and their Components in Profit and Loss Making Firms: UK Evidence S Akbar The University of Liverpool 2007 Online
More information1 The Solow Growth Model
1 The Solow Growth Model The Solow growth model is constructed around 3 building blocks: 1. The aggregate production function: = ( ()) which it is assumed to satisfy a series of technical conditions: (a)
More information(iii) Under equal cluster sampling, show that ( ) notations. (d) Attempt any four of the following:
Central University of Rajasthan Department of Statistics M.Sc./M.A. Statistics (Actuarial)-IV Semester End of Semester Examination, May-2012 MSTA 401: Sampling Techniques and Econometric Methods Max. Marks:
More informationStructural Cointegration Analysis of Private and Public Investment
International Journal of Business and Economics, 2002, Vol. 1, No. 1, 59-67 Structural Cointegration Analysis of Private and Public Investment Rosemary Rossiter * Department of Economics, Ohio University,
More informationTHE IMPACT OF BANKING RISKS ON THE CAPITAL OF COMMERCIAL BANKS IN LIBYA
THE IMPACT OF BANKING RISKS ON THE CAPITAL OF COMMERCIAL BANKS IN LIBYA Azeddin ARAB Kastamonu University, Turkey, Institute for Social Sciences, Department of Business Abstract: The objective of this
More informationKeywords Akiake Information criterion, Automobile, Bonus-Malus, Exponential family, Linear regression, Residuals, Scaled deviance. I.
Application of the Generalized Linear Models in Actuarial Framework BY MURWAN H. M. A. SIDDIG School of Mathematics, Faculty of Engineering Physical Science, The University of Manchester, Oxford Road,
More informationFOREIGN INVESTMENT AND EXPORT PERFORMANCE OF INDIAN TEXTILE AND CLOTHING INDUSTRY IN POST QUOTA REGIME
Indian Journal of Economics & Business, Vol. 15, No. 2, (2016) : 385-391 FOREIGN INVESTMENT AND EXPORT PERFORMANCE OF INDIAN TEXTILE AND CLOTHING INDUSTRY IN POST QUOTA REGIME MEETA MATHUR * AND ANITA
More informationDeterminants of Emerging Market Commercial Bank Stock Returns
Determinants of Emerging Market Commercial Bank Stock Returns Eric Girard * School of Business Siena College Loudonville, NY 12211 Phone: 518.783.4133 Email: egirard@siena.edu Amit Sinha 615 College of
More informationAn Analytical Study on Investors Preference towards Mutual Fund Investment: A Study in Dhaka City, Bangladesh
International Journal of Economics and Finance; Vol. 8, No. 10; 2016 ISSN 1916-971X E-ISSN 1916-9728 Published by Canadian Center of Science and Education An Analytical Study on Investors Preference towards
More informationAnalysis of European Union Economy in Terms of GDP Components
Expert Journal of Economic s (2 0 1 3 ) 1, 13-18 2013 Th e Au thor. Publish ed by Sp rint In v estify. Econ omics.exp ertjou rn a ls.com Analysis of European Union Economy in Terms of GDP Components Simona
More informationExpert Systems with Applications
Expert Systems with Applications 40 (2013) 3970 3983 Contents lists available at SciVerse ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa Measuring firm performance
More informationInvestigation of the critical sources of investment finance in Nigeria: a factor analytical approach
AMERICAN JOURNAL OF SCIENTIFIC AND INDUSTRIAL RESEARCH 200, Science Huβ, http://www.scihub.org/ajsir ISSN: 253-649X doi:0.525/ajsir.200..2.309.33 Investigation of the critical sources of investment finance
More informationEvaluating the Impact of the Key Factors on Foreign Direct Investment: A Study Based on Bangladesh Economy
Evaluating the Impact of the Key Factors on Foreign Direct Investment: A Study Based on Bangladesh Economy Author s Details: (1) Abu Bakar Seddeke, Senior Officer, South Bangla Agriculture and Commerce
More informationPREDICTING STRATEGIC AREAS OF A FINANCIAL INTERMEDIATION SERVICES (SIF) COMPANY USING BSC AND PLS
PREDICTING STRATEGIC AREAS OF A FINANCIAL INTERMEDIATION SERVICES (SIF) COMPANY USING BSC AND PLS Ion Stancu 1*, Ion Alexandru Stancu 2, Laura Elly Naghi 3 and Dragoş Bâlteanu 4 1) Bucharest University
More informationCustomer Preference towards Life Insurance Policies
Volume 118 No. 20 2018, 521-535 ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Customer Preference towards Life Insurance Policies Dr. Gunita Arun Chandhok Director, Guru Nanak College,
More informationINTERMEDIATE MACROECONOMICS
INTERMEDIATE MACROECONOMICS LECTURE 5 Douglas Hanley, University of Pittsburgh ENDOGENOUS GROWTH IN THIS LECTURE How does the Solow model perform across countries? Does it match the data we see historically?
More informationDeterminants of Regional Distribution of FDI Inflows across China s Four Regions
International Business Research; Vol. 5, No. 12; 2012 ISSN 1913-9004 E-ISSN 1913-9012 Published by Canadian Center of Science and Education Determinants of Regional Distribution of FDI Inflows across China
More informationRegulatory Governance and Sector Performance for Electricity Distribution in Latin America*
Regulatory Governance and Sector Performance for Electricity Distribution in Latin America* Luis A. ANDRES The World Bank José Luis GUASCH The World Bank and Sebastián LOPEZ AZUMENDI The World Bank July
More informationTHE MULTIVARIATE REGRESSION MODEL OF THE PRICES OF CHINA S URBAN COMMERCIAL RESIDENCE
THE MULTIVARIATE REGRESSION MODEL OF THE PRICES OF CHINA S URBAN COMMERCIAL RESIDENCE Ming Xuan YU, Dan GAO, Han Jue WANG Business school, RENMIN university of China Abstract: There are various factors
More informationRIDGE REGRESSION ANALYSIS ON THE INFLUENTIAL FACTORS OF FDI IN IRAQ. Ali Sadiq Mohommed BAGER 1 Bahr Kadhim MOHAMMED 2 Meshal Harbi ODAH 3
RIDGE REGRESSION ANALYSIS ON THE INFLUENTIAL FACTORS OF FDI IN IRAQ Ali Sadiq Mohommed BAGER 1 Bahr Kadhim MOHAMMED 2 Meshal Harbi ODAH 3 ABSTRACT Foreign direct investment is considered one of the most
More informationOnline Appendix for. Explaining Corporate Capital Structure: Product Markets, Leases, and Asset Similarity. Joshua D.
Online Appendix for Explaining Corporate Capital Structure: Product Markets, Leases, and Asset Similarity Section 1: Data A. Overview of Capital IQ Joshua D. Rauh Amir Sufi Capital IQ (CIQ) is a Standard
More informationInternational Business 7e
International Business 7e by Charles W.L. Hill adapted by R.Helg for LIUC09 McGraw-Hill/Irwin Copyright 2009 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 7 Foreign Direct Investment
More informationLecture 14. Multinational Firms. 2. Dunning's OLI, joint inputs, firm versus plant-level scale economies
Lecture 14 Multinational Firms 1. Review of empirical evidence 2. Dunning's OLI, joint inputs, firm versus plant-level scale economies 3. A model with endogenous multinationals 4. Pattern of trade in goods
More information[D7] PROBABILITY DISTRIBUTION OF OUTSTANDING LIABILITY FROM INDIVIDUAL PAYMENTS DATA Contributed by T S Wright
Faculty and Institute of Actuaries Claims Reserving Manual v.2 (09/1997) Section D7 [D7] PROBABILITY DISTRIBUTION OF OUTSTANDING LIABILITY FROM INDIVIDUAL PAYMENTS DATA Contributed by T S Wright 1. Introduction
More informationComposition of Foreign Capital Inflows and Growth in India: An Empirical Analysis.
Composition of Foreign Capital Inflows and Growth in India: An Empirical Analysis. Author Details: Narender,Research Scholar, Faculty of Management Studies, University of Delhi. Abstract The role of foreign
More informationNur Fitriany Post Graduate Student of Stikubank University Semarang, Indonesia.
EXPLORING THE FACTORS THAT IMPACT THE ACCUMULATION OF BUDGET ABSORPTION IN THE END OF THE FISCAL YEAR 2013: A CASE STUDY IN PEKALONGAN CITY OF CENTRAL JAVA INDONESIA Nur Fitriany Post Graduate Student
More informationDATABASE AND RESEARCH METHODOLOGY
CHAPTER III DATABASE AND RESEARCH METHODOLOGY The nature of the present study Direct Tax Reforms in India: A Comparative Study of Pre and Post-liberalization periods is such that it requires secondary
More information