A Survey of the Impact of Globalization on Income Distribution Inequality in Islamic Countries

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1 Available online at International Journal of Advanced Studies in Humanities and Social Science Volume 2, Issue2, 2014: A Survey of the Impact of Globalization on Income Distribution Inequality in Islamic Countries Hassan Farazmand 1, Zahra Omidi 2 1 Associate professor of economy and social sciences department of Shahid Chamran University of Ahvaz, Iran 2 MA of economy and social sciences department of Shahid Chamran University of Ahvaz, Iran Abstract Globalization had different effects in economy. On one hand, globalization has led to the gradual global economic growth and social progress and, one the other hand, it played an important role in increasing the injustice and inequality among countries and environmental degradation and difficult competition were the others results. The purpose of this study is to evaluate the impact of globalization and income inequality in 47 Islamic countries during using panel data method. The results show that globalization leads to increasing inequality of these countries. Also, urbanization had a significant positive effect on income inequality and the human development index (HDI) had negatively significant impact on income inequality of these countries. Key words: Globalization, Income Inequality, Islamic Countries, Panel Data, Classification: JEL: F43 O15 C19.C23 Introduction Globalization is one of the important changes of 90s decade. Each of the researchers and theorists by emphasis on some various dimensions of globalization process presented a special definition. Thus, economy globalization and integration of national economy include major changes in trade, financial affairs and direct foreign investment by multi-national companies. The international trade growth is more rapid than global economy and besides increasing the trade of goods, services trade (e.g. banking, information, etc) are developed considerably. The main concern of economists is the effect of globalization on economy and society. The integration of national economy and improving economic growth as from theoretical view it helps the problem of poverty and increase of democracy, it leads to economic instability and inequality growth in the countries.thus, the present study by panel data attempted to test the effect of globalization on income inequality in 47 countries during empirically. The present study was organized in five sections. After the introduction, second section is dedicated to theoretical basics and the third section deals with the local and international researches. The fourth section is about the study methodology, model explication and its estimation and final section is dedicated to conclusion. Theoretical basics According to O Rourke& Williamson (2000), globalization is the international integration of the goods market. Friedman (2000) believed that globalization is stringent integration of the markets, national governments and 211 Page

2 technologies as it was not observed before and the people, companies and national governments are enabled to have access earlier, rapidly, deeply and cheaper to the surrounding world. Stiglitz (2003) defined globalization as close relation of the countries and nations in the world and it is the result of considerable reduction of transportation and communication expenditures and elimination of artificial barriers in the goods, services, capital, knowledge and people. Income distribution is important as welfare of people depends upon the absolute and relative amount of their income. The income distribution method has direct effect on some issues as social and political stability and poverty and growth. Namely, income distribution is based on two views: Income distribution based on production factors and size distribution of income emphasizing on income of people and it doesn t refer to achieving this income. What is important now as the underlying view in income distribution is development view to income distribution that is based on size distribution of income. In most of the previous studies, this hypothesis is raised that trade liberalization and trade policies are one of the income inequalities reasons. There are three approaches in this case: One neoclassic theory of international trade (Heckscher- Ohlin theory) forming the theoretical basic of these studies. According to this theory, the countries are specialized in production of the goods that have production factors of the goods considerably. Two famous factors based on Heckscher- Ohlin theory and in direct relation with trade, wages and price of the other factors of production are price equality of production factors and Stolper Samuelson theory. The price equality theory of production factors emphasized on the fact that liberal trade make the relative and absolute price of production factors among the countries equal and it leads to the equality of real wage of labor force and real interest rate of capital in two countries. Thus, liberal trade depending upon the price of production factors in trade countries affect income distribution. Stolper Samuelson theory investigated the condition of tariff on income distribution among the production factors and found that import tariff increased the price of production factor in which country, production factor is relatively rare. One of the other effective factors of trade liberalization on income distribution is technology progress approach that to apply it in production, we need skillful labor force and by trade liberalization, based on high degree of complementary relation between skillful workers and capital and high degree of substitution between non-skillful labor force and capital, increases the demand and wage of skilful labor force and reduction of demand and wage of less skillful labor force. Thus, the gap of wage between the very skillful workers and less-skillful workers is increased and inequality in these countries is increased. Three distinct concepts are considered for inequality in globalization: 1- Inter-Country Inequality: When it is said that globalization increased inequality in America or England, this type of inequality is considered. 2- Cross-Country Inequality: It refers to the average difference of income ( or GDP) of per capita of countries. When it is said how globalization affected economic growth rates of countries or they have convergence or no t? This meaning of inequality is considered. 3- World Inequality: It includes two above concepts. This inequality refers to the income inequality among all people in world community ignoring their residence. Empirical Studies Foreign Studies 212 Page

3 Mah J. S (2003) investigated the effect of globalization on Gini coefficient in Korea. In this study by the data during , it was found that despite the predictions, globalization didn t have significant effect on Gini oceffiicnet of Korea. He investigated the effect of income per capita changes, economic freedom ratio and direct foreign investment on coefficient and obtained weak support of Kuznets' hypothesis and estimated return point in Korea economy ranging 4000 to 6000 $.Milanovic (2003) in a study compared the global effects on income distribution in wealthy and poor countries. The results showed that the effect of open doors of economy on income distribution depends upon the initial income level of the countries as in the countries with low income, the rich people benefit the open economy.anderson (2005) investigated the open economy degree on inequality in developing countries and believed that the increase of open economy via affecting the price factors ratio and assets, gender and geographical inequality and income re-distribution can affect inequality. Bhasin (2005) in a study evaluated the impact of financial reform after liberalization on income distribution and poverty in African country, Ghana. Trade liberalization results in decreased fiscal revenue of the government and he used the impact of financial reforms instead. General Equilibrium Model (CGE) model was used and it was shown that the elimination of import and export tariffs on goods and services, accompanied by an increase in foreign aid improves the income distribution of households but elimination of import and export barriers with the reduced foreign aids worsen income distribution. Jakobsson (2006) in a paper investigated that whether increase of inequality is associated with openness of trade. The researcher applied the information of countries during and based on the distribution of production factors among the countries presented some hypotheses based on Heckscher- Ohlin theory and estimated the equations of the hypotheses. The results showed that openness only in 1990 had weak effect on inequality and the results analysis didn t show that openness in developed countries reduced inequality and in developing countries lead to increasing inequality. Tian, Wang & Dayanandan (2008) investigated the impact of globalization in income distribution in China. The present study was done during and it showed that worsening income distribution in this country is not the effect of economy globalization. Bergh & Nilsson (2010) studies the impact of globalization and economic liberalization on income inequality in 80 countries during by normalized data of income distribution and economic liberalization index of Fraser Institute and the results showed that there was a direct relation between trade liberalization and income inequality. Local Studies Njai Meidani (2003) in a study investigated the impact of globalization of economy on growth, employment and income distribution in Iran by co-integration method of Johansen and Juselius. The results of the study showed that globalization process didn t have any effect on Iran economy growth and a few effect on reduction of unemployment rate and this process led to the income inequality and worsening injustice distribution of income in Iran. Keshavarz Hadad Nejatahi Moharami (2006) investigated the effect of globalization on trade liberalization and reduction of tariffs on wage inequality in Iran based on Stolper Samuelson theory and applied micro data in household level during by econometric methods of panel data with limited dependent variable of Tobit found that reduction of tariff rate reduced the wage of non-skillful people and increase of the wage of semi-skillful and skillful people. Najarzade and Mahdavi Rasekh (2010) investigated the effect of globalization on income distribution in D8 member states. In this study, by panel data during , it was shown that trade liberalization during the mentioned period led to the improvement of income distribution and reduction of inequality in the studied countries. Tayebi and Maleki (2011) investigated trade openness and income inequality in Iran and Page

4 main trade partners of Iran by panel data during and Spilimbergo & Londono econometric model was estimated by relative stock of production factors. The results of the study showed that trade openness increased inequality in the countries using a few educated labor force. Model explication, data and study method Model explication and data Based on theoretical and empirical studies of Tian, Wang & Dayanandan (2008) and Bergh & Nilsson (2010) investigated the impact of globalization on income inequality in 47 Islamic countries and the model is explained as: Where Logarithm of Gini coefficient (LGINI), Logarithm of urbanization (LURB), Logarithm of Human Development Index (LHDI) and Logarithm of globalization (LGOL) and i is the studied countries and t is year. The KOF Index of Globalization was introduced in 2002 (Dreher, 2006) and is updated and described in detail in Dreher, et al., (2008). The overall index covers the economic, social and political dimensions of globalization. Here, globalization is conceptualized as a process that erodes national boundaries, integrates national economies, cultures, technologies and governance and produces complex relations of mutual interdependence. The study applied KOF Index of Globalization and the data of these variables were provided from World Bank and and sites du ring (1) Study methodology Panel data method is a method to integrate cross section and time series data. The combination of time series with cross section statistics not only provided useful information to estimate econometric models but also based on the results, planning and policy making can be considered. Model diagnostic tests When, panel data are used, various tests are done to define the good estimation method. The most common tests are Chow and Hausman tests. Chow test is used to test ordinary least square of fixed effects. The assumptions are as: (2) Dummy variable in the fixed effects model. In this test, null hypothesis showed the equal coefficients and intercept in the study data. Thus, rejecting null hypothesis indicated the use of panel data and supporting the null hypothesis indicated the use of integrated ordinary least square. Null hypothesis based on constrained values and H1 is based on unconstrained values. Chow test statistics based on the sum of squared errors in the constrained and unconstrained models is as followings: 214 Page

5 (3) This statistics has distribution F with N-1 and NT-N-K degree of freedom. If in chow test, it is determined that for all cross sections or time in the study, separated intercepts are considered, the estimation with random group effects or time series should be selected. The main hypothesis in fixed effects model is that error component is correlated with explanatory variables while in random effects model, it is assumed that no correlation is between error component and explanatory variables. Hausman test applied Chi-square, if the test statistics is greater than 5%, at significance level 95%, random effects are preferred to fixed effects, otherwise fixed effects are selected. The above process is briefly shown as followings: Figure 1- Diagnostic tests in pooled data CHOW test Fixed effect model Random effect model Pooling all data Hausman test LM test Unit root test The first principle in working with time series and pooled data is the investigation of reliability of the data. If this principle is ignored, spurious regress is creased. Thus, some of the most common unit root tests of pooled data are explained. Levin & Lin (LL) test Levin & Lin (LL) showed that in panel data, unit root test of these data had high power compared to unit root test for each cross section separately. Wu (2000) in his study showed that using unit root tests in panel data as Dickey Fuller test, Augmented Dickey Fuller test (ADF) and Phillips Perron test had low statistical power to unit rot tests of panel data. Levin & Lin (2002) presented unit root test as followings: i,t = i X i, t-1 + t + i + i. t =1,2, N (4) = 1,2, T 215 Page

6 Where N is the number of cross sections, T time, auto-correlated parameter for each section, is time effect, i is fixed coefficient for each cross section and i.t disturbance terms with normal distribution (dependent variable coefficient for all the sections was similar ( i = ) and in model explication, is considered as time series. Based on ADF test, we have: i,t = i X i, t-1 + t + i + ij i, t-j + i. t (5) Where li is lag length. LL test is pooled test of ADF test in time series. This test has high power in cross section heterogeneity and variance Heteroscedasticity of disturbance terms. The hypotheses of this test are as: 0: i =0 (6) 1: i = 0 In these hypotheses, the greater the T,N, the more test statistics is approached to normal with zero mean and variance one. Based on the statistics and short and long-term coefficients of the variables, the test statistics is calculated as: = N (0,1) (7) Where is standard deviation and is standard deviation of long-term normalized equation. Also,, are calculated mean and standard deviation, respectively by Levin and Lin test by lag length and the number of variables and T is the average number of lags in each section. The calculated statistics was compared with the statistics of table of significance level of Levin and Lin. If this statistics is smaller than table statistics, the unit root hypothesis for the variable is not rejected. Im, Pesaran & Shin (IPS) test Unit root test of Im, Pesaran & Shin (IPS) for each cross section presented separate convergence rate. Based on the study of Maddala & wu (1999), the test need balanced panel and similar lag is used for all of them. By varied (as test coefficient) for each cross section, unit root test is explained as: i, t = i + i Y i, t + i, t =1,2, N (8) 216 Page

7 = 1,2, T In this test, null hypothesis indicated the non-convergence of countries (unit root) and H1 showed the stationary of at least one panel member. H 0 : 1= 2 = N = =1 (9) H 1 : 1 = 2 = N = 1 Instead of pooling data, a separate unit root test for each cross section was used. Thus, for each section, a t-statistics and mean and variance were presented. T statistics value is defined as mean of Augmented Dickey Fuller test (ADF): = (10) Unit root IPS test is as followings: = N(0,1) (11) After the calculation of this statistics, if the calculated value is smaller than table statistics, null hypothesis regarding the unit root is not rejected. IPS test hypotheses are unit root test hypotheses with the difference that in H1 despite LL test, it is assumed that cross section data have no equal value Co-integration tests The investigation of co-integration of variables is important in pooled data. To do co-integration test of pooled data, Kao (1999) and Pedroni (1999) after estimation of the long-term effect of the variables as it is done in time series and cross section data, the following statistics are applied for co-integration: (12) (13) In the above effect, γ is long-term error regression coefficient on error lag of model estimation by pooled method (eit) is as followings: (14) 217 Page

8 Parameter N in statistics and indicated the cross sections and is standard impact coefficient (14). The extracted statistics both had normal distribution with zero mean and variance one. The hypotheses of co-integration test of pooled data are as followings: (1 Null hypothesis showed the non co-integration between the variables and H1 showed co-integration of the variables in all the cross sections. Kao (1999) presented co-integration test of Augmented Dickey Fuller test with the assumption that co-integration vectors are homogenous in each section as: (16) Where e it is the long-term effect estimation with pooled data method and P is the number of lags in ADF test and its size depends upon elimination of autocorrelation among the error components. Also, is difference coefficient of test lags and is estimated equation error. In other words, in this test like and tests are done after long-term effect, calculation estimation error and by effect above ADF test. The assumptions of this test are like the assumptions of and tests and the test statistics had t standard distribution (Pedroni, 1999). In other words, after effect calculation (16), significance of γ coefficient is tested by distribution table of standard t. Model estimation The results of stationary tests of model variables To investigate the reliability of the variables, Lin and Levin and Im, Pesaran & Shin methods were used. The results of the tests in two cases with intercept and with intercept and trend are shown in the following table. Table 1- The results of stationary tests of model L I Variable Level, intercept Level, intercept & Level, intercept Level, intercept & trend trend statistic Prob. statistic Prob. Statistic Prob. statistic Prob. Lgini Page

9 Lgol Lhdi Lurb Source. Study calculations Based on the above table, all the variables of Lin and Levin method with trend or without trend are stationary but by Im, Pesaran & Shin methods, except logarithm of gini coefficient that is stationary in trend and without trend cases, other variables are not stationary. Thus, it is probable that spurious regression is considered. To avoid this problem, co-integration between the model variables should be investigated. The results of co-integration test In stationary tests, co-integration of the model is investigated. As it was said, in this study, Cao test (1991) is used for co-integration. Null hypothesis of this test indicated non co-integration method. The results of the test showed that non cointegration hypothesis at significance level 5% is rejected and the variables are co-integrated in long-run. Table 2- The results of Kao test Prob. t-statistic Kao ADF Source. Study calculations As there is long-term relation between the variables of the study, the model par ammeters are estimated. The first stage of model estimation is Chow test: Chow test Chow test is used to determine fixed effect model in total data pooling. The results of this test are shown in Table 3. As is shown, the results indicated rejection of null hypothesis and using panel method. Table 3- The results of Chow test Effect test statistic d.f Prob. Cross-section F (46,304) Cross-section chi-square Source. Study calculations 219 Page

10 Hausman test To determine using fixed effect model vs. random effect model, Hausman test is used. This test is formed based on the association or the lack of association between estimated regression error and independent variables. If there is such association, fixed effect model, and if there is no association, the random effect model is used. In this test, null hypothesis showed the lack of association between independent variables of estimation error and H1 showed the association. Thus, based on the results of the following table, null hypothesis is rejected and the mode is fixed effect. Table 4- The results of Hausman test Prob. Statistic d.f Effect test Cross-section Random Source. Study calculations The results of estimation by fixed effects method Based on Table 5, it can be said that estimated model had good fit and main criteria of regression acceptance as adjusted coefficient of determination, expectation of coefficients and significance of single and total regression showed the suitable fitted regression. Thus, based on results of analysis, do economic analysis. As is shown, the results showed that all the coefficients are significant. Increase of 1% in globalization increased Gini coefficient as It means that globalization worsened the income distribution in Islamic countries. As it was expected, the results of the study showed that by the increase of one percent in human development index, Gini coefficient is reduced as By the increase of 1% in urbanization value of Gini coefficient is increased as R2 showed that explanatory power of explanatory variables is high and independent variables explained 91% of the changes of Gini coefficient. Independent variables Table 5- The estimation results LGINI Coefficients Statistic P value LGOL LHDI LURB C Statistic F P- value R Source. Study calculations 220 Page

11 Conclusion The present study investigated the effect of globalization in inequality distribution in 47 selected Islamic countries during by panel data technique for model estimation. To do this, by KOF globalization index that is a new approach to globalization, they study continued. The results of model estimation showed that by the 1% increase of globalization, Gini coefficient is increased as 0.15, it means that globalization worsened income inequality distribution in the studied countries but the increase of human development index reduced income inequality but one percent increase in urbanization index increased income inequality of these countries as 0.68%. References 1. AndersoE,."Openness and Inequality in Developing Countries: A Review of Theory and Recent Evidence", Journal ofworld Development, 33(7), pp , (2005). 2. Baltagi Badi H,."Econometric analysis of panel data", Third edition, ISBN , (2005). 3. Bergh A,. NilssonT,."Do Liberalization and Globalization Increase Income Inequality?", European Journal ofpolitical Economy, 26(4), PP ,(2010). 4. BhasianV.K,."Trade liberalization Remittances, poverty and income Distribution of Households in Ghana", Econometric modeling conference, (2005). 5. Chow G.C., "Tests of equality between sets of coefficients in two linear regressions", Econometrica28, , Dreher A,."Does Globalization Affect Growth? Evidence from a new Index of Globalization", Applied Economics 38, 10: , (2006). 7. DreherA,. GastonN,. Martens P",.Measuring Globalization - Gauging its Consequence", New York: Springer,(2008). 8. Hausman J.A,. D Wise,."Attrition bias in experimental and panel data: The Gary income maintenance experiment", Econometrica47, , JakobssonA,."Trade Openness and IncomeInequality", Bachelor Thesis, Supervisors: Yves Bourdet, JoakimGulstrand, June Kao C., "Spurious regression and residual-based tests for co-integration in panel data", Journal ofeconometrics 90, 1 44, Keshavarz Hadad, Gh; Nejathi Moharami, Z. Trade liberalization and wage inequality in Iran during Economic research journal. No. 76: Levin A., C.F. Lin,. C Chu,."Unit root test in panel data: Asymptotic and finite sample properties", Journal of Econometrics 108, 1 25, Maddala G.S,. S.Wu,."A comparative study of unit root tests with panel data and a new simple test", Oxford Bulletin of Economics and Statistics 61, , MahJai.S,."A Note on Globalization and Income Distribution: the Case of Korea", ,Journal of Asian Economics. No, 14. PP ,(2003). 15. MilanovicB,."Can We Discern the Effect of Globalization on Income Distribution? Evidence from Household Surveys",Development Research Group, World Bank, Washington, (2003). 16. Najarzade, R; Mahdavi, Rasekh. The study of the impact of globalization on income distribution in D8 member states. Journal of trade research. No. 54: Najimeidani, A. The impact of globalization on growth, employment and income distribution in Iran. PhD thesis of Tarbiat Modares University. Human Sciences department Page

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