Research Paper Studying the Effect of Education, Trade, and FDI on Income Inequality in Iran

Management and Administrative Sciences Review Volume: 3, Issue: 4 (Special Issue), Pages: 733-745 (June 2014) Academy of Business & Scientific Research www.absronline.org/journals Research Paper Studying the Effect of Education, Trade, and FDI on Income Inequality in Iran Hossein Khandani Rafsanjani 1 *, Mohammadreza Zeinolabedini 2, Mohammad Mir 3, and Iman Moghadam Far 4 1. Msc studen of Economic Development and Planning, Faculty of Administrative Sciences and Economics, Isfahan University, Isfahan, Iran. 2. Msc studen of Economic Sciences, Faculty of Administrative Sciences and Economics, Isfahan University, Isfahan, Iran. 3. Economy Expert, Chabahar Maritime University, Chabahar, Iran. 4. Msc studen of Economic Sciences, Faculty of Law, Political Science and Economics, Islamic Azad University-South Tehran Branch, Tehran, Iran. This paper examines the impact of foreign direct investment and Trade on income distribution in Iran. In previous studies, the effect of FDI on labor demand and capital spillovers on wage inequality in the Iran has been studied. However, extensive research has not been done exactly with this title. And just some articles titled review of the impact of trade liberalization and globalization on income distribution have been carried out by researchers. According to studies, one of these solutions is paying attention to inflows of foreign capital and workforce training and the effects that these items can have on the distribution of income. So, studying distributional effects of these currents can prove to be crucial. Especially, being aware of the distributional effects of these flows and foreign multinational companies can provide useful information about economic policy to deal with this. The period under study was from 1980 to 2012. The variables examined in this study are based on two models studied Figini and Franco which examines the effect of FDI on income inequality in some countries. The dependent variable of this study is Gini coefficient (a measure of income distribution) and independent variables include: FDI percentage of gross domestic production (GDP) and trade openness indicator, GDP per capita, inflation and education whose impact on income distribution is measured. This study, after assessing the models and implementing stability and detection tests, to avoid spurious regression, it was concluded that the rate of literacy through its effect on the population of skilled labor (educated) over the years have improved the distribution income. Trade Openness and Economic Growth (per capita GDP) have also exacerbated inequality. FDI has not significant and certain effects on income distribution. Kuznets hypothesis is also confirmed In Iran. Keywords: Education, Income inequality, FDI, Openness, Trade *Corresponding author: Hossein Khandani Rafsanjani, MA studen of Economic Development and Planning, Isfahan University, Isfahan, Iran. E-Mail: Khandanihossein69@gmail.com 733

Effect of education, trade, and FDI on income inequality in Iran Research Paper INTRODUCTION In the 1980s, a group of developing countries, especially in Southeast Asian countries, by liberalization of financial markets, were able to attract investment many developing countries. Majority of these assets which were in portfolio form brought significant growth and employment for economists. But the crisis of 1997 that led to capital flight from the country (which is known as the financial crisis), caused changes to occur in the literature of financial markets and capital, and the recommendations of international institutions in developing countries (Fisher, 2003). The openness of capital markets in these countries has caused the governments not to have proper control over domestic capital and by a shock in the international economy, capital flight from these countries began. Thus, despite this financial crisis, most economists (Fisher, Frames 2003, Lal and others 2002) concluded that the most appropriate method to attract foreign investment is by encouraging foreign direct investment. So, the country that can attract FDI by financial liberalization has a higher degree of financial openness than countries that fail to attract such funds. The impact of globalization on how to measure inequality depends completely on, male or female of upper class versus middle class, middle class versus lower class, person the family, or wealth, income or education level. Despite forecasting from opponents of globalization that inequality will increase, if look at the income disparity between those who are well educated and less educated people it is undoubtedly true. But the main reason is not globalization. The main cause of rising inequality, is shifting focus on skill in the third industrial revolution. Unskilled labor value reduces more than before and its reason is technology (Taro, pp. 287, 2007). Inflows of FDI and multinational corporations can have different effects on the economy, the distribution of income and development in country. The entry of foreign investors brings technology and efficiency to the country and is one of the requirements for economic growth and development for each country. But it seems necessary to examine the positive and negative effects to realize them and minimize potential damage. In particular, our country is grappling with income inequality and poverty challenges so knowledge of the distributional effects of these flows and foreign multinational companies can provide useful information on economic policy. In continuation, the theoretical framework is first identified and then a closer look at the subject of this study, previous studies and their results are discussed. And finally the model is presented and the results are estimates and interpreted. Background of Research Paus and Robinson (1990), Barro (2000), Mandel (1954), and Fenzetra & Hanson (1999), Newtel and Heshmati (2006), Egza (2002 ), Ali Abzal Qadiri (2003), Christopher Ehrat (2004), Bhagawti, Dehehjia (1994) and Salvatore (2007), are examples of studies that have investigated the relationship between globalization and income distribution. In this article some results of these studies will be referred to. Eun Lee (2006), has studies the income inequality and globalization in 14 European countries over the period from 1951 to 1992. By using panel data he reached the conclusion is that FDI increases income inequality. There is also an overall picture for growth, equity and globalization in Europe, of course the European growth model differs based on the structure of different countries. Mah (2002), in his paper examines the theories of globalization and income distribution in Korea during the period from 1975 to 1995. According to this study, on distribution of income in Korea, offers weak evidence to support Kuznets. But none of the changes in the degree of openness and foreign direct investment in Korea show significant effects on income distribution. Mundell (1957), in 1957 assumed that the inflow of FDI reduces income inequality in developing countries. Gorg and Figini (1997), have studied globalization, multinational corporations and pay income inequality in Ireland. And reach the conclusion that FDI inflows and the disparities in wages grow in the company, but over time the simple bluecollar workers (unskilled) gain more skills and 734

Manag.Adm. Sci. Rev. Volume: 3, Issue: 4 (SI), Pages: 733-745 become (skilled labor) white-collar, regular income inequality is reduced. Beyer, Rojas and Vergara (1999), examine the relationship between trade reform and the lack of pay equity in Chile and based on Heckscher-Ohlin model they conclude that the relative reduction in price of goods that require a lot of labor leads to an increase in wage inequality in this country. And increasing the education level of workers reduce the wage inequality in this country. And openness of trade increases the wage gap between skilled and unskilled workers. Paus and Robinson (1999) examining the real performance of wages with trade openness, concluded that income inequality is caused by increased openness of trade and FDI, and globalization is one of the factors of income inequality among different segments of society. Feenstra and Hanson (1997), using data from Mexico, while studying foreign direct investment and wage inequality show that FDI inflow into developing countries increases the wages of skilled staff compared to low-skilled workers resulting in income inequality between skilled and unskilled workers. Mahler, Jesuit and DD Roscoe (1999) by investigating the impact of trade and FDI on income inequality in 1991 understood that FDI flows are not important in explaining inequalities. Gorgi and Borhanipour using the collective method of Johansen Juselius and trade intensity index, tried to examine the relationship between globalization and income distribution between the years 1969 to 2004. They have shown that due to the current structure of the Iran, globalization has increased income inequality, and worsen the situation of income distribution. (Ibrahim and Mohammed Borhanipour Georgia, 2007). Ebadi and Shahiky examining trade flows and investment income inequalities in the world using nonparametric method over the period from 1960 to 2000, concluded that global inequality has increased from 1976 onwards, entropy index has almost remained constant 1975 onwards. But the first order Shannon index has increased and it shows that increasing inequality in the share of GDP leads to increased global inequality (Jafar Ebadi and Mohammad Nabi Shahiky Tash, 2006). Keshavarz Haddad has examined globalization using trade liberalization and wage inequality in Iran. Using Stolper Samuelson Theory and panel data method for the period from 2001 to 2003 concludes that the reduction in tariff rates reduces wages of unskilled workers and increases wage of semi-skilled and skilled workers and furthers the wage gap. LITERATURE REVIEW Trade, FDI and employment and wages Liberalization of financial markets means free entry and exit of capital between countries, or in other words, greater flexibility in the external capital account of the balance of payments of a country, means increasing openness of financial transactions. Capital flows between countries in the current economy can be done in two ways, as portfolio capital flows (portfolio investment), which is mainly due to the difference in interest rates between countries and capital flows will be into the country that has a higher interest rate. However, this process depends on many variables: economic, political, etc, including exchange rate depends. Some researchers suggest all these cases with the cost of capital transfers (Eckel 2004, Haufler 1997). Second Trade and capital flows is foreign direct investment where overseas investors use their investments in shapes of physical equipment, land, etc., and the factors mainly affecting absorption of this investment are investment security, profits, market in the host country and the abundance of cheap labor. Impact of financial transactions and capital flows between countries on employment and the labor market, in terms of theoretical and based on labor demand function, which is a function of demand for products derived from the production, is thus; the capital and result of new investments, leading to the growth of productivity and, ultimately, will increase the demand for labor and, on the contrary, the outflow of capital, investment, will decrease the demand for labor. Foreign direct investment, is divided into two categories: Foreign Direct Vertical Investment Rafsanjani et al.

Effect of education, trade, and FDI on income inequality in Iran Research Paper Foreign Direct Horizontal Investment Foreign Direct Vertical Investment (Markusen 1984, Horstman and Markusen 1992, Brainard 1993, Markusen and Veables 2000, January Santis and Astahler 2004), and horizontal foreign direct investment (Helpman 1984, Helpman and Krugman 1985). Horizontal FDI is mainly done among the advanced industrial countries, which have the same factors of production facilities and the balance. In this case, by the creation of new agents in other countries by multinational corporations instead of trade and export of goods or services, transfer agency is transferred to that country. This motion depends on the market of that country and avoiding trade costs. Thus, horizontal foreign direct investment replaces trade. This type of FDI ha a high volume (Brainard 1997 Blonigen 2001, Markusen and Maskas 2000). Vertical FDI which is specifically important is made between countries that are not identical in terms of production facilities and supply factors. Vertical FDI happens when an industry is in need of both skilled labor and unskilled labor. In this case, transferring part of the plant requiring semiskilled and unskilled labor to a country with abundant unskilled labor is inexpensive and will be helpful. Many economists and foreign direct investment introduce the ratio of FDI to GDP of a country as an indicator of "financial openness or financial integration". From among the results and evidence that can be presented in this regard, Rama (2003) study is an example, which reflects the positive impact of FDI on wages in the short term, so that in the short term, an increase of 10 percent in ratio of FDI to GDP (GDP), wages will increase 2 percent, but over time, the effect of FDI on wages gets lower and the effect of trade openness increases over time and can make its effect neutral, so that after about five years, the effect of FDI on wages is not significant. The same study has shown that foreign direct investment, will lead to increased employment of university graduates. And interestingly, by increase of financial openness or the ratio of FDI to GDP growth, the rate of college education has increased as well. Also, Greene, D. Carson and Ariyaachy (2000), found that combining employment and wages before and after financial liberalization in Brazil was in favor of skilled labor and also increased the rate of college education.. METHODOLOGY In this study to evaluate the effects of FDI on wage inequality and income distribution, trade openness indicator are used, which are closely associated with capital flows and entering of multinational companies, particularly, Mandel index in the form of FDI on GDP (with a two year lag). In this study, the Gini index is used as an indicator of income distribution in Iran, which is used because it explicitly shows the changes in income distribution, and data of this indicator is more available. Most important variables influencing income distribution were selected according to the principles and theoretical literature related to the impact of FDI on income distribution in different countries, and studies on topics almost identical to this study and the previous external and internal studies done Variables used in this study are based on two Franco (Chiara Franco, Elisa Gerussi, 2010) in 2013 and Figini and Gorg in 2006 that examined the impact of FDI and trade on income inequality. Most important variables influencing income distribution are: the variable per capita GDP and its square, Mandel Index (FDI / GDP), trade openness index, education (literacy rate) and inflation. After different estimating and reiterating, it was found that there is a nonlinear and meaningless relationship between foreign direct investment and the Gini coefficient so its square was removed from the final model. X M GINI B0 B1LR B2FDI B3FDI ^2 B3( ) GDP B4PGDP B5PGDP^2 B6INF Ui (1) According to this, the model under study can be presented as follows. The basic model: GINI B0 B1LR B2FDI B2TRADE B3PGDP B4PGDP^2 B5INF Ui (2) 736

Manag.Adm. Sci. Rev. Volume: 3, Issue: 4 (SI), Pages: 733-745 Gini: Gini Coefficient. LR: Literacy rate. FDI: foreign direct investment percentage of GDP. TRADE: The sum of imports and exports as percentage of GDP. PGDP: Per capita GDP. INF: Inflation. Ui: Disturbance term Explanation of model variables According to the Kuznets hypothesis, there is an inverted U-shaped relationship between economic development and income distribution, and GDP per capita and GDP per capita variables suggests Kuznets hypothesis. Inequality and income distribution in the early stages of development, increases economic growth. This hypothesis coincides with the arrival of GDP per capita and its squared test was therefore justified in relying on the theoretical and empirical justification for this, in this study, the variables of GDP and its square are included in the model simultaneously. To realize this assumption it is necessary that the sign of the GDP be negative and square root sign be positive. Openness, as one of the indicators reflecting the degree of globalization of economy is inserted in the model. It can be expected that sign of the coefficient of trade openness, considering ideas of Heckscher-Ohlin and Stolper Samuelson, to be negative. Because according to this theory, trade liberalization between countries will lead to a reduction in income inequality (Toan NM, 2004). Several theories about the relationship between education and income distribution effect on income distribution of skilled labor and knowledge are discussed. This means that increasing the skills of the labor force in the globalization process helps improve the distribution of income (Benabou R, 1994). In this study due to the unavailability of data on skilled labor and their skill level, education level indicator variable is used instead. This means that the rate of literacy has been used as education level. Also, people with low income in different communities often suffer more than others from inflation (Colin L & Heng, 2000, p157); as these people with fixed incomes may not be able to react to inflationary conditions, their real income decreases. However, at the same time real property of owners with durable assets (such as land and housing) increases due to inflationary situation and helps unequal distribution of income (Budd EC & Seiders, 2004). So, due to the negative impact of inflation on income distribution, the expected sign of inflation is positive in model. In today's world, with the entry of FDI and multinational companies due to their superior technology compared to foreign companies have often increased the demand for skilled labor relative to the unskilled hence increased the inequality. In the present study, given that capital input does not effect immediately in the same year on economic variables and essentially one or two years is necessary to influence economic variables, the variable was entered with delay of two years. Method: In this study, the Gini index is used as a measure of income inequality. And independent variables whose impact on the Gini coefficient will be studied include foreign direct investment, trade openness index (total trade in% of GDP), inflation, GDP per capita and the literacy rate. In this study to evaluate the effects of FDI on wage inequality and income distribution openness indicators are used. The parameters associated with the flow of capital and multinational companies. Particularly Mandel indicator that is in form of FDI on GDP. In this study, the Gini index is used as an indicator of income distribution in Iran, which is used because it explicitly shows the changes in income distribution, and data of this indicator is more available. Statistical data of Gini coefficient, GDP per capita, literacy rate and inflation are deduced from the Central Bank of Iran (Bank time series), and indexes of openness and foreign direct investment (Mandel index) is derived from the United Nations. Years of research are from 1980 to 2012 due to lack of information in the index before 1980. Model estimates are through Ordinary least squares in Eviews 8 software environment. THE RESEARCH METHOD In this study, the Gini index is used as a measure of income inequality. And independent variables whose impact on the Gini coefficient will be studied include foreign direct investment, trade openness index (total trade in% of GDP), inflation, GDP per capita and the literacy rate. In this study Rafsanjani et al.

Effect of education, trade, and FDI on income inequality in Iran Research Paper to evaluate the effects of FDI on wage inequality and income distribution openness indicators are used. The parameters associated with the flow of capital and multinational companies. Particularly Mandel indicator that is in form of FDI on GDP. In this study, the Gini index is used as an indicator of income distribution in Iran, which is used because it explicitly shows the changes in income distribution, and data of this indicator is more available. Statistical data of Gini coefficient, GDP per capita, literacy rate and inflation are deduced from the Central Bank of Iran (Bank time series), and indexes of openness and foreign direct investment (Mandel index) is derived from the United Nations. Years of research are from 1980 to 2012 due to lack of information in the index before 1980. Model estimates are through Ordinary least squares in Eviews 8 software environment. Testing the Stationary of model variables After reviewing the model variables using Augmented Dickey-Fuller (ADF) test, it was found that all the variables are non- constant and get constant by measuring the time difference (Table 1). On the other hand, if in one model variables were non- constant, instead of first differencing (or higher order differencing), it can be constant and then use the model to estimate the model based on the new variables, in this case spurious regression problem can be solved. [TABLE 1 HERE] Now the question is whether there is another problem. When we use subtracting in estimating coefficients of the model, we lose valuable information regarding the level of variables. Although constancy condition of a regression relationship of variables can be determined by subtracting the difference, by subtracting the firstorder (or higher) we lose the long-term relationship between the time series. (This longterm relationship between two time-series is due to levels of the two variables and not due to the difference of from their first order). Here cointegration helps us to estimate regression without fear of being false based on the estimated time-series variables. Therefore, in this study, extended Engel Granger tests and reviews Stationary of Residual sentences are used. Since the variables of the model have the same degree of accumulated and wastes are constant, without fear of false regressions level of variables can be used (Table2). RESULTS [TABLE 2 HERE] After reviewing the data constancy and having no problem with pseudo-regression, model was estimated using Eviews 8 software and software output are presented in Table 3. Given that the probability of F is below 0.05, validity of regression results is accepted and we can interpret the results.. [TABLE 3 HERE] As observed, the validity of the regression according to F-statistic is accepted. By reviewing R^2 we come to the conclusion that about 0.49% of the variability of the Gini coefficient model by independent variables (FDI, trade openness, GDP per capita, literacy rates and inflation), are explained. To ensure the absence of serial autocorrelation LM test is used and due to its high probability of 0.05 the H0 based on the absence of serial autocorrelation is approved. And Ramsey test, homoskedasticity Test ARCH, and Normality show the normality and homoskedasticity of residuals. Diagnostic Tests: According to the diagnostic tests in Table 4, it can be concluded that the model does not have a problem in terms of the classical assumptions. [TABLE 4 HERE] To Cumulative sum of recursive residuals (CUSUM) and cumulative sum of squares of recursive residuals (CUSUMSQ) offer normalized residue to investigate the structural stability of the parameters in the study period. Hence if the cumulative residual plot is between the two straight lines (95%), the null hypothesis of lack of structural failure is accepted. This is true about the graph of the cumulative squared residuals (Tashkini, 2005). By examining the two graphs it is determined that the two residual diagrams are between the upper and lower bounds thus the null 738

Manag.Adm. Sci. Rev. Volume: 3, Issue: 4 (SI), Pages: 733-745 hypothesis expressing the absence of structural breaks is accepted (Fig 1&2). DISCUSSION [FIG 1&2 HERE] According to the T-statistic, the variables of literacy rate, trade, GDP per capita and its square are significant at the 5 % level. And other variables such as FDI during the study, according to statistics, as of Mahler (Mahler, 1999) and Mah (Mah, 2002), did not have a significant effect on income inequality. But as the theory predicted, literacy rate and GDP per capita is negative and means squared inverse relationship between inequality and these variables. In this study like the results of Bir, Rojas (1999) study, increased levels of education and inequality is reduced. As also discussed in theory, as the population of people with higher education and skills grows, given that the number of unskilled people is reduced and semi-skilled skilled people increases income inequality is thus reduced. As it was previously mentioned, by the entry of foreign direct investment and multinational companies, given that most of these companies have more advanced technology than domestic firms, they need relatively higher skilled workforce. As a result, the demand for skilled labor increases and, consequently, their salary will increase and the demand for unskilled labor will also decline and the effect of overflow of capital will exacerbate this. Because domestic companies will also increase their demand for a more skilled workforce. And as unskilled labor gets more in number the difference in income inequality in society will increase. Regarding the square of per capita GDP, it can be stated that according to Kuznets theory, liberation in the early stages of growth will increase inequality in society and gradually will improve income distribution. Exactly due to the same reason the coefficient of per capita GDP is positive and per capita GDP squared is negative. Hence, according to the statistic T it can be said that this hypothesis has been confirmed in Iran. It is true that the inflation coefficient in the model is not statistically significant but the inflation variable in the model is positive as expected based on the theory. Openness is also one of the indicators reflecting the degree of globalization of economy has entered the models. It can be expected that the sign of coefficient of trade openness is also, considering theorys of Heckscher-Ohlin and Stolper- Samuelson, positive in short term and negative long run. However, many economists believe that this effect depends on the level of development, however, during the years under review (1980-2012) in this research like the studies of Gorgi and Borhani pour (2008), Bayer, Rojas, and Vergara, (1999) in Chile, paus and Robinson (1999) and one study in Vietnam in 2004 is positive, indicating a direct relationship with the dependent variable in the sense that the current structure of the economy due to the increase in the index of income inequality has increased, causing worsening of income distribution. CONCLUSIONS According to the results from software output and estimating foreign direct investment, trade liberalization, per capita GDP, inflation has a positive and direct relationship with the independent variable (Gini coefficient). This means inequality increases by increasing these variables. Literacy rate variables, and per capita GDP squared have a negative relation with the Gini coefficient. This means that the increase in these variables, decreases Gini coefficient and distribution and income inequality reduce and vice versa. With a unit increase in the literacy rate and the square of GDP per capita, the Gini coefficient variable or income inequality decrease respectively 4357.914 and 0.001263 and decreases in other words, the distribution of income gets better. On the other hand, a unit increase in the index of trade liberalization, GDP per capita increases income inequality, and Gini coefficient of 0.000901 and 53.05204 and worsens the distribution of income in country. This study like the study of Mah (2002) in Korea and Mahler (1999) concluded that the flow of FDI does not show a significant effect in explaining inequality and income distribution. The Kuznets hypothesis is also confirmed in Iran. Finally, it is Rafsanjani et al.

Effect of education, trade, and FDI on income inequality in Iran Research Paper recommended that in educational policy and improving educational opportunities the issue of workforce training and general education expenditures be included in the budget and government spending. And special attention be given to them in macroeconomic policy formulation and development of the government to reduce some of the harmful effects of income distribution, capital flow, and globalization. REFERENCES Barro, R. J. 2000. Inequalityand growth in a panel of countries. Journal of Economic Growth, 5(1): 5-32. Benabou, Roland. 1994. Education, Income Distribution, and Growth: The Local Connection, Cambridge, Mass, National Bureau of Economic Research, Working Paper 4798. Beyer, H. & Rojas, P. and Vergara, R. 1999. Trade Liberalization and Wage Inequality Journal of Development Economic, 59: 1: 103-123. Bhagwati, Jagdish; Dehejia, Vivek H. 1994. Free Trade and Wages of the Unskilled- Is Marx Striking Again?, in: Jagdish Bhagwati and Marvin H. Kosters(Eds): Trade and Wages, Washington, D.C:36-75. Blonigen. B.A. 2001. In search of substitution between foreign production and employment: Home country experience in the United States and Sweden, Economic Journal, 107:1787-1797. Brainard, S.L. 1993. A simple theory of multinational corporations and trade with a trade- off between proximity and concentration, NBER Working Paper, n.4269. Brainard, S.L. 1997. An empirical assessment of the proximity concentration trade-off between multinational sales and trade, American Economic Review, 87: 520-540. Blonigen, B. and Slaughter, M. 1999. Foreign affiliate activity and US skill upgrading, NBER Working Paper 7040. Presented at St Andrews RES 2000. Budd, Edward. C & Seiders, David. F, 2004, The Impact of Inflation on the Distribution of Income and Wealth The American Economic Review, Vol.61, pp.128-138. Carsten,Eckel. 2000. Labour Market adjustments to Globalization: unemployment Versus Relative Wages, Department of Economic and Business administration university of passou (Grmany). Colin Lixin XU, Heng-fu ZOU. 2000. Explaining the Changes of Income Distribution in China, China Economic Review, 11: 149-170. Ebadi and Shahiki. 2006. Reviewing current investments and income inequality in the world through a nonparametric method over the period from 1960 to 2000. Economic Research, March-April 1385; - (72): 1-30. EunLee, jong. (2003). Inequality and Globalization in Europe, Journal of Policy Modelling, 28, 791-796. Feenstra, R. And Hanson, G. 1996. Globalization, Outsourcing and Wage Inequality, American Economic Review, 86: 240 45. 740

Manag.Adm. Sci. Rev. Volume: 3, Issue: 4 (SI), Pages: 733-745 Feenstra, R.C., & Hanson, G. 1997. Foreign direct investment and relative wages: evidence from Mexico Journal of International Economics, 42 (3/4): 371-393. Figini, P., & Gorg, H. 2006. Does Foreign Direct Investment Affect Wage Inequality? An Empirical Investigation, IZA Discussion Paper NO. 2336. Figini, P., & Gorg, VH. 1999. Multinational companies and wage inequality in the host country: the case of lerland. Weltwirtschaftlichaftliches Archive, Band 135, Heft 4:135-145. Franco CH, Gerussi E. 2013, Trade, foreign direct investments (FDI) and income inequality: Empirical evidence from transition countries, The Journal of International Trade & Economic Development: An International and Comparative Review, Volume 22, Issue 8, 2013. Gorji and Borhani Poor. 2007. Investigating the relationship between globalization and income distribution between the years 1348 to 1383. Economic Research Forums Spring 1387 - No. 34, 26: 99-124. Hanson, Gorden. 2004. Globalization, labour Income and poverty in Mexicouniversity of California. Haufler, Andreas. 1997. Factor Taxation,Income Distribution and Capital Market Integration,in: Scandinavian Journal of Economics,99:3,425-446. Helpman, E. 1984. A simple theory of trade with multinational corporations, Journal of Political Economy, 92: 451-571. Helpman, E., Krugman, P.R. 1985. Market Structures and Foreign Trade, MIT Press, Cambridge. Helpman, E., Markusen, J.R. 1992. Endogenous market structures in International trade (natura facit saltum), Journal of International Economics, 32:109-129. Heshmati A. 2006. Measurement of a Multidimensional Index of Globalization, Global Economy Journal 6(2), 1-30. Keshavarz, H, G. 2006. Evaluation of globalization and trade liberalization measures of wage inequality. Journal of Economic Studies, No. 76, December: 189-219. Mah, j. 2002. The impact of globalization on income distribution: the Korean experience, journal Applied Economics Letters, Volume 9 (2002), Issue 15: 1007-1009. Mahler, V. A., D. K. Jesuit and D. D. Roscoe. 1999. Exploring the Impact of Trade and Investment on Income Inequality: A Cross-National Sectoral Analysis of the Developed Countries, Comparative Political Studies, 32 (3): 363-395. Markusen, J.R. 1984. Multinationals, multiplant economies, and the gaint from trade, Journal of International Economics, 16: 205-226. Markusen, J.R., Maskus, K.E. 2000. Discriminating among alternative theories of the multinational enterprise, Rafsanjani et al.

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Manag.Adm. Sci. Rev. Volume: 3, Issue: 4 (SI), Pages: 733-745 APPENDIX Table 1. Dickey-Fuller test with 95% confidence level with one time difference variables Dickey-Fuller Statistic 5% Level Stationary Cointegration GINI* -1.099020-1.952066 non-stat - D(GINI)* -7.860696-1.952066 Stat I(1) PGDP -0.862426-2.957110 non-stat - D(PGDP) -3.718668-2.991878 Stat I(1) FDI - 1.975292-2.957110 non-stat - D(FDI) - 5.810574 2.960411- Stat I(1) OPEN -1.726665-2.960411 non-stat - D(OPEN) -3.797593-2.963972 Stat I(1) INF* -1.137017-1.951687 non-stat - D(INF)* -5.978774-2.963972 Stat I(1) LR -2.834613-2.957110 non-stat - D(LR) -3.485851-2.960411 Stat I(1) * None Intercept and Trend Table 2. Test Dickey - Fuller on Residual of model t-statistic Prob Augmented Dickey-Fuller test statistic -5.195078 0.0002 Test critical values: 1% -3.670170 5% -2.963972 10% -2.621007 Rafsanjani et al.

Effect of education, trade, and FDI on income inequality in Iran Research Paper variables Coefficient Table 3. Results of model Std. Error t-statistic Prob c 0.297090 0.063459 4.681573 0.0001 FDI 28914.23 467884.3 0.061798 0.9512 TRADE 0.000901 0.000381 2.360783 0.0267 PERGDP 53.05204 19.81575 2.677266 0.0132 PERGDP^2-4357.914 1665.391-2.616751 0.0151 LR -0.001263 0.000314-4.026643 0.0005 INF 0.055092 0.030733 1.792568 0.0857 Prob(F)= 0.007718 F=3.863782 D.W=1.545333 R^2=0.491339 Test Table 4. Diagnostic tests and satatistics Statistic prob Normality Test LM Test heteroskedasticity Test: ARCH Ramsey test 4.156569 2.122023 1.862188 1.815618 0.1251 0.1437 0.1832 0.1910 744

Manag.Adm. Sci. Rev. Volume: 3, Issue: 4 (SI), Pages: 733-745 15 10 5 0-5 -10-15 90 92 94 96 98 00 02 04 06 08 10 12 CUSUM 5% Significance Fig1. Cumulative sum of recursive residuals test (CUSUM) 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0-0.2-0.4 90 92 94 96 98 00 02 04 06 08 10 12 CUSUM of Squares 5% Significance Fig 2. cumulative sum of squares of recursive residuals test (CUSUMSQ) Rafsanjani et al.