Globalization
- 1. Globalization and Economic Inequality between countries Furmansky Lev The Impact of globalization on national economy - 13401 Prof. Ben-David, Nissim July 2014 By
- 2. 1 INTRODUCTION There is a common opinion that Globalization is one of the main reasons for financial inequality between the countries and abuse of “poor South” by “rich North”. Indeed, the industrial revolution and colonialism brought about great economic divergence between the countries. The large extent, and the gap can be explained by the huge differences between countries in the world in terms of geographical location, size of territory, population and population density, climatic conditions, mineral resources, political system and the consequencesofthe colonial policy and the slave trade.Betweenthe beginning of the 19th century and the middle of the 20th, the average percapita income gap betweenthe richer, more industrial “North” and the less developed “South” rose from a factor of 3 or 4 to a factor of 20 or more (Milanovic, 2012). However with the economical liberalization in the majority of the countries, the globalization process posetively affected in closure this gap. For the past two decades,however, showing per capita income in emerging and developing economies takenas,a whole has grown almost three times asfastasin advanced economies (Dervis, September 2012). The hypothesis called convergence in economics also called catch –up effect while poor per capita income less developed countries tend to grow at faster rates than richer economies. This process could not exist without globalization. According to neoclassical theory, which dates back to the views of modern economy liberals, competitive market itself tends to general equilibrium and stability. Thus, Globalization could be explained as a natural, inevitable process that eliminating economic and trade barriers tends to equalize economies acrossthe globe by sharing acquired factors like technology, labor qualification, knowledge, etc. By using those factors, economic globalization, especially enhanced by trade liberalization and financial deregulation, hasbrought national economies ever closer together. Emerging and developing economy countries that made steps forward to economic liberalization succeeded to absorb new technology and know – how and today attract capital and participate in global markets. However, with the convergence of per capita income in economies across the borders, high inequality within countries and a wider gap between the worlds of richest and poorest citizen appear both in developing and developed economies. Income has become concentrated in the very top of most of the countries. Factors like the nature of technological change, the increased skill premium, the huge expansion of the global market and the associated winner-take-all characteristics of many markets, the mobility of capital in contrast to the relative immobility of labor, particularly unskilled labor, and a declining influence of unions - have all led to increased income concentration at the very top. Certainly, convergence resulting from the rapid catch-up growth affecting a large majority in the emerging and developing countries is giving rise to a rapidly growing global middle class. However,in many countries the income of 1% of top earners became considerably higher than a decade ago. In the United States, the share of the top 1 percent has close to tripled over the past three decades, now accounting for about 20 percent of total U.S. income (Alvaredo F. T., 2012). Today income inequality is on the rise, and so is public pressure for governments to do something about it through their tax and spending policies. From the other hand, fiscal constraints are tight and raising economic growth a priority. Many specialist in financial organizations are trying to find the right recipe how to decrease inequality without affecting the growth. How governments should actin order to achieve
- 3. 2 both factors. Some experts convinced that the right fiscal redistribution could reduce inequality and even lead to the higher growth. In this work, I have tried to find communality between GINI coefficient and number of factors that selected based on five assays dealing with that problem. The main motivation was to find parameters that commutatively affect the per capita income inequality in countries. LITERATURE REVIEW A large literature written today dealing with income inequality problem in the world. Development and economic growth of emerging countries, especially from Asia, made inequality between the countries topics in literature less relevant. International economic organizations, specialists, researchers, critics focused toady on a new problem. Income gaps data between rich and poor population in the countries became a main website topic of the largest economic organizations. In this work, I have picked five articles and reports that lead me during learning this problem. Follow, is their short description that I tried to arrange in logical order from global trends in economics through the statistic data to the researchers report problem solving suggestions. “CONVERGENCE,INTERDEPENDENCEAND DIVERGENCE” (DERVIS,SEPTEMBER 2012) This paper adds to the existing body of economic research that discusses international differences in income inequality. Kemal DerviŞ, Vice President and Director of Global Economy and Development at the Brookings Institution In his article “Convergence, Interdependence and Divergence” (Dervis, September 2012) presenting three fundamental trends that in his view characterize the world economy today. The calculations and conclusions the author made based on IMF World Economic Outlook 2012. First Dervis shows that since 1990 the pace of per capita income growth in emerging and developing economies hasacceleratedin a sustainable manner and is substantially above that in advancedeconomies. The author call this trend - “New Convergence”. He remains that in the past century due to industrial revolution and colonialism there was a great divergence between North and South. However, Dervis claim that the world entered to a new age of convergence where the emerging and developing countries catch-up the developed once. He presents that despite the 1997–98 Asian crisis, for the past two decades, per capita income in emerging and developing economies taken as a whole has grown almost three times as fast as in advanced economies. The trend growth rates calculated using a statistical technique and the Hodrick-Prescott filter presented in the work in charts. Kemal Dervis explains this new convergence process by three developments: The first trend is globalization – through strengthened trade links and rising foreign direct investment—facilitates catch-upgrowth aslatecomersimport and adapt knowhow and technology. Second, the demographic transition of most emerging and many developing economies that accompanied slower population growth supported greater capital intensity and faster per capita
- 4. 3 growth. In addition, author presentsthe advantage of the younger economically active population in emerging economies whereas this ratio is much worse in Europe and Japan for example. A third significant cause of convergence is the higherproportion ofincome investedby emerging and developing countries — 27.0 percentof GDP over the past decade comparedwith 20.5 percent in advanced economies. Per Dervis in addition to increase in productivity of labor, it can also increase total factor productivity by incorporating newknowledge and production techniques and facilitate transition from low-productivity sectors such as agriculture to high-productivity sectors such as manufacturing, which accelerates catch-up growth. Second fundamental trend in the world economy author sees in cyclical interdependence. Despite that emerging and developing economies long-term trend growth rates have delinked from those of advanced economies over the past 20 years, this has not led cyclical movements around the trend to delink. Dervis shows that in example of crisis in early 2008 when it seemed that emerging markets would grow rapidly regardless of what happened in the United States and Europe. However, the panic in western markets affected strait the Asian market. Dervis shows that share of trade in global economic activity has increased,the changesin demand in one country resulting from macroeconomic developments in another. In addition, there is a channel that works through increasingly global, huge, and complex financial markets. While there is the impact of policies in one country on another because of the large volume of trade and financial linkages in today’s economy between the countries. Third trend that actually slightly change the focus of my work from “inequality between countries” to “inequality within countries”, is divergence. Dervis says that with the convergence of per capita incomes in emerging and developing economies there is a rise in gap between the richest and poorest citizens across the entire world. Certainly, convergence resulting from the rapid catch-up growth affecting a large majority in the emerging and developing countries is giving rise to a rapidly growing global middle class. However, there is a trend that income has become concentrated at the very top in many countries. An especially author distinguish the dramatic divergence between the top 1 percent and the rest is a new reality. This situation clear in USA and European countries as well as in China and India. Dervis uses The World Top Incomes Database (Alvaredo and others, 2012) to show that shift. DIVIDED WE STAND:WHY INEQUALITY KEEPS RISING © OECD (OECD,2011) The second text source that reviewed is the 2011 OECD overview report of two decades data research works dealing with income inequality problem in OECD countries. The overview summarizes the key findings of the analytical chaptersofthis report.It sketchesa brief portrait of increasing income inequality in OECD countries and the potential driving forces behind it. It reviews changes in these driving forces and examines their relative impact on inequality. In particular, it looks at the role of globalization and technological changes, regulatory reforms in labor and product markets, changing household structures, and changes in tax and benefit regulations. It assesses what governments can do about increasing inequality and concludes by examining possible specific policy avenues.
- 5. 4 In a big picture, the review showing unequal growth in household income in OECD countries when 10% of the richest households grew much faster than those of the poorest 10%. The average GINI coefficient stood at an average of 0.29 in OECD countries in the mid-1980s. By the late 2000s, however, it had increased by almost 10% to 0.316. Significantly, it rose in 17 of the 22 OECD countries. Globalization impact on wage inequality. Increases in household income inequality, have been largely driven by changes in the distribution of wages and salaries, which account for 75% of household incomes among working-age adults. This was due to both growing earnings’ sharesat the top and declining sharesatthe bottom. Earnersin the top 10% have beenleaving the middle earnersbehind more rapidly than the lowest earnershave beendrifting away from the middle. Over the past decades,OECD countries underwent significant structural changes, driven by their closer integration into the global economy and to rapid technological progress. Those globalization changes contained the share of global trade in goods and services, rise in foreign direct investments and technological share. In that background, OECD countries have seen substantial growth in the number of multinational corporations as well as their overseas operations, which reflects greater offshore outsourcing of their activities. These changes often brought highly skilled workers greater rewards than low-skilled ones and thus affected the way earnings from work were distributed. The rising gap between the earnings of the highly skilled and those of the low-skilled springs from several factors. First, a rapid rise in the integration of trade and financial markets generated a relative shift in labor demand in favor of highly skilled workers. Second, technological progress shifted production technologies in both industries and services in favor of skilled labor. The impact of regulatory reforms In the two decades, most OECD countries carried out regulatory reforms to strengthen competition in the markets for goods and services and to make labor markets more adaptable. For example, loose in employment protection legalization, relax in anti-competitive product-market regulations, changes in wage setting mechanisms and the share of union members among workers. A number of countries cut unemployment benefit replacement rates and, in an attempt to promote employment among low- skilled workers, some reduced taxes on labor for low-income workers. On the one hand, past empirical evidence points to the significant positive impact of reforms on employment levels. On the other hand, most policy and institutional reforms also contributed to widening wage disparities, as more low-paid people entered employment and the highly skilled reaped more benefits from a more dynamic economy.
- 6. 5 Household structure impact. This report suggests that household structure changes played a much more modest part in rising inequality than changes related exclusively to the labor market. A trend toward smaller households is therefore likely to increase earnings and income inequality. There are more single-headed households with and without children today than ever before. Smaller households are less able to benefit from the savings associated with pooling resources and sharing expenditures. The analysis suggests that the increase in men’s earnings disparities was the main factor driving household earnings inequality. Increased employment opportunities for women, however, worked in the opposite direction in all countries, contributing to a more equal distribution of household earnings. There was also in all countries a rise in the phenomenon known as “assortative mating”, that is to say people with higher earnings having their spouses in the same earnings bracket. That explains that in couple households, the wives of top earners were those whose employment rates increased the most. The impact of capital and self-employment income A much-debated driver of income inequality in OECD countries is the distribution of incomes from capital, property, investment and savings, and private transfers. Such distribution has grown more unequal over the past two decades. Self-employment can also have an impact on overall earnings inequality because the income it generates is much more unevenly distributed than wages and salaries. Taxes and benefit systems become less effective Finally, income taxes and cash transfers became less effective in reducing high levels of market income inequality in half of OECD countries, particularly during the late 1990s and early 2000s. Public cash transfers, as well as income taxes and social security contributions, played a major role in all OECD countries in reducing market-income inequality. Together, they estimated to reduce inequality among the working-age population. However, while market income inequality continued to rise after the mid-1990s, much of the stabilizing effect of taxes and benefits on household income inequality declined. Three instruments that government uses for the cash redistribution reviewed in this report. • Benefits - Changes in the numbers of unemployed and reforms to benefit eligibility criteria appear to have been particularly important factors, whereas benefit targeting seems to have played less of a role. • Income taxes - trends towards lower income taxes,on the one hand, and progressive taxation, on the other, had opposite effects on redistribution and partly cancelled each other out. • Social security contributions - because of their relatively flat-rate structure they redistributed very little. As a result, social contributions did not play a major role in altering redistribution directly, despite their growing importance as a revenue source.
- 7. 6 As a result, tax-benefit policies offset some of the large increases in market-income inequality, although they appear to have become less effective at doing so since the mid-1990s. THE TOP 1 PERCENT IN INTERNATIONAL AND HISTORICAL PERSPECTIVE (ALVAREDOF. A., 2013) The third article deals with one more trend in modern economies. The sharp increase in the top 1 percent income share in many countries drawing much public attention in recent years. It has had a noticeable effecton overall income inequality and hasrepresenteda challenge to the economics profession to explain it. Based on pre-tax and pre-transfer market income per family reported on tax returns, the share of total annual income received by the top 1 percent has more than doubled from 9 percent in 1976 to 20 percent in 2011. In this article, the authors highlight four main factors. The first is the impact of tax policy, which has varied over time and differs across countries. The second factor is a richer view of the labor market, where the authors contrast the standard supply side model with one where pay is determined by bargaining and the reactions to top rate cuts. The third factor is capital income that raised back to the level as it was in the beginning of the century. The fourth, little investigated, element is the correlation between earned income and capital income, which has substantially increased in recent decades. Taxes and Top Shares In this part of the article the authors shows graphically the changes in top income tax rate in leading economics countries during the twenties century. The inverted U-shape chart shows the very high tax rates in the middle of the century and it decrease in the last ears. The authors shows that there is a strong correlation between the reductions in top tax rates and the increases in top 1 percent pre-tax income shares. This negative correlation explained in the article in a variety of possible ways. First, when top tax rates declined, those with high incomes had less reason to seek out tax avoidance strategies. This argument has more recently been used to deny that any real increase in income concentration actually took place - that it is a pure statistical artifact. Additional explanation also based on behavior change caused by top tax rate cut. In previous studies proved that, lower tax rates stimulate economic activity among top earners involving more work, greater entrepreneurship, and the like. In this scenario, lower top tax rates would lead to more economic activity by the rich and hence economic growth. In the end authors presents number of article dealing with optimal income tax analysis and arguing that the optimal top income tax rate should stands on 0.5 percent. Richer Models of Pay Determination The extent to which top earners exercised bargaining power may have interacted with the changes in the tax system. When top marginal tax rates were very high, the net reward to a highly paid executive for bargaining for more compensation was modest. When top marginal tax rates fell, high earners started bargaining more aggressively to increase their compensation. Another scenario in which the
- 8. 7 improved information and communications technology and globalization were increasing the demand for high-skilled labor and the deregulation of finance and of other industries was both raising the demand for skill at the top and changing the rules under which compensation had been calculated in the past. Another model says that managers are concerned with their remuneration. Where top tax rates were high, top corporate executives may have concentrated on securing a larger share of the profits, in which case increases in remuneration, or bonuses, may have come at the expense of employment and growth. Capital Income and Inheritance In the article, “capital income” defined as rents, dividends, interest, and realized capital gains. The decline of top capital incomes is the main driver of the falls in top income shares that occurred in many countries early in the twentieth century. The authors says that today many element of capital income is not covered in the income tax data. For example, in times past, a number of income tax systems included imputed rents of homeowners in the income tax base, but today imputed rents are typically excluded. The taxation of income and wealth transfers can cause the share of top wealth- holders to fall. Conclusions Since the increase in top share have seen not in all high-income countries, the authors hence not to blame on that forces common to advanced countries, like the impact of new technologies and globalization on the supply and demand for skills. The author suggestion is to focus on solution of four highlighted in the article factors that have contributed to the growing income shares at the very top of the income distribution REDISTRIBUTION,INEQUALITY,AND GROWTH (JONATHAND. OSTRY, 2014) A key message of this study is that design of Fiscal policy's matters and can significantly reduce income inequality, as it seems from advanced economies practice. At the same time, there has been a diversity of experience: if poorly designed or pushed too far, redistributive policies have proven to be distortive. However, experience also shows that some redistributive fiscal policies can in fact help improve efficiency and support growth, such as those that enhance the human capital of low-income households. This suggests that the devil is in the details. First, the authors looking for some historical macroeconomic evidence say about the relationship between inequality, redistribution, and growth proofing that reduction on inequality have the negative impact on growth. Facing on that they checked different specific elements of redistributive fiscal policies in different country contexts that proofs that attempting to draw lessons about the most efficient ways to redistribute. In order to adequate econometric specification and a correct measurement of the different effects, authors build a framework that simultaneously analyzes the effects of redistributive transfers and
- 9. 8 inequality on growth. Simultaneous analysis required cross-country data on both inequality before taxes and transfers (so-called “market inequality”) and inequality after taxes and transfers (“net inequality”). To calculate redistributive transfers—defined as the difference between the Gini coefficient for market and for net inequality the authors used cross-country dataset (Solt, 2009) that carefully distinguishes net from market inequality. Growth rate and it duration was analyzed over five-year principal and the findings can be summarized as follows. First, societies that are more unequal tend to redistribute more. Among OECD countries, more inequality tends to be associated on average roughly one-for-one with higher redistribution, such that there is almost no overall correlation between net and market inequality. While the effect is weaker in non-OECD countries, it is nevertheless still present. It is thus important to distinguish between market and net inequality in trying to understand the growth-inequality nexus and to separately control for redistribution in growth-inequality work. Inequality continues to be a robust and powerful determinant both of the pace of medium-term growth and of the duration of growth spells, even controlling for the size of redistributive transfers. It would still be a mistake to focus on growth and let inequality take care of itself, not only because inequality may be ethically undesirable but also because the resulting growth may be low and unsustainable. Second, lower net inequality seems to drive faster and more durable growth for a given level of redistribution. OECD countries example shows that using right redistribution resulting lower net inequality and durable growth. Third, there is surprisingly little evidence for the growth-destroying effects of fiscal redistribution at a macroeconomic level authors do find some mixed evidence that very large redistributions may have direct negative effects on growth duration, such that the overall effect—including the positive effect on growth through lower inequality—may be roughly growth-neutral. But for non-extreme redistributions, there is no evidence of any adverse direct effect. The average redistribution, and the associated reduction in inequality, is thus associated with higher and more durable growth. ON CULTURE AND INCOME INEQUALITY:REGRESSION ANALYSIS OF HOFSTEDE’S INTERNATIONAL CULTURAL DIMENSIONS AND THE GINI COEFFICIENT.(MALINOSKI, 2012) The idea of the last fifth article, which was reviewed, is to find the income inequality root cause in national culture or people behavior that possibly affecting the gap between the rich and poor citizens in the country. The author was inspired Guiso et al. article (Guiso, 2006) that argue that economists have historically been reluctant to view culture as a determinant of economic occurrences. The reviewed study explores the relationship between international cultures and income inequality using data from 75 countries. The dependent variable, income inequality, was the most reset GINI Coefficient. The independent variable was the dimensions taken from Hofstede’s Dimensions of Culture learning (Hofstede, 1980) in which in 1980 he observed quantifiable differences among
- 10. 9 cultures at a group level using a set of four intangible dimensions that later raised to five. The dimensions are Power distance (PDI) measures the extent to which power is distributed, and describes the level of hierarchy and regard for authority in a particular society. Individualism (IND) measures the degree to which individuals base their actions on self-interest as opposed to the interests of a collective group. Masculinity (MAS) measures the tendency of a culture to favor aggressive “masculine” values, which emphasize competition and ambition as opposed to more caring “feminine” values, which emphasize the quality of life. Uncertainty avoidance (UAI) measures a society’s tolerance for ambiguity and risk. Finally, long-term orientation (LTO) describes a society’s preference for short-term fulfillment of social obligations rather than long-term values like perseverance. The author used regression analysis framework to determine whether a relationship exists between income inequality and Hofstede’s dimensions. Seven different regression models were tested to observe the effects of Hofstede’s Dimensions on income inequality. From the analysis the author concluded that: 1. There is moderate positive relationship between power distance scores and GINI coefficients. That observation suggests that more hierarchical societies have more income inequality. 2. There is a distinct negative relationship between individualism and the GINI coefficient. This could be because more collectivistic societies may be more susceptible to productivity inefficiencies from group freeriding. Citizens in more individualistic societies may be more inclined to work harder to ensure a sufficient standard of living for themselves. 3. It is doubtful that there is a relationship between masculinity scores and income inequality due to very weak but positive result. 4. Also slight negative relationship between uncertainty avoidance scores and income inequality. 5. There is moderately strong negative relationship between long-term orientation scores and the GINI coefficient. That suggest that cultures that are “future-focused” may save more for the future, decreasing income gaps among citizens. The conclusion of the study suggests that cultures that exhibit either collectivist or short-term orientation tendencies may suffer from higher levels of income inequality. Author shows the example by box plotting the data by region as Latin American nations that have individualism and long-term orientation average scores below the total mean and its correlation with higher that world average GINI coefficient. The author assume that these findings have important implications for both policymakers and multinational corporations. It is seemingly difficult for one or two entities to reprogram the cultural norms of a country in order to foster a more equal economic climate. However, policymakers and employers can try to implement several incentives to achieve a more self-sufficient and future-focused society. Employers can start to offer promotions and pay raises for individua l performance reviews, rather than group performance reviews. This will incentivize individuals to become more self-sufficient, reducing the inefficiencies of group performance. Policymakers can also push its citizens to be more future-focused by stressing the need to save and highlighting the benefits of strategic human capital investments. If a society realizes the long-term benefits of human capital investments like education, it may lead to a more equal economic climate. Finally, multinational corporations can use the relationship between culture and income inequality to offer a greater number of products in different pricing tiers for individuals in highly unequal
- 11. 10 countries. This would capture a greater share of the market in a particular country. Similarly, they can consolidate tiers in more equal countries. MODEL AND FRAMEWORK After a review of relevant literature, the main five of them previously described, I was able to decide which options, in my opinion, may have a correlation and impact on income inequality. The World Bank (World Bank Open Data, 2014) database was used as data source, with the exception of OECD countries GINI coefficient that was pulled from OECD database. The dependent variable, income inequality, is measured using the most recent GINI coefficient available for each nation in the data set. Since GINI coefficients are not reported on an annual basis, the most recent GINI coefficient available for each country has been selected. Unfortunately, number of independent variables, which were planned to pick for the analysis, were excluded because of scarce and unreliable data available. However, eight parameters that contained full data for 93 countries updated to year 2012 has been picked for analysis. THE INDEPENDENT VARIABLES EXPLAIN: Gross National Income (GNI) per capita at purchasing power parity (PPP). The PPP version of GNI was picked in order to use the fair and constant variable for cross-country compare of net income. Tax Rate (%GDP) and Health expenditure, total (% of GDP) was picked in order to check the redistribution hypothesis suggested by Jonathan D. Ostry (Jonathan D. Ostry, 2014). Unemployment (% of labor force) were picked as to variables after idea to split it by gender in order to check the findings from OECD report (OECD, 2011) that female employment reduces the income inequality when mail employment works in opposite direction. FDI income (BoP) as per first article (Dervis, September 2012) foreign investments should also positively affect in gap closure. International Trade in services (% of GDP) was picked in order to check the globalization impact in trade between the countries. Finally Population Growth (annual %) was picked as basic parameter due to its high reliability and wide effect on other parameters. I use a regression analysis framework to determine whether a relationship exists between the GINI and selected variable as described above. In addition, control parameter “D” indicating development has been added in order to check the impact of development in the countries. Half of the countries with lower GNI per capita received 0 value for D when 2nd half received 1. After that all the independent values has been multiplied to “D” in order to check the development impact on the dependent variable. Specifically, I use a level-level model to fit the data to produce a constant elasticity measurement. Eleven regression statistics calculations recovered in order to receive the expected output where the P-value ≥ 0.05. All other variables with hi P-value were excluded after each run gradually. Follow, presented the first and the last regression calculation outputs.
- 12. 11 FIRST REGRESSIONRUN SUMMARY OUTPUT Regression Statistics Multiple R 0.739245 R Square 0.546484 Adjusted R Square 0.449733 Standard Error 6.922995 Observations 92 ANOVA df SS MS F Significa nce F Regression 16 4331.4 5 270.71 56 5.6483 98 9.43E-08 Residual 75 3594.5 89 47.927 85 Total 91 7926.0 39 Coefficie nts Standa rd Error t Stat P- value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 15.9338 6.1802 44 2.5781 83 0.0118 93 3.622119 28.245 47 3.6221 19 28.245 47 GNI per capita, PPP 0.001977 0.0004 98 3.9699 3 0.0001 63 0.000985 0.0029 69 0.0009 85 0.0029 69 Tax Rate (%GDP) 0.176503 0.2557 45 0.6901 55 0.4922 27 -0.33297 0.6859 73 - 0.3329 7 0.6859 73 Unemployment Male (% of m - labor force) 1.190155 0.3454 37 3.4453 66 0.0009 37 0.502011 1.8783 0.5020 11 1.8783 Unemployment female (% of f - labor force) -1.04223 0.2954 46 - 3.5276 4 0.0007 2 -1.63079 - 0.4536 7 - 1.6307 9 - 0.4536 7 FDI income (BoP) -5.7E-12 2.78E- 11 - 0.2060 8 0.8372 84 -6.1E-11 4.97E- 11 -6.1E- 11 4.97E- 11 Poulation Growth (annual%) 8.108425 1.4813 39 5.4737 15 5.6E- 07 5.157447 11.059 4 5.1574 47 11.059 4 Health expenditure, total (% of GDP) 0.027958 0.3953 88 0.0707 11 0.9438 16 -0.75969 0.8156 11 - 0.7596 9 0.8156 11 Trade in services (% of GDP) -0.03096 0.0348 89 - 0.8873 6 0.3777 2 -0.10046 0.0385 43 - 0.1004 6 0.0385 43
- 13. 12 D 21.1916 8.3227 57 2.5462 23 0.0129 41 4.611817 37.771 38 4.6118 17 37.771 38 D*GNI per capita, PPP -0.00234 0.0005 19 - 4.5117 2 2.34E- 05 -0.00338 - 0.0013 1 - 0.0033 8 - 0.0013 1 D*Tax Rate (%GDP) -0.29239 0.3180 52 - 0.9193 3 0.3608 73 -0.92598 0.3411 99 - 0.9259 8 0.3411 99 D*Unemploy Male -1.39086 0.6261 26 - 2.2213 8 0.0293 43 -2.63817 - 0.1435 5 - 2.6381 7 - 0.1435 5 D*Unemploy female 1.588458 0.5528 27 2.8733 38 0.0052 8 0.487171 2.6897 45 0.4871 71 2.6897 45 D*FDI income 2.57E-11 4.3E- 11 0.5978 74 0.5517 25 -6E-11 1.11E- 10 -6E-11 1.11E- 10 D*Poulation Growth (annual%) -3.95403 2.1765 24 - 1.8166 7 0.0732 63 -8.28988 0.3818 33 - 8.2898 8 0.3818 33 D*Health expenditure, total (% of GDP) 0.567618 0.8301 9 0.6837 2 0.4962 59 -1.08621 2.2214 41 - 1.0862 1 2.2214 41 LAST REGRESSION RUN SUMMARY OUTPUT Regression Statistics Multiple R 0.6928 07 R Square 0.4799 81 Adjusted R Square 0.4366 46 Standard Error 7.0048 36 Observations 92 ANOVA df SS MS F Signific ance F Regression 7 3804.34 9 543.4 785 11.07 609 7.66E- 10 Residual 84 4121.68 9 49.06 773 Total 91 7926.03 9
- 14. 13 Coeffic ients Standard Error t Stat P- value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 22.164 3 3.5081 6.318 035 1.21E -08 15.1880 6 29.14 054 15.188 06 29.140 54 GNI per capita, PPP 0.0014 21 0.00040 7 3.489 697 0.000 773 0.00061 1 0.002 231 0.0006 11 0.0022 31 Unemployment Male (% of m - labor force) 0.8768 72 0.27330 9 3.208 354 0.001 89 0.33336 7 1.420 377 0.3333 67 1.4203 77 Unemployment female (% of f - labor force) - 0.6290 9 0.23711 4 - 2.653 12 0.009 535 - 1.10062 - 0.157 56 - 1.1006 2 - 0.1575 6 Poulation Growth (annual %) 6.4175 18 1.03416 8 6.205 486 1.98E -08 4.36096 1 8.474 076 4.3609 61 8.4740 76 D 12.903 14 4.91362 3 2.625 993 0.010 265 3.13186 1 22.67 442 3.1318 61 22.674 42 D*GNI per capita, PPP - 0.0020 1 0.00044 6 - 4.501 73 2.15E -05 -0.0029 - 0.001 12 -0.0029 - 0.0011 2 D*Health expenditure, total (% of GDP) 1.3524 89 0.53432 7 2.531 202 0.013 231 0.28992 2 2.415 056 0.2899 22 2.4150 56 The final regression results contains only the relevant variables that affect the GINI coefficient. By using the Model showed below, we can see the correlation between the actual GINI coefficient and the predicted GINI coefficient that we received using the Model: 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝐺𝐼𝑁𝐼 = 𝛼 + 𝛽1 ∗ 𝐺𝑁𝐼 𝑝𝑒𝑟 𝐶𝑎𝑝 + 𝛽2 ∗ 𝑈𝑛𝑒𝑚 𝑀𝑎𝑙𝑒 + 𝛽3 ∗ 𝑈𝑛𝑒𝑚 𝐹𝑒𝑚𝑎𝑙𝑒 + 𝛽4 ∗ 𝑃𝑜𝑝 𝐺𝑟𝑜𝑤 + 𝛽5 ∗ 𝐷 + 𝛽6 ∗ 𝐷 ∗ 𝐺𝑁𝐼 𝑝𝑒𝑟 𝐶𝑎𝑝 + 𝛽7 ∗ 𝐷 ∗ 𝐻𝑒𝑎𝑙𝑡ℎ 𝑒𝑥𝑝. Figure 1: Correlation plot between actual GINI against Predicted GINI 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 80 90 100 Predicted GINI to GINI Correlation GINI Pred GINI
- 15. 14 THE ELASTICITY GINI GNI per capita, PPP Unemployment Male (% of m - labor force) Unemployment female (% of f - labor force) Population Growth (annual %) D D*GNI per capita, PPP D*Health expenditure, total (% of GDP) Average 37.56913 17573.59 8.3 9.746739087 1.036672 0.5 14733.37 4.271751845 Coefficients 0.001421 0.876872 -0.62909192 6.417518 12.90314 -0.00201 1.352489068 Elasticity 0.66479 0.193724 -0.16320832 0.177083 0.171725 -0.78785 0.153783109 DATA ANALYSIS Figure 2: Plot of GNI per Capita, PPP against the GINI coefficient Ascatterplot of GNI percapita, PPP scores and GINI coefficients shows that there is negative relationship between GNI and GINI coefficient. That shows that countries with higher income have smaller gap betweenthe richest and poorest citizens. This canbe probably due to right redistribution in those countries. 0 10000 20000 30000 40000 50000 60000 70000 0 10 20 30 40 50 60 70 GINI vs GNI per capita, PPP
- 16. 15 Figure 3: Plot Population Growth (annual %) against the GINI coefficient A scatterplot of Population Growth scores and GINI coefficients shows that there is strong positive relationship between those two parameters. That probably can predict that high population growth cannot be supported by enough employment vacancies. That causes for a rise of unemployment or employment with low wage rate. Figure 4: Plot of GINI coefficient against Unemployment Male (percentage of m - labor force) A scatterplot analysis of unemployment male scores and GINI coefficients shows a weak negative relationship between them. This is consistent to OECD report in 2011 where one of the finding suggests that rise in women employment positively effect on income inequality -3 -2 -1 0 1 2 3 4 0 10 20 30 40 50 60 70 GINI vs Poulation Growth (annual%) 0 5 10 15 20 25 30 35 0 10 20 30 40 50 60 70 GINI vs Unemployment Male (% of m - labor force)
- 17. 16 Figure 5: Plot of GINI coefficient against Unemployment Female (% of m - labor force) A scatterplot analysis of unemployment male scores and GINI coefficients shows a weak positive relationship between them. This is also consistent to OECD report in 2011 where one of the finding suggests that rise in men employment negatively effects on income inequality. CONCLUSION According to the finding in this study, four parameterscanbe highlighted that probably effectsthe income inequality. Those also find a connection to the conclusions suggested in literature described above. Two of them shows strong correlation against GINI coefficient. Negative relationship between GNI and GINI coefficient probably indicate that for governments in countries with higher income it’s easier to achieve their distributional goals in order to decrease income inequality. An earlier reviewed study of IMF researchers (Jonathan D. Ostry, 2014) found that on average, fiscal redistribution has been associated with higher growth, because it helps reduce inequality. Countries with lower GNI suffer from fiscal constraints that limits the policymakers to get to economic efficiency by fighting with income inequality without damaging the economic growth because in order to redistribute, ministers of finance need what to redistribute. In addition to fiscal policy, that plays a significant role in reducing income inequality (Jonathan D. Ostry, 2014), income taxes also play an important role. When in advanced economies the income taxes decrease inequality—as measured by the GINI coefficient by an average of around one- third (Michael Keen,2014), In developing economies, low levels of taxes and spending limit the extent of fiscal redistribution, as does the incomplete access of the poor to public education and health. These facts can also find logical connection to the strong positive relationship between Population Growth scores and GINI coefficients found in this study. High population growth in developing countries increases the demand on the labor market and as result increasing the low wage population, that as result 0 5 10 15 20 25 30 35 0 10 20 30 40 50 60 70 GINI vs Unemployment female (% of f - labor force)
- 18. 17 increasing the gap between lower-income groups and higher income groups. It is correlate with findings (Dervis, September 2012) that slower population growth supported greater capital intensity and faster per capita growth. However, it can be advantage of the countries with high ratio of the economically active population to the total population. By the careful design of taxes and spending, with a special focus on those at both the top and the bottom of the income distribution, governments can enhance the human capital of low-income households and minimize the gap between those two groups. As we saw in the literature it can be done by utilized property taxes, implement more progressive personal income taxes, targeting social benefits, strengthening incentives to work, and expanding the access of low-income families to education and health. BIBLIOGRAPHY Alvaredo, F. A. (2013). The Top 1 Percent in International and Historical Perspective. Journal of Economic Perspectives, 27(3): 3-20. Alvaredo, F. T. (2012, June 1). Retrieved from “The World Top Incomes Database” : http://g- mond.parisschoolofeconomics.eu/topincomes Dervis, K. (September 2012). Convergence, Interdependence,and Divergence. Finance & Development , 10-14. Guiso, L. S. (2006). Does culture affect economic outcomes? Journal of Economic Perspectives 20(2):,23-48. Hofstede, G. (1980). Culture’s Consequences:International differences in work -related values. Beverly Hills, CA: Sage. Jonathan D. Ostry, A. B. (2014, April ). Redistribution,Inequality,and Growth. INTERNATIONAL MONETARY FUND. Malinoski, M. (2012). On Culture and Income Inequality: Regression Analysis of Hofstede’s International Cultural Dimensions and the Gini Coefficient. Xavier Journal of Politics,Vol 3, No 1. Michael Keen, S. G. (2014, March 19, 2014). Meeting Rising Pressures to Address Income Inequality—A User’s Guide. Retrieved from iMFdirect: http://blog-imfdirect.imf.org/2014/03/19/meeting-rising-pressures-to- address-income-inequality-a-users-guide/ Milanovic, B. (2012). "Global Inequality: From Class to Location, from Proletarians to Migrants".In Vol. 3, No. 2, (pp. 125–34.). OECD. (2011). Divided We Stand Why Inequality Keeps Rising . © OECD. World Bank Open Data. (2014, July). Retrieved from The World Bank: http://data.worldbank.org/