Is globalization responsible for the recent rise
of inequality?
Master thesis
Author: Baoqiang Zhang Student number: s3738531
Student mail: b.zhang.15@student.rug.nl Supervisor: Dr.X.Ye
Abstract
This paper analyzes the relationship between the different dimensions of globalization and inequality in 113 countries between 1990 and 2015. My results suggest that the increase of trade globalization will reduce inequality when countries’ real per capita GDP is higher than $8,200. In low-income countries, I do not find a significant link between trade globalization and within-country inequality. A similar effect is found for social globalization. However, no significant link can be established between the political dimensions of globalization and inequality.
1. Introduction
In recent years, the integration of the world, or the so-called globalization, has reached an unprecedented level. Under this context, the advantages and disadvantages of globalization have become one of the hot topics. At present, it is widely accepted that the acceleration and deepening of globalization will bring more trading opportunities, promote the spread of science and technology between countries, and most importantly, boost the economic growth of a country (Dreher, 2006; Doucouliagos and Ulubasoglu, 2006). The rapid growth of developing countries such as China in the last decades provides convincing evidence for this point of view. In addition, there is a prevalent belief that globalization has significant contributions to the reduction in global poverty.
Although globalization is considered as a cornerstone of the world’s rapid economic growth, the distributional impact of globalization remains controversial. Many people believe that the rising level of globalization will increase inequality. During the past few decades, the issue of inequality has long plagued policymakers in most countries, especially in developing countries. This is because higher inequality will reduce national happiness, hindering national development and it has also become trigger of the recent populist rebound (IMF, 2014). So, income inequality and globalization are both the main concerns of the authorities during this year, and it is useful to study the relations between the two.
In previous research, Jaumotte, Lall and Papageorgiou (2013) argue that globalization is the main driven force of the recent rise in inequality. The uneven distribution of economic growth brought about by opening trade has widened the income gaps between the regions that are closely integrated with world economy and those backward. Moreover, globalization raises the income of high-educated labor by promoting the spread of science and technology, which further enlarges the income gap within the labor force. In addition, financial globalization tends to offer more investment opportunities, which will bring benefits to those high-income people, as they usually have more funds available for investment. By contrast, those low-income people usually have insufficient funds for investment, thus they will not receive benefits which further widened the gap between the wealthy and the poor.
dynamics within a single country. Little literature uses large panel data on a wide range of countries. In addition, studies in this field pay more attention to trade and financial globalization while the effects of political and social dimensions of globalization on inequality has not attracted much attention.
This paper uses the KOF index proposed by Dreher (2006) to analyze the relationship between four dimensions of globalization and inequality. The KOF indexes provide separate measure for four dimensions of globalization. This paper contributes to the literature by investigating whether various dimensions of globalization have similar impacts on inequality. Specifically, globalization at other dimensions such as financial and cultural globalization can affect inequality through many different channels such as foreign direct investment, cultural, religious, belief and government policy. Focusing merely on the impact of trade liberalization may omit other important aspects of globalization and this paper also explores the impact of the political and social dimension of globalization.
In addition, rise globalization may have different impacts on inequality between developing and developed countries. The Kuznets-style argument indicates that in countries with higher development levels, the better redistribution mechanism promotes an evener distribution of economic growth brought by globalization to citizens. Thus, the increasing of globalization will increase the average income of a country but not inequality. However, in countries at the low and medium development levels, the rising level of globalization is likely to bring a higher income to only a subset of population with higher skills and with access to the globalizing activities. Thus, I predict that the effect of globalization on inequality will be different in countries at different development levels. My paper will test this hypothesis by looking at the marginal effect of globalization, conditional on the different levels of income of the countries.
This paper uses a large panel dataset covering 113 countries from 1995 to 2015 to analyze the impact of globalization of each dimension on the level of inequality within the country. Both static (fixed effects) and dynamic panel analyses (GMM) are adopted. The dynamic model includes one period lag of inequality and is used to control for the persistence of inequality over time.
in high-income countries, in lower income countries, globalization level has no significant effect on inequality. To be more specific, when a country’s real GDP per capita is higher than $8,200, an increase of trade globalization will reduce inequality and the effect is statistically significantly at 5% level for countries with real GDP per capita higher than approximately $14,000. In addition, I find that Increasing social globalization reduces inequality only in high-income countries. No significant link is found between the other dimensions of globalization and inequality.
The remainder of the paper is organized as follows: Section 2 provides a brief review of the existing theoretical and empirical literature which studies the link between globalization and inequality. Section 3 describes the econometric specification and provides the data description, identifying the main control variable which can be used to explain inequality. After that, section 4 presents the empirical result and discusses my findings. The final section concludes.
2. Literature review
In this chapter, I first provide a quick look at the recent rising trend of inequality. provides some brief review of the literature which focuses on the effects of different dimensions of globalization on inequality. Next, I provides a brief review of the literature which focuses on the effects of different dimensions of globalization on inequality and the Heckscher-Ohlin (HO) model. Then, I discuss other potential channels through which inequality is influenced by different dimensions of globalization. A respective review is conducted of the empirical literature to study how inequality is affected by trade, finance, social and political dimensions of globalization.
2.1 Raising levels of inequality
which suggests that the high-income groups receive relatively more income growth compared with low-income groups. Similarly, the report issued by the World Bank also confirms that the overall level of within-countries inequality has been increased in the past few decades (World Bank, 2001).
The level of inequalities can be illustrated by some shocking facts. For example, Milanovic (1999) stated that “The richest 1% of people in the world receive as much
as the bottom 57% in 1993, or in other words, less than 50 million income-richest people receive as much as 2.7 billion poor in 1993.” In addition, Jäntti and Sandström
(2005) argued that the inequality level between different countries have been exacerbated significantly since 1990, which is largely attributed to the disproportionate increase in income earned by the wealthiest 20% people relative to the rest of the population. Gottschalk and Smeeding (1997) indicate that the distribution of personal income becomes more unequal in many high income countries after 1980, especially in the United Kingdom and the United States. Evidence from China also indicates that China's economic integration into the world is accompanied by an increasing regional inequality, because the income gap between coastal and inland areas in China has increased dramatically since the mid-1980s (Zhang and Kanbur, 2001).
Alderson and Nielsen (2002) finds that inequality has exacerbated in 10 out of 16 OECD countries between 1967 and 1992. Besides, Sánchez‐Reaza and Rodríguez‐Pose (2002) studied how inequality has evolved in Mexico. According to their research, the increasing level of Mexico's overall GDP growth rate after 1990 is accompanied by a rising level of inequality. Therefore, the aforementioned evidence leads to the conclusion that the severity of inequality exhibited an increasing trend across most countries over recent years. The effect is not limited to developing countries, but is also the case in many advanced countries.
2.2 Heckscher-Ohlin (HO) model and its criticism
The following section briefly reviews the Heckscher-Ohlin (HO) model which is a widely known theoretical model that can be used to predict the effect of openness on its inequality level.
advantages. Wood (1995) illustrated this model with an example that includes two countries, A and B, and two production factors, skilled and unskilled labor. Country A is abundant in unskilled labor and country B in skilled labor. After both countries decided to trade internationally, the country with abundant unskilled laborers, country A, will focus on producing and exporting low skill intensive products, which helps increase the relative demand for and the wage of unskilled laborers. As a result, wage distribution in country A trends to become more equal after joining the international market. In contrast, skilled labour is going to benefit in country B and its inequality will rise.
Therefore, the model predicted that if international trade happens in developing countries, it should benefit the lower skills population and reduce the skill premiums which can decrease the income gap in developing countries, but it should exert an opposite effect on developed countries.
However, plenty of existing literature points to an opposite conclusion. For example, Han, Liu and Zhang (2012) state that after China's access to the WTO in 2001 which is a famous event representing China’s increasing integration into the international community, the education premiums in China increased thereby increasing the wage of skilled labor but not benefiting unskilled labor. The inequality level in China has increased rather than decreased. In addition, Kanbur and Zhang (2005) argued that China's income inequality has worsened alongside a higher degree of trade openness, pointing out that technological upgrading leads to an increase in demand for highly skilled employees (Kanbur and Zhang, 2005).
Sánchez‐Reaza and Rodríguez‐Pose (2002) indicate that after the signing of NAFTA, the industrial form changed from traditional labor-intensive processing to more capital-intensive manufacturing across various parts of the United States and Mexico, which stimulated the demand for skilled labor and increased skill premium in Mexico leading to the increase of inequality. Research of Hanson (2007) also suggests that the benefits created by trade is uneven distribution among different regions thus increase the inequality due to the varying level of regional exposure to globalization, the high-exposure area in Mexico (common border between Mexico and the United States) has received more benefits and increases in income. However, the reform causes shock to the livelihood of the residents in those regions with a lower level of exposure.
will benefit and the level of inequality will be reduced after they enter the international market. However, the empirical evidence contradicts the prediction obtained from the HO model. In these countries, opening to trade has increased their inequality levels.
2.3 How trade globalization affecting inequality through other channels
Literature mentions two channels through which trade globalization increases inequality: 1. globalization promotes the spread of technology between countries, resulting in increased competitiveness and the rise in demand for highly skilled labor both in developed and developing countries, which increase the inequality. 2. The benefits of globalization cannot be evenly distributed across all regions in a country, some regions are growing faster than other, therefore it increases inequality.
For most economists, the technological progress resulting from globalization is widely regarded as an important cause of rising inequality. For example, Johnson and Stafford (1993) argued that globalization can reduce the cost of introducing new technologies and encourage the replacement between labor and capital and between skilled and unskilled labor. The rate of technical progress may be an endogenous response to the need of maintaining competitiveness in a global marketplace. Therefore, due to the recent general increase in the level of globalization in various countries, technological progress has accelerated, reducing the demand for low-skilled labor and wages, and increasing inequality. Their opinion has been supported by Jaumotte, Lall and Papageorgiou (2013) who claimed that technological advancement is conducive to enhancing productivity and increasing revenue, and globalization can promote the diffusion of technology, which means the more liberalized trade can accelerate the spread of technology, thereby increasing the wages of high skilled laborer but not low skilled which further increases the inequality.
levels of development differently.
Researches focusing on economic geography show that the benefits of international trade often cannot be evenly distributed across regions. In the case of China, trade leads to a concentration of capital and highly skilled laborers in the more open coastal cities and increased regional income disparities with the west (Zhang and Zhang, 2003). Chiquiar (2008) suggested that after Mexico joining the North American Trade Agreement (NAFTA), Mexican regions with a greater access to international markets appear to demonstrate a higher wage level compared to the rest of the country. The regional closed to the United States receive more wage premium and obtain more job opportunities thus growth quickly compared with another regional.
2.4 Financial globalization and inequality
The single dimension of trade openness is insufficient to capture the incidence of other aspects of economic globalization, such as the extent of capital controls and the amount of Foreign Direct Investment (FDI). Dreher (2006) argues that financial globalization is potentially important, and financial globalization can affect inequality in different ways compared to trade. Focusing solely on trade openness can affect the perception of the relationship between globalization and inequality.
First, a large amount of literature suggested that increasing FDI would benefit skilled labor thus increasing inequality. For example, Driffield and Taylor (2000) indicated that the entry of MNEs has not only intensified the competitiveness of the domestic market, forcing many local companies increase their productivity, but also contributed to technology spillovers. Cheung and Ping (2004) suggested that host countries use FDI as an instrument for importing relevant technology and knowledge from developed countries, and the technology spillovers from FDI can also stimulate other local firms willingness to innovate and enhance local firms' innovation ability. These effects will increase the demand for highly skilled labor in both local firms and MNEs and thus raise the wage gap. Feenstra and Hanson (1997) argued through offshore and FDI developed countries produce ever increasingly high-quality goods, during which, the demand for unskilled workers has reduced. However, the demand for skilled labor in the developing countries also increases, since the relatively unskilled activities in developed countries are relatively skilled for the developing countries. Hence, FDI may exert the same effect on the labor markets of both investing and host countries, and it works similarity as a skilled-biased technological change.
capacity of a country via taxation, which will, in turn, increase inequality. Investors now can transfer their income and assets to other countries in an easier way and this process is harder to be traced by the domestic government. This makes tax evasion easier and harder to regulate and detect thus benefits the assets holder and high wage earner, who have more income and assets and a stronger incentive to evade taxes. Genschel (2005) states that tax systems of OECD countries are invented in a context of separated national markets, and is not well prepared for a globalized world financial market. Alstadsæter, Johannesen and Zucman (2019) argue that rich people have been provided with greater motivation and more sufficient means for tax evasion, finding that the richest 0.01% of residents evade about 25% of taxes. Given these, the increase in financial globalization contributes to the net income of those richest, which leads to an increase in inequality.
Finally, the emergence of offshore capital markets further increases the appeal of international tax evasion by reducing the risks related to the exchange rates of currencies. Genschel (2005) argued that the offshore capital markets allow investors to invest abroad without risk of currency depreciation, thereby evading domestic capital income tax. In addition, financial globalization affects inequality through “creative” international capital flows making use of the heterogeneous taxation policies across countries. Since taxes are closely related to a country's redistribution capacity, increasing tax evasion will reduce the country's redistribution capacity thus increase inequality.
2.5 How political globalization affect inequality
thereby indirectly affecting the inequality level of a country. A good institution such as the labor union can increase the bargaining power of the lower-skilled worker thus indirectly increase their income (Bradley, Huber, Moller, Nielsen and Stephens, 2003).
Furthermore, the international political status of a country also interacts with its international trade and financial market. Pollins (1989) suggested that bilateral trade flow is significantly influenced by political relations between two countries, because trading with a reliable partner can reduce political risks. This conclusion has been supported by Morrow, Siverson and Tabares (1998) who found trade flows are higher among countries with similar interests, especially allies and countries within a common world organization. Similarity, Krifa-Schneider and Matei (2010) found that FDI inflow is negatively correlated to national political risk, and countries low in political globalization will have fewer trade partners and higher trade costs.
2.6 Social dimension of globalization and inequality
When it comes to the social dimension of globalization, five major areas are often involved, namely, education, employment, social security, equity for special groups, and health (Clark-Bennett, 2004). In addition to the factors that are directly related with income, social globalization also has effects on, for instance safety, culture identity, tolerance or exclusion, and family and community cohesion.
Social globalization may increase inequality through the monopolization of media and information. Mackay (2004) argued that because of the increasing importance of culture and information in the world, the gap between information and equipment has led to an increasing gap between those possessing information equipment and information sources. In other words, the gap in information accessibility has increased the inequality. In addition, the global media market is dominated by 10 multinational cultural companies, such as Time Warner, Disney, Bertelsmann, etc., which are equipped with scale advantages. Even more, the characteristics of mergers, acquisitions and joint ventures in the cultural field have led to monopoly, further deepening inequality (Mackay, 2004).
To conclude, while most of the previous literature has studied the link between globalization and inequality, but, the conclusions are not uniform. In addition, the effects of the social and political dimension of globalization need to be further explored.
3. The empirical model
In order to answer my research question of whether globalization is associated with the recent increase of within-country inequality, both static fixed effect analysis and dynamic panel analysis are used in my paper. Except for a few studies, existing literature studying the determinants of inequality is based on static specifications or time series analysis of a single country. In the static analysis, fixed effect estimation is adopted in most of the studies in order to eliminate all unobserved time invariant country heterogeneity. But within a country, income inequality is unlikely to have a dramatic change within a short period of time. The structure of income distribution and the level of inequality is highly persistent; the change in globalization will only lead to a partial change in inequality. Therefore, dynamic panel analysis can be useful to capture the high degree of persistence of inequality.
3.1 Static panel analysis
To analyze the static effect of globalization on inequality, the following empirical model is formulated, where countries are represented by i and time period represent by t.
Inequalityit= Globalit Xit iyt it (1)
Inequalityitis the measure of household income inequality level in year t and country i.
dimensions of KOF index, Xit is a vector of independent variables and i is the
individual time-invariant country’s fixed effect, capturing the factors which will influence country’s inequity but will not change over time; yt is the year dummies, capturing the macroeconomics shocks that affect inequality in all countries at a same period. itrepresents the error term. The Hausman test is used to determine whether
random effect or fixed effect should be adopted. The results support the use of the fixed effect method in my case.
3.2 Dynamic panel analysis: system GMM
A large number of existing literature uses static panel analysis. However, inequality is dynamic by nature, with a high degree of time-persistence. Namely inequality will not change dramatically in a short time, and is highly sticky. For example, the studies by Rodríguez-Pose (2010) and Meschi and Vivarelli (2009) both find the coefficient of lagged inequality is quite high in the dynamic regression of the determinants of inequality, and sometimes close to one. The lagged inequality can be used to capture the slow adjustment process of inequality. The specifications of the dynamic panel model are present in equation (2) below.
Inequalityit= Inequalityi,t-1 Globalit Xit iyt it (2)
The Inequalityi,t-1is the lagged dependent variable for one period. Parameter ranges
between 0 and 1 and captures the adjustment speed of inequality. Other variables are the same as the equation (1) which is described above.
3.3 The econometric methodology
4. Data
The panel dataset includes 113 countries from 1990 to 2015, covering 26 years in total. It is an unbalanced panel because the data for some countries or for some periods are not available. I choose to focus on the 1990-2015 which is the period when the globalization process began to develop rapidly in developing countries. The country list can be found in Appendix a.
4.1 The measure for inequality -- the EHII index
The inequality index used in my literature is the Estimated Household Income Inequality Data Set (EHII) index which is developed by James Galbraith and Kum (2003) and their associates from the University of Texas Inequality Project, which is widely known as the (UTIP) database.
UTIP database is a time-series/cross-country dataset that provides comparable and consistent measurements of income inequality both across countries and through time. The most widely used indicator of inequality is the Gini coefficient, ranging from 0 (complete equality) to 1 (most unequal) (Gastwirth, 1972). However, a cross-country comparison of income inequality by using the Gini coefficient faces limitations since the construction method of Gini coefficient in each country is based on different income definitions and different units (such as income/expenditure; individual and households). This gives rise to the issues on data comparability, such that the reported inequality index across countries can not be easily compared in a meaningful way. So directly using the Gini index for cross-country analysis may lead to instability and bias in my results. In order to overcome this problem, I adopt the EHII index which is comparable and consistent across countries.
The EHII Index is built based on the information collected from Deninger and Squire (D&S) and UTIP-UNIDO database. Author adjusts the inter-industry wage inequality data of UTIP-UNIDO according to the proportion of the employed population in each industry to derive the new EHII index (see Galbraith and Kum, 2005, for detailed explanation of the data calculation). The index covers more than 150 countries, from 1963 through 2015, including more than 4,000 observations. Data are ranging from 0 (complete equality) to 100 (high inequality).
4.2 Different dimensions of globalization: the KOF index
stakeholders (Sapkota, 2010). Some recent empirical literature prefers to use alternative indicators such as trade flows, foreign direct investment flows, and import/export tax rates as a proxy to measure the countries’ globalization. However, most research only focuses on an individual dimension of globalization, therefore most fails to take into account the overall impact.
To overcome this, I use the KOF index as a main explanatory variable to measure the globalization of a country. Data is developed by Dreher (2006), which is considered the most comprehensive indicator of globalization. The latest version of the KOF globalization index is available for 195 countries from 1970 to 2017, with the index measuring globalization on a scale of 0 to 100. Higher value represents a higher globalization level. The overall KOF globalization index is a multifaceted index encompassing much more than trade openness and international capital flows. It include the following three dimensions:
1. Economic Globalization is characterized by the market flows of goods, capital, information, and services. It can be subdivided into two dimensions: Trade globalization and Financial globalization. The trade globalization index is constructed with several different variables, including trade in goods, trade in services, trade partner diversity, tax and trade agreements. The index for financial globalization is based on foreign direct investment flows, international debts, reserves, income payments and the number of International investment agreements.
2. Social Globalization is characterized by the diffusion of government policies. The number of migration, communication and exchange of high technology, and the number of the famous brands and international visitors are used as proxy variables to construct the index for culture and social globalization.
3. Political Globalization is characterized by a countries international status and reputation. Variables such as the number of embassies in a country, the rate of participation in UN peacekeeping missions and the number of members in international organizations are used to measure the political globalization of a country.
4.3 Other control variables for inequality
A number of control variables on inequality determinants are included in the specifications. First, within-country inequality can be affected by real GDP per capita. Kuznets (1955) illustrated that the relationship between inequality and economic development presents an inverted U-shape, the inequality tend to rise at the initial stages of development, but after reaching a threshold of income, inequality tends to decrease when the country grows further. This argument is also endorsed by the World Bank in its World Development Report 2009. Thus, I expect a nonlinear relationship between real GDP per capita and inequality. Taking this into account, I added the log of national real GDP per capita and its square to the baseline model. Data is collected from the World Bank.
Second, a higher education level indicates an increase in the supply of highly skilled labor in the society. Theoretically, the impact of education level on inequality is ambiguous. Rodríguez ‐ Pose and Tselios (2009) suggested a statistically insignificant long-term relationship between income inequality and educational attainment. They claimed that providing more opportunities of education will not help to lower the inequality. However, some other studies argue that increasing supply of high skilled labor can decrease the education premium (e.g. Meschi and Vivarelli, 2009). Topel (1997) argues that technological advancement usually benefits the high-skilled employees in large companies the most. The fewer high-skilled employees the country has, the lower the average level of education is, and the higher the skill premium will be, which leads to a larger income gap between the rich and the poor. Hence, when the average educational level of a country rises, the wage premium of skills will decrease thereby reducing inequality.
With the method proposed by Wood and Ridao-Cano (1999), I use the average years of schooling of the adult (over-15) population as a proxy of education level. The educational attainment dataset was developed by Barro and Lee (2013), covering 146 countries from 1950 to 2010. The data is only available every five years. In order to match the annual data of the other variables, the interpolation method is used to fill the data gap, in which I assume that the number of years of education increases linearly while the annual growth rate is constant. Similarly, since the original dataset is only available up to 2010, an extrapolation method has been adopted to calculate the years of education after 2010.
they have. Large countries tend to have a more complex population distribution spreaded on a larger land area, and a more diverse ethnic groups, all of which increase the difficulty of inter-regional management, thereby relating to a higher level of inequality. Besides, Campante and Do (2007) argued that a country with a larger population base will need more sophisticated redistribution mechanisms to reduce its inequality. The increasing number of populations makes redistribution more difficult. Therefore, the logarithm of population is added as the control variable; the data were taken from the World Bank.
Fourth, the inflation rate is included in my model to measure the macroeconomic environment of countries’ income distribution. It was found in lots of papers that a high inflation is associated with higher inequality (Lundberg and Squire, 2003). To be specific, the rising level of inflation would erode real wages and lead to a lower purchasing power. This will harm all wage earners. But on the other hand it will not be that harmful to the capital owners, since the value of capital will boost alongside with inflation. However, for low-income families, they usually hold cash or savings therefore they will suffer more losses in a higher inflation environment. The change of inflation therefore brings different impacts to different income groups (Albanesi, 2007). Moreover, Bulíř (2001) indicated that the relationship between price stability and income inequality is nonlinear. The reduction in inflation rate from hyperinflation significantly lowers income inequality, but further reducing it to a very low level does not seem to have much additional effect. To control for the potential effect of inflation on inequality, I include it as a control variable and the data was taken from World development indicators of the World Bank.
I further control the unemployment rate of each individual country which is also obtained from the WDI database. As revealed by empirical literature, the rising unemployment rate has an significantly positive impact on inequality (Cysne and Turchick, 2012). Galbraith and Garcilazo (2004) analyze the relationship between pay inequality and unemployment in Europe, and their results have demonstrated that a higher income inequality is associated with a rising unemployment rate. Besides, this has a more considerable impact on women and young workers. In many countries, the social redistribution system remains flawed. Their citizens usually have no entitlement to employment subsidies or can receive only a small amount of subsidy. Therefore, the increase in the unemployment rate causes individual incomes to shrink, thus increases inequality.
expansionary fiscal policy has played a significant role in reducing income inequality in advanced economies. Increasing government spending can benefit the lower earners through direct transfer such as public pensions and universal child benefits to reduce the inequality. Evidence shows that the expansionary fiscal policy decreased the inequality level in 25 OECD countries by almost 30 percent from 1985 to 2005 Bastagli, Coady and Gupta (2012). Therefore my specification also includes the control variable of government expenditure as a percentage of GDP, which is sourced from the Government Finance Statistics Yearbook from the IMF.
Finally, as discussed earlier, I interact the log of real GDP levels with each dimension of globalization indexes to test whether globalization affects high- and low-income countries differently.
Table 1 below presents the summary statistics of variables. Table 1: Descriptive Statistics
Variable Obs Mean Std.Dev. Min Max
Inequality level EHII index 2,016 43.567 6.687 24.924 62.850 Overall Globalization level 2,898 60.764 16.316 19.200 91.313 KOF Economic 2,890 57.201 16.966 14.263 95.285 KOF Trade 2,890 56.076 17.954 12.784 96.967 KOF Financial 2,890 58.899 18.240 8.591 98.202 KOF Political 2,916 66.729 21.358 3.199 98.586 KOF Social 2,916 58.019 20.773 6.535 92.265 Inflation rate 2,686 5.922 6.604 -27.632 33.541 Education level 2,704 8.361 2.739 0.930 15.490 Log Population 2,931 16.166 1.706 11.462 21.039
Log real GDP per capita
2,857 9.304 1.172 6.084 11.813
5.Results and interpretation 5.1 Static panel analysis
In this section, the fixed effect estimation is used to investigate whether the increasing level of inequality is significantly related to the increasing level of globalization in various countries. Table 2 and 3 below present the results obtained when various specifications of equation (1) are estimated by the fixed-effect model. Note that all specifications are based on the fixed effect models, because the results of the Hausman test suggest the fixed effect model is more suitable than random effect.1 The dependent variable (EHII index) is the level of inequality and the main control variable is the different dimensions of globalization level (KOF). Columns differ according to the control variables included and the dimensions of globalization.
*, **, *** correspond to 10, 5, and 1% significance levels respectively. standard errors present in brackets. All specifications include country fixed effects.
EHII index (1) (2) (3) (4) (5) (6) (7) Overall Globalization 0.155*** (0.013) 0.027 (0.019) -0.030 (0.023) 0.772*** (0.145) Economic Globalization 0.087*** (0.010) 0.0105 (0.010) -0.009 (0.011) Real GDP -20.953*** (2.384) -30.189*** (3.248) -26.014*** (3.372) -15.811*** ( 2.386) -28.514*** (3.010) -27.878*** (3.018) -42.465*** (4.435) Real GDP square 0.990*** (0.125) 1.544*** (0.160) 1.308*** (0.167) 0.826*** (0.127) 1.474*** (0.154) 1.390*** (0.154) 2.384*** (0.253) Overall Globalization*GDP -0.084*** (0.015) Population 4.650*** (0.712) 3.552*** (0.766) 4.865*** (0.698) 3.452*** (0.777) 2.266*** (0.790) Inflation 0.009 (0.012) 0.016 (0.013) 0.007 (0.012) 0.018 (0.013) 0.009 (0.013) Education 0.219** (0.098) 0.073** (0.110) 0.248*** (0.094) 0.058 (0.109) 0.030 (0.108) Unemployment 0.105*** (0.021) 0.104*** (0.021) 0.109*** (0.020) 0.100*** (0.021) 0.102*** (0.021) Government spending 0.0004 (0.013) 0.004 (0.013) 0.001 (0.013) 0.003 (0.013) -0.003 (0.013) Observations 1,962 1,218 1,218 1,962 1,218 1,218 1,218 Countries 111 74 74 111 74 74 74 R square 0.116 0.236 0.262 0.086 0.235 0.261 0.282
Year dummy no no yes no no yes yes
*,**, *** correspond to 10, 5, and 1% significance levels respectively. standard errors present in brackets. All specifications include country fixed effects.
In column 1 and 4 of table 2 and 3, only real GDP per capita and its square are added in the specification. The result indicates that the aggregated index of globalization has a significant and positive impact on inequality, which confirms the results of most previous empirical works, that is, a higher globalization level will increase inequality. Regressions 2 and 5 introduce other control variables that have been discussed in the previous section. After including all these control variables, there is no significant association between the aggregated globalization level and inequality. Only the social dimension of globalization has positive and significant effects on inequality. I further added year dummies in my regression. The results are shown in column 3 and 6 in table 2 and 3. After including the year dummies, the coefficient of overall globalization is insignificant at the 10 % level and the sign becomes negative. That means an increase in globalization is found to reduce the inequality, but the effect is
EHII index (1) (2) (3) (4) (5) (6) Political Globalization 0.095*** (0.010) -0.007 (0.013) -0.021 (0.014) Societal Globalization 0.111*** (0.012) 0.074*** (0.020) 0.0152 (0.028) Real GDP -21.512*** (2.434) -27.810*** (3.192) -26.254*** (3.225) -20.426*** (2.432) -36.153*** (3.672) -29.564*** (4.172) Real GDP square 1.108*** (0.127) 1.455*** (0.159) 1.318*** (0.161) 0.994*** (0.127) 1.798*** (0.176) 1.463*** (0.203) Population 4.928*** (0.710) 3.688*** (0.772) 3.936*** (0.739) 3.500*** (0.772) Inflation 0.003 (0.012) 0.017 (0.013) 0.010 (0.012) 0.020 (0.012) Education 0.267*** (0.094) 0.069 (0.109) 0.071 (0.107) 0.042 (0.112) Unemployment 0.115*** (0.020) 0.102*** (0.021) 0.098*** (0.020) 0.096*** (0.021) Government spending 0.0004 (0.013) 0.004 (0.013) -0.005 (0.013) 0.003 (0.013) Observations 1,970 1,218 1,218 1,970 1,218 1,218 R square 0.096 0.235 0.262 0.087 0.243 0.261 Number of countries 112 74 74 112 74 74
Year dummy no no yes no no yes
not statistically significant. For the other three dimensions of globalization, political and economic globalization have a negative sign, while social globalization presents a positive sign, but none of them are statistically significant at the 10% level.
Furthermore, as discussed in the introduction, I assume that the impact of globalization on inequality may depend on the country’s development level represent by real GDP per capita. Namely it has been hypothesized that an increase in globalization is more likely to reduce inequality in developed countries than in developing ones. To test this hypothesis, I create an interaction between overall globalization level and GDP, and the results are shown in column 7 of table 2. I find that globalization does have different impacts on the countries with different development levels, presenting a Kuznet style result. In developed countries ( high real GDP per capita level), increasing globalization tends to reduce inequality, while in developing countries, a higher globalization level may increase inequality. After investigate the marginal effects of globalization on the inequality,2 I conclude that increasing globalization level will decrease the country’s inequality when its real GDP per capita level is higher than 8,200 dollars, and this negative effect is statistically significant at 5% level of countries with real GDP per capita higher than approximately 14,000 dollars (i.e. log real GDP of about 9 and 9.5). In contrary, if the countries’ real GDP per capita is lower than 8,200 dollars, increasing the level of overall globalization will increase the inequality.
The marginal effect graph corresponding to the column 7 is presented in Appendix b, figure 1. When we look at the magnitude, results indicate that for the highest income countries whose real GDP per capita is higher than 60,000 dollars (i.e. Log GDP=11), a 1 percentage point increase in overall globalization level will be associated with a 0.15 percentage point decrease in the EHII index on average. When country’s real GDP per capita level is lower than 1,100 dollars (i.e. Log GDP=7), the marginal effect of globalization on inequality will become positive and 1 percentage point increase in the aggregated globalization index is associated with a 0.185 percentage point increase in the EHII.
With respect to control variables, the education level does not have the expected signs. Present positive signs in all columns indicate that the increased supply of skilled labors tends to increase inequality and significantly different from zero at 5% level. Other control variables shown in table 2 and 3 are consistent with that in many existing literature. After adding the year dummy, higher Inflation rate worsens the income distribution, but that is not statistically significantly different from zero. Real
5.2 Dynamic panel analysis
The method of estimation is system GMM. Robust standard errors present in brackets. Education, Inflation, log real GDP per capita, log real GDP per capita square, log population, government spending, and unemployment rate are entered into the instrument matrix views as strictly exogenous. Lagged term of inequality put in the gmmstyle bucket as the endogenous variable. *, **, *** correspond to 10, 5, and 1% significance levels respectively.
Inequality level (EHII) (1) (2) (3) (4) (5) (6)
Lag. inequality l year 0.673*** (0.142) 0.654*** (0.109) 0.605*** (0.147) 0.718*** (0.094) 0.681*** (0.096) 0.740*** (0.138) Overall Globalization -0.048* (0.026) –Economic Globalization -0.057** (0.024) —Trade Globalization -0.051* (0.028) —Financial Globalization -0.027* (0.014) –Political Globalization -0.027 (0.017) –Social Globalization -0.069 (0.063)
Real GDP per capita -10.911 (20.97) -6.999 (18.197) -28.829 (31.244) -15.220 (16.396) -13.008 (24.077) -6.481 (28.201) Real GDP square 0.514 (1.077) 0.292 (0.921) 1.406 (1.580) 0.743 (0.842) 0.609 (1.225) 0.322 (1.407) Population 0.243** (0.119) -0.320 (0.309) 0.021*** (0.313) 0.141* (0.075) 0.358** (0.136) 0.012 (0.118) Inflation 0.016 (0.015) -0.020 (0.032) 0.018 (0.030) 0.019 (0.014) 0.021 (0.014) 0.002 (0.02) Education -0.167 (0.134) -0.334* (0.167) -0.135 (0.194) -0.223 (0.171) -0.201 (0.167) -0.051 (0.109) Unemployment 0.039 (0.026) -0.002 (0.039) 0.064 (0.600) 0.036 (0.026) 0.040 (0.030) 0.031 (0.043) Government spending 0.004 (0.021) 0.016 (0.039) 0.004 (0.022) -0.002 (0.013) 0.001 (0.013) -0.002 (0.013)
Year dummies yes yes yes yes yes yes
Observations 1,113 1,113 1,113 1,113 1,113 1,113
Number of countries 74 74 74 74 74 74
AR(1) test p-value 0.000 0.000 0.000 0.000 0.000 0.000
AR(2) test p-value 0.499 0.410 0.424 0.470 0.517 0.492
Hansen test, p-value 1.000 1.000 1.000 1.000 1.000 1.000
Table 4 presents the results of the dynamic panel regressions. The dynamic panel analysis is conducted using System GMM, and all the columns include yearly fixed effects. The dynamic model includes one-year lag of the dependent variable. And to check whether the number of lag is appropriate, AR(1) and AR(2) tests are performed, and I find that the hypothesis null of AR(1) has been rejected and AR(2) not rejected. So the specification with one-year lag is correctly specified. Furthermore, the Hansen test is used to test the validity of using lagged difference and level of the independent variables as instruments as in the System GMM regression (over-identification restrictions). If the null hypothesis is not rejected, then the instrument are valid, which is the case here.
As I mentioned in section 2, static panel analysis failed to control for the persistence of inequality. Therefore, the results obtained by the dynamic panel regression may be different from the fixed effect method. In table 4 column 1, by far the most important determinant for inequality is the lagged inequality in the previous year; the coefficient equals 0.67 and is strongly significant. This confirms the existence of a high degree of persistence in inequality. And when I look at the effects of globalization, overall effect of globalization on inequality is negative and it has a statistically significant influence on inequality at the 10% level, indicating that the rise of aggregate globalization index is associated with a decline in the level of inequality, which is different from the result obtained by the fixed effect model. Recall that in the fixed effect model, after including the year dummies, the increase in overall globalization level does not statistically significantly affect inequality.
Columns 2, 5 and 6 in table 4 show the impacts of the three sub-dimensions of globalization on inequality, respectively. The results indicate that the negative effect of overall globalization on inequality is mainly driven by economic globalization. When looking at social and political globalization, the estimates are negative but not statistically significantly different from zero.
Economic globalization involves two aspects: trade globalization and financial globalization. As discussed above, some empirical literature claims that increasing trade globalization will reduce inequality level, but financial globalization is associated with an increase in inequality (e.g. Jaumotte, Lall and Papageorgiou, 2013). Since I find that economic globalization plays the most important role in explaining the link between globalization and inequality, in this part, I decompose economic globalization into trade and financial globalization and study whether these two types of globalization have different impacts on inequality. Columns 3 and 4 in table 4 show the results.
The coefficients of both dimensions are negative and statistically significant at the 10% level. Accordingly, in my sample, the growth in both trade and financial globalization has a comparable effect in reducing inequality. No clear evidence can be found that financial globalization and trade globalization have opposite effects on inequality.
Next, similar to the static panel analysis, I am interested in whether the increased globalization may have different consequences on inequality for countries at different development levels. I create an interaction between globalization and GDP to test whether the financial and trade dimension of globalization in this case has the same impact on inequality in countries with different development levels. Table 5 below shows the results of this analysis, which suggests that the increasing levels of trade openness tend to increase income inequality for lower income countries, but in countries with high development levels, increasing globalization tends to reduce the inequality. The cutoff value is again 8,200 dollars (i.e. Log real GDP=9). This is consistent with the conclusions obtained in the static panel analysis. However, a statistically significant marginal effect of trade globalization is only found in the rich countries. More specifically, it is found that when a country’s per capita real GDP exceeds $22,000 (i.e. Log real GDP=10), one percentage point increase in trade globalization level will reduce inequality by 0.063 percentage point. When a country’s per capita real GDP exceeds US $60,000 (i.e. Log real GDP=11), one percentage point increase in trade globalization level will reduce inequality by 0.127 percentage point. However, the marginal effect of globalization is not significant when the national real GDP per capita is less than $22,000.3 Recall that I found the effects of financial and trade globalization on inequality are very similar without including the interaction term with real GDP. However, here I found the result is insignificant, indicating that the impact of financial globalization on equality does not significantly change with the degree of national development level.
The method of estimation is system GMM. Robust standard errors present in brackets. Education, Inflation, log real GDP per capita, log real GDP per capita square, log population, government spending, and unemployment rate are entered into the instrument matrix views as strictly exogenous. Lagged term of inequality put in the gmmstyle bucket as the endogenous variable. *, **, *** correspond to 10, 5, and 1% significance levels respectively.
Inequality level (1) (2) (3) (4)
Lag. inequality l year 0.425*** (0.126) 0.604*** (0.137) 0.647*** (0.144) 0.547*** (0.160) Trade Globalization 0.575* (0.305) Trade Globalization *GDP -0.064** (0.031） Financial Globalization 0.363 (0.557) Financial Globalization *GDP -0.039 (0.056) Political Globalization -0.124 (0.536) Political Colonization *GDP 0.010 (0.054) Social Globalization 0.477 (0.416) Social Globalization *GDP -0.063 (0.041)
Real GDP per capita -44.366 (27.833) -12.465 (26.424) -2.112 (44.904) -24.175 (21.998) Real GDP square 2.401 (1.467) 0.717 (1.506) 0.016 (2.190) 1.444 (1.169) Population 0.224* (0.134) 0.099 (0.146) 0.272 (0.1240) -0.036 (0.130) Inflation rate 0.037*** (0.013) 0.027* (0.015) 0.025 (0.020) -0.010 (0.016) Education level -0.525** (0.221) -0.445 (0.292) -0.412 (0.341) -0.110 (0.102) Unemployment 0.067 (0.042) 0.025 (0.042) 0.020 (0.058) 0.033 (0.026) Government spending 0.008 (0.016) -0.013 (0.016) -0.009 (0.025) -0.004 (0.012)
Year dummies yes yes yes yes
Observations 1,113 1,113 1,113 1,113
Countries number 74 74 74 74
AR(1) test 0.000 0.001 0.001 0.002
AR(2) test 0.495 0.509 0.536 0.555
Hansen test 1.000 1.000 1.000 1.000
6. Conclusion
This paper is aimed to examine the link between globalization and inequality. Both dynamic and static analyses on a sample of 113 countries during 1990-2015 are used to estimate the relationship between globalization and inequality. In addition to the aggregate level of globalization, I also investigated the effect of different dimensions of globalization in trade, financial, society and politics.
Result reveals that only trade and financial dimensions of globalization pose a statistically significant negative impact on inequality, while political and social dimensions do not statistically significantly influence inequality. In addition, I test whether the increased globalization may have different impacts on inequality for countries at different levels of development. I found that trade globalization significantly reduces inequality for the high income countries with real GDP per capita exceeding $14,000. But for low income countries trade globalization does not show a statistically correlation with inequality, although the sign is positive for countries with real GDP per capita lower than $8,200. A similar pattern is found for social globalization. But for financial globalization, I can not find a different effect on inequality for countries with different income levels.
In conclusion, developed countries are characterized by some features that are likely to make the increase of globalization contribute to the reduction of inequality. However, for developing worlds, increasing globalization level will not contribute to reducing within-country inequality.
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Appendix a
The 113 countries which are include in our dataset are present in below.
Appendix b
Figure 1 to 5 below presents the marginal effect graph of overall, trade, financial, political and social dimension globalization level on inequality. We can see that in figure 1 the graph shows a downward trend. Indicate that as the country’s GDP rises, the impact of overall level globalization on inequality changes from positive to negative. Figure 2 below shows the marginal effect of trade globalization. Similar to overall globalization, the graph of the margin effect of trade globalization on inequality also presents a downward trend. In high income countries (Log real GDP>9), increased trade openness will reduce inequality. However, in low income countries (Log real GDP<9), increased trade openness will increase inequality, although this relationship is not statistically significant.
Figure1: The Marginal Effects of overall KOF Globalization level on income inequality
Log real GDP per capita
Figure2: The Marginal Effects of Trade Globalization level on income inequality
Log real GDP per capita
Figure 3: The Marginal Effects of Financial Globalization level on income inequality
Log real GDP per capita
Note: Base on specification 2 in the table 5. 90% confidence interval is shown.
Figure 4: The Marginal Effects of Political Globalization level on income inequality
Log real GDP per capita
Figure 5: The Marginal Effects of Social Globalization level on income inequality
Log real GDP per capita
