Income inequality in transition states
revisited: A case of the Soviet
commonwealth
J.S. Overal
S2507765
jesseoveral@gmail.com
Supervisor: Dr. A.A. Erumban
Co-Assessor: T.M. Harchaoui
Date: 20.06.2017
Abstract
Income inequality in transition states has often been a topic of interest in economics literature. Whereas income inequality has been a widely-discussed topic in economics, its influence in transition economies still lacks detail. The Soviet Union’s collapse and subsequent transition from communism into individual capitalist states is often seen as one of the most renowned events in global economic history where this occurred. Existing literature investigating this transition commonly have a narrow focus and generalize results obtained. Consequently, a broader range of indicators is investigated and an attempt is made to assess the size of bias created through generalization. Results indicate export complexity, economic growth and to small extent inflation and trade lead to decreased income inequality. Female participation, social globalization and grey economies lead to increased income inequality. Finally, depending on the indicator, a high degree of variance between countries assumed to be similar is found
Table of content
1. Introduction ... 1
2. Defining and Measuring inequality ... 3
3. Income inequality in the former Soviet Union - a historical overview ... 4
4. Literature review ... 6
4.1 Economic growth, Globalization & Trade ... 7
4.2 Institutions (Governmental, Financial, Technological, Labor market and
Education) ... 10
4.3 Demographical factors ... 13
4.4 Socio-Economic changes ... 15
Intergenerational income mobility ... 15
3. Methodology and Data ... 17
3.1. Descriptive statistics ... 21
3.2 Results ... 26
Conclusions ... 33
Limitations ... 35
1 1. Introduction
A steady state based on communism transiting into one based on capitalism is intriguing in itself. However, what is even more interesting is the impact of this transition on several economic consequences. Significant interest in the economics literature lies in measuring development through income inequality. Whereas income inequality has been a widely-discussed topic in economics and has been widely widely-discussed by prominent economists to the likes of Piketty and Kuznet, its influence in transition economies still lacks detail. Kuznet in 1955 already clearly expressed the importance of understanding the many multifaceted aspects of income inequality.
The Soviet Union’s collapse is often seen as one of the most renowned events in the global economic history, in which a state transited from communism into capitalism. Several implications of this transition have been widely discussed in papers discussing transition economies. However, questions regarding inequality in transition economies have been less researched, and much of this research is now outdated due to the data restrictions of that time. Furthermore, many articles aim only to find correlations between income inequality and a small number of predictor variables. This thesis aims to broaden the horizon of inequality research in transition economies by considering the impact of a much broader set of predictor variables on income inequality. This is important due to the large effect this could have on literature on transition economies and income inequality in general. Furthermore, in addition to analyzing the general impact of a select number of indicators on income inequality, this thesis will further explore the magnitude of the impact of these variables in individual countries to identify the extent to which predictors have a varying impact on income inequality in former Soviet countries. While much of existing literature has generalized results, finding the influence of causes on income inequality in eastern Europe and Central Asia taken together or in separate regions, individual differences have not been taken into account. This would not be an issue if differences were (almost) non-existent, however Halmos (2011), Johnson et al. (1997) and Alam et al. (2005), among others, found a clear difference in the correlation of indicators of income inequality in a number of countries in the former Soviet Union. Would this be the case for a broader range of countries and/or influencers of income inequality, then this severely flaws any research on this applied topic. Having said that, individual country analysis will not be implemented, however the impact of common coefficients on individual countries will be inspected so that the differences in the magnitude of impact across countries can be quantified.
2 however it does provide us with the significance of the bias, thus, knowledge to what extent existing results could be taken for facts. Fifth, together with the previous statement, a large range of articles have compared the countries in question to other groups of countries. For example, central European and other eastern European countries, north-eastern European countries with the central Asian countries. However, differences among eastern European countries or among central Asian countries were never considered. This research provides an answer to the question how representative existing results are for the region, while completing existing literature by researching the effect of additional predictors on income inequality.
A quick look at the GINI coefficients supports the assumption that indeed differences among the countries in the same region do exist1. Considering all variables found in an extensive literature review, as long as data is available, an attempt is made to find how similar the transitions in former soviet countries really were. To discover the importance of mentioned indicators in determining income inequality, and the extent of the bias created through generalization. This paper does not provide a means to avoiding generalization completely. However, it should aid in diminishing generalization and provide a better understanding of how similar the former Soviet countries actually were.
Alexeev and Gaddy (1993) provide two interesting scenarios that could help explain the link between the transition to a capitalist state and increases or decreases in income inequality. The first mentions a current state of high income inequality triggers a popular opinion for lower income inequality as most people disagree with the current state. In the case of former Soviet countries this would be most applicable to the southern and eastern European, but especially central Asian countries. However, another theory states lower income inequality with a higher mean income might lead to a faster transition to capitalism due to its better starting point, as more factors are already in place to transit into a capitalist system. Furthermore, as the population of such countries might look more positively at the idea of capitalism due to the fact most of the population already has a more decent standard of living and a higher perception of similar chances for everyone. Taken together, this could explain income inequality trends to a certain extent, however, variances within segments are not explained. Additionally, these ideas remain just theories and are not accepted as truths in this thesis.
To fully understand how income inequality changed following the disbandment of the communist system it is critical to take into account a number of factors. These factors will be discussed in more detail in the literature review, explaining what previous literature has found on the topic. To start, hyperinflation in some countries is often seen to be a significant indicator of income inequality. Furthermore, explanations involve a different impact of exports, which is different between states, different richness in natural resources could also be a very significant influence in this region. Other factors include: economic growth, education, social mobility, female participation rate, birth rate, wealth transfers, export bundle/complexity, institutions, privatization of state-owned enterprises, allowing access to foreign enterprises/FDI, wage differentials/polarization, new technology-inflow through FDI also results in polarization, higher wages (at bottom or top) in foreign-owned businesses,
3 grey/shadow economies. Political freedom, property rights, rule of law, economic freedom and civil-freedom all also indirectly lead to higher income inequality. Finally, also tax rates and government expenditure matter. As found specially to influence inequality in former Soviet countries, social exclusion of minorities influences income inequality. All together this shows a wide range of variables influencing income inequality directly or indirectly in the Soviet commonwealth. Nevertheless, much of existing literature only implements one or few of these variables and as such ignore the effect others might have in the same area.
Difficulty of this thesis lies in finding enough and reliable data for all former Soviet countries find reliable results. To start, for the GINI coefficients, the dependent variable in this thesis, certain countries have no data available from before the end of the Soviet Union: Albania, Armenia, Azerbaijan, Georgia and Tajikistan. Poland, Hungary, Bulgaria, Czech Republic, Romania, Belarus, Russian Federation, Ukraine, Turkmenistan, Uzbekistan, Moldova, Lithuania, Estonia, Latvia, Kyrgyz Republic and Kazakhstan, however, do have this required data. Consequently, this provides a starting point for countries that are included in this paper.
The remainder of the paper is structured as follows. First, chapter 2 discusses the definition and measurement of income inequality. Chapter 3 provides a historical overview of the geographical area of interest. Chapter 4 reviews the existing literature on the subject. In this chapter, globalization, economic growth and trade, institutions, demographic factors and socio-economic factors are discussed. This leads to a chapter 5, where a number of hypotheses are assessed, which are subsequently tested using a panel regression. Following the regression analysis, chapter 6 presents the results, which are afterwards multiplied by the actual scores for each year and taken on average for every six years to assess the diversity of the countries included. To conclude, results the impact in each country and show that indeed numerous variables have a significantly different effect in each country. Concluding remarks are presented in chapter 7.
2. Defining and Measuring inequality
Kuznet (1955) describes the importance of income inequality in his 1955 paper: “Since this distribution2 is a focal point at which the functioning of the economic system impinges upon the human being who are the living members of society and for whom and through whom the society operates”. This makes it a critical determinant in understanding behavioral patterns of people, producers, consumers etc. Consequently, understanding the many different aspects of income inequality is fundamental in understanding inequality and therewith the functioning of society (Kuznet, 1955). First, briefly the concept of income inequality is discussed. Whereas wealth inequality refers to inequality among citizens in net. worth: assets minus liabilities, income inequality refers to income from assets, including human assets. As such income inequality entails wages, salaries, profits and interest on a savings’ account and further assets3
2
4 Much explaining the significance of income inequality is discussed in later stages of this paper, however it is important to understand why income inequality is used instead of other varieties of financial inequality. There are a number of reasons as to why income inequality is a preferred measure of inequality over wealth inequality in measuring financial inequality in former Soviet Union states. First, income inequality is the most commonly used measure of financial inequality. Consequently, literature on the topic is bound to be more extensive than for other measures. This, logically, is a virtuous cycle in economics literature. In this case, however, another advantage of this method appears more critical. Due to former Soviet Union countries having a history in communism, possession of assets, officially, simply did not exist. This complicates the attribution of an increase of wealth inequality to any one indicator, or any factors commonly influencing wealth inequality. Therefore, measuring the effect of the transition on wealth inequality in all applicable countries was deemed impossible. Income inequality as well is affected by the policy prior to the transition. However, it was existing and is simply affected by a larger range of factors4. A third fundamental reason for using income inequality follows the primary aim of this research. Due to suspicions of too much ambiguity in current literature on income inequality involving former Soviet Union transition states, it is critical to review income inequality specifically instead of any other inequality.
In this thesis Gini coefficients are implemented as the common measure of income inequality within the countries of interest. The Gini coefficient entails the area between the Lorenz curve5 and a line of total equality (uniform distribution line). The coefficient is measured on a scale of 0-1, where 0 implies total income equality and 1 implies total income inequality. Gini coefficients are used due to its large usage in existing literature (Worldbank, 2016) and similarly to the reasoning for income inequality to review other literature, this again increases value significantly.
3. Income inequality in the former Soviet Union - a historical overview
Before properly discussing the causes of the increase in income inequality in the former Soviet Union it is important to obtain a basic historical background knowledge on the geographical area of interest to fully grasp how factors influenced income inequality in the countries of interest for this research. Alexeev and Gaddy (1993) found income inequality decreasing in the final ten years of the Soviet Union. Subsequently, in this section and the next, aim is to find what literature has found is the cause of the sharp increase that occurred in the following years. And if possible, what caused the differing ways in which this increase occurred in different countries.
The Union of Soviet Socialist Republics (U.S.S.R), or in short the Soviet Union existed from 1917/22 until 1991. It consisted of many countries during its lifespan, of which
4 For example, Johnson (1997) explains it was already quite possible to obtain different incomes under communist regime.
5
5 15: Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan, were still part until the Soviet Union collapsed, with Moscow as their capital. Back in that time, the Soviet Union was the biggest country in the world (by area) and covered over 100 nationalities. Until March 1990, the Soviet Union was led by the Communist Bolshevik Party, which was the “leading and guiding force of Soviet society and the nucleus of its political system.” Consequently, one of their aims was to keep inequality as low as possible. The U.S.S.R tried to obtain this through several ways among which: nationalizing productive and income-producing assets and allocating resources in a “rational” way, per their socialist perspective. Furthermore, the value of money was destroyed through the creation of hyperinflation by printing large amounts of money, and the abolishing of trade unions and private trade was forbidden. In 1985 Gorbachev took office, from that time onwards, economic liberalization started to become more important through use of his Perestroika and Glasnost policies, followed by the exclusion of the party apparat as the sole leading force behind economic policies and state legislature (multi-party system). After this, no clear choice regarding a centrally planned economy or free-market economy was made and the economy of the Soviet Union went into decline. Despite the fact Gorbachev advocated liberalization, income inequality was still found to decrease in the final 10 years of the Soviet Union´s existence, 1980-1989. (Alexeev & Gaddy, 1993) Post 1991, the U.S.S.R ceased to exist, while numerous countries left the Soviet Union in its final two years. Subsequently, a commonwealth of independent states was put in place. However, the communist party ceased to be in control of the market in many countries. In most this occurred around 1991/1992.
Following the fall of the U.S.S.R the government-created low inequality, unemployment and product prices increased dramatically and became more volatile as new governments of individual countries moved away from communism towards a capitalist society. This transition happened much faster in the more developed republics. (Encyclopedia Britannica)
As the foundation of this research is based upon the idea of differences in increasing inequality among former Soviet Union countries, it is essential to understand where this conclusion was derived from. This conclusion was drawn after a first literature review involving a number of sources6 discussing income inequality in this geographical area and a brief research by the author. This brief research used GINI coefficients as found in the Worldbank database (2016) with data between the time-period of 1989 and 2013 and visualizing this. Detailed results can be found in Figure 1. The result is the following: Inequality in former Soviet states initially all increased, however the flow that followed has been significantly contrasting among all countries involved. This gives the impression countries (socio-)economically responded quite differently to the abandonment of the Soviet Union, or at the very least, changes in factors influencing income inequality had a different level of impact on inequality in these countries.
6 Figure 1
Development of GINI coefficients across countries
Source: Own calculations. Data from World Development Indicators (Worldbank, 2016)
4. Literature review
Investigating the influence of many variables on income inequality as well as the difference of the impact of each variable among countries individually requires finding what variables influence income inequality. From data and literature many variables impacting income inequality are found. Two aspects of variables are discussed. First, variables generally found to influence income inequality across the world. Followed by indicators found to have an effect specifically in the former Soviet Union. While all variables might be more, or less present in the countries in question, only those which have been found to be much more present are discussed. Substantially reviewing all variables individually, we aim to provide a broad overview of factors of influence on income inequality and a number of differences between countries based on the most applicable current academic literature.
7 great transformation” and followed after national economies (partially) opened towards the global market. Following “the great transformation” a large number of factors influencing income inequality changed in the degree of their presence. Such factors include: economic growth, education, social mobility, female participation rate, birth rate, wealth transfers, export bundle/complexity, institutions, hyperinflation, richness in natural resources, privatization of state-owned enterprises, opening to foreign enterprises/FDI, wage differentials due to polarization, new technology-inflow, grey/shadow economies, economic freedom, civil-freedom, tax rates and government expenditure. Finally, also demographic changes occurred. Many of these factors resulted from, or led to, industrialization and de-modernization. (Simai, 2006) This section aims to align discussion of the predictors in the Soviet Union with those found across the world. First, economic growth, globalization and openness to trade are discussed. This is then followed by the impact of institutional factors on income inequality. Furthermore, demographical factors’ influence will be explained, whereas this literature review will be concluded with a brief discussion of socio-economic changes.
4.1 Economic growth, Globalization & Trade
Much of previous literature has claimed economic growth has a positive relationship with income inequality. (Lorenzi, 2016) In contrast, income inequality was also found to slow down economic growth (Deaton, 2012) This contradiction creates a continuous discussion. Economic growth is stimulated through political freedom, property rights, the rule of law, education, social mobility, and wealth transfers. (Lorenzi, 2016) Examples of more concrete causes of economic growth (possibly) leading to increased inequality involve e.g. Russian federation’s increase in competitiveness due to the high devaluation of the ruble. (Gregory & Lazarev, 2004) This logically created an improved competitive position. In general7, economic growth does appear to have a positive effect on income inequality, due to its disproportionate positive effect on the top income group8. This is explained by extensive literature on the link between the top percentile and economic growth. As economic growth increases, a logical consequence involves higher pay-outs for top income groups, leading to higher income inequality. (Roine et al., 2009) However, this effect might not applicable to the former Soviet Union. A different perspective states continuous growth leads to a larger middle income group and larger savings for lower income groups and, therefore, lower income inequality. This effect is found to be different for highly developed and lower developed countries (Barro, 2000). Developing countries are more likely to find a negative correlation between economic growth and income inequality if all other factors are similar. Taking every aspect into consideration the logical result is that economic growth affects income inequality slightly. This expectation is confirmed by Barro (2000)
In the appendix Figure A1 depicts a figure as created by Roaf et al. (2014), which illustrates the initial sharp decline in GDP following the exit from the Soviet Union for all states. This decline subsequently rapidly recovers. After this, for each country/set of countries
7
Lorenzi, P. 2016, Roine et al., 2009
8 at their own pace, the GDP increases past the level of GDP each country had at the moment before the collapse. One can see the sharpest decline in the Baltics, followed by the sharpest rise as well. Meanwhile, countries such as Bulgaria and Romania (the red line) following a more gradual trend. As to academic research on effect of income inequality in the region, Halmos (2011) found a significant effect of GDP growth on income inequality, while Bandelj & Mahutga (2010) did not find a significant outcome.
A factor partially determining economic growth more clearly appears to be correlated with inequality. Hartmann et al. (2017) find that inequality appears to strongly related with a country’s export complexity. They observe that nations which export more complex products, measured by the Economic Complexity Index, exhibit lower income inequality levels than countries whose export products consist of more simple goods. Even after controlling for aggregate measures of income, institutions, export concentration and human capital, the relationship remains significant. In addition, the authors showed that when economic complexity increases, decreases in income inequality tend to follow. The previous result can be explained in a variety in ways. One significant reason in which it increases income inequality is that export complexity is a proxy for production structure. This way it could potentially explain a part of the high income inequality in Russia.9 According to Hartmann et al. (2017), the crude petroleum sector was correlated with a high Gini coefficient. Assuming the same relation in the former Soviet Union would explain the high Gini coefficient in countries with a high natural resource usage. The export structure thus explains part of the income inequality since the property rights of the natural resources belong to a select elite. Furthermore, most citizens do not profit a lot from the natural resources since the sector’s capability to provide jobs is limited, and hence effectively increasing income inequality. McMillan and Rodrik (2014) confirm this assumption, as shifting investment to natural resources sectors was found to be growth reducing due to the sector’s low absorptive power. Furthermore, Shelley, L. (1995) found during the Soviet Union era, people in the natural resource sector earned significantly more than those serving in the military sector. This was explained by the inability to acquire assets or higher general income. Employees working in firms with improved prospects could profit from lucrative business, due to its possibility for civilian use. Following the transition this advantage related to employment in the natural resource sector diminished. Another explanation for export complexity influencing income inequality is that complexity is a very concrete and strong proxy for a country’s quality in institutions, for example education (Hartmann et al., 2017).
Openness to trade, as measured by imports, appears to lead to an increase in income inequality. Heshmati (2006) and Miller (2001) state imports consisting of products produced by unskilled labor has led to a significant drawback in demand for unskilled labor within national markets. This fact, together with the fact imports as a share of GDP in the U.S. has gone up, explains how wages at the bottom have decreased in the U.S. Furthermore, also with evenly divided increases in imports, it is likely additional imports lead to increased income inequality. This is a consequence of import-competing industries tending to consist of labor-intensive industries such as manufacturing sectors. Meanwhile, export-competing industries usually do not. (Gordon & Dew-Becker, 2008) Finally, Gordon & Dew-Becker (2008) state
9
9 the effect of imports on income inequality decreases as countries develop, as countries leave import-substitutable products’ sectors for products less prone to competition.
Globalization leads to changes in income inequality through resource allocation, market competition and access to new technologies (Lee, 2006), frequently through foreign direct investment. Freeman (2010) also shows FDI and portfolio flows help explain income inequality. Freeman argues increased FDI flows lead to higher income inequality through the improved allocation of physical capital (machinery) and financial capital. Consequently, skill-specific technological change occurs, which leads to the higher skill-premium/wage polarization, as mentioned previously. Thus, this factor has significant overlap with technology. Adams (2008), however, disagrees on the assumed effect on income inequality, as FDI leads to higher economic growth. An increase in in-flow of FDI might have a negative effect on the income inequality of a developing country due to the increase in middle income groups. Furthermore, through an increase of technology demand increases for the same middle income group, thus diminishing income inequality. However, explaining the reason why the effect of FDI on income inequality is ambiguous globally is possible with Adams’ paper. (2008) Adams (2008) finds the effect of FDI on income inequality is strongly affected by regional differences. Consequently, this strengthens the need for this thesis.
10 Meanwhile, results by Bergh and Nilsson (2010) show, similarly to Freeman, globalization generally leads to higher income inequality. However, contradictory to Freeman they find this effect was largely derived from social globalization, a phenomenon measured by them through outgoing phone-traffic, internet users and the number of IKEA’s and Mcdonald’s restaurants per capita. Following Atkinson (1997) social globalization might lead to differentiated social norms, leading for example to changes in union behavior and other international integration, consequently leading to increased income inequality. As found by Bergh and Nilsson (2010), this is especially the case in developing countries.
A usually assumed to be critical model for openness to trade is the Heckscher-Ohlin model, which assumes opening up to trade will lead to decreased inequality in developing countries10 and the other way around for developed countries. This assumption is based on the idea growth lowers the number of low-income workers in a population and widens the middle-income group caused by integration in the world market and demand being highest for these sectors. More clearly explained, developed countries use their capital/high-skilled labor abundance and developing countries their low-skilled labor abundance as external demand increases for countries’ abundant factors. Trade openness can be measured in a variety of ways, of which two involve trade as a percentage of total GDP and average tariff protection is another. Trade-GDP ratio appears to have a significantly negative effect on income inequality, while average tariff protection has a slightly positive impact on income inequality, however this effect is negligible (Roine et al., 2009). Finally, we can conclude it is relatively easy to state openness to trade generally leads to lower inter-country inequality. However, within countries openness to trade, which is discussed in this thesis, appears to lead to higher income inequality within developed countries and lower income inequality in less-developed countries. (Roine et al., 2009 and Adams, 2008) As explained by Bergh & Nilsson (2010) openness to trade is linked to greater income inequality in case of technology in-/outflow. However, as expected through Heckscher-Ohlin, trade between developing countries might not foster income inequality, due to this trade not creating the same knowledge/technology flow. (Meschi & Vivarelli, 2008)
4.2 Institutions (Governmental, Financial, Technological, Labor market and Education)
As explained by Tintin, (2013), foreign direct investment is largely dependent on the quality of institutions. Economic freedoms in an open market economy, the countries’ ability and skill to cope with challenges and vulnerability, political rights and civil liberties are found to have a positive relation with the degree of FDI (Tintin, 2013) and as such could indirectly influence income inequality in the region. Aside from institutions affecting income inequality indirectly, a number was also found to have a more direct effect. Perugini and Pompei (2015) discovered structural reforms in institutions were one critical reason for increased competitiveness in former Soviet countries. The duration of these transitions and the sequence
10
11 of reforms, according them, also were deemed of significant importance regarding income inequality.
One major reform is the example of privatization of state-owned natural resource-owning companies in Russia, leading to the creation of a small number of oligarchs. Furthermore, a reason that could significantly cause the increase in income inequality is the fact prices of oil, gas and other energy sources increased significantly and rather unexpectedly around 1998. (Perugini & Pompei, 2015) Other reforms that could influence income inequality to be considered involve government spending and taxation, financial institutions and their development, technological progress, labor market liberalization and finally educational reforms.
Government spending tends to assist lower income percentiles, lowers the relative income of the 91-99th percentile and leaves the top 1 percentile unharmed (Roine et al., 2009) and thus, has a decreasing effect on income inequality. According to Johnson et al. (1997), low government spending leads to neglect of public goods ‘Such public goods include law and order, effective tax and regulatory institutions, and relatively uncorrupt public administration.’ Consequently, lower standards of these institutions bring companies to switch from one country to another and from formal sectors to informal sectors. Furthermore, another form of government spending involves social safety nets. In case of absence of such safety nets, a logical consequence is poor people becoming poorer. Government spending is often closely and positively related to the height of taxes within a country.
Much research has aimed to describe the role of taxes in the increase of income inequality. Income inequality is strongly affected by a progressive tax-system, where more progressive taxation tends to be linked negatively to income inequality. (Piketty & Saez, 2003) One reason explaining how progressive taxation theoretically could lead to increased income inequality is wages of top-earners increasing to avoid having them look for a different job with lower taxation. (Slemrod, 1996) As Slemrod (1996) discusses, higher taxation for top incomes encourages top income-earners11 to avoid taxation completely. However, tax evasion as well leads to lower (statistical) income inequality. Consequently, it is a minor complication to finding income inequality. Piketty & Saez (2003) mention therefore that in either way progressive taxation generally has been found to lead to lower inequality in income. Johnson et al. (1997) also put focus on the avoidance of taxes, but from a company’s perspective. In this case, a company would possibly leave a country with a progressive tax system. As this results in decreasing FDI, this would lead to diminishing income inequality. As found by Mollick (2012) during the war times taxes on top bracket-wages did increase12 along with lowering income inequality. As such a more logical and perhaps less paranoid view than Slemrod’s on the causal relationship between progressive taxation and decreased income inequality involves simple redistributive factors. It is acknowledged this remains a highly ambiguous subject and this is not a concluded discussion, essentially, due to progressive taxation being a broader concept than including income taxes alone. 13 One limitation of current literature on the subject involves reverse causality. As Rosser et al. (2000) explain, in
11
And companies with higher profits as explained by Johnson et al. (1997) 12
To finance the war
12 a society with high income inequality trust in the government and other institutions is generally lower. Consequently, firms and individuals switch to informal sectors. Strong evidence strengthening the claim, however, remained absent. Nevertheless, shadow economies will be further discussed at a later stage.
According to Dabla-Norris et al. (2015), better functioning financial systems, or “Financial deepening” at an early stage of development leads to increases in income inequality as wealthier parts of the population have better access to means of financing. As countries become more developed this effect on income inequality decreases. This mitigating effect is derived from the fact general access to external financing improves, also for less-wealthy parts of the population. Consequently, as external financing becomes more available poorer people gain access to improved loans, consisting of lower interest rates, subsequently leading to more opportunities for the lower income-classes and converging income inequality. This is partly confirmed by Roine et al. (2009) finding a widening effect on income inequality deriving from financial development measured by stock market capitalization and bank deposits. Roine et al. (2009) confirm a stronger effect for less-developed countries.
13 Labor market institutions influence the ease of transferring labor across borders and/or distances. As these institutions become more flexible it becomes easier for employees to be moved where they are required, leading to improved reallocation of human resources. Consequently, the skill-premium is likely to go up due to specific skills owned by a select number of people, while more widely available skills are now more easily tapped into. Furthermore, lower participation rates in, and the general existence of, less labor unions leads to lower bargaining power of primarily the lower educated, leading to greater inequality (Gordon, Dew-Becker, 2008). Presence of labor unions and participation rates here within, generally lead to lower income inequality, as the caused increased inequality between different sectors is smaller than the diminished inequality within sectors (Card et al., 2004).14 Gordon & Dew-Becker (2008) state unionization over the period of 1973-2001, contributed to a 14% increase in variances of male wages. The same effect is seen for a lower minimum wage, the lower it is in comparison to the median wage, the greater the inequality as found by Jaumotte and Osorio-Buitron (2015). More concretely, Gordon and Dew-Becker (2008) found consistent evidence showing minimum wage had a more significant positive effect on income inequality for the female population, while unionization had a bigger effect on that for males. Gabaix and Landier (2008) found a clear correlation between market capitalization of top firms in the U.S. and CEO wages, showing more liberalization leads to higher income inequality. On average labor market regulation causes a decline in income inequality (Calderón and Chong, 2009)
Education is inseparably correlated with income inequality through its impact on income of countries’ populations as a whole. However, research has found widely differing opinions regarding its exact impact. One factor mentioned previously involves higher education leading to jobs which are complemented by automation, while lower education leads to jobs to be replaced by automation (Acemoglu, 1998). In one way, this means higher inequality, as mainly people who can afford better education will benefit from this and consequently earn more in return. In contrast, however, a larger number of higher-educated leads to more competition amongst the higher-educated, subsequently resulting in lower wages, as skill-premiums are erased (Gregorio & Lee, 2002). Meanwhile, lower educated would likely start to earn more due to lower availability of lower-educated personnel. Furthermore, Muller (2002) found a relation between the number of high-school dropouts and mortality and income inequality. Gregorio & Lee (2002) similarly found concluding evidence higher education and especially a more equal division of education lead to lower income inequality. Finally, O’Neill (1995) found substantial evidence education’s ambiguous effect on income inequality is explained by geographical factors. Whereas developed countries experience a decrease in income inequality following improved education, this is not the case in developing countries, where the rate of return on education is significantly lower.
4.3 Demographical factors
Demographical factors include all factors influencing income inequality that arise from characteristics of the populations of the countries in question. These factors range from
14 fertility and mortality to the age structure of a country and the number of women in the working population. Finally, demographic factors also include migration.
As birth rates increase and therefore a population growth or a shift in the make-up of a country’s population occurs, this generally leads to a reallocation of resources across these demographical factors. “Intergenerational allocation of resources” as called by Lam (2001). Similarly, a higher mortality rate logically has the same effect as population decreases, or at least a reallocation occurs as employees retire or pass away. Furthermore, history has found on average lower educated people tend to have more children, as such a higher birth rate is often caused by a sharper increase of lower educated people than high educated people. Following this conclusion, it is reasonable to assume a higher birth rate, means a larger number of lower educated people and consequently leads to higher income inequality. (Dahan & Tsiddon, 1998, Chu & Koo, 1990) Following up this conclusion, also Pestieau (1989) found that as lower intelligence is correlated with larger families, the size of families is positively related to income inequality. In contrast, educated people tend to become wealthier, on average, live longer, and as such, a high mortality rate could be a sign of a large number of lower educated people and the final conclusion; also mortality15 is positively related to increased income inequality. (Pestieau, 1989)
Even though age structure is largely caused by fertility and mortality, this specifically remains a significant determinant of income inequality as well. As explained in Lam (2001), a workforce largely dominated by an older group of people might have a different level of income inequality than one dominated by a younger group. Wage does have a proven relationship with the age of a population, but it remains for now uncertain that it also is related to income inequality. (Lam, 2001)
The participation rate of segments in countries’ populations differ significantly. Particularly, the participation rate of women in the workforce is critical as women make up half of countries’ populations. Furthermore, a sharp increase in income inequality worldwide has occurred relatively recently. Simultaneously, a sharp increase in the female participation rate in the workforce was also identified. (Cancian & Reed, 1999) This trend, together with the simple logic presented beforehand regarding the possible impact half of the world’s population could have on income inequality. First, it presents a plausible image of a decrease in income inequality caused by a higher participation rate of a country’s female population. However, results are conflicting, largely due to the trend in increases of income inequality that occurred simultaneously. Results from prior research have remained relatively conflicting. The main conclusions, however, drawn from a higher female participation rate, are that income inequality does decrease slightly. This conclusion derives from the large impact of an increase in participation of women in society. One explanation found a decrease in income inequality among women over recent decades (Gordon & Dew-Becker, 2008), which was caused by minimum wages. They claim the rise in income inequality would have been even higher without the increased participation rate. (Gordon & Dew-Becker, 2008) Opposing views state due to the high correlation between high incomes earned by countries’ male population and high wages earned by their wives, as found in a study by Cancian and
15 Reed (1999) an increase in female participation could also have a positive effect on inequality.
Immigration and emigration as well shape the structure of countries’ populations. As explained by Williamson (2006) it is the skill-selectivity of immigration that matters. Immigrants arrive in a country and generally start to work there, creating a higher level of competition on the labor market. Emigration has exactly the opposite effect. Together this has a positive or negative effect on income inequality, depending on the fact if the additional competition arises in the high-skilled, or the low-skilled end. This undoes, largely, the theory of scarcity as explained by Heckscher and Ohlin16. For immigration to the US labor market, Pestieau (1982) found an unequalizing effect of immigration. This effect was caused by immigration largely existing of low-educated immigration. Part of the inequality increase is explained by the extra competition caused at the bottom and thus, lower wages in lower and middle-class jobs, leaving the higher educated population relatively unharmed. However, the most significant explanation for the phenomenon is the fact low-educated immigrants in the US tend to earn less than nationals of the country. As such an extra dimension is given to the low-income groups and income inequality is increased. This is further explained by Ottaviano and Peri (2006) showing the effect of immigration on low-income nationals is negligible17, but is much higher for immigrants in similar occupations. For middle-income groups the effect is significant in both cases.
4.4 Socio-Economic changes
Socio-Economic changes consist of (intergenerational) income mobility, previously discussed social globalization and social exclusion of minorities.
Intergenerational income mobility
As found by Piketty (2013), inheritances are a significant reason for wealthier classes remaining wealthy. Meanwhile, Hertz (2005) found similar results for especially the black population in the U.S. and lower income classes in general. Where he discovered an intergenerational correlation approaching 0.4. Taken together, a clear positive relation is identified between intergenerational income mobility and income inequality.
Finally, the topic of social exclusion of minorities as a reason for income inequality. Occasionally this phenomenon appears to be a recent phenomenon. However, Bandelj and Mahutga (2010) tried to identify socio-economic changes that influenced income inequality in post-socialist Europe. They discuss discrimination against ethno-national minorities leads to an increase in income inequality. During the communist rule minorities like the Roma were relatively well integrated into society. Under communist rule they benefitted from ‘full employment, industrialization, socialist urbanization, the housing policy, 'free' healthcare, and
16 Due to an increase in globalization and transferability of human capital, large numbers of low-skilled labor in contrast to land is available. This could create income inequality. Due to easier emigration, this effect has become less important
16 mass education’, which all worked to corporate the Roma more into society (Magyari et al., 2001). However, these factors partially fell away after the transition to a free market. This led the minorities to be more vulnerable to ethno- national exclusion. which is what happened to ethnic Russians in the Baltic states of Estonia and Latvia (Bandelj and Mahutga, 2010). Ethnic Russians in Estonia could only acquire Estonian citizenship if they knew the local language and history. However, the problem was that they did not speak the language since Russian used to be the official language during the Soviet era. Additionally, it also became mandatory to possess the ability to communicate in Estonian to acquire a job. As a result, ethnic Russians were unable to find work and therefore inequality between ethnic groups increased. Bandelj and Mahutga (2010) test for this hypothesis and conclude that having ethno-national minorities in a country is significant in explaining inequality in a country. Hence, social exclusion of minorities could potentially lead to increased levels of inequality.
Finally, other possible causes vary widely from all previous mentioned causes of income inequality. These, however, are quite simply defined. Some phenomena do not belong to any categories, or do, but occur rapidly or only have a brief effect on a country’s income inequality. Such phenomena can include hyperinflation, depressions, war, a baby boom, or a large natural disaster. Factors having a positive effect on income inequality include the availability of natural resources and capitalization (Roine et al., 2009) for example, leading to higher income inequality. Enforcing property rights (IPR) is also positively correlated to income inequality in both developing and developed countries. (Adams, 2008) Finally, corruption tends to affect income inequality positively (Gupta, 2001)
17 globalization is expected to lead to increased income inequality resulting from social norms, due to the developing-country setting. Following these social norms differences in wages are expected to be more acceptable.
Contribution to the literature with this thesis is derived from several factors. First, a wide range of predictors have been included instead of the common narrow focus on few. This provides a much broader view on the topic, aiming to avoid ambiguity. Secondly, more data is currently available and is applied, which should provide more reliable data than previous studies have been able to achieve, leading to improved conclusions. Related to the previous sentence, a number of factors have an ambiguous relation with income inequality. It is possible this paper could shed more light on this aspect of income inequality and hopefully provide more clarity on the correlation between these predictors and income inequality. Furthermore, this paper will provide conclusions regarding the effect of these known predictors for income inequality in former Soviet countries. Generating knowledge on the causes of shifting income inequality in developing countries is highly important in transition economics literature, and therefore expanding existing knowledge on this topic is. However, the main contribution of this paper to existing literature is that it will discover if these predictors have different contributions to income inequality across similar countries, where former Soviet countries especially. To conclude, this thesis assists in assessing the reliability of existing literature on income inequality up to this moment and tests the quality of income inequality literature with focus on the former Soviet Union.
3. Methodology and Data
“When several changes are occurring simultaneously, however, it becomes difficult to identify the contribution of the individual factors, and to assess their relative importance to the overall trend in inequality” (Mookherjee & Shorrocks, 1982)
This unfortunately will remain the main limitation of the research performed. However, differences in the effect of certain variables in a variety of countries should still show up in the research results. As the latter is the main goal of this paper the objective is to set the research up as such that data for as many variables is required and compared amongst all possible countries. Subsequently, it is deemed possible to find differences in the impact of these predictor variables on the dependent variable.
As mentioned, the objective of this thesis is to assess reliability of current existing literature on the subject of income inequality, to find differences between predictors of income inequality across former Soviet countries and to provide a final conclusion on the topic of what actually causes income inequality.
18 ambiguous effect on income inequality. However, potentially could be very important and will consequently be used in the regression. Inflation could play a significant part in explaining income inequality due to hyperinflation following the transition from communism to capitalism. Immigration is expected to have a negative effect on income inequality due to immigration commonly consisting of a relatively high number of lower educated immigrants who perform lower-paid tasks. Female participation is expected to cause a slight decrease in income inequality. This is largely due to increases in household income expected in low-income and middle-low-income group, despite the fact research found a high correlation between high-earning men and the income earned by their wives. Natural resource usage is expected to have a positive correlation with income inequality, essentially due to privatization, oligarchs and low wages in these sectors. The latter is also the explanation for a negative correlation for export complexity, as wages are higher in more complex sectors. Birth rate is expected to be positively correlated with income inequality, due it being closely aligned with a low education. Next, social globalization is expected to lead to increased income inequality, due to our developing country-setting, following social norms finding a difference in wages more acceptable. Economic globalization and political globalization are expected to be insignificant due to prior results obtained by Bergh & Nilsson (2010). Shadow economies are expected to be positively related to income inequality due to falling tax rates and weakened safety nets. Finally, education is expected to have a slight positive effect on income inequality in former Soviet countries, again due to the developing country-setting.
More indicators were discussed in the literature review. Such indicators include government expenditure, taxation, development of financial institutions, corruption and intergenerational income mobility. Despite the fact regressions have been performed using capitalization and portfolio investment as indicators. Due to a lack of data these had to be dropped out of the regression.
Taken together factors for which sufficient data was available can be categorized in four groups. Globalization, trade and economic growth is first, institutions, demographical factors and socio-economic factors. These categories, individual factors, reasons for factors’ usage, measurement and sources can be found in Table 1. Based on this information we formulate the following model:
IE , = β0 + β1 FDIinflow , + β2 EconGrowth , + β3 Inflation , + β4 Trade , + β5 Tariff , + β6 FemPart , + β7 NatResDepl , + β8 ExpCompl , + + β9 NetMig , + β10 SocGlob , + β11 birth rate , + β12 Educ , + β13 EconGlob , +
β14 PolGlob , + ε ,
19 country as a percentage of the gross national income to calculate the effect on natural resource usage on income inequality. ExpCompl measures the effect of export complexity measured by total product output divided by the product diversity within a country. NetMig discusses the net migration into a country. SocGlob, EconGlob and PolGlob discuss the varieties of globalization: Social globalization, Economic globalization and political globalization. Here, social globalization is calculated through personal contacts, information flows and cultural proximity. Economic globalization is measured through financial flows such as FDI, Portfolio investment and trade and restrictions and (hidden) import barriers and thus encompasses several indicators. Furthermore, political globalization is measured through a country’s number of embassies, treaties and its membership in international organization. Finally, birth rate is birth rate per 1000 people and education is a variable measuring the number of citizens having concluded a bachelor degree.
Data is taken over the period 1988-2005. As in common in literature investigating an abrupt change, some years prior to the change must be included. Taking into account only the final year before the transition would leave room for speculation, that perhaps an indicator had a certain effect, due to the expectation the transition was soon to come. Sources from where the data was derived for the regression analysis include the World Bank Development Indicators Database, which was used due to its completeness and its use in Bergh & Nilsson (2010), UNCTAD FDI database, IMF, the World Income Inequality Database (WIID), the KOF index by ETH Zürich for social globalization, MIT: Observatory of economic complexity for export complexity and the Barro-Lee Database for education. The use of these databases is not flawless. This is especially the case with the WIID database and other databases measuring income inequality. Limitations applicable to these kinds of databases derives frequently from the fact it is set up from several data sources. These data sources occasionally experience differences in definition of the measurement and construction of income inequality. Consequently, literature (Bergh & Nilsson, 2011) recommends WIID as the best option after the Luxemburg Income Study (LIS), which was incomplete regarding former Soviet countries. After LIS, the WIID database is the most complete for the years investigated and has often been used in existing literature. FDI flow is found in the UNCTAD FDI database which is, similarly to WIID, the most frequently used database for this indicator. KOF Index by ETH Zurich was used due to its exclusiveness in having available complete globalization data for all three kinds of globalization, and its usage in Bergh and Nilsson (2010), while MIT’s Observatory of economic complexity was used due to its exclusiveness on export complexity. The Barro-Lee database for education has been used for its completeness in information on bachelor degrees and its use in Meschi & Vivarelli (2009). Meanwhile, World Bank data was used for other data due to its completeness and the fact it is frequently found in existing literature. Finally, some limitations have to be taken into account for this database. Similarly to the WIID database, also the World Bank data is based on a variety of external sources. Although these sources often include reliable databases such as IMF, difficulty lies in determining differences in measurement among sources.
20 importance of this methodology lies in its addition of a larger number of predictors. Combining the availability of more data and the determination of new indicators of income inequality in recent literature, it is possible to discover which indicators had a significant effect on income inequality following the transition of communism into capitalism in the former Soviet Union. Furthermore, after this regression has taken place it is possible to compare the scores for each country by multiplying coefficients with the scores on each indicator of each country. All together, this methodology enables us to investigate the importance of global indicators of income inequality in the transition-setting of the former Soviet Union and assess the reliability of existing literature by determining inter-country comparability within a region that is regularly generalized.
Table 1. Variables, definitions and their expected effect on income inequality
Variable Expected
sign
Measurement Explanation
(Measurement)
Data source
Income inequality GINI coefficient Most common indicator for
income inequality18
World Bank &
World Income Inequality Database (UN) Independent variables FDI inflow +/- % of GDP. Investment in equity, enterprises, debt and reverse investment
Measures openness to foreign investment
World Bank,
UNCTAD
GDP growth +/- Annual growth in %,
in local currency.
Proxy for economic growth World Bank
Inflation - Consumer price
index. Annual %
Measures inflation, critical especially in case of hyperinflation
World Bank/IMF
Trade +/- % of GDP “sum of exports and imports of
goods and services”
World Bank
Tariff rate +/- Tariffs weighted by
product import shares
Measures protectionism/ Openness to trade
World Bank
Net. Migration - Immigrants–
Emigrants (5-year period)
To measure the effect of migration World Bank
Female participation - % of total 15+ female
population
To measure effect of a higher female participation
World Bank
Natural resource usage + Naturalresource
depletion (% of Gross National Income)
To measure the effect of
dependency on natural resources
World Bank
Social globalization + Personal contacts,
information flows
and cultural
proximity19
Social globalization was found to influence income inequality by Bergh and Nilsson (2010)
KOF index by
ETH Zürich.
Economic globalization +/- Actual flows (FDI,
Portfolio investment,
Usage in Bergh and Nilsson
(2010). All-encompassing
KOF index by
ETH Zürich.
18
For more information, read the introduction and start of literature review
21 trade) and restrictions
((hidden) import
barriers)
indicator covering national economic indicators.
Political globalization +/- Nr. of embassies,
membership in
international
organizations, treaties
Usage in Bergh and Nilsson (2010). Proxy for political situation of countries.
KOF index by
ETH Zürich.
Export complexity - Export complexity
scores by MIT
Country’s productive output/ Product diversity
MIT: Observatory
of economic
complexity (OEC)
Birth rate + Rate per 1000 people. Possible “Baby Boom” effect /
Increase in population in absence of migration
World Bank
Education - Concluded tertiary
education. (5-year period)
To measure the effect of higher education on income inequality
Barro-Lee Database
Shadow economy + DYMIMIC estimates Schneider (2009)
3.1. Descriptive statistics
The descriptive statistics in Table 2 show the data from 1988 to 2005. Countries included in the research are: Belarus, Bulgaria, Czech Republic, Estonia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Poland, Romania, Russia, Ukraine and Uzbekistan. Slovak Republic, Tajikistan and Turkmenistan lacked the required data and similar countries were available, consequently they were excluded. One limitation requires some consideration. Ukraine did not possess any data for the tariff indicator. To resolve this problem, the most similar country, Russia’s data was also used for Ukraine. Investigating the dataset shows that FDI inflow became more prominent after opening their countries’ borders. Similar phenomena appear for shadow economies and with a delay of a number of years also for the growth of national GDPs. Meanwhile, logically, the opposite occurred for net. migration as a sharp increase in emigration in many countries followed the disbandment of the Soviet Union. At the same time, also inflation went up sharply, often up to the level of hyperinflation, immediately following the end of communism. In the years that followed, one notices significant contrasts among the variables included, which explain the sharp trends that were seen for income inequality in Figure 1. Most noticeably one can see the contrasts in economic growth varying from -22.93 to 13.50 growth rates, inflation illustrating the impact of hyperinflation. Other important differences are found for migration and natural resource usage. High GINI levels are visible for Kyrgyz Republic, whereas also Russia, Uzbekistan and Moldova experience GINI levels of over 40%.
Table 2. Descriptive statistics
(1) (2) (3) (4) (5)
22 Year 270 1,997 5.198 1,988 2,005 GINIUNCTAD 267 31.42 5.670 19.40 53.70 Birthrate 270 13.31 5.881 7.600 35.30 FDIinflow 203 3.661 3.358 -0.180 22.33 EconGrowth 217 1.933 6.655 -22.93 13.50 Inflation 211 106.5 408.0 -1.146 4,735 trade 221 86.76 28.65 26.26 158.4 tariff 228 3.722 2.461 0 11.28 NetMig 270 -3,552 134,171 -302,349 503,943 FemPart 239 63.19 6.034 49.20 75.10 NatResDeplGNI 178 4.725 9.417 0.0268 55.97 SocGlob 241 51.13 16.30 21.76 81.42 ExpCompl 210 0.575 0.508 -0.800 1.640 ShadEco 240 33.93 9.698 13.40 55.30 Educ 270 9.442 4.651 3.300 22.30 EcoGlob 241 55.08 14.83 20.71 91.83 PolGlob 241 56.09 25.27 6.264 92.85
Due to data restrictions, many variables lack some observations. This causes a bias; however, this was predicted due to much data being unavailable around the time of the transition. Despite this limitation, more data is still available than was at the time of much of existing literature, causing the importance of this research to persist.
Prior to implementing the panel regression, it is important to avoid multicollinearity. Multicollinearity was avoided in first place by an eye-ball test with usage of a correlation matrix. This eye-ball test was followed by a VIF test. Table 3 presents the correlation matrix.
23 Following the eye-ball test economic globalization has a relatively high correlation with FDI inflow, trade, tariff rates and social globalization. This was to be expected due economic globalization having included the first three indicators in its calculation. Political
globalization has a relatively high correlation with social globalization and export complexity. A high correlation is furthermore found for social globalization and export complexity.
Despite some relatively high scores, the result is inconclusive.
To be certain that there was no multicollinearity problem a test using Variance inflation factors was performed which can be found in the Appendix as Table A1. Following the VIF scores no multicollinearity problem was determined. The only two variables with VIF scores of over five are social globalization and economic globalization. To be certain these variables do not affect the results significantly three regressions have been implemented: one regression including all indicators, one excluding economic globalization and one excluding social globalization were performed.
Before running the panel regression, a Hausman test is used to determine whether to use a fixed effects model or a random effects model. This test concluded to use a random effects model.
Considering previous statements the following are the results of a panel regression of the following Equation 1 :I_INEQ , = β0 + β1 FDIinflow , + β2 EconGrowth , + β3 Inflation , + β4 Trade , + β5 Tariff , + β6 FemPart , + β7 NatResDepl , + β8 ExpCompl , + + β9 NetMig , + β10 SocGlob , + β11 Birthrate , +
β12 ShadEco , β13 Educ , + β14 EconGlob , + β15 PolGlob , + ε , Where c stands for country and t is time (years: 1988-2005).
Table 4
24 (3.45e-06) FemPart 0.238*** (0.0827) NatResDeplGNI -0.0686 (0.0510) SocGlob 0.116** (0.0536) ExpCompl -4.883*** (1.269) ShadEco 0.227*** (0.0741) Educ 0.0884 (0.131) EcoGlob 0.00430 (0.0719) PolGlob 0.00650 (0.0300) Constant 10.87 (8.140) Observations 151 R-squared 0.370
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
As seen in Table 4, birth rate, FDI inflow, inflation, trade, tariff rate, net migration, natural resource usage, education, economic globalization and political globalization all initially result in an insignificant result. New regressions are implemented where first economic globalization and then social globalization have been excluded to confirm that
multicollinearity is not a problem. Both regressions did not change existing results. Therefore, it can be concluded multicollinearity was not a problem. To continue, tariff rate and trade in theory both measure openness to trade and tariff rate has a high insignificance level (0.751). it was decided to leave out tariff rate to investigate if this would significantly affect the results. This suspicion was confirmed as shown in Table 5. Observations increased logically, as tariff rate consisted of flawed data and inflation and trade became significant.
Equation 2: IE , = β0 + β1 FDIinflow, + β2 EconGrowth , + β3 Inflation , + β4 Trade , + β5 FemPart , + β6 NatResDepl , + β7 ExpCompl , + + β8 NetMig , + β9 SocGlob , + β10 birth rate , + β11 ShadEco , + β12 Educ , + β13 EconGlob , + β14 PolGlob , + ε ,
Table 5
Results of Panel Data regression Equation 2
(1)
VARIABLES I_INEQ
Birthrate -0.152
25 FDIinflow -0.159 (0.129) EconGrowth -0.228*** (0.0865) Inflation -0.00284* (0.00162) trade -0.0471** (0.0226) NetMig -1.31e-06 (3.28e-06) FemPart 0.203*** (0.0773) NatResDeplGNI -0.0668 (0.0474) SocGlob 0.0963* (0.0495) ExpCompl -4.932*** (1.175) ShadEco 0.232*** (0.0723) Educ 0.0444 (0.120) EcoGlob 0.0520 (0.0521) PolGlob 0.00940 (0.0284) Constant 13.02 (7.877) Observations 163 R-squared 0.344
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
