Tag: Inequality
What Ukrainians Expect From Reforms
Author: Tom Coupé, KSE.
Ukraine needs reforms badly. However, there is a huge difference in how the government, the expert community, and the general public understand reforms. According to a recent survey conducted by a prominent Ukrainian newspaper, people expect that reforms should, in the first place, improve their personal wellbeing. However, research findings beware that in the short run structural changes in the country can worsen economic performance and increase inequality. To reduce the pain of unmet expectations and popular discontent, the government should openly communicate any difficulties to come, and wisely mix the most painfull measures, like the increase of tariffs for the use of public infrastructure, with empowering changes that give citizens a sence of progress, like actions that strengthen democracy and help SMEs to flourish.
Growing Inequalities in Workplace Amenities
Inequality is considered to be a serious detrimental factor for societies’ development. It has been shown to undermine the health of the population, cause civil unrest, and slow down countries’ economic growth. Nizalova’s (2014) paper shows that the focus on the purely monetary component in the studies of inequality is too narrow. In Ukraine, which has had almost no change in income/wage inequality since 1994, the inequality in other workplace dimensions has soared. Nizalova finds that workers in establishments paying higher hourly wages have enjoyed (i) relatively greater reductions in the total workplace injury burden, (ii) greater retention of various benefits/amenities, and (iii) relatively larger increases in wage payment security (de-creased wage arrears). These findings document a high degree of an unequal shift away from work-centered provision of social services, not counter-balanced by the government, and highlight the importance of timely policy intervention as a possible cause of societal disturbances.
Inequality in income, health, and political rights has been on the agenda of many governments and international organisations. It has been shown to lead to tensions in society that can grow into civil unrest, and is named one of the top global risks in the World Economic Forum Global Risk Report, 2013. Country-level comparisons by epidemiologists have documented that more unequal countries have (i) higher rates of mental illness, drug use, and homicide, (ii) a larger incarceration rate, (iii) a larger share of obese population, (iv) higher school drop-out rates, lower socio-economic mobility, lower child wellbeing, and (v) a lower level of trust (Wilkinson and Pickett, 2010). At the macro level, inequality has also been shown to impede sustainable growth (Ostry and Berg, 2011).
Yet, in Ukraine, in spite of a number of continuing severe problems with population health, labor markets, infrustructure, etc., inequality has not been high on the agenda, except for occasional concerns raised by some international organisations and researchers. In our view, there are at least three reasons for this.
First of all, most of the attention in inequality discussions is paid to income inequality. However, in Ukraine after a significant increase in this indicator by the mid-nineties, there has been hardly any dynamics, with the exception of extreme increases in incomes/wealth of a few oligarchs.
Second, and this relates to inequality in any dimension, when people in power are predominantely concerned with self-enrichment, and citizens are not showing their dissatisfaction, or the government has “effective” means of dealing with this dissatisfaction (imprisonment, physical elimination, etc.), as has been the case in Ukraine for many years, those at the lower end of the income distribution have the least chances to attract attention.
Finally, we believe that the reason international organisations have not given much attention to Ukrainian inequality must be related to the fact that the situation in many areas of life has been so dire, i.e. the level of “well-offness” is so low throughout the distribution that the overall level was considered more important than the distribution.
A recent paper by Olena Nizalova (2014) examines the importance of the non-monetary dimensions of work in studies regarding inequality in total returns to work. Nizalova’s paper exploits a unique data set collected by the International Labour Office in Ukraine to study whether there has been a significant change in the non-monetary components of inequality. If this is the case, it can explain the growing tensions in society where the changes in income/wage inequality have been limited.
Non-monetary aspects of inequality
A few academic studies have explored the issue of income/wage inequality in Ukraine and Russia (Ganguli and Terrell, 2006; Galbraith, Krytynskaia, and Wang, 2004; Gorodnichenko, Peter, and Stolyarov, 2010; Lokshin and Ravallion, 2005), and found that, if anything, the change in inequality after 1995 has been quite modest. These results are in line with the dynamics of wage inequality in Ukraine presented in Figure 1, which pictures the ratio of wages in 2nd, 3rd, and 4th quartiles of the wage distribution against those in the 1st quartile.
Figure 1. Log Differences in Hourly Wages Relative to the Lowest Paying Quartile
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
However, the measures used in the earlier studies may not reflect the true inequality levels in the society. Indeed, they are omitting the contribution of the non-monetary dimension of work to the overall inequality.
The study of non-monetary working conditions is important for several reasons. First, work is central to people’s lives not only because a major share of household income in most countries comes from labor earnings (Guerriero, 2012), but also because individuals spend a considerable part of their time at work. Thus, earnings inequality can inappropriately reflect the true level of the total inequality in the labor market.
Second, the importance of this direction of research is further highlighted by the development of the ILO “Decent work agenda”. One of its aims is to promote both inclusion and productivity by ensuring that women and men enjoy working conditions, which satisfy several criteria. These criteria include that working conditions are safe, allow adequate free time and rest, take into account family and social values, provide for reasonable compensation in case of lost or reduced income, and permit access to adequate healthcare.
Lastly, inequality in working conditions, and in particular workplace injuries, may directly translate into income and wealth inequality, and, indirectly, affect inequality in future generations.
Ukraine: Inequality in Non-Monetary Work Dimensions Matters
The analysis in Nizalova (2014) shows that establishments that pay higher wages, tend to provide safer and, in general, better working conditions than establishments that pay lower wages. In addition, the latter are much more likely to experience difficulties with the payment of wages and have a higher percentage of workers with severe (more than 3 months) wage arrears. This suggests that the wage inequality may be further exacerbated by the inequality in non-monetary work dimensions.
A further distributive analysis demonstrates that the inequality in non-moneraty work dimensions has been changing noticeably over time. In particular, Figure 2 shows that the burden of workplace injuries, measured as total work days lost due to injuries per 100 Full Time Equivalent (FTE) employees, over time has shifted from being concentrated in the top part of the wage distribution to the lowest part (the way to interpret Figure 2 and all subsequent figures is as follows: the diagonal line in all figures corresponds to the equal distribution of the mentioned workplace characteristic across the wage distribution. The further the actual distribution curve (in red) is from the diagonal, the more unequal it is, with the curve below the diagonal indicating a concentration of the characteristic among higher paying enterprises and the curve above the line – concentration of the characteristic in the lower end of the wage distribution).
Figure 2: Concentration Curves – Total Injury Burden by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Moreover, the distribution of employer-provided benefits has also changed from being almost equally spread across the wage distribution to being more concentrated in the upper part (Figure 3).
Figure 3: Concentration Curves – Amenity Scores by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Notice that this result is not driven by any one particular amenity – it is observed across the whole range of indicators (for example, see Figures 4-6).
Figure 4: Distribution of Transportation Subsidy Provision by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Figure 5: Distribution of Kindergarden Subsidy Provision by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Figure 6: Distribution of Health Service Provision by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Similarly, wage arrears’ (non-payments) concentration has changed from being almost equally distributed across all wage levels to being more concentrated among lower paying establishments (Figure 7).
Figure 7: Distribution of Wage Arrears by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Further, the analysis of distributional shifts in the establishment characteristics over the corresponding period shows significant changes only with respect to firm size, export status, and some sectoral shifts.
Overall, the findings of the paper document an emergence of sizeable inequality in the workplace characteristics in the Ukrainian labor market: workers in poorly paying establishments are facing disproportionately larger risks of on-the-job injury, worse provision of amenities, as well as less security in timely payments of earning.
Conclusion
Although further research on causes of growth in multidimensional inequality in returns to work is required, this study provides two important lessons for the research community and policy makers.
First of all, it highlights the importance of a multi-dimensional approach to labor market returns, since a focus on monetary compensations only may significantly underestimate the true inequality in a society.
Secondly, it draws attention to the need of developing adequate governmental policies to address the inequality of workplace-centered provisions of social services during the transition to market economy. By prioritizing measures to facilitate provision of affordable housing, health care, kindergartens, as well as training opportunities, the government could mitigate increasing inequalities. This would allow the government to avoid significant tensions and conflicts in society, which is an important pre-requisite for ongoing sustainable development.
References
- Bockerman, Petri and Pekka Ilmakunnas. 2006. “Do job disamenities raise wages or ruin job satisfaction?” International Journal of Manpower 27 (3):290–302.
- Clark, Andrew E. and Claudia Senik. 2010. “Who Compares to Whom? The Anatomy of Income Comparisons in Europe.”Economic Journal 120 (544):573–594.
- Galbraith, James K., Ludmila Krytynskaia, and Qifei Wang. 2004. “The Experience of Rising Inequality in Russia and China during the Transition.” European Journal of Comparative Economics 1 (1):87–106
- Ganguli, Ina and Katherine Terrell. 2006. “Institutions, markets and men’s and women’s wage inequality: Evidence from Ukraine.” Journal of Comparative Economics 34 (2):200–227
- Gorodnichenko, Yuriy, Klara Sabirianova Peter, and Dmitriy Stolyarov. 2010. “Inequality and Volatility Moderation in Russia: Evidence from Micro-Level Panel Data on Consumption and Income.” Review of Economic Dynamics 13 (1):209–237
- Guerriero, Marta. 2012. “The Labour Share of Income around the World. Evidence from a Panel Dataset.” URL http://www.sed.manchester.ac.uk/idpm/research/publications/wp/depp/documents/deppwp32.pdf. Working Paper
- Hamermesh, DS. 1999. “Changing inequality in markets for workplace amenities.”Quarterly Journal of Economics 114 (4):1085–1123.
- Hensler, Deborah R., M. Susan Marquis, Allan Abrahamse, Sandra H. Berry, Patricia A. Ebener,Elizabeth Lewis, Edgar Lind, Robert J. MacCoun, Willard G. Manning, Jeannette Rogowski, and Mary E. Vaiana. 1991. “Compensation for Accidental Injuries in the UnitedStates.” RAND Corporation Report Series R3999, Santa Monica, CA: RAND Corporation. URL http://www.rand.org/pubs/reports/R3999
- Keogh, J. P., I. Nuwayhid, J. L. Gordon, and P. W. Gucer. 2000. “The impact of occupational injury on injured worker and family: outcomes of upper extremity cumulative trauma disorders in Maryland workers.” American journal of industrial medicine 38 (5):498–506. Research Support, U.S. Gov’t, P.H.S
- Lokshin, Michael and Martin Ravallion. 2005. “Rich and powerful?: Subjective power and welfare in Russia.” Journal of Economic Behavior & Organization 56 (2):141–172.
- Marquis, M. S. and W. G. Manning. 1999. “Lifetime costs and compensation for injuries.” Inquiry: a journal of medical careorganization, provision and financing 36 (3):244–254. Research Support, Non-U.S. Gov’t.
- Nizalova, Olena Y., 2014. “Inequality in Total Returns to Work in Ukraine: Taking a Closer Look at Workplace (Dis)amenities,” IZA Discussion Papers 8322, Institute for the Study of Labor (IZA).
- Ostry, Jonathan David and Andrew Berg. 2011. “Inequality and Unsustainable Growth: Two Sides of the Same Coin?” IMF Staff Discussion Notes 11/08, International Monetary Fund.
- Rosen, Sherwin. 1986. “The Theory of Equalizing Differences.” In Handbook of Labor Economics, edited by O. Ashenfelter, R. Layard, P.R.G. Layard, and D.E. Card, v.2, chap. 12. North-Holland, 641–692.
- Senik, Claudia. 2009. “Direct evidence on income comparisons and their welfare effects.”Journal of Economic Behavior&Organization 72 (1):408–424.
- Wilkinson, R. and K. Pickett. 2010.The Spirit Level:Why Equality is Better for Everyone. Penguin Books Limited.
Preferences for Redistribution in Post-Communist Countries
Public attitudes toward inequality and the demand for redistribution can often play an import role in terms of shaping social policy. The literature on determinants of the demand for redistribution, both theoretical and empirical, is extensive (e.g., Meltzer and Richard 1981, Alesina and Angelotos 2005). Usually, due to data limitations, transition countries are usually considered to be a homogeneous group in empirical papers on the demand for redistribution. However, new data on transition countries allow us to look more deeply into the variation within this group, and to look at which factors are likely to play a significant role in shaping a society’s preferences over redistribution.
The data we use are from the second round of the EBRD and WB Life in Transition Survey (LiTS) (EBRD Transition Report 2011). This is a survey of nationally representative samples consisting of at least 1000 individuals in each of the 29 transition countries.[1] In addition, and for comparison purposes, this survey also covers Turkey, France, Germany, Italy, Sweden and UK. Furthermore, in six of the countries surveyed – Poland, Russia, Serbia, Ukraine, Uzbekistan and UK – the sample consists of 1500 individuals.
Redistribution is, in general, a complex issue, which can take various forms and rely on different mechanisms. In this policy brief, we will only focus on two forms of public attitudes towards redistribution. The first is direct income redistribution from the rich to the poor and public preferences for or against this form of redistribution. The second is indirect redistribution through the provision of public goods, some of which favor certain groups of population over others. In particular, we will consider preferences over extra government spending allocations in the areas of education, healthcare, pensions, housing, environment and public infrastructure. Generally, we would like to explore in greater detail to what extent there are differences across countries in terms of public preferences over redistribution and what might explain differences both within and across societies.
Both survey rounds include questions regarding public preferences towards income redistribution, direct (from the rich to the poor) and indirect (through government spending towards certain public goods). Data for exploring public preferences for direct redistribution can be obtained from a question in the survey that asks respondents to score from 1 to 10 whether they prefer more income inequality or less. More specifically, in the LiTS 2010, the question is the following:
Q 3.16a “How would you place your views on this scale: 1 means that you agree completely with the statement on the left “Incomes should be made more equal”; 10 means that you agree with the statement on the right “We need larger income differences as incentives for individual effort”; and if your views fall somewhere in between, you can choose any number in between?
Note, however, that we use the reverse of this so that 10 represents greater equality and 1 represents wider differences. Bearing this in mind, figure 1 shows the average scores for redistribution preferences for a selection of the countries for 2010 and shows a sizeable variation ranging from 4.4 (more inequality) in Bulgaria to 7.87 (greater equality) in Slovenia. The mean for Russia is 6.92.
The data also allows for a comparison to be made between these preferences in transition countries and in the developed economies covered in the survey. For instance, Russians are on average close to Germans in their preferences for redistribution, while Estonians and Belarusians prefer less redistribution and are closer to the British, on average.
Figure 1. Preferences for Direct Redistribution
Indirect measures of attitudes towards redistribution can add further depth to these societies’ preferences. In particular, the indirect measures in the 2010 survey are derived from a question that asks respondents to rate from 1 to 7 their first priorities for extra government spending.
Q 3.05a “In your opinion, which of these fields should be the first priority for extra government spending: Education; Healthcare; Housing; Pensions; Assisting the poor; Environment (including water quality); Public infrastructures (public transport, roads, etc.); Other (specify)”?
The country averages for these indirect measures for 2010 are presented in Figure 2. The graph reveals a sizeable cross-country variation. For instance, 43.5% of respondents in Mongolia preferred channeling extra government money to education, while 48.7% of respondents in Armenia selected higher healthcare spending. Almost 39% of respondents in Azerbaijan chose assistance to the poor as the first priority for government spending, while the corresponding figure was only 8.3% in Bulgaria and 4% in the Czech Republic. More than 34% of the Russians choose healthcare as their first priority, another 20% choose education, 15% would like the money to be channeled to housing, 14.5% to pensions, 11% to support the poor, 3% to support environment, and only 2% to public infrastructure (2010).
These numbers highlight that there are sizeable differences across the transition countries regarding preferences for redistribution. Also, regarding the form of indirect redistribution in terms of preferences over how government budgets should be prioritized and allocated. Several groups of factors or determinants are typically listed in academic literature to help explain what drives public preferences over the degree and form of redistribution. In the first group of factors, there are various determinants at the individual level. Within the group of individual determinants, self-interest or rational choice of a degree of redistribution favorable to the individual with usual (individual) preferences are stressed. Alternatively, motives behind a preference for redistribution can be related to social preferences (preferences for justice or equity) and reciprocity. Within this general group of self-interest, attitudes towards risks can be stressed as a crucial factor behind demands for social insurance and hence for indirect forms of redistribution. Individuals’ prospects of upward mobility, expectations about their future welfare or ‘tunnel effect’ in shaping their views and preferences over redistribution are also underlined. Also, the commonly held beliefs about the causes of prosperity and poverty are considered to be important in shaping the public’s attitudes under the umbrella of social preferences.
The literature covers possible institutional determinants for preferences towards redistribution and emphasizes the role of the level of inequality in a society and typically relates to the median voter hypothesis in democracies. It is also stressed that welfare regimes (liberal, conservative) can play a role in shaping the level of public support for redistribution.
Figure 2. Preferences for Indirect Redistribution
A closer examination of the data and estimates of the factors shaping individuals preferences over redistribution in the 2010 survey, are consistent with motives involving strong self-interests of the respondents.[2] Those from richer households have less support for redistribution, with the result being robust to the measure of household income used. The past trend in household income positions is insignificant, while the higher the expected income position of household in the coming four years, the less supportive the respondents are of income redistribution (elasticity -0.1). Those who experienced severe hardships with the recent crisis tend to support redistribution more than those who had little problems or not at all (elasticity 0.13).
Furthermore, the role of preferences towards uncertainty is confirmed: the higher the (self-reported) willingness to take risks, the less likely the individual is to support or favor redistribution. Respondents with tertiary education are less inclined to support redistribution of income from the rich to the poor, compared to those with secondary education (elasticity is -0.4). Having a successful experience with business start-ups also decreases demand for income redistribution from the rich to the poor (elasticity -0.3). Those living in rural areas are more in favor of redistribution compared to metropolitan areas, while living in urban areas shows the same level of support for redistribution as those living in metropolitan areas. In each of these cases, it appears that those who would benefit the most from redistribution favor it more than those who view it as coming at their expense, or possible expense in the future.
Beliefs regarding the origins of success and poverty are also shown to be statistically significant and negative, as predicted: those who believe effort and hard work or intelligence and skills are the major factors for success are less supportive of income redistribution (elasticity -0.16). Those who consider laziness and lack of will power the major factors for people’s lack of success are also, consistently, less supportive of redistribution (elasticity -0.2).
It also turns out that better democratic institutions are correlated with a higher demand for redistribution. The result is robust across the measures used, i.e. it does not seem to depend on the particular measure used. The size of the effect is quite pronounced: a one standard deviation increase in the democracy measure increases demand for redistribution from 16 percentage points, when the voice and accountability measure is used, to 33 and 36 percentage points when controls of the executives and democracy index are used.
Furthermore, the better the governance institutions, as measured by the rule of law and control of corruption indexes, the higher is the demand for redistribution. However, the result is not robust to the various measures used. Government effectiveness appears to be insignificant (though with a positive direction), and the regulatory quality measure is insignificant but with a negative direction. The size of the effects is again quite pronounced. A one standard deviation increase in the rule of law measure increases demand for redistribution by 17 percentage points, and a one standard deviation increase in the control of corruption measure increases demand for redistribution by 27 percentage points.
The higher the level of inequality, the larger is the demand for redistribution as might be expected. This result is robust across all measures used. The size of the effect varies from 16 to 18 percentage points in response to a one standard deviation increase.
A regression analysis of preferences towards indirect redistribution also shows that self-interest motives are very pronounced, but there are traces of social preferences as well. In particular, younger people (age 18-24) would like to have more subsidized education and housing at the expense of healthcare and pensions in comparison with the age 35-44 reference group. Those in the age 25-34 group would like to redistribute public spending to housing and environment at the expense of education, pensions and public infrastructure. Respondents in the age 45-54 group would also like to redistribute additional spending from education but to pensions. The two groups of older people (age 55-64 and 65+) would like to shift extra spending from education and housing to healthcare and pensions. The group of age 65+ would also like to shift money from assistance to the poor.
Respondents with tertiary education (in comparison with holders of a secondary degree) favor extra spending for education, environment and public infrastructure at the expense of healthcare, pensions and assisting to the poor, thus revealing additional elements of social motivations. Respondents with primary education, when compared to holders of secondary degree, would like to redistribute public money from education to pensions and assistance to the poor. Respondents with poor health favor additional spending on healthcare and pensions at the expense of education.
High skilled (in terms of occupational groups) respondents would like to redistribute public money from pensions to education. Those with market relevant experience of being successful in setting up a business tend to support education and public infrastructure at the expense of housing and pensions, though the result lack statistical power.
Respondents from households with higher income support extra spending for education, environment and public infrastructure at the expense of healthcare, pensions and assistance to the poor; again pointing to the other elements of possible social motivations. Those with a self-reported positive past trend in income position tend to support spending extra money on the environment at the expense of assistance to the poor (the latter lacks statistical power). If the respondent lives in its own house or apartment, s/he tends to support redistribution from housing and assistance to the poor, to healthcare and pensions.
Respondents whose households were strongly affected by the crisis would like expenditure on environment and public infrastructure to be reduced. Those with higher self-reported willingness to take risks would redistribute extra public money to education at the expense of healthcare and housing.
Respondents who believe that success in life is mainly due to effort and hard work, intelligence and skills favor education at the expense of assistance to the poor and public infrastructure, suggesting they might view education as the key to escape poverty. Those who think that laziness and lack of willpower are the main factors behind poverty would, unsurprisingly, redistribute extra public money from assistance to the poor to healthcare.
Males (as compared to females) favor extra spending on education, housing, environment and public infrastructure at the expense of healthcare. The self-employed favor extra spending of public money to pensions at the expense of housing. There is no difference across respondents living in metropolitan, rural or urban locations.
A regression analysis shows that better democratic institutions are correlated with higher support for allocation of additional public spending to education and healthcare, environment and public infrastructure. The effects are larger for education and healthcare: one standard deviation in the democracy index increases the support for spending money on education by 3 percentage points, for healthcare by 3.1 percentage points, and only by 0.4 and 0.6 percentage points for environment and public infrastructure, respectively. This reallocation is at the expense of assistance to the poor (3.5 percentage points), housing (2.6 percentage points) and pensions (1.1 percentage points). The pattern is robust to the measure of democratic institutions used, though the marginal effects vary slightly depending on the measure.
The influence of governance institutions is similar. Respondents in countries with better governance institutions favor allocation of extra public money to education (3.2 percentage points in response to one standard deviation in government effectiveness), health care (2.9 percentage points), environment (0.9 percentage points) and public infrastructure (0.6 percentage points). The reallocation is at the expense of assistance to the poor (4.2 percentage points), housing (3.3 percentage points) and pensions (0.2 percentage points). The pattern is also robust to the measure of governance institutions with the marginal effects varying slightly depending on the measure.
The higher the level of inequality in a country, the higher the demand for spending extra public money for education at the expense of assistance to the poor, pensions and public infrastructure. A one standard deviation increase in the index, increases demand for spending extra public money on education by 3.8 percentage points, and decreases spending on assistance to the poor by 2 percentage points, pensions by 1.9 percentage points, and public infrastructure by 0.06 percentage points. The results are robust to the inequality measure used.
Overall, the analysis provides empirical evidence that transitional countries are not homogeneous with respect to preferences for redistribution, with sizeable variations in country averages and in public preferences. The study of individual determinants of preferences for redistribution confirms a dominant role of self-interest, with some indications of social sentiments as well. In addition to the usual measures used in individual level analysis, these data allow better control for both positive and negative personal and household experience. The study of institutional determinants also confirms the role of income inequality in shaping public attitudes. In particular, higher inequality is confirmed to increase the demand for direct income redistribution. A novel motive of the paper is the influence of democracy and governance institutions on demand for redistribution. Better democracy and governance institutions are likely to stimulate demand for income redistribution, revealing both higher societal demand for redistribution and appreciation of the potential capability of the government to implement redistribution effectively.
The study of individual determinants of indirect demand for redistribution adds to the overall picture and confirms not only the self-interest motives but also social preferences especially pronounced among people with tertiary education and in high income groups. Better democratic and governance institutions stimulate redistribution of public money towards education, healthcare, environment and public infrastructure, while weaker democratic and governance institutions increases demand for allocation of public money to assistance to the poor, housing and pensions.
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References
Meltzer, A., Richards, S., 1981. “A Rational Theory of the Size of Government”. Journal of Political Economy 1989, 914–927.
Alesina, A., Angeletos, G.M., 2005. “Fairness and Redistribution”. The American Economic Review, 95(4), 960-98
[1] The countries covered were: Albania, Armenia, Azerbaijan, Belarus, Bosnia, Bulgaria, Croatia, Czech Republic, Estonia, FYROM, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyzstan, Latvia, Lithuania, Moldova, Mongolia, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Tajikistan, Turkey, Ukraine and Uzbekistan.
[2] The basic empirical equation to study individual determinants of public preferences towards income redistribution is the OLS with country fixed effects (for direct redistribution) and multinomial regression with country fixed effects (for indirect measures). When studying the influence of institutions, the equations are transformed to replace country fixed effects with an institutional measure (one at a time). To control for the basic economic differences, average GDP per capita was included.