The transition from centrally planned to free-market economies in 1989 initiated a period of social and economic upheaval in post-communist countries, which affected healthcare quality, expenditures, and outcomes. We use data from the Life in Transition Survey (LiTS) to demonstrate that in spite of improvements across various measures of these facets of the healthcare system, it remains the first choice for additional government spending among the public in all countries of the region included in this study. Preferences in priorities for extra budget spending were similar among men and women in respective countries, but the preference for additional healthcare spending was stronger among women than men. The transition countries are compared with Germany and Italy – two Western European LiTs survey participants, countries with higher spending, and better healthcare outcomes.
Across the globe, the outbreak of the COVID-19 pandemic has brought a new spotlight to the preparedness of healthcare systems for profound shocks (Anser et al, 2020). Critical care is a particularly costly element of healthcare provision, and thus, under-resourced systems are uniquely susceptible to spikes in mortality resulting from an oversaturation of intensive care units during an epidemiological crisis of this sort. (Fowler et al, 2008; Mannucci et al, 2020) Considering the widespread discussion surrounding health system capacity and the necessity for implementing economically painful lockdowns when those limits are reached, pressure from society to increase public spending may grow even further. With these developments in mind, in this policy paper, we confront past expressions of preferences regarding public expenditures with changes in government spending on healthcare between 2006 and 2017. The analysis draws on the one hand on the data from the Life in Transition Survey (LiTS), and on the other on publicly available data on government expenditures and outcomes.
In the context of preferences for additional public spending, we present a descriptive summary of trends in government expenditures on healthcare in Armenia, Belarus, Estonia, Georgia, Latvia, Lithuania, Moldova, Poland, Russia, and Ukraine. We include Italy and Germany as wealthier Western benchmarks, for which the data became available in the second wave of the survey in 2010. Data on public healthcare spending shows that despite a clear and strong public preference for increased investment in healthcare provision, additional spending as a proportion of total government expenditures between 2006 and 2017 has been moderate in most countries, and even negative in some. It must be underlined that expenditures are not always reflected in healthcare outcomes, quality, and coverage. Issues of efficiency, system design, and underlying health conditions of the population play a significant role in the returns on investment. For instance, the United States has spent drastically more per capita on healthcare than any other country and yet ranked lowest in the Healthcare Access and Quality (HAQ) Index among comparable countries (Fullman et al, 2016). However, due to the focus of the survey on government spending, we emphasize government expenditures on healthcare as a pertinent measure, especially in relation to overall GDP, per capita spending, and the public budget as a whole.
There is mounting evidence that one of the most important elements in the mitigation of COVID-19 mortality is the ability to expand system capacity and acquire the necessary equipment (e.g. respirators, ventilators) while ensuring that there is equitable access to measures for spread prevention (e.g. testing) (Khan et al, 2020; Ranney et al, 2020; Wang and Tang, 2020). The increasing pressure on healthcare systems, coupled with the additional fiscal strain resulting from the economic fallout of the pandemic, could lead to further divergence between public preferences and government spending on healthcare.
Healthcare Systems During the Transition
The ability of transition countries to absorb the risks and short-term economic shocks associated with pivoting from a centrally planned to a free-market economy has had dramatic implications for healthcare systems. Although countries in this region were divergent in terms of underlying health conditions, levels of expenditures, and health outcomes, most of them fell victims to deficient funding and additional health risks associated with the initial increases in poverty that were commonplace (Adeyi et al, 1997)
Compared to other transition countries, Georgia and Armenia faced a sharper economic collapse as well as armed conflicts, which caused scarcity in the availability of public healthcare providers and spikes in out-of-pocket expenses. Belarus was slower in the implementation of economic reforms and faced issues of fiscal sustainability further down the line (Balabanova et al, 2012). However, following this short tumultuous period, countries transitioning away from centrally planned economies have generally invested heavily in healthcare since the early 1990s. In many cases, these investments were facilitated by rapid GDP growth and accompanied by significant improvements in life expectancy. For example, between 1989 and 2012, Latvia, Lithuania, and Poland increased their per capita healthcare expenditures by more than 1,000 PPP per year, with an increase in life expectancy ranging from 1.7 years in Lithuania to 5.8 years in Poland (Jakovljevic et al, 2015). Despite heterogeneous and extensive reforms in many of these countries, as well as mixed results in measurements of efficiency and outcomes, healthcare expenditures consistently rank as the top priority for further government spending among both men and women in each country. This consistency lends itself to further policy considerations.
Preferences for Government Spending in Transition Countries
As is demonstrated by Figure 1, in 2016, healthcare was the most common answer to the question – “Which field should be the first priority for extra government spending?”- for all ten post-transition countries included in our analysis (the other options were: education, housing, pensions, assisting the poor, public infrastructure, the environment, and other). The survey was carried out on a representative sample that covers approximately 1,000+ respondents from each of the 29 countries in wave I and up to 1,500+ respondents from each of the 34 countries in wave III (EBRD: LiTS, 2020). Despite intercountry differences, in 2016 healthcare persisted as the top priority for both men and women in every transition country we studied apart from Belarus. While healthcare remained the top priority on average, men expressed a higher preference for additional investment in education. In the countries where preferences for health were particularly strong, healthcare was the first priority for as many as 53.5% of Latvians, 47.7% of Poles, and 43.9% of Moldovans (Figure 1a). Notwithstanding some fluctuations in scale, these preferences were not only common across countries but also across time, with people expressing very similar preferences in the first two waves of the survey in 2006 and 2010. (See Annex Figure A1 and Figure A2). While healthcare remained a popular choice in Germany and Italy, spending on healthcare as a percentage of GDP was nearly twice that of any transition country in Germany. There, education outweighed healthcare among men and women in both available waves (II and III), while pensions surpassed healthcare among men in the latter wave. In Italy, despite a more comparable level of healthcare spending relative to the transition countries, a drastic shift took place as healthcare fell from being the first priority by a large margin of 24.9 percentage points (pp) in 2010 to becoming the second priority after pensions in 2016. This can likely be attributed to the prominence of pensions as a major political campaign issue following the austerity-driven reforms of 2011 (Alfonso and Bulfone, 2019).
Figure 1: 1a (left) : Preferences for additional government spending, 2016. / 1b (right): Preferences for additional healthcare spending by gender, 2016
Moreover, it is evident that men and women within countries have rather similar preferences, as far as extra government spending is concerned. Not only is healthcare the first priority in all ten transition countries, but their second, third, and fourth choices are also very similar. When digging deeper into the differences that do exist, in every country except for Georgia women had a stronger preference for healthcare than men, and by as much as 8.8 pp, 8.4 pp, 7.8 pp, and 7.9 pp in Latvia, Germany, Belarus, and Russia respectively (Figure 1b). Conversely, in every case except for Georgia and Ukraine, men had a stronger preference for additional spending on education than women, most notably in Armenia – by 7.8 pp, Germany – by 5.7 pp, Lithuania – by 4.6 pp and Poland – by 3.9 pp. It is apparent that despite rapid investment in healthcare over the first two decades of the transition, there remains a widespread desire for further expansion of expenditures in this area.
Trends in Government Expenditures, 2006-2017
Considering the primacy of healthcare as the priority for additional government spending in all ten studied transition countries, we look at trends in aggregate statistics on government expenditures on healthcare over the surveyed period to explore the extent to which these preferences have been reflected in government spending. Taking the most basic measure into account in Figure 2a, i.e. public health expenditures as a percentage of GDP, among the transition countries only Georgia and Estonia have significantly increased their healthcare expenditures, by 1.6 pp and 1.2 pp, respectively. Lithuania, Poland, and Russia saw more moderate increases in the range of 0.6 pp and 0.2 pp. Other countries have remained essentially stagnant, apart from Moldova and Ukraine which saw a notable drop of 0.8 pp. Considering that this measure is sensitive to fluctuations in GDP growth, we also consider public health spending as a proportion of all government expenditures (see figure A3 in the Annex), which is a better indicator of government priorities for additional spending from 2006 until 2017. Georgia was the only transition country with a significant increase in healthcare spending proportional to total government expenditures, nearly doubling it from 5.2% to 9.5%. Belarus, Estonia, Lithuania, Poland have implemented a more moderate redirection of the budget towards healthcare, increasing proportional expenditures by a factor of 1.26, 1.15, 1.21, and 1.21 respectively. In spite of public preferences, Armenia decreased the proportional share of the budget dedicated to healthcare by as much as 2.6 pp, Moldova, Russia, and Ukraine by 1.3 pp, and Latvia by 0.8 pp. Regardless of the direction of the trend, notwithstanding some slight convergence, no transition country spent as much of its budget on healthcare as Italy and Germany. The latter spent nearly two to four times as much on healthcare as a proportion of total expenditures compared to the studied transition countries, and this gap has been widening relative to all of those included in the analysis, apart from Georgia.
Figure 2: Public healthcare expenditures (% of GDP)
While expenditures per capita are less indicative of government priorities in the budget, they are a better comparative measure for assessing the changes in healthcare provision, barring differences in efficiency. This comes with a huge caveat, namely that it is well established in the literature that additional healthcare expenditures often translate into “small to moderate” direct improvements in healthcare quality and outcomes due to inefficient spending or underlying factors (e.g. lifestyle choices, poverty) that are not addressed by investment in the healthcare system itself (Hussey et al, 2013; Self and Grabowski, 2003). Nevertheless, this measure is more likely to translate to an improvement in the quality of care each person receives, and the data paints a more positive picture considering the clear preference of both men and women for higher spending. In Figure 3 we present healthcare expenditures per capita in USD, and apart from Italy and Ukraine, all of the countries have significantly increased spending between 2006 and 2017. While expenditures per capita in transition countries are dwarfed by Germany and Italy, Estonia, Georgia, and Lithuania have more than doubled their expenditures, and Armenia has more than tripled. Belarus, Latvia, Poland, Moldova, and Russia have also significantly increased their per capita spending on healthcare, by factors in the range of 1.54 and 1.91. However, while expenditures per capita is one indicator of improving healthcare quality, it does not identify government priorities and is largely dependent on overall economic growth (Fuchs, 2013; Bedir, 2016).
Figure 3: Health care expenditure per capita, USD
In every country we include, increasing healthcare expenditure per capita is accompanied by advancements in many measures of healthcare outcomes for men and women. Between 2006-2017, life expectancy at birth increased across the board, with men in Russia experiencing the greatest improvement of 7.1 years (Figure 4a). These are promising trends – for women, life expectancy at birth improved by a larger margin in each transition country than in Germany or Italy, and the same can be said for men in every country apart from Armenia. Furthermore, the Healthcare Access and Quality (HAQ) index, which is composed of 32 indicators related to preventable causes of mortality, has improved across all 12 countries between 2005-2016. The change was most notable in Armenia, Belarus, Estonia, and Russia, constituting as much as 8.7, 10.2, 8.9, and 8.9 points out of a hundred, respectively (Figure 4b). These trends indicate convergence in the quality of healthcare as they significantly outpaced improvements in the HAQ index in Italy (3.1 points) and Germany (3.9 points). As of 2016, among the countries of interest, Georgia (67.1 points) and Moldova (67.4) had the lowest scores, while Germany (92.0) and Italy (94.9) scored highest, as could be expected based on healthcare spending measures presented in Figures 2 and 3.
Figure 4: 4a (left): Change in life expectancy, 2006-2017 / 4b (right): HAQ index
However, as presented in Figure 5, there is no clear relationship between the strength of the preference for additional healthcare spending and the scale of expansion in spending. Taking three of the four countries (Armenia, Belarus, and Russia) with the greatest improvement in the HAQ index as an example, there was virtually no change in healthcare spending as a percentage of GDP over the same period. These countries were also different in terms of how strong the preferences were for additional spending on healthcare as the first priority in 2006.
Figure 5: Public preferences and government healthcare spending (% of GDP)
As we have demonstrated in this brief, in the ten post-communist countries for which we have analyzed LiTS data, there was a consistent and common preference for healthcare as the first priority for extra government spending between 2006 and 2016. We also find that in each country except Georgia, on average, women had a stronger preference for additional public healthcare spending, supporting a wealth of literature that suggests that women utilize healthcare services more frequently and spend more out of pocket on healthcare than men (Owens, 2008; Cylus et al, 2011; Williams et al, 2017). However, over the period we study, these preferences have not translated directly into a reallocation of budgetary resources. The countries with the strongest preferences for additional healthcare spending in 2006 did not experience the highest increases in any of the discussed measures of public healthcare expenditures since then.
People living in Italy and Germany chose an increase in public spending on healthcare as their first priority less frequently than residents of post-transition countries. Better understanding these differences requires further research, but there is likely a combination of factors that play into this effect. For one, wealthier Western countries performed better when looking at simple measures of healthcare outcomes such as life expectancy and deaths from non-communicable diseases (WHO, 2020), and hence other priorities may have gained in salience. Furthermore, they allocated a greater proportion of the public budget towards healthcare. This in part stems from the significant challenges associated with the transition following 1989. Healthcare systems in post-communist countries experienced a fiscal shock when joining the global economy, with the loss of centrally controlled price mechanisms causing an increase in the relative prices of healthcare inputs such as medicines and equipment (Obrizan, 2017). This was exacerbated by a shrinking capability of governments to spend more on healthcare related to the general economic shocks at that time and led to the passing over of costs to patients in the form of out-of-pocket expenses (Balabanova, et al. 2012). Although access to healthcare and the quality of that care have improved after the transition (Romaniuk and Szromek, 2016), these have failed to converge towards Western European countries on a number of substantial measures up to this point. Before the commencement of the COVID-19 pandemic, government healthcare spending did not reflect the preferences of the public in any of the ten studied transition countries. The outbreak of the pandemic has not only intensified the pressure on the healthcare system but also brought about a number of negative economic consequences. This combination can be expected to simultaneously increase the strain on the public budget and necessitate difficult decisions of reallocation at a time when fiscal sustainability during a global recession is already being brought under question (Creel, 2020).
- Adeyi, O., Chellaraj, G., Goldstein, E., Preker, A. and Ringold, D., 1997. Health status during the transition in Central and Eastern Europe: development in reverse?. Health Policy and Planning, 12(2), pp.132-145.
- Afonso, A. and Bulfone, F., 2019. Electoral coalitions and policy reversals in Portugal and Italy in the aftermath of the eurozone crisis. South European Society and Politics, 24(2), pp.233-257.
- Anser, M.K., Yousaf, Z., Khan, M.A., Nassani, A.A., Alotaibi, S.M., Abro, M.M.Q., Vo, X.V. and Zaman, K., 2020. Does communicable diseases (including COVID-19) may increase global poverty risk? A cloud on the horizon. Environmental Research, 187, p.109668.
- Balabanova, D., Roberts, B., Richardson, E., Haerpfer, C. and McKee, M., 2012. Health Care Reform in the Former Soviet Union: Beyond the Transition. Health services research, 47(2), pp.840-864.
- Bedir, S., 2016. Healthcare expenditure and economic growth in developing countries. Advances in Economics and Business, 4(2), pp.76-86.
- Creel, J., 2020. Fiscal space in the euro area before Covid-19. Economics Bulletin, 40(2), pp.1698-1706.
- Cylus, J., Hartman, M., Washington, B., Andrews, K. and Catlin, A., 2011. Pronounced gender and age differences are evident in personal health care spending per person. Health Affairs, 30(1), pp.153-160.
- Fuchs, V.R., 2013. The gross domestic product and health care spending. N Engl J Med, 369(2), pp.107-109.
- EBRD, 2020. Life in Transition Survey (LiTS). European Bank for Reconstruction and Development.
- Fowler, R.A., Adhikari, N.K. and Bhagwanjee, S., 2008. Clinical review: critical care in the global context–disparities in burden of illness, access, and economics. Critical Care, 12(5), p.225.
- Fullman, N., Yearwood, J., Abay, S.M., Abbafati, C., Abd-Allah, F., Abdela, J., Abdelalim, A., Abebe, Z., Abebo, T.A., Aboyans, V. and Abraha, H.N., 2018. Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016. The Lancet, 391(10136), pp.2236-2271.
- Global Burden of Disease Collaborative Network, 2018. Global Burden of Disease Study 2016 (GBD 2016) Healthcare Access and Quality Index Based on Amenable Mortality 1990–2016. Seattle, United States: Institute for Health Metrics and Evaluation (IHME).
- Hussey, P.S., Wertheimer, S. and Mehrotra, A., 2013. The association between health care quality and cost: a systematic review. Annals of internal medicine, 158(1), pp.27-34.
- Mannucci, E., Silverii, G.A. and Monami, M., 2020. Saturation of critical care capacity and mortality in patients with the novel coronavirus (COVID-19) in Italy. Trends in Anaesthesia and Critical Care.
- Jakovljevic, M.B., Vukovic, M. and Fontanesi, J., 2016. Life expectancy and health expenditure evolution in Eastern Europe—DiD and DEA analysis. Expert Review of Pharmacoeconomics & Outcomes Research, 16(4), pp.537-546.
- Obrizan, M., 2017. Does EU membership prevent crowding out of public health care? Evidence from 28 transition countries.
- Owens, G., 2008. Gender differences in health care expenditures, resource utilization, and quality of care. Journal of Managed Care Pharmacy, 14(3), pp.2-6.
- Ranney, M.L., Griffeth, V. and Jha, A.K., 2020. Critical supply shortages—the need for ventilators and personal protective equipment during the Covid-19 pandemic. New England Journal of Medicine, 382(18), p.41.
- Romaniuk, P. and Szromek, A.R., 2016. The evolution of the health system outcomes in Central and Eastern Europe and their association with social, economic and political factors: an analysis of 25 years of transition. BMC health services research, 16(1), p.95.
- Self, S. and Grabowski, R., 2003. How effective is public health expenditure in improving overall health? A cross–country analysis. Applied Economics, 35(7), pp.835-845.
- Wang, Z. and Tang, K., 2020. Combating COVID-19: health equity matters. Nature Medicine, 26(4), pp.458-458.
- Williams, J.S., Bishu, K., Dismuke, C.E. and Egede, L.E., 2017. Sex differences in healthcare expenditures among adults with diabetes: evidence from the medical expenditure panel survey, 2002–2011. BMC health services research, 17(1), p.259.
- World Bank, 2020. Data Bank: World Development Indicators. Washington D.C., World Bank Group.
- World Health Organization, 2020. Global Health Observatory (GHO) data.
Note: Annex included in the attached PDF.
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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. 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. 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.
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
 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.
 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.