This policy brief summarizes the findings in a paper where individual exit trajectories of Russians from the labor market to economic inactivity are examined using survival analysis methods based on the Russian Longitudinal Monitoring Survey for 1995-2015. Among other results, the analysis shows that the statutory retirement age has a significant impact on the time of exit from the labor market for both men and women, but the effect is very high for women. This is an interesting and unexpected result, given no penalty for working beyond the pension age of those already retired, the five-year difference in statutory retirement age between males and females, and the low pension age in Russia on an international scale. This questions the painlessness of rising the retirement age for women, should the decision finally be taken.
An ageing population, combined with a slowdown in economic growth, challenges the Russian public finances with an increased deficit of the Pension fund. In addition, the persistently negative natural population growth against the backdrop of ageing has predetermined a decline in the working-age population in the foreseeable future. Older cohorts are therefore becoming a potentially attractive source to increase the size of the labor force. All this has actualized the discussion about the need to increase the Russian retirement age (see, for instance, Maleva and Sinyavskaya, 2010). However, little is known about the labor market situation of older age groups and, in particular, about the process of their exit from the labor market
The Russian pension system, unlike the pension systems of many developed countries, hardly penalizes continuation of work after reaching retirement age and documenting a pension (working pensioners lose only pension indexation). The changes in pension law that have entered into effect since 2015 encourage continued work without recourse to retirement, but there have been few responses to the innovation so far. Coupled with the low pension replacement rate (i.e., the proportion of wages substituted by pension), this makes the process of leaving the labor market nontrivial, since a large number of people of retirement age remain on the labor market after reaching retirement age.
Denisova (2017) examines individual exit trajectories of Russians from the labor market to pension-age economic inactivity applying survival analysis to the Russian Longitudinal Monitoring Survey (RLMS-HSE). The major research questions are the following: What determines the length of stay of older age groups in the Russian labor market? What is the role of the statutory retirement age in this process?
Data and research methodology
The RLMS-HSE for the period of over 20 years, from 1995 to 2015, is the empirical basis of the research (http://www.cpc.unc.edu/rlms). I limit the sample to age 45-72 as there is practically no retirement by age before age 45, and 72 years is the upper boundary of the working age definition internationally accepted by statisticians. I exclude from the sample those who are on retirement and did not work or seek work for the entire period of observation, since their decision to end working activity remained outside the observation period.
An episode in the survival analysis of exit from the labor market into pension-age inactivity is an episode of working life. The analytical time in this case is the age of the respondent. The failure event (the moment of exit from the labor market to pension-age economic inactivity) is defined by the simultaneous fulfillment of three conditions: the respondent does not work, does not look for a job, and receives retirement pension. Only the final exits from the labor market into inactivity are considered, while temporary exits are disregarded.
I evaluate proportional hazard models, which suggest that exogenous economic factors shift the baseline hazard function (which reflects the average entire sample hazard rate at each age) proportionally. A semi-parametric Cox model specification with robust errors clustered at individual level is used.
The vector of explanatory characteristics includes education; marital status; experience in the labor market (work at an enterprise with a state share; entrepreneurship versus work for wages); health characteristics (subjective and objective); settlement type; and attainment of statutory retirement age. In all cases, I control for the year of the survey.
Given the differences in the behavior of men and women in the labor market, the regression analysis is run separately for the subsamples of men and women. The statistical significance of the differences in returns to factors between men and women is tested based on the results of the full sample regression with interaction terms.
Averaged process of exit from the labor market
The averaged process of leaving the labor market pending on age is conveniently described through so-called Kaplan-Mayer’s survival function (an estimate of the survival process). As seen from Figure 1, the process of exit prior to age 55 for women and 60 for men is very slow, while the rate of exit becomes almost permanent and slows down after 70 years. Men stay in the labor market longer: 25% of women leave the labor market at the age of 58 years, whereas for men this age is 60. The threshold of 75% of the sample that left the labor market is reached in the sample of women by the age of 70, and 71 for men.
Determinants of exit
The analysis of older cohorts’ exit from the labor market via survival methods confirms important determinants of the process, previously identified in literature. The impacts of health and of financial incentives are in this group of results.
Figure 1. Survival functions, men and women
Health status is the key factor for men’s exit into inactivity: the exit to inactivity is accelerated by 71 percentage points for males with bad health, whereas for women this factor is statistically irrelevant.
A higher per capita household income is correlated with later exit from the labor market. A higher income from the main place of employment has no statistically significant effect when we control for household income and is at an extended boundary (15%) of statistical significance if we do not. Both variables indirectly reflect the pension replacement rate, and I interpret the results as an indirect confirmation that workers at the top part of the income distribution, being inadequately insured by the pension system, remain on the labor market longer.
The identified peculiarities of the exit to pension-age inactivity of the Russian elderly are of major interest. Unlike many developed countries, only highly skilled persons remain in the labor market longer than others, while the behavior of middle-skilled groups, and skilled and unskilled workers does not statistically differ between them.
Employment at state-owned enterprises slows down women’s exit to inactivity but is not significant for men. Self-employment and entrepreneurship prolong the presence in the labor force, by 41 percentage points for women.
The regression analysis demonstrates that the statutory retirement age has a significant impact on the time of exit from the labor market for both men and women, and the effect is significantly higher for women: the hazard rate of inactivity rises by 63 percentage points when a woman reaches 55 years, and by 25% when a man reaches 60. For men, an effect comparable in size is the self-assessment of health as poor.
The results, on the one hand, confirm those for developed countries: health status is the key factor for men’s exit into inactivity, and financial motives have a significant impact. At the same time, the peculiarities of the Russian labor market are reflected in a differing labor market exit process of various professional groups, in the sense that self-employment and entrepreneurship and work at state enterprises postpone exit into inactivity. The high sensitivity of women to the statutory retirement age, which by 2.5 times exceeds the sensitivity of men, is one of the new and unexpected results, taking into account that the statutory retirement age for women in Russia is very low by international standards. This questions the painlessness of rising the retirement age for women, should the decision finally be taken. Indeed, given the very low pension age for females, an (gradual) increase in the retirement age for women would seem not to raise strong objections. However, our result testifies that the normative border of the retirement age has a decisive influence on women’s choice of time of exit from the labor market, even under control (as far as data permits) on differences in education, situation in the labor market and family circumstances. In this situation, the process of rising the retirement age, if such a decision is taken, can be rather painfully accepted by those who so strongly focus on its current meaning in their life plans.
- Denisova, Irina, 2017, “Exit of senior age cohorts from the labor market: survival analysis approach” – forthcoming in Population and Economics.
- Maleva T.M., Sinyavskaya O.V., 2010 “Raising the retirement age: pro et contra, Journal of the New Economic Association, No. 8, pp. 117-139.
Belarus proudly calls itself a social state. Indeed, Belarus boasts one of the lowest poverty and inequality levels in the region. Fiscal policy in Belarus is equalizing and pro-poor, effectively redistributing income from rich to poor. As in Russia and many other Post-Soviet states, the equalizing effect of the fiscal policy in Belarus is mostly attributable to the pension system. Some of the other social policies are highly inefficient, failing to redistribute income. The prominent examples are utility subsidies and student stipends, which mainly benefit the upper part of the income distribution. The lack of adequate unemployment benefits is an opportunity to improve the efficiency of the social support system in Belarus.
The Constitution of Belarus characterizes Belarus as a social state, and Belarus takes its social state status seriously. The economic growth in the beginning of the 2000’s was strongly pro-poor (Chubrik, 2007). Poverty according to the national definition (calorie-based poverty line, which in 2015 corresponded to $10.67 PPP per day) declined from 42% in 2000 to 5.7% in 2016, while the poverty according to the international threshold of $3.1 per day in PPP terms is fully eradicated. Belarus also has one of the lowest levels of income inequality in the region with a Gini coefficient of only 0.27 (UNDP, 2016).
How much of the pro-poor and equalizing effects could be attributed to the government policy? Probably it is impossible to give a complete answer to the question. Many non-formalized and not easily quantifiable government policies lead to the decrease in poverty and inequality. For example, the policy of support to state-owned enterprises might have redistributive effects through job creation. However, the absence of access to relevant data makes it impossible to estimate the effects of the policy.
Some of the government policies, on the other hand, are easily quantifiable with available data. Bornukova, Chubrik and Shymanovich (2017) analyze the redistributive effects of fiscal policies in Belarus using the Commitment to Equity methodology (Lustig, 2016). The authors find that the direct taxes and transfers in Belarus (taxes, transfers, and subsidies) are equalizing and pro-poor, lowering the national poverty headcount by 17 percentage points and the income Gini coefficient from 0.41 to 0.27. The high equalizing effect of the fiscal policies in Belarus surpasses those in other developing countries, including Russia where the direct taxes and subsidies reduced the income Gini coefficient by 0.13 (Lopez-Calva et al., 2017). The remaining discussion in this brief is based on the results from Bornukova, Chubrik and Shymanovich (2017), if not otherwise stated.
Fiscal policies and their redistributive effects
The two types of direct personal taxes – the personal income tax and the social contributions tax – are both almost flat in Belarus. To fight tax evasion, the Belarusian authorities introduced flat tax rates in 2009, following a successful experiment in Russia. The personal income tax has some small exemptions for families with children, while the social contributions tax has a lower rate for agriculture employees. However, the effect of these deductions is relatively small: the direct taxes decrease the Gini coefficient by only 0.015.
The indirect taxes – the value-added tax, the import duties, and the excises – are weakly regressive, putting the burden of taxation on the poor. This is particularly true for the alcohol and tobacco excises. Again, the main purpose of these taxes is to penalize unwelcome behavior, and not to redistribute income, hence the result is not unexpected, and common for many countries. Overall the indirect taxes in Belarus increase the Gini coefficient by 0.05.
Direct transfers are responsible for most of the equalizing effects of the fiscal policies. This is not surprising, given that the main purpose of the direct transfers is to fight poverty and provide support for those in need. However, most of the transfers are not need-based or targeted to the poor. Instead they are assigned to households based on their socio-economic characteristics aside income, such as age and maternity status.
Pensions are the main factor of reducing poverty and inequality. They reduced the Gini coefficient by 0.11 and decreased poverty (according to national definition) by 19 percentage points. The incredible effectiveness of the pensions is largely explained by the absence of other sources of income of the retirees. The majority of them does not work, and have no other pension savings or passive income. Pensions in Belarus are also redistributive in nature since they only weakly depend on one’s income during the working life.
Different benefits and privileges also decrease poverty and inequality, but at a much smaller scale. The childcare benefits (for families with children aged 0-3 years) contribute most to the effects, decreasing the Gini coefficient by 0.013 and poverty by 3 percentage points. The variety of privileges does not contribute much due to their relatively small size.
Utilities and transport subsidies are also important elements of the social support system, and their existence is usually justified by the necessity to support those in need. Since the utilities subsidies are incorporated into tariffs and available for everyone independent of need, they are in fact benefitting the rich (i.e. people with big apartments and houses).
Figure 1. Incidence of utilities subsidies by income deciles
As seen on Figure 1, upper deciles receive more support through utilities subsidies, and this support is quite substantial, often surpassing $1 per day in PPP. However, as a share of income the utilities subsidies are still progressive, and they in fact decrease the Gini coefficient by the tiny amount of 0.006, and decrease poverty (as any handout). The same is true for transport subsidies.
What could be improved?
Due to the flat nature of direct taxation and an absence of well-targeted needs-based transfers, some of the people in need still fall through the cracks. 1.9% of the population actually becomes poor after we account for the direct taxes and transfers. This headcount increases to 3.3% if we account for indirect taxes.
Another important issue is the efficiency of government transfers and subsidies in fighting poverty and inequality. It is not surprising that pensions have the largest equalizing contribution, as the government spends almost 11% of GDP on pensions. If we account for this fact and look at the efficiency (effect on poverty and inequality per dollar spent), pensions are not the leading program. It is in fact surpassed by different kinds of child support. Given that mothers in Belarus are allowed to take 3 years of unpaid maternity leave, which decreases household income, childcare benefits are relatively efficient.
The unexpected leader in efficiency is unemployment benefits, despite (or maybe due to) their negligible size. Shymanovich (2017) shows that unemployed face high risks of poverty, suggesting that an increase in the size of unemployment benefits and an easier access may bring huge benefits. The current minuscule size of the benefits (around $10-15 per month) is still enough to lift some people out of poverty, and has important equalizing effects, generating the biggest “bang for the buck” out of all benefits.
The student grants (stipends), the utilities subsidy and the transport subsidy have very low efficiency. These programs relocate a lot of funds to the upper deciles of the income distribution. Our calculations show that if all benefits, privileges and subsidies were not available to those in the top two income deciles, the Belarusian budget could save 1.4% of GDP.
Fiscal policies in Belarus are quite effective in redistributing income. Bornukova, Chubrik and Shymanovich (2017) show that the direct taxes and transfers in Belarus result in a decrease of poverty by 17 percentage points, and decrease the Gini coefficient of inequality from 0.41 to 0.27. The pension system has the most important contribution, decreasing poverty by 19 percentage points, and the Gini coefficient by 0.11.
However, the absence of a needs-based, well-targeted social support system leads to many inefficiencies. Direct and indirect taxes lead to impoverishment of 3.3% of population, which is not compensated by direct transfers.
The absence of targeting also leads to 1.4% of GDP redistributed towards the two upper income deciles through benefits, privileges and subsidies. This is, of course, highly inefficient. Better targeting could allow saving these funds or redirecting them to unemployment benefits – the most efficient but a very small benefits program so far.
- Bornukova, Kateryna, Alexander Chubrik and Gleb Shymanovich, 2017. “Fiscal Incidence in Belarus: a Commitment to Equity Analysis”, BEROC Working Paper Series, WP no. 42
- Chubrik, Alexander, 2007. “GDP Growth and Income Dynamics: Who Reaps the Benefits of Economic Growth in Belarus?” In Haiduk, K., Pelipas, I., Chubrik, A. (Eds.) Growth for All? Economy of Belarus: The Challenges Ahead; IPM research Center
- Lopez-Calva, L. F., Lustig, N., Matytsin, M., Popova, D., 2017. “Who Benefits from Fiscal Redistribution in Russia?”,in The Distributional Impact of Fiscal Policy: Experience from Developing Countries, edited by Gabriela Inchauste and Nora Lustig (Washington: World Bank, forthcoming).
- Gleb Shymanovich, 2017. “Poverty and Vulnerable Groups in Belarus: The Consequences of 2015-2016 Recession (in Russian)”, IPM Research Center Bulletin
- UNDP, 2016. “Regional Human Development Report 2016: Progress at Risk”, United Nations Development Programme, Istanbul Regional Hub, Regional Bureau for Europe and the CIS
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.