Tag: Housing

Homeownership and Material Security in Later Life

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Many previous studies show that homeownership is related to various aspects of well-being, although the causal nature of this relationship is difficult to identify. We analyze the association between homeownership and material security, measured through subjective expectations of being better or worse off in the future, using data from 15 European countries. Our findings show that homeowners have a higher level of material security than renters, with larger differences among those living in big cities. We find that material security increases with the value of owner’s property and at the same time find no significant relationship with education, income or financial situation. We interpret the results as support for one of the most commonly emphasized mechanisms behind the positive effects of homeownership for well-being – that homeownership provides a particular form of material security in old age.


Vast empirical literature links homeownership to numerous outcomes, such as well-being, health or mobility (Costa-Font, 2008; Dietz and Haurin, 2003; Rohe and Stewart, 1996 among others). In most cases the specific causal link with homeownership per se is however difficult to demonstrate. This because homeownership, especially in old age, usually reflects the financial resources accumulated over the life course through labor market history, as well as health and family developments (Angelini et al., 2013). This means that many unobservable characteristics can obscure the relationship between homeownership and welfare outcomes and bias the estimated parameters.

Material security is an important aspect of well-being, facilitating longer-term planning of financial decisions, smoothing of expenditures across periods of lower contemporaneous incomes and allowing exceptional spending when faced with various negative shocks. It seems particularly relevant in old age when people’s ability to adjust their current income to their specific needs is significantly reduced, and material needs increasingly depend on health.  As people age and as their ability to maintain labor market activity diminishes, the material resources available to them, and the security these can provide, are increasingly composed of pensions and accumulated assets. Among the latter, fixed assets, and in particular ownership of one’s home, play a very special role, as they provide some financial backup and secure a flow of regular consumption in the form of accommodation.

It is reasonable to expect that homeownership would influence well-being through the channel of material security, particularly in old age. Surprisingly, the findings in the literature directly exploring this mechanism are so far scarce. We address this gap using data collected in the Survey of Health, Ageing and Retirement in Europe (SHARE) on individuals aged 50 years and above. We take advantage of the 2006 edition of the survey from 14 European countries and Israel and develop a measure of perceived future material security using two consecutive questions on ‘the chances that five years from now the standard of living [of the participant] will be better/worse than today’. Participants reported the estimated chances on a scale from 0 to 100, where 0 means ‘absolutely no chance’ and 100 denotes ‘absolutely certain’. In line with previous behavioral literature, we calculate a difference between the chances of being better vs. worse off, and recode into a categorical variable with 5 outcomes spanning from ‘very likely worse off’, through ‘rather likely worse off’, ‘equally likely’, ‘rather likely better off’ and ‘very likely better off’ (more details in Garten et al., 2022). In our sample, ‘equally likely’ is the most frequent category (30 percent of total responses), and being either ‘very’ or ‘rather likely worse off’ was more frequently reported than being better off (48 percent of total responses coded as either outcome for being worse off as compared to 22 percent for the two categories of being better off).

The Impact of Homeownership on Expectations of Future Standard of Living

We regress the measure of perceived material security on an extended vector of characteristics including basic demographics, education, marital status, labor market status, the relative position in the distributions of income and financial assets, and physical and mental health. Our main variable of interest is a categorical measure of homeownership, where individuals are split between renters and homeowners, who are further divided based on the country-specific quartiles of their home value. This measure is then interacted with being a big city resident. Below we present some selected results, which are reported in full in Garten et al. (2022).

In Figure 1 we report the results for each outcome of perception of material security for owner occupiers (depending on the value of their home) as compared to renters, by place of residence. The correlation with material security is particularly strong among those living in cities. However, among other respondents, those in the top quartile of the home value distribution are also more likely to report being optimistic about their material conditions in the future. For big city dwellers, the differences between renters and home owners are statistically significant already for owners with home values in the second quartile of the distribution, and the effects carry through to higher quartiles. The differences for selected perceptions of material security are not only statistically significant but also large in magnitude in the case of city dwellers who own the most expensive properties. As compared to renters they are 3.7 percentage points more likely to expect that their future situation will be either ‘rather’ or ‘very likely’ better. Among those living in big cities, 17.5 percent and 8.5 percent respectively, declare these positive expectations. This means that proportionally, the estimated 3.7 percentage points correspond to respective increases of 21.2 percent and 43.3 percent. 

Figure 1. Marginal effects of homeownership for outcomes of perception of material security

Note: Results presented as marginal effects based on estimations using the ordered probit model with 95% confidence intervals. More details available in Garten et al. (2022).

We relate the marginal effect of owning a property in the top quartile of the home value distribution, as compared to owners with properties in the bottom quartile or renters, to the effect resulting from: higher education, being in the top income quartile or in the top financial assets quartile. While education, income and financial assets affect the perception of future material situation in the expected direction, the estimated relationships are statistically insignificant, and their magnitude is much lower compared to the estimated relationship with homeownership.


Relative to renters, individuals owning their homes tend to have higher levels of well-being across numerous dimensions (see Garten et al., 2022 for an overview). Due to the complex nature of the accumulation of wealth and its interaction with different spheres of life over the life cycle, the identification of the causal character of this relationship is a nearly impossible task. Although many mechanisms behind this relationship have been suggested, few have actually been put to the test against real-life data. Therefore, better understanding of these mechanisms might be a way to verify the hypothesis that homeownership actually matters for well-being.

Our findings confirm that homeowners – in particular those living in big cities – enjoy a higher level of self-perceived material security and are more likely to express optimism about their material standard of living in the future as compared to renters. Such feeling of security for the coming years may contribute to a more general positive outlook, and consequently to the higher reported levels of well-being and life-satisfaction observed in the literature. The examined relationship is especially strong among those in the top quartile of the distribution of property values, although for dwellers in big cities the effect is also strong for those with lower property value. While these findings cannot be interpreted as strictly causal, we suggest that owning a home offers a very particular type of material security in old age and that this security might be an important mechanism leading to the observed positive relationship between homeownership and overall well-being.


The authors wish to acknowledge the support of the German Science Foundation (DFG, project no: BR 38.6816-1) and the Polish National Science Centre (NCN, project no: 2018/31/G/HS4/01511) in the joint international Beethoven Classic 3 funding scheme – project AGE-WELL. For the full list of acknowledgements see Garten et al. (2022).


Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Do Condominiums Pay Less for Heating?

Kiev in snow during. the winter with condominiums under heating

In Ukraine, a widely shared perception is that housing utility costs are too high. In this policy brief, we study if these costs can be alleviated by introducing a modern form of housing management practice, condominiums. We find that condominiums in old houses (built before 1991) pay 22% less for heating compared to old non-condominiums. Among new houses (built after 1991), we find that condominiums pay 29% less for heating.  Considering the dynamics of condominium formation in 2018-2020, old houses do not show any significant immediate effect of condominium formation on heating costs relative to that of non-condominiums. However, condominium formation among new houses leads to a relative 18% decrease in heating costs. In addition, among condominiums in old houses, participation in an overhaul co-financing program is associated with a 15% lower heating bill. The immediate effect of the program in 2018-2020 is a 16% relative decrease in heating costs for old condominiums and 37% – for new ones.

Heating Costs and Condominiums

In recent years, the cost of housing utilities has been a common concern among Ukrainians. According to a recent survey, 80% of Ukrainians believe that tariffs on utilities are too high.

The form of housing management is a factor that could affect utility costs. Experiences from Slovakia, Hungary, Poland, and Romania in the 1990s suggest that state-owned housing maintenance companies are often associated with inefficient management. Residential buildings that are owned and managed collectively by its dwellers (hereafter referred to as condominiums) are more likely to choose a more efficient private housing maintenance company (Banks, O’Leary et. al., 1996). For instance, in Slovakia’s second-largest city, Kosice, one-third of houses that were privatized in the 1990s chose private maintenance companies with competitive prices. Residents perceived the services as “far more effective” (ibid).

This brief summarizes our analysis of the relationship between heating costs and the form of housing management in Ukraine. Analyzing a large sample of houses in Kyiv, we show that condominiums are associated with lower heating costs, both among the older houses, built before Ukrainian independence in 1991, and among newer houses.

Types of Housing Management Practices in Ukraine

The different housing management practices in Ukraine can be roughly divided into three types. The most commonly used practice is when housing maintenance is carried out by a municipally owned company (commonly referred to as ZhEK – “zhilischno-eksplotazionnaja kontora”, housing maintenance office). Usually, houses that have the ZhEK-type management were built before Ukrainian independence and have kept this practice since Soviet times. The second practice is when housing maintenance is done by a private company affiliated with the building developer. This management type is usually used by houses built after Ukrainian independence that did not form condominiums. These two practices are similar in the sense that dwellers are not directly involved in the decision-making, all decisions are made by the municipal or private company, respectively.

The third type of housing management practice, relatively new for Ukraine, is condominium ownership (the Ukrainian term for it is ОСББ, translated as “Association of Co-owners of Multi-Apartment House”). In a condominium, unlike in the previous two types, the house is managed collectively by the dwellers; in particular, they have the freedom to choose and/or change utility providers, invest in major overhaul, and participate in co-financing programs.

Houses with Condominiums Pay Less for Heating

In our analysis, we use monthly data on housing costs between 2018 and 2020 collected from the Ukrainian municipal enterprise Kyivteploenergo. The data covers more than 70% of residential buildings in Kyiv and includes information on heating costs per square meter, whether or not the house is a condominium, and other house-characteristics (including the source of heating production; the presence of the meter; type of the meter number of service days per month; and share of heat consumption by legal entities).

In addition, we have information on the year of building construction retrieved from the real estate portal LUN, and condominium formation date between 2018-2020, as well as data on house participation in overhaul co-financing programs obtained from the Kyiv state administration.

Our final sample contains 7957 houses. Since we only are interested in apartment housing, we exclude residential buildings with an area below 500 m2, which would normally correspond to a small private house (these constitute only a small part of our sample). The share of condominiums in the sample is 11%, the share of houses with ZhEK is 81% and the share of houses managed by private companies is 8%.

Figure 1. Median costs for heating per m2 across housing management types and house age.

Source: Authors’ calculations. Old houses are those built before 1991, the year of Ukrainian independence, and new houses are built after 1991.

Figure 1 provides preliminary evidence towards our hypothesis, showing that the median heating costs are lower in condominiums, independent of the year of construction.

In our first econometric model, we use an OLS-approach to compare utility costs across different types of housing and management models, while controlling for a number of observable characteristics.  We find that condominiums in old houses pay 22% less for heating than old non-condominium. Similarly, we find that condominiums in new houses pay 29% less for heating compared to new non-condominiums.

The lower heating costs observed in condominiums may have several explanations:

  • First, condominium-type management could be more flexible in its response to weather conditions. Considering that they are profit-maximizing, heating providers in Ukraine tend to overheat houses during the heating season; it could be that condominiums reduce consumption of heating on the warmer days to a greater extent than other houses. In other words, condominiums could increase the efficiency of heating use.
  • Second, it could be that condominiums have lower heating costs because they improve energy efficiency, for example, by installing individual heating points (an automatized unit transferring heat energy from external heat networks to the house heating, hot water supply, ventilation, etc.), new windows, or even insulating the house.

Is There an Immediate Effect?

The next step in our econometric analysis is to study the effect of condominium formation during 2018-2020. Here, we investigate whether non-condominium houses that became condominiums experienced changes in heating costs by utilizing a fixed-effects regression model. This approach not only allows us to assess the immediate effect of condominium formation but also controls for unobservable house-specific characteristics that are constant over time, such as differences in building materials.

For new houses, we find that condominium formation decreases heating costs by 18% compared to other new houses. For old houses, we find that the corresponding effect is statistically insignificant.

This estimation only evaluates the effect of condominium formation in a relatively short timeframe, between 2018 and 2020. While the data coverage does not allow us to give a precise quantitative assessment for a long-run effect, we argue that the positive impact of condominium formation on heating costs could potentially be higher in the longer-run. Indeed, our previous OLS estimation assesses the average utility costs for all condominiums in the sample (including those formed prior to 2018).  It shows that the gap in heating costs between all condominiums and non-condominiums is higher than the corresponding gap derived from our fixed-effects estimation (22% for the old houses and 29% – for the new ones). While this difference in results can be driven by several reasons (e.g., fixed effect estimation taking into account unobservable house-specific characteristics), a stronger long-term effect could be among them.

Concerning the results for new vs. old houses, it might be the case that new houses are technically equipped to be more flexible when it comes to adjusting costs (e.g., are able to switch the heating on/off), while old houses might be inferior in this regard. If this is the case, old houses would only experience lower costs after some thermo-modernization, such as installing individual heating points.

Heating Costs and the Co-financing Program

Since 2015, the Kyiv city council offers a program that helps condominiums to finance major overhauls with the intent to improve the energy efficiency of the residential sector. Applicants compete in planning thermo-modernization projects where winning condominiums are awarded financing covering 70% of the overhaul cost.

Our results show that for old houses with condominiums, those who at some point participated in the co-financing program pay on average 15% less for heating compared to non-participants. The corresponding effect for new houses with condominiums is not significantly different from zero.

However, the immediate effect of program participation is present in both new and old houses with condominiums. Old and new condominiums that took part in the program in 2018-2019 experienced an immediate reduction in heating costs by 16% and 37% respectively.

Figure 2. The number of houses participating in the 70/30 co-financing program across the years.

Authors’ calculations.

There are several potential explanations as to why we observe an immediate effect but no effect of ever participating in the program for the new houses with condominiums.

First, it could be that new houses with condominiums that are not participating in the program are investing in overhaul anyway, although somewhat delayed compared to investments made by participating new condominiums. The average difference in heating costs between participants and non-participants would then be visible in the short-run and fade away after a few years. If this is the case, the program is financing houses that would have invested in overhaul anyway, even without co-financing. This explanation is partly supported by the fact that the share of the new houses condominiums among participants is 32%, while the corresponding share is 15% among all houses. In other words, old houses with condominiums, that are usually in a worse condition, are underrepresented in the program.

If this is the case, the share of old houses with condominiums among participants should be increased.  Given that the purpose of the program is to improve the energy efficiency of residential buildings, its efficient implementation implies encouraging overhauls in houses that are otherwise unable to fund it. In other words, the program should incentivize people living in energy-inefficient housing to form condominiums and undertake overhauls to improve their energy efficiency, rather than finance houses who are already doing well in that regard. To improve on such selection issues, the program could change the co-financing proportions, making participation more beneficial to old houses with condominiums, e.g.  80/20 – for old and 60/40 – for new condominiums.

Second, the new houses with condominiums that participate in the program might be in a much worse state before participation than those that do not. Program part-taking could make participants catch up to the average level of energy-efficiency (or perhaps do slightly better). If this is the case, the program fulfills its function in the sense that it targets the most energy-inefficient houses.

Government Policies That Should Be Changed

Above, we argue that the formation of condominiums leads to efficiency gains in energy use and cuts utility costs for dwellers. Given the design of the overhaul co-financing program, the Kyiv city council seems to recognize these benefits as well. However, there is a range of government policies currently in place that discourage people from condominium formation.

For example, there are cases when the government finances 100% of overhaul costs using a subvention (“subvention for socio-economic development”). In 2020, 17 houses in Kyiv got overhaul expenses funded by this type of subvention. At the same time, 85 houses that participated in the co-financing competition did not receive any state funding (there were 100 winners among 185 participants).

Considering that this type of subvention predominantly finances non-condominiums, we argue that this policy creates the wrong incentives.  Dwellers will likely refrain from forming condominiums in the hope of eventually being selected for an overhaul fully financed by the state, instead of forming condominium and getting only part of overhauls expenses covered (70% of the overhaul funding if winning co-finance program competition, and no funding otherwise).

In addition, this subvention typically has a “pork-barrel” nature since it is often allocated to the constituencies of the ruling party’s MPs. State financed overhauls are often used as an advertisement tool to get popular support. This creates an additional problem in the sense that subvention is targeted to politically loyal regions and not necessarily to regions in need of support.

Along this line of reasoning, we suggest that this pork-barrel subvention should be cancelled and housing-overhauls should instead be funded through co-financing programs. The government should implement programs similar to the “70/30” and further encourage people to adopt condominium ownership.


Motivated by the common perception that utility costs are excessively high, we study one possible way of reducing the utility bill – condominium housing management.

Our analysis shows that old houses with condominiums pay 22% less for heating compared to old non-condominiums. For new houses, we find that condominiums pay 29% less in heating costs than non-condominiums. In addition, old houses with condominiums that participate in Kyiv’s co-financing program pay 15% less than other old condominiums. That is, condominium formation combined with the co-financing program could save more than one-third of a resident’s heating costs.

Our analysis suggests the following policy implications:

  • Condominiums have a positive effect on energy efficiency, and utility cost savings, and should thus be promoted to the population as a preferable form of house management practice.
  • State and municipal governments should provide incentives for condominium formation through, e.g., overhaul co-financing programs. Other state-provided forms of overhaul financing, such as pork-barrel subvention, should be cancelled.
  • Co-financing programs should combine better targeting (e.g., to those houses that are in greater need of overhaul) with sufficient incentives for condominium formation.


  • Hamaniuk, Oleksii; and Andrii Doschyn, 2020.  “Let’s reduce the cost of heating by a third!” – ACMH and co-financing program for buildings”, https://voxukraine.org/en/let-s-reduce-the-cost-of-heating-by-a-third-acmh-and-co-financing-program-for-buildings/
  • Banks, Christopher, Sheila O’Leary, and Carol Rabenhorst, 1996.  Review of urban & regional development studies, vol. 8, issue 2. https://doi.org/10.1111/j.1467-940X.1996.tb00114.x

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Preferences for Redistribution in Post-Communist Countries

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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.[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.


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.