Tag: Transition countries
Important Policy Lessons from Swedish-Russian Capital Flows Data
A recent study of capital flows between Sweden and Russia provides many policy lessons that are highly relevant for the current economic situation in Russia. In line with studies on other countries, bilateral FDI flows were more stable than portfolio flows, which is important for a country looking for predictable external sources of funding. However, much of the FDI flows came with trade and growth of the Russian market. The sharp decline in imports and fall in GDP is therefore bad news also when it comes to attracting FDI. The conclusion is (again) that institutional reforms and reengaging with the West are crucial policies to stimulate both the domestic economy and encourage much-needed FDI.
In a recent paper (Becker 2016), I take a detailed look at the trends and nature of bilateral capital flows between Sweden and Russia over that last 15 years. Although the paper focuses on the capital flows of a relatively small country like Sweden with Russia, it sheds some light on more general theoretical and empirical issues associated with FDI and portfolio flows that are highly relevant for Russia today.
Measuring Bilateral FDI
One general qualifier for studies of bilateral capital flows is however the reliability of data; Not only is a significant share of international capital flows routed through offshore tax havens which makes identifying the true country of origin and investment difficult, but also many investing companies are multinationals (MNEs) with operations and shareholders in many countries so it is hard to have a clear definition of what is a “Swedish” or a “Russian” company. In addition, when different official data providers, in this case Statistics Sweden (SCB) and the Central Bank of Russia (CBR), report capital flows on the macro level, there are large discrepancies.
Private companies also gather company level data on FDI that can be aggregated and compared with the macro level FDI data. This data is on gross FDI flows and should not be expected to be the same as the net macro level FDI flows data but is a bit of a “reality check” of the macro data.
Figure 1. Average annual FDI flows
Sources: SCB, CBR, fDi Market, MergerMarkets
The reported annual average flow of FDI from Sweden to Russia varies from around USD500 million to USD1.2 billion depending on the data source. Russian flows to Sweden are rather insignificant regardless of the source but the different sources do not agree on the sign of the net flows (Figure 1).
The differences between data sources suggest that some caution is warranted when analyzing bilateral FDI flows. With this caveat in mind, there are still some clear patterns in the capital flows data from Sweden to Russia that emerge and carries important policy lessons in the current Russian economic environment.
FDI vs. Portfolio Investments
There is a large literature discussing the distinguishing features of FDI and portfolio flows (see Becker 2016 for a summary). Some of the key macro economic questions include which type of flows provides most international risk sharing; are most stable over time; or most likely to contribute to balance of payments crises when the flows go in reverse. In addition, there are potential differences in terms of the amount of international knowledge transfers and how different types of capital flows respond to institutional factors.
Figure 2. FDI and portfolio investments
Source: SCB
Figure 2 shows that FDI has been much more stable than portfolio flows in the years prior to and after the global financial crisis as well as in more recent years. Although all types of capital flows respond negatively to poor macroeconomic performance, and the stock of portfolio investments swing around much faster than FDI investments, i.e., portfolio flows go in reverse more easily and can contribute to external crises. This makes FDI a more preferable type of capital flow for Russia.
FDI and Trade Go Together
Since FDI is a desired type of capital flow, it is important to understand its driving forces. The first question to address is whether FDI and trade are substitutes or complements. Since the bulk of FDI comes from MNEs that operate in many countries, we can imagine cases both when FDI supports existing trade and cases when it is aimed at replacing trade by moving production to the country where the demand for the goods is high.
In the case of Sweden and Russia, the macro picture is clear; FDI has increased very much in line with Swedish exports to Russia (Figure 3). Both of these variables are of course closely correlated with the general economic development in Russia, but even so, the very close correlation between FDI and trade over the last 15 years suggests that they are compliments rather than substitutes.
Figure 3. Swedish Exports and FDI to Russia
Source: SCB
Most FDI is Horizontal
FDI flows are often categorized in terms of the main motivating force for MNEs to engage in cross-border investment: vertical (basically looking for cheaper inputs), horizontal (expanding the customer base), export-platform (producing abroad for export to third countries) or complex (a mix of the other reasons) FDI.
Looking at the sectoral composition of FDI from Sweden to Russia (Figure 4), most investments have come in sectors where it is clear that MNEs are looking to expand their customer base. Even in the case of real estate investments, a large share is IKEA developing new shopping centers that host their own outlets together with other shops. Communication and financial services are also mostly related to service providers looking for new customer. Only a small share is in natural resource sectors that would be more in line with vertical FDI, while there are very few (if any) examples of MNEs moving production to Russia to export to third countries.
Figure 4. Sectors of Swedish FDI to Russia
Source: SCB
Policy conclusions
The above figures on bilateral capital flows from Sweden to Russia carry three important policy messages: 1) FDI is more stable than portfolio flows; 2) Trade goes hand in hand with FDI; and 3) FDI to Russia has mostly been horizontal and driven by an expanding customer base.
In the current situation where Russia should focus on policies to attract private capital inflows, the goal should be to attract FDI. Instead, the government is now looking for portfolio inflows in the form of a USD3 billion bond issue. But FDI is a more stable type of international capital than portfolio flows and also come with the potential of important knowledge transfers both in terms of new technologies and management practices.
However, as we have seen above, FDI inflows have in the past been correlated with increased trade and an expanding Russian market. In the current environment, where imports with the West declined by 30-40 percent in the last year, GDP fell by around 4 percent, and the drop in consumers’ real incomes have reached double digits in recent months, it is hard to see any macro factors that will drive FDI inflows.
Instead, attracting FDI in this macro environment requires policy changes that remove political and institutional barriers to investments. The first step is to fulfill the Minsk agreement and contribute to a peaceful solution in Ukraine that is consistent with international laws. This would not only remove official sanctions but also provide a very serious signal to foreign investors that Russia plays by the international rulebook and is a safe place for investments from any country.
The second part of an FDI-friendly reform package should address the institutional weaknesses that in the past have reduced both foreign and domestic investments. It is telling that many papers that look at the determinants of FDI flows to transition countries include a ‘Russia dummy’ that is estimated to be negative and both statistically and economically significant (see e.g. Bevan, Estrin and Meyer, 2004 and Frenkel, Funke, and Stadtmann, 2004). One factor that reduces the significance of the ‘Russia dummy’ is related to how laws are implemented. Other studies point to the negative effect corruption has on FDI.
Reducing corruption and improving the rule of law are some of the key reforms that would have benefits far beyond attracting FDI and has been part of the Russian reform discussion for a very long time. It was also part of the reform program that then-President Medvedev presented to deal with the situation in 2009 together with a long list of other structural reforms that would help modernize the Russian economy and society more generally.
As the saying goes, don’t waste a good crisis! It is time that Russia implements these long-overdue reforms and creates the prospering economy that the people of Russia would benefit from for many generations.
References
- Becker, T, 2016, “The Nature of Swedish-Russian Capital Flows”, SITE Working paper 35, March.
- Bevan, A, Estrin, S & Meyer, K 2004, “Foreign investment location and institutional development in transition economies”, International Business Review, vol. 13, no. 1, pp.43-64.
- Frenkel, M, Funke, K & Stadtmann, G 2004, “A panel analysis of bilateral FDI flows to emerging economies”, Economic Systems, vol. 28, no. 3, pp. 281-300.
Does Gender Matter for the Innovativeness of SMEs?
This policy brief summarizes the results of an on-going research project on the gender aspect of companies’ innovativeness in transition countries. The aim of this work is to examine whether there is a gender gap in innovative behavior within the sector of small and medium-sized enterprises (SMEs). The results suggest that the propensity to innovate is higher among companies with a presence of a female owner. This finding preserves for 5 measures of innovativeness. Thus, female involvement in business might be beneficial for the innovative sustainable development of economy.
The role of small and medium-sized enterprises (SMEs) has increased lately and they are considered one of the main engines of economic growth (Radas and Bosic, 2009). Research on transition economies and development has emphasized the need for strong a SME sector, since it often acts as the backbone of the economy (Lukasc, 2005) and is the largest contributor of employment (Omar et al., 2009). Another important channel through which the SME sector contributes to development is through their innovative activities. Sustainable economic development requires competitive and successful industries. Being innovative is one way to achieve this goal. However, the innovativeness of sectors and industries depends not only on the actions of the largest companies, but also on the SME sector and individual entrepreneurs. Indeed, the latter are often argued to be more dynamic and more ambitious (Chalmers, 1989; Li and Rama, 2015).
The decision to follow an innovative strategy often depends on the company’s leader, their experience and other managerial characteristics. However, the experience of the leader is not the only factor affecting managerial actions – gender also appears to matter (Daunfeldt and Rudholm, 2012). In the absence of clear answers and knowledge about female managerial characteristics, including their innovativeness (Alsos et al., 2013), it is difficult to evaluate their role in modernizing the business society and to distinguish their competitive advantages or disadvantages over male managers and business owners.
The role becomes even more ambiguous for the transition, post-communist economies. The labor market under USSR officially provided equal rights to women. However, in practice women were treated differently than men. While women often had to do the same work as men, the patriarchal society remained with men being regarded as the main decision makers, and women being fully responsible for housework and childcare. This can explain the low presence of women in top-managerial positions and women’s weaker business ties and networks (Welter et al., 2004).
The question of gender and innovation in entrepreneurship has recently starting to attract attention. Earlier, innovativeness was strongly connected and associated with high-tech companies. Thus, innovation research mostly focused on technology-based and capital-intensive industries (Dauzenberg, 2012; Marlow and McAdam, 2012). As a result, innovation behavior in less capital-intensive SMEs was almost entirely overlooked. This can also explain the lack of focus on gender, as men usually dominated the capital-intensive industries (Ljunggren et al., 2010). In an ongoing research project, I am trying to expand the understanding of gender differences in innovation and SME entrepreneurship with a focus on transition economies and the CIS block in particular.
The idea is to estimate owners’ and CEOs propensity to implement innovations in the organization. The specification of the model follows the literature and uses a probit technique that allows for an estimation of these propensities while taking into account other influencing factors and individual characteristics of firms, their owners and CEOs, which likely affect innovative decisions. The data I use come from the 5th wave of the Business Environment and Enterprise Performance Survey (BEEPS) conducted in 2012-2013. The final dataset covered 5254 SMEs from 30 European and East Asia countries.
The main variable of interest is the innovativeness of the enterprise, proxied by 5 different indicators. The measures of implemented innovative activities are: 1) whether the firms introduced a new product or service during the last 3 years; 2) whether there was any new production process implemented; 3) whether there were any spending on research and development; 4) whether were was an introduction of a new marketing strategy and method; and 5) whether an enterprise implemented new methods in operational management. The usage of 5 indicators instead of one allows me to see whether there is any specific feature of innovativeness that differs by gender.
The list of control variables covers information on the gender of the CEO and owners, number of years of experience of the CEO, age of the firm, type of ownership, focus on internal and external markets, as well as the usage of foreign technologies and certification. I also have information on the share of skilled labor force, the share of females in the organization, and whether the organization bears additional costs on external consulting services and training of employees. Information on industry, country, size of the organization and type of residence is also available.
Unfortunately, the data lacks information on the number of owners, which will prohibit me from estimating the clear gender effects and limits the analysis to the effect of gender diversity among owners.
The obtained results (see Table 1) show that having a female as the only, or one of the, owner(s) increases the propensity of going into uncertainty and implementation of a new good/service by 4.5% in the CIS region and 6.7% in the non-CIS block. However, the effect of having a female CEO is insignificant. This finding contradicts the literature on gender differences in the willingness to take on risk (Wagner, 2001; He et al., 2007; Eckel et al., 2008; Croson and Gneezy, 2009) that mostly demonstrates that women, on average, are more risk-averse than men.
A similar effect is observed for the implementation of a new business process or marketing strategy. The only insignificant difference is the spending on R&D in CIS countries and new managerial methods in non-CIS block. However, these measures of innovativeness raise doubts regarding its applicability for SME sector. A shift from high-intense productions towards services makes it less useful to spend enormous sums of money on technological research. Instead, other innovative actions like the development of human capital are of greater importance.
Table 1. Propensity to innovate
Source: Author’s own estimation.
Conclusion
The results show that having a female owner or gender diversity in the ownership structure positively affects the propensity of the organization to follow innovative behaviors and strategies. Therefore, promoting female entrepreneurship and gender equality in ownership seem positive for increasing the innovativeness of companies, and the economy in general, in both the CIS and non-CIS block.
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References
- Alsos, G.A., Hytti, U., and Ljunggren, E. 2013.Gender and Innovation: State of the Art and a Research Agenda.International Journal of Gender and Entrepreneurship, 5(3):236-256.
- Chalmers, N. 1989. Industrial Relations in Japan: The Peripheral Workforce. London: Routledge.
- Croson, R. and Gneezy, U. 2009. “Gender Differences in Preferences”.Journal of Economic Literature.Volume 47, #2.
- Daunfeldt, S., O., and Rudholm, N., (2012). Does gender diversity in the boardroom improve firm performance? Department of Economics, Dalarna University, SE-781 88 Borlänge, Sweden; and HUI Research, SE-103 29 Stockholm, Sweden.
- Dautzenberg, K. 2012. Gender differences of business owners in technology-based firms.International Journal of Gender & Entrepreneurship,4:79–98.
- Eckel, C. and Grossman, P. 2008. “Men, Women and Risk Aversion: Experimental Evidence”. Handbook of Experimental Economic Results.Elsevier.Volume 1, #7.
- He, X., Inman, J.J. and Mittal, V. (2007), “Gender jeopardy in financial risk taking”, Journal of Marketing Research, 44: 414-24.
- Li, Y., and Rama, M. 2015. Firm Dynamics, Productivity Growth, and Job Creation in Developing Countries: The Role of Micro- and Small Enterprises. The World Bank Research Observer, 30: 3-38.
- Ljundggren, E., Alsos, G.A., Amble, N., Ervik, R., Kvidal, T., Wiik, R. 2010. Gender and innovation: Learning from regional VRI projects. Nordland Research Institute, Norway.
- Lukacs, E. 2005. The economic role of SMEs in world economy, especially in Europe. European Integration Studies, 4(1): 3-12.
- McAdam, M. and Marlow, S. 2008.The Business Incubator and the Female High-Technology Entrepreneur: A Perfect Match? Paper presented at the 2008 International Council for Small Business World Confrence, recipient of the 2008 Best Paper Award for Women Entrepreneurship.
- Omar, S. S., Arokiasamy, L., & Ismail, M. 2009. The background and challenges faced by the small and medium enterprises. A human resources development perspectives. International Journal of Business and Management, 4(10): 95-102.
- Radas, S., and Božić, Lj. 2009.The Antecedents of SME Innovativeness in an Emerging Transition Economy. Technovation, 29: 438-450.
- Wagner, M.K. (2001), “Behavioral characteristics related to substance abuse and risk-taking, sensation-seeking, anxiety sensitivity and self-reinforcement”, Addictive Behaviors , Vol. 26, pp. 115-20.
- Welter, F., Smallbone, D., Isakova, N., Aculai, E. and Schakirova, N. 2004. Social Capital and Women Entrepreneurship in Fragile Environments: Does Networking Matter? Paper presented at Babson College-Kauffman Foundation Entrepreneurship Research Conference, University of Strathclyde.
Preferences for Redistribution in Post-Communist Countries
Public attitudes toward inequality and the demand for redistribution can often play an import role in terms of shaping social policy. The literature on determinants of the demand for redistribution, both theoretical and empirical, is extensive (e.g., Meltzer and Richard 1981, Alesina and Angelotos 2005). Usually, due to data limitations, transition countries are usually considered to be a homogeneous group in empirical papers on the demand for redistribution. However, new data on transition countries allow us to look more deeply into the variation within this group, and to look at which factors are likely to play a significant role in shaping a society’s preferences over redistribution.
The data we use are from the second round of the EBRD and WB Life in Transition Survey (LiTS) (EBRD Transition Report 2011). This is a survey of nationally representative samples consisting of at least 1000 individuals in each of the 29 transition countries.[1] In addition, and for comparison purposes, this survey also covers Turkey, France, Germany, Italy, Sweden and UK. Furthermore, in six of the countries surveyed – Poland, Russia, Serbia, Ukraine, Uzbekistan and UK – the sample consists of 1500 individuals.
Redistribution is, in general, a complex issue, which can take various forms and rely on different mechanisms. In this policy brief, we will only focus on two forms of public attitudes towards redistribution. The first is direct income redistribution from the rich to the poor and public preferences for or against this form of redistribution. The second is indirect redistribution through the provision of public goods, some of which favor certain groups of population over others. In particular, we will consider preferences over extra government spending allocations in the areas of education, healthcare, pensions, housing, environment and public infrastructure. Generally, we would like to explore in greater detail to what extent there are differences across countries in terms of public preferences over redistribution and what might explain differences both within and across societies.
Both survey rounds include questions regarding public preferences towards income redistribution, direct (from the rich to the poor) and indirect (through government spending towards certain public goods). Data for exploring public preferences for direct redistribution can be obtained from a question in the survey that asks respondents to score from 1 to 10 whether they prefer more income inequality or less. More specifically, in the LiTS 2010, the question is the following:
Q 3.16a “How would you place your views on this scale: 1 means that you agree completely with the statement on the left “Incomes should be made more equal”; 10 means that you agree with the statement on the right “We need larger income differences as incentives for individual effort”; and if your views fall somewhere in between, you can choose any number in between?
Note, however, that we use the reverse of this so that 10 represents greater equality and 1 represents wider differences. Bearing this in mind, figure 1 shows the average scores for redistribution preferences for a selection of the countries for 2010 and shows a sizeable variation ranging from 4.4 (more inequality) in Bulgaria to 7.87 (greater equality) in Slovenia. The mean for Russia is 6.92.
The data also allows for a comparison to be made between these preferences in transition countries and in the developed economies covered in the survey. For instance, Russians are on average close to Germans in their preferences for redistribution, while Estonians and Belarusians prefer less redistribution and are closer to the British, on average.
Figure 1. Preferences for Direct Redistribution
Indirect measures of attitudes towards redistribution can add further depth to these societies’ preferences. In particular, the indirect measures in the 2010 survey are derived from a question that asks respondents to rate from 1 to 7 their first priorities for extra government spending.
Q 3.05a “In your opinion, which of these fields should be the first priority for extra government spending: Education; Healthcare; Housing; Pensions; Assisting the poor; Environment (including water quality); Public infrastructures (public transport, roads, etc.); Other (specify)”?
The country averages for these indirect measures for 2010 are presented in Figure 2. The graph reveals a sizeable cross-country variation. For instance, 43.5% of respondents in Mongolia preferred channeling extra government money to education, while 48.7% of respondents in Armenia selected higher healthcare spending. Almost 39% of respondents in Azerbaijan chose assistance to the poor as the first priority for government spending, while the corresponding figure was only 8.3% in Bulgaria and 4% in the Czech Republic. More than 34% of the Russians choose healthcare as their first priority, another 20% choose education, 15% would like the money to be channeled to housing, 14.5% to pensions, 11% to support the poor, 3% to support environment, and only 2% to public infrastructure (2010).
These numbers highlight that there are sizeable differences across the transition countries regarding preferences for redistribution. Also, regarding the form of indirect redistribution in terms of preferences over how government budgets should be prioritized and allocated. Several groups of factors or determinants are typically listed in academic literature to help explain what drives public preferences over the degree and form of redistribution. In the first group of factors, there are various determinants at the individual level. Within the group of individual determinants, self-interest or rational choice of a degree of redistribution favorable to the individual with usual (individual) preferences are stressed. Alternatively, motives behind a preference for redistribution can be related to social preferences (preferences for justice or equity) and reciprocity. Within this general group of self-interest, attitudes towards risks can be stressed as a crucial factor behind demands for social insurance and hence for indirect forms of redistribution. Individuals’ prospects of upward mobility, expectations about their future welfare or ‘tunnel effect’ in shaping their views and preferences over redistribution are also underlined. Also, the commonly held beliefs about the causes of prosperity and poverty are considered to be important in shaping the public’s attitudes under the umbrella of social preferences.
The literature covers possible institutional determinants for preferences towards redistribution and emphasizes the role of the level of inequality in a society and typically relates to the median voter hypothesis in democracies. It is also stressed that welfare regimes (liberal, conservative) can play a role in shaping the level of public support for redistribution.
Figure 2. Preferences for Indirect Redistribution
A closer examination of the data and estimates of the factors shaping individuals preferences over redistribution in the 2010 survey, are consistent with motives involving strong self-interests of the respondents.[2] Those from richer households have less support for redistribution, with the result being robust to the measure of household income used. The past trend in household income positions is insignificant, while the higher the expected income position of household in the coming four years, the less supportive the respondents are of income redistribution (elasticity -0.1). Those who experienced severe hardships with the recent crisis tend to support redistribution more than those who had little problems or not at all (elasticity 0.13).
Furthermore, the role of preferences towards uncertainty is confirmed: the higher the (self-reported) willingness to take risks, the less likely the individual is to support or favor redistribution. Respondents with tertiary education are less inclined to support redistribution of income from the rich to the poor, compared to those with secondary education (elasticity is -0.4). Having a successful experience with business start-ups also decreases demand for income redistribution from the rich to the poor (elasticity -0.3). Those living in rural areas are more in favor of redistribution compared to metropolitan areas, while living in urban areas shows the same level of support for redistribution as those living in metropolitan areas. In each of these cases, it appears that those who would benefit the most from redistribution favor it more than those who view it as coming at their expense, or possible expense in the future.
Beliefs regarding the origins of success and poverty are also shown to be statistically significant and negative, as predicted: those who believe effort and hard work or intelligence and skills are the major factors for success are less supportive of income redistribution (elasticity -0.16). Those who consider laziness and lack of will power the major factors for people’s lack of success are also, consistently, less supportive of redistribution (elasticity -0.2).
It also turns out that better democratic institutions are correlated with a higher demand for redistribution. The result is robust across the measures used, i.e. it does not seem to depend on the particular measure used. The size of the effect is quite pronounced: a one standard deviation increase in the democracy measure increases demand for redistribution from 16 percentage points, when the voice and accountability measure is used, to 33 and 36 percentage points when controls of the executives and democracy index are used.
Furthermore, the better the governance institutions, as measured by the rule of law and control of corruption indexes, the higher is the demand for redistribution. However, the result is not robust to the various measures used. Government effectiveness appears to be insignificant (though with a positive direction), and the regulatory quality measure is insignificant but with a negative direction. The size of the effects is again quite pronounced. A one standard deviation increase in the rule of law measure increases demand for redistribution by 17 percentage points, and a one standard deviation increase in the control of corruption measure increases demand for redistribution by 27 percentage points.
The higher the level of inequality, the larger is the demand for redistribution as might be expected. This result is robust across all measures used. The size of the effect varies from 16 to 18 percentage points in response to a one standard deviation increase.
A regression analysis of preferences towards indirect redistribution also shows that self-interest motives are very pronounced, but there are traces of social preferences as well. In particular, younger people (age 18-24) would like to have more subsidized education and housing at the expense of healthcare and pensions in comparison with the age 35-44 reference group. Those in the age 25-34 group would like to redistribute public spending to housing and environment at the expense of education, pensions and public infrastructure. Respondents in the age 45-54 group would also like to redistribute additional spending from education but to pensions. The two groups of older people (age 55-64 and 65+) would like to shift extra spending from education and housing to healthcare and pensions. The group of age 65+ would also like to shift money from assistance to the poor.
Respondents with tertiary education (in comparison with holders of a secondary degree) favor extra spending for education, environment and public infrastructure at the expense of healthcare, pensions and assisting to the poor, thus revealing additional elements of social motivations. Respondents with primary education, when compared to holders of secondary degree, would like to redistribute public money from education to pensions and assistance to the poor. Respondents with poor health favor additional spending on healthcare and pensions at the expense of education.
High skilled (in terms of occupational groups) respondents would like to redistribute public money from pensions to education. Those with market relevant experience of being successful in setting up a business tend to support education and public infrastructure at the expense of housing and pensions, though the result lack statistical power.
Respondents from households with higher income support extra spending for education, environment and public infrastructure at the expense of healthcare, pensions and assistance to the poor; again pointing to the other elements of possible social motivations. Those with a self-reported positive past trend in income position tend to support spending extra money on the environment at the expense of assistance to the poor (the latter lacks statistical power). If the respondent lives in its own house or apartment, s/he tends to support redistribution from housing and assistance to the poor, to healthcare and pensions.
Respondents whose households were strongly affected by the crisis would like expenditure on environment and public infrastructure to be reduced. Those with higher self-reported willingness to take risks would redistribute extra public money to education at the expense of healthcare and housing.
Respondents who believe that success in life is mainly due to effort and hard work, intelligence and skills favor education at the expense of assistance to the poor and public infrastructure, suggesting they might view education as the key to escape poverty. Those who think that laziness and lack of willpower are the main factors behind poverty would, unsurprisingly, redistribute extra public money from assistance to the poor to healthcare.
Males (as compared to females) favor extra spending on education, housing, environment and public infrastructure at the expense of healthcare. The self-employed favor extra spending of public money to pensions at the expense of housing. There is no difference across respondents living in metropolitan, rural or urban locations.
A regression analysis shows that better democratic institutions are correlated with higher support for allocation of additional public spending to education and healthcare, environment and public infrastructure. The effects are larger for education and healthcare: one standard deviation in the democracy index increases the support for spending money on education by 3 percentage points, for healthcare by 3.1 percentage points, and only by 0.4 and 0.6 percentage points for environment and public infrastructure, respectively. This reallocation is at the expense of assistance to the poor (3.5 percentage points), housing (2.6 percentage points) and pensions (1.1 percentage points). The pattern is robust to the measure of democratic institutions used, though the marginal effects vary slightly depending on the measure.
The influence of governance institutions is similar. Respondents in countries with better governance institutions favor allocation of extra public money to education (3.2 percentage points in response to one standard deviation in government effectiveness), health care (2.9 percentage points), environment (0.9 percentage points) and public infrastructure (0.6 percentage points). The reallocation is at the expense of assistance to the poor (4.2 percentage points), housing (3.3 percentage points) and pensions (0.2 percentage points). The pattern is also robust to the measure of governance institutions with the marginal effects varying slightly depending on the measure.
The higher the level of inequality in a country, the higher the demand for spending extra public money for education at the expense of assistance to the poor, pensions and public infrastructure. A one standard deviation increase in the index, increases demand for spending extra public money on education by 3.8 percentage points, and decreases spending on assistance to the poor by 2 percentage points, pensions by 1.9 percentage points, and public infrastructure by 0.06 percentage points. The results are robust to the inequality measure used.
Overall, the analysis provides empirical evidence that transitional countries are not homogeneous with respect to preferences for redistribution, with sizeable variations in country averages and in public preferences. The study of individual determinants of preferences for redistribution confirms a dominant role of self-interest, with some indications of social sentiments as well. In addition to the usual measures used in individual level analysis, these data allow better control for both positive and negative personal and household experience. The study of institutional determinants also confirms the role of income inequality in shaping public attitudes. In particular, higher inequality is confirmed to increase the demand for direct income redistribution. A novel motive of the paper is the influence of democracy and governance institutions on demand for redistribution. Better democracy and governance institutions are likely to stimulate demand for income redistribution, revealing both higher societal demand for redistribution and appreciation of the potential capability of the government to implement redistribution effectively.
The study of individual determinants of indirect demand for redistribution adds to the overall picture and confirms not only the self-interest motives but also social preferences especially pronounced among people with tertiary education and in high income groups. Better democratic and governance institutions stimulate redistribution of public money towards education, healthcare, environment and public infrastructure, while weaker democratic and governance institutions increases demand for allocation of public money to assistance to the poor, housing and pensions.
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References
Meltzer, A., Richards, S., 1981. “A Rational Theory of the Size of Government”. Journal of Political Economy 1989, 914–927.
Alesina, A., Angeletos, G.M., 2005. “Fairness and Redistribution”. The American Economic Review, 95(4), 960-98
[1] The countries covered were: Albania, Armenia, Azerbaijan, Belarus, Bosnia, Bulgaria, Croatia, Czech Republic, Estonia, FYROM, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyzstan, Latvia, Lithuania, Moldova, Mongolia, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Tajikistan, Turkey, Ukraine and Uzbekistan.
[2] The basic empirical equation to study individual determinants of public preferences towards income redistribution is the OLS with country fixed effects (for direct redistribution) and multinomial regression with country fixed effects (for indirect measures). When studying the influence of institutions, the equations are transformed to replace country fixed effects with an institutional measure (one at a time). To control for the basic economic differences, average GDP per capita was included.
Recent Dynamics of Returns to Education in Transition Countries
While, in an international comparison, transition countries spend a relatively large share of their GDP on education, and the population in transition countries is fairly highly educated, the returns to education in transition countries have been found to be relatively low, especially in comparison to other developing countries. In our paper, ‘Recent Dynamics of Returns to Education in Transition Countries’, we investigate whether the economic boom that transition countries experienced up to the 2008 financial crisis, increased the returns to education in these countries. Theories of skilled-biased technical change typically predict that periods of fast economic growth go together with an increase in the relative demand for skilled labor and hence an increase in the returns to education.
Using data from the 2007 wave of the International Social Survey Program (ISSP), the estimated return to an additional year of schooling in transition countries varied between a low 5.2 percent in Ukraine to a high of about 10 % in Poland (see Figure 1). Returns in transition countries were relatively low compared to developing countries in the ISSP sample, and on average not unlike OECD countries.
Figure 1. Returns to Education by Countries, 2007 Wave – Basic Specification
Note: Coefficients of the years of schooling variable in earning regressions. Dependent variables are monthly earnings. Specification includes: potential experience (linear and squared), dummy for gender. Source: Ukraine – ISSP 2008, all other countries – ISSP 2007.
The estimated dynamics in returns to education in the period 2002-2007 further suggest that the economic boom that took place in that period did not affect people with different amounts of education in different ways. Returns to education increased slightly in some transition countries and decreased slightly in others, but overall returns to education remained relatively moderate. More specifically, from table 2 we can see a decrease in returns in Bulgaria, Latvia and Poland, and an increase in the Czech Republic, Russia, Slovakia and Slovenia. Both increases and decreases are small in size however.
Table 1. Dynamics of Returns, Basic Specification
Note: Coefficients of the years of schooling variable in earning regressions with few controls as specified in the text.Source: Estimates for 1991-2002 are from Flabbi et al. (2008); estimates for 2007 and for Ukraine are by the authors.
A more detailed analysis for Ukraine using data from the Ukrainian Longitudinal Monitoring Survey, confirmed that economic growth did not have a major impact on the returns to education. The analysis for Ukraine however does suggest that, while in 2003 a secondary degree resulted in a somewhat higher wage, just having secondary education was no longer a differentiating factor in 2007.Moreover, only academic education made a difference, possibly because less and less people were paid very small wages (i.e. less than the official minimum wage).
The relatively limited importance of education for success on the labor market does not only show itself in the low estimated returns to education, it is also clear from the opinions people express about the factors that are important to get ahead. Table 3 gives the percentage of people who say a given factor is essential, important or fairly important to get ahead in a given country (based on the 2009 ISSP).
Table 2. To get ahead, it is essential, important or fairly important to
In most transition countries in the sample, most people think that hard work and ambition is the key to get ahead. Ukraine is no exception with hard work being thought to be essential, important or fairly important by about 94 percent of the respondents. Having a good education is thought to be at least fairly important by only about 73 percent of the respondents, with four other factors, besides hard work, scoring better on this criterion: having political connections, having ambition, having a wealthy family and knowing the right people. Also for the other transition countries in our sample, good education ranks only 5th, 6th or 7th.
Optimists could interpret these results as implying that at least education does not create the same social inequalities in the transition countries as it does in some other countries. Pessimists, on the other hand, who see education as an important driver of economic growth, will argue that low returns to education mean there is a low incentive for people to invest in education and that it is better to have education as a source of inequality rather than political or social connections, or having a wealth family.
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Is School Network Optimization An Opportunity for Education in Transition Countries?
Author: Tom Coupé, KEI.
After the fall of the Soviet Union, transition countries have faced an unprecedented demographic shock, with increasing mortality and emigration, but also with a serious drop in fertility. This negative shock to fertility has translated in an increasingly smaller number of school-aged children, considerably reducing school size and class size over time (Berryman, 2000). In addition, given that this drop in children of school age did not go together with a decline of the number of schools, teachers or classes, student-teacher ratios have decreased substantially. As a consequence, transition countries are now in the situation where they have a disproportionately large number of schools, teachers and classes. This oversized system does not appear to have led to great results in terms of the quality of education.