Project: FREE policy brief
Can Anti-Smoking Campaigns Increase Obesity? Evidence from Belarus
Authors: Aliaksandr Amialchuk, University of Toledo, and Kateryna Bornukova, BEROC.
In this brief, we discuss the possible effects of an anti-tobacco campaign on obesity levels in Belarus based on results of Amialchuk et al (2012). Both smoking and obesity are among the main health concerns in Belarus. Negative correlation between smoking and body weight is well documented, but can anti-tobacco campaign cause an increase in obesity rates? Results of studies from developed countries provide mixed evidence. In Amialchuk et al (2012), we use household survey data from Belarus to establish the link between smoking and body mass index (BMI). We use cigarette prices and regional smoking prevalence as instruments for smoking, and find a negative effect of smoking on BMI. Moreover, using the quantile regression approach, we find that smoking has different effects on body weight for different BMI quantiles, with the largest negative effect in the upper part of the conditional BMI distribution. These findings suggest that anti-tobacco campaigns may slightly increase obesity rates, and campaigns should therefore ideally also include measures to promote a healthy lifestyle. On the other hand, the potentially modest weight gain from an anti-tobacco campaign is likely to be more than offset by the general improvements in health.
Smoking and Obesity in Belarus
Smoking prevalence in Belarus, like in many other transitional countries, is quite high. According to the Belarusian Household Survey of Income and Expenditure from 2010, the smoking rate was 26%, with a much higher prevalence of among men (49.3%) compared to women (9.5%).[1]
Despite the troubling levels of smoking prevalence, little has been done to combat smoking in Belarus. While most of the post-Soviet economies liberalized the tobacco industry, it remains under government control in Belarus. The profits of the state-owned cigarette producers, along with tobacco taxes, constitute an important part of Belarusian budget revenues. This might explain why the Belarusian government has not engaged in anti-tobacco campaigns in the past. However, Belarus is currently implementing Anti-Tobacco Plan for 2011-2015 in cooperation with the World Health Organization.
The Anti-Tobacco Plan includes a variety of anti-tobacco actions and measures. In particular, the government has plans to gradually increase tobacco taxes, introduce smoking-free zones and restrict smoking in public places, along with a massive informational campaign about the dangers of smoking and ways to quit. These measures have the potential to lead to a significant decrease in smoking prevalence. However, an unintended consequence of these policies might be an increase in overweight and obesity rates.
In fact, obesity is another important health problem of Belarus. In 1996-2008, (the period of analysis in Amialchuk et al (2012)), the mean BMI among adults was 26, which suggests that an average Belarusian adult is just on the borderline between healthy weight and overweight. In particular, 34% of adults are overweight, while approximately 15% of adults are obese. Moreover, the distribution of weight status has undergone substantial changes over time: the percentage of individuals in the right tail of the BMI distribution has increased over time, with the percentage of obese increasing faster than the percentage of overweight individuals.
The Link between Smoking and Obesity
The negative relationship between smoking and body weight is well-documented in the medical literature. This inverse relationship is mostly attributed to how smoking affects body weight by boosting metabolism and suppressing appetite. However, causality is usually difficult to establish: for example, a smoking person may also be more likely to eat unhealthy foods and care less about their health in general. Nevertheless, most of the previous studies have found a significant negative effect of smoking on body weight.
Since in many developed countries, the decrease in smoking prevalence coincided in time with the surge in both overweight and obesity rates, the question arises whether anti-smoking campaigns are in part responsible for the increase in obesity rates. However, the evidence on the effects of anti-tobacco campaigns on overweight/obesity rates in developed countries is mixed. Some studies do not find any significant effect on obesity (Nonnemaker et al, 2009).
Evidence from Belarus
As mentioned above, smoking behavior and BMI may be jointly determined, and to deal with the challenge of establishing causality, we utilize the method of instrumental variables analysis. We employ two instrumental variables in our estimation: (i) the mean number of cigarettes smoked per day in the same year-region-gender- and education group as the respondent, and (ii) the average yearly price per pack of cigarettes in the region where the respondent lives. Gilmore et al. (2001) identify important demographic and socio-economic differences in smoking rates, which dictates our use of gender and education categories (below secondary, secondary, university degree) to construct groups of observations that will be followed over time. The use of region as a grouping variable allows us to capture the social norm associated with smoking at the regional level. We exclude the individual’s own cigarette smoking when we create group-level means. Group-specific smoking prevalence is likely to be predictive of the individual’s own smoking preferences, but is unlikely to have a direct effect on individual’s weight status other than through the effect on individual’s smoking. After accounting for the fixed differences in average smoking among regions, gender, and education groups within each year, the source of variation that is available to identify the effect of the instrument on individual’s smoking is the differences in smoking prevalence among various interactions of year, region, gender and education categories.
We use lagged prices as instrument for current year cigarette consumption of the individuals in order to account for the addictive and inelastic nature of demand for smoking and the inability to quickly change smoking behavior after a price change. Furthermore, we use natural log of cigarette prices in order to account for the potentially non-linear effect on the number of cigarettes smoked. Cigarette prices are likely to influence an individual’s BMI only through its effect on smoking.
Other controls in our regressions include total personal income; household size; age; gender; single vs. married indicator; indicators of self-reported health status (good health, fair health, and poor health indicators); number of medical visits in the last 3 months; indicator for having been hospitalized in the last 12 months; indicator for whether health affects ability to work; sports practicing indicator; indicators for the educational attainment (university diploma, secondary education); and indicators for being currently employed, having ever worked, and being a student.
Our endogeneity-corrected estimates suggest that one additional cigarette per day would decrease BMI by roughly 0.23 units, and would reduce the probability of being overweight by approximately 2.5%. Furthermore, there is a small but significant effect on the likelihood of being obese: an additional cigarette smoked per day decreases the probability of being obese by 1.3%. Our results suggest an important implication that smoking is inversely related to body weight, and has some effect on obesity rates.
We also explore the difference in the effect of smoking on body weight across different quantiles of conditional BMI distribution. The largest effect is obtained for the 75th and 90th percentiles, and the smallest effects for the 10th and 25th percentiles. Smoking has a large effect on the body weight of individuals who are at the upper tail of the BMI distribution. These findings suggest that a reduction in smoking rate may lead to an increase in obesity rates by inducing weight gain among the population near the top end of the conditional BMI distribution.
While we found evidence of a possible increase in obesity rates resulting from the anti-tobacco campaign, it is important to remember that adverse health effects of smoking are numerous and the health benefits of smoking cessation are far in excess of the risk of weight gain. The current high prevalence of smoking and number of overweight individuals in Belarus constitute a major public health concern. Our results suggest that the prevalence of overweight and obesity might be exacerbated by the anti-tobacco campaign. From a policy perspective, an increase in obesity rates among the general population may be a reasonable concern for policy instruments targeted at reducing the overall smoking rates. It would therefore be wise to promote healthy eating habits and sports together with the anti-smoking campaign. However, the potentially modest weight gain from anti-tobacco campaign only is likely to be more than offset by the general health improvements associated with a decline in smoking rates.
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References
- Amialchuk, A., K. Bornukova, M. Ali, 2012. Smoking and Obesity Revisited: Evidence from Belarus. BEROC Working Paper Series, WP no. 19
- Gilmore, A.B., McKee, M., Rose, R., 2001. Prevalence and determinants of smoking in Belarus: A national household survey, 2000. European Journal of Epidemiology 17: 245-253
- Nonnemaker, J., Finkelstein, E., Engelen, M., Hoerger, T., Farrelly, M., 2009. Have efforts to reduce smoking really contributed to the obesity epidemic? Economic Inquiry 47, 366–376
[1] The social norms explain difference in smoking rates of men and women. In younger population, however, gender differences in smoking rates are less pronounced.
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.
Optimal Economic Policy and Oil Price Shocks in Russia
Significant oil price fluctuations are an important factor influencing real economic variables, especially in the countries with large dependency on export of natural resources. Under such fluctuations, it is natural to consider the possibility of economic policy to fine tune the real economy, achieve inflation stability, and to weaken the negative influence of oil price shocks. In terms of monetary policy, authorities realize the existence of many channels through which oil market is related to the real sectors and inflation. The Central Bank of Russia should analyze the necessity to react to oil prices and to change the effect of them on the real economic variables.
The most typical way of reaction to oil prices in the Russian Federation is accumulation of reserves at the Reserve Fund. The Stabilization Fund (was later in 2008 separated into the Reserve Fund and the National Welfare Fund) was created in 2004 based on the initiative of Mr. Alexey Kudrin, who was a Minister of Finance at the time. The idea of the fund is to direct the revenue from oil export to the budget, but only when the price of oil does not exceed a pre-specified level, and the residual income should be accumulated in the fund.
In addition, the Central Bank of Russia may respond with its refinancing rate to the changes of the oil price via an augmented oil price Taylor rule or indirectly without inclusion of a commodity quota into the monetary policy rule.
We consider whether the Central Bank of Russia should formally establish the policy of responding to the changes of the oil price. The key evaluation criterion for selecting the optimal response is the minimization of inflation and GDP fluctuations.
Taking into account the results of an applied Dynamic Stochastic General Equilibrium model estimated for the Russian economy, we suggest that the Central Bank, optimally, should include the oil price in its interest rate Taylor monetary rule. That is, it should react to oil price quotas but only in the case of stabilization fund absence. This suggested optimal monetary policy implies a positive direct response to oil price shocks; a 1% oil price increase (decrease) should trigger CBR to raise (decrease) the refinancing rate by 0.1%. In the case of stabilization fund presence, there is no need to respond to changes in the oil price since the former stabilizes the situation when the oil price fluctuates too much.
The main potential limitation of this study is the problem of model quality against the real data. In addition, other monetary policy instruments may be tested against the reaction to changes in the oil price.
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Transportation Infrastructure and Labor Market Integration: the Moscow Oblast Case
The model of city organization proposed by von Thünen in the beginning of the XIXth century, and then formalized by Alonso followed by Muth and Mills (see Ner (1986)), is one of the most “successful” models in economics in terms of practical applications. The model explains why the gradient of population density and land rents decline from the city center towards the periphery. In fact, almost all modern cities fit this pattern, i.e. the model invented two centuries ago is capable of describing today’s spatial structure of cities. Even though von Thünen’s original idea of a city center as a single “marketplace” is no longer realistic, a multitude of factors beyond this make central locations nevertheless attractive. If firms are located near each other, they can take advantage of a common labor pool, easier access to consumers and suppliers, shared infrastructure, and knowledge spillovers, to name but a few advantages. Access to the center brings tangible economic benefits to both labor and capital and these benefits exceed possible losses due to increased competition, and so the von Thünen mechanism still works today, albeit through different channels.
In cities, there are generally two types of spatial organizations possible with respect to household income. If the advantages of amenities in a city center are not very strong, rich people tend to choose to locate in suburbs in order to consume higher quality housing. Such patterns are typical in US cities. If the advantages of a center are strong, the rich choose to live in the center. (Brueckner et al. (1999)) Due to historical circumstances, such patterns are typical of European or Russian cities. In these cases we observe a declining gradient of income; the further we move from the center, the further residents’ average income falls.
There are two forces at work shaping this declining gradient of wage. First, poor people sort themselves into suburban locations. Second, residents of the suburbs who want to take advantage of the labor market in the center face a barrier involving commuting costs. Many of them forgo high-wage opportunities that require tedious everyday commuting and therefore remain poor as a consequence.
An apparent policy solution to reduce income inequality would be to reduce transportation costs. The higher transportation costs are, the steeper the gradient of income. Fast and convenient transportation promotes the integration of local labor markets, gives the residents of the suburbs more, and often better, job opportunities, and works toward equalization of income across the agglomeration. Moreover, as transportation costs decline, the geographic area of agglomeration grows, which opens new opportunities for real estate development as well as new possibilities for rural residents to commute and participate in large labor market.
We conducted a study at CEFIR (Mikhailova et al. (2012)) comparing the spatial patterns of average wages in the Moscow agglomeration with several agglomerations in Western Europe. We considered municipal-level data for Moscow Oblast and for 25 agglomerations in Sweden, Germany, and Netherlands. In the sample of municipalities that are served by suburban train system, we estimated how average wages in a given municipality respond to different lengths of travel times to the city center.
Figure 1 shows the estimated wage-travel time relationship for Moscow Oblast and Figure 2 for the selected European cities.
Figure 1. Average Wage and Travel Time to the City Center, Moscow 
The residents of the Moscow agglomeration are at a clear disadvantage according to the data shown above. Residents of Moscow Oblast, even those who live in relative proximity to the city, loose drastically in terms of average wage. Doubling the travel time (say, from 20 min to 40 min, which is the range most commuters fit into) results in a 25% drop in the average wage for residents in Moscow Oblast compared to only a 5% drop in Europe. The wage in a municipality, from which it would take 90 minutes to travel to the city center, is almost half of the average wage inside Moscow’s Ring Road whereas in Europe 90 minutes translates into a 10% loss of in average wages.
A 90 minutes travel time could be considered as a realistic limit to the size of an agglomeration. This is roughly the maximum distance over which a typical working commuter would be willing to travel each day in each direction. A 90 minute commute in Europe represents approximately a 100 kilometer distance. In Moscow Oblast, however, it is only 63 km. So, Moscow Oblast loses in the effective “reach” of suburban transportation: people who live further than roughly 60 km from the center cannot practically commute.
Even for the same commuting time, the difference in wages between center and suburban municipality is much smaller in Europe (see Figures 1 and 2). This means that a commute for the same time length (in terms of railroad transport) presents a larger barrier for the residents of Moscow Oblast. This is obviously an over simplification of the situation since taking into account only commuting times as the measure of costs we ignore many other critical factors such as price (relative to income), the convenience of schedule and travel comfort, alternative modes of transportation, etc. Suburban trains in Moscow Oblast run infrequently, they are overcrowded, and alternative transportation modes (car or bus) face considerable delays due to road congestion. All of these additional factors serve to reduce the labor market opportunities of the Moscow Oblast residents and make wage inequality even deeper.
Figure 3 presents wage-distance gradients for the Moscow agglomeration under different scenarios using a hypothetical “European” gradient to show what could be the case if changes were made to reduce barriers to transportation bringing the Moscow agglomeration in line with European standards. The graphs end at a distance that corresponds to a typical 90-minute commuting time under various scenarios ranging from the status quo to the best case, where Moscow Oblast replicates European standards. The red curve represents the upper bound estimate of the possible effect of investments to improve the transportation infrastructure to bring Moscow regional transportation network in line with the quality of a typical European agglomeration. The residents of Moscow region could gain up to 24% more in terms of current average wages if this were to take place. The purple curve, however, presents a more modest scenario assuming that the structure of Moscow regional transportation network remains the same, but the travel time were to be cut by 20%. Even in this case, the gains to Moscow Oblast residents are about 3% of wages which is very significant economically for an area populated by 5.5 million people.
Figure 3. Wage Distance Gradient 
Further, it is important to note that to take advantage of labor market integration residents do not necessarily all have to commute to work to the center. The mere possibility of commuting creates arbitrage opportunities in the labor market and puts upward pressure on wages. As a result, it is important for economic policy to constantly improve transportation infrastructure even if the private benefits of increased usage are modest.
In the end, our analysis did not touch on the other benefits from transportation infrastructure. Apart from labor market integration, improvements in transportation infrastructure promote real estate development (Baum-Snow (2007), Garcia-López(2012)) and expand the market for goods and services. We leave these questions for further research.
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References
- Baum-Snow, Nathaniel (2007) “Did Highways Cause Suburbanization?” The Quarterly Journal of Economics 122(2): 775-805
- Brueckner, Jan K., Jacques-François Thisse, and Yves Zenou (1999) “Why is central Paris rich and downtown Detroit poor?: An amenity-based theory.” European Economic Review 43.1: 91-107.
- Garcia-López, Miquel-Àngel (2012) “Urban spatial structure, suburbanization and transportation in Barcelona”, Journal of Urban Economics, Volume 72, Issues 2–3, September–November, Pages 176-190
- Mikhailova, T, V. Rudakov and N. Zhuravlyova (2012) “Economic effects from the Moscow Oblast suburban railroad infrastructure development” («Экономические эффекты от развития инфраструктуры пригородного железнодорожного сообщения в Московской области»), project report, CEFIR.
- Ner, J. B. (1986). The structure of urban equilibria: A unified treatment of the Muth-Mills model. Handbook of regional and urban economics: Urban economics, 2, 821.
For Some Mothers More Than Others: How Children Matter for Labor Market Outcomes When Both Fertility and Female Employment Are Low
Authors: Krzysztof Karbownik and Michal Myck, CenEA.
Wide spread entry of women into the labor force has been one of the most pronounced socio-economic developments in the 20th century, and high levels of female employment are crucial from the point of view of continued economic growth and financial stability of many welfare systems (Galor and Weil, 1996). At the same time, demographic changes determined by the current and future fertility levels will play a vital role in shaping these developments and will affect the costs of social programs. Given the potentially strong link between female employment and family size, it seems that understanding the relationship between the two ought to be at the heart of policy discussions, especially in countries that are characterized by both low fertility and low female employment. In particular, in light of rising unemployment in low-fertility countries, which have been most severely affected by the economic crisis such as Greece, Spain and Latvia, our findings may serve as a guide with respect to the relationship between fertility and labor supply in an environment, which will be more common in Europe in the near future.
Becoming Entrepreneur in Belarus: Factors of Choice
This policy brief summarizes two papers by Maryia Akulava on entrepreneurship development in Belarus and outlines which factors affect the choice of becoming self-employed in Belarus. While one of the papers, “Choice of Becoming Self-Employed in Belarus: Impact of Monetary Gains”, focuses on the role of pecuniary benefits, the other paper, “Portrait of Belarusian Entrepreneur”, adopts a broader perspective by accounting for individual, sociological, and institutional factors.
Although the Belarusian government has repeatedly declared the importance of private entrepreneurship for the national economy, its role remains rather modest. In terms of private sector development, Belarus lags severely behind other post-socialist countries. Yet, over the last decade, some positive dynamics have been recorded. In particular, the number of small and medium enterprises (SMEs) per 1,000 people increased from 2.5 in 2003 to 7.2 in 2010. Still, this ratio is rather small in comparison with other post-socialist economies (Table 1) [3; 4; 5; 6].
Table 1. Number of Small Enterprises (SEs) per 1,000 People
| Number of SEs per 1000 people | |
| Belarus | 7.2 |
| Russia | 11.3 |
| Ukraine | 17 |
| Kazakhstan | 41 |
| United Kingdom | 46 |
| Germany | 37 |
| Italy | 68 |
| France | 35 |
| EU countries | 45 |
| United States | 74.2 |
| Japan | 49.6 |
Regarding the growth rates of SEs and individual entrepreneurs (IEs), the numbers leave much to be desired. Specifically, in 2009, the number of SMEs and IEs amounted to 62,700 and 216,000 respectively, while in 2011 – to 72.200 and 232,000. Therefore, despite the efforts of the authorities to encourage the development of private initiative, the number of SEs and IEs only increased by 15.2 and 7.4%, respectively.
Next, private sector employment remains rather low. It amounts to approximately 13%, while in the developed economies this figure varies between 60 and 70%. For instance, in the U.S., it amounts to 60%, in Germany and in France – around 65-70%, and in Japan – 85%. On the other hand, transition economies have smaller shares, including Russia – 17%, Kazakhstan – 20.6%, and Ukraine – up to 28.8%, [7].
Some important indicators are provided in Table 2 [8].
Table 2. Share of Small and Medium Business in Economic Indicators of Belarus
| Share of small sector | 2003 | 2008 | 2009 | 2010 |
| GDP | 8.2 | 11.2 | 11.4 | 12.4 |
| Volume of industrial production | 8.4 | 8.3 | 9.2 | 9.4 |
| Exports | 18.2 | 31.4 | 34.3 | 38.9 |
| Retail trade turnover | 9.2 | 27.8 | 29.5 | 28.2 |
| Economically active labor force | 13 | 13 | 13 | 13.1 |
Table 2 reveals an increased contribution of private entrepreneurs to the national economy. At the same time, the share of labor employed in the private sector remains unchanged at the level of 13%. This fact suggests that self-employment remains relatively unattractive for salaried workers.
So, what are the drivers of people’s choice? On the one hand, people might be reluctant to become entrepreneurs because of the prevailing social and cultural attitudes, or the lack of necessary experience. Post-socialist economies all share the legacy of planning and suppression of private initiative. On the other hand, government’s policies and regulations might ‘cool down’ enthusiasm or people simply have had or heard of some bad experiences. Thus, it is important to think of the reasons behind people’s choice and formulate policies to encourage entrepreneurship development in Belarus.
Who Is a Belarusian Entrepreneur?
In Belarus, entrepreneurs are active mainly in the non-manufacturing sector, including trade (30% of all entrepreneurs), provision of different services (16.5%), construction (13%), logistics (7%), and real estate (7%). The most common reasons to start your own business include a sudden, but attractive, business opportunity (66%), and the availability of funding for project implementation (33%).
As for the gender and age profiles of Belarusian entrepreneurs, 64% are men and 36% are women, with an average age of around 40-42 years. The majority of entrepreneurs is religious (54%), married (69%), and has children (75%). Around 65% have higher education, and about one third of them were among the top 10% students of their classes. Entrepreneurs report a good health status: 64% of them consider themselves as ‘healthy’. This is not surprising, given that entrepreneurship in Belarus is ‘survival for the fittest’. An entrepreneur has to be ready to take risks, be energetic, active and to continuously search for new business opportunities. Moreover, entrepreneurs are optimists, who evaluate themselves as successful (77%) and happy (81%) people.
Sociological characteristics reveal strong reliance on social networks. In general, the number of relatives or friends involved in the business activities is about two times larger than for salaried workers. Besides that, a much larger share of entrepreneurs consider their parents wealthy and successful (45% and 82%), compared with employees (34% and 37%, respectively).
Belarusian entrepreneurs stay in business because they like what they do (53%), and think that their work is important for society (29%). Profits and income remain a strong, but are not a decisive reason (25%).
Although entrepreneurs and employees do not differ substantially in terms of their attitudes towards family, friends, health, financial stability, religion, and so on, there is still a notable distinction. Specifically, entrepreneurs tend to praise work, power and influence over other people, and also like political freedom. In addition, they value their function of a service provider to other people.
Moreover, entrepreneurs have more trust to colleagues, other business people and subordinates than salaried workers. This is not surprising, given the importance of horizontal networks mentioned above. It is important to note that more than 30% of respondents expressed their trust to political authorities despite the government-induced difficulties for entrepreneurship development in Belarus.
Analysis of institutional infrastructure for doing business detects a negative relationship between a publicly-stated favorable attitude of authorities towards entrepreneurs and their decision to work in the private sector. This can be explained in following way: a priori, the government’s stance on entrepreneurship is evaluated positively, or at least considered as not harmful. Moreover, a person considers himself as being too small to attract the ‘extractive attention’ of the authorities. However, a posteriori, entrepreneurs revise their initial views. Their experience tells us that the government’s attitude is far from welcoming.
As for corruption, the attitude is ambiguous. On the one hand, entrepreneurs generally disfavor corruption. On the other hand, those who seek to expand their businesses consider corruption a way to avoid ‘unnecessary troubles’ and to overcome barriers created by the excessive ‘red tape’ in the economy.
What Are The Obstacles For Doing Private Business In Belarus?
Belarusian entrepreneurs consider the following factors as barriers to business development: (i) inflation and macroeconomic instability (55%), (ii) lack of financing (31%), (iii) high taxes (27%) and complexity of tax system (18%), (iv) legal vulnerability (23%), and (v) toughness of state administrative regulation inspections, licensing and certification requirements (19%). These barriers are largely of macroeconomic and regulatory nature. Moreover, authorities conduct a policy of close-to-full formal employment. This policy is aimed at securing jobs for people even at loss-making and poorly performing companies, which are kept afloat by subsidizes and directed loans. As a result, employees prefer to trade risks of working in the private sector, for a stable employment in the sector of state-owned enterprises.
As for the main barriers, which impede business start ups financial constraints are the most common factor (33%), followed by high risks (25%), the lack of necessary business skills, a clear understanding what to do in the market (15% and 13% respectively), and unwillingness to work a lot (16%). In other words, financial constrains along with the lack of business education are the two most important domestic barriers.
These findings correspond to the results of the research on the impact of pecuniary benefits on entrepreneurs. In that study, education does not appear to have a significant influence on the level of earnings by entrepreneurs. The latter are ‘self-trained’ by the experience of starting a business in the uncertain environment of the 1990s and matured in the course of doing their business in unfriendly conditions. However, as the economy evolves, activities and contracts become more sophisticated. To survive in the changing environment, entrepreneurs have to acquire new skills and learn new methods and concepts of doing business.
So far, it appears that the quality of education obtained by the entrepreneurs does not match the skills required in the Belarusian economy. Thus, it is important to organize seminars, to hold training and to run business education programs for the future and current entrepreneurs in order to upgrade their skills and thus to contribute to their improved performance on the market.
Conclusion
An efficient development of the private sector in Belarus requires a drastic improvement of the domestic business environment. In order to encourage domestic entrepreneurship, the authorities should improve macroeconomic management and cut much of the ‘red tape’. Entrepreneurship possesses a great potential to contribute to growth and development. Surveys reveal that government policies constrain the development of the domestic private sector. Moreover, the high tax burden should be reduced, and some fiscal ‘sweeteners’ could be offered for business startups. In addition, a somewhat higher priority should be given to the improvement of the quality of business education, and make it more accessible for the current and future business people. If implemented, all these measures would supposedly have a fostering impact on the development of a dynamic private sector in Belarus.
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References
Akulava M. 2012. “Choice of Becoming Self-Employed in Belarus: Impact of Monetary Gains”.
Akulava M. 2012. “Portrait of Belarusian Entrepreneur”. Work in progress.
Djankov S., Miguel E., Qian Y., Roland G. and Zhuravskaya E. 2005. “Who are Russia’s Entrepreneurs?” Journal of the European Economic Association, MIT Press. Volume 3 (2-3), 04/05.
Djankov S., Miguel E., Qian Y., Roland G. and Zhuravskaya E. 2006. “Entrepreneurship in China and Russia Compared” Journal of the European Economic Association, MIT Press. Volume 4 (2-3), 04/05.
http://netherlands.mfa.gov.by/_modules/_cfiles/files/sme_belarus_2011_1670.pdf
http://www.tambov-rosnou.ru/monograf/files/ind4.htm
http://www.erce.ru/internet-magazine/magazine/27/389/
http://www.mspbank.ru/files/documents/Ukraine.pdf
Sulakshin S. “State Economic Policy and Economic Doctrine of Russia. To Smart and Ethic Economy”. Т. II.
http://netherlands.mfa.gov.by/_modules/_cfiles/files/sme_belarus_2011_1670.pdf
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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The Eurasian Customs Union among Russia, Belarus and Kazakhstan: Can It Succeed Where Its Predecessor Failed?
In 2010, Russia, Belarus and Kazakhstan formed the Eurasian Customs Union and imposed the Russian tariff as the common external tariff of the Customs Union. This resulted in almost doubling the external average tariff of the more liberal Kazakhstan. Russia has benefited from additional exports to Kazakhstan under the protection of the higher tariffs in Kazakhstan. However, estimates reveal that the tariff changes have resulted in substantial transfers from Kazakhstan to Russia since importers in Kazakhstan now purchase lower quality or higher priced Russian imports which are protected under the tariff umbrella of the common external tariff. Transfers from the Central Asian countries to Russia were the reason the Eurasian Economic Community (known as EurAsEC) failed, so this bodes badly for the ultimate success of the Eurasian Customs Union. What is different, however, is that the Eurasian Customs Union and its associated Common Economic Space aim to reduce non-tariff barriers and improve trade facilitation, and also to allow the free movement of capital and labor, liberalize services, and harmonize some regulations. Estimates by my colleagues and I show that if substantial progress could be made in trade facilitation and reducing non-tariff barriers, this could make the Customs Union positive for Kazakhstan and other potential Central Asian members. Unfortunately, so far the Customs Union has made these matters worse. On the other hand, Russia’s accession to the World Trade Organization will eventually substantially reduce the transfers from Kazakhstan to Russia, but this will need a strong political commitment from Russia which we have not yet seen. If that Russian political leadership is forthcoming, the Eurasian Customs Union could nonetheless succeed where its predecessor has failed.
In January 2010, Russia, Belarus and Kazakhstan formed the Eurasian Customs Union. Two years later, the three countries agreed to even closer economic ties, by signing the agreement to form a “common economic space.” Regarding tariffs, the key change was that the three countries agreed to apply the tariff schedule of the Customs Union as their common external tariff for third countries. With few exceptions, the initial common external tariff schedule was the Russian tariff schedule. Kazakhstan negotiated exceptions from the common external tariffs for slightly more than 400 tariff lines, but was scheduled to phase out the exceptions over a period of five years (World Bank, 2012). In addition, the members agreed to have the Customs Union determine the rules regarding sanitary and phyto-sanitary standards (SPS) and standards on good. Fearing transshipment of goods from China through Kazakhstan and from the European Union through Belarus, Russia negotiated and achieved agreement on stricter controls on the origin of imports from countries outside of the Customs Union. The common economic space (CES) stipulates that, in principle, there will be free movement of labor and capital among the countries, there will be liberalization of services on the CES and coordination of some regulatory policies such as competition policy.
In February 2012, the Eurasian Economic Commission began functioning. It is intended to act as the regulatory authority for the Customs Union in a manner similar to the European Commission for the European Union.
The Economics of Tariff Changes — Gains for Russia and Losses for Kazakhstan
Some proponents of the Eurasian Customs Union have argued that as a result of the Customs Union firms in the three countries will have improved market access through having tariff free access to the markets in all three countries. Prior to 2010, however, along with other countries in the Commonwealth of Independent States (CIS), the three countries had agreements in place that stipulated free trade in goods among them. Thus, the Customs Union could not provide improved market access due to reducing tariffs on goods circulating among the three countries.
Since the common external tariff was essentially the Russian tariff, there was little change in incentives regarding tariffs in Russia. The big change occurred in Kazakhstan, who had a much lower tariff structure than Russia prior to implementing the Customs Union tariff. Despite the exemptions, Kazakhstan almost doubled its tariffs in the first year of the Customs Union (see World Bank, 2012). The increase in tariffs on many items which were not produced in Kazakhstan but produced in Russia, led to a substantial increase in imports from Russia and displacement of imports from Europe. Many of Russia’s manufacturing firms, which were not competitive in Kazakhstan prior to the Customs Union, were now able to expand sales to the Kazakhstani market. This represents gains for Russian industry. Given the deeper manufacturing base in Russia compared with most of the CIS countries and the resulting uneven benefits of the common external tariff in favor of Russia, acceptance of the common external tariff has been a fundamental negotiating position of Russia regarding acceptance of members in the Customs Union.
Some cite the expanded Russian exports in Kazakhstan as evidence of success of the Customs Union. But the displacement of European imports, to higher priced or lower quality imports from Russia, represents a substantial transfer of income from Kazakhstan to Russia and is an example of what economists call “trade diversion”. Moreover, it is the reason the World Bank (2012) has evaluated the tariff changes of the Customs Union as a loss of real income for Kazakhstan.
Furthermore, the three countries together (and even a broader collection of CIS countries) constitute too small a market to erect tariff walls against external competition. They would lose the benefits of importing technology from advanced countries and would rely on high priced production from within the Customs Union. Some would argue that there are political benefits of trade to be taken into account, but experience has shown that when a customs union is inefficient and the benefits and the costs of the customs union are very unequal, the customs union can inflame conflicts (see Schiff and Winters, 2003, 194-195).
Non-Tariff Barriers — Extremely Costly Methods of Regulating Standards Worsened by the Customs Union
Non-tariff barriers, in the form of sanitary and phyto-sanitary (SPS) conditions on food and agricultural products and technical barriers to trade (TBTs) on goods, are a very significant problem of the Customs Union. There are standards based trade disputes between Belarus and Russia on several products, including milk, meat, buses, pipes and beer (see Petrovskaya, 2012). Anecdotal evidence indicates that Kazakhstani exporters complain bitterly regarding the use by the Russian authorities of SPS and TBTs measures, either to extract payments or for protection.
If the Customs Union could make substantial progress on reducing these barriers, it would be a significant accomplishment. My colleagues and I have estimated that progress on the non-tariff barriers and trade facilitation could outweigh the negative impact of the tariff changes for Kazakhstan (see World Bank, 2012). Unfortunately, so far the Customs Union has taken a step backward on both non-tariff barriers and trade facilitation.
A big problem in reducing standards as a non-tariff barrier is that standards regulation, in all three countries, is still primarily based on the Soviet system. As a holdover from the Soviet era, mandatory technical regulations are employed where market economies allow voluntary standards to apply. This regulatory system makes innovation and adaption to the needs of the market very costly as firms must negotiate with regulators when they want to change a product or how it is produced. Legislation in both Russia and Kazakhstan calls for conversion to a system of voluntary standards, but this is happening too slowly in all three countries. The problem is that the Customs Union has worsened the situation. Technical regulations are now decided at the level of the Customs Union, so firms that previously negotiated with their national standards authority, have had to now get agreement from the Customs Union. This has reportedly caused further delays, impeding innovation and the ability of firms to meet the demands of the market.
A second problem with efforts to reduce the non-tariff barriers is that the Customs Union is trying to harmonize standards of the three countries by producing mandatory technical regulations. The alternative is to use Mutual Recognition Agreements (MRAs). Experience has shown that no customs union has been able to broadly harmonize standards based on mandatory technical regulations, with the exception of the European Union. In fact, even in the European Union, they have had to use MRAs and only harmonized technical regulations after decades of work. While each member of the Customs Union is expected to create a system of mutual recognition of certificates of conformity, these certificates are not presently recognized in the other countries of the Customs Union. There is little hope for a significant reduction in standards of non-tariff barriers unless the system of mutual recognition is more widely recognized and adopted.
Trade Facilitation —Participation in International Production Chains Made More Difficult by the Customs Union
Customs posts between the member countries have been removed and this has reduced trade costs for both exporters and importers in the three countries. Russia’s concerns regarding transshipment have, however, led to an opposite impact on trade with third countries, i.e., the costs of trading with countries outside the Customs Union have increased. Participation in international production chains has become a key feature of modern international production and trade. If goods cannot move easily in and out of the country, multinational firms will look to other countries to make their foreign direct investment and for international production sharing. Addressing this significant problem will take a change of emphasis on the part of Russia.
Russian WTO Accession —Liberalization That Will Significantly Reduce Transfers to Russia
It has apparently been agreed by the Customs Union members that the common external tariff of the Customs Union will change to accommodate Russia’s WTO commitments. As a result, the applied un-weighted average tariff will fall in stages from 10.9 percent in 2012 to 7.9 percent by the year 2020 (see Shepotylo and Tarr, forthcoming).[1] This will have the effect of lowering the trade diversion costs of Kazakhstan. In addition, the Customs Union will be expected to adapt its rules on standards to conform to commitments Russia made as part of its WTO accession commitments. In the case of Belarus, it remains to be seen if it will implement the changes, as this will increase competition for its industries.
Conclusion — the Need to Russia to Exercise Political Leadership for Standards and Trade Facilitation Reform for Success of the Customs Union
In 1996, the same three countries formed a customs union. Later the same year, they were joined by Kyrgyzstan, then by Tajikistan and in 2005 by Uzbekistan. As Michalopoulos and I (1997) anticipated, the earlier Customs Union failed because it imposed large costs on the Central Asian countries, which had to buy either lower quality (including lower tech goods) or higher priced Russian manufactured goods under the tariff umbrella. The present Customs Union also started with the Russian tariff, which protects Russian industry and suffers from the same problem that led to the failure of the earlier Customs Union. Nonetheless, the present Customs Union could succeed. Crucially, due to Russia’s accession to the WTO, the tariff of the Customs Union will fall by about 40 to 50 percent.[2] This will make the Customs Union a more open Customs Union, very significantly reduce the transfers from Kazakhstan to Russia, and thereby reduce the pressures from producers and consumers in Kazakhstan on their government to depart from enforcement of the tariffs of the Customs Union. Further, the present Customs Union aims to reduce non-tariff barriers and improve trade facilitation, as well as it has “deep integration” on its agenda, i.e., services liberalization, the free movement of labor and capital and some regulatory harmonization. Although, to date, the Customs Union has moved backwards on non-tariff barriers and trade facilitation, one could optimistically hope for substantial progress. In the important area of non-tariff barriers, given the common history of Soviet mandatory standards, Russia will have to take the lead in moving the Customs Union toward a system of voluntary standards where no health and safety issue are involved, and toward a system of mutual recognition agreements and away from commonly negotiated technical regulations. On trade facilitation, Russia will have to reverse its pressure and find a way to allow the freer movement of goods with third countries while addressing its transshipment concerns.
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References
- Michalopoulos, Constantine and David G. Tarr (1997), “The Economics of Customs Unions in the Commonwealth of Independent States,” Post-Soviet Geography and Economics, Vol. 38, No. 3, 125-143.
- Petrovskaya, Galina (2012), “Belarus, Rossia, Ukraina. Obrechennye na torgovye konflikty” (Belarus, Russia, Ukraine. Doomed for trade conflicts), Deutsche Welle, June 14. www.dw.de/dw/article/0,,16023176,00.html.
- Schiff, Maurice and L. Alan Winters (2003), Regional Integration and Development, Washington DC: World Bank and Oxford University Press.
- Shepotylo, Oleksandr, and David G. Tarr (2008), “Specific tariffs, tariff simplification and the structure of import tariffs in Russia: 2001–2005,” Eastern European Economics, 46(5):49–58.
- Shepotylo, Oleksandr, and David G. Tarr (forthcoming), “Impact of WTO Accession on the Bound and Applied Tariff Rates of Russia,” Eastern European Economics.
- Shymulo-Tapiola, Olga (2012), “The Eurasian Customs Union: Friend or Foe of the EU?” The Carnegie Papers, Carnegie Endowment for International Peace, October. Available at: www.CarnegieEurope.eu,
- World Bank (2012), Assessment of Costs and Benefits of the Customs Union for Kazakhstan, Report Number 65977-KZ, Washington DC, January 3, 2012. Available at: http://documents.worldbank.org/curated/en/2012/01/15647043/assessment-costs-benefits-customs-union-kazakhstan
[1] The final “bound rate” of Russia is higher at 8.6 percent on an un-weighted average basis; but there are about 1,500 tariff lines where the applied rate of Russia is below the bound rate. The applied weighted average tariff will fall from 9.3 percent in 2012 to 5.8 percent in 2020.
[2] Russian tariffs fall more on an un-weighted average basis than they do on a weighted average basis. See Shepotylo and Tarr (forthcoming).
Corruption in Eastern Europe as Depicted by Popular Cross-Country Corruption Indicators
In recent years, variously defined indicators of corruption from different sources have aimed at raising awareness about corruption and to provide researchers with better data for analyzing the causes and consequences of corruption. Most of them have achieved spectacular popularity, and are regularly cited in news reports on corruption around the world. However, in a 2006 study for the World Bank, Stephen Knack warns that the particular properties and limitations of these indicators are often neglected by data users, often leading to wrong interpretations and sometimes puzzling disagreements about the actual situation in a country or a region and its changes over time. The first part of this brief summarizes the main conclusions of this study; the second part presents updated data from different sources on recent corruption trends in the new EU members and the neighbors to the east, as a clear exemplification of the issues discussed.
Existing corruption indicators differ in many ways: where the original information or evaluation comes from, how they are built, who are their constituencies or audiences, as well as which of the many aspects of corruption they intend to capture. For these reasons, no single indicator or data source is best for all purposes.
The corruption indicators can be subdivided into three main groups: those based on surveys, either of firms or households, those reporting expert assessments, and finally, the recently popular composite indexes.
Two examples of firms’ surveys that will be presented below are the Business Environment and Enterprise Performance Survey (BEEPS) and the World Economic Forum (WEF) “Executive Opinion Survey”. Similar enterprise surveys have been conducted by the World Bank and in the IMD World Competitiveness Yearbook. However, BEEPS and WEF are more systematic and better comparable across countries and years, have broader coverage and disclose more information about their definitions and methodology, which makes them, in a sense, more research-friendly.
Surveys are relatively well-suited for evaluating the administrative corruption since they measure the prevalence of corruption as experienced by users of government services. They can also measure some aspects of state capture by asking about perceived undue influence over laws and regulations that affect business. However, surveys are definitely less effective in assessing the prevalence of corrupt transactions that occur entirely within the state, for example when politicians bribe bureaucrats or when funds are illegally diverted. Many types of conflict of interest are also not easily captured by surveys. For example, the equity stakes of public officials or employment promises to them by the firms (World Bank, 2000).
Expert assessments of corruption have been most widely used for comparisons across countries and over time because of bigger coverage in both dimensions. A large and growing number of organizations provide such assessments. Some examples are Freedom House’s Nations in Transit (NIT), the International Country Risk Guide (ICRG), the World Bank’s Country Policy and Institutional Assessment (CPIA). Corruption ratings from these sources are based on the assessment by a network of correspondents with country-specific expertise. In some cases, the final ratings are subsequently determined centrally by a smaller group of people. The organizations that are behind these indicators may be very different, with potential implications for what their ratings are measuring. Some are advocacy NGOs. Others are for-profit companies marketing their product to multi-national investors and paying subscribers. Most subscribers to the ICRG, for example, are more interested in conditions faced by foreign investors than in those faced by local residents. Corruption ratings produced by development agencies are also potentially influenced by their constituents (if for example they take into account the consequences for funds allocation decisions or relations with local partners).
An important difference as compared to the firms or households surveys is that corruption assessments place less emphasis on experience and more on perceptions. Moreover, the respondents in a firms’ survey can be asked more specific and objective questions because they comprise a more homogeneous group. For example, a typical question can be “Was an informal gift or payment expected or requested to this establishment, in reference to the application for an electrical connection?” (from the BEEPS 2009 questionnaire). Instead, a questionnaire directed to a group that includes public officials, academics, journalists, etc. must frame questions in such a way that they can be answered meaningfully by all of them, which necessitates broader questions.
More recently, composite indexes have gained popularity. Well known examples include Transparency International’s widely-cited “Corruption Perceptions Index” and the World Bank Institute (WBI) “Control of Corruption” index (Kaufmann, Kraay and Mastruzzi, 2008). Although the statistical methods used to produce them vary somewhat, both indexes standardize several corruption indicators such as ICRG, CPIA and even survey outcomes, to place them on a comparable scale, then aggregate them, so as to obtain a single value for each country. As a result, composite indexes suffer from the same problem as the corruption measures from individual sources such as ICRG, NIT or CPIA: if any component of a composite index is constructed in an opaque manner, the composite index will be opaque as well. Further limitations are introduced by the process of aggregation. Composite indexes have no explicit definition, but instead are defined implicitly by what goes into them. The sources used in constructing these composite indexes change over time, and from country to country in a given year. For any pair of countries the index values are very likely to reflect differing implicit definitions of corruption.
The standardization procedure used to place different indicators on a common scale precludes the ability to track changes meaningfully over time. A final issue with the composite indexes is the interdependence of expert sources. If expert assessments display high correlations driven by the fact that they consult each other’s ratings – or that they all base their ratings on the same information sources – this can undermine the main premise of the aggregation methodology that more sources produce more accurate and reliable estimates. The addition of another expert-based source containing little new information – relying on the same information sources as its competitors, or even checking their ratings – can actually reduce the accuracy of the composite index.
A general caveat in the use of corruption indicators, beyond the weaknesses of individual types discussed above, concerns the importance of their intended use. For some purposes, broader measures may be preferable: for example, a researcher studying the relation between corruption and economic growth may have no particular view on exactly which aspects of corruption most impair growth, and is hence content with a general measure. For other purposes, however, narrower measures may be required. For example, a donor funding projects in a country may be interested in a measure of corruption in public procurement, while a donor providing budget support might prefer a measure of the likelihood of funds diversion to unintended purposes. The design of effective anti-corruption reforms requires narrow measures to identify specific problem areas and track progress over time, and so on.
Finally, it is important to remember that some indicators are more suitable than others for measuring changes over time. Broad, multi-dimensional indicators are potentially problematic in this respect, because there is no way to ensure that the implicit weights given to the various dimensions do not change over time. Some indicators have no fixed and explicit criteria provided for each ratings level, so there is no way of ensuring that the same numerical rating means the same corruption level from one year to the next.
With this background in mind, it is easy to understand why, while it is often possible to form a broad assessment on the general situation and trends in corruption, different sources might often disagree markedly on specific countries, and in particular on which countries have improved and which have not. The evidence from different sources on recent corruption trends reported below provides a clear example in this respect. We are going to focus on the new EU members (Estonia, Bulgaria, Romania, Slovenia, Slovakia, Czech Republic, Hungary, Poland, Lithuania, Latvia), indicated as EU-group, and the non-Baltic former Soviet Republics (Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine, Uzbekistan), indicated as CIS-group [1].
Levels and Trends in Corruption for the New EU-Members and the Eastern Neighbors
The Business Environment and Enterprise Performance Survey (BEEPS) is a nationally-representative survey of business firms assessing corruption and other problems faced by businesses in the ECA region. The BEEPS is sponsored by the European Bank for Reconstruction and Development (EBRD) and the World Bank, and has covered almost every country in the region since 1999. The two most recent waves with a good coverage of our countries are 2005 and 2009. The surveys typically contain multiple questions pertaining to narrower aspects of corruption, and so do the BEEPS.
Looking at the outcomes of the BEEPS, the most dramatic change between 2005 and 2009 is in the “bribe tax”, the share of annual sales paid in “informal payments or gifts to public officials to get things done”. The average in the new EU members increased more than four-fold from .72% to 3.11% of firm revenues. A positive value for the bribe tax was reported by 28.12% of firms in 2005, increasing to 62.1% in 2009, although this might simply reflect an increasingly open attitude in answering the survey. The corresponding increases for the CIS-group are more moderate, from 1.31% to 4.26% bribe tax and from 40.7% to 60.1% of firms declaring positive values. The only country where the bribe tax did not increase is Poland, although data for 2009 are not available for Belarus, Georgia, Tajikistan, Ukraine and Uzbekistan. The biggest increases are reported in Estonia and Slovenia, although they started from the lowest levels within the EU-group (0.29 and 0.17 respectively). These are two countries that, as we will see later, are consistently singled out as the best performers by the other indicators. This apparent contradiction might be due to a different reporting attitude in these countries. Similarly, the lowest level of bribe tax in the CIS-group for 2009 is reported in Russia (1.31), while the highest levels (8.8) in Azerbaijan, a country that according to other indicators is doing relatively well.
Besides the bribe tax, among the numerous other questions on corruption issues in the BEEPS, most show evidence of a modest improvement. For example, in 2005 about 13.4% (24%) of firms in the EU- (CIS-) group reported that paying bribes was frequently, usually or always necessary to get things done, and this figure is down to 6.75% (18.8%) in 2009. Most questions about specific public services also show evidence of a decline in the incidence of bribe paying, e.g. when paying taxes, dealing with customs and the courts.
The assessment on the fairness of the courts got worse in both areas, but at the same time it is considered a big obstacle by fewer businesses as compared to 2005. Also, the share of businesses that admit to paying a kickback payment to obtain a government contract, and the share of sales required for this payment, decreased over this period, markedly for the new EU members, though only slightly for the former Soviet Republics.
Slovenia and Estonia are the champions also in this respect, as well as Armenia in the CIS-group. Kickback payments are most expensive in Latvia (3.06% of sales) and Russia (4.65%). Corruption was however cited as the biggest obstacle to doing business by an increasing share of firms everywhere [2]. In the CIS-group as a whole, the share of firms that consider corruption the biggest obstacle to business increased from 6.4% in 2008 to 8.16% in 2009. The individual countries with the biggest shares are Romania (9.5%) and Azerbaijan (17.8% of firms), respectively in the first and second group. Ironically, the biggest increase between 2005 and 2009 is in Poland, the only country where the reported bribe tax actually decreased. This highlights how tricky it is to aggregate the information from these sources, given that very different aspects of the situation in a country are captured by each item.
More difficulties emerge with respect to evaluating change over time since different measures often move in opposite directions for a given country. For example, both Hungary and Azerbaijan experienced the biggest increases in bribe tax, but also a sharp decrease in kickback payments for government contracts and it is hard to balance the one against the other. This is also a reason why the picture emerging from these data does not necessarily agree with the aggregate indicators discussed below, although they are in part based on the very same outcomes of the surveys.
The World Economic Forum (WEF) “Executive Opinion Survey” is another cross-country survey of firm managers. The sample in each country is selected with a preference for executives with international experience, who tend to be from larger and exporting firms. The questions are designed to elicit “the expert opinions of business leaders” on corruption and other issues, and focus much less on direct firms’ experiences. Moreover, the aim is solely to produce country-level measures of the business climate, and not firm-level analyses. Cross-country rankings on several corruption questions from this survey are published in WEF’s annual Global Competitiveness Report. Ratings are computed as the simple average of all executives’ responses.
The 2011 WEF data include 7 variables related to corruption, all scaled from a low value of 1 to a high value of 7: Diversion of public funds, Irregular payments and bribes, Judicial independence, Favoritism in decisions of government officials, Burden of government regulation, Transparency of government policymaking and Ethical behavior of firms. The sample includes a total of 142 countries, including many developed countries, and covering most of the countries we have been addressing above. Both the average rating and the average rank are slightly higher for the EU-group, but the similar average hides quite a bit of variation between different countries and in different dimensions.
In particular, the CIS-group ranks higher with respect to both the extent to which government regulation is perceived as a burden for business, and the perceived transparency of policymaking, and the two averages are extremely close when it comes to the assessment of favoritism in officials’ decisions. The largest difference between the two groups seems to be the prevalence of irregular payments and bribes, in accordance with the evidence from the BEEPS.
Nevertheless, some countries in the second group, like Georgia and Tajikistan, have a higher average ranking than most of the new EU members and position themselves extremely well even in global terms in some dimensions. For example, Georgia is number 7 in the world with respect to the (absence of) burden of regulation, although not many more reach the upper quartile or even the upper half of the ranking. On the other hand, some of the new EU countries do very poorly in some respects, like the Slovak Republic ranking 135th (of 142) in terms of favoritism by public officials and the Czech Republic being 124th in diversion of public funds.
Compared to 2010, the EU-group saw a slight worsening in their rating, while the CIS-group improved. More in detail, half of the countries in the first group went down, including some quite substantial drops (Estonia, Poland and Slovenia, down by more than 1 point) while the others improved, though not spectacularly. All but one country (Georgia) in the second group improved their average rating from 2010, the biggest progress taking place in Azerbaijan with 1.5 points.
As opposed to the surveys discussed above, the NIT, CPIA and ICRG each provide a single measure of corruption, intended to reflect a mix of various aspects of corruption.
The NIT index is mostly concerned with the impact of corruption on business. It measures the corruption with on a 1-7 scale, 1 being the best possible rating and 7 being the worst, with quarter-point increments allowed.
The ranges of variation in the ratings during the last five years for the two regions do not overlap at all: all of the new EU countries positioned themselves always below a score of 4, while all the countries in the CIS-group stayed well above this threshold. This implies that the best performers within this group (Georgia and Armenia) have a consistently lower rating than the worst performing EU countries (Bulgaria and Romania). However, the trend over time in this period is very similar across the two regions. Both the averages are very flat, with a slight upward trend (i.e. to the worse). In the EU-group, this reflects the fact that five countries saw worsening in their rating, three saw no change at all and only two (Estonia and Lithuania) a slight progress. The lowest (and hence best) score is Estonia and Slovenia’s 2.25. Also in the other group only two countries – Armenia and Georgia – improved their rating. They also have the lowest scores in the region, 5.25 and 4 respectively. Six countries kept a stable rating while four got worse. The highest (and hence worst) score, 6.75, goes to Turkmenistan and Uzbekistan.
The CPIA question “Transparency, Accountability and Corruption in the Public Sector”, is assessed on a 1-6 scale, where a lower level corresponds to a worse situation in terms of corruption. This index focuses on less developed countries, so the EU-group is not covered. The most recent available data are for the period 2008-2011, during which four out of the six developing regions in the world improved.
In contrast to the stagnation with slight worsening described by the NIT, the ECA region is the one that sees the steepest improvement in the CPIA rating, increasing to 2.87 in 2011. This contrasting assessment can be explained by the fact that only six of our CIS-group countries are included in the CPIA sample: Armenia, Azerbaijan, Georgia, Kyrgyzstan, Moldova and Uzbekistan. If we look at the average only in those six, also the NIT rating improved by about the same relative amount (1.5% of the value range). The two indexes do not agree, though, on the individual countries that they reward with a higher or punish with a lower score. In particular, only Georgia improved in both ratings, while Uzbekistan, for example, got a better CPIA score but a worse NIT score; Armenia and Azerbaijan, that respectively improved and worsened in the NIT assessment, are completely stable in the CPIA, and the opposite is true for Moldova.
Unlike the CPIA, the ICRG sample includes most developed countries. The focus of the ICRG is to establish the relative incidence of corrupt transactions. Its corruption ratings range from a minimum value of 0 to a maximum of 6, where higher rating corresponds to a better situation.
The latest available data are however not as recent as for the other indicators discussed here. In the three years up to 2007, the mean rating remained stable in both groups, around 2.5 in the new EU members and 1.8 in the former soviet countries, although only three of eleven countries from this group are included. Also in this case, the two ranges of values for the two regions do not overlap.
In the EU, Lithuania’s rating went down while Poland’s went slightly up. Estonia and Slovenia are again the best performers together with Hungary. The lowest rating in the region goes to Bulgaria, just as in the NIT evaluation, together with Latvia. All the three countries in the CIS-group made improvements, although from dismally low levels. This is not in contrast to the other assessments, since the data refer to an earlier period. The highest score of the three is Moldova’s (low) 1.5.
Both of the widely-known composite indexes of corruption (TI and WBI index) show large differences between the EU members and their eastern neighbors. The average score, varying from 1 to 10 and from -2.5 to 2.5, respectively, are much higher for the first than for the second group. Similarly the ranks – from 1 (best) to 182 (worst) for TI, reversed scale from 0 (worst) to 100 (best) for WBI – reflect a much worse situation in the CIS-group. However, the former Soviet countries improved their WBI rank between 2009 and 2010, as opposed to the new EU members which saw a slight drop. Although changes over time for these indexes should be taken with caution, this is coherent with the 2010-2011 comparison in the WEF.
The two indexes also agree on best and worst performer, respectively; Estonia and Bulgaria in the first group (Slovenia was best performer in 2009 according to WBI) and Georgia and Turkmenistan (on par with Uzbekistan according to TI) for the second. Both the largest improvement (Lithuania) and the largest backslide (Slovenia) from 2009 happened in the EU-group, but a larger share of the CIS-group countries experienced improvements, which is reflected by a smaller drop in the average score. The main difference between the two indexes is that WBI uses more sources and reports a value even for cases when only one source is available (TI requires a minimum of three sources), obtaining as a consequence a broader coverage. Otherwise, the two indexes are quite correlated, and subject to the same problems.
Summing up, all the indicators agree, not surprisingly, that the situation looks much brighter in the EU-group than in the CIS-group. Although, it is not clear that they are keeping up the good work in the most recent years. There is relatively more evidence of improvement over time in the CIS-group, despite the dismal starting point. Only few countries emerge unequivocally as good or bad performers. One example being the coherently positive performance of Georgia; for most of the other countries, the picture is mixed.
Given the variety and breadth of indicators, this conclusion was very much expected. Corruption is such a broad and multidimensional phenomenon that different indicators and different assessments are bound to result in different, often contrasting pictures. Unless one is very clear on which specific aspect is in focus, and sticks consequently with one particular measure, any conclusion based on general comparisons of corruption indicators both between countries and over time should be taken with serious cautiousness.
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References
- Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. (2008). “Governance Matters VII: Aggregate and Individual Governance Indicators, 1996-2007.” World Bank Policy Research Working Paper 4654. Washington: World Bank (June 24).
- Knack, Stephen. (2006). Measuring Corruption in Eastern Europe and Central Asia: A Critique of the Cross-Country Indicators. World Bank Policy Research Working Paper No. 3968, Washington,D.C.
- World Bank (2000). Anticorruption in Transition: a Contribution to the Policy Debate. Washington DC: World Bank.
- European Bank for Reconstruction and Development, Business Environment and Enterprise Performance Survey (BEEPS), http://www.ebrd.com/pages/research/economics/data/beeps.shtml
- World Economic Forum (WEF), Executive Opinion Survey, https://wefsurvey.org/index.php?sid=28226&lang=en&intro=0
- Freedom House, Nations in Transit, http://www.freedomhouse.org/report-types/nations-transit
- PRS Group, International Country Risk Guide, http://www.prsgroup.com/icrg.aspx
- Transparency International, Corruption Perception Index, http://www.transparency.org/research/cpi/overview
- World Bank Group, Country Policy and Institutional Assessment, http://www.worldbank.org/ida
[1] Turkmenistan and Uzbekistan are only unofficial members of the official Commonwealth of Indipendent States (CIS), and Georgia is not a member any longer since 2009.
[2] Bigger obstacles in the EU-group are the level of tax rates (19% of firms), access to finance and an inadequately educated workforce (11% each), along with political instability (10%). The biggest concern for most firms in the CIS-group is instead market practices from competitors in the informal sector.
Inter-Regional Convergence in Russia
There was no inter-regional convergence in Russia during the 1990s but the situation changed dramatically after 2000. While interregional GDP per capita gaps still persist, the differentials in incomes and wages decreased substantially. Interregional fiscal redistribution has never played a major role in Russia, so understanding interregional convergence requires an analysis of internal capital and labor mobility. The capital market in Russia’s regions is integrated in a sense that local investment does not depend on local savings. Also, the barriers to labor mobility have come down. The situation is very different from the 1990s when many poor Russian regions were in a poverty trap: potential workers wanted to leave those regions but could not afford to finance their move. After 2000 (especially later in the first decade), these barriers were no longer binding. Overall economic development, as well as the development of financial and real estate markets, allowed even the poorest Russian regions to grow out of the poverty trap. This resulted in some convergence in the Russian labor market; the interregional gaps in incomes, wages and unemployment rates are now comparable to those in Europe.
Russia’s Regions are Finally Converging
Large interregional differences have always been an important feature of Russia’s transition to a market economy. This has been explained by the pre-transition geographical allocation of population and of physical capital that was determined by non-market forces. Soviet industrialization policies often pursued political or geopolitical goals. Even when they reflected economic realities, the economic decision-making was distorted substantially by central planning, price-setting and subsidies. In addition, the allocation of production was intended to serve a different country – the Soviet Union (or even the whole Council for Mutual Economic Assistance countries) rather than Russia alone. Moreover, believing in economies of scale rather than in competition, Soviet planners created many monotowns.[1] These towns, cities or even regions relied on a single industry. Therefore economic restructuring and inter-sectoral reallocation implied not only moving workers or capital between employers in one town, but also required moving workers or capital between cities.
Despite the need for geographical reallocation during the transition to a market economy, the differentials between Russian regions remained high (and even increased!) throughout the 1990s. However, after 2000 (especially later in the first decade) there was substantial convergence in incomes and wages (Figure 1). By 2010, this resulted in reduction of the inter-regional differences in incomes in line with European levels. In Figure 2, while inter-regional differences in Russia are still substantially above those in the US and Western Europe, they are comparable to those in the EU.
Figure 1. Differences among Russian Regions in Terms of Logarithms of Real Incomes, Real Wages, Unemployment, Real GDP Per Capita
Source: Guriev and Vakulenko (2012). Note: All variables measured as population-weighted standard deviations.
Figure 2. Income Differentials in Russia, Europe and the US
Interestingly, despite income convergence, there was no convergence in GDP per capita among Russia’s regions. Inter-regional dispersions in GDP per capita remain high not only by European standards, but also by standards of less developed countries. Indeed, in Figure 3, Russia is placed in the international context using the data recently developed by Che and Spilimbergo (2012).
Che and Spilimbergo calculate interregional differences for 32 countries in a compatible way and plot them against GDP per capita (averaged out for 1995-2005, in real PPP-adjusted dollars). Their main finding is that that there is a negative correlation between interregional differences and GDP per capita.
Since Russia was not in Che and Spilimbergo’s dataset, Guriev and Vakulenko (2012) reproduced their calculations for Russia, both for the 1995-2005 average (as they do for the other countries) but also for the individual years 1995, 2000, 2005 and 2010. It turns out that while Russia was “abnormally uniform” in the early 1990s, it did experience substantial divergence in the late 1990s. There was continuing, albeit weaker, divergence even in the early 2000s – so Russia became “abnormally unequal” given its GDP level. Even though there was some convergence late in the first decade, Russia is still “abnormally unequal”. Given the fast economic growth since 2000, Russia should have become substantially “more uniform” – at least given the downward-sloping relationship between income and inter-regional inequality in Che-Spilimbergo’s data.
Source: Che and Spilimbergo (2012). Note: The trend line is calculated without Russia.
Why didn’t income convergence happen in the 1990s and only start after 2000? Why hasn’t GDP convergence taken place? Large interregional differences are consistent with reduced income, wage, and unemployment differentials if the factors of production (labor and capital) have become more mobile while the productivity differences (due to geography, political and economic institutions, and inherited differences in infrastructure) remain in place. Therefore, in order to understand income convergence, an understanding of labor and capital mobility is needed.
Interregional Labor Mobility in Russia
Andrienko and Guriev (2004) studied internal migration flows in Russia in the 1990s and showed that the lack of convergence was explained by a “poverty trap”. In general, Russians did move from poorer to richer regions. However, in Russia’s very poor regions (in about 30% of the regions hosting about 30% of Russia’s population) the potential outgoing migrants wanted, but could not afford, to leave; so for these regions, an increase in income would have resulted in higher rather than lower outmigration.
What changed since 2000? Why did barriers to mobility come down? There are multiple potential explanations: (i) economic growth simply allowed most of Russia’s regions to grow out of the poverty trap; (ii) the development of financial and real estate markets reduced the transactions costs of moving therefore reducing the importance of the poverty trap; (iii) the development of capital markets increased capital mobility; (iv) federal redistribution reduced interregional differences.
According to Guriev and Vakulenko (2012), federal redistribution played a very minor role, while the other three explanations are consistent with the data. Our analysis of capital flows is, however, limited by the lack of detailed data, but our study of panel data on net capital inflows and investment shows that, first, capital does flow to regions with higher returns to capital and with lower wages and incomes, thus contributing to convergence. Second, investment in Russia’s regions is not correlated with savings which suggests that Russia’s capital market is not regionally segmented. As our data on capital are limited to the period after 2000, we cannot compare the recent years to those during the 1990s, but at least we can argue that recently, the capital market was functioning well and was contributing to convergence.
It is striking to what extent the poverty trap and liquidity constraints used to be, but are no longer, binding for labor mobility. Figure 4 is a graphical illustration of the poverty trap. Based on a semiparametric estimation with region-to-region fixed effects it shows the relationship between income in the origin region and migration (both in logarithm). Each dot on this graph represents migration from one region to another in a given year (during 1995-2010). As discussed above, the relationship is non-monotonic. If the sending region is poor, an increase in income results in higher out-migration; for richer regions, a further increase in income results in lower migration. The peak is at log income equal to 8.7 which amounts to average income equal to exp(8.7) ≈ 6003 in 2010 rubles and 1.02 of the Russian average subsistence levels in 2010. The regions to the left of the peak are in the poverty trap while the regions to the right are in a “normal mode” where liquidity constraints are not a substantial barrier to migration.
While in the 1990s tens of regions were below this threshold (and therefore were locked in the poverty trap), by 2010 only one region was below this threshold. In this sense, overall economic growth allowed Russian regions to overcome liquidity constraints by simply growing out of the poverty trap. We ran additional tests to show that financial development also contributed to relaxing liquidity constraints.
Figure 4. Income in the Origin Region and Migration[3]
What Next?
Should we be worried about high interregional differentials in GRP per capita? Not necessarily. In order to ensure inter-regional convergence in incomes and wages, convergence in GDP per capita is not required. As long as barriers to labor and capital mobility are removed, mobility (or even a threat of mobility) protects workers. Therefore, the very fact of remaining large inter-regional dispersion in GDP per capita should not serve by itself as a justification for government intervention (e.g. region-specific government investment).
As reducing barriers to mobility is important for convergence, this is exactly where policies can contribute the most. Developing financial and housing markets and improving investor protection are better policies for reducing inter-regional differences in income; these factors have already reduced income differentials among Russian regions.
We should, however, provide an important caveat. Our analysis was done at the regional level. We therefore do not address the sub-regional level and have nothing to say on the need for town-level government interventions. There may well be many cases where individual towns (e.g. so called mono-towns) are locked in poverty traps. In those cases government intervention may be justified and desirable. Our results show that poverty traps did exist in Russia in the 1990s at the regional level. These may well still exist at the town level even now. We cannot extrapolate the quantitative value of the income threshold we identified for the poverty traps from regional level to the town level but our analysis provides very clear qualitative criteria for government intervention. If the average citizen of a town would benefit from moving out but cannot finance the move (e.g. because his/her real estate is worthless), then the government can and should step in through supporting financial intermediaries that could finance the move. Therefore our analysis is fully consistent with the rationale for the government’s mono-towns restructuring program.
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References
- Andrienko, Yuri, and Sergei Guriev (2004). “Determinants of Interregional Mobility in Russia: Evidence from Panel Data.” Economics of Transition, 12 (1), 1-27.
- Che, Natasha, and Antonio Spilimbergo (2012). “Structural reforms and regional convergence.” CEPR Discussion Paper No. 8951.
- Guriev, Sergei and Elena Vakulenko (2012). “Convergence among Russian regions.” Background paper for the World Bank’s Eurasia Growth Project.
[1] Russian law defines monotowns as town where at least 25% employment is in a single firm. Even now, the Russian government’s Program for the Support of Monotowns lists 335 monotowns (out of the total of 1099 Russia’s towns and cities) with the total of 25% of Russia’s urban population. [2] EU (19): Belgium, Czech Republic, Germany, Estonia, Ireland, Greece, Spain, France, Italy, Latvia, Lithuania, Netherlands, Austria, Poland, Portugal, Slovakia, Finland, Sweden, United Kingdom. For EU (19) we consider only those NUTS-2 units for which there is data for each year. Western Europe: Austria, Belgium, Germany, Ireland, Greece, France, Italy, Netherlands, Norway, Portugal, Finland, Sweden, United Kingdom. [3] The graph shows the relationship between the logarithm of the real income in the sending region and the logarithm in migration controlling for income in the receiving region, unemployment and public goods in both sending and receiving, year dummies and other factors influencing migration. Moscow and Saint Petersburg are excluded.