Location: Russia
Managed Competition in Health Insurance Systems in Central and Eastern Europe
This policy brief summarizes common trends in the development of health care systems in the Czech Republic, Slovakia, and Russia in late 1990s–early 2000s. These countries focused on regulated competition between multiple health insurance companies. However, excessive regulation led to various deficiencies of the model. In particular, improvements in such quality indicators of the three health care systems as infant and under-five mortality are unrelated to the presence of multiple insurers or insurer competition.
A number of transition countries in Central and Eastern Europe and the former Soviet Union introduced health care systems with compulsory enrollment, obligatory insurance contributions unrelated to need and coverage according to a specified package of medical services. This so-called social health insurance (SHI) model (Culyer, 2005) is regarded as a means for achieving universal coverage, stable financial revenues, and consumer equity (Balabanova et al. 2012; Gordeev et al., 2011; Zweifel and Breyer, 2006; Preker et al., 2002). While most transition countries chose to only have a single health insurance provider on the market, the Czech Republic, Slovakia, and Russia allowed competitive (and often private) insurers in the new system. However, the evidence from the three countries shows excessive regulation of health insurers and limited instruments for insurer competition within indebted post-reform health care systems (Naigovzina and Filatov, 2010; Besstremyannaya, 2009; Medved et al., 2005). Consequently, the three countries may have been over-enthusiastic in putting large emphasis on market forces in the reorganization of health care systems in economies with a legacy of central planning (Diamond, 2002).
This brief addresses the results of Besstremyannaya (2010), which assesses the impact of private health insurance companies on the quality of health care system. While various performance measures reflect different goals of national and regional health care systems (Joumard et al., 2010; Propper and Wilson, 2006; OECD, 2004; WHO, 2000), aggregate health outcomes directly related to the quality of health care are commonly infant and under-five mortality (Lawson et al., 2012; Gottret and Schieber, 2006; Wagstaff and Claeson, 2004; Filmer and Pritchett, 1999). Consequently, Besstremyannaya’s (2010) analysis regards mortality indicators as variables reflecting the overall quality of health care system.
The estimations employ data on Russian regions in 2000-2006. The results indicate that regions with only private health insurers have lower infant and under-five mortality. However, given the low degree of competition on the social health insurance market in Russia, we hypothesize that this effect is mostly driven by positive institutional reforms in those regions. Indeed, incorporating the effect of institutional financial environment, we find that the impact of private health insurers becomes insignificant.
Development of a Social Health Insurance Model in the Czech Republic, Slovakia, and Russia
At the beginning of their economic transition, the Czech Republic, Slovakia, and Russia established a model for universal coverage of citizens by mandatory health insurance (Balabanova et al., 2012; Medved et al., 2005; Sheiman, 1991). The revenues of the new SHI system came from a special payroll tax and from government payments for health care provision to the non-working population. The main reason for combining certain features of taxation-based and insurance-based systems was the desire to establish mandatory health insurance as a reliable source of financing in an environment with unstable budgetary revenues (Lawson and Nemec, 2003; Preker et al., 2002; Sheiman, 1994). The insurance systems instituted in the three transition countries correspond to the major SHI principles implemented in Western Europe: contributions by beneficiaries according to their ability to pay; transparency in the flow of funds; and free access to care based on clinical need (Jacobs and Goddard, 2002).
The Czech Republic, Slovakia, and Russia placed emphasis on regulated competition, decreeing that SHI should be offered by multiple private insurance companies with a free choice of the insurer by consumers. Managers of private insurance companies were assumed to perform better than government executives (Lawson and Nemec, 2003; Sinuraya, 2000; Curtis et al., 1995), so an intermediary role for private insurance companies was seen as a key instrument for introducing market incentives and improving the quality of the health care system (Sheiman, 1991).
However, the activity of health insurance companies in the three countries was heavily regulated, since the content of benefit packages, size of subscriber contributions, and the methods of provider reimbursement were decided by government, and tariffs for health care were frequently revised (Lawson et al., 2012; Rokosova et al., 2005; Zaborovskaya et al., 2005; Praznovcova et al., 2003; Hussey and Anderson, 2003). In particular, Russian health care authorities enforced rigid assignments of areas, whose residents were to be served by a particular health insurance company (Twigg, 1999) and imposed informal agreements with health insurance companies to finance providers regardless of the quality and quantity of the health care (Blam and Kovalev, 2006). As a result, the three countries experienced an initial emergence of a large number of health insurance companies, followed by mergers between them, resulting in high market concentration (Sergeeva, 2006; Zaborovskaya et al., 2005; Medved et al., 2005).
In Russia, the Health Insurance Law (1991) specified that until private insurers appeared in a region, the regional SHI fund or its branches could play the role of insurance companies. Therefore, several types of SHI systems emerged in Russian regions in the 1990s and early 2000s: the regional SHI fund might be the only agent on the SHI market; the regional SHI fund might have branches, acting as insurance companies; SHI might be offered exclusively by private insurance companies; or SHI might be offered by both private insurance companies and branches of the regional SHI fund (Figure 1). The variety of SHI systems reflects the fact that many regions opposed market entry by private insurance companies (Twigg, 1999). Indeed, the boards of directors of regional SHI funds usually included regional government officials (Tompson, 2007; Tragakes and Lessof, 2003) who were reluctant to reduce government control over SHI financing sources (Blam and Kovalev, 2006; Twigg, 2001). The controversy with health insurance legislation created a substantial confusion at the regional and the municipal level (Danishevski et al., 2006).
Figure 1. Health insurance agents in Russia in 2000-2006, (number of regions) 
This context suggests that Russian regions provide an interesting study field to address the impact of private health insurance companies on the quality of health care system. In particular, the wide variety of SHI systems across Russian regions, as well as the gradual introduction of the health insurance model in Russia provide a sufficient degree of variation in practices and outcomes to allow for a well-specified empirical analysis.
Data and Results
In our analysis we use data on Russian regional economies between 2000 and 2006 (as based on data availability). Our measures of health outcomes are given by the pooled regional data on infant and under-five mortality. Our key explanatory variable is the presence of only private health insurers in the region. Arguably, the coexistence of public and private health insurance companies does not enable effective functioning of private health insurers owing to their discrimination by the territorial health insurance fund. Therefore, in the empirical estimations we focus on the presence of only private health insurers in the region, regarding it as a measure of effective health insurance model. The analysis also employs a variety of important socio-economic and geographic variables influencing health outcomes (per capita gross regional product (GRP), share of private and public health care expenditure in gross regional product, share of urban population, average temperature in January).
The results of the first set of our empirical estimations demonstrate that the presence of only private health insurers in a region leads to lower infant and under-five mortality. Furthermore, an increase in the share of private health care expenditure in GRP leads to a decrease in both mortality indicators. The result is consistent with numerous findings about the association between personal income and health status in Russia (Balabanova et al., 2012; Sparling, 2008).
Prospective reimbursement of health care providers is associated with a decrease in infant and under-five mortality. The finding suggests the existence of a quasi-insurance mechanism in the Russian SHI market. Operating in an institutional environment where provider reimbursement is based on prospective payment, private insurance companies in effect shift a part of their risk to providers (Glied, 2000; Sheiman, 1997; Chernichovsky et al., 1996).
Table 1. Factors leading to decreased infant and under-five mortality in Russia
Although our analysis shows that the presence of only private health insurers is statistically associated with improvements in infant and under-five mortality, we believe that the influence is indirect. Namely, the overall positive institutional environment in the region may result in both a decrease of mortality indicators and a lower coercion of regional authorities towards the presence of private health insurance companies.
To test this hypothesis, we use financial risk in a region as a measure of institutional environment and incorporate it in the analysis through an instrumental variable approach. (We measure financial risk by an expertly determined rank ordered variable by RA expert rating agency; this variable reflects the balance of the budgets of enterprises and governments in the region, with lower ranks corresponding to smaller risk.)
In line with our hypothesis, the results suggest that the presence of private health insurance companies now becomes insignificant in explaining infant and under-five mortality.
Discussion
The existing literature suggests that the improvement in infant and under-five mortality in the Czech Republic, Slovakia, and Russia can be attributed primarily to an increase of health care spending (Gordeev et al. 2011; Besstremyannaya, 2009; Lawson and Nemec, 2003) rather than being an effect of the social health insurance model with multiple competing insurers. It should be noted that insufficient government payments for the non-working population and a decline of the gross domestic product in the early transition years left SHI systems in the three countries indebted (Naigovzina and Filatov, 2010; Sheiman, 2006; Medved et al., 2005), which undermined the development of the managed competition in the health care provision.
In Russia (and also in the Czech Republic and Slovakia) there is little competition between insurers, and surveys show that the main factors causing consumers to change their health insurance company are change of work or residence, and not dissatisfaction with the insurer (Baranov and Sklyar, 2009). The fact that law suits on defense of SHI patient rights are rarely submitted to courts through health insurers (Federal Mandatory Health Insurance Fund, 2005) may also be evidence of the failure of Russian health insurance companies to win customers on the basis of their competitive strengths.
Summary and Policy Implications
The above findings as well as the other mentioned literature suggest that improvements of infant and under-five mortality in the Czech Republic, Slovakia, and Russia are not associated with the positive role of managed competition in the social health insurance system. In particular, in Russia the decrease in infant and under-five mortality is likely to be related to financial environment, rather than the existence of insurance mechanisms or competition between health insurance companies. One possible explanation of this absence of effect may come from the excessive regulation of the private insurance markets, as well as the insufficient competition between insurers. Importantly, the health insurance reform, implemented in Russia in 2010, both addressed underfinancing (by raising payroll tax rates) and took a step towards fostering provider competition, by allowing private providers to enter the social health insurance market (Besstremyannaya 2013). However, insurance companies are still not endowed with effective instruments for encouraging quality by providers, which may greatly undermine their efficiency.
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References
- Balabanova D, Roberts B, Richardson E, Haerpfer C, McKee V. 2012. Health Care Reform in the Former Soviet Union: Beyond the Transition. Health Services Research 47(2): 840-864.
- Baranov IN, Sklyar TM. 2009. Problemy strakhovoi modeli zdravookhraneniya na primere Moskwy i Sankt-Peterburga (Problems of insurance model in health care: the example of Moscow and Saint Petersburg). In X International Conference on the Problems of Development of Economy and Society, Yasin E.G (ed), Moscow: Higher School of Economics, vol.2.
- Besstremyannaya GE. 2013. Razvitie systemy obyazatelnogo meditsinskogo strakhovaniya v Rossijskoi Federatsii (Development of the Mandatory Health Insurance system in the Russian Federation) Federalizm 3: 201-212
- Besstremyannaya GE. 2010. Essays in Empirical Health Economics. PhD thesis. Keio University (Tokyo).
- Besstremyannaya GE. 2009. Increased public financing and health care outcomes in Russia. Transition Studies Review 16: 723-734.
- Blam I, Kovalev S. 2006. Spontaneous commercialization, inequality and the contradictions of the mandatory medical insurance in transitional Russia. Journal of International Development 18: 407–423.
- Culyer AJ (2005) The Dictionary of Health Economics, Edward Elgar.
- Danishevski K, Balabanova D, McKee M, Atkinson S. 2006. The fragmentary federation: experiences with the decentralized health system in Russia. Health Policy and Planning 21: 183–194.
- Gordeev VS, Pavlova M, Groot W. 2011. Two decades of reforms. Appraisal of the financial reforms in the Russian public healthcare sector. Health Policy 102(2-3): 270-277.
- Hussey P, Anderson GF. 2003. A comparison of single- and multi-payer health insurance systems and options for reform. Health Policy 66: 215-228.
- Jacobs R, Goddard M. 2002. Trade-offs in social health insurance systems. International Jthenal of Social Economics 29(11): 861-875.
- Lawson C, Nemec J, Sagat V. 2012. Health care reforms in the Slovak and Czech Republics 1989-2011: the same or different tracks? Ekonomie a management 1, 19-33.
- Lawson C, Nemec J. 2003. The political economy of Slovak and Czech health policy: 1989-2000. International Political Science Review 24(2): 219-235.
- Medved J, Nemec J, Vitek L. 2005. Social health insurance and its failures in the Czech Republic and Slovakia: the role of the state. Prague Economic Papers 1:64-81.
- Praznovcova L, Suchopar J, Wertheimer AI. 2003. Drug policy in the Czech Republic. Jthenal of Pharmaceutical Finance, Economics and Policy 12(1): 55-75.
- Preker AS, Jakab M, Schneider M. 2002. Health financing reforms in Central and Eastern Europe and the former Soviet Union, in Funding Health Care: Options for Europe, Mossalos E., Dixon A., Figueras J., Kutzin J. (Eds.), European Observatory on Health Care Systems Series: Open University Press, 2002.
- Rokosova M, Hava P, Schreyogg J, Busse R. 2005. Health care systems in transition: Czech Republic. Copenhagen, WHO Regional Office for Europe on behalf of the European Observatory on Health Systems and Policies.
- Sheiman I. 1991. Health care reform in the Russian Federation. Health Policy 19: 45–54.
- Sheiman I. 2006. O tak nazyvaemoi konkurentnoi modeli obyazatelnogo meditsinskogo strahovaniya (On so-called competitive model of mandatory health insurance). Menedzher Zdravoohraneniya 1: 52-58.
- Sheiman I. 1997. From Beveridge to Bismarck: Health Financing in the Russian Federation’. In Innovations in Health Care Financing, Schieber G. (ed.), Discussion Paper 365, 1997, Washington DC: The World Bank.
- Sinuraya T. 2000. Decentralization of the health care system and territorial medical insurance coverage in Russia: friend or foe? European Jthenal of Health Law 7:15–27.
- Sparling AS. 2008. Income, drug, and health: evidence from Russian elderly women. PhD dissertation. University North Carolina at Chapel Hill, UMI Dissertations Publishing.
- Tompson W. 2007. Healthcare reform in Russia: problems and perspectives. Working Papers 538, OECD Economics Department
- Tragakes E, Lessof S. 2003.Russian Federation, Health Care Systems in Transition, The European Observatory, WHO, Europe.
- Twigg J. 1999. Obligatory medical insurance in Russia: the participants’ perspective. Social Science and Medicine 49: 371–382.
- Twigg, JL. 2001. Russian healthcare reform at the regional level: status and impact. Post-Soviet Geography and Economics 42: 202–219.
- Zaborovskaya AS, Chernets VA, Shishkin SV. 2005. Organizatsiya upravleniya i finansirovaniya zdravoohraneniyem v subjektah Rossijskoi Federatsii v 2004 godu (Organization of management and finance of healthcare in Russian regions in 2004)
- Zweifel P, Breyer F. The economics of social health insurance. In The Elgar Companion to Health Economics, Jones A. (ed.), Edward Elgar, 2006.
- Wagstaff A. 2010. Social health insurance reexamined. Health Economics 19: 503–517.
The Customs Union Between Russia, Belarus and Kazakhstan: Some Evidence from the New Tariff Rates and Trade Flows
Author: Arevik Mkrtchyan, European University Institute.
This brief addresses the Customs Union between Russia, Belarus and Kazakhstan that was established in 2010. It argues that the external tariff schedule reflects a compromise between the interests of its members rather than simple expansion of Russian influence on the CU partners, and that the reduction in trade costs due to elimination of internal borders, benefits both the members of the CU and their external trade partners. Moreover, the impact of alleviated non-tariff trade costs on trade flows is strong and significant, while the tariff impact is insignificant for all members.
The European Commission against Gazprom: Should Gas Contracting Arrangement Be Changed?
This policy brief discusses EC’s claim that Gazprom abuses its dominant position. I argue that parts of the claim, like denying Third Party Access, are warranted but others related to the contracts offered by Gazprom to different Member States need not be. In fact, major market players in Europe offer similar contracting forms. In this case, the literature on the competitive effect of long-term supply contracts have stressed that such effect depends on the exact contract arrangement. For example, offering multi-years contract may indeed increase the competition on one part of the market. Having a gas supply contract with a price fully linked to the price of a gas hub may on the other hand reduce the competition among big gas suppliers. Hence, the assessment of Gazprom’s abuse of dominant position should be based on a careful analysis of the many contracting forms that have been agreed between Gazprom and customers in the Member States.
On the 4th of September 2012, the European Commission (EC) opened a proceeding against Gazprom, investigating whether Gazprom has abused its dominant market position in Central and Eastern Europe’s gas supply (see http://europa.eu/rapid/press-release_IP-12-937_en.htm?locale=en). The allegation relies on two different points. First, Gazprom has been accused of denying access to its network pipeline when requested by competing gas supplier. Second, the contractual arrangement offered by Gazprom itself has been under scrutiny. A Gazprom contract usually includes a “destination clause”, that forbids any gas reselling by the buyer. Moreover, the typical Gazprom contract usually specifies a fixed quantity (with a take or pay clause) at a price indexed to the oil price (see Sartori, 2013 for a more extensive description of the EC’s proceeding.)
The objective of this policy brief is to discuss the EC’s claim of Gazprom’s abuse of dominant position. I argue that while the denial of Third Party Access appears as an obvious case of abuse of dominant position, the contractual arrangements offered by Gazprom need not be.
Characterization of Gazprom’s Abuse of Dominant Position
Denying access to Gazprom’s pipelines limits competition and thereby benefits Gazprom as controlling a pipeline constitutes a natural monopoly. This fact has been recognized for a long time with the requirement for a third party access to gas networks in the EU Gas Directive (Directive 2009/73/EC). The first part of the proceeding thus seems to be justified.
The EC proceeding also found that the contractual arrangements offered by Gazprom reflected an abuse of dominant position. The claim is that Gazprom locked in its customers. When signing a contract with Gazprom, buyers agreed on a fixed quantity irrespective of their “real” consumption (“take or pay” clause) and are not allowed to resale ex post excess quantity on the market (“destination clause”). Given that gas contracts usually are signed for many years, the lock-in period can be long. Moreover, the price of the gas contract is usually pegged to the oil price so that it reflects current supply and demand conditions for oil rather than for gas. One implication is that the contracted gas prices did not reflect the severe drop in the gas market price in 2008 (BP report, 2012).
The EC’s allegation that Gazprom has abused its dominant position is thus based not only on the fact that Gazprom is denying third party access to its pipelines but also on the long term contracts with a fixed quantity and an oil indexed price.
Next, I argue that the second part of the claim is questionable. Forcing Gazprom to propose contracts with flexible quantities, shorter contract lengths and no indexation to the oil price may not limit the abuse of Gazprom’s dominance. Depending on the exact contract arrangement (quantity, duration, and indexation), the abuse of dominant position could be more or less severe.
Contract Arrangement and Market Competition
It is important to stress that the major gas suppliers of Europe, like Sonatrach or Statoil, offer similar contract arrangements. So, are long-term supply contract arrangements pro or anticompetitive given that all major competitors use such contracts? The answer to this question typically depends on the contractual details. In what follows, I discuss briefly when contracts provided by major market players could alleviate the abuse of dominant position.
It has been shown that firms may have less incentive to exercise market power, if they have large contract positions (e.g. Allaz and Vila, 1993). Intuitively, a firm obtains a leadership position by selling contracts before going on the spot market. Motivated by this opportunity, all players participate in the contract market and as a consequence compete more aggressively overall. Offering long-term supply contracts may therefore enhance competition among gas suppliers.
The competitive effect of long-term supply contract may not always be present when suppliers and buyers repeatedly sign contracts. In a dynamic setup, it has been shown that allowing contracting for major players may reduce competition. Contracting could be used to reduce demand elasticity by increasing spot market exposure (e.g. Mahenc and Salanié, 2004). Contracting could also increase the likelihood and severity of collusion (Ferreira, 2003; Le Coq, 2004; Liski and Montero, 2006). The reason is that a collusive agreement is easier to sustain in a dynamic setup if firms offer contracts. A collusive strategy is sustainable provided that firms have no incentives to cheat, i.e. the repeated collusive profits exceed the immediate profit from the deviation and the price war following defection. The short run gains from cheating are reduced if all firms have signed contracts as the defecting firm will not capture the demand already covered by competitors’ contract sales. Compared to the case with no contracts, this reduces the gains from defection without changing the punishment path, and therefore makes collusion easier to sustain. In a dynamic setup, offering contracts may therefore increase the likelihood of collusion.
Green and Le Coq (2010) have shown, however, that the anti-competitive effect of contracts depends on their duration. The longer the contracts last, the more difficult it is to sustain collusion. Intuitively, a deviation from the collusive agreement will trigger punishments, which depend on the contract duration. The longer the contract lasts, the smaller would be the punishment profit, which would increase the incentive to deviate.
The contract price’s format also matters when estimating the anti-competitive effect of any contract arrangement. The stronger the degree of indexation to the spot price the easier it is to sustain collusion (Le Coq, 2013). In particular, if a contract price would be fully indexed on a gas spot (hub) price, irrespective of the contract’s duration, it is always easier to collude. The intuition underlying this result is two-fold.
First, given that the contracted quantities are not traded in the spot market, contracts reduce the size of the market that a deviator can serve when undercutting the rival’s price. Second, given that the contract’s price equals the spot price, the contract does not affect profit levels in the punishment phase. Consequently, profits in the punishment phase can be driven down to zero just as in the case when there is no contract market. Moreover, contracts with others forms of indexation have the same qualitative effects, provided that the indexation to the spot price is sufficiently strong. Interestingly, with full indexation, the anti-competitive effect of supply contract holds even if contracted quantities are flexible (can be renegotiated).
To conclude, changing the contract arrangement between Gazprom and European customers may not alleviate the abuse of Gazprom’s dominant position. A detailed analysis of the (many) contract arrangements offered by Gazprom needs to be conduct first to be able to make such claim.
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References
- Allaz, B., Vila, J.-L., 1993. Cournot competition, forward markets and efficiency. Journal of Economic Theory 59 (1), 1–16.
- BP Statistical Review of World Energy June 2012
- Directive 2009/73/EC of the European Parliament and of the Council concerning common rules for the internal market in natural gas and repealing Directive 2003/55/EC, OJ L 211.
- Ferreira, J.L., 2003. Strategic interaction between futures and spot markets. Journal of Economic Theory 108 (1), 141–151.
- Liski, M., Montero, J.-P., 2006. Forward trading and collusion in oligopoly. Journal of Economic Theory 131 (1), 212–230.
- Le Coq, C., 2004. Long-term supply contracts and collusion in the electricity market. Stockholm, SSE/EFI Working Paper Series in Economics and Finance 552.
- Le Coq, C., 2013 Supply Contracts and Competition on the Spot: How indexation and duration matter? Mimeo.
- Le Coq, C., R. Green, 2010 The Length of Contracts and Collusion International Journal of Industrial Organization 28(1), 21-29, 2010.
- Mahenc, P., Salanié, F., 2004. Softening competition through forward trading. Journal of Economic Theory 116 (2), 282–293.
- Sartori N., 2013. The European Commission vs. Gazprom: An Issue of Fair Competition or a Foreign Policy Quarrel? IAI working paper 13103
Alcohol Consumption and Mortality
Many studies have shown that alcohol consumption is the main cause of death among working age Russian males and, in particular, among those younger than 40 (see Bhattacharya et al., 2013, Brainerd and Cutler, 2005, Denisova, 2010, Leon et al., 2007, Triesman, 2010, Yakovlev, 2013a, 2013b). A noteworthy example that illustrates this point is the decrease in male mortality rates during the Gorbachev anti-alcohol campaign. During five years of this campaign, which restricted sales and increased the price of alcohol, alcohol consumption fell by 40%. During the same period, male mortality rates fell by 25%. Furthermore, this trend reversed at end of the Gorbachev anti-alcohol campaign with the liberalization of the alcohol market and surge in mortality by the end of 1990s and beginning of 2000s (see Triesman, 2010 and Bhattacharya et al., 2013). These trends appear to be consistent with the idea that access to more alcohol is related to higher rates of male mortality.
Despite recent regulatory measures imposed by the Russian government to end this trend, male live expectancy remains low: it is 4 years below world average and below poor countries, such as North Korea or Yemen.
The economic literature emphasizes several features of alcohol consumption that are important for policy makers. First, alcohol, and especially hard alcohol, is a relatively elastic good. This implies that an increase in the price of alcohol as well as other costs (such as time costs) will result in an even larger drop in alcohol consumption relative to the price drop. If they are linked, this should also be associated with a fall in mortality rates (see Cook and Moore, 2000, Leung and Phelps, 1993).
Second, alcohol is a “social” good (Kremer and Levy, 2008, Krauth, 2005, Yakovlev, 2013a). People like to drink with others. Drinking often takes place in groups of peers, and peer decisions on whether to drink or not affect personal decisions related to drinking. Peer effects are especially strong among younger generations. The presence of peer effects implies the presence of a so-called social multiplier: the effect of government policy (for example, alcohol taxation) will be higher in the presence of peer effects. A policy such as a rise in taxation will not only affect an individual by encouraging them to consume less, but also have a spillover effect on his or her peers resulting in them drinking less as well. This should, overall, generate a larger decrease in alcohol consumption than would be the case through the effect on individuals alone (i.e. if people choose to drink based purely on their own preferences without paying attention to their peers or social groups). As it was shown by Yakovlev (2013a), for males below age 30, the peer effect increases the price elasticity of alcohol consumption by 50%. This means that a government policy, such as an increased alcohol tax, should generate a 50% higher decrease in alcohol consumption for the younger generation. Furthermore, this should also lead to an even larger reduction of mortality rates.
A third aspect of alcohol consumption is that alcohol is a habit-forming good (see Cook and Moore, 2000). The consumption of alcohol, as well as consumption of certain types of alcoholic beverages, tends to form habits related to these goods. These habits are strong and they potentially affect personal consumption even decades later. If a person starts to consume alcohol in their youth, this means that they are likely to continue and be more likely to consume alcohol in later years simply because they have a past history of consuming this product.
These three aspects have several policy implications. First, due to habits and peer effects, government policies aiming to reduce mortality rates by decreasing alcohol consumption will potentially have greater impact on younger generations than on older. This is simply because peer effects tend to be stronger among youths, but also because decreased consumption earlier in life will reduce the chances of consuming alcohol later in life and have, as a consequence, even longer term effects on society’s level of alcohol consumption. Thus, policy makers should pay special attention on younger groups of the population, in particular, policy tools such as the restriction of alcohol sales near schools and other educational facilities if the goal is to reduce the negative impact of alcohol on life expectancy. Second, the effect of this policy could be long lasting: once habits form, patterns of consumption could be affected for many years afterwards. In other words, the full effects of a policy aiming to curb alcohol consumption to improve mortality rates will not be immediately observed. Instead, part of the change in the future would be attributed to past changes in alcohol consumption.
Another aspect of alcohol consumption of importance for mortality rates concerns the habits individuals form regarding what types of alcoholic beverages, such as beer or vodka (see Yakovlev, 2013b), they drink. This has policy implications since not all beverages have the same degree of harm. If an individual consumes beer during his or her teens, she or he would likely prefer beer ten (or even more) years later. If she or he starts with vodka, she or he will likely prefer vodka. Moreover, Yakovlev (2013b) shows that beer and vodka are substitutes: an increase in the price of beer will decrease the consumption of beer and increase the consumption of vodka, or vice versa. Because beer is a less harmful alcoholic beverage than vodka, an increase in the relative price of vodka with respect to beer should improve public health to the extent that people switch to consuming a less harmful form. In addition, this effect should be stronger in the long run with individuals forming habits toward beer consumption at the expense of the more harmful vodka and, overall, we should expect morality rates to be improved as a result, although not by as much as in the case when people stop or do not consume alcohol.
There are several other features of alcohol consumption worth mentioning but which will not be addressed in detail in this brief. Alcohol consumption is correlated with not only personal health and well-being, but also with the well-being of others: it is associated with negative externalities such as crime, violence, and traffic accidents etc. Alcohol consumption also exhibits several “non-fully-rational” features such as time inconsistency or myopia (Gruber and Koszegi, 2001). In this case, a restriction on the times when alcohol sales are permitted could be a possible effective policy tool to reduce heavy drinking. This happens because people tend to underestimate how much they would like to drink in the future or want to drink less in the future than they expect, and thus prefer not to store alcohol at home. Finally, alcohol consumption is a substitute for other activities, such as sports (Tsai, 2013). Promoting these activities could encourage people to switch from alcohol consumption to healthier behavior, and, conversely, reducing alcohol consumption could foster greater levels of participation in sports activities.
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Literature
- Bhattacharya, Jay, Christina Gathmann, and Grant Miller. 2013. “The Gorbachev Anti-Alcohol Campaign and Russia’s Mortality Crisis” AEJ: Economic Policy 2012
- Cook, Philip J. and Moore, Michael J. 2000. “Alcohol”, Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed. ), Handbook of Health Economics, edition 1, volume 1, chapter 3.
- Brainerd, Elizabeth and David Cutler, 2005, “Autopsy on an Empire: Understanding Mortality in Russia and the Former Soviet Union.” Journal of Economic Perspectives, American Economic Association, vol. 19(1), pages 107-130,Winter.
- Denisova, Irina. 2010. “Adult mortality in Russia: a microanalysis”, Economics of Transition, Vol. 18(2), 2010, 333-363.
- Gruber, Jonathan and Botond K˝oszegi. 2001. “Is Addiction ‘Rational?’ Theory and Evidence.” Quarterly Journal of Economics (2001), 116(4), pp. 1261-1305.
- Kremer, Michael, and Dan Levy. 2008. “Peer Effects and Alcohol Use among College Students.” Journal of Economic Perspectives, 22(3): 189–206.
- Krauth, Brian. 2005. “Peer effects and selection effects on smoking among Canadian youth.” Canadian Journal of Economics/Revue canadienne d’économique, Volume 38, Issue 3, pages 735–757, August 2005.
- Leon, David, Lyudmila Saburova, Susannah Tomkins, Evgueny Andreev, Nikolay Kiryanov, Martin McKee, and Vladimir M Shkolnikov. 2007. “Hazardous alcohol drinking and premature mortality”
- Leung S. F., and Phelps, C. E. “My kingdom for a drink…?” A review of estimates of the price sensitivity of demand for alcoholic beverages. In: Hilton, M. E. and Bloss, G., eds. Economics and the Prevention of Alcohol-Related Problems. NIAAA Research Monograph No. 25, NIH Pub. No. 93–3513. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism, 1993. pp. 1–32.
- Tsai, 2013, “Peer effects in physical training.” NES, mimeo
- Yakovlev, Evgeny 2013, “Peers and Alcohol: Evidence from Russia”, NES/CEFIR working paper
- Yakovlev, Evgeny 2013, “USSR Babies: Who drinks vodka in Russia”, NES/CEFIR working paper
Fact or Fiction? The Reversal of the Gender Education Gap Across the World and the Former Soviet Union
In this policy brief, I discuss the reversal of the gender education gap in many countries around the world – a fact that is still not widely known, although is increasingly gaining attention. I describe recent studies that have documented this fact for both developed and developing countries and have provided evidence on the trend. As there has not been much analysis of the education gap in the former Soviet Union countries, I present some measures of the education gap in the USSR and FSU countries, and compare them to other countries around the world. Finally, I discuss the potential causes of the reversal identified in the literature and how the reversal of the gap is related to other gender disparities.
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.
Accountability in Russia
This policy brief summarizes two recent research papers that are related to obstacles to political accountability in modern Russia and potential ways to overcome these obstacles. The first paper provides a rigorous assessment of the extent of electoral fraud in Moscow city during the parliamentary elections held on December 4, 2011. Using random assignment of independent observers, we estimate the actual share of votes for the incumbent United Russia party to be at least 11 percentage points lower than the official count (35.6 percent instead of 46.5 percent). A less rigorous, but more realistic estimate is 21 percentage points. These results suggest that electoral accountability in Russia is limited. The second paper demonstrates that even in an environment with low electoral accountability and limited freedom of media, alternative accountability mechanisms may emerge. In particular, anti-corruption campaigns in social media may affect the valuation of state-controlled companies, so that market forces put a disciplining effect on the managers of SOEs. We study consequences of blog postings of a popular Russian anti-corruption blogger and shareholder activist Alexei Navalny on the stock prices of state-controlled companies. In an event-study analysis, we find a negative effect of company-related blog postings on both daily abnormal returns and within-day 5-minute returns. We use the incidence of distributed denial-of-services (DDoS) attacks to show that the effect is not driven by the endogenous timing of blog postings. We also show that there are long-term effects of certain types of posts on stock returns, trading volume, and volatility. Overall, our evidence implies that blog postings about corruption in state-controlled companies have a negative causal impact on stock performance of these companies.
To study the extent of electoral fraud we employ data from a large-scale field experiment that allows us to estimate the amount of electoral fraud in the city of Moscow during Russian parliamentary elections in December 2011. In particular, we exploit randomized assignment of independent observers to polling stations. Prior to the parliamentary elections the independent NGO Citizen Observer (Grajdanin-nabludatel) trained more than 500 volunteer observers in the city of Moscow. The observers were sent to 156 randomly selected polling stations. The polling stations were selected using a systematic sampling technique. In particular, polling stations were divided by electoral districts. Within each district, polling stations were sorted according to their official number assigned by Central Election Committee. Every 25th polling station within electoral district starting from the 1st was assigned for observation, resulting in a sample of 185 polling stations. The Citizen Observer’s network recruited enough observers to cover 156 of the 185 polling stations, which corresponds to 4.9 percent of the 3,164 ordinary polling stations in Moscow.[1] To make sure that this procedure does not lead to a biased sample because of some hidden periodicities we check that in the previous parliamentary elections in 2007 polling stations selected using a similar procedure were not different from other polling stations.
Comparison of the share of votes received by different parties and the turnout between polling stations with independent observers from Citizen Observer (treatment group) and without observers (control group) is presented in Figure 1. The results indicate that the presence of observers led to a decrease in the share of votes for United Russia of 10.8 percentage points and the turnout at the polling stations with observers was lower by 6.5 percentage points.
Figure 1. Vote Shares in 2011 
Notes: The figure is reproduced from Enikolopov, Ruben, Vasily Korovkin, Maria Petrova, Konstantin Sonin, and Alexei Zakharov (forthcoming) “Electoral Fraud in Russian Parliamentary Elections in December 2011: Evidence from a Field Experiment.” Proceedings of the National Academy of Sciences.
The above results are likely to provide a lower bound on the extent of the electoral fraud, since the presence of observers at the polling stations did not fully prevent fraud. To provide more information on the extent of the fraud, we divide all treatment stations into three groups: those in which observers reported no serious violations (75 polling stations), those in which serious violations were reported, but the observers received the final protocol (43 polling stations), and those in which all observers were not able to get the official protocol of the vote count (38 polling stations), which happened if the observers were dismissed from the polling station or the heads of electoral commissions illegally refused to give a signed copy of the protocol.
Figure 2 shows the distribution of vote shares for United Russia at polling stations from these three groups. For observations in the control group the distribution seems to be bimodal with two peaks – one around 25 percent of votes and another one around 55 percent of votes. The distribution for the precincts with observers also has two peaks, with the first one around 25 percent of votes. Note, however, that the second mode of this distribution, around 50 percent of votes, is noticeably smaller as compared with the control group. Moreover, for the polling stations in the treatment group in which observers reported no serious violations the distribution becomes unimodal with the peak around 25 percent of votes for United Russia. Thus, the results are consistent with the following hypothesis: the distribution of vote shares for United Russia in the control group is simply a mixture of two distributions that correspond to polling stations without large electoral fraud (for which the distribution is centered around 25 percent of votes) and polling stations with substantial electoral fraud (for which the distribution is centered around 55 percent of votes). Note also that a similar pattern is observed for the distribution of turnout across three groups of precincts, but not for the distribution of vote shares for other parties.
Figure 2. Distribution of votes for United Russia 
Notes: The figure is reproduced from Enikolopov, Ruben, Vasily Korovkin, Maria Petrova, Konstantin Sonin, and Alexei Zakharov (forthcoming) “Electoral Fraud in Russian Parliamentary Elections in December 2011: Evidence from a Field Experiment.” Proceedings of the National Academy of Sciences.
To assess the overall influence of the electoral fraud in Moscow on the outcome of Russian parliamentary elections, we also estimate the total number of votes that United Russia received due to electoral fraud. As both vote share of a ruling party and turnout were affected by electoral fraud, we look at the number of votes for each party as a share of registered voters in precincts with and without observers. Based on these numbers, our conservative estimate of the number of votes, which United Russia received at the ordinary precincts in Moscow due to electoral fraud, is equal to 635,000. This is a lower bound for the size of electoral fraud as it assumes that the presence of observers fully prevented any fraud, and at least anecdotal evidence suggests that it is not always the case. If we use results from the polling stations in which observers report no serious violations as an alternative estimate, the number of stolen votes increases up to 1,090,000.
The results presented above indicate that because of electoral fraud, voting does not constitute an efficient mechanism to replace those in power, and, therefore, electoral accountability in Russia does not work to discipline politicians in the office. Other means to hold politicians and public officials accountable are also limited, since traditional media is often censored and politics is generally not competitive. We ask the question whether in such environment there is any alternative ways to hold public officials accountable, and, in particular, if new media, such as blogs, can make a difference. Specifically, we study whether blog postings of a popular Russian blogger, shareholder activist, and, subsequently, one of the leaders of emerging opposition to President Putin’s regime, Alexei Navalny, have had an impact on stock performance of the companies whose wrongdoings he uncovered and made public.
First, we show that daily abnormal returns of the companies Navalny wrote about were significantly lower after Navalny’s posts about them. The results hold if we control for mentions of these companies in other types of media (business newspapers, online newspapers, and blogs) and for company-year and year-month fixed effects. In addition to looking at daily abnormal returns, we show similar results for 5-minute abnormal returns even controlling for trading-day fixed effects (see Figure 3). The magnitude of this effect is quite sizable with a daily decline of 0.5 p.p. after an average blog posting, and a daily decline of 0.9 p.p. after an important blog posting.
Figure 3. 5-minute Abnormal Returns and Navalny’s Blog Postings, Non-Trading Time (Evenings and Weekends) Excluded
We also provide evidence that the impact of blogging on stock performance is causal. Although the results described above are consistent with the negative impact of blogging, they could be explained, e.g., by selective exposure. To identify the causal effect of blog postings we use an external variable, distributed denial-of-service (DDoS) attack on a blog service, as a source of exogenous variation. During the period under study (between January 2008 and August 2011), these DDoS attacks, allegedly, were not specifically targeting the Navalny’s blog, but they affected the accessibility of the whole blog platform, and the Navalny’s blog was also affected. As a result, DDoS attacks either prevented Navalny from writing a post or prevented his readers from reading his blog, but there was no obvious reason why they might influence fundamental determinants of stock prices of the companies Navalny wrote about.
In a reduced form model, we find significant positive effect of DDoS attacks on daily abnormal returns of the companies Navalny wrote about. This effect is stronger for the companies Navalny was more focused on (the latter result holds even with DDoS attack fixed effects). Quantitatively, the effect of DDoS attack is similar to the absence of the post or to the presence of the post with no information about the company in question. We also show that though DDoS effect is increasing in Navalny’s attention to the companies he was writing about, it is not increasing in the amount of general news attention to these companies.
Finally, in addition to the short-term effects we just described, we look at the longer-term one-month effects of blog postings. We find that although there were no long-term effects of the ordinary postings, there were negative and significant long-term effects of the most important postings, as proxied by at least 5 mentions of a company in the post. In addition, during the month after a blog posting, there was a larger volatility of stock returns and a larger trading volume. It appears that the number of transactions, controlling for trading volume, was significantly larger in both the short-term and longer-term perspective. Smaller average transactions are consistent with more individual, in contrast to institutional trading, which suggest that short-run effects of blog posting are driven by attention effects, rather than provision of new information. Overall, all our results are consistent with a negative causal impact of blog postings on stock performance of state-controlled companies, and imply that potentially there is a disciplining effect on the behavior of public officials who manage these companies. Thus, our results suggest that posting in online social networks can affect the stock performance of state-controlled companies, and, as a result, can become an unusual alternative mechanism to putting additional checks on the behavior of government officials even if political competition remains limited, and traditional media remain controlled.
The report is based on two papers: Enikolopov, Ruben, Vasily Korovkin, Maria Petrova, Konstantin Sonin, and Alexei Zakharov (2012) “Electoral Fraud in Russian Parliamentary Elections in December 2011: Evidence from a Field Experiment.” Proceedings of the National Academy of Sciences, 109 (52); Enikolopov, Ruben, with Maria Petrova and Konstantin Sonin “Do Bloggers Have any Real Influence? Event Study of Blog Postings by a Russian Activist Shareholder and Blog Service DDoS Attacks,” CEPR Working Paper.
[1] The sample excludes 210 precincts that had a special status, as they were located in hospitals, military units, or pre-trial detention facilities. These polling stations were excluded from the analysis since sending observers there was not always possible, and it was not clear if these polling stations were sufficiently similar to each other to use randomization. The number of votes cast at these polling stations, however, stood at only 1.8 percent of total votes in Moscow.
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).
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.