Tag: Russia

The Effects of Sanctions

Posted on May 10, 2022 | Policy Brief

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Sanctions imposed on Russia after its invasion of Ukraine are argued to be the strongest and farthest-reaching imposed on a major power after WWII, more numerous and more comprehensive than all other measures currently in force against all other sanctioned countries. A question often asked, which is hard to answer, is whether sanctions are effective. In the present case, the effect most associate with success would be a swift end of the hostilities, perhaps accompanied by a regime change in Russia. But even when it seems these prizes are out of reach, sanctions certainly have effects, all too often glossed over by the debate but nonetheless of significance.

Why Are Sanctions Seen as Ineffective?

Sanctions are restrictions imposed on a country by one or more other countries with the intent to pressure in effect some desirable outcome, or conversely to condemn and punish some undesired action already taken. When evaluating sanctions, therefore, the focus is naturally on whether they succeed to discourage this particular course of action, or to remove the decision-makers responsible for it. And on this account, sanctions are overwhelmingly seen as unsuccessful. However, a few complications cloud this conclusion.

First of all, sanctions that are implemented already failed at the threat stage. If the threat of a well-specified and credible retribution did not deter the receiving part from pursuing the sanctioned course of action, it is because they reckoned that they can afford to ignore it. So, unless this punishment goes beyond what was expected, in scope or in time, its implementation will also fall flat. This implies that any effort to evaluate sanctions retrospectively suffers from the negative selection problem, when almost exclusively cases of failure, intended in this particular sense, are observed.

Second, sanctions are a rather blunt instrument, that often cannot be targeted with the precision one would desire. Even though sanctions have over time become “smarter”, in the sense that stronger efforts are made to target the regime, or elites that may have the clout to actually affect the regime (think the oligarchs in Russia), they often fail to reach or affect in a meaningful way those individuals that are the real objective, for various reasons. Instead, they can cause significant “collateral damage”, to groups of a population that often are quite far removed from any real decisional power, including those in the sending countries, and even third parties who are extraneous to the situation. The damage inflicted to those parties can only in very special circumstances be part of a causal link eventually impacting the intended outcome. For instance, citizens struggling in an impoverished economy could be led to a riot, or in some other way put pressure on their government – but this implies that the country is sufficiently free for riots to take place or for voters’ opinions to be taken into consideration.

To this, it should be added that, once a course of action has been taken, it might be not obvious how to change or undo it, notwithstanding the signaled displeasure from the sanctioning parties. Sanctions are therefore rarely working in isolation. When positive outcomes are achieved, it is often the case that diplomatic channels were kept open and clear incentives offered for a way out. But then it might be unclear whether it was the sanctions or something else that led to the success.

Other Effects of Sanctions

The pitfalls highlighted above, which make it tricky to answer whether sanctions are effective at reaching their aim, also apply when studying other effects that sanctions might have. There is of course a range of outcomes that might be affected: in this literature we find studies looking at inequality (Afesorgbor et al., 2016), exchange rates (Dreger et al., 2016), trade (Afesorgbor, 2019; Crozet et al. 2020), the informal sector (Early et al, 2019), military spending (Farzanegan, 2019), women’s rights (Drury, 2014), and many more. But as it often happens the most studied outcome is GDP, as this is a measure that efficiently summarizes the whole economy and correlates very nicely with many other outcomes we care about.

Suppose then that we would like to investigate what is the effect of sanctions on a target country’s GDP.  One problem is identifying an appropriate counterfactual; to observe what would have happened in the target country in the absence of sanctions. It is also an issue that the incidence of international sanctions is often a product of a series of events in the target or sender country (e.g. the Iraqi invasion of Kuwait or the apartheid system in South Africa), which also have impacts on the economy that would need to be isolated from the impact of sanctions themselves.

A variety of econometric techniques can be of help in this situation. One first idea is to use, as a reference, cases where sanctions were almost implemented. Gutmann et al. (2021) compare countries under sanctions to countries under threat of sanctions, while Neuenkirch and Neumeier (2015) contrast implemented sanctions to vetoed sanctions, in the context of UN decisions. Both studies find a relatively sizeable negative impact on GDP, in a large group of countries over a long period of time. In the first study, the target country’s GDP per capita decreases on average by 4 percent over the two first years after sanctions imposition and shows no signs of recovery in the three years after sanctions are removed. The second study estimates a reduction in GDP growth that starts at between 2,3 and 3,5 percent after the imposition of UN sanctions and, although it decreases over time, only becomes insignificant after ten years. It should be considered that a lower growth rate compounds over time: experiencing a slower growth even by only 1 percent over ten years implies a total loss of almost 15 percent. As a comparison, the average GDP loss due to the Covid-19 pandemic is estimated to be 3,4 percent in 2020.

These studies have limitations. Countries under threat of sanctions are probably making efforts to avoid punishment, which might imply that these countries are precisely the ones who would be most negatively affected by the sanctions. If so, the impact found by Gutmann et al. (2021) is probably underestimated. Neuenkirch and Neumeier (2015) only look at UN sanctions, which on one hand might give a larger impact because of the multilateral coordination. But on the other hand, the issue of an appropriate counterfactual emerges again: countries whose sanctions are vetoed might be larger, more influential, and better connected within the international community or to some of the major powers, which may also affect their economic success in other ways.

Kwon et al. (2020) adopt a different technique and come to a different conclusion. They use an instrumental variable (IV) approach and find that standard OLS overestimates the negative effect of sanctions, in other words, that sanctions’ effects are less negative than we think. They find an instantaneous effect on per capita GDP that becomes insignificant in the long run, just as if sanctions never happened.

Our confidence in these estimates hinges upon the validity of the IV used. In this case, the actual imposition of sanctions is replaced by its estimated likelihood based on sender countries’ variation in institutions and diplomatic policies (which are exogenous to the target country’s economic developments) and pre-determined country-pair characteristics (trade and financial flows, travels, colonial ties). Therefore, episodes where sanctions are imposed because the sender country happens to be in a period of hawkish foreign policy and because the target does not have strong historical relations with them are contrasted to episodes in which the opposite is true, and sanctions are therefore not implemented, everything else being equal.

The results also show that there is heterogeneity across types of sanctions, with trade sanctions having both a short and long run negative impact, while smart sanctions (i.e. sanctions targeted on particular individuals or groups) have positive effects on the target country’s economy in the long run.  This is quite an important point in itself. Often, sweeping statements about effectiveness of “sanctions” lump all the different measures together, and fail to appreciate that there may be substantial differences. However, the effect of one or another type of sanctions will vary depending on the structure of the economy that is hit.

A third approach is the synthetic control method. Here the researcher tries to replicate as closely as possible the path of economic development in the target country up to the point of sanctions’ implementation, using one or a weighted average of several other countries. In this way, evolution after sanctions’ inception can be compared between the actual country and its synthetic control. Gharehgozli (2017) builds a replica of Iran based on a weighted combination of eight OPEC member countries, two non-OPEC oil-producing countries and three neighboring countries, that match a set of standard economic indicators for Iran over the period 1980-1994. The study finds that over the course of three years the imposition of US sanctions led to a 17.3 percent decline in Iran’s GDP, with the strongest reduction occurring in 2012, one year after the intensification of sanctions (2011-2014) was initiated.

This is a stronger effect than those presented earlier. However, it only speaks to the special case of Iran, rather than estimating a broader global average effect. Another study focusing on Iran (Torbat, 2005) makes the important point that the effect of sanctions varies by type: financial sanctions are found to be more effective (in lowering Iran’s GDP) than trade sanctions – which contrasts with what is found to be true on average by Kwon et al. (2020).

Finally, the relation between economic damage and the effectiveness of sanctions in terms of reaching their goals is debatable. In a theoretical model, Kaempfer et al. (1988) suggest that this relation might even be negative and that the most effective sanctions are not necessarily the most damaging in economic terms. The sanctions most likely to facilitate political change in the target country are those designed to cause income losses on groups benefiting from the target country’s policies, according to the authors.

The Effect of Sanctions on Russia

Are these results from previous studies useful to form expectations about the effects of the current sanctions on Russia? The invasion of Ukraine which started at the end of February was a relatively unexpected event, at least in character and scale, in contrast to what can be said in the majority of situations involving sanctions. However, the context leading up to it was not one of normality either. Besides the global pandemic, Russia was already under sanctions following the Crimean Crisis in 2014. The impact of those economic sanctions, and of the counter-sanctions imposed by Russia as retaliation, is still unclear – and will be in all probability completely dwarfed by the current sanction wave as well as other exogenous shocks, such as significant changes in oil prices in this period. Kholodilin et al. (2016) estimated the immediate loss of GDP in Russia to be 1,97 percent quarter-on-quarter, while no impact on the aggregate Euro Area countries’ GDP could be observed. A Russian study (Gurvich and Prilepsky, 2016) forecasted for the medium term a loss of 2,4 percentage points by 2017 as compared to the hypothetical scenario without sanctions. This pales in comparison to the magnitude of consequences that are being contemplated now. Even the potentially optimistic, or at least conservative, assessment of the current situation by the Russian Federation’s own Accounts Chamber, in the words of its head Alexei Kudrin, suggests that: “For almost one and a half to two years we will live in a very difficult situation.” At the end of April, they published revised forecasts on the economic situation, among which the one for GDP is shown below. Russian Central Bank chief Elvira Nabiullina also sounded bleak, speaking in the State Duma: “The period when the economy can live on reserves is finite. And already in the second – the beginning of the third quarter, we will enter a period of structural transformation and the search for new business models.” The World Bank has forecasted that Russia’s 2022 GDP output will fall by 11.2% due to Western sanctions. These numbers do not yet factor in the announcement of the sixth EU sanction package, which famously includes an oil embargo (see an earlier FREE Policy Brief on the dependency of Russia on oil export).

Figure 1. Revised forecasts of growth rates for the Russian economy

Source: Macroeconomic survey of the Bank of Russia, April 2022.

Are these estimates realistic, and what would have been the counterfactual development without sanctions? If we believe the studies reviewed in the previous section, and also taking into account the unprecedented scale and reach of the current sanctions, at least the time horizon, if not the size, of the consequences forecast by Russian authorities is, though substantial, certainly underestimated. But there is too much uncertainty at the moment, hostilities are still ongoing and sanctions are not being lifted for quite some time in any foreseeable scenario. One reason why these sanctions are not likely to be relaxed, and why their impact is expected to be more severe than in most cases, is that a very broad coalition of countries is backing them. Not only this but the sanctioning countries see Russia’s conduct as a potential threat to the existing world order, so their motivation to contrast it is particularly strong relative to, say, the cases of Iran, North Korea, or Burma.

Moreover, these loss estimates do not yet factor in the announcement of the sixth EU sanction package, which famously includes an oil embargo. Oil is a fundamental driver of growth in Russia. An earlier FREE Policy Brief shows how two-thirds of Russia’s growth can be explained by changes in international oil prices. This is not because oil constitutes such a large share of GDP but because of the secondary effect oil money generates in terms of domestic consumption and investment. Reducing export revenues from the sale of oil and gas will therefore have significant effects on Russia’s GDP, well beyond what the first-round effect of restricting the oil sector would imply.

In short, it is too early to venture an assessment in detail, however, the scale of loss that can be expected is clear from these and many other indicators. In the longer run, it will only be augmented by the relative isolation in which Russia has ended up, implying lower investments and subpar capital inputs at inflated prices, and by the ongoing brain drain (3,8 million people have already left the country since the war began).

Conclusion

In conclusion, the debate about economic sanctions as a tool of foreign policy is often restricted to a binary question: do they work or not? There is ample support in the literature studying sanctions to say that this question is too simplistic. Even if we do not see immediate success in reaching the main aim of the sanction policy, they do cause damage, in many dimensions, and such damage is non-negligible. The political will and the regime behind it may be unaffected, but the resources they need to continue with their course of action will unavoidably shrink in the longer run.

References

  • Afesorgbor, S. K. (2019). The impact of economic sanctions on international trade: How do threatened sanctions compare with imposed sanctions?. European Journal of Political Economy, 56, 11-26.
  • Afesorgbor, S. K., & Mahadevan, R. (2016). The impact of economic sanctions on income inequality of target states. World Development, 83, 1-11.
  • Crozet, M., & Hinz, J. (2020). Friendly fire: The trade impact of the Russia sanctions and counter-sanctions. Economic Policy, 35(101), 97-146.
  • Dreger, C., Kholodilin, K. A., Ulbricht, D., & Fidrmuc, J. (2016). Between the hammer and the anvil: The impact of economic sanctions and oil prices on Russia’s ruble. Journal of Comparative Economics, 44(2), 295-308.
  • Drury, A. Cooper and Dursun Peksen. “Women and economic statecraft: The negative impact international economic sanctions visit on women.” European Journal of International Relations 20 (2014): 463 – 490.
  • Early, B., & Peksen, D. (2019). Searching in the shadows: The impact of economic sanctions on informal economies. Political Research Quarterly, 72(4), 821-834.
  • Farzanegan, Mohammad Reza. (2019). “The Effects of International Sanctions on Military Spending of Iran: A Synthetic Control Analysis.” Organizations & Markets: Policies & Processes eJournal .
  • Gharehgozli, O. (2017). An estimation of the economic cost of recent sanctions on Iran using the synthetic control method. Economics Letters, 157, 141-144.
  • Gurvich E., Prilepskiy I. (2016). The impact of financial sanctions on the Russian economy.  Voprosy Ekonomiki. ;(1):5-35. (In Russ.) https://doi.org/10.32609/0042-8736-2016-1-5-35
  • Gutmann, J., Neuenkirch, M., and Neumeier, F., 2021. ”The Economic Effects of International Sanctions: An Event Study” CESifo Working Paper No. 9007
  • Kaempfer, W. H., & Lowenberg, A. D. (1988). The theory of international economic sanctions: A public choice approach. The American Economic Review, 78(4), 786-793.
  • Kholodilin, Konstantin A. and Netsunajev, Aleksei. (2016) Crimea and Punishment: The Impact of Sanctions on Russian and European Economies. DIW Berlin Discussion Paper No. 1569, SSRN: https://ssrn.com/abstract=2768622
  • Kwon, O., Syropoulos, C., & Yotov, Y. V. (2020). Pain and Gain: The Short-and Long-run Effects of Economic Sanctions on Growth. Manuscript.
  • Neuenkirch, M., & Neumeier, F. (2015). The impact of UN and US economic sanctions on GDP growth. European Journal of Political Economy, 40, 110-125.
  • Torbat, A. E. (2005). Impacts of the US trade and financial sanctions on Iran. World Economy, 28(3), 407-434.
  • World Bank. (2022). “War in the Region” Europe and Central Asia Economic Update (Spring), Washington, DC: World Bank.

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

What does the Gas Crisis Reveal About European Energy Security?

Posted on January 24, 2022 | Policy Brief

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The recent record-high gas prices have triggered legitimate concerns regarding the EU’s energy security, especially with dependence on natural gas from Russia. This brief discusses the historical and current risks associated with Russian gas imports. We argue that decreasing the reliance on Russian gas may not be feasible in the short-to-mid-run, especially with the EU’s goals of green transition and the electrification of the economy. To ensure the security of natural gas supply from Russia, the EU has to adopt the (long-proclaimed) coordinated energy policy strategy.

In the last six months, Europe has been hit by a natural gas crisis with a severe surge in prices. Politicians, industry representatives, and end-energy users voiced their discontent after a more than seven-fold price increase between May and December 2021 (see Figure 1). Even if gas prices somewhat stabilized this month (partly due to unusually warm weather), today, gas is four times as expensive as it was a year ago. This has already translated into an increase in electricity prices, and as a result, is also likely to have dramatic consequences for the cost and price of manufacturing goods.

Figure 1. Evolution of EU gas prices since Oct 2020.

Source:  https://tradingeconomics.com/commodity/eu-natural-gas.

These ever-high gas prices have triggered legitimate concerns regarding the security of gas supply to Europe, specifically, driven by the dependency on Russian gas imports. Around 90% of EU natural gas is imported from outside the EU, and Russia is the largest supplier. In 2020, Russia provided nearly 44% of all EU gas imports, more than twice the second-largest supplier, Norway (19.9%, see Eurostat). The concern about Russian gas dependency was exacerbated by the new underwater gas route project connecting Russia and the EU – Nord Stream 2. The opponents to this new route argued that it will not only increase the EU’s gas dependency but also Russia’s political influence in the EU and its bargaining power against Ukraine (see, e.g., FT). Former President of the European Council Donald Tusk stated that “from the perspective of EU interests, Nord Stream 2 is a bad project.”.

However, neither dependency nor controversial gas route projects are a new phenomenon, and the EU has implemented some measures to tackle these issues in the past. This brief looks at the current security of Russian gas supply through the lens of these historical developments. We provide a snapshot of the risks associated with Russian gas imports faced by the EU a decade ago. We then discuss whether different factors affecting the EU gas supply security have changed since (and to which extent it may have contributed to the current situation) and if decreasing dependence on Russian gas is feasible and cost-effective. We conclude by addressing the policy implications.

Security of Russian Gas Supply to the EU, an Old Problem Difficult to Tackle

Russia has been the main gas provider to the EU for a few decades, and for a while, this dependency has triggered concerns about gas supply security (see, e.g., Stern, 2002 or Lewis, New York Times, 1982). However, the problem with the security of Russian gas supplies was extending beyond the dependency on Russian gas per se. It was driven by a range of risk factors such as insufficient diversification of gas suppliers, low fungibility of natural gas supplies with a prevalence of pipeline gas delivery, or use of gas exports/transit as means to solve geopolitical problems.

This last point became especially prominent in the mid-to-late-2000s, during the “gas wars” between Russia and the gas transit countries Ukraine and Belarus. These wars led to shortages and even a complete halt of Russian gas delivery to some EU countries, showing how weak the security of the Russian gas supply to the EU was at that time.

Reacting to these “gas wars”, the EU attempted to tackle the issue with a revival of the “common energy policy” based on the “solidarity” and “speaking in one voice” principles. The EU wanted to adopt a “coherent approach in the energy relations with third countries and an internal coordination so that the EU and its Member States act together” (see, e.g., EC, 2011). However, this idea turned out to be challenging to implement, primarily because of one crucial contributor to the problem with the security of Russian gas supply – the sizable disbalance in Russian gas supply risk among the individual EU Member States.

Indeed, EU Member States had a different share of natural gas in their total energy consumption, highly uneven diversification of gas suppliers, and varying exposure to Russian gas. Several Eastern-European EU states (such as Bulgaria, Estonia, or Czech Republic) were importing their gas almost entirely from Russia; other EU Member States (such as Germany, Italy, or Belgium) had a diversified gas import portfolio; and a few EU states (e.g., Spain or Portugal) were not consuming any Russian gas at all. Russian natural gas was delivered via several routes (see Figure 2), and member states were using different transit routes and facing different transit-associated risks. These differences naturally led to misalignment of energy policy preferences across EU states, creating policy tensions and making it difficult to implement a common energy policy with “speaking in one voice” (see more on this issue in Le Coq and Paltseva, 2009 and 2012).

Figure 2. Gas pipeline in Europe.

Source: S&G Platt. https://www.spglobal.com/platts/en/market-insights/blogs/natural-gas/010720-so-close-nord-stream-2-gas-link-completion-trips-at-last-hurdle

The introduction of Nord Stream 1 in 2011 is an excellent example of the problem’s complexity. This new gas transit route from Russia increased the reliability of Russian gas supply for EU countries connected to this route (like Germany or France), as they were able to better diversify the transit of their imports from Russia and be less exposed to transit risks. The “Nord Stream” countries (i.e., countries connected to this route) were then willing to push politically and economically for this new project. Le Coq and Paltseva (2012) show, however, that countries unconnected to this new route while simultaneously sharing existing, “older” routes with “Nord Stream” countries would experience a decrease in their gas supply security. The reason for this is that the “directly connected” countries would now be less interested in exerting “common” political pressure to secure gas supplies along the “old” routes.

This is not to say that the EU did not learn from the above lessons. While the “speaking in one voice” energy policy initiative was not entirely successful, the EU has implemented a range of actions to cope with the risks of the security of gas supply from Russia. The next section explains how the situation is has changed since, outlining both the progress made by the EU and the newly arising risk factors.

Security of Russian Gas Supply to the EU, a Current Problem Partially Addressed

Since the end of the 2000s, the EU implemented a few changes that have positively affected the security of gas supply from Russia.

First, the EU put a significant effort into developing the internal gas market, altering both the physical infrastructure and the gas market organization. The EU updated and extended the internal gas network and introduced the wide-scale possibility of utilizing reverse flow, effectively allowing gas pipelines to be bi- rather than uni-directional. These actions improved the gas interconnections between the EU states (and other countries), thereby making potential disruptions along a particular gas transit route less damaging and diminishing the asymmetry of exposure to route-specific gas transit risks among the EU members. Ukraine’s gas import situation is a good illustration of the effect of reverse flow. Ukraine does not directly import Russian gas since 2016, mainly from Slovakia (64%), Hungary (26%), and Poland (10%) (see https://www.enerdata.net/publications/daily-energy-news/ukraine-launches-virtual-gas-reverse-flow-slovakia.html). The transformation of the gas market organization brought about the implementation of a natural gas hub in Europe and change in the mechanism of gas price formation. It is now possible to buy and sell natural gas via long-term contracts and on the spot market. With the gas market becoming more liquid, it became easier to prevent the gas supply disruption threat.

Second, Europe has made certain progress in diversifying its gas exports. According to Komlev (2021), the concentration of EU gas imports from outside of the EU (excluding Norway), as measured by the Herfindahl-Hirschman index, has decreased by around 25% between 2016 and 2020. While the imports are still highly concentrated, with the HHI equal to 3120 in 2020, this is a significant achievement. A large part of this diversification effort is the dramatic increase in the share of liquified natural gas (i.e., LNG) in its gas imports – in 2020, a fair quarter of the EU gas imports came in the form of LNG. An expanded capacity for LNG liquefaction and better fungibility of LNG would facilitate backup opportunities in the case of Russian gas supply risks and improve the diversification of the EU gas imports, thereby increasing the security of natural gas supply.

However, the above developments also have certain disadvantages, which became especially prominent during the ongoing gas crisis. For example, the fungibility of LNG has a reverse side: LNG supplies respond to variations in gas market prices across the world. This change has intensified the competition on the demand side – Europe and Asia might now compete for the same LNG. This is likely to make a secure supply of LNG – e.g., as a backup in the case of a gas supply default or as a diversification device – a costly option.

In turn, new mechanisms of gas price formation in Europe included decoupling the oil and gas prices and changing the format of long-term gas contracts. The percentage of oil-linked contracts in gas imports to the EU dropped from 47% in 2016 to 26% in 2020. In particular, 87% of Gazprom’s long-term contracts in 2020 were linked to spot and forward gas prices and only around 13% to oil prices (Komlev, 2021). This gas-on-gas linking may have contributed to the current gas crisis: Indeed, it undermined the economic incentives of Gazprom to supply more gas to the EU spot market in the current high-price market. Shipping more gas would lower spot prices and prices of hub-linked longer-term contracts for Gazprom. In that sense, the ongoing decline in Russian gas supplies to the EU may reflect not (only) geopolitical considerations but economic optimization.

Similarly, this new mechanism also finds reflection in the ongoing situation with the EU gas storage. The current EU storage capacity is 117 bcm, or almost 20% of its yearly consumption, and thus, can in principle be effective in managing the short-term volume and price shocks. However, the current gas crisis has shown that this option might be far from sufficient in the case of a gas shortage (see, e.g., Zachmann et al., 2021).  One of the reasons for this insufficiency can be Gazprom controlling a sizable share of this storage capacity (see https://www.europarl.europa.eu/doceo/document/E-9-2021-004781_EN.html). For example, Gazprom owns (directly and indirectly) almost one-third of all gas storage in Germany, Austria, and the Netherlands.  Combining this storage market position with a long-term gas contract structure may also lead to strategic behavior for economic (on top of potential political) purposes.

Last but not least, the EU gas market is likely to be characterized by increased demand due to the green transition agenda (see Olofsgård and Strömberg, 2022). Being the least carbon-intensive fossil fuel, natural gas has an important role in facilitating green transition and increasing the electrification of the economy. For example, Le Coq et al. (2018) argues that gas capacity should be around 3 to 4 times the current capacity by 2050 for full electrification of transport and heating in France, Germany, or the Netherlands. In such circumstances, the EU is not likely to have the luxury to diminish reliance on Russian gas.

Conclusions and Policy Implications

Keeping the above discussion in mind, should the EU try to diminish its dependence on Russian gas to improve its energy security? This may be true in theory, but in practice, this might be too costly, at least in the short-to-medium run.

The current situation on the EU gas market suggests that simply cutting gas imports from Russia is likely to lead to high prices both in the energy sector and, later, in other sectors of the economy due to spillovers. Substituting gas imports from Russia with gas from other sources, such as LNG, is likely to be very costly and not necessarily very reliable. Alternative measures, e.g., improving interconnections between the EU Member States or controlling transit issues via the use of reverse flow technology, are effective but have limited impact. Simply cutting down gas demand is not a viable strategy. Indeed, with the EU pushing for a green transition and the electrification of the economy, the EU’s gas imports may have to increase. Russian gas may play an important role in this process.

As a result, we believe that the solution to keep the security issue of Russian gas supply at bay lies in the area of common energy policy. It is essential that the EU implements and effectively manages a coordinated approach in dealing with Russian gas supplies. The EU is the largest buyer of Russian gas, and given Russian dependency on hydrocarbon exports, such a synchronized approach would give the EU the possibility to exploit its “large buyer” power. While the asymmetry in exposure to Russian gas supply risks among the EU Member States is still sizable, the improvements in the functioning of the internal gas market and gas transportation within the EU make their preferences more aligned, and a common policy vector more feasible. Furthermore, recent EU initiatives on creating “strategic gas reserves” by making the Member States share their gas storage with one another would further facilitate such coordination. Implementing the “speaking in one voice” gas import policy will allow the EU to fully utilize its bargaining power vis-à-vis Gazprom and spread the benefits of new gas routes from Russia – such as Nord Stream 2 – across its Member States.

References

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

Social Distancing and Ethnic Diversity

Posted on December 14, 2021 | Policy Brief

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Voluntary social distancing plays a vital role in containing the spread of the disease during a pandemic. As a public good, it should be more commonplace in more homogeneous and altruistic societies. For healthy people, social distancing offers private benefits, too. If sick people are more likely to stay home, healthy ones have fewer incentives to do so, especially if asymptomatic transmission is perceived to be unlikely. This interplay may lead to a stricter observance of social distancing guidelines in more diverse, less altruistic societies. Consistent with this prediction, we find that mobility reduction following the first local case of COVID-19 was stronger in Russian cities with higher ethnic fractionalization and cities with higher levels of xenophobia and we confirm that mobility reduction in the United States was also higher in counties with higher ethnic fractionalization. Our findings highlight the importance of creating strategic incentives for different population groups in crafting effective public policy.

During the COVID-19 pandemic, governments in almost all affected countries have imposed restrictions aimed at promoting social distancing. However, enforcing these restrictions is logistically and politically costly. The effectiveness of these measures depends heavily on people voluntarily observing social distancing guidelines. The conventional wisdom is that informal social norms are more difficult to sustain in ethnically diverse societies (Alesina and La Ferrara, 2000; Algan et al., 2016). In Egorov et al. (2021), we challenge this notion by showing that during the COVID-19 pandemic ethnic diversity has increased prosocial behavior in Russia and the United States.

At least at the beginning of the pandemic, most people considered themselves healthy. For them, the decision to stay home has been driven more by the fear of getting infected than by the desire to avoid infecting others. The likelihood of getting infected is higher if sick people cannot be expected to self-isolate, which, in turn, depends on their prosocial considerations. If people are subject to out-group biases and care less about people from other groups, then the sick are less likely to engage in social distancing in more diverse places. This makes people who consider themselves healthy more likely to self-isolate. Since healthy people constitute a majority, at least in the early stages of a pandemic, we expect to see more social distancing in more diverse societies. Generally, in these circumstances, the private benefits of those who consider themselves healthy align with social objectives.

In Egorov et al. (2021) we formalize this argument and provide causal evidence of the differential decline in social distancing based on ethnic diversity in Russia and the United States.

Method

Our theory predicts that people engage in social distancing more in places with higher ethnic fractionalization when the probability of getting infected becomes nontrivial. To test this prediction empirically, we use two approaches. First, we report difference-in-differences estimates, where we compare cities with higher and lower levels of ethnic fractionalization before and after the first reported case of COVID-19 infection in their region. Second, we combine the difference-in-differences approach with a two-stage least-squares approach, in which the timing of the first reported case is instrumented using measures of preexisting migration.

One potential concern with the first approach is that the timing of the first case is not fully random. For example, regions could report late COVID-19 cases because their medical capacity precluded them from correctly identifying the virus in time, or because their testing policies could be ineffective, or because their administration was prone to conceal the first cases for a longer time. To deal with these potential confounds in the first approach we use predicted timing of the first case. Specifically, we use the fact that travel connections between various cities and Moscow (where the first major outbreak occurred) could affect the timing of the first case in those cities’ respective regions. We rely on internal migration as a proxy for these types of connections (Mikhailova and Valsecchi, 2020; Valsecchi and Durante, forthcoming) and use a shift-share instrument for internal cross-regional migration to deal with the endogeneity of migration.

Data and Results

To measure social distancing, we use data on people’s movements provided by Russia’s largest technology company, Yandex, which tracks individuals’ cell phones with its mobile apps. In particular, we use daily averages of the Yandex Isolation Index, which aggregates data on people’s movements at the city level and is analogous to the Google Mobility Index. The index is calibrated for each city to be 0 for the busiest hour of the working day, and 5 for the quietest hour of the night before the coronavirus outbreak. We use daily data for 302 cities with a population over 50,000 from February 23, 2020, through April 21, 2020.

Information on the first reported case of COVID-19 in each region is taken from the government-agency website that contains official information about the pandemic. Data on ethnic fractionalization is based on the 2010 Census. Information on interregional migration and control variables comes from the Russian Federal State Statistics Service.

Figure 1. Isolation Over Time for Places with High and Low Ethnic Fractionalization

Source: Egorov et al. (2021)

Figure 1 shows no visible difference in the behavior of people in cities with low and high levels of ethnic fractionalization before the first coronavirus case. In both groups of cities, people have engaged in more social distancing since the discovery of the first case. However, after one week, people in more fractionalized cities have been more likely to stay home than people in less fractionalized cities. The effect does not manifest itself immediately after the discovery of the first case, which likely reflects the fact that a certain time is needed to disseminate information about the discovery of the coronavirus in the region. Moreover, the growth in self-isolation in more fractionalized cities is somewhat lower in the first days after the discovery of the first case, which may be driven by people catching up on unfinished tasks that require mobility, such as last-minute purchases, in anticipation of more stringent self-isolation in the future.

The results of the difference-in-differences and IV estimation confirm the results of the visual analysis. The magnitudes of the IV estimation imply that a one-standard-deviation increase in ethnic fractionalization leads to 3.7% higher social distancing following the report of the first local COVID-19 case. In other words, a one-standard-deviation increase in ethnic fractionalization can explain 5.7% of the average mobility reduction after the report of the first case or, alternatively, 4.7% of the weekday-weekend gap for an average locality.

To make sure that the results are not Russia- specific, we also show that ethnic fractionalization led to a bigger reduction in mobility following the first local COVID-19 case using the United States county-level data.

Conclusion

Overall, the results in Egorov et al. (2021) highlight the role of ethnic diversity in voluntary adherence to socially beneficial norms, such as self-isolation and social distancing during a pandemic. We show that people in more diverse places were more likely to restrict their mobility following the reports of the first local COVID-19 cases.

Our study has important implications for government policy. It highlights not only that the propensity of different groups of people to engage in prosocial behavior may differ but also that there may be important strategic effects. In the context of the pandemic, decisions by healthy and sick individuals to self-isolate are strategic substitutes. This means, for example, that in a homogeneous society with high levels of tolerance, extensive testing would allow people to learn that they are sick and self-isolate, enabling the rest to go out with little fear. In a heterogeneous society with low levels of tolerance, the same policy may spur people who learn that they are contagious to go out more because they have little to lose, with the exact opposite implications for the healthy population.

References

  • Alesina, A., La Ferrara, E., 2000. Participation in heterogeneous communities. Quarterly Journal of Economics. 115, 847–904.
  • Algan, Y., Hémet, C., Laitin, D.D., 2016. The social effects of ethnic diversity at the local level: a natural experiment with exogenous residential allocation. Journal of Political Economics. 124, 696–733.
  • Egorov, G., Enikolopov, R., A., Makarin, and M. Petrova. 2021. Divided We Stay Home: Social Distancing and Ethnic Diversity” Journal of Public Economics. 194: 104328.
  • Mikhailova, T., Valsecchi, M., 2020. Internal migration and Covid-19 (in Russian). In: Economic Policy in Times of Covid-19, New Economic School, pp. 26–33.
  • Valsecchi, M., Durante, R., forthcoming. Internal Migration Networks And Mortality In Home Communities: Evidence From Italy During The Covid-19 Pandemic. European Economic Review.

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

Russian Exporters in the Face of the COVID-19 Pandemic Crisis

Posted on November 8, 2021 | Policy Brief

20211108 Russian Exporters in the Face of the COVID-19 Image 01

This brief summarizes the results of recent work on the effects of the COVID-19 pandemic on Russian exporting companies (Volchkova, 2021).  We use data from the CEFIR NES survey of exporters conducted in 2020. 72% of respondents reported that they were affected by the crisis. We scrutinize this impact. Contrary to popular wisdom, we observe little difference in delays of inputs by domestic and foreign suppliers. On the other hand, exporters experienced more disruptions in their sales in foreign destinations than in the domestic market. Possible reasons for this may be due to restrictions on international travel.

Introduction

According to experts at the Gaidar Institute (Knobel, Firanchuk, 2021), in 2020, Russia’s non-resource non-energy exports, decreased by 4.3%, while export prices fell by 4.1 % on average. The export of high-tech goods decreased by 14% due to a reduction in the physical volume of export. These changes in export intensity are mainly associated with the COVID-19 pandemic crisis. But are exporting firms more affected by the crisis than firms only active in the domestic market? What are the main channels through which the crisis influenced exporters?  And how do exporters adjust to the COVID-19 related shocks?

The analysis in this brief is based on forthcoming publication in the Journal of New Economic Association (Volchkova, 2021). We use data from a survey of Russian non-resource exporters conducted in 2020. We show that involvement in international trade did not affect the company’s vulnerability to the crisis on the production side: supply delays were equally likely to occur from domestic and foreign suppliers. These findings are consistent with Bonadio et al. (2021) who consider a numerical multi-sectoral model for 64 countries around the world linked by supply chains. They show that, in the face of the employment shocks associated with quarantine measures and switching to a remote work format, the contribution of global chains to the decline of real GDP is about one quarter. Importantly, the authors show that the “re-nationalization” of supply chains does not make countries more resilient to shocks associated with quarantine measures on the labor market because these shocks are also bad for domestic industries.

At the same time, our results indicate that exporting companies are exposed to additional risks associated with the need to adjust to shocks in the sales markets. According to the data, exporters find it more difficult to adjust their sales in foreign markets than in the domestic one. This is consistent with the fact that, during the pandemic, all countries introduced a strict ban on international travel, reducing the possibility of establishing new business ties through personal contacts. Similarly, Benzi et al. (2020) show a significant negative effect of international travel restrictions on the export of services.

Survey of Non-resource Exporters

The survey of exporters was carried out in June – November 2020 by CEFIR NES. The primary purpose of the survey was to identify and estimate barriers to the export of non-primary non-energy products. In the context of the developing economic crisis caused by the COVID-19 pandemic, we have added several questions to identify how the crisis influenced companies’ operations and how the respondent firms adjusted to the new conditions.

The survey was conducted using a representative sample of Russian exporting firms. As a control group, we interviewed non-exporting firms with (observable) characteristics (region, industry, labor productivity) similar to those of the surveyed exporters. Altogether, 928 exporting companies and 344 non-exporting companies were interviewed during the field stage of the study.

Most exporting companies that took part in the survey produce food products, chemicals, machinery and equipment, electrical equipment, metal products, and timber. On average, a surveyed exporter had 827 full-time employees; 25% of the firms had fewer than 26 employees. More than half of the surveyed exporting firms (53%) are also importers: 81% import raw materials and other inputs, 66% import equipment, and 22% import technology. Most interviewed exporters sell their products both abroad and on the domestic market. On average, an enterprise supplies 67% of its output to the domestic market and 32% abroad.

Impact of the COVID-19 Crisis on Firms’ Performance

Among exporters that participated in the survey, 25% reported that their business was not affected by the COVID-19 crisis, while 72% of respondents stated that the crisis did have an impact. Like any crisis, the COVID-19 pandemic created problems for some enterprises and provided new beneficial opportunities for others. According to the data, exporting businesses were significantly more likely to be negatively affected by the crisis than their non-exporting counterparts, and the impact of the crisis was not correlated with the size of the enterprise. Figure 1 presents the exporters’ answers to the question of how their sales in the domestic and foreign markets have changed with the COVID-19 pandemic.

The distribution of changes in sales volume in domestic and foreign markets significantly differ from each other. Estimates of the mean values of changes in sales volumes also differ significantly: the average drop in sales in the domestic market was 5%, while for the external market, it reached 17%. Hence, in times of the COVID-19 crisis, opportunities for growth were less prominent in foreign markets than in the domestic one, while significant market losses were more frequent.

Figure 1. Change in sales of export companies associated with the COVID-19 pandemic

Source: Survey of non-resource exporters, CEFIR NES, 2020.

Adjustment to the Crisis

The most frequently used crisis adjustment measure was employees transition to remote work – it was reported by 70% of the surveyed companies. 25% of exporters were forced to suspend their work during the crisis, while 72% were not. 14% of respondents stated they had to cut their payroll expenditures and other non-monetary benefits for employees (food, insurance, etc.), 12% of companies sent workers on unpaid leave. Only 6.5% of export firms had to lay off workers, while 91% handled the crisis without layoffs.

Comparing exporters’ answers with those of non-exporters while controlling for enterprise size, we conclude that exporting firms were more rigid in their adjustment  to the crisis. They were significantly more likely to suspend enterprise activities, dismiss of employees, send workers on unpaid leave, and reduce of wages. Also, these events were more likely to occur for smaller companies than for larger ones.

At the same time, flexible adjustment measures such as remote work were equally likely to be used by exporters and non-exporters, as well as by firms of different sizes. In general, Russian exporters of non-primary goods maintained their efficiency mainly by adjusting the labor relations to the new epidemiological conditions rather than by reducing employee-related expenses.

Dealing with Counterparties

Delays in the supply of components and raw materials were reported by 36% of the surveyed companies, and such delays were equally likely for shipments from abroad and domestic shipments. There is a perception that international supply chains in the context of the pandemic crisis are an additional risk factor. Our results indicate that domestic and international supply chains were equally challenged in 2020. Nevertheless, non-exporting companies faced the problem of delayed deliveries significantly less often than exporters did, and about 60% of companies experienced no problems at all on the input supply side.

27% of surveyed exporters stated that they delayed payments to counterparties. Non-exporting companies reported these reactions much less frequently regardless of firm size.

On the sales side, half of the surveyed exporters experienced delays in payments from their customers during the pandemic crisis. Non-exporting enterprises encountered the problems with the same frequency, and companies of all sizes were affected by this obstacle equally.

The cases of planned purchases cancellation on behalf of buyers were reported by 34% of exporting companies. Exporters experienced these problems significantly more often than non-exporters, and smaller companies experienced them much more often than larger ones.

Crossing international borders presented a certain problem for Russian exporters when it concerns product delivery. Just over half of the respondents indicated that they had to delay deliveries due to difficulties with border crossing. However, about the same share of companies (48%) reported that they delayed products delivery due to the introduction of lockdowns. Thus, during the COVID-19 pandemic, exporters’ operations were complicated to the same extent by problems related to border crossings as by those associated with lockdown regimes.

Conclusion

It is widely believed that international exposure of companies in the context of the COVID-19 pandemic crisis creates additional risks. Our study shows that, regarding existing inputs supply, international relations pose problems for Russian companies just as often as relations with domestic partners. As far as sales are concerned, adjustment to the crisis was better on the domestic market than on foreign markets. A possible explanation of this phenomenon is that, in addition to the shocks associated with quarantine measures in the labor market, access to foreign markets was hampered by restrictions on international travel, which is essential for readjusting contractual relations to explore new opportunities brought by crises (Cristea, 2011). Without personal interaction, new contracts were more difficult to launch. Thus firms’ opportunities to adjust foreign sales were more restricted than the ones in the domestic market.

References

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

Media mentions: Key takeaways from this policy brief have been published by one of the most influential media outlets in Russia Kommersant – Коммерсант: «Ковид сильнее ударил по экспортерам». Исследование ЦЭФИР РЭШ. 

Does the Russian Stock Market Care About Navalny?

Posted on March 29, 2021 | Policy Brief

Moscow citi in the sunset representing Russian stock market and Navalny

Alexei Navalny is the most prominent opposition leader in Russia today. During 2020, he entered not only the domestic Russian news flows, but was a major news story around the world following his horrific Novichok poisoning in August. This brief investigates the response in the Russian stock market to news about Navalny. For many significant Navalny news stories, the stock market experienced large negative returns that are not explained by the regular factors that move the market. Although the causality and permanency of these negative excess returns in the stock market are difficult to pin down completely, a first look at the numbers suggests that the short-run drops in the stock market on the days with most significant news regarding Navalny translates into several billion dollars in lost market value on the Russian stock market. In other words, for people that care about their stock market investments and the health of the Russian economy more generally, it makes a lot of sense to care about the health of Navalny.

Introduction

Alexei Navalny has become the leading political opponent to the current regime in Russia. His visibility (and possibly support) has been growing as he has endured poisoning, recovery in hospital, and court rulings that have imposed a harsh prison term. At the same time, Navalny and his team have posted new material online to make his case that both the president and other Russian leaders are seriously corrupt.

The question addressed in this brief is whether the news regarding Navalny affected the Russian stock market. The reasons for such a response may vary between different investors but could include a fear of international sanctions against Russia; an aversion to keeping investments in a country that put a nerve agent in the underwear of a leading opposition leader; or that news of a national security service poisoning one of its own citizens could trigger domestic protests that create instability.

This brief only investigates if Navalny-related news or events are taken into account at the macro level in the stock market and if so, how important the news seem to be relative to other news as drivers of the stock market index. However, there is a long list of related questions that are subjects for upcoming briefs that include differential effects across sectors and companies as well as identifying what dimensions of the news stories investors responded to.

Navalny in the News

Since August 2020, news regarding Alexei Navalny’s health and his role as the most important opposition leader in Russia have featured prominently in media around the world. There are different ways to analyze the significance of Navalny in the news and here the readily available measure provided by Google trends will be used. Figure 1 shows a global search on the keyword “Navalny” over the period July 1, 2020 to March 13, 2021 relative to total searches, where the maximum level in the period is normalized to 100 and other values are scaled to this. While the numbers on the graph are just relative measures, not telling much about the actual popularity, or market relevance of searches, the spikes in Figure 1 have very clear connections to major news stories as will be detailed below.

Figure 1. Google trends on Navalny

Source: Google trends, global search on Navalny on 2021-03-18

Four episodes stand out in Figure 1 and are marked by red numbers:

1 (August 20-25, 2020) is associated with Navalny falling ill on the flight from Tomsk to Moscow which led to an emergency landing in Omsk and then going to Germany for specialist treatment where it was stated that he had been poisoned.

2 (September 2-3, 2020) is when the German government said that the poison Navalny was exposed to was Novichok, which was also confirmed by laboratories in Sweden and France.

3 (January 17-25, 2021) is an extended period covering the arrest of Navalny as he returned to Russia on January 17; the publication of the YouTube video on “Putin’s palace”; and the street protests that followed.

4 (January 31-February 5) is a period covering a new weekend of public protests and then on February 2, Navalny being sentenced to prison for not complying with parole rules when he was in a coma in Germany. At the tail end of this period, Navalny’s chief of staff announced that street protests will be suspended due to thousands of arrests and police beatings.

Russian Stock Market Reactions

Using stock markets to investigate the value of political news is not new; for example, Fisman (2001) looks at how news regarding Suharto’s health differentially impacted firms that were connected to Suharto versus those that were not. On a topic more closely related to this brief, Enikolopov, Petrova, and Sonin (2018), show that Navalny’s blog posts on corruption negatively affect share prices for the exposed state-controlled companies. Looking at the overall stock market index rather than individual shares in Russia, Becker (2019) analyzes stock market reactions to Russia invading Crimea.

To get a stock market valuation effect of Navalny news that is as clean as possible, we need to filter out other factors that are known to be important drivers of the stock market. In the case of Russia’s dollar denominated stock market index RTS (short for Russia Trading System), we know from Becker (2019) that it is sensitive to movements in global stock markets and international oil prices. The former factor is in line with other stock markets around the world and the oil dependence of the Russian economy makes oil prices a natural second factor (see Becker, 2016).

Figure 2 shows how the RTS index moves with the global markets (proxied by S&P 500 index) and (Brent) oil prices in this period. The correlations of returns are around 0.4 between the RTS and both S&P500 and oil prices respectively. This figure is also the answer to the obvious argument that the stock market was doing very well in the time period of Navalny in the news, so he could not be a major concern to investors. As we will show below, this argument goes away when the effects of the exogenous factors are removed.

To filter out these exogenous factors, we follow the approach in Becker (2019) and regress daily returns on the RTS on daily returns of the exogenous variables. We then compute the residuals from the estimation to arrive at the excess returns that are utilized in the subsequent analysis. For more details on this, see Becker (2020). Since the estimated model provides the foundation for the subsequent analysis, it is important to note that all of the coefficients are statistically significant, and that results are robust to changes in the estimation period and exclusion of lagged values of the exogenous variables.

Figure 2. RTS and exogenous factors

Source: Data on RTS from the Moscow Exchange (MOEX), S&P500 from Nasdaq, and Brent oil prices from the US energy information administration.

With a time-series of excess returns for the Russian stock market, we can look at the stock market reactions to the four Navalny episodes identified in Figure 1. These periods cover some days for which we cannot compute excess returns since there are days when there is no trading, but all dates in the period are shown in Figure 3 to provide a full account of what stock market data we have for the events. In addition to excess returns during the events that are shown in blue, the day before and the day after the events are shown in light grey. In the first three episodes, the cumulative returns during the events windows were minus 6.2, minus 2.4, minus 6.0 percent, while in the fourth event window it was plus 0.8 (although in this period, the day after Navalny was sentenced to jail, the excess return was minus 1.7).

The correlations between news and excess returns in this brief are based on daily data. Since many things can happen during a day, the analysis is not as precise as in the paper by Enikolopov, Petrova, and Sonin (2018), where the authors claim that causality is proven by the minute by minute data. Although we have to be more modest in claiming that we have identified a causal relationship going from Navalny news to negative stock market returns, the daily data used here provides enough evidence to claim that there is a strong association pointing in this direction. If we take all four events and translate the cumulative excess returns in percent (which is 14) into dollars by using the market capitalization on the RTS at the time of the events (on average around 200 billion dollars), this amounts to a combined loss in market value of over 27 billion dollars.

Figure 3. Excess returns and Navalny news

Source: Excess returns from author’s calculations based on data from the Moscow Exchange (MOEX), Nasdaq, and the US energy information administration. The chart indicates days for which we cannot compute excess returns since not all days are trading days.

We may think that excess returns of this magnitude are common and that what we pick up for the four Navalny episodes are regular events in the market. To investigate this and other potential factors that have been important to explain excess returns in this time period, Table 1 provides a list of all the days when the excess return in the market was minus 2 percent or worse. Between August 2020 and mid-March 2021, there were eight such days. The table also shows what could be an associated Navalny event on or close to those dates as well as other competing factors or news that could explain the large negative returns on these days.

Out of 8 days with strong negative returns, the first three days are very clearly associated with major news regarding the poisoning of Navalny. The fourth day is close to Navalny’s release from the hospital but also when there are discussions about U.S. views on Iran and Ukraine. Two of the days are in the time period of the protests following Navalny’s video on “Putin’s palace” and two more days are related to important international institutions speaking out regarding first the poisoning with Novichok and then about the prison term of Navalny.

Although we would need a more fine-grained look at market data to make a final judgment on the most important drivers of the excess returns of a specific day, the fact that every single day with large negative excess returns is on or close to a Navalny news story is again pointing in the direction of a stock market that reacts to news about Navalny. Furthermore, the most significant drops with less competing news are associated with events that have a direct connection to Navalny’s health and how his life was put in danger. In the list of competing news are Nord Stream, Biden affecting the oil and gas industry, and a law regarding the taxation of digital currencies. They are likely to be of at least some relevance for stock market valuations and could account for certain days or shares of poor performance of the RTS, but it is hard to ignore the general impression of Navalny being important for the stock market in this period.

Table 1. Days with RTS excess returns of minus 2% or worse (August 1, 2020 to March 12, 2021)

Source: Excess returns from author’s calculations based on data from the Moscow Exchange (MOEX), Nasdaq, and the US energy information administration. News comes from internet searches on Navalny and relevant dates.

Conclusions

Although it is difficult to prove causality and rule out all competing explanations, this investigation has shown a strong association between major news regarding Navalny and very poor performance of the Russian stock market. Every day since August 2020 that had excess returns of minus 2 percent or worse is more or less closely associated with significant news on Navalny. More than that, almost all days with significant Navalny news during this period, – as captured by high search intensity of Navalny on Google, – are associated with a poorly performing stock market. In particular, this holds for the day of his poisoning and the following days with comments by international doctors, politicians, and institutions regarding the use of Novichok to this end.

It could be noted that a 1 percent decline in the RTS equates to a loss in monetary terms of around 2 billion USD in this time period since the market capitalization of the RTS index was on average around 200 billion USD. The combined decline in the events shown in Figure 3 is 14 percent and for the days listed in Table 1, it is 21 percent, i.e., corresponding to market losses of somewhere between 28 and 42 billion USD. Even if only a fraction of this would be directly associated with news on Navalny, it adds up to very significant sums that some investors have lost. One may argue that the losses are only temporary and recovered within a short time period (which would still need to be proven), but for the investors that sold assets on those particular days, this is of little comfort. At a minimum, events like these contribute to increased volatility in the market that in turn has a negative effect on capital flows, investments, and ultimately economic growth (Becker, 2019 and 2020). For anyone caring about the health of their own investments or the Russian economy, it makes sense to care about the health of Navalny.

References

  • Becker, Torbjörn, 2016. “Russia and Oil — Out of Control”, FREE policy brief.
  • Becker, Torbjörn, 2019. “Russia’s Real Cost of Crimean Uncertainty”, FREE policy brief, June 10.
  • Becker, Torbjörn, 2020. “Russia’s macroeconomy—a closer look at growth, investment, and uncertainty”, Ch 2 in Putin’s Russia: Economy, Defence And Foreign Policy, ed. Steven Rosefielde, Scientific Press: Singapore.
  • Enikolopov, Ruben, Maria Petrova, and Konstantin Sonin, 2018, “Social Media and Corruption”, American Economic Journals: Applied Economics, 10(1): 150-174.
  • Fisman, Raymond, 2001, “Estimating the Value of Political Connections.” American Economic Review, 91 (4): 1095-1102.
  • Google trends data.
  • Moscow Exchange (MOEX), RTS index data.
  • Nasdaq, S&P 500 data.
  • U.S. Energy Information Administration, 2021, data on Brent oil prices.

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

Understanding Russia’s GDP Numbers in the COVID-19 Crisis

Posted on March 9, 2021 | Policy Brief

20210308 Understanding Russia GDP Numbers FREE Network Policy Brief Image 02

Russia’s real GDP fell by a modest 3 percent in 2020. The question addressed here is how a major oil-exporting country can go through the COVID-19 pandemic with a decline of this magnitude when oil prices fell by 35 percent at the same time as the domestic economy suffered from lock-downs. The short answer is that it is mainly a statistical mirage. The aggregate real GDP decline would have been almost three times greater than in the official statistics if changes in exports were computed in a way that better reflects their value. In particular, the real GDP calculation uses changes in volumes rather than values to omit inflation, but for exports, it thus ignores large changes in international oil prices. In the end, what the government, companies, and people in Russia can spend is much more closely related to how much money is earned on its exports than how many barrels of oil the country has sold to the rest of the world. More generally, this means that real GDP growth in Russia is not a very useful statistic in years with large changes in oil prices, as was the case in 2020, since it does not properly reflect changes in real income or spending power. When policymakers, journalists, and scholars now start to compare economic developments across countries in the covid-19 pandemic, this is something to bear in mind.

Introduction

The world is closing the books on 2020 and it is time to take stock of the damage done by the COVID-19 pandemic thus far. A year into the pandemic, over 100 million cases have been confirmed and almost 2.5 million people have died worldwide according to ECDC (2021) statistics. Russia has not been spared and Rosstat reported 4 million infected and over 160 000 dead in 2020.

Human suffering in terms of lost health and lives is certainly the main concern in the pandemic, but on top of that comes the damage done to economies around the world. Falling incomes, lost jobs, closed businesses, and sub-par schooling will create significant health and other problems even in a fully vaccinated world for years to come.

Understanding how real GDP has fared in the crisis does not capture all of these aspects, but some. With the IMF’s latest World Economic Outlook update on economic performance out in January 2021, it is easy to start comparing GDP growth across countries (IMF, 2021). GDP growth is a standard measure of past performances in general, but the numbers for 2020 may also enter various domestic and international policy discussions of what does and does not work in protecting economies in the pandemic. For countries that seem to have fared better than their peers, the growth numbers are likely going to be used by incumbent politicians to boost their ratings or by consumers and business leaders making plans for the future.

In short, real GDP numbers are important to most economic and political actors, domestically and globally, with or without a crisis unfolding. It is therefore important to understand how Russia, a major oil exporter with significant losses of lives and incomes in the pandemic, could report a real GDP decline of only 3 percent in 2020 (Rosstat, 2021). Although this is not far from the global average reported by the IMF (2021), it is far better than the 7.2 percent drop in the Euro area, 10 percent fall in the UK, or 7.5 to 8 percent declines of its BRICS peers, South Africa and India. This brief provides the details to understand that Russia’s performance is more of a statistical artifact than a fundamental reflection of the health of the Russian economy.

Oil prices, GDP growth, and the ruble

Russia’s dependence on exporting oil and other natural resources is well documented (see for example Becker, 2016a and 2016b) and often discussed by Russian policymakers and pundits. In particular, changing international oil prices is a key determinant of growth in the Russian economy. Even if the level of real GDP disconnected from oil prices somewhere between 2009 and 2014 (Figure 1), the link between real GDP growth and changes in oil prices persists (Figure 2).

Figure 1. Russia real GDP and oil prices

Source: Author’s calculations based on U.S. Energy Information Administration and Rosstat.

The empirical regularity that still holds is that, on average, a 10 percent increase (decline) in oil prices leads to around 1.4 percent real GDP growth (fall), see Becker (2016a). With a 35 percent decline in oil prices in 2020, this alone would lead to a drop in GDP of around 5 percent.

Figure 2. GDP growth and oil price changes

Source: Author’s calculations based on U.S. Energy Information Administration and Rosstat.

One factor that has a fundamental impact on how the relationship between oil prices and different measures of GDP changes over time is the ruble exchange rate. For a long period, Russia had a fixed exchange rate regime with only occasional adjustments of the rate. A stable exchange rate was the nominal anchor that should instill confidence among consumers and investors. However, when changes in the oil prices were too significant, the exchange rate had to be adjusted to avoid a complete loss of foreign exchange reserves. This was evident in the 90’s with the crisis in 1998 and later in the global financial crisis in 2008/09. Eventually, this led to a flexible exchange rate regime and in 2014, Russia introduced a flexible exchange rate regime together with inflation targeting as many other countries had done before it.

As can be seen in Figure 3, this has important implications for how changes in international oil prices in dollars are translated into rubles. Note that the figure shows index values of the series that are set to 100 in the year 2000 so that values indicate changes from this initial level. Starting in 2011, but more prominently since 2014, the oil price in rubles has been at a significantly higher level compared to the oil price measured in dollars, which is of course due to the ruble depreciating. This affects the government’s budget as well as different measures of income in rubles. However, if oil prices in dollars change, this affects the real spending power of Russian entities compared with economic actors in other countries regardless of the exchange rate regime. Moving to a flexible exchange rate regime was inevitable and the right policy to ensure macroeconomic stability in Russia when oil prices went into free fall. Nevertheless, it does not change the fundamental economic fact that falling oil prices affect the real income of an oil-exporting country. It also makes it even more important to understand how real GDP is calculated.

Figure 3. Oil prices and exchange rate indices

Source: Author’s calculations based on U.S. Energy Information Administration and Central Bank of Russia.

The components of real GPD

GDP is an aggregate number that can be calculated from the income or expenditure side. The focus in this brief is on the expenditure side of GDP. The accounting identity at play is then that GDP is equal to private consumption plus government consumption plus investments (that can be divided into fixed capital investments plus change in inventories) plus exports minus imports (where exports minus imports is also called net exports). Being an accounting identity, it should add up perfectly but in the real world, components on both the income and expenditure sides are estimated and things do not always add up as expected. This generates a statistical discrepancy in empirical data.

Another important note on real GDP (rather than nominal GDP measured in current rubles) is that the focus is on how quantities change rather than prices or ruble values. The idea is of course to get rid of inflation and focus on, for example, how many refrigerators are consumed this year compared to last year and not if the price of refrigerators went up or down. This may sound obvious, but it comes with its own problems concerning implementation and interpretations. For Russia, real GDP becomes problematic because its main export is oil (gas and its related products). The price of oil is just one of many drivers of Russia’s inflation but is an extremely important driver of its export revenues and growth as has been discussed above. On top of that, oil prices are volatile and basically impossible to control for Russia or even the OPEC.

So why does this matter for understanding Russia’s real GDP growth in 2020? The answer lies in how the different components of real GDP are computed. To make this clear, the evolution of the components between 2019 and 2020 is shown in Table 1.   

Table 1. Russia’s GDP components from the expenditure side

Source: Author’s calculations based on data from Rosstat

In short, private consumption fell by close to 9 percent in 2020 compared to 2019; government consumption increased by 4 percent; gross fixed capital formation declined by 6 percent while inventories increased by 26 percent; exports lost 5 percent, but imports went down by 14 so that net exports showed an increase of 65 percent! To calculate the impact these changes have on aggregate GDP growth, we need to multiply with the share of GDP for a component to arrive at the impact on GDP growth in the final column of Table 1.

Although there are some issues to resolve with both government consumption and inventory buildup, to understand real GDP growth in 2020, it is crucial to understand what happened to exports and imports in real GDP data. First of all, how does this data compare with the balance of payments data that measures exports and imports in dollar terms or the data that show the value of exports of oil, gas, and related products? Table 2 makes it clear that the numbers do not compare at all! Again, this is due to real GDP numbers being based on changes in volumes rather than values while the trade date reports values in dollars (that can be translated to rubles by using the market exchange rate).

In the real GDP statistics, net exports show growth of 66 percent in 2020, compared to declines of 37 to 44 percent if merchandise trade data is used. Going into more detail, real GDP data has exports declining by 5 percent, while other indicators fall by between 11 and 37 percent. It is similar with imports (that enter the GDP calculation with a negative sign); the import decline recorded in real GDP is 14 percent, while trade data suggest a 6 percent decline in dollar terms but an increase of 7 percent in nominal ruble terms.

Table 2. Trade statistics

Source: Author’s calculations based on Rosstat, Central Bank of Russia and BOFIT

What would it mean if we use some of these alternative growth rates for exports and imports (while keeping other components in line with official statistics) to calculate aggregate GDP growth in 2020? The rationale for keeping other components unchanged is that this provides a first-round effect of changing trade numbers on real GDP growth.

To make this calculation, the GDP shares of exports and imports (or net exports) in 2019 are needed. Table 1 shows that these numbers are 27 and 24 percent (or a net 3 percent) of total GDP. Multiplying the share of a GDP component with its growth rate gives the contribution of the component to overall GDP growth. The calculations based on different trade data are shown in Table 3. The last line of the table is what GDP growth would have been with these alternative trade data. Note that the real GDP growth number is -2.9 percent when we use the individual components of GDP decomposition (rather than the official headline number -3.1 real GDP growth when using aggregate GDP) so this is shown here to make the table consistent with the alternative calculations. In the last column of Table 3, oil and gas exports are assumed to make up for half of exports and this number disregards changes in other exports or imports to isolate the effect of changes in the value of oil and gas exports from other changes.

The summary of this exercise is that with more meaningful trade data used in calculating GDP growth, Russia would have recorded a decline of around 9 percent rather than 3 percent. This is of course a partial analysis focusing on the trade part of real GDP since this effect is very striking. Other components of the calculation may also have issues that need to be adjusted to arrive at a more realistic growth number. Still, even the current estimate is not unrealistic. For example,  household consumption fell by around 9 percent, which would be consistent with a GDP decline of 9 percent that is not recovered in the future in a permanent income model.

Table 3. GDP growth contributions from alternative trade data

Source: Author’s calculations based on U.S. Energy Information Administration and Rosstat

Conclusions

Real GDP growth numbers are important to understand economic developments in a country and provide the foundation for many types of economic decisions. The numbers are also used to compare the economic performance of different countries and evaluate policy responses in the COVID-19 pandemic we are currently part of.

The problem with Russia’s reported growth of minus 3 percent is not that the real GDP calculation is wrong per se, but it is clearly the wrong metrics to use for understanding how incomes and purchasing powers of Russian households, companies, and the government changed in 2020. If we instead use trade data that better reflect plummeting oil prices in international markets, alternative estimates of Russia’s real growth show a GDP decline of (at least) 9 percent.  This is a three times larger drop than the official number of minus 3 percent. This is important to keep in mind when Russia’s economic performance in the pandemic is compared with other countries or while discussing the economic realities of people living in Russia.

References

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

The Shadow Economy in Russia: New Estimates and Comparisons with Nearby Countries

Posted on March 16, 2020 | Policy Brief

Image of a metro elevator going upwards representing shadow economy of Russia

We apply a new method to measure the shadow economy in Russia during the period 2017-2018 and provide evidence on the main factors that influence involvement in the shadow economy. Drawing on a methodology developed by Putnins and Sauka (2015), we estimate that the size of the shadow economy in Russia is 44.7% of GDP in 2018. This is similar to the size of the shadow economy in countries such as Kyrgyzstan, Kosovo, Ukraine, and Romania, but higher than the level of the Baltic countries. Our findings are largely consistent with other less direct approaches for estimating the shadow economy. An advantage of our approach is that it can provide more detailed information on the components of the shadow economy.

Introduction and Approach to Measuring the Shadow Economy

The aim of the Shadow Economy Index, which has now been estimated in a number of countries, is to measure the size of shadow economies and explore the main factors that influence participation in the shadow economy. We use the term “shadow economy” to refer to all legal production of goods and services produced by registered firms that is deliberately concealed from public authorities (OECD, 2002; Schneider, Buehn and Montenegro, 2010).

The Shadow Economy Index draws on a survey-based methodology developed by Putnins and Sauka (2015). It combines estimates of business income that has been concealed from authorities, unregistered employees, and ‘envelope’ wages. The approach exploits the fact that entrepreneurs and business leaders are in a unique position in that they have knowledge about the amount of business income that is concealed from authorities, the number of employees that work for them unofficially, and the extent to which they pay wages informally to avoid taxes.

The challenge for such methods is to elicit maximally truthful responses about these sensitive issues, otherwise, the size of the shadow economy will be underestimated. To address this challenge, we use a number of survey and data collection techniques shown in previous studies to be effective in eliciting more truthful responses (e.g. Gerxhani, 2007; Kazemier and van Eck, 1992; Hanousek and Palda, 2004). While the full details can be found in Putnins and Sauka (2015), they include confidentiality with respect to the identities of respondents, framing the survey as a study of satisfaction with government policy, phrasing misreporting questions indirectly about “similar firms in the industry” rather than the respondent’s actual firm, gradually introducing the most sensitive questions after less sensitive questions, excluding inconsistent responses, and controlling for factors that correlate with a potential untruthful response such as tolerance towards misreporting.

The Index measures the size of the shadow economy as a percentage of GDP. Computing the Index involves three steps:

  • (i) estimate the degree of underreporting of employee remuneration and underreporting of firms’ operating income using the survey responses;
  • (ii) estimate each firm’s shadow production as a weighted average of its underreported employee remuneration and underreported operating income, with weights reflecting the proportions of employee remuneration and firms’ operating income in the composition of GDP; and
  • (iii) calculate a production-weighted average of shadow production across firms.

The survey about shadow activity in Russia from 2017 to 2018 was conducted between February and March 2019. We use random stratified sampling to construct samples that are representative of the population of firms in Russia drawing on the official company register and covering all regions in Russia. In total, 500 phone interviews were conducted with owners, directors, and managers of companies in Russia. We use the same methodology to collect data in other countries, which we compare with Russia, conducting a minimum of 500 interviews in each country.

Size of the Shadow Economy in Russia and Nearby Countries

The estimated size of the shadow economy in Russia is 44.7% of GDP in 2018. Our estimates suggest that the year before, in 2017, the shadow economy was slightly larger with 45.8% of GDP, although the annual change is not statistically significant. For comparison with nearby countries, using the same approach, high levels of shadow economy are also found in Kyrgyzstan (44.5% of GDP in 2018), Kosovo (39.5% of GDP in 2018), Ukraine (38.2% of GDP in 2018), and Romania (33.35% of GDP in 2016), but considerably lower levels are found in the Baltic countries, especially Estonia (16.7% of GDP in 2018). See Table 1 for the full set of estimates.

The estimates using our direct micro-level approach to measuring the shadow economy are largely consistent with other less direct approaches for estimating the size of the shadow economies, such as Schneider (2019). An advantage of the direct micro-level approach is that it is able to provide more detailed information on the components of the shadow economy, which we turn to next.

Components and Determinants of the Shadow Economy in Russia

We find that envelope wages and underreporting of business profits stand out as the two largest components of the Russian shadow economy. Underreporting of salaries or so-called ‘envelope wages’ in Russia are approximately 38.7% of the true wage on average in 2018, whereas approximately 33.8% of business income (actual profits) are underreported. Unofficial employees in Russia as a percentage of the actual number of employees are estimated 28.2% in 2018.

Some companies in Russia, rather than simply concealing part of the income or employees, are completely unregistered and therefore also contribute to the shadow economy. We estimate that such companies make up 6.1% of all enterprises in Russia.

Our findings also suggest that there is a very high level of bribery in Russia: the magnitude of bribery (percentage of revenue spent on ‘getting things done’) is estimated to be 26.4%, whereas the percentage of the contract value that firms typically offer as a bribe to secure a contract with the government in Russia is 20.6% in 2018. We also find that more than one-third of companies in Russia pay more than 25% of the revenue or contract value in bribes.

We find that the size of the shadow economy in all sectors of the Russian economy is close to 40% with somewhat higher levels in the construction and wholesale sectors, controlling for other factors. Using regression analysis, we find that entrepreneurs that view tax evasion as a tolerated behaviour tend to engage in more informal activity, as do entrepreneurs that are more dissatisfied with the tax system and the government. This result offers some insights into why the size of the shadow economy in Russia is so large – it is at least in part due to relatively high dissatisfaction of entrepreneurs with the business legislation and the government’s tax policy. We also find some evidence that higher perceived detection probabilities and, in particular, more severe penalties for tax evasion reduce the level of tax evasion, suggesting increased penalties and better detection methods as possible policy tools for reducing the size of the shadow economy.

Finally, while firms of all sizes participate in the shadow economy, we find that younger firms tend to do so to a greater extent than older firms. The results support the notion that young firms use tax evasion as a means of being competitive against larger and more established competitors.

Acknowledgments

This research was supported by a Marie Curie Research and Innovation Staff Exchange scheme within the H2020 Programme (grant acronym: SHADOW, no: 778118).

References

  • Gerxhani, K. (2007). “Did you pay your taxes?” How (not) to conduct tax evasion surveys in transition countries. Social Indicators Research 80, pp. 555-581.
  • Hanousek, J. and Palda, F. (2004). Quality of government services and the civic duty to pay taxes in the Czech and Slovak Republics, and other transition countries. Kyklos 57, pp. 237-252.
  • Kazemier, B. & van Eck, R. (1992). Survey investigations of the hidden economy. Journal of Economic Psychology 13, pp. 569-587.
  • Lechmann, E. and D. Nikulin (2017). Shadow Economy Index in Poland. Gdansk University of Technology, Poland: Gdansk.
  • Lysa, O. et al. (2019) Shadow Economy Index in Ukraine. SHADOW: an exploration of the nature of informal economies and shadow practices in the former USSR region. Kyiv International Institute of Sociology, Ukraine: Kyiv.
  • Mustafa, I., Pula J.S., Krasniqi, B., Sauka, A., Berisha, G., Pula, L., Lajqui, S. and Jahja, S. (2019) Analysis of the Shadow Economy in Kosova. Kosova Academy of Sciences and Arts, Kosova: Pristina.
  • OECD, 2002. Measuring the Non-Observed Economy: A Handbook. OECD, Paris, France.
  • Putnins, T.J. and Sauka, A. (2019). Shadow Economy Index for the ‘Baltic Countries 2019-2018. SSE Riga: Riga, Latvia.
  • Putnins, T.J., A. Sauka and A. Davidescu (2020, forthcoming). Shadow Economy Index for Moldova and Romania, 2015-2018. SSE Riga, National Scientific Research Institute for Labour and Social Protection.
  • Putnins, T.J. and Sauka, A. (2015). Measuring the shadow economy using company managers. Journal of Comparative Economics 43, pp. 471-490.
  • SIAR (2019). Shadow Economy Index for Kyrgyzstan. SHADOW: an exploration of the nature of informal economies and shadow practices in the former USSR region. SIAR research and consulting, Kyrgyzstan: Bishkek.
  • Schneider, F. (2019) Calculation of the Size and Development of the Shadow Economy of 35 Mostly OECD Countries up to 2018. Unpublished manuscript.
  • Schneider, F., Buehn, A. and Montenegro, C. (2010). New estimates for the shadow economies all over the world. International Economic Journal 24, pp. 443-461.

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

A Decade of Russian Cross-Domain Coercion Towards Ukraine: Letting the Data Speak

Posted on February 17, 2020 | Policy Brief

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Russia’s coercion towards Ukraine has been a topic of major international events, meetings and conferences. It regularly makes the headlines of influential news outlets. But the question remains open – do we really understand it? We diligently collect and analyze data to make informed decisions in practically all domestic issues but is the same done for international relations? This research paper introduces a number of tools and methods that could be used to study Russia’s coercion towards Ukraine beyond its most visible manifestation, looking into latent trends and relations that could reveal more.

Introduction

For the past decade, Ukraine has been in the headlines of the major world news outlets more frequently than ever before. Ukrainian-Russian relations have been and still remain the topic of international summits and other events. The occupation of a part of Ukraine’s territory has been denounced and Russia’s coercion towards Ukraine is now generally accepted as a fact. But what do we really know about the underlying empirics and dynamics and how can this multi-domain assertiveness be measured and tracked? This paper presents a number of data-driven approaches that allow looking beyond the headline stories to identify and track various dimensions of Russia’s coercion towards Ukraine and the dynamics of its development.

Academic Interest

Mapping the landscape of scholarly literature reveals a number of interesting results. First, the body of works studying Russia’s coercion towards Ukraine remains relatively modest. It quintupled in 2014 but afterwards the interest started tapering off. A search for papers on this topic in Scopus and Web of Science with a very precise query (to increase the accuracy of search) and publication time of 2009-2019 returned 155 papers most of which were published in or after 2014.

Figure 1. Scholarly publications on Russian-Ukrainian relations.

Source: WoS and Scopus, 2009-2019

A closer look at the content of these works with the use of a bibliometric software called CiteSpace shows that the majority of papers focus on Putin, once again emphasizing the significant role of his personality in Russia’s coercion towards Ukraine. The second largest cluster has the “great power identity” as its main theme and presumably looks beyond actions of Russia to identify its ideological grounds. Another group of publications is devoted to sanctions, pointing to their important role in studying Russian-Ukrainian relations.

Figure 2. The landscape of topics in scholarly publications on Russian-Ukrainian Relations.

Expressions of Coercion

The “practical” side of Russia’s coercion towards Ukraine is also frequently associated with the personality of Vladimir Putin and his attitudes towards Ukraine. To analyze this perception further, we created a corpus of speeches of Russian presidents published on the Kremlin website, filtered them to keep only those that mention Ukraine, divided them into pre-2014 and 2014 and after, and then analyzed them using an LDA topic modeling algorithm. This algorithm is based on the assumption that documents on similar topics use similar words. So, the latent topics that a certain document covers can be identified on the basis of probability distributions over words. Each document covers a number of topics that are derived by analyzing the words that are used in it. In simple terms, the model assigns each word from the document a probabilistic score of the most probable topic that this word could belong to and then groups the documents accordingly.

Figure 3. Speeches of Russian presidents before 2014, LDA topic modeling.

Figure 4. Speeches of Russian presidents in 2014 and after, LDA topic modeling.

Quite surprisingly, we discovered that the overall rhetoric of speeches is very similar for the two periods. Although some speeches do differ and the later corpus includes new vocabulary to reflect some changes (i.e “Crimea”, “war”) the most common words remain practically the same. Thus, regardless of the apparent shift in relations between the two countries, Russian leadership still relies on the same notions of collaboration, interaction, joint activities, etc. The narrative of “brotherhood” between the nations persists despite and beyond the obvious narrative of conflict.

To include a broader circle of Russia’s leadership we also looked at the surveys of the Russian elite conducted regularly by a group of researchers led by William Zimmerman and supported by various funders over the years (in 2016 – the National Science Foundation and the Arthur Levitt Public Affairs Center at Hamilton College). Seven waves of the survey already took place; the most recent one in 2016. The respondents were the representatives of several elite groups (government, including executive and legislative branches, security institutions, such as federal security service, army, militia, private business and state-owned enterprises, media, science and education; for practical reasons from Moscow only).

The survey revealed a number of interesting observations. For instance, while the prevailing Russian opinion on Russia’s occupation of Crimea had been that it was not a violation of international law, a closer look at the demographic characteristics of respondents shows that they were not as coherent as it might seem from the outside. While the “green” answers from respondents with backgrounds such as media or private business may have been anticipated, the number of members of the legislative and especially executive branch and the military that had at least some doubt on the legality was surprisingly quite sizable, and they even demonstrated some support of the “violation of law” interpretation.

Figure 5. Elite and public opinion on Russia’s annexation of Crimea.

Comparing these elite opinions to the public opinion poll by Levada Center conducted in the same year shows that even the general public is slightly more likely to choose the most extreme “full legality” option than the respondents from the executive branch.

Beyond the elite or general opinion polls, we tried to develop a metric that might allow us to track Russian sensitivities towards Ukraine. For that, we examined two different ways of expressing “in Ukraine” in Russian language: ‘на Украине’ (the ‘official’ Russian expression) vs. ‘в Украине’ (the version preferred by Ukrainians). [In English, this can be compared so saying ‘Ukraine’ vs ‘the Ukraine’.]

Our first visual plots how many search queries were done on Google Search with both versions over the last decade.

Figure 6. Search queries for “в Украине” (green) versus “на Украине” (red), Google Trends, 2009-2019.

We can clearly observe that during less turbulent times the more politically sensitive version is much more common. This however drastically changes during the peaks of Russia’s coercion towards Ukraine when the number of searches with the less politically correct term increases significantly.

A different trend can be observed if we look at official media publications stored in the Factiva database. We estimated the ratio of search volumes for each term and observed that until the beginning of 2013, about a third of articles and news reports used “in Ukraine”. This changed around January 2013 when the ratio starts to decrease for “in Ukraine” searches and plummets to a mere 10% of outlets still preferring this term.

Figure 7. The ratio of “в Украине” to “на Украине” occurrences in large Russia media (2009 – 2019), Factiva.

Tracking Coercion Itself

What is the track record of Russia’s actual coercion over this decade? For this, we turn to a few recent datasets that try to systematically capture verbal and material actions (words and deeds): the automated event datasets. The largest one of those, called GDELT (Global Database of Events, Language, and Tone), covers the period from 1979 to the present, and contains over three quarters of a billion events. It is updated every fifteen minutes to include all “events” reported in the world’s various news outlets. To exclude multiple mentions of the same event by one newswire, the events are “internally” deduplicated. The events are not compared across newswires.

An event consists of a “triple” coded automatically to represent the actor (who?), the action (what?) and the target (to whom?) as well as a number of other parameters such as type (verbal or material; conflict or cooperation; diplomatic, informational, security, military, economic), degree of conflict vs cooperation etc. Other similar datasets include ICEWS (Integrated Crisis Early Warning System) and TERRIER (Temporally Extended, Regular, Reproducible International Events Records). For this analysis, we filtered out only those events in which Russia was the source actor and Ukraine was the target country. We present two metrics: (1) the percentage of all world events that this subset of events represents and (2) the monthly averages of the Goldstein score, which captures the degree of cooperation or conflict of an event and can take a value from -10 (most conflict) to +10 (most cooperation). Also, to add a broader temporal perspective, we looked beyond the last decade. It can be clearly seen that the number of events before 2013 was significantly lower, especially in “material” domains. Some verbal assertions from Russia towards Ukraine happened during the Orange Revolution and so-called “gas wars”.

The situation changes radically starting from 2013. The proportion of events increases with some especially evident peaks (i.e. during the occupation of Crimea). The verbal events remain quite neutral while the actions towards Ukraine move from some fluctuations to steadily conflictual.

Figure 8. Russia’s negative assertiveness towards Ukraine, 2000-2019.

Measuring Influence

We have seen that the past decade was exceptional in the scale of Russian assertiveness towards Ukraine. But what do we know about Russia’s influence on Ukraine and Ukraine’s dependence on Russia? Influence measures the capacity of one actor to change the behavior of the other actor in a desired direction. In an international context this often concerns the relations between countries. Influence can be achieved by various means, one of which is to increase the dependence of the target country upon the coercive one. This strategy is frequently employed by Russia willing to regain and/or increase control over the former post-Soviet countries. The Formal Bilateral Influence Capacity (FBIC) Index developed by Frederick S. Pardee (Center for International Future) looks at several diplomatic (i.e. intergovernmental membership), economic (trade, aid) and security (military alliances, arms import) indicators allowing to identify the level of dependence of one country upon another. This is especially interesting from a comparative perspective. Figure 9 shows that countries such as Armenia and Belarus remain highly dependent on Russia. For half of the decade, Ukraine was number three on this list. Today the situation has changed. Ukraine’s dependence on Russia has gradually decreased and has become even smaller than Moldova’s, moving closer to the steadily low level of dependence of Georgia. This may signify a positive trend and a break of a decade-long relationship of dependence.

Figure 9. Dependence of post-Soviet countries on Russia, FBIC.

Conclusion

Consequently, Russia and Ukraine have become much more visible in the international academic and policy research efforts. This can be measured through a number of instruments, including a comprehensive mapping of the academic landscape itself with regard to salience and topics that are being studied, analysis of the word choice (that could be represented by the use of the terms to describe events in Ukraine by the government media and Google search users (“на Украине” versus “в Украине”); speeches of Russian presidents that use the same rhetoric of collaboration when talking about Ukraine despite the obvious change in relationships) and material coercion (significant increase in number of assertive conflictual Russia’s actions towards Ukraine). Some findings do give hope for change: the opinions of the Russian elite on recent Russian actions towards Ukraine while remaining generally unfavourable are not as cohesive as it might appear and Ukraine’s dependence on Russia has decreased significantly.

Disclaimer

This research is a part of a larger research effort titled RuBase funded by the Carnegie Foundation of New York and implemented jointly by The Hague Centre for Strategic Studies and Georgia Tech with the help of the Kyiv School of Economics StratBase team in Ukraine. The ‘Ru’ part of the title stands for Russia; and ‘base’ has a double meaning – both the knowledge base built during the project, and the (aspirationally) foundational nature of this effort. The project intends to look beyond the often-shallow traditional understanding of coercion and apply innovative tools and instruments to study coercion in its multifaceted form. This is only a small selection of the tools that have been successfully tested in the course of this (ongoing) research project and applied to the study of Russia’s coercion in different domains. The prospects of any progress in resolving the Russian-Ukrainian conflict are currently slim, thus further work that would allow identifying patterns and trends that the human eye may oversee to understand Russia better and develop an informed foreign policy strategy both for Ukraine and the West is crucially important.

References

The Russian Food Embargo: Five Years Later

Posted on October 14, 2019 | Policy Brief

20191014 The Russian Food Embargo FREE Network Policy Brief Image 01

In this brief, we report the results of a quantitative assessment of the consequences of counter-sanctions introduced by the Russian government in 2014 – Russian food embargo. We consider several affected commodity groups: meat, fish, dairy products, fruit and vegetables. Applying a partial equilibrium analysis to the data from several sources, including Rosstat, Euromonitor, UN Comtrade, industry reviews etc. as of 2018, we obtain that consumers’ total loss amounts to 445 bn Rub, or 3000 Rub per year for each Russian citizen. This is equivalent to a 4.8% increase in food expenditure for those who are close to the poverty line. Out of this amount, 84% is distributed towards producer gains, 3% to importers, while the deadweight loss amounts to 13%. Based on industry dynamics, we identify industries where import substitution policies led to positive developments, industries where these policies failed and group of industries where partial success of import substitution was very costly for consumers.

The full text of the underlying paper is forthcoming in the Journal of the New Economic Association in October 2019.

In August 2014, in response to sectoral sanctions against Russia, the national government issued resolution No. 778, which prohibited import of processed and raw agricultural products from the United States, the EU, Ukraine and a number of other countries (Norway, Canada, Australia, etc.).  The goal was to limit market access for countries, which supported sectoral sanctions. The other rhetoric of the counter-sanctions was to support domestic producers via trade restrictions, or by other words – import substitution.

This brief provides an update of welfare analysis of counter-sanctions based on partial equilibrium  model of domestic market. The initial estimations based on 2016 data can be found in another FREE Policy Brief here. This time we compare the consumption, outputs and prices of the counter sanctioned goods as of 2018 relative to 2013. The estimated consumer surplus changes, producer gains and prices are reported in Table 1.

Table 1. Welfare effects of counter-sanctions in 2018 relative to 2013.

Data sources: Rosstat, Euromonitor, UN COMTRADE
* Negative losses correspond to gains
** Negative gains correspond to losses
Green color was used to mark the commodity groups with a noticeable consumption growth in 2013-2018 and red color those with consumption decrease.

Effect on production

From the point of view of price dynamics, on the one hand, and consumption and output, on the other, the studied products can be divided into three groups.

The first group which we call “Success of import substitution”  includes goods for which real prices (in 2013 level) increased by 2016 but afterwards, the growing domestic production ensured that by 2018 prices fell below the level of 2013 with a corresponding increase in consumption. This group includes tomatoes, pork, poultry and, with some reservation, beef. For beef, growing domestic production pushed prices down after 2016, but the level of consumption and prices have not yet reached the pre-sanction level.

For the second group, import substitution has not resulted in a price decrease, we call this group “Failure of import substitution”. For products in this group, the initial increase in prices by 2016 was not reverted afterwards. Their consumption decreased significantly compared to 2013, and domestic production either continued to fall after 2016, or its growth turned out to be fragile. This group includes apples, cheese, fish, as well as condensed milk and processed meat.

We call the third group “Very expensive import substitution”. It includes fromage, sour milk, milk and (to a lesser extent) butter. This group is characterized by increase in consumption and output in the period 2016–2018, but real prices over this period still remain very high.

Effect on consumers

By comparing the losses and gains of consumers in different categories of goods due to changes in real prices and real consumption, our analysis provides the following monetary equivalents. For all considered counter-sanctioned product groups, with the exception of poultry, pork and tomatoes, consumer losses are around 520 billion rubles per year (in 2013 prices). In three product groups (poultry, pork, tomatoes), in which there was a decrease in prices and a significant increase in consumption, the consumer gains are equivalent to 75 billion rubles per year. Thus, the total negative effect from counter-sanctions for the consumers amounted to 445 billion rubles a year, or about 3000 rubles for a person per year.

Given the cost of the minimum food basket, defined in Russia as 50% of the subsistence level, the impact of counter-sanctions on the budgets of Russian consumers can be estimated as follows. 3000 rubles account for approximately 4.8% of the annual cost of the minimum food basket. The minimum food basket is a set of food products necessary to maintain human health and ensure its vital functions that is established by law. In other words, one can say that 3000 rubles a year are equivalent to a 4.8% increase in food expenditure for those who are close to the poverty line.

Consumer surplus losses were significantly redistributed in favor of domestic production, totaling 374 billion, or 2500 rubles per year per person. Another 56 billion rubles (or 390 rubles per person) correspond to the deadweight loss, i.e., reflect the inefficiency increase of the Russian economy, and 16 billion rubles (110 rubles per person) is the equivalent of redistribution in favor of foreign producers, who get access to Russian market with higher priced products than before counter-sanctions.

Effect on foreign partners

As a result of the selective embargo, the geography of Russian imports of the affected goods has changed. Traditional suppliers of these goods, primarily from Europe, were replaced by suppliers from other countries due to trade diversion. Given the changes in the composition of importers after the imposition of sanctions, we single out countries that have lost and countries that have gained access to the Russian market. We use the change in trade volumes from the respective countries as indicators of growth and decrease in share of these importers in the Russian market. Below we consider in detail the three groups of goods with the largest gains for importers in 2018 compared with 2013: cheese, apples, butter.

Cheese imports decreased significantly after the imposition of counter-sanctions, in 2018 accounting for only 42% of their dollar value in 2013. The total gain of importers due to the growth of domestic prices in 2013-2018 amounted to 17.3 billion rubles (Table 1) and was distributed among following importing countries: Belarus (78%), Argentina (6%), Switzerland (4%), Uruguay (3%), Chile (3%), other countries (6%). Countries that lost their shares of the Russian cheese market included Ukraine, Holland, Germany, Finland, Poland, Lithuania, France, Denmark, Italy and Estonia. As mentioned earlier, domestic production and Belarusian imports were not able to fully compensate for imports from countries on the counter-sanctions list, and in 2016-2018 cheese consumption in Russia decreased significantly.

Apple imports after the initial drop in 2016 partially recovered in 2018, amounting to 66% of their dollar volume in 2013. The total gain of importers in 2018 compared to 2013 amounted to 15.0 billion rubles (Table 1); it was distributed between Serbia (22%), Moldova (19%), China (13%), Turkey (10%), Iran (10%), Azerbaijan (7%), South Africa (4%), Chile (3%), Brazil (3%) and other countries (9%). Poland suffered the most from the ban on apple imports; it accounted for about 80% of all losses. Other losers from counter-sanctions include Italy, Belgium and France. The reorientation of trade flows did not completely replace Polish imports, so apple consumption in 2016-2018 was significantly lower than in 2013.

Imports of butter in 2018 was also below the level of 2013 (67% of dollar value). The gain of importers in 2018 compared to 2013 amounted to 11.2 billion rubles and was distributed among the following  trading partners: Belarus (90%), Kazakhstan (4%), Kyrgyzstan (3%) and other countries (3%). Among the countries bearing most of the negative burden of the diversion of trade, one should mention Finland and Australia.

Conclusions

Five year after counter-sanctions were put in place Russian consumers continue paying for them out of their pockets. While few industries have demonstrated a positive effect of import substitution policies, most are not effective enough to revert the price dynamics.

References

Short-Run and Long-Run Effects of Sizeable Child Subsidy: Evidence from Russia

Posted on October 7, 2019 | Policy Brief

20191007 Short-Run and Long-Run FREE Network Policy Brief Image 01

How to design the optimal pro-natalist policy is an important open question for policymakers around the world. Our paper utilizes a large-scale natural experiment aimed to increase fertility in Russia. Motivated by a decade-long decrease in fertility and population, the Russian government introduced a sequence of sizable child subsidies (called Maternity Capitals) in 2007 and 2012. We find that the Maternity Capital resulted in a significant increase in fertility both in the short run and in the long run. The subsidy is conditional and can be used mainly to buy housing. We find that fertility grew faster in regions with a shortage of housing and with a higher ratio of subsidy to housing prices. We also find that the subsidy has a substantial general equilibrium effect. It affected the housing market and family stability. Finally, we show that this government intervention comes at substantial costs.

In all European and Northern American countries the fertility is below the replacement level (United Nations, 2017). Following this concern, most of the developed countries have implemented various large scale and expensive pro-natalist policies. Yet, the effectiveness of these policies is unclear, and the design of the optimal pro-natalist policy remains a challenge.

There are several important open research questions on the evaluation of these programs. The first is whether these programs can induce fertility in the short-run and/or in the long-run horizon. Indeed, very few of these expensive and large-scale policies are proved to be an effective tool to increase fertility (Adda et al, 2017). The next set of questions deals with further evaluation of the programs: What are the characteristics of families that are affected by this policy? How costly is the policy, i.e. how much is the government paying per one birth that is induced by the policy? Finally, what are the non-fertility related effects of these policies? While most of the studies that analyze the effect of pro-natalist policies concentrate on fertility and mothers’ labor market outcomes, these, usually large-scale, policies may have important general equilibrium and multiplier effects that may affect economies both in the short run and long run (Acemoglu, 2010).

In our paper we utilize a natural experiment aimed to increase fertility in Russia to address these questions.

Motivated by a decade-long decrease in fertility and depopulation, the Russian government introduced a sizable conditional child subsidy (called Maternity Capital). The program was implemented in two waves. The first wave, the Federal Maternity Capital program, was enacted in 2007. Starting from 2007, a family that already has at least one child, and gives birth to another, becomes eligible for a one-time subsidy. Its size is approximately 10,000 dollars, which exceeds the country’s average 18-month wage and exceeds the country’s minimum wage over a 10-year period. The recipients of the subsidy can use it only on three options: on housing, the child’s education, and the mother’s pension. Four years later, at the end of 2011, Russian regional governments introduced their own regional maternity programs that give additional – on the top of the federal subsidy – money to families with new-born children.

In our paper, we document that the Maternity Capital program results in a significant increase in fertility rates both in the short run (by 10%) and in the long run (by more than 20%). This effect can be seen from both within-country analysis and from comparing the long-term growth of fertility rates in Russia with Eastern and Central European countries that face similar economic conditions and had similar pre-reform fertility trends. Like Russia, Eastern European countries experienced a drop in fertility rates right after the collapse of the Soviet Union and had similar trends in fertility up until 2007. Our results show that while having similar trends in fertility before 2007, afterward Russia significantly surpassed all the countries from this comparison group.

Figure 1 illustrates the effect of the Maternity Capital on birth rates. The top two panels show monthly birth rates (simple counts and de-seasoned); the bottom panels show total fertility rates in Russia versus Eastern European countries, and versus the European Union and the US.

Figure 1. Total Fertility Rate, Russia, Eastern European countries, USA and EU.

Source: Sorvachev and Yakovlev (2019), and http://www.fertilitydata.org/.

The effects of the policy are not limited to fertility. This policy affects family stability: it results in a reduction in the share of single mothers and in the share of non-married mothers.

Also, the policy affects the housing market. Out of three options (education, housing and pension), 88% of families use Federal Maternity Capital money to buy housing. We find that the supply of new housing and housing prices increased significantly as a result of the program. Confirming a close connection between the housing market and fertility, we find that in regions where the subsidy has a higher value for the housing market, the program has a larger effect: the effect of maternity capital was stronger, both in the short run and long run, in regions with a shortage of housing, and in regions with a higher ratio of subsidy to price of apartments (i.e. those regions where the real price of subsidy as measured in square meters of housing is higher).

Figure 2 below shows the effect of Federal Maternity Capital on birth rates in different regions. It shows no effect on fertility in Moscow, small effect in Saint-Petersburg; whereas the sizable effect of maternity capital in other Russian regions.

Figure 2. Effect of Federal Maternity capital, by regions

Source: Sorvachev and Yakovlev (2019), and http://www.gks.ru/.

These results suggest that cost-benefit analysis of such policies should go beyond the short-run and long-run effects on fertility. Ignoring general equilibrium issues may result in substantial bias in the evaluation of both short-run and long-run costs and benefits of the program.

While there are many benefits of the program, we show that this government intervention comes at substantial costs: the government’s willingness to pay for an additional birth induced by the program equals approximately 50,000 dollars.[1]

For more detailed evaluation of the results see Evgeny Yakovlev and Ilia Sorvachev, 2019, “Short-Run and Long-Run Effects of Sizable Child Subsidy: Evidence from Russia”, NES working Paper # 254, 2019.

References

  • Acemoglu, Daron 2010 “Theory, General Equilibrium, Political Economy and Empirics in Development Economics”, Journal of Economic Perspectives, 24(3), pp. 17-32. 2010
  • Adda, Jérôme, Christian Dustmann and Katrien Stevens 2017. “The Career Costs of Children”. Journal of Political Economy, 125, 2, 293-337.
  • Ilia Sorvachev and Evgeny Yakovlev, 2019, “Short-Run and Long-Run Effects of Sizable Child Subsidy: Evidence from Russia”, NES working Paper #254 and LSE IGA Research Working Paper Series 8/2019

[1] Roughly, the WTP (US$50,000) exceeds nominal US$10,000 subsidy because the government pays for all (100%) families that give birth to a child to induce additional (20%) increase in fertility. See paper for more accurate elaboration.