Tag: Sanctions
Belarus Under War Sanctions
Numerous developed countries have imposed tough sanctions on Belarus, as the Belarusian regime has become part of the Russian aggression against Ukraine. At the same time, economic relations with Ukraine have been disrupted. These shocks have simultaneously disturbed the Belarusian economy and triggered a severe recession. Thanks to several positive effects from the external environment, some success from measures undertaken by the authorities to stabilize output, and some degree of resilience – all seasoned with a large portion of good luck – the situation of the Belarusian economy is however “not that bad”. Nonetheless, the Belarusian economy is experiencing its worst economic crisis since the mid-1990s, and the current path of the economy is highly unstable and associated with numerous risks and threats. In economic terms, it is likely the case that the full costs from the sanctions are yet to be paid.
Sanctions, Multiple Shocks and Their Potential Implications
As the Belarusian regime has become part of the Russian war on Ukraine many developed countries have adopted tough sanctions against Belarus. These sanctions include an embargo on a large share of Belarusian exports and imports, prohibitions and restrictions on transportation of goods of Belarusian origin, restrictions on and/or blocking actions regarding financial operations and settlements, a freeze of parts of the Belarusian international reserves, and numerous restricting and blocking actions against banks, companies and individuals. Such sanctions, combined with a new external environment, cause powerful indirect effects with foreign companies exiting the Belarusian market and refusing business with Belarusian counterparts. Additionally, some Belarusian businesses and employees have left the country. On top of this, economic relations with Ukraine, formerly Belarus’s second largest trading partner, have been virtually reduced to zero.
In economic terms, the above mentioned may be treated as a bundle of simultaneous powerful shocks to the national economy, differing in direction, mechanics, size, and persistence. These shocks may be grouped into three clusters.
The first cluster covers demand shocks, and in particular export shocks. According to our assessments, the exogenous demand shock following the sanctions may reduce Belarusian exports (in physical terms) by 40 percent, compared to previous steady-state levels. This figure should however be seen as a potential lower bound which may be realized if no measures to mitigate the impact from the sanctions are undertaken. Belarusian authorities and businesses are however doing their best trying to find new buyers for the “vanishing” exports, bypass restrictions in order to connect to “old” buyers, and establish new logistic and financial chains. The extent to which these attempts may be successful depends on the global environment, the degree of the price competitiveness of Belarusian producers, and numerous non-economic factors. Additionally, all factors affecting exports are unstable and volatile. Exports under these new conditions are therefore less sustainable and may fluctuate in an extremely wide range. Shocks to consumption and investments stemming from weakened sentiment and expectations further amplify the demand shocks.
The second cluster of shocks relates to the supply side of the economy. It includes business closures, emigration that weakens labor supply, and production bottlenecks due to the inaccessibility of imports. Supply shocks are hard to quantify, but we perceive them as persistent and cumulative. Business closures and emigration have irrevocable effects on the national economy (at least in the medium-term), and a continuation of such drop-outs will likely amplify the size of the shock.
The third cluster combines different primarily nominal shocks: price, exchange rate, financial stability and fiscal ones. Such shocks have become permanent companions to the Belarusian economy under the sanctions, and they are volatile in terms of size. As a result, the corresponding economic indicators are likely to also become highly unstable.
This bundle of adverse shocks shifts the economy down from the previous, close to steady-state, trajectory. A new trajectory is however far from predetermined. Firstly, it depends on the effectiveness of the government in curbing the shocks stemming from the sanctions, as the actual path of the economy may be considerably affected by monetary or fiscal policy and other interventions. Secondly, some positive exogenous shocks may partially offset the effects from adverse ones. Lastly, the economy, at least for a while, may resist through exploitation of accumulated buffers (such as, international reserve assets, financial reserves of State-owned enterprises that were accumulated under favorable conditions in 2021 etc.).
Considering the worst possible assumptions regarding the above mentioned issues, our model-based simulations predict a severe recession of about 20 percent (as compared to the output peak in 2021-Q2). This recession is accompanied by a sharp increase in inflation (which in turn is highly likely to be supplemented by a full-fledged financial crisis). This simulation should however be regarded as the potential rock bottom. Whether it will become reality or not critically depends on the Belarusian government’s policies.
Policy Response by the Authorities
The root cause of the problem, namely the provision of Belarusian territory for the Russian army, has never been publicly discussed by Belarusian officials. Instead, the government has focused on strategies which treat the symptoms, rather than focusing on curing the disease itself. The main coping strategies that were publicly discussed include: 1) expected increase in Russian support and exports to Russia 2) re-orientation of exports towards Asian and developing markets 3) greater mobilization of domestic resources and 4) monetary, fiscal and other stimuli.
The Russia-related initiatives are often beyond convention and include some radical proposals. These are, for instance, accelerating the establishment of sea terminals in Russian ports, promoting exports to Russia, and requesting greater financial support from Russia linked to the so-called “deep integration” package (mainly in the form of energy subsidies, import substitution investments and direct subsidies). Adherence to these proposals would mean that Belarusian authorities de facto accept serving as a Russian protectorate and correspondingly take on the role of a puppet government.
Belarusian authorities have reached some success from choosing the “Russian track” as the debt payments to Russia were postponed, new cheap gas and oil prices were granted and export to Russia increased by 15 percent in the first 8 months of 2022. The Belarusian regime’s $7 billion compensation claim for incurred economic losses due to the war has however been rejected by Russia so far.
The coping strategy of export re-orientation serves primarily as a rhetoric intervention as China and other Asian countries considered by the government cannot fully replace the European market. For many Belarusian exports, the EU was a premium, high-margin market while re-orientation means at best lower margins. The success of re-orientation depends on the degree of price competitiveness, which can change greatly over time. The only success from this strategy to date is the re-orientation of 10 percent of potash exports to China via railroad (incurring greater transportation costs).
The third strategy “greater mobilization of domestic resources” firstly assumes more interference with the business activity of State-owned enterprises (SOE). Despite severe demand shocks these are pressured by the government to maintain production and/or salaries, the latter in order to support output via sustained consumer demand. Further, a “discipline” component of the strategy is implemented through renewed catch-pay-and-release practices. In effect, businessmen are arrested based on anti-corruption or tax fraud criminal charges. They are then offered to pay certain amounts to the state and released if they choose to pay.
Since late spring, when direct financial shocks have been suppressed, the authorities have intensified stimulus measures to the economy. In the fiscal sphere, these are aimed at promoting exports and mainly provided on an individual or sectoral basis. To a large extent, these stimuli may be seen as partial compensation to SOEs for their output-supporting role. In the monetary sphere a specific environment in which the Russian ruble is appreciated vs. the US dollar, despite the worldwide strength of the latter, has allowed the authorities to implement a “magic” (but highly likely temporary) solution: The Belarusian national currency is manipulated to depreciate vs. the Russian ruble (both in nominal and real terms) but appreciate vs. the US dollar. The former leads to a great increase in price competitiveness (as Russia is today the dominant trading partner), while the latter serves as a buffer for fragile prices and provides financial stability. Moreover, the authorities have excessively softened monetary policy, trying to spur domestic credit. These measures lead to heightened inflation pressure, which is however somehow suppressed by reinvigorated direct price controls.
Current Situation and Future Implications
Until now, the Belarusian economy places far from the potential rock bottom. By the end of the second quarter in 2022, output losses (vs. the output peak in 2021-Q2) amounted to about 5.5 percent. By the end of 2022, they are however expected to increase to about 8.5 percent (vs. the 2021-Q2 output peak). The Belarusian economy is stuck in a heightened inflation environment – with the inflation being as high as 20 percent in annual terms. Although the inflation is considerably higher than in “normal times”, it is still not a disaster (considering the much higher projected level under the worst-case scenario and the background of 40-year peak in global inflation). Moreover, the current situation is still far from a full-fledged financial crisis, despite some financial turbulence.
The position of the economy as “not that bad”, is a result of existing buffers, positive effects from the external environment and some immediate efficiency from actions undertaken by the authorities to stabilize output – all seasoned with a large portion of good luck. For instance, the jump in price competitiveness accounts for a large share of curbing efforts that counter the sanctions. This is, in turn, due to a combination of high global prices, low and frozen energy prices for Belarus, and a very specific and unstable stance on monetary policy underpinned by direct price controls. Some buffer savings that Belarusian SOEs succeeded to accumulate during the period of the so-called “foreign trade miracle” in late 2020 and 2021 also play an important role. Last but not least, the Belarusian authorities seem to have succeeded in the partial curbing of the export shock. Since the beginning of summer, there are some signs of recovery in exports which most likely reflects a partial recovery of exports within the most sensitive domains: oil products and potash fertilizers (corresponding statistics have been blocked out).
However, the “not that bad” position of the economy does not mean good. According to all standard metrics, Belarus is currently experiencing a severe economic crisis. The notion that it could be even more severe is bad news, not good ones. Moreover, the current situation is extremely unstable and fragile. The economy is facing numerous distortions, contradictions and risks, all of which can still shift the scenario of the crisis from the “not that bad” situation to the worst possible.
Conclusion
The Belarusian regime’s involvement in the Russian aggression against Ukraine have propelled Belarus into the most severe economic crisis since the mid-1990s. Until recently, fortunate external economic circumstances, a specific policy mix and a good portion of luck have allowed for a partial mitigation of the crisis. The situation is however extremely unstable and the full effects from the sanctions are likely yet to be realized.
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.
Sanctions Enforcement and Money Laundering
With sanctions becoming an increasingly important tool in ostracising autocratic regimes from western markets, the need for effective enforcement of Anti-Money Laundering (AML) policies is increasing. The global AML regime will be the backbone in detecting evasion of sanctions. This regime has, however, been widely criticised as ineffective. In this brief, we discuss issues with the current AML regime and propose a reward scheme for whistleblowers to enable asset seizures. A powerful feature of our proposal is that it does not rely on the effectiveness of the AML regime.
Introduction
Before Russia’s invasion of Ukraine, we wrote a FREE Policy brief expressing concerns over the ability of the current Anti Money Laundering (AML) regime to keep money launderers out of the international financial system. In the brief, we concluded that “The ease with which criminals have evaded present detection methods should cause concern about the effectiveness of sanctions”. The issue has now received renewed attention as the current sanctions against Russia will only be effective if it is difficult or costly to circumvent them. Sanctions evasions have a lot of similarities with money laundering, and the methods for detecting both is very similar, such that the proposal we discuss in this brief is applicable to both.
While an initial shock due to unexpected sanctions may generate disruptions, prohibited goods can later be imported/exported through third-party intermediaries in non-sanctioned countries to circumvent the sanctions. False labelling of origin, misinvocing, etc., are likely to occur and may be very difficult to detect. Analogously, sanctioned individuals’ assets may shift hands, and be laundered through shell companies without known beneficial owners.
In this brief, we consider a way to enhance enforcement, as outlined in a recent paper (Nyreröd, Andreadakis, and Spagnolo, 2022). The approach builds upon the US Kleptocracy Asset Recovery Rewards Program which offers up to $5 million “for information leading to seizure, restraint, or forfeiture of assets linked to foreign government corruption” (US Treasury, 2022).
The AML Regime
To justify the enforcement mechanism we later propose, some background on the AML regime is necessary. The global standard-setter for AML is the Financial Action Taskforce (FATF), which has since 1989 issued recommendations to countries on how to combat money laundering and terrorist financing. While initially focusing on drug money, the regime expanded in the last decades and has now received increased attention as it will be an important tool in ensuring sanctions against Russian oligarchs are effective.
The regime imposes numerous obligations on financial and other entities as they must assess risks and conduct due diligence along various dimensions, collect documents, and send reports to the national Financial Intelligence Unit. This regime has been widely criticized. Widespread AML non-compliance within banks, lack of rigorous supervision and enforcement by national supervisors and high costs relative to verifiable benefits are some of the issues that have been identified (Spagnolo and Nyreröd 2021; Nyreröd, Andreadakis and Spagnolo, 2022). The World Bank estimates that between 2 and 5 percent of global GDP is laundered annually, and that only around 0.2 percent of the proceeds from crime, laundered via the financial system, are seized and frozen (UNODC, 2011). Researchers have also been critical – for example Pol (2020), cites 22 papers that have “identified gaps between the intentions and results of the modern anti-money laundering effort, including its core capacity to detect and prevent serious profit-motivated crime and terrorism” (p.103).
Recent responses by the European Commission and others have focused on ensuring compliance within covered entities. Yet, increasing compliance with current AML rules may be costly and non-sufficient to stem the flows of illicit money in the international system. Even if widespread compliance within covered entities is obtained, and the AML procedures are effective, this may not be enough – even minimal non-compliance rates may result in major damages. We have seen how Danske Bank Estonia, a relatively small branch, managed to transfer around $230 billions of suspicious funds within the span of a couple of years (Bruun and Hjejle, 2018).
Some have suggested providing whistleblower rewards to those who report significant violations of AML rules by covered institutions (Spagnolo and Nyreröd, 2021; Scarcella, 2021). Yet, such rewards are only desirable if the AML regime is effective in achieving its policy objectives, which is not a given (we elaborate on this in Nyreröd, Andreadakis and Spagnolo, 2022). Enhanced compliance with the AML regime does not necessarily entail increased detection and deterrence of e.g., money laundering. Numerous laundering methods exist that circumvent the reporting rules required under AML. A better option may be to incentivize facilitators of money laundering to provide information leading directly to asset seizures, as they have the best information that can lead to such forfeitures.
Incentivizing Facilitators
Money laundering is a derivative crime and requires what is called a “predicate offense” (such as human trafficking, drug sales, or corruption) that generates illegal money whose source needs to be obscured. The EU Directive (2018/1673) stipulates 22 categories of criminal activities that constitute predicate offenses.
There is a large infrastructure facilitating money laundering including financial advisers, real estate agents, tax advisors, and lawyers – crucial to criminals seeking to launder money. Bill Browder, famous for his work on advocating the Magnitsky Act, describes how he was aided by Alexander Perepilichnyy, a financial adviser for individuals involved in a large tax theft in Russia. Perepilichnyy helped launder the money for those involved in the tax theft, but eventually turned whistleblower when he provided bank statements to Browder that led to the freezing of $11 million related to this fraud (Browder 2022, p. 39). His information provided a “road-map” to even be able to start investigating where the illegally stolen assets had ended up. Perepilichnyy later died while jogging near London in 2012, which some believe was a murder in retaliation for blowing the whistle. A reward scheme would aim at people like Perepilichnyy, persons who are unrelated to the predicate offense, yet have information on the source and location of illicit funds.
Reward Programs in AML
The US has used whistleblower reward schemes in several regulatory areas including tax, procurement fraud, and securities fraud. These programs offer 10-30 percent of the recoveries or fines to whistleblowers that bring information crucial to issue the fines or recover public funds. Rewards to whistleblowers are therefore paid by the wrongdoing party, not the taxpayer.
These programs have received increased attention as several studies have found that they are effective at uncovering and deterring wrongdoing (Dyck, 2010; Wiedman and Zhu, 2018; Raleigh, 2020; Leder-Luis, 2020; Dey et al., 2021; Berger and Lee, 2022, see Nyreröd and Spagnolo, 2021 for a review). Agencies managing these programs have widely praised them, and studies show they are highly cost effective. More countries are also starting to experiment with offering rewards for information.
A salient feature of the US programs is that some degree of culpability in the wrongdoing does not disqualify an individual from an award. In 2012, Bradley Birkenfeld received $104 million under the Internal Revenue Service’s reward program despite serving a jail sentence for his involvement in facilitating tax evasion. In fact, when one of the most effective and famous whistleblower laws was enacted, the US Senator who tabled the bill argued that the bill aimed at “setting a rogue to catch a rogue” which “is the safest and most expeditious way I have ever discovered of bringing rogues to justice” (Howard, 1863).
Motivated by these experiences, we propose that AML should incorporate a whistleblower reward scheme, targeting those facilitating money laundry, with three central pillars:
Witness protection: aim at shielding whistleblowers and their families from negative consequences, if there are concerns that they might become victims of retaliation, harassment, or mistreatment of any kind. If the whistleblower is based in a hostile country, guaranteed asylum should be granted.
Leniency: offer immunity for any reported offense related to money laundering, but not for any other crime. Without immunity, a whistleblower will have no incentive to turn to authorities as they would immediately incriminate themselves and risk jailtime for money laundering.
Large, scaling, and mandatory rewards: offer large, mandatory rewards that scale with the level of recoveries. As noted above, successful US programs pay 10-30 percent of the recoveries to whistleblowers. In the money laundering case, this percentage range may be lowered. Also, similarly to whistleblowers’ rewards in other cases, AML rewards would come from confiscated funds.
Numerous other design dimensions are important, but due to space limitations we refer the reader to other lengthier pieces that go into further detail (Nyreröd, Andreadakis and Spagnolo, 2022; Spagnolo and Nyreröd, 2021; Nyreröd and Spagnolo, 2021; Engstrom 2018).
Conclusion
The Russian aggression against Ukraine and the subsequent sanctions have put increased emphasis on the ability and effectiveness of the current AML regime to detect money laundering. Justified concerns about this regime have been raised, and its performance record is still under question. Programs offering whistleblowers witness protection, leniency, and large rewards could be an effective complement to this regime.
References
- Berger, P. and Lee, H. (2022), “Did the Dodd-Frank Whistleblower Provision Deter Accounting Fraud?”, Journal of Accounting Research, early view, available at: https://doi.org/10.1111/1475-679X.12421
- Browder, B. (2022b). Freezing Order, Simon & Schuster, New York, NY.
- Bruun and Hjejle. (2018). “Report on the Non-Resident Portfolio at Danske Bank’s Estonian Branch”. Danske Bank.
- Dey, A., Heese, J. and G. Pérez-Cavazos. (2021). “Cash-for-Information Whistleblower Programs: Effects on Whistleblowing and Consequences for Whistleblowers”, Journal of Accounting Research, Vol. 59, No.5, pp.1689-1740.
- Dyck, A., Morse, A. and Zingales, L. (2010). “Who Blows the Whistle on Corporate Fraud?”, The Journal of Finance, Vol. 65, No.6, pp.2213-2253.
- Engstrom, D. (2018). “Bounty Regimes.” In Arlen, J. (ed.) Research Handbook on Corporate Crime and Financial Misdealing, Edward Elgar.
- Howard, J.M. (1863). Congressional Globe, Senate, 37th Congress, 3rd Session, pp. 955-956.
- Leder-Luis, J. (2020). “Whistleblowers, Private Enforcement, and Medicare Fraud”, Working Paper, Massachusetts Institute of Technology, available at: https://sites.bu.edu/jetson/files/2020/07/False-Claims-Act-Paper.pdf.
- Nyreröd, T. and Spagnolo, G. (2021). “Myths and numbers on whistleblower rewards”, Regulation and Governance, Vol. 15, No.1, pp.82-97.
- Nyreröd, T., Andreadakis, S. and Spagnolo, G. (2022). “Money laundering and sanctions enforcement: large rewards, leniency, and witness protection for whistleblowers”, The Journal of Money Laundering Control, early view available at: https://www.emerald.com/insight/content/doi/10.1108/JMLC-05-2022-0068/full/html
- Pol, R. (2020). “Responses to money laundering scandal: evidence-informed or perception-driven?”, Journal of Money Laundering Control, Vol.23, No.1, pp.103-121.
- Raleigh, J. (2020). “The Deterrent Effect of Whistleblowing on Insider Trading”, University of Minnesota Working Paper, available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3672026.
- Scarcella, G. (2021). “Qui Tam and the Bank Secrecy Act: A Public-Private Enforcement Model to Improve Anti-Money Laundering Efforts”, Fordham Law Review, Vol. 90, No.3, pp.1359- 1395.
- Spagnolo, G. and Nyreröd, T. (2021). “Financial Incentives to whistleblowers: a short survey”, Sokol, D. and van Rooij, B. (Ed.), Cambridge Handbook of Compliance, Cambridge University Press, Cambridge UK, pp.341-351.
- Spagnolo, G. and Nyreröd, T. (2021a). “Money Laundering and Whistleblowers”, report written for Centre for Business and Policy Studies (SNS), available at: https://snsse.cdn.triggerfish.cloud/uploads/2021/11/money-laundering-and-whistleblowers.pdf.
- UNODC. (2011). “Estimating Illicit Financial Flows Resulting from Drug Trafficking and Other Transnational Organized Crimes”, Research Report, United Nations Office on Drugs and Crime, available at: https://www.unodc.org/documents/data-and-analysis/Studies/Illicit-financial-flows_31Aug11.pdf.
- US Treasury. (2022). “U.S. Departments of Treasury and Justice Launch Multilateral Russian Oligarch Task Force”, March 16, available at: https://home.treasury.gov/news/press-releases/jy0659.
- Wiedman, C. and Zhu, C. (2018). “Do the SEC Whistleblower Provisions of Dodd-Frank Deter Aggressive Financial Reporting?”, 2018 Canadian Academic Accounting Association Annual Conference, available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3105521.
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 Effects of Sanctions
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.
Russia’s Real Cost of Crimean Uncertainty
The annexation of Crimea has real costs to the Russian economy beyond what is measured by some items in the armed forces’ budget; social spending in the occupied territories; or the cost of building a rather extreme bridge to solve logistics issues. Russia’s real cost of the annexation of Crimea is also associated with the permanent loss of income that the entire Russian population is experiencing due to increased uncertainty, reduced capital flows and investment, and thus a growth rate that is significantly lower than it would have been otherwise. Since the years of lost growth are extremely hard to make up for in later years, there will be a permanent loss of income in Russia that is a significant part of the real cost of annexing Crimea and continuing the fighting in Eastern Ukraine. It is time to stop not only the human bleeding associated with Ukraine, but also the economic.
Estimating the real cost of Russia’s annexation of Crimea and the continued involvement in Eastern Ukraine is complicated since there are many other things going on in the Russian economy at the same time. In particular, oil prices fell from over $100/barrel in late 2013 to $30/barrel in 2016 (Figure 1). Becker (2016) has shown that 60-80 percent of the variation in GDP growth can be explained by changes in oil prices, so this makes it hard to just look at actual data on growth to assess the impact of Crimea and subsequent sanctions and counter sanctions.
Figure 1. Russian GDP and oil price
Source: Becker (2019)
The approach here is instead to focus on one channel that is likely to be important for growth in these circumstances, which is uncertainty and its impact on capital flows and investment.
From uncertainty to growth
The analysis presented here is based on several steps that link uncertainty to GDP growth. All the details of the steps in this analysis are explained at some length in Becker (2019). Although this brief will focus on the main assumptions and estimates that are needed to arrive at the real cost of Crimea, a short description of the steps is as follows.
First of all, in line with basic models of capital flows, investors that can move their money across different markets (here countries) will look at relative returns and volatility between different markets. When relative uncertainty goes up in one market, capital will leave that market.
The next step is that international capital flows affect investment in the domestic market. If capital leaves a country, less money will be available for fixed capital investments.
The final step is that domestic investments is important for growth. Mechanically, in a static, national accounts setting, if investments go down, so does GDP. More long term and dynamically, investments have a supply side effect on growth, and if investments are low, this will affect potential as well as actual growth negatively.
These steps are rather straightforward and saying that uncertainty created by the annexation of Crimea leads to lower growth is trivial. What is not trivial is to provide an actual number on how much growth may have been affected. This requires estimates of a number of coefficients that is the empirical counterparts to the theoretical steps outlined here.
Estimates to link uncertainty to growth
In short, we need three coefficients that link: domestic investments to growth; capital flows to domestic investments; and uncertainty to capital flows.
There are many studies that look at the determinants of growth, so there are plenty of estimates on the first of these coefficients. Here we will use the estimate of Levine and Renelt (1992), that focus on finding robust determinants of growth from a large set of potential explanatory variables. In their preferred specification, growth is explained well by four variables, initial income, population growth, secondary education and the investments to GDP ratio. The coefficient on the latter is 17.5, which means that when the investment to GDP ratio increases by 10 percentage points, GDP grows an extra 1.75 percentage points per year. Becker and Olofsgård (2018) have shown that this model explains the growth experience of 25 transition countries including Russia since 2000 very well, which makes this estimate relevant for the current calculation.
The next coefficient links capital flows to domestic investments. This is also a subject that has been studied in many empirical papers. Recent estimates for transition countries and Russia in Mileva (2008) and Becker (2019) find an effect of FDI on domestic investments that is larger than one, i.e., there are positive spillovers from FDI inflows to domestic investments. Here we will use the estimate from Becker (2019) that finds that 10 extra dollars of FDI inflows are associated with an increase of domestic investments of 15 dollars.
Finally, we need an estimate linking uncertainty with capital flows. There are many studies looking at risk, return and investment in general, and also several studies focusing on international capital flows and uncertainty. Julio and Yook (2016) look at how political uncertainty around elections affect FDI of US firms and find that FDI to countries with high institutional quality is less affected by electoral uncertainty than others. Becker (2019) estimates how volatility in the Russian stock market index RTS relative to the volatility in the US market’s S&P 500 is associated with net private capital outflows. The estimate suggests that when volatility in the RTS goes up by one standard deviation, this is associated with net private capital outflows of $30 billion.
These estimates now only need one more thing to allow us to estimate how much Crimean uncertainty has impacted growth and this is a measure of the volatility that was created by the annexation of Crimea.
Measuring Crimean uncertainty
In Becker (2019), the measure of volatility that is used in the regression with net capital outflows is the 60-day volatility of the RTS index. Since we now want to isolate the uncertainty created by Crimea related events, we need to take out the volatility that can be explained by other factors in order to arrive at a volatility measure that captures Crimean induced uncertainty. In Becker (2019) this is done by running a regression of RTS volatility on the volatility of international oil prices and the US stock market as represented by the S&P 500. The residual that remains after this regression is the excess volatility of the RTS that cannot be explained by these two external factors. The excess volatility of the RTS index is shown in figure 2.
It is clear that the major peaks in excess volatility are linked to Crimea related events, and in particular to the sanctions introduced at various points in time. From March 2014 to March 2015, there is an average excess volatility of 0.73 standard deviations with a peak of almost 4 when the EU and the USA ban trade with Crimea. This excess volatility is our measure of the uncertainty created by the annexation of Crimea.
Figure 2. RTS excess volatility
Source: Becker (2019)
From Crimean uncertainty to growth
The final step is simply to use our measure of Crimean induced uncertainty together with the estimates that link uncertainty in general to growth.
The estimated excess volatility associated with Crimea is conservatively estimated at 0.7 standard deviations. Using this with the estimate that increasing volatility by one standard deviation is associated with $30 billion in capital outflows, we get that the Crimean uncertainty would lead to $21 billions of capital outflows in one quarter or $84 billions in one year. If this is in the form of reduced FDI flows, we have estimated that this means that domestic investments would fall by a factor of 1.5 or $126 billions.
In this period, Russia had a GDP of $1849bn and fixed capital investments of $392bn. This means that $126 billions in reduced investments correspond to a reduction in the investments to GDP ratio of 7 percentage points (or that the investments to GDP ratio goes from around 21 percent to 14 percent).
Finally, using the estimate of 17.5 from Levine and Renelt, this implies that GDP growth would have been 1.2 percentage points higher without the estimated decline in investments to GDP.
In other words, the Crimean induced uncertainty is estimated to have led to a significant loss of growth that has to be added to all the other costs of the annexation of Crimea and continued fighting in Eastern Ukraine. Note that recent growth in Russia has been just barely above 1 percent per year, so this means that growth has been cut in half by this self-generated uncertainty.
Of course, the 1.2 percentage point estimate of lost growth is based on many model assumptions, but it provides a more sensible estimate of the cost of Crimea than we can get by looking at actual data that is a mix of many other factors that have impacted capital flows, investments and growth over this period.
Policy conclusions
The annexation of Crimea and continued fighting in Eastern Ukraine carry great costs in terms of human suffering. In addition, they also carry real costs to the Russian economy. Not least to people in Russia that see that their incomes are not growing in line with other countries in the world while the value of their rubles has been cut in half. Some of this is due to falling oil prices and other global factors that require reforms that will reorient the economy from natural resource extraction to a more diversified base of income generation. This process will take time even in the best of worlds.
However, one “reform” that can be implemented over night is to stop the fighting in Eastern Ukraine and work with Ukraine and other parties to get out of the current situation of sanctions and counter-sanctions. This would provide a much-needed boost to foreign and domestic investments required to generate high, sustainable growth to the benefit of many Russians as well as neighboring countries looking for a strong economy to do trade and business with.
References
- Becker, T, (2019), “Russia’s macroeconomy—a closer look at growth, investment, and uncertainty”, forthcoming SITE Working paper.
- Becker, T. and A. Olofsgård, (2018), “From abnormal to normal—Two tales of growth from 25 years of transition”, Economics of Transition, vol. 26, issue 4.
- Becker, T. (2016), “Russia and Oil – Out of Control”, FREE policy brief, October.
- Julio, B. and Yook, Y. (2016), ‘Policy uncertainty, irreversibility, and cross-border flows of capital’, Journal of International Economics, Vol. 103, pp. 13-26.
- Levine, R. and Renelt, D. (1992). ‘A Sensitivity Analysis of Cross-Country Growth Regressions’, American Economic Review, 82(4), pp. 942–963.
- Mileva, E. (2008), ‘The Impact of Capital Flows on Domestic Investment in Transition Economies, ECB Working Paper No. 871, February.
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.
Losers and Winners of Russian Countersanctions: A welfare analysis
In this brief we provide a quantitative assessment of the consequences of countersanctions introduced by the Russian government in 2014 in response to sectoral restrictive measures initiated by a number of developed countries. Commodity groups that fell under countersanctions included meat, fish, dairy products, fruit and vegetables. By applying a basic partial equilibrium analysis to data from several sources, including Rosstat, Euromonitor, UN Comtrade, industry reviews etc., we obtain that total consumers’ loss due to countersanctions amounts to 288 bn Rub or 2000 rubles per year for each Russian citizen. Producers capture 63% of this amount, importers 26%, while deadweight loss amounts to 10%. 30% of the transfer from Russian consumers toward importers was acquired by Belarus. The gain of Belarusian importers of cheese is especially impressive – 83% of total importer’s gains on the cheese market.
In August 2014, in response to sectoral sanctions initiated by some countries 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.).
Russian countersanctions were, in particular, imposed on meat, fish, dairy products, fruit and vegetables. Later the list of counter sanctioned goods was edited: inputs for the production of baby food and medicines have been deleted from the ban list, while new items were added. Salt was added to the list in November 2016 and animal fats in October 2017.
The popular idea behind the countersanctions was to limit market access for countries, which supported sectoral sanctions. The other rhetoric of the countersanctions was to support domestic producers via trade restrictions, or by other words – import substitution.
We apply a basic partial equilibrium analysis in order to evaluate the effect of countersanctions on the welfare of main stakeholders – consumers, producers and importers. The overall results are in line with general microeconomic consequences of trade restrictions in a small open economy, that is, we observe a decline in consumer surplus, increase in producer surplus and redistribution across importers. Perhaps, even more interestingly, we are able to provide a numerical assessment of redistribution effects between Russian consumers and producers, on the one hand, and among importers from different countries, on the other.
Partial equilibrium welfare analysis
We apply a framework of the classical analysis of import tariff increases to Russian countersanctions. Countersanctions resulted in increased domestic prices, declining consumption and increased domestic production. Given the increase in prices and declined volumes of consumption, we evaluate the losses by consumers as a decline in consumer surplus. Respectively, given the increase in prices and increase in domestic output we identify the producers gains as an increase in producer surplus. The only difference with a classical analysis is the lack of increase in government revenues. In this case increases in domestic prices were driven by restrictions on trade with historical partners which were substituted by more costly producers. Given the changes in the composition of importers after sanctions, we identify countries which lost and gained access to the Russian market. We use changes in volumes of trade as a measure of respective gains and losses. Figure 1 presents all relevant concepts.
In order to measure all relevant welfare changes, we rely on consumption, production and price data from Rosstat and Euromonitor, trade data from the UN Comtrade database. We use data for 2013 as a benchmark before countersanctions and compare it to 2016. The measures of own price elasticities of Russian demand and supply were taken from the literature. We use real price (in terms of 2013 prices) and volume information for consumption and supply in 2016 as the resulting points on the supply (point C) and demand (point A) curves as shown on Figure 1. Then we restore the consumption and production points on these curves (points F and B) as they would have been in 2013 given the own price elasticities of demand and supply and price level as of 2013.
Figure 1. Visualization of deadweight losses, consumer and producer surplus changes
Welfare analysis
Data
We consider 12 commodity groups that were included in 2014 in the countersanctions list: pork, cheese, poultry, apples, beef, tomatoes, processed meat, fromage frais, butter, oranges, condensed milk, grapes, cream, sour milk products, milk, and bananas.
Prices and volumes information are taken from Rosstat official statistics, which in a few cases were adjusted by data from Euromonitor. Import values were obtained from the UN Comtrade database. The summary of the original data and results of welfare analyses are reported in table 1. Below we discuss in details the situation in three markets – beef, apples and cheese.
Table 1. Summary table of the welfare effects of countersanctions
| Group | Price (RUR per kg, 2013) | Production (thous. tons) | Consumption (thous. tons) | Elasticity | Consumer losses, RUR mn | Producer surplus, RUR mn | Deadweight loss, RUR mn | Importer gains, RUR mn | ||||
| 2016 | 2013 | 2016 | 2013 | 2016 | 2013 | demand | supply | |||||
| Beef | 376 | 357 | 238 | 240 | 600 | 897 | -0.78 | 0.1 | 11311 | 4388 | 234 | 6690 |
| Poultry | 109 | 108 | 4468 | 3610 | 4577 | 4084 | -0.78 | 0.45 | 3263 | 3173 | 13 | 77 |
| Pork | 286 | 289 | 2042 | 1299 | 2282 | 1919 | -0.78 | 0.2 | -7167 | -6447 | 38 | -757 |
| Milk | 55 | 47 | 5540 | 5386 | 5704 | 5595 | -0.93 | 0.3 | 48234 | 42507 | 4443 | 1284 |
| Butter | 343 | 271 | 251 | 225 | 340 | 340 | -0.93 | 0.18 | 27468 | 17680 | 3370 | 6419 |
| Cheese | 358 | 283 | 605 | 435 | 748 | 764 | -0.93 | 0.28 | 63493 | 44259 | 8437 | 10797 |
| Fromage frais | 233 | 190 | 407 | 371 | 456 | 457 | -0.93 | 0.3 | 21803 | 17104 | 2600 | 2099 |
| Apples | 84 | 70 | 324 | 313 | 986 | 1665 | -0.85 | 0.1 | 15225 | 4562 | 1238 | 9425 |
| Bananas | 61 | 47 | 0 | 0 | 1141 | 1165 | -0.9 | 0.1 | 18967 | 0 | 2315 | 16652 |
| Oranges | 65 | 59 | 0 | 0 | 932 | 1059 | -0.9 | 0.1 | 6054 | 0 | 272 | 5782 |
| Grapes | 175 | 131 | 174 | 101 | 366 | 459 | -0.85 | 0.1 | 18312 | 7527 | 2351 | 8435 |
| Tomatoes | 82 | 65 | 1130 | 863 | 1583 | 1718 | -0.97 | 0.1 | 28824 | 18177 | 3290 | 7357 |
Data sources: Rosstat, Euromonitor, UN COMTRADE
Bold figures were used to mark the commodity groups with a noticeable consumption growth in 2013-2016, italic figures – for those with consumption decrease, and underlined – for groups where consumption changed insignificantly during the period.
Beef
The Russian beef market experienced a drastic decrease in consumption during two years under countersanctions. In 2013 constant prices, the average real of 1 kg of beef increased by 5.3% from 357 Rub/kg in 2013 up to 376 Rub/kg in 2016. Domestic output decreased by 0.8% and to 238 thousand tons in 2016 from 240 in 2013. Domestic consumption decreased by 33.1% to 600 thousand tons in 2016 from 897 in 2013. Our estimations indicate that consumer losses amount to 11.3 bn Rub or 3.5% of beef consumption in 2013; producers’ gains are 4.4 bn Rub or 1.4%; deadweight losses are estimated at 0.2 bn Rub or 0.07%; and importers’ gains equal 6.7 bn Rub or 2.1%.
Out of total 6.7 bn Rub of importers’ gains, importers from Belarus acquire the major share (88%) – 5.9 bn Rub. Importers of beef from India and Colombia gained 0.4 bn Rub (6% of total) and 0.3 bn Rub (5%) respectively. Beef importers from Mongolia gained 0.03 bn Rub, from Kazakhstan – 0.01 bn Rub. Importers of beef from Brazil, Paraguay, Australia, Uruguay, Ukraine, Lithuania, Poland, and Argentina lost market shares in over the period 2013-2016.
Cheese
Average real price for 1 kg of cheese increased by 26.5% up to 358 Rub/kg in 2016 from 283 Rub/kg in 2013, both in constant 2013 prices. Domestic output increased by 39.1% to 605 thousand tons in 2016 from 435 thous. tons in 2013. Domestic consumption decreased by 2.1% to 748 thous. tons in 2016 from 764 thous. tons in 2013. Our results indicate the following effects of countersanctions on cheese market: consumers’ losses amounted to 63.5 bn Rub or 29.4% of cheese consumption in 2013; producer’s gain is 44.3 bn Rub or 20.5%; deadweight loss is estimated at 8.4 bn Rub or 3.9%; importers’ gains equal 10.8 bn Rub or 5.0%.
Out of a total 10.8 bn Rub of importer’s gains on the cheese market, importers of cheese from Belarus acquired the major share (82.9%) – 9.0 bln Rub, importers of cheese from Argentina gained 0.5 bn Rub (4.8% of total importers’ gain), importers from Uruguay gained 0.4 bn Rub (3.9%), Swiss cheese importers gained 0.2 bn Rub, importers from Armenia – 0.2 bn Rub (1.8%). While importers of cheese from Ukraine, the Netherlands, Germany, Finland, Poland, Lithuania, France, Denmark, Italy, and Estonia lost market access over 2013-2016.
Apples
In 2013 constant prices, average real price for 1 kg of apples increased by 20.0% up to 84 Rub/kg in 2016 from 70 Rub/kg in 2013. Domestic output increased by 3.5% to 324 thous. tons in 2016 from 313 thous. tons in 2013. Domestic consumption decreased by 40.8% to 986 thous. tons in 2016 from 1665 thous. tons in 2013. According to our analysis, the effects of countersanctions on the apple market are the following: consumers’ losses amounted to 15.2 bn Rub or 13.1 of apple consumption in 2013; producer’s gain is 4.6 bn Rub or 3.0%; deadweight loss is estimated at 1.2 bn Rub or 1.1%; importers’ gains equal 9.4 bln Rub or 8.1%.
Out of a total 9.4 bn Rub of importer’s gains, importers from Serbia acquired the major share (49.7%) – 4,7 bn Rub, importers of apples from China gained 1.6 bn Rub (16.7% of total importers’ gains), those importing from Macedonia gained 0.8 bn Rub (8.4%), from Azerbaijan 0.6 bn Rub (6.0%), and from South Africa 0.4 bn Rub (4.5% of total importers’ gains). While importers of apples from Poland, Italy, Belgium, and France lost market access.
Overall effects for 12 commodity groups
We calculated the welfare effects for 12 commodity groups: beef, poultry, milk, cheese, cottage cheese, ton butter, dairy products, apples, bananas, oranges, grapes and tomatoes.
Total consumers’ loss due to countersanctions amounts to 288 bn Rub, producers gain 63% out of this amount (182 bn Rub), 26% of total consumers’ loss is redistributed to importers (75 bn Rub), deadweight losses amount to 10% (31 bn Rub).
Distribution of importers’ gains
Belarus is the major beneficiary of Russians countersanctions: its exporters gain 29.4 bn Rub (38%), Ecuador’s exporters are in the second place with 16.4 bln Rub (21). Exporters from Serbia gained 5.1 bn Rub (7%).
Conclusion
There is no doubt that countersanctions were paid out of the pockets of Russian consumers: our estimation of total consumer losses amounts to 288 billion rubles, i.e. each Russian citizen paid 2000 rubles per year. Out of this sum, Russian producers received 144 billion rubles, i.e. transfer from Russian consumers to producers equals 1260 rubles per person per year. Among Russian sectors, major gains and associated increases in production happened in pork industries (50%), poultry (20%), dairy products (10-30%), fruit and vegetables (10-50%).
The transfer from Russian consumers toward importers from non-sanctioned countries equals 75 billion rubles a year (520 rubles per person per year), out of which 30% was acquired by Belarusian importers. Countersanctions lead to deadweight losses in the efficiency of Russian economy equal to 31 billion rubles or 215 rubles per person per year.
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 Bleak Economic Future of Russia (audio test)
Is the Russian economy “surprisingly resilient” to sanctions and actions of the West? The short answer is no. On the contrary, the impact on Russian growth is already very clear while the economic downturn in the EU is small. The main effects from the sanctions are yet to be realized, and the coming sanctions will be even more consequential for the Russian economy. The biggest impacts are however those in the longer run, beyond the sanctions. Mr. Putin’s actions have led to a fundamental shift in the perception of Russia as a market for doing business. The West and especially EU countries are on a track of divesting their economic ties to Russia (in particular in, but not only, energy markets) and the country is simultaneously losing significant shares of its human capital. All these effects mean that the long-term economic outlook for Russia is not just a business cycle type recession but a lasting downward shift.
Introduction
The global economic outlook at the moment seems rather bleak. According to the International Monetary Fund’s (IMF) most recent World Economic Outlook, global growth is expected to slow from above 6 percent in 2021, to 3.2 percent this year, and 2.7 percent in 2023. For the US and the Euro area the corresponding numbers are slightly above a 5 percent growth in 2021, between 2 and 3 percent in 2022, while barely reaching 1 percent in 2023. At the same time inflation is up and central banks are trying to curb this by raising interest rates.
From an EU perspective it is an open question what proportion of the lower growth is caused by the economic consequences of the Russian invasion of Ukraine. Certainly, energy prices are affected as well as issues relating to natural resources and agricultural products (though the consequences of shortages in these goods are far larger for Middle Eastern, North African and Sub-Saharan countries). But it is not the case that all of the economic problems in the EU are due to the changed economic relations with Russia.
In assessing the economic impact of Russia’s war, and in particular the impact of sanctions, it is important to focus on both expectations as well as proportions. A widespread narrative portrays Russia’s relative economic resilience (compared to the expectations of some in March/ April 2022) as the Russian economy being surprisingly unaffected, while the EU is depicted as being badly hit, especially by high energy prices. In a European context, the Swedish daily newspaper Dagens Nyheter claims that “experts are surprised over Russia’s resilience” and the Economist, a British weekly newspaper, recently portrayed recession prospects for Europe as “Russia climbs out”. We argue that such point of view is misleading. To get a more balanced image of what is unfolding it is important to think both about the expected consequences of sanctions, including how long some of them take to have an effect, but also (and maybe most important when thinking about the long run), what economic consequences are now unfolding beyond the impact of sanctions.
Sanctions Against Russia
Let us start with what sanctions are in place, what types of impact these have had so far and what can be expected in the future. There are three types of sanctions currently in place. First, and most impactful in the short run, are limitations on financial transactions, especially those imposed on the Central Bank. In this category there are also the restrictions on other Russian banks disconnecting them from a key part of the global payment system, SWIFT, as well as measures targeting other assets: divestments from funds, investment withdrawals, asset freezes, and other impediments to financial flows. The main short-term aim of these actions was to reduce the Russian government’s alternatives to finance the army and their military operations. Second there are sanctions on trade in goods and services. At the moment these target particularly technology imports and energy and metals exports. These take a longer time to be felt and are potentially more costly to the sanctioning countries as well. They also contribute, in principle, to reduced resources for war. Besides affecting the government’s budget, both financial and trade sanctions disturb ordinary people’s lives as well and might create discontent and protests. A third group of sanctions are so-called sanctions of inconvenience such as limitations to air traffic, closure of air space, exclusion form sport and cultural events, restrictions of movement for both officials and tourists, and others, which aim at disconnecting the target country from the rest of the world. These are partly symbolic in nature, but can also impact popular opinion, including among the elites. However, a potential problem is that such sanctions can push opinion in either of two opposite directions: against the target regime in sympathy with the sanctioning parties; or against what is now perceived as an external enemy in a so-called rally-around-the-flag effect.
Along these dimensions the sanctions have so far had mixed effects in relation to the objectives listed above. We will return to this issue below, but in short, the sanctions on the Central Bank and the financial system, albeit powerful, fell short of causing anything like a collapse of the Russian financial system. Some of the trade restrictions, together with other global economic events, created an environment where lost trade volumes for Russia were compensated by price increases in resources and energy exports. When it comes to restrictions on imports of many high-tech components, these are certainly being felt in the Russian economy although still not fully. Public perceptions in Russia are hard to judge from the outside, especially given the problems of voiced opposition in the country, while public perceptions in sanctioning countries have mainly been favorable as people want to see that their governments are “doing something”.
What Do We Know About Sanctions in General?
A key question when judging whether sanctions “work” is to study what a reasonable benchmark can be. As discussed in a previous FREE Policy Brief (2012), sanctions don’t enjoy a reputation of being very effective. This is true both in the research literature as well as in the public opinion. There are reasons for this that have to do with both how “effectiveness” is intended and the limits that empirical enquiries necessarily face in trying to answer the question of effectiveness. This does not mean, however, that sanctions have no effect. Another FREE Policy Brief (2022) summarizes a selection of the most credible research in this area. In short, a majority of studies find that sanctions affect the population in target countries through shortages of various kind (food, clean water, medicine and healthcare), resulting in lower life expectancy and increased infant mortality. The types of effects are comparable to the consequences of a military conflict. In the cases where it has been possible to credibly quantify the damage to GDP, estimates are in the range of 2 to 4 percent of reduced annual growth over a fairly long period (10 years on average and up to 3 years after the lifting of sanctions). One has to keep in mind that lower growth rates compound over time, so that the total loss at the end of an average period is quite substantial. As a comparison, the latest estimate of the total loss in global GDP from the Covid-19 crisis stands at “just” -3.4 percent. Other studies find similarly significant negative effects on other economic outcomes such as employment rate, international trade, public expenditure, the value of the country’s currency, and inequality. There is of course variation in the effects depending on the type of sanctions and also on the structure of the target economy. Trade sanctions tend to have a negative effect both in the short and long run, while smart sanctions (i.e. sanctions targeting specific individuals or groups) may even have positive effects on the target country’s economy in the long run.
Sanctions and the Current State of the Russian Economy
When it comes to the Russian economy’s performance in these dire straits, the very bleak forecasts from spring 2022 have since been partly revised upwards. Some are surprised that the collective West has not been able to deliver a “knock-out blow” to the Russian economy. In light of what we know about sanctions in general this is perhaps not very surprising. Also, one can recall that even a totally isolated Soviet economy held up for quite some time. This however does not mean that sanctions are not working. There are several explanations for this. As already mentioned, some of the restrictions imply by their very nature some time delay; large countries normally have stocks and reserves of many goods – and on top of this Mr. Putin had been preparing for a while. Also, the undecisive and delayed management of energy trade from the EU reduced the effectiveness of other measures, in particular the impact of financial restrictions. Continued trade in the most valuable resources for the Russian government together with spikes in prices (partly due to the fact that the embargo was announced several months ahead of the intended implementation) flooded the Russian state coffers. This effect was also enlarged by the domestic tax cuts on gasoline prices in many European countries in response to a higher oil price (Gars, Spiro and Wachtmeister, 2022). This is soon coming to an end, but at the moment Russia enjoys the world’s second largest current account surplus.
The phenomenal adaptability of the global economy is also playing in Russia’s favor: banned from Western markets, Russia is finding new suppliers for at least some imports. However, although they are dampening and slowing the blow at the moment, it is difficult to envision how these countries can be substitutes for Western trade partners for many years to come.
The Russian Economy Beyond Sanctions
Given all of this, the impact on the Russian economy is not nearly as small as some commentators claim. Starting with GDP, an earlier FREE Policy Brief (2016) shows how surprisingly well Russia’s GDP growth can be explained by changes in international oil prices. This is true for the most recent period as well, up until the turn of the year 2021-2022 and the start of hostilities, as shown in Figure 1. Besides the clear seasonal pattern, Russian GDP (in Rubles) closely follows the BRENT oil price. This simple model, which performs very well in explaining the GDP series historically, generates a predicted development as shown by the red dotted line. Comparing this with the figures provided by the Russian Federal State Statistics Service, Rosstat, for the first two quarters of 2022 (which might in themselves be exaggeratedly positive) indicates a loss by at least 8 percent in the first and further 9 percent in the second quarter. In other words, GDP predicted by this admittedly simple model would have been 19 percent higher than what reported by Rosstat in the first half of 2022. As a comparison, Saudi Arabia – another highly oil dependent country – saw its fastest growth in a decade during the second quarter, up by almost 12 percent.
Figure 1. Russian GDP against predictions
Source: Authors’ calculations on GDP in rubles based on figures from Rosstat and the BRENT oil price series. Note that GDP is denominated in Rubles to avoid confusion due to the USD/Rubles exchange rates being volatile (given the lack of trade post invasion) and thus hard to interpret.
Other indicators point in the same direction. According to a report published by researchers at Yale University in July this year, Russian imports, on which all sectors and industries in the economy are dependent, fell by no less than ~50 percent; consumer spending and retail sales both plunged by at least ~20 percent; sales of foreign cars – an important indicator of business cycle – plummeted by 95 percent. Further, domestic production levels show no trace of the effort towards import substitution, a key ingredient in Mr. Putin’s proposed “solution” to the sanctions problem.
Longer Term Trends
There are many reasons to be concerned with the short run impact from sanctions on the Russian economy. Internally in Russia it matters for the public opinion, especially in parts that do not have access to reports about what goes on in the war. Economic growth has always been important for Putin’s popularity during peace time (Becker, 2019a). In Europe it matters mainly because a key objective is to make financing the war as difficult as possible, but also to ensure public support for Ukraine. A perception among Europeans that the Russian economy is doing fine despite sanctions is likely to decrease the support for these measures. However, the more important economic consequences for Russia are the long-run effects. Many large multinational firms have left and started to divest from the country. There has always been a risk premium attached to doing business in Russia, which showed up particularly in terms of reduced investment after the annexation of Crimea in 2014 (Becker, 2019b). But for a long time hopes of a gradual shift and a large market potential kept companies involved in Russia (in some time periods more, in others less). This has however ended for the foreseeable future. Many of the large companies that have left the Russian market are unlikely to return even in the medium term, regardless of what happens to sanctions. Similarly, investments into Russia have been seen as a crucial determinant of its growth and wellbeing (Becker and Olofsgård, 2017), and now this momentum is completely lost.
Energy relations have been Russia’s main leverage against the EU although warnings about this dependency have been raised for a long time. In this relationship, there has also been a hope that Russia would feel a mutual dependence and that over time it would shift its less desirable political course. With the events over the past year, this balancing act has decidedly come to an end, if not permanent, at least for many years to come. The EU will do its utmost not to rely on Russian energy in the future, and regardless of what path it chooses – LNG, more nuclear power, more electricity storage, etc. – the path forward will be to move away from Russia. Of course, there are other markets – approximately 40 percent of global GDP lies outside of the sanctioning countries – so clearly there are alternatives both for selling resources and establishing new trade relationships. However, this will in many cases take a lot of time and require very large infrastructure investments. And perhaps more important, for the most (to Russia) valuable imports in the high-tech sector it will take a very long time before other countries can replace the firms that have now pulled out.
Yet another factor that will have long-term consequences is that many of these aspects are understood by large parts of the Russian population, and those with good prospects in the West have already left or are trying to do so. It has been a long-term goal for those wanting to reform the Russian economy, at least in the past 20 years, to attract and put to fruition the high potential that have been available in terms of human capital and scientific knowledge. However, these attempts have not succeeded and the recent developments have put a permanent end to those dreams.
Conclusion
In the latest IMF forecast, countries in the Euro area will grow by 3.1 percent this year and only 0.5 percent in 2023. In January the corresponding numbers stood at 3.9 percent and 2.5 percent. This drop, caused in large part by the altered relations with Russia, is certainly non negligible, and especially painful coming on the heels of the Covid-19 crisis. However, it is an order of magnitude smaller than the “missed growth” Russia is experiencing. When judging the impact from sanctions on the Russian economy overall, the correct (and historically consistent) counterfactual displays a sizable GDP growth driven by very high energy and commodity prices. Relative to such counterfactual, the sanctions effect is already very noticeable. In the coming months, economic activity will slow down and many European household will feel the consequences. In this climate it will be important that, when assessing the situation with Russia perhaps performing better than expected, the following is kept in mind. Firstly, Russia is still doing much worse compared to the EU as well as to other oil-producing countries. Secondly, and even more important, what matters are the longer run prospects. And these are certainly even worse for the Russian economy.
References
- Becker, T. (2019a). Economic growth and Putin’s Approval Ratings – The Return of the Fridge https://freepolicybriefs.org/2019/02/25/economic-growth-and-putins-approval-ratings-the-return-of-the-fridge/ FREE Policy Brief
- Becker, T. (2019b). Russia’s Real Cost of Crimean Uncertainty https://freepolicybriefs.org/2019/06/10/russias-real-cost-of-crimean-uncertainty/FREE Policy Brief
- Becker, T. and Olofsgård, A. (2017). From abnormal to normal – Two tales of growth from 25 years of transition, SITE Working paper 43, September.
- Becker, T. (2016). Russia and Oil – Out of Control https://freepolicybriefs.org/2016/10/31/russia-oil-control FREE Policy Brief
- Gars, J., Spiro, D. and Wachtmeister, H. (2022). The effect of European fuel-tax cuts on the oil income of Russia. Nat Energy 7, pp. 989-997 https://www.nature.com/articles/s41560-022-01122-6
- Perotta Berlin, M. (2022). The Effect of Sanctions https://freepolicybriefs.org/2022/05/10/effects-economic-sanctions/ FREE Policy Brief
- Perotta Berlin, M. (2012). Do Economic Sanctions Work? https://freepolicybriefs.org/2012/03/19/do-economic-sanctions-work/ FREE Policy Brief
- Sonnenfeld, J., Tian, S., Sokolowski, F., Wyrebkowski, M. and Kasprowicz, M. (2022). Business Retreats and Sanctions Are Crippling the Russian Economy. http://dx.doi.org/10.2139/ssrn.4167193
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.