Project: FREE policy brief
Energy Storage: Opportunities and Challenges
As the dramatic consequences of climate change are starting to unfold, addressing the intermittency of low-carbon energy sources, such as solar and wind, is crucial. The obvious solution to intermittency is energy storage. However, its constraints and implications are far from trivial. Developing and facilitating energy storage is associated with technological difficulties as well as economic and regulatory problems that need to be addressed to spur investments and foster competition. With these issues in mind, the annual Energy Talk, organized by the Stockholm Institute of Transition Economics, invited three experts to discuss the challenges and opportunities of energy storage.
Introduction
The intermittency of renewable energy sources poses one of the main challenges in the race against climate change. As the balance between electricity supply and demand must be maintained at all times, a critical step in decarbonizing the global energy sector is to enhance energy storage capacity to compensate for intermittent renewables.
Storage systems create opportunities for new entrants as well as established players in the wind and solar industry. But they also present challenges, particularly in terms of investment and economic impact.
Transitioning towards renewables, adopting green technologies, and developing energy storage can be particularly difficult for emerging economies. Some countries may be forced to clean a carbon-intensive power sector at the expense of economic progress.
The 2021 edition of Energy Talk – an annual seminar organized by the Stockholm Institute of Transition Economics – invited three international experts to discuss the challenges and opportunities of energy storage from a variety of academic and regulatory perspectives. This brief summarizes the main points of the discussion.
A TSO’s Perspective
Niclas Damsgaard, the Chief strategist at Svenska kraftnät, gave a brief overview of the situation from a transmission system operator’s (TSO’s) viewpoint. He highlighted several reasons for a faster, larger-scale, and more variable development of energy storage. For starters, the green transition implies that we are moving towards a power system that requires the supply of electricity to follow the demand to a much larger extent. The fact that the availability of renewable energy is not constant over time makes it crucial to save power when the need for electricity is low and discharge it when demand is high. However, the development and facilitation of energy storage will not happen overnight, and substantial measures on the demand side are also needed to ensure a more dynamic energy system. Indeed, Damsgaard emphasized that demand flexibility constitutes a necessary element in the current decarbonization process. However, with the long-run electrification of the economy (particularly driven by the transition of the transport industry), extensive energy storage will be a necessary complement to demand flexibility.
It is worth mentioning that such electrification is likely to create not only adaptation challenges but also opportunities for the energy systems. For example, the current dramatic decrease in battery costs (around 90% between 2010 and 2020) is, to a significant extent, associated with an increased adoption of electric vehicles.
However, even such a drastic decline in prices may still fall short of fully facilitating the new realities of the fast-changing energy sector. One of the new challenges is the possibility to store energy for extended periods of time, for example, to benefit from the differences in energy demand across months or seasons. Lithium-ion batteries, the dominant battery technology today, work well to store for a few hours or days, but not for longer storage, as such batteries self-discharge over time. Hence, to ensure sufficient long-term storage, more batteries would be needed and the associated cost would be too high, despite the above-mentioned price decrease. Alternative technological solutions may be necessary to resolve this problem.
Energy Storage and Market Structure
As emphasized above, energy storage facilitates the integration of renewables into the power market, reduces the overall cost of generating electricity, and limits carbon-based backup capacities required for the security of supply, creating massive gains for society. However, because the technological costs are still high, it is unclear whether the current economic environment will induce efficient storage. In particular, does the market provide optimal incentives for investment, or is there a need for regulations to ensure this?
Natalia Fabra, Professor of Economics and Head of EnergyEcoLab at Universidad Carlos III de Madrid, shared insights from her (and co-author’s) recent paper that addresses these questions. The paper studies how firms’ incentives to operate and invest in energy storage change when firms in storage and/or production have market power.
Fabra argued that storage pricing depends on how decisions about the storage investment and generation are allocated between the regulator and the firms operating in the storage and generation markets. Comparing different market structures, she showed as market power increases, the aggregate welfare and the consumer surplus decline. Still, even at the highest level of market concentration, an integrated storage-generation monopolist firm, society and consumers are better off than without energy storage.
Fabra’s model also predicts that market power is likely to result in inefficient storage investment.
If the storage market is competitive, firms maximize profits by storing energy when the prices are low and releasing when the prices are high. The free entry condition implies that there are investments in storage capacity as long as the marginal benefit of storage investment is higher than the marginal cost of adding an additional unit of storage. But this precisely reflects the societal gains from storage; so, the competitive market will replicate the regulator solution, and there are no investment distortions.
If there is market power in either generation or storage markets, or both, the investment is no longer efficient. Under market power in generation and perfectly competitive storage, power generating firms will have the incentive to supply less electricity when demand is high and thereby increase the price. As a result, the induced price volatility will inflate arbitrage profits for competitive storage firms, potentially leading to overinvestment.
If the model features a monopolist storage firm interacting with a perfectly competitive power generation market, the effect is reversed. The firm internalizes the price it either buys or sells energy, so profit maximization makes it buy and sell less energy than it would in a competitive market, in the exact same manner as the classical monopolist/monopsonist does. This underutilization of storage leads to underinvestment.
If the model considers a vertically integrated (VI) generation-storage firm with market power in both sectors, the incentives to invest are further weakened: the above-mentioned storage monopolist distortion is exacerbated as storage undermines profits from generation.
Using data on the Spanish electricity market, the study also demonstrated that investments in renewables and storage have a complementary relationship. While storage increases renewables’ profitability by reducing the energy wasted when the availability is excess, renewables increase arbitrage profits due to increased volatility in the price.
In summary, Fabra’s presentation highlighted that the benefits of storage depend significantly on the market power and the ownership structure of storage. Typically, market power in production leads to higher volatility in prices across demand levels; in turn, storage monopolist creates productive inefficiencies, two situations that ultimately translate into higher prices for consumers and a sub-optimal level of investment.
Governments aiming to facilitate the incentives to invest in the energy storage sector should therefore carefully consider the economic and regulatory context of their respective countries, while keeping in mind that an imperfect storage market is better than none at all.
The Russian Context
The last part of the event was devoted to the green transition and the energy storage issue in Eastern Europe, with a specific focus on Russia.
Alexey Khokhlov, Head of the Electric Power Sector at the Energy Center of Moscow School of Management, SKOLKOVO, gave context to Russia’s energy storage issues and prospects. While making up for 3% of global GDP, Russia stands for 10% of the worldwide energy production, which arguably makes it one of the major actors in the global power sector (Global and Russian Energy Outlook, 2016). The country has a unified power system (UPS) interconnected by seven regional facilities constituting 880 powerplants. The system is highly centralized and covers nearly the whole country except for more remote regions in the northeast of Russia, which rely on independent energy systems. The energy production of the UPS is strongly dominated by thermal (59.27%) followed by nuclear (20.60%), hydro (19.81%), wind (0.19%), and solar energy (0.13%). The corresponding ranking in capacity is similar to that of production, except the share of hydro-storage is almost twice as high as nuclear. The percentage of solar and wind of the total energy balance is insignificant
Despite the deterring factors mentioned above, Khokhlov described how the Russian energy sector is transitioning, though at a slow pace, from the traditional centralized carbon-based system towards renewables and distributed energy resources (DER). Specifically, the production of renewables has increased 12-fold over the last five years. The government is exploring the possibilities of expanding as well as integrating already existing (originally industrial) microgrids that generate, store, and load energy, independent from the main grid. These types of small-scaled facilities typically employ a mix of energy sources, although the ones currently installed in Russia are dominated by natural gas. A primary reason for utilizing such localized systems would be for Russia to improve the energy system efficiency. Conventional power systems require extra energy to transmit power across distances. Microgrids, along with other DER’s, do not only offer better opportunities to expand the production of renewables, but their ability to operate autonomously can also help mitigate the pressure on the main grid, reducing the risk for black-outs and raising the feasibility to meet large-scale electrification in the future.
Although decarbonization does not currently seem to be on the top of Russia’s priority list, their plans to decentralize the energy sector on top of the changes in global demand for fossil fuels opens up possibilities to establish a low-carbon energy sector with storage technologies. Russia is currently exploring different technological solutions to the latter. In particular, in 2021, Russia plans to unveil a state-of-the-art solid-mass gravity storage system in Novosibirisk. Other recently commissioned solutions include photovoltaic and hybrid powerplants with integrated energy storage.
Conclusion
There is no doubt that decarbonization of the global energy system, and the role of energy storage, are key in mitigating climate change. However, the webinar highlighted that the challenges of implementing and investing in storage are both vast and heterogenous. Adequate regulation and, potentially, further government involvement is needed to correctly shape incentives for the market participants and get the industry going.
On behalf of the Stockholm Institute of Transition Economics, we would like to thank Niclas Damsgaard, Natalia Fabra, and Alexey Khokhlov for participating in this year’s Energy Talk. The material presented at the webinar can be found here.
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.
For a Better Budget Management of Infrastructure Investments
Many developing countries rely on investment-to-GDP metrics as a sign of progress towards their development goals. Unfortunately, too often the focus on investment pushes aside the issues of adequately maintaining existing infrastructure. The result could be disastrous to human lives, health, and well-being. Lack of maintenance of existing infrastructure is a well-known problem, not only in developing economies but also in some developed countries. However, how much the government should plan to spend on maintenance over the lifetime of infrastructure assets is neither a simple nor straightforward question. In this policy brief, we examine the cases of two transition economies – Georgia and Estonia – and provide a more general discussion of the challenges and possible solutions to infrastructure maintenance issues. We argue that relevant research along with properly aligned incentives could help the countries overcome these problems and optimize infrastructure spending.
Introduction
The efficiency of infrastructure investment has gotten quite some attention in the past years. A recent book by G. Schwartz et al. (2020) shows that countries waste about 1/3 (and some even more) of their infrastructure spending due to inefficiencies. With poor management, the major budgetary efforts undertaken to make room for infrastructure investments go to waste. The question of how much the country should plan to spend on maintenance over the lifetime of infrastructure assets is neither simple nor straightforward. In two recent ISET-PI blog posts, Y. Babych and L. Leruth (2020a, b) stress the importance of striking the right balance between new infrastructure investments and the rehabilitation and maintenance of existing infrastructure. Without this balance, the up-keep of public infrastructure could either be too expensive for the budget to handle, or, at the other extreme, would quickly deteriorate to the point where it is no longer operational and needs to be rebuilt from the ground up (which is the case in many developing countries, including Georgia, Armenia, Ukraine, and others). This policy brief focuses on the reasons why developing (and even some developed) countries tend to invest too little in public infrastructure maintenance and what can be done to solve this problem. We first examine the cases of Georgia and Estonia, two post-Soviet transition economies with different approaches to infrastructure maintenance financing. This analysis is then followed by a more general discussion about the infrastructure maintenance challenges and potential solutions.
Maintenance vs. Investment: the Cases of Georgia and Estonia
Developing countries tend to use investment (public or private) as a share of GDP to measure their economic progress and prospects. Georgia is one of the countries that has invested a lot in public infrastructure. Public investment grew sharply between 2003-2007 to 8% of GDP and settled at 6% of GDP after 2017 (PIMA GEO 2018). The capital stock is about 90% of GDP. In comparison, in Estonia, another post-Soviet economy, public investment was about 4% of GPD, whereas the capital stock was 57% of GDP in 2015. Yet, the quality of Georgia’s public infrastructure is much lower than in Estonia (Georgia is in 69th place globally according to Global Competitiveness Index 2017-2018, while Estonia is in 32nd place). The reason for this is quite simple: management, especially the maintenance of public infrastructure. Both countries recently went through a Public Investment Management Assessment (PIMA), a comprehensive framework developed by the IMF to assess infrastructure governance. The results suggest that Georgia is much weaker than Estonia in planning, budgeting, and maintenance. (A complete summary of the assessment results can be found here).
Georgia’s case is far from unique. The country belongs to the vast majority of emerging economies that have not efficiently linked their medium- and long-term infrastructure plans within a sustainable fiscal framework. Moreover, infrastructure planning deficiencies spread way beyond the emerging markets: Allen et al. (2019) estimate that 56% of all world countries do not have a proper Public Investment Program.
Why is Infrastructure Maintenance a Challenge for Many Countries?
Even though maintenance, rehabilitation, and new investments are intrinsically linked, the practical process of integrating these three infrastructure components is complex. Blazey et al. (2019), for example, identify the following reasons:
- Political economy reasons—governments will opt for a ribbon-cutting rather than maintaining existing assets;
- Fiscal reasons—budget funding for operations and maintenance is prone to be cut when fiscal space is limited;
- Institutional reasons—in many countries, separate agencies still prepare investment and current expenditure budgets;
- Capacity reasons— up-to-date information on the state of assets may not be readily available.
A number of international studies (usually sectorial) point to the high cost of neglecting maintenance. A study on the upkeep of bridges and roads in the US shows that 1$ of deferred maintenance will cost over 4$ in future repairs. The same holds for airports. In Africa, the World Bank estimates that timely road expenditure of $12 billion spent in the 80s would have saved $45 billion in reconstruction costs during the next decade. It is not only rehabilitation costs that increase with poor maintenance: user costs can increase dramatically (Escobal and Ponce, 2003); health costs in terms of injuries or deaths; and ecological costs (the water lost daily because of leaks could satisfy the needs of 200 million people according to the World Bank, 2006).
Conceptually, however, the link between maintenance, rehabilitation, and new investments is simple to understand. Figure 1 below, adopted from Thi Hoai Le et al. (2019), clarifies this point. As discussed in Babych and Leruth (2020b), when planned maintenance activities (such as planned repair, upkeep, etc.) are insufficient, then the rate at which infrastructure is deteriorating will be high, and the unplanned maintenance costs will increase as well. This response would, in turn, result in a higher total cost. If the amount of planned maintenance activities is excessive, then the unplanned costs may be low, but the total cost is higher than optimal. In order to strike the optimal balance, there need to be just enough planned maintenance activities.
Figure 1. Optimal zone of maintenance.
Conceptually simple maybe, but the devil(s) is (are) in the details. We have already listed above some of the reasons why integration is complex. Data availability is another issue raised by numerous Public Investment Management Assessments made by the IMF. The reporting standards are simply not built in a way that would allow for the compilation of maintenance and rehabilitation data (although aggregate estimates of investment data are available). In any case, the Government Finance Statistics Manual of the IMF (2014) does not separate maintenance expenditure, which is undoubtedly an area that requires further deepening. More fundamentally perhaps, as pointed out long ago by Schick (1966), there is an additional issue relating to governance philosophy: “planning and budgeting have run separate tracks and have invited different perspectives, the one conservative and negativistic, the other innovative and expansionist …”. Finally, with governments looking for the ‘cheap’ route through public-private partnerships (PPPs) to finance infrastructure development, fiscal risks have increased in advanced and emerging economies in the early 2000s (IMF, 2008). To our knowledge, there have been no systematic assessments of PPP-related fiscal risks since IMF’s report in 2008, but as fiscal positions have deteriorated with the Covid-19 pandemic, PPP projects are likely even riskier today.
What Can Be Done to Improve Infrastructure Maintenance?
Leaving the data, PPPs, and inter-departmental culture issues aside, several considerations that emerge from a closer look at Figure 1 can feed the policy discussions. Let us first consider the notion of planned maintenance (the orange line). In principle, as a project is developed, the cost of maintenance is projected over its life cycle. If the infrastructure is maintained accordingly, its life span may even exceed the projections. At the time the project is conceived, a schedule of maintenance expenditure is also planned and integrated into the analysis. In the figure above, one would expect that these cost assumptions are located in the ‘optimal maintenance zone’ with a limited amount to be spent on unplanned maintenance later on. This level of planned maintenance should then be integrated as a ‘given’ in all subsequent budgets. Usually, as we have already mentioned, it is not.
If we now move to ‘unplanned’ maintenance (the line in blue), we are really referring to situations when infrastructure must be brought back to shape after months (or even years) of neglect. In some cases, this can no longer be labeled as maintenance, and it becomes rehabilitation. Reduce regular maintenance a bit more and the authorities must start over.
Finally, the continuity of the curves is misleading: it is wrong to say that things are necessarily smooth even in the optimal zone.
Let us look more closely at the leading causes and the ways to overcome the problems that arise when optimizing maintenance expenditure.
Setting benchmarks: One explanation for the shortage of maintenance planning outlined above is the lack of information on the practical implementation of such planning. There are too few studies on maintenance expenditure for policymakers to set benchmarks and develop reliable estimates. The existing studies in this area tend to focus on OECD countries (where data availability is less of a constrain) and on the transportation sector (roads, rail, etc.) perhaps because the private sector is more often involved (see, for example, the American Society of Civil Engineers from 2017, that concluded that 9 percent of all bridges are structurally deficient). Some studies have looked at buildings (e.g., Batalovic et al., 2017 or the Ashrae database, 2021) and unsurprisingly concluded that the age of the construction and its height are significant variables to explain maintenance outlays. However, we are not aware of studies that would, for example, distinguish between different types of maintenance in order to limit overall costs. We are neither aware of studies investigating which organizational arrangements are the most efficient (as discussed by Allen et al., 2019). The bottom line is that there is not much to use as a benchmark, and an effort must be made to build reliable estimates.
Policy dialogue on maintenance is needed: The abovementioned considerations of the consequences of delayed, unplanned, and sometimes unexpected maintenance bring us to our next point. Things break down when they are not maintained (and sometimes break down when they are maintained too), and such long-term aspects must be more present in the policy dialogue with developing countries. Clearly, delaying maintenance increases fiscal costs in the short- and longer-term (Blazey et al., 2019).
The smoothness of the curves in Figure 1 can be misleading because insufficient maintenance may suddenly trigger a major problem (a bridge or a dam can collapse, as it happened in Italy and in India recently,) and this will entail high costs, even disasters involving in human lives. The major collapses of nuclear plants (as in Chornobyl, Ukraine, and more recently in Fukushima, Japan) are other examples of the same problem. In addition, studies estimate that poor maintenance of transmission lines could be one of the reasons for electricity blackouts (Yu and Pollitt, 2009). In fact, the lack of maintenance increases the speed at which the value of the existing capital of infrastructure is eroding. While politicians may well hope that this will not happen during their tenure, the probability of a failure increases as maintenance decreases.
On top of the above, inefficiency in maintenance expenditures can be aggravated by wrongly set incentives, both for domestic actors and foreign donors. Indeed, the latter play an important role in infrastructure investment in many developing countries. In Georgia, for example, 40% of infrastructural projects are funded by foreign donors. Setting the right incentives for both parties, as well as their interplay, are thus of immense importance.
Aligning the incentives: Incentives are against maintenance. As pointed out by Babych and Leruth (2020a), capital investment and rehabilitation look good on paper. Maintenance, on the other hand, is considered a current expenditure item in the Government Finance Statistics (GFS) (IMF, 2014). Spending more on maintenance will therefore not look good since 1) more maintenance will reduce government savings in the short term; 2) spending less on maintenance will increase the need for virtuous-looking investment expenditure in the medium and long term. Yet, in spite of the lack of clear benchmarks, donors can play an essential role by stressing the need to systematically integrate maintenance in the budget and in the Medium-Term Expenditure Framework (MTEF). To some extent, it is already the case. In Georgia, projects that are funded by donors tend to follow better appraisal procedures. However, ex-post audits are irregular – e.g., no individual projects audits were completed by State Audit Office during 2015-2017 (PIMA GEO, 2018). If donors could include these audits in their dialogue, it would clearly be helpful. Training subnational governments in proper maintenance management would be even more critical as capacities tend to be weaker than in the center.
Overcoming a potential moral hazard problem of donor involvement: Excessive donor involvement in new investments could also be counterproductive. Donors should carefully examine the need to build new infrastructure and first consider the possibility of performing some rehabilitation while holding the authorities accountable for the maintenance of existing ones. If the authorities are expecting a donor to eventually replace a piece of infrastructure that does not function, the incentives to maintain it are greatly reduced.
Conclusion
- Developing economies, but also emerging ones like Georgia, as well as Armenia, Ukraine and others, would benefit from proper incentives and support from the international donors to integrate maintenance into the infrastructure planning framework;
- This is especially important for local governments, who lack the financial and human capital resources to maintain local infrastructure properly, making regions outside of the capital city less attractive places to invest or live in;
- Given the absence of transparent and comparable sources of information about the composition of maintenance expenditures – for example, the Government Finance Statistics (IMF), which does not distinguish between maintenance and rehabilitation expenditures, – donors could insist that governments compile these expenditures and report on them, at least for the major projects;
- The culture of maintaining rather than rehabilitating or replacing is directly linked to the sustainable development goals and the circular economy concept. In light of their commitment to Agenda 2030, the international community and the national governments in countries like Georgia should consider prioritizing and implementing the set of reforms suggested in their respective PIMAs.
References
- Allen, R., M. Betley, C. Renteria and A. Singh, “Integrating Infrastructure Planning and Budgeting,” in Schwartz et al. (2020), pp. 225-244 (2019).
- American Society of Civil Engineers, Infrastructure Report Card, Reston, Va, (2017).
- ASHRAE, Purpose of The Service Life and Maintenance Cost Database, available at., (2021).
- Babych, Y., and L. Leruth, “Tbilisi: a Growing City with Growing Needs,” ISET-PI Blog available at, (2020a).
- Babych, Y., and L. Leruth, “To Prevent, to Repair, or to Start Over: Should Georgia Put’ Maintenance’ Ahead of ‘Investment’ in Its Development Dictionary?,” ISET-PI Blog available at, (2020b).
- Batalovic, M., K. SokolijaM. Hadzialic, and N. Batalovic, “Maintenance and Operation Costs Model for University Buildings,” Tehnicki Vjesnik, 23(2), pp. 589-598, (2017).
- Blazey, A., F. Gonguet, and P. Stokoe, “Maintaining and Managing Public Infrastructure Assets,” in Schwartz et al. (2020), pp. 265-281 (2019).
- Escobal, J. and C. Ponce, “The Benefits of Rural Roads: Enhancing Income Opportunities for the Rural Poor,” Working Paper 40, Grupo de Analysis Para el Desarrollo (GRADE), Lima, Peru, (2003).
- IMF, “Fiscal Risks—Sources, Disclosure, and Management,” Fiscal Affairs Department, Washington DC,(2008).
- IMF, GFS, Government Finance Statistics Manual, IMF, Washington DC, (2014).
- PIMA EST, Republic of Estonia: Technical Assistance Report-Public Investment Management Assessment, IMF, Washington DC, (2019).
- PIMA GEO, Republic of Georgia: Technical Assistance Report-Public Investment Management Assessment, IMF, Washington DC, (2018).
- Rozenberg, J., and M. Fay, eds, “Beyond The Gap: How Countries Can Afford The Infrastructure They Need While Protecting The Planet,” Sustainable Infrastructure Series, The World Bank, Washington DC, (2019)
- Schick, A., “The Road to PPB: The Stages of Budget Reform,” Public Administration Review, 26(4), pp. 243-258, (1966).
- Schwartz, G., M. Fouad, T. Hansen, and G. Verdier, Well Spent : How Strong Infrastructure Governance Can End Waste in Public Investment, IMF, Washington DC, (2020).
- Thi Hoai Le, A., N. Domingo, E. Rasheed, and K. Park, “Building Maintenance Cost Planning and Estimating: A Literature Review,” 34th Annual ARCOM Conference, Belfast, UK (2019).
- World Bank, The Challenge of Reducing Non-Revenue Water in Developing Countries – How The Private Sector Can Help,” Water Supply and Sanitation Board Discussion Paper Series No 8, Washington DC, (2006).
- Yu, W., and M. Pollitt, “Does Liberalization Cause More Electricity Blackouts?,” EPRG Working Paper 0827, Energy Policy Research Group, University of Cambridge, United Kingdom, (2009).
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.
On Corporate Wrongdoing in Europe and Its Enablers
In the last two decades, several instances of prolonged and severe corporate wrongdoing by European companies have come to light: from Dieselgate to corruption, money laundering through large European banks, recidivist bid and price rigging, and most recently Wirecard. What allowed European firms to engage in so much wrongdoing? In this brief, we consider some important institutional drivers behind corporate wrongdoing, focusing on the European countries with the largest share of corporate infringers.
The Harm from and Extent of Corporate Wrongdoing in the EU
In June 2020, the German firm Wirecard AG’s stock price fell from €104 to below €2 in the span of nine days after the firm admitted it could not locate $2 billion missing from its accounts. The firm has since then been accused of a wide range of infringements including money laundering, corruption, and fraudulent inflation of profits and sales, with some allegations going back over a decade. The Germany financial supervisor BaFin has been criticized as allegations about fraud had been made several times in prior years. Yet, BaFin failed to identify the problem and even banned short-selling of the stock, as well as accused journalists who were critical of the firm of market manipulation.
This scandal occurred against a backdrop of several other prolonged corporate scandals and has led many to wonder how extensive corporate wrongdoing is and how to combat it more effectively.
Corporate wrongdoing has a range of negative effects in competitive markets that are frequently overlooked in the public debate. Beyond the immediate damages of corporate wrongdoing, such as the draining of public resources in the case of tax evasion, money laundering, corruption, air pollution and associated health harm in the case of environmental law violations, there are also more general negative effects of corporate wrongdoing.
It attracts investors to the worst part of the industry, as firms that engage in profitable wrongdoing often do better than their competitors. Also, it forces out honest competitors and increases market entry thresholds for new competitors. These effects become more pronounced when the wrongdoing is prolonged, so, in an ideal world, regulators need to act fast.
Instead, several recent cases of European corporate wrongdoing lasted for many years before being detected and sanctioned, and there is a worrying degree of recidivism in several regulatory areas, including financial regulation with several banks being recidivists, but also in antitrust (Marvão, 2016).
What are the drivers and enablers behind these many prolonged cases of wrongdoing, and why do firms feel emboldened to engage in recidivism?
One way to gain some insight is to identify European countries whose firms are most frequently fined for wrongdoing and review the legal, cultural, and political contexts of those countries.
We tackle this issue by using data from Violationtracker, a database with over 400 000 actions by US enforcement agencies and prosecutors (such as the Securities and Exchange Commission and the Department of Justice). Many of these sanctions are against firms with headquarters in EU countries. In Nyreröd and Spagnolo (2021a), we added the fines for firms with headquarters in all respective EU countries for the period 2000-2020. After excluding countries like Switzerland, well known as homes of extensive financial crime linked to their status of international tax havens and off-shore centers, we find that the United Kingdom is the gold medalist in corporate wrongdoing, with Germany coming in second place.
Table 1. Fines across the top six EU countries (2000-2020).
Interestingly, the top of the ranking is preserved no matter which metrics we use. In Nyreröd and Spagnolo (2021a) we weigh the fines by population, GDP, and exports to the US, and the UK and Germany remain stable at the top, with the UK’s first position becoming more pronounced. Therefore, we focus on these two countries, although many of the problems we identify apply to a varying degree to most other EU countries.
Because of the recent headlines made by the Wirecard case we start with the runner-up, Germany.
Germany
The Wirecard case follows a long tradition of large “household” names such as Siemens, Deutsche bank, Thyssenkrupp, and Volkswagen that have engaged in systemic wrongdoing over extended periods of time and are responsible for most of the fines shown in Table 1.
In one of the largest corruption scandals in history, Siemens was fined $1.6 billion by the Department of Justice in 2008 for systematically paying bribes to government officials around the world, amounting to more than $1.4 billion since the mid-1990s. According to the Securities and Exchange Commission’s investigation, bribery at Siemens was “standard operating procedure” for decades, and the SEC concluded that “the company’s tone at the top […] created a corporate culture in which bribery was tolerated and even rewarded at the highest levels of the company”(SEC, 2008).
In 2015 the Dieselgate scandal unraveled, where it was discovered that several car manufacturers had installed “defeat devices” to cheat emissions tests. Volkswagen had installed the device in 11 million vehicles, some of which emitted up to 40 times more than emissions standards allowed (Gates et al, 2017).
Germany’s largest lender Deutsche Bank has since 2000 paid a whopping $18 billion in fines in the US for alleged infringements ranging from facilitating money laundering and tax evasion, to concealing bribe payments and misleading investors (DoJ, 2021). This is by far the greatest amount paid by any EU bank in the period 2000 – 2020 (Violationtracker.org, 2021)..
Finally, there is the steel conglomerate ThyssenKrupp, which was handed a €479 million fine for bid-rigging by the European Commission in 2007, the highest EU bid-rigging fine ever at the time. The size of the fine was motivated by the fact that, in 2007, Thyssenkrupp was already a repeat offender. In 2019, Thyssenkrupp and three other steel manufacturers were fined $719 million for price-rigging between 2002 to 2016. The firm has also been accused of bribe payments on several occasions (see Nyreröd and Spagnolo 2021a for details).
In reviewing local factors that have enabled these incidents, we find that Germany appears to have a particularly lenient stance toward corporate wrongdoing and a notably hard one against whistleblowers disclosing it. With respect to corruption, for example, bribe payments could be deducted from tax in Germany up until 1999 if paid to foreign officials, and up until 2002 if paid to recipients in the business world (Berghoff, 2017). In October of 2003, the United Nations adopted the Convention Against Corruption. On average, European countries had ratified this treaty halfway through 2007, but Germany was one of the last to ratify the treaty, it did it only in 2014 (UNODC, 2020).
Perhaps more importantly, Germany’s institutional environment seems focused on punishing and deterring whistleblowers, rather than listening to their reports in order to fight corporate wrongdoing. This is likely a crucial enabler of the prolonged wrongdoing we discuss in more depth in Nyreröd and Spagnolo (2021a). It is well known that whistleblowers are essential to detecting corporate wrongdoing (ACFE, 2020). Yet, Germany has some of the worst whistleblower protection laws in the EU (Transparency International 2013, Wolfe et al 2014), and one of the worst records in Europe in terms of mistreating the (obviously few) whistleblowers that dared to denounce corporate wrongdoing (Worth 2020a).
The German opposition to the protection of(truth-telling) whistleblowers from employers’ retaliation was on full display when a public consultation was held on the new EU Directive on whistleblower protection (2019/1937). German industry representatives were particularly active in arguing against it, suggesting that whistleblower protection is not necessary and that the new regulations are a clear signal of mistrust towards companies (BDI, 2019). The German parliament discussed improving the poor whistleblower protections in 2013 but did not enact any improvement of whistleblower protection laws. There are several instances of retaliation against truth-telling whistleblowers where they had very little legal recourse (Worth 2020a; Nyreröd and Spagnolo, 2021a).
The hostile regulatory and political environment to whistleblowers is likely a main factor that has enabled so many German corporations to engage in such prolonged wrongdoing with no records of employees reporting it.
The United Kingdom
We now turn to the winner of our contest, the UK. Over $26 billion of the total fines paid by UK firms in Table 1 is accounted for by the British Petroleum’s (BP) Deep Horizon oil spill in 2010 in the Mexican Gulf. It is estimated that 5 million barrels of oil were released into the ocean, a spill regarded as one of the largest environmental disasters in history.
Internal investigations at BP during the decade preceding this spill had warned senior BP managers that the company repeatedly disregarded safety and environmental rules and risked a serious accident if it did not change its ways. A 2004 inquiry found a pattern of intimidating workers who raised safety or environmental concerns (Lustgarten and Knutson, 2010). The company allegedly flouted safety standards by neglecting aging equipment, delayed inspections to cut production costs, and falsified inspection records. Even before the 2010 spill, officials at the US Environmental Protection Agency had considered debarring BP from receiving government contracts (Lustgarten, 2012). Since 2000, BP has been fined 158 times for environment-related offenses in the US, and again over 60 times since the oil spill in 2010.
Then there is the UK banking sector, with many large banks continuously engaging in wrongdoing, and seemingly more so than elsewhere. CASS (2020: 6) shows how, since 2011, the conduct costs of UK banks have far exceeded that of banks based in the US and Euro area when compared to GDP. In 2017, conduct costs for UK banks represented 0.88% of the UK’s annual GDP, while conduct costs for US and Euro area banks represented around 0.10% or less. In 2018, the conduct costs for UK banks shrank and constituted around 0.55% of the UK’s annual GDP.
In 2010, it was discovered that HSBC had systematically laundered money for some of the bloodiest drug cartels in history through its Mexican subsidiary. Despite numerous internal warnings, complaints from regulators, and internal flags, HSBC Mexico continued laundering money for organizations like the Sinaloa cartel, who not only flood the US with illegal drugs but is considered responsible for the gruesome killings of tens of thousands of people, often innocent civilian casualties at home. The UK’s then-chief financial minister, George Osborne, pleaded with the US Treasury Secretary and others that they do not impose criminal sanctions on HSBC (US Congress 2016).
Another major scandal involving UK banks that have cost regular people billions of pounds was the misselling of “payment protection insurance”. This aggressively marketed insurance had profitability of approximately 90% (Laris, 2020). Several barriers were created to inhibit people from claiming the insurance, such as contract exclusions or administrative barriers, and many people who bought these insurances either did not need them or were unsuitable. As of January 2011, UK banks and financial institutions had paid out £37.5 billion in compensation to customers who were wrongly sold the insurance (Coppola, 2019).
One of the main drivers of corporate wrongdoing in the UK appears to have been the lack of effective corporate sanctions. The “identification principle” requires the identification of a directing mind and will of the company (typically a director), and then proving criminal liability through this person’s conduct and state of mind. This principle has been singled out by several experts as making it “impossibly difficulty” for prosecutors to find companies guilty of serious crimes, especially crimes in large companies with devolved business structures (The Law Commission, 2015: 15). Several UK institutions, such as the UK’s Serious Fraud Office and the Crown Prosecution Service, have also pointed to the identification principle as a central hurdle to their ability to bring corporate prosecutions (Corruption Watch, 2019).
Moreover, effective business lobbying and close connection between politicians, regulators and the financial sector have been prevalent in the UK for a long time and may have exacerbated the already accommodating regulatory environment. Several well-known high-level politicians that affected financial regulation and its implementation for years ended up being hired with handsome pay by financial institutions afterwards (see Nyreröd and Spagnolo 2021a for details).
Regarding regulators, Miller & Dinan (2009: 29) notes that of the 36 people that served on the board of the Financial Services Authority (FSA) between 2000 and 2009, 26 of the members had connections at board or senior level with the banking and finance industry either before or after their term of office, whilst nine continued to hold appointments in financial corporations while they were at the FSA”.
The UK also has an outdated and ineffective whistleblower protection law, the “public interest disclosure act” of 1988 (see e.g., Lewis 2008, Thomas Reuter Foundation and Blueprint for Free Speech 2016, All Parliamentary Committee 2020). At the same time, important UK regulatory agencies have been proactive in neglecting the mounting independent academic research highlighting the effectiveness of the US whistleblowers rewards programs (see Nyreröd and Spagnolo 2021b).
Conclusion
Corporate wrongdoing appears widespread in Europe, and recent cases have been prolonged, severe, and sometimes industry-wide.
The UK and Germany stand out, but other EU countries are no angels. In the case of Germany, an acute aversion to whistleblowers by government institutions appears as a central driver that has enabled corporate wrongdoing. With respect to the UK, ineffective corporate sanctions laws, regulatory/political capture, and a lack of whistleblowers, appear to have driven or enabled firms to engage in prolonged corporate wrongdoing. Similar enablers and drivers are likely present in other EU countries to varying degrees.
There is now an EU Directive on whistleblowing, requiring all member states to put in place retaliation protections for those reporting on corporate wrongdoing. But protections have proven insufficient in a variety of ways and are unlikely to be a game-changer in terms of combating corporate wrongdoing (see e.g., GAP and IBA, 2021).
In the light of the strong independent evidence on the effectiveness of whistleblower reward programs at increasing detection and deterring wrongdoing (see, e.g., Nyreröd and Spagnolo 2021b for a survey), EU Member States seriously concerned about corporate wrongdoing should consider introducing them in a wide variety of regulatory areas.
References
- All Parliamentary Committee. (2020). “Making whistleblowing work for society.”
- ACFE. (2020). “Report to the nations: 2020 Global Study on Occupational Fraud and Abuse”
- BDI. (2019). “Neue Vorschriften zum Schutz von Whistleblowern – Unternehmen werden nicht geschont”, May 15th.
- Berghoff, H. (2017). “‘Organised irresponsibility’? The Siemens corruption scandal of the 1990s and 2000s”, Business History, 60(3), 423-445.
- Bundeskartellamt. (2019). “Fines imposed on steel manufacturers”, December 12th
- CASS. (2020). “The CBR Conduct Costs Project”
- Coppola, F. (2019). “The U.K.’s Biggest Financial Scandal Bites Its Biggest Bank – Again”, July 31st
- Corruption Watch. (2019). “Corporate Crime Gap How the UK Lags the US in Policing Corporate Financial Crime”.
- DoJ. (2021). ”Deutsche Bank Agrees to Pay over $130 Million to Resolve Foreign Corrupt Practices Act and Fraud Case” Press Release, January 8th
- GAP and IBA. (2021). “Are whistleblowing laws working? A global study of whistleblower protection litigation” Government Accountability Project (GAP), International Bar Association (IBA).
- Gates, G., Ewing, J., Russell, K., Watkins, D. (2017). “How Volkswagen’s ‘Defeat Devices’ Worked”, March 16th
- Lewis, D. (2008). “Ten Years of Public Interest Disclosure Legislation in the UK: Are Whistleblowers Adequately Protected?”, The European Identity in Business and Social Ethics: The Eben 20th Annual Conference in Leuven, 82(2), 497-507.
- Laris, G. (2020) “Scandal or Repetitive Misconduct: Payment Protection Insurance (PPI) and the not so Little “Skin in Lending Games””, May 14th
- Lustgarten, A. (2012). “EPA Officials Weigh Sanctions Against BP’s U.S. Operations”, November 28th
- Lustgarten, A., and Knutson, R. (2010). ”Years of Internal BP Probes Warned That Neglect Could Lead to Accidents”, June 7th
- Mariuzzo, F., Ormosi, P., Majied, Z. (2020). “Fines and reputational sanctions: The case of cartels”, International Journal of Industrial Organization, 69.
- Marvão, C. (2016). “The EU Leniency Programme and Recidivism”, Review of Industrial Organization, 48(1), 1-27.
- Nyreröd, T. Spagnolo, G. (2021a). “Surprised by Wirecard? Enablers of Corporate Wrongdoing in Europe”, SITE Working Paper No. 54.
- Miller, D. and Dinan, W. (2009). “Revolving Doors, Accountability and Transparency – Emerging Regulatory Concerns and Policy Solutions in the Financial Crisis”, Public Governance Committee.
- Nyreröd, T. Spagnolo, G. (2021b). “Myths and Numbers on Whistleblower Rewards”, Regulation and Governance, 15(1), 82-97.
- SEC. (2008). “SEC Files Settled Foreign Corrupt Practices Act Charges Against Siemens AG for Engaging in Worldwide Bribery With Total Disgorgement and Criminal Fines of Over $1.6 Billion” Litigation Release No. 20829, December 15.
- SEC. (2012). “BP to Pay $525 Million Penalty to Settle SEC Charges of Securities Fraud During Deepwater Horizon Oil Spill”, Press Release November 15th
- The Law Commission. (2015). “Criminal Liability in Regulatory Contexts”, A Consultation Paper.
- Transparency International. (2013). “Whistleblowing in Europe, Legal Protections for Whistleblowers in the EU.”
- Thomas Reuter Foundation and Blueprint for Free Speech. (2016). “Protecting Whistleblowers in the UK: A New Blueprint”.
- UNODC. (2020). “Signature and Ratification Status”, February 6.
- US Congress (2016). “Too Big to Jail: Inside the Obama Justice Department’s Decision not to Hold Wall Street Accountable”.
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.
Does the Russian Stock Market Care About 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
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
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
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)
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.
Inequality in the Pandemic: Evidence from Sweden
Most reports on the labor-market effects of the first wave of COVID-19 have pointed to women, low-skilled workers and other vulnerable groups being more affected. Research on the topic shows a more mixed picture. We contribute to this discussion. Using monthly official unemployment data in Sweden we find that across wage levels, occupations with lower salaries display higher increases in unemployment, and low-wage occupations are also more difficult to do from home. The job loss probability is also higher in sectors with a higher concentration of workers born outside of the EU and those aged below 30. But we find no evidence of a gender unequal impact in Sweden. Overall, our results point to higher effects for low-wage groups but small gender differences overall.
Introduction
The ongoing Covid-19 pandemic has affected the health of millions of people worldwide. But it has also had an enormous impact on economic and living conditions through government policies aimed at containing the spread of the infection. While, at the onset of the pandemic, government officials, mainstream media, and even celebrities labeled COVID-19 “the great equalizer” (Mein, 2020), the reality has proven quite different, with the most vulnerable groups of the population appearing to be the most harmed by both the health and the economic crises (see, for instance, The World Economic Forum, Joseph Stiglitz in this IMF article, and The World Bank). In this brief, we focus on one specific economic impact of the pandemic, namely its effect on unemployment status, and we study the extent to which this impact has been unequal across different groups of the Swedish society. Our analysis uses administrative data and segments the population by wage, gender, age, and foreign-born status.
Covid-19 and Inequality in the Labor Market
An extensive review of the emerging literature on the effect of the pandemic on different kinds of inequality is beyond the scope of this brief. However, a number of studies are especially relevant to put our analysis in context, as they are focused on the unequal labor market impacts of the crisis and study real-time data. Based on these studies, a number of patterns emerge. First, the effect of the pandemic on the increased probability of job loss appears stronger for low-skilled workers, as proxied by education level (see e.g., Adam-Prassl et al., 2020, Gaudecker et al. 2020, Casarico and Lattanzio 2020). Gaudecker et al. (2020) also observe that in the Netherlands the negative education gradient has been mitigated by the government identifying some sectors of the economy as essential since some of these sectors are characterized by a high concentration of low-educated workers. Second, the evidence of unequal gender impacts on the probability of job loss is mixed. While survey information from the UK and the US reveals that labor market outcomes for women have more severely deteriorated during the crisis (Adams-Prassl et al., 2020), there is no evidence of unequal impacts by gender in Germany (Adams-Pras et al., 2020) and Italy (Casarico and Lattanzio, 2020). Other papers confirm that the effect on labor-market outcomes by gender varies across contexts (see, e.g., Hupkau and Petrongolo, 2020).
Analysis of Labor Market Data From Sweden
Our analysis of the Swedish labor market provides a valuable contribution to the existing findings for a number of reasons. First, despite rising inequality over the past decades, Sweden is characterized by relatively low income inequality (e.g. OECD, 2019), high participation of women in the labor market, and high level of society inclusiveness (e.g. Gottfries, 2019, OECD 2016) among OECD countries. Second, unlike the majority of countries worldwide, throughout the pandemic, Sweden has not adopted stay-at-home orders that would have separated sectors of the economy between “essential” and “non-essential”. As a result, sectors that were typically shut down in other countries, for instance, the hospitality industry, were not ordered to close during the first wave of the pandemic and have then only faced partial limitations during the second wave. Importantly, schools below the secondary level were never closed. Third, as we will describe in more detail below, the availability of administrative information on unemployment claims on a monthly basis allows studying the “real-time” development of unemployment throughout the pandemic for the universe of employees in the Swedish labor market.
Data
We use data from the registry of unemployed individuals kept by the Swedish Public Employment Service (Arbetsförmedlingen), the government agency responsible for the functioning of the Swedish labor market. The incentives for laid-off individuals to register with the Employment Service are high since the registration is directly connected to the right to claim various (relatively generous) unemployment benefits. As such, the data arguably includes a large share of employees who lost their job over the period studied. Based on the high incentives to register as unemployed, we also assume that the probability to register does not differ the segments of the population that we consider. The data does not include some self-employed who for various reasons choose not to register, but this group is not believed to be significant. Also, furloughed workers do not count as unemployed. This group was significant, especially in the very early stages of the pandemic, but still small relative to all unemployed. As of July 2020, they represented 13% of the total pool of unemployed individuals in Sweden (Swedish Agency for Economic and Regional Growth, 2021).
The population-wide coverage is the main advantage of our data vis-à-vis the survey information used in many recent studies of the labor market throughout the pandemic (other studies using administrative data are Casarico and Lattanzi, 2020, studying the Italian labor market, and Forsythe et al., 2020, who analyze the US case).
We consider everyone registered as unemployed/seeking employment each month from January 2019 to July 2020. The data is grouped by 4-digit occupational classification (there are about 440 occupations at this level) and each occupational group is further broken down by sex, age, and foreign-born status (specifically, Sweden born, foreign EU born, and foreign non-EU born.) We then merge this data with information on the average wage by occupational group and gender in 2019, as reported by Medlingsinstitutet and publicly available at Statistics Sweden. This measure, although not being at the individual level, allows us to develop a relatively precise proxy of wages by occupation that we use to rank unemployment by wage deciles.
Evidence
With the data described above, we build the following measure of the change in job-loss probability (JLP) between February and July 2020, adjusted for seasonality:
where u is the number of workers in 4-digit occupational sector who registered as unemployed in a month over the average number of employed in the same sector in 2017 and 2018 (data available at Statistics Sweden). Put it simply, ΔJLP is a sector-level indicator of the change in job loss probability due to the pandemic; it measures the change in chances of job loss between February and July 2020, i.e. between five months after the start of the pandemic and the month before its onset, as compared to the equivalent change the year before. We thus account for seasonal factors by differencing out the job loss probability during the same months of 2019, when the pandemic was neither occurring nor anticipated. Below we use ΔJLP to show differences in the impact of the pandemic on the chances of job loss for different groups of the Swedish society.
Job loss probability by wage deciles. We leverage information on sector-level average wages and the number of employees to partition occupational sectors into (approximate) wage deciles. The purpose of such a partition is to rank sectors as being typically “low-” or “high-” wage within the Swedish context. As we document in Figure 1, the pandemic has increased the probability of job loss across all sectors of the economy; however, this increase in percentage points is higher the lower is the average sector wage, with the category of least-paid workers being the most likely to lose their job. This category includes occupations such as home-based personal care and related workers, cleaners and helpers in offices, hotels and other establishments, or restaurant and kitchen helpers. Considering that the pre-pandemic probability of becoming unemployed was already largest for this group (19.7% compared to the average 6% in 2019), the existing inequality in the labor market has been exacerbated by the Covid-19 crisis. In our regression analysis that is available by request, we also find that accounting for an index of the share of tasks that can be performed from home, defined at 2-digit occupational level, does not explain away the negative and significant relationship between wages and job loss probability. Although, we confirm previous evidence that the probability of losing jobs is lower among occupations that can be performed from home. The substantial contraction in economic activity in some sectors of the economy seems to be the driver of the unequal distribution of job losses.
Figure 1. Change in job loss probability by wage decile between February and July
Job loss probability by gender. Figure 1 also documents that, even though the change in job loss probability is higher in sectors dominated by women, the likelihood of men losing jobs has increased more in these sectors. As a result, in the regression analysis we find that there is no significant association between the share of women in a sector and the sector-level change in job loss probability.
Job loss probability by foreign status and age. We find that workers who are born outside of EU countries are significantly more likely to transition into unemployment during the pandemic (see Figure 2). The difference is striking. Based on our indicator, considering male workers the pandemic has raised the probability of job loss by roughly 7 p.p. more for non-EU citizens as compared to non-Swedish EU citizens, and by 9 p.p. more compared to Swedish citizens. These differences are only slightly smaller for women. Another group particularly affected is that of workers in the age group below 30 (result available upon request). Such patterns are due to foreign-born and younger workers being more concentrated in those low-wage sectors that also appear, based on our analysis, to be more impacted by the pandemic in terms of job loss probability
Figure 2. Change in job loss probability by foreign status between February and July 2020
Conclusion
Our analysis of administrative monthly data on the number of workers who register as unemployed in Sweden confirms previous evidence that the Covid-19 crisis has not been “the great equalizer”. While the pandemic has increased the probability of losing jobs across all sectors, the most affected in Sweden are those workers in occupations where the lowest wages were paid before the pandemic. Considering other demographic characteristics, vulnerable groups that were most impacted by the crisis are workers born outside of the EU and workers aged below 30. However, we do not find evidence of a gender-unequal impact of the pandemic in terms of the probability of job loss. There may of course be many other aspects to the issue along gender lines. For example, on one hand, there might be gender-unequal effects that we cannot observe in our data, for instance in the number of hours worked, temporary unemployment, and level of stress due to increased childcare responsibility. On the other hand, since schools in Sweden stayed open throughout the pandemic, the concerns related to increased childcare responsibility, which have led to identifying mothers as most vulnerable in other countries, do not necessarily apply to the Swedish context.
Sweden has adopted a number of measures to shield workers from the worst effects of the pandemic. As the country plans the recovery, special attention should be devoted to the opportunities for re-employment for the most vulnerable groups. Absent such focus, the economy emerging from the crisis might be less inclusive and equal than it has been before the pandemic, with important consequences for many societal outcomes that are generally linked to labor market inclusiveness.
References
- Adams-Prassl, A., Boneva, T., Golin, M., & Rauh, C. (2020). ”Inequality in the Impact of the Coronavirus Shock: New Survey Evidence for the UK”. Journal of Public Economics, 189, 104245.
- Casarico, A., & Lattanzio, S. (2020). ”The heterogeneous effects of COVID-19 on labor market flows: Evidence from administrative data”. Covid Economics, 52, 152-174.
- Forsythe, E., Kahn, L. B., Lange, F., & Wiczer, D. (2020). ”Labor demand in the time of COVID-19: Evidence from vacancy postings and UI claims”. Journal of Public Economics, 189, 104238.
- Gottfries, N. (2019). “The labor market in Sweden since the 1990s”. IZA World of Labor 2018: 411.
- Hupkau, C., & Petrongolo, B. (2020). ”Work, care and gender during the Covid‐19 crisis”. Fiscal studies, 41(3), 623-651.
- OECD (2016). ”Promoting Well-being and Inclusiveness in Sweden”, Better Policies, OECD Publishing, Paris.
- OECD (2019). ”OECD Economic Surveys: Sweden 2019”, OECD Publishing, Paris.
- Swedish Agency for Economic and Regional Growth, Database, 10 Mar. 2021.
- Von Gaudecker, H. M., Holler, R., Janys, L., Siflinger, B., & Zimpelmann, C. (2020). ”Labour supply in the early stages of the CoViD-19 Pandemic: Empirical Evidence on hours, home office, and expectations”. IZA Discussion Paper No. 13158.
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.
Laissez-faire Covid-19: Economic Consequences in Belarus
Despite its traditional paternalistic role, the Belarusian government chose minimal reaction to the Covid-19 pandemic. No meaningful economic or social measures were taken in response to the pandemic. We explore a unique dataset to document how major Covid-related shocks affected the earnings of Belarusians in 2020. We utilize the differential timing and sectoral effects of the shocks to identify the impact of Covid-19 on individual socioeconomic outcomes. Not surprisingly, we find that Covid-related shocks increase the probability of an income reduction. This effect is most pronounced for those employed in the private sector. In the absence of a social security net, vulnerable groups had to cope with the economic consequences of the pandemic on their own.
Introduction
Belarus had its first official case of Covid-19 registered on February 27 and its first death on March 31. At first, the increase in newly registered cases was slower than in most other countries, but at the beginning of April Belarus started to catch up. The peak of the first wave was recorded on May 18 with 943 new daily cases. According to the official statistics, the second wave started in September 2020 and was much more severe than the first one, reaching 1,890 new daily cases by the end of December.
Belarusian authorities did not undertake any substantial interventions, such as lockdowns, to fight the spread of the pandemic. Nevertheless, there were several other key mechanisms through which Covid-19 affected the Belarusian economy. The population’s reaction to the risks of contamination led to a substantial fall in mobility that resulted in decreased sales in retail and services requiring physical interaction. For example, sales in the restaurant industry decreased by 20% in 2020. Lockdowns in major international trade partners such as Russia have led to a decrease in demand for Belarusian exports of goods and transportation services. In the face of these economic challenges, the government focused its attention on supporting full employment and production in state-owned enterprises while ignoring the rest of the economy.
In this brief, we present evidence of the economic effects of Covid-19 in Belarus. We employ a unique dataset on socioeconomic outcomes collected by BEROC to study how individuals are affected by Covid-related shocks in mobility and exports. In order to isolate the effects of these shocks on the well-being of Belarusians, we exploit their timing and sectoral differences.
Measuring Covid-related Shocks
Figure 1 depicts changes in the Yandex self-isolation index which measures the use of Yandex services, including Yandex traffic monitoring and customer mobility compared to the average pre-pandemic day (Yandex DataLens, 2021). Individual everyday mobility started to decline in mid-March, and as the first wave of the pandemic gained momentum, mobility reached its lowest point at the end of April. It started to decline again in November-December 2020 following the second wave.
Figure 1. Yandex self-isolation index in Belarus, 2020
Belarus is a small and open economy with Russia as its main trading partner. The lockdown in Russia that lasted from the end of March until mid-May along with the spring lockdowns in Europe caused a major contraction in external demand for Belarusian goods. Figure 2 shows total physical exports and non-energy physical exports in 2020. The largest difference between total and non-energy exports can be observed in January, February, and March during which Russia and Belarus had an oil-supply dispute. To focus on the effects of the pandemic we use non-energy physical exports to approximate Covid-related exogenous shocks to the economy.
Figure 2. Physical export indices, Belarus
Income Dynamics
To measure the impact of Covid-19 on Belarusian society, BEROC, in cooperation with the marketing and opinion research company SATIO, conducted a series of online surveys representative of the urban population of Belarus (Covidonomics, 2021). The five waves of the 2020 survey were carried out on April 17-22, May 8-11, June 8-15, September 11-16, and November 25-30.
Respondents were asked about recent changes to their income, and also to specify the reasons for income reduction (if this was the case), including depreciation of the ruble, salary cut, furlough, etc. Figure 3 depicts the percentage of individuals who reported an income reduction in the previous month for reasons other than currency depreciation by sector of employment. The income reductions peaked in April-June, with the situation relatively stabilizing by September.
Figure 3. Income dynamics by sector
The fact that the share of respondents reporting termination peaked at 2.9% in May indicates that firms did not use employment reduction to adapt to the pandemic environment. A big share of respondents employed in the service sector reported domestic demand contraction (fewer orders/clients) as a key factor for their income reduction. The industries that took the hardest hit were hospitality-retail and transportation. In early spring, manufacturing appeared to be one of the most affected industries. However, as exports started to recover in June, the share of manufacturing workers that reported an income reduction decreased significantly, becoming one of the lowest across industries.
Identifying the Effects of Covid-19 Shocks
In this section, we estimate the probability of facing a reduction in individual income as well as the likelihood of being furloughed due to the Covid-19 pandemic.
In 2020, the Belarusian economy suffered due to the oil-supply dispute with Russia, the Covid-19 pandemic, and the national political crisis. To isolate the effects of Covid-19 from those driven by the oil dispute and the political crisis, we add interactions between Covid-related shocks and dummies indicating industries affected by those shocks. This implies three interactions with different binary indicators: exports and manufacturing, exports and transportation, and mobility and hospitality/retail.
To estimate these effects, we use a fixed-effects probit regression controlling for sector of employment, education, age, and gender.
Table 1. Probability of income reduction and furlough
Table 1 shows that individuals employed in the hospitality and retail industry face higher risks of an income reduction due to decreased mobility caused by self-isolation behavior. A 10-percentage-point increase in the self-isolation index is associated with a 1.3 percentage point increase in the probability of income reduction for those employed in the retail and hospitality industry. The interaction term between exports and the manufacturing dummy also appears to be statistically significant for various specifications. A 10-percentage-point decline in physical volumes of exports is associated with a 8.6 percentage point increase in the probability of income reduction for manufacturing workers.
Notably, the private sector employment coefficient shows strong statistical significance which highlights the choice of the authorities to support SOEs, with little to no support for the private sector. Being employed in the private sector increases the probability of facing an income reduction by 7.9 percentage points.
The Gender Dimension
Despite concerns that women experience larger economic losses due to consequences of the pandemic (Dang and Nguyen, 2021; Alon et al., 2020b), we do not find a statistically significant effect of gender in our sample. In particular, our results offer no evidence of women being more likely to experience an income reduction during the pandemic, similar to findings in Germany (Adams-Prassl et al. 2020c).
While job losses were uncommon during the Covid-19 crisis in Belarus, being furloughed was one of the most common reasons for an income reduction (11.3% of respondents reported being furloughed in May). We also investigate the separate channels through which individuals lose income due to the Covid-related shocks. Notably, the only channel of income reduction that is more prevalent among women than men is through furlough. This finding is consistent with Adams-Prasslet al. (2020a) who argue that this discrepancy can be explained by gender differences in childcare responsibilities.
Conclusion
Belarus is close to unique in having almost no government response to the Covid-19 pandemic. Despite the absence of lockdowns and other restrictions, the Belarusian economy has experienced several Covid-associated shocks. Due to the economy’s openness to trade, it was seriously affected by export contractions. Belarusians have voluntarily reduced their mobility to minimize health risks which has affected the hospitality and retail industry.
We utilize the differential timing and sectoral impact of Covid-related shocks to estimate the pandemic’s effect on the socioeconomic outcomes of individuals. By using a unique dataset, we find evidence that the pandemic increased the likelihood of income reductions for Belarusians, mainly due to the effects of decreased mobility and fall in exports. We also find that those employed in the private sector were more likely to suffer from negative shocks, reflecting the policy choice of the Belarusian government to only provide economic support to the state sector. Finally, we show that, while women are as likely as men to see their income reduced, they are significantly more likely to be furloughed.
Many Belarusians saw their well-being deteriorating as a result of the Covid-19 pandemic. In the absence of unemployment benefits and other social protection mechanisms (Umapathi, 2020), those economically affected had to bear the cost of the shocks on their own.
References
- Adams-Prassl, A., Boneva, T., Golin, M., and Rauh, C. (2020a). Furloughing. Fiscal Studies, 41(3):591–622.
- Adams-Prassl, A., Boneva, T., Golin, M., and Rauh, C. (2020b). Inequality in the impact of the coronavirus shock: Evidence from real time surveys. Journal of Public Economics, 189:104245.
- Covidonomics project (2020). BEROC and Satio. http://covideconomy.by/
- Dang, H.-A. H. and Nguyen, C. V. (2021). Gender inequality during the Covid-19 pandemic: Income, expenditure, savings, and job loss. World Development, 140:105296.
- Umapathi, N. (2020). Social protection system in Belarus: perspective. Bankovskiy Vestnik, (3):75–80. (in Russian).
- Yandex (2021) Yandex DataLens, https://datalens.yandex.ru/
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
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
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
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
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
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
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
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
- Becker, Torbjörn, 2016a. “Russia’s Oil Dependence and the EU”, SITE Working paper 38.
- Becker, Torbjörn, 2016b. “Russia and Oil — Out of Control”, FREE policy brief.
- BOFIT, 2021, Weekly report 7 on Russia.
- Central Bank of Russia, 2021, data on exchange rates.
- ECDC, 2021, data on covid-19 infections and deaths.
- IMF, 2021, World Economic Outlook update, January.
- Roststat, 2021, Russia GDP data.
- U.S. Energy Information Administration, 2021, data on 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.
Addressing the COVID-19 Pandemic: Vaccination Efforts in FREE Network Countries
There are great expectations that vaccinations will enable a return to normality from Covid-19. However, there is massive variation in vaccination efforts, vaccine access, and attitudes to vaccination in the population across countries. This policy brief compares the situation in a number of countries in Eastern Europe, the Baltics, the Caucasus region, and Sweden. The brief is based on the insights shared at a recent webinar “Addressing the COVID-19 pandemic: Vaccination efforts in FREE Network countries” organized by the Stockholm Institute of Transition Economics.
Introduction
As of February 16, 2021, the total number of confirmed COVID-19 deaths across the globe has reached 2.45 million according to Our World in Data (2021). Rapid implementation of vaccination programs that extend to major parts of the population is of paramount importance, not only from a global health perspective, but also in terms of economic, political, and social implications.
Eastern Europe is no exception. Although many countries in the region had a relatively low level of infections during the first wave of the COVID-19 pandemic in the spring of 2020, all have by now been severely affected. Vaccination plays a key role for these economies to bounce back, especially as many of them depend on tourism, trade, and other sectors that have been particularly hurt by social distancing restrictions.
Figure 1. Cumulative confirmed COVID-19 cases (top panel) and deaths per million (bottom panel) in the FREE Network region
Against this background, the Stockholm Institute of Transition Economics invited representatives of the FREE Network countries to discuss the current vaccination efforts happening in Eastern Europe, the Baltics, and the Caucasus (the represented countries were Belarus, Georgia, Latvia, Poland, Russia, Sweden, and Ukraine). This brief summarizes the main points raised in this event.
Vaccination Status
In Latvia, Poland, and Sweden, the second wave of infections started to pick up in November 2020 and peaked according to most COVID-19 impact measures in early 2021. As all three countries are members of the EU and take part in its coordinated efforts, they have all received vaccines from the same suppliers (i.e. Astra/Zeneca, Moderna, and Pfizer/BioNTech).
Latvia had problems early on with getting the vaccination process off the ground. The health minister was blamed for the slow start since he declined orders from Pfizer/BioNTech in the early stages, and was forced to resign. As of February 16, two doses per 100 people have been distributed primarily to medical staff, social care workers, and key-state officials.
Figure 2. Cumulative COVID-19 vaccination doses per 100 people
With the first phase starting in late December, Sweden has by February 16th, 2021, fully vaccinated 1,05% of the population while experiencing serious problems with delivery and implementation. As planning and delivery of vaccines are centralized while the implementation is decided regionally, there have been some unclarities regarding who stands accountable for issues that emerge. Guidelines, issued by the Public Health Agency of Sweden, for how to prioritize different groups have been changed a couple of times. Currently, the (non-binding) recommendation is to prioritize vaccinating people living in elderly care homes, as well as personnel working with this group, followed by those above 65 years of age, health care workers, and other risk groups.
Looking at regional statistics there are significant differences in vaccinating people across regions with an average of 70% usage rate of delivered vaccines, and with lows at 40-60%, see figure 3. Reasons for this remain unclear.
Figure 3. Distributed relative to delivered vaccines across counties (län) in Sweden.
Poland has so far been somewhat more efficient than Sweden in its vaccination efforts. Despite turbulent political events over the last couple of months, it has managed to distribute 5.7 doses per 100 people. The country has just finished the first phase of the national vaccination plan, which focused on vaccinating healthcare personnel, and has now entered the second phase with a shifted focus towards elderly care homes, people above 60 years of age, military, and teachers.
Among the countries that are not members of the EU, and thus, not taking part in its coordinated vaccination efforts, the vaccination statuses are more diverse.
Russia was fast in developing and approving the Sputnik V vaccine. The country started vaccinating in early December, although only people in the age of 18-60 in prioritized occupations such as health care workers, people living and working in nursing homes, teachers, and military. At the start of 2021, the program extended to people above 60 and, on January 16, all adults were given the possibility to register themselves and get vaccinated within one week. There are no precise data at the moment, but the fraction of the population vaccinated is likely to be higher than 1%.
Others in the region have faced greater challenges in signing contracts with vaccine suppliers. Georgia and Ukraine are still waiting to secure deliveries and have not yet started to vaccinate. Being outside the EU agreements and with public and political mistrust towards Sputnik V and Russia alternatives are being explored. Georgia has ordered vaccines through the COVAX platform (co-led by Gavi, the Coalition for Epidemic Preparedness Innovations (CEPI) and WHO) but there are concerns about potential delays in deliveries. In terms of prioritizing groups once vaccinations can start, both Ukraine and Georgia have set similar priorities as other countries, with extra focus on health-care and essential workers, age-related risk groups, and people with chronic illnesses.
While Belarus’ official figures on the death toll have been widely perceived as unrealistic from the beginning, the most accurate and recent data shows an excess deaths rate of about 20% in July. The country has no precise data on vaccinations, but some reports have emerged based on interviews with government officials in the Belarusian media. These suggest that around 20,000 imported doses of Sputnik V have been distributed mainly to medical professionals and an additional 120,000-140,000 doses have been promised by Russia.
Main Challenges
The discussion during the Q&A session at the webinar concerned the economic and political implications of vaccinations in the region.
Pavlo Kovtoniuk, the Head of Health Economics Center at KSE in Ukraine, stressed the importance of a coordinated vaccination effort in Europe with regards to geopolitics. There is a clear EU vs Non-EU divide in the vaccination status across European countries. The limited vaccine availability in Non-EU countries such as Ukraine, Georgia, and Belarus offers opportunities for more influential nations like Russia and China to pressure and affect domestic policy in these countries.
Also highlighting the fact that no one is safe until everybody is safe, Lev Lvovskiy, Senior Research Fellow at BEROC in Minsk, noted that vaccination efforts in Europe are important for recovery in small open economies like Belarus as many of its trade partners currently have imposed temporary import restrictions.
Similar to the political crisis happening alongside the pandemic in Belarus, the challenges we see in Poland – protests against the recent developments regarding abortion rights and attempts by the government to limit free media – have deflated the urgency to vaccinate in terms of its future economic and political implications, according to Michal Myck, director of CenEA in Szczecin.
Looking forward, another major challenge for the region is vaccine skepticism. Not only do many countries have to build proper infrastructure that can administer vaccines at the required scale and pace, but also make sure that people actually show up. In Latvia, Poland, Georgia, Russia, and Ukraine, polls show that less than 50% of the population are ready to vaccinate. Sergejs Gubin, Research Fellow at BICEPS in Riga, highlighted that there can be systematic variation in the willingness to vaccinate within countries as e.g. Russian-speaking natives in Latvia have been found to be less prone to vaccinate on average. Also, most of the skepticism in Georgia has been more directed towards the Chinese and Russian vaccine than towards those approved by the EU, according to Yaroslava Babych who is lead economist at ISET in Tbilisi.
Even though vaccine skepticism is an issue in Russia too, Natalya Volchkova, Director of CEFIR at New Economic School in Moscow, pointed to the positive impact of “bandwagon effects” in vaccination efforts. When one person gets vaccinated, that person can spread more accurate information about the vaccine to their social circle, resulting in fewer and fewer people being skeptical as the share of vaccinated grows. In such a scenario vaccine skepticism can fade away over time, even if initial estimates suggest it is high in the population.
Concluding Remarks
Almost exactly a year has passed since Covid-19 was declared a pandemic. The economic and social consequences have been enormous. Now vaccines – developed faster than expected – promise a way out of the crisis. But major challenges, of different types and magnitudes across the globe, still remain. As the seminar highlighted, there are important differences across transition countries. Some countries (such as Russia) have secured vaccines by developing them, but still face challenges in producing and distributing vaccines. Others have secured deliveries through the joint effort by the EU, but this has also had its costs in terms of a somewhat slower process (compared to some of the countries acting on their own) and sharing within the EU. For some other countries, like Belarus, Ukraine, and Georgia, the vaccination is yet to be started. All in all, the choice and availability of vaccines across the region illustrates how economic and geopolitical questions remain important. Finally, for many of the region countries vaccine skepticism and information as well as disinformation are important determinants in distributing vaccines. Summing up, the combination of these factors once again reminds us that how to best get back from the pandemic is truly a multidisciplinary question.
List of Participants
- Iurii Ganychenko, Senior researcher at Kyiv School of Economics (KSE/Ukraine)
- Jesper Roine, Professor at Stockholm School of Economics (SSE) and Deputy Director at the Stockholm Institute of Transition Economics (SITE/ Sweden)
- Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC/ Belarus)
- Michal Myck, Director of the Centre for Economic Analysis (CenEA/ Poland)
- Natalya Volchkova, Director of the Centre for Economic and Financial Research New Economic School (CEFIR NES/ Russia)
- Pavlo Kovtoniuk, Head of Health Economics Center at Kyiv School of Economics (KSE/Ukraine)
- Sergej Gubin, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS/ Latvia)
- Yaroslava V. Babych, Lead Economist at ISET Policy Institute (ISET PI/ Georgia)
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.
Video of the FREE Network webinar “Addressing the Covid-19 Pandemic: Vaccination Efforts in Free Network Countries“
Media Freedom in Eastern Europe
In recent years, press freedom in many Eastern European countries has increasingly come under threat. This policy brief provides an overview of the importance of a free press for democracy and the challenges to media freedom in these European transition economies.
Introduction
Freedom of expression – which encompasses media freedom – is a fundamental human right enshrined in most countries’ constitutions. Yet for many of their citizens, it is more of an aspiration than a reality. Following the dissolution of the Soviet Union, a number of countries in Eastern Europe embarked on a process of democratisation and accession to the European Union – for which one of the prerequisites is a free press.
Figure 1 shows a measure of press freedom for the eight Eastern European countries that joined the EU in 2004. These countries saw a general improvement in press freedom from the early 1990s to the early 2000s. But since then, experiences have diverged and in 2017 only Estonia and the Czech Republic showed better scores on press freedom than when they first joined the EU. This pattern of backsliding is not confined to the media, but is also evident in other measures of democracy.
Figure 1. Media Freedom in Eastern Europe
Media and Democracy
A free press and a strong democracy are mutually reinforcing. Research, from mainly Western democracies, shows that the media plays an important role in informing the electorate and holding politicians accountable. For example, Snyder and Strömberg (2010) find that U.S. voters are less informed about their Congressmen when they are covered less in the local press. This is ultimately damaging for voters, as these politicians work less for their constituency and these constituencies also receive less federal funding.
Investigative journalism can play an important role in uncovering corruption and other forms of wrongdoing by politicians. For instance, using the Panama Papers and other leaked documents, journalists uncovered 11,562 offshore entities linked to Russia, 2943 linked to Latvia, and 103 linked to Sweden (see: Offshore Leaks Database). While there are legitimate uses for these offshore entities, the lack of transparency surrounding offshore finance also facilitates tax evasion and money laundering. The revelations of offshore holdings became an embarrassment to many politicians, with some forced to resign. In Russian media, the allegations that the leaks document suspected money laundering by President Putin were characterised as US propaganda (Hoskins and Shchelin, 2018).
Figure 2 shows the relationship between the length of time a country’s leader has been in office and its press freedom score in 2020. While there is no systematic relationship between leader tenure length and press freedom in Western Europe (in blue), across Eastern Europe (in red), countries whose leader has been in power for longer tend to have less media freedom. This correlation is likely to reflect three factors: 1) media coverage can affect a government’s chances of staying in power; 2) a longer-lived government might be more able to control the media and 3) a host of other factors, such as the public’s political engagement and the strength of democratic institutions, could influence both freedom of the press and the longevity of governments.
Figure 2. Media Freedom and Leader Tenure
Electoral Effects of the Media
A number of papers show the causal effects of (biased) media coverage in shaping support for political parties. For instance, watching Fox News increases voting for the Republican party in the US (DellaVigna and Kaplan, 2007; Martin and Yurukoglu, 2017).
Enikolopov, Petrova, and Zhuravskaya (2011) investigate the influence of NTV (the only national TV channel that was at the time independent of the government) on voting in the 1999 parliamentary election in Russia. They find that areas with greater access to NTV were significantly less likely to vote for the government party and more likely to vote for opposition parties.
Biased media can also be used as a foreign policy tool. Peisakhin and Rozenas (2018) find that Ukrainian areas that received Russian TV had on average greater support for pro-Russian parties and candidates in the 2014 elections.
The media landscape in many CEE countries is highly polarised and politicised. Kostadinova (2015) cites research showing that in some former communist countries many journalists still rely on government officials as news sources. In other countries, media in opposition to the communist regimes emerged at the end of the 1980s, such as in Poland where the Gazeta Wyborcza became one of the leading daily newspapers.
Government Control of the Media
Governments have many ways of controlling the media in their country. At the extreme, governments can own and run media outlets, dictate their contents, and censor any dissenting voices. While political and media systems across CEE are diverse, they share some common experiences that might explain their current fragility.
Transitions in Media Ownership
In the Eastern Bloc, the mass media was owned and tightly controlled by the state and used as a tool for propaganda. After the fall of communism, many state-owned media were privatised – along with other state-owned enterprises. Foreign (mostly western European) media conglomerates purchased a significant fraction of media outlets in a number of countries.
While private and foreign ownership of the media can reduce the government’s ability to influence media content, the experience of CEE was not entirely positive. Stetka (2012) argues that while foreign owners brought capital and technology, they were less concerned with transplanting Western journalistic and professional standards. Dobek-Ostrowska (2015) claims that this focus on profit led to the tabloidisation of news across the CEE.
Following the global financial crisis in 2007/2008, foreign investors started to pull out of the CEE media markets and are being replaced by local owners who often have strong links with the government. This is evident in Hungary, where businessmen close to the government have been buying up independent media outlets, including its largest news website, one of two national commercial TV channels, and all regional newspapers (Bede, 2018). The Polish government also aims to “re-nationalise” its media. Plans by a state-run oil company to buy one of the country’s largest media publishers from its German owners were recently approved.
Elsewhere, domestically owned and previously independent media outlets are also being bought by new pro-government owners. In Russia, the formerly independent NTV from the above example was taken over by a state-owned company in 2001 and started to cover the ruling party in the run-up to the following elections in a similarly favourable way to state-controlled TV channels. Gehlbach (2010) argues that Putin’s media strategy is to exert tight control over the news coverage of these three main national television networks, while allowing media outlets with less reach to operate more independently.
In some countries of the region, there is limited information about the ultimate owner of media outlets. Within the EU, Latvia, Hungary, the Czech Republic, Slovakia and Cyprus, are assessed as high risk in terms of transparency of media ownership (Brogi et al. 2020). In 2009, the Swedish company Bonnier sold Diena – one of Latvia’s largest newspapers – to an initially undisclosed investor. A year later, a Latvian businessman acquired a controlling stake in the paper.
Government Advertising
Around the world, traditional news media is facing increased competition from digital platforms and becoming highly dependent on advertising revenue, including advertising from the government and pro-government businesses According to the Centre for Media Pluralism and Media Freedom, there are no clear and fair criteria for the distribution of state advertising to the media in the majority of EU countries – especially those in Eastern Europe (with the exception of Estonia).
Szeidl and Szucs (2021) document how the Hungarian government targeted advertising to friendly media outlets and how these media in turn covered the government more positively. They also present suggestive evidence that a similar favour exchange between government and the media occurs in nine other Eastern European countries, including Poland.
Two weeks ago, many private Polish media outlets coordinated a media blackout to protest government plans to tax advertising revenues. The media companies complained that the tax would cost them $270m a year, while public media received twice as much from taxpayers.
Public Service Media
The establishment of public service media forms an integral part of the EU’s agenda for promoting press freedom. While public service media are an important and trusted source of unbiased information in many western European countries, they generally play a smaller role in the Eastern European media markets. Furthermore, no laws are guaranteeing the independence of public service media from the government in eastern EU countries, with the exception of the Baltic states and Slovenia (see Centre for Media Pluralism and Media Freedom).
Intimidation of Journalists
Governments can also ensure positive coverage by intimidating editors and journalists. Since 1992, 91 journalists were killed, imprisoned, or went missing in Russia, 18 in Ukraine, 15 in Belarus, and 8 in Georgia (data by the Committee to Protect Journalists). While not all of these cases reflect government action, several recent examples illustrate how the judicial system may be used against journalists. For instance, according to the CPJ, ten journalists were imprisoned in November 2020 for covering protests against President Lukashenko in Belarus and one journalist was charged with high treason and espionage in Russia in July 2020.
There are also fears that governments can use defamation laws to deter and punish unwelcome media reports. For instance, the head of Poland’s ruling party filed a libel charge against two journalists from the Gazeta Wyborcza for reporting about his alleged involvement in a real estate project (see, e.g. Council of Europe media freedom alert).
Conclusion
The media plays a vital role in shaping the public debate and holding those in power accountable to the wider population. This power of the media also increases the risk that governments attempt to influence media content.
In recent years, many countries in CEE have seen press freedom come increasingly under threat, undermining some of the progress made since the dissolution of the Soviet Union. Part of the present fragility of media freedom in Eastern Europe may be due to their historical experience. During the transition from communism, many formerly state-owned media companies were sold to private and often foreign owners. In the past decade, local business interests with strong ties to the government started to buy up large shares of the media market in a number of Eastern European countries. Meanwhile, public service media have been less successful at establishing themselves as important and unbiased sources of information across Eastern Europe compared to Western Europe. To ensure positive media coverage, many governments adopt a carrot and stick approach: state advertising revenues and intimidation of individual journalists.
Article 19 of the Universal Declaration of Human Rights states that “everyone has the right to freedom of opinion and expression; this right includes freedom to hold opinions without interference and to seek, receive and impart information and ideas through any media and regardless of frontiers”. To ensure these fundamental rights, there need to be transparent and fair rules governing the ownership, management, and financing of media outlets and safeguards for individual journalists.
References
- Bede, Márton, 2018. “As elections loom, stakes are raised for Hungarian media.” International Press Institute.
- Brogi, Elda, Roberta Carlini, Iva Nenadic, Pier Luigi Parcu and Mario Viola de Azevedo Cunha, 2020. ”Monitoring Media Pluralism in the Digital Era.”, Centre for Media Pluralism and Media Freedom Report.
- DellaVigna, Stefano, and Ethan Kaplan. “The Fox News effect: Media bias and voting.” Quarterly Journal of Economics 122, no. 3 (2007): 1187-1234.
- Dobek-Ostrowska, Bogusława, 2015. “25 years after communism: four models of media and politics in Central and Eastern Europe”. In Democracy and media in Central and Eastern Europe 25 years on, 11-46. Publisher: Peter Lang Edition Editors: Bogusłąwa Dobek-Ostrowska & Michał Głowacki
- Enikolopov, Ruben, Maria Petrova and Ekaterina Zhuravskaya, 2011. “Media and political persuasion: Evidence from Russia.” American Economic Review, 101(7), pp. 3253-85.
- Gehlbach, Scott, 2010. “Reflections on Putin and the Media“, Post-Soviet Affairs, 26:1, 77-87.
- Hoskins, Andrew and Pavel Shchelin, 2018. “Information war in the Russian media ecology: the case of the Panama Papers.” Continuum, 32:2, 250-266.
- Kostadinova, Petia, 2015. “Media in the New Democracies of Post-Communist Eastern Europe.” East European Politics and Societies, 29 (2), 453–66.
- Martin, Gregory J., and Ali Yurukoglu, 2017. “Bias in cable news: Persuasion and polarization.” American Economic Review 107, no. 9: 2565-99.
- Peisakhin, Leonid and Arturas Rozenas. 2018. “Electoral Effects of Biased Media: Russian Television in Ukraine.” American Journal of Political Science, 62: 535-550.
- Snyder, James M., and David Strömberg, 2010. “Press Coverage and Political Accountability.” Journal of Political Economy, 118 (2), 355-408.
- Stetka, Vaclav. “From multinationals to business tycoons: Media ownership and journalistic autonomy in Central and Eastern Europe.” The International Journal of Press/Politics, 17: 4, 433-456.
- Szeidl, Adam, and Ferenc Szucs, 2010. “Media capture through favor exchange.” Econometrica, 89 (1): 281-310.
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.
Ukraine’s Integration into the EU’s Digital Single Market
This brief is based on a study that investigates how Ukraine’s integration into the EU Digital Single Market (DSM) could affect EU-Ukraine bilateral trade as well as Ukraine’s GDP growth. The major benefits of integration are expected to come from 1) reduction of cross-border regulatory barriers and restrictions to EU-Ukraine digital trade 2) acceleration of the development of Ukraine’s digital economy in line with EU standards. According to the results, enhanced regulatory and digital connectivity between Ukraine and the EU is expected to increase Ukraine’s exports of goods and services to the EU by 11.8-17% and 7.6-12.2% respectively. At the same time, the acceleration of the digital transformation of the Ukrainian economy and society will produce a positive effect on its productivity and economic growth – a 1%-increase in the digitalization of the Ukrainian economy and society may lead to an increase in its GDP by 0.42%.
Background
Integration into the EU has been one of the key topics on Ukraine’s political agenda for a number of years. Recently, more emphasis has been put on an essential component of issue – integration into the EU’s Digital Single Market (DSM). The DSM is a strategy aimed at uniting and enhancing digital markets and applying common approaches and standards in the digital sphere across the EU. The Ukraine-EU Summit, held on October 6, 2020, stressed the paramount importance of the digital sector in boosting its economic integration and regulatory approximation under the EU-Ukraine Association Agreement. Implementation of the provisions of this agreement, in particular the updated Annex XVII-3, would introduce the latest EU standards in the field of electronic communications in Ukraine. The country is also gradually approximating its regulations with regard to other components of the EU DSM – electronic identification, electronic payments and e-payment systems, e-commerce, protection of intellectual property rights on the Internet, cybersecurity, protection of personal data, e-government, postal services, etc. These steps will, in turn, ensure Ukraine’s gradual integration into the EU’s Digital Single Market, which will facilitate digital transformations within the country and open a new window of opportunity for individuals and businesses.
This brief summarizes the results of our recent work (Iavorskyi, P., et al., 2020), in which we estimate the effect that Ukraine’s integration into DSM could have on EU-Ukraine bilateral trade as well as Ukraine’s GDP growth.
Benefits of Integration into the EU DSM
The EU DSM strategy comprises three pillars: (1) better access for consumers and businesses to digital goods and services across Europe; (2) creating the right conditions and a level playing field for digital networks and innovative services to flourish; (3) maximizing the growth potential of the digital economy (EC, 2021).
These goals suggest that the major benefits of Ukraine’s integration into the DSM are likely to come from 1) reduction of cross-border regulatory barriers and restrictions to EU-Ukraine trade, 2) acceleration of the development of Ukraine’s digital economy in line with EU standards.
Indeed, the trade of goods and services is increasingly becoming “digital” – i.e., involving “digitally enabled transactions in goods and services that can be either digitally or physically delivered” (OECD, 2019). Trade digitalization (e.g., electronic contracts, electronic payments, e-customs, etc.) simplifies export and import procedures, reduces trade costs for exporters, and creates new opportunities for trade with the EU, in particular for SMEs. Therefore, the reduction of regulatory restrictions on cross-border digital trade reduces the overall level of restrictiveness of trade in goods and services.
Thus, digitalization is expected to facilitate and intensify the total EU-Ukraine trade in goods and services. It is also anticipated to increase the productivity of Ukraine’s economy which will have a positive impact on the country’s economic growth.
Major benefits include lower prices and greater access to EU online markets for Ukrainian consumers and business, digital innovative products and services, greater online consumer protection, lower transaction costs for businesses, improved quality and transparency of public digital services and e-government as well as an intensification of innovation development in Ukraine.
At the same time, Ukraine’s integration into the DSM entails several obligations: to align national legislation and standards with EU legislation and standards; to ensure institutional and technical capacity as well as interoperability of digital systems. For businesses in Ukraine, this means facing new EU requirements aimed at improving consumer and personal data protection, as well as increased competition from European companies in digital markets. However, these changes are necessary if the country wants to build a common economic space with the EU, especially given the growing impact of digital technologies on international trade and economy.
Ukraine in International Digital Rankings
Many international digital development rankings show that Ukraine lags behind EU countries, including its neighbors that recently joined the EU.
According to the UN e-Government Development Index (EGDI) for 2020, Ukraine ranks 69th among 193 countries and is included in the group of countries with high levels of e-government development. It received the lowest scores for Telecommunications Infrastructure and Online Services, and the highest for Human Capital. Nevertheless, Ukraine is lagging behind its neighboring EU members, – Poland, Hungary, Slovakia, Romania, Bulgaria, Lithuania, etc., – which belong to the group of countries with very high levels of e-government development (UN, 2020).
In the Network Readiness Index (NRI) ranking for 2019, Ukraine ranked 67th among 121 countries. As for the components of the index, Ukraine ranks worst in the following indicators: Future technologies (82nd out of 121), ICT Use by Government and Online Government Services (87th), and Regulatory Environment (72nd). Neighboring EU countries have higher rankings (Poland – 37, Latvia – 39, Czech Republic – 30, Croatia – 44). Other neighboring countries do somewhat better than Ukraine (Turkey is ranked 51st, Russia – 48th) or occupy positions close to Ukraine (Belarus – 61, Moldova – 66, Georgia – 68) (Portulans Institute, 2019).
In 2019, the country ranked 60th among 63 countries included in the World Digital Competitiveness Ranking (WDCR) rating. Just as in the other rankings, Ukraine scored well in the Knowledge component (40th among 63 countries), while in terms of Technology and Future Readiness it was at the bottom (61st and 62nd position respectively) (IMD, 2019).
Hence, it is primarily the technological and regulatory issues, that need to be addressed in order to improve Ukraine’s digital position in the region and the world.
Methodology
Measuring Ukraine’s Digitalization level
In order to estimate the impact of digitalization, a Composite Digitalization Index is calculated for Ukraine, the EU, and other countries included in the model. This index is based on 11 digital indicators, combined into five components that characterize different areas of the digital economy and society – Connectivity, Use of the Internet by citizens, Human capital, Integration of digital technology by businesses, and Digital public services.
Our results confirm that the level of digital development in Ukraine is far below the EU average. It also lags behind the new EU Member States, which have a lower level of digital development compared to the other EU countries. As of 2018, the widest gaps between Ukraine and the EU average are found in Digital Public Services, Connectivity and Use of Internet by citizens. At the same time, Ukraine performed better in Human Capital and Integration of digital technology by businesses.
Measuring Digital Services Trade Restrictiveness in Ukraine
To assess the impact of digital regulatory barriers on trade, we use the Digital Services Trade Restrictiveness Index (Digital STRI) (OECD, 2020). It quantifies the regulatory barriers in five different policy areas (communication infrastructure, electronic transactions, electronic payments, intellectual property, other restrictions) that affect trade in digital services (Ferencz, J., 2019). OECD calculates Digital STRI for OECD countries and some non-OECD countries. As Ukraine is not included in this index, we estimate it for 2016-2018 using the OECD methodology.
Our estimations show that the level of digital services trade restrictiveness in Ukraine is much higher than the EU average. The regulatory differences in the digital sphere between Ukraine and the EU increase the cost of cross-border digital transactions between countries.
For Ukraine, most barriers are related to cross-border electronic payments and settlements, protection of intellectual property rights on the internet, cross-border electronic transactions (for example, the divergence of the national requirements for foreign trade agreements, including electronic ones, from international practices and standards, lack of practical mechanisms for the application of the electronic digital signature in foreign trade contracts, lack of mutual recognition of electronic identification and electronic trust services between Ukraine and major trading partners, etc.), other barriers (requirements for the use of local software and cryptography, etc.). These regulatory restrictions significantly hinder the development of cross-border cooperation and Ukraine’s integration into the European and global digital space.
Ukraine’s integration scenarios
In the event of Ukraine’s integration into the EU DSM, the country’s regulatory environment and digital development are expected to gradually approach the EU averages. We model it through assuming that the regulatory differences between Ukraine and the EU (captured by the Digital STRI Heterogeneity Indices – see OECD, 2020) will be decreasing, and level of digitalization in the country (captured by the Digitalization Index – OECD, 2020) will converge towards that of EU-DSM members.
We considered three integration scenarios that imply high, medium, and low levels of Ukraine’s approximation to the regulatory environment and digital development of the EU. For instance, the high scenario implies the highest level of Ukraine’s digital development and the lowest level of regulatory differences between Ukraine and the EU.
Models
We study the effect of reduced regulatory differences in the digital sphere on Ukraine-EU trade using a gravity model – one of the traditional approaches in the international trade literature. A gravity model predicts bilateral trade flows based on the size of the economy and trade costs between countries (affected by distance, cultural differences, FTAs, tariffs, etc.)
The study uses the following specification of the model for exports of goods and services in 2016-2018:
• Dependent variable – the total export flow of goods and services from country into country j (all possible pairs of countries).
• Independent variables – distance between countries and common characteristics (borders, language, law), existence of a free trade agreement, level of tariff protection (for goods), level of regulatory heterogeneity in the digital sphere between the two countries, and a set of fixed effects for each country.
We also estimate how digital development affects technical modernization, productivity, and economic growth. Technically, we use a Cobb-Douglas production function to describe each country’s output and model its total factor productivity component as a function of digital development (captured by the Digitalization index).
Results
The results suggest that Ukraine’s integration into the EU DSM will be beneficial for both Ukraine and the EU. Under all integration scenarios, bilateral trade between Ukraine and the EU is expected to intensify considerably due to enhanced regulatory and digital connectivity between the two.
Ukraine’s total exports of goods and services to the EU are estimated to grow by 11.8-17% ($2.4-3.4 billion) and 7.6-12.2% ($302.5-485.5 million), respectively – a cumulative increase throughout the period of implementation of reforms aimed at regulatory and digital approximation of Ukraine to the EU.
Figure 1. The impact of Ukraine’s integration into the EU’s DSM on the exports of services from Ukraine to the EU*: three integration scenarios
Figure 2. The impact of Ukraine’s integration into the EU’s DSM on exports of goods from Ukraine to the EU*: three integration scenarios
The EU would increase its exports of goods and services to Ukraine by 17.7-21.7% ($4.1-5 billion) and 5.7-9.1% ($191-305 million), respectively.
The acceleration of Ukraine’s digital development will bring productivity gains that would transform into higher GDP growth. It is estimated that a 1% increase in Ukraine’s digitalization level is expected to raise its GDP by 0.42%. As a result, the country’s gradual approximation to EU levels of digitalization would result in additional Ukraines GDP growth of 2.4-12.1% ($3.1-15.8 billion), depending on the scenario.
Figure 3. Impact of digitalization on Ukraine’s GDP growth: three digitalization increase scenarios
Conclusion
According to our estimations, improved digitalization and reduction of regulatory barriers in the digital sphere between Ukraine and the EU will have a positive effect on trade for both Ukraine and the EU. There is also a significant potential for economic growth to be attained in Ukraine by increasing digitalization and productivity of various spheres of the economy and society.
Realization of this potential would, however, require a substantial regulatory approximation on the Ukrainian side to achieve alignment with the EU DSM. The main emphasis needs to be put on electronic identification and transactions, payment systems and electronic payments, protection of intellectual property rights on the internet, cybersecurity, and personal data protection.
References
- European Commission, 3.02.2021. Shaping the Digital Single Market.
- Ferencz, J., 2019. The OECD Digital Services Trade Restrictiveness Index, OECD Trade Policy Papers, No. 221, OECD Publishing, Paris.
- Iavorskyi, P., et al., 2020. Ukraine’s integration into the EU’s Digital Single Market: potential economic benefits
- IMD, 2019. World Digital Competitiveness Ranking 2019.
- Marcus, J., Petropoulos, G., and Yeung, T., 2019. Contribution to Growth: The European Digital Single Market Delivering economic benefits for citizens and businesses. CEPS Special Report.
- OECD, 2020. Digital Services Trade Restrictiveness Index and Digital STRI Heterogeneity Indices.
- OECD, 2019. Digital trade. Trade policy brief.
- Official Journal of the European Union, 2014. “EU-Ukraine Association Agreement.
- Portulans Institute, 2019. Network Readiness Index 2019, Washington D.C., USA.
- UN, 2020. E-Government Development Index (EGDI) 2020.
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.