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Regional Economic Development Along the Polish-German Border: 1992-2012
In this brief, we summarize the results of a recent analysis focused on the regional economic development in Poland and Germany along the Oder-Neisse border (Freier, Myck and Najsztub 2021a). Economic activity is approximated by satellite night-time light intensity, a comparable proxy available for regions on both sides of the frontier consistently between 1992 and 2012. This period covers the time of economic transformation and the first eight years of Poland’s membership in the European Union. We find that convergence in overall activity across the border has been complete: Polish municipalities that used to be economically much weaker have caught up with those on the German side of the Oder and the Neisse rivers.
Introduction
The question of the harmonious development of economic activity is at the heart of the European integration project (Art. 2, Treaty of Rome, 1957), and the Maastricht Treaty (1992) made economic convergence between member states an explicit objective. In a forthcoming paper (Freier et al. 2021), we take a new approach to the question of regional European integration.
This brief derives from a recent publication in Applied Economics (Freier et al. 2021a), in which we examine the degree of regional economic convergence along the German-Polish border by taking advantage of satellite night-time illumination data covering the period between 1992 and 2012. The data allows us to study detailed regional patterns of economic development along the river-delimited part of the frontier and further inland.
The seminal work by Henderson et al. (2012) was the first to use night-time light intensity data which covers the entire globe to measure economic activity. Unlike traditional regional economic indicators, light intensity data is independent of administrative border reforms and has been collected in a consistent format over the studied two decades.
Our analysis suggests that, over the analysed period from 1992-2012, there has been essentially full convergence in economic activity between municipalities on both sides of the Polish-German border. While the average value of night-time illumination in our selected group of municipalities in 1992 was 3.7 (on a scale between 0 and 63) in Poland and 7.7 in Germany, the respective values were 9.0 and 9.7 by 2012, and the latter difference is not statistically significant. This convergence suggests a much stronger rate of growth in economic activity on the Polish side of the border. Additionally, we show that within Germany, the distance to the border has much less relevance for economic activity compared to Poland, where it reflects interesting trends. In 1992, Polish towns farther from the border showed significantly higher economic performance. Within Poland, this gap has been greatly reduced over the 20 years we analyse, with regions closer to the border growing much faster compared to those farther away.
Night Lights Along the Polish-German Border
In our dataset, we include municipalities that are located within 100 km from the river delimited part of the PL-DE border. To avoid the sensitivity of the analysis to top censoring of the night-time light intensity data, we removed regional capital cities: Berlin (with surrounding municipalities), Dresden, Gorzów Wielkopolski, and Zielona Góra. This leaves us with 488 municipalities on the German side of the border and 193 municipalities on the Polish side.
The night lights data series, provided by the National Oceanic and Atmospheric Association (NOAA), starts as early as 1992 and continues in a consistent, comparable format to 2012. The data is independent of the administrative structures of local governments, which over time have changed on both sides of the border. This allows us to aggregate the night-time lights information for municipalities using the most recent available administrative borders. This data is essentially the only source of information on economic activity that is consistently available and comparable on both sides of the border over such a long period of time.
The night-time lights data has been applied widely as a proxy of economic development on the country and regional level (Henderson et al., 2012; Bickenbach et al., 2016). Clearly, the intensity of night-time lights does not capture the entire spectrum of economic activity. It has been pointed out that the relationship between night-time light intensity and conventional measures of economic development, such as GDP, is likely to differ depending on a region’s stage of economic development (Hu and Yao, 2019). However, we focus on mostly rural and sparsely populated areas (where there is little risk of top censoring of the data), and compare dynamics between regions that are similar in terms of their stage of economic development, geography, and weather. All these factors support the use of night lights as a proxy for regional development in our application (a number of technical steps are necessary to validate and calibrate the data for use in our analysis, see: Freier et al. 2021).
Economic Convergence Along the PL-DE Border
To understand the overall development of economic activity over the period of interest, we map the changes in the night-time light intensity in Figure 1. The colour scale on the map represents differences in light emissions between 1992 and 2012, with the range going from -40 to 40. A negative value indicates a reduction, and a positive value highlights an increase in light intensity. The negative values have been coloured in a blue-green scale (-40 to 0), while positive values in a red scale (0 to +40).
Figure 1. Night lights: changes in light intensity between 1992 – 2012 along the Polish-German border
As notable in Figure 1, the red areas are predominant. This exemplifies that between 1992 and 2012, nearly all municipalities in this area witnessed positive economic development as manifested in the intensity of night-time lights. We have a few areas that reflect negative dynamics on the German side of the border. This is mainly due to the regional implications of shutting down activity in agriculture and traditional industries as they were unable to compete with West-German technology and productivity. In Poland, green-blue areas are essentially non-existent, illustrating a universally positive economic development over the studied period. This difference in the pace of changes in light intensity between the German and the Polish side reflects a process of rapid convergence of economic development between municipalities on both sides of the border. These developments are represented in Figure 2 which shows the difference between the night-time light intensity in Germany and Poland by year and provides a test for its statistical significance. The estimation is done on mean log pixel values per municipality and clearly highlights the steep path of convergence. In the early nineties, the difference in mean light intensity was around 100 percent – i.e., the mean difference was as high as the mean level of lights on the Polish side of the border. Already ten years later it reduced to around 50 percent and disappeared by the end of the analysed period. It is notable that, after an initial steep convergence, the difference in light intensity had a period of stagnation between 2002 and 2008. Interestingly, the full convergence which followed coincides with Poland’s entry into the Schengen agreement in December 2007. As seen in Figure 2, the difference in the average night-time light intensity between Poland and Germany was statistically insignificant and essentially zero since 2009.
Figure 2. Difference in mean night-time lights between Germany and Poland over time
Regional Development and Distance from the Border
Thanks to its high degree of geographical precision, the night-time lights data allows us to study the detailed spatial patterns within each country and, in particular, the relationship between distance to the border and economic activity. This is done by looking across the years 1992 to 2012 and examining three-year windows at each end of the analysed period. Our results, which are reported in Table 1, confirm a strong positive relationship between economic activity and distance to the border on the Polish side of the Oder-Neisse rivers. Overall, Polish regions farther from the border show a greater degree of economic activity, but this relationship has substantially diminished over time. While in Germany, economic activity was higher in regions farther from the border and increasing at the average rate of about 0.3% per km, this rate was about three times higher in Poland, falling from about 1.2% per km in 1992-94 to 0.6% in 2010-2012.
Table 1. Total night-time lights along the Polish-German border, 1992-2012
Table 2 reports changes in light intensity between the beginning and the end of a specific period. Here, we find some interesting and perhaps disconcerting results on the relationship between the distance to the border and changes in light intensity. While the distance-to-border coefficient in the Polish case for the full period is negative, suggesting that regions closer to the border were catching up to the more developed regions farther away, the corresponding coefficient for the final three years is positive. This means that, in the years 2010-2012, economic development was faster in municipalities farther away from the border. Although the relationship is not very strong (the change in light intensity grows by about 0.1% per kilometre of distance to the border), it still suggests a reversal in the fortunes of municipalities close to the border on the Polish side. This result points towards the fact that homogeneity of development cannot be taken for granted and that physical distance might continue to play a role in determining the regional rate of growth in the future.
Table 2. Changes in night-time lights along the Polish-German border: 1992-2012
Conclusion
In this brief, we report results from a forthcoming paper (Freier et al. 2021) in which we evaluate regional development in municipalities on the German and Polish side of the Oder-Neisse border between 1992 and 2012, using night lights data as a proxy for economic activity. We find that driven by rapid growth in Polish municipalities and somewhat sluggish growth in German ones, the light intensity levels across the Oder-Neisse border show no significant differences by the end of our observation period. This is despite significant initial differences just 20 years earlier and the fact that municipalities on the German side also experienced increases in economic activity. In as far as economic development can be proxied by the intensity of night-time illumination, it seems that economic convergence between regions on both sides of the border was complete by 2012.
We also show interesting patterns regarding the relationship between economic activity and distance from the border. For Germany, this relationship is weakly positive and remains stable throughout the analysed period. In Poland, distance is strongly and positively correlated with light emissions at the beginning of the period, hence indicating that municipalities farther from the border show higher average economic activity. By 2012, however, the border regions have closed most of the gap and the distance to the border is a substantially weaker predictor of economic activity, suggesting a much more homogenous pattern of activity.
Acknowledgements
This brief draws on results reported in Freier et al. (2021a). The authors gratefully acknowledge the support of the Polish National Science Centre (NCN), project number: 2016/21/B/HS4/01574. For the full list of acknowledgements and references see Freier et al. (2021a).
References
- Bickenbach F, Bode E, Nunnenkamp P and Söder M (2016) Night Lights and Regional GDP. Review of World Economics 152(2): 425–47.
- Freier, R., Myck, M., Najsztub, M (2021a) Lights along the frontier: convergence of economic activity in the proximity of the Polish-German border, 1992-2012. Applied Economics, available online: doi: 10.1080/00036846.2021.1898534.
- Freier, R., Myck, M., Najsztub, M (2021b) Night lights along the PL-DE border 1992-2012. Dataset used in Freier et al. (2021a), Zenodo, DOI: 10.5281/zenodo.4600685.
- Henderson JV, Storeygard A and Weil DN (2012) Measuring Economic Growth from Outer Space. American Economic Review 102(2): 994–1028.
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.
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).
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- 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)
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- 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).
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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.
Why Are Women Underrepresented in Politics: Exploring Causes and Solutions
Why are women underrepresented in politics? Despite progress in gender equality, women are still significantly underrepresented in political offices worldwide, especially in higher-level positions. This issue has drawn increasing attention in both academic and policy circles.
Recent research in economics and political science explores the key reasons why women are underrepresented in politics, often categorizing them into “supply-side” and “demand-side” factors. Supply-side factors include women’s potentially lower willingness or ability to run for political office, influenced by social norms, family responsibilities, or lack of political networks. On the demand side, voter and party leader biases against women candidates play a significant role in limiting opportunities for women to hold political office.
Understanding why women are underrepresented in politics is critical for designing effective policies that address the gender gap in political representation. Solutions such as gender quotas, political leadership training for women, and reforms to reduce bias have been proposed. We review some of these strategies and assess their effectiveness based on available evidence.
By tackling the root causes of why women are underrepresented in politics, we can create more inclusive political systems that better reflect the diverse populations they serve.
Country Reports
Belarus country report (EN) | Belarussian language version (BY) |
Georgia country report (EN) | Georgian language version (GE) |
Latvia country report (EN) | Latvian language version (LV) |
Poland country report (EN) | Polish language version (PL) |
Russia country report (EN) | Russian language version (RU) |
Ukraine country report (EN) | Ukrainian language version (UA) |
Women in Politics: Why Are They Under-represented?
Women are generally under-represented in political offices worldwide, and their under-representation becomes larger in more senior positions. Of the four dimensions considered in the World Economic Forum’s Gender Equality Index (namely, Economic Opportunity and Participation, Educational Attainment, Health and Survival and Political Empowerment), the dimension called Political Empowerment, which measures the extent to which women are represented in political office, records the poorest performance, with only 25% of an hypothetical 100% gap having been closed to date.
Importantly, although there is large variation across countries, gender inequality in political empowerment is documented in every region worldwide, including in those countries that are most socially and economically advanced. Sweden, for instance, while having a good record of women’s representation in most institutions (women currently represent 47.5% of the Parliament members, 54.5% of the ministers, and about 43% of the municipal councilors), has never had a woman as Prime minister, and only one-third of its mayors are female. Countries in Eastern Europe and Central Asia have only closed 15% of a hypothetical 100% gender gap in political empowerment, according to the World Economic Forum, by far their worst performance among the four sub-indexes that compose the overall Gender Equality Index.
Given the persistent under-representation of women in political institutions, where important decisions that shape societies are taken, economists and political scientists, among others, are increasingly interested in understanding the causes of the gender gap in political representation. In this brief, some of the recent academic literature on this question is summarized, and some policies that may help to close the gender gaps in political representation are reviewed.
Table 1. World Economic Forum Gender Equality Index. Regional Performance in 2020, by Sub-index
Why Are Women Under-represented in Political Office?
Broadly speaking, three main reasons are most often explored, namely women’s unwillingness to become politicians, voters’ bias, and parties’ bias. Below an overview of some of the work that has addressed each of these three factors is provided.
Gender Gaps in Political Ambition
Large-scale surveys have documented that women who, based on their professional and economic credentials, are potential political candidates, report lower ambition to occupy executive offices than comparable men (Fox and Lawless, 2004). The main reasons for the gender gap in ambition appear to be that
- (a) women are less encouraged to run for office than men and
- (b) women are less likely to believe that they are qualified for office than men.
Women’s tendency to shy away from competition (Niederle and Vesterlund, 2007) may also play a role since the political selection process is likely perceived as highly competitive. As Preece and Stoddard (2015) find by using two experiments, priming individuals to consider the competitive nature of politics lowers women’s interest in running for political office, whereas it has no effect on the interest of men.
Women’s willingness to advance in their political careers can also be influenced by family and relational considerations. Recent work from Folke and Rickne (2020) shows that in Sweden female politicians who are promoted to mayor (i.e. the highest office in municipal politics) experience a significant increase in the likelihood of divorcing their partner, whereas this is not the case for men. If women face higher costs for their career achievements, as the evidence in Folke and Rickne (2020) suggests, they may be discouraged from pursuing such objectives.
While there is evidence that women may on average be less willing to advance to top positions than men, it is not clear how quantitatively relevant this factor is to account for the lack of women in power. The introduction of gender quotas in candidate lists in different countries worldwide can be informative in this sense. If women’s under-representation in electoral lists is mostly due to the lack of qualified female politicians, some electoral lists (in most cases representing specific political parties) may not be able to run due to the introduction of a quota, and the average “quality” of lists, measured by some relevant (to voters) characteristics of their members, would decrease. The literature finds no evidence of either of these two responses to quotas (see Baltrunaite et al., 2014, Besley et al., 2017, Bagues and Campa, 2020). On the contrary, in Italy (Baltrunaite et al., 2014) and Sweden (Besley et al., 2017) quotas appear to have improved the “quality” of the elected politicians.
Voters’ Bias
Krook (2018) observes that the existing work in political science regarding the importance of voters’ bias in explaining women’s underrepresentation in politics leads to ambivalent conclusions. Results in the most recent economics literature confirm this assessment. Barbanchon and Sauvagnat (2019) compare votes received by the same female candidate in French parliamentary elections across different polling stations within an electoral district and find that votes for women are lower in municipalities with more traditional gender-role attitudes. They interpret this pattern as evidence of voters’ discrimination and conclude that voters’ bias matters quantitatively in explaining women’s under-representation among politicians. Conversely, Bagues and Campa (2020) find no evidence of voters’ bias against women, based on voters’ reaction to the introduction of a gender quota for electoral lists in Spain. Specifically, they study how the quota impacts the electoral performance of lists that were more affected by the quota – i.e. that were forced to increase their share of female candidates by a larger extent, due to their lower level of feminization pre-quota. They do not find evidence that such lists have worsened their relative electoral performance due to the quota. Put differently, there is no evidence that voters lower their electoral support of a list when its share of female candidates increases for exogenous reasons.
Survey data on voters’ attitudes can also help in gauging the extent to which voters discriminate against women. Based on data from the latest wave of the World Value Survey (WVS, 2017-2020), in Western Europe typically less than 20% of survey respondents express agreement with the statement “Men make better political leaders than women do” (e.g. 5% in Sweden, 9% in Denmark and Germany, 12% in Finland and France, 19% in Italy; only in Greece the share of the agreement is higher than 20%, at 26%). As shown in Figure 1, these percentages are substantially higher in Eastern Europe and Central Asia.).
Figure 1. Share of survey respondents who report to “Agree” or “Strongly Agree” with the statement “Men make better political leaders than women do”.
It bears noting, however, that answers to the WVS are not always informative about the extent to which voters’ bias prevails in a country. Where the percentage of respondents who think that men make better political leaders than women is close to or above 50%, as e.g. in Armenia, Georgia, or Russia, voters’ bias is likely to be an important factor. However, in countries with lower levels of agreement, such as for instance Poland, drawing conclusions is harder, since the WVS does not measure the share of respondents who think that women make better political leaders than men do.
Parties’ Bias
Party leaders, who often are key players in the selection of politicians, may prefer to promote male rather than female candidates. If they are aware of voters’ bias against women, preferring male candidates is consistent with a votes-maximizing strategy. However, party leaders may also act as gate-keepers and hold women back even in absence of voters’ bias. Esteve-Volart and Bagues (2012) find evidence of an agency problem between voters and parties by looking at Spanish elections. While parties tend to nominate women in worse positions on the ballot, there is no evidence that women attract fewer votes than men; moreover, when the competition is stiffer, women’s position on the ballot improves. These two facts lead the authors to conclude that the disadvantage women face can likely be attributed to parties’ rather than voters’ bias.
When considering all these factors, it is also important to note that the systematic under-representation of women in political institutions is likely self-reinforcing, due to gendered group dynamics. In the laboratory, women in male-majority teams appear significantly less likely to put their name forward as team-leaders than women in female-majority teams; they anticipate, correctly, lower support from team members (see Born et al., 2019). Female mayors in Italy are significantly more likely to be removed by their municipal councils than their comparable male colleagues; importantly, this is especially true when the share of male councilors is particularly large (Gagliarducci and Paserman, 2011). These studies suggest that, since the political arena has been historically male-dominated, gendered group dynamics can create vicious cycles of women’s under-representation.
Which Policies Can Be Used to Increase Women’s Representation in Political Institutions?
Different policies can be considered to address the various factors accounting for women’s under-representation in politics. In an attempt to address the ”supply-side’’ aspect of women’s under-representation, various non-profit organizations have offered training programs aimed at providing women with knowledge, skills, and networks to build political careers (see, for instance, NDI 2013). While reviewing the existing literature on these programs is beyond the scope of this brief, to the best of the author’s knowledge, there is little to no research-based evidence on the quantitative impact of training on women’s advancements in politics. Non-profit organizations, political parties, and researchers may fruitfully collaborate to implement and systematically test training programs.
Gender quotas are the most commonly used policy intervention, especially those regulating the composition of candidate lists, and they have been extensively studied; overall the literature suggests that quotas are more or less effective in empowering women depending on their design and the context where they are used (see Campa and Hauser, 2020 for a more comprehensive review of the economics literature on gender quotas and related policy implications). Given the nuances in the functioning of quotas, countries or regions that consider their adoption should consult with experts who know the ins and outs of such policies and combine their expertise with local knowledge of the relevant context.
The structure and distribution of power within parties are likely crucial for improving women’s political representation. Some scholars have devoted attention to the role of women’s organizations within parties. Theoretically, such organizations should favour the creation of networks and offer mentorship services, which are likely crucial to climb the career ladder in politics. In Sweden, a coalition of women from both the right and the left is credited for having pressed the Social Democrats’ into adopting their internal zipper quota by threatening to form a feminist party (see Besley et al., 2017). Women’s wings within political parties could play a similar role. Kantola (2018) notes that women’s organizations seem to be currently deemed as outdated, at least in European parties; Childs and Kittilson (2016), on the other hand, find that their presence does not seem to harm women’s promotion to executive roles within parties, a concern that has been associated with the existence of such organizations. In countries with public funding of political parties, specific funds could be directed to women’s organizations within parties.
Folke and Rickne (2020) also note that, since women in top jobs appear to face more relational and family constraints than men, policies that improve the distribution of economic roles within couples could help address the under-representation of women in positions of political power; their observation underlines the crucial role of gender-role attitudes in affecting women’s empowerment in any area of society. How can these attitudes change? An increasing amount of research is being devoted to answering this question. Campa and Serafinelli (2019), for instance, show that a politico-economic regime that puts emphasis on women’s inclusion in the labor market can change some of these attitudes. More research from different contexts and on specific policies will hopefully provide more guidance for policy makers on this important aspect, but the message from the existing research is that gender-role attitudes can be changed, and therefore policy-makers should devote attention to interventions that can influence the formation of such attitudes.
In many Western democracies, the rate of progress in women’s access to top political positions has proven especially slow. This history of Western democracies and the existence of the self-reinforcing mechanisms described above can serve as a lesson for countries in transitions, where new political organizations and institutions are emerging. In absence of specific policies that address women’s under-representation at lower levels very early on, it would likely take a very long time before gender gaps are closed at higher levels of the political hierarchy.
In conclusion, the authors observe that constant monitoring of the gender gaps in political institutions is important, even in presence of clear upward trends, since progress is rarely linear and therefore needs continuous nurturing.
About FROGEE Policy Briefs
FROGEE Policy Briefs is a special series aimed at providing overviews and the popularization of economic research related to gender equality issues. Debates around policies related to gender equality are often highly politicized. We believe that using arguments derived from the most up to date research-based knowledge would help us build a more fruitful discussion of policy proposals and in the end achieve better outcomes.
The aim of the briefs is to improve the understanding of research-based arguments and their implications, by covering the key theories and the most important findings in areas of special interest to the current debate. The briefs start with short general overviews of a given theme, which are followed by a presentation of country-specific contexts, specific policy challenges, implemented reforms and a discussion of other policy options.
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“