Project: FREE policy brief

Inequality in the Pandemic: Evidence from Sweden

Posted on March 22, 2021 | Policy Brief

Condominium houses near the water representing inequality during the covid19 pandemic

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

Source: Author’s own calculation, for data sources see Data Section.

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

Source: Author’s own calculation, for data sources see Data section

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

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

Posted on March 15, 2021 | Policy Brief

20210301 Addressing the COVID-19 Pandemic FREE Network Policy Brief Image 04

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

Source: Yandex. The average value during 24 Feb-8 March 2020 set to 100. Seven-day rolling average.

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

Source: Belstat. December 2019=100.

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

Percentage of respondents reporting income reductions in the previous month for reasons other than currency depreciation, Source: BEROC/SATIO data

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

Source: Own estimates from BEROC/Satio data. Controls include age, sector of employment, and education level.

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

Posted on March 9, 2021 | Policy Brief

20210308 Understanding Russia GDP Numbers FREE Network Policy Brief Image 02

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

Introduction

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

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

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

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

Oil prices, GDP growth, and the ruble

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

Figure 1. Russia real GDP and oil prices

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

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

Figure 2. GDP growth and oil price changes

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

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

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

Figure 3. Oil prices and exchange rate indices

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

The components of real GPD

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

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

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

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

Source: Author’s calculations based on data from Rosstat

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

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

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

Table 2. Trade statistics

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

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

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

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

Table 3. GDP growth contributions from alternative trade data

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

Conclusions

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

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

References

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

Addressing the COVID-19 Pandemic: Vaccination Efforts in FREE Network Countries

Posted on March 1, 2021 | Policy Brief

Preparing Covid Vaccine on Pink Surface representing COVID-19 vaccination

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

Source: John Hopkins University CSSE COVID-19 visualizations: Ourworldindata.org/coronavirus

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

Source: Our world in data, last updated February 24th, 2021. This is counted as a single dose, and may not equal the total number of people vaccinated. Visualizations: Ourworldindata.org/coronavirus

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.

Source: Authors’ calculations based on data collected by the Public Health Agency of Sweden. Last updated February 14th, 2021.

Poland has so far been somewhat more efficient than Sweden in its vaccination efforts. Despite turbulent political events over the last couple of months, it has managed to distribute 5.7 doses per 100 people. The country has just finished the first phase of the national vaccination plan, which focused on vaccinating healthcare personnel, and has now entered the second phase with a shifted focus towards elderly care homes, people above 60 years of age, military, and teachers.

Among the countries that are not members of the EU, and thus, not taking part in its coordinated vaccination efforts, the vaccination statuses are more diverse.

Russia was fast in developing and approving the Sputnik V vaccine. The country started vaccinating in early December, although only people in the age of 18-60 in prioritized occupations such as health care workers, people living and working in nursing homes, teachers, and military. At the start of 2021, the program extended to people above 60 and, on January 16, all adults were given the possibility to register themselves and get vaccinated within one week. There are no precise data at the moment, but the fraction of the population vaccinated is likely to be higher than 1%.

Others in the region have faced greater challenges in signing contracts with vaccine suppliers. Georgia and Ukraine are still waiting to secure deliveries and have not yet started to vaccinate. Being outside the EU agreements and with public and political mistrust towards Sputnik V and Russia alternatives are being explored. Georgia has ordered vaccines through the COVAX platform (co-led by Gavi, the Coalition for Epidemic Preparedness Innovations (CEPI) and WHO) but there are concerns about potential delays in deliveries. In terms of prioritizing groups once vaccinations can start, both Ukraine and Georgia have set similar priorities as other countries, with extra focus on health-care and essential workers, age-related risk groups, and people with chronic illnesses.

While Belarus’ official figures on the death toll have been widely perceived as unrealistic from the beginning, the most accurate and recent data shows an excess deaths rate of about 20% in July. The country has no precise data on vaccinations, but some reports have emerged based on interviews with government officials in the Belarusian media. These suggest that around 20,000 imported doses of Sputnik V have been distributed mainly to medical professionals and an additional 120,000-140,000 doses have been promised by Russia.

Main Challenges

The discussion during the Q&A session at the webinar concerned the economic and political implications of vaccinations in the region.

Pavlo Kovtoniuk, the Head of Health Economics Center at KSE in Ukraine, stressed the importance of a coordinated vaccination effort in Europe with regards to geopolitics. There is a clear EU vs Non-EU divide in the vaccination status across European countries. The limited vaccine availability in Non-EU countries such as Ukraine, Georgia, and Belarus offers opportunities for more influential nations like Russia and China to pressure and affect domestic policy in these countries.

Also highlighting the fact that no one is safe until everybody is safe, Lev Lvovskiy, Senior Research Fellow at BEROC in Minsk, noted that vaccination efforts in Europe are important for recovery in small open economies like Belarus as many of its trade partners currently have imposed temporary import restrictions.

Similar to the political crisis happening alongside the pandemic in Belarus, the challenges we see in Poland – protests against the recent developments regarding abortion rights and attempts by the government to limit free media – have deflated the urgency to vaccinate in terms of its future economic and political implications, according to Michal Myck, director of CenEA in Szczecin.

Looking forward, another major challenge for the region is vaccine skepticism. Not only do many countries have to build proper infrastructure that can administer vaccines at the required scale and pace, but also make sure that people actually show up. In Latvia, Poland, Georgia, Russia, and Ukraine, polls show that less than 50% of the population are ready to vaccinate. Sergejs Gubin, Research Fellow at BICEPS in Riga, highlighted that there can be systematic variation in the willingness to vaccinate within countries as e.g. Russian-speaking natives in Latvia have been found to be less prone to vaccinate on average. Also, most of the skepticism in Georgia has been more directed towards the Chinese and Russian vaccine than towards those approved by the EU, according to Yaroslava Babych who is lead economist at ISET in Tbilisi.

Even though vaccine skepticism is an issue in Russia too, Natalya Volchkova, Director of CEFIR at New Economic School in Moscow, pointed to the positive impact of “bandwagon effects” in vaccination efforts. When one person gets vaccinated, that person can spread more accurate information about the vaccine to their social circle, resulting in fewer and fewer people being skeptical as the share of vaccinated grows. In such a scenario vaccine skepticism can fade away over time, even if initial estimates suggest it is high in the population.

Concluding Remarks

Almost exactly a year has passed since Covid-19 was declared a pandemic. The economic and social consequences have been enormous. Now vaccines – developed faster than expected – promise a way out of the crisis. But major challenges, of different types and magnitudes across the globe, still remain. As the seminar highlighted, there are important differences across transition countries. Some countries (such as Russia) have secured vaccines by developing them, but still face challenges in producing and distributing vaccines. Others have secured deliveries through the joint effort by the EU, but this has also had its costs in terms of a somewhat slower process (compared to some of the countries acting on their own) and sharing within the EU. For some other countries, like Belarus, Ukraine, and Georgia, the vaccination is yet to be started. All in all, the choice and availability of vaccines across the region illustrates how economic and geopolitical questions remain important. Finally, for many of the region countries vaccine skepticism and information as well as disinformation are important determinants in distributing vaccines. Summing up, the combination of these factors once again reminds us that how to best get back from the pandemic is truly a multidisciplinary question.

List of Participants

  • Iurii Ganychenko, Senior researcher at Kyiv School of Economics (KSE/Ukraine)
  • Jesper Roine, Professor at Stockholm School of Economics (SSE) and Deputy Director at the Stockholm Institute of Transition Economics (SITE/ Sweden)
  • Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC/ Belarus)
  • Michal Myck, Director of the Centre for Economic Analysis (CenEA/ Poland)
  • Natalya Volchkova, Director of the Centre for Economic and Financial Research  ­New Economic School (CEFIR NES/ Russia)
  • Pavlo Kovtoniuk, Head of Health Economics Center at Kyiv School of Economics (KSE/Ukraine)
  • Sergej Gubin, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS/ Latvia)
  • Yaroslava V. Babych, Lead Economist at ISET Policy Institute (ISET PI/ Georgia)

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

Video of the FREE Network webinar “Addressing the Covid-19 Pandemic: Vaccination Efforts in Free Network Countries“

Media Freedom in Eastern Europe

Posted on February 22, 2021 | Policy Brief

Photography of Magazine representing Media Freedom Eastern Europe

In recent years, press freedom in many Eastern European countries has increasingly come under threat. This policy brief provides an overview of the importance of a free press for democracy and the challenges to media freedom in these European transition economies.

Introduction

Freedom of expression – which encompasses media freedom – is a fundamental human right enshrined in most countries’ constitutions. Yet for many of their citizens, it is more of an aspiration than a reality. Following the dissolution of the Soviet Union, a number of countries in Eastern Europe embarked on a process of democratisation and accession to the European Union – for which one of the prerequisites is a free press.

Figure 1 shows a measure of press freedom for the eight Eastern European countries that joined the EU in 2004. These countries saw a general improvement in press freedom from the early 1990s to the early 2000s. But since then, experiences have diverged and in 2017 only Estonia and the Czech Republic showed better scores on press freedom than when they first joined the EU. This pattern of backsliding is not confined to the media, but is also evident in other measures of democracy.

Figure 1. Media Freedom in Eastern Europe

Media and Democracy

A free press and a strong democracy are mutually reinforcing. Research, from mainly Western democracies, shows that the media plays an important role in informing the electorate and holding politicians accountable. For example, Snyder and Strömberg (2010) find that U.S. voters are less informed about their Congressmen when they are covered less in the local press. This is ultimately damaging for voters, as these politicians work less for their constituency and these constituencies also receive less federal funding.

Investigative journalism can play an important role in uncovering corruption and other forms of wrongdoing by politicians. For instance, using the Panama Papers and other leaked documents, journalists uncovered 11,562 offshore entities linked to Russia, 2943 linked to Latvia, and 103 linked to Sweden (see: Offshore Leaks Database). While there are legitimate uses for these offshore entities, the lack of transparency surrounding offshore finance also facilitates tax evasion and money laundering. The revelations of offshore holdings became an embarrassment to many politicians, with some forced to resign. In Russian media, the allegations that the leaks document suspected money laundering by President Putin were characterised as US propaganda (Hoskins and Shchelin, 2018).

Figure 2 shows the relationship between the length of time a country’s leader has been in office and its press freedom score in 2020. While there is no systematic relationship between leader tenure length and press freedom in Western Europe (in blue), across Eastern Europe (in red), countries whose leader has been in power for longer tend to have less media freedom. This correlation is likely to reflect three factors: 1) media coverage can affect a government’s chances of staying in power; 2) a longer-lived government might be more able to control the media and 3) a host of other factors, such as the public’s political engagement and the strength of democratic institutions, could influence both freedom of the press and the longevity of governments.

Figure 2. Media Freedom and Leader Tenure

Source: Freedom of the Press, Freedom House. This figure shows the Freedom of the Press total score for the 8 central and eastern European countries that joined the EU in 2004 from 1993 to 2017. A country with a score between 0 and 30 (31 and 60) is designated as having a free (partly free) press.

Electoral Effects of the Media

A number of papers show the causal effects of (biased) media coverage in shaping support for political parties. For instance, watching Fox News increases voting for the Republican party in the US (DellaVigna and Kaplan, 2007; Martin and Yurukoglu, 2017).

Enikolopov, Petrova, and Zhuravskaya (2011) investigate the influence of NTV (the only national TV channel that was at the time independent of the government) on voting in the 1999 parliamentary election in Russia. They find that areas with greater access to NTV were significantly less likely to vote for the government party and more likely to vote for opposition parties.

Biased media can also be used as a foreign policy tool. Peisakhin and Rozenas (2018) find that Ukrainian areas that received Russian TV had on average greater support for pro-Russian parties and candidates in the 2014 elections.

The media landscape in many CEE countries is highly polarised and politicised. Kostadinova (2015) cites research showing that in some former communist countries many journalists still rely on government officials as news sources. In other countries, media in opposition to the communist regimes emerged at the end of the 1980s, such as in Poland where the Gazeta Wyborcza became one of the leading daily newspapers.

Government Control of the Media

Governments have many ways of controlling the media in their country. At the extreme, governments can own and run media outlets, dictate their contents, and censor any dissenting voices. While political and media systems across CEE are diverse, they share some common experiences that might explain their current fragility.

Transitions in Media Ownership

In the Eastern Bloc, the mass media was owned and tightly controlled by the state and used as a tool for propaganda. After the fall of communism, many state-owned media were privatised – along with other state-owned enterprises.  Foreign (mostly western European) media conglomerates purchased a significant fraction of media outlets in a number of countries.

While private and foreign ownership of the media can reduce the government’s ability to influence media content, the experience of CEE was not entirely positive.  Stetka (2012) argues that while foreign owners brought capital and technology, they were less concerned with transplanting Western journalistic and professional standards.  Dobek-Ostrowska (2015) claims that this focus on profit led to the tabloidisation of news across the CEE.

Following the global financial crisis in 2007/2008, foreign investors started to pull out of the CEE media markets and are being replaced by local owners who often have strong links with the government. This is evident in Hungary, where businessmen close to the government have been buying up independent media outlets, including its largest news website, one of two national commercial TV channels, and all regional newspapers (Bede, 2018). The Polish government also aims to “re-nationalise” its media. Plans by a state-run oil company to buy one of the country’s largest media publishers from its German owners were recently approved.

Elsewhere, domestically owned and previously independent media outlets are also being bought by new pro-government owners. In Russia, the formerly independent NTV from the above example was taken over by a state-owned company in 2001 and started to cover the ruling party in the run-up to the following elections in a similarly favourable way to state-controlled TV channels. Gehlbach (2010) argues that Putin’s media strategy is to exert tight control over the news coverage of these three main national television networks, while allowing media outlets with less reach to operate more independently.

In some countries of the region, there is limited information about the ultimate owner of media outlets. Within the EU, Latvia, Hungary, the Czech Republic, Slovakia and Cyprus, are assessed as high risk in terms of transparency of media ownership (Brogi et al. 2020). In 2009, the Swedish company Bonnier sold Diena – one of Latvia’s largest newspapers – to an initially undisclosed investor. A year later, a Latvian businessman acquired a controlling stake in the paper.

Government Advertising

Around the world, traditional news media is facing increased competition from digital platforms and becoming highly dependent on advertising revenue, including advertising from the government and pro-government businesses According to the Centre for Media Pluralism and Media Freedom, there are no clear and fair criteria for the distribution of state advertising to the media in the majority of EU countries – especially those in Eastern Europe (with the exception of Estonia).

Szeidl and Szucs (2021) document how the Hungarian government targeted advertising to friendly media outlets and how these media in turn covered the government more positively.  They also present suggestive evidence that a similar favour exchange between government and the media occurs in nine other Eastern European countries, including Poland.

Two weeks ago, many private Polish media outlets coordinated a media blackout to protest government plans to tax advertising revenues. The media companies complained that the tax would cost them $270m a year, while public media received twice as much from taxpayers.

Public Service Media

The establishment of public service media forms an integral part of the EU’s agenda for promoting press freedom. While public service media are an important and trusted source of unbiased information in many western European countries, they generally play a smaller role in the  Eastern European media markets. Furthermore, no laws are guaranteeing the independence of public service media from the government in eastern EU countries, with the exception of the Baltic states and Slovenia  (see Centre for Media Pluralism and Media Freedom).

Intimidation of Journalists

Governments can also ensure positive coverage by intimidating editors and journalists. Since 1992, 91 journalists were killed, imprisoned, or went missing in Russia, 18 in Ukraine, 15 in Belarus, and 8 in Georgia (data by the Committee to Protect Journalists). While not all of these cases reflect government action, several recent examples illustrate how the judicial system may be used against journalists. For instance, according to the CPJ, ten journalists were imprisoned in November 2020 for covering protests against President Lukashenko in Belarus and one journalist was charged with high treason and espionage in Russia in July 2020.

There are also fears that governments can use defamation laws to deter and punish unwelcome media reports. For instance, the head of Poland’s ruling party filed a libel charge against two journalists from the Gazeta Wyborcza for reporting about his alleged involvement in a real estate project (see, e.g. Council of Europe media freedom alert).

Conclusion

The media plays a vital role in shaping the public debate and holding those in power accountable to the wider population. This power of the media also increases the risk that governments attempt to influence media content.

In recent years, many countries in CEE have seen press freedom come increasingly under threat, undermining some of the progress made since the dissolution of the Soviet Union. Part of the present fragility of media freedom in Eastern Europe may be due to their historical experience. During the transition from communism, many formerly state-owned media companies were sold to private and often foreign owners. In the past decade, local business interests with strong ties to the government started to buy up large shares of the media market in a number of Eastern European countries. Meanwhile, public service media have been less successful at establishing themselves as important and unbiased sources of information across Eastern Europe compared to Western Europe.  To ensure positive media coverage, many governments adopt a carrot and stick approach: state advertising revenues and intimidation of individual journalists.

Article 19 of the Universal Declaration of Human Rights states that “everyone has the right to freedom of opinion and expression; this right includes freedom to hold opinions without interference and to seek, receive and impart information and ideas through any media and regardless of frontiers”. To ensure these fundamental rights, there need to be transparent and fair rules governing the ownership, management, and financing of media outlets and safeguards for individual journalists.

References

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

Ukraine’s Integration into the EU’s Digital Single Market

Posted on February 15, 2021 | Policy Brief

Blue EU flags in front of European Commission representing Ukraine’s integration into the single market

This brief is based on a study that investigates how Ukraine’s integration into the EU Digital Single Market (DSM) could affect EU-Ukraine bilateral trade as well as Ukraine’s GDP growth.  The major benefits of integration are expected to come from 1) reduction of cross-border regulatory barriers and restrictions to EU-Ukraine digital trade 2) acceleration of the development of Ukraine’s digital economy in line with EU standards. According to the results, enhanced regulatory and digital connectivity between Ukraine and the EU is expected to increase Ukraine’s exports of goods and services to the EU by 11.8-17% and 7.6-12.2% respectively. At the same time, the acceleration of the digital transformation of the Ukrainian economy and society will produce a positive effect on its productivity and economic growth – a 1%-increase in the digitalization of the Ukrainian economy and society may lead to an increase in its GDP by 0.42%.

Background

Integration into the EU has been one of the key topics on Ukraine’s political agenda for a number of years. Recently, more emphasis has been put on an essential component of issue – integration into the EU’s Digital Single Market (DSM). The DSM is a strategy aimed at uniting and enhancing digital markets and applying common approaches and standards in the digital sphere across the EU. The Ukraine-EU Summit, held on October 6, 2020, stressed the paramount importance of the digital sector in boosting its economic integration and regulatory approximation under the EU-Ukraine Association Agreement. Implementation of the provisions of this agreement, in particular the updated Annex XVII-3, would introduce the latest EU standards in the field of electronic communications in Ukraine. The country is also gradually approximating its regulations with regard to other components of the EU DSM – electronic identification, electronic payments and e-payment systems, e-commerce, protection of intellectual property rights on the Internet, cybersecurity, protection of personal data, e-government, postal services, etc. These steps will, in turn, ensure Ukraine’s gradual integration into the EU’s Digital Single Market, which will facilitate digital transformations within the country and open a new window of opportunity for individuals and businesses.

This brief summarizes the results of our recent work (Iavorskyi, P., et al., 2020), in which we estimate the effect that Ukraine’s integration into DSM could have on EU-Ukraine bilateral trade as well as Ukraine’s GDP growth.

Benefits of Integration into the EU DSM

The EU DSM strategy comprises three pillars: (1) better access for consumers and businesses to digital goods and services across Europe; (2) creating the right conditions and a level playing field for digital networks and innovative services to flourish; (3) maximizing the growth potential of the digital economy (EC, 2021).

These goals suggest that the major benefits of Ukraine’s integration into the DSM are likely to come from 1) reduction of cross-border regulatory barriers and restrictions to EU-Ukraine trade, 2) acceleration of the development of Ukraine’s digital economy in line with EU standards.

Indeed, the trade of goods and services is increasingly becoming “digital” – i.e., involving “digitally enabled transactions in goods and services that can be either digitally or physically delivered” (OECD, 2019). Trade digitalization (e.g., electronic contracts, electronic payments, e-customs, etc.) simplifies export and import procedures, reduces trade costs for exporters, and creates new opportunities for trade with the EU, in particular for SMEs. Therefore, the reduction of regulatory restrictions on cross-border digital trade reduces the overall level of restrictiveness of trade in goods and services.

Thus, digitalization is expected to facilitate and intensify the total EU-Ukraine trade in goods and services. It is also anticipated to increase the productivity of Ukraine’s economy which will have a positive impact on the country’s economic growth.

Major benefits include lower prices and greater access to EU online markets for Ukrainian consumers and business, digital innovative products and services, greater online consumer protection, lower transaction costs for businesses, improved quality and transparency of public digital services and e-government as well as an intensification of innovation development in Ukraine.

At the same time, Ukraine’s integration into the DSM entails several obligations: to align national legislation and standards with EU legislation and standards; to ensure institutional and technical capacity as well as interoperability of digital systems. For businesses in Ukraine, this means facing new EU requirements aimed at improving consumer and personal data protection, as well as increased competition from European companies in digital markets. However, these changes are necessary if the country wants to build a common economic space with the EU, especially given the growing impact of digital technologies on international trade and economy.

Ukraine in International Digital Rankings

Many international digital development rankings show that Ukraine lags behind EU countries, including its neighbors that recently joined the EU.

According to the UN e-Government Development Index (EGDI) for 2020, Ukraine ranks 69th among 193 countries and is included in the group of countries with high levels of e-government development. It received the lowest scores for Telecommunications Infrastructure and Online Services, and the highest for Human Capital. Nevertheless, Ukraine is lagging behind its neighboring EU members, – Poland, Hungary, Slovakia, Romania, Bulgaria, Lithuania, etc., – which belong to the group of countries with very high levels of e-government development (UN, 2020).

In the Network Readiness Index (NRI) ranking for 2019, Ukraine ranked 67th among 121 countries. As for the components of the index, Ukraine ranks worst in the following indicators: Future technologies (82nd out of 121), ICT Use by Government and Online Government Services (87th), and Regulatory Environment (72nd). Neighboring EU countries have higher rankings (Poland – 37, Latvia – 39, Czech Republic – 30, Croatia – 44). Other neighboring countries do somewhat better than Ukraine (Turkey is ranked 51st, Russia – 48th) or occupy positions close to Ukraine (Belarus – 61, Moldova – 66, Georgia – 68) (Portulans Institute, 2019).

In 2019, the country ranked 60th among 63 countries included in the World Digital Competitiveness Ranking (WDCR) rating. Just as in the other rankings, Ukraine scored well in the Knowledge component (40th among 63 countries), while in terms of Technology and Future Readiness it was at the bottom (61st and 62nd position respectively) (IMD, 2019).

Hence, it is primarily the technological and regulatory issues, that need to be addressed in order to improve Ukraine’s digital position in the region and the world.

Methodology

Measuring Ukraine’s Digitalization level

In order to estimate the impact of digitalization, a Composite Digitalization Index is calculated for Ukraine, the EU, and other countries included in the model. This index is based on 11 digital indicators, combined into five components that characterize different areas of the digital economy and society – Connectivity, Use of the Internet by citizens, Human capital, Integration of digital technology by businesses, and Digital public services.

Our results confirm that the level of digital development in Ukraine is far below the EU average. It also lags behind the new EU Member States, which have a lower level of digital development compared to the other EU countries. As of 2018, the widest gaps between Ukraine and the EU average are found in Digital Public Services, Connectivity and Use of Internet by citizens. At the same time, Ukraine performed better in Human Capital and Integration of digital technology by businesses.

Measuring Digital Services Trade Restrictiveness in Ukraine

To assess the impact of digital regulatory barriers on trade, we use the Digital Services Trade Restrictiveness Index (Digital STRI) (OECD, 2020). It quantifies the regulatory barriers in five different policy areas (communication infrastructure, electronic transactions, electronic payments, intellectual property, other restrictions) that affect trade in digital services (Ferencz, J., 2019). OECD calculates Digital STRI for OECD countries and some non-OECD countries. As Ukraine is not included in this index, we estimate it for 2016-2018 using the OECD methodology.

Our estimations show that the level of digital services trade restrictiveness in Ukraine is much higher than the EU average. The regulatory differences in the digital sphere between Ukraine and the EU increase the cost of cross-border digital transactions between countries.

For Ukraine, most barriers are related to cross-border electronic payments and settlements, protection of intellectual property rights on the internet, cross-border electronic transactions (for example, the divergence of the national requirements for foreign trade agreements, including electronic ones, from international practices and standards, lack of practical mechanisms for the application of the electronic digital signature in foreign trade contracts, lack of mutual recognition of electronic identification and electronic trust services between Ukraine and major trading partners, etc.), other barriers (requirements for the use of local software and cryptography, etc.). These regulatory restrictions significantly hinder the development of cross-border cooperation and Ukraine’s integration into the European and global digital space.

Ukraine’s integration scenarios

In the event of Ukraine’s integration into the EU DSM, the country’s regulatory environment and digital development are expected to gradually approach the EU averages. We model it through assuming that the regulatory differences between Ukraine and the EU (captured by the Digital STRI Heterogeneity Indices – see OECD, 2020) will be decreasing, and level of digitalization in the country (captured by the Digitalization Index – OECD, 2020) will converge towards that of EU-DSM members.

We considered three integration scenarios that imply high, medium, and low levels of Ukraine’s approximation to the regulatory environment and digital development of the EU. For instance, the high scenario implies the highest level of Ukraine’s digital development and the lowest level of regulatory differences between Ukraine and the EU.

Models

We study the effect of reduced regulatory differences in the digital sphere on Ukraine-EU trade using a gravity model – one of the traditional approaches in the international trade literature. A gravity model predicts bilateral trade flows based on the size of the economy and trade costs between countries (affected by distance, cultural differences, FTAs, tariffs, etc.)

The study uses the following specification of the model for exports of goods and services in 2016-2018:

• Dependent variable – the total export flow of goods and services from country into country j (all possible pairs of countries).

• Independent variables – distance between countries and common characteristics (borders, language, law), existence of a free trade agreement, level of tariff protection (for goods), level of regulatory heterogeneity in the digital sphere between the two countries, and a set of fixed effects for each country.

We also estimate how digital development affects technical modernization, productivity, and economic growth. Technically, we use a Cobb-Douglas production function to describe each country’s output and model its total factor productivity component as a function of digital development (captured by the Digitalization index).

Results

The results suggest that Ukraine’s integration into the EU DSM will be beneficial for both Ukraine and the EU. Under all integration scenarios, bilateral trade between Ukraine and the EU is expected to intensify considerably due to enhanced regulatory and digital connectivity between the two.

Ukraine’s total exports of goods and services to the EU are estimated to grow by 11.8-17% ($2.4-3.4 billion) and 7.6-12.2% ($302.5-485.5 million), respectively – a cumulative increase throughout the period of implementation of reforms aimed at regulatory and digital approximation of Ukraine to the EU.

 Figure 1. The impact of Ukraine’s integration into the EU’s DSM on the exports of services from Ukraine to the EU*: three integration scenarios

Source: Authors’ own calculations. The current level of Ukraine’s exports of services to the EU – as of 2018

Figure 2. The impact of Ukraine’s integration into the EU’s DSM on exports of goods from Ukraine to the EU*: three integration scenarios

Source: Authors’ own calculations. The current level of exports of Ukrainian goods to the EU as of 2018

The EU would increase its exports of goods and services to Ukraine by 17.7-21.7% ($4.1-5 billion) and 5.7-9.1% ($191-305 million), respectively.

The acceleration of Ukraine’s digital development will bring productivity gains that would transform into higher GDP growth. It is estimated that a 1% increase in Ukraine’s digitalization level is expected to raise its GDP by 0.42%. As a result, the country’s gradual approximation to EU levels of digitalization would result in additional Ukraines GDP growth of 2.4-12.1% ($3.1-15.8 billion), depending on the scenario.  

Figure 3. Impact of digitalization on Ukraine’s GDP growth: three digitalization increase scenarios

Source: own calculations. The left axis – GDP growth (%), the right axis – the level of digitalization. The current level of digitalization of Ukraine as of 2018.

Conclusion

According to our estimations, improved digitalization and reduction of regulatory barriers in the digital sphere between Ukraine and the EU will have a positive effect on trade for both Ukraine and the EU. There is also a significant potential for economic growth to be attained in Ukraine by increasing digitalization and productivity of various spheres of the economy and society.

Realization of this potential would, however, require a substantial regulatory approximation on the Ukrainian side to achieve alignment with the EU DSM. The main emphasis needs to be put on electronic identification and transactions, payment systems and electronic payments, protection of intellectual property rights on the internet, cybersecurity, and personal data protection.

References

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.

Political Implications of the Rise of Mobile Broadband Internet

Posted on February 8, 2021 | Policy Brief

Image of 5g broadband tower representing implications mobile internet

In the last ten years, the world has experienced the dramatic rise of mobile broadband internet brought by third-generation (3G) and fourth-generation (4G) mobile networks. This has resulted in major political changes – reduced confidence in governments around the world, lower voting shares of incumbent political parties, and the rise of populists. The empirical evidence is consistent with both the optimistic view of 3G internet (the “Liberation Technology”) and the pessimistic one (the “Disinformation Technology”). 3G internet helps to expose actual corruption; however, it also contributes to electoral successes of populist opposition.

The Spectacular Rise of 3G

Communication technologies have undergone a dramatic change in the last 10-15 years. According to the International Telecommunications Union (ITU), there were only 4 active mobile broadband subscriptions per hundred people in the world in 2007, while this number reached 75 per hundred in 2020. The growth of mobile broadband internet – provided by the third and fourth generation of mobile networks (3G and 4G, respectively) – was the main driver of growth in broadband access. The number of fixed broadband subscriptions per hundred people has only increased from 5 to 15 percent in the same period of time.

Relative to the previous generations of mobile technology, 3G provides a qualitatively different way of using the internet. First, it is broadband access on the go, available wherever the user is rather than at a fixed point at home or in the office. Second, it allows for downloading and uploading photos and videos. Before 3G, mobile technology only allowed exchanging text messages along with limited and slow access to the web. Third, it is the technology that is best suited for social media. While social networks started before 3G and were initially accessed on fixed broadband, today most Facebook, Twitter and YouTube users are mobile.

Liberation Technology or Disinformation Technology?

What are the political implications of the spread of this new technology around the world? Initially, political scientists were excited about the internet as a “Liberation Technology”, especially after it played an important role in the Arab Spring. Internet – and in particular mobile internet –helped pro-democracy activists in autocratic states to disseminate critical information about the government, expose corruption, and coordinate protests.

Later on, however, it became clear that social media also provided a platform for the dissemination of false news and hate speech – thus supporting the rise of populists. This led to a rethinking of the role of mobile internet – and rechristening it into a “Disinformation Technology.”

Which view, the optimistic or the pessimistic one, is correct? In Guriev et al. (2021), we study the impact of the expansion of 3G around the world on attitudes to government and electoral outcomes.

Exposing Actual Corruption

In order to explore the effects on confidence in government, we use data from Gallup World Poll surveys of 840,537 individuals from 2,232 subnational regions in 116 countries from 2008 to 2017. In each region and year we calculate the population-weighted average access to mobile broadband relying on the network coverage data from Collins Bartholomew’s Mobile Coverage Explorer.

First, we find that increased access to 3G internet causes lower confidence in government, judiciary, honesty of elections, and a lower belief that the government is not corrupt. As shown in Figure 1, the magnitudes are substantial. In our paper, we show that a decade-long 3G expansion has the same effect on government approval as a 2.2 percentage-point rise in the national unemployment rate.

Figure 1. Mobile Broadband Access and Government Approval.

Source: Guriev et al. (2021), Table 1, authors’ calculations.

This effect is only present when there is no online censorship and stronger when traditional media are not free. Furthermore, the spread of 3G makes people think that the government is corrupt when the actual corruption is high. In the cleanest countries of the world, the effect is actually positive – better access to information may help citizens to understand that other countries are much more corrupt relative to their own. 

This positive impact is, however, limited to about 10% of the world’s countries. On average, the effect of 3G on the perception that government is clean is negative (see Figure 1). There are two potential explanations. First, as suggested by Gurriv (2018), before the arrival of the fast internet, the elites controlled the media and, as a result, the public was not fully aware of the elites’ corruption. 3G helped to expose this corruption and corrected the pre-3G positive bias. The second explanation is related to the negative bias of social media where critical messages spread faster and deeper (see the references in Guriev et al. 2021).

Another potential explanation is that social media promote overall negative and pessimistic attitudes. We show that this conjecture is not consistent with the evidence: the spread of 3G does not reduce life satisfaction or expected future life satisfaction.

Helping European Populists

The evidence above is consistent with the view that mobile broadband internet and social media help to expose misgovernance and corruption. These findings are in line with the optimistic view of mobile broadband internet as a “Liberation Technology.” However, it turns out that the pessimistic view of “Disinformation Technology” may also be correct.

We examine the impact of 3G expansion on the outcomes of 102 parliamentary elections in 33 European democracies between 2007 and 2018. Using subnational data, we show that the spread of 3G, not surprisingly, decreases the vote share of incumbents substantially (see Figure 2).

 Figure 2. The impact of 3G expansion on incumbent vote share in Europe.

Source: Guriev et al. (2021), Figure VIII.

Figure 3. The impact of 3G expansion on opposition vote share in Europe.

Source: Guriev et al. (2021), Figure IX.

If incumbents lose votes, who picks them up? We show that the main beneficiaries of 3G expansion are the populist opposition parties, both on the left and right (Figure 3). The non-populist opposition does not gain.

Why do populists benefit from the spread of mobile broadband and social media? One explanation is that social media is decentralized and has no entry barriers. It is not the first time in history that populist politicians have relied on new communication technology to circumvent mainstream media controlled by the elites (e.g. the US late 19thcentury populists used telegraph and railroads, the Nazis in Germany used radio). It may also be the case that populist messages may be simpler, and thus, better suited for a short and catchy communication on social media. For example, another pan-European family of anti-system parties, the Greens, do not benefit from the spread of the 3G internet at all (see Figure 3): their narrative is more complex, asking voters to take responsibility for the planet.

Fact-Checking Alternative Facts

Many populist politicians point to actual corruption of the incumbent elites, but some also spread false narratives or “alternative facts.” (It was Donald Trump’s Counselor Kellyanne who, in January 2017, when asked to comment on false statements by Trump’s Press-Secretary about his inauguration, famously said that these were not falsehoods but “alternative facts.”) What can be done to stop the dissemination of these falsehoods on social media? Can fact-checking by mainstream media and independent organizations help?

In two studies, Barrera et al. (2020) and Henry et al. (2021), we carry out two randomized online experiments to identify the causal effects of alternative facts spread by populist politicians and their fact-checking. The findings are as follows: (i) alternative facts are highly persuasive; (ii) fact-checking helps to correct factual beliefs – but do not change voting intentions; even though the voters understand that the populists misrepresent the facts, they still support their agenda; (iii) fact-checking, however, substantially reduces sharing of alternative facts on social media; (iv) the impact of fact-checking on sharing is equally strong regardless of whether the users are forced to view the fact-checking information or are simply given an option to click on a fact-checking link; (v) asking users to re-confirm their intention to share alternative facts with an additional click greatly reduces sharing.

Our results suggest that fact-checking may not be as effective as fact-checkers themselves hope, but can help slow down the dissemination of falsehoods on social media. Furthermore, our analysis delivers clear policy implications – both providing fact-checking (even in the form of accompanying alternative facts with fact-checking links) and requiring additional clicks before sharing can be very effective.

Conclusion

The findings from our analysis of the worldwide spread of mobile broadband internet in the last decade are consistent with both optimistic and pessimistic views. On the one hand, 3G internet does help expose actual corruption. On the other hand, it helps populist opposition to gain votes. Likely, the latter result is eventually due to the populists’ abuse of online platforms for spreading disinformation. We show that the propagation of falsehoods on social media can be at least partially slowed down by fact-checking.

References

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

Pollution and the COVID-19 Pandemic: Air Quality in Eastern Europe

Posted on February 1, 2021 | Policy Brief

Factory with chimney smoke representing air-quality Eastern Europe

The COVID-19 pandemic has drawn attention to a pre-existing threat to global health: the quality of air in cities around the world. Prolonged exposure to air pollution has been found to increase the mortality rate of COVID-19. This is a particular concern for much of Eastern Europe, where emissions regularly exceed safe levels. This policy brief analyses recent data on air quality in the region and the factors that explain a persistent East-West divide in pollution in Europe. It concludes by evaluating to what extent lockdowns in 2020 provided a temporary respite from pollution in the region. 

Introduction

The WHO estimates that air pollution causes seven million premature deaths every year (WHO 2018). COVID-19 has further amplified these health risks, as air pollution can increase both the chance of catching respiratory diseases and their severity. At the same time, the pandemic has resulted in lockdowns and a general slowdown in economic activity which are widely perceived as having led to a temporary improvement in air quality.

This brief provides an overview of recent trends in air quality in Eastern European cities using data from the World Air Quality Index. It addresses three questions:

  1. How did air pollution in Eastern Europe compare to Western Europe prior to the pandemic?
  2. What are the main sources of air pollution in Eastern European cities and can they be addressed by policymakers?
  3. Was there a significant improvement in air quality in 2020 as a result of COVID-19?

Air Pollution in Eastern Europe

Most measures of air quality in Europe show a stark East-West divide. Map 1 plots the share of days in 2019 where air pollution, as measured by PM 2.5 (fine particulate matter), exceeded levels classified as unhealthy for the general population. On average, cities to the east of the former Iron Curtain experienced over 100 such days, compared to an average of 20 days in Western Europe. These averages mask significant variation within both regions; Tallinn was among the best performing cities while Naples was among the worst.

Map 1

Source: Author’s calculations based on data from the World Air Quality Index COVID-19 dataset. Above the threshold AQI of 150, PM 2.5 levels are classified as unhealthy to the general population by the US EPA.

The gap in air quality between Eastern and Western Europe has been linked to differences in health outcomes for decades. Shortly after the fall of the Soviet Union, Bobak and Feachem (1995) found that air pollution accounted for a significant share of the Czech Republic and Poland’s mortality gap with respect to Western Europe. The European Environment Agency’s 2020 report provides estimates for ‘years of life lost’ attributable to different pollutants. Figure 1, which plots these estimates for PM 2.5, highlights the fact that Eastern European countries, in particular those in the Balkans, continue to experience significantly higher mortality related to pollution, as compared to their Western European counterparts.

Figure 1

Source: estimates from EEA Air Quality in Europe report 2020

Sources of Air Pollution

A number of factors contribute to the pattern of pollution shown on Map 1, not all of which are under policymakers’ direct control. For example, two of the cities on the map with the unhealthiest air – Sarajevo and Skopje – are surrounded by mountains that prevent emissions from dissipating.

In addition to immutable geographic factors, policies elsewhere may also be contributing to pollution in the region. Stricter regulations in Western Europe can have adverse effects if they result in polluting industries migrating eastwards. Bagayev and Lochard (2017) show that as EU countries adopt new air pollution regulations, the share of their imports from Eastern Europe and Central Asia in pollution-intensive sectors increases. Stricter rules can also result in outdated technology being exported to other countries. A Transport & Environment report found that over 30,000 high-emission diesel cars were exported from Western Europe to Bulgaria in 2017 and argued that such flows will continue as Western European cities impose Low Emission Zones and diesel bans (Transport & Environment 2018).

Power generation, and in particular coal power, is likely to be the single most important determinant of the gap in air quality between Eastern and Western European cities. Coal power accounts for over 60% of electricity production in Poland, Serbia, Bosnia Herzegovina, and North Macedonia, and remains an important energy source in the majority of Eastern European countries (BP 2020). Many of the coal power plants in the region have been operating for decades and are not equipped with modern desulphurisation technology that would help to reduce their emissions. A report by the Health and Environment Alliance found that 16 coal power plants in the Western Balkans collectively produce more emissions than the 250 power plants in the European Union, while only being able to generate 6% of the power (Matkovic Puljic et al. 2019).

Countries in the region are taking steps to reduce their dependence on coal power. In September 2020, the Polish government struck an agreement with labour unions that would see coal phased out by 2049. Coal accounts for 75% of Poland’s current electricity and Map 1 shows that air in the Upper Silesian Coal Basin, in the south of the country, is particularly polluted. Despite such commitments, Western European countries have in recent years been faster at transitioning away from coal. If this trend continues, the gap in air quality may even increase in the short run.

Did COVID-19 Improve Air Quality?

Last spring, a number of headlines from around the world featured the phrase “A breath of fresh air” (e.g. Reuters, The Economic Times, EUIdeas). These articles described measurable improvements in air quality in cities with government-mandated lockdowns. Recent academic publications have confirmed these reports in a variety of settings including the US (Berman and Ebisu 2020), China (Chen et al. 2020), and Korea (Ju et al. 2020).

While Eastern Europe was less affected by the initial wave of COVID-19 than Western Europe, most countries imposed lockdowns and social distancing measures that can be expected to have affected air quality. Figure 2 uses daily data from the World Air Quality Index for 221 European cities to compare average air pollution in 2020 to 2019. Overall, these plots suggest that air quality did improve in Eastern European cities relative to the previous year. However, not all types of pollutants declined and the declines are slightly smaller on average than in Western European cities. Panels A, B, and C plot air quality indices for fine particulate matter (PM 2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) respectively. Dots below the line represent cities where the average air quality index was lower (indicating less pollution) in 2020 than in 2019. The declines are largest for NO2 – a gas that is formed when fuel is burned. The reduction in traffic and transportation in all European cities is likely to have contributed to this drop. By contrast, there were no statistically significant declines in SO2. This may be due to the fact that power generation, which is the source of most SO2 emissions, was less affected by lockdowns than transportation.

Figure 2

Panel A

Panel B

Panel C

Source: Author’s calculations based on the World Air Quality Index COVID-19 dataset. Each marker represents a city. Markers below the 45-degree line represent cities where emissions for the respective category of pollutant were lower in 2020 than in 2019. For reasons of presentation, outliers were excluded from panels B and C.

The variation in COVID-19 prevalence over the course of 2020 is visible when tracking pollution over time. Figure 3 shows that average daily NO2 emissions in Western European cities dropped most from March to June of 2020, during the first wave of the pandemic. NO2 levels were comparable to the previous year in July and August when case numbers fell and restrictions were lifted. In the last months of the year, as the second wave hit, NO2 emissions once more dropped below the previous year’s average. This pattern is similar for Eastern European cities but the decline in NO2 in the first half of the year is less pronounced.

Figure 3

Source: Author’s calculations based on the World Air Quality Index COVID-19 dataset. Lines show the seven day moving average of the ratio between average NO2 emissions in 2020 and 2019.

Conclusion

The COVID-19 epidemic has highlighted the health costs of air pollution. The preliminary evidence suggests that long-term exposure to pollution increased COVID-19 mortality rates (Cole et al. 2020, Wu et al. 2020). This is a particular concern for countries across Eastern Europe which – at the time of writing – are still grappling with the second wave of the pandemic in Europe. Many people in this region have been exposed to polluted air for decades.

The pandemic has also demonstrated that air quality can improve relatively quickly when human behaviour changes. The data described in this brief suggest that Eastern Europe was no exception in this regard, although the declines were confined to some categories of pollutants. Achieving a more general, and sustained improvement in air quality will require a shift from coal power towards cleaner forms of energy.

Stimulus packages aimed at a post-pandemic economic recovery can provide an opportunity for policy to reorient the economy and accelerate such a shift. The consultancy Vivid Economics, which rated G20 member countries’ proposed stimulus packages in terms of their environmental impact, found that the ‘greenest’ stimulus proposals are those of the European Commission, France, UK, and Germany. Russia is one of the worst performers on this index (Vivid Economics 2020). Whether governments in Eastern Europe are able to take advantage of this opportunity will depend on their respective fiscal space and whether they make improving air quality a priority.

References

  • Bagayev, Igor, and Julie Lochard, 2017. “EU air pollution regulation: A breath of fresh air for Eastern European polluting industries?.” Journal of Environmental Economics and Management 83: 145-163.
  • Berman, Jesse D., and Keita Ebisu. 2020 “Changes in US air pollution during the COVID-19 pandemic.” Science of the Total Environment 739: 139864.
  • BP 2020 “Statistical Review of World Energy – all data, 1965-2019“
  • Bobak, Martin, and Richard GA Feachem. 1995. “Air pollution and mortality in central and eastern Europe: an estimate of the impact.” The European Journal of Public Health , no. 2: 82-86.
  • Cole, Matthew, Ceren Ozgen, and Eric Strobl, 2020. “Air pollution exposure and COVID-19.”.
  • Chen, Kai, Meng Wang, Conghong Huang, Patrick L. Kinney, and Paul T. Anastas, 2020. “Air pollution reduction and mortality benefit during the COVID-19 outbreak in China.” The Lancet Planetary Health 4, no. 6: e210-e212.
  • European Environment Agency 2020. “Air Quality in Europe – 2020 report“, EEA Report No 9/2020
  • Matkovic Puljic, V., D. Jones, C. Moore, L. Myllyvirta, R. Gierens, I. Kalaba, I. Ciuta, P. Gallop, and S. Risteska. 2019. “Chronic coal pollution–EU action on the Western Balkans will improve health and economies across Europe.” HEAL, CAN Europe, Sandbag, CEE Bankwatch Network and Europe Beyond Coal, Brussels.
  • Ju, Min Jae, Jaehyun Oh, and Yoon-Hyeong Choi. 2020. “Changes in air pollution levels after COVID-19 outbreak in Korea.” Science of The Total Environment 750: 141521.
  • Transport & Environment, 2018. “Briefing: Dirty diesels heading east”
  • Vivid Economics, 2020. “Greenness of Stimulus Index” December 2020 update
  • World Air Quality Index, 2021. “Worldwide COVID-19 dataset“
  • World Health Organization, 2018. “WHO Global Ambient Air Quality Database (update May 2018)”
  • Wu, Xiao, Rachel C. Nethery, Benjamin M. Sabath, Danielle Braun, and Francesca Dominici, 2020. “Exposure to air pollution and COVID-19 mortality in the United States.” medRxiv

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.

Optimal Recommendation System with Competing Sellers

Posted on January 25, 2021 | Policy Brief

Person Holding Pineapple Fruit Near Red Wall Representing Optimal Recommendation System

Many e-commerce platforms that connect buyers and sellers employ recommendation systems to help customers find products and services. Such platforms seek to maximize their profits which mainly comes from a commission on sales made via the platform. This may create incentives for platforms to use a recommendation strategy that suppresses competition among sellers and keeps prices and the resulting commission high. At the same time, the huge success of platforms such as Amazon suggests that they also care about customer satisfaction. Thus, the platform has an incentive to recommend goods that are cheaper and a better match for customer’s tastes. This requires not only sufficient competition between sellers but also that sellers act to improve the fit of their product to customer needs. Since these actions are typically costly, a high commission may disincentivize sellers to undertake them, thereby negatively affecting customers. Therefore, in designing the recommendation system and deciding on commissions, the platform should carefully balance the pro-competitive customer care and anti-competitive incentives to keep high prices and profits.

Introduction

When we search for a product on an e-commerce platform, such as Amazon or AliExpress, the default search outcome often contains a list of recommended products sold by vendors that are selected by the platform. The order of these sellers is, of course, not random – the platform’s decision on which sellers to recommend is strategic and there could be different forces driving such a strategy. For example, since the platform charges commission on sales, it may have an incentive to recommend the most expensive seller among those who sell similar products. At the same time, such a recommendation strategy, and high(er) prices in general, may negatively affect customer satisfaction from the marketplace and lead to a loss of its customer base. This is not in the best interest of the platform, especially if it wants to achieve long-term sustainability and growth.

The behavior of sellers adds a further layer to these considerations. Indeed, sellers are likely to adjust their pricing behavior and competitive strategies in response to a platform recommendation system.

These considerations give rise to two questions: First, how should an e-commerce platform design its recommendation system, or in other words, how does it optimally choose which sellers to recommend, which commission rate to set, etc.? Second, how does the presence of this system affect the competition and prices?

Further, a seller’s strategy may depend not only on the presence of recommendations but also on the commission rate set by the platform. Sellers usually have an option to perform costly actions in order to improve the match of their product to customers’ needs. For example, sellers may disclose more information on the characteristics of a good they are selling: spend time and money on detailed descriptions of their goods, or provide high-resolution photos. Though these actions are usually left at sellers’ discretion, they may substantially increase a customer’s satisfaction by improving the match between the purchased product and customer’s preferences.

In turn, a better fit may create a more loyal customer base for the seller, giving her more market power and increased profits. However, if the platform sets a high commission rate, sellers will have less incentive to undertake such costly actions (since the platform eats up a large share of the return to this action). This raises the questions – what is the optimal commission rate chosen by the platform, and how does the optimal commission rate affect sellers’ incentives to disclose information about their goods?

Another issue that arises here concerns the optimal precision of the recommendation system, that is, its ability to pin down customers’ tastes correctly. When the e-commerce platform deals with heterogeneous buyers, it should assess buyer’s preferences prior to making a recommendation. Although almost all research in Computer Science regarding recommendation systems focuses on how to make the precision as high as possible, I show that the highest level of precision may not be optimal from the platform’s perspective. Intuitively, this is because highly precise recommendation systems differentiate customers effectively, which in turn could give sellers local monopoly power and translate into higher prices. At the same time, an inaccurate recommendation system cannot distinguish customers with different preferences and views, which intensifies the competition by allowing sellers to compete for all customers.

In Fedchenko (2020), I address the abovementioned and other related issues on recommendation systems of e-commerce platforms. This brief summarizes the main findings of the study.

Model Description and Findings

In my model, I consider a platform that is designing a recommendation system. That is, for each seller, the platform chooses what share of customers end up receiving a recommendation to buy from this seller. This choice depends on the seller’s price, the quality of the good (if disclosed by the seller), and the buyers’ tastes. The platform also sets the commission rate it charges the sellers. I focus only on direct recommendations (i.e., the platform gives each buyer a unique recommendation). Although, in reality, platforms usually provide users with a ranking of alternatives, I assume that buyers always choose the top-ranked alternative which is equivalent to a single recommendation.

The model also assumes that a platform seeks to maximize the weighted sum of its profit (driven by commissions) and aggregate consumer surplus (motivated by the platform’s willingness to build a steady customer base). The (exogenous) weight assigned to the aggregate consumer surplus is referred to as the platform’s degree of consumer orientation (DCO). DCO is a measure of how much the platform cares about customer satisfaction and it plays an important role in determining the platform’s optimal recommendation strategy. In turn, customers have higher satisfaction if they buy a good that better fits their tastes, has higher quality, and is sold at a lower price.

Recommendation System Affects Competition

My model demonstrates that the presence of a recommendation system that charges sellers commission on sales (i.e. makes the platform have a stake in sellers’ profits) “softens” competition, and, in turn, increases prices. This effect is stronger the more a platform cares about its profits relative to customer satisfaction. The force that drives this result has already been touched upon in the introduction: if the platform has a stake in sellers’ profits, it will occasionally recommend sellers with higher prices. However, since the platform also cares about consumer surplus (which decreases if the price goes up) these high-priced recommendations will not go to all buyers, and therefore, the overall price level will not become too high. Still, the sellers are encouraged to set higher prices in this scenario, as compared to the hypothetical case in which customers know about the sellers without the platform.

Optimal Commission vs. Information Disclosure

The relationship between the commission rate and the seller’s decision on how much information to disclose is nontrivially affected by the DCO. If the DCO is high, then a higher commission rate causes sellers to disclose less information about their goods in equilibrium. If the DCO is low, the relationship is reversed: a higher commission rate increases the amount of disclosed information. This result stems from the interplay between two counteracting forces. On one hand, an increase in the commission rate decreases a seller’s return to providing disclosure, and hence, discourages sellers from making the effort to disclose. On the other hand, a higher commission rate increases the platform’s stake in the sellers’ profits and, as a result, softens competition, increases sellers’ prices and profits, and thus makes it more worthwhile for sellers to provide disclosure of their goods.

An interesting implication of this result is that for a high DCO, the optimal commission rate for a platform should be as small as possible (just enough for the platform to cover the operational cost).

Optimal Precision

Next, I show that a lower precision (i.e., ability of the recommendation system to pin down buyers’ tastes) weakens the effect of the presence of a recommendation system on competition. This happens since more imprecise recommendations effectively increase the share of “undecisive” customers and, thereby, the appeal to capture that market share. As a result, the competition for those customers will intensify.

Imprecision also affects the amount of product information sellers choose to disclose in equilibrium. However, the direction of this effect depends on the cost of disclosure: if the cost is low, a more precise recommendation system may increase the amount of disclosed information, while the result is reversed if the cost is high. The reason for that is as follows: The platform has two sources of information to infer whether a particular seller fits a certain buyer – the buyer’s preferences and the seller’s information on the quality of the product (if disclosed). If the buyer’s taste is measured imprecisely, while the seller’s information is more precise, it is optimal for the platform to focus on the latter when designing a recommendation system. This, in turn, would motivate sellers to disclose more information about their products.  In the case of low disclosure costs, this positive effect on disclosure more than offsets the direct negative effect of imprecision brought about by harsher competition and lower profits. In the case of high costs, the direct effect dominates.

I also show that some imprecision, in fact, can be optimal for the platform. Perfect precision softens the competition and results in increased prices for consumers. This negative effect on consumer satisfaction outweighs the benefits of a perfect match between seller and buyer. So, consumers prefer a certain degree of imprecision over perfect precision, which in turn, makes the platform unwilling to implement perfect precision. In other words, it is optimal to “sacrifice” some customers (i.e., not recommending them the best fitting alternative) in order to intensify the competition among sellers and, eventually, benefit all customers through lower prices.

Conclusion

The presence of a recommendation system on an e-commerce platform that charges sellers commissions on sales may cause softer competition and lead to higher prices and profits of sellers, as well as increased earnings for the platform. At the same time, it can sometimes be optimal for a platform to set a low commission rate since it would guarantee that sellers disclose more information about their goods which would improve the match between customers’ tastes and the goods they buy. If customer satisfaction is important for a platform, the indirect positive effect on customer satisfaction of a low commission rate, via sellers’ decisions, may outweigh the direct negative effect on the platform’s and sellers’ profits. Similarly, a recommendation system with some degree of imprecision can be beneficial for customers since it does not allow sellers to get local monopoly power. So, increasing the precision in the measurement of customers’ tastes – which seems to be the focus of many ongoing computer science studies devoted to recommendation systems, – may not actually be in the best interest of a platform.

In the modern era of digitalization, the use of e-commerce platforms is on the rise. Moreover, the ongoing COVID-19 pandemic has increased the use of such platforms even further. Understanding the implications of the strategies used by these platforms, such as recommendation systems, on prices, competition, and societal welfare is, thus, a necessary component for developing efficient regulation principles.

References

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

The Southern Urals as a Touchstone for Soviet Wartime Performance

Posted on January 18, 2021 | Policy Brief

Image of a Soviet tank representing soviet war performance

As time passes and archives open, ever more topics in Russian military-economic history can be studied with primary sources. One such theme is the colossal evacuation of industrial enterprises and equipment from July 1941 onwards. Thousands of railway cars and lorries carried equipment, raw materials, as well as personnel from Ukraine, the Baltics, and western regions of the Russian Federation to the Urals and beyond. A recent documentary collection Put’ k Pobede (The Road to Victory) opens new areas for research on the southern Urals. These regional sources illustrate and add details to documents from the federal archives on the history of the Soviet military-industrial complex. Successful evacuation of industrial capacity eastwards was a decisive factor for the Soviet endurance and finally its victory in 1945. However, many empirical questions remain to be answered and analytical calculations to be done, on how in fact the Soviet system managed simultaneously to successfully evacuate factories eastwards and thousands of troop transports westwards to the fronts.

New Frontiers for Research on the Soviet War effort, 1939–1945

The role of the new industrial centers in the Urals and Siberia for the Soviet defense potential has been recognized long ago (1). From the mid-1920s, Soviet military leaders included projections for full mobilization of industrial and human resources in contingency plans for the case of war. Evacuation projects outlined which important factories were to be re-located from close-to-border areas (within the range of enemy air bombings) to well-prepared interior locations (2). Industrial plans in the late 1930s put significant emphasis on the enhancing of defense-related production, as well as on modernization of the armed forces (3).

Checking the blueprints for IS heavy tank at the Kirov Tank Factory in Cheliabinsk.

In the early 2000s, a grand research project started on the history of the Russian and Soviet military-industrial complex by exploring the main federal archives (GARF, RGAE, RGVA, and others). The project has so far resulted in five volumes that cover the period from 1914 till 1942. The first volumes show the evolution of the Russian defense industries until the mid-1930s, with special emphasis on how military considerations influenced the five-year plans for 1928–32 and 1933–37. The fourth volume starts (p. 5–85) with a historical preface by Professor Andrei Sokolov (1941–2015), who was also the author of a most informative study of the military-industrial complex. It contains documents for the crucial period up to June 1941 (4). The fifth volume reproduces relevant documents from several archives concerning the first war-years 1941 and 1942. (5)

How did Soviet security concerns change in the first stage of World War Two? In August 1939, the Red Army won a momentous victory over the Japanese forces at Khalkhin-Gol in Mongolia. Japan thereafter gave up their invasion plans against the Soviet Far East, and shifted its aggression southwards to the Philippines and Indochina. Thus, the risk diminished considerably of the USSR facing a two-front war, with tough enemy coalitions in Europe as well as in the East. (6). This strategic significance of the Red Army’s victory was apparently missed in Berlin. In 1940, the German military leaders paid attention mostly to the poor performance of the Soviet army in the Winter War against Finland (7). Encouraged by an easy victory over France by June 1940, Hitler ordered Wehrmacht to plan for war against Russia.

When the Soviet leaders in 1939 concluded a non-aggression pact with Germany, they obviously calculated that France and Great Britain were to wage a long-drawn-out war against Germany for many years, yet with uncertainty as to who would be the winning one. The drastically changed outlook after the sudden defeat of France in 1940 challenged the Soviet leaders to speed up already expansive plans for military-industrial production.

The American engineer John Scott who had worked as a welder in Magnitogorsk in the 1930s, and thereafter as a correspondent in Moscow for a British newspaper, compiled a massive dossier for the Research and Analysis department of the American intelligence O.S.S. (Office of Strategic Studies). His 1943 exhaustive “Heavy industry in the Soviet Union east of the Volga; a report prepared for the Board of Economic Warfare” covered a unique amount of data on new industrial enterprises obtained from open sources. While stationed in Stockholm as O.S.S. agent later in World War II, under the cover of a Time-Life correspondent, John Scott lectured in many cities in Sweden over his best-selling book “Behind the Urals”, which in Swedish had the more pertinent subtitle “The secret of the endurance of the Russian defense” (8). Scott emphasized that Stalinist forced drive in the 1930s had created completely new industrial zones far beyond the borders, out of reach for even long-range German air raids. This had been a revelation for many Westerners. British and American military attachés in Moscow were profoundly mistaken in 1941 when they predicted a rapid German victory. As Hitler’s Operation Barbarossa came to a standstill in the winter of 1941-42, Western assessments of the real Soviet military-industrial capabilities had to be reconsidered (9).

Relocation of a Minor Industrial Nation – the 1941-42 Evacuations

A crucial factor – likewise often neglected in Western historiography – for the Soviet military-industrial endurance was the evacuation of industry. In an unprecedented way, another Soviet defense-industrial basis would rapidly emerge east of Volga, in the Urals and in Siberia.

A fundamental Russian 12-volume work on the Great Patriotic war describes main traits of the industrial evacuation (10). Already a few days after the German invasion, the situation on the fronts forced the Soviet leadership to consider completely unexpected scenarios. It was soon obvious that the German invasion could not be stopped, as the principal Red Army doctrine had expected, at the borders. All pre-war considerations of how to mobilize the Soviet military-industrial potential were up for revision. The unforeseen disasters on Soviet territory, not covered in pre-war plans for industrial mobilization, led to the formation of a council for evacuation of factories. Tens of thousands industrial workers and millions in the civilian population must be evacuated.

The massive evacuations of entire factories, or at least the most crucial equipment, started already in July 1941 from the Baltic republics, Ukraine, and Russia’s Western regions. The council on the evacuation sent directives concerning which factories to relocate eastwards and to which cities.

Evacuated equipment installed, under open skies, even before the factory walls were built!

Evacuation organs were responsible for rail, road, and river transports, as well as for the integration of evacuated resources to existing factories or to new building sites.

Facilities and stock that could not be evacuated were destroyed so as not to fall into the hands of the enemy (“scorched earth policy”). Most complicated from a logistic point of view was the evacuation of the industrial, transport, and energy production facilities. These had to be constantly re-adapted as the military situation changed with the German armies’ further advance towards Moscow, Leningrad, and in Ukraine in particular. Troop transports towards the fronts had priority; thus, evacuation trains sometimes had to wait on sidetracks for many days.

Assembly of engines at the Urals Automotive Factory (UAZ) in the Miass city.

Hundreds of thousands of civilians were evacuated from Ukraine, southern and western parts of the Russian Federation, and sent to Uzbekistan and other interior regions. Western literature has described few aspects of the evacuation, with emphasis on problems by influx of thousands of refugees, e.g. in the cities of Kirov (now Viatka) and Tashkent (11).

Mentioned should be the successful evacuation of the country’s cultural treasures. One telling example is how the staff of the Hermitage museum and hundreds of volunteers in Leningrad managed to pack down much of the museum’s exhibits. Over a million works of art were sent in special trains to Sverdlovsk (now Ekaterinburg), where they were safely stored until 1945. Remaining paintings and sculptures were stored in the underground of the Hermitage. When evacuation could not be accomplished, German occupation forces plundered art collections, and thousands of war trophies sent home by Nazi generals.

An Innovative Source Collection Volume from Cheliabinsk

In regional studies more complex, detailed analyses of the evacuation, its successes and failures have been presented. A documentary collection Put’ k Pobede (The Road to Victory) from the Cheliabinsk State Archives (OGAChO), shows how formerly restricted topics can be studies as archive holdings are declassified. The Road to Victory contains over sixty photocopied documents. It gives short biographies of industrial managers and contains many pertinent photographs from enterprises. The interested reader of the photocopies will find a great amount of new information that calls for analysis (12). One of the primary findings in the archives is that the number of enterprises, whole or parts thereof, set up and restarted in Cheliabinsk and other cities in the southern Urals were 329 enterprises from 27 different ministries (commissariats). That is substantially larger a figure than the previously assumed number of enterprises. The leading historian on this topic, Marina Potiomkina, professor at the G.I. Nosov Magnitogorsk State Technical University, gives a thorough presentation of how evacuated enterprises in fact managed to integrate into the existing factories (13). The dimensions of this emergency relocation of entire industrial plants are enormous. Often German troops were approaching closely and the factories were under bombardment. One striking example is the report on evacuation from Zaporozhie to Magnitogorsk in 1941 as the front skirmishes already threatened several factories.

Historians like to unscramble interesting information from seemingly peripheral, marginal notes in such documents. There are lots of “food for thought” in the commentaries by the wartime managers. The reader furthermore gets a clear perspective on the massive change of the urban landscape in the region. The new administrative structure is reflected in biographies of leading managers and designers, in detailed information on every known evacuation site, as well as in the characterization of affiliate people’s commissariats (ministries) that were moved from Moscow to Cheliabinsk. Important wartime reports with photos, diagrams, and drawings are reproduced in a rich illustrative section of this book. The documentary clarifies how the relocation of equipment from the Kirov Works in Leningrad to the Tractor Factory in Cheliabinsk laid the foundations for the consolidated tank industry in the Urals. Contemporary correspondence reflects both complaints and achievements, in particular under the most severe conditions in winter 1941–42.

A meeting at the Cheliabinsk Kirov Factory: Tank industry minister Isaak Zaltsman (2d from left), Region party secretary Nikolai Patolichev (4thfrom left), chief tank designer Zjozef Kotin (9th from left).

At the end of the war in 1945 many cadres, engineers, and workers could return to their home cities in western parts of Russia. The Cheliabinsk region had undergone dramatic changes. It was then a mix of the original factories, established in the 1930s or even earlier. To this was added trainloads of evacuated equipment from Leningrad, Kharkov, and other cities. New branches, in particular of defense-related industries thus formed the basis for the postwar planning. Any of the documents in Put’ k Pobede can serve as a starting point for discussions concerning the undoubtedly strong aspects of the Soviet command economy, on the one hand, and also on which reforms might have been called for even at that time period, on the other hand.

In conclusion and forward-looking, it should be mentioned that Professor Potiomkina has recently surveyed the entire historiography of Soviet wartime industrial evacuation. Her article includes not only her own and others’ works on the Urals, but also an impressive number of contributions from other regions. Her evaluation of the character of the evacuation calls for a stricter methodology, for a common conceptualization, and for a better grasp of the primary sources, in order to estimate the relative weight of planning versus improvisation, of success stories as compared to failures in the evacuation process. (14)

Note: Illustrations reproduced with permission by Cheliabinsk Regional Archive (OGAChO).

References

  • (1) Compare my previous SITE Policy Briefs in 2015, https://www.hhs.se/sv/om-oss/news/site-publications/2015/research-of-formerly-secret-archives-sheds-new-light-on-the-soviet-wartime-economy/  and https://freepolicybriefs.org/2015/05/04/new-light-on-the-eastern-front-contributions-from-russia-to-the-70th-anniversary-of-the-victory-in-europe-in-world-war-two/; see also Samuelson, Tankograd (Swedish, English or Russian version, chapters 7, 8 and 9.
  • (2) Meliia, Aleksei, Mobilizatsionnaia podgotovka narodnogo khoziaistva SSSR, [Mobilization preparedness of the Soviet economy], Moscow: Alpina Biznes Buks, 2004.
  • (3) For a most recent work, see Robert W. Davies et altere, The Industrialisation of Soviet Russia 7: The Soviet economy and the Approach of war, 1937–1939, by, London 2018, referred to in previous Policy Brief: https://freepolicybriefs.org/wp-content/uploads/2020/07/freepolicybriefs20200702-1.pdf
  • (4) Sokolov, Andrei K. Ot Voenproma k VPK: Sovetskaia voennaia promyshlennost 1917–iiun 1941, [From Voenprom to VPK: Soviet military industry 1917–June 1941], Moscow: Novyi Khronograf, 2012, chapter IV. Compare Sokolov (ed.), Oboronno-promyshlennyi kompleks SSSR nakanune Velikoi Otechestvennoi voiny (1938 – iium 1941), [The Defence-industry complex of the USSR prior to the Great Patriotic war (1938 – June 1941], vol. IV, Moscow 2014.
  • (5) Artizov, Andrei (ed.) et altere, Oboronno-promyshlennui kompleks SSSR v gody Velikoi Otechestvennoi voiny, iiun 1941–1942, [The Defence-industry complex of the USSR during the Great Patriotic war, June 1941–1942], Moscow:  Compare lecture by RGAE Director Elena A. Tiurina on this documentary volume, Оборонно-промышленный комплекс СССР в годы Великой Отечественной войны – Российское историческоеобщество (historyrussia.org) .
  • (6) Goldman, Stuart D., Nomonhan, 1939; The Red Army’s Victory That Shaped World War II, Naval Institute Press, Annapolis 2012, for analysis of this decisive battle that was previously neglected in Western historiography.
  • (7) Compare Carl Van Dyke, The Soviet Invasion of Finland, 1939–1940, London: Routledge, 1997, for a pioneer study based on declassified Soviet archival sources, that shows lessons Stalin and his generals drew from the Winter War 1939–40.
  • (8) See John Scott, Behind the Urals: An American Worker in Russia’s City of Steel, London, 1989, new edition in with foreword by Stephen Kotkin). Idem, Vad gör Ryssland bortom Ural?: Hemligheten med det ryska försvarets kraft, Stockholm: Natur och Kultur 1943. Scott’s O.S.S. study of prewar industry in the Urals and Siberia is in the Library of Congress, Washington, DC (Manuscript Division).
  • (9) For the – mostly mistaken! – Western estimates of Soviet military capabilities before the fascist invasion as well as  many months later in 1941 – 42, compare Martin Kahn, The Western Allies and Soviet Potential in World War II: Economy, Society and Military Power, London: Routledge 2019.
  • (10) Velikaia Otechestvennaia voin 1941–1945 godov. Tom 7. Ekonomika i oruzhie voiny, [The Great Patriotic war, 1941–1945. Volume 7: The Economy and Armaments of the War], Moscow 2013, “Mobilizatsiia ekonomiki SSSR i perekhod k ekonomike voennogo vremeni”, p. 60 – 117; “Evakuatsiia kak sostavnaia chast perestroika ekonomiki v voennoe vremia”, p. 118 – 144; “Sozdanie ekonomicheskikh predposylok dlia korennogo pereloma v voine”, p. 145 – 196.
  • (11) Larry E. Holmes, Stalin’s World War II Evacuations: Triumph and Troubles in Kirov, University Press of Kansas 2017; Rebecca Manley, To the Tashkent Station: Evacuation and Survival in the Soviet Union at War, Cormell University Press 2009.
  • (12) Nikolai A. Antipin et altere (ed.), Put’ k Pobede: Evakuatsiia promysjlennosti predpriiatii v Cheliabinskuiu oblast v godu Velikoi Otechestvennoi voine 194 –1945 gg., [The Road to Victory: The Evacuation of industrial factories to the Cheliabinsk region during the Great Patriotic war 1941–1945], Cheliabinsk 2020.
  • (13) See Marina N. Potiomkina, in Put’ k Pobede, p. 7–21; idem, Evakuatsiia v gody Velikoi Otechestvennoi voiny na Urale: liudi i sudby, [Evacuation in the Urals during the Great Patriotic war: People and destinies], Magnitogorsk 2002; idem, Evakuatsiia naseleniia v gody Velikoi Otechestvennoi voiny na Ural: Gendernoe izmerenie, [The Evacuation of the populations to the Urals during the Great Patriotic war: The Gender dimension], Magnitogorsk 2019; idem, Demograficheskii aspect evakuatsii naseleniia v sovetskii tyl v gody Velikoi Otechestvennoi vony, [The Demographic aspect of the evacuation of the population to the Soviet interiors during the Great Patriotic war], Magnitogorsk 2019.
  • (14) Potiomkina, Marina N. & Aleksei Yu. Klimanov, ”Sovremennaia otechestvennaia istriografiia i perspektivy izuchenija promyshlennoi evakuatsii perioda Belikoi Otechestvennoi voiny”, [Contemporary Russian historiography and perspectives on the study of industrial evacuation in the Great Patriotic War], Noveishaia istoria Rossii, Tom 10, No 3, 2020.

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