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Green Concerns and Salience of Environmental Issues in Eastern Europe

Posted on June 7, 2021 | Policy Brief

Flooded street in Germany representing climate change risk perceptions

Changes in individual behavior are an essential component of the planet’s effort to reduce carbon emissions. But such changes would not be possible without individuals acknowledging the threat of anthropogenic climate change. This brief discusses the climate change risk perceptions across Europe. We show that people in Eastern Europe are, on average, less concerned about climate change than those in Western Europe. Using detailed survey data, we find evidence that the personal experience of extreme weather events is a key driver of green concern, and even more so in the non-EU Eastern part of Europe. We argue that this association might be explained by the relatively low quality and informativeness of public messages concerning global warming in this part of Europe. If information is scarce or perceived as biased, personal experience will resonate more.

Introduction

Climate change is one of the main threats to humanity. Tackling it entails a combined effort from all parts of society, from regulatory changes and industries adopting new greener business models to consumers adjusting their behavior. While an individual’s contribution to climate change may appear insignificant, research shows that the aggregate effect of mobilizing already known changes in consumer behavior may allow the European Union (EU) to reduce its carbon footprint by about 25% (Moran et al., 2020).

However, the first step for people to adjust their consumption patterns is to acknowledge the threat of anthropogenic climate change. Public ignorance about climate change’s impacts remains high across the world. Furthermore, citizens of more polluting countries are often relatively less concerned about climate change. This lack of awareness is not well-understood, in part due to the multi-dimensional local factors affecting it (Farrell et al., 2019).

This brief discusses the potential drivers of climate risk perceptions, focusing on the differences between Western Europe, Eastern European states that are part of the EU, and non-EU Eastern European countries. We first present the climate change concerns across these regions. We then discuss to which extent the country’s pollution exposure measures and individuals’ socio-economic characteristics can explain these differences. We show that the personal experience of extreme weather events is a key driver of green concern, and even more so in the non-EU part of Eastern Europe. We relate this result to the relatively low salience and informativeness of public messages concerning climate in this part of Europe and discuss potential policy implications.

Green Concerns and Pollution Exposure Across Europe

Figure 1 compares, across Europe, the share of poll respondents who see climate change as a major threat, based on the data from the Lloyd’s Register Foundation World Risk Poll 2020.  While there is a significant variation in climate risk perception within each region, respondents in Eastern Europe are, on average, less concerned about climate change than those in Western Europe. We observe a similar pattern between the EU and non-EU parts of Eastern Europe. 

Exposure to pollution does not seem to clearly explain these differences. Moreover, the patterns of correlation between climate concern and pollution differ across regions and measures of pollution exposure. The left panel of Figure 2 presents averages across the regions for two pollution measures: carbon emissions (which is, perhaps, reflecting climate threat in general) and air quality (which is more directly associated with health risks). We can see that CO2 emissions are the highest in the non-EU part of Eastern Europe, the least environmentally concerned region. Still, the EU part of Eastern Europe has the lowest average emissions per capita across the three regions (this ranking likely results from the interaction between reliance on fossil fuels, industrial structure, and level of development across the three regions). At the same time, when it comes to the average air quality (measured as the percentage of population exposed to at least 10 micrograms of PM2.5/m3), the non-EU EasternEuropean region is doing better than its EU counterpart, which is more climate concerned. Here, better average air quality in the non-EU Eastern European region is due to its relatively low population density, and consequently, low PM2.5 exposure in large parts of Russia. (See, more on the air quality gap within the EU in Lehne, 2021).

Figure 1: Climate concerns in Eastern and Western Europe

Source: Authors’ calculations based on Lloyd’s Register Foundation World Risk Poll 2020, question 5 “Do you think that climate change is a very serious threat, a somewhat serious threat, or not a threat at all to the people in this country in the next 20 years?”. Averages are calculated with population-representative weights.

The right panel of Figure 2 shows correlations between (country-level) climate concerns and pollution. For CO2, the correlation is negative in all three regions, suggesting that, within each region, more emitting countries are less concerned. This negative correlation, however, is the strongest in the EU-part of Eastern Europe and almost absent in the non-EU part. The differences between the regions are even more striking for the correlation between climate concerns and air quality: both in Western Europe and in the EU part of Eastern Europe, citizens of countries with worse air quality are more concerned about climate change. However, in non-EU Eastern Europe, the relation is the exact opposite: lower concerns about climate change go hand-in-hand with worse quality of air.

Figure 2: Emissions vs. Climate concerns in Eastern and Western Europe, 2018

Source: Authors’ calculations based on www.climatewatchdata.org, OECD and World Risk Poll 2020. The climate concern variable is a country-level weighted average of answers “Very high risk” to the World Risk Poll 2020 question 5, see note to Figure 1.

Green Concerns and Socio-economic Characteristics

Lower climate concerns in EU-part of the Eastern bloc have been documented before; they are often explained by the Eastern-European economies’ high reliance on coal and other fossil fuels, low-income levels, and other immediate problems that lower the priority of climate issues (e.g., Lorenzoni and Pidgeon 2006, Poortinga et al., 2018, or Marquart-Pyatt et al., 2019). Additionally, the literature suggests that climate beliefs are linked to individuals’ socio-economic characteristics, such as level of education, income, or gender (see, e.g., Poortinga, 2019), which may be different across the regions.

However, the regional differences in climate beliefs also persist when we use individual-level data and control for respondents’ individual characteristics, as well as for country-level variables, such as GDP per capita, oil, gas, and coal dependence of the economies, and exposure to emissions (at the country level, as our individual data does not have this information). This is illustrated in Column 1 of Table 1.

Table 1: Climate change beliefs determinants, individual-level cross-section data.

Source: This is an outcome of logistic regression. Experience =1 if the respondent answered “yes” to the World Risk Poll 2020 question L8D “Have you or someone you personally know, experienced serious harm from severe weather events, such as floods or violent storms in the past TWO years?” Media Freedom is based on 2018 Freedom House data, and scores media between 0 (worst) and 4 (best). Controls include age, gender, education, personal feelings about household income, income quantile, urban/rural, size of household, number of children under 15, las well as log of GDP per capita, log of CO2 per capita, mean exposure to PM2.5, and oil, gas and coal rents as a share of GDP. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

In what follows, we explore another key driver, the personal experience of extreme weather events. While there is a sizable literature on the effect of experience on climate beliefs, that factor was never, to our knowledge, considered to understand the difference in climate risk perception between the EU- and non-EU parts of Eastern Europe.  

Green Concern and Salience of Environmental Issues

In line with the recent climate risk perceptions literature (e.g., Van der Linden, 2015), we show that personal experience increases the likelihood of considering climate change as a major threat across all three regions (see column 2 in Table 1). The association is stronger in the EU part of Eastern Europe and even more so in the non-EU part (even if the difference between the last two is not statistically significant). This finding is confirmed when we control for (observable and unobservable) country-specific effects, such as social norms, via the inclusion of country-level fixed effects. In this case, extreme weather events make respondents more climate-conscious within each country (Column 3 of Table 1). In this specification, the effect differs statistically between the two groups of Eastern-European countries, even if only at a 10% significance level. To put it differently, the impact of personal experience with extreme weather events seems to close a sizable part of the gap in climate risk perceptions across the regions and more so in the non-EU part of Eastern Europe.

Our preferred explanation for this finding is that personal experience resonates with the quality and informativeness of public messages concerning global warming. If information is scarce or perceived as biased, personal experience will resonate more. The low political salience of environmental issues in Eastern Europe, inherited from its Soviet past (McCright, 2015), and lower media quality in Eastern Europe (see e.g., Zuang, 2021) are likely to affect the quality of public discourse concerning the risks of climate change, and, consequently, the information available to individuals.

The climate-related legislative effort across Eastern Europe reflects the low political importance of climate change in the region. According to the data from Grantham Research Institute on Climate Change and the Environment, non-EU transition countries, on average, have adopted 8 climate-related laws and policies, while the corresponding figure is 11.5 for EU transition countries and 18 for the countries in Western Europe. Further, Figure 3 shows a positive correlation between climate change concerns and the number of climate-related laws for Western Europe and the EU-part of Eastern Europe but a negative one for the non-EU part of Eastern Europe and Caucasus countries. One possible interpretation of these differences is that climate change is relatively low on the political agenda of (populist) regimes in the non-EU part of Eastern Europe, as climate-related legislative activity (proxied by, admittedly rough, a measure of the number of laws) does not reflect the intensity of population climate preferences.

Figure 3: Climate concern vs. Climate legislation

Source: Authors’ calculations based on climate legislation data from Grantham Research Institute on Climate Change and the Environment, and World Risk Poll 2020

Regarding the influence of media quality, column (4) of Table 1 shows that the effect of personal experience on climate change concern is negatively correlated with media freedom. One interpretation could be that individuals in countries with freer media infer less from their extreme weather experience because more accurate media coverage about climate risks improves the population’s knowledge on the issue.

Of course, the causality of the climate belief-experience relationship could also go in the other direction – people who are more concerned about climate change could be more likely to interpret their personal experience as weather-related extreme events. It is impossible to distinguish with the data at hand. However, Myers et al. (2013) show that both channels are present in the US, and the former channel dominates for the people less engaged in the climate issue. Stretching this finding to the Eastern Europe case, we argue that more precise information on the importance of climate change may partially have the same effect as experience – i.e., it will increase people’s awareness and concern about the consequences of global warming.

Conclusion

This brief addresses the differences in climate change beliefs between Eastern and Western Europe, as well as within Eastern Europe. It discusses the determinants of these differences and stresses the importance of personal experience, especially in the non-EU part of Eastern Europe. It relates this finding to the relatively low accuracy of information and quality of public discourse about climate change in the region.

We know already that tackling climate change requires reliable and accurate sources of information. This is especially crucial given what we outline in this brief. This issue resonates with the current social science analysis of the diffusion of climate change denial (see e.g., Farell et al., 2019, on the significant organized effort in spreading misinformation about climate change). Such contrarian information that relays uncertainty and doubt regarding the severity of the global climate change threat could have a severe impact, especially in situations with low political salience of climate change, like in non-EU Eastern Europe. A significant effort of both governments and civil society is needed to provide adequate information and mobilize the population in our common fight against climate change.

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.

Jurisdictional Competition for FDI in Developing and Developed Countries

Posted on May 31, 2021 | Policy Brief

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This brief is based on research studying jurisdictional competition between countries and its influence on the inflow of foreign direct investments (FDI). The study compares jurisdictional competition among the developing Central and Eastern European (CEE) countries with competition among developed EU countries. As instruments of jurisdictional competition for FDI, we consider governments’ efforts to improve the rule of law, corporate governance, and tax policies. The results suggest the presence of proactive jurisdictional competition via the quality of corporate governance regulation both in the CEE and the EU countries. The CEE states also attract FDI by competing in tax policies.

Introduction

The determinants of FDI inflows have been examined in numerous studies. A substantial number of them consider the influence of institutions, which are defined as particular organizational entities, procedural devices, and regulatory frameworks (IMF, 2003).

The quality of institutions is a particularly important FDI determinant for less-developed countries because the poor institutional quality and weak law enforcement increase the costs of running a business, create barriers for financial market efficiency, and increase the probability of foreign assets expropriation (Blonigen, 2005).

However, governments interested in attracting FDI to boost job creation, new technologies, and tax revenues to their countries are not only concerned about the internal institutional environment. They are also competing with other countries in attracting foreign investments, engaging in what is often referred to as “jurisdictional competition”. In a broad sense,  this can be thought of as governments’ efforts to outcompete one another in offering foreign companies more favorable institutional and fiscal conditions for capital placements.

This brief summarizes the results of a study on the jurisdictional competition for FDI among the developing CEE and among developed EU countries (Mazol and Mazol, 2021). The research explores the precondition for proactive jurisdictional competition between economies for FDI – namely, how the economic and institutional environment within a country impacts the inflow of FDI both domestically and to its neighboring states, – by using a spatial econometric approach. The brief emphasizes the difference in the FDI policy responses implemented by developing CEE and developed EU countries.

Data and Methodology

In our econometric analysis, we use the FDI inward stock (i.e., the value of capital and reserves in the economy attributable to a parent enterprise resident in a different economy) as the dependent variable. The explanatory variables indicating jurisdictional competition include quality of corporate governance, rule of law, political stability, and tax policy. We employ balanced panel datasets for 26 developing CEE countries and 15 developed EU countries for the period 2006-2018. The dataset is derived from the World Bank and UNCTAD databases.

The analysis is based on a panel spatial Durbin error model (SDEM) with fixed effects (LeSage, 2014). Parameter estimates in the SDEM contain a range of information on the relationships between spatial units (in our case, countries). A change in a single observation associated with any given explanatory variable will affect the spatial unit itself (a direct effect) and potentially affect all other spatial units indirectly (a spillover effect) (Elhorst, 2014). The spatial spillover effect is viewed here as the impact of the change in the institutional or economic factor in one country on the performance of other economies (LeSage & Pace, 2009).

In our case, the direct effect is the effect on the FDI in country i of the changes in the studied instrument of jurisdictional competition in country i. The spillover effect is the change in FDI in country j following a change in the studied instrument of jurisdictional competition in country i.

Results

The results of our estimation are suggestive of a proactive jurisdictional competition in taxes among the CEE countries and in corporate governance quality both among the CEE and EU countries. Analyses of other factors (i.e., political stability and rule of law) show no significant interrelation between policy measures implemented by neighboring countries in order to attract FDI.

The precondition for the presence of proactive jurisdictional competition in a particular factor is to have statistical significance in both its direct and spillover effects (Elhorst and Freret, 2009). Such findings may indicate that policy measures in one economy trigger a policy response in a neighboring economy, which, in turn, influences the level of FDI in both countries.

Table 1. Estimation results of SDEM models – direct effects

Notes: *** – significance at 1% level, **  – significance at 5% level, *  – significance at 10% level. ln – denotes the logarithm of the underlying variable. lagt – denotes lagged underlying variable by one period (year) in time. Values of t statistics in parenthesis. CEE countries: Albania, Armenia, Azerbaijan, Belarus, Estonia, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Macedonia, Moldova, Poland, Romania, Russia, Serbia, Slovakia, Slovenia, Tajikistan, Ukraine, Uzbekistan. EU countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland. Source: Author’s estimates based on World Bank and UNCTAD databases.

Our results for the direct and indirect response to a tax policy in CEE countries illustrate this logic. Decreasing tax_rateincreases FDI to the CEE economy enacting this change (see Table 1), as well as to its neighboring countries (see Table 2). This finding is consistent with jurisdictional competition in taxes. That is, a reduction in domestic tax_rate may entail a decrease in the tax rate of a neighboring economy, resulting in a subsequent increase in FDI. (To explicitly confirm the suggested channel, further tax policy analysis would be needed). Interestingly, our results suggest that jurisdictional competition in taxes is only present among CEE economies, but not among EU countries.

In turn, an increase in corp_governance, a measure of corporate governance quality, increases FDI in neighboring countries both in the EU and in the CEE region (see Table 2).  A possible interpretation is that an increase in corp_governance in one country may entail an increase in corp_governance in its neighboring economies, resulting in a subsequent increase in FDI.  This result suggests proactive competition via corporate governance policy both among the EU countries and the CEE countries.

However, the direct effect differs between the regions. In the EU, an increase in corp_governance increases FDI to the EU economy in question, in line with common wisdom (see Table 1). At the same time, in the CEE region, an increase in corp_governance is followed by a decrease in FDI to that country.

Table 2. Estimation results of SDEM models – spillover effects

Notes: ***  – significance at 1% level, **  – significance at 5% level, *  – significance at 10% level. ln – denotes the logarithm of the underlying variable. Values of t statistics in parenthesis. lagt_lags – denotes spatially lagged underlying variable (multiplied by spatial weight matrix) lagged by one period (year) in time. Source: Author’s estimates based on World Bank and UNCTAD databases.

One potential explanation for the negative direct effect of corporate governance quality on FDI in the CEE economies is that improved corporate governance practices can block certain types of FDI, leaving behind foreign investors with a lower “threshold for corruption”. This may decrease FDI to the CEE country in question. However, once the jurisdictional competition results in an improvement of corporate governance across the region, it ultimately has a positive spillover effect.

The above explanation is in line with the theory of regulatory capture (Stigler, 1971), which suggests that the decisions made by public officials might be shaped and sometimes distorted by the efforts of rent-seeking interest groups to increase their influence.

Finally, the estimates do not indicate that the other studied institutional factors, rule of law and political stability, are applied as instruments of jurisdictional competition as neither groups of countries show significant spillover effects. The results, however, show that these factors influence the FDI inflow via the direct effect. More specifically, an increase in political_stability positively influences the FDI inflow to the economies in question, both in CEE and the EU, while rule_of_law is positive and significant only for the CEE countries. If investors are not as responsive to changes in rule_of_law when the initial level is high, the fact that EU countries typically have a higher rule_of_law value compared to CEE countries might explain why this estimate is insignificant for the EU countries.

Conclusion

This brief, first, presents new evidence on the relationship between different economic and institutional factors and FDI using a spatial econometric approach; second, it analyzes the possible existence of jurisdictional competition among developing CEE countries and developed EU countries as well as its effect on FDI.

The results suggest proactive jurisdictional competition in FDI determinants such as corporate governance quality and tax rates. CEE countries competing with one another use both these instruments of jurisdictional competition, while EU countries compete only via corporate governance quality. Furthermore, foreign investors are not sensitive to the quality of rule of law in the EU countries, while this instrument is more important for the FDI inflow to CEE economies.

Our results stress that officials responsible for the FDI policy implementation should pay more attention to the policies undertaken by neighboring governments as such external policies can make their own strategies to attract FDI to their economy less effective.

References

  • Blanton, S., and R. Blanton. (2007). What Attracts Foreign Investors? An Examination of Human Rights and Foreign Direct Investment. The Journal of Politics, 69(1), 143-155.
  • Blonigen, B. (2005). A Review of the Empirical Literature on FDI Determinants. Atlantic Economic Journal, 33(4), 383-403.
  • Elhorst, J. (2014). Spatial Econometrics from Cross-Sectional Data to Spatial Panels. Berlin: Springer.
  • Elhorst, J., and S. Freret. (2009). Evidence of Political Yardstick Competition in France Using a Two-Regime Spatial Durbin Model with Fixed Effects December. Journal of Regional Science, 49(5), 931-951.
  • IMF (2003). World Economic Outlook 2003. International Monetary Fund: Washington DC.
  • LeSage, J. (2014). What Regional Scientists Need to Know About Spatial Econometrics? Working Paper, Texas State University-San Marcos, San Marcos.
  • LeSage, J., and R. Pace. (2009). Introduction to Spatial Econometrics. Boca Raton, FL: CRC Press, Taylor and Francis Group.
  • Mazol, A., and S. Mazol. (2021). Competition of Jurisdictions for FDI: Does Developing and Developed Countries Response Different to Economic Challenges? BEROC Working Paper Series, WP no. 73.
  • Stigler, G. (1971). The Theory of Economic Regulation. Bell Journal of Economic and Management Science, 2, 3-21.

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.

Enemies of the People

Posted on May 24, 2021 | Policy Brief

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From the early days of the Soviet Union, the regime designated the educated elite as Enemies of the People. They were political opponents and considered a threat to the regime. Between the late 1920s and early 1950s, millions of enemies of the people were rounded up and forcedly resettled to remote locations within the GULAG, a system of labor camps spread across the Soviet Union. In recent research (Toews and Vezina, 2021), we show that these forced relocations have long-term consequences on local economies. Places close to camps that hosted more enemies of the people among prisoners are more prosperous today. We suggest that this result can be explained by the intergenerational transmission of education and a resulting positive effect on local development, which can still be observed to this day.

Historical Background

Targeting the educated elite, collectively referring to them as Enemies of the People and advocating their imprisonment, can be traced back to the beginning of the Russian Revolution in 1917. After consolidating power a decade later, Stalin launched the expansion of the GULAG system, which at its peak consisted of more than a hundred camps with over 1.5 million prisoners (see Figure 1). A large number of historians extensively described this dark episode in Russian history (Applebaum (2012), Khlevniuk (2004), and Solzhenitsyn (1974)). During the darkest hours of this episode, the Great Terror, 1.5 million enemies were arrested in just about two years. While half were executed immediately, the other half were forcedly allocated to GULAG camps spread across the Soviet Union and mixed with non-political prisoners (see Figure 2). Enemies accounted for about a third of GULAG prisoners after the Great Terror. As a result, education levels were higher in the GULAG than in society. In 1939, the share of GULAG prisoners with tertiary education was 1.8%, while, according to the Soviet Census of the same year, only 0.6% of the population had tertiary education.

After Stalin’s death, labor camps started closing rapidly, but many ex-prisoners settled close to the campsites. New cities were created and existing cities in the proximity of camps started growing fast (Mikhailova, 2012). Enemies remained once freed for a combination of political, economic, and psychological reasons. Politically, they were constrained in their choice of location by Stalin-era restrictions on mobility. Economically, they had few outside options and could keep on working for the camps’ industrial projects. On the psychological level, prisoners had become attached to the location of the camp, as Solzhenitsyn (1974) clearly puts it: “Exile relieved us of the need to choose a place of residence for ourselves, and so from troublesome uncertainties and errors. No place would have been right, except that to which they had sent us.”.

Figure 1. Location and size of camps in the Soviet Gulag system

Notes: The circles are proportional to the prisoner population of camps. The data is from the State Archive of the Russian Federation (GARF) and Memorial

Enemies of the People and Local Prosperity

At the heart of our analysis is a dataset on GULAG camps which we collected at the State Archive of the Russian Federation (GARF). It allows us to differentiate between prisoners who were imprisoned for political reasons (Enemies of the People) and those arrested for non-political crimes. The share of enemies varied greatly across camps, and we argue that this variation was quasi-random. We back this up by the historical narrative, according to which the resettlement process was driven by political rather than economic forces, suggesting that strategic placements played little role in the allocation of enemies (Khlevniuk (1995) and Ertz (2008)). Moreover, while the forced nature of allocation to camps allows us to rule out endogenous location decisions, we also show that neither economic activities nor geographic attributes, such as climatic conditions, soil quality, or the availability of resources, predict the share of enemies across camps.

To estimate the long-run effects of enemies on local prosperity, we link the location of camps in 1952, the year before Stalin’s death and at the peak of the GULAG system, to post-Soviet data covering the period 2000-2018.

Figure 2. The rise and fall of the Gulag

Notes: The solid line shows the number of Gulag camps while the dashed line shows the total number of prisoners in the Gulag. The two vertical dash lines indicate the years that can define the start and end of the Gulag, starting with Stalin’s 5-year plan in 1928 and ending with Stalin’s death in 1953. The shaded areas show specific periods of marked change for the Gulag, starting with dekulakization in 1929, when when Stalin announced the liquidation of the kulaks as a class and 1.8 million well-off peasants were relocated or executed. The Great Terror of 1936-1938, also referred to as the Great Purge, is the most brutal episode under Stalin’s rule, when 1.5 million enemies were arrested, and half of them executed. The Gulag’s prisoner population went down during WW2, as non-political prisoners were enlisted in the Red Army, and as the conditions in camps deteriorated and mortality increased. Source: Memorial.

In particular, the camp level information is linked to the location of firms from the Russian firm census (2018), data on night-lights (2000-2015), as well as data from household and firm-level surveys (2016 and 2011-2014, respectively). Our results suggest that one standard deviation (28 percentage point) increase in the share of enemies of the people increases night-lights intensity per capita by 58%, profits per employee by 65%, and average wages by 22%. A large number of specifications confirm the relationship depicted in Figure 3, which illustrates the positive association between the share of enemies across camps and night-lights intensity per capita.

Figure 3. Share of enemies vs. night lights per capita across Gulags

Notes: The scatters show the relationship between the share of enemies in camps in 1952 and night lights per capita within 30 km of camps in 2000 and 2015. Each circle is a 30km-radius area around a camp, and the size of the circles is proportional to the camps’ prisoner populations. The biggest circle is Khabarovsk. The solid lines show the linear fit, and the shaded areas show the 95% confidence interval. Areas near camps with a higher share of enemies have brighter night lights per capita both in 2000 and 2015. The data on Gulags is from the State Archive of the Russian Federation (GARF) and the data on night lights is from the DMSP-OLS satellite program and made available by the Earth Observation Group and the NOAA National Geophysical Data Center. The data on population is from the gridded population of the world from SEDAC.

Intergenerational Transmission

We suggest that the relationship between enemies and modern prosperity is due to the long-run persistence of high education levels, notably via intergenerational transmission, and their role in increasing firm productivity. For the identification of the intergenerational link, we rely on a household survey collected by the EBRD in which interviewees are explicitly asked whether their grandparents have been imprisoned for political reasons during Soviet times. Exploiting this information, we show that the grandchildren of enemies of the people are today relatively more educated. We also find that grandchildren of enemies are more likely to be residing near camps that had a higher share of enemies of the people among prisoners in 1952. An alternative explanation for our results could be that the leadership of the Soviet Union may have strategically chosen to invest more during the post-GULAG period in locations that had received more enemies to exploit complementarities between human and physical capital. We find no evidence for this mechanism. We document that Soviet investment in railroads, factories of the defence industry, or universities was, if anything, lower in places with a large share of enemies.

Conclusion

We show that the massive and forced re-allocation of human capital that took place under Stalin had long-run effects on local development. Sixty years after the death of Stalin and the demise of the GULAG, areas around camps that had a higher share of enemies are richer today, as captured by firms’ wages and profits, as well as by night-lights per capita. We argue that the education transferred from forcedly displaced enemies of the people to their children and grandchildren partly explains variation in prosperity across localities of Russia. This can be seen as a historical natural experiment that identifies the long-run persistence of higher education and its effect on long-run prosperity. Sadly, it also highlights how atrocious acts by powerful individuals can shape the development path of localities over many generations.

Bibliography

  • Applebaum, A., Gulag: A History of the Soviet Camps, Penguin Books Limited, 2012.
  • Ertz, Simon. Making Sense of the Gulag: Analyzing and Interpreting the Function of the Stalinist Camp System. No. 50. PERSA Working Paper, 2008.
  • Khlevnyuk, Oleg, “The objectives of the Great Terror, 1937–1938.” In Soviet History, 1917–53, pp. 158-176. Palgrave Macmillan, London, 1995.
  • Khlevnyuk, Oleg, The History of the Gulag: From Collectivization to the Great Terror Annals of Communism, Yale University Press, 2004.
  • Mikhailova, Tatiana, “Gulag, WWII and the long-run patterns of Soviet city growth,” 2012.
  • Solzhenitsyn, Aleksandr, The Gulag Archipelago, 1918-56: An Experiment in Literary Investigation, New York: Harper Row, 1973.
  • Toews, Gerhard, and Pierre-Louis Vézina. “Enemies of the people.” (2021).

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.

Carbon Tax Regressivity and Income Inequality

Posted on May 17, 2021 | Policy Brief

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A common presumption in economics is that a carbon tax is regressive – that the tax disproportionately burdens low-income households. However, this presumption originates from early research on carbon taxes that used US data, and little is known about the factors that determine the level of regressivity of carbon taxation across countries. In this policy brief, I explore how differences in income inequality may determine the distribution of carbon tax burden across households in Europe. The results indicate that carbon taxation will be regressive in high-income countries with relatively high levels of inequality, but closer to proportional in middle- and low-income countries and in countries with low levels of income inequality.

Introduction

Climate change is one of the main challenges facing us today. To reduce emissions of greenhouse gases, and thereby mitigate climate change, economists recommend the use of a carbon tax. The environmental and economic efficiency of carbon taxation is often highlighted, but the equity story is also of importance: who bears the burden of the tax?

How the burden from a carbon tax is shared across households is important since it affects the political acceptability of the tax. For instance, the “Yellow Vests” protests against the French carbon tax started due to concerns that the tax burden is disproportionately large on middle- and working-class households. Research in economics also shows that people prefer a progressive carbon tax (Brännlund and Persson, 2012).   

In this brief, I explore what we know about the distributional effects of carbon taxes and analyze the link between carbon tax regressivity and levels of income inequality in theory and in application to Sweden as well as other European countries.

Carbon Tax Burden Across Households

It is a common finding in the economics literature that carbon taxes are, or would be, regressive (Hassett et al., 2008; Grainger and Kolstad, 2010). However, most of the earlier literature is based on US data, and the US is unrepresentative of an average high-income country in terms of variables that are arguably important for carbon tax incidence. Compared to most countries in Europe, income in the US is high but unequally distributed, carbon dioxide emissions per capita are high, the gasoline tax rate is low, and the access to public transport is poor. If we want to understand the likely distributional effects of carbon taxes across Europe, we thus need to look beyond the US studies.

A recent study by Feindt et al. (2020) examines the consumer tax burden from a hypothetical EU-wide carbon tax. They find that the distributional effect at the EU-level is regressive, driven by the high carbon intensity of energy consumption in relatively low-income countries in Eastern Europe. At the national level, however, carbon taxation in Eastern European countries is slightly progressive due to car ownership and transport fuel being luxuries. Conversely, in high-income countries – where transport fuel is a necessity – carbon taxation is slightly regressive.

That the incidence of carbon and gasoline taxation varies across countries with different levels of income, has been found in numerous studies (Sterner, 2012; Sager, 2019). To understand the source of this variation, we need to identify the determinants of the incidence of carbon taxes.

The Role of Income Inequality

In a recent paper, I, together with Giles Atkinson at the London School of Economics, present a simple model where the variation in the carbon tax burden across countries and time can be determined by two parameters: the level of income inequality and the income elasticity of demand for the taxed goods (Andersson and Atkinson, 2020). The income elasticity specifies how the demand for a good, such as gasoline, responds to a change in income. If the budget share decreases as income increase, we refer to gasoline as a necessity. If the budget share increases with income, we refer to gasoline as a luxury good. Our model predicts that rising inequality increases the regressivity of a carbon tax on necessities. Similarly, we will see a more progressive incidence if inequality increases and the taxed good is a luxury.

To mitigate climate change, a carbon tax should be applied to goods responsible for the majority of greenhouse gas emissions: transport fuel, electricity, heating, and food. To estimate the distribution of carbon tax burden, we must then first establish if these goods are necessities or luxuries, respectively. Gasoline is typically found to be a luxury good in low-income countries but a necessity in high-income countries (Dahl, 2012). Food, in the aggregate, is consistently found to be a necessity. A carbon tax on food would, however, mainly increase the price of red meat – beef has a magnitude larger carbon footprint than all other food groups – and red meat is generally a luxury good, even in high-income countries (Gallet, 2010). Lastly, electricity and heating are necessities, with little variation across countries in the level of income elasticities.  A broad carbon tax would thus likely be regressive in high-income countries, but more proportional, maybe even progressive, in low-income countries. The overall effect in low-income countries depends on the relative budget shares of transport fuel and meat (luxuries) versus electricity and heating (necessities). A narrow carbon tax on transport fuel has a less ambiguous incidence: it will be regressive in high-income countries where the good is a necessity and proportional to progressive in low-income countries where the good is a luxury.  

The income elasticities of demand, however, only provide half of the picture. To understand the degree of regressivity from carbon taxation, we also need to take into account the level of income inequality in a country. Our model predicts that a carbon tax on necessities will be more regressive in countries with relatively high levels of inequality. And increases in inequality over time may turn a proportional tax incidence into a regressive one.

To test our model’s prediction, we analyze the distributional effects of the Swedish carbon tax on transport fuel and examine previous studies of gasoline tax incidence across high-income countries. 

Empirical Evidence from Sweden

The Swedish carbon tax was implemented in 1991 at $30 per ton of carbon dioxide and the rate was subsequently increased rather rapidly between 2000-2004. Today, in 2021, the rate is above $130 per ton; the world’s highest carbon tax rate imposed on households. The full tax rate is mainly applied to transport fuel, with around 90 percent of the revenue today coming from gasoline and diesel consumption.

 Figure 1. Carbon tax incidence and income inequality in Sweden

Sources: Andersson and Atkinson (2020). Gini coefficients are provided by Statistics Sweden.

Using household-level data on transport fuel expenditures and annual income between 1999-2012, we find that the Swedish carbon tax is increasingly regressive over time, which is highly correlated with an increase in income inequality. Figure 1 shows the strong linear correlation between the incidence of the tax and the level of inequality across our sample period. The progressivity of the tax is measured using the Suits index (Suits, 1977), a summary measure of tax incidence that spans from +1 to -1. Positive (negative) numbers indicate that the tax is overall progressive (regressive) and a proportional tax is given an index of zero. The level of income inequality, in turn, is summarized by the Gini coefficient (0-100), with higher numbers indicating higher levels of inequality.

In 1991, when the Swedish carbon tax was implemented, income inequality was relatively low, with a Gini of 20.8. If we extrapolate, the results presented in Figure 1 indicate that the tax incidence in 1991 was proportional to slightly progressive. Since the early 1990s, however, Sweden has experienced a rise in inequality. Today, the Gini is around 28 and the carbon tax incidence is rather regressive. This can be a potential concern if people start to perceive the distribution of the tax burden as unfair and call for reductions in the tax rate.

Empirical Evidence Across High-Income Countries

Figure 2 presents the results of our analysis of previous studies of gasoline tax incidence across high-income countries. Again, we find a strong correlation with inequality; the higher the level of inequality, the more regressive are gasoline taxes.  In the bottom-right corner, we locate the results from studies on gasoline tax incidence that have used US data. The level of inequality in the US has been persistently high, and the widespread assumption that gasoline and carbon taxation is regressive is thus based to a large part on studies of one highly unequal country. Looking across Europe we find that the tax incidence is more varied, with close to a proportional outcome in the (relatively equal) Nordic countries of Denmark and Sweden.

Figure 2. Gasoline tax incidence and income inequality in OECD countries

Sources: Andersson and Atkinson (2020). Gini coefficients are from the SWIID database (Solt, 2019).

Conclusion

A carbon tax is economists’ preferred instrument to tackle climate change, but its distributional effect may undermine the political acceptability of the tax. This brief shows that to understand the likely distributional effects of carbon taxation we need to take into account the type of goods that are taxed – necessities or luxuries – and the level and direction of income inequality. Carbon taxation will be closer to proportional in European countries with low levels of inequality, whereas in countries with relatively high levels of inequality the carbon tax incidence will be regressive on necessities and progressive for luxury goods.

This insight may explain why we first saw the introduction of carbon taxes in the Nordic countries. Finland, Sweden, Denmark, and Norway all implemented carbon taxes between 1990-1992, and income inequality was relatively, and historically, low in this region at the time. Policymakers in the Nordic countries thus didn’t need to worry about possibly regressive effects. Looking across Europe today, many of the countries that have relatively low levels of inequality have either already implemented carbon taxes or, due to the size of their economies, have a low share of global emissions. In countries that are responsible for a larger share of global emissions – such as, the UK, Germany, and France – inequality is relatively high, and they may find it to be politically more difficult to implement carbon pricing as the equity argument becomes more salient and provides opportunities for opponents to attack the tax.

To increase the political acceptability and perceived fairness of carbon pricing, policymakers in Europe should consider a policy design that offsets regressive effects by returning the revenue back to households, either by lump-sum transfers or by reducing tax rates on labor income.   

References

  • Andersson, Julius and Giles Atkinson. 2020. “The Distributional Effects of a Carbon Tax: The Role of Income Inequality.” Grantham Research Institute on Climate Change and the Environment Working Paper 349. London School of Economics.  
  • Brännlund, Runar and Lars Persson. 2012. “To tax, or not to tax: preferences for climate policy attributes.” Climate Policy 12 (6): 704-721.
  • Dahl, Carol A. 2012. “Measuring global gasoline and diesel price and income elasticities.” Energy Policy 41: 2-13.
  • Feindt, Simon, et al. 2020. “Understanding Regressivity: Challenges and Opportunities of European Carbon Pricing.” SSRN 3703833.
  • Gallet, Craig A. 2010. “The income elasticity of meat: a meta-analysis.” Australian Journal of Agricultural and Resource Economics 54(4): 477-490.
  • Grainger, Corbett A and Charles D Kolstad. 2010. “Who pays a price on carbon?” Environmental and Resource Economics 46(3): 359-376.  
  • Hassett, Kevin A, Aparna Mathur, and Gilbert E Metcalf. 2009. “The consumer burden of a carbon tax on gasoline.” American Enterprise Institute, Working Paper.
  • Sager, Lutz. 2019. “The global consumer incidence of carbon pricing: evidence from trade.” Grantham Research Institute on Climate Change and the Environment Working Paper 320. London School of Economics.  
  • Thomas, Sterner. 2012. Fuel taxes and the poor: the distributional effects of gasoline taxation and their implications for climate policy. Routledge.
  • Suits, Daniel B. 1977. “Measurement of tax progressivity.” American Economic Review 67(4): 747-752.

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.

Five Years in Operation: the Polish Universal Child Benefit

Posted on May 10, 2021 | Policy Brief

Family in the golden hour representing Child Benefits

Over the last five years, Polish families with children have been entitled to a relatively generous benefit of approximately €110 per month and child. Initially granted for every second and subsequent child in the family regardless of income and for the first child for low-income families, the benefit was made fully universal in 2019. With the total costs amounting to as much as 1.7% of Poland’s GDP, the benefit reaches the parents of 6.7 million children and significantly affects these families’ position in the income distribution. Its introduction has led to a substantial reduction in the number of children living in poverty. However, since families with children are more likely to be among households in the upper half of the income distribution, out of the total cost of the benefit, a proportionally greater share ends up in the wallets of high-income families. While the implementation of the benefit has significantly changed the scope of public support to families in Poland, there are many lessons to be learnt and some important revisions to be undertaken to achieve an effective and comprehensive support system.

Introduction

One of the principal commitments in the 2015 Polish parliamentary elections of the then-main opposition party – Law and Justice (Prawo i Sprawiedliwość, PiS), was introducing a generous child benefit. The purpose of this benefit was to support families and encourage higher fertility, which had been one of the lowest in the European Union for a long time. Following PiS’s electoral victory, the new government introduced a semi-universal child benefit of approximately €110 per month (exactly 500 PLN per month, thus the Polish nickname of “the 500+ benefit”) in April 2016. Initially, the benefit was granted for every second and subsequent child in the family regardless of income and for the first child in low-income families. Since July 2019 (nota bene three months before the next parliamentary elections), it was made universal – all parents with children under the age of 18 are entitled to 500PLN per month for every child.  The benefit is relatively generous (for comparison, it accounts for 17.9% of the minimum wage in Poland in 2021), and universal coverage implies substantial costs for the government budget, totalling about 41bn PLN per year (1.7% of the Polish GDP).

Over the last five years, a number of analyses of the consequences of the benefit’s introduction have been conducted. These have encompassed a variety of socio-economic outcomes for Polish families with children – from a comprehensive assessment of these consequences (Magda et al. 2019) to analyses focused on specific effects of the benefit, such as the impact on women’s economic activity (Magda et al. 2018, Myck 2016, Myck and Trzciński 2019) or poverty (Brzeziński and Najsztub 2017, Szarfenberg 2017). The fifth anniversary of the benefit’s implementation seems to be a good opportunity for a summary and update of previous evaluations of the distributional consequences and financial gains for households resulting from this policy (an overview of all the previous CenEA analyses of the child benefit can be found in CenEA 2021). The results presented in this brief are based on analyses conducted using the Polish microsimulation model SIMPL on data from the 2019 CSO Household Budget Survey (more details in Myck et al. 2021). It should be noted that the analyses do not account for the impact of the Covid-19 pandemic on the material situation of households, as the data was collected before the outbreak. As previous studies suggest, the consequences for households of the pandemic and the series of resulting lockdowns varied greatly depending on various factors, such as the sources of income, sector, and form of employment, thus making it impossible to estimate precisely (Myck et al. 2020a).

The Child Benefit on Household Incomes

Due to its universal character, the distributional consequences of the child benefit payments are directly related to the position of households with children aged 0-17 in the income distribution relative to those without. As households with children are more likely to be in the upper half of the distribution (taking into account the demographic structure of households through income equivalisation), out of the total budget expenditure on the benefit, a proportionally greater share goes to high-income families (Table 1). Families with children in the two highest income decile groups (i.e., belonging to the 20% of households with the highest income) currently receive almost 25% of the total annual expenditure on the child benefit. On the other hand, among the 20% of households with the lowest incomes, families with children receive only 11.7% of the total annual cost of the benefit.

Table 1. Household gains resulting from the child benefit by income decile groups

Source: Myck et al. 2021. Notes: Income decile groups – ten groups each covering 10% of the population, from households (HH) with the lowest disposable income to the most affluent households, calculated on the basis of equivalised incomes.

Compared to the poorest 10% of households, families with children in the highest income decile receive 2.5 times more of the total funds allocated to the benefit.

It is also worth noting that the proportion of benefit in the disposable income is relatively evenly distributed if one considers all households in a given decile (with and without children). The proportional benefits in the first nine income deciles are in the range of 3.4% and 5.3% and only fall to 1.9% in the highest income group. A significant differentiation of the benefit in proportional terms can only be seen when accounting solely for households with children within each income decile. The benefit amounts to as much as 26.9% of the disposable income of households with children in the first decile, and the effect falls in subsequent groups – from 18.9% and 16.4% in the second and third deciles, to only 4.1% in the top decile.

The Child Benefit and the Position of Families With Children in the Income Distribution

Taking into account the magnitude of the policy, the position of families with children in the income distribution relative to other households may, to some extent, be the result of receiving the benefit itself. It is, therefore, reasonable to ask what role the benefit plays in shaping this relative position in the income distribution. Figure 1 presents the number of children under 18 in households by income decile groups when the benefit is included in total household income (left panel) and in a hypothetical scenario when the child benefit payment is withdrawn (right panel). As we can see, the withdrawal of the benefit would cause a substantial change in the relative position of families with children in the income distribution, significantly increasing the number of children in the lowest income groups. While in the current system, the poorest 10% of households include 342 thousand children aged 0-17, this number would be 553 thousand in a system without the benefit. However, the benefit also changes the relative position of high-income households with children. In the current system, the richest 10% of households include 762 thousand children. Subtracting the benefit from their household income would reduce this number to 687 thousand.

Figure 1. The child benefit and its impact on the position of families with children in the income distribution

Source: Myck et al. 2021.

Thus, even when taking into account the income distribution without the benefit, the number of children among the richest 10% of households is almost 25% higher than the number of children in the poorest 10% of households. Looking at the income distribution after including the benefit, there are more than twice as many children in the richest 10% of households than among the poorest 10%. This, in turn, inevitably means that, out of the total cost of the benefit, over twice as much money is transferred to households belonging to the richest deciles as compared to the funds transferred to families belonging to the poorest 10% of households.

Discussion

With the total costs amounting to 1.7% of Poland’s GDP, the child benefit introduced in April 2016 substantially raised the level of direct financial support for families with children. As shown in this brief, the benefit reaches the parents of 6.7 million children aged 0-17 and significantly affects the position of these families in the income distribution. While, on the one hand, a large proportion of families with children have incomes high enough to be in the highest income groups even without this support , the lowest decile group would include over 200 thousand more children in the absence of the benefit. This confirms that the child benefit alone contributes to a significant improvement in the material conditions of families with children and to a significant reduction in poverty (cf. Brzezinski and Najsztub, 2017; Szarfenberg, 2017). However, the scale of this reduction is modest given the size of the resources involved. This is not surprising given that the bulk of the total costs of the benefit comes from the 2019 program extension to cover all children regardless of family incomes, which largely ended up in the wallets of higher-income families (Myck et al. 2020b). One of the key goals of the benefit upon introduction was to increase the number of births in Poland by easing the material conditions of families with children. Yet despite a radical increase in the level of support, the number of births in Poland over the period 2017-2020 has essentially remained the same as that forecasted by the Central Statistical Office in its long-term population projection of 2014 (Myck et al. 2021). It is thus difficult to consider the benefit a success in terms of this major objective. Moreover, the withdrawal of the income threshold has largely eliminated the negative disincentive effects of the benefit with regard to employment (Myck and Trzcinski 2019). However, it is unclear whether the post-pandemic economic situation will allow for an increase in female labour force participation, which declined following the introduction of the benefit in 2016 (Magda et al., 2018).

The effects of every socio-economic programme should be assessed by comparing cost-equivalent alternatives. Despite all gains the “500+” child benefit has brought to millions of families in Poland over the last five years, the flagship programme of the ruling Law and Justice party does not fare well in this perspective. The need for change seems much broader than the reform of the benefit alone. The benefit was introduced on top of two other financial support mechanisms focused on families with children, namely family allowances and child tax credits, and the three elements have been operating in parallel since 2016. A number of suggestions on creating a streamlined, comprehensive system have been made a long time ago (e.g., Myck et al. 2016). However, a major restructuring of the entire support system with clearly defined socio-economic policy goals in mind seems all the more justified now, when many families may require additional assistance due to the difficult financial situation related to the Covid-19 pandemic.

Acknowledgement:

This Policy Brief draws on the CenEA Commentary published on 31.03.2021 (Myck et al. 2021). It has been prepared under the FROGEE project, with financial support from the Swedish International Development Cooperation Agency (Sida). The views presented in the Policy Brief reflect the opinions of the Authors and do not necessarily overlap with the position of the FREE Network or Sida.

References

  • Brzeziński, M., Najsztub, M. 2017. The impact of „Family 500+” programme on household incomes, poverty and inequality”, Polityka Społeczna44(1): 16-25.
  • CenEA 2021. Childcare benefit 500+ in CenEA analyses. https://cenea.org.pl/2021/04/06/childcare-benefit-500-in-cenea-analyses/
  • Magda, I., Brzeziński, M., Chłoń-Domińczak, A., Kotowska, I.E., Myck, M., Najsztub, M., Tyrowicz, J. 2019. „Rodzina 500+– ocena programu i propozycje zmian”. (“Child benefit 500+: the evaluation of the programme and suggestions for changes”), IBS report.
  • Magda, I., Kiełczewska, A., Brandt, N. 2018. “The Effects of Large Universal Child Benefits on Female Labour Supply”, IZA Discussion Paper No. 11652.
  • Myck, M. 2016. “Estimating Labour Supply Response to the Introduction of the Family 500+ Programme”. CenEA Working Paper 1/2016.
  • Myck, M., Król, A., Oczkowska, M., Trzciński, K. 2021. “Świadczenie wychowawcze po pięciu latach: 500 plus ile?”(„The child benefit after 5 years – 500 plus what?”), CenEA Commentary 31/03/2021.
  • Myck, M., Kundera, M., Najsztub, M., Oczkowska, M. 2016. „25 miliardów złotych dla rodzin z dziećmi: projekt Rodzina 500+ i możliwości modyfikacji systemu wsparcia” („25 billion PLN to families with children: Family 500+ programme and possible modifications of the family support system”),  CenEA Commentary 18/01/2016.
  • Myck, M., Oczkowska, M., Trzciński, K. 2020a. “Household exposure to financial risks: the first wave of impact from COVID-19 on the economy”, CenEA Commentary 23/03/2020.
  • Myck, M., Oczkowska, M., Trzciński, K. 2020b. „Kwota wolna od podatku i świadczenie wychowawcze 500+ po pięciu latach od prezydenckich deklaracji” („Tax credit and child benefit 500+ after five years since electoral declarations”, in PL), CenEA Commentary 22/06/2020.
  • Myck, M., Trzciński, K. 2019. “From Partial to Full Universality: The Family 500+ Programme in Poland and its Labor Supply Implications”, ifo DICE Report 17(03), 36-44.
  • Szarfenberg, R. 2017. “Effect of Child Care Benefit (500+) on Poverty Based on Microsimulation”, Polityka Społeczna 44(1): 25-30.

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.

Regional Economic Development Along the Polish-German Border: 1992-2012

Posted on May 3, 2021 | Policy Brief

Image of Europe at night from sky via NASA representing regional economic development

In this brief, we summarize the results of a recent analysis focused on the regional economic development in Poland and Germany along the Oder-Neisse border (Freier, Myck and Najsztub 2021a). Economic activity is approximated by satellite night-time light intensity, a comparable proxy available for regions on both sides of the frontier consistently between 1992 and 2012. This period covers the time of economic transformation and the first eight years of Poland’s membership in the European Union. We find that convergence in overall activity across the border has been complete: Polish municipalities that used to be economically much weaker have caught up with those on the German side of the Oder and the Neisse rivers.

Introduction

The question of the harmonious development of economic activity is at the heart of the European integration project (Art. 2, Treaty of Rome, 1957), and the Maastricht Treaty (1992) made economic convergence between member states an explicit objective. In a forthcoming paper (Freier et al. 2021), we take a new approach to the question of regional European integration.

This brief derives from a recent publication in Applied Economics (Freier et al. 2021a), in which we examine the degree of regional economic convergence along the German-Polish border by taking advantage of satellite night-time illumination data covering the period between 1992 and 2012. The data allows us to study detailed regional patterns of economic development along the river-delimited part of the frontier and further inland.

The seminal work by Henderson et al. (2012) was the first to use night-time light intensity data which covers the entire globe to measure economic activity. Unlike traditional regional economic indicators, light intensity data is independent of administrative border reforms and has been collected in a consistent format over the studied two decades.

Our analysis suggests that, over the analysed period from 1992-2012, there has been essentially full convergence in economic activity between municipalities on both sides of the Polish-German border. While the average value of night-time illumination in our selected group of municipalities in 1992 was 3.7 (on a scale between 0 and 63) in Poland and 7.7 in Germany, the respective values were 9.0 and 9.7 by 2012, and the latter difference is not statistically significant. This convergence suggests a much stronger rate of growth in economic activity on the Polish side of the border. Additionally, we show that within Germany, the distance to the border has much less relevance for economic activity compared to Poland, where it reflects interesting trends. In 1992, Polish towns farther from the border showed significantly higher economic performance. Within Poland, this gap has been greatly reduced over the 20 years we analyse, with regions closer to the border growing much faster compared to those farther away.

Night Lights Along the Polish-German Border

In our dataset, we include municipalities that are located within 100 km from the river delimited part of the PL-DE border. To avoid the sensitivity of the analysis to top censoring of the night-time light intensity data, we removed regional capital cities: Berlin (with surrounding municipalities), Dresden, Gorzów Wielkopolski, and Zielona Góra. This leaves us with 488 municipalities on the German side of the border and 193 municipalities on the Polish side.

The night lights data series, provided by the National Oceanic and Atmospheric Association (NOAA), starts as early as 1992 and continues in a consistent, comparable format to 2012. The data is independent of the administrative structures of local governments, which over time have changed on both sides of the border. This allows us to aggregate the night-time lights information for municipalities using the most recent available administrative borders. This data is essentially the only source of information on economic activity that is consistently available and comparable on both sides of the border over such a long period of time.

The night-time lights data has been applied widely as a proxy of economic development on the country and regional level (Henderson et al., 2012; Bickenbach et al., 2016). Clearly, the intensity of night-time lights does not capture the entire spectrum of economic activity. It has been pointed out that the relationship between night-time light intensity and conventional measures of economic development, such as GDP, is likely to differ depending on a region’s stage of economic development (Hu and Yao, 2019). However, we focus on mostly rural and sparsely populated areas (where there is little risk of top censoring of the data), and compare dynamics between regions that are similar in terms of their stage of economic development, geography, and weather. All these factors support the use of night lights as a proxy for regional development in our application (a number of technical steps are necessary to validate and calibrate the data for use in our analysis, see: Freier et al. 2021).

Economic Convergence Along the PL-DE Border

To understand the overall development of economic activity over the period of interest, we map the changes in the night-time light intensity in Figure 1. The colour scale on the map represents differences in light emissions between 1992 and 2012, with the range going from -40 to 40. A negative value indicates a reduction, and a positive value highlights an increase in light intensity. The negative values have been coloured in a blue-green scale (-40 to 0), while positive values in a red scale (0 to +40).

Figure 1. Night lights: changes in light intensity between 1992 – 2012 along the Polish-German border

Notes: municipalities along the PL-DE river border up to 100 km to the border; municipalities marked in grey treated as outliers and excluded from analysis due to high proportion of top-coded lights pixels in 1992; municipality borders as of 2013 (DE) and 2012 (PL). Source: GeoBasis-DE / BKG 2013, PRG 2012, DMSP OLS v4, OpenStreetMap, own calculations. For details see Freier et al. (2021).

As notable in Figure 1, the red areas are predominant. This exemplifies that between 1992 and 2012, nearly all municipalities in this area witnessed positive economic development as manifested in the intensity of night-time lights. We have a few areas that reflect negative dynamics on the German side of the border. This is mainly due to the regional implications of shutting down activity in agriculture and traditional industries as they were unable to compete with West-German technology and productivity. In Poland, green-blue areas are essentially non-existent, illustrating a universally positive economic development over the studied period. This difference in the pace of changes in light intensity between the German and the Polish side reflects a process of rapid convergence of economic development between municipalities on both sides of the border. These developments are represented in Figure 2 which shows the difference between the night-time light intensity in Germany and Poland by year and provides a test for its statistical significance. The estimation is done on mean log pixel values per municipality and clearly highlights the steep path of convergence. In the early nineties, the difference in mean light intensity was around 100 percent – i.e., the mean difference was as high as the mean level of lights on the Polish side of the border.  Already ten years later it reduced to around 50 percent and disappeared by the end of the analysed period. It is notable that, after an initial steep convergence, the difference in light intensity had a period of stagnation between 2002 and 2008. Interestingly, the full convergence which followed coincides with Poland’s entry into the Schengen agreement in December 2007. As seen in Figure 2, the difference in the average night-time light intensity between Poland and Germany was statistically insignificant and essentially zero since 2009.

Figure 2. Difference in mean night-time lights between Germany and Poland over time

Notes: Difference in log of average pixel values per municipality; year fixed effects included, weighted by municipality area; 95% CI. Source: see Figure 1.

Regional Development and Distance from the Border

Thanks to its high degree of geographical precision, the night-time lights data allows us to study the detailed spatial patterns within each country and, in particular, the relationship between distance to the border and economic activity. This is done by looking across the years 1992 to 2012 and examining three-year windows at each end of the analysed period. Our results, which are reported in Table 1, confirm a strong positive relationship between economic activity and distance to the border on the Polish side of the Oder-Neisse rivers. Overall, Polish regions farther from the border show a greater degree of economic activity, but this relationship has substantially diminished over time. While in Germany, economic activity was higher in regions farther from the border and increasing at the average rate of about 0.3% per km, this rate was about three times higher in Poland, falling from about 1.2% per km in 1992-94 to 0.6% in 2010-2012.

 Table 1. Total night-time lights along the Polish-German border, 1992-2012

Notes: Notes: municipalities along the PL-DE river border up to 100 km to the border; municipality borders as of 2013 (DE) and 2012 (PL); mean municipal total lights calculated using average pixel values per municipality and weighted by municipality area. Standard errors in parentheses, statistical significance: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Source: see Figure 1.

Table 2 reports changes in light intensity between the beginning and the end of a specific period. Here, we find some interesting and perhaps disconcerting results on the relationship between the distance to the border and changes in light intensity. While the distance-to-border coefficient in the Polish case for the full period is negative, suggesting that regions closer to the border were catching up to the more developed regions farther away, the corresponding coefficient for the final three years is positive. This means that, in the years 2010-2012, economic development was faster in municipalities farther away from the border. Although the relationship is not very strong (the change in light intensity grows by about 0.1% per kilometre of distance to the border), it still suggests a reversal in the fortunes of municipalities close to the border on the Polish side. This result points towards the fact that homogeneity of development cannot be taken for granted and that physical distance might continue to play a role in determining the regional rate of growth in the future.

Table 2. Changes in night-time lights along the Polish-German border: 1992-2012

Notes: Notes: municipalities along the PL-DE river border up to 100 km to the border; municipality borders as of 2013 (DE) and 2012 (PL); mean municipal total lights calculated using average pixel values per municipality and weighted by municipality area. Standard errors in parentheses, statistical significance: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Source: see Figure 1.

Conclusion

In this brief, we report results from a forthcoming paper (Freier et al. 2021) in which we evaluate regional development in municipalities on the German and Polish side of the Oder-Neisse border between 1992 and 2012, using night lights data as a proxy for economic activity. We find that driven by rapid growth in Polish municipalities and somewhat sluggish growth in German ones, the light intensity levels across the Oder-Neisse border show no significant differences by the end of our observation period. This is despite significant initial differences just 20 years earlier and the fact that municipalities on the German side also experienced increases in economic activity. In as far as economic development can be proxied by the intensity of night-time illumination, it seems that economic convergence between regions on both sides of the border was complete by 2012.

We also show interesting patterns regarding the relationship between economic activity and distance from the border. For Germany, this relationship is weakly positive and remains stable throughout the analysed period. In Poland, distance is strongly and positively correlated with light emissions at the beginning of the period, hence indicating that municipalities farther from the border show higher average economic activity. By 2012, however, the border regions have closed most of the gap and the distance to the border is a substantially weaker predictor of economic activity, suggesting a much more homogenous pattern of activity.

Acknowledgements

This brief draws on results reported in Freier et al. (2021a). The authors gratefully acknowledge the support of the Polish National Science Centre (NCN), project number: 2016/21/B/HS4/01574. For the full list of acknowledgements and references see Freier et al. (2021a).

References

  • Bickenbach F, Bode E, Nunnenkamp P and Söder M (2016) Night Lights and Regional GDP. Review of World Economics 152(2): 425–47.
  • Freier, R., Myck, M., Najsztub, M (2021a) Lights along the frontier: convergence of economic activity in the proximity of the Polish-German border, 1992-2012. Applied Economics, available online: doi: 10.1080/00036846.2021.1898534.
  • Freier, R., Myck, M., Najsztub, M (2021b) Night lights along the PL-DE border 1992-2012. Dataset used in Freier et al. (2021a), Zenodo, DOI: 10.5281/zenodo.4600685.
  • Henderson JV, Storeygard A and Weil DN (2012) Measuring Economic Growth from Outer Space. American Economic Review 102(2): 994–1028.

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

Energy Storage: Opportunities and Challenges

Posted on April 26, 2021 | Policy Brief

Wind turbines in a sunny desert representing energy storage

As the dramatic consequences of climate change are starting to unfold, addressing the intermittency of low-carbon energy sources, such as solar and wind, is crucial. The obvious solution to intermittency is energy storage. However, its constraints and implications are far from trivial. Developing and facilitating energy storage is associated with technological difficulties as well as economic and regulatory problems that need to be addressed to spur investments and foster competition. With these issues in mind, the annual Energy Talk, organized by the Stockholm Institute of Transition Economics, invited three experts to discuss the challenges and opportunities of energy storage.

Introduction

The intermittency of renewable energy sources poses one of the main challenges in the race against climate change. As the balance between electricity supply and demand must be maintained at all times, a critical step in decarbonizing the global energy sector is to enhance energy storage capacity to compensate for intermittent renewables.

Storage systems create opportunities for new entrants as well as established players in the wind and solar industry. But they also present challenges, particularly in terms of investment and economic impact.

Transitioning towards renewables, adopting green technologies, and developing energy storage can be particularly difficult for emerging economies. Some countries may be forced to clean a carbon-intensive power sector at the expense of economic progress.

The 2021 edition of Energy Talk – an annual seminar organized by the Stockholm Institute of Transition Economics – invited three international experts to discuss the challenges and opportunities of energy storage from a variety of academic and regulatory perspectives. This brief summarizes the main points of the discussion.

A TSO’s Perspective

Niclas Damsgaard, the Chief strategist at Svenska kraftnät, gave a brief overview of the situation from a transmission system operator’s (TSO’s) viewpoint. He highlighted several reasons for a faster, larger-scale, and more variable development of energy storage. For starters, the green transition implies that we are moving towards a power system that requires the supply of electricity to follow the demand to a much larger extent. The fact that the availability of renewable energy is not constant over time makes it crucial to save power when the need for electricity is low and discharge it when demand is high. However, the development and facilitation of energy storage will not happen overnight, and substantial measures on the demand side are also needed to ensure a more dynamic energy system. Indeed, Damsgaard emphasized that demand flexibility constitutes a necessary element in the current decarbonization process. However, with the long-run electrification of the economy (particularly driven by the transition of the transport industry), extensive energy storage will be a necessary complement to demand flexibility.

It is worth mentioning that such electrification is likely to create not only adaptation challenges but also opportunities for the energy systems. For example, the current dramatic decrease in battery costs (around 90% between 2010 and 2020) is, to a significant extent, associated with an increased adoption of electric vehicles.

However, even such a drastic decline in prices may still fall short of fully facilitating the new realities of the fast-changing energy sector. One of the new challenges is the possibility to store energy for extended periods of time, for example, to benefit from the differences in energy demand across months or seasons. Lithium-ion batteries, the dominant battery technology today, work well to store for a few hours or days, but not for longer storage, as such batteries self-discharge over time. Hence, to ensure sufficient long-term storage, more batteries would be needed and the associated cost would be too high, despite the above-mentioned price decrease. Alternative technological solutions may be necessary to resolve this problem.

Energy Storage and Market Structure

As emphasized above, energy storage facilitates the integration of renewables into the power market, reduces the overall cost of generating electricity, and limits carbon-based backup capacities required for the security of supply, creating massive gains for society. However, because the technological costs are still high, it is unclear whether the current economic environment will induce efficient storage. In particular, does the market provide optimal incentives for investment, or is there a need for regulations to ensure this?

Natalia Fabra, Professor of Economics and Head of EnergyEcoLab at Universidad Carlos III de Madrid, shared insights from her (and co-author’s) recent paper that addresses these questions. The paper studies how firms’ incentives to operate and invest in energy storage change when firms in storage and/or production have market power.

Fabra argued that storage pricing depends on how decisions about the storage investment and generation are allocated between the regulator and the firms operating in the storage and generation markets. Comparing different market structures, she showed as market power increases, the aggregate welfare and the consumer surplus decline. Still, even at the highest level of market concentration, an integrated storage-generation monopolist firm, society and consumers are better off than without energy storage.

Fabra’s model also predicts that market power is likely to result in inefficient storage investment.

If the storage market is competitive, firms maximize profits by storing energy when the prices are low and releasing when the prices are high. The free entry condition implies that there are investments in storage capacity as long as the marginal benefit of storage investment is higher than the marginal cost of adding an additional unit of storage. But this precisely reflects the societal gains from storage; so, the competitive market will replicate the regulator solution, and there are no investment distortions.

If there is market power in either generation or storage markets, or both, the investment is no longer efficient. Under market power in generation and perfectly competitive storage, power generating firms will have the incentive to supply less electricity when demand is high and thereby increase the price. As a result, the induced price volatility will inflate arbitrage profits for competitive storage firms, potentially leading to overinvestment.

If the model features a monopolist storage firm interacting with a perfectly competitive power generation market, the effect is reversed. The firm internalizes the price it either buys or sells energy, so profit maximization makes it buy and sell less energy than it would in a competitive market, in the exact same manner as the classical monopolist/monopsonist does. This underutilization of storage leads to underinvestment.

If the model considers a vertically integrated (VI) generation-storage firm with market power in both sectors, the incentives to invest are further weakened: the above-mentioned storage monopolist distortion is exacerbated as storage undermines profits from generation.

Using data on the Spanish electricity market, the study also demonstrated that investments in renewables and storage have a complementary relationship. While storage increases renewables’ profitability by reducing the energy wasted when the availability is excess, renewables increase arbitrage profits due to increased volatility in the price.

In summary, Fabra’s presentation highlighted that the benefits of storage depend significantly on the market power and the ownership structure of storage. Typically, market power in production leads to higher volatility in prices across demand levels; in turn, storage monopolist creates productive inefficiencies, two situations that ultimately translate into higher prices for consumers and a sub-optimal level of investment.

Governments aiming to facilitate the incentives to invest in the energy storage sector should therefore carefully consider the economic and regulatory context of their respective countries, while keeping in mind that an imperfect storage market is better than none at all.

The Russian Context

The last part of the event was devoted to the green transition and the energy storage issue in Eastern Europe, with a specific focus on Russia.

Alexey Khokhlov, Head of the Electric Power Sector at the Energy Center of Moscow School of Management, SKOLKOVO, gave context to Russia’s energy storage issues and prospects. While making up for 3% of global GDP, Russia stands for 10% of the worldwide energy production, which arguably makes it one of the major actors in the global power sector (Global and Russian Energy Outlook, 2016). The country has a unified power system (UPS) interconnected by seven regional facilities constituting 880 powerplants. The system is highly centralized and covers nearly the whole country except for more remote regions in the northeast of Russia, which rely on independent energy systems. The energy production of the UPS is strongly dominated by thermal (59.27%) followed by nuclear (20.60%), hydro (19.81%), wind (0.19%), and solar energy (0.13%). The corresponding ranking in capacity is similar to that of production, except the share of hydro-storage is almost twice as high as nuclear. The percentage of solar and wind of the total energy balance is insignificant

Despite the deterring factors mentioned above, Khokhlov described how the Russian energy sector is transitioning, though at a slow pace, from the traditional centralized carbon-based system towards renewables and distributed energy resources (DER). Specifically, the production of renewables has increased 12-fold over the last five years. The government is exploring the possibilities of expanding as well as integrating already existing (originally industrial) microgrids that generate, store, and load energy, independent from the main grid. These types of small-scaled facilities typically employ a mix of energy sources, although the ones currently installed in Russia are dominated by natural gas. A primary reason for utilizing such localized systems would be for Russia to improve the energy system efficiency. Conventional power systems require extra energy to transmit power across distances. Microgrids, along with other DER’s, do not only offer better opportunities to expand the production of renewables, but their ability to operate autonomously can also help mitigate the pressure on the main grid, reducing the risk for black-outs and raising the feasibility to meet large-scale electrification in the future.

Although decarbonization does not currently seem to be on the top of Russia’s priority list, their plans to decentralize the energy sector on top of the changes in global demand for fossil fuels opens up possibilities to establish a low-carbon energy sector with storage technologies. Russia is currently exploring different technological solutions to the latter. In particular, in 2021, Russia plans to unveil a state-of-the-art solid-mass gravity storage system in Novosibirisk. Other recently commissioned solutions include photovoltaic and hybrid powerplants with integrated energy storage.

Conclusion

There is no doubt that decarbonization of the global energy system, and the role of energy storage, are key in mitigating climate change. However, the webinar highlighted that the challenges of implementing and investing in storage are both vast and heterogenous. Adequate regulation and, potentially, further government involvement is needed to correctly shape incentives for the market participants and get the industry going.

On behalf of the Stockholm Institute of Transition Economics, we would like to thank Niclas Damsgaard, Natalia Fabra, and Alexey Khokhlov for participating in this year’s Energy Talk. The material presented at the webinar can be found here.

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

For a Better Budget Management of Infrastructure Investments

Posted on April 19, 2021 | Policy Brief

Aerial photo of buildings and roads representing infrastructure investments

Many developing countries rely on investment-to-GDP metrics as a sign of progress towards their development goals. Unfortunately, too often the focus on investment pushes aside the issues of adequately maintaining existing infrastructure. The result could be disastrous to human lives, health, and well-being. Lack of maintenance of existing infrastructure is a well-known problem, not only in developing economies but also in some developed countries. However, how much the government should plan to spend on maintenance over the lifetime of infrastructure assets is neither a simple nor straightforward question. In this policy brief, we examine the cases of two transition economies – Georgia and Estonia – and provide a more general discussion of the challenges and possible solutions to infrastructure maintenance issues. We argue that relevant research along with properly aligned incentives could help the countries overcome these problems and optimize infrastructure spending.

Introduction

The efficiency of infrastructure investment has gotten quite some attention in the past years. A recent book by G. Schwartz et al. (2020) shows that countries waste about 1/3 (and some even more) of their infrastructure spending due to inefficiencies. With poor management, the major budgetary efforts undertaken to make room for infrastructure investments go to waste. The question of how much the country should plan to spend on maintenance over the lifetime of infrastructure assets is neither simple nor straightforward. In two recent ISET-PI blog posts, Y. Babych and L. Leruth (2020a, b) stress the importance of striking the right balance between new infrastructure investments and the rehabilitation and maintenance of existing infrastructure. Without this balance, the up-keep of public infrastructure could either be too expensive for the budget to handle, or, at the other extreme, would quickly deteriorate to the point where it is no longer operational and needs to be rebuilt from the ground up (which is the case in many developing countries, including Georgia, Armenia, Ukraine, and others). This policy brief focuses on the reasons why developing (and even some developed) countries tend to invest too little in public infrastructure maintenance and what can be done to solve this problem. We first examine the cases of Georgia and Estonia, two post-Soviet transition economies with different approaches to infrastructure maintenance financing. This analysis is then followed by a more general discussion about the infrastructure maintenance challenges and potential solutions.

Maintenance vs. Investment: the Cases of Georgia and Estonia

Developing countries tend to use investment (public or private) as a share of GDP to measure their economic progress and prospects. Georgia is one of the countries that has invested a lot in public infrastructure. Public investment grew sharply between 2003-2007 to 8% of GDP and settled at 6% of GDP after 2017 (PIMA GEO 2018).  The capital stock is about 90% of GDP. In comparison, in Estonia, another post-Soviet economy, public investment was about 4% of GPD, whereas the capital stock was 57% of GDP in 2015. Yet, the quality of Georgia’s public infrastructure is much lower than in Estonia (Georgia is in 69th place globally according to Global Competitiveness Index 2017-2018, while Estonia is in 32nd place).  The reason for this is quite simple:  management, especially the maintenance of public infrastructure. Both countries recently went through a Public Investment Management Assessment (PIMA), a comprehensive framework developed by the IMF to assess infrastructure governance. The results suggest that Georgia is much weaker than Estonia in planning, budgeting, and maintenance. (A complete summary of the assessment results can be found here).

Georgia’s case is far from unique. The country belongs to the vast majority of emerging economies that have not efficiently linked their medium- and long-term infrastructure plans within a sustainable fiscal framework. Moreover, infrastructure planning deficiencies spread way beyond the emerging markets: Allen et al. (2019) estimate that 56% of all world countries do not have a proper Public Investment Program.

Why is Infrastructure Maintenance a Challenge for Many Countries?

Even though maintenance, rehabilitation, and new investments are intrinsically linked, the practical process of integrating these three infrastructure components is complex. Blazey et al. (2019), for example, identify the following reasons:

  • Political economy reasons—governments will opt for a ribbon-cutting rather than maintaining existing assets;
  • Fiscal reasons—budget funding for operations and maintenance is prone to be cut when fiscal space is limited;
  • Institutional reasons—in many countries, separate agencies still prepare investment and current expenditure budgets;
  • Capacity reasons— up-to-date information on the state of assets may not be readily available.

A number of international studies (usually sectorial) point to the high cost of neglecting maintenance. A study on the upkeep of bridges and roads in the US shows that 1$ of deferred maintenance will cost over 4$ in future repairs. The same holds for airports. In Africa, the World Bank estimates that timely road expenditure of $12 billion spent in the 80s would have saved $45 billion in reconstruction costs during the next decade. It is not only rehabilitation costs that increase with poor maintenance: user costs can increase dramatically (Escobal and Ponce, 2003); health costs in terms of injuries or deaths; and ecological costs (the water lost daily because of leaks could satisfy the needs of 200 million people according to the World Bank, 2006).

Conceptually, however, the link between maintenance, rehabilitation, and new investments is simple to understand. Figure 1 below, adopted from Thi Hoai Le et al. (2019), clarifies this point. As discussed in Babych and Leruth (2020b), when planned maintenance activities (such as planned repair, upkeep, etc.) are insufficient, then the rate at which infrastructure is deteriorating will be high, and the unplanned maintenance costs will increase as well. This response would, in turn, result in a higher total cost. If the amount of planned maintenance activities is excessive, then the unplanned costs may be low, but the total cost is higher than optimal. In order to strike the optimal balance, there need to be just enough planned maintenance activities. 

Figure 1. Optimal zone of maintenance.

Source: Thi Hoai Le et al., (2019).

Conceptually simple maybe, but the devil(s) is (are) in the details. We have already listed above some of the reasons why integration is complex. Data availability is another issue raised by numerous Public Investment Management Assessments made by the IMF. The reporting standards are simply not built in a way that would allow for the compilation of maintenance and rehabilitation data (although aggregate estimates of investment data are available). In any case, the Government Finance Statistics Manual of the IMF (2014) does not separate maintenance expenditure, which is undoubtedly an area that requires further deepening.  More fundamentally perhaps, as pointed out long ago by Schick (1966), there is an additional issue relating to governance philosophy: “planning and budgeting have run separate tracks and have invited different perspectives, the one conservative and negativistic, the other innovative and expansionist …”. Finally, with governments looking for the ‘cheap’ route through public-private partnerships (PPPs) to finance infrastructure development, fiscal risks have increased in advanced and emerging economies in the early 2000s (IMF, 2008). To our knowledge, there have been no systematic assessments of PPP-related fiscal risks since IMF’s report in 2008, but as fiscal positions have deteriorated with the Covid-19 pandemic, PPP projects are likely even riskier today.

What Can Be Done to Improve Infrastructure Maintenance?

Leaving the data, PPPs, and inter-departmental culture issues aside, several considerations that emerge from a closer look at Figure 1 can feed the policy discussions. Let us first consider the notion of planned maintenance (the orange line). In principle, as a project is developed, the cost of maintenance is projected over its life cycle. If the infrastructure is maintained accordingly, its life span may even exceed the projections. At the time the project is conceived, a schedule of maintenance expenditure is also planned and integrated into the analysis. In the figure above, one would expect that these cost assumptions are located in the ‘optimal maintenance zone’ with a limited amount to be spent on unplanned maintenance later on. This level of planned maintenance should then be integrated as a ‘given’ in all subsequent budgets. Usually, as we have already mentioned, it is not.

If we now move to ‘unplanned’ maintenance (the line in blue), we are really referring to situations when infrastructure must be brought back to shape after months (or even years) of neglect. In some cases, this can no longer be labeled as maintenance, and it becomes rehabilitation. Reduce regular maintenance a bit more and the authorities must start over.

Finally, the continuity of the curves is misleading: it is wrong to say that things are necessarily smooth even in the optimal zone.

Let us look more closely at the leading causes and the ways to overcome the problems that arise when optimizing maintenance expenditure.

Setting benchmarks: One explanation for the shortage of maintenance planning outlined above is the lack of information on the practical implementation of such planning.  There are too few studies on maintenance expenditure for policymakers to set benchmarks and develop reliable estimates. The existing studies in this area tend to focus on OECD countries (where data availability is less of a constrain) and on the transportation sector (roads, rail, etc.) perhaps because the private sector is more often involved (see, for example, the American Society of Civil Engineers from 2017, that concluded that 9 percent of all bridges are structurally deficient). Some studies have looked at buildings (e.g., Batalovic et al., 2017 or the Ashrae database, 2021) and unsurprisingly concluded that the age of the construction and its height are significant variables to explain maintenance outlays. However, we are not aware of studies that would, for example, distinguish between different types of maintenance in order to limit overall costs. We are neither aware of studies investigating which organizational arrangements are the most efficient (as discussed by Allen et al., 2019). The bottom line is that there is not much to use as a benchmark, and an effort must be made to build reliable estimates.

Policy dialogue on maintenance is needed:  The abovementioned considerations of the consequences of delayed, unplanned, and sometimes unexpected maintenance bring us to our next point. Things break down when they are not maintained (and sometimes break down when they are maintained too), and such long-term aspects must be more present in the policy dialogue with developing countries. Clearly, delaying maintenance increases fiscal costs in the short- and longer-term (Blazey et al., 2019).

The smoothness of the curves in Figure 1 can be misleading because insufficient maintenance may suddenly trigger a major problem (a bridge or a dam can collapse, as it happened in Italy and in India recently,)  and this will entail high costs, even disasters involving in human lives. The major collapses of nuclear plants (as in Chornobyl, Ukraine, and more recently in Fukushima, Japan) are other examples of the same problem. In addition, studies estimate that poor maintenance of transmission lines could be one of the reasons for electricity blackouts (Yu and Pollitt, 2009). In fact, the lack of maintenance increases the speed at which the value of the existing capital of infrastructure is eroding. While politicians may well hope that this will not happen during their tenure, the probability of a failure increases as maintenance decreases.

On top of the above, inefficiency in maintenance expenditures can be aggravated by wrongly set incentives, both for domestic actors and foreign donors. Indeed, the latter play an important role in infrastructure investment in many developing countries. In Georgia, for example, 40% of infrastructural projects are funded by foreign donors. Setting the right incentives for both parties, as well as their interplay, are thus of immense importance.

Aligning the incentives: Incentives are against maintenance. As pointed out by Babych and Leruth (2020a), capital investment and rehabilitation look good on paper. Maintenance, on the other hand, is considered a current expenditure item in the Government Finance Statistics (GFS) (IMF, 2014). Spending more on maintenance will therefore not look good since 1) more maintenance will reduce government savings in the short term; 2) spending less on maintenance will increase the need for virtuous-looking investment expenditure in the medium and long term. Yet, in spite of the lack of clear benchmarks, donors can play an essential role by stressing the need to systematically integrate maintenance in the budget and in the Medium-Term Expenditure Framework (MTEF). To some extent, it is already the case. In Georgia, projects that are funded by donors tend to follow better appraisal procedures. However, ex-post audits are irregular – e.g., no individual projects audits were completed by State Audit Office during 2015-2017 (PIMA GEO, 2018). If donors could include these audits in their dialogue, it would clearly be helpful. Training subnational governments in proper maintenance management would be even more critical as capacities tend to be weaker than in the center.

Overcoming a potential moral hazard problem of donor involvement: Excessive donor involvement in new investments could also be counterproductive. Donors should carefully examine the need to build new infrastructure and first consider the possibility of performing some rehabilitation while holding the authorities accountable for the maintenance of existing ones. If the authorities are expecting a donor to eventually replace a piece of infrastructure that does not function, the incentives to maintain it are greatly reduced.

Conclusion

  • Developing economies, but also emerging ones like Georgia, as well as Armenia, Ukraine and others, would benefit from proper incentives and support from the international donors to integrate maintenance into the infrastructure planning framework;
  • This is especially important for local governments, who lack the financial and human capital resources to maintain local infrastructure properly, making regions outside of the capital city less attractive places to invest or live in;
  • Given the absence of transparent and comparable sources of information about the composition of maintenance expenditures – for example, the Government Finance Statistics (IMF), which does not distinguish between maintenance and rehabilitation expenditures, – donors could insist that governments compile these expenditures and report on them, at least for the major projects;
  • The culture of maintaining rather than rehabilitating or replacing is directly linked to the sustainable development goals and the circular economy concept. In light of their commitment to Agenda 2030, the international community and the national governments in countries like Georgia should consider prioritizing and implementing the set of reforms suggested in their respective PIMAs.

References

  • Allen, R., M. Betley, C. Renteria and A. Singh, “Integrating Infrastructure Planning and Budgeting,” in Schwartz et al. (2020), pp. 225-244 (2019).
  • American Society of Civil Engineers, Infrastructure Report Card, Reston, Va, (2017).
  • ASHRAE, Purpose of The Service Life and Maintenance Cost Database, available at., (2021).
  • Babych, Y., and L. Leruth, “Tbilisi: a Growing City with Growing Needs,” ISET-PI Blog available at, (2020a).
  • Babych, Y., and L. Leruth, “To Prevent, to Repair, or to Start Over: Should Georgia Put’ Maintenance’ Ahead of ‘Investment’ in Its Development Dictionary?,” ISET-PI Blog available at, (2020b).
  • Batalovic, M., K. SokolijaM. Hadzialic, and N. Batalovic, “Maintenance and Operation Costs Model for University Buildings,” Tehnicki Vjesnik, 23(2), pp. 589-598, (2017).
  • Blazey, A., F. Gonguet, and P. Stokoe, “Maintaining and Managing Public Infrastructure Assets,” in Schwartz et al. (2020), pp. 265-281 (2019).
  • Escobal, J. and C. Ponce, “The Benefits of Rural Roads: Enhancing Income Opportunities for the Rural Poor,” Working Paper 40, Grupo de Analysis Para el Desarrollo (GRADE), Lima, Peru, (2003).
  • IMF, “Fiscal Risks—Sources, Disclosure, and Management,” Fiscal Affairs Department, Washington DC,(2008).
  • IMF, GFS, Government Finance Statistics Manual, IMF, Washington DC, (2014).
  • PIMA EST, Republic of Estonia: Technical Assistance Report-Public Investment Management Assessment, IMF, Washington DC, (2019).
  • PIMA GEO, Republic of Georgia: Technical Assistance Report-Public Investment Management Assessment, IMF, Washington DC, (2018).
  • Rozenberg, J., and M. Fay, eds, “Beyond The Gap: How Countries Can Afford The Infrastructure They Need While Protecting The Planet,” Sustainable Infrastructure Series, The World Bank, Washington DC, (2019)
  • Schick, A., “The Road to PPB: The Stages of Budget Reform,” Public Administration Review, 26(4), pp. 243-258, (1966).
  • Schwartz, G., M. Fouad, T. Hansen, and G. Verdier, Well Spent : How Strong Infrastructure Governance Can End Waste in Public Investment, IMF, Washington DC, (2020).
  • Thi Hoai Le, A., N. Domingo, E. Rasheed, and K. Park, “Building Maintenance Cost Planning and Estimating: A Literature Review,” 34th Annual ARCOM Conference, Belfast, UK (2019).
  • World Bank, The Challenge of Reducing Non-Revenue Water in Developing Countries – How The Private Sector Can Help,” Water Supply and Sanitation Board Discussion Paper Series No 8, Washington DC, (2006).
  • Yu, W., and M. Pollitt, “Does Liberalization Cause More Electricity Blackouts?,” EPRG Working Paper 0827, Energy Policy Research Group, University of Cambridge, United Kingdom, (2009).

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

On Corporate Wrongdoing in Europe and Its Enablers

Posted on April 13, 2021 | Policy Brief

20210413 On Corporate Wrongdoing in Europe FREE Network Image 01

In the last two decades, several instances of prolonged and severe corporate wrongdoing by European companies have come to light: from Dieselgate to corruption, money laundering through large European banks, recidivist bid and price rigging, and most recently Wirecard. What allowed European firms to engage in so much wrongdoing? In this brief, we consider some important institutional drivers behind corporate wrongdoing, focusing on the European countries with the largest share of corporate infringers.

The Harm from and Extent of Corporate Wrongdoing in the EU

In June 2020, the German firm Wirecard AG’s stock price fell from €104 to below €2 in the span of nine days after the firm admitted it could not locate $2 billion missing from its accounts. The firm has since then been accused of a wide range of infringements including money laundering, corruption, and fraudulent inflation of profits and sales, with some allegations going back over a decade. The Germany financial supervisor BaFin has been criticized as allegations about fraud had been made several times in prior years. Yet, BaFin failed to identify the problem and even banned short-selling of the stock, as well as accused journalists who were critical of the firm of market manipulation.

This scandal occurred against a backdrop of several other prolonged corporate scandals and has led many to wonder how extensive corporate wrongdoing is and how to combat it more effectively.

Corporate wrongdoing has a range of negative effects in competitive markets that are frequently overlooked in the public debate. Beyond the immediate damages of corporate wrongdoing, such as the draining of public resources in the case of tax evasion, money laundering, corruption, air pollution and associated health harm in the case of environmental law violations, there are also more general negative effects of corporate wrongdoing.

It attracts investors to the worst part of the industry, as firms that engage in profitable wrongdoing often do better than their competitors. Also, it forces out honest competitors and increases market entry thresholds for new competitors. These effects become more pronounced when the wrongdoing is prolonged, so, in an ideal world, regulators need to act fast.

Instead, several recent cases of European corporate wrongdoing lasted for many years before being detected and sanctioned, and there is a worrying degree of recidivism in several regulatory areas, including financial regulation with several banks being recidivists, but also in antitrust (Marvão, 2016).

What are the drivers and enablers behind these many prolonged cases of wrongdoing, and why do firms feel emboldened to engage in recidivism?

One way to gain some insight is to identify European countries whose firms are most frequently fined for wrongdoing and review the legal, cultural, and political contexts of those countries.

We tackle this issue by using data from Violationtracker, a database with over 400 000 actions by US enforcement agencies and prosecutors (such as the Securities and Exchange Commission and the Department of Justice). Many of these sanctions are against firms with headquarters in EU countries. In Nyreröd and Spagnolo (2021a), we added the fines for firms with headquarters in all respective EU countries for the period 2000-2020. After excluding countries like Switzerland, well known as homes of extensive financial crime linked to their status of international tax havens and off-shore centers, we find that the United Kingdom is the gold medalist in corporate wrongdoing, with Germany coming in second place.

Table 1. Fines across the top six EU countries (2000-2020).

Note: Author’s calculation based on data retrieved from Violationtracker.org. Number of fines in parentheses.

Interestingly, the top of the ranking is preserved no matter which metrics we use. In Nyreröd and Spagnolo (2021a) we weigh the fines by population, GDP, and exports to the US, and the UK and Germany remain stable at the top, with the UK’s first position becoming more pronounced. Therefore, we focus on these two countries, although many of the problems we identify apply to a varying degree to most other EU countries.

Because of the recent headlines made by the Wirecard case we start with the runner-up, Germany.

Germany

The Wirecard case follows a long tradition of large “household” names such as Siemens, Deutsche bank, Thyssenkrupp, and Volkswagen that have engaged in systemic wrongdoing over extended periods of time and are responsible for most of the fines shown in Table 1.

In one of the largest corruption scandals in history, Siemens was fined $1.6 billion by the Department of Justice in 2008 for systematically paying bribes to government officials around the world, amounting to more than $1.4 billion since the mid-1990s. According to the Securities and Exchange Commission’s investigation, bribery at Siemens was “standard operating procedure” for decades, and the SEC concluded that “the company’s tone at the top […] created a corporate culture in which bribery was tolerated and even rewarded at the highest levels of the company”(SEC, 2008).

In 2015 the Dieselgate scandal unraveled, where it was discovered that several car manufacturers had installed “defeat devices” to cheat emissions tests. Volkswagen had installed the device in 11 million vehicles, some of which emitted up to 40 times more than emissions standards allowed (Gates et al, 2017).

Germany’s largest lender Deutsche Bank has since 2000 paid a whopping $18 billion in fines in the US for alleged infringements ranging from facilitating money laundering and tax evasion, to concealing bribe payments and misleading investors (DoJ, 2021). This is by far the greatest amount paid by any EU bank in the period 2000 – 2020 (Violationtracker.org, 2021)..

Finally, there is the steel conglomerate ThyssenKrupp, which was handed a €479 million fine for bid-rigging by the European Commission in 2007, the highest EU bid-rigging fine ever at the time. The size of the fine was motivated by the fact that, in 2007, Thyssenkrupp was already a repeat offender. In 2019, Thyssenkrupp and three other steel manufacturers were fined $719 million for price-rigging between 2002 to 2016. The firm has also been accused of bribe payments on several occasions (see Nyreröd and Spagnolo 2021a for details).

In reviewing local factors that have enabled these incidents, we find that Germany appears to have a particularly lenient stance toward corporate wrongdoing and a notably hard one against whistleblowers disclosing it. With respect to corruption, for example, bribe payments could be deducted from tax in Germany up until 1999 if paid to foreign officials, and up until 2002 if paid to recipients in the business world (Berghoff, 2017). In October of 2003, the United Nations adopted the Convention Against Corruption. On average, European countries had ratified this treaty halfway through 2007, but Germany was one of the last to ratify the treaty, it did it only in 2014 (UNODC, 2020).

Perhaps more importantly, Germany’s institutional environment seems focused on punishing and deterring whistleblowers, rather than listening to their reports in order to fight corporate wrongdoing. This is likely a crucial enabler of the prolonged wrongdoing we discuss in more depth in Nyreröd and Spagnolo (2021a). It is well known that whistleblowers are essential to detecting corporate wrongdoing (ACFE, 2020). Yet, Germany has some of the worst whistleblower protection laws in the EU (Transparency International 2013, Wolfe et al 2014), and one of the worst records in Europe in terms of mistreating the (obviously few) whistleblowers that dared to denounce corporate wrongdoing (Worth 2020a).

The German opposition to the protection of(truth-telling) whistleblowers from employers’ retaliation was on full display when a public consultation was held on the new EU Directive on whistleblower protection (2019/1937). German industry representatives were particularly active in arguing against it, suggesting that whistleblower protection is not necessary and that the new regulations are a clear signal of mistrust towards companies (BDI, 2019). The German parliament discussed improving the poor whistleblower protections in 2013 but did not enact any improvement of whistleblower protection laws. There are several instances of retaliation against truth-telling whistleblowers where they had very little legal recourse (Worth 2020a; Nyreröd and Spagnolo, 2021a).

The hostile regulatory and political environment to whistleblowers is likely a main factor that has enabled so many German corporations to engage in such prolonged wrongdoing with no records of employees reporting it.

The United Kingdom

We now turn to the winner of our contest, the UK. Over $26 billion of the total fines paid by UK firms in Table 1 is accounted for by the British Petroleum’s (BP) Deep Horizon oil spill in 2010 in the Mexican Gulf. It is estimated that 5 million barrels of oil were released into the ocean, a spill regarded as one of the largest environmental disasters in history.

Internal investigations at BP during the decade preceding this spill had warned senior BP managers that the company repeatedly disregarded safety and environmental rules and risked a serious accident if it did not change its ways. A 2004 inquiry found a pattern of intimidating workers who raised safety or environmental concerns (Lustgarten and Knutson, 2010). The company allegedly flouted safety standards by neglecting aging equipment, delayed inspections to cut production costs, and falsified inspection records. Even before the 2010 spill, officials at the US Environmental Protection Agency had considered debarring BP from receiving government contracts (Lustgarten, 2012). Since 2000, BP has been fined 158 times for environment-related offenses in the US, and again over 60 times since the oil spill in 2010.

Then there is the UK banking sector, with many large banks continuously engaging in wrongdoing, and seemingly more so than elsewhere. CASS (2020: 6) shows how, since 2011, the conduct costs of UK banks have far exceeded that of banks based in the US and Euro area when compared to GDP. In 2017, conduct costs for UK banks represented 0.88% of the UK’s annual GDP, while conduct costs for US and Euro area banks represented around 0.10% or less. In 2018, the conduct costs for UK banks shrank and constituted around 0.55% of the UK’s annual GDP.

In 2010, it was discovered that HSBC had systematically laundered money for some of the bloodiest drug cartels in history through its Mexican subsidiary. Despite numerous internal warnings, complaints from regulators, and internal flags, HSBC Mexico continued laundering money for organizations like the Sinaloa cartel, who not only flood the US with illegal drugs but is considered responsible for the gruesome killings of tens of thousands of people, often innocent civilian casualties at home. The UK’s then-chief financial minister, George Osborne, pleaded with the US Treasury Secretary and others that they do not impose criminal sanctions on HSBC (US Congress 2016).

Another major scandal involving UK banks that have cost regular people billions of pounds was the misselling of “payment protection insurance”. This aggressively marketed insurance had profitability of approximately 90% (Laris, 2020). Several barriers were created to inhibit people from claiming the insurance, such as contract exclusions or administrative barriers, and many people who bought these insurances either did not need them or were unsuitable. As of January 2011, UK banks and financial institutions had paid out £37.5 billion in compensation to customers who were wrongly sold the insurance (Coppola, 2019).

One of the main drivers of corporate wrongdoing in the UK appears to have been the lack of effective corporate sanctions. The “identification principle” requires the identification of a directing mind and will of the company (typically a director), and then proving criminal liability through this person’s conduct and state of mind. This principle has been singled out by several experts as making it “impossibly difficulty” for prosecutors to find companies guilty of serious crimes, especially crimes in large companies with devolved business structures (The Law Commission, 2015: 15). Several UK institutions, such as the UK’s Serious Fraud Office and the Crown Prosecution Service, have also pointed to the identification principle as a central hurdle to their ability to bring corporate prosecutions (Corruption Watch, 2019).

Moreover, effective business lobbying and close connection between politicians, regulators and the financial sector have been prevalent in the UK for a long time and may have exacerbated the already accommodating regulatory environment. Several well-known high-level politicians that affected financial regulation and its implementation for years ended up being hired with handsome pay by financial institutions afterwards (see Nyreröd and Spagnolo 2021a for details).

Regarding regulators, Miller & Dinan (2009: 29) notes that of the 36 people that served on the board of the Financial Services Authority (FSA) between 2000 and 2009, 26 of the members had connections at board or senior level with the banking and finance industry either before or after their term of office, whilst nine continued to hold appointments in financial corporations while they were at the FSA”.

The UK also has an outdated and ineffective whistleblower protection law, the “public interest disclosure act” of 1988 (see e.g., Lewis 2008, Thomas Reuter Foundation and Blueprint for Free Speech 2016, All Parliamentary Committee 2020). At the same time, important UK regulatory agencies have been proactive in neglecting the mounting independent academic research highlighting the effectiveness of the US whistleblowers rewards programs (see Nyreröd and Spagnolo 2021b).

Conclusion

Corporate wrongdoing appears widespread in Europe, and recent cases have been prolonged, severe, and sometimes industry-wide.

The UK and Germany stand out, but other EU countries are no angels. In the case of Germany, an acute aversion to whistleblowers by government institutions appears as a central driver that has enabled corporate wrongdoing. With respect to the UK, ineffective corporate sanctions laws, regulatory/political capture, and a lack of whistleblowers, appear to have driven or enabled firms to engage in prolonged corporate wrongdoing. Similar enablers and drivers are likely present in other EU countries to varying degrees.

There is now an EU Directive on whistleblowing, requiring all member states to put in place retaliation protections for those reporting on corporate wrongdoing. But protections have proven insufficient in a variety of ways and are unlikely to be a game-changer in terms of combating corporate wrongdoing (see e.g., GAP and IBA, 2021).

In the light of the strong independent evidence on the effectiveness of whistleblower reward programs at increasing detection and deterring wrongdoing (see, e.g., Nyreröd and Spagnolo 2021b for a survey), EU Member States seriously concerned about corporate wrongdoing should consider introducing them in a wide variety of regulatory areas.

References

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

Does the Russian Stock Market Care About Navalny?

Posted on March 29, 2021 | Policy Brief

Moscow citi in the sunset representing Russian stock market and Navalny

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

Introduction

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

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

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

Navalny in the News

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

Figure 1. Google trends on Navalny

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

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

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

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

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

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

Russian Stock Market Reactions

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

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

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

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

Figure 2. RTS and exogenous factors

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

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

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

Figure 3. Excess returns and Navalny news

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

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

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

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

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

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

Conclusions

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

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

References

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

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