Tag: Russia
Does the Russian Stock Market Care About Navalny?
Alexei Navalny is the most prominent opposition leader in Russia today. During 2020, he entered not only the domestic Russian news flows, but was a major news story around the world following his horrific Novichok poisoning in August. This brief investigates the response in the Russian stock market to news about Navalny. For many significant Navalny news stories, the stock market experienced large negative returns that are not explained by the regular factors that move the market. Although the causality and permanency of these negative excess returns in the stock market are difficult to pin down completely, a first look at the numbers suggests that the short-run drops in the stock market on the days with most significant news regarding Navalny translates into several billion dollars in lost market value on the Russian stock market. In other words, for people that care about their stock market investments and the health of the Russian economy more generally, it makes a lot of sense to care about the health of Navalny.
Introduction
Alexei Navalny has become the leading political opponent to the current regime in Russia. His visibility (and possibly support) has been growing as he has endured poisoning, recovery in hospital, and court rulings that have imposed a harsh prison term. At the same time, Navalny and his team have posted new material online to make his case that both the president and other Russian leaders are seriously corrupt.
The question addressed in this brief is whether the news regarding Navalny affected the Russian stock market. The reasons for such a response may vary between different investors but could include a fear of international sanctions against Russia; an aversion to keeping investments in a country that put a nerve agent in the underwear of a leading opposition leader; or that news of a national security service poisoning one of its own citizens could trigger domestic protests that create instability.
This brief only investigates if Navalny-related news or events are taken into account at the macro level in the stock market and if so, how important the news seem to be relative to other news as drivers of the stock market index. However, there is a long list of related questions that are subjects for upcoming briefs that include differential effects across sectors and companies as well as identifying what dimensions of the news stories investors responded to.
Navalny in the News
Since August 2020, news regarding Alexei Navalny’s health and his role as the most important opposition leader in Russia have featured prominently in media around the world. There are different ways to analyze the significance of Navalny in the news and here the readily available measure provided by Google trends will be used. Figure 1 shows a global search on the keyword “Navalny” over the period July 1, 2020 to March 13, 2021 relative to total searches, where the maximum level in the period is normalized to 100 and other values are scaled to this. While the numbers on the graph are just relative measures, not telling much about the actual popularity, or market relevance of searches, the spikes in Figure 1 have very clear connections to major news stories as will be detailed below.
Figure 1. Google trends on Navalny
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.
Understanding Russia’s GDP Numbers in the COVID-19 Crisis
Russia’s real GDP fell by a modest 3 percent in 2020. The question addressed here is how a major oil-exporting country can go through the COVID-19 pandemic with a decline of this magnitude when oil prices fell by 35 percent at the same time as the domestic economy suffered from lock-downs. The short answer is that it is mainly a statistical mirage. The aggregate real GDP decline would have been almost three times greater than in the official statistics if changes in exports were computed in a way that better reflects their value. In particular, the real GDP calculation uses changes in volumes rather than values to omit inflation, but for exports, it thus ignores large changes in international oil prices. In the end, what the government, companies, and people in Russia can spend is much more closely related to how much money is earned on its exports than how many barrels of oil the country has sold to the rest of the world. More generally, this means that real GDP growth in Russia is not a very useful statistic in years with large changes in oil prices, as was the case in 2020, since it does not properly reflect changes in real income or spending power. When policymakers, journalists, and scholars now start to compare economic developments across countries in the covid-19 pandemic, this is something to bear in mind.
Introduction
The world is closing the books on 2020 and it is time to take stock of the damage done by the COVID-19 pandemic thus far. A year into the pandemic, over 100 million cases have been confirmed and almost 2.5 million people have died worldwide according to ECDC (2021) statistics. Russia has not been spared and Rosstat reported 4 million infected and over 160 000 dead in 2020.
Human suffering in terms of lost health and lives is certainly the main concern in the pandemic, but on top of that comes the damage done to economies around the world. Falling incomes, lost jobs, closed businesses, and sub-par schooling will create significant health and other problems even in a fully vaccinated world for years to come.
Understanding how real GDP has fared in the crisis does not capture all of these aspects, but some. With the IMF’s latest World Economic Outlook update on economic performance out in January 2021, it is easy to start comparing GDP growth across countries (IMF, 2021). GDP growth is a standard measure of past performances in general, but the numbers for 2020 may also enter various domestic and international policy discussions of what does and does not work in protecting economies in the pandemic. For countries that seem to have fared better than their peers, the growth numbers are likely going to be used by incumbent politicians to boost their ratings or by consumers and business leaders making plans for the future.
In short, real GDP numbers are important to most economic and political actors, domestically and globally, with or without a crisis unfolding. It is therefore important to understand how Russia, a major oil exporter with significant losses of lives and incomes in the pandemic, could report a real GDP decline of only 3 percent in 2020 (Rosstat, 2021). Although this is not far from the global average reported by the IMF (2021), it is far better than the 7.2 percent drop in the Euro area, 10 percent fall in the UK, or 7.5 to 8 percent declines of its BRICS peers, South Africa and India. This brief provides the details to understand that Russia’s performance is more of a statistical artifact than a fundamental reflection of the health of the Russian economy.
Oil prices, GDP growth, and the ruble
Russia’s dependence on exporting oil and other natural resources is well documented (see for example Becker, 2016a and 2016b) and often discussed by Russian policymakers and pundits. In particular, changing international oil prices is a key determinant of growth in the Russian economy. Even if the level of real GDP disconnected from oil prices somewhere between 2009 and 2014 (Figure 1), the link between real GDP growth and changes in oil prices persists (Figure 2).
Figure 1. Russia real GDP and oil prices
Source: Author’s calculations based on U.S. Energy Information Administration and Rosstat.
The empirical regularity that still holds is that, on average, a 10 percent increase (decline) in oil prices leads to around 1.4 percent real GDP growth (fall), see Becker (2016a). With a 35 percent decline in oil prices in 2020, this alone would lead to a drop in GDP of around 5 percent.
Figure 2. GDP growth and oil price changes
Source: Author’s calculations based on U.S. Energy Information Administration and Rosstat.
One factor that has a fundamental impact on how the relationship between oil prices and different measures of GDP changes over time is the ruble exchange rate. For a long period, Russia had a fixed exchange rate regime with only occasional adjustments of the rate. A stable exchange rate was the nominal anchor that should instill confidence among consumers and investors. However, when changes in the oil prices were too significant, the exchange rate had to be adjusted to avoid a complete loss of foreign exchange reserves. This was evident in the 90’s with the crisis in 1998 and later in the global financial crisis in 2008/09. Eventually, this led to a flexible exchange rate regime and in 2014, Russia introduced a flexible exchange rate regime together with inflation targeting as many other countries had done before it.
As can be seen in Figure 3, this has important implications for how changes in international oil prices in dollars are translated into rubles. Note that the figure shows index values of the series that are set to 100 in the year 2000 so that values indicate changes from this initial level. Starting in 2011, but more prominently since 2014, the oil price in rubles has been at a significantly higher level compared to the oil price measured in dollars, which is of course due to the ruble depreciating. This affects the government’s budget as well as different measures of income in rubles. However, if oil prices in dollars change, this affects the real spending power of Russian entities compared with economic actors in other countries regardless of the exchange rate regime. Moving to a flexible exchange rate regime was inevitable and the right policy to ensure macroeconomic stability in Russia when oil prices went into free fall. Nevertheless, it does not change the fundamental economic fact that falling oil prices affect the real income of an oil-exporting country. It also makes it even more important to understand how real GDP is calculated.
Figure 3. Oil prices and exchange rate indices
Source: Author’s calculations based on U.S. Energy Information Administration and Central Bank of Russia.
The components of real GPD
GDP is an aggregate number that can be calculated from the income or expenditure side. The focus in this brief is on the expenditure side of GDP. The accounting identity at play is then that GDP is equal to private consumption plus government consumption plus investments (that can be divided into fixed capital investments plus change in inventories) plus exports minus imports (where exports minus imports is also called net exports). Being an accounting identity, it should add up perfectly but in the real world, components on both the income and expenditure sides are estimated and things do not always add up as expected. This generates a statistical discrepancy in empirical data.
Another important note on real GDP (rather than nominal GDP measured in current rubles) is that the focus is on how quantities change rather than prices or ruble values. The idea is of course to get rid of inflation and focus on, for example, how many refrigerators are consumed this year compared to last year and not if the price of refrigerators went up or down. This may sound obvious, but it comes with its own problems concerning implementation and interpretations. For Russia, real GDP becomes problematic because its main export is oil (gas and its related products). The price of oil is just one of many drivers of Russia’s inflation but is an extremely important driver of its export revenues and growth as has been discussed above. On top of that, oil prices are volatile and basically impossible to control for Russia or even the OPEC.
So why does this matter for understanding Russia’s real GDP growth in 2020? The answer lies in how the different components of real GDP are computed. To make this clear, the evolution of the components between 2019 and 2020 is shown in Table 1.
Table 1. Russia’s GDP components from the expenditure side
Source: Author’s calculations based on data from Rosstat
In short, private consumption fell by close to 9 percent in 2020 compared to 2019; government consumption increased by 4 percent; gross fixed capital formation declined by 6 percent while inventories increased by 26 percent; exports lost 5 percent, but imports went down by 14 so that net exports showed an increase of 65 percent! To calculate the impact these changes have on aggregate GDP growth, we need to multiply with the share of GDP for a component to arrive at the impact on GDP growth in the final column of Table 1.
Although there are some issues to resolve with both government consumption and inventory buildup, to understand real GDP growth in 2020, it is crucial to understand what happened to exports and imports in real GDP data. First of all, how does this data compare with the balance of payments data that measures exports and imports in dollar terms or the data that show the value of exports of oil, gas, and related products? Table 2 makes it clear that the numbers do not compare at all! Again, this is due to real GDP numbers being based on changes in volumes rather than values while the trade date reports values in dollars (that can be translated to rubles by using the market exchange rate).
In the real GDP statistics, net exports show growth of 66 percent in 2020, compared to declines of 37 to 44 percent if merchandise trade data is used. Going into more detail, real GDP data has exports declining by 5 percent, while other indicators fall by between 11 and 37 percent. It is similar with imports (that enter the GDP calculation with a negative sign); the import decline recorded in real GDP is 14 percent, while trade data suggest a 6 percent decline in dollar terms but an increase of 7 percent in nominal ruble terms.
Table 2. Trade statistics
Source: Author’s calculations based on Rosstat, Central Bank of Russia and BOFIT
What would it mean if we use some of these alternative growth rates for exports and imports (while keeping other components in line with official statistics) to calculate aggregate GDP growth in 2020? The rationale for keeping other components unchanged is that this provides a first-round effect of changing trade numbers on real GDP growth.
To make this calculation, the GDP shares of exports and imports (or net exports) in 2019 are needed. Table 1 shows that these numbers are 27 and 24 percent (or a net 3 percent) of total GDP. Multiplying the share of a GDP component with its growth rate gives the contribution of the component to overall GDP growth. The calculations based on different trade data are shown in Table 3. The last line of the table is what GDP growth would have been with these alternative trade data. Note that the real GDP growth number is -2.9 percent when we use the individual components of GDP decomposition (rather than the official headline number -3.1 real GDP growth when using aggregate GDP) so this is shown here to make the table consistent with the alternative calculations. In the last column of Table 3, oil and gas exports are assumed to make up for half of exports and this number disregards changes in other exports or imports to isolate the effect of changes in the value of oil and gas exports from other changes.
The summary of this exercise is that with more meaningful trade data used in calculating GDP growth, Russia would have recorded a decline of around 9 percent rather than 3 percent. This is of course a partial analysis focusing on the trade part of real GDP since this effect is very striking. Other components of the calculation may also have issues that need to be adjusted to arrive at a more realistic growth number. Still, even the current estimate is not unrealistic. For example, household consumption fell by around 9 percent, which would be consistent with a GDP decline of 9 percent that is not recovered in the future in a permanent income model.
Table 3. GDP growth contributions from alternative trade data
Source: Author’s calculations based on U.S. Energy Information Administration and Rosstat
Conclusions
Real GDP growth numbers are important to understand economic developments in a country and provide the foundation for many types of economic decisions. The numbers are also used to compare the economic performance of different countries and evaluate policy responses in the COVID-19 pandemic we are currently part of.
The problem with Russia’s reported growth of minus 3 percent is not that the real GDP calculation is wrong per se, but it is clearly the wrong metrics to use for understanding how incomes and purchasing powers of Russian households, companies, and the government changed in 2020. If we instead use trade data that better reflect plummeting oil prices in international markets, alternative estimates of Russia’s real growth show a GDP decline of (at least) 9 percent. This is a three times larger drop than the official number of minus 3 percent. This is important to keep in mind when Russia’s economic performance in the pandemic is compared with other countries or while discussing the economic realities of people living in Russia.
References
- Becker, Torbjörn, 2016a. “Russia’s Oil Dependence and the EU”, SITE Working paper 38.
- Becker, Torbjörn, 2016b. “Russia and Oil — Out of Control”, FREE policy brief.
- BOFIT, 2021, Weekly report 7 on Russia.
- Central Bank of Russia, 2021, data on exchange rates.
- ECDC, 2021, data on covid-19 infections and deaths.
- IMF, 2021, World Economic Outlook update, January.
- Roststat, 2021, Russia GDP data.
- U.S. Energy Information Administration, 2021, data on oil prices.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
The Shadow Economy in Russia: New Estimates and Comparisons with Nearby Countries
We apply a new method to measure the shadow economy in Russia during the period 2017-2018 and provide evidence on the main factors that influence involvement in the shadow economy. Drawing on a methodology developed by Putnins and Sauka (2015), we estimate that the size of the shadow economy in Russia is 44.7% of GDP in 2018. This is similar to the size of the shadow economy in countries such as Kyrgyzstan, Kosovo, Ukraine, and Romania, but higher than the level of the Baltic countries. Our findings are largely consistent with other less direct approaches for estimating the shadow economy. An advantage of our approach is that it can provide more detailed information on the components of the shadow economy.
Introduction and Approach to Measuring the Shadow Economy
The aim of the Shadow Economy Index, which has now been estimated in a number of countries, is to measure the size of shadow economies and explore the main factors that influence participation in the shadow economy. We use the term “shadow economy” to refer to all legal production of goods and services produced by registered firms that is deliberately concealed from public authorities (OECD, 2002; Schneider, Buehn and Montenegro, 2010).
The Shadow Economy Index draws on a survey-based methodology developed by Putnins and Sauka (2015). It combines estimates of business income that has been concealed from authorities, unregistered employees, and ‘envelope’ wages. The approach exploits the fact that entrepreneurs and business leaders are in a unique position in that they have knowledge about the amount of business income that is concealed from authorities, the number of employees that work for them unofficially, and the extent to which they pay wages informally to avoid taxes.
The challenge for such methods is to elicit maximally truthful responses about these sensitive issues, otherwise, the size of the shadow economy will be underestimated. To address this challenge, we use a number of survey and data collection techniques shown in previous studies to be effective in eliciting more truthful responses (e.g. Gerxhani, 2007; Kazemier and van Eck, 1992; Hanousek and Palda, 2004). While the full details can be found in Putnins and Sauka (2015), they include confidentiality with respect to the identities of respondents, framing the survey as a study of satisfaction with government policy, phrasing misreporting questions indirectly about “similar firms in the industry” rather than the respondent’s actual firm, gradually introducing the most sensitive questions after less sensitive questions, excluding inconsistent responses, and controlling for factors that correlate with a potential untruthful response such as tolerance towards misreporting.
The Index measures the size of the shadow economy as a percentage of GDP. Computing the Index involves three steps:
- (i) estimate the degree of underreporting of employee remuneration and underreporting of firms’ operating income using the survey responses;
- (ii) estimate each firm’s shadow production as a weighted average of its underreported employee remuneration and underreported operating income, with weights reflecting the proportions of employee remuneration and firms’ operating income in the composition of GDP; and
- (iii) calculate a production-weighted average of shadow production across firms.
The survey about shadow activity in Russia from 2017 to 2018 was conducted between February and March 2019. We use random stratified sampling to construct samples that are representative of the population of firms in Russia drawing on the official company register and covering all regions in Russia. In total, 500 phone interviews were conducted with owners, directors, and managers of companies in Russia. We use the same methodology to collect data in other countries, which we compare with Russia, conducting a minimum of 500 interviews in each country.
Size of the Shadow Economy in Russia and Nearby Countries
The estimated size of the shadow economy in Russia is 44.7% of GDP in 2018. Our estimates suggest that the year before, in 2017, the shadow economy was slightly larger with 45.8% of GDP, although the annual change is not statistically significant. For comparison with nearby countries, using the same approach, high levels of shadow economy are also found in Kyrgyzstan (44.5% of GDP in 2018), Kosovo (39.5% of GDP in 2018), Ukraine (38.2% of GDP in 2018), and Romania (33.35% of GDP in 2016), but considerably lower levels are found in the Baltic countries, especially Estonia (16.7% of GDP in 2018). See Table 1 for the full set of estimates.
The estimates using our direct micro-level approach to measuring the shadow economy are largely consistent with other less direct approaches for estimating the size of the shadow economies, such as Schneider (2019). An advantage of the direct micro-level approach is that it is able to provide more detailed information on the components of the shadow economy, which we turn to next.
Components and Determinants of the Shadow Economy in Russia
We find that envelope wages and underreporting of business profits stand out as the two largest components of the Russian shadow economy. Underreporting of salaries or so-called ‘envelope wages’ in Russia are approximately 38.7% of the true wage on average in 2018, whereas approximately 33.8% of business income (actual profits) are underreported. Unofficial employees in Russia as a percentage of the actual number of employees are estimated 28.2% in 2018.
Some companies in Russia, rather than simply concealing part of the income or employees, are completely unregistered and therefore also contribute to the shadow economy. We estimate that such companies make up 6.1% of all enterprises in Russia.
Our findings also suggest that there is a very high level of bribery in Russia: the magnitude of bribery (percentage of revenue spent on ‘getting things done’) is estimated to be 26.4%, whereas the percentage of the contract value that firms typically offer as a bribe to secure a contract with the government in Russia is 20.6% in 2018. We also find that more than one-third of companies in Russia pay more than 25% of the revenue or contract value in bribes.
We find that the size of the shadow economy in all sectors of the Russian economy is close to 40% with somewhat higher levels in the construction and wholesale sectors, controlling for other factors. Using regression analysis, we find that entrepreneurs that view tax evasion as a tolerated behaviour tend to engage in more informal activity, as do entrepreneurs that are more dissatisfied with the tax system and the government. This result offers some insights into why the size of the shadow economy in Russia is so large – it is at least in part due to relatively high dissatisfaction of entrepreneurs with the business legislation and the government’s tax policy. We also find some evidence that higher perceived detection probabilities and, in particular, more severe penalties for tax evasion reduce the level of tax evasion, suggesting increased penalties and better detection methods as possible policy tools for reducing the size of the shadow economy.
Finally, while firms of all sizes participate in the shadow economy, we find that younger firms tend to do so to a greater extent than older firms. The results support the notion that young firms use tax evasion as a means of being competitive against larger and more established competitors.
Acknowledgments
This research was supported by a Marie Curie Research and Innovation Staff Exchange scheme within the H2020 Programme (grant acronym: SHADOW, no: 778118).
References
- Gerxhani, K. (2007). “Did you pay your taxes?” How (not) to conduct tax evasion surveys in transition countries. Social Indicators Research 80, pp. 555-581.
- Hanousek, J. and Palda, F. (2004). Quality of government services and the civic duty to pay taxes in the Czech and Slovak Republics, and other transition countries. Kyklos 57, pp. 237-252.
- Kazemier, B. & van Eck, R. (1992). Survey investigations of the hidden economy. Journal of Economic Psychology 13, pp. 569-587.
- Lechmann, E. and D. Nikulin (2017). Shadow Economy Index in Poland. Gdansk University of Technology, Poland: Gdansk.
- Lysa, O. et al. (2019) Shadow Economy Index in Ukraine. SHADOW: an exploration of the nature of informal economies and shadow practices in the former USSR region. Kyiv International Institute of Sociology, Ukraine: Kyiv.
- Mustafa, I., Pula J.S., Krasniqi, B., Sauka, A., Berisha, G., Pula, L., Lajqui, S. and Jahja, S. (2019) Analysis of the Shadow Economy in Kosova. Kosova Academy of Sciences and Arts, Kosova: Pristina.
- OECD, 2002. Measuring the Non-Observed Economy: A Handbook. OECD, Paris, France.
- Putnins, T.J. and Sauka, A. (2019). Shadow Economy Index for the ‘Baltic Countries 2019-2018. SSE Riga: Riga, Latvia.
- Putnins, T.J., A. Sauka and A. Davidescu (2020, forthcoming). Shadow Economy Index for Moldova and Romania, 2015-2018. SSE Riga, National Scientific Research Institute for Labour and Social Protection.
- Putnins, T.J. and Sauka, A. (2015). Measuring the shadow economy using company managers. Journal of Comparative Economics 43, pp. 471-490.
- SIAR (2019). Shadow Economy Index for Kyrgyzstan. SHADOW: an exploration of the nature of informal economies and shadow practices in the former USSR region. SIAR research and consulting, Kyrgyzstan: Bishkek.
- Schneider, F. (2019) Calculation of the Size and Development of the Shadow Economy of 35 Mostly OECD Countries up to 2018. Unpublished manuscript.
- Schneider, F., Buehn, A. and Montenegro, C. (2010). New estimates for the shadow economies all over the world. International Economic Journal 24, pp. 443-461.
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.
A Decade of Russian Cross-Domain Coercion Towards Ukraine: Letting the Data Speak
Russia’s coercion towards Ukraine has been a topic of major international events, meetings and conferences. It regularly makes the headlines of influential news outlets. But the question remains open – do we really understand it? We diligently collect and analyze data to make informed decisions in practically all domestic issues but is the same done for international relations? This research paper introduces a number of tools and methods that could be used to study Russia’s coercion towards Ukraine beyond its most visible manifestation, looking into latent trends and relations that could reveal more.
Introduction
For the past decade, Ukraine has been in the headlines of the major world news outlets more frequently than ever before. Ukrainian-Russian relations have been and still remain the topic of international summits and other events. The occupation of a part of Ukraine’s territory has been denounced and Russia’s coercion towards Ukraine is now generally accepted as a fact. But what do we really know about the underlying empirics and dynamics and how can this multi-domain assertiveness be measured and tracked? This paper presents a number of data-driven approaches that allow looking beyond the headline stories to identify and track various dimensions of Russia’s coercion towards Ukraine and the dynamics of its development.
Academic Interest
Mapping the landscape of scholarly literature reveals a number of interesting results. First, the body of works studying Russia’s coercion towards Ukraine remains relatively modest. It quintupled in 2014 but afterwards the interest started tapering off. A search for papers on this topic in Scopus and Web of Science with a very precise query (to increase the accuracy of search) and publication time of 2009-2019 returned 155 papers most of which were published in or after 2014.
Figure 1. Scholarly publications on Russian-Ukrainian relations.
Source: WoS and Scopus, 2009-2019
A closer look at the content of these works with the use of a bibliometric software called CiteSpace shows that the majority of papers focus on Putin, once again emphasizing the significant role of his personality in Russia’s coercion towards Ukraine. The second largest cluster has the “great power identity” as its main theme and presumably looks beyond actions of Russia to identify its ideological grounds. Another group of publications is devoted to sanctions, pointing to their important role in studying Russian-Ukrainian relations.
Figure 2. The landscape of topics in scholarly publications on Russian-Ukrainian Relations.
Expressions of Coercion
The “practical” side of Russia’s coercion towards Ukraine is also frequently associated with the personality of Vladimir Putin and his attitudes towards Ukraine. To analyze this perception further, we created a corpus of speeches of Russian presidents published on the Kremlin website, filtered them to keep only those that mention Ukraine, divided them into pre-2014 and 2014 and after, and then analyzed them using an LDA topic modeling algorithm. This algorithm is based on the assumption that documents on similar topics use similar words. So, the latent topics that a certain document covers can be identified on the basis of probability distributions over words. Each document covers a number of topics that are derived by analyzing the words that are used in it. In simple terms, the model assigns each word from the document a probabilistic score of the most probable topic that this word could belong to and then groups the documents accordingly.
Figure 3. Speeches of Russian presidents before 2014, LDA topic modeling.
Figure 4. Speeches of Russian presidents in 2014 and after, LDA topic modeling.
Quite surprisingly, we discovered that the overall rhetoric of speeches is very similar for the two periods. Although some speeches do differ and the later corpus includes new vocabulary to reflect some changes (i.e “Crimea”, “war”) the most common words remain practically the same. Thus, regardless of the apparent shift in relations between the two countries, Russian leadership still relies on the same notions of collaboration, interaction, joint activities, etc. The narrative of “brotherhood” between the nations persists despite and beyond the obvious narrative of conflict.
To include a broader circle of Russia’s leadership we also looked at the surveys of the Russian elite conducted regularly by a group of researchers led by William Zimmerman and supported by various funders over the years (in 2016 – the National Science Foundation and the Arthur Levitt Public Affairs Center at Hamilton College). Seven waves of the survey already took place; the most recent one in 2016. The respondents were the representatives of several elite groups (government, including executive and legislative branches, security institutions, such as federal security service, army, militia, private business and state-owned enterprises, media, science and education; for practical reasons from Moscow only).
The survey revealed a number of interesting observations. For instance, while the prevailing Russian opinion on Russia’s occupation of Crimea had been that it was not a violation of international law, a closer look at the demographic characteristics of respondents shows that they were not as coherent as it might seem from the outside. While the “green” answers from respondents with backgrounds such as media or private business may have been anticipated, the number of members of the legislative and especially executive branch and the military that had at least some doubt on the legality was surprisingly quite sizable, and they even demonstrated some support of the “violation of law” interpretation.
Figure 5. Elite and public opinion on Russia’s annexation of Crimea.
Comparing these elite opinions to the public opinion poll by Levada Center conducted in the same year shows that even the general public is slightly more likely to choose the most extreme “full legality” option than the respondents from the executive branch.
Beyond the elite or general opinion polls, we tried to develop a metric that might allow us to track Russian sensitivities towards Ukraine. For that, we examined two different ways of expressing “in Ukraine” in Russian language: ‘на Украине’ (the ‘official’ Russian expression) vs. ‘в Украине’ (the version preferred by Ukrainians). [In English, this can be compared so saying ‘Ukraine’ vs ‘the Ukraine’.]
Our first visual plots how many search queries were done on Google Search with both versions over the last decade.
Figure 6. Search queries for “в Украине” (green) versus “на Украине” (red), Google Trends, 2009-2019.
We can clearly observe that during less turbulent times the more politically sensitive version is much more common. This however drastically changes during the peaks of Russia’s coercion towards Ukraine when the number of searches with the less politically correct term increases significantly.
A different trend can be observed if we look at official media publications stored in the Factiva database. We estimated the ratio of search volumes for each term and observed that until the beginning of 2013, about a third of articles and news reports used “in Ukraine”. This changed around January 2013 when the ratio starts to decrease for “in Ukraine” searches and plummets to a mere 10% of outlets still preferring this term.
Figure 7. The ratio of “в Украине” to “на Украине” occurrences in large Russia media (2009 – 2019), Factiva.
Tracking Coercion Itself
What is the track record of Russia’s actual coercion over this decade? For this, we turn to a few recent datasets that try to systematically capture verbal and material actions (words and deeds): the automated event datasets. The largest one of those, called GDELT (Global Database of Events, Language, and Tone), covers the period from 1979 to the present, and contains over three quarters of a billion events. It is updated every fifteen minutes to include all “events” reported in the world’s various news outlets. To exclude multiple mentions of the same event by one newswire, the events are “internally” deduplicated. The events are not compared across newswires.
An event consists of a “triple” coded automatically to represent the actor (who?), the action (what?) and the target (to whom?) as well as a number of other parameters such as type (verbal or material; conflict or cooperation; diplomatic, informational, security, military, economic), degree of conflict vs cooperation etc. Other similar datasets include ICEWS (Integrated Crisis Early Warning System) and TERRIER (Temporally Extended, Regular, Reproducible International Events Records). For this analysis, we filtered out only those events in which Russia was the source actor and Ukraine was the target country. We present two metrics: (1) the percentage of all world events that this subset of events represents and (2) the monthly averages of the Goldstein score, which captures the degree of cooperation or conflict of an event and can take a value from -10 (most conflict) to +10 (most cooperation). Also, to add a broader temporal perspective, we looked beyond the last decade. It can be clearly seen that the number of events before 2013 was significantly lower, especially in “material” domains. Some verbal assertions from Russia towards Ukraine happened during the Orange Revolution and so-called “gas wars”.
The situation changes radically starting from 2013. The proportion of events increases with some especially evident peaks (i.e. during the occupation of Crimea). The verbal events remain quite neutral while the actions towards Ukraine move from some fluctuations to steadily conflictual.
Figure 8. Russia’s negative assertiveness towards Ukraine, 2000-2019.
Measuring Influence
We have seen that the past decade was exceptional in the scale of Russian assertiveness towards Ukraine. But what do we know about Russia’s influence on Ukraine and Ukraine’s dependence on Russia? Influence measures the capacity of one actor to change the behavior of the other actor in a desired direction. In an international context this often concerns the relations between countries. Influence can be achieved by various means, one of which is to increase the dependence of the target country upon the coercive one. This strategy is frequently employed by Russia willing to regain and/or increase control over the former post-Soviet countries. The Formal Bilateral Influence Capacity (FBIC) Index developed by Frederick S. Pardee (Center for International Future) looks at several diplomatic (i.e. intergovernmental membership), economic (trade, aid) and security (military alliances, arms import) indicators allowing to identify the level of dependence of one country upon another. This is especially interesting from a comparative perspective. Figure 9 shows that countries such as Armenia and Belarus remain highly dependent on Russia. For half of the decade, Ukraine was number three on this list. Today the situation has changed. Ukraine’s dependence on Russia has gradually decreased and has become even smaller than Moldova’s, moving closer to the steadily low level of dependence of Georgia. This may signify a positive trend and a break of a decade-long relationship of dependence.
Figure 9. Dependence of post-Soviet countries on Russia, FBIC.
Conclusion
Consequently, Russia and Ukraine have become much more visible in the international academic and policy research efforts. This can be measured through a number of instruments, including a comprehensive mapping of the academic landscape itself with regard to salience and topics that are being studied, analysis of the word choice (that could be represented by the use of the terms to describe events in Ukraine by the government media and Google search users (“на Украине” versus “в Украине”); speeches of Russian presidents that use the same rhetoric of collaboration when talking about Ukraine despite the obvious change in relationships) and material coercion (significant increase in number of assertive conflictual Russia’s actions towards Ukraine). Some findings do give hope for change: the opinions of the Russian elite on recent Russian actions towards Ukraine while remaining generally unfavourable are not as cohesive as it might appear and Ukraine’s dependence on Russia has decreased significantly.
Disclaimer
This research is a part of a larger research effort titled RuBase funded by the Carnegie Foundation of New York and implemented jointly by The Hague Centre for Strategic Studies and Georgia Tech with the help of the Kyiv School of Economics StratBase team in Ukraine. The ‘Ru’ part of the title stands for Russia; and ‘base’ has a double meaning – both the knowledge base built during the project, and the (aspirationally) foundational nature of this effort. The project intends to look beyond the often-shallow traditional understanding of coercion and apply innovative tools and instruments to study coercion in its multifaceted form. This is only a small selection of the tools that have been successfully tested in the course of this (ongoing) research project and applied to the study of Russia’s coercion in different domains. The prospects of any progress in resolving the Russian-Ukrainian conflict are currently slim, thus further work that would allow identifying patterns and trends that the human eye may oversee to understand Russia better and develop an informed foreign policy strategy both for Ukraine and the West is crucially important.
References
- Boschee, Elizabeth et al. (2019). “ICEWS Automated Daily Event Data.” (November 12, 2019).
- Clarivate Analytics (2019). “Web of Science Core Collection.” Web of Science Group. (January 20, 2020).
- Dow Jones (2019). “Factiva – Global News Monitoring & Search Engine.” Dow Jones. (December 2, 2019).
- Elsevier (2019). “Scopus.” (December 3, 2019).
- Google (2018). “Google Trends – The Homepage Explained – Trends Help.” (January 20, 2020).
- Holynska, Khrystyna, Yevhen Sapolovych, Mikhail Akimov, and Stephan De Spiegeleire (2019). “Events Datasets and Strategic Monitoring: Method Piece” (Forthcoming). The Hague Centre For Strategic Studies.
- Levada-Center (2019). “Levada Center.” (December 3, 2019).
- Moyer, Jonathan D., Tim Sweijs, Mathew J. Burrows, and Hugo Van Manen (2018) “Power and Influence in a Globalized World.” Atlantic Council. (November 26, 2019).
- OU Event Data (2018). “Terrier (Temporally Extended, Regular, Reproducible International Event Records)”. (January 29, 2020).
- The GDELT Project (2015). “GDELT 2.0: Our Global World in Realtime.” GDELT Blog. (October 11, 2018).
- Zimmerman, William, Sharon Werning Rivera, and Kirill Kalinin. (2019). “Survey of Russian Elites, Moscow, Russia, 1993-2016”. Version 6.” (November 26, 2019).
- Президент России (2019). “Президент России.” Президент России. (November 26, 2019).
The Russian Food Embargo: Five Years Later
In this brief, we report the results of a quantitative assessment of the consequences of counter-sanctions introduced by the Russian government in 2014 – Russian food embargo. We consider several affected commodity groups: meat, fish, dairy products, fruit and vegetables. Applying a partial equilibrium analysis to the data from several sources, including Rosstat, Euromonitor, UN Comtrade, industry reviews etc. as of 2018, we obtain that consumers’ total loss amounts to 445 bn Rub, or 3000 Rub per year for each Russian citizen. This is equivalent to a 4.8% increase in food expenditure for those who are close to the poverty line. Out of this amount, 84% is distributed towards producer gains, 3% to importers, while the deadweight loss amounts to 13%. Based on industry dynamics, we identify industries where import substitution policies led to positive developments, industries where these policies failed and group of industries where partial success of import substitution was very costly for consumers.
The full text of the underlying paper is forthcoming in the Journal of the New Economic Association in October 2019.
In August 2014, in response to sectoral sanctions against Russia, the national government issued resolution No. 778, which prohibited import of processed and raw agricultural products from the United States, the EU, Ukraine and a number of other countries (Norway, Canada, Australia, etc.). The goal was to limit market access for countries, which supported sectoral sanctions. The other rhetoric of the counter-sanctions was to support domestic producers via trade restrictions, or by other words – import substitution.
This brief provides an update of welfare analysis of counter-sanctions based on partial equilibrium model of domestic market. The initial estimations based on 2016 data can be found in another FREE Policy Brief here. This time we compare the consumption, outputs and prices of the counter sanctioned goods as of 2018 relative to 2013. The estimated consumer surplus changes, producer gains and prices are reported in Table 1.
Table 1. Welfare effects of counter-sanctions in 2018 relative to 2013.
Data sources: Rosstat, Euromonitor, UN COMTRADE
* Negative losses correspond to gains
** Negative gains correspond to losses
Green color was used to mark the commodity groups with a noticeable consumption growth in 2013-2018 and red color those with consumption decrease.
Effect on production
From the point of view of price dynamics, on the one hand, and consumption and output, on the other, the studied products can be divided into three groups.
The first group which we call “Success of import substitution” includes goods for which real prices (in 2013 level) increased by 2016 but afterwards, the growing domestic production ensured that by 2018 prices fell below the level of 2013 with a corresponding increase in consumption. This group includes tomatoes, pork, poultry and, with some reservation, beef. For beef, growing domestic production pushed prices down after 2016, but the level of consumption and prices have not yet reached the pre-sanction level.
For the second group, import substitution has not resulted in a price decrease, we call this group “Failure of import substitution”. For products in this group, the initial increase in prices by 2016 was not reverted afterwards. Their consumption decreased significantly compared to 2013, and domestic production either continued to fall after 2016, or its growth turned out to be fragile. This group includes apples, cheese, fish, as well as condensed milk and processed meat.
We call the third group “Very expensive import substitution”. It includes fromage, sour milk, milk and (to a lesser extent) butter. This group is characterized by increase in consumption and output in the period 2016–2018, but real prices over this period still remain very high.
Effect on consumers
By comparing the losses and gains of consumers in different categories of goods due to changes in real prices and real consumption, our analysis provides the following monetary equivalents. For all considered counter-sanctioned product groups, with the exception of poultry, pork and tomatoes, consumer losses are around 520 billion rubles per year (in 2013 prices). In three product groups (poultry, pork, tomatoes), in which there was a decrease in prices and a significant increase in consumption, the consumer gains are equivalent to 75 billion rubles per year. Thus, the total negative effect from counter-sanctions for the consumers amounted to 445 billion rubles a year, or about 3000 rubles for a person per year.
Given the cost of the minimum food basket, defined in Russia as 50% of the subsistence level, the impact of counter-sanctions on the budgets of Russian consumers can be estimated as follows. 3000 rubles account for approximately 4.8% of the annual cost of the minimum food basket. The minimum food basket is a set of food products necessary to maintain human health and ensure its vital functions that is established by law. In other words, one can say that 3000 rubles a year are equivalent to a 4.8% increase in food expenditure for those who are close to the poverty line.
Consumer surplus losses were significantly redistributed in favor of domestic production, totaling 374 billion, or 2500 rubles per year per person. Another 56 billion rubles (or 390 rubles per person) correspond to the deadweight loss, i.e., reflect the inefficiency increase of the Russian economy, and 16 billion rubles (110 rubles per person) is the equivalent of redistribution in favor of foreign producers, who get access to Russian market with higher priced products than before counter-sanctions.
Effect on foreign partners
As a result of the selective embargo, the geography of Russian imports of the affected goods has changed. Traditional suppliers of these goods, primarily from Europe, were replaced by suppliers from other countries due to trade diversion. Given the changes in the composition of importers after the imposition of sanctions, we single out countries that have lost and countries that have gained access to the Russian market. We use the change in trade volumes from the respective countries as indicators of growth and decrease in share of these importers in the Russian market. Below we consider in detail the three groups of goods with the largest gains for importers in 2018 compared with 2013: cheese, apples, butter.
Cheese imports decreased significantly after the imposition of counter-sanctions, in 2018 accounting for only 42% of their dollar value in 2013. The total gain of importers due to the growth of domestic prices in 2013-2018 amounted to 17.3 billion rubles (Table 1) and was distributed among following importing countries: Belarus (78%), Argentina (6%), Switzerland (4%), Uruguay (3%), Chile (3%), other countries (6%). Countries that lost their shares of the Russian cheese market included Ukraine, Holland, Germany, Finland, Poland, Lithuania, France, Denmark, Italy and Estonia. As mentioned earlier, domestic production and Belarusian imports were not able to fully compensate for imports from countries on the counter-sanctions list, and in 2016-2018 cheese consumption in Russia decreased significantly.
Apple imports after the initial drop in 2016 partially recovered in 2018, amounting to 66% of their dollar volume in 2013. The total gain of importers in 2018 compared to 2013 amounted to 15.0 billion rubles (Table 1); it was distributed between Serbia (22%), Moldova (19%), China (13%), Turkey (10%), Iran (10%), Azerbaijan (7%), South Africa (4%), Chile (3%), Brazil (3%) and other countries (9%). Poland suffered the most from the ban on apple imports; it accounted for about 80% of all losses. Other losers from counter-sanctions include Italy, Belgium and France. The reorientation of trade flows did not completely replace Polish imports, so apple consumption in 2016-2018 was significantly lower than in 2013.
Imports of butter in 2018 was also below the level of 2013 (67% of dollar value). The gain of importers in 2018 compared to 2013 amounted to 11.2 billion rubles and was distributed among the following trading partners: Belarus (90%), Kazakhstan (4%), Kyrgyzstan (3%) and other countries (3%). Among the countries bearing most of the negative burden of the diversion of trade, one should mention Finland and Australia.
Conclusions
Five year after counter-sanctions were put in place Russian consumers continue paying for them out of their pockets. While few industries have demonstrated a positive effect of import substitution policies, most are not effective enough to revert the price dynamics.
References
- Kuznetsova, Polina; and Natalya Volchkova, 2019. “How Much Do Counter-Sanctions Cost: Welfare Analysis”, Journal of New Economic Association, N3(43), pp 173-183. (in Russian)
Short-Run and Long-Run Effects of Sizeable Child Subsidy: Evidence from Russia
How to design the optimal pro-natalist policy is an important open question for policymakers around the world. Our paper utilizes a large-scale natural experiment aimed to increase fertility in Russia. Motivated by a decade-long decrease in fertility and population, the Russian government introduced a sequence of sizable child subsidies (called Maternity Capitals) in 2007 and 2012. We find that the Maternity Capital resulted in a significant increase in fertility both in the short run and in the long run. The subsidy is conditional and can be used mainly to buy housing. We find that fertility grew faster in regions with a shortage of housing and with a higher ratio of subsidy to housing prices. We also find that the subsidy has a substantial general equilibrium effect. It affected the housing market and family stability. Finally, we show that this government intervention comes at substantial costs.
In all European and Northern American countries the fertility is below the replacement level (United Nations, 2017). Following this concern, most of the developed countries have implemented various large scale and expensive pro-natalist policies. Yet, the effectiveness of these policies is unclear, and the design of the optimal pro-natalist policy remains a challenge.
There are several important open research questions on the evaluation of these programs. The first is whether these programs can induce fertility in the short-run and/or in the long-run horizon. Indeed, very few of these expensive and large-scale policies are proved to be an effective tool to increase fertility (Adda et al, 2017). The next set of questions deals with further evaluation of the programs: What are the characteristics of families that are affected by this policy? How costly is the policy, i.e. how much is the government paying per one birth that is induced by the policy? Finally, what are the non-fertility related effects of these policies? While most of the studies that analyze the effect of pro-natalist policies concentrate on fertility and mothers’ labor market outcomes, these, usually large-scale, policies may have important general equilibrium and multiplier effects that may affect economies both in the short run and long run (Acemoglu, 2010).
In our paper we utilize a natural experiment aimed to increase fertility in Russia to address these questions.
Motivated by a decade-long decrease in fertility and depopulation, the Russian government introduced a sizable conditional child subsidy (called Maternity Capital). The program was implemented in two waves. The first wave, the Federal Maternity Capital program, was enacted in 2007. Starting from 2007, a family that already has at least one child, and gives birth to another, becomes eligible for a one-time subsidy. Its size is approximately 10,000 dollars, which exceeds the country’s average 18-month wage and exceeds the country’s minimum wage over a 10-year period. The recipients of the subsidy can use it only on three options: on housing, the child’s education, and the mother’s pension. Four years later, at the end of 2011, Russian regional governments introduced their own regional maternity programs that give additional – on the top of the federal subsidy – money to families with new-born children.
In our paper, we document that the Maternity Capital program results in a significant increase in fertility rates both in the short run (by 10%) and in the long run (by more than 20%). This effect can be seen from both within-country analysis and from comparing the long-term growth of fertility rates in Russia with Eastern and Central European countries that face similar economic conditions and had similar pre-reform fertility trends. Like Russia, Eastern European countries experienced a drop in fertility rates right after the collapse of the Soviet Union and had similar trends in fertility up until 2007. Our results show that while having similar trends in fertility before 2007, afterward Russia significantly surpassed all the countries from this comparison group.
Figure 1 illustrates the effect of the Maternity Capital on birth rates. The top two panels show monthly birth rates (simple counts and de-seasoned); the bottom panels show total fertility rates in Russia versus Eastern European countries, and versus the European Union and the US.
Figure 1. Total Fertility Rate, Russia, Eastern European countries, USA and EU.
Source: Sorvachev and Yakovlev (2019), and http://www.fertilitydata.org/.
The effects of the policy are not limited to fertility. This policy affects family stability: it results in a reduction in the share of single mothers and in the share of non-married mothers.
Also, the policy affects the housing market. Out of three options (education, housing and pension), 88% of families use Federal Maternity Capital money to buy housing. We find that the supply of new housing and housing prices increased significantly as a result of the program. Confirming a close connection between the housing market and fertility, we find that in regions where the subsidy has a higher value for the housing market, the program has a larger effect: the effect of maternity capital was stronger, both in the short run and long run, in regions with a shortage of housing, and in regions with a higher ratio of subsidy to price of apartments (i.e. those regions where the real price of subsidy as measured in square meters of housing is higher).
Figure 2 below shows the effect of Federal Maternity Capital on birth rates in different regions. It shows no effect on fertility in Moscow, small effect in Saint-Petersburg; whereas the sizable effect of maternity capital in other Russian regions.
Figure 2. Effect of Federal Maternity capital, by regions
Source: Sorvachev and Yakovlev (2019), and http://www.gks.ru/.
These results suggest that cost-benefit analysis of such policies should go beyond the short-run and long-run effects on fertility. Ignoring general equilibrium issues may result in substantial bias in the evaluation of both short-run and long-run costs and benefits of the program.
While there are many benefits of the program, we show that this government intervention comes at substantial costs: the government’s willingness to pay for an additional birth induced by the program equals approximately 50,000 dollars.[1]
For more detailed evaluation of the results see Evgeny Yakovlev and Ilia Sorvachev, 2019, “Short-Run and Long-Run Effects of Sizable Child Subsidy: Evidence from Russia”, NES working Paper # 254, 2019.
References
- Acemoglu, Daron 2010 “Theory, General Equilibrium, Political Economy and Empirics in Development Economics”, Journal of Economic Perspectives, 24(3), pp. 17-32. 2010
- Adda, Jérôme, Christian Dustmann and Katrien Stevens 2017. “The Career Costs of Children”. Journal of Political Economy, 125, 2, 293-337.
- Ilia Sorvachev and Evgeny Yakovlev, 2019, “Short-Run and Long-Run Effects of Sizable Child Subsidy: Evidence from Russia”, NES working Paper #254 and LSE IGA Research Working Paper Series 8/2019
[1] Roughly, the WTP (US$50,000) exceeds nominal US$10,000 subsidy because the government pays for all (100%) families that give birth to a child to induce additional (20%) increase in fertility. See paper for more accurate elaboration.
Social Media and Xenophobia
We study the causal effect of social media on hate crimes and xenophobic attitudes in Russia, using variation in social media penetration across cities. We find that higher penetration of social media leads to more ethnic hate crimes, but only in cities with a high baseline level of nationalist sentiment prior to the introduction of social media. Consistent with a mechanism for the coordination of crimes, the effects are stronger for crimes with multiple perpetrators. We show that social media penetration also had a persuasive effect on young and uneducated individuals, who became more likely to have xenophobic attitudes.
In recent years, the world has witnessed a large increase in expressions of hate, particularly of xenophobia. Candidates and platforms endorsing nationalism and views associated with intolerance toward specific groups have also gathered increased popular support both in the U.S. and across Europe. There is a lot of speculation about the potential drivers of this increase in the expression of hate. In our recent paper (Enikolopov et al, 2019) we study the role of social media in this process. This brief introduces the topic and offers a short outline of our findings.
Conceptually, social media could foster hate being expressed through different channels. First, social media reduces the cost of coordination. For example, there is evidence that it facilitates political protest (Enikolopov, Makarin, Petrova, 2018). Coordination facilitated through social media might be particularly relevant for illegal and stigmatized activities, such as hate crime: social media might make it easier to find like-minded people (through targeted communities and groups); it might also reduce the cost of asking or exposing oneself by providing a more anonymous forum for social interactions. Social media might also influence people’s opinions: tolerant individuals might be more exposed to intolerant views, while intolerant individuals might end up in an “echo chamber” (Sunstein 2001, 2017, Settle 2018) that make their views even more extreme. In our paper, we study the causal effect of social media exposure on xenophobic crimes and xenophobic attitudes in Russia and provide evidence on the particular mechanisms behind these effects.
The challenge in identifying a causal effect of social media is that access and consumption of social media are not randomly assigned. To surmount this challenge, we follow the approach of Enikolopov et al. (2018) and exploit a feature of the introduction of the main Russian social media platform – VKontakte (VK). This social media, which is analogous to Facebook in functionality, was the first mover on the Russian market and secured its dominant position with a user share of over 90% by 2011. VK was launched in October 2006 by Pavel Durov, its founder, who at that time was an undergraduate student at St Petersburg State University (SPbSU). Initially, users could only join the platform by invitation, through a student forum of the University, which was also created by Durov.
As a result, the vast majority of the early users of VK were students of SPbSU. This, in turn, made their friends and relatives more likely to open an account. And since SPbSU attracted students from around the country, this sped up the development of VK in the cities, from which these students were coming from. Network externalities magnified these effects and, as a result, the idiosyncratic variation in the distribution of the home cities of Durov’s classmates had a long-lasting effect on VK penetration. Following this logic, we use fluctuations in the distribution of student of SPbSU across cities as an instrument for the city-level penetration of VK. We then evaluate the effect of higher VK penetration on hate crimes and hate attitudes, combining data on hate crimes for the period between 2007 and 2015 collected by a reputable Russian NGO SOVA with survey data on hate attitudes.
Previous findings indicate that whether information from media induces people to be involved in the active manifestation of xenophobic attitudes or not depends on predispositions of the population. For example, Adena et al (2015) demonstrate that radio propaganda by the Nazis in the 1930’s was effective only in areas with a historically high levels of anti-Semitism. The role of the underlying level of nationalism is likely to be even stronger for social media, in which the content of the media itself directly reflects the attitudes of the population. This is particularly relevant for hate crimes committed by multiple perpetrators, in which social media can facilitate the coordination of such crimes.
Thus, we test whether the effect of social media depends on the pre-existing level of nationalism. To get at this underlying sentiment, we break cities by their level of support for the Rodina (“Motherland”) party, which ran in the national 2003 elections (the last parliamentary elections before the creation of VK) on an explicit nationalistic, xenophobic platform.
We find that penetration of social media leads to more ethnic hate crimes, but only in cities with a high baseline level of nationalist sentiment prior to the introduction of social media. For example, in cities with a maximum level of support of Rodina an increase in the number of VK users by 10% lead to an increase in ethnic hate crimes by 20%, while it had no significant effect on hater crime in cities with minimal support of Rodina. There is also no evidence that future social media penetration is related to ethnic hate crimes before the creation of social media, regardless of the level of pre-existing nationalistic attitudes.
Further evidence is consistent with social media playing a coordination role in hate crimes. The effect of social media is stronger for crimes perpetrated by multiple individuals (as opposed to crimes committed by a single person), where coordination is more important. These heterogeneous effects are also not consistent with results being simply driven by a higher likelihood of hate crime in places with higher social media penetration, unless this effect were present precisely in cities with higher support for Rodina and for crimes with multiple perpetrators, for example – which we find unlikely.
Having found evidence of a causal effect of social media on ethnic hate crimes, consistent with a mechanism of coordination, we turn next to the impact of social media on xenophobic attitudes. We designed and organized an online survey, and launched it in the summer of 2018, reaching 4,327 respondents from 64 cities. To measure xenophobic attitudes, we examined answers to the question “Do you feel irritation of dislike for individuals from some other ethnicities?” Note that, unlike the coordination of hate crimes, the persuasive effects of social media are not necessarily expected to be strongest in cities with higher baseline nationalistic sentiment since individuals on social media can get as easily connected to people outside their city. In fact, it is conceptually possible that the persuasion would be stronger in cities with lower baseline nationalistic sentiment: individuals might have previously been less aware of and less exposed to these types of views before the introduction of social media.
Since there might be a stigma in reporting xenophobic attitudes even in anonymous surveys, we use a “list experiment” to approximate “truly-held” xenophobic attitudes. In particular, the list experiment works as follows: first, respondents are randomly assigned either into a control group or a treatment group. Respondents in all groups are asked to indicate the number of policy positions they support from a list of positions on several issues. Support for any particular policy position is never indicated, only the total number of positions articulated on the list that a respondent supports. In the control group, the list includes a set of contentious, but not stigmatized, opinions. In the treatment group, the list includes all the contentious opinions from the control list, but also adds the opinion of interest, which is potentially stigmatized. The degree of support for the stigmatized opinion can be assessed by comparing the average number of issues supported in the treatment and control groups. The question of interest, randomly added to half of the questionnaires, was “Do you feel irritation of dislike for individuals from some other ethnicities?”.
The results indicate that the average share of people who agree with the statement is 37%. While there is no significant effect of social media penetration on xenophobic attitudes for the whole sample, there is a significant effect for important subsamples, which are at a higher risk of being involved in hate crime, such as respondents with lower levels of education or young respondents. Of course, the individuals that became more likely to engage in hate crime are not necessarily the same that have been persuaded to have more xenophobic attitudes (especially given the question used to assess attitudes) – though it is possible that some individuals who would have been close to committing crimes in the absence of social media might have been persuaded enough to switch their behavior in the presence of social media.
At the same time, we do not find that social media leads to an increase in xenophobic attitudes when measured with a direct question. The results are confirmed if we use a much larger, nationally representative survey of more than 30,000 respondents conducted by one of the biggest Russian survey companies FOM in 2011. In principle, it is possible that social media not only changed real attitudes but also the perception of the social acceptability of expressing these attitudes. However, we do not find any evidence that social media reduces the stigma of admitting xenophobic attitudes. The fact that we find the effect of social media on actual attitudes, but not on the expressed ones suggests, that if anything the stigma increased, at least for the respondents who acquired xenophobic attitudes as a result of social media influence. This highlights the importance of using a survey method that reduces concerns with social acceptability, such as list experiments.
Overall, our results indicate that social media lead to an increase in both ethnic hate crimes and xenophobic attitudes in Russia. However, the effect on hate crime is observed only in cities in which there was already a high level of nationalism. Additional evidence indicates that this effect is driven both by facilitating the coordination of nationalists and by persuading people to become more xenophobic. These findings contribute to a growing body of evidence that social media is a complex phenomenon that has both positive and negative effects on the welfare of people (see also Allcott et al, 2019), which has to be taken into account in discussing policy implications of the recent changes in media technologies.
References
- Allcott, H., Braghieri, L., Eichmeyer, S., Gentzkow, M. (2019) “The Welfare Effects of Social Media”, Working paper.
- Burzstyn, L., Egorov, G., Enikolopov, R., Makarin, A. (2019) “Social Media and Xenophobia: Evidence from Russia”, Working paper.
- Enikolopov, R., Makarin, A., Petrova, M. (2018) “Social Media and Protest Participation: Evidence from Russia“, Working paper.
- Settle, J. E. (2018) Frenemies: How Social Media Polarizes America. Cambridge University Press.
- Sunstein, C. (2001) Republic.com. Princeton University Press.
- Sunstein, C. (2017) Republic: Divided Democracy in the Age of Social Media. Princeton University Press.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Russia’s Real Cost of Crimean Uncertainty
The annexation of Crimea has real costs to the Russian economy beyond what is measured by some items in the armed forces’ budget; social spending in the occupied territories; or the cost of building a rather extreme bridge to solve logistics issues. Russia’s real cost of the annexation of Crimea is also associated with the permanent loss of income that the entire Russian population is experiencing due to increased uncertainty, reduced capital flows and investment, and thus a growth rate that is significantly lower than it would have been otherwise. Since the years of lost growth are extremely hard to make up for in later years, there will be a permanent loss of income in Russia that is a significant part of the real cost of annexing Crimea and continuing the fighting in Eastern Ukraine. It is time to stop not only the human bleeding associated with Ukraine, but also the economic.
Estimating the real cost of Russia’s annexation of Crimea and the continued involvement in Eastern Ukraine is complicated since there are many other things going on in the Russian economy at the same time. In particular, oil prices fell from over $100/barrel in late 2013 to $30/barrel in 2016 (Figure 1). Becker (2016) has shown that 60-80 percent of the variation in GDP growth can be explained by changes in oil prices, so this makes it hard to just look at actual data on growth to assess the impact of Crimea and subsequent sanctions and counter sanctions.
Figure 1. Russian GDP and oil price
Source: Becker (2019)
The approach here is instead to focus on one channel that is likely to be important for growth in these circumstances, which is uncertainty and its impact on capital flows and investment.
From uncertainty to growth
The analysis presented here is based on several steps that link uncertainty to GDP growth. All the details of the steps in this analysis are explained at some length in Becker (2019). Although this brief will focus on the main assumptions and estimates that are needed to arrive at the real cost of Crimea, a short description of the steps is as follows.
First of all, in line with basic models of capital flows, investors that can move their money across different markets (here countries) will look at relative returns and volatility between different markets. When relative uncertainty goes up in one market, capital will leave that market.
The next step is that international capital flows affect investment in the domestic market. If capital leaves a country, less money will be available for fixed capital investments.
The final step is that domestic investments is important for growth. Mechanically, in a static, national accounts setting, if investments go down, so does GDP. More long term and dynamically, investments have a supply side effect on growth, and if investments are low, this will affect potential as well as actual growth negatively.
These steps are rather straightforward and saying that uncertainty created by the annexation of Crimea leads to lower growth is trivial. What is not trivial is to provide an actual number on how much growth may have been affected. This requires estimates of a number of coefficients that is the empirical counterparts to the theoretical steps outlined here.
Estimates to link uncertainty to growth
In short, we need three coefficients that link: domestic investments to growth; capital flows to domestic investments; and uncertainty to capital flows.
There are many studies that look at the determinants of growth, so there are plenty of estimates on the first of these coefficients. Here we will use the estimate of Levine and Renelt (1992), that focus on finding robust determinants of growth from a large set of potential explanatory variables. In their preferred specification, growth is explained well by four variables, initial income, population growth, secondary education and the investments to GDP ratio. The coefficient on the latter is 17.5, which means that when the investment to GDP ratio increases by 10 percentage points, GDP grows an extra 1.75 percentage points per year. Becker and Olofsgård (2018) have shown that this model explains the growth experience of 25 transition countries including Russia since 2000 very well, which makes this estimate relevant for the current calculation.
The next coefficient links capital flows to domestic investments. This is also a subject that has been studied in many empirical papers. Recent estimates for transition countries and Russia in Mileva (2008) and Becker (2019) find an effect of FDI on domestic investments that is larger than one, i.e., there are positive spillovers from FDI inflows to domestic investments. Here we will use the estimate from Becker (2019) that finds that 10 extra dollars of FDI inflows are associated with an increase of domestic investments of 15 dollars.
Finally, we need an estimate linking uncertainty with capital flows. There are many studies looking at risk, return and investment in general, and also several studies focusing on international capital flows and uncertainty. Julio and Yook (2016) look at how political uncertainty around elections affect FDI of US firms and find that FDI to countries with high institutional quality is less affected by electoral uncertainty than others. Becker (2019) estimates how volatility in the Russian stock market index RTS relative to the volatility in the US market’s S&P 500 is associated with net private capital outflows. The estimate suggests that when volatility in the RTS goes up by one standard deviation, this is associated with net private capital outflows of $30 billion.
These estimates now only need one more thing to allow us to estimate how much Crimean uncertainty has impacted growth and this is a measure of the volatility that was created by the annexation of Crimea.
Measuring Crimean uncertainty
In Becker (2019), the measure of volatility that is used in the regression with net capital outflows is the 60-day volatility of the RTS index. Since we now want to isolate the uncertainty created by Crimea related events, we need to take out the volatility that can be explained by other factors in order to arrive at a volatility measure that captures Crimean induced uncertainty. In Becker (2019) this is done by running a regression of RTS volatility on the volatility of international oil prices and the US stock market as represented by the S&P 500. The residual that remains after this regression is the excess volatility of the RTS that cannot be explained by these two external factors. The excess volatility of the RTS index is shown in figure 2.
It is clear that the major peaks in excess volatility are linked to Crimea related events, and in particular to the sanctions introduced at various points in time. From March 2014 to March 2015, there is an average excess volatility of 0.73 standard deviations with a peak of almost 4 when the EU and the USA ban trade with Crimea. This excess volatility is our measure of the uncertainty created by the annexation of Crimea.
Figure 2. RTS excess volatility
Source: Becker (2019)
From Crimean uncertainty to growth
The final step is simply to use our measure of Crimean induced uncertainty together with the estimates that link uncertainty in general to growth.
The estimated excess volatility associated with Crimea is conservatively estimated at 0.7 standard deviations. Using this with the estimate that increasing volatility by one standard deviation is associated with $30 billion in capital outflows, we get that the Crimean uncertainty would lead to $21 billions of capital outflows in one quarter or $84 billions in one year. If this is in the form of reduced FDI flows, we have estimated that this means that domestic investments would fall by a factor of 1.5 or $126 billions.
In this period, Russia had a GDP of $1849bn and fixed capital investments of $392bn. This means that $126 billions in reduced investments correspond to a reduction in the investments to GDP ratio of 7 percentage points (or that the investments to GDP ratio goes from around 21 percent to 14 percent).
Finally, using the estimate of 17.5 from Levine and Renelt, this implies that GDP growth would have been 1.2 percentage points higher without the estimated decline in investments to GDP.
In other words, the Crimean induced uncertainty is estimated to have led to a significant loss of growth that has to be added to all the other costs of the annexation of Crimea and continued fighting in Eastern Ukraine. Note that recent growth in Russia has been just barely above 1 percent per year, so this means that growth has been cut in half by this self-generated uncertainty.
Of course, the 1.2 percentage point estimate of lost growth is based on many model assumptions, but it provides a more sensible estimate of the cost of Crimea than we can get by looking at actual data that is a mix of many other factors that have impacted capital flows, investments and growth over this period.
Policy conclusions
The annexation of Crimea and continued fighting in Eastern Ukraine carry great costs in terms of human suffering. In addition, they also carry real costs to the Russian economy. Not least to people in Russia that see that their incomes are not growing in line with other countries in the world while the value of their rubles has been cut in half. Some of this is due to falling oil prices and other global factors that require reforms that will reorient the economy from natural resource extraction to a more diversified base of income generation. This process will take time even in the best of worlds.
However, one “reform” that can be implemented over night is to stop the fighting in Eastern Ukraine and work with Ukraine and other parties to get out of the current situation of sanctions and counter-sanctions. This would provide a much-needed boost to foreign and domestic investments required to generate high, sustainable growth to the benefit of many Russians as well as neighboring countries looking for a strong economy to do trade and business with.
References
- Becker, T, (2019), “Russia’s macroeconomy—a closer look at growth, investment, and uncertainty”, forthcoming SITE Working paper.
- Becker, T. and A. Olofsgård, (2018), “From abnormal to normal—Two tales of growth from 25 years of transition”, Economics of Transition, vol. 26, issue 4.
- Becker, T. (2016), “Russia and Oil – Out of Control”, FREE policy brief, October.
- Julio, B. and Yook, Y. (2016), ‘Policy uncertainty, irreversibility, and cross-border flows of capital’, Journal of International Economics, Vol. 103, pp. 13-26.
- Levine, R. and Renelt, D. (1992). ‘A Sensitivity Analysis of Cross-Country Growth Regressions’, American Economic Review, 82(4), pp. 942–963.
- Mileva, E. (2008), ‘The Impact of Capital Flows on Domestic Investment in Transition Economies, ECB Working Paper No. 871, February.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Economic Growth and Putin’s Approval Ratings —The Return of the Fridge
This brief discusses how the approval ratings of president Putin covaries with economic growth. In most years the relationship between approval ratings for Putin and growth looks like approval ratings for politicians in most countries so that when growth is higher, the president is more popular. Or to use an American expression “it’s the economy stupid”. The caveat in Russia is that external events at times overshadow the importance of growth to the extent that the president’s ratings stay high and can even go up despite a faltering economy. In a time of low Russian growth, this is not good news for geopolitics unless Putin can be convinced to focus on policies that generate high, sustainable growth instead of international turbulence. That said, it is clear that poor economic growth carries a political cost also in Russia. The only sustainable way of maintaining high approval ratings for the president is by fostering economic growth since in the contest between “the TV and the fridge”, the fridge will eventually win.
Russia is a complex country and culture. Instead of the simple American saying “it’s the economy stupid”, Russians talk about “the TV vs the fridge”. This translates into that concerns about the economic situation can be made irrelevant by propaganda so that voters turn their eyes away from the half-empty fridge to follow how Russia’s armed forces fight the enemy in foreign countries.
The propaganda messages have of course varied over the years, but it seems that external enemies that threaten the nation are at the heart of many of the messages. This theme in propaganda is of course not unique to Russia, but it seems to carry more weight in Russia than in other countries.
The observation that propaganda is used and that it seems to work to a relatively large extent at times can lead to the conclusion that “it’s not the economy stupid” when it comes to approval ratings of the Russian leadership.
This observation is tempting as another piece of evidence on how Russia is different and unique, but this brief will show that in most times, it is indeed “the economy stupid” also in Russia.
Putin’s ratings and growth
The idea that Russia is different in that growth would not be important for the president’s approval rating can be justified empirically when we look at the full series of approval ratings of Putin as measured by the Levada center and corresponding quarterly growth rates going back to 1999 (Figure 1).
Instead of showing a strong positive correlation as we would expect, the correlation is negative 0.3. However, a more careful look at the observations in the scatter plot suggests that there are a few clusters of observations that create this negative correlation. In the figure, three distinct clusters are marked; first there is the period when Russia rebounded strongly from the 1998 crisis in 1999-2000, with growth rates that have not been seen before or after that time in Russia. The growth was an artefact of the previous massive decline in income in combination with a large devaluation, and later followed by oil price increases. This happened in Putin’s initial time in the highest offices when he was prime minister, interim president and then elected president. Although Putin enjoyed high ratings as a consequence, it was not in line with the extreme growth rates that were the result of events preceding his tenure and can thus be regarded as outliers.
The second cluster is related to the global financial crisis in 2008/09 when Russian growth took a major hit as oil prices collapsed and the exchange rate was not allowed to appreciate correspondingly. However, this crisis was blamed (as in many other countries) on the US and the West and did not cost Putin in terms of approval ratings.
The final cluster is related to the annexation of Crimea and ongoing involvement in the conflict in Eastern Ukraine. This period also coincides with a sharp drop in oil prices that taken together led to negative growth that then remained low for a prolonged period. We should note that before the annexation of Crimea, growth rates in 2013 were very low at 1-2 percent with approval ratings going down to 63 percent, which was an all-time low since Putin’s first year in office.
Figure 1. Ratings and growth
Source: Becker (2019)
If we purge the data from the three exceptional episodes that we have identified above, we get Figure 2. Note that the scale has not been changed from Figure 1. Now there are no observations of negative growth rates, but the distribution of growth rates is still rather spread out, going from around 1 to 9 percent growth. The spread of the growth distribution is important since it allows us to identify the relationship between growth and approval ratings more clearly.
The relationship between approval ratings and growth in Figure 2 is strongly positive with a correlation coefficient of 0.7, and in line with what we would expect in other countries. This is a quite remarkable shift from the negative correlation in Figure 1. Note that if approval ratings in 2014 had been behaving as in “normal” years, the regression line would have put them around 60 percent instead of the actual approval rating that peaked at 86 percent after the annexation of Crimea. Such is the strength of the TV.
Figure 2. Ratings and growth
Source: Becker (2019)
This is very clear evidence that Russia is a “normal” country in “normal” times, but that there are also times when other forces overshadow this normalcy.
Policy conclusions
Are there any policy conclusions that can be drawn from the stark contrast between figures 1 and 2? The answer is a very clear “yes”, both for the Russian leadership but also for the rest of the world that has economic interests and security concerns with Russia.
For the Russian president, the message is that it pays in terms of high approval ratings to generate growth and “keeping the fridge well stocked”. It is also clear that the high popularity rating that was seen after the annexation of Crimea has been followed by several years of poor growth. A forthcoming brief discusses how the increased uncertainty created by this event has led to lower capital inflows, lower domestic investments and lower growth.
Not surprisingly, the sustained low growth has started to show in terms of falling approval ratings. The polls at the end of 2018 and early 2019 (for when there is not yet data on growth rates) indicate a significant decline in approval ratings, down to 64 percent from over 80 percent at the end of 2017. This is linked to protests over pension reforms, but they in turn are a result of lower government revenues in an economy that lacks growth.
In other words, if growth does not return before the propaganda loses its appeal, this will eventually result in falling approval ratings for the president, which is what we are seeing now.
There are potentially also some policy conclusions for Russia’s foreign investors, trading partners and neighbors. When growth in Russia is low and no credible reform programs are on the horizon, expect external actions that take the attention away from poor economic performance while increasing the level of uncertainty both in Russia and abroad.
For the more pro-active external actors, finding ways to support Russia’s return to growth through dialogue on real economic reforms could perhaps be both politically feasible and of mutual interest to Russia and the West. There are clearly some geopolitical issues that may interfere with this process, but it should still remain high on the wish list of regular people in Russia and elsewhere. Let the fridge rule!
References
- Becker, T., 2019. “Russia’s macroeconomy—a closer look at growth, investment, and uncertainty”, forthcoming SITE Working Paper.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Losers and Winners of Russian Countersanctions: A welfare analysis
In this brief we provide a quantitative assessment of the consequences of countersanctions introduced by the Russian government in 2014 in response to sectoral restrictive measures initiated by a number of developed countries. Commodity groups that fell under countersanctions included meat, fish, dairy products, fruit and vegetables. By applying a basic partial equilibrium analysis to data from several sources, including Rosstat, Euromonitor, UN Comtrade, industry reviews etc., we obtain that total consumers’ loss due to countersanctions amounts to 288 bn Rub or 2000 rubles per year for each Russian citizen. Producers capture 63% of this amount, importers 26%, while deadweight loss amounts to 10%. 30% of the transfer from Russian consumers toward importers was acquired by Belarus. The gain of Belarusian importers of cheese is especially impressive – 83% of total importer’s gains on the cheese market.
In August 2014, in response to sectoral sanctions initiated by some countries against Russia, the national government issued resolution No. 778, which prohibited import of processed and raw agricultural products from the United States, the EU, Ukraine and a number of other countries (Norway, Canada, Australia, etc.).
Russian countersanctions were, in particular, imposed on meat, fish, dairy products, fruit and vegetables. Later the list of counter sanctioned goods was edited: inputs for the production of baby food and medicines have been deleted from the ban list, while new items were added. Salt was added to the list in November 2016 and animal fats in October 2017.
The popular idea behind the countersanctions was to limit market access for countries, which supported sectoral sanctions. The other rhetoric of the countersanctions was to support domestic producers via trade restrictions, or by other words – import substitution.
We apply a basic partial equilibrium analysis in order to evaluate the effect of countersanctions on the welfare of main stakeholders – consumers, producers and importers. The overall results are in line with general microeconomic consequences of trade restrictions in a small open economy, that is, we observe a decline in consumer surplus, increase in producer surplus and redistribution across importers. Perhaps, even more interestingly, we are able to provide a numerical assessment of redistribution effects between Russian consumers and producers, on the one hand, and among importers from different countries, on the other.
Partial equilibrium welfare analysis
We apply a framework of the classical analysis of import tariff increases to Russian countersanctions. Countersanctions resulted in increased domestic prices, declining consumption and increased domestic production. Given the increase in prices and declined volumes of consumption, we evaluate the losses by consumers as a decline in consumer surplus. Respectively, given the increase in prices and increase in domestic output we identify the producers gains as an increase in producer surplus. The only difference with a classical analysis is the lack of increase in government revenues. In this case increases in domestic prices were driven by restrictions on trade with historical partners which were substituted by more costly producers. Given the changes in the composition of importers after sanctions, we identify countries which lost and gained access to the Russian market. We use changes in volumes of trade as a measure of respective gains and losses. Figure 1 presents all relevant concepts.
In order to measure all relevant welfare changes, we rely on consumption, production and price data from Rosstat and Euromonitor, trade data from the UN Comtrade database. We use data for 2013 as a benchmark before countersanctions and compare it to 2016. The measures of own price elasticities of Russian demand and supply were taken from the literature. We use real price (in terms of 2013 prices) and volume information for consumption and supply in 2016 as the resulting points on the supply (point C) and demand (point A) curves as shown on Figure 1. Then we restore the consumption and production points on these curves (points F and B) as they would have been in 2013 given the own price elasticities of demand and supply and price level as of 2013.
Figure 1. Visualization of deadweight losses, consumer and producer surplus changes
Welfare analysis
Data
We consider 12 commodity groups that were included in 2014 in the countersanctions list: pork, cheese, poultry, apples, beef, tomatoes, processed meat, fromage frais, butter, oranges, condensed milk, grapes, cream, sour milk products, milk, and bananas.
Prices and volumes information are taken from Rosstat official statistics, which in a few cases were adjusted by data from Euromonitor. Import values were obtained from the UN Comtrade database. The summary of the original data and results of welfare analyses are reported in table 1. Below we discuss in details the situation in three markets – beef, apples and cheese.
Table 1. Summary table of the welfare effects of countersanctions
| Group | Price (RUR per kg, 2013) | Production (thous. tons) | Consumption (thous. tons) | Elasticity | Consumer losses, RUR mn | Producer surplus, RUR mn | Deadweight loss, RUR mn | Importer gains, RUR mn | ||||
| 2016 | 2013 | 2016 | 2013 | 2016 | 2013 | demand | supply | |||||
| Beef | 376 | 357 | 238 | 240 | 600 | 897 | -0.78 | 0.1 | 11311 | 4388 | 234 | 6690 |
| Poultry | 109 | 108 | 4468 | 3610 | 4577 | 4084 | -0.78 | 0.45 | 3263 | 3173 | 13 | 77 |
| Pork | 286 | 289 | 2042 | 1299 | 2282 | 1919 | -0.78 | 0.2 | -7167 | -6447 | 38 | -757 |
| Milk | 55 | 47 | 5540 | 5386 | 5704 | 5595 | -0.93 | 0.3 | 48234 | 42507 | 4443 | 1284 |
| Butter | 343 | 271 | 251 | 225 | 340 | 340 | -0.93 | 0.18 | 27468 | 17680 | 3370 | 6419 |
| Cheese | 358 | 283 | 605 | 435 | 748 | 764 | -0.93 | 0.28 | 63493 | 44259 | 8437 | 10797 |
| Fromage frais | 233 | 190 | 407 | 371 | 456 | 457 | -0.93 | 0.3 | 21803 | 17104 | 2600 | 2099 |
| Apples | 84 | 70 | 324 | 313 | 986 | 1665 | -0.85 | 0.1 | 15225 | 4562 | 1238 | 9425 |
| Bananas | 61 | 47 | 0 | 0 | 1141 | 1165 | -0.9 | 0.1 | 18967 | 0 | 2315 | 16652 |
| Oranges | 65 | 59 | 0 | 0 | 932 | 1059 | -0.9 | 0.1 | 6054 | 0 | 272 | 5782 |
| Grapes | 175 | 131 | 174 | 101 | 366 | 459 | -0.85 | 0.1 | 18312 | 7527 | 2351 | 8435 |
| Tomatoes | 82 | 65 | 1130 | 863 | 1583 | 1718 | -0.97 | 0.1 | 28824 | 18177 | 3290 | 7357 |
Data sources: Rosstat, Euromonitor, UN COMTRADE
Bold figures were used to mark the commodity groups with a noticeable consumption growth in 2013-2016, italic figures – for those with consumption decrease, and underlined – for groups where consumption changed insignificantly during the period.
Beef
The Russian beef market experienced a drastic decrease in consumption during two years under countersanctions. In 2013 constant prices, the average real of 1 kg of beef increased by 5.3% from 357 Rub/kg in 2013 up to 376 Rub/kg in 2016. Domestic output decreased by 0.8% and to 238 thousand tons in 2016 from 240 in 2013. Domestic consumption decreased by 33.1% to 600 thousand tons in 2016 from 897 in 2013. Our estimations indicate that consumer losses amount to 11.3 bn Rub or 3.5% of beef consumption in 2013; producers’ gains are 4.4 bn Rub or 1.4%; deadweight losses are estimated at 0.2 bn Rub or 0.07%; and importers’ gains equal 6.7 bn Rub or 2.1%.
Out of total 6.7 bn Rub of importers’ gains, importers from Belarus acquire the major share (88%) – 5.9 bn Rub. Importers of beef from India and Colombia gained 0.4 bn Rub (6% of total) and 0.3 bn Rub (5%) respectively. Beef importers from Mongolia gained 0.03 bn Rub, from Kazakhstan – 0.01 bn Rub. Importers of beef from Brazil, Paraguay, Australia, Uruguay, Ukraine, Lithuania, Poland, and Argentina lost market shares in over the period 2013-2016.
Cheese
Average real price for 1 kg of cheese increased by 26.5% up to 358 Rub/kg in 2016 from 283 Rub/kg in 2013, both in constant 2013 prices. Domestic output increased by 39.1% to 605 thousand tons in 2016 from 435 thous. tons in 2013. Domestic consumption decreased by 2.1% to 748 thous. tons in 2016 from 764 thous. tons in 2013. Our results indicate the following effects of countersanctions on cheese market: consumers’ losses amounted to 63.5 bn Rub or 29.4% of cheese consumption in 2013; producer’s gain is 44.3 bn Rub or 20.5%; deadweight loss is estimated at 8.4 bn Rub or 3.9%; importers’ gains equal 10.8 bn Rub or 5.0%.
Out of a total 10.8 bn Rub of importer’s gains on the cheese market, importers of cheese from Belarus acquired the major share (82.9%) – 9.0 bln Rub, importers of cheese from Argentina gained 0.5 bn Rub (4.8% of total importers’ gain), importers from Uruguay gained 0.4 bn Rub (3.9%), Swiss cheese importers gained 0.2 bn Rub, importers from Armenia – 0.2 bn Rub (1.8%). While importers of cheese from Ukraine, the Netherlands, Germany, Finland, Poland, Lithuania, France, Denmark, Italy, and Estonia lost market access over 2013-2016.
Apples
In 2013 constant prices, average real price for 1 kg of apples increased by 20.0% up to 84 Rub/kg in 2016 from 70 Rub/kg in 2013. Domestic output increased by 3.5% to 324 thous. tons in 2016 from 313 thous. tons in 2013. Domestic consumption decreased by 40.8% to 986 thous. tons in 2016 from 1665 thous. tons in 2013. According to our analysis, the effects of countersanctions on the apple market are the following: consumers’ losses amounted to 15.2 bn Rub or 13.1 of apple consumption in 2013; producer’s gain is 4.6 bn Rub or 3.0%; deadweight loss is estimated at 1.2 bn Rub or 1.1%; importers’ gains equal 9.4 bln Rub or 8.1%.
Out of a total 9.4 bn Rub of importer’s gains, importers from Serbia acquired the major share (49.7%) – 4,7 bn Rub, importers of apples from China gained 1.6 bn Rub (16.7% of total importers’ gains), those importing from Macedonia gained 0.8 bn Rub (8.4%), from Azerbaijan 0.6 bn Rub (6.0%), and from South Africa 0.4 bn Rub (4.5% of total importers’ gains). While importers of apples from Poland, Italy, Belgium, and France lost market access.
Overall effects for 12 commodity groups
We calculated the welfare effects for 12 commodity groups: beef, poultry, milk, cheese, cottage cheese, ton butter, dairy products, apples, bananas, oranges, grapes and tomatoes.
Total consumers’ loss due to countersanctions amounts to 288 bn Rub, producers gain 63% out of this amount (182 bn Rub), 26% of total consumers’ loss is redistributed to importers (75 bn Rub), deadweight losses amount to 10% (31 bn Rub).
Distribution of importers’ gains
Belarus is the major beneficiary of Russians countersanctions: its exporters gain 29.4 bn Rub (38%), Ecuador’s exporters are in the second place with 16.4 bln Rub (21). Exporters from Serbia gained 5.1 bn Rub (7%).
Conclusion
There is no doubt that countersanctions were paid out of the pockets of Russian consumers: our estimation of total consumer losses amounts to 288 billion rubles, i.e. each Russian citizen paid 2000 rubles per year. Out of this sum, Russian producers received 144 billion rubles, i.e. transfer from Russian consumers to producers equals 1260 rubles per person per year. Among Russian sectors, major gains and associated increases in production happened in pork industries (50%), poultry (20%), dairy products (10-30%), fruit and vegetables (10-50%).
The transfer from Russian consumers toward importers from non-sanctioned countries equals 75 billion rubles a year (520 rubles per person per year), out of which 30% was acquired by Belarusian importers. Countersanctions lead to deadweight losses in the efficiency of Russian economy equal to 31 billion rubles or 215 rubles per person per year.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.