Estimates of Russia's potential output

Janíčko, Martin; Maleček, Petr; Janíčko, Pavel Article Estimates of Russia's potential output Ekonomika Provided in Cooperation with: Vilnius University Press Suggested Citation: Janíčko, Martin; Maleček, Petr; Janíčko, Pavel (2021) : Estimates of Russia's potential output, Ekonomika, ISSN 2424-6166, Vilnius University Press, Vilnius, Vol. 100, Iss. 2, pp. 84-100, https://doi.org/10.15388/Ekon.2021.100.2.4 This Version is available at: https://hdl.handle.net/10419/323099 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ 84 Ekonomika ISSN 1392-1258 eISSN 2424-6166 2021, vol. 100(2), pp. 84–100 DOI: https://doi.org/10.15388/Ekon.2021.100.2.4 Estimates of Russia’s Potential Output Petr Maleček Faculty of Economics, Prague University of Economics and Business, Czech Republic E-mail: [email protected] Martin Janíčko Faculty of Economics, Prague University of Economics and Business, Czech Republic E-mail: [email protected] Pavel Janíčko College of Regional Development and the Banking Institute – AMBIS, Czech Republic E-mail: [email protected] Abstract. Considering the specifics of the Russian economy such as dependency on oil, gas drilling and production, including the current context of the Western sanctions, COVID-19 pandemic as well as distinct potential output development, the main aim of this paper is to quantify the recent output gap for Russia. We use three mainstream methodologies: the Hodrick-Prescott filter as a benchmark, the Kalman filter to follow and the Cobb-Douglas production function. The sample time span ranges from 1995Q1 until 2020Q3, while all calculations are performed on quarterly frequencies. The analysis suggests that given the low fixed investment ratios, limited R&D spending in non-military sectors and adverse demographic development, under a “no policy change” scenario, there might soon be even more downward pressures on the country’s potential output growth. The economy may continue increasing only at a snail’s pace even after a possible withdrawal of the Western sanctions and the end of the COVID-19 pandemic. Keywords: Russian economy; potential output; production function; Hodrick-Prescott filtering; Kalman filtering 1. Introduction Russia’s economy has not been faring well as of late. In particular, the Western sanctions following the annexation of Crimea and relatively weak energy prices have been the main culprits of the general economic slowdown, manifested itself by the recession in 2015, followed by a lacklustre GDP growth until now. Looking at a longer time span, the country already had to overcome major financial and economic crisis in 2008 and 2009, while prior to that it had been coping with major economic and social turbulences caused by abrupt privatization by a handful of chosen oligarchs, swift liberalization, frequent intentional dismantlement and/or weakening of Received: 16/02/2021. Revised: 24/04/2021. Accepted: 26/04/2021 Copyright © 2021 Petr Maleček, Martin Janíčko, Pavel Janíčko. Published by Vilnius University Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Contents lists available at Vilnius University Press Petr Maleček, Martin Janíčko, Pavel Janíčko. Estimates of Russia’s Potential Output 85 elementary institutional mechanisms, and exploding corruption during the 1990s. Also, the skyrocketing inequality in the same period, partly propelled by lifting price controls over 90% of traded goods, made things even worse. These were exactly the problems that resulted in huge output losses and significant social and political unrest between 1992 and 1996, peaking with the 1998 financial crisis, which itself went straight to university textbooks on finance and economics. For illustration, in the time span of 1990 and 1998, Russia’s real GDP plummeted by approximately 45% (World Bank, 2021).Yet, with the beginning of the new millennium, Russia started to be perceived as a relatively normally functioning emerging economy which was achieved inter alia by overall institutional stabilisation and rising openness of the economy to international trade and investment inflows. Still, the rising oil price was likely the chief factor in the country’s economic development between 2000 and 2008, while the high oil price supported the economy also beyond that period, particularly between 2010 and the first half of 2014, after it reached an all-time high in 2013 (Kluge, 2019). Left aside the difficult access to the global financial markets as well as subdued oil prices, the most pronounced, purely domestic, challenge will perhaps be the negative demographic trend and population ageing (although demographic trends might sometimes be less straightforward than conventionally thought (Blanchard & Quah, 1989)), which is for the time being partially mitigated by immigration flows particularly from the former Soviet republics. Russia’s working-age population declined by around one million during the last years, although employment stagnated in the same period. This heavily weighs on the country’s labour component of the aggregate production function and may even worsen the situation in the years to come unless the labour force is compensated elsewhere: according to the United Nations, the working-age population may drop to only about 90 million in 2030 (UN World Population Aging Report, 2019). Only recently did the government hike the legal retirement age by five years to 60 and 65 for females and males, respectively. The main upside of the shrinking labour force is that the unemployment rate stays low even during recessions as labour becomes relatively scarce with respect to to other production factors. This could be seen during both the Great Recession in 2008 as well as the current situation in which the headline unemployment rate remains below 7% in 2020 (ROSSTAT, 2020), an enviable value for many Western European countries. The economy remains heavily dependent on oil drilling/production and the share of exports of natural resources of various kinds fluctuates between 16 and 20% (OECD Database, 2021). At the same time, natural resources account for about 80 to 85% of total exports (OECD Database, 2021). Russia’s reserves-to-production ratio stands at 20, while it reaches 89 in Kuwait and 64 in Saudi Arabia (Worldometer, 2021). Meanwhile, the gross capital formation remains low compared not only to the Western countries, but also to the Eastern and Central Europe. This is deemed quite surprising, namely given the general need for higher investment in drilling and extraction. Hence, such lack of productive investment combined with several other negative factors, predominantly present in the institutional functioning of the country, may potentially prevent Russia’s GDP growth from accelerating further and will likely require an appropriate economic policy action. ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2021, vol. 100(2) 86 As for 2020, the Russian economy is estimated to have declined by 3.1% - the most since 2009 (IMF, 2021). The country’s economic development was adversely affected by the coronavirus pandemic and falling oil prices. Russia is expected to return to growth in 2021 and 2022 (IMF, 2021). According to the World Bank (2020), the health crisis caused by the COVID-19 pandemic slowed down the business activity in the country, but also the demand for oil, Russia’s main export commodity. The unemployment rate in Russia rose sharply in response to restrictions on movement imposed throughout the country to prevent the spread of coronavirus. However, even with a positive outlook for further growth, GDP will not reach pre-pandemic levels sooner than in late 2022. We show that the estimation of Russia’s potential output can be performed with three standard methods, namely Hodrick-Prescott filter, Kalman filter, and the Cobb-Douglas production function with constant returns to scale. All approaches show relatively similar results and identify a negative output gap in Russia’s economy in 1999, 2003, 2009, 2015 and 2020. The results also suggest the economy might begin entering a protracted period of low-potential growth due to low capital-labour ratios, dismal demographics and slow increases in total factor productivity. We consider the estimation of current growth of Russia’s potential output as the article’s main contribution to the relevant research field. The main contribution particularly resolves the scarcity of up-to-date estimates in this respect which is discussed more broadly in the third section. Furthermore, the value added of this paper lies within disentangling the causes of sluggish growth even before the COVID-19 pandemic, principally by means of decomposition of growth into cyclical and structural factors. With respect to the latter, we also perform a deeper analysis considering developments in the capital accumulation and the labour market. The main novelty of the article resides in the following: we connect the theoretical underpinnings, actual economic situation and quantitative methods to come up with consistent estimation of Russia’s potential output over a relatively long time horizon. Likewise, we analyse three potential output methods, compare and contrast them where necessary. Finally, we use the most up-to-date data allowing us to identify the main episodes and underlying characteristics, stemming from labour market and elsewhere affecting the potential output development. We also believe that, given the relatively consistent results, the methods we used have proved to be appropriate. The text can be useful in offering a reasonable snapshot on Russia’s potential output for the researchers, experts and/or policy makers since, to the best of our knowledge, no other text with similar complexity exists until now. The rest of the article is structured as follows: the next section explores the major structural issues of Russia’s economy in the current macroeconomic context. Section 3 presents previous research conducted on the topic and the data used. Section 4 details the estimation methods. Section 5 summarises results and provides their comparison. The last section concludes the analysis. Petr Maleček, Martin Janíčko, Pavel Janíčko. Estimates of Russia’s Potential Output 87 2. Russian Economy: Structural Issues After seven straight years of expanding at a relatively decent clip, fuelled by increasing oil prices as well as prices of other natural resources and reforms which curbed the power of oligarchs and lobby groups, the Russian economy contracted by nearly 2% in 2015 (ROSSTAT, 2020). This is also due to the combination of the weak oil prices and Western sanctions following the annexation of Crimea as well as Russian countersanctions targeting mainly the Western agricultural and food products. Both sets of sanctions have been in place until mid-2020, but, given the current political and geopolitical context, they will likely be extended beyond that period. Still, even in the years before 2015, Russia’s economy decelerated markedly due to a weak fixed capital accumulation, partially driven by the rising and volatile interest rates on corporate loans. As a result, with low profitability and high interest rates, businesses found it hard to finance major investment projects, and even those companies that could invest in Russia, often preferred to invest abroad rather than domestically. A relative brisk economic policy response helped Russia avoid even more protracted slump, but the country still ranks poorly in the effectiveness of its state administration, judicial independence, property rights and effectiveness of its financial markets. Combined with all the “exogenous factors”, all these shortcomings are weighing on the near-term economic development of the country and it is worth noting that other countries of the so-called BRICS group are currently ranked higher in most of these gauges. High interest rates on corporate loans and subdued demand for output also prevent the stimulation and picking up of longer-term capital formation. In addition to this, Russia’s entry into the WTO makes it clear that the domestic industries will have to compete with less expensive imports, and that will require higher productivity, diversification and improved competitiveness. In the short run, agriculture, automobile sector, engineering, pharmaceuticals, and industries producing high-tech equipment will be hurt the most by foreign competition. Nevertheless, over the next several years, Russia’s entry into the WTO should enable the country to more efficiently allocate resources within the economy and will contribute to the increase of the competitiveness of the Russian goods and services. Likewise, slowly but surely, increasing oil prices will support the country’s revenues and increase the margin for manoeuvre in case further fiscal expansion is necessary. Finally, the economy has been accommodating to the Western sanctions and developed some sort of self-sufficiency, albeit at relatively high initial costs, including corruption. Since the economic crisis of 2015 and 2016, the Russian government has recovered its fiscal and monetary reserves in the context of the rising oil and gas prices. However, the government is trying to maintain an acceptable budget deficit and to implement some fiscal adjustments (cuts in pensions and tax increases, toll growth). For example, (Schwabe, 2019) argues that “(…) Russia has also partially shifted reserves from US dollar-denominated holdings to the euro and even to Chinese currency, but also to holdings of gold reserves, which rose to the equivalent of $110 billion. The National Welfare Fund owned by the government is now worth over $120 billion and is expected to soon reach 7 percent of GDP. ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2021, vol. 100(2) 88 This is a formal threshold at which the government could start to use part of the funds.” At the same time, the country significantly reduced its external debt, and only this year the combined external debt of the private and public sectors stopped diminishing and again increased to $480 billion (Schwabe, 2019). As such, Russia is in a favourable situation where all foreign debt of private and public entities is fully covered by foreign reserves. This has made Russia more resilient to the new external shocks, such as a further decline in oil prices or new sanctions. It follows that the Russian economy does not have to worry about high instability even at present, when expectations for future growth are not very optimistic. Still, it remains to be seen whether the production in the oil sector will not start declining from 2021 onwards due to depletion of oil fields and a lack of modern technology. Demographic challenges also threaten Russia’s economic growth. Given the extremely low birth rate in the 1990s, there is currently a shortage of young workers in the labour market, while many baby boomers are entering retirement age. Migration compensated a part of the workforce, but in the period between 2018 and 2020 the number of migrants dropped as Russia became less attractive for migrant workers (Foltynova, 2020). Many of these problems could be mitigated or offset by a suitable economic policy, but it seems that, because of persistent political rigidity and populism, these opportunities are not being properly exploited. The Western economists recommend Russia could invest in education and health care to increase the productivity and longevity of its shrinking workforce. The domestic and international experts have repeatedly submitted this proposal during Putin’s time in power. However, the Russian government continues prioritizing spending on security services and military. The concern is also how to distribute the ownership structures in the most important sectors to maximize their contribution to effective growth and competitiveness. Viable programs may include large-scale infrastructure projects, mainly government-funded, in the hope that investments and hence economic growth will strengthen in the coming years. 3. Previous Research and Data Literature devoted to the Russian potential output is rather scarce, and, given the need for recent data, also relatively obsolete and dating mostly to the period before the Global Financial and Economic Crisis (GFC). One of the examples is (Michalides & Millios, 2009) who attempted to estimate Total Factor Productivity shifts in the Russian economy during the time span from 1994 to 2006. They also calculated the potential output and output gap using the Cobb-Douglas production function and the Hodrick-Prescott filter. Equally, some of the previous papers, e.g. (Oomes & Dynnikova, 2006), elaborate on the importance of utilization rates of primary factor inputs. Similarly, (Hanson, 2009) studies the changing structure of the Russian economy and projects its development until 2020time horizon. Further, (Izyumov & Vahaly, 2008) came up with a study about levels and trends in capital accumulation in the Commonwealth of Independent States (CIS) since their transition to the market/capitalist economies. Focusing on the 1992-2005 period, they have ascertained that Russia remains the most capitalised CIS country with capital-labour Petr Maleček, Martin Janíčko, Pavel Janíčko. Estimates of Russia’s Potential Output 89 ratio (K/L) of about $40,000 per worker. Finally, (Kuboniwa, 2010) discusses the sources of growth in the Russian economy now and during the financial crisis of 1998-1999. She confirms, inter alia, that the country’s performance in manufacturing and trade sectors still heavily depends on the changes in oil prices. Finally, (Zubarev & Trunin, 2017) estimate Russia’s output gap, following the phase-in of the Western sanctions, and suggest that a slowdown in the potential output growth took place. Most of the previous research confirms that Russia’s potential output has been drifting to a slower growth trajectory. The data used in this paper have been extracted from the Moody’s Analytics historical data platform DataBuffet (raw data can be found in the Appendix) and the Federal State Statistics Service of the Russian Federation. They covered a period between 1995Q1 until 2020Q3, thus partially taking into account also the impact of the COVID-19 pandemic with the onset in the first quarter of 2020. The end of the time series marks the last full year of available data at our disposal at the time of creation of the article. In certain cases, the times series have been extended with predictions in order to receive more precise data and avoid some methodological issues. Where necessary, data have been transformed into the Seasonally Adjusted Annual Rates (SAAR) to account for changes in data caused by seasonal variations. 4. Methods for Estimating Potential Output in Russia Bearing in mind the several important particularities of the Russian economy, as outlined in the previous section, let us now turn to the estimation of the potential product and the output gap, using quarterly data. We chose three methods that are commonly used in this respect: the Hodrick-Prescott filter, the Kalman filter and the production function. 4.1. Hodrick-Prescott filter As our reference method, we use the frequently employed Hodrick-Prescott filter. (Hodrick & Prescott, 1997) The idea of this approach is to disentangle real GDP into its trend part and cyclical part by means of penalization of the trend component growth rate. We set the only free parameter (the penalization parameter lambda) to 1600, which has become mainstream while analysing quarterly data. It holds that the larger the penalization parameter, the smoother it yields the trend. In an extreme case of lambda approaching infinity, the series will exhibit a linear trend. In a conventional representation, real GDP yt is composed of the trend part gt and cyclical part ct, i.e. it has to hold that: ttt cgy += (1) The trend part is then obtained by minimising the equation below with respect to gt. This can be written as follows: ( ) ( ) ( ) [ ] ∑∑ − = −+ = −−−+− 1 2 2 11 1 2T t tttt T t tt gggggy λ (2) ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2021, vol. 100(2) 90 4.2. Kalman filter The Kalman filter or the linear quadratic estimation (LQE), is a significantly more sophisticated method than the Hodrick-Prescott filter. This method is not a purely mechanical filtering technique, but it has more structural character and is recursive. Its use could thus be wide-ranged, and it goes well beyond economics or econometrics. The general set-up of the Kalman filter could be written as follows: 1t tt t Fv ξξ += + (3) '' t tt t t y Ax H w ξ =++ (4) where ξ stands for unobserved variables that are to be estimated, yt represents all observed variables, whilst t v and t w are two white noise vectors. Further, Ft , Ht' and At' stand for coefficient matrixes. The main general objective is, based on the initial state x0 with normal distribution and historical observations, to derive optimal estimates of the future states by using maximum likelihood method. In our case, the equation for real output is thus as follows: ttt yyy= + (5) where t y is the output gap and t y is the trend. Equation (5) can be further split up into two separate equations – the autoregressive state equation and the trend state equation: 11t tt y cy v − = + (6) 2 1 tt t yy c µ − = ++ (7) where c1 and c2 are constants and μt is a random variable. On the one hand, the main advantage of the Kalman filter is its ability to consider the effects of all the variables in the model and also the fact that it is less skewed by the historical data than other (statistical) filtering methods. Owing to its structural character, there is also consistency between the model estimates and structural features of the described reality. On the other hand, as it has already been mentioned, the filter is complicated in the sense of initial values for the model. An inappropriate choice of parameters, for example, may partially or completely invalidate the filtering results. For the Russian economy data, the parameters are calibrated. The state space is defined by the system of equations in the following form: 1t t vt xx v φσ − = + (8) t t et yx e σ = + (9) where νt and et denote independent standard Gaussian variables. In what follows, we consider a standard New Keynesian Model in the following form: Petr Maleček, Martin Janíčko, Pavel Janíčko. Estimates of Russia’s Potential Output 91 1() t tt t t t E yy v π π βπ κ + = + −+ (10) 11 () y t tt t tt t y Ey i E v γπ ++ = −− + (11) tt i φπ = (12) 1t tt y yu ρ − = + (13) where πt denotes the inflation rate, yt stands for the change in the real output, 1t tt y yu ρ − = + denotes change in the potential output, it is the money market interest rate, and νt and ut stand for normally-distributed estimation errors. The calibrated parameters denoting sensitivities in the system are mostly based on (Kreptsev & Seleznev, 2018) and take the following form: { } { } 222 , , , , , , , 0.99,0.15,1.85,1.5,0.95,1,1,0.01 yu π βκγφρσ σ σ = (14) The solved model is put in the state space form, while changing the variance would affect the behaviour of the Kalman smoothener. For example, smaller variance of measurement errors would put more weight to the variance of structural innovations. According to the filet, the output gap is suggested to be marginally negative as of late, following the events that happened in 2014. Likewise, the filtered series is relatively less smooth than elsewhere which is given by the very nature of the multivariate filter set-up. As can be observed in Figure 1 further in the text, by using the Kalman filter, the output gap was positive between the years 2000 and 2009, while often hovering around zero or being even negative since that period. In the most recent years, we can see that the gap tends to shrink, which is in line with the recent normalisation of Russia’s GDP growth. 4.3. Production function To develop upon the statistical nature of the HP filter by gaining more insight into the drivers of potential output growth, the next commonly used method for determining the output gap is the production function. In this paper, we will use the two-factor CobbDouglas production function, implicitly treating technological progress as Hicks-neutral, similarly to (d’Auria et al., 2010) and (Giorno, Richardson, Roseveare, & van den Noord, 1995). Thereafter, the potential output is a function of potential capital stock Kt, potential employment Lt, i.e., the level where there should be no demand inflationary pressures, and potential total factor productivity (TFP) At. βα tttt LKAY ⋅⋅= (15) In line with the most empirical studies, we will employ constant returns to scale, just as in the original Solow model. According to (Michaelides & Millos, 2009), the labour content of the Russian economy has been stable, oscillating around 50%, we thus set both alfa and beta constant at 0.5. For the capital stock, we use the perpetual inventory method (PIM), which suggests that the current capital stock is the sum of the past capital stock adjusted for depreciation and present real fixed investments. We set the annual depreciation ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2021, vol. 100(2) 98 Appendix 1. Raw Data Used Nominal GDP Real GDP Gross F. Cap. Form. Unempl. rate Total Employment Population 15-64 bil. RUB. SAAR bil. 2016 RUB. SAAR bil. 2016 RUB. SAAR %. LFS. SA mil. persons. SA mil. persons 1995Q1 1121.9 45544.9 8554.3 2.75 66.82 99.04 1995Q2 1433.6 45900.0 11238.5 3.06 66.85 99.07 1995Q3 1752.1 46428.6 9027.0 3.31 66.75 99.09 1995Q4 1760.8 45376.7 10566.7 3.50 66.52 99.13 1996Q1 2041.2 44525.6 8445.1 3.69 66.29 99.19 1996Q2 2137.8 44327.2 7577.9 3.88 66.06 99.26 1996Q3 2214.2 43873.3 7131.8 4.11 65.35 99.36 1996Q4 2284.0 43973.8 8349.3 4.37 64.14 99.46 1997Q1 2325.0 44329.3 7880.0 4.64 62.95 99.57 1997Q2 2509.3 43960.1 7326.8 4.92 61.76 99.69 1997Q3 2527.3 45120.6 7506.9 6.24 61.82 99.85 1997Q4 2687.8 45563.7 6717.8 8.50 63.23 100.03 1998Q1 2631.8 43671.8 7167.1 10.54 64.63 100.26 1998Q2 2730.5 43527.1 6983.1 12.41 66.01 100.51 1998Q3 2948.4 41139.1 6684.3 13.25 66.84 100.77 1998Q4 3439.1 41404.2 5333.5 13.12 67.07 101.02 1999Q1 4190.3 42880.9 6786.0 13.00 67.29 101.24 1999Q2 4792.5 44898.4 6958.1 12.87 67.51 101.43 1999Q3 5651.9 45854.6 6875.0 12.62 67.12 101.60 1999Q4 5893.5 46405.8 6544.2 12.21 66.06 101.76 2000Q1 6930.5 48839.0 7835.7 11.80 65.02 101.90 2000Q2 7310.6 49224.5 7824.6 11.17 65.26 102.02 2000Q3 8084.0 49731.4 8006.4 10.66 65.32 102.13 2000Q4 8565.6 50468.4 8099.6 10.28 65.20 102.23 2001Q1 8722.9 51143.2 8381.6 9.84 64.93 102.31 2001Q2 9218.3 51680.5 8659.2 9.37 65.09 102.37 2001Q3 9724.3 52763.7 8863.7 9.26 65.37 102.43 2001Q4 10049.5 52736.6 9000.9 8.97 65.63 102.47 2002Q1 10447.2 53113.2 8626.6 8.03 66.89 102.51 2002Q2 11235.9 53909.7 8807.7 8.27 66.74 102.53 2002Q3 13012.7 55154.2 9133.3 8.11 67.00 102.54 2002Q4 11800.8 55953.2 9291.6 8.55 66.55 102.53 2003Q1 13153.5 57165.4 9649.4 8.65 66.17 102.50 2003Q2 13678.1 58176.1 10057.2 8.72 66.17 102.43 2003Q3 13811.7 58612.6 10302.4 8.63 66.44 102.35 2003Q4 14877.9 60254.0 10758.0 8.28 67.14 102.25 2004Q1 16554.9 61320.7 11349.8 8.51 66.95 102.15 2004Q2 17350.1 62822.4 11563.6 8.00 67.65 102.06 2004Q3 17249.9 62931.9 11394.8 7.98 67.66 101.97 Petr Maleček, Martin Janíčko, Pavel Janíčko. Estimates of Russia’s Potential Output 99 Nominal GDP Real GDP Gross F. Cap. Form. Unempl. rate Total Employment Population 15-64 bil. RUB. SAAR bil. 2016 RUB. SAAR bil. 2016 RUB. SAAR %. LFS. SA mil. persons. SA mil. persons 2004Q4 20679.1 63982.5 11862.0 8.17 67.56 101.89 2005Q1 20763.1 65596.8 12662.7 7.54 68.06 101.83 2005Q2 22021.2 66256.4 12461.9 7.53 68.29 101.77 2005Q3 22641.9 67011.4 13192.8 7.59 68.57 101.72 2005Q4 25495.9 68171.3 12796.0 7.37 69.02 101.66 2006Q1 27385.3 70397.8 13963.4 6.89 69.20 101.60 2006Q2 27658.6 71633.9 14774.2 7.48 68.78 101.54 2006Q3 29104.8 72474.5 15558.8 7.07 69.20 101.50 2006Q4 30324.5 74207.5 15519.7 6.78 69.49 101.49 2007Q1 32640.7 76106.9 16629.2 6.18 70.72 101.53 2007Q2 34719.9 77844.0 17639.3 6.06 70.68 101.61 2007Q3 36834.9 78360.5 18001.8 5.99 70.68 101.71 2007Q4 40089.5 81012.8 19684.6 5.77 71.00 101.82 2008Q1 41450.4 83105.6 20662.1 5.88 71.38 101.93 2008Q2 44010.4 84011.8 20814.0 5.59 71.40 102.04 2008Q3 44873.2 83379.3 20416.6 6.21 70.86 102.15 2008Q4 40927.7 79915.5 19481.6 7.15 70.37 102.27 2009Q1 39551.8 75475.8 17353.6 8.28 69.38 102.40 2009Q2 39793.8 74656.2 16754.3 8.70 69.19 102.54 2009Q3 40672.3 76183.8 17127.9 8.29 69.39 102.67 2009Q4 41562.3 77836.0 17797.7 7.93 69.68 102.78 2010Q1 46835.8 78345.1 17795.6 8.00 69.14 102.87 2010Q2 46820.0 79750.5 18296.7 7.50 69.97 102.93 2010Q3 49243.2 79772.9 18544.4 7.01 70.27 102.96 2010Q4 51693.9 80063.1 18442.2 6.81 70.36 102.96 2011Q1 55906.5 80506.4 18297.6 6.88 70.26 102.92 2011Q2 58878.8 81336.4 18939.2 6.56 70.81 102.82 2011Q3 60552.6 82330.0 19403.6 6.41 71.18 102.70 2011Q4 62845.9 83348.4 20005.2 6.12 71.18 102.57 2012Q1 64988.9 84599.6 20289.6 5.89 70.80 102.45 2012Q2 66435.8 84944.4 20468.4 5.46 71.89 102.31 2012Q3 67423.4 85382.4 20850.4 5.31 71.76 102.16 2012Q4 68516.4 85439.6 20786.8 5.14 71.73 101.98 2013Q1 69242.6 86072.4 21063.6 5.44 71.58 101.77 2013Q2 70026.0 86365.6 21148.8 5.47 71.38 101.58 2013Q3 71654.2 86400.4 21045.6 5.52 71.26 101.44 2013Q4 72769.3 86899.6 20924.0 5.48 71.36 101.43 2014Q1 74742.8 86893.2 20852.0 5.21 71.47 101.56 2014Q2 78888.0 87312.0 20814.8 5.09 71.50 101.77 2014Q3 79843.3 87034.4 20566.4 5.14 71.45 101.97 ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2021, vol. 100(2) 100 Nominal GDP Real GDP Gross F. Cap. Form. Unempl. rate Total Employment Population 15-64 bil. RUB. SAAR bil. 2016 RUB. SAAR bil. 2016 RUB. SAAR %. LFS. SA mil. persons. SA mil. persons 2014Q4 81468.2 86829.2 20146.8 5.20 71.67 102.07 2015Q1 81788.4 85945.2 19626.4 5.43 72.25 102.00 2015Q2 81994.1 85369.2 18612.8 5.61 72.30 101.79 2015Q3 84718.6 85534.4 18151.6 5.52 72.43 101.51 2015Q4 85254.4 85314.0 17915.6 5.72 72.27 101.21 2016Q1 83350.4 85476.0 17767.6 5.63 72.08 100.97 2016Q2 84810.6 85522.0 18237.6 5.70 72.33 100.76 2016Q3 86022.4 85608.4 18612.8 5.48 72.60 100.57 2016Q4 87833.2 86301.6 19223.6 5.34 72.54 100.39 2017Q1 90809.4 86530.4 19324.4 5.33 72.19 100.21 2017Q2 90750.6 87069.2 19882.0 5.24 72.05 100.02 2017Q3 91913.9 87632.4 19367.6 5.23 72.08 99.82 2017Q4 93596.3 87653.2 19874.8 5.04 72.24 99.62 2018Q1 100775.2 88814.4 20050.0 4.88 72.49 99.43 2018Q2 104447.5 89086.0 19384.8 4.81 72.47 99.24 2018Q3 106612.6 89413.6 20257.2 4.78 72.31 99.05 2018Q4 106134.1 89798.4 19472.8 4.76 72.15 98.86 2019Q1 109965.2 89375.2 19615.2 4.66 71.84 98.68 2019Q2 109402.6 91620.0 20318.8 4.62 71.65 98.50 2019Q3 111903.7 90332.0 20165.6 4.58 71.62 98.31 2019Q4 108938.1 90025.2 20327.2 4.55 71.94 98.11 2020Q1 111573.0 89595.6 19154.8 4.48 71.72 97.90 2020Q2 96492.0 87058.0 17372.4 6.06 70.19 97.67 2020Q3 108281.3 87664.4 15539.2 6.57 69.85 97.45 Source: Moody’s Analytics Data Buffet