Corresponding author: Pavel Pavlov ( pavlov@ranepa.ru ) © 2019 Nonprofit partnership “Voprosy Ekonomiki”.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BYNCND 4.0), which permits to copy and distribute the article for noncommercial purposes, provided that the article is not altered or modified and the original author and source are credited.
Citation:
Drobyshevsky S, Idrisov G, Kaukin A, Pavlov P, SinelnikovMurylev S (2018) Decomposition of growth rates for the Russian economy. Russian Journal of Economics 4(4): 305327. https://doi.org/10.3897/j.ruje.4.33617

In this paper, we present a methodology of GDP growth rate decomposition adapted for the Russian economy. We calculated the indicators for structural unemployment (NAWRU) and total factor productivity in Russia. We estimated the structural, foreign trade and cyclical components of GDP growth rates under various macroeconomic scenarios for the period from 2018 through 2024. The study shows that a significant contribution to growth rates for the period 2018 through 2024 will be made by the sum of the business cycle and random shock component, which, combined with the revitalization of investments in 2017, may indicate the beginning of a new cycle of economic growth in Russia. In the scenarios reviewed, the contribution from the foreign trade component will be negative from 2018 to 2024. The calculations indicate further stagnation of structural growth rates in the Russian economy from 2018 to 2024 at the level of approximately 1.5 p.p. in all of the basic macroeconomic scenarios reviewed. This points to the inexpediency in postponing structural reforms to create conditions for Russia’s economy to achieve growth rates that exceed world averages.
economic growth, total factor productivity, NAWRU, terms of trade, business cycle
This paper elaborates on the studies examining the decomposition of growth rates for the Russian economy, which rely on isolating the contributions by fundamental factors of production. The paper is based on the approaches used in the OECD output gap estimation methodology (
Several concepts are usually identified in literature related to measuring and estimating an economy’s output: actual, potential, structural and longterm average annual output (smoothed output). Each concept is clarified below and the concepts of potential and structural output are key for purposes of the following material.
The terms for potential output are defined differently in different studies.
Potential output is generally considered in concert with the concept of the natural rate of unemployment.
Based on this approach, we can determine the economy’s structural output as the level of production of products and services corresponding to the structural (natural) rate of unemployment. In turn, the term potential output would best be described with a definition that is most consistent with its connotation: potential output is the output of goods and services corresponding to the production possibility frontier of a given economy,
The trajectory of an economy’s structural output is determined by the trends in fundamental production factors, i.e. labor, capital and total factor productivity (TFP) and represents the trajectory for the maximum output level of goods and services that can be maintained sustainably in the long run. The trajectory of actual output relative to potential output is determined by the combined effects of fundamental and cyclical (shortterm) factors. An economy tends to approach its potential output level during periods of “overheating”: potential output corresponds to the maximum possible (for a given economy) proximity to the full utilization of production possibilities
The longterm average annual (smoothed) output is a series of trend values statistically isolated in a given manner (e.g. with a filter) from a series of actual output values. At a glance, smoothed output trends correspond to structural output trends adjusted for deviations that, essentially, represent shortterm or mediumterm output gaps. If the output gap associated with the business cycle phase and favorable or unfavorable foreign trade conditions is zero over a certain interval, then the series of structural output values may align with the series of longterm average annual (smoothed) output values.
Actual output is the observable output of goods and services recorded in statistics by public authorities. At any point in time, it differs from the structural output by the size of the output gap. In this paper, we also assume that the output gap is the sum of the contributions from the cyclical and foreign trade components.
An economy’s aggregate output can be expressed as the sum of businesssector and publicsector output. The public sector usually encompasses economic activities corresponding to the following types:
In the System of National Accounts (SNA), the GDP indicator is calculated in market prices and includes the sum of indicators of the gross value added (GVA) produced by the businesssector and publicsector industries in basic prices (including product subsidies, but excluding taxes thereon), as well as the sum of net product taxes. The SNA considers product taxes in aggregate, while their analytical distribution amongst the economy’s industries does not take place in official statistics due to the specific aspects of indirect tax (VAT, import taxes) assessment. When isolating the components of publicsector and businesssector output in the GDP structure based on official statistics (SNA), it should be noted that net product taxes indicator follows the same trend as businesssector real output index (Fig.
Businesssector real output and net product tax indices (2002 = 100).
Source: Authors’ calculations based on Federal State Statistics Service (Rosstat) data.
The components of businesssector and publicsector output can be isolated within the GDP structure, taking into account the above aspects of the SNA and patterns in the joint output and tax trends, as follows:
Y = Y ^{b} + Y ^{pub} (1)
where: Y — gross domestic product; Y ^{b} — businesssector output: sum of GVA in the businesssector industries + net product taxes (in this case, the economy’s business sector is viewed as the payer of the majority of product taxes); Y ^{pub} — publicsector output: sum of GVA of the publicsector industries (this approach is arbitrary to some extent, as a certain portion of market services involving the payment of product taxes is produced by the public sector).
It should be added that publicsector output is calculated mostly based on budget expenditure for financing the production of services in education, healthcare, culture, sports, government administration, national security, social security and social services (thus, most of the publicsector output is calculated using the cost method). In addition, its output also includes the added value corresponding to extrabudgetary revenues from budgetfunded, autonomous, and public institutions and to the revenues of businesssector organizations providing services in education, healthcare, culture, sports, mass media etc.
As shown in Figure
Businesssector and publicsector real output indices (2002 = 100).
Source: Authors’ calculations based on Rosstat data.
Results of unit root tests (period: 2002–2016).
Time series  Real output index for Russia’s public sector  Real output index for Russia’s business sector  
Test statistics  DFGLS (Elliott–Rothenberg–Stock)  KPSS (LMStat.)  DFGLS (Elliott–Rothenberg–Stock)  KPSS (LMStat.)  
Test statistic value  –0.556  –3.298  0.579  0.074  –1.675  –1.676  0.530  0.153  
1% significance level  –2.741  –3.770  0.739  0.216  –2.741  –3.770  0.739  0.216  
5% significance level  –1.968  –3.190  0.463  0.146  –1.968  –3.190  0.463  0.146  
10% significance level  –1.604  –2.890  0.347  0.119  –1.604  –2.890  0.347  0.119  
Constant  +  +  +  +  
Constant and linear trend  +  +  +  +  
Null hypothesis  Unit root  Unit root  Series is stationary  Series is stationary  Unit root  Unit root  Series is stationary  Series is stationary  
Test result at 5% significance level  Hypothesis is not rejected  Hypothesis is rejected  Hypothesis is rejected  Hypothesis is not rejected  Hypothesis is not rejected  Hypothesis is not rejected  Hypothesis is rejected  Hypothesis is rejected 
The deviation in output growth rates from periodaverage values is substantially lower in the public sector than in the business sector (this is true for both individual intervals and for the period as a whole). In addition, publicsector output was not subjected to sharp changes during the period under review. The empirical facts, cited above, enable us, for purposes of this paper, to adopt one of the assumptions from the original OECD methodology: publicsector output is always considered to be structural. In this case, we used the assumption that the values of actual, structural and potential output in the public sector are equal. At the same time, certain segments may experience noticeable fluctuations in the level of added value produced (e.g. in education). However, historical data on actual government expenditure indicate that the overall publicsector output changes to a significantly lesser degree than businesssector output, i.e. there is mostly a redistribution of funding between government expenditure. Under this assumption, the size of output gap for an economy equals the size of the output gap in the business sector. It should be noted that, in going from analyzing indicator levels to analyzing their growth rates, the deviation in actual output growth rates from structural growth rates becomes a similar indicator to the output gap.
3. Methodology for decomposing Russian GDP growth rates
The methodology for decomposing GDP growth rates, adapted for the case of Russia’s economy in
In this study, we suggest a number of additions and changes to the methodology, which can improve the quality of decomposition for Russian GDP growth rates. First, due to distinctive features of the dynamics, businesssector output and publicsector output will be considered separately:
The proposed methodology of GDP growth rate decomposition is an algorithm consisting of three main steps (as in
A. The calculation of structural GDP growth rates consists of the following steps.
1. Expressing aggregate output Y as the sum of businesssector output Y ^{b} and publicsector output Y ^{pub} — see equation (1).
2. Modeling businesssector output based on the Cobb–Douglas production function in a logarithmic expression:
ln Y ^{b} = ln E ^{b} + α ln K ^{b} + (1 – α)ln L^{b} (2)
where: E ^{b} — businesssector total factor productivity level; K ^{b} — businesssector capital stock;
3. Calculation of the Solow residual series for businesssector output (in a logarithmic expression):
ln E ^{b} = ln Y ^{b} – α ln K ^{b} – (1 – α)ln L^{b} (3)
4. Smoothing the Solow residual series with the Hodrick–Prescott filter. The suitability of this filter for smoothing the Solow residual series, from which growth rates are interpreted as an indicator of TFP trends, is based on the fact that, due to the nature of this indicator’s calculation, its composition includes more than just trends in technological progress: the Solow residual series also reflects supply and demand fluctuations, the utilization rate of production factors and the impact of sharp changes in oil prices. The smoothing procedure is aimed at reducing the impact of these nontechnological factors on TFP growth rates.
5. The calculation of the businesssector potential employment level, provided that actual rate of unemployment equals to structural rate of unemployment NAWRU, L^{b}^{*} is made according to the following equation (4):
L^{b} ^{*}= LFS (1 – NAWRU) – L ^{pub} (4)
where: LFS — size of economy’s labour force; NAWRU — nonaccelerating wage rate of unemployment (structural rate of unemployment); L ^{pub} — size of employment in the public sector.
The structural rate of unemployment is calculated using the methodology proposed in
NAWRU = U – (DU / D^{3} logW) × D^{2} logW (5)
where: U — rate of unemployment; W — average monthly nominal wage; and D is the firstdifference operator.
In this paper we use the structural unemployment indicator smoothed by the Hodrick–Prescott filter. Thus, expression (4) was transformed into:
L^{b} ^{*}= LFS (1 – NAWRU ^{HP}) – L ^{pub} (6)
6. The final expression (6) was used to calculate the employment level in the business sector (at the level of structural unemployment NAWRU ^{HP}) . The trend in the smoothed structural unemployment NAWRU ^{HP} indicator is represented in Fig.
Actual and structural unemployment in Russia under various macroeconomic scenarios.
Source: Authors’ calculations.
7. Modeling structural output for the business sector:
ln Y ^{b}^{*} = ln E ^{b}, ^{HP} + α ln K ^{b} + (1 – α)ln L^{b}^{*} (7)
where: Y ^{b}^{*}— businesssector structural output; E ^{b}, ^{HP}— Solow residual smoothed by the Hodrick–Prescott filter; K ^{b}— businesssector capital stock, taken with a 1year lag;
8. Calculation of structural GDP level,
Y ^{*} = Y ^{b}^{*} + Y ^{pub} (8)
9. Calculation of structural GDP growth rates, g_{t}^{s}:
g_{t}^{s} = Y ^{*} _{t} / Y ^{*} _{t} _{–1} (9)
10. Smoothing structural GDP growth rates.
B. The algorithm for calculating the effect of the terms of trade on GDP growth rates (the foreign trade component of the growth rates) consists of the following steps.
1. We calculated the difference between actual and structural GDP growth rates, g_{t}^{res}:
g_{t}^{res} = g_{t} – g_{t}^{s} (10)
where: g_{t} — actual GDP growth rates at time t; g_{t}^{s} — structural GDP growth rates at time t.
2. “Residual” growth rates not attributable to the fundamental factors are modeled based on the equation:
g_{t}^{res} = γ _{0} + γ_{1}tot_{t} + τ_{t}, (11)
where: tot_{t} — terms of trade index for the Russian economy at time t; and τ_{t} is the free term.
Terms of trade index series is calculated by the World Bank and the OECD.
where: P_{t}^{oil} — actual level of the Urals oil price at time t; — longterm average annual Urals oil price at time t.
3. The theoretical value of the equation for g_{t}^{res} is considered to be the foreign trade component of GDP growth rates.
C. The value of the residuals τ_{t}, obtained through an econometric evaluation of equation (11), is considered to be the cyclical component of economic growth rates.
According to the GDP decomposition methodology described above for the period from 2000 to 2017, the TFP series were calculated for the business sector and for the Russian economy as a whole (Fig.
Estimates of TFP growth rates in the business sector and in the Russian economy using actual data (without forecasts).
Source: Authors’ calculations.
To obtain the trend values for TFP growth rates, we used the procedure for smoothing dynamic series with the Hodrick–Prescott filter, characterized first by inertia and second by sensitivity to adding new observations to the initial time series. Adding new observations may cause a retrospective revaluation of the results of smoothing the dynamic series, especially near the most recent values (the socalled “endpoint bias” effect; see
According to the calculations, a local minimum for the unsmoothed series of TFP growth rates for the Russian economy can be identified approximately around 2015 (see Fig.
It should be noted that the varying estimates of TFP growth rate trends (as well as varying estimates of smoothed Solow residuals series, caused by similar reasons) may lead to a change in the estimate of the level and growth rates of the structural GDP indicator (see equations (7) – (9)). Along with the scheme of algorithm described above, a disturbance appearing at this stage will also apply to the estimates for the foreign trade and cyclical components of output growth rates. In other words, the estimates for the contributions of various components to the growth rates in the Russian economy are largely dependent on the data used and, more precisely, on the period of estimation selected.
Fig.
Decomposition of growth rates of Russia’s GDP from 2000 to 2017 (%).
Source: Authors’ calculations.
The value of the change in the TFP growth rate trend estimates and the structural component of GDP growth rates, in the general case, will be less the further the observation is from the endpoint of the time series to be smoothed. In other words, adding new observations enables us to move potential changes associated with the “wagging” effect closer to the end of the extended time series and produces alternative retrospective estimates of the TFP growth rates and the value of individual GDP growth rate components. This problem was reflected in the OECD papers: as a solution, it was proposed to use updated estimates of the output gap as new statistics emerged to enable a more accurate calculation (
The problem associated with the sustainability of the GDP growth rate decomposition results can be illustrated as follows. We built estimates of the structural, foreign trade and cyclical components of economic growth rates for the period from 2010 to 2013, based on the data available as of the end of 2013, 2014, 2015, 2016 and 2017 (Figs
Range of estimates for the structural component of growth rates, 2010–2013 (%).
Source: Authors’ calculations.
Range of estimates for the foreign trade component of growth rates, 2010–2013 (%).
Source: Authors’ calculations.
Range of estimates for the cyclical component of growth rates, 2010–2013 (%).
Source: Authors’ calculations.
As we can see, adding new observations (2014–2017 data) causes a retrospective revaluation of all growth rate components. The range of estimates for the structural component of growth rates varied between 0.98 and 1.69 p.p., foreign trade, between 0.74 and 0.93 p.p. and cyclical, between 0.38 and 0.90 p.p. Estimates of the economic growth components for earlier periods (2010) are usually revised to a significantly lower degree than for the more recent ones (2013). The estimate for the structural component of growth rates is the most sensitive to adding new observations; the estimate for the foreign trade component is the least sensitive.
Thus, the estimated contributions of various components to GDP growth rates may become distorted closer to the end of the time series, especially in the case of further trend reversal. Due to the equivalence between actual GDP growth rates and the sum of the structural, foreign trade and cyclical components, any error in the estimate of any one of them will cause errors in the estimates of the others. For example, if Russia’s economy begins to grow faster in the future with the same cyclical component, part of that growth can be attributed to the accelerated TFP, while the local minimum point in its trend will be associated with the current period. If the growth rates are low, they will correspond to a stagnation of TFP trend values and structural growth rates.
The contribution of the various components to GDP growth rates can be modeled taking these aspects into account, using the hypotheses regarding the anticipated macroeconomic scenarios. The parameters for the economic development scenarios can be borrowed from the official forecast by the Russian Ministry of Economic Development.
The parameters for the official macroeconomic scenarios (baseline and conservative) for 2018 to 2024 are given in Table
Macroeconomic scenarios from 2018 to 2024
Indicator  2018  2019  2020  2021  2022  2023  2024 
Baseline Scenario  
Real GDP growth rate, %  1.8  1.3  2.0  3.1  3.2  3.3  3.3 
Workforce population, millions  75.8  75.8  75.9  76.0  75.9  76.2  76.3 
Unemployment rate, %  4.8  4.8  4.7  4.7  4.6  4.6  4.6 
Consumer price index, year end  103.4  104.3  103.8  104.0  104.0  104.0  104.0 
Nominal accrued average monthly wages of employees in organizations, RUB/month  43,008  45,639  48,099  51,256  54,801  58,543  62,617 
Fixed capital volume index*  102.3  102.4  102.8  103.2  103.5  103.7  104.0 
Urals oil prices (global), USD/bbl  69.6  63.4  59.7  57.9  56.4  55.1  53.5 
Conservative Scenario  
Real GDP growth rate, %  1.8  1.0  1.9  2.5  2.9  3.0  3.0 
Workforce population, millions  75.8  75.8  75.9  76.0  75.9  76.2  76.3 
Unemployment rate, %  4.8  4.9  4.8  4.8  4.7  4.7  4.7 
Consumer price index, year end  103.4  104.6  104.0  104.0  104.0  104.0  104.0 
Nominal accrued average monthly wages of employees in organizations, RUB/month  43,008  45,631  48,138  51,066  54,413  57,946  61,840 
Fixed capital volume index*  102.3  102.3  102.7  102.9  103.1  103.3  103.4 
Urals oil prices (global), USD/bbl  69.6  56.0  42.5  43.3  44.2  45.0  45.9 
The conservative scenario involves lower oil prices and lower GDP growth rates combined with higher unemployment and inflation rates. GDP trend scenarios, according to forecasts by the Russian Ministry of Economic Development, are shown in Fig.
Real GDP growth rates under various macroeconomic scenarios for 2018 to 2024 (%).
Source: Russian Federation Ministry of Economic Development.
These scenarios for 2018 to 2024 are accompanied below by a decomposition of Russian GDP growth rates during the period from 2007 to 2017. The decomposition of growth rates from 2007 through 2017, based on actual data for economic trends from 2018 to 2024, will obviously differ from those obtained below, if it differs from the forecast.
The estimates for structural growth rates under various macroeconomic scenarios at the end of 2017 are 1.4 to 1.5 p.p. The deceleration between 2007 and 2017 (Fig.
Structural growth rates under various macroeconomic scenarios (%).
Source: Authors’ calculations.
The consistent deceleration of smoothed TFP growth rates, observed since the early and mid2000s (Fig.
TFP growth rates in the Russian economy based on various decomposition methodologies.
Source: Authors’ calculations.
The local minimum point for the smoothed TFP growth rates (TFP trend) falls during the period from 2014 through 2017. An additional factor in the decelerating TFP trend since 2014 has been sectoral
In the short and medium term, those events helped to shape negative expectations by economic agents. The latter, combined with the increased costs to raise capital due to credit rationing and increased interest rates for Russian companies, as well as the higher costs of imported investment goods caused by the ruble devaluation in December 2014, led to lower investments. The first protracted “investment pause” since 1999, observed between 2013 and 2016,
At the end of 2017, the growth in fixed capital investments at comparable prices was 4.4%, which may indicate the end of the “investment pause”. With a certain lag, this could be helped by the import substitution programs which were activated in response to the sectoral sanctions in 2015 and also in the capital goods segment (see more in
In case of the conservative economic development scenario, recovery of the TFP trend is expected to be slower than in the baseline scenario with a corresponding impact on GDP structural growth rates (Fig.
TFP growth rates in the Russian economy under various macroeconomic scenarios.
Source: Authors’ calculations.
From 2013 through 2017, the amount of retired fixed capital did not exceed the amount of new fixed capital, but deceleration of the growth rates trend for the capital stock in the Russian economy provided an additional reason for deceleration of the structural GDP growth rates in recent years (Fig.
The economy’s structural growth rate is under considerable influence from unfavorable demographic conditions. According to the “medium case” demographic forecast by Rosstat in the period from 2019 to 2035, the workingage population will decrease by 3.10 million.
Workingage population in the “medium case” longterm demographic forecast (thousands of people).
Note: This forecast does not take into account a scenario for increasing the retirement age. Source: Rosstat.
The gradual decrease in the workingage population under particular conditions can be viewed as an incentive for businesses to renew capital to preserve output levels or growth rates and to invest in laborsaving technologies including automation and robotics. Capital renewal enables a noncontradictory combination of reduced labor resources, increased capital stock and TFP growth within a single macroeconomic scenario.
At the same time, with no capital renewal or TFP growth, ensuring predicted structural growth rates in the economy’s output will require greater migration. Currently, the increase in migration forecast by Rosstat for 2019 to 2035 is around 208,000 to 282,000 people per year (Fig.
The foreign trade component trend was determined based on the Urals oil price forecast by the Russian Ministry of Economic Development (Figs
Oil prices: actual values and forecasts for 2018 to 2024 (Urals, USD/bbl).
Note: According to assumptions by the Russian Ministry of Economic Development, pressure on the oil market will be exerted by increased production of the OPEC+ deal countries, as well as by an increase in shale oil production in the United States. Yet in the short term, a further rise in oil prices is possible under the influence of declining supply from some large oilproducers, such as Iran, due to the influence of USA sanctions. The longterm average annual price is calculated for the baseline scenario. Source: Authors’ calculations.
Foreign trade component of GDP growth rates from 2007 to 2024 under various macroeconomic scenarios (%).
Source: Authors’ calculations.
From 2007 to 2014 (except for 2009), high oil prices contributed to a positive foreign trade component in GDP growth rates. Following a short period of stabilization at around USD 100/bbl from 2011 to 2014, oil prices began to fall sharply.
The drop in oil prices in 2015 below longterm average annual values caused the foreign trade component to fall below zero. The signing of a cartel agreement to limit oil production by OPEC countries and 11 oilexporting countries, including Russia, interrupted the price decline in 2016. In 2017, the agreement to limit production was renewed until the end of 2018, which supported oil prices. In addition, on May 8, 2018, the United States declared their exit from the Iranian nuclear program treaty and in August, they declared the renewal of sanctions to restrict, amongst other things, supplies of Iranian oil to the global market. In June 2018, as part of the OPEC+ deal, it was decided to increase oil production by 1 million bbl per day for the purposes of limiting prices for energy resources and deceleration in the development of shale oil and gas production around the world. However, in December 2018, in view of a growing imbalance between global oil supply and demand, and weakening oil prices, the cartel participants agreed to cut oil production by 1.2 million bbl per day since the beginning of 2019, which means a restoration of 2016 quotas.
According to the forecast under review, oil prices will remain below longterm average annual prices in the near future, which will correspond with a negative value for the foreign trade component. Under various macroeconomic scenarios, losses from deteriorating terms of trade will be around 1 p.p. of GDP growth rates per year.
The trends in the cyclical component are shown in Fig.
Affected by the global financial crisis of 2008 and 2009, the cyclical component of GDP growth rates dropped below zero. At the end of 2008, a transition began towards the downward phase of the business cycle. In 2009, the cyclical component was reduced by a random shock caused by a number of overlapping adverse factors, including changing expectations by economic agents (investor pessimism contributed to a 13.5% reduction in investment due to passing the lowest point of the global financial crisis in 2009), as well as by the decline in demand for Russia’s main exports.
The postcrisis period was characterized by a transition to recovery growth. A slight increase in positive expectations by economic agents also occurred (e.g. between March and October 2012, the business confidence level in the manufacturing sector (according to Rosstat) hit positive territory for the first time since the 2008–2009 crisis). Nevertheless, these trends were unstable due to the gradual reduction of the cyclical growth rate component into negative values.
From 2013 to 2015, the cyclical recession deepened, aggravated by a number of random shocks. The “investment pause” in the Russian economy began in 2013. In 2014, the intensified sanction and counter sanction policy heightened the risks associated with economic activity in Russia, both for domestic and, especially, for foreign investors (read more in
Although the RF Central Bank had pursued the key rate reduction policy since Q1, 2015, real interest rates on loans in 2016, calculated based on the actual inflation rate, remained at their highest levels since 2007. Despite the fact that the real interest rates, calculated based on the expected inflation rate, were rather low during the same period, under conditions of high uncertainty, including with respect to the effectiveness of inflation targeting policy, investments continued to decline.
In 2017, for the first time in Russia’s modern history, the target inflation rate was achieved. Its decrease encouraged the “anchoring” of inflation expectations and a lowering in the level of uncertainty, offsetting the adverse impact of high real interest rates. Investments and the economy as a whole responded positively to the disinflation policy. Investments increased in 2017 and the growth component related to the business cycle moved into positive territory.
The cyclical component of GDP growth rates is expected to trend positively from 2018 to 2024. At the same time, the economic situation remains uncertain due to the potential for new sanctions against Russian economic agents, including personal sanctions and visa restrictions that affect the interests of Russian entrepreneurs.
Thus, the decomposition of GDP growth rates shows that, under different macroeconomic scenarios, there is a high probability of stabilization of structural growth rates component of Russia’s economy at the level of approximately 1.5 p.p. per annum. A significant source of growth will also be a component of business cycle, which is expected to be around 2.0 p.p. per annum (through the period 2020–2024). Thus, in the baseline and conservative scenarios prepared by the Russian Ministry of Economic Development, economic growth rates will be positive due to the combination of the structural and cyclical components. Meanwhile, if the unfavorable foreign trade situation persists, which has a negative impact on growth rates (around –1 p.p. per annum) and, given the likelihood of new sanctions that may negatively impact business conditions in Russia, there is potential to shift from slow growth to stagnation or even recession.
During the late 1990s and early 2000s, the structural growth rates for the Russian economy were around 5.0–5.5 p.p., driven by the posttransformation recovery growth and improved terms of trade. The lack of institutional transformations, or their slow pace, caused a gradual deceleration in structural growth rates to 1.5 p.p. by 2017.
The positive contribution by foreign trade conditions, which was up to 3.1 p.p. of the GDP growth rate during the period from 2000 to 2014 (except for 2009), factored into the slow pace of institutional transformation in Russia during that time. Creating obstacles for developing social institutions during the periods of favorable commodity market conditions is the basic mechanism of the resource curse, which results in lower longterm output growth rates in the economy (see
From 2015 to 2017, for the first time in quite a long period, the foreign trade component of economic growth rates became negative due to falling global oil prices. It remains at around 1 p.p. per year since 2019.
The cyclical component of Russia’s economic growth rate over the period under review was highly variable, caused by different random and sometimes overlapping (as in 2008 and 2009 or in 2014) shocks. On the whole, in developing economies, the business cycle has the properties of a stochastic process, which complicates its identification and periodization. Nevertheless, an analysis of the decomposition of Russia’s GDP growth rates enables us to propose the following periodization of the business cycle phases during the time under review:
In the absence of the random shocks of 2014, the business cycle could have reached the bottom in 2013, while the period of reduced investment might have lasted only 1 or 2 years. Then, the cyclical component could have shifted into positive territory in 2015 or 2016. In fact, the sum of the business cycle and random shock components only achieved positive values in 2017, reaching between 1.1 p.p. and 1.2 p.p.
The actual growth rate of the Russian economy of 1.5% in 2017, according to our estimates, resulted from the negative impact of global prices for Russia’s basic exported goods on the one hand and from the positive impact of the structural and cyclical components of GDP growth rates on the other. The fact that a significant impact on growth rates in 2017 was exerted by the sum of the business cycle and random shock components, combined with increasing investment, may support the statement that this is the beginning of a new economic growth cycle in the Russian economy. However, it may be unstable under the effect of random shocks, including those caused by new sanctions.
The decomposition indicates stagnation in Russia’s structural GDP growth rates from 2019 to 2024 for the macroeconomic scenarios under review.