Global oil glut and sanctions: The impact on Putin’s Russia
Yelena Tuzova a,n
, Faryal Qayum b
a
MUFG Union Bank, 400 California Street, 12th Floor, San Francisco, CA 94104, United States
b
School of Social Science, Policy and Evaluation, Claremont Graduate University, 160 East Tenth Street, Claremont, CA 91711, United States
H I G H L I G H T S
 The impact of the recent decline in oil prices and western sanctions is analyzed.
 A vector autoregression model is used to do the forecast for Russia.
 The real GDP is likely to contract by 19 percent over the next two years.
a r t i c l e i n f o
Article history:
Received 19 June 2015
Received in revised form
28 November 2015
Accepted 8 December 2015
Available online 24 December 2015
Keywords:
Oil prices
Russian economy
Vector autoregressive model
Forecast
a b s t r a c t
The Russian economy is highly responsive to oil price ﬂuctuations. At the start of 2014, the country was
already suffering from the weak economic growth, partly due to the ongoing crisis in Ukraine and
Western sanctions. The recent plunge in global oil prices put even further strain on the Russian economy.
This paper analyzes the dynamic relationship between oil price shocks, economic sanctions, and leading
macroeconomic indicators in Russia. We apply a vector autoregression (VAR) to quantify the effects of oil
price shocks as well as western economic sanctions on real GDP, real effective exchange rate, inﬂation,
real ﬁscal expenditures, real consumption expenditures, and external trade using quarterly data from
1999:1 until 2015:1. Our results show a signiﬁcant impact of oil prices on the Russian economy. We
predict that Russia’s economic outlook is not very optimistic. If sanctions remain until the end of 2017,
the quarter-to-quarter real GDP will contract on average by 19 percent over the next two years.
& 2015 Elsevier Ltd. All rights reserved.
1. Introduction
For much of the past decade, oil prices have been high –
bouncing around $100 per barrel since 2010 – due to soaring oil
consumption in countries like China and conﬂicts in key oil nations
like Iraq. Oil production in conventional ﬁelds could not keep
up with demand, so prices spiked. High prices beneﬁted oil exporters
like Russia at the expense of oil importers. Soaring oil
prices spurred companies in the US and Canada to start drilling for
new, hard-to-extract crude in North Dakota’s shale formations and
Alberta’s oil sands. Then, over the last year, demand for oil in
places like Europe, Asia, and the US began tapering off, thanks to
weakening economies and new efﬁciency measures. Added to this
is the fact that the oil cartel OPEC decided not to cut production as
a way to prop up prices. By late 2014, world oil supply was on track
to rise much higher than actual demand, as shown in Fig. 1. Since
summer of 2014, the price of crude oil has declined by more than
half. If back in June 2014, the price of Brent crude oil was up
around $111 per barrel, in January 2015, it had fallen down to $48
per barrel, as can be seen in Fig. 2.
At the start of 2014, Russia was already suffering from weak
economic growth due to the ongoing crisis in Ukraine. In November
2013 Ukraine’s President Viktor Yanukovych refused to
sign a European Union Association Agreement (EUAS), which
meant to create a framework for cooperation between Ukraine and
the European Union (EU). Viktor Yanukovych’s rejection sparked
mass protests on the streets of Kiev. Russia backed ousted Yanukovych,
annexed Crimea in March of 2014 and invaded eastern
Ukraine. In response, the US and Europe levied sanctions on
Russian government ofﬁcials through assets freezes, visa bans, and
controls on exports of energy technology that would have helped
Russia develop its Arctic. Countering such actions Russia banned
food imports from the West. Fig. 3 shows a detailed timeline for
Ukraine-related sanctions.
The Ukraine crisis with several waves of Western economic
sanctions imposed on Russia combined with a 50-percent drop in
the global oil prices, Russia’s key commodity, put even further
strain on the Russian economy. After the country’s 1998 ﬁnancial
crisis, most of the oil produced has come from drilling and redrilling
old Soviet oil ﬁelds, squeezing more black gold out of the
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/enpol
Energy Policy
http://dx.doi.org/10.1016/j.enpol.2015.12.008
0301-4215/& 2015 Elsevier Ltd. All rights reserved.
n
Corresponding author.
E-mail addresses: yelena.tuzova@unionbank.com (Y. Tuzova),
faryal.qayum@cgu.edu (F. Qayum).
Energy Policy 90 (2016) 140–151
same ground. Over many years, almost no efforts were made to
develop new ﬁelds. The oil wealth is drying up. In response to
falling oil prices, the Russian economy started to fall into recession.
Ofﬁcial data shows that in 2014 the real GDP grew by only
0.4 percent. Over the last year, the ofﬁcial annual inﬂation rate
increased from 6 percent to 9 percent. Food prices climbed by 25
percent. Between June and December 2014, the Russian ruble
declined in value by 59 percent relative to the U.S. dollar. If in
2009–2013 private-sector net capital outﬂows averaged $57 billion
annually, in 2014 it increased sharply to $152 billion, according to
Fig. 1. World oil supply and demand. Source: International Energy Agency.
Fig. 2. Brent crude oil price. Source: Global Financial Data.
Fig. 3. Timeline for Ukraine-related sanctions. Source: Peterson Institute for International Economics.
Y. Tuzova, F. Qayum / Energy Policy 90 (2016) 140–151 141
Standard & Poor’s. In December last year, the Central Bank of
Russia (CBR) pushed interest rates all the way up to 17 percent.
Apparently, a big drop in the price of oil and geopolitical problems
have been very devastating to the Russian economy.
Falling oil prices paired with international sanctions imposed
on Russia have drawn considerable attention of politicians and
economists over the last year. But despite the general recognition
of the importance of both issues, no empirical studies exist that
numerically quantify the effects of both the oil price shock and the
imposition of economic sanctions on Russian macroeconomic dynamics.
To some extent, data problems partly explain the lack of
empirical macroeconomic work on Russia’s economy. The time
series for Russia are at times either missing or inconsistent. Although
we were able to ﬁnd a few papers that study the relationship
between oil prices and Russia’s macroeconomic performance,
all of them cover the time when energy prices had a
tendency to grow, leaving the macroeconomic effects of falling oil
prices outside the analysis. The economic sanctions literature is
not more optimistic either. To the best of our knowledge, most
research on sanctions is policy-oriented and primarily discusses
the effectiveness and usefulness of sanctions as a substitute for
war. But none of the papers we have seen provides a theoretical
model that allows us to numerically estimate the impact of sanctions
on economic growth in Russia.
The contribution of this paper is to propose a tractable, quantitative,
macroeconomic framework that quantiﬁes the impact of
the most recent decline of oil prices together with the imposition
of economic sanctions on the Russian economy. Identifying and
simultaneously estimating the effects of falling oil prices and
Western sanctions is crucial since it would help us measure its
effects on GDP and its main components and possibly prescribe
better policies to prevent future economic crises. We use a vector
autoregression methodology (VAR) and employ the most recent
quarterly data sets for Brent oil prices, Russian GDP, household and
government consumption expenditures, investment, exports and
imports of goods and services, inﬂation and real effective exchange
rate that became available in late spring 2015. We use a dummy
variable to represent economic sanctions, assuming a value of
1 after 2014:2 and 0 otherwise.1
The results indicate that the
Russian economy is highly responsive to both oil price ﬂuctuations,
which conﬁrms the common perception of Russia’s dependence
on oil, and economic sanctions. In the end, we provide a
two-year economic forecast for 2015–2017.
The remainder of the paper is organized as follows. Section 2
discusses previous literature relevant to our study. Section 3 deals
with the data issues and describes a VAR methodology. Section 4
outlines the forecast for Russia for 2015–2017. Section 5 contains
the concluding remarks.
2. Literature review
There exists a plethora of economic studies investigating the
impact of oil price ﬂuctuations on macroeconomic performance in
industrialized countries and emerging economies. Most of these
studies concentrate on the effect of oil prices on the economic
growth, inﬂation dynamics, investment, current account balance,
and the exchange rate. Since the oil crisis of the 1970s, economists
have been trying to estimate the effects of oil price volatility in oil
importing as well as oil exporting economies, both small and large.
As it is well understood, the ﬁndings differ depending on whether
the economy is an oil-exporter or oil-importer. In addition, since
oil prices had a tendency to rise for much of the last decade, most
of the existing literature analyzed the high oil price phenomenon.
Let us review some of the work before we proceed to the model.
Small oil importing countries are price takers in the international
market due to their size. Their demand is not of a signiﬁcant
magnitude, which does not empower them to exert inﬂuence on
the international market. Thus, they take oil prices as given. For
such countries, high oil prices are undoubtedly associated with
low economic growth. High energy prices adversely affect consumer
spending through disposable income, fuel the higher costs
of production, lower proﬁts, and, as a result, cause the growth rate
to fall (see, e.g., Hamilton, 1983, 1996, 2003; Burbidge and Harrison,
1984; Mork, 1989; Mork et al., 1994; Federer, 1996; Finn,
2000; Jiménez-Rodriguez and Sánchez, 2005; Prasad et al., 2007;
Jayaraman and Choong, 2009; Korhonen and Ledyaeva, 2010;
Bjornland, 2000; Farzanegan and Markwardt, 2009; Özlale and
Pekkurnaz, 2010). As an example, Aydin and Acar (2011) analyzed
the economic effects of oil price shocks in Turkey and conﬁrmed
that high oil prices cause reduction in output and consumption.
According to Özlale and Pekkurnaz (2010), most of the small open
oil importing economies do not succeed in generating enough
savings, which is necessary to ensure high investment levels and
sustainable growth. The increased dependency on energy imports
destabilizes these economies and results into high ratios of current
account deﬁcits. Furthermore, with the increase in oil prices,
money demand also increases, which causes inﬂation to rise and
investments to fall (see, e.g., Eryiğit, 2012; Tang et al., 2010).
Unlike small economies, large oil importing economies – the
economies that have the market power to affect world oil markets
– are less sensitive to oil price shocks. Research shows that in
countries like the U.S., Europe and China, while the impact of oil
price ﬂuctuations is still present, the negative effects of rising oil
prices pale in comparison to small oil importing economies. It is
true that any shift in oil price results in substantial revisions in
these countries’ national budgets, but, as shown in Zaouali (2007),
the negative effect is not as severe due to strong investment and
foreign capital inﬂows that can offset the adverse effects of high oil
prices.
On the other hand, oil exporting countries, like OPEC, Russia,
Norway, and Canada, beneﬁt from high oil price. High oil prices
help net oil exporters generate high proﬁts (see, e.g., Mork et al.,
1994; Bjornland, 2000; Korhonen and Ledyaeva, 2010; Rautava,
2004; Ross DeVol, 2015). In this regard, Mork et al. (1994) and
Bjornland (2000) showed a positive effect of oil price volatility on
the Norwegian economy. Rautava (2004) reported a positive effect
of oil price increase on the Russian economy. He found that a 10
percent increase in oil prices leads to a 2.2 percent growth in
Russia’s GDP. Ito (2010) also studied the impact of the rising oil
prices on the Russian economy and reported a 0.46 percent growth
in Russia’s GDP in response to a 1 percent increase in oil prices.
According to Beck et al. (2007), the positive effect of rising oil
prices on Russia’s GDP growth increases over time, but it can be
hampered by the real effective exchange rate appreciation, which
stimulates imports. On the contrary, negative oil price shocks adversely
affect output growth. For instance, Cukrowski (2004) argued
that for Russia low oil prices have the potential to destabilize
the overall economy through a setback to output and ﬁscal revenue.
In addition, Mehrara (2008) found that in heavily oil-dependent
countries, oil revenue shocks affect output asymmetrically,
that is, output growth is adversely affected by the negative
oil shocks whereas positive oil shocks play a limited role in economic
growth. Farzanegan and Markwardt (2009) also found an
asymmetric relationship between oil price shocks and industrial
production.
1
The authors consider three scenarios. The ﬁrst scenario assumes that the US
and EU sanctions will be in place throughout 2017. That is, the dummy variable
takes on a value of 1 from 2014:2 until 2017:4. In the second scenario, the sanctions
are valid until 2016:4. The third and last scenario is when sanctions are removed at
the end of 2015:4.
Y. Tuzova, F. Qayum / Energy Policy 90 (2016) 140–151142
Some research suggests that oil prices tend to inﬂuence the
exchange rates. As an example, Akram (2004) and Rautava (2004)
studied the cases of Norway and Russia and found that for oil
exporting economies, an increase in oil price results in an exchange
rate appreciation. Farzanegan and Markwardt (2009)
found that an increase (decrease) in oil prices appreciates (depreciates)
the real effective exchange rate in Iran. On the other
hand, Ito (2010) found that a rise in oil price causes the Russian
currency to depreciate both in the short run and long run. Méndez-Carbajo
(2011) found that for small open economies like that
of the Dominican Republic, the rise in oil prices causes depreciation
of the local currency.
There are a few economic studies that concentrate on the indirect
effects of oil price rise from both the exporters’ and importers’
perspectives. For example, high oil prices lower aggregate
income in oil importing countries and reduce the export demand
of oil supplied by oil producing countries. At the same time,
households and ﬁrms start consuming more oil produced domestically
and by doing so help local producers generate higher
earnings (see, e.g., Abeysinghe 2001; Korhonen and Ledyaeva
2010).
Our analysis is closely related to Rautava (2004)’s and Ito (2008,
2010)’s research but has several innovations. For instance, using a
VAR model and a cointegration framework, Rautava (2004) examines
the effect of oil price and real exchange rate changes on
GDP and ﬁscal revenues. He uses quarterly data on real GDP, federal
government revenue, the real effective exchange rate and oil
prices from 1995:1 until 2001:3. Real GDP, federal government
revenue, and the real effective exchange rate are modeled as endogenous
variables whereas international oil prices are treated as
an exogenous variable. All are expressed in logarithmic form. Ito
(2008) uses the co-integrated VAR to investigate the effects of oil
price on real GDP, inﬂation, and interest rate. He also uses quarterly
data that spans from 1995:3 until 2007:4. Ito (2010) extends
his previous work on the impact of oil prices on the macroeconomic
performance in Russia and now includes real oil prices,
real GDP, inﬂation and real effective exchange rate from 1994:1
until 2009:3. Compared to the previous research done on Russia,
our analysis covers the period from 1991:1 until 2015:1. We use
quarterly data on real GDP and all major GDP components (seasonally
adjusted) expressed in real terms and model them as the
ﬁrst difference. International oil prices are treated as an endogenous
variable in our model. We also introduce a new dummy
variable for sanctions. We select these variables because they are
the most commonly used in the business cycle theory.
Since we attempt to model sanctions, let us brieﬂy describe
some research work done on economic sanctions. Many scholars
argue that sanctions are largely ineffective (see, e.g. Galtung, 1967;
Knorr, 1975; Bienen and Gilpin, 1980; Von Amerongen, 1980;
Lindsay, 1986; Doxey, 1987; Pape, 1997; Haass, 1997). The success
rate of sanctions ranges from less than 5 percent historically to
approximately 34–38 percent at best (see, e.g., Hufbauer et al.,
1990; Pape, 1997; Drezner, 1999). Politicians and policy makers
largely consider them as a substitute for war but always debate
about their effectiveness. Drezner (1999) uses game theory to
predict whether to impose sanctions or not, and if implemented,
how effective those sanctions are. He argues that the imposition of
sanctions causes a deadweight loss of utility for both the sender
country and target country, and thus both countries try to ﬁnd a
compromise and make an agreement before imposition. He suggests
that if the sender country incurs small economic costs in
relation to GDP in imposing sanctions, while the target country
incurs tremendous losses, the large gap in opportunity costs
makes both the sender more likely to impose sanctions and the
target country more likely to concede.
Now, what are the incurring costs of economic sanctions for
Russia? The multilateral economic sanctions due to the Russia–
Ukraine geopolitical tensions have hit the Russian economy
through three main channels. First, these tensions led to massive
capital outﬂows, deteriorating Russia’s capital and ﬁnancial account
balance. Further, falling oil prices caused the ruble to lose
half of its value against the US dollar. The depreciation of the ruble
increased inﬂationary pressures, resulting in a signiﬁcant tightening
of monetary conditions. This increased costs to borrowing,
further restricting access to domestic credit for both investors and
consumers. Second, the sanctions restricted Russia’s access to international
ﬁnancial markets, as most Western ﬁnancial markets
were closed to Russian banks and companies. Third, business and
consumer conﬁdence deteriorated as a result of increased uncertainty.
further contracting consumption and investment activities.
Lastly, foreign direct investment into Russia fell signiﬁcantly
in the ﬁrst three quarters of 2014. Compared to the same quarters
in 2011–2013, foreign direct investment decreased by 47 percent
(World Bank Group, 2015). The sanctions have also had substantial
impact on trade ﬂows. Russia’s ban on food imports from Western
countries and the weakening exchange rate resulted in a plunge in
imports.
3. Econometrics analysis
To quantify the impact of a recent fall in oil prices on the
Russian economy, we collect quarterly data for the period of
1999:1 to 2015:1. The variables used in the model include: inﬂation
rate (INFL), measured by the percentage changes of consumer
price index (CPI, 2010¼100); real effective exchange rate (REER,
2010¼100); real oil prices (ROP); real GDP at constant 2010 prices
(RGDP_2010), real household consumption expenditure (RCP), real
government consumption expenditure (RCG), real investment (RI),
real exports (RX), and real imports (RM). The consumer price index
is used as a deﬂator to obtain real ﬁgures. Identiﬁable seasonality
is present in almost all variables, except real oil prices, real
effective exchange rate and inﬂation. As such, they are seasonally
adjusted (SA) using a moving-average multiplicative decomposition.
Thus, there is no need to include seasonal dummies in the
model.
For oil prices, we use quarterly Brent crude oil prices, provided
by the International Energy Agency (IEA), and then deﬂate them
by CPI. It could well be argued that West Texas Intermediate oil
prices or Urals oil prices could also be employed. Since these three
oil price measures are highly correlated, utilizing Brent crude oil
does not change the main ﬁndings of the paper. The rest of the
data is taken from the IMF International Financial Statistic (IFS)
and Global Financial Data (GFD) online portals.
We have chosen the vector autoregression (VAR henceforth),
developed by Sims (1980), to analyze the relationship of selected
variables with each other. In general, a VAR is an n-equation,
n-variable model in which each variable is in turn explained by its
own lagged values, plus (current) and past values of the remaining
−n 1 variables. The multivariate generalization of an autoregressive
process can be written as
∑ γ Ω= + + + ϵ ϵ ~ ( )
=
−
( − )Z A A Z SANC i i d, . . . 0,t
i
p
i t i t t t0
1
1
where Zt is an ×n 1 vector containing each of the n variables
included in the VAR; A0 is an ×n 1 vector of intercept terms; Ai,
= … −i p1, , 1, is an ×n n matrices of coefﬁcients; and εt is an
×n 1 vector of error terms for = …t T1,2, , . In addition, εt is an
independently and identically distributed (i.i.d.) with zero mean,
ε( )=E 0t and an ×n n symmetric variance–covariance matrix Ω,
Ω(ϵ ϵ′ ) =E .t t The variable SANCt is the sanction variable used as
Y. Tuzova, F. Qayum / Energy Policy 90 (2016) 140–151 143
dummies in the empirical speciﬁcation. It takes on a value of
1 from 2014:2, which is the time when the US and EU imposed the
ﬁrst round of sanctions on Russia.
Macroeconomic time series are often non-stationary. To do
proper forecasts, all series must be stationary. Considering the
small sample size, we apply the Dickey–Fuller t-test to check for
stationarity. Assuming the null hypothesis of unit root is adopted,
if the t-statistic in absolute value is smaller than all the critical
values, the data are non-stationary. On the other hand, if the tstatistic
is larger than the critical values at 1%, 5% and 10% signiﬁcance
levels, then the data are stationary. Test results are
shown in Table 1.
The results of the Dickey–Fuller t-test indicate that the series
are non-stationary when the variables are deﬁned in levels, except
inﬂation (INFL). All non-stationary series are expressed in the ﬁrstdifference
form.
The number of coefﬁcients in each equation of a VAR is proportional
to the number of variables in the VAR. We have to acknowledge
the fact that the more variables we select, the more
lags we use and the higher the amount of estimation error, which
can result in a deterioration of the accuracy of the forecast. We
select nine (endogenous) variables for the VAR model. A constant
term and a dummy variable are treated as exogenous. Compact
form of the system of equations becomes
γ= + + + ϵ
=
_
_
−
⎡
⎣
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎤
⎦
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
Z A A Z SANC
Z
DREALBRT R
DRGDPSA
DRCPSA
DRCGSA
DRISA
DRXSA
DRMSA
INF
DREER
,
where
2010
.
t t t t
t
t
t
t
t
t
t
t
t
t
0 1 1
To determine the optimal lag selection, we have used Akaike
information criterion (AIC). In this study, the optimal lag is one.
The results are shown in Table 2.
After estimating the model over the sample period from 1999:2
to 2015:1, we obtain impulse response functions for periods
1 through 10 for each of the nine shocks. Table 3 provides the
impulse responses of selected variables to shocks in the real Brent
crude oil (DREALBRT_R) price. Note that all variables are expressed
in the ﬁrst difference.
Table 3 shows that the effect of one-standard-deviation shock
in the ﬁrst difference of real price of oil (DREALBRT_R) on eight
variables used in the model. As shown in Table 3, DREALBRT_R
(equal to 2.73 units) induces a contemporaneous increase in
DRGDPSA_2010 of 2,023,233,465.72 units and a contemporaneous
increase in DRXSA of 1,287,261,821.77 units. After one period,
DREALBRT_R is still 0.40 units above its mean, while
DRGDPSA_2010 is still 1,990,564,828.35 units higher. But after two
periods DRGDPSA_2010 drops signiﬁcantly to 29,727,966.21. This
can be clearly seen in Fig. 4(a).
One difﬁculty with the impulse responses reported above is
that they are not standardized to account for differences in the
units of measure. Thus, we adapt our program segment and divide
each response by the standard deviation of the appropriate residual
variance. The standardized impulse responses are plotted in
Fig. 4(b). We can see that once the real price of oil falls, all of the
variables, excluding inﬂation, also decrease.
In Table 4, the ﬁrst column in the output is the standard error of
forecast for this variable in the model. The remaining columns
provide the variance decomposition. In each row, they add up to
100 percent. In our sample, 30.51 percent of the variance of the
one-step forecast error of the ﬁrst difference of real GDP
(DRGDPSA_2010) is due to the oil price ﬂuctuations (DREALBRT_R)
and 69.5 percent is due to the innovation in the ﬁrst difference of
real GDP itself (DRGDPSA_2010). However, the more interesting
information is at the longer steps, where the interactions among
the variables start to become felt. We have truncated this table to
10 lags to keep its size manageable, but ordinarily one should
examine at least four year’s worth of steps. According to Table 4,
the four principal factors driving real GDP (DRGDPSA_2010) are
GDP itself (DRGDPSA_2010), real oil prices (DREALBRT_R), real
consumption (DRCPSA), real investment (DRISA), and real imports
(DRMSA). As Table 4 shows, the DRGDPSA explains 50.1 percent of
its 10-step ahead forecast error variance, DREALBRT_R explains
35.5 percent, DRCPSA explains 4.9 percent, DRISA explains 3.1 percent,
and ﬁnally DRMSA explains 3.1 percent of the forecast error
variance in DRGDPSA_2010. The other variables have negligible
explanatory power for DRGDPSA_2010.
4. Economic forecasting
We use our nine-variable VAR model to do forecasting for
2015–2017. Sanctions are treated as an exogenous variable. Table 5
provides the forecast for the selected variables under different
scenarios. In Table 5(a) we show the numerical estimates of the
Table 1
Dickey–Fuller unit root test.
Variables ConstantþTrend
REALBRT_R À3.27456
DREALBRT_R À6.69103**
RGDPSA_2010 À1.57442
DRGDPSA_2010 À7.72990**
RCPSA À0.69865
DRCPSA À4.62250**
RCGSA À0.71368
DRCGSA À7.43639**
RISA À1.83076
DRISA À8.74126**
RXSA À2.78860
DRXSA À6.09431**
RMSA À2.73976
DRMSA À6.77091**
REER 1.58758
DREER À6.39567**
INFL À4.26737**
Note: Unit-root computations are
made using RATS 8.2, which computes
the Dickey–Fuller t-test. The regression
includes a time-trend and a constant
with zero lags.
*
Signiﬁcant at the 5% level.
**
Signiﬁcant at the 1% level.
Table 2
VAR lag selection.
Lags AIC criterion
0 14,689.4715
1 14,602.5515a
2 14,701.9951
3 15,036.7172
4 15,840.4248
a
Indicates lag order
selected by the criter-
ion.
Y. Tuzova, F. Qayum / Energy Policy 90 (2016) 140–151144
Table 3
Impulse responses of selected variables to the shocks in the real crude oil and real GDP.
Entry DREALBRT_R DRGDPSA_2010 DRCPSA DRCGSA DRISA DRXSA DRMSA INFL DREER
Responses to shock in DREALBRT_R
1 2.73027138 2,023,233,465.722774 266,073,643 60,140,048 609,914,343 1,287,261,821.766380 302,075,229 À0.28870282 0.15747918
2 0.40897450 1,990,564,828.346367 413,101,822 183,652,103 820,832,100 1,071,935,591.824393 412,917,771 À0.79227807 1.00455772
3 À0.23930261 29,727,966.212621 154,320,833 54,978,095 92,055,019 À88,717,943.631890 204,666,216 À0.70740403 0.21770700
4 À0.18301081 À24,165,269.039806 21,355,558 12,431,314 1,306,116 À133,029,393.351509 À29,177,471 À0.37680297 À0.15510611
5 À0.02906369 À232,308,714.258568 À65,475,526 À36,049,638 À133,071,915 À32,465,444.049045 À33,609,067 À0.21240715 À0.13045393
6 0.01387325 31,779,482.646454 À9,169,118 6,551,658 45,027,953 À12,829,504.097800 À11,032,479 À0.13751743 0.05222322
7 0.00438291 À7,968,163.435865 8,525,682 À4,423,573 À16,074,980 4,114,813.496807 1,154,205 À0.07646555 0.00798352
8 0.00048017 22,911,265.185820 7,279,399 5,128,967 16,832,810 2,708,994.663106 7,473,572 À0.06450937 0.00575156
9 À0.00222638 À1,793,724.804574 À114,304 À1,038,472 À3,933,628 1,624,012.267901 802,411 À0.04086011 À0.00759604
10 À0.00270838 À2,535,470.766582 À1,017,087 61,689 1,023,888 À1,968,291.390965 À149,386 À0.02719712 0.00297373
Responses to shock in DRGDPSA_2010
1 0.00000000 3,053,216,134.011144 456,183,744 285,946,575 1,993,216,284.285092 831,563,487 292,978,110 0.069082366 0.31447761
2 0.13381359 À1,105,545,801.875696 À143,270,599 À171,184,790 À663,190,637.658722 93,833,319 263,413,320 0.159486164 À0.12527412
3 À0.16411145 830,967,952.913152 149,519,768 164,496,349 652,172,016.055992 À145,803,174 À26,081,669 0.151428257 0.48842956
4 À0.04876871 À561,627,235.380180 À40,454,776 À104,210,127 À457,352,551.220062 À20,850,837 À24,207,680 0.236968109 À0.33163584
5 0.02827048 216,741,831.695320 À4,135,408 51,442,491 208,050,065.790845 À4,076,864 9,049,981 0.046598114 0.07315162
6 0.00822688 À101,416,633.051128 À17,026,988 À30,399,918 À107,617,664.543601 17,927,849 À17,912,975 0.096699187 À0.05330242
7 À0.00029524 38,734,365.757877 4,861,048 12,990,570 49,635,550.339819 À7,635,412 6,250,490 0.033845749 0.05383231
8 0.00283138 À4,933,846.083888 4,342,105 À3,470,974 À16,374,800.338876 1,898,807 À1,277,002 0.035878303 À0.02033096
9 0.00046001 2,387,727.910251 À2,161,328 1,108,306 3,938,899.843218 3,384,195 1,355,336 0.015149347 0.00032342
10 0.00051934 À983,341.447877 À831,535 À406,280 À841,530.971709 À736,921 À941,180 0.012302762 0.00005704
Note: All variables are expressed in ﬁrst differences. Sanctions are imposed throughout 2017:04.
Y.Tuzova,F.Qayum/EnergyPolicy90(2016)140–151145
forecast for the ﬁrst difference of the selected macroeconomic
variables when sanctions are imposed throughout 2017:4. Table 5
(b) and Table 5(c) show the forecasts when sanctions are imposed
until 2016:4 and 2015:4, respectively.
Every variable, except inﬂation, is expressed in terms of the
ﬁrst differences and listed in the corresponding column. To get a
better idea of how each macroeconomic variable will respond to
the shock of oil prices and economic sanctions, we have expressed
Fig. 4. (a) Impulse responses to shocks in the Brent crude oil. Note: All variables are expressed in ﬁrst differences. (b). Standardized impulse responses to shocks in the Brent
crude oil. Note: All variables are expressed in ﬁrst differences.
Table 4
Decomposition of variance for series DRGDPSA_2010.
Step Std. error DREALBRT_R DRGDPSA_2010 DRCPSA DRCGSA DRISA DRXSA DRMSA INFL DREER
1 3,662,731,551.425322 30.513 69.487 0.000 0.000 0.000 0.000 0.000 0.000 0.000
2 4,444,387,346.025604 40.784 53.382 3.367 0.281 0.662 0.610 0.527 0.009 0.379
3 4,710,318,918.611965 36.313 50.637 4.881 0.515 2.939 1.333 2.788 0.014 0.582
4 4,761,715,278.036043 35.535 50.941 4.803 0.696 3.060 1.310 3.052 0.014 0.589
5 4,775,792,802.937173 35.563 50.847 4.806 0.709 3.042 1.313 3.057 0.013 0.650
6 4,779,645,623.589170 35.510 50.810 4.850 0.719 3.052 1.312 3.075 0.014 0.659
7 4,780,078,196.240484 35.504 50.807 4.851 0.721 3.054 1.311 3.078 0.014 0.659
8 4,780,169,721.516589 35.505 50.806 4.851 0.721 3.054 1.312 3.079 0.014 0.659
9 4,780,187,733.477399 35.505 50.805 4.851 0.721 3.054 1.312 3.079 0.014 0.660
10 4,780,190,144.225545 35.505 50.805 4.851 0.721 3.054 1.312 3.079 0.014 0.660
Note: All variables are expressed in ﬁrst differences. Sanctions are imposed throughout 2017:04.
Y. Tuzova, F. Qayum / Energy Policy 90 (2016) 140–151146
Table 5
Russia’s forecast for 2015–2017.
Entry DREALBRT_R DRGDPSA_2010 DRCPSA DRCGSA DRISA DRXSA DRMSA INFL DREER
(a)
2015:02 À7.44260926 À5,447,462,294.105593 À2,103,801,338.228843 42,275,928.249365 1,499,878,256.162802 À6,137,156,675.920204 À2,687,434,870.561536 20.87139504 5.66561882
2015:03 À2.64877117 À8,513,485,117.918602 À774,614,938.937302 À1,326,609,256.012073 À6,002,703,918.270302 À2,188,958,917.390739 À1,178,141,915.001744 22.73587185 À8.17128731
2015:04 À1.23054989 À2,070,590,506.559486 À1,815,423,628.353830 À377,224,590.718124 À1,302,364,010.766618 1,043,623,324.131592 À317,560,024.337248 19.73425351 À6.86757481
2016:01 À2.05222139 À4,784,149,025.978889 À2,235,784,082.574301 À914,778,684.612624 À2,885,712,153.786951 272,640,620.413795 À760,407,997.463221 19.58585247 À5.22415433
2016:02 À2.53176228 À4,314,288,835.379646 À1,704,923,297.377430 À646,683,745.828699 À2,139,363,591.552138 À562,247,709.038651 À745,015,152.746771 19.54964058 À4.21889518
2016:03 À2.34327452 À4,434,138,873.681705 À1,631,450,413.521368 À720,954,412.090546 À2,598,569,002.273516 À342,978,226.751355 À684,613,775.159366 19.51115066 À5.38479128
2016:04 À2.26610126 À4,303,279,358.982455 À1,795,126,179.026073 À706,683,303.319217 À2,449,377,804.115297 À153,563,702.218105 À667,958,258.167885 19.24814771 À5.38449084
2017:01 À2.31090784 À4,416,943,234.674351 À1,816,879,947.343030 À727,517,147.458624 À2,479,678,297.454096 À234,121,418.263431 À710,439,599.652244 19.20804783 À5.17519189
2017:02 À2.33466580 À4,402,484,828.580368 À1,768,797,572.204194 À716,550,460.554144 À2,463,251,634.884093 À280,034,261.519112 À706,053,781.190586 19.19983757 À5.12725143
2017:03 À2.31762248 À4,378,456,458.446338 À1,760,169,010.434468 À714,736,416.873584 À2,470,119,203.003137 À257,080,873.599083 À693,585,329.072257 19.17003770 À5.20354348
2017:04 À2.31468178 À4,377,345,789.731367 À1,773,722,378.557308 À716,276,502.498826 À2,468,150,074.009276 À242,997,244.102967 À693,761,084.634377 19.14025092 À5.20925137
Note: All variables are expressed in ﬁrst differences. Sanctions are imposed throughout 2017:04.
(b)
2015:02 À7.44260926 À5,447,462,294.105593 À2,103,801,338.228843 42,275,928.249365 1,499,878,256.162802 À6,137,156,675.920204 À2,687,434,870.561536 20.87139504 5.66561882
2015:03 À2.64877117 À8,513,485,117.918602 À774,614,938.937302 À1,326,609,256.012073 À6,002,703,918.270302 À2,188,958,917.390739 À1,178,141,915.001744 22.73587185 À8.17128731
2015:04 À1.23054989 À2,070,590,506.559486 À1,815,423,628.353830 À377,224,590.718124 À1,302,364,010.766618 1043,623,324.131592 À317,560,024.337248 19.73425351 À6.86757481
2016:01 À2.05222139 À4,784,149,025.978889 À2,235,784,082.574301 À914,778,684.612624 À2,885,712,153.786951 272,640,620.413795 À760,407,997.463221 19.58585247 À5.22415433
2016:02 À2.53176228 À4,314,288,835.379646 À1,704,923,297.377430 À646,683,745.828699 À2,139,363,591.552138 À562,247,709.038651 À745,015,152.746771 19.54964058 À4.21889518
2016:03 À2.34327452 À4,434,138,873.681705 À1,631,450,413.521368 À720,954,412.090546 À2,598,569,002.273516 À342978,226.751355 À684,613,775.159366 19.51115066 À5.38479128
2016:04 À2.26610126 À4,303,279,358.982455 À1,795,126,179.026073 À706,683,303.319217 À2,449,377,804.115297 À153,563,702.218105 À667,958,258.167885 19.24814771 À5.38449084
2017:01 0.47046801 À1,049,248,841.873494 À549,427,473.283015 53,703,211.173034 139,319,204.344126 À867,987,966.397836 À493,366,471.028275 17.44400143 2.48473261
2017:02 0.43050711 2,050,649,101.974288 1,222,070,420.355390 349,092,231.383311 492,296,736.279352 98,281,884.570170 51,150,148.186564 15.87942908 2.03898316
2017:03 0.46927319 1,884,443,036.688169 1,063,443,829.770251 391,821,264.710449 369,055,755.284581 645,294,588.953555 584,505,583.248454 13.51408982 0.60744420
2017:04 0.23780890 1,947,482,927.771046 799,215,233.608767 383,901,902.805882 579,221,445.521476 521,764,235.384573 406,651,411.320090 12.20284453 0.80731778
Note: All variables are expressed in ﬁrst differences. Sanctions are imposed throughout 2016:04.
(c)
2015:02 À7.44260926 À5,447,462,294.105593 À2,103,801,338.228843 42,275,928.249365 1,499,878,256.162802 À6,137,156,675.920204 À2,687,434,870.561536 20.87139504 5.66561882
2015:03 À2.64877117 À8,513,485,117.918602 À774,614,938.937302 À1,326,609,256.012073 À6,002,703,918.270302 À2,188,958,917.390739 À1,178,141,915.001744 22.73587185 À8.17128731
2015:04 À1.23054989 À2,070,590,506.559486 À1,815,423,628.353830 À377,224,590.718124 À1,302,364,010.766618 1,043,623,324.131592 À317,560,024.337248 19.73425351 À6.86757481
2016:01 0.72915445 À1,416,454,633.178031 À968,331,608.514285 À133,558,325.980967 À266,714,651.988729 À361,225,927.720610 À543,334,868.839252 17.82180606 2.43577017
2016:02 0.23341063 2,138,845,095.175009 1,285,944,695.182153 418,958,946.108756 816,184,779.611308 À183,931,562.949369 12,188,776.630379 16.22923209 2.94733940
2016:03 0.44362115 1,828,760,621.452805 1,192,162,426.683353 385,603,269.493487 240,605,956.014203 559,397,235.801284 593,477,137.161345 13.85520278 0.42619640
2016:04 0.28638942 2,021,549,358.519957 777,811,433.140002 393,495,101.985491 597,993,715.415455 611,197,777.269437 432,454,237.786582 12.31074133 0.63207831
2017:01 0.06140676 1,268,684,386.183923 715,916,723.154845 286,379,591.739295 261,603,924.556316 274,654,162.951124 286,184,460.550059 11.72036357 1.08526187
2017:02 0.07192420 1,509,547,897.947765 865,739,584.565195 350,512,107.488358 439,628,762.734651 177,686,542.732506 323,240,005.573094 11.29626926 1.15069423
2017:03 0.12280268 1,533,148,199.323487 868,680,686.299200 335,252,700.145074 364,544,034.793390 286,064,187.138597 354,462,827.068396 10.97098746 0.92436904
2017:04 0.11457556 1,554,921,859.024626 839,518,305.684831 341,111,548.646016 412,154,748.184806 300,981,488.851411 360,936,509.036645 10.71644247 0.97941184
Note: All variables are expressed in ﬁrst differences. Sanctions are imposed throughout 2015:04.
Y.Tuzova,F.Qayum/EnergyPolicy90(2016)140–151147
all variables in terms of their actual levels. Figs. 5–7 show a graphical
representation of our forecast for real GDP, inﬂation, and the
main GDP components for 2015–2017.
As shown in Fig. 5, the Russian economy is currently experiencing
a slowdown due to the fall in the price of oil and Western
sanctions. From 2014:4 to 2015:1, the real GDP (seasonally adjusted)
fell from 2014:4 to 2015:1 by 37.92 percent at an annual
rate. If sanctions continue to be implemented throughout 2017, our
model predicts that on average the quarter-to-quarter real GDP (at
2010 prices) will fall at an annual rate of 21.74 percent in 2015,
16.32 in 2016, and 19.21 in 2017. If sanctions are to be removed at
the end of 2016, the year of 2017 will look much better. The
quarter-to-quarter real GDP may grow on average at a 5.45 percent
annual rate in 2017. Finally, if the US and EU agree to remove the
sanctions at the end of 2015 (which is highly unlikely), we predict
that on average in 2016 we may see a quarter-to-quarter real GDP
growth at a 4.33 percent annual rate and a 5.15 percent annual rate
in 2017.
In retaliation to ﬁnancial and trade sanctions brought by the
EU, US and other countries, Russia banned imports of a wide range
of U.S. and European foods (beef, pork, poultry, ﬁsh, fruit, vegetables,
cheese, milk and other dairy products). Moreover, the decline
in the value of the Russian ruble, beginning in the second half
of 2014, sparked fears of a new wave of ﬁnancial crisis. As the
ruble plunged at the end of last year, millions of Russian consumers
made panic purchases. People rushed out to buy imported
cars, refrigerators, washing machines, TV sets and other major
home appliances before they became even more expensive. The
weaker ruble and Western sanctions on food imports pushed up
inﬂation. According to IFS, inﬂation rate in Russia jumped from
7.68 percent to 9.58 percent in the last quarter of 2014 and to 16.2
percent in the ﬁrst quarter of 2015, as shown in Fig. 6. We predict
that the inﬂation will be around 19.5 percent over the next two
years, which will certainly be above Russian Central Bank’s
4.5 percent inﬂation target. We think that the Central Bank of
Russia will try to keep inﬂation low and slow down consumer
price growth. To earn market participants’ conﬁdence and attract
investment, the CBR is likely to lower the discount rate by
switching to a ﬂoating exchange rate regime and abandoning interventions.
This is exactly what happened at the beginning of
Fig. 5. Forecast of real GDP (seasonally adjusted) for 2015–2017.
Fig. 6. Forecast of inﬂation (seasonally adjusted) for 2015–2017.
Y. Tuzova, F. Qayum / Energy Policy 90 (2016) 140–151148
Fig. 7. (a) GDP components in rubles (seasonally adjusted) for 2015–2017 with sanctions imposed until 2017. (b) GDP components in rubles (seasonally adjusted) for 2015–
2017 with sanctions imposed until 2016. (c) GDP components in rubles (seasonally adjusted) for 2015–2017 with sanctions imposed until 2015.
Y. Tuzova, F. Qayum / Energy Policy 90 (2016) 140–151 149
2015, when the CBR lowered the discount rate from 17 percent to
12.5 percent.
High prices caused a big decline in household consumption,
personal savings, investment, and government spending. Imports
were reduced by the contracting domestic demand and European
sanctions. Due to a big depreciation of the ruble, we expect the
prices of current imports to double in the future. This is likely to
mean a shift from high-quality goods from Europe to lower quality
goods from China, India, and Indonesia. Russia’s export income
declined due to the fall in oil prices. As for investment, the fact that
the EU froze ﬁve state-controlled banks out of its capital market
made it nearly impossible to send money overseas. The economy
continues to grapple with serious inefﬁciencies in factor allocation,
ruble depreciation, monetary tightening, capital ﬂight, extinguished
investment and a heightened perception of risk. In
Fig. 7(a)–(c) we show the dynamic response of inﬂation, real
household consumption, real investment, government spending
and real exports and imports under different scenarios. Our model
shows that if sanctions remain, in the ﬁrst quarter of 2016 the real
consumption will fall at a 13.7 percent annual rate, the real investment
will fall at a 66.5 percent annual rate, real government
spending will decrease by 15.6 percent annual rate, exports will
rise at a 2.84 percent annual rate, and imports will contract at a
10.5 percent annual rate. The real effective exchange rate will drop
at a 28 percent annual rate. In the face of increased uncertainty, it
is hard to predict for sure the long-term behavior of GDP and its
main components. Thus, we advise the reader to treat these
forecasts with caution. We also think that as long as the Kremlin
continues its aggression in eastern Ukraine, there is no reason to
anticipate that the West will ease its ﬁnancial and trade sanctions
against Russia. As so, the economic future does not look too good.
5. Conclusions and policy implications
Before the global ﬁnancial crisis of 2008–2009, Russia was
among the fastest growing emerging countries due to high oil
prices. However, the shale oil boom in the US and Canada, low
demand in China, and petroleum efﬁciency in the advanced
countries caused the global crude oil prices to fall by more than 50
percent last year. The decline in oil prices severely hurt Russia’s
economy. Using the vector autoregression analysis, we construct
impulse response functions and variance decomposition to estimate
the effect of oil prices and sanctions on Russia’s macroeconomic
variables. The results conﬁrm that Russia is heavily affected
by oil price ﬂuctuations and economic restrictions as most
of its export revenues come from petroleum products. We conclude
that over the next two years, Russia’s economy will not grow
at all due to the harm caused by sanctions and a sharp decline in
oil prices.
What should Russia do to grow back again? Following the
principle consequences of a natural resource curse, Russia should
not entirely depend upon its natural resources. Recall that the
abundance of natural resources can harm the resource rich
countries through the so-called Dutch disease and lead to an increase
in corruption and deterioration of institutions. As Kalcheva
and Oomes (2007) suggest, the symptoms of the Dutch disease are
certainly present in Russia. Russia is a resource dependent economy
and has little incentives to expand alternative industries
especially while oil prices are high. The relatively high oil prices
make prices of other goods relatively more expensive, which
weaken consumer demand and make alternative sectors uncompetitive.
Further, high wages in the resource extraction industry,
and the difﬁcult living conditions in the remote regions
make those regions unattractive for alternative industry workers.
Russia would not have been so adversely affected by the falling oil
prices if it had developed a successful diversiﬁcation plan. But, as
Esanov (2012) and Gelb and Grasmann (2010) emphasized, a
successful diversiﬁcation plan requires political commitment,
consistent policies, ﬁnancial resources, and investment in human
capital. Misaligned economic policies, inadequate diversiﬁcation
strategies, and weak institutions always hold back private investment
and discourage economic growth. As the World Bank suggested,
in order to secure future growth, Russia will have to ﬁnd
the way to expand other tradable industries. Otherwise, the longrun
perspective of the Russian economy will not be very
optimistic.
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