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Citation: Li, Z.; Li, T. Economic
Sanctions and Regional Differences:
Evidence from Sanctions on Russia.
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sustainability
Article
Economic Sanctions and Regional Differences: Evidence from
Sanctions on Russia
Zhentao Li 1and Tianzi Li 2,*
1Northeast Asian Studies College, Jilin University, Changchun 130012, China; ztli18@mails.jlu.edu.cn
2Northeast Asian Research Center, Jilin University, Changchun 130012, China
*Correspondence: litianzi@jlu.edu.cn
Abstract:
The objective of this study was to analyze the relationship between economic sanctions and
regional differences within Russia from three perspectives: regional favoritism of the political elite,
industry development, and trade costs. Using the nighttime lights in Russia, we found a correlation
between economic sanctions and regional differences. First, as sanctions increased, the lights of
Moscow, St. Petersburg, and provincial capitals were brighter than those of the rest of the country.
Second, the lights of manufacturing cities were brighter as sanctions increased. However, under the
influence of sanctions, the lights of mining areas of Russia were dimmer than those of other areas.
Finally, there were relatively more economic activities in areas close to the Chinese border. The lights
of Blagoveshchensk were brighter than that of the rest of the country. In addition, the relationship
between economic sanctions and the brightness of lights had the characteristics of stages. There was
a negative correlation with the brightness of Russian lights in the early stages of economic sanctions.
However, this negative correlation disappeared in the later stages.
Keywords: economic sanctions; regional differences; import substitution; nighttime lights
1. Introduction
Economic sanctions have become one of the most important tools of statecraft in
international politics [
1
], aiming to change certain policies of target countries by causing
economic losses [
2
]. In March 2014, western countries imposed economic sanctions on
Russia in response to the Crimean crisis. Because the government of the Russian Federation
did not implement the Minsk Agreement and interfered in the U.S. election and the Ames-
bury nerve agent incident, western countries have expanded and strengthened economic
sanctions against Russia. Although the period of economic sanctions imposed on Russia by
the west has been relatively short, it is possible to answer these questions initially: how
effective are the economic sanctions imposed on Russia by western countries? How do
economic sanctions affect the regional development of Russia? Do economic sanctions
promote Russian import substitution industrialization?
The research on the impact of western sanctions on Russia has mainly been from
the macro perspective of GDP, trade, exchange rate, and FDI. For example, Tuzova and
Qayum confirmed that sanctions had an adverse effect on Russian economic growth [
3
].
The International Monetary Fund (IMF) in 2015 reported that sanctions may cause Russia’s
short-term GDP to drop by 1.0–1.5% and that the medium-term accumulated loss may reach
9.0% of GDP [
4
]. Economic sanctions affect not only GDP but the investment environment.
In 2014, Russia’s net private capital outflows totaled 152 billion USD [
5
]. Crozet and Hinz
analyzed the impact of sanctions on Russian trade and found that the Russian Federation
lost US$53 billion under western sanctions [
6
]. Kholodilin and Netsunajev showed that
economic sanctions impacted not only on Russia’s economic growth but the real effective
exchange rate [7].
Unlike in most macro studies. Ahn and Ludema used company-level survey data.
They showed that target companies lost about one-third of their operating income and more
Sustainability 2022,14, 6112. https://doi.org/10.3390/su14106112 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 6112 2 of 23
than half of their assets compared with companies that were not sanctioned [
8
]. Golikova
and Kuznetsov, based on the latest survey of manufacturing companies, confirmed that
sanctions were harmful not only to target companies but to all companies that performed
better in foreign trade with the E.U. and Ukraine [
9
]. Shida further confirmed that about
half of the corporate managers interviewed felt the negative impact of sanctions to a
certain extent. Moreover, there were no regional differences in the impact of sanctions on
companies. The sanctions even had impacts on companies close to the Asia–Pacific region
with close ties with Asian countries [10].
Previous studies mainly analyzed the impact of economic sanctions from the macroeco-
nomic level and the micro level of the company. The sanctions against North Korea provide
a good example. During the sanctions, the brightness of lights in the capital and provincial
capitals was relatively bright because of the close connection of these cities with the ruling
elite [
11
]. However, as an external shock, the impact of economic sanctions on Russian
internal economic differences has not been effectively verified. The relationship between
economic sanctions and regional differences can be explained from several perspectives.
First is regional favoritism. Political elites allocate limited resources to regions they deem
important. Second, economic sanctions usually act as protective tariffs and promote the
development of import substitution industries [
12
], which in turn affect economic activities
in manufacturing areas. Finally, sanctions may also affect regional differences through
trade costs.
In this paper, we attempted to empirically examine the relationship between economic
sanctions and the brightness of Russian lights and contribute to the literature in three
ways. First, the target countries of previous sanctions were usually small [
13
–
15
]. However,
the target for this time is the country with the largest territories and one that was once a
superpower in the world. Moreover, Russia is rich in natural resources and relatively sound
industrial types. Studying economic sanctions against Russia may increase understanding
of the effectiveness of economic sanctions. Second, measuring the sanctions index is
a challenge for the academic community. This paper built a comprehensive sanctions
index based on Russia’s sanctioned economic sectors and incorporated economic sanctions
into the model to test the impact of sanctions. Finally, early research focused on the
relationship among economic growth, oil prices, exchange rates, trade, corporate earnings,
and economic sanctions. Little attention has been paid to the relationship between sanctions
and the internal regions of the target country. Therefore, this paper analyzed the impact
of economic sanctions on Russia from the perspective of the internal regions of the target
country, making up for the deficiencies of previous research.
In fact, research on regional economic differences has always faced data problems. The
first reason is that core economic statistics such as GDP may be artificially interfered with.
On the one hand, there are problems such as inconsistent statistical standards and inaccurate
price indices for GDP. On the other hand, political pressure from local governments can
lead to falsified GDP figures [
16
]. Second, it is difficult to calculate the economic data of
specific regions, cities, or subdivision geographic units within a country. This makes it
difficult to break the administrative unit to reflect internal regional economic activities
within a country. The nighttime lights are not interfered with by human factors relative to
the GDP data. They can reflect some information that is difficult to include in the GDP data,
such as the informal economy or underground economy, which are accurately recorded [
17
].
In addition, the nighttime lights have a high geographic spatial resolution, providing more
abundant and accurate geographic unit information [
18
]. Prior research has used nighttime
lights for socioeconomic analysis [
19
–
21
]. Therefore, this paper used nighttime lights to
measure the level of regional development and analyzed the impact of economic sanctions
on the brightness of lights in Russia by constructing an economic sanctions index.
The results of this paper are as follows. There was a negatively correlation with
the brightness of Russian lights in the early stages of economic sanctions. However, this
negative correlation disappeared under continuous sanctions. From the perspective of
Russia’s internal regional differences, we found that the lights in Moscow, St. Petersburg,
Sustainability 2022,14, 6112 3 of 23
and provincial capitals were brighter than those in the rest of the country. This shows that
under the effect of regional favoritism, political elites allocated limited resources to Moscow
and St. Petersburg, forming a unique economic geographic structure. In addition, as
sanctions increased, the lights in manufacturing cities became relatively brighter, indicating
that economic sanctions promoted the development of import substitution industries.
However, our research also found that as sanctions increased, the brightness of the lights in
Russia’s domestic mineral areas dimmed. Finally, as sanctions increased, the Russian city
of Blagoveshchensk near the Chinese border became relatively brighter. This shows that
under continuous economic sanctions, China–Russia economic and trade relations grew
closer. However, in areas closely related to the sanctions-sending countries, the lights of
the Kaliningrad Oblast, west port city, and west port area did not dim.
Our analysis is organized as follows: the second section introduces the theory and
hypotheses, as well as analysis of the relationship between economic sanctions and regional
differences from three perspectives. The third section introduces data and methods. The
fourth section presents the estimation results, including the tests of robustness. The final
section presents conclusions.
2. Theoretical Analysis and Research Hypothesis
2.1. Regional Favoritism
One explanation for the relationship between economic sanctions and regional dif-
ferences in Russia may be regional favoritism. Some political leaders choose preferential
policies for their favorite regions, which is regional favoritism. In the acquisition and
distribution of resources, the regions favored by political leaders have a promoting effect
in infrastructure investment [
22
], project approval [
23
], and transfers [
24
,
25
]. Because
of the economic sanctions of western countries and falling oil prices, Russia’s domestic
economy began to experience a severe recession. Moscow and St. Petersburg are more
important than other cities in the Russian Federation. In addition, Moscow and St. Pe-
tersburg have gathered many companies from different economic areas, including many
companies sanctioned by western countries, which constitute key sectors of the Russian
economy. In addition, Moscow and St. Petersburg are closely connected with political
elites. The government of the Russian Federation is at the top of this political and economic
system [
26
]. In the process of fighting crisis, resources can be allocated to areas deemed
important by the political leadership through a series of administrative and institutional
means. Therefore, Moscow and St. Petersburg naturally receive more policy and financial
support. Ades and Glaeser further explained the determinants leading to the absolute
dominance of big cities. They believed that high tariffs, high-cost domestic trade, and
low-level international trade had further strengthened the population concentration of
large cities and shaped the unique economic geographic structure in which large cities
dominate in closed economies [27].
Although western countries have imposed sanctions on Russia’s economic elites and
key industries, these sanctions may affect the economic well-being of citizens by slowing
economic growth more generally. In 2012, Russia resumed direct elections of local chief
executives [
28
]. Therefore, votes of ordinary people are increasingly important to local
political elites. Most provincial capital cities have a larger population and a better economic
foundation than other cities in the state. To revive the state’s economy and gain support
from local voters for the government, local political elites transferred limited resources to
the area around their provincial capitals. Therefore, following hypothesis was presented:
Hypothesis 1 (H1):
When sanctions increase, Russia’s domestic political elites allocate limited
resources to large cities and provincial capitals. The estimated results of Moscow, St. Petersburg,
and provincial capitals should be positive.
Sustainability 2022,14, 6112 4 of 23
2.2. Industry Development
Another explanation for the relationship between economic sanctions and regional
differences in Russia is the industrial development. Import substitution usually refers to
the protection and development of domestic manufacturing by restricting the import of
industrial manufactured products, promoting domestic industrialization [
29
,
30
]. Selden
argued that economic sanctions usually act as protective tariffs and lead to import substitu-
tion industrialization [
12
]. In 2014, the Crimean crisis led to western countries imposing
economic sanctions on Russia. A senior Russian official stated that economic sanctions
may become a powerful impetus for Russia to develop its industry and find economic and
trade partners [
31
]. Russia cannot obtain military technology or energy-related equipment
or technology from western countries, even slightly more sensitive civilian technologies.
Therefore, Russia’s national security is threatened. In response, Russian government offi-
cials formulated economic policies and developed import substitution industries to reduce
the impact of western sanctions. The government committee on import substitution was es-
tablished in August 2015. Subsequently, a detailed import substitution plan was proposed,
initially including more than 2000 projects in 19 economic fields. This increased resource
allocation to areas targeted by western sanctions, such as the national defense and oil and
gas equipment industries. In addition, relevant policy support was provided to enterprises
participating in import substitution. These policies included preferential loans, preferential
access to national procurement funds, and financial support for import substitution in the
form of tax cuts [
26
]. Historically, Zimbabwe has adopted an import substitution strategy
that could make the country prosper even in the face of severe economic sanctions [32].
For the Russian energy sector, western countries’ restrictions on Russian energy ex-
traction technology affect not only the development of new oil and gas wells but the
oil production efficiency of oil wells. In addition, financial restrictions on the Russian
energy sector affect the operations of energy companies. Although economic sanctions
have stimulated the development of Russia’s import substitution industry, Russia cannot
replace energy extraction technology and equipment of western countries with domestic
products in the short term. The long-term development of Russian energy sector has been
affected by technological constraints and financial sanctions. Therefore, we proposed the
following hypothesis:
Hypothesis 2 (H2):
When sanctions are increased, compared with other areas of the country, the
lights in manufacturing cities should be brighter. However, the lights in mining areas should be
dimmer than that in other parts of the country.
2.3. Trade Cost
Trade costs may be an important factor in explaining the relationship between eco-
nomic sanctions and regional differences in Russia. Economic sanctions can be considered
as prohibitive tariffs [
33
], increasing the relative trade costs of the target and sender country.
Meanwhile, they reduce the relative trade costs of the target country and the countries not
involved in the sanctions. Therefore, economic sanctions have changed the trade costs in
different regions, which in turn has caused differences in regional economic development.
For example, after Mexico and the United States signed a free trade agreement, trade tariffs
between the United States and Mexico were reduced, and Mexico’s production activities
near the U.S. border increased [
34
]. Economic sanctions have strained relations between
Russia and western countries. To seek new impetus for the national development, Russia
began to accelerate the pace of eastward advancement [
35
] and strengthen cooperation
with Asia–Pacific countries including China, the world’s largest emerging economy. This
was to make up for the economic losses caused by the recent deterioration in relations with
western countries [10].
Sustainability 2022,14, 6112 5 of 23
Russia is the third largest trading partner of the E.U., and the E.U. is Russia’s largest
trading partner and most important foreign investor [
36
]. Gould-Davies showed that if
the sanction sender country and the target country were partners rather than opponents
in the past, and there was a strong trade relationship between them, then the sanctions
would be more likely to have an effect [
37
]. In fact, EU sanctions against Russia are limited
to trade in specific commodities. The traded goods subject to sanctions include an arms
embargo, prohibiting the export of dual-use goods, and restricting Russia’s access to certain
sensitive technologies and services that can be used in oil production and exploration [
38
].
Other countries have successively introduced similar sanctions. However, the EU has
not imposed trade restrictions on Russia’s core economic sectors, mainly the export of
oil, natural gas, and raw materials [
39
]. The sanctions imposed by the EU have a limited
effect on the increase in trade costs and only selectively increase trade costs. Therefore, the
following hypothesis was presented:
Hypothesis 3 (H3):
Economic sanctions have increased Russia’s trade ties with nonsanctioning
countries and should therefore brighten the lights in areas that have trade ties with nonsanctioning
countries. The scope of E.U. trade sanctions against Russia is narrow, and the increase in trade
costs is limited. Therefore, sanctions in regions with close trade relations with the E.U. may not dim
the lights.
3. The Data and Methods
3.1. Data
The data of nighttime lights were from the Visible Infrared Imaging Radiometer
Suite (VIIRS) Day/Night Band (DNB) produced by the Earth Observations Group (EOG)
(https://eogdata.mines.edu/nighttime_light/annual/v20/ (accessed on 28 May 2021)).
This version of the nighttime lights eliminates temporary burning, aurora, volcano and
other background noises. We set the negative DN value of grid cells to 0 and finally
obtained the annual nighttime lights. Then, we transformed the processed nighttime lights
into an Albers equal-area projection. In this paper, we took the arithmetic mean of each
2
×
2 grid cells as a 1
×
1 grid and used it as the geographic unit for analysis. This process
generated 6,447,786 grid cells in Russia. Existing studies have used nighttime lights to
study the impact of sanctions on the Rostov region. Brock showed that despite sanctions,
Rostov’s rural areas and cities near the Ukrainian border still showed good economic
performance because of active military activities and import substitution policies [
40
]. The
administrative division data in this paper were from the OMS [
41
]. We identified the
manufacturing cities based on the Russian atlas and related data [
42
,
43
]. The port data
were from Humdata [
44
]. The port area with the largest area was the center on the port,
with a radius of 2 km to determine the port area. The U.S. Geological Survey provided
the coordinate information for the mining areas. In this paper, the area within 3 km of the
coordinates was identified as the mining area. The oil and gas areas were from the U.S.
Geological Survey [
45
]. There may have been overlaps between oil and gas areas and urban
areas. Therefore, we used Liu to identify oil and gas areas that overlapped with urban
areas [46].
According to the territorial characteristics of Russia, the administrative hierarchy of
cities, and Lee [
11
], this paper divided Russia into many areas: Moscow, St. Petersburg,
provincial capitals, other cities (excluding municipalities, provincial capitals, manufac-
turing cities, and port cities), manufacturing cities, mining areas, port cities, port areas,
and the Kaliningrad Oblast. We merged the nighttime lights with Russia’s administrative
boundaries through Geographic Information System (GIS) software to identify the province
and county of each grid cell. Grid cells were further identified by dummy variables indicat-
ing grid cells of Moscow, St. Petersburg, provincial capitals, other cities, manufacturing
cities, mining areas, Russian border areas close to China, port cities, and port areas. It
is worth explaining Blagoveshchensk and Kaliningrad Oblast. Blagoveshchensk is the
largest city on the border between China and Russia. With western countries imposing
Sustainability 2022,14, 6112 6 of 23
sanctions on Russia, the economic and trade exchanges between China and Russia have
increased. Kaliningrad Oblast is a region in the westernmost part of Russia. It is completely
disconnected from the Russian mainland and has relatively close economic and trade
relations with Europe. Economic sanctions from western countries can reduce economic
and trade ties with the region. However, the Russian government can pay more attention
to Kaliningrad Oblast because of its special geographical location. We divided the port
cities and port areas into two parts to distinguish the impact of sanctions on port city and
port areas in different regions.
3.2. Sanctions Index
As a result of the referendum in March 2014, Crimea declared independence from
Ukraine and then joined the Russian Federation. In the same month, western countries
announced that they would impose sanctions on Russian and Ukrainian officials who were
responsible for the “breaking the sovereignty of Ukraine” by the Russian government.
Since then, western countries have imposed sanctions on Russia. The development of
sanctions can be roughly divided into three stages.
The first stage was the warning sanctions stage. In March 2014, U.S. President Barack
Obama signed Executive Orders 13660, 13661, and 13662 to impose sanctions on specially
designated nationals and blocked persons (SDN). These included Russian politicians,
military officers, separatists, and oligarchs who endangered the territorial integrity and
the sovereignty of Ukraine. The United States adopted restrictive measures to prohibit
travel and freeze the assets of certain individuals and groups. Subsequently, U.S. allies
also adopted similar sanctions. Diplomatic sanctions included the cancellation of the G8
meeting held in Sochi, Russia and the suspension of Russia’s membership in the G8.
The second stage was the substantive sanctions stage. The United States Department
of the Treasury’s Office of Foreign Assets Control (OFAC) imposed sectoral sanctions on
16 July 2014 targeting the finance, energy, and defense sectors of Russia. The financial
sanctions restricted Russian financial institutions’ access to U.S. capital markets for financ-
ing. They also shortened the maturity of new bonds issued by six Russian banks from 90
days to 30 days. Energy-related sanctions prohibited U.S. companies from cooperating
with important Russian energy companies and prohibited Russian from obtaining certain
sensitive technologies and services that can be used for oil production and exploration.
The export of dual-use goods to Russia was prohibited, and the assets of Russian defense
companies in the United States were frozen. Similar sanctions were imposed by Australia,
Canada, Japan, Norway, Switzerland, and Ukraine.
The third stage was the expansion and strengthening stage. In 2016, Russia intervened
in the U.S. election, and the United States increased economic sanctions against Russia. The
U.S. Treasury Department announced the freezing of the assets of dozens of large Russian
entities in the United States. In August 2018, the United States strengthened its sanctions
against Russia because Russia used nerve agents to assassinate British citizen Sergei Skripal
and his daughter Yulia Skripal. The sanctions included ending arms sales and financing
restrictions on arms sales and expanding export restrictions on specific commodities.
Constructing a reasonable sanction index is a challenge for scholars. Dreger first at-
tempted to evaluate the impact of economic sanctions on Russia by constructing a sanctions
index [
47
]. Kholodilin and Netsunajev and Bali conducted further research [
7
,
48
]. Dreger
divided sanctions into (1) sanctions against individuals, (2) sanctions against specific en-
tities, and (3) sanctions against the entire economic sector and then set weights of 1, 2,
and 3 according to their degree of impact [
47
]. However, this weight setting may be too
subjective because the sanctions against the economic sector are implemented through
sanctions on entities in this sector. For example, sanctions against specific entities, with
a weight of 2, include those against military–industrial entities. However, the military
entity belongs to the defense sector, and the weight should be set to 3. The target areas of
western countries’ sanctions against Russia have mainly been concentrated in the financial,
energy, and defense sectors, which are very important to Russia’s domestic economy. There-
Sustainability 2022,14, 6112 7 of 23
fore, from the perspective of the target country, this paper constructed a comprehensive
sanctions index by classifying the sanctioned sectors of the target. This method can better
measure the impact of economic sanctions on the target country. Although the sanctions
index constructed in this paper is not perfect, it can provide a reference for studying the
impact of sanctions on a country. The sanctions index in this paper was constructed as
follows. The sanctions index
St
refers to the cumulative sum of the number of sanctions
events each year. The Suomi National Polar-Orbiting Partnership (NPP) Satellite with
VIIRS sensor was launched in October 2011. NPP was a new satellite used to monitor
nighttime light. Limited by light data availability, we set 2012 as the first year in this study,
with a sanctions index of zero. The sanctions index Stis
St=∑T
t=2012 ∑I
i=1(Sf in
it +Smil
it +Seng
it +Sother
it )(1)
According to the sector on which sanctions were imposed on Russia by western coun-
tries, sanctions were divided into financial sector sanctions
Sf in
it
, military sector sanctions
Smil
it
, energy sector sanctions
Seng
it
, and other sanctions
Sother
it
. Other sanctions refer to
sanctions from western countries on individuals and entities that are not in the financial,
military, or energy sectors.
i
represents the sender country. We first took financial sanctions
as an example. If there are any financial-related sanctions in year t in the United States, the
European Union, Canada, Australia, etc., the financial sanction value
Sf in
n
increases by 1. If
there is no new financial sanction in year t, the financial sanction value
Sf in
n
is 0. If financial
sanctions are reduced in the t year, the financial sanction value
Sf in
n
is reduced by 1. In
addition, this paper constructed a sanction index in the form of 0 and 1 to test its robustness.
Statistical information on sanctions against Russia is shown in Table A1 of Appendix A.
It is worth noting that the sanctions index was constructed without considering
sanction threats and Russian countersanctions. There were two reasons why. First, the
United States is the leader in economic sanctions against Russia, and other countries
and organizations are followers. Although Washington announced that it would impose
sanctions on Russia, the formal sanctions against Russia were signed shortly afterwards.
Therefore, it was difficult to eliminate the threats of sanctions. Second, Russia’s main
countersanction is to restrict the import of agricultural products and food from sender
countries, and the effect of this on the brightness of the lights is negligible. Therefore, the
threat of sanctions against Russia and Russian countersanctions were not considered in
this paper.
The relationship between the sanctions index and the Russian GDP during the sample
period is shown Figure 1. Since western countries imposed sanctions on Russia in 2014,
Russia’s GDP growth has slowed. During the sample period, Russia’s GDP reached its
lowest point in 2015. After 2016, Russia’s GDP began to grow. The relationship between
the sanctions index and total Russian trade during the sample period is shown in Figure 2.
Before the sanctions, Russia’s foreign trade was already in recession. Economic sanctions
accelerated the decline in Russian trade. From 2017 to 2019, Russia’s total foreign trade
began to slowly recover. The relationship between the sanctions index and the brightness
of lights in Russia is shown in Figure 3. Because western countries imposed sanctions on
Russia in 2014, the average brightness was dimmer than that in the previous year. However,
the average light brightness value of Russia began to fluctuate and rise in 2016. The change
in the brightness of lights was basically similar to changes in GDP.
Sustainability 2022,14, 6112 8 of 23
Sustainability 2022, 14, x FOR PEER REVIEW 8 of 25
year. However, the average light brightness value of Russia began to fluctuate and rise in
2016. The change in the brightness of lights was basically similar to changes in GDP.
Figure 1. Sanctions index and GDP. Data source: World Bank, GDP (2010 constant price USD).
Figure 2. Sanctions index and Russia’s trade. Data source: IMF.
Figure 1. Sanctions index and GDP. Data source: World Bank, GDP (2010 constant price USD).
Sustainability 2022, 14, x FOR PEER REVIEW 8 of 25
year. However, the average light brightness value of Russia began to fluctuate and rise in
2016. The change in the brightness of lights was basically similar to changes in GDP.
Figure 1. Sanctions index and GDP. Data source: World Bank, GDP (2010 constant price USD).
Figure 2. Sanctions index and Russia’s trade. Data source: IMF.
Figure 2. Sanctions index and Russia’s trade. Data source: IMF.
Sustainability 2022, 14, x FOR PEER REVIEW 9 of 25
Figure 3. Sanctions index and the brightness of lights of Russia.
3.3. Methodology
This paper used a panel model to study the relationship between economic sanctions
and regional differences. The basic regression was
(ℎ)=+1−1 +2+3 +++ (2
)
where lightit is the light-brightness value of each grid cell i in year t. Since a substantial
share of the value of grid cells in Russia was zero, we followed the literature and added
0.01 before taking natural logarithms to avoid removing a large number of 0-valued cells
[49]. = {
1,
2,
3,…} is the set of dummy variables that identify grid cell characteristics.
In the base regression, included dummy variables equal to 1 if the grid cells were in
Moscow, Saint Petersburg, a provincial capital, another city, a manufacturing city, a min-
ing area, a port city, or a port area. Because of the time-lag effect of sanctions on the bright-
ness of lights in Russia, this paper lagged the sanctions index by one period. Furthermore,
this avoided a reverse causal relationship between sanction and brightness. −1 indicates
that the sanctions index lags one period, and the coefficient of interest is 1. If the lights
in a specified region are brighter than those in other areas as the sanctions increase, 1 is
positive. It is worth noting that Russia’s crude oil exports account for more than half of its
total exports, nearly 7.6% of Russia’s GDP. The significant changes in oil prices during our
study may have resulted in regional differences. Therefore, the oil price was controlled in
the model: represents the oil price, and the coefficient is 2. This paper used Brent
crude oil prices to represent the international crude oil price, because they are used to set
the price of most of the world’s internationally traded crude oil supplies. It is worth noting
that Russia’s fiscal budget is based on the price of crude oil in the previous year. However,
during the sanctions period, international crude oil prices fluctuated significantly, and
Russia timely adjusted its fiscal budget to reduce fiscal expenditures based on changing
international crude oil prices. Therefore, this paper did not lag the international oil prices.
Taking into account the impact of other factors on the brightness of lights, combined
with the availability of data, we controlled the population size (pop), road length (road),
fixed assets (assets), and regional economic scale in the model. represents the afore-
mentioned control variable. The population size is expressed by the total population of
the area. Anderson found that there was a correlation between population and the bright-
ness of lights [50]. The length of the road is expressed by the total length of the illuminated
part of the street, lane, and embankment at the end of the year. The longer the illuminated
road, the brighter the lights may be in that area. Fixed assets are represented by the total
Figure 3. Sanctions index and the brightness of lights of Russia.
Sustainability 2022,14, 6112 9 of 23
3.3. Methodology
This paper used a panel model to study the relationship between economic sanctions
and regional differences. The basic regression was
ln(lightit )=α+β1Dilnst−1+β2Dilnoilt+β3Xit +µi+δt+εct (2)
where light
it
is the light-brightness value of each grid cell iin year t. Since a substantial
share of the value of grid cells in Russia was zero, we followed the literature and added 0.01
before taking natural logarithms to avoid removing a large number of 0-valued cells [
49
].
Di
= {
D1
i
,
D2
i
,
D3
i
,
. . .
} is the set of dummy variables that identify grid cell characteristics.
In the base regression,
Di
included dummy variables equal to 1 if the grid cells were in
Moscow, Saint Petersburg, a provincial capital, another city, a manufacturing city, a mining
area, a port city, or a port area. Because of the time-lag effect of sanctions on the brightness
of lights in Russia, this paper lagged the sanctions index by one period. Furthermore, this
avoided a reverse causal relationship between sanction and brightness.
st−1
indicates that
the sanctions index lags one period, and the coefficient of interest is
β1
. If the lights in
a specified region are brighter than those in other areas as the sanctions increase,
β1
is
positive. It is worth noting that Russia’s crude oil exports account for more than half of
its total exports, nearly 7.6% of Russia’s GDP. The significant changes in oil prices during
our study may have resulted in regional differences. Therefore, the oil price was controlled
in the model:
oilt
represents the oil price, and the coefficient is
β2
. This paper used Brent
crude oil prices to represent the international crude oil price, because they are used to set
the price of most of the world’s internationally traded crude oil supplies. It is worth noting
that Russia’s fiscal budget is based on the price of crude oil in the previous year. However,
during the sanctions period, international crude oil prices fluctuated significantly, and
Russia timely adjusted its fiscal budget to reduce fiscal expenditures based on changing
international crude oil prices. Therefore, this paper did not lag the international oil prices.
Taking into account the impact of other factors on the brightness of lights, combined
with the availability of data, we controlled the population size (pop), road length (road),
fixed assets (assets), and regional economic scale in the model.
Xit
represents the aforemen-
tioned control variable. The population size is expressed by the total population of the area.
Anderson found that there was a correlation between population and the brightness of
lights [
50
]. The length of the road is expressed by the total length of the illuminated part of
the street, lane, and embankment at the end of the year. The longer the illuminated road, the
brighter the lights may be in that area. Fixed assets are represented by the total fixed assets
of commercial and noncommercial organizations. Fixed assets include houses, buildings,
transportation vehicles, and other equipment related to production and business activities.
Therefore, the greater the total fixed assets, the more brightness is produced. The above
data came from the Federal State Statistics Service (https://www.gks.ru/dbscripts/munst/
(accessed on 1 May 2021)). The size of the regional economy is an important factor affecting
the brightness of the lights. However, the Russian Federal Statistical Office lacks GDP data
for the municipal areas. Wang believed that there was a significant positive correlation
between night light intensity and regional GDP. This indicates that night light intensity can
replace GDP under certain conditions [
51
]. Therefore, this paper used the regional average
value of light brightness (avgntl) to replace the economic level of the municipal area to
control the economic scale of the region. The financial expenditure of local governments
may affect the brightness of lights in the region. However, the financial revenue of local
governments is affected by economic sanctions and oil prices. This paper did not consider
the financial expenditures of local governments to prevent commonality. The year fixed
effects
δt
control the unobserved satellite characteristics as well as unobserved annual
patterns in the data. The grid cell fixed effects
µi
control for time-invariant, location-specific
factors. Standard errors were clustered at the municipal district level to study the correla-
tions between grid cells within the region and across time. All regression estimates were
based on Stata 15. The descriptive statistics of the variables are shown in Table 1.
Sustainability 2022,14, 6112 10 of 23
Table 1. Descriptive statistics.
Variable Mean Std. Dev. Min Max
ln (lights) −4.4419 0.8873 −4.6052 9.7777
ln (Sanction index) 2.4035 1.8772 0 4.1589
ln (oil price) 4.2690 0.3369 3.7759 4.7147
Moscow 0.0024 0.0489 0 1
St Petersburg 0.0001 0.0093 0 1
Provincial capital 0.0021 0.0454 0 1
Other city 0.0161 0.1259 0 1
Manufacturing city 0.0124 0.1105 0 1
Mining area 0.2389 0.4264 0 1
With 10 km of Chinese border 0.0021 0.0456 0 1
Blagoveshchensk 0.0002 0.0141 0 1
Kaliningrad 0.0008 0.0283 0 1
East port city 0.0001 0.0074 0 1
East port area 0.0000 0.0019 0 1
West port city 0.0002 0.0142 0 1
West port area 0.0000 0.0021 0 1
ln (avgntl) 1.2868 0.8708 0 5.3764
ln (pop) 9.5621 1.3403 0 16.3397
ln (road) 3.3853 1.0602 0 8.7639
ln (assets) 13.9477 1.8219 0 20.1361
4. Empirical Results
4.1. Benchmark Regression Results
The basic regression results are shown in Table 2. Columns (1) and (2) are pool
regression results. The Hausmann test results in columns (3) to (6) rejected the assumption
of random effects, so the fixed effects were used in this paper. Columns (3) to (6) report
the regression results for the entire sanctions period from 2012 to 2019. We added control
variables in column (4) and a lag term for the brightness of the light in column (6).
First, column (3) of Table 2examines the relationship between sanctions index and
the brightness of the lights. Column (3) of Table 2shows that the sanctions index was
positive and significant at the 1% level. As sanctions increased, the brightness of the lights
in Russia did not dim because the target country reduced the impact of economic sanctions
on the brightness of lights through the adjustment of economic structure [
52
]. Column (5)
of Table 2shows the impact of economic sanctions on the brightness of lights in different
regions of Russia. The results showed that the estimated coefficients of the interactive terms
in Moscow, St. Petersburg, and provincial capitals were positive and significant at the 1%
level. The coefficient estimates of interactive terms in other cities were positively correlated
with the brightness of lights. However, there was no statistical significance. This result
verified Hypothesis 1. Political elites allocate limited resources and public goods to areas
they deem important according to their private political and economic interests, widening
the gap in the brightness of lights between Moscow, St. Petersburg and capital cities and
other areas of the country.
Column (5) of Table 2shows that the coefficient estimates of interactive terms in
manufacturing cities were positive and significant at the 1% level. The difference in the
brightness of lights between manufacturing cities and other areas of the country increased
with sanctions. This was consistent with Hypothesis 2. Economic sanctions prompted the
reallocation of resources to domestic industries and the development of import substitution
industries, thereby reducing Russia’s foreign dependence [
53
]. The estimated coefficients
of the interactive terms in mining areas were negative and statistically significant. On
one hand, the sanctions on energy technology affected not only the development of new
“frontier” oil deposits (such as the Bazhenov and Domanik formations in Western Siberia
and the Urals) but technology used in enhanced recovery of oil in “brownfield” deposits. On
the other hand, financial restrictions impacted the operations of Russian energy firms [
54
].
Under the dual constraints of technical sanctions and financial restrictions, the long-term
Sustainability 2022,14, 6112 11 of 23
development of Russia’s energy sector has been affected. Therefore, when the sanctions
increased, the brightness of the lights in the mining area became dimmer relative to other
areas of the country. This result verified Hypothesis 2.
Table 2. Basic regression results.
(1) (2) (3) (4) (5) (6)
Variables ln (Lights) ln (Lights) ln (Lights) ln (Lights) ln (Lights) ln (Lights)
Sanction index 0.0116 *** 0.00615 ***
(0.00299) (0.00110)
Regional Favoritism
Moscow ×Sanction index 0.292 *** 0.244 *** 0.246 *** 0.207 ***
(0.0195) (0.0196) (0.0196) (0.0166)
St. Petersburg ×Sanction index 0.305 *** 0.112 *** 0.110 *** 0.0961 ***
(0.00456) (0.000867) (0.00106) (0.00118)
Provincial capital ×Sanction index 0.222 *** 0.0757 *** 0.0751 *** 0.0474 ***
(0.0250) (0.0146) (0.0142) (0.0117)
Other city ×Sanction index 0.0408 * 0.0240 * 0.0208 0.0155
(0.0225) (0.0142) (0.0147) (0.0109)
Industrialization
Manufacturing city ×Sanction index 0.0570 *** 0.0300 *** 0.0310 *** 0.0229 ***
(0.00925) (0.00791) (0.00758) (0.00651)
Mining area ×Sanction index −0.00261 −0.00975 *** −0.0109 *** −0.00874 ***
(0.00201) (0.00152) (0.00163) (0.00152)
Trade Cost
Within 10 km of Chinese border ×
Sanction index 0.0101 *** −0.00199 −0.00210 −0.00263
(0.00246) (0.00272) (0.00278) (0.00230)
Blagoveshchensk ×Sanction index 0.0717 *** 0.0860 *** 0.0832 *** 0.0647 ***
(0.0225) (0.00709) (0.00729) (0.00285)
Kaliningrad ×Sanction index 0.103 *** 0.148 *** 0.144 *** 0.127 ***
(0.0250) (0.0336) (0.0335) (0.0293)
East port city ×Sanction index 0.179 *** 0.0148 0.0173 −0.00123
(0.0336) (0.0236) (0.0236) (0.0254)
East port area ×Sanction index 0.290 *** 0.135 0.138 0.106
(0.0303) (0.163) (0.163) (0.140)
West port city ×Sanction index 0.0958 ** 0.0616 * 0.0586 ** 0.0384 *
(0.0375) (0.0319) (0.0298) (0.0224)
West port area ×Sanction index 0.306 *** 0.0396 ** 0.0395 ** 0.0181
(0.0128) (0.0170) (0.0173) (0.0153)
Lagged ln (lights) 0.150 ***
(0.00688)
Oil price Yes Yes Yes Yes Yes Yes
Control variables Yes Yes Yes No Yes Yes
Individual FE No No Yes Yes Yes Yes
Year FE No No No Yes Yes Yes
Constant −3.834 *** −4.390 *** −4.374 *** −4.468 *** −4.342 *** −3.756 ***
(0.155) (0.0780) (0.226) (0.00633) (0.190) (0.140)
Hausman test – – 306,360.55 40,884.97 178,779.61 2.74 ×107
p-value – – 0.0000 0.0000 0.0000 0.0000
Observations 51,665,696 51,665,696 51,665,696 51,665,696 51,665,696 45,207,484
R-squared 0.060 0.135 0.005 0.014 0.014 0.035
Notes: *** p< 0.01, ** p< 0.05, * p< 0.1. Standard errors are clustered at the municipal district level. Although
there may be autocorrelation between the regional average value of light brightness (avgntl) and ln (lights), we
removed the avgntl and retained other control variables. The coefficient of the regression result changed slightly,
and the significance level did not change.
Sustainability 2022,14, 6112 12 of 23
Column (5) shows that the coefficient estimate of interactive terms in Blagoveshchensk
was positive and significant at the 1% level. Economic sanctions reduced the relative
trade costs with nonsanctioning countries, and economic activities in regions with close
trade ties with nonsanctioning countries relatively increased. Therefore, the brightness of
Blagoveshchensk’s lights became brighter. This was consistent with Hypothesis 3. As eco-
nomic sanctions increased, the coefficient estimates of the interactive terms in Kaliningrad
Oblast, west port cities, and west port areas were positive and statistically significant. This
result was consistent with previous Hypothesis 3. E.U. trade sanctions against Russia were
limited to dual-use goods and energy exploration equipment and had little impact on other
trade, especially in raw materials, oil and gas. Therefore, the brightness of the lights in the
Kaliningrad Oblast, west port cities, and west port areas did not dim.
Additionally, one can naturally assume the autoregressive process of nighttime lights
data where the current value depends past values. We examined the dynamic changes in
nighttime lights in column (6) of Table 2by additionally controlling for one-year lagged
lights. Although the lag term that included the brightness value of the night light reduced
the magnitude of the estimation, except that the coefficient of the interaction term of west
port areas was not significant, the other interaction terms had similar estimation results
and statistical significance as the previous ones.
4.2. Robustness
We added 0.01 before taking natural logarithms of the nighttime lights in previous
estimated results. Because most of the grid cells were zero, we added different values to
affect the estimation results before taking the logarithm. In columns (1), (2), (4), and (5) of
Table 3, we added 0.05 and 0.1 to the independent values of the function before calculating
the logarithm. The results were statistically and qualitatively similar to those in column (5)
of Table 2. Although the value of 0.01 was quite different from those of 0.05 and 0.1, the
regression results were still robust.
After Russia annexed Crimea in 2014, western countries began to impose sanctions
on Russia. After 2014, the government of Russian Federation made Far East development
an important part of the national strategy. This may have led to differences in the spatial
distribution of the domestic economy. In column (6) of Table 3, we controlled for the
Russian Far East. The results showed that the interaction terms in the Far East are positive
and statistically significant. The regression coefficients of other interaction terms have not
changed because of the addition of the interaction terms in the Far East, and the results are
still robust.
Blagoveshchensk and the area within 10 km of the Chinese border were not included
in column (7) of Table 3. By excluding these regions, this paper focused on regions that were
unlikely to be affected by economic spillovers from China and across borders. Excluding
the regions within 10 km of the Chinese border and Blagoveshchensk in column (7) of
Table 3, the regression results showed that there was no change in other interaction terms.
We obtained similar results to those in column (5) of Table 2. The results were still robust.
The sanctions index was calculated by accumulating documents issued by the coun-
tries sending sanctions. There were omissions in our collection of documents from countries
sending sanctions against Russia. We thus transformed the sanctions index into a dummy
variable, that is, the sanctions index is 1 after 2014. Columns (3) and (8) of Table 3show the
regression results using dummy variables to replace the sanction index. Despite replacing
the sanction index with dummy variables, we obtained similar results to those in column (5)
of Table 2. The results were still robust.
Sustainability 2022,14, 6112 13 of 23
Table 3. Robustness test: main regression results.
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Variables Add 0.05 Add 0.1 Dummy for
Sanction Index Add 0.05 Add 0.1 Control for the
Far East
Exclude
Borders
Dummy for
Sanction Index 2SLS
Sanction index 0.00451 *** 0.00374 *** 0.0198 ***
(0.000809) (0.000685) (0.00427)
Moscow ×Sanction index 0.200 *** 0.179 *** 0.245 *** 0.246 *** 0.974 *** 0.276 ***
(0.0160) (0.0146) (0.0196) (0.0196) (0.0782) (0.0211)
St. Petersburg ×Sanction index 0.113 *** 0.114 *** 0.109 *** 0.110 *** 0.434 *** 0.113 ***
(0.000793) (0.000673) (0.00114) (0.00106) (0.00427) (0.00130)
Provincial capital ×Sanction index 0.0652 *** 0.0604 *** 0.0737 *** 0.0751 *** 0.262 *** 0.112 ***
(0.0116) (0.0105) (0.0141) (0.0142) (0.0570) (0.0168)
Other city ×Sanction index 0.0164 0.0143 0.0193 0.0208 0.0752 0.0287
(0.0111) (0.00951) (0.0147) (0.0147) (0.0556) (0.0198)
Manufacturing city ×Sanction index 0.0243 *** 0.0212 *** 0.0293 *** 0.0310 *** 0.118 *** 0.0449 ***
(0.00601) (0.00529) (0.00759) (0.00758) (0.0310) (0.00909)
Mining area ×Sanction index −0.00842 *** −0.00721 *** −0.0122 *** −0.0109 *** −0.0436 *** −0.0132 ***
(0.00122) (0.00106) (0.00170) (0.00163) (0.00691) (0.00187)
Within 10 km of Chinese border ×
Sanction index −0.00157 −0.00138 −0.00367 −0.00816 −0.00377
(0.00192) (0.00157) (0.00282) (0.0118) (0.00305)
Blagoveshchensk ×Sanction index 0.0612 *** 0.0514 *** 0.0816 *** 0.328 *** 0.0940 ***
(0.00777) (0.00792) (0.00731) (0.0206) (0.0106)
Kaliningrad ×Sanction index 0.102 *** 0.0846 *** 0.142 *** 0.144 *** 0.608 *** 0.161 ***
(0.0256) (0.0221) (0.0335) (0.0335) (0.135) (0.0387)
East port city ×Sanction index 0.0121 0.00873 0.0159 0.0173 0.0289 0.0360
(0.0192) (0.0175) (0.0235) (0.0236) (0.102) (0.0254)
East port area ×Sanction index 0.108 0.0954 0.137 0.138 0.553 0.157
(0.130) (0.116) (0.163) (0.163) (0.688) (0.173)
West port city ×Sanction index 0.0461 * 0.0399 * 0.0570 * 0.0586 ** 0.217 * 0.0794 **
(0.0242) (0.0216) (0.0298) (0.0298) (0.116) (0.0390)
West port area ×Sanction index 0.0425 ** 0.0438 ** 0.0381 ** 0.0395 ** 0.117 * 0.0688 ***
(0.0171) (0.0171) (0.0173) (0.0173) (0.0710) (0.0259)
Far East ×Sanction index −0.0117 ***
(0.00119)
Oil price Yes Yes Yes Yes Yes Yes Yes Yes Yes
Control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes
Individual FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year FE No No No Yes Yes Yes Yes Yes Yes
Constant −2.853 *** −2.194 *** −4.372 *** −2.822 *** −2.166 *** −4.307 *** −4.342 *** −4.339 ***
(0.161) (0.134) (0.231) (0.136) (0.113) (0.185) (0.190) (0.193)
Cragg–Donald Wald F statistic 2.0 ×107
Kleibergen–Paap rk Wald F statistic 3562.050
Observations 51,665,696 51,665,696 51,665,696 51,665,696 51,665,696 51,665,696 51,547,592 51,665,696 51,665,696
R-squared 0.006 0.006 0.005 0.017 0.019 0.015 0.014 0.014 0.014
Notes: *** p< 0.01, ** p< 0.05, * p< 0.1. Standard errors are clustered at the municipal district level.
Sustainability 2022,14, 6112 14 of 23
Finally, missing variables can lead to errors in the estimation results. This paper
adopted a fixed-effect model and added a set of control variables that may have affected
the brightness of the light. However, the impact of economic sanctions on the brightness
of the light may still have endogenous interference with missing variables. Therefore,
we introduced instrumental variables to further alleviate concerns about endogenous
interference. External events directly led to the imposition of economic sanctions by
western countries on Russia. External events included intervention in the sovereignty
and territorial integrity of Ukraine, intervention in the U.S. election, and the poisoning of
Sergei and Yulia Skripal. These events were driven mainly by Russian diplomacy rather
than regional economic conditions within Russia. Therefore, we constructed a simple
index to accumulate the number of external events. This external event index was used
as an instrumental variable for the sanctions index. The external event index is shown in
Figure A1 in the Appendix A. In practice, we used the regional dummy
×
instrumental
variable in the regression as the instrumental variable of the regional dummy
×
sanction
index. In column (9) of Table 3, the Cragg–Donald Wald F value was 2.0
×
10
7
, and the
Kleibergen–Paap rk Wald F value was 3562, which was much greater than 10 [
55
]. The
null hypothesis could be rejected, and there was no weak instrumental variable problem.
Column (9) shows the regression results of the second stage of 2SLS. The results were
similar to the regression results in column (5) of Table 2. The results were still robust.
4.3. Results for Different Periods
In this section, we examined the impact of economic sanctions on the brightness of
lights in Russia at different periods. Columns (1) and (2) of Table 4are the regression
estimation results at the early stages of sanctions (2012 to 2015). Columns (3) and (4) of
Table 4are the regression estimation results at the later stages of sanctions (2016 to 2019).
The regression results in column (1) show that in the early stage of sanctions, the sanction
index was negatively correlated with the brightness of lights. Column (3) shows that in the
later stages of sanctions, the sanctions index was positively correlated with the brightness
of lights. All of the above results were significant at the 1% level. The regression result in
column (2) shows that various regions were affected by the negative impact of sanctions
in the early stages of sanctions. Column (4) shows that this negative influence gradually
disappeared in the later stages of sanctions. Our research results were consistent with the
research of Dizaji and van Bergeijk. Sanctions are more harmful in the early stages because
during that time, the target country has not yet found a way to adjust its economy [52].
In the early stages of sanctions, the coefficient of Moscow’s interaction term was
negative. In the later stages of sanctions, the coefficient was positive. The coefficients of
the interaction term in St. Petersburg were positive in both in the early and later stages
of sanctions. In the early stages of sanctions, the coefficients of the interaction term in
provincial capitals and other cities were negative. In the later stages of sanctions, the
coefficients were positive. Moscow and St. Petersburg are the first and second largest
cities in Russia, respectively. They are important economic centers of Russia with relatively
complete industrial types. Studies have shown that diversified industrial structures are
more resistant to external shocks [
56
]. However, Moscow has about half of all financial
institutions in the country [
57
]. Economic sanctions affect the commercial activities of
financial institutions by restricting financing, directly or indirectly affecting the regions
where financial institutions are located. In addition, most of the sanctioned companies
were located in Moscow [
58
]. Therefore, sanctions had a negative impact on the brightness
of Moscow’s lights in the early stages. Because the industrial structure of cities such as
provincial capitals is not as robust as that of St. Petersburg, their ability to withstand
economic sanctions is relatively weak. Therefore, sanctions had a negative impact on the
brightness of lights in provincial capitals and other cities in the early stages.
Sustainability 2022,14, 6112 15 of 23
Table 4. Results for different time periods.
(1) (2) (3) (4)
2012–2015 2012–2015 2016–2019 2016–2019
Variables ln (lights) ln (lights) ln (lights) ln (lights)
Sanction index −0.0707 *** 0.116 ***
(0.00775) (0.0101)
Regional Favoritism
Moscow ×Sanction index −0.337 *** 1.080 ***
(0.0596) (0.218)
St. Petersburg ×Sanction index 0.101 *** 0.205 ***
(0.00532) (0.0102)
Provincial capital ×Sanction index −0.747 *** 1.225 ***
(0.0876) (0.279)
Other city ×Sanction index −0.190 * 0.302 **
(0.0985) (0.153)
Industrialization
Manufacturing city ×Sanction index −0.204 *** 0.474 ***
(0.0419) (0.131)
Mining area ×Sanction index 0.0322 *** −0.0900 ***
(0.00952) (0.0149)
Trade Costs
With 10 km of Chinese border
×
Sanction index
0.0252 *** −0.0783 ***
(0.00940) (0.0235)
Blagoveshchensk ×Sanction index −0.213 ** 0.272 ***
(0.107) (0.0561)
Kaliningrad ×Sanction index −0.258 *** 0.770 ***
(0.0690) (0.238)
East port city ×Sanction index −0.501 *** 0.456 *
(0.110) (0.257)
East port area ×Sanction index −0.308 ** 0.458
(0.135) (0.418)
West port city ×Sanction index −0.583 *** 0.531
(0.161) (0.390)
West port area ×Sanction index −0.384 *** 1.012 **
(0.0794) (0.496)
Oil price Yes Yes Yes Yes
Control variables Yes Yes Yes Yes
Individual FE Yes Yes Yes Yes
Year FE No Yes No Yes
Constant −3.628 *** −5.145 *** −4.709 *** −4.461 ***
(0.580) (0.577) (0.0881) (0.0627)
Hausman test 110,267.10 108,304.35 30,0571.46 148,347.56
p-value 0.0000 0.0000 0.0000 0.0000
Observations 25,832,848 25,832,848 25,832,848 25,832,848
R-squared 0.007 0.013 0.002 0.003
Notes: *** p< 0.01, ** p< 0.05, * p< 0.1. Standard errors are clustered at the municipal district level.
In the early stages of sanctions, the coefficient of interaction terms in manufacturing
cities is negative. In the later stages of sanctions, the coefficient of interaction terms in
manufacturing cities was positive. This result further proves that economic sanctions
promoted the development of Russia’s import substitution industry. Western countries’
sanctions on Russia’s energy sector mainly hit the long-term development of Russia’s
energy industry. In the early stages of sanctions, sanctions had minimal impact on Russian
oil production [
59
]. Therefore, the coefficient of the interaction term in the mining area was
positive in the early stages of the sanctions. The coefficient estimate of the interaction term
in mining areas was negative in the later stages of sanctions.
Sustainability 2022,14, 6112 16 of 23
The sanctions imposed by western countries because of the Ukraine crisis separated
Russia from the West. This accelerated the reorientation of economic and foreign policy,
as well as diplomatic relations, towards the East [
35
,
60
,
61
]. In particular, this strength-
ened economic and trade cooperation with China. Therefore, the brightness of lights in
Blagoveshchensk and east port cities became brighter than that in other areas in the later
stages of sanctions. For the interaction items of Russia close to the border with China, the
coefficient was positive in the early stages of sanctions and negative in the later stages of
sanctions. This shows an unexpected result that we need to explain carefully. Affected by
the spillover of the Chinese economy, the coefficient of the interaction term in the areas
within 10 km of the Chinese border was positive at the beginning of the sanctions. As
economic growth in Northeast China slowed, the spillover effect weakened. In addition, in
the context of severe population loss in the Far East [
54
], economic sanctions can lead to an
accelerated shrink in the Russian population close to the Chinese border. Therefore, as the
sanctions increased, the coefficient of the interaction term in the areas within 10 km of the
Chinese border became negative.
For the interaction items of Kaliningrad Oblast, west port cities, and west port areas,
the estimated result was negative at the early stages of sanctions because sanctions increased
trade uncertainty. However, the estimated result was positive at the later stages of sanctions.
In response to the uncertainty caused by sanctions, foreign companies may have been
overly cautious and interrupted business relations with all Russian partners, even those
partners who were not clearly sanctioned [
62
]. This inadvertently affected the export of
nonembargoed products from western countries to Russia [
6
]. Therefore, in the early stages
of sanctions, sanctions had a negative impact on the brightness of lights in the Kaliningrad
Oblast, west port cities, and west port areas. Trade uncertainty caused by sanctions fades
over time, and western countries gradually resumed trade with Russia. Therefore, the
coefficient of the interaction term in Kaliningrad Oblast and west port were positive in the
later stages of sanctions.
4.4. Results for Different Sectors of Sanctions
The regression results for different sanctions are shown in Table 5. This paper divided
the sanctions against Russia into financial, energy, military, and other sanctions to examine
the impact of different types of sanctions on the brightness of Russia. The results showed
that different types of sanctions obtained similar results, as shown in Table 5. A possible
reason why is that the different types of sanctions were interrelated. Therefore, it was
difficult to attribute regional differences to certain sanctions [11].
Table 5. Results using different sectors of sanctions.
(1) (2) (3) (4)
Variables Finance Military Energy Other
Moscow ×Sanction index 0.359 *** 0.356 *** 0.383 *** 0.338 ***
(0.0285) (0.0284) (0.0303) (0.0268)
St. Petersburg ×Sanction index 0.161 *** 0.160 *** 0.172 *** 0.152 ***
(0.00156) (0.00152) (0.00166) (0.00145)
Provincial Capital ×Sanction index 0.112 *** 0.109 *** 0.124 *** 0.109 ***
(0.0207) (0.0204) (0.0220) (0.0194)
Other city ×Sanction index 0.0309 0.0303 0.0338 0.0297
(0.0218) (0.0213) (0.0237) (0.0207)
Manufacturing city ×Sanction index 0.0458 *** 0.0444 *** 0.0500 *** 0.0433 ***
(0.0111) (0.0109) (0.0118) (0.0103)
Mining area ×Sanction index −0.0160 *** −0.0156 *** −0.0170 *** −0.0149 ***
(0.00237) (0.00233) (0.00250) (0.00219)
With 10 km of Chinese border ×Sanction index −0.00314 −0.00293 −0.00346 −0.00292
(0.00404) (0.00398) (0.00424) (0.00372)
Sustainability 2022,14, 6112 17 of 23
Table 5. Cont.
(1) (2) (3) (4)
Variables Finance Military Energy Other
Blagoveshchensk ×Sanction index 0.122 *** 0.121 *** 0.130 *** 0.115 ***
(0.0110) (0.0110) (0.0126) (0.0113)
Kaliningrad ×Sanction index 0.209 *** 0.205 *** 0.219 *** 0.192 ***
(0.0489) (0.0483) (0.0521) (0.0457)
East port city ×Sanction index 0.0271 0.0271 0.0332 0.0299
(0.0339) (0.0337) (0.0357) (0.0313)
East port area ×Sanction index 0.201 0.200 0.214 0.189
(0.235) (0.234) (0.247) (0.217)
West port city ×Sanction index 0.0868 ** 0.0850 ** 0.0942 ** 0.0829 **
(0.0437) (0.0432) (0.0474) (0.0417)
West port area ×Sanction index 0.0590 ** 0.0590 ** 0.0686 ** 0.0604 **
(0.0250) (0.0251) (0.0278) (0.0242)
Oil price Yes Yes Yes Yes
Control variables Yes Yes Yes Yes
Individual FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
Constant −4.344 *** −4.343 *** −4.345 *** −4.346 ***
(0.190) (0.190) (0.189) (0.189)
Observations 51,665,696 51,665,696 51,665,696 51,665,696
R-squared 0.014 0.014 0.015 0.015
Notes: *** p< 0.01, ** p< 0.05. Standard errors are clustered at the municipal district level.
5. Conclusions
Since March 2014, western countries have imposed economic sanctions on Russia
for more than seven years. In addition, the period has been extended, and sanctions
against Russia have also increased. The impact of economic sanctions on Russia’s domestic
economy is a matter of concern for not only the target country but the sending countries.
This paper used nighttime lights and constructed a sanctions index, which can increase the
understanding of the impact of economic sanctions on Russia.
First, we found a complex relationship between economic sanctions and the brightness
of lights in Russia. There were negative correlations with the brightness of Russian lights in
the early stages of economic sanctions. However, the government of the Russian Federation
took measures to gradually adapt to the economic sanctions, and this negative relationship
disappeared as the sanctions continued. This shows that long-term sanctions do not
necessarily change the political process [
47
]. Therefore, from the perspective of resolving
the Ukrainian crisis, the effectiveness of sanctions is questionable.
Second, this study improves our understanding of the relationship between interna-
tional policies and regional differences. In particular, it provides evidence for studying
the relationship between economic sanctions and regional differences within Russia. The
regression results show a very clear picture. Moscow, St. Petersburg, and provincial capitals
are the political and economic centers of Russia. As sanctions increased, the brightness
of the light in those areas was brighter than that in other areas in the country. Russia’s
import substitution industrialization was promoted as economic sanctions increased. The
lights in manufacturing cities became relatively brighter. However, the results also showed
that the brightness of the lights in Russia’s natural resource areas dimmed because of the
impact of sanctions. The lights in Blagoveshchensk were brighter than those in other areas
as economic sanctions increased. However, in areas closely related to the sanction-sending
countries, the lights of Kaliningrad Oblast, west port cities, and west port areas did not
dim. We explained the relationship between economic sanctions and regional differences
from the following three perspectives: (1) regional favoritism of political elites, i.e., political
elites allocating limited resources and public goods to regions that they deem important;
(2) sanctions affecting regional economic activities through industry development; and
Sustainability 2022,14, 6112 18 of 23
(3) sanctions changing the relative trade costs between countries, and trade shifting to the
regions that benefit the most.
The relationship between regional differences and sanctions is well established. In
particular, sanctions have widened the development gap between regions. This shows that
the impact of sanctions on the economy of the target country exceeds the expected political
goals. Even if the affected economies returned to the expected economic growth trajectory
after the sanctions were lifted, it would become an additional burden to address the growing
regional differences. This may slow down recovery efforts [
63
] and hinder the sustainable
development of the Russian domestic economy. This paper empirically analyzed the
relationship between economic sanctions and regional differences in Russia. This provides
empirical support for the formulation of Russian regional sustainable development policies.
The results in this paper are important for us to understand the impact of economic
sanctions on Russia, but there were some limitations. This paper took Russia as an example
to analyze the relationship between economic sanctions and various regions in the country.
Because of the uniqueness of Russia, the conclusions of this paper cannot be applied to all
countries that are subject to sanctions. In addition, this paper used only a fixed-effects panel
data model to study the relationship between economic sanctions and the brightness of
lights in Russia. We assumed that the current light-brightness value depended on the past
value. The basic principle of GMM is to use the lag terms of a set of explanatory variables
as instrumental variables of the relevant variables in the equation, according to theory and
experience. However, this paper involved panel data with a sample size of 6,458,212
×
8,
and it was difficult for this paper to use GMM estimator for dynamic estimation, because
the sample size was too large and exceeded the computing power of ordinary computers.
Shida indicated that the difficulty of evaluating the Russian economy lay in the complexity
of the influencing factors, such as the external shocks of economic sanctions, the drop in
crude oil prices, and the high dependence of the internal industrial structure on natural
resources [
10
]. These factors are intertwined and interact, making it difficult to quantify
the impact of sanctions separately from other impacts. However, this paper provides a
useful attempt to analyze the impact of economic sanctions on Russia’s domestic regional
differences by constructing a sanctions index, controlling for international crude oil prices
and other control variables, and using the nighttime lights. The current research was based
on a panel of data covering only 8 years, with a relatively short timescale. However, it
often takes a long time to show the impact of industrial upgrading. Therefore, the impact
of economic sanctions on Russian industrial upgrading is a direction for future research. In
addition, this paper used the nighttime lights to analyze the impact of economic sanctions
on regional development at a macro level. However, we did not consider the impact of
economic sanctions on Russian residents and the income of different groups in Russia.
Studying the impact of economic sanctions on the income of Russian residents will be
another focus of future research.
Author Contributions:
Conceptualization: Z.L. and T.L.; methodology: Z.L.; software: Z.L.;
writing—original
draft preparation, Z.L.; writing—review and editing, Z.L. and T.L.; supervision,
Z.L. and T.L.; project administration, T.L.; funding acquisition, T.L. All authors have read and agreed
to the published version of the manuscript.
Funding:
This work was supported by the Natural Science Fund of China under Grant 41201109 and
the Humanities and Social Sciences Key Research Base of major projects of the Ministry of Education
in China under Grant 17JJDGJW006.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: The authors are grateful to the three reviewers for their helpful comments.
Conflicts of Interest: The authors declare no conflict of interest.
Sustainability 2022,14, 6112 19 of 23
Appendix A
Table A1. Chronology of the sanctions on Russia.
Year Sender Content Finance Energy Military Other
2014 AUS
Asset freeze and travel restrictions of specific persons/entities;
sanctions on financial entities: Bank Rossiya, Invest Capital Bank,
and SMP Bank; sanctions on defense entities: Almaz-Antey.
+ + +
2014 CAN
Asset freeze and travel restrictions of specific persons/entities;
sanctions on energy entities: OAO Novatek, prohibition of export
of equipment related to oil exploration or production; sanctions
on financial entities: Gazprombank OAO, VEB, Bank of Moscow,
Russian Agricultural Bank, VTB Bank OAO, and Sberbank;
sanctions on defense entities: Almaz-Antey, Bazalt, JSC Concern
Radio-Electronic Technologies, JSC Concern Sozvezdie, JSC MIC
NPO Mashinostroyenia, and others.
+ + + +
2014 CHE
Asset freeze and travel restrictions of specific persons/entities;
sanctions on financial entities: Gazprombank, Rosselkhozbank,
Sberbank, Vnesheconombank, and VTB Bank; sanctions on energy
entities: Rosneft, Transneft, and Gazprom Neft; restrictions on
export to Russia of certain items for use in petroleum exploration
and production; sanctions on defense entities: Uralvagonzavod,
United Aircraft Corporation, OPK Oboronprom, and others.
+ + + +
2014 EU
Asset freeze and travel restrictions of specific persons/entities;
restrictions on access to E.U. primary and secondary capital
markets for certain financial institutions: Sberbank, VTB Bank,
Gazprombank, Vnesheconombank, and Rosselkhozbank;
sanctions on energy entities: Chernomorneftega, Rosneft,
Gazpromneft, and Transneft; restrictions on transactions to
defense entities: Oboronprom, United Aircraft Corporation,
Uralvagonzavod, Almaz Antey, Bazalt, and others.
+ + + +
2014 JPN
Sanctions on five state-owned banks in Russia: Sberbank, VTB
Bank, Vnesheconombank, Gazprombank, and Russian
Agricultural Bank; arms embargo.
+ +
2014 NOR
Asset freeze and travel restrictions of specific persons/entities;
sanctions on five state-owned banks in Russia: Sberbank, VTB
Bank, Vnesheconombank, Gazprombank, and Russian
Agricultural Bank; sanctions on defense entities: Oboronprom,
United Aircraft Corporation, Uralvagonzavod, JSC Sirius, Bazalt,
and others; sanctions on energy entities: Rosneft, Transneft, and
Gazprom Neft; prohibition of exports of certain technologies used
in the petroleum industry.
+ + + +
2014
UKR,
ALB,
ISL,
MNE
Asset freeze and travel restrictions of specific persons/entities. +
2014 USA
Asset freeze and travel restrictions of specific persons/entities;
sanctions on financial entities: Bank of Moscow, Gazprombank
OAO, Russian Agricultural Bank, Sberbank, VEB, and VTB Bank;
prohibition of export of certain technologies used in the
petroleum industry; sanctions on energy entities: Novatek,
Rosneft, Vnesheconombank, and Gazprombank; sanctions on
defense entities: Almaz-Antey, Rostec, Bazalt, and others.
+ + + +
Sustainability 2022,14, 6112 20 of 23
Table A1. Cont.
Year Sender Content Finance Energy Military Other
2015 AUS
Restrictions on export to or import from Russia of arms and
related materiel; restrictions on export to certain technologies
used in the petroleum industry; restrictions on commercial
dealing with certain capital financial market instruments issued
by certain Russian state-owned entities.
+ + +
2015 CAN
Asset freeze and travel restrictions of specific persons/entities;
sanctions on defense entities: JSC United Aircraft Corporation
and Rostec subsidiaries; restrictions on financing of energy
entities: Rosneft, OJSC Gazprom, OJSC Gazprom Neft, OJSC
Surgutneftegas, and Transneft OAO.
+ + +
2015 CHE
Asset freeze and travel restrictions of specific persons/entities;
prohibition of import of firearms and their components,
accessories, ammunition, and ammunition components
from Russia.
+ +
2015 EU Asset freeze and travel restrictions of specific persons/entities. +
2015 UKR
Asset freeze and travel restrictions of specific persons/entities;
sanctions on financial entities: Moscow Bank, Gazprom Bank,
Genbank, Adelantbank, Smartbank, Marshall Capital Partners,
Russian National Commercial Bank, Tempbank, and others;
sanctions on defense entities: Russian Helicopters, Almaz-Antey,
Helicopter Service Company, Ulan-Ude Aviation Plant,
and others.
+ + +
2015 USA
Asset freeze and travel restrictions of specific persons/entities;
sanctions on financial entities: VTB Bank subsidiaries and Sberban
subsidiaries; sanctions on energy entities: subsidiaries of Rosneft;
sanctions on defense entities: subsidiaries of Rostec, Izhevsk y
Mekhanichesky Zavod JSC, Kontsern Izhmash, and others.
+ + + +
2016 CAN
Asset freeze and travel restrictions of specific persons/entities;
sanctions on defense entities: Izhevsky Mekhanichesky Zavod
JSC, JSC Tecmash, Ruselectronics JSC, Shvabe Holding JSC.
+ +
2016 UKR
Asset freeze and travel restrictions of specific persons/entities;
sanctions on payment entities: Zolota Korona, Kolibri, Unistream,
Anelik, Blizko, and others;
sanctions on energy entities: Bashneft, Novocherkaska GRES,
Spezneftegaz, and Transmashholding; sanctions on defense
entities: Rosteh, Rosoboroneksport, Rostvertol, Izhmash,
and others.
+ + + +
2016 USA
Asset freeze and travel restrictions of specific persons/entities;
sanctions on subsidiaries of Bank of Moscow, Gazprombank, and
Russian Agricultural Bank; sanctions on energy entities: Gazprom
and Novatek subsidiaries.
+ + +
2017 EU Asset freeze and travel restrictions of specific persons/entities. +
2017 UKR
Asset freeze and travel restrictions of specific persons/entities;
sanctions on five Ukrainian banks with the capital of Russian
state-owned banks: Sberbank PJSC, VS Bank PJSC,
Prominvestbank PJSC, VTB Bank PJSC, and BM Bank PJSC.
+ +
2017 USA
Authorization of certain products to be exported to entities under
the Russian Federal Security Agency. -
2017 USA
Asset freeze and travel restrictions of specific persons/entities;
Sanctions on financial entities: Taatta AO, IS Bank AO, VVB PAO,
and others; sanctions on Transneft subsidiaries; sanctions on
defense entities: Molot-Oruzhie OOO and others.
+ + + +
Sustainability 2022,14, 6112 21 of 23
Table A1. Cont.
Year Sender Content Finance Energy Military Other
2018 EU Asset freeze and travel restrictions of specific persons/entities. +
2018 UKR
Asset freeze and travel restrictions of specific persons/entities;
sanctions on energy entities: Rosneft, Lukoil, and Transoil;
sanctions on payment institutions: WebMoney and Moscow
Exchange MICEX-RTS transactions.
+ + +
2018 USA
Asset freeze and travel restrictions of specific persons/entities;
sanctions on energy entities: subsidiaries of Surgutneftegas;
sanctions on Rosoboronexport OJSC.
+ + +
2018 USA OFAC authorization of trading licenses related to RUSAL and
EN + Group. -
2019 USA
Asset freeze and travel restrictions of specific persons/entities;
financing restrictions; restrictions on the export of chemical and
biological dual-use products to Russia.
+ + +
2019 EU Asset freeze and travel restrictions of specific persons. +
2019 CAN
Asset freeze and travel restrictions of specific persons/entities;
sanctions on energy entities: Stroygazmontazh LLC, Ugolnye
Tekhnologii OOO, and Gaz-Alyans OOO; sanctions on defense
entities: Sukhoi Aviation JSC, JSC Russian Aircraft Corporation
MiG, Tupolev CJSC, and others.
+ + +
2019 UKR Asset freeze and travel restrictions of specific persons/entities;
sanctions on military, energy, and financial entities. + + + +
Note: “+” means increase sanctions; “-“ means decrease sanctions. Document source: https://www.treasury.gov
(accessed on 28 May 2021); https://eeas.europa.eu; https://www.international.gc.cales (accessed on 26 May
2021); https://dfat.gov.au (accessed on 28 May 2021); https://www.mof.go.jp (accessed on 28 May 2021); https://
lovdata.no/register/lovtidend (accessed on 25 May 2021); https://www.seco.admin.ch (accessed on 21 May 2021);
http://www.rnbo.gov.ua/documents/ (accessed on 28 May 2021); https://sanctionsnews.bakermckenzie.com/
(accessed on 24 May 2021).
Sustainability 2022, 14, x FOR PEER REVIEW 23 of 25
Figure A1. The external event index over time.
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