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Measuring Sustainability in the Russian Arctic: An Interdisciplinary Study

by Votrin, Valery, PhD

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The indicators are meant to give a quantitative characteristic to the progress towards
sustainable development of the region, based on the publicly available national statistical data.
The next sections discuss and analyse the dynamics of each indicator.
4.2 Economic Dimension
4.2.1 Gross Regional Product per Head – Oil Prosperity
Change in gross domestic product (GDP) is the basic aggregated indicator used
traditionally in measuring the country’s economic development. Together with change in fixed
assets, this is a major indicator for economic growth. Its regional modifications, gross regional
product (GRP) and GRP per head, are extremely important for measuring economic growth and
overall well-being at regional level and are usually included in most sustainability indicator sets.
At the same time, GRP per head is a poor indicator as it can and does show positive change
due to economic activities which reduce quality of life and quality of the environment.
In Russia, GRP has become the major regional development indicator since the country
adopted the System of National Accounts (SNA) in 1994. However, while Rosstat is the only
feasible data source, the quality of its regional data remains dubious. As Solanko (2003) points
out, in many instances it is even unclear exactly how regional data on production, incomes and
prices are collected and what the precise relationship is between regional and national figures.
For instance, while Rosstat reported that in 2001 national GDP grew by 5%, the average
reported GRP increase was 6%. Thus, total GRP of all Russian regions is quite different from
the Russian GDP since the Russian GRP does not include the added value of non-market
community services provided by the government (national security, etc.), other non-market
community services provided from the federal budget, banking services where the banking
activities extend beyond regional borders, and regional foreign trade. Total GRP comprises only
85 per cent of the total Russian GDP (Granberg et al, 1998).
The significant shadow economy sector in Russia strongly affects both data on GDP and
GRP. Some experts estimate that the shadow economy sector in Russia has increased up to 40
per cent during the reform period, and the actual use and sales of some types of natural
resources are 2 or 3 times higher than those reported by the official sources (Bobylev and
Soloviova, 2003). Rosstat has included estimates of hidden or non-formal economy into official
GRP statistics beginning from 1997, “hidden or non-formal activity” being “firstly, economic
activities allowed by law but hidden or understated by volume for the purposes of evading taxes,
and secondly, individual production activities performed without licence and based on informal
relations between actors where goods or services are completely or partially produced for own
consumption or for sale”. Illegal economic activity forbidden by the existing legislation is not
registered by Rosstat (Granberg et al, 1998).
It is the normal practice for Rosstat to include the data on GRP and GRP per head for
the AO into the data for the corresponding oblast or krai. Four of the six Russian Arctic areas


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are the AO (Nenets, Yamal-Nenets, Taimyr, and Chukotka), with the latter being the
autonomous area with special status (Chukotka seceded from Magadan Oblast in the early
1990s and is the only okrug for which the GRP data are available). Alternative GRP estimates
(Granberg et al, 1998) demonstrate that some oil and gas producing AO (Yamal-Nenets,
Khanty-Mansiysk, and Taimyr) top the list of the regions with the highest GRP per capita, and
almost all Russian Arctic autonomous areas are the drivers of economic development of their
The aggregate GRP per head for the six Russian Arctic regions steadily rose between
1999 and 2002 concurrently with growing industrial output and natural resource production. In
fact, the growth began after 1998, the year of the banking default in Russia, with the sharp rise
in GRP per head since 2000. GRP per head by region is shown in Table 4.2.
Table 4.2. Gross regional product per head in the Russian Arctic regions and Russia,
1999 to 2002, thousand roubles
Region 1999 2000 2001 2002
Murmansk 43332 58797 59395 71281
Arkhangelsk 26541 202222 182388 149153
Tyumen 110475 251988 368265 554075
Krasnoyarsk 42430 42987 43557 44175
Sakha 65847 83259 103606 118463
Chukotka 43173 56761 117332 178459
Russia 28492 42902 53709 66111
Source: Rosstat (2004)2
As the table suggests, Tyumen Oblast with Yamal-Nenets AO was the leader in GRP per

head formation, with 110475 roubles, or 4092 USD in 1999 and growing by average 30 per cent
annually. As Granberg and Zaitseva (2003) indicate, there was no case of decrease in GRP in
the Russian North West including Murmansk Oblast between 1999 and 2001. The Russian
Arctic regions have clearly followed this trend which is true for the rest of Russia as well. Not
surprisingly, resource rich Tyumen, Arkhangelsk, Sakha and Chukotka performed better,
highlighting the importance of the initial regional industrial structure, whereas Murmansk and
Krasnoyarsk with high shares of heavy engineering were lagging behind. Mikheeva (1999) and
Popov (2001) point out at the difference between regional performance as measured by the
change in output and regional performance measured by the change in real income in the
Russian regions. In such conditions, budgetary expenditure is a more explanatory variable for
the behaviour of real incomes than for change in GRP per head. For example, in 1999 Tyumen
Oblast had GRP per head which was 3 times higher than personal income per head. This fact
can be explained by the oblast’s donor status in inter-regional government financial flows when


Data for oblasts together with autonomus okrugs.
Russian roubles per US dollar: 27,0 (1999), 28,2 (2000), 30,1 (2001), 31,8 (2002).


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value added created in Tyumen was redistributed to the other regions through state taxation
and transfers of business income (Popov, 2001).
Table 4.3 compares the Russian Arctic regions with other Russian geographic areas in
terms of the aggregate GRP per head.
Table 4.3. Aggregate GRP per head in the Russian Arctic and other Russian regions,
1999 to 2002, thousand roubles
Region 1999 2000 2001 2002
Russian Arctic 331798 696013 874543 1115606
Central Russia 401639 581172 732866 930347
Caucasus 109015 155473 200784 238366
Siberia 247847 355859 453694 549518
Far East3 215323 280298 364579 456865
Source: author’s calculations based on Rosstat (2004)
It turns out that after 1999 the aggregate GRP per head of the six Russian Arctic regions
outstrips even that of 18 Central Russian regions, including Moscow, which are considered
notoriously prosperous. Sharp rise in income and GRP per head after 1999 can be accounted
for by the initial conditions (the share of resource industries in total output, the index of resource
potential, and initial level of GRP per head), measures of the institutional capacity of regional
governments (massive investments into fixed assets, mainly in oil sector and other natural
resource industries, and the share of shadow economy in the region) and relatively high price
and wage level (Popov, 2001; Dolinskaya, 2002; Solanko, 2003).
The value of traditional macroeconomic indicators such as GDP and GRP as the
sustainability measure is being questioned today by the experts in many countries, including
Russia. However, for Russia the problem of doubling of GDP and GRP by exploiting natural
resources is more acute. Bobylev (2005) argues that the macroeconomic indicators are based
on technogenic development that exploits nature, and their future use will be restricted when
natural resources are depleted and the environment is irreversibly polluted. The author is in
favour of more environmentally friendly economic measures such as genuine savings and
stresses that in recent years this indicator has been negative in Russia, mainly due to the
depletion of the raw material base and the inefficient, nature-exploiting structure of the Russian
economy. It is worth noting, however, that as a sustainability indicator genuine savings were
used only in one of the two regional sustainability indicator sets in Russia (Tomsk) which can be
explained both by the economic orientation of Russia’s current environmental policies and by
reasonable doubt with regard to the relevance of genuine savings to sustainable development,
as found in Hueting and Reijnders (2004). An indicator such as the Ecological Footprint needs
to be included into the core set in order to account for the environmental damage related to
consumption of resources.


Without Sakha and Chukotka.


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The quality of Russian regional data on GRP per head is certainly a question. Solanko
(2003) suggests that the indicators “monetary income per head” and “value of industrial
production” that both closely correlate with GRP and have readily available data from 1990
onwards, can be used to provide a more accurate picture of change in real income in the
Russian regions. In absence of a more appropriate and coherent economic indicator, GRP
linked to other economic and social indicators can be used to assess the overall well-being in
Russia both at federal and regional levels. The role of a comprehensive sustainability
assessment instrument like the indicator set is crucial in comparing different and often
contradictory sets of data to analyse the regional sustainability process.

4.2.2 Rate of Renewal of Fixed Assets – Fixing Regional Economy?
Fixed assets are the major material element of national wealth of a country or region.
Rate of Renewal of Fixed Assets (RRFA) is important for the complex analysis of dynamics and
use of fixed assets during certain period of time. With regard to the environment, the
depreciation of fixed assets entails many techogenic accidents and disasters (Kozlovskaya,
As a sustainability indicator, RRFA is extremely important for Russia where large
proportion of machinery and equipment is old and needs replacement. It was used both in the
federal (Bobylev and Makeyenko, 2002) and regional (ERM, 2003; Kozlovskaya, 2003)
sustainability indicator sets. The indicator’s positive dynamics may show improvements in
economic structure, technologic renewal, and mitigation of adverse environmental impact. Rapid
economic growth in 1999-2000 in Russia was largely due to the involvement of spare
production facilities, or fixed assets, without introducing new ones. By 2000, the use of
production facilities reached its highest point of 50 per cent for the last 10 years. The
relationship between the depreciation and age structure of fixed assets can highlight the need
for urgent renewal of fixed capital. In many industries, further expansion of production depends
on the investment dynamics. As stated above, the depreciation of fixed assets results in
numerous industrial accidents. Today, in many sectors as much as 60 to 80 per cent of
equipment is depreciated, and some 50,000 accidents a year occur in Russia’s oil and gas
fields on trunk and field pipeline facilities due to the depreciation of equipment. For the same
reason, accidents occur frequently at the chemical plants which in most cases has severe
consequences for the environment (Bobylev and Makeyenko, 2002; Bobylev, 2005).
Table 4.4 shows change in RRFA for the whole Russia between 1970 and 2002.
Table 4.4. Dynamics of Rate of Renewal of Fixed Assets in Russia, 1970 to 2002, %

1970 1980 1985 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003
10,6 8,1 6,9 6,9 1,7 1,4 1,1 1,2 1,1 1,5 1,6 1,8 1,8

Source: Rosstat (2004)


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For the last 30 years, RRFA in all of Russia decreased dramatically, in the first 20 years
to decrease twofold and in the next 10 years fourfold. Compared to the value for the whole
Russia, change in RRFA in the Russian Arctic was rather positive, as shown in Figure 4.1.
Figure 4.1. Rate of Renewal of Fixed Assets in the Russian Arctic







Nenets AO
Yamal Nenets AO


1998 1999 2000 2001 2002 2003 2004

Source: Rosstat (2004)
RRFA in the six Russian Arctic regions from 1998 to 2004 tends to increase, with sharp
rise in three major oil and gas producing regions – Nenets AO, Yamal-Nenets AO, and Taimyr
where RRFA grew by average 2 per cent a year.
The high value of the indicator so different from the average Russian figures is
accounted for by the initial conditions, the investment climate and especially resource-orientated
nature of the regional economies. Domestic and foreign investments are the largest significant
factor contributing to the renewal of fixed assets that change faster than those in non-resource
manufacturing regions. Fuel producing industries remain the main target of investors. In 2002,
out of 100 roubles of the cost of fixed assets in fuel producing sector, about 19 roubles were
invested. In most manufacturing industries, RRFA is considerably lower than in fuel producing
sector. In 2001, RRFA in the Russian fuel producing industry was 3,5 whereas in nonferrous
industry it was 2,8, in machine industry, metalworking and electric power industry 0,9, in
chemical and petrochemical industry 0,8 and in light industry only 0,6 (Gorst et al, 2003).
Investments were one of the major factors of rapid economic growth in Russia between
1999 and 2001, being largely the consequence of extremely favourable conditions for Russian
exporters in international market (Bobylev and Makeyenko, 2002; Molchanov, 2003). Table 4.5
shows investments as percentage of Russian GDP between 1991 and 2002.


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Table 4.5. Investments as GDP percentage in Russia, 1992 to 2002, %

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
14,0 15,8 17,8 16,7 17,5 16,5 15,1 14,8 16,9 17,7 16,2
Source: Rosstat (2004)
During the 1990s, investments as GDP percentage in Russia grew steadily and reached
their peak in 2001 with 18 per cent of national GDP. However, despite all favourable factors the
investment climate in Russia in 2000 remained as it was. Lack of structural reforms affected the
normalisation of relationships between financial sector and the real sector. Gaps in legislation
regulating property rights, corporate governance, competitiveness and transparency hindered
investment activities in Russia. Therefore, with increasing investments and expanding domestic
financing sources, the flight of capital from Russia abroad had not diminished (Bobylev and
Makeyenko, 2002). In 2002, investments lost their role as one of the driving forces of the
Russian economy. Export-orientated sectors, especially fuel producing industry and black
metallurgy have experienced the most dramatic 28 per cent reduction in investments compared
with 2001, mainly because the resource exporters used their profit as the source of financing,
and the abolition of investment tax preference in 2002 has struck them most severely
(Molchanov, 2003).
Table 4.6. Investments as percentage of gross regional product in the Russian Arctic,
1999 to 2002, %

Region 1999 2000 2001 2002
Murmansk 13,1 15,0 18,6 14,6
Nenets AO 18,2 21,1 55,2 84,3
Yamal-Nenets AO 58,0 65,0 49,0 46,7
Taimyr 11,0 13,2 61,0 45,3
Sakha 15,0 19,7 21,7 22,5
Chukotka 7,6 11,3 21,8 42,1
Source: Rosstat (2004)
One of the peculiar features of investment process in Russia after 2000 was the
reduction in investments registered by official statistics which was largely due to the fact that a
considerable portion of investments has gone into the shadows in investors’ effort to evade
taxes (Molchanov, 2003). Table 4.6 shows a significant drop in investments in the main oil
producing region, Yamal-Nenets AO, in 2002 (18 per cent compared with 2000) and a
somewhat contradictory rise in investments in other resource rich regions, namely Nenets AO
(63 per cent compared with 2000), Taimyr (32 per cent compared with 2000) and Chukotka (31
per cent compared with 2000). RRFA, however, steadily rose in all six Russian Arctic regions at
that time to be significantly higher than average Russian one. It turns out that investments into
fixed assets of resource extracting sectors in the Russian Arctic did not cease to grow, despite
the Rosstat data. Thus, the correlation between RRFA and investments as GRP percentage in
the Russian Arctic regions provides a better picture of fixed asset dynamics and investment

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Such investment policies that increase imbalances between resource-exploiting and
manufacturing sectors greatly strain the environmental situation in Russia and in particular in
the resource rich Russian Arctic. As Bobylev (2005) puts it, structural shifts in the economy
toward nature-exploiting sectors result in a “dirty” economic recovery which is currently under
way in Russia. Where there is only one criterion of efficiency – obtaining high growth rates of
enormous profit primarily by extracting natural resources, there can be no talk about sustainable
development either at federal or regional level. Rather than investments in nature depleting
sectors, investments in science, education, public health, and technology development need to
be increased sharply.

4.2.3 Employees and Unemployees
Unemployment is one of the most acute socio-economic problems and is generally in the
top ten urgent issues crucial for society. Unemployment rate is used virtually in every
sustainability indicator framework, whether it is national, regional or community sustainability
indicator set, and indicates the scale of socio-economic strain in the labour market.
For Russia, a new phenomenon emerged in the early 1990s: official, or registered
unemployment. As Bragin and Osakovskii (2005) argue, it did not become a dominant factor in
the labour market and its scale was not catastrophic, as “the unemployment rate did not exceed
13 to 14 per cent at any time during this period”. The authors argue that this was because the
labour market’s initial adjustment through decreased real wages rather than through reduced
numbers of employees and the gradual establishment of balanced supply and demand of labour
resources in the labour market because of the reduction in job vacancies which diminished to 1
to 2 per cent of the total number of employees in Russia.
Official data do not seem to contradict this statement. As Table 4.7 below suggests,
unemployment rate in Russia did not exceed 14 per cent from 1998 and 2003 and began to
diminish after 1999, whereas registered unemployment was even lower and stabilised in the
recent years. The main factor in Russia is the shift from self-employment (whether
entrepreneurial or subsistence) to wage employment, accompanied by a decline in inequality
among wage earners. One reason explaining this decline is the reduction in arrears, which has
been a feature of the economic recovery in the FSU (Alam et al, 2005). Job flows in Russia
have been remarkably low, with job creation lagging behind job destruction for most of the past
decade (Rutkowski et al, 2005).
Table 4.7. Unemployment (U) versus Registered Unemployment (RU) in the Russian
Arctic and Russia, 1995 to 2003, %
Region 1998 1999 2000 2001 2002 2003
Murmansk 21,1 4,0 16,4 3,3 12,8 4,0 12,8 4,0 10,2 4,0 10,0 6,0
Nenets AO 11,5 6,5 20,0 3,3 10,9 3,3 7,2 4,3 7,2 4,0 8,6 7,7

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Yamal-Nenets AO 11,2 2,8 10,1 2,4 7,9 2,7 7,1 2,9 6,9 2,5 5,5 4,7
Taimyr 15,5 4,1 9,6 4,1 5,7 4,2 7,3 5,2 7,6 6,3 6,9 5,2
Sakha 13,4 1,2 13,8 1,1 11,3 1,3 8,2 1,3 7,1 1,3 9,4 2,5
Chukotka 4,9 4,0 9,3 3,7 10,0 2,7 7,4 1,9 4,6 2,3 4,8 4,1
Russian Arctic average 12,9 5,0 13,2 3,8 9,8 3,0 8,3 3,0 7,3 3,3 7,5 3,4
Russia 13,2 2,9 13,0 1,7 10,5 1,4 9,1 1,6 8,1 2,1 8,6 2,3
Source: Rosstat (2004)
However, Tchetvernina et al (2001) indicate that, while registered unemployment
declined and the gap between total and registered unemployment in Russia became sevenfold
in the late 1990s, in reality the situation is not so simple or straightforward. Apart from the
general economic situation in any given region, the registered unemployment rate is strongly
influenced by the factors such as government unemployment policy, the work efficiency and
type of work carried out by individual regional and district employment services, peculiarities of
labour legislation, and the local and regional legislative initiatives. Between 1996 and 2000, all
these factors contributed to the underestimation of registered unemployment versus the real
scale of the phenomenon in Russia.
Thus, despite recent declines, Russia can no longer be called a low-unemployment
economy. The rate and duration of unemployment are high (relative to some transition
countries), and regional variations in unemployment are quite large. The Russian workers with
low levels of education, obsolete skills, and older age have the highest rates of unemployment
and the longest duration of unemployment. High payroll taxes in Russia (higher than OECD but
lower than most Central and Eastern European countries) may also contribute to higher
unemployment by raising the cost of labour. The sharp decline in unemployment shows that the
labour market has been more flexible in responding to economic growth than it has in other
CEE countries. One reason may be the lack of enforcement of restrictive legislation such as
high minimum wages or restrictive termination conditions found in other CEE countries (World
Bank, 2003).
In addition, the unemployment issue in Russia rests largely on a definitional question
whether those in subsistence agriculture are included as employed. The Rosstat definition of
employment includes individuals engaged in home production only if they sell their products but
not if the production is for own consumption. If subsistence agricultural workers are counted as
employed, the unemployment rate would decline to 8 per cent, and employment would increase
by 12 per cent (World Bank, 2003). Remarkably, the restructuring of remaining large farms did
not lead to significant income gains in rural areas but it has not resulted in open unemployment
either which can be explained by the more capital-intensive nature of agriculture in Russia
(Alam et al, 2005).
The Russian Arctic as a whole shows similar trend as suggested above by Bragin and
Osakovskii (2005). Figure 4.2 shows that registered unemployment in the region exceeded 13

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per cent only once, in 1999. Since 1999, unemployment rate in the region dropped from 13,2
per cent to 7,3 per cent in 2002 and 7,5 per cent in 2003. Registered unemployment rate
decreased from 5 per cent in 1998 to 3 per cent in 2000 and 2001 where it stabilised at 3 to 3,5
per cent in subsequent years.

Figure 4.2. Unemployment rates in the Russian Arctic







1998 1999 2000 2001 2002 2003

Unemployment Registered unemployment
Source: author’s calculations based on Rosstat (2004)
This finding agrees with the one in Popov (2001) who found that unemployment in the
Far East (including some Russian Arctic regions, particularly Sakha and Chukotka) remained
mostly at levels close to or below the national average. Regional unemployment rates illustrate
this thesis. As Table 4.7 demonstrates, after 1999 labour market trends continue to improve in
almost all Russian Arctic regions, with the stabilised unemployment rate both at federal and
regional levels in 2003.
As Bragin and Osakovskii (2005) indicate, while growing labour shortages can be
noticed in the country’s main industrial and commercial centres such as Moscow and St
Petersburg, the unemployment rate is over 10 per cent of the labour force in one out of three
Russian regions. In the Russian Arctic, Murmansk Oblast is particularly affected. Yet according
to the same authors, Chukotka is one of the ten regions (along with Moscow and St Petersburg)
which had the lowest unemployment rate in 2003 (4.8 per cent in 2003 compared with 7.4 per
cent in 2001), and no Russian Arctic region was among the ten regions, mainly those in
Northern Caucasus, with the highest unemployment rate in 2003.
There continues to be a large regional variation in unemployment levels in Russia.
During the 1990s, a pronounced tendency to widening regional differences in unemployment
rates coupled with overall unemployment growth was observed. In 2000, the situation had


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slightly improved but unemployment rate above 15 per cent still persisted in 31 regions. It
should be noted that no Russian Arctic region was among them. In Moscow and St. Petersburg,
open unemployment is negligible (less than 4 per cent), while it is alarmingly high in the
Northern Caucasus regions of Dagestan and Ingushetia (24 and 44 per cent respectively). High
unemployment regions concentrated in eastern and western Siberia and the Northern Caucasus
have lower expenditure per capita, high poverty rates, high birth rates, and a high industrial
share of output. High unemployment rates in high industrial share regions or particular state
sectors (e.g. railways) indicate that unemployment in these regions and in mono-company
towns might be exacerbated (in the short run) by economic restructuring, which will require
social policy focus (Rutkowski et al, 2005; Tchetvernina et al, 2001; World Bank, 2003).
As with other Rosstat based indicators, there is a problem of inadequate statistical
methodology, with a host of latent processes in the Russian labour market being significantly
underrepresented by the official statistics as highlighted by Tchetvernina et al (2001) who point
out that various forms of hidden (latent) and informal employment call for new tools and
changes in the methodology of the Rosstat Labour Force Survey. Most of the hidden
unemployment can be revealed through the use of standard statistical methods but the main
problem in analysing hidden unemployment lies in the inadequacy of information processing
and presentation implemented by Rosstat.
The relatively low unemployment rate in the Russian Arctic compared to other Russian
regions is mainly due to the fact that since the Soviet times people have moved there to work in
industrial sectors, enjoying a variety of benefits in relation to the work under the harsh northern
conditions. Most people in the resource-rich regions such as Nenets AO, Yamal-Nenets AO,
and Taimyr are employed in the industry, with just a smaller portion working in other sectors of
the economy. Commercial and public sectors as well as services, culture and construction
attract far less workers, highlighting again an unprecedented bias towards nature-exploiting
4.2.4 Energy Use Controversy
Energy use, or energy intensity is a particular indicator of environmental intensity. For
Russia with its uniquely huge energy intensity of the economy, energy use is a key indicator that
can be useful as economic and environmental sustainability indicator both at federal and
regional levels. The indicator is used in many global sustainability indicator sets including
UNCSD (2003) and IAEA energy indicator frameworks (2005).
In Russia, the environment is under pressure largely due to the extraction and
production of primary energy resources. Energy consumption plays a less important role in
adverse socio-economic and environmental impacts. Therefore, leading experts (Bobylev and
Makeyenko, 2002) assert that for Russia it is more effective to use the indicator of energy
production intensity or energy use per unit of GDP, rather than energy consumption indicators


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