The SUST-RUS Database: Regional Social Accounting Matrices for Russia

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The SUST-RUS Database Regional Social Accounting
Matrices for Russia

• Natalia Tourdyeva (CEFIR)
• Marina Kartseva (CEFIR)
• Christophe Heyndrickx (TML)

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Overview of the presentation

• Overview of the SUST-RUS project
• Data sources for the SAM
• Estimation of the Russian input-output table
  (IOT)
• Estimation of the regional SAMs
• SUST-RUS social aspect
• SUST-RUS environmental dimention

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The SUST-RUS project

• SUST-RUS is a CGE model for the assessment of
  sustainability policies of the Russian Federation
• European 7th framework programmes project
• Consortium consists of 6 members
• CEFIR (Moscow, Russia) (coordinator),
• TML (Leuven, Belgium)
• ZEW (Mannheim, Germany)
• IET (Moscow, Russia)
• Urals State University USU (Yekaterinburg,
  Russia)
• Voronezh State University VSU (Voronezh,
  Russia)
• Far Eastern Center for Economic Development
  FECED (Vladivostok, Russia)

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The SUST-RUS project

• Three pillar approach sustainable development
  refers to progress in economic, social and
  environmental systems.

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The SUST-RUS project
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The SUST-RUS project

• Russia is represented by 7 federal districts,
  trading among each other and with the ROW
• In each region there are
• 32 types of producers, 3 types of households,
  government, and an investment sector
• 4 factors
• 3 types of labour and capital
• SUST-RUS database consists of a multiregional SAM
  for year 2006, which follows the model structure
• with addition of fuel energy use data in natural
  terms (in toe), as well as emissions data (CO2,
  NOx, VOC, SO2, PM) by industry and region.

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Data sources for the SAM

• 2006 Russian make matrix and use matrix in
  consumer prices, both have 11 sectors (1-letter
  NACE)
• No regional input-output tables
• Interregional trade data, international trade
  data on regional level, regional output and value
  added data by sector, as well as SNA data on the
  country level.
• Thus, there is a problem of the country-level IO
  table disaggregation
• We need 2-letter NACE disaggregation 32 sectors.

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Estimation of the Russian IOT

• There are different methods for
  updating/projecting/disaggregating IO tables
• RAS method (UN (1999), McDougall (1999))
• Cross-entropy minimization (Golan, Judge,
  Robinson (1994) Robinson, Cattaneo, El-Said
  (2000), etc.)
• GRAS (Harthoorn and van Dalen (1987), Kuroda
  (1988), Temurshoev, Webb, Yamano(2010))
• We used CE minimization method, with a prior and
  a set of constrains.
• Set up a prior with sufficient disaggregation
• Use all relevant country-level data for
  constrains.

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Estimation of the Russian IOT

• For the prior matrix (Abar in CE literature)
• Detailed 1995 Russian SIOT (product by product)
  in basic prices. This table consists of 110
  sectors defined in old Russian classification
  OKONH, not compatible with ISIC or NACE
• Aggregated 2003 Russian SIOT with 23 sectors (old
  Russian classification OKONH)
• Estimation of the Abar matrix with CE
  minimization techniques
• Methodology is quite close to the GTAP 7 Russian
  IO table estimation, but slightly different list
  of sectors

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Estimation of the Russian IOT

• Estimated Abar matrix
• Symmetric IO table, product-by-product
• 32 NACE industries
• Constrains for the Russian IOT estimation should
  be expressed in terms of SIOT in
  product-by-product format in basic prices.
• Thus we have to go from 11-sector use matrix for
  2006 in consumer prices to basic prices, and then
  to symmetric matrix.
• Two assumptions were made we assumed that share
  of markups is the same as in the 2003, and we
  used commodity technology assumption for SIOT
  estimation.

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Estimation of the Russian IOT

• Finally, we have everything for CE method
  estimation of Russian country SIOT for 2006 (32
  NACE industries).
• Prior matrix (Abar)
• 2006 constrains (11-sector SIOT), production by
  sectors, SNA data, VA data, etc.
• Result of the CE method is the core matrix for
  regional SAM estimation,
• we use top-down approach, assuming technology is
  the same in all regions and coincide with
  country-wide technology.

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Estimation of the Russian IOT
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Estimation of the regional SAMs

• Interregional trade data
• 1999-2006 data on regional exports of 245
  commodity groups by origin and by destination.

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Estimation of the regional SAMs

• Interregional trade data suggests that majority
  of trade between Russian regions goes through
  Moscow or Central region.
• Since SUST-RUS model does not allow for regional
  re-export, we corrected aggregated data flows.
• Final balancing of all regional SAMs was done
  with CE minimization methods.
• The first version of the SUST-RUS database is
  available on the sust-rus.org site (deliverable
  2).

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SUST-RUS social aspect

• We are currently working on implementing social
  aspects in regional SAMs
• 3 types of households by income groups and
• 3 types of labour by ILO classification in each
  region
• Data comes from the Russian Longitudinal
  Monitoring Survey (RLMS), which is a series of
  nationally representative surveys designed to
  monitor the effects of Russian reforms on the
  health and economic welfare of households and
  individuals in the Russian Federation.

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SUST-RUS environmental

• The database includes fuel consumption in natural
  terms (toe) for all sectors and regions of the
  SUST-RUS model.
• This data comes from Russian industrial fuel
  consumption database (11-TER). Regional
  distribution in the SUST-RUS database is done
  according to each regions production.
• For each region and sector fuel consumption is
  differentiated by 4 types of fuel (coal, oil, gas
  and petrochemicals).

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SUST-RUS environmental

• Important note on methodology we follow
  approach, proposed by the WIOD project
  researchers (Deliverable 4) for estimating
  energy use
• The raw data on energy use allow allocation of
  autoproduction of energy and heat to the NACE
  sectors were it took place.
• Thus our energy use data differs from energy
  balances by IEA for Russia.

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Fuel use by sector
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SUST-RUS environmental

• CO2 emissions are calculated according to UNFCCC
  methodology on the basis of fuel use data.
• Our estimate of CO2 emission from combustion in
  2005 is 1,300,360.89 Gg (thousand tonnes) of
  CO2
• Total GHG emission from combustion according to
  Russian national report in Ggr (thousand tonnes)
  of CO2-equivalent
• 2005 1 345 755,47
• 2006 1 391 269,49

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CO2 emissions by sector
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CO2 emissions by electricity generation

• Source of CO2 emissions from electricity
  generation by fuel type.

Fuel type used for electricity generation (NACE sector 40.1) Share in CO2 emissions Share in energy consumption
Gas 67 71
Petrochemicals 4 4
Crude oil 0 0
Coal 29 24
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• Thank you