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

1. The SUST-RUS Database: Regional Social Accounting Matrices for Russia Natalia Tourdyeva (CEFIR) Marina Kartseva (CEFIR) Christophe Heyndrickx (TML)

2. 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

3. The SUST-RUS project • SUST-RUS is a CGE model for the assessment of sustainability policies of the Russian Federation • European 7th framework programme’s 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)

4. The SUST-RUS project ‘Three pillar’ approach: sustainable development refers to progress in economic, social and environmental systems.

5. Goods and services markets Goods and services Consumer expenses Taxes Taxes Households Government Firms Goods and services Goods and services Profit/Factor income Factors Factor markets Money flows Goods flows The SUST-RUS project

6. 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.

7. 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.

8. 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.

9. 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

10. 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.

11. 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.

12. Estimate 2006 use matrix in producer prices. Assumption: structure of mark-ups is the same as in 2003. Estimate 2006 symmetric input-output matrix in basic prices with commodity technology assumption. Run a cross-entropy minimization procedure; disaggregate the estimated symmetric input-output matrix for 2006, with 2003 priors on coefficients. Estimation of the Russian IOT

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

14. 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).

15. 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.

16. 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 region’s production. • For each region and sector fuel consumption is differentiated by 4 types of fuel (coal, oil, gas and petrochemicals).

17. 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.

18. Fuel use by sector

19. 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

20. CO2 emissions by sector

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

22. Thank you