1. Workshop Inter-industry Accounts WP 1 Groningen, 15-16 September 2005 Intrapolating SU-Tables with Bi-Proportional Methods Kurt Kratena, WIFO

2. The Framework of SUT Commodity balances for the value of total supply ( VS i ) and the value of total uses ( VU i ) by commodity i : (purchaser prices)

3. The Framework of SUT Intermediate Demand with as the price of the composite good: Row Sum of Intermediate Demand: Column Sum of Intermediate Demand: VX j = VY j – VK j - VL j - T j Estimating

4. Column Margin of Intermediate Demand Estimating 1.Time Series of Gross Output (basic prices) by industries 2.Supply Tables (Make Matrices) for IOT/SUT years product mix-matrix D with column sum = 1 and elements d ij for j industries and i commodities Main Changes in D: shift between the main diagonal (the 'characteristic' production) and the other elements

5. Column Margin of Intermediate Demand Estimating 1.Time Series of trade statistics (including balance of payments data for services) 2.Link between annual import growth in trade statistics & import growth between IOT/SUT years a) straightforward for commodities b) link for BoP categories

6. Column Margin of Intermediate Demand Estimating at purchaser prices 1.Exports similar to imports 2.Conversion matrix for private consumption 3.Commodity shares matrix for assets & capital formation matrix ( consistency with WP 3 ! ) 4.Link NPSIH and government consumption to output

7. Row Margin of Intermediate Demand Starting Values for Intermediate Demand Time series (1976 – 2003) for input categories for j industries: Energy, materials, freight, repair, processing, rent&leasing, other services. Conversion matrix

8. Private Consumption Conversion Matrix (from Statistics Austria) 1.Conversion matrix for 2001: Two Altetnative Bi-Proportional Methods: a)Applying RAS (derive r i and s i ) and extra/intrapolate r i and s i (Alcala, Antille, Fontela,1999), e.g. to 1995 b)Directly extra/intrapolate r i and adjust VC 2000 so that VC* i = VC NA * i Matrix of identical elements.

9. Gross Capital Formation Capital Formation: Assets & Commodities 1.Investment by commodities = Row sum of Capital Formation matrix (Statistics Austria) for 2000: 2.Link between Investment (industries*assets) I A,2000 as in WP3 and Investment (industries*commodities) Commodity shares of assets w ikj (by i commodities, k assets and j industries) 3.Adjusting the row sum by r i (e.g. 1995) and then adjust in order to guarantee VI* i = VI NA * i Matrix of identical elements.

10. Gross Capital Formation Concordance of Assets & Commodities in Austria Input for the Commodity shares of assets Includes NACE categories that are non-zero in the Austrian Matrix of investment industries * commodities

11. First Empirical Results for Austria Data Availability (IOTs and SUTs): 1990 (ESA 1979), 1995,1997,1999,2000, 2001. - Filling the gaps: 1996 and 1998 - Using 1990 only as a benchmark for results - Backcasting from 1995 to 1976 - Problems: External Trade Data before 1988, Estimating Trade & Transport Margins and Taxes less Subsidies on Products, FISIM (not only a reallocation, but a change in the output level of NACE 65)

12. First Empirical Results for Austria Total or Intermediate Demand ? Change in %, 1995 - 2000

13. First Empirical Results for Austria Total or Intermediate Demand ? Change in %, 1995 - 2000

14. First Empirical Results for Austria Row adjustment factor ( r i ), Private Consumption 1976

15. First Empirical Results for Austria Row adjustment factor ( r i ), Private Consumption 1976

16. First Empirical Results for Austria Sum adjustment factor ( f )

17. First Empirical Results for Austria Row adjustment factor ( r i ), Gross Capital Formation 1995

18. Further Empirical Work for Austria - Full backcasting to 1976 of final demand categories plus imports with established methodology - Extra/intrapolation of commodity output (supply matrices) - Estimation of time series of trade&transport margins and taxes less subsidies - Implementing the input structure data to achieve a first guess of intermediate demand matrix - Application of RAS to the intermediate demand matrix