Transcript Results of WP1: Scenario formulation

Workshop Inter-industry Accounts WP 1
Groningen, 15-16 September 2005
Intrapolating SU-Tables with
Bi-Proportional Methods
Kurt Kratena, WIFO
The Framework of SUT
Commodity balances for the value of total
supply (VSi) and the value of total uses
(VUi) by commodity i: (purchaser prices)
VS i  piY Yij  piM M i  TRi  Ti
j
VU i  piY  X ijY  piY  X ifY  piM  M ij  piM  M if
j
f
j
f
The Framework of SUT
Q
Intermediate Demand with pi as the price
of the composite good:
Row Sum of Intermediate Demand:
VX i  piQ  X ij
j
piQ  X ij  pi Yij  piM M i  TRi  Ti  piQ  X if
j
j
f
Column Sum of Intermediate Demand:
VXj = VYj – VKj - VLj - Tj
 Estimating
pi Yij
j
piM M i
piQ  X if
f
TRi Ti
Column Margin of Intermediate Demand
Estimating pi Yij
j
1.
2.
Time Series of Gross Output (basic prices) by industries
Supply Tables (Make Matrices) for IOT/SUT years
 product mix-matrix D with column sum = 1 and
elements dij for j industries and i commodities
Main Changes in D: shift between the main diagonal (the
'characteristic' production) and the other elements
Column Margin of Intermediate Demand
Estimating piM M i
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
Column Margin of Intermediate Demand
Estimating piQ  X if at purchaser prices
f
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
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
VX j (i) 2000  BX ,2000VX j (k )
Private Consumption
Conversion Matrix (from Statistics Austria)
1.
Conversion matrix for 2001:
NA
VC2000  BC,2000VC2000
Two Altetnative Bi-Proportional Methods:
a) Applying RAS (derive ri and si) and extra/intrapolate ri
and si (Alcala, Antille, Fontela,1999), e.g. to 1995
C
C
NA
VC1995  Rˆ1995
BC , 2000 Sˆ1995
VC1995
b) Directly extra/intrapolate ri and adjust VC2000 so that
C
VC*i = VCNA *i  Matrix Fˆ1995
of identical elements.
C
C
NA
VC1995  Fˆ1995
( Rˆ1995
BC , 2000VC1995
)
Gross Capital Formation
Capital Formation: Assets & Commodities
1.
Investment by commodities = Row sum of Capital
Formation matrix (Statistics Austria) for 2000:
VI 2000  I I ,2000 i
2.
3.
Link between Investment (industries*assets) IA,2000 as in
WP3 and Investment (industries*commodities) 
“Commodity shares of assets” wikj (by i commodities,
k assets and j industries)
Adjusting the row sum by ri (e.g. 1995) and then adjust
I
in order to guarantee VI*i = VINA *i  Matrix Fˆ1995
of
identical elements.
Gross Capital Formation
Concordance of Assets & Commodities in Austria
Input for the “Commodity shares of assets”
Assets
NACE
Agriculture
IT
CT
Other Machinery
Transport Equipment
Structures
Software
01, 02, 05
30
32
27-33 (exc. 30, 32)
34,35, 50
14-26, 36, 45, 70, 73,74, 92
72
Includes NACE categories that are non-zero in the Austrian
Matrix of investment “industries * commodities”
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)
First Empirical Results for Austria
Total or Intermediate Demand ?
Change in %, 1995 - 2000
NACE
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Final Demand
26,7
21,9
18,7
28,3
37,4
100,3
27,3
56,6
46,0
6,3
47,4
43,3
27,4
67,9
50,2
54,5
Intermediate
Demand
9,1
27,4
35,2
26,3
30,9
21,7
41,9
33,7
29,7
16,5
21,8
22,3
34,1
137,2
38,7
78,5
First Empirical Results for Austria
Total or Intermediate Demand ?
Change in %, 1995 - 2000
NACE
70
71
72
73
74
75
80
85
90
91
92
93
Final Demand
29,5
31,9
141,1
109,2
33,4
11,1
12,3
4,4
73,7
16,0
28,2
18,6
Intermediate
Demand
26,8
51,3
62,1
22,5
49,2
32,8
153,2
18,1
21,5
88,1
32,5
First Empirical Results for Austria
Row adjustment factor (ri), Private Consumption 1976
NACE
10
15
16
17
18
19
23
36
40
52
39,0204
1,1059
1,0786
0,5699
0,6327
1,0621
1,0404
0,8431
0,8570
1,2217
First Empirical Results for Austria
Row adjustment factor (ri), Private Consumption 1976
NACE
55
60
61
62
63
64
66
70
80
85
91
92
93
95
0,9546
1,2094
2,0734
0,3845
0,8183
0,6053
0,8221
0,9676
3,6840
1,6500
4,2952
0,8070
0,7932
1,6640
First Empirical Results for Austria
Sum adjustment factor (f)
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
1,0412
1,0425
1,0474
1,0519
1,0506
1,0529
1,0559
1,0565
1,0563
1,0553
1,0544
1,0516
1,0486
1,0462
1,0421
1,0375
1,0363
1,0344
1,0246
1,0169
1,0224
1,0000
First Empirical Results for Austria
Row adjustment factor (ri), Gross Capital Formation 1995
NACE
27
28
29
30
31
32
33
34
35
36
45
70
72
73
74
92
0,9740
0,8206
1,3161
1,2163
1,1549
1,0633
1,0534
0,9777
1,3858
0,7909
1,0075
0,1873
0,9758
1,1134
1,1397
1,0239
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