1. South African National Accounts benchmarking experience
Gerhardt Bouwer

2. Background Shared responsibility in compilation of
South African national accounts
Stats SA responsible for production, income
SARB responsible for expenditure
Close liaison between two organizations
on reconciliation of their estimates
Stats SA and SARB jointly benchmarks
South Africa’s estimates of national accounts

3. South African benchmarking experience
Previous benchmark saw a lot of changes
in data, data sources and methodology
These changes resulted in large level
changes in data
These changes influenced the national
accounts time series data

4. Business Sampling Frame (BSF)
Stats SA developed a new BSF, based on
Income Tax and VAT database
Survey data used for benchmark based on
new BSF, while survey data before
benchmark was based on previous used
Business Address Register (BAR)
The new BSF showed that SA economy
was on a much higher level

5. Business Sampling Frame (BSF) cont.
Year
2000
Pre-Benchmark
level (R’million)
After Benchmark
% change
in level
Output at basic prices
16,9

6. Business Sampling Frame (BSF) cont.
Value added (current
prices) - Year 2000
Pre-Benchmark
level (R’million)
After Benchmark
% change
in level
Mining
54 951
63 391
15,4
Manufacturing
5,9
Trade, hotels, restaurants
14,4
Other personal services
48 979
51 382
4,9
All Industries
3,7

7. Business Sampling Frame (BSF) cont.
1998
1999
2000
2001
2002
2003
Pre-Benchmark:
GDP (R’billion)
739
801
888
983
1 121
1 209
After Benchmark:
742
814
922
1 020
1 165
1 251
% change in level
0,5
1,6
3,8
3,7
3,9
3,5

8. Economic Activity Survey (EAS)
Stats SA started to use EAS with benchmark
EAS formed cornerstone of the calculation
of national accounts estimates
Confrontation of data in SUT framework
suggested that the distribution of economic
activity between different industrial groups
was not correct

9. Economic Activity Survey (EAS) cont.
Two main explanation for this:
Incorrect classifications
Enterprise vs establishment

10. Supply and use tables (SUT)
Started to use SUT to calculate annual
GDP estimates
SUT balanced on 95-industry, 48-commodity
Confrontation between approaches on
commodity level, previously only on
big totals

11. Double-deflation Annual sets of SUT’s were developed
for each year, which made it possible
to introduce double-deflation
Specific price indices could be linked to
corresponding commodity groups
Derivation of a weighted intermediate
consumption and output for each industry
Previously deflation by single price index

12. Quarterly estimates – Denton Method
Quarterly estimates based on annual
nominal and real estimates
Preserve as much as possible of the short
term movements in the new series
Quarterly ratio average to annual ratios
for each year

13. Conclusion Large level changes in data should be worked
in over longer period than only two years
More frequent annual data than periodical
data might influence time series
Establishment vs enterprise will always
create problems
Changing of methodology might also
influence time series

14. End