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Statistical Co-Ordination in South Africa

Statistical Co-Ordination in South Africa Statistics South Africa Legal Framework For Co-ordination

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Statistical Co-Ordination in South Africa

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  1. Statistical Co-Ordination in South Africa Statistics South Africa

  2. Legal Framework For Co-ordination • The Statistics Act (No. 6 of 1999) defines the role of the Statistician-General both as the coordinator of national statistics nationwide and as the developer and enforcer of statistical standards • South African Cabinet approved the National Statistics System (NSS) in Jan 2002

  3. Partnerships • Education • Reserve Bank • Home Affairs • Dept of Agriculture • Mining • Labour • Trade and Industry • South African Revenue Service

  4. Difficulties of Implementing the NSS • Internal capacity ( both in Stats SA and other government departments) • Political authority • Lack of clear policy framework

  5. Co-ordination of Economic Statistics Important Supply side co-ordination with Reserve Bank ( National Accounts and Selected Surveys) South African Revenue Service ( Business Register) Other players: Trade and Industry ( Systems of Registrations) Labour ( UIF)) Municipalities ( Financial Data for Government Accounts)

  6. SARS • 1999 – Amendment to tax law to a allow Stats SA to access all tax records • Independent Business Register ( co-operation of 4 government departments) • Data Quality uneven • Most important admin data – SARS – VAT data • Key challenge – single business identifier

  7. Stats SA and SARB High Level of co-ordination and co-operation Unusual division of labour StatSA responsible for estimating GDP from the production side SARB estimates from the expenditure side? Has various implications for further division of labour

  8. StatsSA – Production Side • ) Estimates of GDP annually and quarterly. • 2) The Department of Agriculture (DEA) compiles all the basic and detailed data regarding the production account for the agriculture industry while the final estimate is made by Stats SA in close co-operation with DEA. • 3) StatSA relies on Reserve Banks Estimates in finance, construction and government for its quarterly GDP estimates. • 4) StatSA relies on the SARB estimates of finance and government for its annual GDP estimates • 5) Mining ( to cut down on response burden)- data directly from department of minerals and energy

  9. SARB – Expenditure Side • Estimates of GDP annually and quarterly. • Income and outlay accounts for institutional sectors, annually for corporations, annually and quarterly for general government and households. • Balance of payments, annually and quarterly • Financial accounts (flow of funds), annually and quarterly • SARB monitors the estimates of GDP from the produc­tion side by making their own estimates although not as detailed as Stats SA. • compiles all the basic and detailed data and the production account for the finance and insurance industries and for the general government sector.

  10. Government Accounts • With the exception of municipal data, the SARB obtains and estimate all other levels of government on a quarterly basis. This information is required for their GDP estimates (as calculated by the SARB). • The SARB quarterly bulletin also has tables on the finances of all levels of government (income and expenditure); cash-flow statements, ownership of government debt, ets – Stats SA does not collect this type of information. • For the purposes of compiling the National financial account, the SARB also obtains information from various sectors (foreign, monetary, Public Investment Commission, etc).

  11. Pros and Cons • Labour Market – scarce skills, higher salary scales makes SARB more attractive place • a large critical mass of well paid statisticians and economists work on national accounts and in the process provides more integrity to GDP estimates. • Stats SA thinly spread and highly dependent on one or two individuals

  12. Pros/Cons • The main advantage of this model is that it provides more confidence in the calculation of GDP to the public where estimates are calculated independently of each other signaling that there is some consistency ( assuming that the residual is small). • Requires high level of co-ordination and co-operation – this is effective up to certain point • Can you realistically separate the two processes? • Different demands for transparency.

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