HLEG thematic workshop on Measurement of Well Being and Development in Africa, 12-14 November 2015, Durban, South Africa, More information at: www.oecd.org/statistics/measuring-economic-social-progress

1. South Africa’s National Accounts:
Structure vs Conjuncture
by Rashad Cassim
South African Reserve Bank
Measurement of wellbeing and
development in Africa Conference

2. Background and objectives of theBackground and objectives of the
presentationpresentation
• Needless to say that the most important organising framework for any
country producing official statistics is the system of national accounts.
• While GDP remains the most important output derived from a national
accounts framework, there are a variety of facets to the system including a
series of extended accounts such as the accumulation account and the
financial accounts.
• There are also further ‘second generation’ extensions such as productivity
accounts, environmental accounts, tourism satellite accounts and more
recently happiness accounts – these have become increasingly important
in response to the growing need to find more appropriate indicators of well-
being.
• While these extensions represent an important advancement in response
to the growing needs of policy makers, what is often forgotten, is that even
in the core GDP accounts, improving statistical accuracy remains an ever
present challenge.

3. Background (continued)
The presentation ultimate aims to demonstrate the following :
An important part of understanding links between the economy and
the environment, for example, hinges on a consistent set of national
accounts – the basis of which is the core set of accounts – the
GDP.
We should, however, not lose sight of the fact that getting GDP
measures and its components right, is not trivial and there are many
challenges that a middle-income country like South Africa, let alone
developing countries, face in getting a set of conventional economic
indicators right?
The aim here is to give an appreciation of measurement difficulties
of parts of the economy we take for granted.

4. Prioritisation: Policy demands and
investment in data
 Assessment of where to devote resources in producing official
economic and social data from the perspective of a practitioner.
 Tensions between devoting resources in social data vs
economic data often expressed in business surveys vs
household surveys.
 Tensions are not only between social and economic data but
between high frequency economic data and structural long term
economic data.
 Put differently, should we gear up our statistical infrastructure to
track as accurately as we can, the business cycle or sacrifice
this for something else – like putting more resources into
estimating the value add of the informal sector, conduct area
sampling to better understand small enterprises?

5. Prioritisation
 The key challenge facing countries with limited resources is
the ever challenging dilemma of what kind of data to
produce for what policy objective?
 The nature of official statistical compilation is such that
decisions to go in one direction entails sunk costs
 Where do we most bang for our buck – or the marginal cost
of investing in new data vs the marginal benefit?
 To answer these questions, I argue that an important
starting point is to embark on a thorough review of a
country’s national accounts (needs) and all the source data
that feeds into?

6. Preconditions to measuring GDP
 Quarterly and annual accounts most comprehensive
measure of the state of the economy, notwithstanding its
limitations in measuring wellbeing.
 Direct link between macro-economic or fiscal and monetary
policy and comprehensive national accounts particularly at
quarterly frequency.
 These are perhaps less important , for example, for a long
term structural reform agenda that both requires robust and
consistent time series data as well as more disaggregated
data.
 Timely and quarterly GDP numbers bias change in GDP
over size or level of the GDP?

7. Trade-off between Structure and Conjuncture
 Entire system of national accounts in most countries is first and
foremost dependent on providing credible measures of economic
change?
 Measures of change in GDP creates demands for costly high
frequency indicators that is reconciled after several years with more
accurate data.
 Operates on the assumption that we have sufficient confidence in
change such as GDP growth than we have on the level of economic
activity.
 How can you be confident when you not sure about levels?
 Areas where measures of economic activities is uncertain is the
value added contribution of the informal sector and illegal economic
activity
 Impact on change is small?

8. Aggregation vs Detail
 High frequency data is oriented towards obtaining the best
possible estimate of aggregate economic indicators – but this
comes at cost of obtaining more detail, which in its own right may
not be important for policy making.
 For example, it is extremely difficult to provide detailed industry
data at quarterly frequency but this problem to some extent exists
for annual data too?
 Part of the problem is that close to 5000 large firms account for
70% of turnover in the SA economy- they are involved in the
different economic activities this requires both sophisticated
survey capacity, as well co-operations of large firms

9.  Separating and industry from economic activity – e.g. is
the construction sector in South Africa underestimated
because we have not been measuring it as an industry
and not an activity?
 A further trade-off exists, between national economic data
and provincial and local economy data- makes for
interesting policy debates when it comes to GDP or
measures of for that matter household income and
expenditure surveys?
Aggregation vs Detail (Continued)

10.  In order to produce defensible GDP numbers, countries need to
do the following:
 Firstly, compile a coherent and internally consistent set of
quarterly national accounts produced from all three conceptually
equivalent sides (income, expenditure and production);
 secondly, the production- and expenditure-based GDP estimates
must be confronted simultaneously in creating them – a practice
that does not exists in South Africa currently; and
 thirdly, the residual between them will be controlled and
progressively reduced as data sources are improved, and the
residual will carry no positive or negative bias.
Data preconditions for Monetary and Fiscal Policy

11. Preconditions (Continued)
 The ability to develop a coherent set of accounts depends not
only on a well designed national accounts architecture, but
also on the quality of source data that feeds into it.
 While both the level and GDP is based on a complex array of
national account standards and practices, it is measured on
the basis of a variety of data sources ranging from fairly
reliable data to often very crude indicators.
 Not surprisingly, data is particularly weak in the services (no
observable prices) and knowledge economy, but is also
relevant for traditional parts of the economy such as
investment and inventories.
 Has particular significance for tracking the business cycle.

12.  Major effort has been put into getting conjuncture right in South
Africa? In other words, constantly working at improving the
accuracy of quarterly GDP numbers?
 These require a great deal of time and investment into ensuring
that policy makers can base their decisions on more accurate
data then is the case currently?
 It may come at a cost of putting effort into more extended data
such as productivity accounts, environmental accounts –
although some of this is being done in South Africa and within
the official statistical agency.
Agenda to Improve GDP in South Africa

13. South Africa’s GDP: Unfinished Agenda

14. Economics Statistics Research Agenda
 Cluster of areas where the scope for measuring the South
African economy at the aggregate level, let alone at more
detailed level are as follows:
 Fixed capital formation, construction and productivity;
 Nominal and real and the difficulties of finding appropriate
deflators; and
 Services economy particularly business services as these
become increasingly important and contribute over 70 per cent
of total value-added in SA.

15. Prices and Quantities (Deflators)
 Official economic data is available in current and constant
values – but it is the latter that often matters
 There is, however, very little appreciation as to how volume
measures are derived and the extent to which they can bias
estimates?
 Appropriate deflators require detailed information on products
and prices – these exists to a limited extent in manufactures but
not in services – has specific implications for services output ?
 For example, the producer price index – a key source of
deflation for the national accounts is a considered a luxury and
does not exist in many developing countries and has only in the
past few years been revamped in South Africa

16. Implications of Inadequate Deflators
 Real economy activity and growth could be materially
different;
 Very obscure issue in the policy debate, but has profound
significance for measuring economic performance;
 Many inconsistencies in different data sets are not at the
current or nominal level but at constant?
 Large research agenda is needed to improve deflators – in
South Africa in a variety of areas (exports/imports, capital
equipment, business services).

17. Source Data
 Adequate source data is the second, or the most important
leg on which credible GDP estimates hang on.
 From the production side of GDP there are several areas
where source data is largely inadequate:
Examples include the following:
 Real estate;
 Personal services; and
 Business and professional services.

18. Source Data GDP Exports: Investment
 In South Africa, or for most countries one of the most important
drivers of the business cycle is change in investment and
inventories.
 Investment and inventories at quarterly frequency are most
difficult because it requires more commitment from survey
respondents than information on monthly sales or costs

 The ramifications of these have important implications for
household savings or income?
 Large complex firms have to record investment projects in ways
that observe international standard industrial classification rules
(ISIC), and this is not a straight forward exercise?

19. Big Data/Administrative Sources
 South Africa is fortunate to rely on a comprehensive tax
data/business register.
 Not the case in many other developing countries.
 However, tax records or other administrative records are
designed for specific purposes.
 Converting these into data for statistical purposes, with
consistency of definitions and coverages and standards
requires a sophisticated infrastructure?
 A solution but not a quick and easy one.

20. Beyond GDP
 There are various areas where the scope for improved
statistical accuracy is considerable:
 Productivity Statistics;
 Better industry classification;
 Savings/balance sheet by Institutional Sector; and
 By size (small- and medium enterprises)

21. Example: The case for improved
productivity statistics
 Currently no authoritive(?) release of annual, let alone
quarterly productivity statistics.
 SARB publishes quarterly labour market statistics.
 Itself derived from a source where coverage is limited?
 Specific challenges in measuring capital productivity,
since we are interested in capital service and not stock.

22. Conclusion
 Aggregate numbers are of limited value in a country with high
firm concentration and inequality.
 However, as a starting point the aggregation as the basis for
control totals, remains a critical precondition for any extended
analysis of the economy beyond GDP.
 An important initiative exists to go beyond crude measures of the
GDP to measure well being remains relevant to a country like
South Africa.
 Notwithstanding this, we should not trivialise GDP – a persistent
challenge of getting better accuracy.
 Any extended work on linking GDP with inequality and the
environment, for example, would be meaningless if the core set
of economic accounts contains inaccuracies.
 Ultimate aim is to convert the presentation into an empirical
paper with more evidence of the limitations of South African
economy data

23. THE END