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I. Historical data for the Consumer Price Index minus the mortgage cost component (or CPIX) for “metropolitan areas”

South Africa adopted inflation targeting in 2000, targeting the consumer price index excluding mortgage interest cost (or CPIX), for “metropolitan and urban areas”. Yet there is no clear technical account of the methodology of construction of the consumer price index (CPI) and CPIX by Statistics South Africa, as published by reputable government statistical agencies in other countries. Aron and Muellbauer. “Construction of CPIX Data for Forecasting and Modelling in South Africa.” (CSAE Working Paper Series/2004-09) has two main goals. First, we aim to enhance transparency by explaining the CPI and CPIX methodology (as we understand it), and to encourage publication of an official technical handbook. We also raise various technical issues concerning CPI construction. Second, while the CPIX (“metropolitan and urban areas”) measure only became relevant to monetary policy setting and wage contracts from 2000, and is published monthly only from 1997, a far longer time series is required for the forecasting and modelling exercises of the South African Reserve Bank (SARB), National Treasury and others. We produce estimates of CPIX (“metropolitan areas”) back to 1970, on a consistent methodology, using monthly price indices, the appropriate weights, and linking correctly when rebasing. The corresponding measure published by Statistics South Africa goes back only to 1994, and shows some troubling discrepancies with our measure. The SARB uses a quarterly approximation to historical CPIX in its forecast models. Our consistent monthly measure should lead to improved inflation modelling.

SACPIX data: Excel file CSV file (comma separated variables) Graph (PDF file)

[When using these data , please reference : Aron, J. and J. Muellbauer. 2004. "Construction of CPIX
Data for Forecasting and Modelling in South Africa." South African Journal of Economics 72 (5): 1-30, December.]

II. Wealth data

In common with many emerging market countries, South Africa’s government does not publish balance sheet wealth estimates on a market value basis, as produced in the U.S., U.K., Japan, and elsewhere. Yet without information on the market values of liquid and illiquid personal sector wealth, it is difficult to explain aggregate consumer spending and saving, consumers’ demand for credit, and the broad money holdings of households. Behavioural equations for these variables are key components of central banks’ macro-econometric models, used in forecasting and policy-making. Understanding the domestic asset value channel of the monetary policy transmission mechanism is especially important for inflation targeting countries. In Aron, Muellbauer and Prinsloo. 2007. " Balance sheet estimates for South Africa's household sector from 1975 to 2005."Working Paper 07/01,Research Department, South African Reserve Bank, we construct aggregate, personal sector wealth estimates at market value for South Africa. Our quarterly estimates derive from published data on financial flows, and various other capital market data, often at book value. Our methods rely, where relevant, on accumulating flow of funds data using appropriate benchmarks, and, where necessary, converting book to market values using appropriate asset price indices. Relating asset to income ratios for various asset classes to asset price movements and rates of return, throws light on the changing composition of personal sector wealth. Most striking are the rise in pension wealth - overtaking gross housing assets in the late 1980s; the rise in household debt; and the relative decline of liquid and housing assets, from the early and mid-1980s, respectively.

[These data are now available from the SA Reserve Bank. Please reference the above paper when using these data.]

III. Financial liberalisation indicator

For a discussion of monetary policy regimes in South Africa, and extensive financial liberalisation, see Aron, J. and J. Muellbauer. 2002. "Estimating Monetary Policy Rules for South Africa”, in Norman Loayza and Klaus Schmidt-Hebbel (eds) "Monetary Policy: Rules and Transmission Mechanisms", Series on Central Banking, Analysis and Economic Policies, Volume 4, Central Bank of Chile, pages 427-475.
    We measure financial liberalisation in consumer credit markets from the 1980s. Proxying this by the ratio of debt to income, as in Bayoumi (EJ, 1993; RESTAT, 1993) and Sarno and Taylor (Journal of Macroeconometrics, 1998), is not ideal because this ratio responds with a lag to deregulation, and it depends also on income expectations, asset levels, uncertainty, and interest rates. Bandiera and others (RESTAT, 2000) propose the technique of principal components to summarize the composite information in a set of dummy variables reflecting different facets of financial liberalization. However, the weights do not reflect the behavioural impact of financial liberalization. A flexible technique linking institutional information with behavioural responses is needed.
    In Aron, J. and J. Muellbauer. 2000. "Personal and Corporate Saving in South Africa.” World Bank Economic Review 14 (3): 509-544, our innovation is to treat financial liberalization as an unobservable indicator entering both household debt and consumption equations. The indicator, FLIB, is proxied by a linear spline function, and the parameters of this function are estimated jointly with the consumption and debt equations (subject to cross-equation restrictions on the coefficients in the spline function). The estimated parameters for FLIB reflect the key institutional changes in credit markets. Our indicator shows strong rises in 1984, 1988, and 1995, with more moderate increases in 1989, 1990, and 1996.
 
SAFLIB data: Excel file CSV file (comma separated variables) Graph (PDF file)

[Please reference the above paper when using these data.]

IV. Openness/Trade liberalisation indicator

There have been extensive changes in South African trade policy. Unfortunately, there is no index of effective protection combining the effects of surcharges, tariffs, and quotas (these last are dominant in South African trade policy until the early-1980s); nor can we directly capture the effects of trade sanctions.
    In Aron and Muellbauer “Inflation dynamics and trade openness.” CSAE Working Paper Series 2007-11 (also CEPR, London, Discussion Paper 6346), we develop a measure for openness, which is derived from a model for the share of manufactured imports in home demand for manufactured goods, where the latter is defined as domestic production plus imports, less exports, for which we have annual data.
    We do not employ the import share itself to measure openness, because it depends on other factors, such as fluctuations in domestic demand and relative prices of imports or the exchange rate. However, our model for the log of the import share controls for these influences.
    The model includes a measure of import tariffs and surcharges, which is one (negative) component of openness. The unmeasured component of quotas and the effect of sanctions are captured in our model by a smooth non-linear stochastic trend, estimated in STAMP (Koopman, and others, Timberlake Consultants Press, 2000). To capture demand side influences (other than home demand for manufactured goods as defined above), the model includes the growth rate of real GDP, the log of the real exchange rate, and a lag in the log of the terms-of-trade, heavily influenced by the price of gold. The latter might reflect sectoral differences in GDP growth, relevant for imports, as well as the relaxation of balance of payments constraints when gold prices are high.
    The influences of the openness variable operate both through the measured effects of import tariffs and surcharges, and through the unobservable effects captured in the stochastic trend (see paper).
 

SAOPEN data (updated June 2007): Excel file CSV file (comma separated variables) Graph (PDF file)

[Please reference the above paper when using these data.]