Estimating and Explaining Changes in Potential Growth in South Africa

Abstract

Estimates of potential output growth in SA have declined from over 3% prior to the Global Financial Crisis (GFC) to just over 2% currently. A similar slowdown has been experienced in several other countries, including most members of the G20. The purpose of this paper is to (i) estimate SA's level of potential output growth both before and after the GFC using a multivariate filter technique and (ii) attempt to explain the apparent decline in the growth potential by investigating the underlying drivers of potential GDP growth using a Cobb-Douglas- type production function. It is found that potential growth has declined to around 2.2% post-GFC. It is also determined that the biggest driver of the post-crisis decline in potential growth has been lower productivity growth.

Notes

1 See Laxton and Tetlow (1992), Benes, Clinton, Garcia-Saltos, Johnson, Laxton, Manchev and Matheson (2010), and Blagrave et al (2015), among others.

2 The prior is that for advanced economies, shocks to output over the cycle is predominantly associated with fluctuations around the trend, i.e. shocks to output are driven mainly by demand/gap shocks. A common rule- of-thumb is that shocks to output will be approximately 1/3 supply/potential and 2/3 demand/gap. While the prior is that there is a more important role for shocks to trend (potential) in explaining the business cycle in emerging economies, the abovementioned calibration is used as a baseline, but the data is allowed to influence the prior through the estimation procedure.

3 Following Blagrave et al. (2015), the confidence bands are plotted in deviations from the model's point estimates.

4 For the construction of the HP-based confidence bands, a Monte Carlo procedure was followed where 5000 draws of GDP were obtained from simulating historical shocks, which were assumed to follow mean-zero Gaussian processes. An HP filter with λ = 6.25 is applied to each sample and +/- 1.96 RMSEs from the assumed true path of the trend growth and cycle components is plotted. Conventional wisdom has become to fix the value of the smoothing parameter, λ, at 1600 (100) for quarterly (annual) frequency data following Hodrick and Prescott's (1997) view. Ravn and Ulhig (2002), while still proposing a fixed lambda, suggest that the HP filter should adjust to the frequency of data. They suggested a value of 6.25 for annual and 1600 for quarterly data. In fact, setting λ = 6.25 for annual data results in trend and cycle series that more closely resemble those obtained when setting λ = 1600 for the same series at a quarterly frequency.

5 For the purposes of this paper, actual capital stock as published by the South African Reserve Bank is used. Alternative formulations, including removing housing stock from the capital variable or multiplying the capital stock by trend capacity utilisation does not materially alter the results.

6 See Cubeddo et al. (2014) for a discussion of this phenomenon for emerging markets in general.