A dynamic, stochastic, Bayesian, provincial input–output model for the South African economy

Abstract

Background: The green economy has long been important in public discourses, as has been the forecasting of macroeconomic phenomena.

Aim: The purpose with this study was to construct a regional input–output model for the South African economy.

Setting: The model is a coupled input–output/system dynamics model. It is dynamic in the sense that sector growth follows the ‘limits to growth’ hypothesis. The model is used to explore the impact on the green economy, and also on poverty indices.

Method: It was constructed using Vensim®, a system dynamics modelling package. Bayesian methods were utilised to estimate realistic values for the multipliers. Type I multipliers for output, income, employment and gross value added (GVA) are estimated. The model was then ‘tested’ by forecasting various resource sectors’ GVA (i.e. agriculture, mining, water, electricity).

Results: The model fits the historical data well, replicating provincial GVA as well as national GVA to an acceptable standard. The multipliers fell within appropriate ranges, and followed a priori expectations.

Conclusion: The model provides ‘highly accurate’ forecasts of various macroeconomic parameters, including the resources sectors. The impact of different multipliers in the three resources sectors on various poverty indices in South Africa was also assessed.

Contribution: The model has great potential for further use in the agricultural, energy and resource sectors, but also has wider application since it provides a means for generating an input– output table for any specific year based on the forecasting of input – output elements.