Household savings, financing and economic growth in South Africa

Abstract

The South African economy is characterised by low levels of household savings which play a very crucial role in stimulating sustained economic growth. At the same time consumers borrow in order to consume. The paper intends to investigate the impact of household savings and financing on economic growth in South Africa. The study is envisaged to assist monetary authorities and policy makers to mitigate this problem. An annual time series data covering the period from 1980 to 2014 is analysed by means of the Vector Error Correction Model approach. The Johansen Cointegration test results confirmed the existence of a long run relationship amongst variables under investigations. Moreover, the results suggest that financing, namely; credit extensions and leasing finance have positive relationships with the country's economic growth while household savings indicate a negative relationship with growth. That being the case, the recommendation is that since it has been established by other studies that a rapid increase in credit is not commonly perceived to be one of the leading indicators of financial instability, policymakers are advised to consider imposing stringent credit control measures so that the demand for financing can be kept under control.

Stokvel saving and banking sector liquidity in South Africa: cointegration and short-run dynamics

“…Gross Domestic Product is the total value of goods and services produced within the borders of an economy or a country during a given period of time measured in market prices (Ribaj and Mexhuani, 2021;Mogale, Mashamaite and Khoza, 2018;Zwane, Greyling and Maleka, 2016;Jagadeesh, 2015).…”

Untitled

“…In our investigation of the Causality between two or more variables, the series must be stationary; that is, the series must have no seasonality, a constant mean, and a constant autocorrelation structure and tend to return to the long-term trend following a shock. We cannot use time series that are nonstationary; that is, those that have a non-constant mean, a non-constant variance, and a non-constant autocorrelation over time (Yuan et al 2007, cited in Akinwale and Grobler, 2019; Asteriou & Hall, 2011, cited in Mongale et al 2018). If we fit regressions that use nonstationary series, our results will be spurious, and their outcomes cannot be used for forecasting or prediction (Granger and Newbold, 1974; cited in Akinwale and Grobler, 2019).…”

“…If we fit regressions that use nonstationary series, our results will be spurious, and their outcomes cannot be used for forecasting or prediction (Granger and Newbold, 1974; cited in Akinwale and Grobler, 2019). Therefore, it is vital to check whether the series is stationary (Mongale et al, 2018). Several tests, including the Augmented Dickey-Fuller (ADF), Phillips-Perron, DFGLS, Levin-Lin-Chu and Im-Pesaran-Shin, are used for testing stationarity, also called unit root tests.…”

Institutional Quality and Economic Growth in Tanzania

“…Lag-order selection criteria for Y Our next step is to check for the stationarity of variables(Mongale et al, 2018) to sieve away non-stationary series, that is, those that have a non-constant mean, a non-constant variance, and a non-constant autocorrelation over time(Yuan et al, 2007 cited in Akinwale and Grobler, 2019; Asteriou and Hall, 2011 cited in Mongale et al…”