Bank Credit And Agricultural Output In South Africa: Cointegration, Short Run Dynamics And Causality

Joseph Chisasa,Daniel Makina

doi:10.19030/jabr.v31i2.9148

Abstract

In this paper we investigate the dynamic relationship between bank credit and agricultural output in South Africa using time series data from 1970 to 2011. Using the Johansen cointegration test, we observe bank credit and agricultural output to be cointegrated. In the long run we find credit and capital formation to have significant positive impact on agricultural output. Employing an ECM, we find that, in the short run, bank credit has a negative impact on agricultural output reflecting the uncertainties of institutional credit in South Africa. However, the ECM coefficient shows that agricultural GDP rapidly adjusts to short term disturbances indicating that there is no room for tardiness in the agricultural sector. The absence of institutional credit will be immediately replaced by availability of other credit facilities from non-institutional sources so that there is no room for possible non-application of intermediate inputs. Conventional Granger causality tests show uni-directional causality from (1) bank credit to agricultural output growth; (2) agricultural output to capital formation; (3) agricultural output to labour; (4) capital formation to credit; (5) capital formation to labour, and a bi-directional causality between credit and labour. Noteworthy is that for the agricultural sector the direction of causality is from finance to growth, i.e., supply-leading, whereas at the macroeconomic level the direction of causality is from economic growth to finance, i.e., demand-leading.

Highlights

T he debate pertaining the relationship between bank credit and agricultural output has been a subject of discussion in recent decades (Carter, 1989; Iqbal et al, 2003) and increasingly so in recent years (Das et al, 2009 and Kumar et al, 2010)
The Johansen Trace cointegration test shows that there are three integrating equations at the 95% confidence level (p-value=0.05) suggesting that credit, rainfall, labour, capital formation and agricultural output are cointegrated. Both the Trace Statistic and the Max-Eigen Statistic are higher than the Eigenvalue and confirming that in the long run, bank credit, labour, capital formation, rainfall and agricultural output are cointegrated
There is no evidence of reverse causality. This means that increasing credit supply to farmers will cause an increase in agricultural production holding other factors constant

Summary

Introduction

T he debate pertaining the relationship between bank credit and agricultural output has been a subject of discussion in recent decades (Carter, 1989; Iqbal et al, 2003) and increasingly so in recent years (Das et al, 2009 and Kumar et al, 2010). Several empirical studies have adopted the Cobb-Douglas (1928) production function to estimate agricultural output function (Enoma, 2010; Sial et al, 2011; Chisasa and Makina, 2013). These studies have largely found credit to have a positive impact on agricultural output. The traditional Cobb-Douglas production function has been observed to portray weaknesses (Tan, 2008; Temple, 2010) which motivate further analysis of the relationship between bank credit and agricultural output. The standard Cobb-Douglas model does not take account of the uncertainty under which farmers operate so that some researchers have modified it by employing the stochastic production frontier approach suggested by Battese (1992)

Methods

Results

Conclusion