4.4.1 Model specification
The model used in this study attempts to capture the relationship between business cycle and credit growth following from Seo (2013). It is assumed that the lending behaviour is related to the business cycle. Domestic GDP growth and change in money supply are used as proxy variables for business cycle (Jokipii and Milne, 2008; Seo, 2013). Seo (2013) adopted a VECM and panel data analysis to capture the hypothesis that fluctuations in bank lending to SMEs and large enterprises are due to business cycle changes in Japan. In this study VAR modelling is used to capture the relationship between bank lending and business cycle because using panel data on individual banks will not be able to accurately elicit the macroeconomic long-run relationship between business cycle and bank lending since some banks might slow down bank lending due to some idiosyncratic factors unrelated to the downturn in the economy.
The model estimated in Jokipii and Milne (2008) is of the form: Equation 4.1
Loant = f [In Loan, GDPt, Control (BISt), Control (ABDt), Control (NIMt)] …4.1
Where ABD is the buffer of credit risk, NIM is the variable for profit making conditions, and GDP is Gross domestic product growth rate.
However, the model to be estimated was modified, and can now be written as: Equation 4.2
Where the dependent variable (Bt ) is total banking loans at time t and the explanatory variables
are the lagged dependent variable(Bt−1 ), business cycle coincident index (COINt ) and other
control variables (Xt ). GDPt-1 is the domestic GDP growth at time (t-1) while MS is the money
supply (M3) at time t-1. The model in this study contains variables which are endogenous to some extent, that is, are determined by at least one of the variables within the model. The VAR/VEC model is adopted in this study since contemporaneous relationships might exist between the variables in the analysis and this model provides an avenue for sorting out such relationships (Bernanke, 1987; Sims, 1980). The model is designed for the verification of the procyclicality hypothesis. Equation 4.2 will be used to verify the hypothesis that credit growth exhibits a procyclical pattern in South Africa.
4.4.2 Data and variable definitions
GDP at level and growth rate: GDP at level (In GDP), ∆ GDP= In GDPt - In GDPt-1 it is assumed that lending behaviour is associated with the business cycle. Therefore, domestic GDP growth at level is used as a proxy for the business cycle. However, domestic GDP growth or real GDP do not capture changes in different sectors of South African economy, and real GDP is usually influenced by agricultural and mining production. However, as macroeconomic shocks affect banks’ lending during economic downturn, bank behaviour might reduce loans during recessions and increase it during booms. GDP is expected to move together with the trend in bank loans.
Composite Coincident Index: The South African Reserve Bank employs about 200 individual time series variables to capture the cyclical movement of the South African economy. These variables are used to examine the current and future economic situation. Business cycles are identified by comparing the turning points of the cyclical components of individual time series with the reference turning points. If the specific turning point tends to coincide with the reference turning point, the relevant variables are called ‘coincident indicators’. If the variables predate the reference point, they are called ‘leading indicators’, and if the variables happen after the reference point they are called ‘lagging variables’. The real GDP is not a very good measurement or proxy for the business cycle because any aggregate changes in real GDP do not capture the invariable changes in other sectors of the economy. Similarly, the real GDP is biased towards changes in agricultural production which might cause a bias to economic activities. Therefore the composite coincident index is a better indicator than real GDP or GDP growth (Mohr, 2012: 76). The South African coincident index is adopted to capture the business cycle index, as it will accurately capture the business cycle and the shocks that will affect bank loans during recessions and boom periods. Money supply (M3 to GDP): Money supply and credit are expected to flow in the same direction. Changes in monetary policy through the South African Reserve Bank are expected to affect bank
lending, hence it is another important variable in the model used in this study which is different from the model adopted by Seo (2013) and Jopikii and Milne (2008).
Private sector credit to GDP (B): Prior studies such as Fourie et al. (2011) and Raputsoane (2014) showed that private sector credit to GDP is important in confirming credit flow during the business cycle. Fourie et al. (2011), Raputsoane (2014) and Bernstein et al. (2014) used similar variables in their analysis.
Inflation (CPI): This is an index for the accumulation of a basket of consumers’ goods and services which is usually used as a proxy for measuring the inflation rate (the persistent increase in general price levels). Inflation is expected to have both negative and positive effects on credit. Borrowers of funds usually gain during inflation but lenders usually have the opposite effect.
Investment (INV): Since this study concentrates on demand side credit, it will be important to see the effect of investment on credit in the long run. Gross fixed capital formation is employed as a proxy for measuring investment. The literature has established that in the BGG model, credit constraint to a borrower can negatively affect the investment and reduce inventories.
Table 4.1: Definition of Variables
VARIABLES A priori expectation DEFINITIONS AND SOURCES
Real Gross Domestic Product (+): The business cycle is
expected to vary directly with credit from the perspective of the credit procyclicality theory
Real Gross Domestic Product annually standardised using natural logarithmic scale and GDP growth rate (Seo, 2013)
Composite coincident index (+/-): The business cycle is
expected to vary directly with credit from perspective of the credit procyclicality theory
The South Africa coincident index that captures the current business cycle in South Africa (SARB)
M3 to GDP (+): Money supply and credit are
expected to flow in the same direction
The ratio of M3 to GDP (SARB)
Investment (+): Investment and credit demand
are expected to flow in the same direction following BGG Model
Gross fixed capital formation: Private business enterprises (Investment)
Consumer Price Index (CPI) (+/-): Inflation is expected to flow in either direction to affect borrowers (firms) positively and lenders negatively
Consumer price index (SARB)
Bank rate (-): Bank’s interest and credit are
expected to move in opposite directions
SARB’s prime rate is used as a proxy for interest rate
Private sector credit to GDP +: this study is interested in the co- movement of business cycle and credit growth
The ratio of private credit provided by banks to GDP (SARB)