RDP 2022-07: The Term Funding Facility: Has It Encouraged Business Lending? 7. What Was the Effect of the Availability of the TFF on Business Lending?

7.1 Model specification

Our second analysis seeks to identify whether the TFF encouraged business lending by financial institutions that were eligible to access the facility. We compare business lending by banks, which were eligible to access the TFF, relative to non-banks, which were not eligible. This analysis captures the broader range of channels through which the TFF can affect business lending, including the additional allowance incentive captured by the first model, as well as by increasing banks' funding certainty and reducing funding costs. The specification is similar to the first model:

(6) Δ L iht = Creditoutstandin g iht Averagecreditoutstandin g ihJan2020 ×100= γ 1 + γ 2 institution_typ e h + v ht

where institution_typeh is a dummy variable equal to 1 if the institution is a bank and 0 if the institution is a non-bank. We also run a version of this regression using fixed-term lending as the dependent variable, although this uses a smaller sample of institutions due to the smaller number of institutions that report outstanding credit by loan type.

Similar to our first set of analysis, we weight the regressions for the baseline and fixed-term model by each institution's level of business credit in the base period.

7.2 Results

For our second question (Equation (6)), we find statistically significant, positive effects on aggregate bank business lending (relative to non-bank lending) for a small number of the 20 post-TFF time periods in our sample (Figure 12; Table C13). These coefficients are generally largest in the early months of the pandemic. In addition, we run regressions for SME and large business credit separately (using data that has the breakdown of business credit by size but a smaller sample of institutions). These models show positive and statistically significant effects on large business credit growth for banks (for most time periods) but insignificant results for SME credit (Figures B9 and B10; Tables C14 and C15).

Figure 12: Business Credit Growth for Banks Relative to Non-banks
Regression coefficient
Figure 12: Business Credit Growth for Banks Relative to Non-banks

Notes: Whiskers show 95 per cent confidence intervals. The filled square marker indicates a significant result.

Sources: APRA; Authors' calculations; RBA

We also run analysis to compare fixed-term business lending for banks and non-banks. We find no statistically significant effects of the TFF on fixed-term credit growth by banks relative to non-banks for all time periods following the introduction of the TFF (Figure B11; Table C16). Taken together, these results suggest that the significant positive effects on aggregate bank business lending in the early months of the pandemic are driven by larger businesses' precautionary drawdowns on credit lines.

In the regressions that are weighted by each institution's level of business credit in the base period, we find positive and statistically significant coefficients on the bank dummy variable for the months March to July 2020 in the baseline model (Figure B12; Table C17). For the fixed-term lending model, we find no statistically significant results of the TFF on fixed-term credit growth by banks relative to non-banks for most time periods following the introduction of the TFF (Figure B13; Table C18).