Drivers of Productivity in the Spanish Banking Sector: Recent Evidence

Abstract

We analyse the drivers of total factor productivity of Spanish banks from early 2000, including the last financial crisis and the post-crisis period. This allows us to study changes in productivity following a major restructuring process in the banking sector such as the one experienced in Spain. Overall, we find that following a period of continued growth, productivity declined after the height of the crisis, though large banks were less affected. We also find that risk, capital levels, competition and input prices were important drivers of the differences in productivity change between banks. Finally, our results suggest that, by the end of our sample period, there was still some room for potential improvements in productivity via exploiting scale economies and enhancing cost efficiency. These opportunities appear to be generally greater for the smaller banks in our sample.

Appendices

Appendix 1: The probability of distress model

We obtain a composite ex-ante risk measure by means of the estimation of the following conditional logit model:

$$ P\left({D}_{it+h}=1|{\boldsymbol{x}}_{it},{D}_{it}=0\right)= Logit\left({\boldsymbol{x}}_{it}|,{D}_{it}=0\right), $$

where, D is a binary variable representing the occurrence of a distress event within a two-year horizon. The occurrence of a distress event is defined as the intervention of a bank, its need of recapitalization with public funds, or capital needs derived from stress tests; x is a vector of bank characteristics and macroeconomic factors including the following variables:

• i. total assets, included in logs;

• ii. NPLs, measured as the ratio of non-performing loans from governments, credit institutions and other sectors to total loans;

• iii. the annual growth rate of the NPL ratio;

• iv. liquidity ratio, computed as the ratio of liquid assets to total assets, where liquid assets are defined as cash and balances with central banks and debt securities issued by resident and non-resident governments;

• v. solvency ratio, computed as CET1 capital divided by total assets, where CET1 capital includes ordinary shares, noncumulative preferred stock and disclosed reserves;

• vi. net interest margin, measured as the ratio of net interest income to earning assets;

• vii. the annual real GDP growth rate; and,

• viii. the annual growth rate of short-run nominal interest rates.

The probability of distress (pd), used as a composite ex-ante risk measure, is a prediction of the probability of a bank experiencing an event of distress, as defined above, within the following two years. Results of estimations are available upon request.

Appendix 2

Table 4 Estimation results of additional models

Fig. 5

TFP growth decomposition of banks transferring nonperforming assets to SAREB

Table 5 Results of profit efficiency models. Mean RTS and ECRTS significance levels are computed with respect to differences from 1 following the delta method of convergence of transformed random variables