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Cooperative Banks Lending During and After the Great Crisis

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New Cooperative Banking in Europe

Abstract

This chapter analyses the lending behaviour of cooperative banks vis-à-vis commercial and savings banks during and after the crisis started in Europe in 2008. The empirical results suggest that a limit to the traditional cooperative banks’ countercyclical attitude exists in the case of economies characterised by prolonged downturns. The analysis shows that cooperative banks outpaced other kinds of banks in terms of credit provision in Germany and in the whole north-east half of the euro area, while no significant differences emerged as concerns the rest of the continent, hit severely by the crisis. The main cause of this phenomenon should be found in the impact of non-performing loans in the capitalisation levels of cooperative banks and in the choice of deleveraging by also containing lending.

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Notes

  1. 1.

    This section is based on Migliorelli and Brunelli (2016).

  2. 2.

    In general, target capital-to-asset ratios are the result of management preferences (risk appetite) and/or regulatory provisions.

  3. 3.

    Conventionally, the stakeholder banking segment is composed of cooperative and savings banks .

  4. 4.

    Other indicators could be used to group countries in economic areas, for example, the GDP growth or the level of domestic production. In many cases, the clustering procedure would have highlighted the division between the economies of the south of Europe and those of the centre and north of the continent.

  5. 5.

    In fact, “Without prejudice to the objective of price stability , the ESCB [European System of Central Banks] shall support the general economic policies in the Union with a view to contributing to the achievement of the objectives of the Union” (Treaty on the Functioning of the European Union, Article 127 (1)). These may include, inter alia, full employment and balanced economic growth.

  6. 6.

    The objective of the ECB to maintain price stability is defined as “a year-on-year increase in the Harmonized Index of Consumer Prices (HICP) for the euro area of below 2%”. The ECB Governing Council clarified in 2003 that in the pursuit of price stability, it aims to maintain inflation rates below, but close to, two per cent over the medium term.

  7. 7.

    The underlying macroeconomic argument is that countries with high levels of debt and unemployment would be better off with a laxer monetary policy (at least in the short-medium term), while countries with low debt and unemployment would mostly be in favour of a more conservative strategy. An expansionary monetary policy and a higher inflation will allow high-debt high-unemployment countries to reduce the weight of their liabilities in real terms and to give a stimulus to the investments through lower interest rates. On the other hand, low-debt low-unemployment countries may find such a solution to be inefficient if standing close to their production saturation levels.

  8. 8.

    In statistical terms, the L2 distance is the square root of the sum of squared differences.

  9. 9.

    The main drawback of using this procedure is that the same weight is assigned to each country, independently of its relative economic relevance for the euro area . Nevertheless, in the final identification, the two main clusters are similar in terms of total GDP. Hence, the potential bias due to the overweight of the small countries’ results being smoothed or absent.

  10. 10.

    For example, the minimum average sum of squares or Ward’s method.

  11. 11.

    Critics to the exceptionally low interest rates were addressed to the ECB by Germany and other north-European countries in particular the period 2011–2016.

  12. 12.

    In 2016, the contract between Bureau Van Dijk and Fitch (the main provider of financial data included in the Bankscope database) terminated. In 2017, Bankscope was acquired by Moody’s Analytics. As a result of these events, this database is no longer available. As Bankscope was one of the reference databases in banking research, some disruption may be expected in the next few years as concerns research based on banks’ financial data.

  13. 13.

    As far as it concerns non-accounting information, the ECB reference interest rate has been taken from the available sources of the ECB, while the GDP growth rates for each country have been taken from Eurostat.

  14. 14.

    On the basis of the Bankscope classification, we use banks’ statements information at the levels C2 (statement of a mother bank integrating the statements of its controlled subsidiaries or branches with an unconsolidated companion present in the Bankscope database), C1 (statement of a mother bank integrating the statements of its controlled subsidiaries or branches with no unconsolidated companion present in the Bankscope database) and U1 (statement not integrating the statements of the possible controlled subsidiaries or branches of the concerned bank with no consolidated companion).

  15. 15.

    In the relation 3.1 ε i,t comprises a fixed error component. In such a specification, to control for a fixed effect is relevant as banks may have fixed strategies concerning loans which may not react to time-varying circumstances.

  16. 16.

    Note also that this bias is not caused by an autocorrelated error process and it arises even if the error process is independent and identically distributed (iid).

  17. 17.

    In more detail, the variables treated as endogenous are r t *SIZE i,t and r t *CAPITAL i,t (lagged and two lagged used as instrument) and r t *BM i (no lagged values used).

  18. 18.

    Considering the policy rate (and other macroeconomic indicators) as an exogenous variable is in line with previous studies (e.g. Ashcraft 2006; Ferri et al. 2014). Nevertheless, endogeneity problems could arise by using EONIA to explain changes in banks’ loan supply and such a use may represents a potential limitation of the analysis.

  19. 19.

    Both the tests reported large Chi2 values, rejecting the null hypothesis of constant variance of the residuals (Breusch-Pagan test) and homoscedasticity (White test).

  20. 20.

    In Ferri et al. (2014), the authors analysed the period 1999–2011 and the two sub-periods 1999–2007 (financial stability ) and 2008–2011 (crisis).

  21. 21.

    In the same respect and again because of the time period analysed, our results cannot precisely predict the behaviour of cooperative banks in periods of increasing interest rates.

  22. 22.

    These dummy variables have been considered as “already differenced exogenous variables” in the Arellano-Bond procedure available in STATA.

  23. 23.

    Furthermore, little public intervention has been deployed in the aftermath of the crisis, so that the empirical investigation is less impacted by external factors.

  24. 24.

    In Italy , the cooperative banking segment is composed of Banche di Credito Cooperativo and Banche Popolari .

  25. 25.

    This is still valid during the period analysed. With the reform of the major Banche Popolari launched in 2015, which transformed these banks in joint-stock companies , the weight of cooperative banks in Italy has considerably reduced. This reform is discussed in detail in Chap. 5.

  26. 26.

    These figures concern the situation before the reform of the major Banche Popolari .

  27. 27.

    Source: Credito Cooperativo and European Cooperative Banks Association. Other metrics may be used to underline the importance of the cooperative banking segment in Italy : at 31/03/2014 more than 400 cooperative banks were active in Italy (counting for more than 50 per cent of total Italian banks) with more than 4000 branches and 37,000 employees.

  28. 28.

    Source: European Commission (2012), “Small Business Act for Europe, Fact Sheet 2012 – Italy” .

  29. 29.

    Source: Eurostat.

  30. 30.

    In their seminal work, Bernanke and Lown (1991) defined the credit crunch as “a significant leftward shift in the supply curve for bank loans, holding constant both the safe real interest rate and the quality of potential borrowers”. In other words, in a situation of credit crunch banks may refuse to supply credit at the same conditions they were used to do in previous periods. Whatever the cause, the main drawback of a credit crunch lays in the fact that it can hamper the launch of new investments by firms and can impact the consumption of households, leading to a further worsening of the economic cycle.

  31. 31.

    For example, Stein (1998) concludes that banks issuing new equity may be perceived underperforming by the investors.

  32. 32.

    This may not be the case for banks that adopt the Internal Ratings-Based (IRB) approach to evaluate their credit positions. By using the IRB approach, the weight to assign to each loan varies depending on the borrower’s credit standing.

  33. 33.

    Author’s estimate on Bankscope data as at 31/12/2013.

  34. 34.

    See, for example, Di Colli and Girardi (2012) and Zago and Dogili (2014).

  35. 35.

    To this extent, the regression results for 2009 for the other banks’ cluster, even though they are not particularly strong in terms of overall significance, seem to validate the capital crunch hypothesis and the adjustment effect that it carries over. In fact, as it is shown in Fig. 3.6, starting from 2010 these banks have succeeded in stabilising their capital-to-asset ratio, which has been maintained almost unchanged till the end of the reference period (end of 2013). This may signal the attainment of a new capital-to-asset target-level in the market. Hence, the absence of capital crunch effects in the period 2010–2013 is coherent with the absence of significant capital stains.

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Migliorelli, M. (2018). Cooperative Banks Lending During and After the Great Crisis. In: Migliorelli, M. (eds) New Cooperative Banking in Europe. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-93578-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-93578-2_3

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