The performance of merging cooperative banks in Germany

Dreusch, Dennis; Reichling, Peter Article The performance of merging cooperative banks in Germany German Economic Review (GER) Provided in Cooperation with: Verein für Socialpolitik / German Economic Association Suggested Citation: Dreusch, Dennis; Reichling, Peter (2025) : The performance of merging cooperative banks in Germany, German Economic Review (GER), ISSN 1468-0475, De Gruyter, Berlin, Vol. 26, Iss. 3, pp. 193-227, https://doi.org/10.1515/ger-2024-0087 This Version is available at: https://hdl.handle.net/10419/331954 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ ger 2025; 26(3): 193–227 Dennis Dreusch* and Peter Reichling The Performance of Merging Cooperative Banks in Germany https://doi.org/10.1515/ger-2024-0087 Received August 6, 2024; accepted February 3, 2025; published online February 17, 2025 Abstract:Motivated by the recent increase in bank mergers, this paper examines the performance of German cooperative banks that merged between 2014 and 2019. We are particularly interested in whether elevated merger rates are due to bank inefficiencies or to challenging policy measures such as low-for-long interest rates. The results indicate that banks that perform relatively worse before and during the low interest environment exhibit a greater probability of becoming a target during this period. Consolidation generally occurs among low performing banks where large and well-capitalized banks merge with their small and inefficient peers. Ultimately, our results attribute the increased number of mergers to inefficiencies in the banking industry, as banks that exited the market were inefficient prior to the adverse low interest rate environment. Keywords: banks; mergers; regulation; low interest environment; efficiency JEL Classification: G21; G34 1 Introduction Over the past decade, a significant number of German banks have exited the market through mergers, which many banks see as the result of excessive regulation and low-for-long interest rates. The shrinking banking landscape thus raises potential concerns that difficult policy measures have driven ex ante good banks out of *Corresponding author: Dennis Dreusch, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany, E-mail: [email protected]. https://orcid.org/0009-0000-7295-6982 Peter Reichling, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany, E-mail: [email protected] Open Access. ©2025 the author(s), published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 International License. 194 —D. Dreusch and P. Reichling the market. This paper examines this issue by estimating the determinants of bank mergers before and during the low interest rate environment, thereby identifying the performance characteristics of merging banks in two time periods. This providesinformationnotonlyonwhethermergingbanksweregoodorbadperformers ex ante, but also on the primary cause of the merger wave: Mergers are likely to be driven by bank inefficiency if target banks perform relatively poorly before and during the regulatory and interest rate changes and eventually exit the market during this adverse period. Conversely, increased merger activity may reflect challenging policies if target banks initially perform well or similarly to their nonmergingpeers,butunderperformin the course of the low interest rateenvironment and eventually leave the market. Given the recent increase in the number of mergers, it seems interesting to ask how adverse operating conditions are shaping and impacting the banking industry. The past decade provided for at least two crucial developments that, possibly each individually, but particularly when viewed together, resulted into a difficult surrounding. First and foremost, the low-for-long interest rate policy in the aftermath of the 2007–2009 financial crisis gradually reduced banks’ profitability and net interest margin and effectively deteriorated their financial leeway (e.g. Borio, Gambacorta, and Hofmann 2017;Busch et al. 2022;Claessens, Coleman, and Donnelly 2018;Genay and Podjasek 2014). Existing work suggests that extended periods of low or negative policy rates induce banks to adapt. Banks increase their risktaking by lowering their loan standards (Maddaloni and Peydró 2011) and raising the portion of risk assets (Delis and Kouretas 2011;Heider, Saidi, and Schepens 2019). Furthermore, banks cut their lending activity (Heider, Saidi, and Schepens 2019), adjust their funding structure, and exchange interest-generating engagements with fee-related and trading activities (Brei, Borio, and Gambacorta 2020). Importantly, the effects are more pronounced for small banks with higher deposit shares (e.g. Claessens, Coleman, and Donnelly 2018;Genay and Podjasek 2014;Heider, Saidi, and Schepens 2019;Kerbl and Sigmund 2016;Meyer 2018). Besides triggering low policy rates, the financial crisis also spurred authorities to thoroughly tighten and expand existing banking regulations. Considering the sheer complexity and impact on the industry, the Basel III reform thereby hardly compares to prior frameworks. In fact, reforms entail substantial costs for banks to establish permissible risk-management systems and comply with novel regulatory standards and metrics (e.g. Bonner and Eijffinger 2015;Dietrich, Hess, and Wanzenried 2014;Handorf 2014;King 2013). Since regulatory burden can manifest in various aspects, such as increased spending on administration and staff training, however, no reliable quantification exists with respect to banks’ incurred cost of regulation (Cochrane 2014; Hoskins and Labonte 2015). The literature shows that banks transpose and respond to regulatory claims for higher capital and liquidity requirements by cutting their The Performance of German Cooperative Banks —195 lending (De Nicolò, Gamba, and Lucchetta 2014;Mésonnier and Monks 2015)and risk-weighted assets (Gropp et al. 2019) and by employing different strategies based on their profitability (Andrle et al. 2017;Cohen and Scatigna 2016). Taken together, banks have faced a difficult surrounding for more than a decade,evokingchanges in asset composition and fundingstructure.Given the relatively sharp decline in the number of independently operating EU banks, however, adverse conditions not only seem to have sparked notable adaptations in financial profiles. They likely also contributed to an accelerated consolidation process in which a substantial number of banks left the market through mergers.1The underlying reasoning is intuitive. Strenuous periods require an effective allocation of resources for banks to remain competitive. Banks failing to cope with detrimental circumstances are then either forced to wind up or merge with other banks. What remains unclear, however, is whether adverse conditions induced ex ante inefficient banks to exit the market in the spirit of Schumpeter (1939) (hereafter the ‘efficiency-view’), or whether they encouraged even initially well-performing banks to leave the market by negatively affecting their lending and deposit business. The uncharted issue of elevated bank exits steps into the line of questions related to the effects of ultra-loose monetary policies and stringent regulations, and is of paramount importance for comprehending developments in the banking landscape. If recent policy measures primarily turn small, albeit initially well performing, banks into merger targets, then this should be of interest to authorities. Likewise, it is important to know if consolidation occurs among low performing banks, which could help reduce over-capacities, enhance profitability, and improve resilience against shocks (ECB 2019). Given these unresolved questions surrounding the recent merger activity, this paper examines the relative performance and other key characteristics of banks that exited the market through mergers in recent years. We do so by considering the merger dynamics in the German cooperative banking industry between 2014 and 2019. We select the German banking market because it is one of the largest in the EU, while the cooperative sector offers an abundant number of independently but similarly operating banks. This naturally mitigates concerns about potential differences in unobserved time-invariant bank characteristics (Mood 2010), and simultaneously satisfies crucial homogeneity assumptions forwell-established efficiency measurement techniques (Dysonetal. 2001). The chosen time span reflects our intention to investigate critical periods. On the one hand, 1According to the European Banking Federation, the number of EU banks between 2013 and 2019 declined by 23 %, which is roughly twice the decline from 2007 to 2013 (10 %). Similar findings are obtained for the number of MFIs in the Euro area: https://www.ecb.europa.eu/ stats/ecb_statistics/escb/html/table.en.html?id=JDF_MFI_MFI_LIST. 196 —D. Dreusch and P. Reichling the Capital Requirements Regulation enters legal force in 2014, gradually imposing stricter standards on banks’ capital and liquidity management. On the other hand, stipulated refinancing rates and standing facilities continue their declining path, further reducing banks’ interest expenses but also their overall profitability. For our empirical analyses, we draw on the rich literature on bank merger determinants. As such, we first estimate a series of standard multinomial logistic models for the period 2014 to 2019, linking various performance measures to the probability of becoming a target or an acquirer. This allows important insights into consolidating banks’ performance characteristics at that juncture. Subsequently, we consider an earlier time period from 2010 to 2012, when interest rates prevail at common levels and novel regulations still remain to be poured into binding law. We reiterate our analyses in similar fashion but this time relating banks’ average performance over the period 2010 to 2012 to the probability of becoming a future target at some point between 2014 and 2019. The idea is to test whether performance, as measured before the low interest environment, significantly determines the probability of becoming a future target. If so, we can rule out the notion that the targets were initially well-performing banks that became underperforming due to the adverse policy actions and eventually disappeared from the market as a result. We provide two main findings. First, banks that perform relatively worse before and between 2014 and 2019 are more likely to exit the market during this period. Controlling for bank size and related merger determinants, the probability of becoming a target increases with a higher cost-income ratio, lower growth rates, and poorer asset management. Second, depending on the performance measure of choice, acquirers perform either similar or worse, but in no case better, than the reference group with no merger occurrence. Consolidation thus tends to occur among low performing banks, where large and well-capitalized banks take over their small and inefficient peers. Based on the findings in this paper, we conclude that the recent merger wave primarily arises from bank inefficiencies: Banks that perform relatively worse over the 2010–2012 period are more likely to become targets in the low interest rate environment. In addition to studies analyzing the effects of banking regulations and lowfor-long interest rate policies, our paper relates to several other streams of literature, including research on bank merger motives and determinants (e.g. Beccalli and Frantz 2013;Focarelli, Panetta, and Salleo 2002;Goddard, McKillop, and Wilson 2009;Hadlock, Houston, and Ryngaert 1999;Hannan and Pilloff 2009;Hannan and Rhoades 1987;Hernando, Nieto, and Wall 2009;Huhtilainen, Saastamoinen, and Suhonen 2022;Koetter et al. 2007;Lanine and Vander Vennet 2007;Moore 1996,1997; Pasiouras, Tanna, and Gaganis 2011;Wheelock and Wilson 2000;Worthington 2004), bank distress and failure (e.g. Berger and Bouwman 2013;Berger, Imbierowicz, and Rauch 2016;Cole and Gunther 1995;Cole and White 2012;DeYoung and Torna 2013; The Performance of German Cooperative Banks —197 Estrella, Park, and Peristiani 2000;Wheelock and Wilson 2000), and the cleansing effect of crises (Spokeviciute, Keasey, and Vallascas 2019). Our paper closely connects to Spokeviciute, Keasey, and Vallascas (2019), which seems to be the only perceivable work investigating the cleansing effects of financial crises within the banking industry. The work predicts the probability of bank failure or acquisition by interacting crises with banks’ cost efficiency. They find that the savings and loan crisis in the mid 1980s and early 1990s increases the exit probability for less efficient US commercial banks more than for the group of efficient banks. However, the 2007–2009 financial crisis escalates the exit probability regardless of banks’ cost efficiency. The work thus provides mixed evidence on the efficiency-view that crises encourage less efficient banks to drop out of the market. Our paper differs from the existing literature in that we examine the link between recent merger activity and (monetary) policy measures. By showing that consolidation occurs among low performing banks, we provide an interesting avenue for research analyzing the belief that “in systems with many weak-performing small banks, consolidation within their domestic system could improve performance” ECB (2019), p. 107. Related, we also extent the literature on bank merger predictability. 2 Mergers Made in Germany The German cooperative banking industry has experienced an extraordinary increase of mergers over the past years. This is illustrated in Figure 1(a),which depicts the annual number of banks exiting the market along with the marginal lending facility as a measure of the ECB’s interest rate policy. Importantly, mergers accelerate after 2011, and peak in 2017 when 57 targets are taken over by 40 acquiring banks. These numbers can be put into perspective by comparing the recent merger activity with the situation around the 2007–2009 financial crisis. Although the crisis is known for its substantial impact on the banking industry, relatively few banks engaged into mergers during that period. Considering the entire time span of our sample from 2013 to 2020, merger dynamics induce an overall decline in the quantity of independent cooperative banks by nearly 25 %.2 The driving motive behind these elevated merger rates could arguably be linked to the low interest environment, which exposes banks to historically low earnings. Such linkage is supported by the simple fact that mergers occur in a time when interest rates concurrently exhibit a declining path. Lower market rates, 2Bundesbank data suggests 1,065 banks by the end of 2013, and 804 banks by the end of 2020. In this respect, note that supervisory agencies posses no direct mandate to enforce mergers. Given numbers hence comprise mergers made on a voluntary basis. 198 —D. Dreusch and P. Reichling Figure 1: The evolution of cooperative bank mergers in Germany. Figure (a) shows recent developments in German cooperative bank merger activity along with the lending facility. Figure (b) depicts the average net interest margin and capitalization for this industry. Shaded areas respectively indicate the 2007–2009 financial crisis and the 2014–2019 low-interest environment as considered in our analysis. The data are taken from the Bundesbank: “Bankstellenstatistik” and “Zeitreihen-Datenbanken”. in turn, come along with a reduction of banks’ net interest margin, as shown in Figure 1(b) by the solid line. In this regard, banks might merge to encounter their decreasing margins by realizing economies of scale or scope (Amel et al. 2004). However, low interest rates may not be the only motive. The recent merger wave also falls in a period in which authorities depart towards a stricter banking regulation. Extensive regulation in terms of the Basel III framework could then impede growth and burden banks with costs (e.g. Dietrich, Hess, and Wanzenried 2014;Mésonnier and Monks 2015), although these costs may be absorbed, at least to some extent, by the compound structures of the German cooperative system. While the quantification of such regulatory costs is difficult, the impact of greater (capital) requirements can be shown clearly by an upward sloping capital ratio, as depicted in Figure 1(b) by the dashed line. Thus, banks might also merge to comply with regulations at fewer costs, for instance by profiting from a diversification of their loan portfolio (Amel et al. 2004). Apart from regulatory motives, mergers can also be based on management restructuring goals and the establishment of a new corporate culture (Gindele et al. 2019). In their recent study, Gindele et al. (2019) explicitly state that mergers among cooperative banks are primarily driven by structural changes in management, rather than attempts to improve profitability. Although the analysis is limited to banks in the German state of Baden-Württemberg between 2009 and 2016, these management motives make it generally unclear whether the target banks are underperforming or distressed. The Performance of German Cooperative Banks —199 As for the current wave of mergers, low interest rates seem to be the dominant motive. Drawing on banks’ websites, press releases, local newspaper articles that comprise interviews with bank executives, and related sources, banks mainly attribute their decision to merge to the low interest environment and an extensive set of regulations, but also to a costly layout of a digital infrastructure. More specifically, among the 228 targets subject to our empirical analysis, 211 banks justify their merger by referring to a combination of at least two out of these three elements. For the remaining 17 banks, we find no information. An exemplary statement reflecting key merger motives is taken from a press release by the Volksbank Untere Saar eG and the Vereinigte Volksbank eG Saarlouis – Losheim am See – Sulzbach/Saar: As for the main reasons of the merger, Soester names the exuberant regulatory requirements, the altered customer behavior due to an increasing digitalization, and the impact of the ongoing low interest environment, which causes declines in earnings year after year. (Press release, accessed on the 11.01.2023 at 11:30) Another example concerns the merger activity between the VR-Bank HunsrückMosel eG and the Vereinigte Volksbank Raiffeisenbank eG: The merger is mainly aimed at meeting the challenges of the low-interest policy and the increasing supervisory regulation. (Website, accessed on the 11.01.2023 at 11:30) Since most statements resemble each other to a great extent, both examples can be taken as representative of the general reasoning in the population. While it could be true that some banks mask other decisive merger intentions behind statements such as the two presented, the disadvantageous nature of the low interest environment is evident. Even if there are cases where unobserved factors ultimately seal the merger, such as excessive losses leading to distressed mergers, it seems likely that such factors are again closely related to the state of profitability, or to the costs of regulation and digitalization, although the latter may again be absorbed by the prevailing network structures in the cooperative sector. Given the high number of mergers, paired with the fact that most banks attribute their merger decision to the fierce operating situation, it appears natural to ask if such environment encourages rather low performing banks to exit the market. To obtain a first, motivating glimpse on the differences between targets and non-merging banks, Table 1 contrasts both groups with regard to crucial bank (performance) characteristics. Beginning with Panel A, which considers all bank-year observations from our primary data as discussed in the subsequent section, we observe a significant difference in total assets. Target banks are on average only half the size of non-merging banks. Moreover, non-merging banks exhibit greater growth rates, are seemingly better capitalized, profit from significantly lower NPL-ratios, and achieve more favorable cost-to-income balances. We 200 —D. Dreusch and P. Reichling Table 1: Performance differences. This table examines performance differences between target banks and non-merging banks. Panel A considers the full sample from 2014 to 2019, for which we observe 599 non-merging banks over 6 years yielding 3,594 bank-year observations. Panel B additionally considers the 2014–2017 sub-sample comprising 599 non-merging banks. All variables are reported in percent, except assets and GRP per capita, which are reported in millions and thousands of Euros, respectively. S.E. refers to the standard error of the mean. The first p-value refers to a two-sample t-test, which tests the H0that the difference in means equals zero. The second p-value refers to a Wilcoxon rank sum test and is reported to address concerns about data normality. Panel A: Full sample Target Non-merging NMean S.E. NMean S.E. Difference tp-value Wp-value Assets 837 389 12.91 3,594 785 33.70 396 0.00 0.00 Asset growth 609 3.73 0.144 2,995 4.69 0.076 0.96 0.00 0.00 Loan growth 609 4.20 0.211 2,995 5.67 0.099 1.47 0.00 0.00 Equity share 837 9.14 0.069 3,594 9.46 0.036 0.32 0.00 0.00 Tier 1 ratio 812 14.6 0.141 3,557 15.1 0.067 0.50 0.00 0.00 NPL ratio 758 1.79 0.061 3,403 1.55 0.025 −0.24 0.00 0.00 Return on assets 837 0.27 0.006 3,594 0.28 0.003 0.01 0.11 0.72 Return on equity 837 2.94 0.056 3,594 2.94 0.029 0.00 0.96 0.26 Cost-income ratio 837 70.0 0.360 3,594 67.0 0.332 −3.00 0.00 0.00 GRP per capita 833 34.7 0.439 3,582 35.4 0.220 0.70 0.16 0.14 Panel B: Sub-sample – Target Non-merging NMean S.E. NMean S.E. Difference tp-value Wp-value Assets 228 402 21.58 2,396 749 38.97 347 0.01 0.05 Asset growth 171 3.56 0.271 1,797 4.43 0.101 0.87 0.01 0.05 Loan growth 171 4.11 0.370 1,797 5.46 0.131 1.35 0.00 0.01 Equity share 228 9.08 0.134 2,396 9.25 0.045 0.17 0.25 0.16 Tier 1 ratio 228 14.3 0.246 2,361 14.6 0.083 0.30 0.23 0.24 NPL ratio 213 1.92 0.142 2,259 1.73 0.032 −0.19 0.09 0.09 Return on assets 228 0.25 0.009 2,396 0.29 0.004 0.04 0.00 0.00 Return on equity 228 2.82 0.091 2,396 3.20 0.036 0.38 0.00 0.01 Cost-income ratio 228 71.0 0.710 2,396 67.0 0.441 −4.00 0.01 0.00 GRP per capita 224 35.2 0.897 2,388 34.4 0.260 −0.80 0.36 0.23 neither observe differences in the profitability, nor in the regional economic output as proxied by the Gross Regional Product per capita. Since Figure 1(b) reveals trends in banks’ capitalization and net interest margin, however, it is likely that comparisons among related variables are flawed. This is because non-merging banks, by definition, remain throughout the sample period and are thus more exposed to The Performance of German Cooperative Banks —207 The resulting efficiency values – for brevity, adjusted values – are hence adjusted for relevant environmental factors. Since an increasing 𝜃indicates decreasing bank inefficiency, we expect a negative relationship between both efficiency measures and the probability of becoming a target.8 Apart from DEA, we also analyze six conventional accounting-based ratios. As for the first five measures, we consider the cost-to-income ratio, the non-interest expenses to asset ratio, the return on equity and assets, and loan growth. These metrics are selected due to their frequent use in related work (e.g. Lanine and Vander Vennet 2007), and recognition as performance measures by supervisors (e.g. ECB 2010). The sixth measure is a liquidity indicator, defined as the sum of cash and central bank holdings as a share of total assets. Although proportionally large stakes of these liquid assets seem to appear desirable at first sight, they might indicate idle resources with poor returns (Koetter et al. 2007). This could be particularly true when considering the low interest sphere with zero or negative marginal deposit facilities. We thus view the liquidity share as an indication of how banks perform regarding their asset management. In this respect, we expect the liquidity share to be positively associated with the probability of becoming a target bank. 4.2 Control Variables Considering the findings of Koetter et al. (2007),Hernando, Nieto, and Wall (2009), Pasiouras, Tanna, and Gaganis (2011) and Beccalli and Frantz (2013), amongst others, the banking literature has converged to a set of bank-specific factors that are reliably associated with bank mergers. To make sure that these common determinants of bank mergers are not driving our performance estimates, we include corresponding factors as controls in the regressions. More precisely, we follow Hernando, Nieto, and Wall (2009) and Pasiouras, Tanna, and Gaganis (2011) and include the natural log of total assets as a measure of bank size. Furthermore, we include the percentage change in total assets as a proxy for growth prospects as well as the ratio of equity to total assets as a proxy for capitalization. Moreover, we consider customer loans as a share of total assets to control for differences in specialization and asset diversification (e.g. Beccalli and Frantz 2013;Huhtilainen, Saastamoinen, and Suhonen 2022). To ensure robustness, we further take into account alternative with larger shares of elder people are affected detrimentally, which could be due to reduced loan demand and greater costs for personal advisory (Conrad, Neuberger, and Trigo 2009). 8Related work often considers profit and cost efficiency, which, however, require data on unit prices and costs. Here, we instead focus on the technical-physical aspects of intermediary efficiency as we lack reliable information on respective variables such as depreciation and number of employees (Cooper, Seiford, and Tone 2007). 208 —D. Dreusch and P. Reichling metrics such as the Tier 1 Capital ratio (instead of the equity share) and the share of securities in total assets (instead of the loan share) as in Koetter et al. (2007). Because bank characteristics may depend on external conditions, we also include economic and demographic information for the county in which each bank operates. We account for the possibility that banks in economically more favorable environments could profit in terms of asset growth and loan quality. In this respect,weadditionallyincludetheunemploymentratetoproxyregionaleconomic strength. We also suspect that urban banks might be larger and could benefit from a productivity premium vis-à-vis banks in rural areas (Andersson, Burgess, and Lane 2007). Therefore, we consider the natural log of the population density as a measure of the urbanization extent. For robustness, we check if a county’s cooperative bank density and market concentration influence our results. As such, we account for the variables bank density, proxied by the number of banks relative to the county’s population size, and HHI, which is the normalized Herfindahl–Hirschman Index measuring customer loan market concentration within each county. 5 Results This section addresses our empirical results. We begin by estimating variations of equation (1) for the period 2014 to 2019 to understand if bank performance significantly associates with the probability of turning into a target or an acquirer. Subsequently, our focus shifts to an earlier point in time, when interest rates prevail at somewhat common levels, and new regulations just begin to unpack their influence on the industry. The idea is to analyze how target banks perform prior to low market rates and fiercer regulations. This could show that adverse conditions are not the primary cause for target banks’ low performance. Instead, initially low performing banks may have a greater probability to exit the market later on. 5.1 Results of the Multinomial Model To examine how performance relates to the likelihood of a bank being a target or an acquirer, we conduct a series of multinomial logistic regressions. Estimations are based on a sample of 201 targets, 169 acquirers, and 599 non-merging banks. Note that the estimations require independence among all bank-year observations. Due to the panel structure, however, this is unlikely to be the case, since banks that continue to exist in some year tcannot have become targets in t−1, and vice versa (Shumway 2001). We tackle this issue by employing standard errors that are clustered at the bank level. This leaves estimated coefficients unchanged but offers robust inference by allowing dependence within clusters. We report our results in The Performance of German Cooperative Banks —209 Table 2. Recall that we discuss the coefficients in terms of RRR, which capture the change in the relative probability given a unit change in the predictor variable. For example, the coefficient on the liquidity share of around 0.092 indicates that a one percentage point increase in this variable increases the probability of being in the target group relative to the probability of being in the reference group by [(e0.092 −1) ×100 %]≈10 %. Two main findings are apparent. First, passing through target group results across columns (T), we notice that targets perform worse than the reference group on nearly all performance measures. Starting with column (1), which captures banks’ unadjusted efficiency as intermediaries, we observe a significant and negative coefficient. Consistent with our expectation, increasing bank efficiency is associated with a decreasing probability of becoming a target. The result particularly implies that target banks are characterized by relatively low efficiency values, indicating their tendency to rely on greater input volumes compared to their nonmerging peers. This finding remains in column (2) when we repeat the estimation but use adjusted values which account for differences in operating environments. Simply put, target banks tend to draw on relatively greater input quantities even when considering their operation in potentially disadvantageous locations. Turning to our set of accounting-based performance measures, we document a significant, albeit economically small, effect for the cost-income ratio in column (3). The probability of becoming a target increases as banks bear larger costs per income unit. This again shows that the targets under consideration are not particularly well performing banks but instead tend to exhibit relatively greater costincome ratios. Similarly, column (4) implies that target banks rather suffer from a greater share of non-interest expenses. This indicates relatively larger spending on staff and administration to manage one asset unit. Continuing with column (5), we observe a positive relationship between banks’ liquidity share and the likelihood of being a target, which echoes the findings of Koetter et al. (2007).Recall that we expect this outcome since excessive cash positions and central bank holdings offer poor returns, especially during periods of low market interest rates. The profitability measure in column (6) turns out insignificant after controlling for common determinants of bank mergers. The outcome does not change if we reiterate the exercise but use the return on assets instead (unreported). Finally, column (7) indicates a negative relationship between loan growth and the probability of being a target such that banks with greater growth rates, anything else equal, are less likely to engage into mergers. Note that we are aware of potential multicollinearity issues between loan and asset growth. We accept this possibility to maintain model consistency. Second, we find mixed evidence for the acquirer group (A). On the one hand, columns(1),(2), (4), and (7) implythatacquirers operateinefficientlyregardingtheir 210 —D. Dreusch and P. Reichling Table 2: Multinomial logit results. This table explores how various performance measures relate to the probability of a bank becoming a target or an acquirer. Estimations are based on a sample of 201 targets, 169 acquirers, and 599 non-merging banks over the period 2015 to 2019. Merger activities and bank-year observations in 2014 are omitted due to the lagged nature of asset growth. All estimations consider year effects as shown at the bottom of the table. Standard errors are clustered at the bank level. P-values are reported in parentheses. (1) (2) (3) (4) (5) (6) (7) TATATATATATATA Efficiency −.∗∗ −.∗∗∗ (.) (.) Efficiency adjusted −.∗∗∗ −.∗∗∗ (.) (.) Cost-income ratio .∗∗ . (.) (.) Expense share .∗∗∗ .∗∗ (.) (.) Liquidity share .∗∗ −. (.) (.) Return on equity −. −. (.) (.) Loan growth −.∗∗∗ −.∗∗∗ (.) (.) ln(assets) −.∗∗∗ .∗∗∗ −.∗∗∗ .∗∗∗ −.∗∗∗ .∗∗∗ −.∗∗∗ .∗∗∗ −.∗∗∗ .∗∗∗ −.∗∗∗ .∗∗∗ −.∗∗∗ .∗∗∗ (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) Asset growth −.∗∗∗ −.∗∗∗ −.∗∗∗ −.∗∗∗ −.∗∗∗ −.∗∗∗ −.∗∗∗ −.∗∗∗ −.∗∗∗ −.∗∗∗ −.∗∗∗ −.∗∗∗ −.∗∗ . (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) Equity share −.∗.∗∗ −.∗.∗∗ −.∗∗ .∗∗ −.∗∗ .∗−.∗∗ .∗∗ −.∗∗ .∗∗ −.∗∗ .∗∗ (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) Loan share −.∗. −.∗∗ . −.∗∗ . −.∗∗ . −.∗∗ . −.∗. −. . (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) Unemployment rate −. −. −. −. −. −. −.∗−.∗−. −. −. −. −. −. (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) ln(population density) .∗−.∗∗ . −.∗∗∗ . −.∗∗ .∗∗ −.∗∗ . −.∗∗∗ . −.∗∗∗ . −.∗∗∗ (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) (.) Year effects Yes Yes Yes Yes Yes Yes Yes N, , , , , , , Pseudo R2. . . . . . . Wald 𝜒2. . . . . . . Pseudo loglikelihood −,. −,. −,. −,. −,. −,. −,. ∗∗∗,∗∗ and ∗Respectively denote statistical significance at the 1 %, the 5 % and the 10 % level. The Performance of German Cooperative Banks —211 input-output allocation, expense share, and loan growth, respectively. On the other hand, columns (3), (5), and (6) suggest that acquirers’ cost-income ratio, liquidity share, and profitability do not significantly differ from those of the reference group. Thus, depending on the indicator of choice, acquirers either perform worse or the same, but in no case better than the reference group. The remaining set of control variables is not of key interest for our purposes. Nevertheless, it may be reassuring to note that the observed effects on all bankspecific factors are generally consistent with those found in related work. In particular, the results uniformly indicate notable differences in bank size and capitalization. Acquirers (targets) are relatively larger (smaller) and better (worse) capitalized, which aligns with the findings of Koetter et al. (2007) and Behr and Heid (2011). Lastly, we stress the robustness of these results across various model specifications. In unreported estimations, the results not only hold when different covariates are used, such as the Tier 1 Capital ratio or the security share. They also remainlargelyunchangedwhenestimationsinclude more than one designated performance measure at a time. For example, the cost-income effects do not change significantly when the liquidity share is added to the estimations. Furthermore, the results remain when we additionally include the merger events in 2014, which are omitted here due to the lagged nature of asset growth. This is shown in column (2) of Table A3, which achieves an extended sample by neglecting the asset growth variable.9Another concern that often goes unnoticed in allied work is the potential interdependence between mergers and regional market concentration. In this vein, we suspect that mergers could more likely occur in counties with a greater cooperative bank density and a more dispersed market structure. To control for this possibility, columns (3) and (4) of Table A3 respectively include the variable bank density, which is the number of banks in a county relative to the county’s population, and the HHI, which is the normalized Herfindahl–Hirschman index that measures the concentration of the market for consumer loans. Our findings hold up against both specifications. Consistent with our expectations, higher bank density and dispersed local market power seem to encourage mergers, but the performance aspect prevails. Finally, we acknowledge that Tobit regressions may yield biased second-stage efficiency measures (Simar and Wilson 2007). Therefore, column (5) uses bias-corrected efficiency as the dependent variable, which is based on the procedure proposed by Simar and Wilson (2007). The previously observed results remain. What we take away from these regressions is that consolidating banks rather perform worse than their non-merging peers, but nuances exist among targets and 9We focus on adjusted efficiency for brevity. 212 —D. Dreusch and P. Reichling acquirers. While targets clearly exhibit financially weak profiles, particularly from a cost perspective, acquirers do benefit from greater size, stronger capitalization, and some performance outcomes that are indistinguishable from the peer group. The results so far provide important insights into the characteristics of consolidating banks, but they are not sufficient to reliably verify the efficiency-view in the German banking market. This is because we have to consider the possibility that the target banks performed well prior to the low interest rate era, but deteriorated mainlyduetotheadverseenvironment.Suchapossibilityissupportedbytheobservation that the targets are often small, regional banks. These banks are typically more reliant on deposit and lending activities and may be more exposed to changes in the interest rate environment (e.g. Claessens, Coleman, and Donnelly 2018;Genay and Podjasek 2014). If so, then the sole reliance on previous analyses would likely yield fallacious conclusions, and impugn the efficiency-view. Therefore, the next section complements the previous analyses by examining bank performance prior to the low-interest period and before the regulatory interventions took legal effect in 2014. 5.2 Results of the Logit Model In order to understand if targets perform relatively worse before our initial investigation period, we draw on our secondary data set. For each of the 218 future targets and 883 other cooperative banks in this sample, we consider averaged values over theperiod2010to2012. We split the analysis into two parts. First, we examine whether targets are underperforming, as in our primary analysis. In this vein, we first estimate equation (2), relating the probability of a bank being acquired within the period 2014 to 2019 to the average bank performance over the years 2010–2012. The goal of this procedure is to show that the low interest sphere does not turn ex ante well performing banks into targets. Instead, banks that operate comparatively worse between 2010 and 2012 are more likely to become targets later on. Second, we assign banks to different groups based on their size and performance, and repeat previous estimations using group indicators. The aim is to understand, in particular, how performance relates to the survival chances of small banks. From an efficiency perspective, we expect small and well performing banks to benefit in terms of lower exit probabilities compared to their underperforming peers. Similarly, the survival probabilities of large and well-performing banks should be significantly higher than for their cohorts of similar size. Before proceeding with the analysis, finally note that we restrict the analysis in this part to our set of accounting-based performance indicators. This is not by choice but rather due to the fact that unit measurements differ for some The Performance of German Cooperative Banks —213 observations, which severely distorts all DEA efficiency values. We also do not cover the non-interest expense share here as the data-set at hand lacks information on administrative expenses. 5.2.1 Target Bank Performance Before the Low-For-Long Interest Rate Era We begin by estimating equation (2) to see if ex ante performance significantly determines future target banks. More precisely, we consider a binary logistic model which links the probability of a bank becoming a target at some point between 2014 and 2019 to the average performance over the period 2010 to 2012. In case that estimated coefficients resemble the values from earlier regressions, the low interest environment and related factors are unlikely to be the cause for target banks’ low performance in the more recent years. In addition to the logistic regressions, we also apply a linear probability model (LPM). Although LPMs suffer from commonly known, undesirable properties, we find that they provide a convenient and affordable way to further reinforce our results. In this vein, we regress the binary variable FutureTarget on the same vector x, again comprising one performance measure at atime. We report our results in Table 3. The first five columns, (1) to (5), show the outcome of the logistic model. Recall that the coefficients for these regressions measure the change in log-odds for a unit-change in the predictor variable. Applying the transformation yields [(e0.060 −1) ×100 %]≈6.2 %, such that a one percentage point increase in the cost-income ratio increases the odds of becoming a target by about 6.2 %. Put differently, banks with a one percentage point higher cost-income ratio are 1.062 times more likely to become a target than banks without this additional increase. Beginning with column (1), we observe a significant and positive effect of the cost-income ratio. This implies that target banks exhibit relatively greater cost-income balances before the emergence of the zero interest environment. The finding aligns with the earlier observation from Table 2 and particularly suggests that the zero interest sphere does not turn well operating banks into targets. Instead, banks with ex ante greater cost-income ratios more likely become targets later on during operationally disadvantageous periods. Continuing with column (2), we find a positive and significant association between banks’ liquidity share and their probability of turning into a future target. This again matches our previous observations such that banks with proportionally larger holdings of idle assets tend to be more likely future targets. In contrast to our primary analysis, columns (3) and (4) document a negative and significant effect of both profitability measures. Initially more profitable banks are less likely to be a target in the future. Turning to column (5), we find a negative and statistically significant effect of loan growth. Greater loan growth reduces the probability of a future market exit. This 214 —D. Dreusch and P. Reichling Table 3: Binary logit results. This table analyzes how performance over the period 2010 to 2012 influences the probability of becoming a future target between 2014 and 2019. The first five columns, (1) to (5), show the outcome of the binary logistic model. Columns (6) to (10) show the results of the linear probability model. Estimations are based on a sample of 218 future targets and 838 other banks assumed to have not engaged into mergers during the 2010–2012 period. The number of observation varies depending on data availability. We do not require information on all variables to prevent a potential sample selection bias. Robust Huber-White standard errors are employed. P-values are reported in parentheses. Logit LPM (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) FT FT FT FT FT FT FT FT FT FT Cost-income ratio 0.060∗∗∗ 0.00874∗∗∗ (0.000) (0.000) Liquidity share 0.023∗∗ 0.00419∗∗ (0.024) (0.021) Return on equity −0.086∗∗ −0.0113∗∗ (0.039) (0.027) Return on assets −1.221∗∗ −0.158∗∗ (0.041) (0.025) Loan growth −0.074∗∗∗ −0.0114∗∗∗ (0.008) (0.007) ln(assets) −0.078∗−0.105∗∗ −0.098∗∗ −0.100∗∗ −0.097∗∗ −0.0118∗−0.0158∗∗ −0.0146∗∗ −0.0148∗∗ −0.0145∗∗ (0.079) (0.029) (0.041) (0.038) (0.043) (0.066) (0.015) (0.024) (0.022) (0.026) Asset growth −0.081∗∗ −0.123∗∗∗ −0.111∗∗∗ −0.111∗∗∗ −0.076∗−0.0124∗∗∗ −0.0181∗∗∗ −0.0163∗∗∗ −0.0165∗∗∗ −0.0101∗ (0.017) (0.000) (0.001) (0.001) (0.051) (0.009) (0.000) (0.001) (0.001) (0.073) Equity share 0.080∗0.029 0.029 0.081∗0.022 0.0119∗0.00533 0.00512 0.0124∗0.00416 (0.068) (0.502) (0.494) (0.086) (0.602) (0.084) (0.441) (0.458) (0.099) (0.542) The Performance of German Cooperative Banks —215 Table 3: (continued) Logit LPM (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) FT FT FT FT FT FT FT FT FT FT Loan share 0.028∗∗ 0.015 0.022∗0.022∗0.020 0.00347∗∗ 0.00206 0.00291∗0.00280∗0.00258 (0.031) (0.194) (0.070) (0.075) (0.105) (0.031) (0.200) (0.068) (0.078) (0.104) Unemployment rate −0.078∗∗ −0.060∗−0.070∗∗ −0.070∗∗ −0.044 −0.0112∗∗ −0.00927∗−0.00981∗−0.00959∗−0.00657 (0.025) (0.074) (0.047) (0.046) (0.180) (0.021) (0.062) (0.054) (0.056) (0.176) ln(population density) 0.031 −0.006 −0.002 −0.001 −0.014 0.00776 −0.000424 −0.000425 −0.000939 −0.00166 (0.737) (0.944) (0.978) (0.987) (0.870) (0.562) (0.975) (0.975) (0.944) (0.900) N960 961 960 960 961 960 961 960 960 961 R20.063 0.037 0.038 0.038 0.040 0.060 0.036 0.036 0.036 0.038 Wald 𝜒252.01 31.84 27.04 27.10 31.74 Pseudo loglikelihood −466.61 −479.61 −478.93 −477.75 −478.05 ∗∗∗,∗∗ and ∗Respectively denote statistical significance at the 1 %, the 5 % and the 10 % level. 216 —D. Dreusch and P. Reichling result again implies that target banks exhibit relatively poor growth rates before the emergence of detrimental conditions. Apart from these performance measures, we make an interesting discovery concerning the equity and loan share. In contrast to previous estimations, both metrics now indicate a statistically marginal but uniformly positive effect. Better capitalized banks and banks holding proportionally more loans in their assets bear a greater probability of becoming a future target. Since small, traditional cooperative banks match this description remarkably well, we see the outcome strongly in line with the literature finding more pronounced interest rate policy effects for smallbanks.In particular,Heider,Saidi, and Schepens(2019),p.3741,findthat“Highdeposit banks are also smaller, have higher equity ratios (6.2 % vs. 5.0 %), higher loans-to-assets ratios” and that the introduction of negative policy rates leads “to more risk-taking and less lending by euro-area banks with a greater reliance on deposit funding”. Related, Claessens, Coleman, and Donnelly (2018), p. 8, find that “small banks have greater difficulty maintaining their NIMs in a low interest rate environment”. Our estimation results complement these findings by showing that banks, initially fulfilling the traditional role of well-capitalized and loan-oriented intermediaries, also entail an increased probability of exiting the market in the course of a lasting low-interest environment. In this respect, the low-for-long interest rate policy could have influenced small banks more drastically. Importantly, however, this does not change the notion that targets already perform relatively worse before such adverse environment. The subsequent section further disentangles performance from bank size and analyzes differences in exit probabilities between small and large banks. Proceeding with columns (6) to (10), which show the estimation results for the LPM, we verify all previous findings. For instance, the coefficient on the cost-income ratio indicates that an increase of the average cost-income ratio by one percentage point is associated with an increase in the probability of becoming a future target by around one percentage point, holding other factors fixed. Banks with higher costincome ratios are therefore more likely to become targets later on. Concluding, we find that targets perform worse before the low-for-long interest rate environment and the Basel III implementation in 2014. This is particularly evident from Table 3, which assigns comparatively low performing banks a greater probability of becoming a future target. Our analysis thus favors the efficiency-view, but so far has fallen short of comparing the survival chances across banks with different sizes and performance levels. We address this issue in the following. The Performance of German Cooperative Banks —223 Table A3: (continued) (1) (2) (3) (4) (5) TA TA TA TA TA ln(population density) 0.0885 −0.307∗∗∗ 0.134 −0.295∗∗∗ 0.0828 −0.204∗∗ 0.0942 −0.315∗∗∗ 0.136 −0.219∗∗ (0.343) (0.001) (0.118) (0.001) (0.399) (0.041) (0.312) (0.001) (0.129) (0.012) Year effects Yes Yes Yes Yes Yes N4,449 5,445 4,449 4,449 4,449 Pseudo R20.060 0.052 0.064 0.062 0.056 Wald 𝜒2194.77 206.18 209.71 193.35 190.10 Pseudo loglikelihood −1,385.69 −1,605.17 −1,379.44 −1,383.46 −1,391.75 ∗∗∗,∗∗ and ∗Respectively denote statistical significance at the 1 %, the 5 % and the 10 % level. 224 —D. Dreusch and P. Reichling References Amel, D., C. Barnes, F. Panetta, and C. Salleo. 2004. “Consolidation and Efficiency in the Financial Sector: A Review of the International Evidence.” Journal of Banking & Finance 28 (10): 2493−519.. Andersson, F., S. Burgess, and J. Lane. 2007. “Cities, Matching and the Productivity Gains of Agglomeration.” Journal of Urban Economics 61 (1): 112−28.. Andrle, M., J. Brada, V. Tomšík, and J. Vlček. 2017. Banks’ Adjustment to Basel Iii Reform: A Bank-Level Perspective for Emerging Europe. Working Paper 17/24. Washington: International Monetary Fund. Banker, R., and R. Morey. 1986. “Efficiency Analysis for Exogenously Fixed Inputs and Outputs.” Operations Research 34 (4): 513−21.. Banker, R., A. Charnes, and W. Cooper. 1984. “Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis.” Management Science 30 (9): 1078−92.. Barth, J., C. Lin, Y. Ma, J. Seade, and F. Song. 2013. “Do Bank Regulation, Supervision and Monitoring Enhance or Impede Bank Efficiency?” Journal of Banking & Finance 37 (8): 2879−92.. Beccalli, E., and P. Frantz. 2013. “The Determinants of Mergers and Acquisitions in Banking.” Journal of Financial Services Research 43: 265−91.. Behr, A., and F. Heid. 2011. “The Success of Bank Mergers Revisited. An Assessment Based on a Matching Strategy.” Journal of Empirical Finance 18 (1): 117−35.. Berger, A., and C. Bouwman. 2013. “How Does Capital Affect Bank Performance during Financial Crises?” Journal of Financial Economics 109 (1): 146−76.. Berger, A., and D. Humphrey. 1997. “Efficiency of Financial Institutions: International Survey and Directions for Future Research.” European Journal of Operational Research 98 (2): 175−212.. Berger, A., B. Imbierowicz, and C. Rauch. 2016. “The Roles of Corporate Governance in Bank Failures during the Recent Financial Crisis.” Journal of Money, Credit, and Banking 48 (4): 729−70.. Bonner, C., and S. Eijffinger. 2015. “The Impact of Liquidity Regulation on Bank Intermediation.” Review of Finance 20 (5): 1945−79.. Borio, C., L. Gambacorta, and B. Hofmann. 2017. “The Influence of Monetary Policy on Bank Profitability.” International Finance 20 (1): 48−63.. Brei, M., C. Borio, and L. Gambacorta. 2020. “Bank Intermediation Activity in a Low-Interest-Rate Environment.” Economic Notes 49 (2): e12164.. Busch, R., H. Littke, C. Memmel, and S. Niederauer. 2022. “German Banks’ Behavior in the Low Interest Rate Environment.” Financial Markets and Portfolio Management 36 (3): 267−96.. Charnes, A., W. Cooper, and E. Rhodes. 1978. “Measuring the Efficiency of Decision Making Units.” European Journal of Operational Research 2(6):429−44.. Chortareas, G., C. Girardone, and A. Ventouri. 2012. “Bank Supervision, Regulation, and Efficiency: Evidence from the European Union.” Journal of Financial Stability 8(4):292 −302.. Claessens, S., N. Coleman, and M. Donnelly. 2018. ““Low-For-Long” Interest Rates and Banks’ Interest Margins and Profitability: Cross-Country Evidence.” Journal of Financial Intermediation 35: 1−16.. Cochrane, J. H. 2014. “Challenges for Cost-Benefit Analysis of Financial Regulation.” The Journal of Legal Studies 43 (S2): S63−S105.. Cohen, B., and M. Scatigna. 2016. “Banks and Capital Requirements: Channels of Adjustment.” Journal of Banking & Finance 69: S56−S69.. Cole, R., and J. W. Gunther. 1995. “Separating the Likelihood and Timing of Bank Failure.” Journal of Banking & Finance 19 (6): 1073−89.. Cole, R., and L. White. 2012. “Déjà Vu All over Again: The Causes of US Commercial Bank Failures This Time Around.” Journal of Financial Services Research 42: 5−29.. The Performance of German Cooperative Banks —225 Conrad, A., D. Neuberger, and G., L. Trigo. 2009. The Impact of Regional and Demographic Factors on the Efficiency of German Savings Banks. Working Paper 111. Rostock: University of Rostock, Institute of Economics. Cooper, W., L. Seiford, and K. Tone. 2007. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, 2nd ed. New York: Springer. De Nicolò, G., A. Gamba, and M. Lucchetta. 2014. “Microprudential Regulation in a Dynamic Model of Banking.” Review of Financial Studies 27 (7): 2097−138.. Delis, M., and G. Kouretas. 2011. “Interest Rates and Bank Risk-Taking.” Journal of Banking & Finance 35 (4): 840−55.. DeYoung, R., and G. Torna. 2013. “Nontraditional Banking Activities and Bank Failures during the Financial Crisis.” Journal of Financial Intermediation 22 (3): 397−421.. Dietrich, A., K. Hess, and G. Wanzenried. 2014. “The Good and Bad News about the New Liquidity Rules of Basel III in Western European Countries.” Journal of Banking & Finance 44: 13−25.. Dombret, A. 2016. Are There Too Many Banks? The Banking Sector after the Financial Crisis. Vienna: Speech given by Andreas Dombret at the General Meeting of the Austrian Society for Bank Research. Drake, L., M. J. Hall, and R. Simper. 2009. “Bank Modelling Methodologies: A Comparative Non-parametric Analysis of Efficiency in the Japanese Banking Sector.” Journal of International Financial Markets, Institutions and Money 19 (1): 1−15.. Dyson, R., R. Allen, A. Camanho, V. Podinovski, C. Sarrico, and E. Shale. 2001. “Pitfalls and Protocols in DEA.” European Journal of Operational Research 132 (2): 245−59.. ECB. 2010. Beyond ROE −How to Measure Bank Performance. Appendix to the Report on eu Banking Structures. Frankfurt am Main: European Central Bank. ECB. 2019. Financial Stability Review, November 2019. Financial Stability Review. Frankfurt am Main: European Central Bank. Estrella, A., S. Park, and S. Peristiani. 2000. “Capital Ratios as Predictors of Bank Failure.” Federal Reserve Bank of New York Economic Policy Review 6(2):33 −52. Farrell, M. J. 1957. “The Measurement of Productive Efficiency.” Journal of the Royal Statistical Society: Series A 120 (3): 253−81.. Fiorentino, E., A. Karmann, and M. Koetter. 2006. The Cost Efficiency of German Banks: A Comparison of SFA and DEA. Discussion Paper 10/2006. Frankfurt am Main: Deutsche Bundesbank. Focarelli, D., F. Panetta, and C. Salleo. 2002. “Why Do Banks Merge?” Journal of Money, Credit, and Banking 23 (4): 1047−66.. Fried, H., S. Schmidt, and S. Yaisawarng. 1999. “Incorporating the Operating Environment into a Nonparametric Measure of Technical Efficiency.” Journal of Productivity Analysis 12: 249−67. . Genay, H., and R. Podjasek. 2014. What is the Impact of a Low Interest Rate Environment on Bank profitability? Chicago Fed Letter 324. Chicago: Federal Reserve Bank of Chicago. Gindele, N., R. Doluschitz, L. Sauter, and P. Müller. 2019. “Fusionen von Volks-und Raiffeisenbanken: Ergebnisse einer empirischen Analyse 2017 in Baden-Württemberg.” Zeitschrift für das Gesamte Genossenschaftswesen 69 (3): 162−78.. Goddard, J., D. McKillop, and J. O. Wilson. 2009. “Which Credit Unions Are Acquired?” Journal of Financial Services Research 36: 231−52.. Gropp, R., T. Mosk, S. Ongena, and C. Wix. 2019. “Banks Response to Higher Capital Requirements: Evidence from a Quasi-Natural Experiment.” Review of Financial Studies 32 (1): 266−99.. Hadlock, C., J. Houston, and M. Ryngaert. 1999. “The Role of Managerial Incentives in Bank Acquisitions.” Journal of Banking & Finance 23 (2−4): 221−49.. Handorf, W. 2014. “The Cost of Bank Liquidity.” Journal of Banking Regulation 15: 1−13.. 226 —D. Dreusch and P. Reichling Hannan, T. H., and S. J. Pilloff. 2009. “Acquisition Targets and Motives in the Banking Industry.” Journal of Money, Credit, and Banking 41 (6): 1167−87.. Hannan, T. H., and S. A. Rhoades. 1987. “Acquisition Targets and Motives: The Case of the Banking Industry.” The Review of Economics and Statistics 69 (1): 67−74.. Heider, F., F. Saidi, and G. Schepens. 2019. “Life below Zero: Bank Lending under Negative Policy Rates.” Review of Financial Studies 32 (10): 3728−61.. Hernando, I., M. Nieto, and L. Wall. 2009. “Determinants of Domestic and Cross-Border Bank Acquisitions in the European Union.” Journal of Banking & Finance 33 (6): 1022−32.. Hoskins, S., and M. Labonte. 2015. An Analysis of the Regulatory Burden on Small Banks. Report Prepared for the Members and Committees of Congress. Washington: Congressional Research Service. Hosmer, D. W., and S. Lemeshow. 2000. Applied Logistic Regression. New York: Wiley. Huhtilainen, M., J. Saastamoinen, and N. Suhonen. 2022. “Determinants of Mergers and Acquisitions Among Finnish Cooperative and Savings Banks.” Journal of Banking Regulation 23 (3): 339−49.. Jensen, M., and R. Ruback. 1983. “The Market for Corporate Control: The Scientific Evidence.” Journal of Financial Economics 11 (1−4): 5−50.. Kerbl, S., and M. Sigmund. 2016. From Low to Negative Rates: An Asymmetric Dilemma. Financial Stability Report 32. Vienna: Austrian Central Bank. King, M. R. 2013. “The Basel III Net Stable Funding Ratio and Bank Net Interest Margins.” Journal of Banking & Finance 37 (11): 4144−56.. Koetter, M., T. Nestmann, S. Stolz, and M. Wedow. 2006. “Still Overbanked and Unprofitable? Two Decades of German Banking.” Credit and Capital Markets −Kredit und Kapital 39 (4): 497−511.. Koetter, M., J. Bos, F. Heid, J. Kolari, C. Kool, and D. Porath. 2007. “Accounting for Distress in Bank Mergers.” Journal of Banking & Finance 31 (10): 3200−17.. Lang, G., and P. Welzel. 1996. “Efficiency and Technical Progress in Banking Empirical Results for a Panel of German Cooperative Banks.” Journal of Banking & Finance 20 (6): 1003−23.. Lanine, G., and R. Vander Vennet. 2007. “Microeconomic Determinants of Acquisitions of Eastern European Banks by Western European Banks.” The Economics of Transition 15 (2): 285−308.. Maddaloni, A., and J.-L. Peydró. 2011. “Bank Risk-Taking, Securitization, Supervision, and Low Interest Rates: Evidence from the Euro-Area and the U.S. Lending Standards.” Review of Financial Studies 24 (6): 2121−65.. Manne, H. 1965. “Mergers and the Market for Corporate Control.” Journal of Political Economy 73 (2): 110−20.. Mésonnier, J.-S., and A. Monks. 2015. “Did the EBA Capital Exercise Cause a Credit Crunch in the Euro Area?” International Journal of Central Banking 11 (3): 75−117. Meyer, E. 2018. “The New Economic Scenario and its Impact on the Cooperative Banking Business Model.” In New Cooperative Banking in Europe: Strategies for Adapting the Business Model Post Crisis, edited by M. Migliorelli, 29−45. Cham: Springer International Publishing. Mood, C. 2010. “Logistic Regression: Why We Cannot Do what We Think We Can Do, and what We Can Do about it.” European Sociological Review 26 (1): 67−82.. Moore, R. R. 1996. “Banking’s Merger Fervor: Survival of the Fittest.” In Federal Reserve Bank of Dallas Financial Industry Studies,9 −15. Moore, R. R. 1997. Bank Acquisition Determinants: Implications for Small Business Credit. Working Paper. Dallas: Federal Reserve Bank of Dallas. Palepu, K. G. 1986. “Predicting Takeover Targets: A Methodological and Empirical Analysis.” Journal of Accounting and Economics 8(1):3−35.. The Performance of German Cooperative Banks —227 Pasiouras, F., S. Tanna, and C. Gaganis. 2011. “What Drives Acquisitions in the EU Banking Industry? The Role of Bank Regulation and Supervision Framework, Bank Specific and Market Specific Factors.” Financial Markets, Institutions & Instruments 20 (2): 29−77.. Reichling, P., and G. Schulze. 2018. “Regional Differences in the Efficiency of German Savings Banks.” Credit and Capital Markets −Kredit und Kapital 51 (3): 445−64.. Schumpeter, J. 1939. Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process. New York: McGraw-Hill. Sealey, C. W., and J. T. Lindley. 1977. “Inputs, Outputs, and a Theory of Production and Cost at Depository Financial Institutions.” The Journal of Finance 32 (4): 1251−66.. Shumway, T. 2001. “Forecasting Bankruptcy More Accurately: A Simple Hazard Model.” Journal of Business 74 (1): 101−24.. Simar, L., and P. W. Wilson. 2007. “Estimation and Inference in Two-Stage, Semi-parametric Models of Production Processes.” Journal of Econometrics 136 (1): 31−64.. Spokeviciute, L., K. Keasey, and F. Vallascas. 2019. “Do Financial Crises Cleanse the Banking Industry? Evidence from US Commercial Bank Exits.” Journal of Banking & Finance 99: 222−36.. Tischer, J. 2018. Quantitative Easing, Portfolio Rebalancing and Credit Growth: Micro Evidence from Germany. Discussion Paper 20/2018. Frankfurt am Main: Deutsche Bundesbank. Wheelock, D. C., and P. W. Wilson. 2000. “Why Do Banks Disappear? The Determinants of US Bank Failures and Acquisitions.” The Review of Economics and Statistics 82 (1): 127−38.. Worthington, A. C. 2004. “Determinants of Merger and Acquisition Activity in Australian Cooperative Deposit-Taking Institutions.” Journal of Business Research 57 (1): 47−57..