Cooperative banks primarily compete with one another because they target niche markets that large banks typically ignore. The current study shows that in this competitive environment, the connection between financial intermediaries affects the operational efficiency of small banks. The findings indicate that the capitalization, diversification strategies, funding costs, liquidity, credit quality, and risk of bank neighbors have spillover effects on technical efficiency. Thus, bank networks trigger a cascading effect that demands the attention of bank stakeholders.
LM test description Statistic P-value Anselin (1988) Conditional test for spatial error autocorrelation (H 0 : spatial error autoregressive coefficient equal to zero) 9.66 0.000 Conditional test for spatial lag autocorrelation (H 0 : spatial lag autoregressive coefficient equal to zero) 20.86 0.000 Baltagi et al. (2003) Joint test (H 0 : absence of random effects and spatial autocorrelation) 2843.9 0.000 Marginal test of random effects (H 0 : absence of random effects) 50.32 0.000 Marginal test of spatial autocorrelation (H 0 : absence of spatial autocorrelation) 17.66 0.000 Conditional test of spatial autocorrelation (H 0 : absence of spatial autocorrelation, assuming random effects are non null) 15.42 0.000 Conditional test of random effects (H 0 : absence of random effects, assuming spatial autocorrelation may or may not be equal to 0) 48.82 0.000 Baltagi et al. (2007) Joint test (H 0 : absence of serial or spatial error correlation or random effects) 2976.2 0.000 One-dimensional conditional test (H 0 : absence of spatial error correlation, assuming the existence of both serial correlation and random effects) 22.48 0.000 One-dimensional conditional test (H 0 : absence of serial correlation, assuming the existence of both spatial error correlation and random effects) 436.77 0.000 One-dimensional conditional test (H 0 : absence of random effects, assuming the existence of both serial and spatial error correlation) 117.59 0.000
In this article we consider the effects of the inclusion of spatial dependence in the empirical model measuring small cooperative banks' risk performance.In the presence of cross-sectional dependence, spatial analysis deals with co-movement among geographical units, allowing for the evaluation of spillover effects and improving econometric models. The article makes several contributions to the literature. First, we support the hypothesis that the inclusion of spatial terms improves small bank soundness models. Second, with the Z Score used as a proxy for bank soundness, we indirectly test the impacts of relationship lending on small firms, which is a classic tool adopted by small banks to assess the creditworthiness of small firms. Third, since we control for banks' market power, we expand the literature on the relationship between bank risk and market competitive pressure. Finally, we find empirical evidence that bank size does affect the financial standing of small banks.
This study examines the determinants of cooperative banks' diversification proclivity, with consideration of the spatial dependence effect. The empirical analysis demonstrates that Italian cooperative banks operate as a network with significant spillover effects that should not be ignored. Indeed, local banks compete in the same market segment, and any shift in their diversification strategy has a cascading effect on neighbouring cooperative banks as a result of customer migration. Finally, we observe that an increase in bank market power results in a decline in local bank lending activity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.