The long-run benefits of bank capital strength are undisputed, but the short-term impacts on lending of stricter capital requirements are less obvious. This column shows that after Basel III was enforced in Italy in 2014, low-capitalised banks slowed down credit to firms and raised interest rates, compared to capital-strong lenders.
Regulators and supervisors impose requirements on
banks to guide their behaviour. Still, the effect of higher capital
requirements on lending is debated, both theoretically and empirically.
The issue is policy relevant, as shown by the attention that
international bodies, including the Financial Stability Board, have
devoted to this topic (Financial Stability Board 2018, 2019). Acharya et
al. (2021) also suggest that regulators’ aggressive loosening can end
up directing credit to suboptimal targets.
In the long run, the benefits of capital strength are hardly
contended: large capital buffers enhance bank resilience, including
against black swans (i.e. unforeseeable events) (Mendicino et al. 2021),
and this should secure more stable lending flows.
By contrast, in the
short run, some scholars suggest that stricter risk-based capital
requirements increase banks’ funding costs; this makes lending less
attractive, at least during a transition phase (e.g. Aiyar et al. 2014,
Aiyar et al. 2014, Acharya et al. 2018, Gropp et al. 2019). Others argue
that an increase in capital requirements might, under some conditions,
reduce average banks’ funding costs and thus create the conditions to
increase bank lending (Begenau 2020, Admati et al. 2013, Bassett and
Berrospide 2018).
Finally, to complete the options on the shelf, recent
papers open the door to a half-way conclusion: capital requirements
depress lending up to a certain bindingness, but this impact becomes
comparatively milder for banks that get particularly constrained; it
could even be reverted for some lenders, for which stricter rules would
eventually produce an increase in lending (Bahaj and Malherbe 2020).
To shed light on the issue, one can exploit quasi-natural
experiments, coupled with granular data on firms, banks, and their
relationships (Galardo and Vacca 2022). To do this, we need a few
ingredients. First, an exogenous shock should break the timeline in a
period before and a period after the shock. Second, it should be
possible to separate the sample of banks into two subsamples, in order
to compare their after-shock reactions: more affected banks versus less
or unaffected banks...
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