Publication - Supply- and demand-side factors in global banking - by Mary Amiti, Patrick McGuire and David E Weinstein
Introduction
The question of what drives international banking flows is of central importance for policymakers and economists trying to understand the international transmission of banking crises. In answering this question, researchers have identified a large number of plausible demand and supply factors.
However, these answers remain incomplete in the sense that regression evidence always involves a residual that typically accounts for the vast majority of the variance. For example, how much weight should policy makers place on a finding that, say, interest rate differentials across countries are correlated with capital flows when the empirical model used to produce this result is able to explain only a tiny share of the aggregate movements in these flows? Such problems become even more severe in periods of financial stress, when statistical relationships that seem strong in normal times break down. This fact makes it very difficult to match theory to data.
This paper solves this problem by providing a methodology, based on Amiti and Weinstein (forthcoming), that generates an exact decomposition of the growth in global bank flows into three components. First, there is a “common” shock that affects all banking relationships and captures common forces. Second, there is a bankingsystem, or “supply”, shock that captures how idiosyncratic shifts in a banking system’s credit supply behaviour. Third, there is a borrower country, or “demand”, shock that captures idiosyncratic shifts in a destination’s borrowing behaviour.2 This method is appropriate for all models of global banking behaviour that specify credit growth from a bank to a borrower as a linear combination of a source banking system shock, a destination shock and possibly a source-destination interaction term.
We apply this methodology to the BIS Consolidated Banking Statistics (CBS), which track the consolidated foreign claims (ie loans, holdings of debt securities and other financial claims) of banks headquartered in 31 reporting countries on counterparties in more than 200 countries. Global bank claims are highly concentrated in only a handful of banking systems and a somewhat larger set of counterparty (borrower) countries. This concentration impacts the data in two important ways. First, the vast majority of bilateral credit flows are small compared to the aggregate flow of the typical banking system, which makes it problematic to use standard regression techniques to infer aggregate behaviour. Second, the small number of large banking systems means that idiosyncratic credit supply shocks in one banking system can have aggregate implications.
Most important from the perspective of policy makers today, the estimates offer an up-to-date decomposition of the growth in foreign claims. This is particularly important for monitoring bank credit to borrowers in emerging economies. For example, while global claims growth slowed in 2015 and 2016, the contraction in credit to emerging Asia and emerging Europe – to China and Russia in particular – was severe. Negative supply shocks, which in part reflect a contraction in European banks’ global balance sheets, contributed somewhat. But the estimates presented here suggest that these countries are unique, with borrower country characteristics (ie demand shocks) accounting for virtually all of the decline.
In order to produce these estimates, we first adjust the data in two ways. The publicly available CBS contain numerous breaks in series, due to bank mergers or changes in reporting methodology, that produce wild swings in the series that swamp the true variation. A second problem arises because banks’ bilateral positions are typically denominated in many currencies but are expressed in US dollars when reported to the BIS. Exchange rate movements change the relative value of the underlying currencies and thus induce changes in outstanding stocks that do not signify the actual extension or withdrawal of credit. We correct the data to render consistently measured supply and demand shocks to the growth rates of bank credit.
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