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Germany’s economy is so bad even sausage factories are closingIWHThe Economist, January 15, 2026
Syndicated loan data provided by DealScan is an essential input in banking research to answer urging questions on bank lending, e.g., in the presence of financial or geopolitical shocks or climate change. However, many data options raise the question of how to choose the estimation sample. We employ a standard regression framework analyzing bank lending during the financial crisis of 2007/08 to study how conventional but varying usages of DealScan affect the estimates. The key finding is that the direction of coefficients remains relatively robust. However, statistical significance depends on the data and sampling choice, and we provide guidelines for applied research.
We examine how banks manage carbon transition risk by selling loans given to polluting borrowers to less regulated shadow banks in securitization markets. Exploiting the election of Donald Trump as an exogenous shock that reduces carbon transition risk, we find that banks engage in regulatory arbitrage and use brown loan securitization to manage their exposure to carbon transition risk. Banks are more likely to securitize brown loans when carbon transition risk is high but keep these loans on their balance sheets when the risk is reduced. In addition, securitization enables banks to offer lower interest rates to polluting borrowers but does not affect the supply of green loans. Our findings are more pronounced among banks with low levels of capitalization, domestic banks, and banks that do not display green lending preferences. We discuss how securitization can weaken the effectiveness of bank climate policies.
Banks play a crucial role in the transition to a low-carbon economy, but they also expose themselves to climate transition risk. This column shows that banks use corporate loan securitisation to shift climate transition risk to less-regulated shadow banking entities. This behaviour affects carbon premia in loan contracts. When banks can use securitisation to manage transition risk, their climate policies that target only activities reflected in their books may not be as effective as bank regulators hope for.
Banks play a special role in the financial system. According to classical banking theory, they help reduce informational asymmetries and serve as liquidity providers. Banks can, at least partially, lower transaction costs that result from information frictions between investors and firms and thereby alleviate firms’ funding constraints (Diamond, 1984). Moreover, banks create liquidity on their balance sheets by financing comparably illiquid assets with relatively liquid liabilities (Diamond and Dybvig, 1983). Integrating credit and liquidity provision functions, banks have been the object of numerous studies on financial intermediation. A particular focus in recent years has been on banks’ behavior as well as on the con- sequences of their actions for the real economy when hit by adverse shocks. Following the global financial crisis, financial shocks that originate from within the financial sec- tor have received wide attention (Cingano et al., 2016; Chodorow-Reich, 2014; Khwaja and Mian, 2008; Paravisini, 2008; Paravisini et al., 2015; Schnabl, 2012). However, banks are also subject to numerous non-financial shocks, which are the focus of this thesis.
We examine how banks manage carbon transition risk by selling loans given to polluting borrowers to less regulated shadow banks in securitization markets. Exploiting the election of Donald Trump as an exogenous shock that reduces carbon risk, we find that banks’ securitization decisions are sensitive to borrowers’ carbon footprints. Banks are more likely to securitize brown loans when carbon risk is high but swiftly change to keep these loans on their balance sheets when carbon risk is reduced after Trump’s election. Importantly, securitization enables banks to offer lower interest rates to polluting borrowers but does not affect the supply of green loans. Our findings are more pronounced among domestic banks and banks that do not display green lending preferences. We discuss how securitization can weaken the effectiveness of bank climate policies through reducing banks’ incentives to price carbon risk.
Syndicated loan data provided by DealScan is an essential input in banking research. This data is rich enough to answer urging questions on bank lending, e.g., in the presence of financial shocks or climate change. However, many data options raise the question of how to choose the estimation sample. We employ a standard regression framework analyzing bank lending during the financial crisis of 2007/08 to study how conventional but varying usages of DealScan affect the estimates. The key finding is that the direction of coefficients remains relatively robust. However, statistical significance depends on the data and sampling choice and we provide guidelines for applied research.
We identify the effect of climate change-related regulatory risks on credit real-location. Our evidence suggests that effects depend borrower's region. Following an increase in salience of regulatory risks, banks reallocate credit to US firms that could be negatively impacted by regulatory interventions. Conversely, in Europe, banks lend more to firms that could benefit from environmental regulation. The effect is moderated by banks' own loan portfolio composition. Banks with a portfolio tilted towards firms that could be negatively a affected by environmental policies increasingly support these firms. Overall, our results indicate that financial implications of regulation associated with climate change appear to be the main drivers of banks' behavior.
This paper provides evidence that banks cut lending to US borrowers as a consequence of a trade shock. This adverse reaction is stronger for banks with higher ex-ante lending to US industries hit by the trade shock. Importantly, I document large heterogeneity in banks‘ reaction depending on their sectoral specialisation. Banks shield industries in which they are specialised in and at the same time reduce the availability of credit to industries they are not specialised in. The latter is driven by low-capital banks and lending to firms that are themselves hit by the trade shock. Banks‘ adjustments have adverse real effects.
