Björn Richter

Job Market Paper

The Profit-Credit Cycle

(with Kaspar Zimmermann)

Bank profitability leads the credit cycle. An increase in return on equity of the banking sector predicts rising credit-to-GDP ratios over the medium term in a panel of 17 advanced economies spanning the years 1870 to 2015. The pattern also holds in bank-level data and for the global financial cycle. The relationship is only partly explained by a balance sheet channel where retained profits relax net worth constraints. Turning to behavioral mechanisms, the results are consistent with a channel that links past profitability through expectation formation to credit expansions. In line with this channel, there is a strong association between past profitability, expectations about future profitability and subsequent credit growth in recent survey data from the US. Looking at the further course of the financial cycle, we find that the run-up to crisis episodes is characterized by high return on equity, high bank profits and dividends relative to GDP, and low provisioning for loan losses. The transition from boom to bust is preceeded by decreasing profitability. Finally, losses realized in a crisis predict the post-crisis recovery of lending.

Working Papers

(with Òscar Jordà, Moritz Schularick and Alan M. Taylor)

NBER Working Paper 23287, CEPR Discussion Paper 11934, FRBSF Working Paper 2017-06

R & R at the Review of Economic Studies

This paper undertakes the first comprehensive analysis of the long-run evolution of the capital structure of modern banking. Based on newly constructed data for banks’ balance sheets in 17 countries since 1870, we document the changing funding mix of banks and study the nexus between capital structure and financial instability over the long run. We find little evidence that higher capital ratios restrain excessive risk-taking and affect the probability of systemic banking crises ex ante. Although capital does little to reduce the probability of crises, it plays an important role in crisis intensity. Economies with better capitalized banking systems have milder crisis recessions and recover faster.

(with Moritz Schularick and Ilhyock Shim)

NBER Working Paper 24989, BIS Working Paper 740, HKIMR Working Paper 18/2018

R & R at the Journal of International Economics

Central banks increasingly rely on macroprudential measures to manage the financial cycle. However, the effects of such policies on the core objectives of monetary policy to stabilise output and inflation are largely unknown. In this paper we quantify the effects of changes in maximum loan-to-value (LTV) ratios on output and inflation. We rely on a narrative identification approach based on detailed reading of policy-makers' objectives when implementing the measures. We find that over a four year horizon, a 10 percentage point decrease in the maximum LTV ratio leads to a 1.1% reduction in output. As a rule of thumb, the impact of a 10 percentage point LTV tightening can be viewed as roughly comparable to that of a 25 basis point increase in the policy rate. However, the effects are imprecisely estimated and the effect is only present in emerging market economies. We also find that tightening LTV limits has larger economic effects than loosening them. At the same time, we show that changes in maximum LTV ratios have substantial effects on credit and house price growth. Using inverse propensity weights to rerandomise LTV actions, we show that these effects are likely causal.

(with Moritz Schularick and Paul Wachtel)

CEPR Discussion Paper 12188

This paper shows that policy-makers can distinguish between good and bad credit booms with high accuracy and they can do so in real time. Evidence from 17 countries over nearly 150 years of modern financial history shows that credit booms that are accompanied by house price booms and a rising loan-to-deposit-ratio are much more likely to end in a systemic banking crisis. We evaluate the predictive accuracy for different classification models and show that the characteristics of the credit boom contain valuable information for sorting the data into good and bad booms. Importantly, we demonstrate that policy-makers have the ability to spot dangerous credit booms on the basis of data available in real time. We also show that these results are robust across alternative specifications and time-periods.