« Back to Results

Credit Booms, Aggregate Demand, and Financial Crises

Paper Session

Sunday, Jan. 7, 2018 1:00 PM - 3:00 PM

Marriott Philadelphia Downtown, Grand Ballroom Salon B
Hosted By: American Economic Association
  • Chair: Luc Laeven, European Central Bank

Predictable Reversals: Credit Booms, Debt Service and Prolonged Slumps

Mathias Drehmann
,
Bank for International Settlements
Mikael Juselius
,
Bank of Finland
Anton Korinek
,
Johns Hopkins University

Abstract

Traditional economic models have had difficulty explaining why booms in household credit have predictable negative after-effects that may last for up to a decade. This paper explains why: when taking on new debt, borrowers commit to a pre-specified path of future debt service. We first show theoretically that there are two key properties of the data that give rise to a pronounced lag between credit booms and debt service: (i) that new borrowing is strongly auto-correlated and (ii) that debt contracts are long term. Then we analyze a panel of household debt in 17 countries and find that on average, the lag between peaks in credit booms and peaks in debt service is four years. Furthermore, we show that this delayed increase in debt service following an impulse to new borrowing explains why credit booms are associated with lower future output growth and higher probability of crisis for up to seven years. Our results thus provide a systematic transmission mechanism from credit expansions to prolonged adverse real effects, highlighting a significant challenge for macroeconomic management.

How Do Credit Supply Shocks Affect the Real Economy? Evidence from the United States in the 1980s

Atif Mian
,
Princeton University
Amir Sufi
,
University of Chicago
Emil Verner
,
Princeton University

Abstract

We study the business cycle consequences of credit supply expansion in the U.S. The 1980’s credit
boom resulted in stronger credit expansion in more deregulated states, and these states experience
a more amplified business cycle. A new test shows that amplification is primarily driven by the
local demand rather than the production capacity channel. States with greater exposure to credit
expansion experience larger increases in household debt, the relative price of non-tradable goods,
nominal wages, and non-tradable employment. Yet there is no change in tradable sector employment.
Eventually states with greater exposure to credit expansion experience a significantly deeper
recession.

Identifying Banking Crises: A Bank Equity Based Approach

Matthew Baron
,
Cornell University
Emil Verner
,
Princeton University
Wei Xiong
,
Princeton University

Abstract

We identify historical banking crises in 47 countries over the period 1800 - 2016 using new historical data on bank equity returns. We argue bank equity crashes provide an objective, quantitative, and theoretically‐motivated measure of banking crises, which can help refine existing historical approaches. We show that bank equity crashes often pick up an impending crisis first, before credit and non‐financial equity measures. We validate our measure by showing that bank equity crashes line up well with other indicators of banking crises (e.g., panics, bank failures, government intervention) and predict the severity of crises. Our approach also uncovers some “new” banking crises not identified by previous historical approaches but which are backed up by the historical narrative. Bank equity returns provide additional forecasting power of macroeconomic outcomes relative to traditional predictors.

When to Lean Against the Wind

Bjorn Richter
,
University of Bonn
Moritz Schularick
,
University of Bonn
Paul Wachtel
,
New York University

Abstract

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 financial 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 policymakers 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.
Discussant(s)
Giovanni Dell'Ariccia
,
International Monetary Fund
Guido Lorenzoni
,
Northwestern University
Mikael Juselius
,
Bank of Finland
Olivier Jeanne
,
Johns Hopkins University
JEL Classifications
  • E3 - Prices, Business Fluctuations, and Cycles
  • G2 - Financial Institutions and Services