Panel Paper: Identifying “Tipping Points” in Consumer Liabilities Using High Frequency Data

Thursday, November 2, 2017
Dusable (Hyatt Regency Chicago)

*Names in bold indicate Presenter

Lowell R. Ricketts, Carlos Garriga and Don E. Schlagenhauf, St. Louis Federal Reserve

In a period where the Federal Reserve Bank is debating normalizing monetary policy by raising interest rates, the balance sheet of the average American household is vulnerable in light of recent behavior in credit markets. In the second quarter of 2016, U.S. consumers increased their credit card balances by a record-setting $34.4 billion. This has continued the trend of growing outstanding credit card balances that started in 2011 and is likely to achieve the $1 trillion mark by the end of 2016. In light of this trend, the obvious question is whether this level of borrowing could expose the U.S. economy to another major deleveraging period if growth slows and the labor market deteriorates. This could push households past the tipping point towards delinquencies, defaults, and adjustments of the household balance sheet. Identifying a priori the level of these tipping points where an individual’s debt is no longer sustainable, given their current income levels and cost of borrowing, could inform monitoring programs to avoid a debt default outcome. By anticipating a household debt crisis, one can mitigate or eliminate some of the consequences that helped cause the Great Recession.

In this paper we define a new notion of a household "tipping point" using principles of economics and a novel high-frequency consumer-level dataset. We attempt to create a metric which identifies when a household reaches the point where their debt holdings become a drag on their activity rather than an engine for growth. We begin by posing a question: how does economics help in the identification of such a tipping-point? The answer is a tipping point occurs when an individual can no longer satisfy their individual budget constraint. This is the point where an individual must turn to bankruptcy or a foreclosure to satisfy their budget constraint. The ratio of the total debt payment to after-tax income has proven to be an informative measure to specify this tipping. However, we show that this metric should be conditioned on such things as available wealth, the liquidity of the assets, ability to raise additional debt, and the structure of this debt. The depth of our data allows us to assess how this ratio behaves based on age groups and income quartiles. We also assessed the ability of the debt payment to income ratio to predict bankruptcy and foreclosure events. A preliminary examination of this measure based on the dynamics of the monthly debt payment to income ratio suggests that this metric has some predictive content when compared to alternative metrics based on the dynamics of the FICO score.