Economic Financial Advisory

Economic Financial Advisory

An additional motivation for our work, particularly our empirical efforts, is that in recent research the extreme events of bankruptcy or outright repudiation play an important role in helping quantify the importance of limited commitment for allocations. Specifically, it is the observable rate of personal bankruptcy that provides a main target for the parameterization of the models. Recent work uses such models to analyze the implications of regulations (especially bankruptcy law) on outcomes. For example, recent reforms, such as the Bankruptcy Abuse Prevention and Consumer Protection Act of 2005 (BAPCPA), and the effects of competing social insurance policies on credit use have been studied through versions of what is now a “standard default model” (e.g., Livshits, MacGee, and Tertilt (2007); Chatterjee et al. (2007)). A consistent finding in this work (see also Athreya, Tam, and Young (2009)) is that debt relief makes credit expensive and so sensitive to borrower circumstances that the overall ability to smooth consumption (and hence ex ante welfare) is substantially worsened.3 But, as noted above, absent clear evidence that the baseline models used in these analyses capture well the time path of overall financial distress, there is reason for concern about the sensitivity of that finding.

Close-up of a Calculator and Pen on a Financial Newspaper. Blue-toned.

Before proceeding, we stress that while our analysis suggests the presence of heterogeneity in discounting, such variation is still a stand-in for a variety of other forces–such as any unobserved demands for consumption within the household arising from a variety of sources. The appropriate interpretation of our findings is therefore not that individuals are necessarily widely varying in their personal levels of patience, but rather that a sizable subset of consumers are persistently rendered effectively impatient by potentially the entire host of additional factors not modeled here. Future work that allows for more detail on household-level shocks, intrahousehold bargaining, and other (persistent) within-household resource variation is therefore essential before reaching conclusions that individuals are to be “implicated” in their fates.4 Indeed, it is for this reason that we avoid any normative analysis in this paper.

Financial distress or household financial “fragility” has received significant attention in recent work and has been the topic of interest with the general public (Picchi, 2015). Interest in the ability of the household to shield itself from susceptibility to shocks through the use of financial markets is, of course, longstanding. However, recent work has been aided by the arrival of more detailed data on household balance sheets (Lusardi, Schneider, and Tufano (2011), Lusardi (2011); Jappelli, Pagano, and Di Maggio (2013); Ampudia, van Vlokhoven, and Dawid (2016); Brunetti, Elena, and Costanza (2016)) and aims to gauge borrowing capacity and resilience to sudden, unforeseen expenditures. Specifically, this work primarily focuses on measuring the ability of households to remain current on incurred debts, as well as questioning of how much borrowing the household could feasibly engage in, within a short-term period, for example, 30 days—especially to cover an unforeseen “expense” (as opposed to a change in income, say). A rough summary of this work might be this: A substantial proportion of households in the United States as well as in the European Union are, by various measures, “fragile” or in—or near—financial distress.

Our work is also clearly related to the far larger body of work concerned with the measurement of liquidity constraints across consumers. Substantively, this work tries to measure the proportion of U.S. households that are liquidity constrained and, therefore, not well-positioned to deal with adverse shocks. These include papers of Jappelli and Pagano (1999), Hall and Mishkin (1982), Zeldes (1989), and others. Gross and Souleles (2002) use exogenous variation in credit line extensions to gauge the fraction that increase their debt in response (and hence can be viewed as having been constrained). They find (perhaps unsurprisingly) that those close to their limits increased borrowing by most, but (and more surprisingly) so did even those further away from their credit limit. A consensus might be that roughly 20% are “constrained” either in terms of excess sensitivity to income or in terms of how they respond to survey questions. Compared to this previous literature, our study uncovers the persistence of financial distress. This has important implications for welfare analysis and policy design, as we will show.

Our work contributes to the research programs above in two ways. First, to our knowledge, we are the first to focus on the empirical dynamics of consumer financial distress, which one might broadly define to be those situations in which the household remains susceptible to any deviation of income from its ex ante expectation. In this sense, our measures are informed by the line of work emphasizing household insurance, particularly Kaplan and Violante (2010), and the “insurance coefficient” approach of Blundell, Pistaferri, and Preston (2008). Our emphasis, relative to the preceding work, is on direct measures of financial conditions that have empirical counterparts.

Second, our work extracts a previously unknown implication from the “standard default model.” We have already noted above that benchmark models of unsecured consumer debt and default over the life cycle imply too little persistence of distress. These include models based primarily on those of Livshits, MacGee, and Tertilt (2007) and Athreya (2008). For example, when distress is measured by severe delinquency (i.e., having a debt 120 days or more past due), models without delinquency or discount factor heterogeneity generate almost no persistence of financial distress at short horizons and very little at long horizons. Our quantitative results show that delinquency is useful in correcting the former, while discount factor heterogeneity helps with the latter.

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