In this survey, we review the quantitative macroeconomic literature analyzing consumer debt and default. We start by providing an overview of consumer bankruptcy law in the US and document the relevant institutional changes over time. We proceed with a comprehensive empirical section, describing...

We examine the ability of policymakers to stimulate household borrowing and spending during the Great Recession by reducing banks' cost of funds. Using panel data on 8.5 million U.S. credit card accounts and 743 credit limit regression discontinuities, we estimate the marginal propensity to...

In the long run, we are all dead. Nonetheless, when studying the short-run dynamics of economic models, it is crucial to consider boundary conditions that govern long-run, forward-looking behavior, such as transversality conditions. We demonstrate that machine learning (ML) can automatically...

We examine the profitability of personalized pricing policies that are derived using different specifications of demand in a typical retail setting with consumer-level panel data. We generate pricing policies from a variety of models, including Bayesian hierarchical choice models, regularized...

We postulate a nonlinear DSGE model with a financial sector and heterogeneous households. In our model, the interaction between the supply of bonds by the financial sector and the precautionary demand for bonds by households produces significant endogenous aggregate risk. This risk induces an...

We study the effect of spatial inequality on economic activity. Given that the relationship is highly simultaneous in nature, we use exogenous variation in geographic features to construct an instrument for spatial inequality, which is independent from any man-made factors. Inequality measures...

Human decision-making differs due to variation in both incentives and available information. This generates substantial challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on...

This paper sheds new light on the role of communication for cartel formation. Using machine learning to evaluate free-form chat communication among firms in a laboratory experiment, we identify typical communication patterns for both explicit cartel formation and indirect attempts to collude...

This paper addresses the steep learning curve in Machine Learning faced by non-computer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage ”once you understand OLS, you can work...

In framework of Rochet and Tirole (2011), I allow for partial merchant internalization and study how MIT threshold is related to levels of inter-change fee that maximize various components of social welfare. I find that cost absorption on the side of issuers and merchant heterogeneity each bias...