Paul Goldsmith-Pinkham

MBS Ratings and the Mortgage Credit Boom

We study credit ratings on subprime and Alt-A mortgage-backed securities (MBS) deals issued between 2001 and 2007, the period leading up to the subprime crisis. The fraction of highly-rated securities in each deal is decreasing in mortgage credit risk (measured either ex-ante or ex-post), suggesting ratings contain useful information for investors. However, we also find evidence of significant time-variation in risk-adjusted credit ratings, including a progressive decline in standards around the MBS market peak between the start of 2005 and mid-2007. Conditional on initial ratings, we observe underperformance (high mortgage defaults and losses, and large rating downgrades) amongst deals with observably higher-risk mortgages based on a simple ex-ante model, and deals with a high fraction of opaque low-documentation loans. These findings hold over the entire sample period, not just for deal cohorts most affected by the crisis.

Social Networks and the Identification of Peer Effects

There is a large and growing literature on peer effects in economics. In the current article, we focus on a Manski-type linear-in-means model that has proved to be popular in empirical work. We critically examine some aspects of the statistical model that may be restrictive in empirical analyses. Specifically, we focus on three aspects. First, we examine the endogeneity of the network or peer groups. Second, we investigate simultaneously alternative definitions of links and the possibility of peer effects arising through multiple networks. Third, we highlight the representation of the traditional linear-in-means model as an autoregressive model, and contrast it with an alternative moving-average model, where the correlation between unconnected individuals who are indirectly connected is limited. Using data on friendship networks from the Add Health dataset, we illustrate the empirical relevance of these ideas.

Liquidity, Bank Runs and Bailouts: Spillover Effects during the Northern Rock Episode

In September 2007, Northern Rock—the fifth largest mortgage lender in the United Kingdom—experienced an old-fashioned bank run, the first bank run in the U.K. since the collapse of City of Glasgow Bank in 1878. The run had been contained by the government’s announcement that it would guarantee all deposits in Northern Rock. This paper analyzes spillover effects during the Northern Rock episode and shows that both the bank run and the subsequent bailout announcement had significant effects on the rest of the U.K. banking system, as measured by abnormal returns on the stock prices of banks. The paper also shows that the effects were a rational response by investors to market news about the liability side of banks’ balance sheets. In particular, banks that rely on funding from wholesale markets were significantly affected, a result consistent with the drying up of liquidity in wholesale markets and the record-high levels of the London Interbank Offered Rate (LIBOR) during the crisis.

Composite Poisson Models for Goal Scoring

Goal scoring in sports such as hockey and soccer is often modeled as a Poisson process. We work with a Poisson model where the mean goals scored by the home team is the sum of parameters for the home teams offense, the road teams defense, and a home advantage. The mean goals for the road team is the sum of parameters for the road teams offense and for the home teams defense. The best teams have a large offensive parameter value and a small defensive parameter value. A level-2 model connects the offensive and defensive parameters for the k teams. Parameter inference is made by imagining that goals can be classified as being strictly due to offense, to (lack of) defense, or to home-field advantage. Though not a realistic description, such a breakdown is consistent with our model assumptions and the literature, and we can work out the conditional distributions and generate random partitions to facilitate inference about the team parameters. We use the conditional Binomial distribution, given the Poisson totals and the current parameter values, to partition each observed goal total at each iteration in an MCMC algorithm.

Bad Credit, No Problem? Credit and Labor Market Consequences of Bad Credit Reports

Credit reports are used in nearly all consumer lending decisions and, increasingly, in hiring decisions in the labor market, but the impact of a bad credit report is largely unknown. We study the effects of credit reports on financial and labor market outcomes using a difference-in-differences research design that compares changes in outcomes over time for Chapter 13 filers, whose personal bankruptcy flags are removed from credit reports after 7 years, to changes for Chapter 7 filers, whose personal bankruptcy flags are removed from credit reports after 10 years. Using credit bureau data, we show that the removal of a Chapter 13 bankruptcy flag leads to a large increase in credit scores, and an economically significant increase in credit card balances and mortgage borrowing. We study labor market effects using administrative tax records linked to personal bankruptcy records. In sharp contrast to the credit market effects, we estimate a precise zero effect of flag removal on employment and earnings outcomes. We conclude that credit reports are important for credit market outcomes, where they are the primary source of information used to screen applicants, but are of limited consequence for labor market outcomes, where employers rely on a much broader set of screening mechanisms.

Gender Representation in Economics Across Topics and Time: Evidence from the NBER Summer Institute

We document the representation of female economists on the conference programs at the NBER Summer Institute from 2001 to 2016. Over the 2013-16 period, women made up 20.6 percent of all authors on scheduled papers. However, there was large dispersion across programs, with the share of female authors ranging from 7.3 percent to 47.7 percent. While the average share of women rose slightly—from 18.5 percent in 2001-04—a persistent gap between the finance, macroeconomics, and microeconomics subfields remains, with women representing 14.4 percent of authors in finance, 16.3 percent of authors in macroeconomics, and 25.9 percent of authors in microeconomics. We examine three channels potentially affecting female representation. First, using anonymized data on submissions, we show that the rate of paper acceptance for women is statistically indistinguishable from that of men. Second, we find that the share of female authors is comparable to the share of women among all tenure-track professors, but is 10 percentage points lower than the share of women among assistant professors. Finally, within conference programs, we find that when a woman organizes the program, the share of female authors and discussants is higher.

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Recent innovations in statistical technology, including in evaluating creditworthiness, have sparked concerns about impacts on the fairness of outcomes across categories such as race and gender. We build a simple equilibrium model of credit provision in which to evaluate such impacts. We find that as statistical technology changes, the effects on disparity depend on a combination of the changes in the functional form used to evaluate creditworthiness using underlying borrower characteristics and the cross-category distribution of these characteristics. Employing detailed data on US mortgages and applications, we predict default using a number of popular machine learning techniques, and embed these techniques in our equilibrium model to analyze both extensive margin (exclusion) and intensive margin (rates) impacts on disparity. We propose a basic measure of cross-category disparity, and find that the machine learning models perform worse on this measure than logit models, especially on the intensive margin. We discuss the implications of our findings for mortgage policy.