The real estate market is highly intermediated, with 90% of buyers and sellers hiring an agent to help them transact a house. However, low barriers to entry and fixed commission rates result in a market where inexperienced intermediaries have a large market share, especially following house price booms. Using rich micro-level data on 10.4 million listings, we show that seller agents’ experience is an important determinant of client outcomes, particularly during real estate busts. Houses listed for sale by inexperienced agents spend more time on the market and have a lower probability of selling. We then study the aggregate implications of the agents’ experience distri- bution on real estate market liquidity by building a theoretical entry and exit model of real estate agents with aggregate shocks. Several policies that raise the barriers to entry for agents are considered: 1) increased entry costs; 2) lower commission rates; and 3) more informed clients. Across each counterfactual, increasing barriers to entry shift the distribution of agents across experience to the right, improves liquidity, and reduces the amplitude of liquidity cycles in the housing market.
The Bartik instrument is formed by interacting local industry shares and national industry growth rates. We show that the Bartik instrument is numerically equivalent to using local industry shares as instruments. Hence, the identifying assumption is best stated in terms of these shares, with the national industry growth rates only affecting instrument relevance. We then show how to decompose the Bartik instrument into the weighted sum of the just-identified instrumental variables estimators, where the weights sum to one, can be negative and are easy to compute. These weights measure how sensitive the parameter estimate is to each instrument. We illustrate our results through three applications: estimating the inverse elasticity of labor supply, estimating local labor market effects of Chinese imports, and using simulated instruments to study the effects of Medicaid expansions.
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.
We document the representation of female economists on the conference programs at the NBER Summer Institute from 2001-2016. Over the period from 2013-2016, 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% since 2001-2004, a persistent gap between finance, macroeconomics and microeconomics subfields remains, with women consisting of 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 indistinguish- able to that of men. Second, we find that the share of female authors is comparable to the share of women amongst all tenure-track professors, but is ten percentage points lower than the share of women among assistant professors. Finally, within conference program, we find that when a woman organizes the program, the share of female authors and discussants is higher.
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 economically significant increases in credit card and mortgage borrowing. Using administrative tax records linked to personal bankruptcy records, we estimate a precise zero effect of flag removal on employment and earnings outcomes. We rationalize these contrasting results by showing that, conditional on basic observables, “hidden” bankruptcy flags are strongly correlated with adverse credit market outcomes but have no predictive power for labor market outcomes.
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 that 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) among 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.
We measure bank supervision using the database of supervisory issues, known as matters requiring attention or immediate attention, raised by Federal Reserve examiners to banking organizations. The volume of supervisory issues increases with banks’ asset size, especially for the largest and most complex banks, and decreases with profitability and the quality of the loan portfolio. Stressed banks are faster at resolving issues, but all else equal, resolving new issues takes longer the more issues a bank faces, which may suggest capacity constraints in addressing multiple supervisory issues. Using computational linguistic methods on the text of the issue description, we define five categorical issue topics. The subset of issues related to capital levels and loan portfolio are the most consequential in terms of regulatory rating downgrades and are directly related to changes in banks’ balance sheet characteristics and profitability. Other issues appear to reflect soft information and are less correlated with bank observables. By categorizing questions asked by analysts at banks’ quarterly earnings calls using the same linguistic approach, we find that market monitors raise issues similar to those of supervisors when the issues are related to hard information (such as loan quality or capital) and public supervisory assessment programs.
Cross-listing on a US exchange does not force foreign firms to follow the exchange’s corporate governance rules. Hand-collected data show that 80% of cross-listed firms opt out of at least one exchange governance rule and those that opt out have a smaller share of independent directors. Cross-listed firms opt out more when coming from countries with weak corporate governance rules, but if these firms are growing and need external financing, they are more likely to comply. For firms in such countries, opting out also lowers firm valuations, decreases the value of cash holdings, and reduces investment sensitivity to market valuations.
This paper estimates the effect of Chapter 13 bankruptcy protection on post-filing financial outcomes using a new dataset linking bankruptcy filings to credit bureau records. Our empirical strategy uses the leniency of randomly-assigned judges as an instrument for Chapter 13 protection. Over the first five post-filing years, we find that Chapter 13 protection decreases an index measuring adverse financial events such as civil judgments and repossessions by 0.316 standard deviations, increases the probability of being a homeowner by 13.2 percentage points, and increases credit scores by 14.9 points. Chapter 13 protection has little impact on open unsecured debt, but decreases the amount of debt in collections by $1,315. We find evidence that both debt forgiveness and protection from debt collectors are important drivers of our results.
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.
We present and discuss preliminary evidence suggesting that credit ratings significantly influenced prices for subprime mortgage-backed securities issued in the period leading up to the recent financial crisis. Ratings are closely correlated with prices even controlling for a rich set of security- and loan-level controls. This incremental variation in ratings has much less predictive power for security defaults, however, based on findings to date from our ongoing research, suggesting prices were excessively sensitive to ratings relative to their informational content.
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.
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 team's offense, the road team's defense, and a home advantage. The mean goals for the road team is the sum of parameters for the road team's offense and for the home team's 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.