We use a new methodology to assess mortgage pricing discrimination faced by minority borrowers. When getting a mortgage, borrowers can choose to avoid closing costs and pay a high interest rate or contribute to closing costs to get a lower rate. While data on both dimensions of mortgage pricing are by now often available, intuitively attractive metrics of lender pricing discrimination currently used by the literature can lead to false positives (seeing discrimination where none exists), false negatives (not seeing discrimination where it does exist), and sometimes contradictory results depending on which methodology is used. This "menu problem" stems from potential heterogeneity in preferences across racial groups and is broadly relevant whenever agents make multi-dimensional trade-offs, but is generally underappreciated. To address this problem, we define (1) a new, robust test statistic for equality in menus and (2) a difference in menus (DIM) metric for assessing whether one group of consumers would prefer to switch to another group's menus, both based on pairwise dominance relationships in the data. We show how these metrics can be computed using methods from optimal transport and devise a new procedure for hypothesis testing in this class of problems based on directional differentiation. We implement our metrics on a new data set matching 2018--2019 Home Mortgage Disclosure Act (HMDA) data to Optimal Blue rate locks. We find robust evidence that Black and Hispanic borrowers were offered menus that, in terms of rates and points by lenders, were worse than the menus offered to non-Hispanic white borrowers for conforming mortgages. Furthermore, the differences we detect are not explained by loan originator compensation and were particularly concentrated among more creditworthy borrowers.
Kristopher Gerardi, Paul Willen and David Hao Zhang
Black and Hispanic homeowners pay significantly higher mortgage interest rates than Non-Hispanic White homeowners. We decompose the racial rate gap into (1) higher rates charged to Black borrowers at origination and (2) racial disparities in the timing of originations. We show that the latter explains most of the gap and is driven by racial differences in mortgage prepayment behavior. Non-Hispanic White borrowers are more mobile and are more likely to exploit falling interest rates by refinancing their mortgages. Observable borrower and mortgage characteristics like income, credit scores, and loan-to-value ratios explain most of the differences in prepayment behavior by race. By driving down mortgage rates, monetary expansions such as the first quantitative easing program (QE1) exacerbates the racial rate gap by disproportionately benefiting Non-Hispanic White borrowers. Alternative mortgage contract designs to the standard fixed-rate mortgage with rate originated above mortgage-backed securities yields may have desirable distributional implications across racial groups.
We examine whether law school alumni relationships between the lawyers and judges affect case outcomes. We show that in the context of medical malpractice lawsuits in Florida, the plaintiff lawyer sharing the same law school as the judge increases the chances of recovery by 2%. Furthermore, the effect is confined to younger lawyers who see a 4% increase in the likelihood of recovery from having been to the same law school as the judge, and is absent in older lawyers. We interpret our results as evidence that lawyers gain school-specific human capital from their law schools which helps in their interactions with judges that graduated from the same school, and that this school-specific human capital become less important further on in the lawyers' careers.
I develop a new method for estimating counterfactuals in dynamic discrete choice models, a widely used set of models in economics, without requiring a distributional assumption on utility shocks. Applying my method to the canonical Rust (1987) setting, I find that the typical logit assumption on utility shocks can lead the researcher to conclude that the agent's counterfactual choice probabilities are much more sensitive to policy changes than what a semi-parametric model would suggest. Therefore, my method may be useful to applied researchers in generating policy counterfactuals that are robust to such distributional assumptions.
We analyze how CEO stock options compensation can be used as a commitment device in oligopolistic competition. We develop a two-stage model where shareholders choose managerial compensation to commit their managers to being aggressive in equilibrium. Our results may explain why some shareholders appear to incentivize ‘excessive’ risk taking through stock options compensation. We analyze how our results are impacted by product quality, marginal cost, product differentiation, and industry concentration. As motivation for our research, we show that there exists positive empirical correlation between industry concentration and options compensation vega within a sample of firms, as suggested by our model.
We analyze the Markov Perfect Equilibria of an infinite-horizon overlapping generations model with consumer lock-in to compare the performance of history-based and uniform pricing in growing and declining markets. Under history-based pricing, firms charge higher prices to locked-in customers and lower prices to new customers. We show that a high exit rate of consumers (sufficiently declining market) constitutes a sufficient condition for history-based pricing to generate higher average prices than uniform pricing, thereby harming consumer welfare. In contrast, a high consumer entry rate (sufficiently growing market) ensures that history-based pricing intensifies competition compared with uniform pricing.
Households still pay rent primarily with paper methods, even though electronic methods are featured more prominently among high-income, high-education, and high-rent households. These patterns may be explained either by the lack of landlord acceptance of electronic methods or by the unwillingness of tenants to pay using these methods.
The dominant methods for paying rent are cash (22 percent), check (42 percent), and money order (16 percent). Electronic methods are still rarely used, at 8 percent for bank account number payment and 7 percent for online banking bill payment, and less than 2 percent for debit and credit cards.
Compared with other large bill payments of more than $200, rental payments are much more likely to be made with paper-based methods than with electronic methods, despite the recent attempts by start-ups to make it easier for landlords to accept electronic payments. They are also much less likely to be automatic.
Check and electronic methods are more frequently used for higher-value transactions and by those with higher income and education.
Zhang, David Hao. "How Do People Pay Rent?" Federal Reserve Bank of Boston, Research Data Report, No. 16-2, Federal Reserve Bank of Boston, Boston, MA, 2016.
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