This paper presents an analysis of mortgage delinquency between 2004 and 2008 using a loan-level data set from a major national mortgage bank. Our analysis highlights two problems underlying the mortgage crisis: a reliance on mortgage brokers who tend to originate lower-quality loans and a prevalence of low-documentation loans—known in the industry as “liar's loans”—that result in borrower information falsification. While over three-quarters of the difference in delinquency rates between bank and broker channels can be attributed to observable loan and borrower characteristics, the delinquency difference between full- and low-documentation mortgages is due to unobservable heterogeneity, about half of it potentially due to income falsification.

This paper examines the properties of instrumental variables (IV) applied to models with essential heterogeneity, that is, models where responses to interventions are heterogeneous and agents adopt treatments (participate in programs) with at least partial knowledge of their idiosyncratic response. We analyze two-outcome and multiple-outcome models, including ordered and unordered choice models. We allow for transition-specific and general instruments. We generalize previous analyses by developing weights for treatment effects for general instruments. We develop a simple test for the presence of essential heterogeneity. We note the asymmetry of the model of essential heterogeneity: outcomes of choices are heterogeneous in a general way; choices are not. When both choices and outcomes are permitted to be symmetrically heterogeneous, the method of IV breaks down for estimating treatment parameters.

This note shows that the local average treatment effect (LATE) assumptions of Angrist and Imbens are weaker than imposing an ordered, discrete-choice selection model if one imposes the standard assumption of constant thresholds in the latter. However, the note extends results of Vytlacil to show that the LATE assumptions are equivalent to an ordered, discrete-choice selection model if one allows for random thresholds in the latter. A nonparametric representation result for ordered, discrete-choice models is produced as a by-product of these results.

This note derives simply computed closed-form expressions for the average treatment effect, the effect of treatment on the treated, the local average treatment effect, and the marginal treatment effect in a latent-variable framework for both normal and nonnormal models. Asymptotic standard errors for versions of these parameters that average over observed characteristics are also obtained. The performances of the derived estimators are also evaluated in Monte Carlo experiments under correct specification and misspecification.

This paper considers two problems that arise in determining the role of cognitive ability in explaining the level of and change in the rate of return to schooling. The first problem is that ability and schooling are so strongly dependent that it is not possible, over a wide range of variation in schooling and ability, to independently vary these two variables and estimate their separate impacts. The second problem is that the structure of panel data makes it difficult to identify main age and time effects or to isolate crucial education-ability-time interactions which are needed to assess the role of ability in explaining the rise in the return to education.

One current educational reform seeks to reward the “value added” by teachers and schools based on the average change in pupil test scores over time. In this paper, we outline the conditions under which the average change in scores is sufficient to rank schools in terms of value added. A key condition is that socioeconomic outcomes be a linear function of test scores. Absent this condition, one can still derive the optimal value-added policy if one knows the relationship between test scores and socioeconomic outcomes, and the distribution of test scores both before and after the intervention. Using the National Longitudinal Survey of Youth, we find a nonlinear relationship between test scores and one important outcome: log wages. We find no consistent pattern in the curvature of log wage returns to test scores (whether percentiles, scaled, or raw scores). This implies that, used alone, the average gain in test scores is an inadequate measure of school performance and current value-added methodology may misdirect school resources.