In this article we characterize and estimate the process for short-term interest rates using federal funds interest rate data. We presume that we are observing a discrete-time sample of a stationary scalar diffusion. We concentrate on a class of models in which the local volatility elasticity is constant and the drift has a flexible specification. To accommodate missing observations and to break the link between "economic time" and calendar time, we model the sampling scheme as an increasing process that is not directly observed. We propose and implement two new methods for estimation. We find evidence for a volatility elasticity between one and one-half and two. When interest rates are high, local mean reversion is small and the mechanism for inducing stationarity is the increased volatility of the diffusion process.

Jurisdictions across the United States have adopted “ban the box” (BTB) policies preventing employers from conducting criminal background checks until late in the job application process. Their primary goal is to increase employment for those with criminal records. If individuals with criminal records view these policies as improving their labor market opportunities, they might move to BTB-adopting places in search of employment. In this paper, we consider BTB's effects on the demographic composition of labor markets and the likelihood that residents report recently moving from other labor markets. We find no evidence that BTB affects migration.

Use It or Lose It: Teaching Literacy in the Economics Principles Course by W. Lee Hansen, Michael K. Salemi and John J. Siegfried. Published in volume 92, issue 2, pages 463-472 of American Economic Review, May 2002

The ability of quantile regression models to characterize the heterogeneous impact of variables on different points of an outcome distribution makes them appealing in many economic applications. However, in observational studies, the variables of interest (e.g., education, prices) are often endogenous, making conventional quantile regression inconsistent and hence inappropriate for recovering the causal effects of these variables on the quantiles of economic outcomes. In order to address this problem, we develop a model of quantile treatment effects (QTE) in the presence of endogeneity and obtain conditions for identification of the QTE without functional form assumptions. The principal feature of the model is the imposition of conditions that restrict the evolution of ranks across treatment states. This feature allows us to overcome the endogeneity problem and recover the true QTE through the use of instrumental variables. The proposed model can also be equivalently viewed as a structural simultaneous equation model with nonadditive errors, where QTE can be interpreted as the structural quantile effects (SQE).

Some projects take time to build or are slow to yield cash flows. This may impact the dynamics of investment and liquidity management, although few studies test their financial implications. We exploit the peculiar advantages of copper mines as a laboratory to identify cash-flow sensitivities. In this context, investment decisions depend on the expectations of the long run price of the commodity, while the spread between the spot price and this long run expectations shifts current cash-flows. For this study we compiled a sample of copper firms between 2002 and 2012. We do not find significant effects of cash flow on current capital expenditures, but we do observe a systematic cash flow sensitivity of cash holdings, meaning that some of these transitory earnings are retained as liquidity. This cash stockpiling is stronger among financially constrained firms. In a context of time-to-build, our findings support financial theories emphasizing the salience of cash as buffer stock for liquidity in preparation for future investment opportunities.

This article presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by a known, observed dissimilarity measure over spatial indices. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the partition into clusters is learned, a cluster-based inference procedure is applied to a statistical hypothesis testing procedure. The procedure proposed in the article allows the number of clusters to depend on the data, which gives researchers a principled method for choosing an appropriate clustering level. The article gives conditions under which the proposed procedure asymptotically attains correct size. A simulation study shows that the proposed procedure attains near nominal size in finite samples in a variety of statistical testing problems with dependent data.

This paper studies mental health spillovers among new mothers. We exploit variation in the mental health of peers in mother groups in the Danish public postnatal care program. We show that municipal nurses assign mothers arbitrarily to groups conditional on a narrow set of well-defined characteristics. Exposure to a depressed peer in the group increases mothers' mental health care uptake by 11 percent two years after birth. We document worse self-reported mental health and labor market outcomes for treated mothers. Exploring heterogeneity, we find suggestive evidence for mental health deterioration, rather than increased demand for health care, as mechanism

We shed light on housing price inertia by investigating how the home-sale mechanism affects housing price dynamics. Using Australian data, we find that auction prices forecast better and display less momentum than negotiated prices. These findings are robust to alternative price measurements and different sample selection corrections. Motivated by microtheory that predicts different weights for buyer and seller values in auction and negotiated prices, we decompose housing prices into two diffusion processes and interpret them as buyer value and seller value, respectively. The seller value updates much more slowly, which could be an important driver of housing price inertia.