The Occupational Cost of Being Illegal in the United States: Legal Status,Job Hazards,and Compensating Differentials

Considerable research and pervasive cultural narratives suggest that undocumented immigrant workers are concentrated in the most dangerous, hazardous, and otherwise unappealing jobs in U.S. labor markets. Yet, owing largely to data limitations, little empirical work has addressed this topic. Using data from the 2004 and 2008 panels of the Survey of Income and Program Participation, we impute legal status for Mexican and Central American immigrants and link their occupations to Bureau of Labor and Statistics (BLS) data on occupational fatalities and occupational hazard data from the U.S. Department of Labor to explore racial and legal status differentials on several specific measures of occupational risk. Results indicate that undocumented workers face heightened exposure to numerous dimensions of occupational hazard – including higher levels of physical strain, exposure to heights, and repetitive motions – but are less exposed than native workers to some of the potentially most dangerous environments. We also show that undocumented workers are rewarded less for employment in hazardous settings, receiving low or no compensating differential for working in jobs with high fatality, toxic materials, or exposure to heights. Overall, this study suggests that legal status plays an important role in determining exposure to job hazard and in structuring the wage returns to risky work.     

A value of information framework for assessing the trade-offs associated with uncertainty,duration, and cost of chemical toxicity testing

A number of investigators have explored the use of value of information (VOI) analysis to evaluate alternative information collection procedures in diverse decision-making contexts. This paper presents an analytic framework for determining the value of toxicity information used in risk-based decision making. The framework is specifically designed to explore the trade-offs between cost, timeliness, and uncertainty reduction associated with different toxicity-testing methodologies. The use of the proposed framework is demonstrated by two illustrative applications which, although based on simplified assumptions, show the insights that can be obtained through the use of VOI analysis. Specifically, these results suggest that timeliness of information collection has a significant impact on estimates of the VOI of chemical toxicity tests, even in the presence of smaller reductions in uncertainty. The framework introduces the concept of the expected value of delayed sample information, as an extension to the usual expected value of sample information, to accommodate the reductions in value resulting from delayed decision making. Our analysis also suggests that lower cost and higher throughput testing also may be beneficial in terms of public health benefits by increasing the number of substances that can be evaluated within a given budget. When the relative value is expressed in terms of return-on-investment per testing strategy, the differences can be substantial.     

A Bayesian method for safety signal detection in ongoing blinded randomised controlled trials

Sponsors have a responsibility to minimise risk to participants in clinical studies through safety monitoring. The FDA Final Rule for IND Safety Reporting requires routine aggregate safety evaluation, including in ongoing blinded studies. We are interested in estimating the probability that the true adverse event rate in the experimental arm exceeds that in the control arm. We developed a Bayesian approach that specifies an informative meta-analytic predictive prior on the event probability in the control arm and an uninformative prior on that in the experimental arm. We combined these priors with a mixture likelihood that considers each patient in the ongoing blinded study may belong to the experimental or control arm. This allowed us to estimate the quantity of interest without unblinding. We evaluated our method by simulation, pairing scenarios that differed only in whether a safety signal was present or missing, and quantifying the ability of our model to discriminate using signal detection theory. Our approach shows benefit. It detects safety signals more reliably with greater sample sizes and for common rather than rare events. Performance does not deteriorate markedly when historical studies exhibit heterogeneous hazards or non-constant hazards. Our method will allow us to monitor safety signals in ongoing blinded studies with the goal of earlier identification and risk mitigation. Our method could be adapted to use informative priors on both arms or predictive covariates where pertinent data exist. We stress that ongoing safety monitoring should involve a multi-disciplinary team where statistical methods are paired with medical judgement.     

Do Interactions between Unemployment Insurance and Sickness Insurance Affect Transitions to Employment?

Previous research suggests that there are substantial interactions between the unemployment and sickness insurance in Sweden. Moral hazard arises in the interplay between these insurance systems, as by reporting sick an unemployed person can postpone unemployment insurance expiration and sometimes receive considerably higher benefits. I examine whether these interactions affect the transition rate to employment. To do this I use a reform that greatly reduced the incentives to transfer to sickness benefits. Although there is evidence that this reform effectively lowered the incidence of sick reports among the unemployed, I do not find that the reduced sick report rate in turn affected the transition rate to employment.     

Inference in Arch and Garch Models with Heavy–Tailed Errors

ARCH and GARCH models directly address the dependency of conditional second moments, and have proved particularly valuable in modelling processes where a relatively large degree of fluctuation is present. These include financial time series, which can be particularly heavy tailed. However, little is known about properties of ARCH or GARCH models in the heavy–tailed setting, and no methods are available for approximating the distributions of parameter estimators there. In this paper we show that, for heavy–tailed errors, the asymptotic distributions of quasi–maximum likelihood parameter estimators in ARCH and GARCH models are nonnormal, and are particularly difficult to estimate directly using standard parametric methods. Standard bootstrap methods also fail to produce consistent estimators. To overcome these problems we develop percentile–t, subsample bootstrap approximations to estimator distributions. Studentizing is employed to approximate scale, and the subsample bootstrap is used to estimate shape. The good performance of this approach is demonstrated both theoretically and numerically.     

AIDE: Adaptive intrapatient dose escalation designs to accelerate Phase I clinical trials

Designing Phase I clinical trials is challenging when accrual is slow or sample size is limited. The corresponding key question is: how to efficiently and reliably identify the maximum tolerated dose (MTD) using a sample size as small as possible? We propose model-assisted and model-based designs with adaptive intrapatient dose escalation (AIDE) to address this challenge. AIDE is adaptive in that the decision of conducting intrapatient dose escalation depends on both the patient's individual safety data, as well as other enrolled patient's safety data. When both data indicate reasonable safety, a patient may perform intrapatient dose escalation, generating toxicity data at more than one dose. This strategy not only provides patients the opportunity to receive higher potentially more effective doses, but also enables efficient statistical learning of the dose-toxicity profile of the treatment, which dramatically reduces the required sample size. Simulation studies show that the proposed designs are safe, robust, and efficient to identify the MTD with a sample size that is substantially smaller than conventional interpatient dose escalation designs. Practical considerations are provided and R code for implementing AIDE is available upon request.     

There Goes the Neighborhood: How Racial Threat Shapes the Formation of Integrated and Segregated Neighborhoods

Neighborhoods in the United States are highly segregated by race. Black–white segregation is particularly acute. Although racial segregation has been declining in recent years, it remains extremely high—especially in the largest metropolitan areas. The historical pattern has been that as African Americans move into a neighborhood, whites move out and thus the neighborhood (gradually or rapidly) transitions from predominantly white to predominantly black. A seemingly contradictory pattern has recently been observed: integrated neighborhoods are becoming more common. Why do some neighborhoods transition from white to black and others become integrated? We use racial competition theory to answer this question. We examine the characteristics of stable, integrated neighborhoods and contrast them with segregated predominantly white, predominantly black, and rapidly transitioning neighborhoods in six large metropolitan areas. We find that the location of black neighborhoods is a key factor in whether an integrated neighborhood remains integrated. Specifically, we find integrated neighborhoods are spatially distant from majority black neighborhoods while transitioning neighborhoods are adjacent to them. Our findings support the “sense of threat” hypothesis of racial competition theory.     

ORGANIZATIONAL CHOICE AND SOME CORRELATES OF INDIVIDUALS' EXPECTANCIES1

This study uses expectancy theory to predict the sorority choices of 121 undergraduate students. Results show that the complete, multiplicative expectancy model is only a slightly better predictor of choice behavior than other versions of the model. Several personal and situational correlates of individuals' expectancies of entering sororities also are examined. Results show significant relationships between expectancy and subjects' self-esteem, perception of value similarity with sorority members, and number of perceived friendships with sorority members.