Moral hazard,monitoring costs,and optimal government intervention

When insurance firms can monitor with non-prohibitive costs the consumption of risk-influencing goods by an insured, they have incentives to tax-subsidize the insured's consumption of the goods. If the government cannot monitor at a lower cost than private insurers, intervention is neither needed nor desirable. Where the government does have a monitoring-cost advantage, it cannot achieve a constrained optimum by commodity tax-subsidies alone. It must also augment the level of insurance and, in some cases, prohibit private tax-subsidies by insurers. Such “invasive” intervention can be avoided if the government regulates the consumption of the risk-influencing goods.     

Testing for genetic susceptibility: What you see is not what you get

Media reports of advances in human genetic research often make exaggerated claims and generate expectations of practical benefits, particularly the imminence of genetic testing. After the discovery of a disease‐related gene, which attracts media attention, additional research is needed to demonstrate the safety and effectiveness of genetic tests. Even more work is needed to develop interventions to prevent or reduce the burden of genetic disease and to demonstrate their safety and effectiveness. Clinical laboratories sometimes offer genetic tests before the tests, or ensuing interventions, have been adequately assessed. Exaggerated reporting is not entirely the fault of the mass media; research institutions and scientists themselves contribute. The need to justify scientific research by exaggerating its effects has increased in recent years as government funding for research (in the U.S.) has become problematic and universities turn to the private sector. If the exaggerations continue unchecked, and if unvalidated tests reach the public, a backlash against science could result. More critical reporting by the media and university public relations offices, more stringent oversight of genetic test development and delivery, and greater awareness of the benefits and risks of tests by health care providers can reduce the problem.     

Computation of the estimated parameters and wald statistic for the generalized growth curve model

Multivariate analysis is difficult when there are missing observations in the response vectors. Kleinbaum (1973) proposed a Wald statistic useful in the analysis of incomplete multivariate data. SUBROUTINE C0EF calculates the estimated parameter matrix g in the generalization of the Potthoff-Roy (1964) growth curve model proposed by Kleinbaum (1973). SUBROUTINE WALD calculates the Wald statistic for hypotheses of the form Hn: H 5 D = 0 as proposed by Kleinbaum (1973).     

Properties of Designs in Experiments on Spatial Lattices

ABSTRACT

When spatial variation is present in experiments, it is clearly sensible to use designs with favorable properties under both generalized and ordinary least squares. This will make the statistical analysis more robust to misspecification of the spatial model than would be the case if designs were based solely on generalized least squares. In this article, treatment information is introduced as a way of studying the ordinary least squares properties of designs. The treatment information is separated into orthogonal frequency or polynomial components which are assumed to be independent under the spatial model. The well-known trend-resistant designs are those with no treatment information at the very low order frequency or polynomial components which tend to have the higher variances under the spatial model. Ideally, designs would be chosen with all the treatment information distributed at the higher-order components. However, the results in this article show that there are limits on how much trend resistance can be achieved as there are many constraints on the treatment information. In addition, appropriately chosen Williams squares designs are shown to have favorable properties under both ordinary and generalized least squares. At all times, the ordinary least squares properties of the designs are balanced against the generalized least squares objectives of optimizing neighbor balance.     

Mobile safety cameras: estimating casualty reductions and the demand for secondary healthcare

We consider a fully Bayesian analysis of road casualty data at 56 designated mobile safety camera sites in the Northumbria Police Force area in the UK. It is well documented that regression to the mean (RTM) can exaggerate the effectiveness of road safety measures and, since the 1980s, an empirical Bayes (EB) estimation framework has become the gold standard for separating real treatment effects from those of RTM. In this paper we suggest some diagnostics to check the assumptions underpinning the standard estimation framework. We also show that, relative to a fully Bayesian treatment, the EB method is over-optimistic when quantifying the variability of estimates of casualty frequency. Implementing a fully Bayesian analysis via Markov chain Monte Carlo also provides a more flexible and complete inferential procedure. We assess the sensitivity of estimates of treatment effectiveness, as well as the expected monetary value of prevention owing to the implementation of the safety cameras, to different model specifications, which include the estimation of trend and the construction of informative priors for some parameters.     

Yarning about gambling in Indigenous communities: an Aboriginal and Islander Mental Health Initiative

Gambling impacts upon the health, wellbeing and finances of many people throughout Australia. This study aimed to explore the socioeconomic and cultural factors linked with gambling in urban and remote Indigenous settings in the Northern Territory to inform the development of a gambling public health strategy. The Aboriginal and Islander Mental Health Initiative developed a semi‐structured questionnaire with Aboriginal partner organisations following consultation. Indigenous consumers of substance use treatment facilities participated in focus group discussions and key informant interviews were conducted with nine service providers at two time points, a year apart. Participants described key strengths in community as family, health and culture, while key worries included substance misuse, health concerns and family disharmony. Regulated gambling and card playing were also identified as important community worries. Financial and family concerns and addictive behaviour were seen as negative consequences of gambling. There was increasing concern linked with card playing and electronic gaming machines and an increased call for awareness campaigns, support from government for change and greater regulation. The findings of this study provide the most recent insight into attitudes, behaviours and consequences linked with Indigenous gambling in the Northern Territory.     

Migrant Selection and the Health of U.S. Immigrants From the Former Soviet Union

Few prior studies have investigated the health of U.S. immigrants from the former Soviet Union (FSU). Utilizing data from the 2000 U.S. census and the 2000–2007 National Health Interview Survey (NIHS), we compare levels of disability of FSU immigrants with U.S.-born whites (ages 50–84). Our findings suggest an “epidemiologic paradox” in that FSU immigrants possess higher levels of education compared with U.S.-born whites, but report considerably higher disability with and without adjustment for education. Nonetheless, FSU immigrants report lower levels of smoking and heavy alcohol use compared with U.S.-born whites. We further investigate disability by period of arrival among FSU immigrants. Changes in Soviet emigration policies conceivably altered the level of health selectivity among émigrés. We find evidence that FSU immigrants who emigrated during a period when a permission to emigrate was hard to obtain (1970–1986) displayed less disability compared with those who emigrated when these restrictions were less stringent (1987–2000). Finally, we compare disability among Russian-born U.S. immigrants with that of those residing in Russia as a direct test of health selectivity. We find that Russian immigrants report lower levels of disability compared with Russians in Russia, suggesting that they are positively selected for health despite their poor health relative to U.S.-born whites.     

Estimation of Weibull parameters from common percentiles

Estimation of Weibull distribution shape and scale parameters is accomplished through use of symmetrically located percentiles from a sample. The process requires algebraic solution of two equations derived from the cumulative distribution function. Three alternatives examined are compared for precision and variability with maximum likelihood (MLE) and least squares (LS) estimators. The best percentile estimator (using the 10th and 90th) is inferior to MLE in variability and to one least squares estimator in accuracy and variability to a small degree. However, application of a correction factor related to sample size improves the percentile estimator substantially, making it more accurate than LS.     

Econometric analysis of realized volatility and its use in estimating stochastic volatility models

Summary. The availability of intraday data on the prices of speculative assets means that we can use quadratic variation-like measures of activity in financial markets, called realized volatility, to study the stochastic properties of returns. Here, under the assumption of a rather general stochastic volatility model, we derive the moments and the asymptotic distribution of the realized volatility error—the difference between realized volatility and the discretized integrated volatility (which we call actual volatility). These properties can be used to allow us to estimate the parameters of stochastic volatility models without recourse to the use of simulation-intensive methods.