The Burden of Risk: Who Dies Because of Cars?

This article argues that those interested in social policy should by definition be interested in issues of transport policy. It analyses data on road traffic fatalities and suggests, in the light of this evidence, that those who benefit least from the motor vehicle seem disproportionately likely, given their relative exposure to the risk, to die in road traffic accidents.     

Application of extreme value theory to the bec family of distributions

Arnold and Strauss (1988) derived a family of bivariate life distributions having the property that the conditional distributions are exponential. Asymptotic distributions for the marginal and bivariate extremes for this family of distributions are derived employing the asymptotic theory of extreme order statistics.     

Inferences on the lognormal mean for complete samples

In many engineering problems it is necessary to draw statistical inferences on the mean of a lognormal distribution based on a complete sample of observations. Statistical demonstration of mean time to repair (MTTR) is one example. Although optimum confidence intervals and hypothesis tests for the lognormal mean have been developed, they are difficult to use, requiring extensive tables and/or a computer. In this paper, simplified conservative methods for calculating confidence intervals or hypothesis tests for the lognormal mean are presented. In this paper, “conservative” refers to confidence intervals (hypothesis tests) whose infimum coverage probability (supremum probability of rejecting the null hypothesis taken over parameter values under the null hypothesis) equals the nominal level. The term “conservative” has obvious implications to confidence intervals (they are “wider” in some sense than their optimum or exact counterparts). Applying the term “conservative” to hypothesis tests should not be confusing if it is remembered that this implies that their equivalent confidence intervals are conservative. No implication of optimality is intended for these conservative procedures. It is emphasized that these are direct statistical inference methods for the lognormal mean, as opposed to the already well-known methods for the parameters of the underlying normal distribution. The method currently employed in MIL-STD-471A for statistical demonstration of MTTR is analyzed and compared to the new method in terms of asymptotic relative efficiency. The new methods are also compared to the optimum methods derived by Land (1971, 1973).     

On the asymptotic distribution of cramer-von mises one-sample test statistics under an alternative

The asymptotic normality of the Cramer-von Mises one-sample test statistic and one of its variants under an alternative cdf is demonstrated. The derivation herein is unique in that it does not require knowledge of the theory of weak convergence of probability measures defined on metrized function spaces, and thus is accessible to a broader class of students and practitioners.     

A note on the central limit theorem for the bootstrap mean

Following Yang (1988), a simple, more self-contained derivation of the asymptotic normality of the bootstrap mean is presented, as well as other asymptotic results. The derivations are appropriate for beginning graduate students in statistics, relying only on fundamental notions of probability theory and analysis,     

Revisiting the mediating role of trust in transformational leadership effects: Do different types of trust make a difference?

This study examines the mediating effects of cognitive and affective trust on the relationship between follower perceptions of transformational leadership behavior and their work outcomes. Using data obtained from 318 supervisor–subordinate dyads from a manufacturing organization located in mainland China, structural equation modeling results revealed that affective trust fully mediated the relationships between transformational leadership and the work outcomes of followers, including their affective organizational commitment, organizational citizenship behaviors (OCBs), and job performance. In contrast, cognitive trust negatively mediated the relationship between transformational leadership and follower job performance, and had insignificant effects on their affective organizational commitment and organizational citizenship behaviors. These findings highlight the importance of affective trust as a mechanism which translates transformational leadership into positive work outcomes for the organization.     

MINIMUM WAGES,IMPORT STATUS,AND FIRMS' INNOVATION: THEORY AND EVIDENCE FROM CHINA

This study explores the heterogeneous effects of minimum wage on innovation of different types of firms. We develop an open‐economy R&D‐based growth model and obtain the following result: raising the minimum wage reduces innovation of firms that use domestic inputs but increases innovation of firms that import foreign inputs. We test this result using city‐level data on minimum wages and firm‐level patent data in China. In accordance with our theory, we find that raising the minimum wage is associated with more innovation by importing firms and less by non‐importing firms. This result survives a battery of robustness checks. (JEL E24, F43, O31)     

Endogenous fertility and human capital in a Schumpeterian growth model

This study develops a scale-invariant Schumpeterian growth model with endogenous fertility and human capital accumulation. The model features two engines of long-run economic growth: R&D-based innovation and human capital accumulation. One novelty of this study is endogenous fertility, which negatively affects the growth rate of human capital. Given this growth-theoretic framework, we characterize the dynamics of the model and derive comparative statics of the equilibrium growth rates with respect to structural parameters. As for policy implications, we analyze how patent policy affects economic growth through technological progress, human capital accumulation, and endogenous fertility. In summary, we find that strengthening patent protection has (a) a positive effect on technological progress, (b) a negative effect on human capital accumulation through a higher rate of fertility, and (c) an ambiguous overall effect on economic growth.     

Adult height and childhood disease

Taller populations are typically richer populations, and taller individuals live longer and earn more. In consequence, adult height has recently become a focus in understanding the relationship between health and wealth. We investigate the childhood determinants of population adult height, focusing on the respective roles of income and of disease. Across a range of European countries and the United States, we find a strong inverse relationship between postneonatal (ages 1 month to 1 year) mortality, interpreted as a measure of the disease and nutritional burden in childhood, and the mean height of those children as adults. Consistent with these findings, we develop a model of selection and stunting in which the early-life burden of undernutrition and disease not only is responsible for mortality in childhood but also leaves a residue of long-term health risks for survivors, risks that express themselves in adult height and in late-life disease. The model predicts that at sufficiently high mortality levels, selection can dominate scarring, leaving a taller population of survivors. We find evidence of this effect in the poorest and highest-mortality countries of the world, supplementing recent findings on the effects of the Great Chinese Famine.     

Bootstrapping a Universal Pivot When Nuisance Parameters are Estimated

In complete samples from a continuous cumulative distribution with unknown parameters, it is known that various pivotal functions can be constructed by appealing to the probability integral transform. A pivotal function (or simply pivot) is a function of the data and parameters that has the property that its distribution is free of any unknown parameters. Pivotal functions play a key role in constructing confidence intervals and hypothesis tests. If there are nuisance parameters in addition to a parameter of interest, and consistent estimators of the nuisance parameters are available, then substituting them into the pivot can preserve the pivot property while altering the pivot distribution, or may instead create a function that is approximately a pivot in the sense that its asymptotic distribution is free of unknown parameters. In this latter case, bootstrapping has been shown to be an effective way of estimating its distribution accurately and constructing confidence intervals that have more accurate coverage probability in finite samples than those based on the asymptotic pivot distribution. In this article, one particular pivotal function based on the probability integral transform is considered when nuisance parameters are estimated, and the estimation of its distribution using parametric bootstrapping is examined. Applications to finding confidence intervals are emphasized. This material should be of interest to instructors of upper division and beginning graduate courses in mathematical statistics who wish to integrate bootstrapping into their lessons on interval estimation and the use of pivotal functions.

[Received November 2014. Revised August 2015.]