Physical exercise,employee fitness and the management of health at work

Abstract

This paper presents some recent data on why organizations invest in exercise and physical fitness programmes for their stiff. These data suggest that, owing to a lack of evaluation studies, organizations act mainly on the basis of assumption and belief. However, from a subsequent review of the available literature on the personal and organizational effects of such programmes, it appears that these assumptions and beliefs are not altogether unfounded. The paper concludes by discussing the ways in which exercise and employee fitness programmes might help die individual and their organization in terms of the management of health at work.     

Second-Order Response Surface Model with Neighbor Effects

This article considers the second-order response surface model in which the experimental units, i.e., plots experience the neighbor effects from immediate left and right neighboring plots assuming the plots to be placed adjacent linearly with no gaps. Conditions have been derived for the estimation of coefficients of second-order response surface model. A method of constructing designs for fitting second-order response surface in the presence of neighbor effects has been developed. The designs so obtained are found to be rotatable.     

Penalized spline joint models for longitudinal and time-to-event data

The joint models for longitudinal data and time-to-event data have recently received numerous attention in clinical and epidemiologic studies. Our interest is in modeling the relationship between event time outcomes and internal time-dependent covariates. In practice, the longitudinal responses often show non linear and fluctuated curves. Therefore, the main aim of this paper is to use penalized splines with a truncated polynomial basis to parameterize the non linear longitudinal process. Then, the linear mixed-effects model is applied to subject-specific curves and to control the smoothing. The association between the dropout process and longitudinal outcomes is modeled through a proportional hazard model. Two types of baseline risk functions are considered, namely a Gompertz distribution and a piecewise constant model. The resulting models are referred to as penalized spline joint models; an extension of the standard joint models. The expectation conditional maximization (ECM) algorithm is applied to estimate the parameters in the proposed models. To validate the proposed algorithm, extensive simulation studies were implemented followed by a case study. In summary, the penalized spline joint models provide a new approach for joint models that have improved the existing standard joint models.     

Product Introduction Strategies under Sequential Innovation for Durable Goods with Network Effects

When network effects are important and technology is rapidly improved, this study explores the relative optimality of five product introduction strategies of a durable goods manufacturer: (1) replacement, (2) skipping, (3) a delayed line, (4) shelving, and (5) line‐extension. Using a two‐period analytical model, we show how the type of compatibility—either full or backward compatibility—and the magnitude of the network effect influence the manufacturer's preference for the above strategies. Our analysis reveals that only the strategies (1)–(3) above can be optimal; and the optimal strategy varies with network strength. Further, the type of compatibility can dramatically change the profitability under each optimal strategy; for instance, while backward compatibility can increase the profitability of replacement under certain conditions, it always reduces the profitability of a delayed line. We also illustrate that if compatibility were a choice, although backward compatibility may be observed widely in practice, the parametric region for its optimality is relatively more restricted than that of full compatibility.     

Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.     

Tests and Confidence Intervals for an Extended Variance Component Using the Modified Likelihood Ratio Statistic

Abstract.  The large deviation modified likelihood ratio statistic is studied for testing a variance component equal to a specified value. Formulas are presented in the general balanced case, whereas in the unbalanced case only the one-way random effects model is studied. Simulation studies are presented, showing that the normal approximation to the large deviation modified likelihood ratio statistic gives confidence intervals for variance components with coverage probabilities very close to the nominal confidence coefficient.     

Does work during childhood affect the health of Guatemalan adults?

The diversity of potential relationships between child labor and health makes the empirical disentanglement of the causal relationship a difficult exercise. This paper examines the long run impact of child labour on health by controlling for unobserved household specific characteristics. In order to control for the unobserved households specific effect, we estimate a conditional fixed effect model using data on siblings constructed from the Guatemalan National Survey of Living Condition. The estimation results reinforce the conventional wisdom that child labor is harmful for health in the long run. The results can be interpreted as a lower bound of the true impact since healthier children are most likely to offer themselves for employment and to be appointed. The views expressed in this paper are those of the authors and do not necessarily represent those of IMF and IMF policy.     

琵琶曲《十面埋伏》与《霸王卸甲》的比较

琵琶乐曲《十面埋伏》和《霸王卸甲》是大家熟悉的两首琵琶武曲,它们虽然在题材上、形式上有相似之处,可仔细比较起来却还是有许多差异。通过对创作特点,演奏特点和审美特点三方面的综合阐述,揭示了两首乐曲的内在联系和不同之处,便于欣赏者更好的了解两首古曲的动人之处。     

An experiment in primary health care provision

Missing data and, more generally, imperfections in implementing a study design are an endemic problem in large scale studies involving human subjects. We present an analysis of an experiment in the interaction between general practitioners and their patients, in which the issue of missing data is addressed by a sensitivity analysis using multiple imputation. Instead of specifying a model for missingness we explore certain extreme ways of departing from the assumption of data missing at random and establish the largest extent of such departures which would still fail to supplant the evidence about the studied effect. An important advantage of the approach is that the algorithm intended for the complete data, to fit generalized linear models with random effects, is used without any alteration.     

买方市场条件下的企业营销对策

买方市场的形成是我国经济发展过程的一个划时代变化，它将对我国企业经营和营销带来严峻挑战。本文分析了我国买方市场的形成、特征及其对企业营销的影响，并就买方市场条件下企业营销对策进行了重点分析。