Are labour inspections effective when labour regulations vary according to the size of the firm? Evidence from Peru

This article analyses the impact of enforcement of four labour standards (pension system enrolment, minimum wage, maximum weekly working hours and written employment contract) on compliance in Peru, where labour regulations and penalties vary according to firm size. The author uses household survey data to analyse a factor not previously studied – adjustment by firms through downsizing to benefit from lower fines and less stringent regulations. The empirical findings indicate that enforcement efforts have little effect on either the degree of compliance or the size of firms.     

A sampling plan for one-shot systems considering destructive inspection

Abstract

The paper is concerned with an acceptance sampling problem under destructive inspections for one-shot systems. The systems may fail at random times while they are operating (as the systems are considered to be operating when storage begins), and these failures can only be identified by inspection. Thus, n samples are randomly selected from N one-shot systems for periodic destructive inspection. After storage time T, the N systems are replaced if the number of working systems is less than a pre-specified threshold k. The primary purpose of this study is to determine the optimal number of samples n*, extracted from the N for destructive detection and the optimal acceptance number k*, in the sample under the constraint of the system interval availability, to minimize the expected cost rate. Numerical experiments are studied to investigate the effect of the parameters in sampling inspection on the optimal solutions.     

Combined multiple testing of multivariate survival times by censored empirical likelihood

In each study testing the survival experience of one or more populations, one must not only choose an appropriate class of tests, but further an appropriate weight function. As the optimal choice depends on the true shape of the hazard ratio, one is often not capable of getting the best results with respect to a specific dataset. For the univariate case several methods were proposed to conquer this problem. However, most of the interesting datasets contain multivariate observations nowadays. In this work we propose a multivariate version of a method based on multiple constrained censored empirical likelihood where the constraints are formulated as linear functionals of the cumulative hazard functions. By considering the conditional hazards, we take the correlation between the components into account with the goal of obtaining a test that exhibits a high power irrespective of the shape of the hazard ratio under the alternative hypothesis.     

Estimation of discrete survival function for error‐prone diagnostic tests

The product limit or Kaplan‐Meier (KM) estimator is commonly used to estimate the survival function in the presence of incomplete time to event. Application of this method assumes inherently that the occurrence of an event is known with certainty. However, the clinical diagnosis of an event is often subject to misclassification due to assay error or adjudication error, by which the event is assessed with some uncertainty. In the presence of such errors, the true distribution of the time to first event would not be estimated accurately using the KM method. We develop a method to estimate the true survival distribution by incorporating negative predictive values and positive predictive values, into a KM‐like method of estimation. This allows us to quantify the bias in the KM survival estimates due to the presence of misclassified events in the observed data. We present an unbiased estimator of the true survival function and its variance. Asymptotic properties of the proposed estimators are provided, and these properties are examined through simulations. We demonstrate our methods using data from the Viral Resistance to Antiviral Therapy of Hepatitis C study.     

Semiparametric Efficient Estimation in the Generalized Odds-Rate Class of Regression Models for Right-Censored Time-to-Event Data

The generalized odds-rate class of regression models for time to event data is indexed by a non-negative constant and assumes thatg(S(t|Z)) = (t) + Zwhere g(s) = log(^-1(s-) for > 0, g₀(s) = log(- log s), S(t|Z) is the survival function of the time to event for an individual with qx1 covariate vector Z, is a qx1 vector of unknown regression parameters, and (t) is some arbitrary increasing function of t. When =0, this model is equivalent to the proportional hazards model and when =1, this model reduces to the proportional odds model. In the presence of right censoring, we construct estimators for and exp((t)) and show that they are consistent and asymptotically normal. In addition, we show that the estimator for is semiparametric efficient in the sense that it attains the semiparametric variance bound.     

库切小说《耻》中白人的生存困境与历史宿命

为库切赢得诺贝尔文学奖奠定基础的《耻》是一部思想深刻的作品,小说以后种族隔离时代的南非为背景,讲述了白人卢里和露茜父女深陷生存困境的故事。他们的遭遇如实地展现了生活在新南非的白人如何一步步丧失话语权,陷入生存“耻”境,最终难逃历史性悲剧宿命的图景。故事背后隐藏着作者对人性的思索和对个体生命历史宿命的感悟。     

经济政策不确定性、企业出口生存与出口转内销

基于2000-2014年中国工业企业数据库、中国海关进出口数据库与Baker等编制的经济政策不确定性指数的匹配数据,本文实证研究了目的国经济政策不确定性对中国企业出口生存的影响。研究发现：目的国经济政策不确定性的提高显著增加了企业出口失败的概率;异质性分析发现,非国有企业、非东部地区企业,以及出口到高收入国家的企业受经济政策不确定性的影响更大;进一步分析发现,经济政策不确定性的提高显著促进了企业“出口转内销”,劳动密集型企业和一般贸易企业“出口转内销”的幅度更大。     

关于高校党风廉政建设的思考

本文通过分析当前高校党风廉政建设的现状, 阐述了努力做好高校纪检监察工作与促进党风廉政建设的关系, 提出了推进党风廉政建设工作的对策。     

Semiparametric Additive Accelerated Life Models

In this paper we investigate the asymptotic properties of estimators obtained for the semiparametric additive accelerated life model proposed by Bagdonavicius & Nikulin (1995). This model generalizes the well known additive hazards model of survival analysis and is close to the general transformation model (see Dabrowska & Doksum, 1988). Asymptotic properties of the estimator of the regression parameter and the estimator of the reliability function are given in the case of right censoring for discretized data and a numerical example illustrates these results.