WAGE DISCRIMINATION ACROSS ETHNIC GROUPS: EVIDENCE FROM ISRAEL

We investigate wage differentials between ethnic groups in Israel, proposing a new methodology and an illustration based on the latest Israeli census. By using separate wage equations for the various occupations, our methodology allows us to decompose the wage differential into three components: one reflecting human capital differences; one reflecting wage discrimination; and a third indicating occupational segregation. We find that 70 percent of the wage gap is due to segregation, 26 percent to wage discrimination and only 4 percent to human capital differences. Evidence is also found for the existence of duality in the Israeli labor market.     

对林业税费的经济学分析

该文运用经济学的基本工具———供给和需求曲线 ,以商品林为研究对象 ,探讨了林业税费的作用机理和内涵。该文认为商品林生产具有正的外部性 ,市场失灵使生产者将不能提供社会真正所需要的商品林数量。在封闭的市场条件下 ,林业税费会直接成为价格的一部分 ,商品林生产者应当能够获得正常利润 ,与税费高低无关 ,但会使一部分林地退出生产。林业补贴是一种负的林业税收。在开放市场经济条件下 ,国内商品林供应量低于没有林业税收时的商品林供应量 ,更低于考虑社会生态需求时的商品林供应量 ,造成巨大的社会福利损失 ,应减免林业税费、增加林业扶持、取消价格管制。该文进一步研究了与林业税费密切相关的森林采伐限额政策 ,认为对商品林应该完全取消采伐限额政策     

浅谈外语学院教学管理工作

教学管理是教学工作运转的支柱,教学管理的优劣影响着教学质量。该文介绍了外语学院教学管理工作的基本内容,包括教学任务的布置落实、教学检查、教学文件的建设、学籍管理,以及结合学院的实际情况完善考试制度、制定成绩管理细则和考试结果奖励办法等。     

Netzwerken: Reflexionen und Erfahrungen am Beispiel eines TrainerInnen und BeraterInnen-Netzwerkes

How are social networks organized and how much structure and openess do they have? The ambiguity of networking and the grade of organization are discussed and described theoretically and with an authentic example.     

绿色管理——企业可持续发展的必然选择

保护生态环境,实现可持续发展是企业在21世纪必须面对的发展战略问题.随着环境污染的日益严重和人们环保意识的不断提高,传统企业管理方式已无法适应人类社会发展的需要.一种新型管理方式——绿色管理应运而生.该文通过对绿色管理的概念、绿色管理产生与发展过程的阐述,揭示了绿色管理是企业可持续发展的必然选择.     

Design optimization using ANOVA

This paper describes the design optimization of a robot sensor used for locating 3-D objects employing the Taguchi method in a computer simulation scenario. The location information from the sensor is to be utilized to control the movements of an industrial robot in a 'pick-and-place' or assembly operation. The Taguchi method, which is based on the Analysis-of-Variance (ANOVA) approach, is utilized to improve the performance of the sensor over a wider operating range. A review of the Taguchi method is presented along with step-by-step implementation details to identify and optimize the design parameters of the sensor. The method allows us to gauge the impact of various interactions present in the sensor system exclusively and permits us to single out those factors that have a dominant influence on the overall performance of the sensor. The investigation suggests that the Taguchi method is a more structured and efficient approach for achieving a robust design compared with the classical full factorial design approach.     

Führung durch Selbstführung

This paper outlines the conceptual bases, strategies, and psychological attributes of leadership by self-leadership. In addition, relations to other theories of leadership are drawn. Dimensions and correlates of self-leadership competence are described and discussed. Various approaches for developing self-leadership competence are discussed as well.     

Simple detection of outlying short time series

Sets of relatively short time series arise in many situations. One aspect of their analysis may be the detection of outlying series. We examine the performance of standard normal outlier tests applied to the means, or to simple functions of the means, of AR(1) series, not necessarily of equal lengths. Although unequal lengths of series implies that the means have unequal variances, that are only known approximately, it is shown that nominal significance levels hold good under most circumstances. Thus a standard outlier test can usefully be applied, avoiding the complication of estimating the time series' parameters. The test's power is affected by unequal lengths, being higher when the slippage occurs in one of the longer series     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

Inference for the measurement of poverty in the presence of a stochastic weighting variable

Empirical applications of poverty measurement often have to deal with a stochastic weighting variable such as household size. Within the framework of a bivariate distribution function defined over income and weight, I derive the limiting distributions of the decomposable poverty measures and of the ordinates of stochastic dominance curves. The poverty line is allowed to depend on the income distribution. It is shown how the results can be used to test hypotheses concerning changes in poverty. The inference procedures are briefly illustrated using Belgian data. An erratum to this article can be found at