Partial strikes under the railway labor act: The need for a doctrine of unprotected concerted activity

In light of use by airline unions of partial-strike tactics, such as concerted refusals to bid for overtime work and so-called ” CHAOS” tactics involving unannounced refusals to fly after passengers have been ticketed and are ready to board, the authors examine whether the Railway Labor Act (RLA) should be interpreted to permit employers to discipline employees for engaging in such tactics, or whether these are a protected form of economic pressure. Although in many respects bargaining duties and economic weapons under the RLA are read consonant with precedents under the National Labor Relations Act (NLRA) (which governs all industries other than rail and air transport), there are a few decisions suggesting that during the period when self-help may be resorted to, employees can engage in partial strike activities as long as they violate no court order but are subject to permanent replacement in limited circumstances. These decisions, the authors submit, fail to take account of Supreme Court decsions since the 1930s that some economic pressures by unions, such as slowdowns and sitins, may not violate the labor laws but nor are they protected by those laws so as to immunize partial strikers from employer discipline. These decisions are not based on unique features of the NLRA. Rather, they give recognition to the background assumptions of Congress that employers may act to protect their property interests as long as they do not run afoul of NLRA or RLA protections and that employees who engage in partial-strike activities are subject to employer discipline even where not strictly necessary to maintain operations. Moreover, these tactics skew the bargaining process by giving employees an essentially risk-free gambit to pressure their economic position through planned disruption of carrier operations. Professor Estreicher is also labor and employment counsel to O’Melveny & Myers, LLP. The views expressed herein are the authors’ and should not be attributed to any organization. Hannah Breshin and Tom Jerman of O’Melveny & Myers, assisted the authors with this article. We also thank Professor Herbert Northrup for his helpful comments.     

Dairy Farm Survival in a Metropolitan Area: Dutchess County,New York, 1984–19901

Abstract The effect of nonfarm development on dairy farm survival in New York's highly developed lower Hudson Valley is analyzed. The major hypothesis is that significant losses of dairy farms during this period are the result of urban development pressures. Contrary to this hypothesis, however, the bivariate and multivariate analyses of data from a mail survey of all commercial dairy operators in Dutchess County as of 1984 and industry survivors, exiters, and new entrants through 1990 demonstrate that nonfarm development has a negligible effect on dairy farm survival; rather, survival is significantly affected by age of operator and by family disruption. Thus, demographic and family process factors are found to be more powerful determinants of dairy farm survival in this particular metropolitan context.     

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.     

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     

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     

Randomized response techniques for complex survey designs

Singh et al. ([13]) pointed out that the Randomized response (RR) technique proposed by Moors ([9]) is not desirable because it fails to protect the confidentiality of the respondents and they provided two alternative strategies free from the above drawback but limited to SRSWOR sampling only. In this paper, generalization of one of the strategies is provided for complex survey designs, wider class of estimators and for quantitative characteristics. Relative efficiency of the modified strategy is tested through empirical investigations. An erratum to this article is available at .     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.