Unemployment,Vacancies and Unfair Dismissals

In most West European economies the annual number of grievance procedures settling individual complaints against unfair dismissals has been increasing since the 1960s. This development has very often been attributed to the enactment of legal regulations restricting the dismissal behaviour of firms. Econometric analyses using data from Germany and Great Britain show that labour market developments, namely the flow into unemployment and the vacancy rate, have a much stronger influence on the cyclical demand for grievance procedures than changes in the “legal infrastructure” of the labour market. Without denying the importance of institutional differences it appears that the individual costs of unemployment (which, ceteris paribus, rise as the flow into unemployment increases and the vacancy rate decreases) are superior predictors of the demand for grievance procedures than institutional changes strengthening or weakening employees' rights.     

Risk preference and employment contract type

Summary.  We explore the possibility that a systematic relationship exists between employment within a particular type of contract and risk preference. We exploit a set of proxies for risk preference, whereby some of the proxies capture risk loving behaviour (expenditure on gambling, smoking and alcohol) whereas others capture risk averse behaviour (expenditure on life and contents insurance, and unearned income). The empirical analysis, based on pooled cross-section data from the UK Family Expenditure Survey, 1997–2000, provides evidence of a systematic relationship between employment contract type and risk preference, with, for example, self-employed workers being more or less likely to engage in the consumption of 'risky' or financial security products respectively. The results are based on the ordered generalized extreme value model, a relatively infrequently used discrete choice model, which allows for ordering and correlation in the alternatives observed.     

The SLA DBIO 100 Poll: 100 Journals Voted by SLA’s BioMedical and Life Sciences Division as the Most Influential over the Last 100 Years

Abstract

An expert opinion poll of the members of the BioMedical and Life Sciences Division (DBIO) of the Special Libraries Association (SLA), conducted on the occasion of the SLA's Centennial (Washington, DC, June 14–17, 2009), identified 100 journals across three major categories (Clinical Medicine and Allied Health Sciences, Journals Primarily Reporting Molecular and Cellular Biology, and Journals of Natural History) as the most influential over the last 100 years. The Top Ten, containing winners from all three of these groups, were also named, as were a “Journal” and a “Publisher” of the Centennial.”     

Reviews

Abstract

Trina Boukin Nolen reviews Dancing with Digital Natives, Stephanie Rosenblatt reviews The Embedded Librarian and Sarah D. Tusa reviews Collection Development in the Digital Age.     

Response to Young and Wolf: goal attainment in urban ecology research

Our critique focuses on the poorly defined key concepts, methodological inconsistencies, circular research design, and over-reaching substantive claims made by Young and Wolf. We suggest that Young and Wolf have provided an assessment of the Urban Ecosystems journal, not of urban ecology as a field. We conclude by identifying questions to guide a bibliometric analysis that focuses on a collaborative and interdisciplinary future of urban ecology (how are participating disciplines contributing to urban ecological research and scholarship; what theories and conceptual frameworks are being used, and how are these theories being tested and modified; and what mixed methodologies are being developed to collect data to address complex urban issues that are inherently interdisciplinary). We take seriously Young and Wolf’s call for a “fundamental discussion as to if and how the intentions of the field have been or need to be updated” and argue that such a discussion requires a more inclusive, rigorous, and meaningful identification of the “core” of urban ecology literature than provided.     

Progress Toward Title IX Compliance: The Effect of Formal and Informal Enforcement Mechanisms*

Objective. This article examines the factors that determine whether an intercollegiate athletic program is in compliance with Title IX, the statute requiring gender equity in educational programs. Methods. I conduct a series of econometric regressions that examine the compliance status of Division I institutions as well as the progress they have made toward compliance. Results. Large institutions and institutions with a lower percentage of female undergraduates are more likely to be in compliance. Institutions with football programs are less likely to be in compliance as are schools in the south. The effect of football revenues, operating budgets, and NCAA sanctions varies across division as well as compliance area. Conclusions. The results of the analysis indicate that current enforcement mechanisms have been relatively ineffective at increasing compliance and that some change either in enforcement or compliance standards is warranted. Additionally, a one‐size‐fits‐all enforcement approach is unlikely to be effective.     

A Bayesian dose‐finding design for drug combination clinical trials based on the logistic model

In early phase dose‐finding cancer studies, the objective is to determine the maximum tolerated dose, defined as the highest dose with an acceptable dose‐limiting toxicity rate. Finding this dose for drug‐combination trials is complicated because of drug–drug interactions, and many trial designs have been proposed to address this issue. These designs rely on complicated statistical models that typically are not familiar to clinicians, and are rarely used in practice. The aim of this paper is to propose a Bayesian dose‐finding design for drug combination trials based on standard logistic regression. Under the proposed design, we continuously update the posterior estimates of the model parameters to make the decisions of dose assignment and early stopping. Simulation studies show that the proposed design is competitive and outperforms some existing designs. We also extend our design to handle delayed toxicities. Copyright © 2014 John Wiley & Sons, Ltd.     

A cautionary case study of approaches to the treatment of missing data

This article presents findings from a case study of different approaches to the treatment of missing data. Simulations based on data from the Los Angeles Mammography Promotion in Churches Program (LAMP) led the authors to the following cautionary conclusions about the treatment of missing data: (1) Automated selection of the imputation model in the use of full Bayesian multiple imputation can lead to unexpected bias in coefficients of substantive models. (2) Under conditions that occur in actual data, casewise deletion can perform less well than we were led to expect by the existing literature. (3) Relatively unsophisticated imputations, such as mean imputation and conditional mean imputation, performed better than the technical literature led us to expect. (4) To underscore points (1), (2), and (3), the article concludes that imputation models are substantive models, and require the same caution with respect to specificity and calculability. The research reported here was partially supported by National Institutes of Health, National Cancer Institute, R01 CA65879 (SAF). We thank Nicholas Wolfinger, Naihua Duan, John Adams, John Fox, and the anonymous referees for their thoughtful comments on earlier drafts. The responsibility for any remaining errors is ours alone. Benjamin Stein was exceptionally helpful in orchestrating the simulations at the labs of UCLA Social Science Computing. Michael Mitchell of the UCLA Academic Technology Services Statistical Consulting Group artfully created Fig. 1 using the Stata graphics language; we are most grateful.     

Exact Goodness‐of‐Fit Testing for the Ising Model

The Ising model is one of the simplest and most famous models of interacting systems. It was originally proposed to model ferromagnetic interactions in statistical physics and is now widely used to model spatial processes in many areas such as ecology, sociology, and genetics, usually without testing its goodness of fit. Here, we propose various test statistics and an exact goodness‐of‐fit test for the finite‐lattice Ising model. The theory of Markov bases has been developed in algebraic statistics for exact goodness‐of‐fit testing using a Monte Carlo approach. However, finding a Markov basis is often computationally intractable. Thus, we develop a Monte Carlo method for exact goodness‐of‐fit testing for the Ising model that avoids computing a Markov basis and also leads to a better connectivity of the Markov chain and hence to a faster convergence. We show how this method can be applied to analyze the spatial organization of receptors on the cell membrane.