Strategic Intersectionality and Political Representation: Female Muslim Councilors in London

This article addresses questions of how race/ethnicity, gender, and religion influence political representation. We use original interview data to test a strategic intersectionality theory developed by Fraga and colleagues (2005) in the case of female Muslim councilors in London, the United Kingdom. The original strategic intersectionality theory proposes that women are more effective advocates for ethnic group interests due to their unique capacity to leverage three primary resources: a substantive policy focus, multiple identity advantage, and gender inclusive advantage. We modify the thesis by analyzing religion as an additional identity marker and further disaggregating the three primary sources of leverage. We use the modified thesis to test whether female Muslim councilors of three London boroughs are more effective advocates for Muslim interests than their non-Muslim colleagues. We find mixed evidence for the presence of the three sources of leverage associated with strategic intersectionality, resulting in a more complex theorizing of this phenomenon than that found in prior research. This study offers a new contribution to the operationalization of intersectionality and the literature on intersectionality and political representation.     

Non‐dominated Time‐Window Policies in City Distribution

Urban freight contributes significantly to pollution, noise disturbance, traffic congestion, and safety problems in city centers. In many cities, local governments have introduced policy measures, in particular time‐access restrictions, to alleviate these problems. However, setting time windows is very challenging due to the conflicting interests and objectives of the stakeholders involved. In this article, we examine whether it is possible to develop time‐window policies that enhance environmental sustainability and distribution efficiencies, while meeting the objectives of the municipalities. We develop a framework for balancing retailer (costs), municipality (satisfaction), and environmental (emissions) objectives, using data envelopment analysis, under different urban time‐window policies. The approach is illustrated by a case study of three Dutch retail organizations, with a large number of stores affected by such time windows. On the basis of an evaluation of 99 different time‐window policies, our results show that harmonizing time windows between neighboring cities leads to the best overall performance. The currently used time‐window policy appears to perform reasonably well, but can be improved on all dimensions . However, harmonizing time‐window policies may be difficult to realize in practice.     

Skill Management in Large‐Scale Service Marketplaces

Large‐scale, web‐based service marketplaces have recently emerged as a new resource for customers who need quick resolutions for their short‐term problems. Due to the temporary nature of the relations between customers and service providers (agents) in these marketplaces, customers may not have an opportunity to assess the ability of an agent before their service completion. On the other hand, the moderating firm has a more sustained relationship with agents, and thus it can provide customers with more information about the abilities of agents through skill screening mechanisms. In this study, we consider a marketplace where the moderating firm can run two skills tests on agents to assess if their skills are above certain thresholds. Our main objective is to evaluate the effectiveness of skill screening as a revenue maximization tool. We, specifically, analyze how much benefit the firm obtains after each additional skill test. We find that skill screening leads to negligible revenue improvements in marketplaces where agent skills are highly compatible and the average service times are similar for all customers. As the compatibility of agent skills weakens or the customers start to vary in their processing time needs, we show that the firm starts to experience sizable improvements in revenue from skill screening. Apparently, the firm can reap the most of these substantial benefits when it runs only one test. For instance, in marketplaces where agents posses uncorrelated skills, the second skill test only brings an additional 2% improvement in revenue. Accounting for possible skill screening costs, we then show the optimality of offering only one test when the compatibility between agent skills is sufficiently low. The results of this study also have important implications in terms of the right level of intervention in the marketplaces we study.     

Muslims and Minority Politics in Great Britain

This paper reviews the history of immigration and demographics of British Muslims and analyzes the relevant characteristics that influence their political representation in the country. Such factors include immigration patterns, demographic characteristics of Muslim groups and coalition versus group competition trends, political mobilization patterns, the politics of race, and the dynamics of party–minority relations. The paper also provides original data on elected Muslims in British local and national government and examines the electoral power, political identity, social behavior, civic and political participation, and representation of Muslims in the larger British community.     

A Decision Support Tool for Predicting Patients at Risk of Readmission: A Comparison of Classification Trees,Logistic Regression,Generalized Additive Models,and Multivariate Adaptive Regression Splines

The number of emergency (or unplanned) readmissions in the United Kingdom National Health Service (NHS) has been rising for many years. This trend, which is possibly related to poor patient care, places financial pressures on hospitals and on national healthcare budgets. As a result, clinicians and key decision makers (e.g., managers and commissioners) are interested in predicting patients at high risk of readmission. Logistic regression is the most popular method of predicting patient‐specific probabilities. However, these studies have produced conflicting results with poor prediction accuracies. We compared the predictive accuracy of logistic regression with that of regression trees for predicting emergency readmissions within 45 days after been discharged from hospital. We also examined the predictive ability of two other types of data‐driven models: generalized additive models (GAMs) and multivariate adaptive regression splines (MARS). We used data on 963 patients readmitted to hospitals with chronic obstructive pulmonary disease and asthma. We used repeated split‐sample validation: the data were divided into derivation and validation samples. Predictive models were estimated using the derivation sample and the predictive accuracy of the resultant model was assessed using a number of performance measures, such as area under the receiver operating characteristic (ROC) curve in the validation sample. This process was repeated 1,000 times—the initial data set was divided into derivation and validation samples 1,000 times, and the predictive accuracy of each method was assessed each time. The mean ROC curve area for the regression tree models in the 1,000 derivation samples was .928, while the mean ROC curve area of a logistic regression model was .924. Our study shows that logistic regression model and regression trees had performance comparable to that of more flexible, data‐driven models such as GAMs and MARS. Given that the models have produced excellent predictive accuracies, this could be a valuable decision support tool for clinicians (healthcare managers, policy makers, etc.) for informed decision making in the management of diseases, which ultimately contributes to improved measures for hospital performance management.     

Differential Peer Effects,Student Achievement,and Student Absenteeism: Evidence From a Large-Scale Randomized Experiment

Using data from a well-executed randomized experiment, I examine the effects of gender composition and peer achievement on high school students’ outcomes in disadvantaged neighborhoods. Results show that having a higher proportion of female peers in the classroom improves girls’ math test scores only in less-advanced courses. For male students, the estimated gender peer effects are positive but less precisely estimated. I also find no effect of average classroom achievement on female math test scores. Males, on the other hand, seem to benefit from a higher-achieving classroom. I propose mechanisms relating to lower gender stereotype influences and gender-specific attitudes toward competition as potential explanations for peer effects findings. Finally, having a higher proportion of female students in the classroom decreases student absenteeism among male students but has no impact on female attendance.     

A Maximum Entropy Joint Demand Estimation and Capacity Control Policy

We propose a tractable, data‐driven demand estimation procedure based on the use of maximum entropy (ME) distributions, and apply it to a stochastic capacity control problem motivated from airline revenue management. Specifically, we study the two fare class “Littlewood” problem in a setting where the firm has access to only potentially censored sales observations; this is also known as the repeated newsvendor problem. We propose a heuristic that iteratively fits an ME distribution to all observed sales data, and in each iteration selects a protection level based on the estimated distribution. When the underlying demand distribution is discrete, we show that the sequence of protection levels converges to the optimal one almost surely, and that the ME demand forecast converges to the true demand distribution for all values below the optimal protection level. That is, the proposed heuristic avoids the “spiral down” effect, making it attractive for problems of joint forecasting and revenue optimization problems in the presence of censored observations.