Competing for jobs: Labor queues and gender sorting in the hiring process

While much research has documented the pattern and extent of sex segregation of workers once they are employed, few studies have addressed the pre-hire mechanisms that are posited to produce sex segregation in employment. While the notion of a labor queue—the rank order of the set of people that employers choose among—plays a prominent role in pre-hire accounts of job sex sorting mechanisms, few studies have examined the ways in which job candidates are sorted into labor queues. In this paper, we explore the mechanisms by which labor queues contribute to the gendering of jobs by studying the hiring process for all jobs at a call center. Being placed in a queue has a clear gendering effect on the hiring process: the sex distribution of applicants who are matched to queues and those who are rejected at this phase diverge, and among those assigned to queues, women are prevalent in queues for low pay, low status jobs. The screening process also contributes to the gendering of the population of hires at this firm. Females are more prevalent among hires than they are among candidates at initial queue assignment. Among high status jobs, however, males are more prevalent than females. Moreover, there are important wage implications associated with matching to queues. While there are large between-queue sex differences in the paid wages associated with allocation to queues, once allocated to queues the wage differences between male and female candidates are nil. Consequently, the roots of gender wage inequality in this setting lie in the initial sorting of candidates to labor queues.     

Interacting multiple try algorithms with different proposal distributions

We introduce a new class of interacting Markov chain Monte Carlo (MCMC) algorithms which is designed to increase the efficiency of a modified multiple-try Metropolis (MTM) sampler. The extension with respect to the existing MCMC literature is twofold. First, the sampler proposed extends the basic MTM algorithm by allowing for different proposal distributions in the multiple-try generation step. Second, we exploit the different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods and builds connections with the rapidly expanding world of adaptive MCMC. We show the validity of the algorithm and discuss the choice of the selection weights and of the different proposals. The numerical studies show that the interaction mechanism allows the IMTM to efficiently explore the state space leading to higher efficiency than other competing algorithms.     

Exposing the white avatar: projections,justifications, and the ever-evolving American racism

In the context of the United States, mainstream entertainment genres continue to recycle dominant racial ideologies typified by the perspectives of white men. American minstrelsy, literature, and film are key sites for whiteness to manifest itself in the design and projection of online personae via white avatars. These projections of whiteness also reify the US racial structure, one that subjects the perspectives of people of color to misrepresentation and further racial marginalization. This theoretical interpretative article employs Critical Race Theory (CRT) and Critical Whiteness Studies (CWS) to investigate how these projections of whiteness are historically rooted and have evolved in the post-racial era. Using popular virtual gaming and social media examples, this paper critically deconstructs how the creation of white personae via avatars maintains and justifies the hegemonic power of whiteness.     

Supplier involvement in integrated product development: evidence from a group of Italian SMEs

Integrated product development (IPD) is gaining increased attention from practitioners and the academic community as a source of innovation and competitive advantage. However undeniable the importance of the issue is as to how the different actors involved in the process interact among themselves and the supporting factors not being well understood even in the SME context, the availability of empirical research and a comprehensive framework to drive this research remains unavailable. This article contributes to these research gaps by providing theoretical and empirical findings. First, a research framework on IPD is identified. The proposed framework represents a conceptualisation of IPD principles as consisting of three groups of integration enablers, which aim to achieve high internal (among departments) and external (with suppliers and customers) integration: (a) organisational enablers, (b) technological enablers and (c) methodologies. Then, a multiple case study approach is used in order to empirically explore external integration with suppliers in product development in 22 SMEs from the machine tool sector.     

A Bayesian Markov-Switching Correlation Model for Contagion Analysis on Exchange Rate Markets

This article develops a new Markov-switching vector autoregressive (VAR) model with stochastic correlation for contagion analysis on financial markets. The correlation and the log-volatility dynamics are driven by two independent Markov chains, thus allowing for different effects such as volatility spill-overs and correlation shifts with various degrees of intensity. We outline a suitable Bayesian inference procedure based on Markov chain Monte Carlo algorithms. We then apply the model to some major and Asian-Pacific cross rates against the U.S. dollar and find strong evidence supporting the existence of contagion effects and correlation drops during crises, closely in line with the stylized facts outlined in the contagion literature. A comparison of this model with its closest competitors, such as a time-varying parameter VAR, reveals that our model has a better predictive ability. Supplementary materials for this article are available online     

A Stochastic Volatility Model With Realized Measures for Option Pricing

Abstract

Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A semi-analytical option pricing framework is developed for this class of models. In addition, we provide analytical filtering and smoothing recursions for the basic specification of the model, and an effective MCMC algorithm for its richer variants. The empirical analysis shows the effectiveness of filtering and smoothing realized measures in inflating the latent volatility persistence—the crucial parameter in pricing Standard and Poor’s 500 Index options.     

Inferences in dichotomous classifications with misclassifications based on sequential repetitive classifications

To create inferences in dichotomous classifications with misclassifications and possibly perform repeated classifications, the maximum likelihood method is commonly used, mainly because of its efficiency in obtaining parameter estimators of a mixture of two binomial distributions. One simpler alternative that is operationally easier is to consider the simple majority method. In this method, each of n items are classified r times as conforming or non-conforming. The final classification of the item is determined by the most frequent class. This method yielded lower mean squared errors than the maximum likelihood and the moments estimators and is asymptotically efficient. In this paper, we introduce a new approach in which the realization of all r repeated classifications of each item may not be needed. Each of n items is sequentially classified as conforming or nonconforming, and the process ceases when the frequency of conforming or non-conforming classification reaches the integer a. We show that, by a Monte Carlo simulation, the last procedure presents a lower mean squared error than the simple majority results for a similar number of r repeated classifications.     

Education and gender bias in the sex ratio at birth: Evidence from India

This article investigates the possible existence of a nonlinear link between female disadvantage in natality and education. To this end, we devise a theoretical model based on the key role of social interaction in explaining people’s acquisition of preferences, which justifies the existence of a nonmonotonic relationship between female disadvantage in natality and education. The empirical validity of the proposed model is examined for the case of India, using district-level data. In this context, our econometric analysis pays particular attention to the role of spatial dependence to avoid any potential problems of misspecification. The results confirm that the relationship between the sex ratio at birth and education in India follows an inverted U-shape. This finding is robust to the inclusion of additional explanatory variables in the analysis, and to the choice of the spatial weight matrix used to quantify the spatial interdependence between the sample districts.