Childbearing and (female) research productivity: a personnel economics perspective on the leaky pipeline

Despite the fact that childbearing is time-consuming (i.e., associated with a negative resource effect), we descriptively find female researchers with children in business and economics to be more productive than female researchers without children. Hence, female researchers with children either manage to overcompensate the negative resource effect associated with childbearing by working harder (positive incentive effect), or only the most productive female researchers decide to go for a career in academia and have children at the same time (positive self-selection effect). Our first descriptive evidence on the timing of parenthood among more than 400 researchers in business and economics from Austria, Germany and the German-speaking part of Switzerland hints at the latter being the case: only the most productive female researchers with children dare to self-select (or are selected) into an academic career. Our results have important policy implications when it comes to reducing the “leaky pipeline” in academia

Capitalizing on Bourdieu : how useful are concepts of `social capital` and `social field` for researching `marginalized` young women?

This article considers Bourdieu&rsquo;s concepts of &lsquo;social capital&rsquo; and &lsquo;social fields&rsquo;, comparing and contrasting his use of these concepts with that of James Coleman and Robert Putnam. It examines how Bourdieu&rsquo;s ideas offer a different way of understanding the lives of economically disadvantaged young women designated as &lsquo;at risk&rsquo; of leaving school early. A micro-level analysis is made of &lsquo;Bluey&rsquo;s story&rsquo;, a narrative derived from data gathered during a three-year research project on young women&rsquo;s negotiations from the margins of education and work in Australia. Deconstructing Bluey&rsquo;s narrative reveals how social capital is deployed (sometimes unsuccessfully) in a range of social fields. Such narratives can thus be used to &lsquo;speak back&rsquo; to educational policies, to provide alternative insights into the issues and needs of economically disadvantaged young women and to challenge current constructs of &lsquo;at risk&rsquo;.<br /

Publish and Prosper: The Financial Impact of Publishing by Marketing Faculty

This paper investigates the impact of research productivity on the salaries of marketing faculty members. We examine how the number of articles published in various types of publications affects faculty members' nine-month salary using a sample of three years of information on 298 marketing professors from 33 research-oriented, public universities. Consistent with research conducted in other disciplines, we find a positive impact of publishing on salary. We estimate the individual salary impact for each of the four top-tier journals in marketing, finding that the biggest impact on salary comes from publishing in . Further, we find that publishing in Tier 1 journals in marketing has a bigger salary impact than in Tier 1 journals outside marketing. Finally, we find that faculty and department characteristics also affect salary. For example, being from a higher ranked research university and being a full professor are each associated with higher salary.research, financial impact, salary, publication

Nested Sequential Monte Carlo Methods

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000