POPAN-4: enhancements to a system for the analysis of mark-recapture data from open populations

We describe recent developments in the POPAN system for the analysis of mark-recapture data from Jolly-Seber type experiments. The previous versions (POPAN-3 for SUN/OS workstations and POPAN-PC for IBM-PC running DOS or Windows) included general statistics gathering and testing procedures, a wide range of analysis options for estimating population abundance, survival and birth parameters, and a general simulation capability. POPAN-4 adds a very general procedure for fitting constrained models based on a new unified theory for Jolly-Seber models. Users can impose constraints on capture, survival and birth rates over time and/or across attribute groups (e.g. sex or age groups) and can model these rates using covariate models involving auxiliary variables (e.g. sampling effort).     

Organization of the planning department in a divisionalized concern

This article is based upon an assignment carried out by the author concerned with organization for planning in a company formed as a result of a merger. Possible organizational solutions are discussed and the recommendations of the author and his colleague outlined.     

慢市一种全新的舒适生活

或许,你听过或看过“慢活”这个名词,却不了解“慢活”是什么?它是全球正刮着的一股新风潮,有一群人极力鼓倡、推行着慢食、慢工、慢动、慢性、慢疗和慢闲运动,以一种正常而平衡的速度生活着……     

Decision support flexible manufacturing systems

The justification of flexible manufacturing systems (FMS) is a topic that has gained a lot of attention due to the strategic effect it has on the competitive stature of industrial firms. The decision to convert to FMS should follow a pattern of feasibility assessments, cost/benefit analysis where consideration of issues such as increased quality, productivity and capability as well as tangible benefits takes place. Difficulties, however, arise in the modeling of the behavior of the proposed FMS. This paper introduces the design of a decision support framework that aids the strategic planner in simulating the performance of proposed FMS and in determining the parameters that affect the costs and benefits, tangible and intangible, of such a system. The decision support system (DSS) allows the user to use subjective evaluations of benefits accruing from intangible considerations of new product design, faster turnaround on design-to-market cycles and new marketing strategies in fragmented markets. The DSS enhances the examination of issues such as changing demand, varied tasks and routings, job and machine flexibility, etc. which affect costs and benefits. It performs these important functions by allowing for the development of scenarios that aid in the evaluation of the effect of the conversion from non-flexible to flexible manufacturing on the organization's financial position.     

Social capital and political fantasy: Robert Putnam’sbowling alone

Theory and Society -     

ARE WINNERS PROMOTED TOO OFTEN? EVIDENCE FROM THE NFL DRAFT 1999–2012

Firms engaging in hiring face recruitment costs. To reduce these costs, firms concentrate their efforts in locations that are perceived as talent rich or have produced successful employees in the past. Such recruitment mechanisms may lead to statistical discrimination if they reduce uncertainty for a subset of candidates or if firms relate current employee attributes with the institution. In this article, I test for statistical discrimination associated with an individual's institutional affiliation that results from targeted hiring practices by using a unique individual‐level data set of National Football League (NFL) draft prospects. I find that conditional on individual ability, individuals from highly ranked college teams are drafted earlier than individuals from lower ranked institutions. Over the length of a player's professional career, a player's college institution has no effect on career success, indicating that certain players are damaged by this recruitment mechanism. Even though players can suffer substantial financial damages as a result of being drafted later in the draft, NFL team performance is not sufficiently affected for teams to exploit this bias. (JEL J71, J31)