On the causal interpretation of acyclic mixed graphs under multivariate normality

In multivariate statistics, acyclic mixed graphs with directed and bidirected edges are widely used for compact representation of dependence structures that can arise in the presence of hidden (i.e., latent or unobserved) variables. Indeed, under multivariate normality, every mixed graph corresponds to a set of covariance matrices that contains as a full-dimensional subset the covariance matrices associated with a causally interpretable acyclic digraph. This digraph generally has some of its nodes corresponding to hidden variables. We seek to clarify for which mixed graphs there exists an acyclic digraph whose hidden variable model coincides with the mixed graph model. Restricting to the tractable setting of chain graphs and multivariate normality, we show that decomposability of the bidirected part of the chain graph is necessary and sufficient for equality between the mixed graph model and some hidden variable model given by an acyclic digraph

Technology-Labor and Fiscal Spending Crowding-in Puzzles: The Role of Interpersonal Comparison

Standard real business cycle models predict a rise in employment following a technology shock. In contrast, numerous empirical studies show that a technology shock leads to a decline in labor input. In this paper, we demonstrate that a flexible price model enriched with interpersonal comparison of consumption expenditures is able to generate a fall in employment in response to a technology shock. The negative labor response is robust to different values assigned to the inverse Frisch elastictiy of labor supply and integrating capital adjustment cost into the model

Income Redistribution, Consumer Credit, and Keeping Up with the Riches

In this study, we set up a dynamic stochastic general equilibrium (DSGE) model with upward looking consumption comparison and show that consumption externalities are an important driver of consumer credit dynamics. Our model economy is populated by two different household types. Investors, who hold the economy\u27s capital stock, own the firms and supply credit, and workers, who supply labor and demand credit to finance consumption. Furthermore, workers condition their consumption choice on the investors\u27 level of consumption. We estimate the model and find a significant keeping up mechanism by matching business cycle statistics. In reproducing credit moments, our proposed model significantly outperforms a model version in which we abstract from consumption externalities

Context-Dependent Diffusion Network for Visual Relationship Detection

Visual relationship detection can bridge the gap between computer vision and natural language for scene understanding of images. Different from pure object recognition tasks, the relation triplets of subject-predicate-object lie on an extreme diversity space, such as \textit{person-behind-person} and \textit{car-behind-building}, while suffering from the problem of combinatorial explosion. In this paper, we propose a context-dependent diffusion network (CDDN) framework to deal with visual relationship detection. To capture the interactions of different object instances, two types of graphs, word semantic graph and visual scene graph, are constructed to encode global context interdependency. The semantic graph is built through language priors to model semantic correlations across objects, whilst the visual scene graph defines the connections of scene objects so as to utilize the surrounding scene information. For the graph-structured data, we design a diffusion network to adaptively aggregate information from contexts, which can effectively learn latent representations of visual relationships and well cater to visual relationship detection in view of its isomorphic invariance to graphs. Experiments on two widely-used datasets demonstrate that our proposed method is more effective and achieves the state-of-the-art performance.Comment: 8 pages, 3 figures, 2018 ACM Multimedia Conference (MM'18

"Spotify for News"? User Perception of Subscription-Based Content Platforms for News Media

Subscription-based news platforms (such as "Apple News+" or "Readly") that bundle content from different publishers into one comprehensive package and offer it to media users at a fixed monthly rate are a new way of accessing and consuming digital journalism. These services have received little attention in journalism studies, although they differ greatly from traditional media products and distribution channels. This article empirically investigates the perception of journalism platforms based on eight qualitative focus group discussions with 55 German news consumers. Results show that the central characteristics these platforms should fulfill in order to attract users are strikingly similar to the characteristics of media platforms from the music and video industries, in particular regarding price points, contract features, and modes of usage. Against this background, the potential and perspectives of a subscription-based news platform for journalism's societal role are discussed

Money for nothing and content for free? Willingness to pay for digital journalism

True to the motto “Money for nothing and content for free”, both up-to-date information and thoroughly researched reporting are principally used free of charge in their digital forms. Considering this, how can journalism be funded sustainably? This study focuses on users and investigates the reasons for their lack of willingness to pay for content, as well as what they do pay for, and why

Verteilungspolitische Implikationen der steuerlichen Begünstigung des 13. und 14. Monatsgehaltes

(no abstract available

Cabin Environment Physics Risk Model

This paper presents a Cabin Environment Physics Risk (CEPR) model that predicts the time for an initial failure of Environmental Control and Life Support System (ECLSS) functionality to propagate into a hazardous environment and trigger a loss-of-crew (LOC) event. This physics-of failure model allows a probabilistic risk assessment of a crewed spacecraft to account for the cabin environment, which can serve as a buffer to protect the crew during an abort from orbit and ultimately enable a safe return. The results of the CEPR model replace the assumption that failure of the crew critical ECLSS functionality causes LOC instantly, and provide a more accurate representation of the spacecraft's risk posture. The instant-LOC assumption is shown to be excessively conservative and, moreover, can impact the relative risk drivers identified for the spacecraft. This, in turn, could lead the design team to allocate mass for equipment to reduce overly conservative risk estimates in a suboptimal configuration, which inherently increases the overall risk to the crew. For example, available mass could be poorly used to add redundant ECLSS components that have a negligible benefit but appear to make the vehicle safer due to poor assumptions about the propagation time of ECLSS failures

An Integrated Physics-Based Risk Model for Assessing the Asteroid Threat

Although most asteroids and other near-Earth objects (NEOs) do not pose a threat to Earths inhabitants, impacts from objects that are just tens of meters in diameter can cause significant damage if they occur over a populated area. This paper forms the foundation of an effort at NASA Ames Research Center to quantify these risks and identify the greatest risk-driving parameters and uncertainties. An integrated risk model that couples dynamic probabilistic simulations of strike occurrences with physics-based models of NEO impact damage factors has been developed to generate casualty estimates for a range of NEO impact properties. Currently, the model focuses on the risk due to blast overpressure damage from airbursts and impacts on land. The model is first used to reproduce results from established sources, and then is extended to perform sensitivity studies that yield greater insights into risk driving parameters. Results show that meteor strength and entry angle play a role for small to mid-size NEOs, and that accounting for the specific target location significantly affects casualty estimates and dominates the risk. Future work will continue to refine and expand the models to better characterize key impact risk factors, include additional types of threats such as tsunamis and climate effects, and ultimately support assessments of potential asteroid mitigation strategies

Die langfristige Entwicklung der Einkommenskonzentration in Österreich, 1957-2008. Teil 1: Literaturüberblick und Beschreibung der Daten

Die Entwicklung der personellen Einkommensverteilung rückt im Zuge der Suche nach den strukturellen Ursachen der aktuellen Finanz- und Wirtschaftskrise immer stärker in das Zentrum des wissenschaftlichen und wirtschaftspolitischen Diskurses. In den meisten entwickelten Ländern, vor allem aber im angelsächsischen Raum, nahm die Konzentration der Einkommen und Vermögen in den letzten zwei Jahrzehnten massiv zu. Auch in Österreich lässt sich für die Lohneinkommen ein ähnliches Bild beobachten. Im Bereich der Lohneinkünfte konnte das oberste Prozent der Einkommensbeziehenden seit 1994 seinen Anteil an der gesamten ausbezahlten Lohnsumme um 12% erhöhen, das oberste Dezil insgesamt um ca. 6%. Eine Analyse der Entwicklung der personellen Einkommensverteilung über alle Einkunftsarten ist für Österreich dagegen mit der derzeit verfügbaren Datengrundlage nur in eingeschränktem Umfang möglich. In der vorliegenden Arbeit werden die Probleme der einzelnen Datenquellen im Zuge einer solchen Analyse dargelegt und ihre Auswirkungen auf die Interpretation der berechneten Konzentrationsmaße eingehend diskutiert. (authors' abstract