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dc.contributor.authorMüller, Henrik-
dc.contributor.authorHornig, Nico-
dc.date.accessioned2020-10-14T17:47:32Z-
dc.date.available2020-10-14T17:47:32Z-
dc.date.issued2020-
dc.identifier.urihttp://hdl.handle.net/2003/39777-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-21669-
dc.description.abstractNews-based indicators are in vogue in economics. But they tend to be applied with little consideration for the properties of news itself. In this paper, we try to shed light on the nature of this type of data. Drawing from established findings in communication science and journalism studies we argue that news-based indicators should be taken with a pinch of salt, since news is a somewhat biased representation of political and social reality. Contrary to economics and other social sciences, journalism tends to be driven by outliers, the outrageous, and the outraged. This structural dissonance between journalism and other disciplines needs to be born in mind when dealing with news content as data, and it is of particular concern in the context of economic developments. While economics and statistics are inherently backward looking, trying to make sense of the (immediate) past using models and probability distributions derived from bygone observations, journalism is about the present, and sometimes about the future. What’s going on right now? And where does it lead us? Seeking answers to these questions makes news a valuable data input, as a measure of what drives society at a given point in time. We show how taking the properties of news into consideration influences the entire process of large-scale news analysis. As an example, we update our Uncertainty Perception Indicator (Müller and Hornig 2020), setting it on a firmer footing by enlarging the newspaper corpus considerably. The new version of the UPI for Germany yields some remarkable results. At the trough of the Covid-19-induced economic crisis in Q2 of 2020, the overall indicator already decreased considerably, although it stayed at elevated levels. Deconstructing the UPI by applying the topic modelling approach Latent Dirichlet Allocation (LDA), shows that the coverage of the pandemic has merged with the issue of climate change and its mitigation. In the past decade or so incalculable politics was the main driver of economic uncertainty perception. Now truly exogenous developments, neither elicited by the economy nor by politics, come to the fore, adding to the sense of an inherently unstable world.en
dc.language.isoende
dc.relation.ispartofseriesDoCMA Working Paper;2-2020en
dc.subjectuncertaintyen
dc.subjecteconomic policyen
dc.subjectbusiness cyclesen
dc.subjectCovid-19en
dc.subjectlatent Dirichlet allocationen
dc.subject.ddc004-
dc.subject.ddc070-
dc.subject.ddc310-
dc.title"I heard the News today, oh Boy"en
dc.title.alternativeAn updated Version of our Uncertainty Perception Indicator (UPI) - and some general thoughts on news-based economic indicatorsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dc.subject.rswkCOVID-19de
dc.subject.rswkUngewissheitde
dc.subject.rswkWirtschaftskreislaufde
dc.subject.rswkStochastisches Modellde
dc.subject.rswkWirtschaftspolitikde
dc.subject.rswkIndikatorde
dcterms.accessRightsopen access-
eldorado.secondarypublicationfalsede
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