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Narrating Inflation
(2025-03-14) Schmidt, Tobias
This paper examines the pivotal role of journalists in shaping economic narratives, focusing on inflation coverage in Germany in 2022. While the media’s influence on disseminating economic narratives is widely acknowledged, little research has focused on journalists, the agents responsible for content production. Using a mixed-method approach combining survey data with media content analysis, this study investigates how economic journalists explain inflation causes and persistence compared to professional economists. The results from surveys conducted during peak inflation (10.4%) show that journalists hold less optimistic views on inflation persistence than experts and that they are more likely to attribute inflation to specific protagonists, particularly the European Central Bank (ECB) and corporate profit-seeking. The ECB’s role emerges as an especially contentious issue among journalists, revealing significant disagreement within the profession. Analysis of media coverage reveals notable alignment between journalists’ perceptions and actual content, especially regarding the emphasis placed on the ECB’s role - despite experts considering monetary policy a relatively minor factor. While this suggests that journalists’ personal narratives actually influence media coverage, the study’s design precludes causal claims. The findings underscore the need for further research into how journalists’
personal narratives impact public discourse on economic matters.
Bivariate analysis of birth weight and gestational age by Bayesian distributional regression with copulas
(2023-10-27) Rathjens, Jonathan; Kolbe, Arthur; Hölzer, Jürgen; Ickstadt, Katja; Klein, Nadja
We analyze perinatal data including biometric and obstetric information as well as data on maternal smoking, among others. Birth weight is the primarily interesting response variable. Gestational age is usually an important covariate and included in polynomial form. However, in opposition to this univariate regression, bivariate modeling of birth weight and gestational age is recommended to distinguish effects on each, on both, and between them. Rather than a parametric bivariate distribution, we apply conditional copula regression, where the marginal distributions of birth weight and gestational age (not necessarily of the same form) and the dependence structure are modeled conditionally on covariates. In the resulting distributional regression model, all parameters of the two marginals and the copula parameter are observation specific. While the Gaussian distribution is suitable for birth weight, the skewed gestational age data are better modeled by the three-parameter Dagum distribution. The Clayton copula performs better than the Gumbel and the symmetric Gaussian copula, indicating lower tail dependence (stronger dependence when both variables are low), although this non-linear dependence between birth weight and gestational age is surprisingly weak and only influenced by Cesarean section. A non-linear trend of birth weight on gestational age is detected by a univariate model that is polynomial with respect to the effect of gestational age. Covariate effects on the expected birth weight are similar in our copula regression model and a univariate regression model, while distributional copula regression reveals further insights, such as effects of covariates on the association between birth weight and gestational age.
BMO ε-regularity results for solutions to Legendre–Hadamard elliptic systems
(2023-06-03) Irving, Christopher
We will establish an ε-regularity result for weak solutions to Legendre–Hadamard elliptic systems, under the a-priori assumption that the gradient ∇u is small in BMO. Focusing on the case of Euler–Lagrange systems to simplify the exposition, regularity results will be obtained up to the boundary, and global consequences will be explored. Extensions to general quasilinear elliptic systems and higher-order integrands is also discussed.
Cognitive profiles of children with isolated and comorbid learning difficulties in reading and math: a meta-analysis
(2023-03-13) Viesel-Nordmeyer, Nurit; Reuber, Julia; Kuhn, Jörg-Tobias; Moll, Kristina; Holling, Heinz; Dobel, Christian
The causes underlying comorbid learning difficulties in reading (RD) and math (MD) are still a matter of debate. Based on current research, two models for the relation of the cognitive profile of isolated and combined learning difficulties (RDMD) are discussed. Regarding the “multi-deficit model”, the profile of RDMD is characterized by the sum of domain-specific core deficits of RD and MD (additivity) as well as shared domain-general risk factors of RD and MD resulting in less severe deficits than expected under additivity (under-additivity). The “three independent disorders model” explains RDMD as a distinct learning disorder, showing a separate cognitive profile with distinct and/or more severe deficits, compared to the sum of RD’s and MD’s profiles (over-additivity). To evaluate these approaches, a meta-analysis including 74 studies, examining children aged 6–12, was conducted. Separate group comparisons for the three subcomponents in the cognitive profiles—reading, math, executive functions (EF)—were considered. Linear hypothesis testing revealed different results regarding the three subcomponents of the cognitive profiles of children with isolated vs. combined learning difficulties: Whereas RDMDs’ deficits in reading and math represented the sum of the deficits in the isolated groups (additivity), there was some evidence that RDMDs’ deficits in EF skills corresponded to under-additivity. Furthermore, group differences in math skills were more pronounced in symbolic than in non-symbolic math tasks, whereas in reading, group differences were larger in phonological processing and reading than in rapid automatized naming and language skills. Results are discussed in terms of intervention options for RDMD.
Challenges of IT freelancers on digital labor platforms: a topic model approach
(2023-10-23) Gussek, Lisa; Grabbe, Alex; Wiesche, Manuel
Freelancers working on digital labor platforms have to deal with many challenges. IT freelancers should be considered separately due to special characteristics such as high skill level or collaborative nature of IT work. Through a Latent Dirichlet Allocation analysis of 2804 forum posts with over 20,000 comments from IT freelancers and additional qualitative analysis, we assign themes to each identified topic, cluster them into aggregated dimensions, and illustrate the interrelationships in a model of IT freelancing. Thus, we examine the challenges of IT freelancers and synthesize them. We extend the challenges of online freelancing already outlined in the literature and identify four IT-specific challenges for IT freelancers. Therefore, we improve our understanding of how IT work is performed on digital labor platforms and which particular aspects should be considered in future research. Furthermore, we highlight implications for practitioners, i.e., IT freelancers on the one hand and platform owners on the other hand.