Pompeu Fabra University

About: Pompeu Fabra University is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 8093 authors who have published 23570 publications receiving 858431 citations. The organization is also known as: Universitat Pompeu Fabra & UPF.

Cities, skills, and regional change

Abstract: One approach to urban areas emphasizes the existence of certain immutable relationships, such as Zipf's or Gibrat's Law An alternative view is that urban change reflects individual responses to changing tastes or technologies This paper examines almost 200 years of regional change in the US and finds that few, if any, growth relationships remain constant, including Gibrat's Law Education does a reasonable job of explaining urban resilience in recent decades, but does not seem to predict county growth a century ago After reviewing this evidence, we present and estimate a simple model of regional change, where education increases the level of entrepreneurship Human capital spillovers occur at the city level because skilled workers produce more product varieties and thereby increase labor demand We find that skills are associated with growth in productivity or entrepreneurship, not with growth in quality of life, at least outside of the West We also find that skills seem to have depressed housing supply growth in the West, but not in other regions, which supports the view that educated residents in that region have fought for tougher land-use controls We also present evidence that skills have had a disproportionately large impact on unemployment during the current recession

Gender inequalities in occupational health related to the unequal distribution of working and employment conditions: a systematic review

Abstract: Gender inequalities exist in work life, but little is known about their presence in relation to factors examined in occupation health settings. The aim of this study was to identify and summarize the working and employment conditions described as determinants of gender inequalities in occupational health in studies related to occupational health published between 1999 and 2010. A systematic literature review was undertaken of studies available in MEDLINE, EMBASE, Sociological Abstracts, LILACS, EconLit and CINAHL between 1999 and 2010. Epidemiologic studies were selected by applying a set of inclusion criteria to the title, abstract, and complete text. The quality of the studies was also assessed. Selected studies were qualitatively analysed, resulting in a compilation of all differences between women and men in the prevalence of exposure to working and employment conditions and work-related health problems as outcomes. Most of the 30 studies included were conducted in Europe (n=19) and had a cross-sectional design (n=24). The most common topic analysed was related to the exposure to work-related psychosocial hazards (n=8). Employed women had more job insecurity, lower control, worse contractual working conditions and poorer self-perceived physical and mental health than men did. Conversely, employed men had a higher degree of physically demanding work, lower support, higher levels of effort-reward imbalance, higher job status, were more exposed to noise and worked longer hours than women did. This systematic review has identified a set of working and employment conditions as determinants of gender inequalities in occupational health from the occupational health literature. These results may be useful to policy makers seeking to reduce gender inequalities in occupational health, and to researchers wishing to analyse these determinants in greater depth.

Many Languages, One Parser

Abstract: We train one multilingual model for dependency parsing and use it to parse sentences in several languages. The parsing model uses (i) multilingual word clusters and embeddings; (ii) token-level language information; and (iii) language-specific features (fine-grained POS tags). This input representation enables the parser not only to parse effectively in multiple languages, but also to generalize across languages based on linguistic universals and typological similarities, making it more effective to learn from limited annotations. Our parser's performance compares favorably to strong baselines in a range of data scenarios, including when the target language has a large treebank, a small treebank, or no treebank for training.