Selection of workers and firm heterogeneity

A model based on differences between workers regarding their preferences for wage and leisure drives the heterogeneity of firms result. The more industrious workers are driven to small firms due to free riding in large firms. An industry consisting of small and large firms turns out to produce more output than an industry consisting of only large firms. Some comparative statics results are derived with respect to the size of large firms, the productivity difference between firms, and monitoring capabilities

Influence of firm size on the competencies required to management engineers in the Jordanian telecommunications sector

This is an Accepted Manuscript of an article published by Taylor & Francis in European Journal of Engineering Education on [13 jun 2016], available online:http://www.tandfonline.com/ doi/abs/10.1080/03043797.2016.1197890.[EN] The objective of this study is to identify the competencies required to achieve success in the transition from higher education to the labour market based on the perceptions of employers. This paper analyses the assessments made by a group of engineering company employers. An item-battery of 20 competencies was grouped into 3 dimensions by using factor analysis. Subsequently, respondents scores were also clustered into three groups and characterised through contingency tables. The competencies demanded by employers were grouped into business and finance, problem-solving and strategic planning. Significant differences were found between responses from employers working in medium and small companies, who placed more importance on competencies related to problem-solving and strategic planning, and employers in big companies, who were more concerned about the difficulties of finding well-trained graduates. The findings from this paper have important implications for research in the areas of higher education and organisations that usually employ graduate engineers.The authors would like to thank the Education, Audiovisual and Culture Executive Agency (EACEA) [Tempus program. Project number 511074] of the European Commission for providing funding for conducting this study. This publication reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.Conchado Peiró, A.; Bas Cerdá, MDC.; Gharaibeh, KM.; Kaylani, H. (2016). 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Assembly of the LongSHOT cohort: Public record linkage on a grand scale

Background: Virtually all existing evidence linking access to firearms to elevated risks of mortality and morbidity comes from ecological and case-control studies. To improve understanding of the health risks and benefits of firearm ownership, we launched a cohort study: the Longitudinal Study of Handgun Ownership and Transfer (LongSHOT). Methods: Using probabilistic matching techniques we linked three sources of individual-level, state-wide data in California: official voter registration records, an archive of lawful handgun transactions and all-cause mortality data. There were nearly 28.8 million unique voter registrants, 5.5 million handgun transfers and 3.1 million deaths during the study period (18 October 2004 to 31 December 2016). The linkage relied on several identifying variables (first, middle and last names; date of birth; sex; residential address) that were available in all three data sets, deploying them in a series of bespoke algorithms. Results: Assembly of the LongSHOT cohort commenced in January 2016 and was completed in March 2019. Approximately three-quarters of matches identified were exact matches on all link variables. The cohort consists of 28.8 million adult residents of California followed for up to 12.2 years. A total of 1.2 million cohort members purchased at least one handgun during the study period, and 1.6 million died. Conclusions: Three steps taken early may be particularly useful in enhancing the efficiency of large-scale data linkage: thorough data cleaning; assessment of the suitability of off-the-shelf data linkage packages relative to bespoke coding; and careful consideration of the minimum sample size and matching precision needed to support rigorous investigation of the study questions

Pathogenesis of aerosolized Eastern Equine Encephalitis virus infection in guinea pigs

Mice and guinea pigs were experimentally exposed to aerosols containing regionally-distinct strains (NJ1959 or ArgM) of eastern equine encephalitis virus (EEEV) at two exclusive particle size distributions. Mice were more susceptible to either strain of aerosolized EEEV than were guinea pigs; however, clinical signs indicating encephalitis were more readily observed in the guinea pigs. Lower lethality was observed in both species when EEEV was presented at the larger aerosol distribution (> 6 μm), although the differences in the median lethal dose (LD50) were not significant. Virus isolation and immunohistochemistry indicated that virus invaded the brains of guinea pigs within one day postexposure, regardless of viral strain or particle size distribution. Immunohistochemistry further demonstrated that neuroinvasion occurred through the olfactory system, followed by transneuronal spread to all regions of the brain. Olfactory bipolar neurons and neurons throughout the brain were the key viral targets. The main microscopic lesions in infected guinea pigs were neuronal necrosis, inflammation of the meninges and neuropil of the brain, and vasculitis in the brain. These results indicate that guinea pigs experimentally infected by aerosolized EEEV recapitulate several key features of fatal human infection and thus should serve as a suitable animal model for aerosol exposure to EEEV

Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Per\ufa

\ua9 2024. The Author(s). Alpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families

Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Perú

Alpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families

Phenotypes Determined by Cluster Analysis and Their Survival in the Prospective European Scleroderma Trials and Research Cohort of Patients With Systemic Sclerosis

Objective: Systemic sclerosis (SSc) is a heterogeneous connective tissue disease that is typically subdivided into limited cutaneous SSc (lcSSc) and diffuse cutaneous SSc (dcSSc) depending on the extent of skin involvement. This subclassification may not capture the entire variability of clinical phenotypes. The European Scleroderma Trials and Research (EUSTAR) database includes data on a prospective cohort of SSc patients from 122 European referral centers. This study was undertaken to perform a cluster analysis of EUSTAR data to distinguish and characterize homogeneous phenotypes without any a priori assumptions, and to examine survival among the clusters obtained. / Methods: A total of 11,318 patients were registered in the EUSTAR database, and 6,927 were included in the study. Twenty‐four clinical and serologic variables were used for clustering. / Results: Clustering analyses provided a first delineation of 2 clusters showing moderate stability. In an exploratory attempt, we further characterized 6 homogeneous groups that differed with regard to their clinical features, autoantibody profile, and mortality. Some groups resembled usual dcSSc or lcSSc prototypes, but others exhibited unique features, such as a majority of lcSSc patients with a high rate of visceral damage and antitopoisomerase antibodies. Prognosis varied among groups and the presence of organ damage markedly impacted survival regardless of cutaneous involvement. / Conclusion: Our findings suggest that restricting subsets of SSc patients to only those based on cutaneous involvement may not capture the complete heterogeneity of the disease. Organ damage and antibody profile should be taken into consideration when individuating homogeneous groups of patients with a distinct prognosis