HIERFSTAT, a package for R to compute and test hierarchical F-statistics

The package HIERFSTAT for the statistical software R, created by the R Development Core Team, allows the estimate of hierarchical F-statistics from a hierarchy with any numbers of levels. In addition, it allows testing the statistical significance of population differentiation for these different levels, using a generalized likelihood-ratio test. The package HIERFSTAT is available at http://www.unil.ch/popgen/softwares/hierfstat.htm.

Do Agglomeration Economies Reduce the Sensitivity of Firm Location to Tax Differentials?

Recent theoretical work in economic geography has shown that agglomeration forces can mitigate 'race-to-the-bottom' tax competition, by partly or fully offsetting firms' sensitivity to tax differentials. We test this proposition using data on firm births across Swiss municipalities. We find that corporate taxes deter firm births less in more spatially concentrated sectors. Firms in sectors with an agglomeration intensity in the top quintile are less than half as responsive to differences in corporate tax burdens as firms in sectors with an agglomeration intensity in the bottom quintile. Hence, agglomeration economies do appear to attenuate the impact of tax differentials on firms' location choices.

Doctoral education's reform in Switzerland and Norway : a public management analyis

This doctoral thesis examines a recent phenomenon in European higher education, namely the reform of doctoral education. On the basis of a number of case studies, consisting of Swiss and Norwegian doctoral schools, and their institutional, national and international context, it demonstrates to what extent changes appear in doctoral education and its governance. Findings indicate new practices regarding doctoral students' recruitment, curricular component, supervision, scientific exchange, follow-up and their career. Doctoral education's character is not anymore exclusively determined by individual supervisors, but increasingly by interdisciplinary and interinstitutional colleges of academics. Finally, general governance patterns are identified: according to the type of scientific discipline and higher education institution, the institution's size and national political system, the field of higher education is more or less dominated by New Public Management or Network Governance characteristics.

Assessing the impact of DRGs on patient care and professional practice in Switzerland (IDoC) : a potential model for monitoring and evaluating healthcare reform.

QUESTIONS UNDER STUDY: The starting point of the interdisciplinary project "Assessing the impact of diagnosis related groups (DRGs) on patient care and professional practice" (IDoC) was the lack of a systematic ethical assessment for the introduction of cost containment measures in healthcare. Our aim was to contribute to the methodological and empirical basis of such an assessment. METHODS: Five sub-groups conducted separate but related research within the fields of biomedical ethics, law, nursing sciences and health services, applying a number of complementary methodological approaches. The individual research projects were framed within an overall ethical matrix. Workshops and bilateral meetings were held to identify and elaborate joint research themes. RESULTS: Four common, ethically relevant themes emerged in the results of the studies across sub-groups: (1.) the quality and safety of patient care, (2.) the state of professional practice of physicians and nurses, (3.) changes in incentives structure, (4.) vulnerable groups and access to healthcare services. Furthermore, much-needed data for future comparative research has been collected and some early insights into the potential impact of DRGs are outlined. CONCLUSIONS: Based on the joint results we developed preliminary recommendations related to conceptual analysis, methodological refinement, monitoring and implementation.

Rgtsp: a generalized top scoring pairs package for class prediction.

SUMMARY: A top scoring pair (TSP) classifier consists of a pair of variables whose relative ordering can be used for accurately predicting the class label of a sample. This classification rule has the advantage of being easily interpretable and more robust against technical variations in data, as those due to different microarray platforms. Here we describe a parallel implementation of this classifier which significantly reduces the training time, and a number of extensions, including a multi-class approach, which has the potential of improving the classification performance. AVAILABILITY AND IMPLEMENTATION: Full C++ source code and R package Rgtsp are freely available from http://lausanne.isb-sib.ch/~vpopovic/research/. The implementation relies on existing OpenMP libraries.