766,078 research outputs found

    Selection Wages: An Illustration

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    Offering higher wages may enable firms to attract more applicants and screen them more carefully. If firms compete in this way in the labor market, "selection wages" emerge. This note illustrates this wage-setting mechanism. Selection wages may engender unconventional results, such as a pre-tax wage compression induced by the introduction of a progressive wage tax

    Selection Wages: An Illustration

    Get PDF
    Offering higher wages may enable firms to attract more applicants and screen them more carefully. If firms compete in this way in the labor market, "selection wages" emerge. This note illustrates this wage-setting mechanism. Selection wages may engender unconventional results, such as a pre-tax wage compression induced by the introduction of a progressive wage tax.wage formation; efficiency wage; incentive wage; mobility; job-specific pay; wage-tax

    Cover illustration: Non-premixed hydrocarbon flame

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    This year’s cover illustration, reproduced here as figure 1, depicts an image formed by a short-time (1/1000 s) exposure of a non-premixed hydrocarbon flame. The flow is driven by the buoyancy forces generated by the density difference from the combustion heat release and resulting temperature rise. The Reynolds number for this buoyancy-induced, turbulent flow is relatively low, estimated at a few thousand

    Over-optimism in bioinformatics: an illustration

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    In statistical bioinformatics research, different optimization mechanisms potentially lead to "over-optimism" in published papers. The present empirical study illustrates these mechanisms through a concrete example from an active research field. The investigated sources of over-optimism include the optimization of the data sets, of the settings, of the competing methods and, most importantly, of the method’s characteristics. We consider a "promising" new classification algorithm that turns out to yield disappointing results in terms of error rate, namely linear discriminant analysis incorporating prior knowledge on gene functional groups through an appropriate shrinkage of the within-group covariance matrix. We quantitatively demonstrate that this disappointing method can artificially seem superior to existing approaches if we "fish for significance”. We conclude that, if the improvement of a quantitative criterion such as the error rate is the main contribution of a paper, the superiority of new algorithms should be validated using "fresh" validation data sets
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