The Distributional impact of dams: Evidence from cropland productivity in Africa

We examine the distributional impact of major dams on cropland productivity in Africa. As our unit of analysis we use a scientifically based spatial breakdown of the continent that allows one to exactly define regions in terms of their upstream/downstream relationship at a highly disaggregated level. We then use satellite data to derive measures of cropland productivity within these areas. Our econometric analysis shows that while regions downstream benefit from large dams, cropland within the vicinity tends to suffer productivity losses during droughts. Overall our results suggest that because of rainfall shortages dams in Africa caused a net loss of 0.96 per cent in productivity over our sample period (1981-2000). However, further dam construction in appropriate areas could potentially lead to large increases in productivity even if rainfall is not plenty.dams, agricultural productivity, Africa

Lie Algebroid Yang Mills with Matter Fields

Lie algebroid Yang-Mills theories are a generalization of Yang-Mills gauge theories, replacing the structural Lie algebra by a Lie algebroid E. In this note we relax the conditions on the fiber metric of E for gauge invariance of the action functional. Coupling to scalar fields requires possibly nonlinear representations of Lie algebroids. In all cases, gauge invariance is seen to lead to a condition of covariant constancy on the respective fiber metric in question with respect to an appropriate Lie algebroid connection. The presentation is kept in part explicit so as to be accessible also to a less mathematically oriented audience.Comment: 24 pages, accepted for publication in J. Geom. Phy

Non-abelian Gerbes and Enhanced Leibniz Algebras

We present the most general gauge-invariant action functional for coupled 1- and 2-form gauge fields with kinetic terms in generic dimensions, i.e. dropping eventual contributions that can be added in particular space-time dimensions only such as higher Chern-Simons terms. After appropriate field redefinitions it coincides with a truncation of the Samtleben-Szegin-Wimmer action. In the process one sees explicitly how the existence of a gauge invariant functional enforces that the most general semi-strict Lie 2-algebra describing the bundle of a non-abelian gerbe gets reduced to a very particular structure, which, after the field redefinition, can be identified with the one of an enhanced Leibniz algebra. This is the first step towards a systematic construction of such functionals for higher gauge theories, with kinetic terms for a tower of gauge fields up to some highest form degree p, solved here for p = 2.Comment: Accepted for Publication in Rapid Communications PRD, submitted originally on April 8, final revised version on June 3

Statistical Sources of Variable Selection Bias in Classification Tree Algorithms Based on the Gini Index

Evidence for variable selection bias in classification tree algorithms based on the Gini Index is reviewed from the literature and embedded into a broader explanatory scheme: Variable selection bias in classification tree algorithms based on the Gini Index can be caused not only by the statistical effect of multiple comparisons, but also by an increasing estimation bias and variance of the splitting criterion when plug-in estimates of entropy measures like the Gini Index are employed. The relevance of these sources of variable selection bias in the different simulation study designs is examined. Variable selection bias due to the explored sources applies to all classification tree algorithms based on empirical entropy measures like the Gini Index, Deviance and Information Gain, and to both binary and multiway splitting algorithms

Variable Selection Bias in Classification Trees Based on Imprecise Probabilities

Classification trees based on imprecise probabilities provide an advancement of classical classification trees. The Gini Index is the default splitting criterion in classical classification trees, while in classification trees based on imprecise probabilities, an extension of the Shannon entropy has been introduced as the splitting criterion. However, the use of these empirical entropy measures as split selection criteria can lead to a bias in variable selection, such that variables are preferred for features other than their information content. This bias is not eliminated by the imprecise probability approach. The source of variable selection bias for the estimated Shannon entropy, as well as possible corrections, are outlined. The variable selection performance of the biased and corrected estimators are evaluated in a simulation study. Additional results from research on variable selection bias in classical classification trees are incorporated, implying further investigation of alternative split selection criteria in classification trees based on imprecise probabilities