Morphological characteristics of sensilla on the female ovipositor of Lype phaeopa (Psychomyiidae; Trichoptera)

An important aspect of the association of Lype phaeopa (Stephens) with submerged wood is the oviposition behavior of adult females, which preferably oviposit their eggs on moist emergent or submerged parts of woody debris with a structured surface. The eggs are commonly deposited in cracks and crevices using the elongated ovipositor. Ovipositor morphology and various sensilla on the tip and along the ovipositor were studied by scanning electron microscopy and their possible function discussed. Structure of these sensilla and pre-oviposition behavior of the females point to a preference for certain oviposition sites on woody debris. This may be a key factor for the distribution and development of the larvae

Endogeneity in Panel Data Models with Time-Varying and Time-Fixed Regressors: To IV or Not IV?

We analyse the problem of parameter inconsistency in panel data econometrics due to the correlation of exogenous variables with the error term. A common solution in this setting is to use Instrumental-Variable (IV) estimation in the spirit of Hausman-Taylor (1981). However, some potential shortcomings of the latter approach recently gave rise to the use of non-IV two-step estimators. Given their growing number of empirical applications, we aim to systematically compare the performance of IV and non-IV approaches in the presence of time-fixed variables and right hand side endogeneity using Monte Carlo simulations, where we explicitly control for the problem of IV selection in the Hausman-Taylor case. The simulation results show that the Hausman- Taylor model with perfect-knowledge about the underlying data structure (instrument orthogonality) has on average the smallest bias. However, compared to the empirically relevant specification with imperfect-knowledge and instruments chosen by statistical criteria, the non-IV rival performs equally well or even better especially in terms of estimating variable coefficients for time- fixed regressors. Moreover, the non-IV method tends to have a smaller root mean square error (rmse) than both Hausman-Taylor models with perfect and imperfect knowledge about the underlying correlation between r.h.s variables and residual term. This indicates that it is generally more efficient. The results are roughly robust for various combinations in the time and cross-section dimension of the data

Regional State Aid Control in Europe: A Legal and Economic Assessment

This paper provides a legal and economic analysis of the European rules for regional State aid according to Article 107 (1) and (3) TFEU. It summarizes the historical evolution and the trends of regional aid rules and describes the economic rationale behind them. The main principles are discussed with reference to recent academic research, leading cases and the State Aid Modernization initiative ("SAM"). The current rules for the assessment of compatibility as laid down in the General Block Exemption and the Regional Aid Guidelines 2014 are critically reviewed in light of these principles

EU Structural Funds and Regional Income Convergence A Sobering Experience

The European Structural and Investment Funds (ESIF) are the prime instrument of EU regional policy. European policy makers place considerable hope into their growth stimulating funding measures to overcome current economic stagnation. Consequently, there is a strong need for credible evidence regarding the programs' effectiveness. Based on an empirical identification strategy linked to modern growth theory, we find that the disbursement of EU structural funds is negatively correlated with regional growth. Incorporating spatial dynamics and decomposing this correlation into a direct and a spatially-indirect component, it is particularly the latter which determines this 'sobering' finding. Regarding the economics behind these results, the obtained negative spatial effect may reflect the role played by policy-induced spatial competition among neighboring regions. It could also highlight the backwardness in technological endowment and economic structures of highly funded regions. In any case, EU structural funding does not seem to contribute effectively to foster income convergence across regions.Die europäischen Struktur und Investmentfonds (ESIF) sind das wichtigste Instrument der EU-Regionalpolitik; die europäische Politik setzt große Hoffnungen in die wachstumsstimulierende Wirkung dieser Fördermaßnahmen zur Überwindung der aktuellen wirtschaftlichen Stagnation. Basierend auf einer empirischen Identifikationsstrategie können in diesem Papier keine Belege für positive Förderwirkungen gefunden werden. Werden räumliche Strukturen berücksichtigt, zeigt sich, dass räumlich indirekte Effekte dieses Ergebnis beeinflussen - also solche Regionen weniger wachsen, deren Nachbarn stark gefördert werden. Neben der potenziellen Erklärung, dass Nachbarn gegenseitig ihre Investoren abwerben, legt dieses Resultat nahe, dass hochgeförderte regionale Cluster unter struktureller und technologischer Rückständigkeit leiden, die durch Wachstumsprogramme nicht überwunden werden. Beide möglichen Erklärungen lassen den Schluss zu, dass die Förderung ihr eigentliches Ziel, die Konvergenz der Regionen, verfehlt

A comparison of two methods of estimating propensity scores after multiple imputation

In many observational studies, analysts estimate treatment effects using propensity scores, e.g. by matching or sub-classifying on the scores. When some values of the covariates are missing, analysts can use multiple imputation to fill in the missing data, estimate propensity scores based on the m completed datasets, and use the propensity scores to estimate treatment effects. We compare two approaches to implement this process. In the first, the analyst estimates the treatment effect using propensity score matching within each completed data set, and averages the m treatment effect estimates. In the second approach, the analyst averages the m propensity scores for each record across the completed datasets, and performs propensity score matching with these averaged scores to estimate the treatment effect. We compare properties of both methods via simulation studies using artificial and real data. The simulations suggest that the second method has greater potential to produce substantial bias reductions than the first, particularly when the missing values are predictive of treatment assignment