Location, investment and regional policy: the contribution of the average effective tax rate theory

For decades, most industrialised countries have implemented some forms of fiscal and financial incentives to stimulate fixed capital formation. Tax cuts and capital grants are of great use in regional policy. Since these instruments mobilise huge amounts of public resources the issue of their efficiency is of particular interest for policymakers. The impact of taxation on investment income was traditionally apprehended through models measuring the effective tax rate on marginal investments. However recent literature, especially Devereux and Griffith (2002), showed the interest of resorting to an alternative tax measure – the effective average tax rate (EATR) - when firms face discrete investment choices that are expected to generate positive economic rent before tax. This effective average tax rate is defined by the difference between the net present value of the rent of the investment before and after taxes scaled by the net present value of the pre-tax income stream. In this sense, the effective average tax rate developed by Devereux and Griffith (2002) seems to be particularly relevant to shed a new light on the relative effectiveness of tax cuts and capital subsidy grants. In this paper we intend to compare the costs for public authorities to lower the corporate tax rate or to grant a capital subsidy. These public costs are directly affected by the variation of the after-tax revenue earned by the shareholder. The extent to which each policy must be implemented depends on the channel chosen by the government to stimulate investment. We pay attention to two of these channels: a reduction of the capital cost and a lowering of the EATR. Finally, in order to illustrate the relevance of our approach, we developed a numerical example for the Belgian case. JEL Classification: H25, H32 and R58

Reproductive conflicts and egg discrimination in a socially polymorphic ant

The ability to discriminate against competitors shapes cooperation and conflicts in all forms of social life. In insect societies, workers may detect and destroy eggs laid by other workers or by foreign queens, which can contribute to regulate reproductive conflicts among workers and queens. Variation in colony kin structure affects the magnitude of these conflicts and the diversity of cues used for discrimination, but the impact of the number of queens per colony on the ability of workers to discriminate between eggs of diverse origin has so far not been investigated. Here, we examined whether workers from the socially polymorphic ant Formica selysi distinguished eggs laid by nestmate workers from eggs laid by nestmate queens, as well as eggs laid by foreign queens from eggs laid by nestmate queens. Workers from single- and multiple-queen colonies discriminated worker-laid from queen-laid eggs, and eliminated the former. This suggests that workers collectively police each other in order to limit the colony-level costs of worker reproduction and not because of relatedness differences towards queens' and workers' sons. Workers from single-queen colonies discriminated eggs laid by foreign queens of the same social structure from eggs laid by nestmate queens. In contrast, workers from multiple-queen colonies did not make this distinction, possibly because cues on workers or eggs are more diverse. Overall, these data indicate that the ability of F. selysi workers to discriminate eggs is sufficient to restrain worker reproduction but does not permit discrimination between matrilines in multiple-queen colonie

Split sex ratios in the social Hymenoptera: a meta-analysis

The study of sex allocation in social Hymenoptera (ants, bees, and wasps) provides an excellent opportunity for testing kin-selection theory and studying conflict resolution. A queen-worker conflict over sex allocation is expected because workers are more related to sisters than to brothers, whereas queens are equally related to daughters and sons. If workers fully control sex allocation, split sex ratio theory predicts that colonies with relatively high or low relatedness asymmetry (the relatedness of workers to females divided by the relatedness of workers to males) should specialize in females or males, respectively. We performed a meta-analysis to assess the magnitude of adaptive sex allocation biasing by workers and degree of support for split sex ratio theory in the social Hymenoptera. Overall, variation in relatedness asymmetry (due to mate number or queen replacement) and variation in queen number (which also affects relatedness asymmetry in some conditions) explained 20.9% and 5% of the variance in sex allocation among colonies, respectively. These results show that workers often bias colony sex allocation in their favor as predicted by split sex ratio theory, even if their control is incomplete and a large part of the variation among colonies has other causes. The explanatory power of split sex ratio theory was close to that of local mate competition and local resource competition in the few species of social Hymenoptera where these factors apply. Hence, three of the most successful theories explaining quantitative variation in sex allocation are based on kin selectio

Multi-domain learning CNN model for microscopy image classification

For any type of microscopy image, getting a deep learning model to work well requires considerable effort to select a suitable architecture and time to train it. As there is a wide range of microscopes and experimental setups, designing a single model that can apply to multiple imaging domains, instead of having multiple per-domain models, becomes more essential. This task is challenging and somehow overlooked in the literature. In this paper, we present a multi-domain learning architecture for the classification of microscopy images that differ significantly in types and contents. Unlike previous methods that are computationally intensive, we have developed a compact model, called Mobincep, by combining the simple but effective techniques of depth-wise separable convolution and the inception module. We also introduce a new optimization technique to regulate the latent feature space during training to improve the network's performance. We evaluated our model on three different public datasets and compared its performance in single-domain and multiple-domain learning modes. The proposed classifier surpasses state-of-the-art results and is robust for limited labeled data. Moreover, it helps to eliminate the burden of designing a new network when switching to new experiments

L'amorçage sémantique masqué en situation de cocktail party.

International audienceCette étude vise à tester l'automaticité du traitement sémantique durant la perception de la parole grâce à la situation de cocktail party. Les participants devaient effectuer une tâche de décision lexicale sur un item cible inséré dans un cocktail de parole. Celui-ci était composé de voix prononçant des mots sémantiquement liés à la cible (voix amorces) , et d'autres voix prononçant des mots sémantiquement indépendants les uns des autres (voix masquante). L'analyse des résultats a montré qu'un effet d'amorçage n'apparaissait que lorsque le nombre de voix amorces était strictement supérieur au nombre de voix masquantes, mettant en évidence un besoin d'intelligibilité de l'amorce et la nature stratégique de l'effet d'amorçage observé

Using auditory classification images for the identification of fine acoustic cues used in speech perception

International audienceAn essential step in understanding the processes underlying the general mechanism of perceptual categorization is to identify which portions of a physical stimulation modulate the behavior of our perceptual system. More specifically, in the context of speech comprehension, it is still a major open challenge to understand which information is used to categorize a speech stimulus as one phoneme or another, the auditory primitives relevant for the categorical perception of speech being still unknown. Here we propose to adapt a method relying on a Generalized Linear Model with smoothness priors, already used in the visual domain for the estimation of so-called classification images, to auditory experiments. This statistical model offers a rigorous framework for dealing with non-Gaussian noise, as it is often the case in the auditory modality, and limits the amount of noise in the estimated template by enforcing smoother solutions. By applying this technique to a specific two-alternative forced choice experiment between stimuli " aba " and " ada " in noise with an adaptive SNR, we confirm that the second formantic transition is key for classifying phonemes into /b/ or /d/ in noise, and that its estimation by the auditory system is a relative measurement across spectral bands and in relation to the perceived height of the second formant in the preceding syllable. Through this example, we show how the GLM with smoothness priors approach can be applied to the identification of fine functional acoustic cues in speech perception. Finally we discuss some assumptions of the model in the specific case of speech perception