Fair tax evasion and majority voting over redistributive taxation

We shed some light on fairness preferences regarding tax evasion. Individuals perceive income inequality which they are responsible for as fair (e.g. work effort) while inequality resulting from factors outside their reach is regarded as unfair (e.g. productivity or wage rate). This affects the incentives to hide income from tax authorities and supply labor. We set up a model where individuals simultaneously choose unreported income and work effort given a linear taxation scheme. We show the conditions for which individuals respond with lower or higher unreported income and work effort when fair tax evasion is introduced. Beyond, it can be shown that unreported income increases while work effort decreases when the tax rate is raised. Finally, we consider a majority voting over redistributive taxation. Thereby, it is shown that the median voter prefers lower (higher) taxation if she evades less (more) taxes than would be fair since raising the tax rate would enlarge (reduce) the deviation from fair tax evasion. This affects the moral cost as peceived by the individuals

Does a work effort norm lead to more efficient taxation in majority voting?

This paper introduces a work effort norm into a three-type ability approach to optimal linear income taxation. According to this social norm type, individuals experience stigma when working more or less than the average. This leads to a smaller dispersion in labor supply. The individual work incentives then induce post-tax income inequality to rise. Based on this, it can be shown that the socially optimal tax rate is unambiguously increasing with the strength of the work effort norm. Turning to majority voting the tax rate preferred by the median voter could decrease when the work effort norm is introduced. We can show that the majority tax rate turns out to be inefficiently low or high. Beyond, for large preference parameters the difference between the first-best tax rate and the majority tax rate seems to diminish. Further, for specific wage distributions there seem to exist a preference strength which ensures efficient taxation. Subsequently, the work effort norm can reduce the inefficiency implemented by majority voting

Fair tax evasion and majority voting over redistributive taxation

We shed some light on fairness preferences regarding tax evasion. Individuals perceive income inequality which they are responsible for as fair (e.g. work effort) while inequality resulting from factors outside their reach is regarded as unfair (e.g. productivity or wage rate). This affects the incentives to hide income from tax authorities and supply labor. We set up a model where individuals simultaneously choose unreported income and work effort given a linear taxation scheme. We show the conditions for which individuals respond with lower or higher unreported income and work effort when fair tax evasion is introduced. Beyond, it can be shown that unreported income increases while work effort decreases when the tax rate is raised. Finally, we consider a majority voting over redistributive taxation. Thereby, it is shown that the median voter prefers lower (higher) taxation if she evades less (more) taxes than would be fair since raising the tax rate would enlarge (reduce) the deviation from fair tax evasion. This affects the moral cost as peceived by the individuals

The perception of distributive fairness and optimal taxation under uncertainty

This paper incorporates a preference for distributive fairness (inequity aversion) into the analysis on optimal redistributive taxation under uncertainty. We can show that introducing or strengthening the taste for distributive fairness does not affect the socially optimal tax rate (social insurance) directly. This merely works through a reduction in individual risk taking (increase in self-insurance) induced by inequity aversion. If the efficacy of self-insurance is sufficiently small, this renders taxation more desirable and therefore enhances the socially optimal tax rate. In other words, self-insurance should be complemented by social insurance in order to impair the psychic disutility stemming from income inequality. Turning to the case of moral hazard it can be shown that optimal self-insurance efforts are again increasing with the strength of inequity aversion while the effect on the optimal tax rate remains unclear

The evolution of phenotypic correlations and “developmental memory”

Development introduces structured correlations among traits that may constrain or bias the distribution of phenotypes produced. Moreover, when suitable heritable variation exists, natural selection may alter such constraints and correlations, affecting the phenotypic variation available to subsequent selection. However, exactly how the distribution of phenotypes produced by complex developmental systems can be shaped by past selective environments is poorly understood. Here we investigate the evolution of a network of recurrent nonlinear ontogenetic interactions, such as a gene regulation network, in various selective scenarios. We find that evolved networks of this type can exhibit several phenomena that are familiar in cognitive learning systems. These include formation of a distributed associative memory that can “store” and “recall” multiple phenotypes that have been selected in the past, recreate complete adult phenotypic patterns accurately from partial or corrupted embryonic phenotypes, and “generalize” (by exploiting evolved developmental modules) to produce new combinations of phenotypic features. We show that these surprising behaviors follow from an equivalence between the action of natural selection on phenotypic correlations and associative learning, well-understood in the context of neural networks. This helps to explain how development facilitates the evolution of high-fitness phenotypes and how this ability changes over evolutionary time

Multisite Phosphorylation of the Sum1 Transcriptional Repressor by S-Phase Kinases Controls Exit from Meiotic Prophase in Yeast.

Activation of the meiotic transcription factor Ndt80 is a key regulatory transition in the life cycle of Saccharomyces cerevisiae because it triggers exit from pachytene and entry into meiosis. The NDT80 promoter is held inactive by a complex containing the DNA-binding protein Sum1 and the histone deacetylase Hst1. Meiosis-specific phosphorylation of Sum1 by the protein kinases Cdk1, Ime2, and Cdc7 is required for NDT80 expression. Here, we show that the S-phase-promoting cyclin Clb5 activates Cdk1 to phosphorylate most, and perhaps all, of the 11 minimal cyclin-dependent kinase (CDK) phospho-consensus sites (S/T-P) in Sum1. Nine of these sites can individually promote modest levels of meiosis, yet these sites function in a quasiadditive manner to promote substantial levels of meiosis. Two Cdk1 sites and an Ime2 site individually promote high levels of meiosis, likely by preparing Sum1 for phosphorylation by Cdc7. Chromatin immunoprecipitation reveals that the phosphorylation sites are required for removal of Sum1 from the NDT80 promoter. We also find that Sum1, but not its partner protein Hst1, is required to repress NDT80 transcription. Thus, while the phosphorylation of Sum1 may lead to dissociation from DNA by influencing Hst1, it is the presence of Sum1 on DNA that determines whether NDT80 will be expressed

Stepwise Acquisition of Pyrimethamine Resistance in the Malaria Parasite

The spread of high-level pyrimethamine resistance in Africa threatens to curtail the therapeutic lifetime of antifolate antimalarials. We studied the possible evolutionary pathways in the evolution of pyrimethamine resistance using an approach in which all possible mutational intermediates were created by site-directed mutagenesis and assayed for their level of drug resistance. The coding sequence for dihydrofolate reductase (DHFR) from the malaria parasite Plasmodium falciparum was mutagenized, and tests were carried out in Escherichia coli under conditions in which the endogenous bacterial enzyme was selectively inhibited. We studied 4 key amino acid replacements implicated in pyrimethamine resistance: N51I, C59R, S108N, and I164L. Using empirical estimates of the mutational spectrum in P. falciparum and probabilities of fixation based on the relative levels of resistance, we found that the predicted favored pathways of drug resistance are consistent with those reported in previous kinetic studies, as well as DHFR polymorphisms observed in natural populations. We found that 3 pathways account for nearly 90% of the simulated realizations of the evolution of pyrimethamine resistance. The most frequent pathway (S108N and then C59R, N51I, and I164L) accounts for more than half of the simulated realizations. Our results also suggest an explanation for why I164L is detected in Southeast Asia and South America, but not at significant frequencies in Africa.Organismic and Evolutionary Biolog

Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design

Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science, but also a very difficult one due to the vast number of possible molecular systems. We propose an Evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favourable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO-LUMO gap; optimization was done over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) datasets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to $10^{6}$ QM calculations and were thus only feasible thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches