Optimal Tax and Expenditure Policy in the Presence of Migration - Are Credit Restrictions Important?

This paper concerns optimal income taxation in the presence of emigration. The basic model is a two-period model where all agents are identical and live in the home country in the first period of life, but where some emigrate at the end of the first period. It is shown that with a binding credit restriction, the government will tax labor income in the first period at a higher rate than otherwise, whereas the labor income tax in the second period is unaffected by emigration. With heterogenous agents, the labor income tax in period two will be affected by emigration.optimal taxation; labor mobility; intertemporal consumer choice

International Cooperation over Green Taxes: On the Impossibility of Achieving a Probability-One Gain

This paper concerns international coordination of environmental taxation.The main contribution of the paper is to provide a frame-work for dynamiccost benefit analysis of environmental tax reforms in a global economy withtransboundary environmental problems. We show that the welfare effects ofgreen tax reform in a multi-country economy may differ substantially fromearlier results associated with representative agent models, where thetransboundary aspect of the environmental problems is neglected.environmental taxation, global external effects.

Technology Transfers and the Clean Development Mechanism in a North-South General Equilibrium Model

This paper analyzes the potential welfare gains of introducing a technology transfer from Annex I to non-Annex I in order to mitigate greenhouse gas emissions. Our analysis is based on a numerical general equilibrium model for a world economy comprising two regions, North (Annex I) and South (non-Annex I). As our model allows for labor mobility between the formal and informal sectors in the South, we are also able to capture additional aspects of how the transfer influences the Southern economy. In a cooperative equilibrium, a technology transfer from the North to the South is clearly desirable from the perspective of a ‘global social planner’, since the welfare gain for the South outweighs the welfare loss for the North. However, if the regions do not cooperate, then the incentives to introduce the technology transfer appear to be relatively weak from the perspective of the North; at least if we allow for Southern abatement in the pre-transfer Nash equilibrium. Finally, by adding the emission reductions associated with the Kyoto agreement to an otherwise uncontrolled market economy, the technology transfer leads to higher welfare in both regions.Climate Policy, Technology Transfer, Kyoto Protocol, General Equilibrium, Clean Development Mechanism

International Cooperation over Green Taxes: On the Impossibility of Achieving a Probability-One Gain

This paper concerns international coordination of environmental taxation.The main contribution of the paper is to provide a frame-work for dynamiccost benefit analysis of environmental tax reforms in a global economy withtransboundary environmental problems. We show that the welfare effects ofgreen tax reform in a multi-country economy may differ substantially fromearlier results associated with representative agent models, where thetransboundary aspect of the environmental problems is neglected

Climate Policy and Development -The Role of Technology Transfers and the Clean Development Mechanism in a North-South Model

Abstract This paper analyzes the potential welfare gains of introducing a technology transfer from Annex I to non-Annex I in order to mitigate greenhouse gas emissions. Our analysis is based on a numerical general equilibrium model for a world economy comprising two regions, North (Annex I) and South (non-Annex I). We consider three different pre-transfer resource allocations; (i) the regions behave as uncontrolled market economies, (ii) the regions behave as Nash competitors, and (iii) the pre-transfer resource allocation is a conditional cooperative equilibrium. As our model allows for labor mobility between the formal and informal sectors in the South, we are also able to capture additional aspects of how the transfer may affect the Southern economy. In the conditional cooperative equilibrium regime, where the resource allocation is decided upon by a global social planner, the welfare gain for the South of introducing a technology transfer outweighs the welfare loss for the North. However, if the regions do not cooperate prior to the introduction of the technology transfer, the incentives for the North of using this option appear to be relatively weak, at least if we allow for abatement efforts carried out by the South prior to introducing the transfer. By adding the requirement for emissions reduction implicit in the Kyoto protocol to the otherwise uncontrolled market economy, the results imply that the technology transfer leads to higher welfare for both regions

Technology Transfers and the Clean Development Mechanism in a North-South General Equilibrium Model

This paper analyzes the potential welfare gains of introducing a technology transfer from Annex I to non-Annex I in order to mitigate greenhouse gas emissions. Our analysis is based on a numerical general equilibrium model for a world economy comprising two regions, North (Annex I) and South (non-Annex I). As our model allows for labor mobility between the formal and informal sectors in the South, we are also able to capture additional aspects of how the transfer influences the Southern economy. In a cooperative equilibrium, a technology transfer from the North to the South is clearly desirable from the perspective of a global social planner, since the welfare gain for the South outweighs the welfare loss for the North. However, if the regions do not cooperate, then the incentives to introduce the technology transfer appear to be relatively weak from the perspective of the North; at least if we allow for Southern abatement in the pre-transfer Nash equilibrium. Finally, by adding the emission reductions associated with the Kyoto agreement to an otherwise uncontrolled market economy, the technology transfer leads to higher welfare in both regions

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

\ua9 2015 The Authors. This is an open access article under the CC BY-NC-ND license. Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Joint Analysis Of Psychiatric Disorders Increases Accuracy Of Risk Prediction For Schizophrenia, Bipolar Disorder, And Major Depressive Disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk