International Tax Competition: A Case for International Cooperation in Globalization

The liberalization of international financial flows and foreign direct investment has induced countries to use diverse measures to attract inflow of foreign capital and foreign direct investment, which is expected to have a positive effect on the growth of GDP and thus a positive effect on social welfare. Tax exemption, reduction of tax rate, tax holiday, or diverse subsidies are some of the most important measures used. In this paper we study international tax cooperation, i.e., countries change and especially reduce tax rate for corporate income or for asset revenues to attract inflow of foreign direct investment. Both theoretical and empirical studies have shown the sensibility of foreign direct investment decision with respect to tax rate differences between home countries and host countries. In general, more inflow of foreign direct investment can be expected if the tax rate of the home country is lower than that of foreign countries. This is the main reason for international tax cooperation. In this paper we propose a simple model to prove the sub-optimal Nash non cooperative solution in a two-country tax-competition game. The model shows that international tax cooperation can improve welfare of the participating countries. How to reach a cooperative solution for an international tax competition game (ITCG) is therefore an important issue for further discussions and studies. International institutions can play a crucial role to reach international tax cooperation or international tax harmonization.     

Child Care “Packaging” Among TANF Recipients: Implications for Social Work

This article reports the results of a study that examined the ways in which current and recent TANF recipients assembled or “packaged” their child care arrangements among formal and informal providers. The findings are drawn from in-person, in-depth interviews with current and former welfare recipients. The study found that most of the respondents used multiple providers within the category of informal child care for reasons including availability, the use of a network of social acquaintances as a problem-solving resource, the need to accommodate irregular work hours, and personal trauma that contributed to a distrust of strangers in formal settings. The findings can affect the efficacy of practice with TANF recipients by contributing to social workers’ understanding of the context of decision-making regarding child care.     

Performance measurement and classification data in DEA: Input-oriented model

This paper presents an improved efficiency measurement tool by modifying the existing data envelopment analysis methodology to permit the incorporation of expert knowledge. A previous paper examined the inclusion of such knowledge within the additive model. This information appeared in the form of a binary classification of a subset of the decision making units under study (e.g. good versus poor performers). In the current paper, we extend this logic to the input-oriented radial projection model. We demonstrate that the inclusion of this and other forms of expert judgment can improve the performance of the DEA tool in the sense that the efficiency scores are more in line with expert/management beliefs.     

Predictive Bayesian Microbial Dose-Response Assessment Based on Suggested Self-Organization in Primary Illness Response: Cryptosporidium parvum

The probability of illness caused by very low doses of pathogens cannot generally be tested due to the numbers of subjects that would be needed, though such assessments of illness dose response are needed to evaluate drinking water standards. A predictive Bayesian dose-response assessment method was proposed previously to assess the unconditional probability of illness from available information and avoid the inconsistencies of confidence-based approaches. However, the method uses knowledge of the conditional dose-response form, and this form is not well established for the illness endpoint. A conditional parametric dose-response function for gastroenteric illness is proposed here based on simple numerical models of self-organized host-pathogen systems and probabilistic arguments. In the models, illnesses terminate when the host evolves by processes of natural selection to a self-organized critical value of wellness. A generalized beta-Poisson illness dose-response form emerges for the population as a whole. Use of this form is demonstrated in a predictive Bayesian dose-response assessment for cryptosporidiosis. Results suggest that a maximum allowable dose of 5.0 x 10(-7) oocysts/exposure (e.g., 2.5 x 10(-7) oocysts/L water) would correspond with the original goals of the U.S. Environmental Protection Agency Surface Water Treatment Rule, considering only primary illnesses resulting from Poisson-distributed pathogen counts. This estimate should be revised to account for non-Poisson distributions of Cryptosporidium parvum in drinking water and total response, considering secondary illness propagation in the population.     

A spatiotemporal auto-regressive moving average model for solar radiation

Summary.  To investigate the variability in energy output from a network of photovoltaic cells, solar radiation was recorded at 10 sites every 10 min in the Pentland Hills to the south of Edinburgh. We identify spatiotemporal auto-regressive moving average models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we show that by approximating using toroidal space and fitting by matching auto-correlations, calculations can be substantially reduced. We find that a first-order spatiotemporal auto-regressive (STAR(1)) process with a first-order neighbourhood structure and a Matern noise process provide an adequate fit to the data, and we demonstrate its use in simulating realizations of energy output.     

Statistical consulting

The paper and the special issue focus on the activity of statistical consulting and its varieties. This includes academic consulting, consulting to and in industry as well as statistics in public media.     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.