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.     

Three-level main-effects designs exploiting prior information about model uncertainty

To explore the projection efficiency of a design, Tsai, et al [2000. Projective three-level main effects designs robust to model uncertainty. Biometrika 87, 467–475] introduced the Q criterion to compare three-level main-effects designs for quantitative factors that allow the consideration of interactions in addition to main effects. In this paper, we extend their method and focus on the case in which experimenters have some prior knowledge, in advance of running the experiment, about the probabilities of effects being non-negligible. A criterion which incorporates experimenters’ prior beliefs about the importance of each effect is introduced to compare orthogonal, or nearly orthogonal, main effects designs with robustness to interactions as a secondary consideration. We show that this criterion, exploiting prior information about model uncertainty, can lead to more appropriate designs reflecting experimenters’ prior beliefs.     

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.     

Asthma Patients' Activities and Air Pollution: A Semiparametric Censored Regression Analysis

Asthma patients' health status may be especially sensitive to some types of air pollution, but the evidence on this is mixed. We explore the effects of ground-level ozone on asthma patient's activities, breaking apart the usual aggregated category of leisure into indoor and outdoor activities, and differentiating those by whether the activities were active or inactive. Applying the semiparametric censored estimation method we demonstrate that even though the period over which activities were observed was relatively low in ozone levels, there is a significant impact of ozone on a few activities. The (non-ozone) economic and demographic variables in the model play significant roles in explaining the allocation of time among seven activities, suggesting the suitability of the approach for other household decision-making contexts.     

Some results on optimality in models with heteroscedastic errors from partial optimum designs

This paper studies optimum designs for linear models when the errors are heteroscedastic. Sufficient conditions are given in order to obtainD-, A- andE-optimum designs for a complete regression model from partial optimum designs for some sub-parameters. A result about optimality for a complete model from the optimality for the submodels is included. Supported by Junta de Andalucía, research group FQM244.