A journey from a corruption port to a tax haven

We sketch a model according to which tax havens attract corporate income generated in corrupted countries. We consider the choice of optimal bribes by corrupt officials and the share of the proceeds of corruption that will be concealed in tax havens. Our framework provides novel welfare implications of tax havens. First, tax havens’ services have a positive effect on welfare through encouraging investment by firms fearing expropriation and bribes in corrupt countries. Second, by supporting corruption and the concealment of officials’ bribes, tax havens discourage the provision of public goods and hence have also a negative effect on welfare. The net welfare effect depends on the specified preferences and parameters. One source of this ambiguity is that the presence of multinational firms in corrupted countries is positively associated with demanding tax havens’ operations. Using firm-level data, we provide new empirical results supporting this hypothesis.     

Corporate tax evasion: The case for specialists

We analyze the role of accounting specialists who help corporations evade/avoid taxes in a game of incomplete information played by a tax authority, corporate taxpayers, and an accounting specialist. In addition to a full equilibrium characterization, we establish that (i) marginal changes in enforcement are not effective when evasion/avoidance is pervasive; (ii) fines on firms as opposed to specialists are more effective in such situations; (iii) reducing auditing costs and increasing “creative accounting” costs are effective in curbing evasion when tax compliance is relatively high.     

Transboundary Problems of Overcoming Natural Disasters for International Regional Organizations in the Eurasian Region

Based on the example of Central Asia, this paper has examined the problems of transborder cooperation among countries in order to reduce damage from natural disasters and suggested a common methodology for assessing social risks.     

Diagnoses by general practitioners: Accuracy and reliability

Only 10% of the results of consultations in primary care can be assigned to a confirmed diagnosis, while 50% remain “symptoms” and 40% are classified as “named syndromes” (“picture of a disease”). Moreover, less than 20% of the most frequent diagnoses account for more than 80% of the results of consultations. This finding, confirmed empirically during the last fifty years, suggests a power law distribution, with critical consequences for diagnosis and decision making in primary care.Our results prove that primary care has a severe “black swan” element in the vast majority of consultations. Some critical cases involving “avoidable life-threatening dangerous developments” (ALDD) such as myocardial disturbance, brain bleeding, and appendicitis may be masked by those often vague symptoms of health disorders ranked in the 20% most frequent diagnoses. The Braun distribution predicts the frequency of health disorders on a phenomenological level and reveals the “black swan” problem, but is not a tool by itself for arriving at accurate diagnoses. To improve predictions and enhance the reliability of diagnoses we propose standards of documentation and a systematic manner by which the risk facing a patient with an uncertain diagnosis can be evaluated (diagnostic protocols).Accepting a power law distribution in primary care implies the following: (1) primary care should no longer be defined only by “low prevalence” properties, but also by its black-swan-incidence-problem. This includes rethinking malpractice and the requirements of malpractice litigations; (2) at the level of everyday practice, diagnostic protocols are tools to make diagnoses more reliable; (3) at the level of epidemiology, Braun’s system of classification is useful for generating valid information by which predictions of risks can be improved.