Private health care as a supplement to a public health system with waiting time for treatment

In this article the authors Michael Hoel and Erik Magnus Sæther consider an economy where most of the health care is publicly provided, and where there is waiting time for several types of treatments. Private health care without waiting time is an option for the patients in the public health queue. This article shows the effects of a tax (positive or negative) on private health care, and derives the socially optimal tax/subsidy. Finally, a discussion of how the size of the tax might affect the political support for a high quality public health system is provided.Private health care; public health care; health queues

Public Health Care with Waiting Time: The Role of Supplementary Private Health Care

We consider an economy where most of the health care is publicly provided,and where there is waiting time for several types of treatments. Privatehealth care without waiting time is an option for the patients in the publichealth queue. We show that although patients with low waiting costs willchoose public treatment, they may be better off with waiting time thanwithout. The reason is that waiting time induces patients with high waitingcosts to choose private treatment, thus reducing the cost of public healthcare that everyone pays for. Even if higher quality (i.e. zero waiting time) canbe achieved at no cost, the self-selection induced redistribution may implythat it is socially optimal to provide health care publicly and at an inferiorquality level. We give a detailed discussion of the circumstances in which it isoptimal to have waiting time for public health treatment. Moreover, we studythe interaction between this quality decision and the optimal tax/subsidy onprivate health care.public health care, private health care, waiting time, healthqueues.

What caused what? A quantitative account of actual causation using dynamical causal networks

Actual causation is concerned with the question "what caused what?" Consider a transition between two states within a system of interacting elements, such as an artificial neural network, or a biological brain circuit. Which combination of synapses caused the neuron to fire? Which image features caused the classifier to misinterpret the picture? Even detailed knowledge of the system's causal network, its elements, their states, connectivity, and dynamics does not automatically provide a straightforward answer to the "what caused what?" question. Counterfactual accounts of actual causation based on graphical models, paired with system interventions, have demonstrated initial success in addressing specific problem cases in line with intuitive causal judgments. Here, we start from a set of basic requirements for causation (realization, composition, information, integration, and exclusion) and develop a rigorous, quantitative account of actual causation that is generally applicable to discrete dynamical systems. We present a formal framework to evaluate these causal requirements that is based on system interventions and partitions, and considers all counterfactuals of a state transition. This framework is used to provide a complete causal account of the transition by identifying and quantifying the strength of all actual causes and effects linking the two consecutive system states. Finally, we examine several exemplary cases and paradoxes of causation and show that they can be illuminated by the proposed framework for quantifying actual causation.Comment: 43 pages, 16 figures, supplementary discussion, supplementary methods, supplementary proof

Causal Geometry

Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, despite causal models being a fundamental part of science and explanation. Here we introduce causal geometry, which formalizes not only how outcomes are impacted by parameters, but also how the parameters of a model can be intervened upon. Therefore we introduce a geometric version of "effective information" -- a known measure of the informativeness of a causal relationship. We show that it is given by the matching between the space of effects and the space of interventions, in the form of their geometric congruence. Therefore, given a fixed intervention capability, an effective causal model is one that matches those interventions. This is a consequence of "causal emergence," wherein macroscopic causal relationships may carry more information than "fundamental" microscopic ones. We thus argue that a coarse-grained model may, paradoxically, be more informative than the microscopic one, especially when it better matches the scale of accessible interventions -- as we illustrate on toy examples.Comment: 12 pages, 4 figure

SPACs off Track: An Empirical Study on Attributes Affecting the Post-Merger Performance of De-SPAC Companies

Special Purpose Acquisition Companies ( acclaimed as a better alternative to theSPACs) —traditional IPO for taking a company public have been booming since 2020. This thesis—analyzes attributes affecting the post-merger performance of SPACs merging between 2020 and early 2022. Throughout this period, SPACs have massively underperformed their benchmarks, except on the first day of trading. Multiple Linear Regression were used to investigate the relationship between independent variables and the dependent variables first-day and two-month return. We discover a significant negative relationship between performance and the redemption rates (investors withdrawal). Further, we find that the market favored young, profitable, and non-healthcare companies, which outperformed their peers in the short run. Contrary to our initial beliefs, the performance correlates similarly with the designated attributes –independent of the time horizon of interest. Substantiated by the “rise of retail investors” in 2020, we further reveal that two weeks of lagged “hype” has a significant positive relationship with post-merger initial performance. Based on the obtained results, we suspected that the redemption rate absorbed the effect of the other predictors. This insight was further evaluated using a variety of machine learning models which concluded two things. First, redemption rates indeed absorb the effects in the OLS models. Second, in an Ordinal Logistic Regression, the other variables were able to predict the redemption rate with an accuracy close to 75%.nhhma

Marine Spatial Planning: Norway´s management plans

Since the adoption of a government white paper on ocean governance in 2001, Norway has worked on the development and implementation of marine spatial planning in the format of regional management plans. Management plans for the Barents Sea and the oceans off northern Norway and the Norwegian Sea were adopted in 2006 and 2009, respectively, and a management plan for the North Sea is planned for 2013. A key aspect of the plans is integrated assessment of the cumulative impacts on marine ecosystem from human activities (fisheries, petroleum, marine transportation, etc) on the one hand, and external sources (climate change, long range pollution) on the other. Another important feature is the identification of valuable and vulnerable areas requiring special management measures. These valuable areas have been used as input to define the spatial measures in the plans which includes routing systems for international ship traffic and zoning plans for petroleum activities. Fishing activities is also partially regulated used spatial measures such as MPAs and temporary closed areas. A monitoring system is set up with indicators and reference levels. The plan has been implemented through the regular governance structure without the establishment of new, formal institutions or new jurisdiction. An inter-- ]ministerial committee oversees the work, guided by three working groups. A revised version of the Barents Sea plan will be adopted late in 2010, taking marine spatial planning in Norway into its second generation. Key words: Marine spatial planning, Norway, Barents Sea, ecosystem approac