Consumption Externalities and Diffusion in Pharmaceutical Markets: Antiulcer Drugs

We examine the role of consumption externalities in the demand for pharmaceuticals at both the brand level and over a therapeutic class of drugs. These effects emerge when use of a drug by others affects its value, and/or conveys information abut efficacy and safety to patients and physicians. This can affect that rate of market diffusion for a new entrant, and can lead to herb behavior whereby a particular drug can dominate the market despite the availability of close substitutes. We use data for H2-antagonist antiulcer drugs to estimate a dynamic demand model and quantify these effects. The model has three components: an hedonic price equation that measures how the aggregate usage of a drug, as well as conventional attributes, affect brand valuation; equations relating equilibrium market shares to quality-adjusted prices and marketing levels; and diffusion equations describing the dynamic adjustment process. We find that consumption externalities influence both valuations and rates of diffusion, but that they operate at the brand and not the therapeutic class level.

Averting catastrophes: the strange economics of Scylla and Charybdis

Faced with numerous potential catastrophes—nuclear and bioterrorism, megaviruses, climate change, and others—which should society attempt to avert? A policy to avert one catastrophe considered in isolation might be evaluated in cost-benefit terms. But because society faces multiple catastrophes, simple cost-benefit analysis fails: Even if the benefit of averting each one exceeds the cost, we should not necessarily avert them all. We explore the policy interdependence of catastrophic events, and develop a rule for determining which catastrophes should be averted and which should not

A Nonconvex Singular Stochastic Control Problem and its Related Optimal Stopping Boundaries

Abstract. Equivalences are known between problems of singular stochastic control (SSC) with convex performance criteria and related questions of optimal stopping; see, for example, Karatzas and Shreve [SIAM J. Control Optim., 22 (1984), pp. 856–877]. The aim of this paper is to inves-tigate how far connections of this type generalize to a nonconvex problem of purchasing electricity. Where the classical equivalence breaks down we provide alternative connections to optimal stopping problems. We consider a nonconvex infinite time horizon SSC problem whose state consists of an un-controlled diffusion representing a real-valued commodity price, and a controlled increasing bounded process representing an inventory. We analyze the geometry of the action and inaction regions by characterizing their (optimal) boundaries. Unlike the case of convex SSC problems we find that the optimal boundaries may be both reflecting and repelling and it is natural to interpret the problem as one of SSC with discretionary stopping

Risk-Seeking versus Risk-Avoiding Investments in Noisy Periodic Environments

We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, $x(t)$, and at each time step invest a particular fraction, $q(t)$, of their budget. The return on investment (RoI), $r(t)$, is characterized by a periodic function with different types and levels of noise. Risk-avoiding agents choose their fraction $q(t)$ proportional to the expected positive RoI, while risk-seeking agents always choose a maximum value $q_{max}$ if they predict the RoI to be positive ("everything on red"). In addition to these different strategies, agents have different capabilities to predict the future $r(t)$, dependent on their internal complexity. Here, we compare 'zero-intelligent' agents using technical analysis (such as moving least squares) with agents using reinforcement learning or genetic algorithms to predict $r(t)$. The performance of agents is measured by their average budget growth after a certain number of time steps. We present results of extensive computer simulations, which show that, for our given artificial environment, (i) the risk-seeking strategy outperforms the risk-avoiding one, and (ii) the genetic algorithm was able to find this optimal strategy itself, and thus outperforms other prediction approaches considered.Comment: 27 pp. v2 with minor corrections. See http://www.sg.ethz.ch for more inf

Priority for the Worse Off and the Social Cost of Carbon

The social cost of carbon (SCC) is a monetary measure of the harms from carbon emission. Specifically, it is the reduction in current consumption that produces a loss in social welfare equivalent to that caused by the emission of a ton of CO2. The standard approach is to calculate the SCC using a discounted-utilitarian social welfare function (SWF)—one that simply adds up the well-being numbers (utilities) of individuals, as discounted by a weighting factor that decreases with time. The discounted-utilitarian SWF has been criticized both for ignoring the distribution of well-being, and for including an arbitrary preference for earlier generations. Here, we use a prioritarian SWF, with no time-discount factor, to calculate the SCC in the integrated assessment model RICE. Prioritarianism is a well-developed concept in ethics and theoretical welfare economics, but has been, thus far, little used in climate scholarship. The core idea is to give greater weight to well-being changes affecting worse off individuals. We find substantial differences between the discounted-utilitarian and non-discounted prioritarian SCC

Booms and busts in commodity markets: bubbles or fundamentals?

This paper considers whether there were periodically collapsing rational speculative bubbles in commodity prices over a 40-year period from the late 1960s. We apply a switching regression approach to a broad range of commodities using two different measures of fundamental values—estimated from convenience yields and from a set of macroeconomic factors believed to affect commodity demand. We find reliable evidence for bubbles only among crude oil and feeder cattle, showing the popular belief that the extreme price movements observed in commodity markets were caused by pure speculation to be unsustainabl

Unraveling the Historical Economies of Scale and Learning Effects for Desalination Technologies

As a technology develops and matures, both economies of scale and the lessons learned through experience drive down the cost over time. This article analyzes and separates the effects of economies of scale and learning through experience on historical cost reductions for three mature desalination technologies: multi‐effect distillation (MED), multi‐stage flash (MSF) distillation, and reverse osmosis (RO). The analysis suggests that learning has been the dominant driver for cost reductions, with learning rates of 23%, 30%, and 12% for MED, MSF, and RO, respectively, when the effects of scale are removed. The highest influence of economies of scale is found for MED, with an exponential scale coefficient of 0.71 and the largest difference between a traditional or scale‐free estimation of the learning rate. MSF and RO showed smaller differences between the traditional and de‐scaled learning rates (only 3%), pointing at learning as the main factor driving their historical cost reductions. However, a trend break observed over the last 10 years mirrors an exhaustion of the potential for technical improvements, as well as an increasing complexity and nonlinearity of the factors influencing the systems' cost. The findings provide useful data and insights for integrated and economic modeling frameworks, while providing guidance to prevent overestimations of the learning effect due to the confounding influence of economies of scale effects associated to historical unit upscaling processes

Production Externalities in the Wood Furniture Industry in Central Java

This paper exploits micro firm level data to examine the impact of spatial clustering and links to foreign buyer networks on firm performance in the wood furniture industry in Central Java, Indonesia. The analysis is based on an annual manufacturing survey. We identify the impact of specialization of the cluster, diversification, and links to foreign buyer networks. For this purpose, a production function framework is developed. The results lend support to the view that clustering of large and medium scale specialized firms improves firm performance, while clustering of small scale specialized firms and clustering of diverse firms are not conducive to firm performance. We also find a clear positive association between involvement in exporting activities and firm performance

Strategic implications of valuation methods

Author's OriginalStrategy is ultimately aimed at creating shareholder value, placing valuation in a central role linking finance and strategy. Focusing on growth options, this paper uses a unique "perfect information" model to examine, from a strategy point of view, the relationship between the market value of the firm and its intrinsic, or DCF, value. Although the research is at the level of the firm, the results have implications at the level of individual strategies and projects, since a firm can be conceptualized as a collection of projects. The findings highlight the relationship between the value of growth options and macroeconomic conditions, industry characteristics, and firm-specific factors. A revised version of this paper has since been published in the journal Advances in Strategic Management. Please use this version in your citations.Alessandri, T. M., Lander, D. M., & Bettis, R. A. (2007), Strategic Implications of Valuation: Evidence from Valuing Growth Options, in Professor Brian Silverman (ed.) Real Options Theory. Advances in Strategic Management, 24, 459-48

Application of Data Mining in Air Traffic Forecasting

The main goal of the study centers on developing a model for the purpose of air traffic forecasting by using off-the-shelf data mining and machine learning techniques. Although data driven modeling has been extensively applied in the aviation sector, little research has been done in the area of air traffic forecasting. This study is inspired by previous research focused on improving the Federal Aviation Administration (FAA) Terminal Area Forecasting (TAF) methodology, which historically assumed that the US air transportation system (ATS) network structure was static. Recent developments use data mining algorithms to predict the likelihood of previously un-connected airport-pairs being connected in the future, and the likelihood of connected airport-pairs becoming un-connected. Despite the innovation of this research, it does not focus on improving the FAA’s existing methodology for forecasting future air traffic levels on existing routes, which is based on relatively simple regression and growth models. We investigate different approaches for improving and developing new features within the existing data mining applications in air traffic forecasting. We focus particularly on predicting detailed traffic information for the US ATS. Initially, a 2-stage log-log model is applied to establish the significance of different inputs and to identify issues of endogeneity and multi-colinearity, while maintaining the simplicity of current models. Although the model shows high goodness of fit, it tested positive for both mentioned issues as well as presenting problems with causality. With the objective of solving these issues, a 3-stage model that is under development is introduced. This model employs logistic regression and discrete choice modelling. As part of future work, machine learning techniques such as clustering and neural networks will be applied to improve this model’s performance