Market Simulation Based Sensitivity Analysis as a Means to Inform Design Effort as Applied to Photovoltaic Panels

Product design success depends on the engineering performance of the product and also on the reaction of external stakeholders such as customers, retailers, and policymakers. This article illustrates how an early-stage engineering design performance model can be incorporated into a decision framework representing customers, retailers, and policymakers to assess the revenue potential for different technologies. Sensitivity analysis is performed for revenue and other stakeholder decision criteria with respect to the design performance measures. We illustrate our approach for photovoltaic panels in the context of the residential solar electricity generation system market in New South Wales, Australia that experienced a variety of federal and state government incentive programs between 2010 and 2012. The analysis is based on engineering performance modeling, discrete choice demand modeling, and cost modeling all with simplifying assumptions.

Discrete Choice Theory and Modeling

This article provides an overview of the methods employed in discrete choice models relevant to food demand analysis. Discrete analysis of food choices can be grouped into two main areas: analysis that focuses on the consumer to assess preferences and welfare, and analysis that focuses on assessing consumer behavior to provide marketing or sales strategies. This article illustrates that discrete choice models of food demand have been estimated from a variety of data sources: choice experiments, experimental economic data, and scanner panel data. It examines the conceptual framework underpinning these discrete choices. It reflects on the relatively unique properties of disaggregate food choices and the corresponding issues for discrete choice demand analysis. This article considers the operationalization of models based on the theoretical microeconomic model. Finally, it provides a brief description of some interesting extensions and future research issues in the area of discrete choice analysis and food demand.

Pecuniary and Non-Pecuniary Incentives in Prescription Pharmaceuticals: The Case of Statins

Health insurance companies seek to influence the type of care patients receive in order to increase value in relation to cost. Traditional health insurance relies primarily on price mechanisms to affect patients' and doctors' choices, whereas managed care plans such as HMOs, as the name implies, affect choices directly thorough various forms of “managed care.” I investigate the effect of pecuniary and non-pecuniary incentives used by health insurance companies to influence prescription decisions in an important class of pharmaceuticals, cholesterol-lowering drugs called Statins, using a discrete-choice demand model on patient-level data. My results suggest that HMOs are significantly more successful at influencing drug choice than traditional indemnity insurers. In conjunction with volume-contingent discounts given by drug producers, this could explain part of the cost-effectiveness differential between HMOs and traditional indemnity insurers.

Understanding Grain Movements for Demand Estimation

Grain producers and handlers in Washington State have benefited from a multimodal transportation network of roads, railroads, and the Columbia–Snake River barge system to move large amounts of grain effectively in a timely and economic manner. The competitive environment of the grain industry brings many changes, including the number of firms and houses, mergers, and modal competitiveness. Additionally, marketing strategies are affected because choices of available transportation modes reflect the decision processes of warehouses or firm managers. This aggregate study of grain marketing and transportation in the Pacific Northwest helps lay the groundwork for subsequent estimates of empirical demand. Such subsequent modeling attempts may include revealed and stated preference analysis in discrete choice demand models. A thorough understanding of the industry and market characteristics should improve empirical estimation efforts and produce more defensible policy analysis. Based on a 90% shipment volume response rate, results show that in the Columbia–Snake River grain situation, one destination absorbs more than 90% of shipments. Modal competition is active; barge has a market share of more than 50%, down 12–16% from 10 years ago. Multiple-car shipments have increased, but not drastically. Rates are consistently competitive over the period. Finally, grain demand is seasonal but generally has been stable over time. The revealed preferences from this aggregate analysis suggest that price elasticity may vary across shippers, times of movement, and modal availability.