Valuing Pharma R&D: The Catch-22 of DCF

There are two principal methods for valuing pharmaceutical R&D projects—discounted cash flow (DCF) and real options valuation (ROV). As typically practiced, DCF valuations tend to be lower than the estimates produced by ROV techniques. Part of the difference, as many have recognized, stems from DCF s limited ability to take account of managers' real option to cut its losses when new information reveals a drug candidate's lack of profit potential. Another reason for the difference, however, is the widespread use in DCF valuations of established success rates that do not distinguish between projects that fail to pass safety or efficacy trials and those that are abandoned for lack of economic viability. If the appropriate success rates are used, the two methods should yield identical project values because they assume the same scenarios. The practical reality, however, is that the two methods deal in a completely different way with the possibility of abandonment for economic reasons. Because ROV accounts for this possibility directly in the model itself, it is much better suited than DCF to this task—indeed, that is the uncertainty that it is designed to deal with. And the fact that 30% of all pharma R&D abandonments are for economic reasons is a strong argument for using ROV rather than DCF to evaluate new drug development.     

Studying the association between air pollution and lung cancer incidence in a large metropolitan area using a kernel density function

In the absence of patient-specific data, composite level data are often used in epidemiological studies. However, since individual exposure levels cannot accurately be inferred from aggregate data, such an approach may lead to erroneous estimates of health effects of potential environmental risk factors. In the present study, we attempt to address this information-loss problem by using the “kernel density function”, which estimates the intensity of events across a surface, by calculating the overall number of cases situated within a given search radius from a target point. The present paper illustrates the use of this analytical technique for a study of association between the geographical distributions of lung cancer cases and SO₂ air pollution estimates in the Greater Haifa Metropolitan Area (GHMA). In the analysis, the results obtained by kernel smoothing are contrasted with those obtained by areal aggregation techniques more commonly used in empirical studies.     

A note on testing for complementarity and substitutability in the case of multiple practices

Recent empirical studies of firm-level performance have tested complementarity in the case of multiple practices. These papers have drawn conclusions using potentially biased estimates of pair-wise interaction effects. We develop a consistent and simple testing framework and test it against alternatives.     

Agricultural productivity,shadow wages and off-farm labor decisions in Nicaragua

The objective of this study is to analyze the decision to work off-farm by male and female farmers in Nicaragua using a three-year unbalanced panel dataset. Shadow income and shadow wages are also estimated. Moreover, to mitigate biases from unobserved individual and farm time-invariant characteristics as well as from sample selection, we apply a semiparametric approach for panel data. Our main findings suggest that shadow wages play a major role in off-farm labor decisions for both males and females. This implies that less labor is allocated to off-farm activities as the opportunity cost for agricultural work goes up. In addition, as the on-farm marginal productivity of households (i.e., shadow income) rises, both males and females reduce the hours they allocate to off-farm activities.     

Preston,Ivan L., The Tangled Web They Weave: Truth,Falsity, & Advertisers,Madison: The University of Wisconsin Press, 1994

Abstract

In an experiment, children in three grade levels (second, fourth, and sixth) viewed four advertisements. The children were shown advertisements with either metaphors in both pictorial and verbal form or a literal equivalent. The sixth graders interpreted more metaphors correctly than the fourth and second graders. In terms of recall of advertised content, the sixth and fourth graders recalled brand names, products, and additional copy better than the second graders. Within each grade level, metaphors did not enhance children's recall of advertised content or perceptions of the understandability of the advertisements and liking of the advertised products. The authors speculate that metaphors are most likely to enhance recall under conditions in which the metaphor serves as a direct representation linking the metaphor to the advertised product. Their findings suggest that advertisers should be aware that young readers may have difficulty interpreting metaphors and that metaphors may not be more effective in terms of children's recall and perception of understandability than literal versions of advertisements.     

Customer satisfaction measurement: comparing four methods of attribute categorisations

The issue of how to weight and categorise service attributes has attracted great attention from academics as well as practitioners. The application of an inappropriate method could lead to misleading interpretations and useless or costly actions. We first review several streams of literature concerning the theoretical background of attribute categorisations in relation to customer satisfaction. We then identify four methods that have been developed to categorise attributes into four classes. In the next step we apply these methods in an empirical study. Criteria for distinguishing the considered approaches conceptually and methodologically are proposed, and implications for future research are discussed.     

Goodness-of-fit tests for Poisson count time series based on the Stein–Chen identity

To test the null hypothesis of a Poisson marginal distribution, test statistics based on the Stein–Chen identity are proposed. For a wide class of Poisson count time series, the asymptotic distribution of different types of Stein–Chen statistics is derived, also if multiple statistics are jointly applied. The performance of the tests is analyzed with simulations, as well as the question which Stein–Chen functions should be used for which alternative. Illustrative data examples are presented, and possible extensions of the novel Stein–Chen approach are discussed as well.     

Variables — Observable,objective, measurable and constructed

Quality & Quantity -     

How star women build portable skills

In May 2004, with the war for talent in high gear, Groysberg and colleagues from Harvard Business School wrote in these pages about the risks of hiring star performers away from competitors. After studying the fortunes of more than 1,000 star stock analysts, they found that when a star switched companies, not only did his performance plunge, so did the effectiveness of the group he joined and the market value of his new company. But further analysis of the data reveals that it's not that simple. In fact, one group of analysts reliably maintained star rankings even after changing employers: women. Unlike their male counterparts, female stars who switched firms performed just as well, in the aggregate, as those who stayed put. The 189 star women in the sample (18% of the star analysts studied) achieved a higher rank after switching firms than the men did. Why the discrepancy? First, says the author, the best female analysts appear to have built their franchises on portable, external relationships with clients and the companies they covered, rather than on relationships rooted within their firms. By contrast, male analysts built up greater firm- and team-specific human capital by investing more in the internal networks and unique capabilities and resources of their own companies. Second, women took greater care when assessing a prospective new employer. In this article, Groysberg explores the reasons behind the star women's portable performance.