Tversky (1972) has proposed a family of models for paired-comparison data that generalize the Bradley-Terry-Luce (BTL) model and can, therefore, apply to a diversity of situations in which the BTL model is doomed to fail. In this article, we present a Matlab function that makes it easy to specify any of these general models (EBA, Pretree, or BTL) and to estimate their parameters. The program eliminates the time-consuming task of constructing the likelihood function by hand for every single model. The usage of the program is illustrated by several examples. Features of the algorithm are outlined. The purpose of this article is to facilitate the use of probabilistic choice models in the analysis of data resulting from paired comparisons.

This paper analyzes whether the opportunity for physicians to dispense drugs increases healthcare expenditures. We study the case of Switzerland, where dispensing physicians face financial incentives to overprescribe and sell more expensive pharmaceuticals. Using comprehensive physician-level data, we exploit the regional variation in the dispensing regime to estimate causal effects. The empirical strategy consists of a doubly-robust estimation that combines inverse probability weighting with regression. Our main finding suggests that dispensing leads to higher drug costs on the order of 34% per patient.

The trade and consumption of bushmeat are a major threat to biodiversity across the tropics. Conservationists have traditionally advocated for stricter regulation and enforcement as a way to control these practices, with less attention given to consumers and the management of the demand. Yet, it is clear that without adequately tackling demand, it is impossible to effectively curb the bushmeat trade. In this paper, we describe an intervention to reduce demand for bushmeat in northern Tanzania. The intervention was centered around the 1‐h radio show My Wildlife – My Community which included 15‐min episodes of the radio drama Temboni. Each episode of the radio drama was accompanied by a 45‐min interactive call‐in show featuring interviews with experts and local information about available community resources. We evaluated this intervention using a Before‐After‐Control‐Impact framework based on longitudinal data from 168 respondents. To account for the fact that respondents volunteered to be exposed to the intervention, in this case the radio show, we used a matching algorithm together with regression to ensure that we could build a credible counterfactual group. Our analysis did not uncover any differences in outcomes between the treatment and control groups, and thus no evidence of the intervention achieving its initial goals. One potential causal mechanism that could have led to this outcome is the low audience penetration rate. Fewer than 40% of respondents listened to the show and among those who did, only about 20% listened to five of more episodes. This research highlights the challenges of implementing and evaluating interventions delivered through mass media in developing countries, and the importance of reporting on interventions even when there is no evidence that they achieved their initial goals. Only through robust evaluation of behavior change interventions and the sharing of lessons learned can conservationists successfully tackle complex issues such as the bushmeat trade.

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