We develop a method which measures the effect of the major international conventions in the area of safety, pollution, search and rescue and work related measures. We further distinguish between the effect of entry into force and the status of ratification of a convention by its parties. We use standard econometric models and base our analysis on a unique dataset of 30 years of monthly data where we correct for other factors which can influence safety such as safety inspections and ship economic cycles. The results show a complex picture where the average time between adoption and entry into force was calculated to be 3.1 years. Overall, the more parties ratify a convention, the more likely safety is improved and pollution is decreased although one can detect a certain level of non compliance. The immediate effect of entry into force presents a mixed picture where most negative effects can be found with legislation in the area of safety management and pollution, followed by technical areas. The effect of legislation in the areas related to working and living conditions and certification and training is smallest. Seasonality can be found with peaks in December and January for all conventions but are less important for pollution.

To examine cross-country diffusion of new products, marketing researchers have to rely on a multivariate product growth model. We put forward such a model, and show that it is a natural extension of the original Bass (1969) model. We contrast our model with currently in use multivariate models and we show that inference is much easier and interpretation is straightforward. Especially if the number of countries is larger than two. In fact, parameter estimation can be done using standard commercially available software.We illustrate the benefits of our model relative to other models in simulation experiments.These experiments show that in the competing models the cross-country effects are actually very difficult to identify from the data. An application to a three-country CD sales series shows the merits of our model in practice.

We present a statistical model for voter choice that incorporates a consideration set stage and final vote intention stage. The first stage involves a multivariate probit model for the vector of probabilities that a candidate or a party gets considered. The second stage of the model is a multinomial probit model for the actual choice. In both stages we use as explanatory variables data on voter choice at the previous election, as well as socio-demographic respondent characteristics. Importantly, our model explicitly accounts for the three types of "missing data" encountered in polling. First, we include a no-vote option in the final vote intention stage. Second, the "do not know" response is assumed to arise from too little difference in the utility between the two most preferred options in the consideration set. Third, the "do not want to say" response is modelled as a missing observation on the most preferred alternative in the consideration set. Thus, we consider the missing data generating mechanism to be non-ignorable and build a model based on utility maximization to describe the voting intentions of these respondents. We illustrate the merits of the model as we have information on a sample of about 5000 individuals from the Netherlands for who we know how they voted last time (if at all), which parties they would consider for the upcoming election, and what their voting intention is. A unique feature of the data set is that information is available on actual individual voting behavior, measured at the day of election. We find that the inclusion of the consideration set stage in the model enables the user to make more precise inferences on the competitive structure in the political domain and to get better out-of-sample forecasts.

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