Model-based econometric techniques of the shadow economy estimation have been increasingly popular, but a systematic approach to getting the best of their complementarities has so far been missing. We review the dominant approaches in the literature-currency demand analysis and MIMIC model-and propose a hybrid procedure that addresses their previous critique, in particular the misspecification issues in CDA equations and the vague transformation of the latent variable obtained via MIMIC model into interpretable levels and paths of the shadow economy. We propose a new identification scheme for the MIMIC model, referred to as 'reverse standarization'. It supplies the MIMIC model with the panel-structured information on the latent variable's mean and variance obtained from the CDA estimates, treating this information as given in the restricted full information maximum likelihood function. This approach allows avoiding some controversial steps, such as choosing an externally estimated reference point for benchmarking or adopting other ad hoc identifying assumptions. We estimate the shadow economy for up to 43 countries, with the results obtained in the range of 2.8-29.9% of GDP. Various versions of our models remain robust as regards changes in the level of the shadow economy over time and the relative position of the analysed countries. We also find that the contribution of (a correctly specified) MIMIC model to the measurement of trends in the shadow economy is marginal as compared to the contribution of the CDA model, confirming the scepticism of some previous literature towards this method.

Estimated indirect cost of diabetes can be a useful input for health technology analyses of drugs or economic impact assessments of public health programmes.

We investigated the spatial variation patterns of voting results in Polish parliamentary election in 2015 across 380 regions. That election was a milestone event in Polish politics that substantially affected Poland’s internal and foreign policy directions and promoted two emerging political parties as runners-up against the well-established ones. While socio-economic, cultural and geographical factors such as economic activity, historical legacies (post-Russian East vs post-German West) and economic dichotomies (cities vs the countryside) explain most variations for most parties, they do not appeared to fit as determinants of the new parties’ support, especially of right-wing populists. Demographic target groups of individual parties appear to be relatively unresponsive to their pre-election offerings. The spatial specification of econometric models considerably improves their statistical properties. We also examined mixed-W models to account for the unobservable spatial effects stemming from the construction of constituencies. Their distinctive sets of candidates added significantly to the explanation of the spatial variation in voting.

In an environment of growing real prices and changing consumption patterns in the tobacco market, the question arises whether the price elasticities of demand may be estimated as constant parameters over multi-annual samples. The authors develop a methodological framework for estimating time-varying demand elasticities in a state-space model, estimated via maximum likelihood based on the Kalman filter. This model is applied to evaluate various, alternative paths of tobacco excise tax rates. Importantly, both in estimation and in simulations, the authors account not only for changes in the level, but also in the structure of excise tax by exploring the market segmentation into a lower and a higher end of the market. This allows the authors to contribute to the existing literature about the optimum structuring of the tax between the specific and ad valorem rates and to analyse the Laffer surface (rather than a curve). The measurement results indicate some growth in the magnitude of price elasticity of demand since 2005, and the simulations show that the differences between the actual and the optimum taxation policy for tobacco products were marginal in the 2014-2018 period.