Satisfaction as a determinant of tourist expenditure

The main aim of this study is to analyse the influence of tourist satisfaction with the tourism offering on tourist expenditure in the destination. Quantitative analysis is based on 523 self-administered questionnaires collected from tourists visiting Rijeka and the Opatija Riviera (Croatia) during the summer of 2014. Principal component analysis was conducted on 22 elements of the offering in order to reduce the dimensionality of satisfaction with the tourism offering, resulting in four dimensions of satisfaction. Regression analysis was conducted with the log of daily expenditure as a dependent variable. The results revealed that only one satisfaction dimension (related to the diversity of facilities) out of the four proved to be a significant predictor of tourist expenditure in the destination. The study also confirms that family income, type of accommodation, past behaviour and length of stay significantly influence daily expenditure.     

Nutrition in socioeconomic planning in LDCs

Nutrition should be seen as an integral part of the national planning effort. The author examines longterm determinants of food consumption in developing countries and shows that progress in improving nutrition is closely related to raising productivity and efficiency in the national economy. Optimum programmes in nutrition should be part of the design of appropriate social and economic development. This should be done at national and regional levels, with particular emphasis on the regional level.     

Achieving destination competitiveness: an importance–performance analysis of Serbia

As a relatively new and under-researched tourism destination, Serbia provides an interesting context to assess destination competitiveness in conditions of global environmental changes and the additional challenges of transition from a socialist economy to a market-based economy. This article uses importance–performance analysis (IPA) to assess the importance of different activities to underpin tourism development in Serbia, as well as the industry's perceived performance in respect of these activities. There are a number of areas in which Serbian tourism industry considers itself to be underperforming in the implementation of activities to maintain destination competitiveness. This article analyses these results in detail using IPA as a diagnostic tool. Particular attention is paid to investigating the implications of the findings for both destination managers and private tourism operators in Serbia that can assist them to develop a focused action agenda to achieve and maintain destination competitive advantage. The approach can be used in other destinations to assess tourism ability to meet the challenges of global trends.     

TEMPORARY EMIGRATION AND WELFARE: THE CASE OF LOW‐SKILLED LABOR

This article studies the implications of temporary emigration for the welfare of a source country. The framework is one of general equilibrium, where the economy's stocks of both capital and labor are endogenously determined by the saving and migration decisions of optimizing agents. Simulations of the model suggest that for realistic values of the parameters, welfare of nonmigrants of the source country is maximized when the migrants are employed abroad for a period in the range of roughly 8–12 years. The ideal duration is found to be an increasing function of the international wage differential, migration costs, and the degree to which the rights of migrants are protected in the host country.     

Markov decision processes under model uncertainty

We introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting. By providing a dynamic programming principle, we obtain a local-to-global paradigm, namely solving a local, that is, a one time-step robust optimization problem leads to an optimizer of the global (i.e., infinite time-steps) robust stochastic optimal control problem, as well as to a corresponding worst-case measure. Moreover, we apply this framework to portfolio optimization involving data of the . We present two different types of ambiguity sets; one is fully data-driven given by a Wasserstein-ball around the empirical measure, the second one is described by a parametric set of multivariate normal distributions, where the corresponding uncertainty sets of the parameters are estimated from the data. It turns out that in scenarios where the market is volatile or bearish, the optimal portfolio strategies from the corresponding robust optimization problem outperforms the ones without model uncertainty, showcasing the importance of taking model uncertainty into account.