Functional Foods and the Young

Factors such as raised consciousness about human health, development of the food industry, and medicine and branches of natural science studying the relation between nutrition and health have led to the popularization of foods with proven health effects. Such foods are called functional foods. Although there is growing interest in functional foods, there are several groups of consumers that are, more than others, interested in functional foods. To examine buying behavior and attitudes toward functional food of young consumers (18–30 years old) and to determine the most interested consumers, we conducted a survey using a self-administered questionnaire on a sample of 570 Croatian students. The majority of the respondents purchase functional foods, usually in supermarkets. Apart from taste, the most important functional food attribute is price/quality ratio. Respondents are buying functional foods because they believe that those foods are healthier and safer than other products. Examined market segments were based on respondents’ attitudes toward functional foods. The results of this research could be used in planning further development of the functional foods market for young consumers.     

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.     

Semiparametric robust estimation of truncated and censored regression models

Many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically censored least squares (SCLS) are sensitive to outliers and data contamination as we document. Therefore, we propose a semiparametric general trimmed estimator (GTE) of truncated and censored regression, which is highly robust but relatively imprecise. To improve its performance, we also propose data-adaptive and one-step trimmed estimators. We derive the robust and asymptotic properties of all proposed estimators and show that the one-step estimators (e.g., one-step SCLS) are as robust as GTE and are asymptotically equivalent to the original estimator (e.g., SCLS). The finite-sample properties of existing and proposed estimators are studied by means of Monte Carlo simulations.     

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.     

Forecasting high-frequency excess stock returns via data analytics and machine learning

Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.     

The role of satisfaction in labor diaspora dynamics: An analysis of BREXIT effects

In diaspora research, people's international mobility is often understood as a response to pull-push forces on an economic macro-level or as part of diasporic waves. However, labor diaspora formations are also influenced by micro-level (i.e., individual perceptions) drivers related to work per se, such as satisfaction. This explorative qualitative study takes a novel angle and focuses on the role of Portuguese nurses’ satisfaction with the evaluation of the Brexit scenario and its effects on this labor diaspora's mobility and stability. The findings illustrate that satisfaction, although affected by uncertainty, mainly relates to perceived working conditions and legislation and the possibilities to communicate with locals and other diasporans. This paper provides suggestions for future research and contributes to the development of theory on staying and human stickiness, explaining the dynamics of exit and entry in (re-)migration decision making.     

Emojis to conversion on social media

Emojis have become popular in online marketing communications. Marketers use emojis to humanize brand voice and elicit an emotional response from target audiences. However, little is known about how emojis are perceived and what kind of reactions they evoke. Therefore, this study aims to verify whether exposure to emojis leads to an increased intention to purchase and whether the use of emojis impacts campaign effectiveness in a real-life environment. In a theory build-up process, we draw upon the Dual Coding Theory and emotional contagion concept and develop seven hypotheses. We performed two data collection studies to test the hypothesized relations. The first study is based on a questionnaire (N = 318), while the second is based on experimental design in a real-life business environment. Surprisingly, we found that using emojis had a negative effect on purchase intention, while the effect was positive when mediated by positive affect. Emojis increased the effectiveness of marketing campaigns for hedonic products and strongly impacted the return on advertising spent. The findings of this study have both theoretical and practical implications in the observed domains, particularly about the type of products emojis are most effective in promoting, gender differences, and real-life consumer behaviour. This is the first study to use an experimental design in a real-life scenario to capture the role of emojis on campaign effectiveness and decipher the differences between genders and their perceptions of emojis. Theoretical and practical implications together with future prospects are discussed.     

How Wealthy are the Rich?

Underreporting and undersampling biases in top tail wealth, although widely acknowledged, have not been statistically quantified so far, essentially because they are not readily observable. Here we exploit the functional form of power law-like regimes in top tail wealth to derive analytical expressions for these biases, and use German microdata from a popular survey and rich list to illustrate that tiny differences in non-response rates lead to tail wealth estimates that differ by an order of magnitude, in our case ranging from 1 to 9 trillion euros. Underreporting seriously compounds the problem, and we find that the estimation of totals in scale-free systems oftentimes tends to be spurious. Our findings also suggest that recent debates on the existence of scale- or type-dependence in returns to wealth are ill-posed because the available data cannot discriminate between scale- or type-dependence, on one hand, and statistical biases, on the other hand. Yet both economic theory and mathematical formalism indicate that sampling and reporting biases are more plausible explanations for the observed data than scale- or type-dependence.     

Online social network fundraising: Threats and potentialities

There has been a growth in online fundraising from crowdfunding apps, like GoFundMe, that propagate fundraising appeals on social networking sites. In the online space, these crowdfunding apps pose a potential threat to the traditional intermediation role of charities. The disintermediation threat is that donors choose crowdfunding intermediaries instead of charities to channel their giving. In this article, we discuss what makes crowdsourced fundraising effective and how charities can adapt to this new dynamic for more effective online fundraising emphasizing two key success factors: brand strength/reputation and managing the donor experience. In addition, we explain the advantages and disadvantages of social media fundraising and giving and propose ways charities can leverage their good reputations and public trust to stimulate reintermediation. Finally, we propose a landscape for future research based on model that emphases the fundraising campaign's ability to stimulate viral sharing within and between online social networks.