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The aim of this chapter is to shed some light on the behavior of Income Inequality and Inequality of Opportunity over time for 26 European countries. The analysis is carried out using microdata collected by the European Union Statistics on Income and Living Conditions (EU-SILC), which incorporates a wide variety of personal harmonized variables, allowing comparability between countries. The availability of this database for years 2004 and 2010 is particularly relevant to assess changes over time in the main inequality indices and the contribution of circumstances to inequality of opportunity. Furthermore, a bootstrap estimation is performed with the aim of testing whether the differences between both years are statistically significant.

This paper studies the cross-country differences in conventional measures of inequality of opportunity in Europe in the space of individual disposable incomes. Exploiting two recent waves of the EUSILC database reporting information on family background (2005 and 2011), we provide estimates of inequality of opportunity in about 30 European countries for two sufficiently distant data points, allowing a check of consistency for country rankings. In addition, we exploit two observations available for most of the countries to explore the relationship between many institutional dimensions and inequality of opportunity, finding evidence of negative correlation with educational expenditure (especially at the pre-primary level) and passive labour market policies.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this chapter, the authors investigate different methods of assessing business cycles (BCs) for the European Union in general and the euro area in particular. First, the authors conduct a Monte Carlo (MC) experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analysed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, the authors used the structural model’s parameters calibrated to the euro area’s real gross domestic product (GDP) and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models (SoM) consisting of popular Hodrick–Prescott, Christiano–Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. The authors find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and BCs; and (iii) the best-performing MC approaches provide a reasonable combination as the SoM. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the BC for real GDP. Second, the authors estimate the BCs for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. This analysis also confirms that the BCs of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronized with the aggregate euro area.

The proliferation of bibliometric review articles is a true reflection of how bibliometrics is gaining popularity and has been widely adopted in various disciplines. The growing interest of scholars has encouraged us to dwell upon the what, why, when, how, and where of bibliometric literature reviews. The study explained the bibliometric review with the standpoint that it can be considered a strong review method for analyzing a large volume of data and scholars can supplement their traditional reviews with bibliometric reviews to strengthen their knowledge. This will help researchers to justify the (a) need for a study on the particular topic; (b) type or method of review chosen; (c) number of articles selected; (d) inclusion and exclusion criterion; (e) method of analysis; and (f) presentation of the findings.

As fake news and other disinformation are spread primarily online and erode trust in experts and institutions, they challenge the role of librarians as information gatekeepers. Experts have advocated for libraries to educate the public to resist misinformation, yet libraries cannot assume sole responsibility for information literacy work. In this chapter, the authors explore several successful information literacy programs in Ukraine, whose fake news problems made global headlines in 2014, when the Russian annexation of Crimea was accompanied by a flood of crude but effective disinformation. The authors look particularly at the Learn to Discern programs established by the international non-profit organization IREX to foster information literacy using techniques grounded in interdisciplinary expertise and carefully evaluated through pilot studies and follow-up evaluations. These programs train instructors through workshops and provide them with materials. In the first program, aimed at the general public, many of the instructors were librarians, and library facilities were heavily used to deliver the public training. In the second program, information literacy was integrated into the public school curriculum and thousands of teachers were trained to deliver expertly designed materials for particular grade levels and subjects. The authors also consider the special challenges posed by the COVID-19 pandemic, both as a source for new forms of misinformation and as a disruptor of training previously delivered in tightly packed libraries and classrooms. These Ukrainian programs demonstrate the potential for fighting fake news and other misinformation on a scale far beyond what could be accomplished by individual libraries acting alone.