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In the last decades, low-cost carriers (LCCs) have generated several changes in the air market for both passengers and airports. Mainly for regional airports, LCCs have represented an important opportunity to improve their connectivity levels and passenger traffic. Furthermore, many regional airports have become key factors to regenerate the local economy by improving accessibility and stimulating several markets, such as tourism. However, the relationship between LCCs and airports is rather complex and the outcomes not always predictable. In order to analyze and understand better such relationship and its outcomes, this chapter discusses the main underlying factors identified in: relation with the regional air market (secondary/primary airports), balance of power (dominated/non-dominated airports), and industrial organization (bases/non-bases). Starting from the proposed Relative Closeness Index, which combines yearly airport passengers and distance between airport pairs, a large sample of European airports is analyzed. Then, a smaller sub-sample – which includes selected, significant case studies referring to mid-sized airports – is discussed in detail. Among the main findings, airports sharing their catchment area with others are in a very risky position, due to the potential mobility of LCCs, while geographically isolated airports in good catchment areas can better counterbalance the power of carriers.

Most decision-making problems in construction are complex and difficult to solve, as they involve multiple criteria and multiple decision makers in addition to subjective uncertainties, imprecisions and vagueness surrounding the decision-making process. In many instances, the decision-making process is based on linguistic terms rather than numerical values. Hence, structured fuzzy consensus-reaching processes and fuzzy aggregation methods are instrumental in multi-criteria group decision-making (MCGDM) problems for capturing the point of view of a group of experts. This chapter outlines different fuzzy consensus-reaching processes and fuzzy aggregation methods. It presents the background of the basic theory and formulation of these processes and methods, as well as numerical examples that illustrate their theory and formulation. Application areas of fuzzy consensus reaching and fuzzy aggregation in the construction domain are identified, and an overview of previously developed frameworks for fuzzy consensus reaching and fuzzy aggregation is provided. Finally, areas for future work are presented that highlight emerging trends and the imminent needs of fuzzy consensus reaching and fuzzy aggregation in the construction domain.

This chapter focuses on an important aspect of economic inequality – the question of how people perceive inequality and whether these perceptions deviate in any meaningful way from statistical measures of inequality. Using a novel approach, the author investigates whether individuals across different countries are able to correctly estimate the shape of income distribution of the country where they reside. The author further investigates whether individuals have the distribution of a particular reference group in mind when they answer questions on inequality. The author finds that perceptions of inequality are frequently shaped by reference groups such as those formed according to educational attainment, age, and gender.

The study constructs a composite indicator to rank macroeconomic performance of countries and a separate composite indicator to rank countries by inequality using the TOPSIS methodology of Multiple Criteria Decision-Making Analysis. The intuitive idea of TOPSIS is to formulate an ideal solution with respect to each individual policy variable; the relative rank of any country is then determined, using a suitable distance metric, such that the best performer simultaneously has the shortest distance from the ideal solution and the farthest distance from the non-ideal. It uses the composite indicator based rankings together with the KOF Globalization Index and sub-indices based rankings to examine the overall relationship between globalization and macroeconomic performance of countries and reduction in inequality; the impacts of trade and financial globalization for 1990–2018 across countries and groups of the globe. It shows that though highly correlated with growth, globalization may not necessarily lead to an improvement in overall macroeconomic performances of countries when one also takes into account unemployment and inflation. Economic globalization is seen here to mostly coincide with rise in income inequality. Observations also support the fact that countries, even if they are not highly integrated may reap sufficient benefits of globalization for macroeconomic performance and inequality diminution given supportive policies.

Transportation plays a central role in facilitating economic activities across sectors and between regions and thus should be essential to business cycle research. In this chapter, we identify four coincident indicators representing different aspects of the transportation sector. Foremost among them is the index of transportation services output (TSI) presented in the previous chapter. Following the long-standing methodology of National Bureau of Economic Research (NBER) business cycle research, the other three indicators that we include are payroll, personal consumption and employment – all pertaining to the transportation sector. Using a composite of the four indicators, we define the classical business cycle and growth cycle chronologies for the transportation sector. We find that, relative to the economy, business cycles in the transportation sector have an average lead of nearly 6 months at peaks and an average lag of 2 months at troughs. Similar to transportation business cycles, growth slowdowns in this sector also last longer than the economy-wide slowdowns by a few months. This study underscores the importance of transportation indicators in monitoring cyclical movements in the aggregate economy.