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This study investigates, both theoretically and empirically, the effects of joint ventures on traffic. Although alliances are a pre-condition for joint ventures, both cooperation agreements are different in their nature. The reason is that alliances are revenue-sharing agreements, whereas joint ventures also involve a cost-sharing commitment. Our empirical analysis focuses on the transatlantic market, including non-stop routings (interhub markets) and one-stopover routings (interline markets). Our theoretical and empirical findings emphasize the relevance of economies of traffic density and reveal a positive effect of joint ventures on traffic, both in interhub and interline markets.

This chapter reviews the key results obtained in previous studies of airline mergers. It is found that the effect of mergers on airfares is dependent on the network configurations of merging airlines. Fare increases are frequently observed on overlapped routes. However, if the networks of two merging airlines are complementary, the expanded network after the merger leads to cost savings, increase in travel options, and improvement in service quality. Therefore, in a deregulated market, with few entry barriers, relaxing merger regulations is likely to improve welfare. However, most welfare evaluations do not incorporate quality changes or dynamic competition effects. Empirical investigations are primarily ex post analysis of mergers that have already passed antitrust reviews. The relationship between market concentration and welfare might be nonlinear and market specific. Therefore, airline mergers and alliances should be reviewed case by case. Methodological improvements are needed in future studies to control for the effects of complicating factors inherent in ex post evaluations.

We investigate the relationship between airline network structure and airport congestion. More specifically, we study the ways in which airlines adjust capacity to delays depending on the network type they operate. We find some evidence suggesting that airlines operating hub-and-spoke structures react less to delays than airlines operating fully connected configurations. In particular, network airlines have incentives to keep frequency high even if this is at the expense of a greater congestion at their hub airports. We also show that airlines in slot-constrained airports seem to react to higher levels of congestion by using bigger aircraft at lower frequencies; thus, we conclude that conditioning the number of available slots on the levels of delays at the airport seems an effective measure that creates the right incentives for airlines to reduce the congestion they generate.

Airline travel is composed of business and nonbusiness travelers, each with different preferences that give rise to differences in demand elasticities and substitution not only across airlines but also airports. In this study, we develop and estimate a model of airline wherein consumers choose which airports and airline to use that allows for unobserved differences between travelers (e.g., business and nonbusiness travelers). The results point to the role that airports themselves play in the ultimate selection of a flight, and that there are strong interactive effects between the airlines’ networks and the consumers’ preferences across airports.

Here we consider the various ways in which airlines integrate their business activities. The thin markets, long distances, poor infrastructure, and challenging terrain over which many airlines based in developing countries operate can make it difficult to reap the economies of scale, scope, and density that carriers in more developed nations enjoy. There also remain institutional barriers to cross border trade in airline services. As a response to this, airlines from developing regions “cooperate” in a number of ways. This may involve multinational ownership, code sharing, or joint ventures. The rationale for these actions, together with discussion of the outcomes of some of them, is considered here.

Housing finance is frequently difficult to provide in developing nations due to unstable macroeconomic conditions and a lack of supportive legal, technological and regulatory frameworks (Lea and Bernstein, 1996). Governments in these countries have, therefore, created a range of organizations and initiatives to improve the flow of capital to the housing market on a footing that is affordable to their populations given the household income levels (Ram and Needham, 2016). Housing, however, is by its very nature a significant investment requiring a considerable capital outlay at the onset (Dasgupta et al., 2014). This makes acquiring it challenging, particularly in underdeveloped nations where saving tendencies are quite low partly because of low-income levels (Keller and Mukudi-Omwami, 2017). As a result, many developing nations struggle with severe housing issues that lead to slums, overcrowding and related health issues.

The theoretical model for analyzing housing finance in Kenya in this study incorporates both demand and supply aspects, drawing from Brueckner’s (1994) framework. This model divides factors influencing demand into certainty and uncertainty conditions faced by households. In terms of certainty, the model considers factors that households can predict reliably. First is income, households are assumed to have stable income, allowing accurate assessment of budget constraints and mortgage decisions. Second is interest rates. While interest rates fluctuate, the model assumes that households have information about current rates, enabling informed decision-making. Finally, existing housing costs, such as rent or mortgage payments, are treated as fixed and predictable, facilitating accurate budget planning. Conversely, uncertainty factors include future income, future interest rates and housing prices. Households face uncertainty regarding future income, which can impact their mortgage repayment ability due to job market changes or unforeseen events. The model does not predict future interest rate changes, which can affect the affordability of mortgages. Furthermore, future fluctuations in housing prices add uncertainty to the benefits of homeownership and mortgage debt. Due to these uncertainties, the model in this study assumes certainty conditions, focusing on households maximizing their utility. In Brueckner’s model, a utility function captures household preferences and well-being linked to consumption choices, specifically between housing (H) and nonhousing goods (N). The utility function helps determine optimal income allocation, influenced by income (M), prices (P) and return on savings (t). The utility maximization problem involves selecting optimal amounts of housing and nonhousing consumption while managing housing credit (C).

The study confirms a significant long-run relationship between house finance and several macroeconomic variables, including interest rates on credit, inflation, unemployment and gross domestic product (GDP). The negative and significant error correction term indicates the presence of an equilibrium relationship, suggesting that the housing finance market in Kenya self-corrects swiftly in response to economic shocks. This efficiency could be attributed to increasing competition among financial institutions or a growing public awareness of housing finance options, implying a relatively well-developed market. Such responsiveness suggests that government policies aimed at influencing housing finance might have a quicker impact. For instance, introducing subsidies to reduce credit rates could rapidly boost housing finance activity (World Bank, 2019). However, the flip side of a fast-adjusting market is potential volatility, where rapid swings in economic factors could lead to significant fluctuations in housing finance availability, posing risks for both lenders and borrowers (Braun et al., 2022). Moreover, a rapid adjustment might not necessarily reflect a perfectly healthy market; it could indicate underlying issues like speculation or easy access to credit, potentially leading to bubbles or financial instability (Agnello et al., 2020).

This study reveals key insights into the determinants of housing finance in Kenya, demonstrating a significant long-run relationship between housing finance and economic variables such as interest rates, inflation, unemployment and GDP. The efficient adjustment of the housing finance market to economic changes suggests that government policies can rapidly influence housing finance, although this responsiveness also implies potential volatility and risks, including financial instability. Policymakers should, therefore, focus on maintaining macroeconomic stability and monitoring the housing market for signs of overheating. Encouraging competition among lenders and diversifying housing finance products can help ensure sustainable market adjustments. Credit interest rates show a modest but positive relationship with housing finance, suggesting that a stable lending environment could stimulate activity. Policymakers should manage credit availability to prevent excessive expansion and instability, enhancing financial inclusion and fostering competition in the banking sector. Inflation positively impacts housing finance, with rising inflation driving demand for real assets like housing. However, significant interest rate hikes by the Central Bank to combat inflation could reduce mortgage affordability. A flexible interest rate policy, along with targeted interventions like subsidized rates for first-time buyers, is necessary to balance market stimulation with inflation control. Unemployment’s negative impact on housing finance underscores the need for robust unemployment benefits and job training initiatives to support financial stability during job losses. Targeted housing finance programs for low- and middle-income earners can also improve mortgage accessibility. The positive correlation between GDP growth and housing finance indicates that economic expansion drives housing demand. Policymakers should prioritize initiatives that promote long-term economic growth, such as infrastructure development and innovation. Finally, the insignificance of savings interest rates in influencing housing finance suggests that traditional monetary policy may have limited effects. Promoting financial literacy and developing tailored savings instruments could strengthen the connection between savings and housing finance over time.

This paper aims to embark a mathematical model based on investigation and comparison of airport pricing policies under various types of competition, considering both per-passenger and per-flight charges at congested airports.

In this model, four-game theoretic strategies are assessed and closed-form formulas have been proved for each of the mentioned strategies. Numerical examples and graphical representations of the optimal solutions are provided to illustrate the models.

The rectitude of the presented formulas is evaluated with sensitivity analysis and numerical examples have been put forward. Finally, managerial implications are suggested by means of the proposed analysis.

The represented model is inherently limited to investigate all the available and influential factors in the field of congestion pricing. With this regard, several studies can be implemented as the future research of this study. The applications of other game theoretic approaches such as Cartel games and its combination with the four mentioned games seem to be worthwhile. Moreover, it is recommended to investigate the effectiveness of the proposed model and formulations with a large-scale database.

The authors formulate a novel strategy that put forwards a four-game theoretic strategy, which helps managers to select the best suitable ones for their specific airline and/or air traveling companies. The authors find that by means of the proposed model, the application of Stackelberg–Bertrand behavior in the field of airport congestion pricing will rebound to a more profitable strategy in contrast with the other three represented methods.