Die Entwicklung der Kfz-Zulassungen in Deutschland: Eine Prognose und Implikationen für die Kraftfahrtversicherung

An Overview of Population Projections—Methodological Concepts, International Data Availability, and Use Cases

“…Besides, further challenges arise from a positive population development. For instance, childcare capacities must be created [1] and local public transport must adapt its infrastructure to a growing number of passengers [65].…”

“…Population projections serve as a quantitative basis for a variety of different applications and actors. In the coming decades, society is facing a radical macroeconomic structural change that will affect all important markets: the labor market in industrialized countries lacks young workers [17], product markets must adapt to structurally changed consumer wishes and increasing longevity [24,64,65], the housing market has to align the housing stock with the requirements and housing desires of older people [66], and savings behavior and the demand for investments on the capital market are transforming [67,68]. In addition to the mentioned labor market trends, the social insurance systems face a greater financial burden on the expenditure side due to rising population figures in the higher and more vulnerable age groups [25,29].…”

The future of mobility and its impact on the automobile insurance industry

“…Demographic change: a Focas and Christidis (2017); b Kuhnimhof, Zumkeller, and Chlond (2013); c Linden and Wittmer (2018); d Lyons and Davidson (2016); e Plazinic and Jovic (2018); f Thiele and Schmidt‐Jochmann (2015); g Thompson, Baldock, and Dutschke (2018); h Weidner et al (2015). …”

“…The academic research on the motor insurance business often deals with the highly competitive market conditions, market growth, and market profitability (e.g., Eling & Luhnen, 2008, 2010; Maichel‐Guggemoos & Wagner, 2018). Attention has also been given to the consequences of individual major mobility “subtrends” or product designs with a focus on usage‐based insurance (e.g., Guillen & Perez‐Marin, 2018; Kraft & Hering, 2017; Sheehan et al, 2017; Weidner, Vanella, & Zuchandke, 2015; Wu, 2019). For instance, Sheehan et al (2017) apply a Bayesian network to examine the potential consequences of autonomous driving for the motor insurance business and the changing risk landscape based on the five levels of automation defined by the Society of Automotive Engineers (SAE).…”

“…For instance, Sheehan et al (2017) apply a Bayesian network to examine the potential consequences of autonomous driving for the motor insurance business and the changing risk landscape based on the five levels of automation defined by the Society of Automotive Engineers (SAE). Weidner et al (2015) use a forecast model to study the (future) motor insurance demand in Germany based on the expected vehicle stock and conclude by identifying the direct and indirect effects on future motor insurance market potentials. Furthermore, Matley, Gandhi, Yoo, Jarmuz, and Peterson (2016) provide a categorization of consequences for the automobile insurance industry regarding customers, products and distribution, but without explicitly distinguishing between the responsible trends.…”