Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Prevalence and Economic Costs of Absenteeism in an Aging Population – A Quasi-Stochastic Projection for Germany

Version 1 : Received: 20 January 2022 / Approved: 21 January 2022 / Online: 21 January 2022 (10:32:54 CET)

A peer-reviewed article of this Preprint also exists.

Vanella, P.; Wilke, C.B.; Söhnlein, D. Prevalence and Economic Costs of Absenteeism in an Aging Population—A Quasi-Stochastic Projection for Germany. Forecasting 2022, 4, 371-393. Vanella, P.; Wilke, C.B.; Söhnlein, D. Prevalence and Economic Costs of Absenteeism in an Aging Population—A Quasi-Stochastic Projection for Germany. Forecasting 2022, 4, 371-393.

Abstract

Demographic change is leading to the aging of German society. As long as the baby boom co-horts are still of working age, the working population will also age - and decline as soon as this baby boom generation gradually reaches retirement age. At the same time, there has been a trend towards increasing absenteeism (times of inability to work) in companies since the zero years, with the number of days of absence increasing with age. We present a novel stochastic forecast approach that combines population forecasting with forecasts of labor force participation trends, considering epidemiological aspects. For this, we combine a stochastic Monte Carlo-based cohort-component forecast of the population with projections of labor force participation rates and morbidity rates. This article examines the purely demographic effect on the economic costs associated with such absenteeism due to the inability to work. Under expected future employment patterns and constant morbidity patterns, absenteeism is expected by close to 5 percent by 2050 relative to 2020, associated with increasing economic costs of almost 3 percent. Our results illustrate how strongly the pronounced baby boom/ baby bust phenomenon determines demographic development in Germany in the midterm.

Keywords

Cohort-Component Method; Multivariate Methods; Time Series Analysis; Monte Carlo Methods; Stochastic Forecasting; Demography; Statistical Epidemiology; Labor Market Research; Health Economics

Subject

Business, Economics and Management, Econometrics and Statistics

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