Forecasting labour supply and population: an integrated stochastic model
Johann Fuchs,
Doris Söhnlein,
Brigitte Weber and
Enzo Weber
Additional contact information
Johann Fuchs: Institute for Employment Research (IAB), Nuremberg, Germany
Doris Söhnlein: Institute for Employment Research (IAB), Nuremberg, Germany
Brigitte Weber: Institute for Employment Research (IAB), Nuremberg, Germany
No 201701, IAB-Discussion Paper from Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]
Abstract:
"This paper presents a stochastic integrated model to forecast the German population and labour supply until 2060. Within a cohort-component approach, the population forecast applies principal components to birth, mortality, emigration and immigration rates. The labour force participation rates are estimated by means of an econometric time series approach. All time series are forecast by bootstrapping. This allows fully integrated simulations and the possibility to illustrate the uncertainties in the form of confidence intervals. Our new forecast confirms the results from former studies. We conclude that even rising birth rates and high levels of immigration cannot break the basic demographic trend in the long run." (Author's abstract, IAB-Doku) ((en))
Keywords: Bundesrepublik Deutschland; Bevölkerungsprognose; Einwanderung; Erwerbsbeteiligung; Erwerbspersonenpotenzial; Erwerbsquote; Geburtenentwicklung; Prognosemodell; Stochastik; Arbeitskräfteangebot; Arbeitsmarktprognose; 1959-2060 (search for similar items in EconPapers)
JEL-codes: C38 J11 J20 J21 J22 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2017
New Economics Papers: this item is included in nep-age, nep-for, nep-lab and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)
Published in/as: Population Research and Policy Review, doi:10.1007/s11113-017-9451-3
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Persistent link: https://EconPapers.repec.org/RePEc:iab:iabdpa:201701
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