Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/234235 
Year of Publication: 
2020
Citation: 
[Journal:] Journal for Labour Market Research [ISSN:] 2510-5027 [Volume:] 54 [Issue:] 1 [Publisher:] Springer [Place:] Heidelberg [Year:] 2020 [Pages:] 1-10
Publisher: 
Springer, Heidelberg
Abstract: 
Contrary to the number of unemployed or vacancies, the number of employees subject to social security contribu-tions (SSC) for Germany is published after a time lag of 2 months. Furthermore, there is a waiting period of 6 months until the values are not revised any more. This paper uses monthly data on the number of people subject to compul-sory health insurance (CHI) as auxiliary variable to better nowcast SSC. Statistical evaluation tests using real-time data show that CHI significantly improves nowcast accuracy compared to purely autoregressive benchmark models. The mean squared prediction error for nowcasts of SSC can be reduced by approximately 20%. In addition, CHI outper-forms alternative candidate variables such as unemployment, vacancies and industrial production.
Subjects: 
Nowcasting
Real-time data
Employees
Social security contributions
Compulsory health insurance
JEL: 
C53
E24
E27
J21
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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