«Public Administration Issues» Journal,

Post. address:
National Research University
Higher School of Economics
20 Myasnitskaya Str., Moscow 101000, Russian Federation
Location address:
of. 307, 4/2, Slavyanskaya sq., Moscow 109074, Russian Federation

Tel./fax: 7 (495) 772-95-90, ext. 12631

E-mail: vgmu@hse.ru 

Science Index rating

32nd place in the SCIENCE INDEX
 for 2019 (more than 4000 journals)

Russian Science Citation Index
two-year  impact factor for 2019: 2,631 
(the citation  of all sources)

Russian Science Citation Index
five-year  impact factor for 2019: 1,725

Ten-year h-index 2019: 31


Journal's Indexing


Research and educational journal
Published quarterly since 2007
ISSN 1999-5431
E-ISSN 2409-5095

Leandro Costa 1, Ricardo Ramos 2, Sérgio Moro 3
  • 1 MsC in Computer Science and Business Management, Instituto Universitário de Lisboa, Lisboa, Portugal, Av.ª das Forças Armadas, 1649-026 Lisboa
  • 2 PhD, Assistant Professor at Universidade Autónoma, Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, Lisboa, Portugal; CICEE – Centro de Investigação em Ciências Económicas e Empresariais, Universidade Autónoma de Lisboa, Portugal, Av.ª das Forças Armadas, 1649-026 Lisboa
  • 3 PhD, Sub-Director of ISTAR-IUL, Assistant Professor at ISCTE-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, Lisboa, Portugal, Av.ª das Forças Armadas, 1649-026 Lisboa

Anticipating Next Public Administration Employee’s Absence Duration

2019. No. 6. P. 23–40 [issue contents]
Absenteeism affects state-owned companies who are obliged to undertake strategies to prevent it, be efficient and conduct effective human resource (HR) management. This paper aims to understand the reasons for Public Administration Employees’ (PAE) absenteeism and predict future employee absences. Data from 17,600 PAE from seven public databases regarding their 2016 absences was collected, and the Recency, Frequency and Monetary (RFM) and Support Vector Machine (SVM) algorithm was used for modeling the absence duration, backed up with a 10-fold cross-validation scheme. Results revealed that the worker profile is less relevant than the absence characteristics. The most concerning employee profile was uncovered, and a set of scenarios is provided regarding the expected days of absence in the future for each scenario. The veracity of the absence motives could not be proven and thus are totally reliable. In addition, the number of records of one absence day was disproportionate to the other records. The findings are of value to the Human Capital Management department in order to support their decisions regarding the allocation of workers and productivity management and use these valuable insights in the recruitment process. Until now, little has been known concerning the characteristics that affect PAE absenteeism, therefore enriching the necessity for further understanding of this matter in this particular.

Citation: Costa L., Ramos R. F. & Moro S. (2019). Anticipating Next Public Administration Employee’s Absence Duration. Public Administration Issue, no 6, (Special Issue II, electronic edition), pp. 23–40 (in English).
ISSN 1999-5431
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