Skip to main content

The rise in absenteeism: disentangling the impacts of cohort, age and time

Abstract

In recent years, a number of welfare state economies, including Norway, have experienced substantial increases in sickness absence. Using longitudinal individual register data for virtually all Norwegian employees, we examine the remarkable rise since the early 1990s, with emphasis on disentangling the roles of cohort, age, and time. We show that individual age-adjusted absence propensities have risen even more than aggregate absence rates from 1993 to 2005, which casts doubt on the popular hypotheses that the rise was due to the inclusion into the workforce of young or marginal workers with weaker work-norms or poorer health.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Notes

  1. Data for all physician-certified absence spells are available only from 2001; hence it is not possible to use these data to examine the developments of absenteeism in the 1990’s. In 2005, the sickness absence paid for by SSA accounted for approximately 75% of all absence days in Norway. A detailed examination of physician-certified absences from 2001 and onwards is provided by Markussen et al. (2011).

  2. We exclude persons employed directly by the state for the reason that their sickness insurance payments were not recorded by the SSA at the beginning of our data period. Note also that until April 1, 1998, the employer covered only the first 14 days of the absence spell (rather than the first 16). Hence, there is a break in our data series in 1998 causing a slight decline in the level of recorded absence.

  3. This rise occurred despite general improvements in self-reported health conditions. According to Statistics Norway’s level-of-living surveys, the proportion of the population reporting poor or very poor health declined from 10% in 1995 to 8% in 2005 among citizens above 45 years, while it remained stable at 3% among younger citizens. Based on a sample survey on self-reported health complaints and actual absence behavior in 1996 and 2003, Ihlebaek et al. (2007) found that while the prevalence of health complaints remained stable, sickness absence rose by 65%.

  4. This model is described and discussed in, e.g., Chamberlain (1984, Section 3.2), Lechner et al. (2008, Section 7.3), Baltagi (2008, Section 11.1), and Hilbe (2009, Section 4.1).

  5. Let (t r ,a r ) be the reference year and reference age, respectively, with c r  = t r  − a r . Take a person in cohort c at time t and age a (so that t − a = c) with time dummies t j (t j  = 1 for j = t) and a j (a j  = 1 for j = a). We then have that \(\sum\nolimits_j {(j-t_r )t_j } -\sum\nolimits_j {(j-a_r )a_j } =t-t_r -(a-a_r )=c-c_r \), which is obviously constant within each individual.

  6. The average within-year difference in the absence rate between men aged 37 and 38 and women aged 42 and 43 (1993–2005) are both around 0.01 percentage points.

  7. Recall that there was a break in the data-series in 1998. Before April 1998, spells exceeding 14 days were recorded. After that date, only spells exceeding 16 days were recorded. This implies that our estimates underrate the rise in 1998 (which probably explains why the rise was particularly low in 1998).

  8. When we drop the data from 2004 and 2005, we lose 11% of the analysis population for men and 10% of the population for women.

References

  • Askildsen JE, Bratberg E, Nilsen ØA (2005) Unemployment, labour force composition and sickness absence. A panel data study. Health Econ 14(11):1087–1101

    Article  Google Scholar 

  • Baltagi BH (2008) Econometric analysis of panel data, 4th edn. Wiley, Chichester

    Google Scholar 

  • Biørn E (2010) Identifying trend and age effects in sickness absence from individual data. Some econometric problems. Memorandum No. 20/2010, Economics Department, University of Oslo

  • Bonato L, Lusinyan L (2007) Work absence in Europe. IMF Staff Pap 54(3):475–538

    Article  Google Scholar 

  • Cameron AC, Trivedi PK (2005) Microeconometrics. Methods and applications. Cambridge University Press, New York

    Book  Google Scholar 

  • Chamberlain G (1984) Panel data. In: Griliches Z, Intriligator MD (eds) Handbook of econometrics, vol 2. North Holland, Amsterdam, pp 1247–1318

    Chapter  Google Scholar 

  • Gans D, Silverstein M (2006) Norms of filial responsibility for aging parents across time and generations. J Marriage Fam 68:961–976

    Article  Google Scholar 

  • Gaure S (2012) Dummy-encoding of inherently collinear variables. Working Paper 1/2012, The Ragnar Frisch Centre for Economic Research

  • Greene WH (2008) Econometric analysis, 6th edn. Prentice Hall, New Jersey

    Google Scholar 

  • Hall BH, Mairesse J, Turner L (2007) Identifying age, cohort, and period effects in scientific research productivity: discussion and illustration using simulated and actual data on French physicists. Econ Innov New Technol 16(2):159–177

    Article  Google Scholar 

  • Hilbe JM (2009) Logistic regression models. Chapman & Hall/CRC, New York

    Google Scholar 

  • Ihlebaek C, Brage S, Eriksen HR (2007) Health complaints and sickness absence in Norway, 1996–2003. Occup Med 57:43–49

    Article  Google Scholar 

  • Kupper LL, Janis JM, Salama IA, Yoshizawa CN, Greenberg BG (1983) Age-period-cohort analysis: an illustration of the problems in assessing interaction in one observation per cell data. Commun Stat Theory Methods 12(23):2779–2807

    Google Scholar 

  • Lechner M, Lollivier S, Magnac T (2008) Parametric binary choice models. In: Matyas L, Sevestre P (eds) The econometrics of panel data, chapter 7. Fundamentals and recent developments in theory and practice, 3rd edn. Springer, Heidelberg

    Google Scholar 

  • Lindbeck A (1995) Hazardous welfare-state dynamics. Am Econ Rev, Papers and Proceedings 85:9–15

    Google Scholar 

  • Lindbeck A, Nyberg S, Weibull JW (1999) Social norms and economic incentives in the welfare state. Q J Econ 114:1–35

    Article  Google Scholar 

  • Markussen S (2009) Closing the gates? Evidence from a natural experiment on physicians’ sickness certification. Memorandum No. 19/2009, Department of Economics, University of Oslo

  • Markussen S (2011) The individual cost of sick leave. J Popul Econ. doi:10.1007/s00148-011-0390-8

    Google Scholar 

  • Markussen S, Røed K, Røgeberg OJ, Gaure S (2011) The anatomy of absenteeism. J Health Econ 30(2):277–292

    Article  Google Scholar 

  • Mason KO, Mason WM, Winsborough HH, Pool WK (1973) Some methodological issues in cohort analysis of archival data. Am Sociol Rev 38(2):242–258

    Article  Google Scholar 

  • Nordberg M, Røed K (2009) Economic incentives, business cycles, and long-term sickness absence. Ind Relat 48(2):203–230

    Article  Google Scholar 

  • Røed K, Fevang E (2007) Organizational change, absenteeism, and welfare dependency. J Hum Resour 42(1):156–193

    Google Scholar 

  • Rodgers WL (1982) Estimable functions of age, period, and cohort effects. Am Sociol Rev 47(6):774–787

    Article  Google Scholar 

  • Ryder NB (1965) The cohort as a concept in the study of social change. Am Sociol Rev 30:843–861

    Article  Google Scholar 

Download references

Acknowledgements

This paper is part of the project “Absenteeism in Norway—Causes, Consequences, and Policy Implications”, financed by the Norwegian Research Council (grant #187924). We thank the Editor and two anonymous referees for useful comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Knut Røed.

Additional information

Responsible editor: Erdal Tekin

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

(PDF 34.4 KB)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Biørn, E., Gaure, S., Markussen, S. et al. The rise in absenteeism: disentangling the impacts of cohort, age and time. J Popul Econ 26, 1585–1608 (2013). https://doi.org/10.1007/s00148-012-0403-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00148-012-0403-2

Keywords

  • Sick leave
  • Work-norms
  • Multicollinearity

JEL Classification

  • C23
  • I38
  • J22