PosterPDF Available

Abstract

As labour markets get older and the supply of the labour force diminishes, companies must adjust their policies to the new reality. In this study, we are ‘mapping’ the entire national economy in terms of age management. We analyse changes in the approach to older workers in Dutch companies using employer surveys conducted in 2009 and 2017. By applying group-comparison latent class analysis on a set of measures aimed at older workers, we specify four clusters of firms that focus on accommodation and improving workability, stimulate early exit, use both approaches, or have no age management at all. Between 2009 and 2017 the popularity of active policy largely increased, while exit-oriented policies and passive strategies reduced. The largest pro-active shift occurred in small organisations, firms with a high share of older employees, and knowledge-intensive companies. The 2009-2017 change can be seen as an effect of (1) policy reforms in the Netherlands; (2) employees working longer; (3) employers’ increased awareness of the effects of age management.
The pro-active shift in age management
Evidence from Dutch companies 2009-2017
Konrad Turek1,2 Kène Henkens1,3,4 Jaap Oude Mulders1
1 Netherlands Interdisciplinary Demographic Institute (NIDI-KNAW & University of Groningen)
2 Jagiellonian University, Poland
3 University Medical Center Groningen (UMCG-RUG), the Netherlands
4 University of Amsterdam, the Netherlands
As labour markets get older and the supply of the labour force
diminishes, companies must adjust their policies to the new reality.
In this study, we are ‘mapping’ the entire national economy in terms
of age management.
We analyse changes in the approach to older workers in Dutch
companies using representative employer surveys conducted in
2009 and 2017.
Acknowledgements:
This work was supported by the grant from European Commission
‘Marie Skłodowska-Curie Actions Individual Fellowship
[748671 LEEP H2020-MSCA-IF-2016/H2020-MSCA-IF-2016].
Data: Comparative surveys of employers: 2009 (n=1,077) and 2017
(n=1,358) representative for the Netherlands.
Method: Three-step group-comparison latent class analysis (LCA)
combined with multinomial logistic model (MNL). Grouping in LCA
by year with constrained measurement. MNL separate for each wave.
Multiple imputation of miss.values.Final N=2,331.
Class indicators:Which of these policies are applied in your
organization (1=currently applied; 0=not)? Ergonomic measures,
Training for older workers, Flexible working hours, Part-time
retirement, Gradual retirement, Early retirement.
Covariates in MNL:Sector, Size, Strong role of labour unions,
Knowledge intensity, Requires regular training, Experienced
shortages, Share of workers 50+, Share of women.
INTRODUCTION DATA AND METHODS
Four clusters of firms that focus on:
Accommodation and improving work-ability [Active]
Stimulate early exit [Exit]
Use both approaches [All]
Or have no age management at all [None]
MAIN FINDINGS
CONCLUSIONS
In this study, we found four approaches to age management in Dutch companies (Active, Exit, All, None)
[Table 1]
Between 2009 and 2017 the popularity of active policy largely increased, while exit-oriented policies and
passive strategies reduced [Figure 1]
The largest pro-active shift occurred in small organisations, firms with a high share of older employees,
and knowledge-intensive companies [Figures 2, 3 & 4]
The 2009-2017 change can be seen as an effect of (1) policy reforms in the Netherlands; (2) employees
working longer; (3) employers’ increased awareness of the effects of age management.
Table 1.
Cluster profiles (Latent Class Analysis)
Clusters
Custer indicators
None Exit All Active
Ergonomic measures 0.02 0.30 0.82 0.68
Training for older 0.01 0.04 0.61 0.48
Flexible hours 0.24 0.26 0.65 0.64
Part-
time retirement
0.03 0.60 0.74 0.05
Gradual retirement
0.02 0.43 0.76 0.12
Early retirement
0.00 0.80 0.85 0.05
0.47
0.21
0.13
0.19
0.30
0.06
0.13
0.52
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
None Exit All Active
2009 2017
Figure 1. Share of companies using specific age management approach (%)
Figures 2, 3 & 4. Change in predicted probability of class membership
(2017-2009, in percentage points, at the pooled-mean values of predictors)
-0.03
-0.14
-0.02
0.23
-0.23 -0.14 -0.03
0.43
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
None Exit All Active
Fig. 3. Knowledge intensive firms
No Yes
Correspondence:turek@nidi.nl
-0.22
-0.12 -0.03
0.39
-0.07
-0.25
0.03
0.32
0.03
-0.14 -0.15
0.26
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
None Exit All Active
Fig. 2. Size of company
1-49
50-249
250+
Notes: Based on multinomial logit model.
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
010 20 30 40 50
Fig. 4. Share of workers 50+
None Exit All Active
Notes: Based on latent class analysis
ResearchGate has not been able to resolve any citations for this publication.
ResearchGate has not been able to resolve any references for this publication.