Labour force participation across Europe: a cohort-based analysis


We use a cohort-based model to analyse the determinants of labour force participation in six European economies, focusing on age and cohort effects as factors explaining differences in participation behaviour across countries. Cohort effects are particularly relevant for women with those born in the late 1960s and early 1970s more likely to participate over the life-cycle. Our results suggest that cohort effects can be interpreted as evolving social norms or preferences towards participating in the labour market according to Fernandez (NBER working paper no. 13373, 2007). We find substantial variation in the estimated age and cohort effects across European countries: cohort effects can account for a substantial part of the recent increase in participation in Spain, the Netherlands and Germany, and a positive, but smaller part of in the increase in participation of the UK, Italy and France. Looking forward, positive cohort effects could help counteract the downward impact of population ageing on participation.

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  1. 1.

    Another related strand of the literature has documented the impact of labour market institutions on employment and unemployment in European countries (for a recent contribution and review of the literature, see Bassanini and Duval 2006 and Bertola et al. 2007 for an age-group-specific analysis). Participation decisions have received less attention in this context. However, Blöndal and Scarpetta (1999) and Duval (2006) focus on older workers and their retirement decisions and Jaumotte (2003) on females. Genre et al. (2005, 2010) use annual data for a panel of European Union countries to estimate participation equations for age and gender groups to identify the impact of labour market institutions in participation decisions. They find that higher union density, more employment protection and more generous unemployment benefits lower participation rates. We argue that changes in institutions alone cannot explain the trends in participation rates observed in the data. We therefore add to the studies mentioned above by considering disaggregated groups and by incorporating the impact of a broader set of factors through the age and cohort effects together with observed determinants of participation in the same model.

  2. 2.


  3. 3.

    See Fig. A1 in the Electronic Supplementary Material.

  4. 4.

    Participation behaviour also varies across other personal characteristics, such as education and skills, immigrant status, wages, etc. We focus on age and gender for reasons of data availability: in particular, LFS data by education categories are only available from the early 1990s onwards. These data show that more educated workers tend to have higher participation rates and that an increase in overall educational attainment over time has coincided with an increase in participation rates, particularly for women. We will partly control for that using youth in education to test the robustness of our results. The LFS does not include information on wages.

  5. 5.

    A detailed description of the sampling methods and adjustment procedures used in the LFS can be found in “The European Union Labour Force Survey—Methods and Definitions, 2001”, the available variables are listed and described in the “EU Labour Force Survey database—User guide”. The change from annual to quarterly periods by Eurostat has resulted in breaks in the LFS in many euro area countries. Therefore, we rely on the more consistent spring (second quarter) data throughout the sample period, except for France (where data refer to the first quarter). Data for Spain and the UK are only available for those above 16.

  6. 6.

    The idea is to generate a series that is representative for overall Germany also before 1991. While this allows us to solve the issue of the break in the series in 1991, it assumes that Eastern and Western participation rates have developed in a comparable way before 1991 and/or that developments in Eastern participation rates have been dominated by Western participation rates as the West accounts for 80 % of the population.

  7. 7.

    The most important difference between our approach and Fallick and Pingle is this logistic transformation of the participation rate for males and females. Further, Fallick and Pingle merge single ages into groups of 2–5 years. Due to the hump-shaped age-participation profile, participation changes strongly between the very young and very old ages. As some cohorts only enter a few age groups at the end of the range, this leads to jumps in the estimated cohort effects for those cohorts that enter more age groups than the predecessor. We avoid this “cycling” of cohort effects using single ages instead of age groups.

  8. 8.

    We do this by replacing the values of the participation rate and the other explanatory variables of the ages affected with means from the rest of the sample. We also restrict the cohort effects of the last eight cohorts to equal the average of the remaining cohorts for the respective age.

  9. 9.

    See for example Beaudry and Lemieux (1999) or Fitzenberger et al. (2004) for a related approach. Articles in Mason and Fienberg (1985) provide an early discussion of basic accounting models.

  10. 10.

    There is a large literature on identifying causal effects of institutions using reforms in various countries or additional information related to the causality of the effects. For example Lechner and Wiehler (2011) evaluate the impact of labour market programs on female participation in Austria by investigating the reduction and postponing of pregnancies.

  11. 11.

    The full estimation results including \(t\) tests are provided in the Electronic Supplementary Material.

  12. 12.

    Full estimation results, including goodness of fit measures are available upon request.

  13. 13.

    While union density, unemployment benefits and labour taxes are included in the equations for all age and gender groups (in working age), the implicit tax on continued work is only included for the oldest workers and the female part-time employment rate for females only.

  14. 14.

    See for their data. Figure A5 in the Electronic Supplementary Material plots educational attainment for our set of countries.

  15. 15.

    Prieto-Rodriguez and Rodriguez-Gutierrez (2000) find these effects to be relevant for women in Spain, in line with our finding of negative business cycle effects for women of all ages.

  16. 16.

    Results are documented in Fig. A4 in the Electronic Supplementary Material. In order to construct a scenario for future labour supply, we use population projections from the New Cronos database by Eurostat (EUROPOP2008). EUROPOP2008 contains statistical information on population projections with reference to projected 1st of January population by sex and single year of age, projected vital events (births and deaths) and assumptions concerning fertility, life expectancy at birth by sex and international migration. We keep observed determinants at their 2007 values and we fix the cohort effects for the young cohorts, i.e. the last eight cohorts of our sample and those that enter the labour market after 2007, at the level of the last cohort effect we estimate, namely those born in 1984.

  17. 17.

    Carone (2005) and Burniaux et al. (2004) emphasize the importance of cohort effects to project participation rates for EU and OECD countries (respectively).

  18. 18.

    Results are available upon request.


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We would like to thank numerous participants and colleagues for helpful comments. The views expressed in this paper are those of the authors and do not necessarily reflect those of the International Monetary Fund.

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Correspondence to Almut Balleer.

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Balleer, A., Gomez-Salvador, R. & Turunen, J. Labour force participation across Europe: a cohort-based analysis. Empir Econ 46, 1385–1415 (2014).

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  • Labour force participation
  • Cohort analysis
  • Labour market institutions
  • Cross-country analysis

JEL Classification

  • J11
  • J21