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Ageing and productivity

Ageing and productivity. Pekka Ilmakunnas Vegard Skirbekk Jan van Ours Matthias Weiss. SET-UP. Setting the Stage The Grand View Empirical studies Absenteeism Working capacity Productivity: team level – plant level Conclusions and policy implications. SETTING THE STAGE.

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Ageing and productivity

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  1. Ageing and productivity Pekka Ilmakunnas Vegard Skirbekk Jan van Ours Matthias Weiss

  2. SET-UP • Setting the Stage • The Grand View • Empirical studies • Absenteeism • Working capacity • Productivity: team level – plant level • Conclusions and policy implications

  3. SETTING THE STAGE

  4. Labour force participation rates; 2003

  5. Population Western Europe 2010-2050 (1000)

  6. Old age dependency ratio & participation rates 50-64 • Old age dep. ratio = Population 65+ / 20-64 • Increase 2000-2050: moderate – very large • Participation rates 50-64: high – low • Extremes • Scandinavian countries, Switzerland, US • Southern Europe (including Italy)

  7. Participation rates 2003 – males

  8. “Older people should be forced to retire when jobs are scarce”

  9. Age of labour market entry and exit by birth cohort and educational attainment; Italy

  10. Duration of labour market attachment

  11. Norway 1801 and 2001, ages 41-90both genders

  12. Setting the Stage - conclusions • Long term developments: people grow older and work fewer years • Work fewer years: preferences, incentives and misperceptions • Future: increase labour supply of older workers • Question: Age  Productivity?

  13. THE GRAND VIEW

  14. Motivation, Energy Region, Industry Type Mental Abilities, Personality Age Physical Abilities, Health, Strength Work Performance Job Experience Firm Characteristics Education Some causes of age-variation in productivity

  15. Components individual productivity • Physical strength • Experience • Cognitive abilities: • Crystallized: verbal ability; age  • Fluid: speed, memory; age 

  16. Importance of various abilities

  17. Productivity variation of the life cycle Combining abilities

  18. Alternative relationships

  19. Changes over time • Supply: better health, better mental performance, longevity • Demand: physical strength less important, reduced working day

  20. The Grand View – conclusions • Abilities not constant over age and cohorts • Various components are affect differently by ageing

  21. Measuring productivity (not easy) • Managers’ ratings of employees • Employees self-assessment • Measure productivity directly • Linked employer-employee datasets

  22. EMPIRICAL STUDIES on AGE and PRODUCTIVITY Extensive margin: absenteeism Subjective measure: work capacity Intensive margin: team & plant level

  23. Absenteeism – data • Germany: Assembly plant of a German car manufacturer – any work day 2003-2004 • Finland: Quality of Working Life Survey (QWLS) 2003

  24. Individual absence rates assembly line German car manufacturer

  25. Age and absenteeism survey of Finnish employees

  26. Absenteeism - conclusions • Age: negative effect on incidence, positive effect on duration; overall positive effect on absence rate • Age diversity: higher absence rates

  27. Working capacity: self assessment Assuming that your top working capacity would score 10 points While your total inability to work would score 0 points How many points would you give your working capacity at the moment?

  28. Top working capacity and the effect of chronic illness

  29. Working capacity - conclusions • Age  Working capacity  • Fall not dramatic, 0.3 points in 10 years • Faster drop for jobs in which physical strength is required • Technological change: influence of age becomes smaller

  30. Productivity – intensive margin • Individual productivity available only in special cases • Plant level & firm level data effects of: • Age • Age diversity

  31. Age – Productivity – team level • DaimlerChrysler; 2003-2004 • Productivity = quality = 1/errors • Decreases with age • Increases with job tenure • More age diverse teams make more mistakes

  32. Age – Productivity – plant level • Finnish Linked Employer-Employee Data (FLEED); 1990-2002 • Industrial plants – total factor productivity • No effect of age diversity

  33. Relationship changes over time

  34. Accounting for plant cohort and tenure effects

  35. Conclusions • Age-productivity relationship complex and multidimensional • Relationship changes over time (across cohorts) and is affected by other factors • Absenteeism  • Work capacity  • Productivity  • Age diversity?

  36. Policy implications • Retirement plans • Anticipating early retirement reduces investments in human capital; actuarial neutral pension schemes • Wage policies • Reduce seniority as a basis for wages • Human resource management • More easy to try to prevent dismissals of older workers than it is to encourage hiring of older workers • Employment Protection Legislation • Experience rating of dismissal costs

  37. Negative effects of age on productivity should not be underestimated

  38. Should not be exaggerated either

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