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

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

Labour force participation rates; 2003

0

10

2030

40

50

60

7080

90

100

15 to 19 20 to 24 30 to 34 35 to 39 40 to 44 45 to 49 50 to 54 55 to 59 60 to 64 65 to 69

EU 15

Italy

Japan

USA

Population Western Europe 2010-2050 (1000)

0

5,000

10,000

15,000

20,000

2050 20

30 2010

2050

2030

2010

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)

Participation rates 2003 – males

80 85 90 95 10010

20

30

40

50

60

70

80

90

Australia

AustriaBelgium

Canada Denmark

Finland

France

Germany

Greece

Iceland

Ireland

Italy

Japan

Lux

Netherlands

NZNorway

Portugal

Spain

Sweden Switzerland

UKUS

Age 50-54

Ag

e 6

0-6

4

“Older people should be forced to retire

when jobs are scarce”

37,5 40 42,5 45 47,5 50 52,5 55 57,5 60 62,5 65 67,5 700

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

USSweden

Denmark

Norway

UKNetherlands

GermanyFrance

Italy

Spain

Employment rate men 55-64

Agre

em

ent w

ith s

tate

ment

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

Italy

Men

0

10

20

30

40

50

60

70

80

15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74

Birth cohort

Ag

e

Duration of labour market attachment

Men

0

10

20

30

40

50

60

1915-19 1940-45 1915-19 1940-45 1915-19 1940-45 1915-19 1940-45 1915-19 1940-45

Finland France Germany Italy UK

Education 1 Education 2 Education 3

Norway 1801 and 2001, ages 41-90

both genders

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?

THE GRAND VIEW

Some causes of age-variation in

productivity

Motivation, Energy

Job Experience

Mental Abilities, Personality

Work PerformancePhysical Abilities, Health, Strength

Education

Age

Firm Characteristics

Region, Industry Type

Components individual productivity

• Physical strength

• Experience

• Cognitive abilities:– Crystallized: verbal ability; age – Fluid: speed, memory; age

Importance of various abilities

0.0

0.1

0.2

0.3

0.4

Ability

Im

port

ance

Productivity variation of the life cycle Combining abilities

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

-19 20-24 25-34 35-44 45-54 55-65Age

Pro

du

cti

vit

y P

ote

nti

al

Alternative relationships

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

-19 20-24 25-34 35-44 45-54 55-65

Age

Pro

du

cti

vit

y P

ote

nti

al

Manager -Communication

Numerical -Analytical

Changes over time

• Supply: better health, better mental performance, longevity

• Demand: physical strength less important, reduced working day

The Grand View – conclusions

• Abilities not constant over age and cohorts

• Various components are affect differently by ageing

Measuring productivity (not easy)

• Managers’ ratings of employees

• Employees self-assessment

• Measure productivity directly

• Linked employer-employee datasets

EMPIRICAL STUDIES on AGE and PRODUCTIVITY

Extensive margin: absenteeism

Subjective measure: work capacity

Intensive margin: team & plant level

Absenteeism – data

• Germany: Assembly plant of a German car manufacturer – any work day 2003-2004

• Finland: Quality of Working Life Survey (QWLS) 2003

Individual absence rates assembly line German car manufacturer

Age and absenteeism survey of Finnish employees

0.2

.4.6

.81

Share

with a

bsences

Age

0.5

11

.52

Num

be

r of

ab

sence

s

Age

05

10

15

20

25

Days o

f absen

ce

Age

05

10

15

Dura

tio

n o

f a

typic

al a

bse

nce

Age

Absenteeism - conclusions

• Age: negative effect on incidence, positive effect on duration; overall positive effect on absence rate

• Age diversity: higher absence rates

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?

01

23

45

67

89

10

Wo

rkin

g c

ap

aci

ty

20 25 30 35 40 45 50 55 60 65Age

Top working capacity and the effect of chronic illness

0.2

.4.6

Pro

bab

ility o

f to

p w

ork

ing c

ap

acity

-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-Age

No chronic illness Chronic illness

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

Productivity – intensive margin

• Individual productivity available only in special cases

• Plant level & firm level data effects of:– Age– Age diversity

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

Age – Productivity – plant level

• Finnish Linked Employer-Employee Data (FLEED); 1990-2002

• Industrial plants – total factor productivity

• No effect of age diversity

Relationship changes over time1

.61

.71

.81

.92

2.1

2.2

2.3

2.4

2.5

log

(TF

P)

30 35 40 45 50Average age

1990 19952000

Accounting for plant cohort and tenure effects

22

.12

.22

.32

.42

.5

log

(TF

P)

30 35 40 45 50Average age

Tenure and cohort contro lled Plant cohort controlledNeither contro lled

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?

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

Negative effects of age on Negative effects of age on productivity should not be productivity should not be

underestimatedunderestimated

Should not be exaggerated eitherShould not be exaggerated either