Lecture 4: Pensions and labour markets · 2020-02-04 · I. Facts Measurement 1 Labour force...

Lecture 4: Pensions and labour markets

Antoine Bozio

Paris School of Economics (PSE)

Ecole des hautes etudes en sciences sociales (EHESS)

Master PPDParis – February 2020

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Introduction

1 What is retirement ?• Withdrawing from the labour force ?• Reducing hours of work ?• Claiming a pension ?

2 Labour vs public economics• Labour economics : supply/demand for labour• Public economics : impact of public pensions/mandatory

savings

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Outline of the lecture

I. Facts about retirement

II. Modelling retirement

III. Impact of pensions on retirement

IV. Early retirement policies

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I. Facts

1 Measurement issues

2 Decline in labour force participation of older men(1880-1985)

3 Large cross-country heterogeneity

4 Increase in labour force participation of older workers since1990

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I. FactsMeasurement

1 Ambiguity of retirement• Retirement and unemployment• Part time work

2 Partial retirement• Progressive decrease in hours of work• Change of job while claiming a pension

3 Retirement as absorbing state ?• Used to be thought that retirement was irreversible• Now not anymore the case

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I. FactsMeasurement

1 Labour force surveys• Since late 1960s• Little information on pensions

2 Retirement surveys• US Retirement history survey (1969-79)• UK Retirement survey (1989-94)

3 New Ageing surveys• Health and retirement survey (HRS) : 1992-2014• English Longitudinal Study of Ageing (ELSA) : 2002-2014• Survey of Health, Ageing and Retirement in Europe

(SHARE) : 2004-2013• Japanese Study of Aging and Retirement (JSTAR) :

2007-20116 / 112

Figure 1 – LFP rates of men aged 65 and over (1850–1990)

0%

10%

20%

30%

40%

50%

60%

70%

80%

1840 1860 1880 1900 1920 1940 1960 1980 2000

US

UK

France

Germany

Source : Costa (1998), Table 2A.2, p. 29.

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Figure 2 – LFP rates of U.S. men aged 18 to 85 (1880–1990)

Source : Costa (1998), Fig. 2-5, p. 12.

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Figure 3 – LFP rates of men aged 60 to 64 (1960-1994)

Source : Gruber and Wise (1999), Fig. 1A, p. 3.

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Figure 4 – LFP rates of men aged 60 to 64 (1960-1994)

Source : Gruber and Wise (1999), Fig. 1B, p. 3.

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Figure 5 – Employment rates of men aged 50 to 74 (1977)

Source : Blundell, Bozio and Laroque (2013).

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Figure 9 – Employment rates of men by mortality rates (1977)


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Figure 10 – Employment rates of men by mortality rates (2007)


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Figure 11 – Employment rates of men aged 55 to 74

0%

10%

20%

30%

40%

50%

60%

1968 1973 1978 1983 1988 1993 1998 2003 2008 2013

US

UK

FR

GER


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II. Modelling retirement

1 A simple lifetime retirement model

2 Option value model

3 Dynamic programming models

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II. Modelling retirementA simple retirement model• Lifetime budget constraint

• Live T years, work L years, retired R = T − L years• Constant wage w , interest rate r = 0• Lifetime consumption C , lifetime leisure R

• Lifetime utility maximisation

maxL

U(R ,C )s.t.C <= (T − R)w

• Standard demand for leisure framework• Leisure is a normal good• Increase in income leads to a decrease in retirement age R

(more leisure)• A wage increase leads to income and substitution effects,

hence ambiguous effect on retirement19 / 112

II. Modelling retirementA simple retirement model

• Income effects• Long-term trend in decreasing retirement age• Could be explained by income effects• Being richer, we consume more leisure

• Limits• Value of market time independent of age• Value of leisure independent of age• No reason for bunching of leisure at the end of life

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II. Modelling retirementOption value model• Stock and Wise (1990)

• Individuals assess utility of retiring at each date• Individuals retire if value of retiring now is higher than

retiring later• Irreversibility of retirement decision

• Model setup• People earns Ys while working in year s• Receive pension Bs(r) when retiring at age r• Value at date t of future income, discounted at rate β,

when retiring at age r :

Vt(r) =r−1∑s=t

βs−tU(Ys) +T∑s=r

βs−tU(Bs(r))

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II. Modelling retirementOption value model

• Optimal retirement age• Gains from postponing retirement :

Gt(r) = EtVt(r)− EtVt(t)• r∗ is the year with highest expected value : r∗ solves

maxr EtVt(r)• Retirement decision if Gt(r

∗) = EtVt(r∗)− EtVt(t) < 0

• Parametrization of the model• U(Ys) = Y γ

s + ωs

• U(Bs) = (κBs(r))γ + ξs• γ is risk aversion relative to income uncertainty• κ is the value of income while working relative to its value

when retired• ωs and ξs are individual random effects

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II. Modelling retirementOption value model

• Estimation technique• Retirement decision is Pr [Gt(r

∗) > 0]• Parameters κ, γ, β estimated by maximum likelihood

• Applications• Stock and Wise (1990) on a pension plan firm data• Lumsdaine, Stock and Wise (1992)• Blanchet and Mahieu (2004) and Mahieu and Walraet

(2005) on French data

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II. Modelling retirementDynamic programming models

• Stochastic dynamic programming models• Dynamic : budget constrain, health, earnings• Stochastic : uncertainty around earnings, health, asset

returns• Structural : generability of results based on structure

• Key papers• Gustman and Steinmeier (Econometrica, 1986)• Rust and Phelan (Econometrica, 1997)• French (RESTUD, 2005)

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III. Impact of pensions on retirement

1 Implicit pension tax

2 Early US estimates

3 Cross-country evidence

4 Recent evidence

5 Evidence on France

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III. Impact of pensions on retirementImplicit pension tax

• Lifetime budget constraint• Live T years, work R years• Constant wage w , interest rate r = 0• Lifetime consumption C = wR

• Actuarially fair pension• Pension b(R), tax rate τ• b(R) = τR

T−R• Then C = (w − τ)R + b(R)(T − R) = wR• No effect on lifetime budget constraint• Actuarially fair system does not affect retirement age under

the assumption of no uncertainty, no myopia, no creditconstraint

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III. Impact of pensions on retirementImplicit pension tax• Not actuarially fair pensions

• C = (w − τ)R + b(R)(T − R)• dC

dR = w − τ − b + (T − R)b′(R)• Substitution and wealth effects

• Implicit tax• (1− t)w = dC

dR• If you delay retirement by 1 year, PDV of consumption

increases by dCdR

• t = w−dC/dRw

• Case with no adjustment in benefit• b′(R) = 0⇒ dC

dR = w − τ − b• If b = 0.75w and τ = 0.25w• Then dC

dR = 0, t = 100%

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III. Impact of pensions on retirementEarly U.S. estimates

• Empirical methods

1 Time-series evidence

LFPt = α + βBt/wt + εt

2 Cross-sectional evidence

LFPi = α + βBi/wi + εi

• Not very conclusive literature• Boskin and Hurd (1978) : strong effects of pensions• Burkhauser and Qinn (1983) : no effect• Burtless (1986) : little impact

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III. Impact of pensions on retirementEarly U.S. estimates• Identification issues

1 Income effect correlated with development of publicpensions (colinearity)

2 Pension is a function of past earnings and past career,likely to influence propensity to retire

• Kruger and Pischke (JOLE, 1992)• Critique of previous literature : lack of proper source of

exogenous identification• Use US SS notch as natural experiment• Find small wealth effect of SS on retirement

• US social security notch• 1977 amendments : lower benefits for those born after Jan.

1917 (“notch babies”)

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Figure 12 – Average monthly SS benefit for workers retiring atage 65

Source : Krueger and Pischke (1992), Fig. 2.

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III. Impact of pensions on retirementRetirement hasard spikes

• Retirement hasard spikes• Retirement hasard at age a is the fraction of people who

retire at age a among those still working at age a− 1

• Spikes are common• US : early retirement age at 62, full-rate at age 65• France : full-rate at age 60 (pre 1993)• Spikes move when pension rules are changed

• Evidence of large pension effects• Either non-rational behaviour or liquidity effects

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Figure 13 – Hasard rate out of labour force in the US

Source : Coile and Gruber (2007), Fig. 1.

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Figure 14 – Hasard rate out of labour force in the US (1960)

Source : Gruber and Wise (1999), Fig. 12, from Burtless and Moffitt (1984).

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Figure 17 – Hasard rate out of labour force in France (1972)

Source : Gruber and Wise (1999), Fig. 9.A, p. 14 ; Blanchet and Pele (1999)

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Figure 18 – Hasard rate out of labour force in France (1986)

Source : Gruber and Wise (1999), Fig. 9.D, p. 15 ; Blanchet and Pele (1999).

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III. Impact of pensions on retirementCross-country evidence

• NBER International social security (ISS) group• Researchers from different countries• Using micro-data in a cross-country framework• Led by Jon Gruber and David Wise

• Gruber and Wise (1998, 1999)• Documenting pension reforms over time• Computation of measure of tax incentive through pension• Correlate pension tax with labour force participation

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Figure 19 – Tax rate on work in France

Source : Gruber and Wise (1999), Fig. 10, p. 16 ; Blanchet and Pele (1999).

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Figure 20 – Hasard rate out of labour force in France

Source : Gruber and Wise (1999), Fig. 11, p. 16 ; Blanchet and Pele (1999).

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Figure 21 – Unused capacity vs. tax force

Source : Gruber and Wise (1999), Fig. 17.B, p. 32.

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III. Impact of pensions on retirementRecent evidence

• Coile and Gruber (RESTAT, 2007)• Use forward-looking pension incentives (Stock and Wise,

1990)• Reduced-form regression framework

• Social security wealth (SSW)• SSWt(r) is the present discounted value (PDV) of pension

wealth at date t, when retiring at r

SSWt(r) =T∑s=r

β(s−t)ps|tBs(r)

• β discount factor ; ps|t probability to survive until sconditioned on having survived until t

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• Accrual• Gain to postpone retirement from 1 year

Accrualt = SSWt(r + 1)− SSWt(r)

• Implicit tax

Taxt = −AccrualtEtYt+1

• Peak value (PV)• Forward-looking incentive measure

PVt = maxx>=t+1

[SSWt(x)]− SSWt(t)

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• Option value (OV)• Following Stock and Wise (1990)• Derive utility measure of option to remain in work at age a

U(a, R) =R∑

u=a

β(u−a) w

1−γ

1− γs(u|a) +

ω∑u=R

β(u−a) (κp(R))1−γ

1− γs(u|a)

• Use previously estimated parameters for γ, κ• Derive option value

OVt = maxr

[U(t; r)]− U(t; t) = U(a; r)− U(a; a)

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III. Impact of pensions on retirementRecent evidence• Regression framework

• Probit estimation

Rit = β0 + β1RWit + β2IVit + β3Xit + εit

• R worker retires, RW PDV of pensions, IV incentivevariable, X controls

• Results on U.S. data• β1 > 0 ; β2 < 0 i.e. higher gains for postponing retirement

reduce retirement probability, while higher pension wealthincreases probability of retirement

• But no account of spikes of hasard rate at 62 and 65

• Other applications• Cross-country (Gruber and Wise, 2004)

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• Earnings test• Limit on earnings while claiming pensions• US earnings test partly removed in 2000• UK earnings rule repelled in 1989• French rules on cumul emploi retraite

• U.S. Earnings test today• 62 < Age < NRA, 50% tax above $13,500• No earnings test above NRA• Normal retirement age (NRA) today 66 (increasing

progressively to 67)• Benefits taxed away will be credited back at NRA

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Figure 22 – Budget constraint of the earnings test

Source : Friedberg (RESTAT, 2000), Fig. 1, p. 49

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Figure 23 – Changes in the US earnings test

Source : Gelber, Jones and Sachs (2019), Fig. 1.

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• Friedberg (RESTAT, 2000)• Exploit changes in the US earnings test• Use CPS data• Assess bunching before the threshold

• Changes exploited

1 1978 increase in the exempt amount for the 65-712 1983 reform removed the earnings test for 70 and 71 years

old3 1990 decline in the tax rate (from 50% to 33%) for the

65-69

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Figure 24 – Earnings distribution at the earnings test (1980-81)

Source : Friedberg (RESTAT, 2000), Fig. 3.

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Figure 25 – Earnings distribution at the earnings test (1983-84)

Source : Friedberg (RESTAT, 2000), Fig. 3.

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• Labour supply estimation• Piece-wise linear budget constraint• log(H) = β0 + β1w(1− τ) + β2Yv + β3X• Exploit the change in earnings test to generate changes in

net earnings and virtual income• To obtain substitution and income elasticities

• Results• Large income and substitution elasticities• Large implied deadweight loss of earnings test• Removing earnings test would lead to 5.3% increase in

hours worked

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• Gelber, Jones and Sachs (AEJ-AE, 2020)• Exploit recent changes in the U.S. earnings test• 2000 removal of the test above NRA• Use bunching estimation techniques (Saez, 2010)

• Results• Significant bunching at kinks• Bunching slowly removed over time (inertia)

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Figure 26 – Earnings histogram by age (1990 to 1999)

Source : Gelber, Jones and Sachs (2019), Fig. 2.A

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Figure 27 – Normalized Excess Mass by Age (1990 to 1999)

Source : Gelber, Jones and Sachs (2019), Fig. 2.B.

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Figure 28 – Earnings histogram by age (1983 to 1999)

Source : Gelber, Jones and Sachs (2019), Fig. 4.A. 56 / 112


• Gelber, Jones, Sachs and Song (forthcoming JHR)• Data from U.S. Social Security Administration : 9 million

individuals over 1968 to 1987• Estimation strategy is difference-in-differences• Comparison of the probability to have positive earnings

conditional on having earnings pre age 63 above or belowthe earnings test

• Results• Large employment effect of the earnings test : -3.3 pp of

employment rate for Americans aged 63-64

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Figure 29 – Probability of positive earnings by age and earningsrelative to exempt amount

Source : Gelber, Jones, Sachs and Song (forthcoming JHR), Fig. 6.58 / 112

Figure 30 – Difference between probability of positive earningsamong those earning above and below the exempt amount, by age

Source : Gelber, Jones, Sachs and Song (forthcoming JHR), Fig. 7.59 / 112

Manoli and Weber (AEJ-EP, 2016)

• Institutional design• Mandated lump-sum cash retirement benefit in Austria

(severance pay)• Amount depends on job tenure at retirement (10, 15, 20

and 25 years)• Use administrative data (Social security data merged with

tax data)

• Evidence• Non-parametric evidence of bunching at tenure thresholds• Discontinuity in retirement decision according to tenure• Estimation of retirement age elasticities

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Figure 31 – Distribution of tenure at retirement

200

400

600

800

1000

Indi

vidu

als

10 15 20 25Years of Tenure at Retirement

Manoli and Weber () Financial Incentives at Retirement

�

��

� ��

Source : Manoli and Weber (2016), Fig. 2.

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Figure 32 – Mean Severance Payments, Responder Sample

.15

.2.2

5.3

.35

Seve

ranc

e Pa

y Fr

actio

n

6 7 8 9 10 11 12 13Years of Tenure at Retirement

A. 10 Year ThresholdManoli and Weber ()

Financial Incentives at Retirement �

�

��

�

Source : Manoli and Weber (2016), slides.

62 / 112


.3.3

5.4

.45

.5Se

vera

nce

Pay

Frac

tion

12 13 14 15 16 17 18Years of Tenure at Retirement

B. 15 Year Threshold



63 / 112


.55

.6.6

5.7

.75

Seve

ranc

e Pa

y Fr

actio

n

17 18 19 20 21 22 23Years of Tenure at Retirement

C. 20 Year Threshold


� � � � ��

�


64 / 112

Manoli and Weber (AEJ-EP, 2016)• Participation semi-elasticity

• Quantify the magnitude of participation response tochange in financial incentives

ε(t0) =1− r(t0)

4(1− τ(t0))

• where r(t0) denotes the probability of retiring at tenurelevel (t − t0) and τ(t0) refers to the implicit tax rate onworking the additional t0 periods until the threshold isreached

• Results• Semi-elasticities between 0.14 and 0.28• highest estimated semi-elasticities at 6 to 9 months prior to

a tenure threshold• potential effectiveness of bonuses targeted at short delays

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Figure 35 – Estimating change in retirement


�

��

�

� � �


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Figure 36 – Participation elasticities

Source : Manoli and Weber (2016), Tab. 3.

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III. Impact of pensions on retirementEvidence on France

• Bozio (2006, 2008)• Exploit the 1993 pension reform• Use of administrative data• Very significant effects of pension rules

• The 1993 pension reform• First reform aiming to increase retirement age• Only affected private sector workers

• Regime general (Cnav) formula• P pension, PC proportionality coefficient, Wref reference

earningsP = τ ∗ PC ∗Wref

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• Pension rate formula• δ the penalty, of 10 %, N1 the required contribution length

and D1 the contribution length of the worker

τ = 0.50∗

[1− δ ∗max

{0,min

[(65−AGE ), (N1−D1)

]}]

• The 1993 pension reform

1 Wref is computed with the best 25 years and not the best10 years

2 N1 increases from 150 quarters to 160 quarters (37.5 to 40years) - N2 remains at 150 quarters

3 Pension benefits are indexed on inflation and not anymoreon wage growth

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Figure 37 – Distribution of contribution length at pension claim

Source : Bozio (2008), Fig. .

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• Estimation strategy• Use gen x contribution length at 60 as instrument for

change in pension benefits• Use cohorts born before 1934 as control groups

• Results

1 Very large claiming elasticity2 Large work elasticity3 Impact on probability of claiming incapacity pensions

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Figure 41 – Impact of 1993 reform on retirement age

-10

12

34

Cla

imin

g ag

e in

crea

se (i

n qu

arte

rs)

130 140 150 160 170 180Contribution length at age 60 (in quarters)

Cohort 1932Cohort 1933

Source : Bozio, Garrouste and Perdrix (2019). 75 / 112


-10

12

34

Cla

imin

g ag

e in

crea

se (i

n qu

arte

rs)

130 140 150 160 170 180Contribution length at age 60 (in quarters)

Cohort 1933Cohort 1936Cohort 1938

Source : Bozio, Garrouste and Perdrix (2019). 76 / 112


-0,5

0

0,5

1

1,5

2

2,5

130-131

132-135

136-138

139-140

141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160


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• Manoli and Weber (2018)• Increase in early retirement age (ERA) in Austria• Men from 60 to 62, Female from 55 to 57• Use administrative data

• Results

1 Very strong effect on claiming age2 Strong effect on retirement decision3 Spillover effects on groups not affected

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• Austrian old age pension system• NRA is 65 for men, 60 for women• ERA is 60 for men, 55 for women• Conditions of 35 years of insurance to qualify for early

retirement pensions

• 2000 and 2004 reforms• Increase in ERA to 62 (men) and 57 (women)• Increase in penalty for early retirement from 3-5% to 8-9%

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Figure 44 – Increase in ERA in Austria (male)

Notes: The first two vertical lines mark the beginning and ending of changes implemented under the 2000 and 2004 pension reform.


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Figure 45 – Increase in ERA in Austria (female)

Notes: The first two vertical lines mark the beginning and ending of changes implemented under the 2000 and 2004 pension reform.


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Figure 46 – Percent Change in benefits at ERA

.02

.03

.04

.05

.06

.07

.08

.09

Perc

ent

1939 1941 1943 1945 1947 1949 1951 1953Birth Cohort

A. Women -- Short Contribution Years

0.0

5.1

.15

Perc

ent

1939 1941 1943 1945 1947 1949 1951 1953Birth Cohort

B. Women -- Long Contribution Years.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9Pe

rcen

t

1934 1936 1938 1940 1942 1944 1946 1948Birth Cohort

C. Men -- Short Contribution Years

0.0

5.1

.15

Perc

ent

1934 1936 1938 1940 1942 1944 1946 1948Birth Cohort

D. Men -- Long Contribution Years

42


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Figure 47 – Age of claim by cohort (male)

Source : Manoli and Weber (2018), Fig. 6A.

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Figure 48 – Fraction Claiming at Age 60 by Cohort andContribution Years (male)

Source : Manoli and Weber (2018), Fig. 9.84 / 112

Figure 49 – Fraction Exiting at Age 60 by Cohort andContribution Years (male)


Figure 50 – Fraction Claiming at Age 55 by Cohort andContribution Years (female)


Figure 51 – Fraction Exiting at Age 55 by Cohort andContribution Years (female)



• RDD for changes in financial incentives• Change in benefits according to quarter of birth• RDD estimate of impact on retirement• Estimates are small and not significant

• RKD for changes in ERA• Estimate change in slopes of retirement age w.r.t. change

in ERA eligibility• Large effects estimated

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Figure 52 – Exit age from the labour force

5656

.557

57.5

5858

.5Ag

e in

yea

rs

1935 1937 1939 1941 1943 1945 1947 1949 1951 1953Birth Cohort

Women -- Short Contribution Years

5656

.557

57.5

5858

.5Ag

e in

yea

rs

1935 1937 1939 1941 1943 1945 1947 1949 1951 1953Birth Cohort

Women -- Full Sample58

58.5

5959

.560

Age

in y

ears

1930 1932 1934 1936 1938 1940 1942 1944 1946 1948Birth Cohort

Men -- Short Contribution Years

5858

.559

59.5

60Ag

e in

yea

rs

1930 1932 1934 1936 1938 1940 1942 1944 1946 1948Birth Cohort

Men -- Full Sample

Reduced Form


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III. Impact of pensions on retirementNorms, reference point

• Behaghel and Blau (AEJ-EP, 2012)• Exploit increase in FRA in the U.S. from 65 to 66• Estimate the effect of the increase in the FRA on the

hazard of exiting employment and claiming OASI pension

• Spikes at FRA• Spike in claiming hazard moved with FRA• Less clear evidence on labour market exit

• Results

1 FRA effect can account for 10-40% of the hazard at age 652 Individuals with higher cognitive ability respond more to

FRA changes3 Suggest reference dependence with loss aversion

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Figure 53 – U.S. SS Benefit Claiming Hazard

Source : Behaghel and Blau (2012), Fig. 2.91 / 112


• Seibold (2017)• Exploit discontinuities in German pension systems :

statutory ages, kinks in incentives• Use bunching techniques to infer elasticities

• Statutory ages• Early retirement age (ERA) : 60 to 65• Full retirement age (FRA) : 63 to 65• Normal retirement age (NRA) : 65

• Results

1 Large effects of statutory ages2 Much larger than financial incentives3 Rationalised with reference dependence

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Table 1 – Statutory ages according to retirement pathwaysin Germany (after 1990s reforms)

Pathways Required contribution FRA ERA

Regular 5 years 65 65Long-term insured 35 years 65 63Women 15 years 65 60Unemployed 15 years 65 60Disability 35 years 63 60

Source : Seibold (2017), Tab. 1.

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Figure 54 – Job Exit Age Distribution in Germany

Source : Seibold (2017), Fig. 1.

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Figure 55 – Evolution of FRA in Germany

Source : Seibold (2017), Fig. 3.B.

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III. Impact of pensions on retirementNorms, reference point• Bunching method

• Measuring the bunching mass B at age R compared tocounterfactual density h0(R) gives the excess mass b

b =B

h0(R)

• Elasticity of retirement age w.r.t. the net-of-tax rate ε

b/R

∆τ/(1− τ)

• Bunching with frictions and statutory ages• Note xi obs. var. and Ds

i dummy for statutory age at i

Bi = B(ε,Dsi , xi )

• Idea is to use different statutory ages, to uncover structuralelasticities ε from frictions or statutory age effects

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Figure 56 – Statutory age vs. pure financial incentive notch

Source : Seibold (2017), Fig. 4.A.

• ERA at age 60 for women born in 1945-46, ε = 4.45

• 15 years contribution notch, ε = 0.12

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Figure 57 – Kinks in Disability vs. Invalidity Pathways


• FRA at age 63 for disabled born in 1945-46, ε = 0.67

• Kinks at age 63 for invalidity due to financial adjustment ofpension (not statutory age), ε = 0.006

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Figure 58 – Excess Mass from Bunching at Statutory Age

Source : Seibold (2017), Fig. 5.A.

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Figure 59 – Excess Mass from Bunching at financial incentiveskinks


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• Reduced-form estimation• Regression of bunching on financial incentives and

statutory ages

bi

Ri

= ε∆τi

1− τi+∑s

βsDsi + γZi + νi

• Observation i corresponds to each discontinuity• βs measures additional bunching for each statutory age

• Results• NRA has largest effect on bunching : βNRA = 0.8• FRA has largest effect on bunching : βFRA = 0.3• ERA has largest effect on bunching : βERA = 0.2• Elasticity ε = 0.1

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IV. Unemployment and pensions

1 Early retirement policies

2 Impact on older workers’ employment

3 Impact on unemployment

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IV. Unemployment and pensionsEarly retirement policies

• Lump of labour idea• Unemployment is caused by excess labour supply• If older workers (women, immigrants) leave the labour

force, they “release” jobs for the unemployed• Very popular idea

• Early retirement policies in Europe• 1970s oil shock, increase in unemployment• Development of early retirement schemes in many

countries (France, Netherlands, Belgium, UK)

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• Early retirement in France• 1972 garanties de ressources : age 60-64• 1977 garanties de ressources demission : : age 60-64• 1981 contrat de solidarite : age 55-60• 1983 reform to lower effective retirement age at 60• 1983 Removal of the incentive to delay retirement

• Large support• Across party support

“Que ceux qui sont les plus ages, que ceux qui onttravaille, partent, fassent la place aux jeunes pour que toutle monde ait du travail.”

Pierre Mauroy, French Prime Minister, 1981• Employer and employee unions

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Figure 60 – Development of early retirement schemes in France

0

500

1000

1500

2000

2500

1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001

Mill

iers

Growth of the program (SS and other schemes)

60-64 ans 55-59 ans DRE 55-59 ans

Retired aged 60-64

DRE: Unemployed exempted from job search

Early retirees aged 55-59

Early retirees aged 60-64

Source : Ben Salem, Blanchet, Bozio and Roger (2010), Fig. .

105 / 112


• Evaluation• Little evaluation at the time (assumption that 1 for 1

reduction in unemployment)• Gruber and Wise (2010) : country case studies• France : Ben Salem et al. (2010)• UK : Job Release Scheme (Banks et al. ,2010)• Denmark : very sharp introduction of early retirement

scheme in 1979

• Results• Very significant negative effect on older worker’s

employment• No effect detected on youth unemployment or employment

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• Cross-country evidence• Diamond (2006) using Gruber and Wise (1999)• Gruber, Milligan and Wise (2010)

• Results• No correlation between tax incentives and unemployment

• Mechanisms ?• Not well understood• Bozio (2006) suggest labour market modelling with impact

of early retirement funding on labour cost of youngerworkers

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Figure 61 – Tax on postponing activity vs labour forceparticipation

Source : Diamond (2006), based on Gruber and Wise (1999).

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Figure 62 – Tax on postponing activity vs unemployment

Source : Diamond (2006), based on Gruber and Wise (1999).

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Lecture 4: Pensions and labour markets · 2020-02-04 · I. Facts Measurement 1 Labour force...

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