The Microeconomic Determinants of Older...
Transcript of The Microeconomic Determinants of Older...
The Microeconomic
Determinants of Older
Employment
Evidence from Belgium and Abroad
Vandenberghe, Vincent (IRES-ESL-UCL)
NBB-BNB
21 Feb 2014
Brussels
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Outline
1. Context
2. Tentative framework: a (labour) supply &
demand story
3. Empirical evidence (with a focus on Belgium)
- Country-level
- Firm-level
4. Some success stories
5. Policy recommendations
1. Context
Ageing (rising health & pension costs)…
… and the understantable desire to expand
an older employment rate that remains
relatively low
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Weakness of older Employment rate in
Belgium
Women
Men
2. Framework
• The existing literature, and most policy
initiatives, have focused on supply-side
determinants (i.e. the role of pension,
early- retirement schemes in fostering
withdrawal for the labour market…)
• But one should not ignore the role of
demand-side barriers (reluctance of firms
to (re)employ older workers) 5
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Wage
Older Employment
Supply
Demand
L2L1
Supply
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Supply-Side Demand-side
- Generosity of replacement
earnings
- (Early) pensions benefits
- Unemployment benefits
- Disability benefits
- Health or family
considerations
- Seniority wages
- Employment protection
- Severance costs
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- Productivity decline
3. The Empirical Evidence
3.1 Country-level evidence
– What OECD/EU-SILC country-level data
reveal
3.2 Firm-level evidence
(Bel-first + social security data)
- The Employment consequences of steeper
wage-age gradients in Belgium
- The productivity and profitability
consequences of larger shares older workers
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3.1. Country-level : Youth vs Old: no evidence
of crowding out … on the contrary
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AUS
AUT
BEL
CAN
CHL
CZE
DNK
ESTFIN
FRA
DEU
GRC
HUN
ISL
IRLISR
ITA
JPN
KOR
LUX
MEX
NLD
NZL
NOR
POL
PRT
SVK
SVN
ESP
SWECHE
TUR
GBR
USAOECD
30
40
50
60
70
80
90
100
30 35 40 45 50 55 60 65 70 75 80
Employment rate, 20-24 year-olds (%)
Percentage of 55-59 year-olds and 20-24 year-olds in employment, 2009)
Employment rate, 55-59 year olds, per cent
Country-level – OECD 2006
OERj= f (BCj, SCHOOLj, PENSIONj, EMPL_PROTj, WAGE SETTINGj,
TAXESj, REL_WAGEj, GENDER*AGE j)
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Countries: AUS,AUT,BEL,CZE,DEU,DNK,ESP,FIN,FRA,GBR,ITA,NLD,NOR,POL,PRT,SVK,SWE,USA
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Belgium
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3.2 Firm-level The Employment consequences
of steeper wage-age gradients in Belgium
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LnWAGE ij = α + βj AGEij + ρ lnYj
Old Employment Outcomej = f( 𝜷𝒋, Controlsj)
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Firm-level –The productivity and profitability
consequences of larger shares older workers
Productivity
lnYit=f( Kit ,Lit , AGE SHARESit)
Gross profits (employability)
ln(Yit/Wit)= g( Kit ,Lit , AGE SHARESit)
Key results
4. Success stories
4.1. Successful firms
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=>BMW “Oldsters Beat Youngsters in BMW
Assembly-Line Test”• In 2007, BMW set up an experimental assembly line with older
employees to see whether they could keep pace. The production
line features stools to spare aging backs, adjustable-height work
benches, and wooden floors instead of rubber to help hips swivel
during repetitive tasks
• The verdict: not only could they keep up, the older workers did a
better job than younger staffers on another line at the same factory
• Today, many of the changes are being implemented at plants across
the company.
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=> Horndal steel-plant in central
Sweden • Between 1930 and 1950 annual growth rate in
productivity of 2.5 percent
• Horndal had a very aged workforce
– In 1930, 34% of the workers at Horndal were
50+
– In 1950, 50% …
• Horndal suggests that workforce ageing is not a
problem for productivity
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4.2 A successful country: Sweden
*Horizon: extension of the right to stay in employmentfrom 65 to 67 (2001), discussions to extend to 69
* Supply-side: The pension reform of the 1990’s (fromdefined benefit to defined contribution) introduces strongfinancial incentives to stay active
Simultaneoulsly, the alternative exit routes have been closed
*Demand-side:
• Moderate seniority wage (compare to Belgium, France), declining wage >55
• Reduced social contribution >65
• More attention to workplace ergonomy
• Higher incidence of training18
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Source: Eurostat, 2007
5. Policy Recommendations
1. Boost supply
1.1. Expand Horizon (legal age of retirement)
1.2. Make it less financially attractive to retire (replacement rate)
1.3. Better control alternative exit routes (disability…)
2. Entice demand
2.1. Combat age-related productivity decline
- Invest in ergonomy/work environment (BMW…) [1]
- Develop training among 40-50 year-old workers (Sweden)
2.2. Labour costs [2]
- Moderate seniority-based wage rises [3]
- Conditional on social partners committing to [1],[2], [3],
selectively reduce tax wedge on older labour
3. Improve intermediation (activate older unemployed)
– “Seek and Ye Shall Find” J. van Ours (2012)
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Appendices
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24
0,1
0,15
0,2
0,25
0,3
0,35
0,4
Source: ISSP2005
A high share of involuntary retirement (55-64)
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Implicit contrats
What needs to change if the (implicit) age of retirement is raised
Age
wage 1
Productivity
6555
wage 2
100
Productivity
Wage
A
B
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Implicit contrats and wage dynamics in case of job change
Seniority 1
wage 1
Productivity
Employer 1 Employer 2
wage 2
Seniority 2
100
Productivity
Wage
55
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Implicit contrats and negative shocks- Impact on departure age
Age/seniority
Productivity
Wage
Wage
Productivity
End of implicit
contract
100
A*
B*
5552
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Average earnings by age: full-time male workers, 2008
Source: D'Addio et al. (2010) based on
OECD Earnings Database.
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Model and Econometrics
Equ.1:average productivity
ln (Yi,t /Li,t )= lnA + α ln QLi,t +ß lnKi,t – lnLi,t
where: Yi,t is the firm’s value added
and QLit a labour quality index à-la-Hellerstein-Neumark
QLit = ∑k µi,j Li,j,t = µi,ref Li,t + ∑j ≠ref (µi,j - µi,ref) Li,j,t
µj reflecting the productivity of type j workers (e.g.
university)
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• Assuming same marginal product across firms,
(no subscript i) and taking logs QL becomes:
ln QLi,t = ln µref + lnLi,t+ ln (1+ ∑j ≠ref (λj - 1) Pi,j,t)
Where
λj≡µj/µref is the relative productivity of type j
workers
Pi,j,t ≡ Li,j,t/Li,t the proportion of type j
workers (eg.young, prime age (ref.), old…)
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Since ln(1+x)≈ x, we can linearize
Ln QLit = ln µref + ln Li,t + ∑j ≠ref (λj - 1) Pi,j,t
And the production function becomes:
ln(Yi,t /Li,t)= lnA+ α [ln µref + ln Li,t + ∑j ≠ref (λj -1) Pijt]
+ ß lnKit – lnLik
or, equivalently
ln (Yit /Lit)= B + (α-1)lit + ∑j ≠ref ηj Pijt + ß kit
where:
– B=lnA+α ln µref
– ηj = α (λj – 1); λj=µj/µref j ≠ref.
– lit=lnLit; kit=lnKit
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Equ.2: average labour costs
Wit /Lit= ∑j πj Lijt / Lit =πref + ∑j ≠ref.(πj - πref) Lijt/ Lit
Taking the logarithm and using log(1+x)≈ x, we
can approximate this by:
ln(Wt /Lit)= Bw + ∑k ≠ref ηWj Pi,j,t
where
– Bw =ln π0
– ηWj = (Φj – 1)
– Φj≡ πj/ πref j ≠ref
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Formulating the key hypothesis test is now
straightforward
Assuming spot labour markets and cost-
minimizing firms the null hypothesis of no
impact on the productivity-labour cost ratio
for type j worker implies ηj = ηwj.
Any negative (or positive) difference between
these two coefficients can be interpreted as
a quantitative measure of the disincentive
(incentive) to employ the category of workers
considered.
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Econometric specificationThe hyp. test = easily implemented if one adopts strictly
equivalent econometric specificationsln (Yit /Lit)= B + (α-1)lit + ∑j ≠ref ηj Pitj + ß kit + γFit + εit
ln (Wit /Lit)= Bw+ (αw-1)lit + ∑j ≠ref ηw
j Pitj + ßw kit + γwFit + εwit
Taking the difference ln (Yit )- ln (Wit )=BG+ αGlit + ∑j ≠ref η
Gj Pitj + ßG kit + γGFit
+ εGit
where:
BG= B -Bw; αG=α-αw ;ηGj= ηj-η
wj;
ßG= ß- ßw ;γG= γ-γw and εGit =εit -εw
it
ηGj = direct estimate of null hypothesis of no impact
on the productivity-labour cost ratio
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ln (Yit /Lit)= B +
+ ß kit + γFit + εit
with εit = ϴi + τit + σit
ϴi unobservable (time-invariant) heterogeneity between firms
τit short-term (asymmetrically) observed productivity shocks
σit random error E(σit) = 0
The identification problem
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One can deal with θi by resorting to first differences
or firm fixed effects
The biggest challenge= coping with τit
=> two methods:
IV: lagged values Pi,j,t-1 ; Pi,j,t-2 as instruments (Aubert
and Crépon, 2003, 2007; van Ours & Stoeldraijer,
2011)
Intermediates-as-proxy/more structural approach
Olley & Pakes (1998), Levinsohn & Petrin (2003),
Ackerman, Caves and Fraezer (2006).
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ACF uses intermediates/materials to proxy the short-term
productivity term
intit =f(τit , kit, qlit)
Assuming this function is monotonic, it can be inverted
τit =f-1(intit, kit, qlit)
Leading to
ln (Yit /Lit)= B+ φ qlit + ß kit + γFit + θi + f-1(intit, kit, qlit) + σit
=> Our specificity it to combine this strategy with fixed
effects to eliminate θi => FE-ACF
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Robustness checks
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Training