The European Unemployment Puzzle: implications from population aging
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Oct 18, 2025
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About This Presentation
We study the link between the evolving age structure of the working population and unemployment. We build a large New Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices and aggregate shocks. Once calibrated to the European economies, we use this model to provid...
Slide Content
Population aging through the lens of DSGE-OLG-NK model: implications for
unemployment and monetary policy in the Euro area
Krzysztof Makarski Sylwia Radomska Joanna Tyrowicz
Warsaw School of Economics
University of Warsaw
FAME|GRAPE
Institute of Economics, Polish Academy of Sciences
UEK, 2025/10/15
1 / 44
unemployment and monetary policy in the Euro area
Krzysztof Makarski Sylwia Radomska Joanna Tyrowicz
Warsaw School of Economics
University of Warsaw
FAME|GRAPE
Institute of Economics, Polish Academy of Sciences
UEK, 2025/10/15
1 / 44
PREmotivation
Aghion & Blanchard, optimal speed of transition (1992)
•Theorizing about transition from public to private ownership (mechanism: synchronization)
•Analogous Caballero & Hammour (1995) for sectoral reallocation (mechanism: adjustment costs)
•One problem:
•And demographics (generational exchange) was key in all transition countries
2 / 44
•Theorizing about transition from public to private ownership (mechanism: synchronization)
•Analogous Caballero & Hammour (1995) for sectoral reallocation (mechanism: adjustment costs)
•One problem:
•And demographics (generational exchange) was key in all transition countries
2 / 44
Motivation
Aging populations tend to have lower unemployment rate
3 / 44
3 / 44
Demographics and unemployment: empirical regularities
We estimate
unemploymentc,t=αc+αt+βypopulation share
15−24
c,t
+βopopulation share
50−64
c,t
+ϵi,t
Eurostat World Bank
data all years same years as Eurostat EU 28 (all years) EU15 (all years)
ˆ
βy−
ˆ
β0 -0.33***-0.20*** -0.34*** -0.32*** -0.18*
(0.07) (0.05) (0.07) (0.06) (0.1)
Observations 800 1389 800 1012 620
R
2
0.71 0.74 0.69 0.55 0.56
•↓15-24 share by 10 pp, unemployment rate↓by approx 4 pp,ceteris paribus
•↑50-64 share by 10 pp, unemployment rate↓by approx 3 pp,ceteris paribus
4 / 44
We estimate
unemploymentc,t=αc+αt+βypopulation share
15−24
c,t
+βopopulation share
50−64
c,t
+ϵi,t
Eurostat World Bank
data all years same years as Eurostat EU 28 (all years) EU15 (all years)
ˆ
βy−
ˆ
β0 -0.33***-0.20*** -0.34*** -0.32*** -0.18*
(0.07) (0.05) (0.07) (0.06) (0.1)
Observations 800 1389 800 1012 620
R
2
0.71 0.74 0.69 0.55 0.56
•↓15-24 share by 10 pp, unemployment rate↓by approx 4 pp,ceteris paribus
•↑50-64 share by 10 pp, unemployment rate↓by approx 3 pp,ceteris paribus
4 / 44
EZ labor force ages fast
Between 2010 and 1970:
•Share of young workers shrinked by
•Share of elderly workers rose by
5 / 44
Between 2010 and 1970:
•Share of young workers shrinked by
•Share of elderly workers rose by
5 / 44
In this paper we ask: how does aging affect labor market?
Our tool: build a large scale NK OLG-DSGE modelwith:
•search & matching frictions
•and realistic population structure
We look into:
•long-term trends
•decompose the role of demographics and changes in the labor market features
•local stochastic properties & the conduct of monetary policy
Preview of the results
•Aging lowers unemployment levels.
•Aging reduces the cost of stabilizing inflation in terms of unemployment volatility.
•Model can account for many aspects => we welcome all the comments about which direction to go.
6 / 44
Our tool: build a large scale NK OLG-DSGE modelwith:
•search & matching frictions
•and realistic population structure
We look into:
•long-term trends
•decompose the role of demographics and changes in the labor market features
•local stochastic properties & the conduct of monetary policy
Preview of the results
•Aging lowers unemployment levels.
•Aging reduces the cost of stabilizing inflation in terms of unemployment volatility.
•Model can account for many aspects => we welcome all the comments about which direction to go.
6 / 44
In this paper we ask: how does aging affect labor market?
Our tool: build a large scale NK OLG-DSGE modelwith:
•search & matching frictions
•and realistic population structure
We look into:
•long-term trends
•decompose the role of demographics and changes in the labor market features
•local stochastic properties & the conduct of monetary policy
Preview of the results
•Aging lowers unemployment levels.
•Aging reduces the cost of stabilizing inflation in terms of unemployment volatility.
•Model can account for many aspects => we welcome all the comments about which direction to go.
6 / 44
Our tool: build a large scale NK OLG-DSGE modelwith:
•search & matching frictions
•and realistic population structure
We look into:
•long-term trends
•decompose the role of demographics and changes in the labor market features
•local stochastic properties & the conduct of monetary policy
Preview of the results
•Aging lowers unemployment levels.
•Aging reduces the cost of stabilizing inflation in terms of unemployment volatility.
•Model can account for many aspects => we welcome all the comments about which direction to go.
6 / 44
In this paper we ask: how does aging affect labor market?
Our tool: build a large scale NK OLG-DSGE modelwith:
•search & matching frictions
•and realistic population structure
We look into:
•long-term trends
•decompose the role of demographics and changes in the labor market features
•local stochastic properties & the conduct of monetary policy
Preview of the results
•Aging lowers unemployment levels.
•Aging reduces the cost of stabilizing inflation in terms of unemployment volatility.
•Model can account for many aspects => we welcome all the comments about which direction to go.
6 / 44
Our tool: build a large scale NK OLG-DSGE modelwith:
•search & matching frictions
•and realistic population structure
We look into:
•long-term trends
•decompose the role of demographics and changes in the labor market features
•local stochastic properties & the conduct of monetary policy
Preview of the results
•Aging lowers unemployment levels.
•Aging reduces the cost of stabilizing inflation in terms of unemployment volatility.
•Model can account for many aspects => we welcome all the comments about which direction to go.
6 / 44
In this paper we ask: how does aging affect labor market?
Our tool: build a large scale NK OLG-DSGE modelwith:
•search & matching frictions
•and realistic population structure
We look into:
•long-term trends
•decompose the role of demographics and changes in the labor market features
•local stochastic properties & the conduct of monetary policy
Preview of the results
•Aging lowers unemployment levels.
•Aging reduces the cost of stabilizing inflation in terms of unemployment volatility.
•Model can account for many aspects => we welcome all the comments about which direction to go.
6 / 44
Our tool: build a large scale NK OLG-DSGE modelwith:
•search & matching frictions
•and realistic population structure
We look into:
•long-term trends
•decompose the role of demographics and changes in the labor market features
•local stochastic properties & the conduct of monetary policy
Preview of the results
•Aging lowers unemployment levels.
•Aging reduces the cost of stabilizing inflation in terms of unemployment volatility.
•Model can account for many aspects => we welcome all the comments about which direction to go.
6 / 44
Model
Model structure: overview
A large scale DSGE-OLG NK model
(Bielecki, Brzoza-Brzezina and Kolasa, 2022)
with search & matching frictions
•80 cohorts of overlapping generations of households (age 20-99)
•Age-specific asset structure: bonds and real assets
•Age-specific labor dynamics
•... with nominal & real frictions...
•sticky prices, investment adjustment costs
•... with labor market frictions...
•two-state model (employed & unemployed)
•search and matching frictions
•wages set in staggered Nash bargaining
•job brokers
•... with fiscal and monetary policy...
•realistic population structure and population growth.
7 / 44
A large scale DSGE-OLG NK model
(Bielecki, Brzoza-Brzezina and Kolasa, 2022)
with search & matching frictions
•80 cohorts of overlapping generations of households (age 20-99)
•Age-specific asset structure: bonds and real assets
•Age-specific labor dynamics
•... with nominal & real frictions...
•sticky prices, investment adjustment costs
•... with labor market frictions...
•two-state model (employed & unemployed)
•search and matching frictions
•wages set in staggered Nash bargaining
•job brokers
•... with fiscal and monetary policy...
•realistic population structure and population growth.
7 / 44
Demographics and population structure
•Agents live forJ=80 periods (corresponds to age 20 - 99)
•Labor market participation until retirement age
¯
J=45 (corresponds to 65)
•young workers aged 20-30
•prime-age workers 31-55
•elderly workers 56-64
•Population sizeNt=
P
J
j=1
Nj,t;νt=
Nt
N
t−1
−1
•Utility function (with habit formation):
Uj,t=
1
1−σ
e
εc,t
(cj,t−ϱ¯cj,t−1)
1−σ
+βωjUj+1,t+1
•The function captures habit persistenceϱ, risk aversionσ, preference shockse
εc,t
, and mortality riskωj
8 / 44
•Agents live forJ=80 periods (corresponds to age 20 - 99)
•Labor market participation until retirement age
¯
J=45 (corresponds to 65)
•young workers aged 20-30
•prime-age workers 31-55
•elderly workers 56-64
•Population sizeNt=
P
J
j=1
Nj,t;νt=
Nt
N
t−1
−1
•Utility function (with habit formation):
Uj,t=
1
1−σ
e
εc,t
(cj,t−ϱ¯cj,t−1)
1−σ
+βωjUj+1,t+1
•The function captures habit persistenceϱ, risk aversionσ, preference shockse
εc,t
, and mortality riskωj
8 / 44
Household budget constraint
Household budget constraint:
cj,t+aj,t= (1−τt)wj,tzjnj,t+χj,tuj,t+
R
a
j,t
πt
aj−1,t−1−Tt+beqj,t
•aj,t- total asset holdings (riskless bonds withR
b
t+ risky assetsR
k
t)
•R
a
j,t- weighted return on household’s portfolio:
R
a
j,t=ω
b
jR
b
t+ (1−ω
b
j)R
k
t
•Portfolio weightsω
b
jvary by age(Bielecki et al., 2022)
•Unintended bequestsbeqj,tshared by agents withj<¯J−10
•Households expected income is a weighted share of earned labor income and unemployment benefit.
9 / 44
Household budget constraint:
cj,t+aj,t= (1−τt)wj,tzjnj,t+χj,tuj,t+
R
a
j,t
πt
aj−1,t−1−Tt+beqj,t
•aj,t- total asset holdings (riskless bonds withR
b
t+ risky assetsR
k
t)
•R
a
j,t- weighted return on household’s portfolio:
R
a
j,t=ω
b
jR
b
t+ (1−ω
b
j)R
k
t
•Portfolio weightsω
b
jvary by age(Bielecki et al., 2022)
•Unintended bequestsbeqj,tshared by agents withj<¯J−10
•Households expected income is a weighted share of earned labor income and unemployment benefit.
9 / 44
Labor market: set up
Two-state model:
•Employed
Wj,t( ˜wj,t) =zj˜wj,t+I
{j<¯J−1}
Et
h
πt+1
Rt+1
ωj((1−ρj)(θwWj+1,t+1(
πt−1
πt
˜wj,t)
+ (1−θw)Wj+1,t+1( ˜wj+1,t+1)) +ρjΥj+1,t+1)
i
(1)
•or unemployed
Υj,t=χt+I
{j<¯J−1}
Et
h
πt+1
Rt+1
ωj(sj,tWj+1,t+1(wj+1,t+1) + (1−sj,t)Υj+1,t+1)
i
(2)
(I
{j<¯j−1}is an indicator for retired tomorrow)
10 / 44
Two-state model:
•Employed
Wj,t( ˜wj,t) =zj˜wj,t+I
{j<¯J−1}
Et
h
πt+1
Rt+1
ωj((1−ρj)(θwWj+1,t+1(
πt−1
πt
˜wj,t)
+ (1−θw)Wj+1,t+1( ˜wj+1,t+1)) +ρjΥj+1,t+1)
i
(1)
•or unemployed
Υj,t=χt+I
{j<¯J−1}
Et
h
πt+1
Rt+1
ωj(sj,tWj+1,t+1(wj+1,t+1) + (1−sj,t)Υj+1,t+1)
i
(2)
(I
{j<¯j−1}is an indicator for retired tomorrow)
10 / 44
Labor supply
•Labor services in periodt
ℓt=
»
X
ι∈{y,p,e}
`
ℓι,t
´
σ
L
−1
σ
L
–
σ
L
σ
L
−1
(3)
where
ℓy,t=
P
j=1,...,10
ℓj,t ←young,
ℓp,t=
P
j=11,...,35
ℓj,t ←prime-age,
andℓe,t=
P
j=36,...,¯J−1
ℓj,t ←elderly.
•Effective labor supply per capita of cohortjin periodtis
ℓj,t=
z
j
Nj,t
Nt
(4)
11 / 44
•Labor services in periodt
ℓt=
»
X
ι∈{y,p,e}
`
ℓι,t
´
σ
L
−1
σ
L
–
σ
L
σ
L
−1
(3)
where
ℓy,t=
P
j=1,...,10
ℓj,t ←young,
ℓp,t=
P
j=11,...,35
ℓj,t ←prime-age,
andℓe,t=
P
j=36,...,¯J−1
ℓj,t ←elderly.
•Effective labor supply per capita of cohortjin periodtis
ℓj,t=
z
j
Nj,t
Nt
(4)
11 / 44
Labor market: search and matching
•New matches are created according to
Mj,t=mj(Uj,t,Vt) =e
ϵ
M,t
σj,m
“
Nj,t
Nt
”
1−ϕj
U
ϕj
j,t
V
1−ϕj
t (5)
withϵM,tdenoting shocks to matching technology.
•Vacancy filling and job finding probability
qj,t=e
ϵ
M,t
σj,m
“
Nj,t
Nt
”
ϑ
−ϕj
j,t
andsj,t=e
ϵ
M,t
σj,mϑ
1−ϕj
j,t
withϑj,t=
VtNt
uj,t
denoting the tightness anduj,tdenoting unemployment.
•This yields labor market flows
nj,t= (1−ρj−1)nj−1,t−1+sj−1,t−1uj−1,t−1 (6)
uj,t=1−nj,t+ρjnj,t (7)
12 / 44
•New matches are created according to
Mj,t=mj(Uj,t,Vt) =e
ϵ
M,t
σj,m
“
Nj,t
Nt
”
1−ϕj
U
ϕj
j,t
V
1−ϕj
t (5)
withϵM,tdenoting shocks to matching technology.
•Vacancy filling and job finding probability
qj,t=e
ϵ
M,t
σj,m
“
Nj,t
Nt
”
ϑ
−ϕj
j,t
andsj,t=e
ϵ
M,t
σj,mϑ
1−ϕj
j,t
withϑj,t=
VtNt
uj,t
denoting the tightness anduj,tdenoting unemployment.
•This yields labor market flows
nj,t= (1−ρj−1)nj−1,t−1+sj−1,t−1uj−1,t−1 (6)
uj,t=1−nj,t+ρjnj,t (7)
12 / 44
Job broker
•Job brokers sell labor services to intermediate good producers in the perfectly competitive market for the
price ofΩt.
•Labor services in periodtare given by the following formula
ℓt=
»
X
ι∈{y,p,e}
`
ℓι,t
´
σ
L
−1
σ
L
–
σ
L
σ
L
−1
(8)
whereℓy,t=
P
j=1,...,10
ℓj,tdenotes young,ℓp,t=
P
j=11,...,35
ℓj,tprime-age, andℓe,t=
P
j=36,...,¯J−1
ℓj,t
elderly.
•and effective labor supply per capita of cohortjin periodtis
ℓj,t=
z
j
Nj,t
Nt
(9)
13 / 44
•Job brokers sell labor services to intermediate good producers in the perfectly competitive market for the
price ofΩt.
•Labor services in periodtare given by the following formula
ℓt=
»
X
ι∈{y,p,e}
`
ℓι,t
´
σ
L
−1
σ
L
–
σ
L
σ
L
−1
(8)
whereℓy,t=
P
j=1,...,10
ℓj,tdenotes young,ℓp,t=
P
j=11,...,35
ℓj,tprime-age, andℓe,t=
P
j=36,...,¯J−1
ℓj,t
elderly.
•and effective labor supply per capita of cohortjin periodtis
ℓj,t=
z
j
Nj,t
Nt
(9)
13 / 44
Job brokers sell labor services to intermediate good producers at priceΩt
•Job brokering agency needs to post vacancy to hirec(Vt) =
κ
2
P¯J−1
j=1
q
2
j,tV
2
t→search is not directed
•The agency receives payment from firmsΩtzjand pays workers˜wj,t
•... with the value of worker
Jj,t( ˜wj,t) = Ω
ι(j),tzj−˜wj,tzj
+ωj(1−ρj)I
{j<
¯
J−1}
Et[
πt+1
Rt+1
(θwJj+1,t+1(
πt−1
πt
˜wj,t) + (1−θw)Jj+1,t+1( ˜wj+1,t+1))](10)
14 / 44
•Job brokering agency needs to post vacancy to hirec(Vt) =
κ
2
P¯J−1
j=1
q
2
j,tV
2
t→search is not directed
•The agency receives payment from firmsΩtzjand pays workers˜wj,t
•... with the value of worker
Jj,t( ˜wj,t) = Ω
ι(j),tzj−˜wj,tzj
+ωj(1−ρj)I
{j<
¯
J−1}
Et[
πt+1
Rt+1
(θwJj+1,t+1(
πt−1
πt
˜wj,t) + (1−θw)Jj+1,t+1( ˜wj+1,t+1))](10)
14 / 44
Wage Setting: Nash Bargaining
•Wages are determined in staggered Nash bargaining - are either re-optimzed or indexed to past inflation
(Gertler and Trigari, 2009)
wj,t= (1−θw) ˜wj,t+θw
πt−1
πt
wj,t−1
•Reflects wage rigidity and cohort-specific re-negotiation.
15 / 44
•Wages are determined in staggered Nash bargaining - are either re-optimzed or indexed to past inflation
(Gertler and Trigari, 2009)
wj,t= (1−θw) ˜wj,t+θw
πt−1
πt
wj,t−1
•Reflects wage rigidity and cohort-specific re-negotiation.
15 / 44
Producers
•Final goods aggregated from differentiated intermediate products
ct+it+gt=
»Z
yt(i)
1
µdi
–
µ
•Intermediate goods firms face Calvo-type price stickiness and produce
yt(i) =e
At
kt(i)
α
ℓt(i)
1−α
−Φ
•Capital producers are subject to investment adjustment cost
(1+νt+1)kt+1= (1−δ)kt+
"
1−
Sk
2
„
it
it−1
−1
«
2
#
it
16 / 44
•Final goods aggregated from differentiated intermediate products
ct+it+gt=
»Z
yt(i)
1
µdi
–
µ
•Intermediate goods firms face Calvo-type price stickiness and produce
yt(i) =e
At
kt(i)
α
ℓt(i)
1−α
−Φ
•Capital producers are subject to investment adjustment cost
(1+νt+1)kt+1= (1−δ)kt+
"
1−
Sk
2
„
it
it−1
−1
«
2
#
it
16 / 44
Firm sector: three types of producers
•Final good producers (perfect competition)
•Intermediate good producers (monopolistic competition,
•Capital good producers (perfect competition,)
Tractability
Why this structure?=⇒consistency with empirics of business cycle
•Price stickiness⇒non-neutral monetary policy
•Investment frictions⇒realistic capital dynamics
•Markups and firm heterogeneity in price setting
17 / 44
•Final good producers (perfect competition)
•Intermediate good producers (monopolistic competition,
•Capital good producers (perfect competition,)
Tractability
Why this structure?=⇒consistency with empirics of business cycle
•Price stickiness⇒non-neutral monetary policy
•Investment frictions⇒realistic capital dynamics
•Markups and firm heterogeneity in price setting
17 / 44
Final good producers
•Operate under perfect competition
•Produce a homogeneous final good using a CES aggregator:
yt=
»
1
υ
Z
υ
0
yt(i)
1
µdi
–
µ
•Demand function for each intermediate good:
yt(i) =
„
Pt(i)
Pt
«µ
1−µ
yt
•Zero profits in steady state
18 / 44
•Operate under perfect competition
•Produce a homogeneous final good using a CES aggregator:
yt=
»
1
υ
Z
υ
0
yt(i)
1
µdi
–
µ
•Demand function for each intermediate good:
yt(i) =
„
Pt(i)
Pt
«µ
1−µ
yt
•Zero profits in steady state
18 / 44
Intermediate good producers
•Monopolistically competitive firms producing differentiated goods
•Cobb-Douglas production:
yt(i) =e
At
(k
s
t(i))
α
ℓt(i)
1−α
−Φ
•Price setting with Calvo rigidity:
•Firms can reset prices with probability 1−θ
•Otherwise, prices indexed to past inflation (ζπ)
•Profit:
Pt(i)
Pt
yt(i)−Ωtℓt(i)−rk,tk
s
t(i)
19 / 44
•Monopolistically competitive firms producing differentiated goods
•Cobb-Douglas production:
yt(i) =e
At
(k
s
t(i))
α
ℓt(i)
1−α
−Φ
•Price setting with Calvo rigidity:
•Firms can reset prices with probability 1−θ
•Otherwise, prices indexed to past inflation (ζπ)
•Profit:
Pt(i)
Pt
yt(i)−Ωtℓt(i)−rk,tk
s
t(i)
19 / 44
Capital Good Producers
•Operate in perfect competition
•Transform final goods into capital, subject to adjustment costs:
(1+νt+1)kt+1= (1−δ)kt+
"
1−
Sk
2
„
it
it−1
−1
«
2
#
it
•Adjustment costs smooth investment responses to shocks
20 / 44
•Operate in perfect competition
•Transform final goods into capital, subject to adjustment costs:
(1+νt+1)kt+1= (1−δ)kt+
"
1−
Sk
2
„
it
it−1
−1
«
2
#
it
•Adjustment costs smooth investment responses to shocks
20 / 44
Government and monetary policy
•Government runs (inconsequentially) balanced budget
Rt
πt
bt+gt= (1+νt+1)bt+1+
X
j
τtwj,tLj,t+
X
j
Nj,tTt (11)
•Monetary policy follows the Taylor rule
Rt
¯
R
=
“
Rt
¯
R
”
γ
R
“
`πt
¯π
´
γπ
`yt
¯y
´
γy
”
1−γ
R
(12)
21 / 44
•Government runs (inconsequentially) balanced budget
Rt
πt
bt+gt= (1+νt+1)bt+1+
X
j
τtwj,tLj,t+
X
j
Nj,tTt (11)
•Monetary policy follows the Taylor rule
Rt
¯
R
=
“
Rt
¯
R
”
γ
R
“
`πt
¯π
´
γπ
`yt
¯y
´
γy
”
1−γ
R
(12)
21 / 44
We use this model to
•Deterministic simulations of transition across model parameters.
•Population structure
•Labor market parameters
•Stochastic simulations around local steady state for a given population structure.
Shocks to: preferences, technology (TFP) and monetary policy
•Impulse response functions
•Monetary policy frontier
see detailes
22 / 44
•Deterministic simulations of transition across model parameters.
•Population structure
•Labor market parameters
•Stochastic simulations around local steady state for a given population structure.
Shocks to: preferences, technology (TFP) and monetary policy
•Impulse response functions
•Monetary policy frontier
see detailes
22 / 44
Calibration
Calibration
•Demographic data: Eurostat and EUROPOP
•Standard structural parameters: taken from literature or to match data moments
•Vacancy data from the OECD (averaged to Eurozone by population)
•Life-cycle features calibrated from individual level data:
•Age-specific productivity: HFCS and PSID
•Age-specific labor market flows: EU LFS (findings and separations)
•Age-specific asset holdings HFCS
•The main calibration was made on the pre-covid data from 2015s.
23 / 44
•Demographic data: Eurostat and EUROPOP
•Standard structural parameters: taken from literature or to match data moments
•Vacancy data from the OECD (averaged to Eurozone by population)
•Life-cycle features calibrated from individual level data:
•Age-specific productivity: HFCS and PSID
•Age-specific labor market flows: EU LFS (findings and separations)
•Age-specific asset holdings HFCS
•The main calibration was made on the pre-covid data from 2015s.
23 / 44
Labor market flows in EZ: job finding rate (left) and separation rate (right)
24 / 44
24 / 44
Calibration: Labor market
Table 1:Target statistics in the data and the model for EZ
variable 1995s 2015s description
model data model data
uyoung 18% 18%16.8%16.8% unemployment rate for young
uprime age8.7%8.7%8.6% 8.7%unemployment rate for prime age individuals
uelderly 8% 8% 7% 7% unemployment rate for elderly
syoung 41% 41% 41% 40% job finding rate for young
sprime age35% 36% 38% 41% job finding rate for prime age individuals
selderly24% 24% 32% 32% job finding rate for elderly
ϑ 0.1 - 0 .1 0 .1 labor market tightness
Note: the unemployment data for Europe includes NEETs.
25 / 44
Table 1:Target statistics in the data and the model for EZ
variable 1995s 2015s description
model data model data
uyoung 18% 18%16.8%16.8% unemployment rate for young
uprime age8.7%8.7%8.6% 8.7%unemployment rate for prime age individuals
uelderly 8% 8% 7% 7% unemployment rate for elderly
syoung 41% 41% 41% 40% job finding rate for young
sprime age35% 36% 38% 41% job finding rate for prime age individuals
selderly24% 24% 32% 32% job finding rate for elderly
ϑ 0.1 - 0 .1 0 .1 labor market tightness
Note: the unemployment data for Europe includes NEETs.
25 / 44
Performance of our model1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
0
2
4
6
8
10
12
model
data - HP filtered
data
26 / 44
0
2
4
6
8
10
12
model
data - HP filtered
data
26 / 44
Model EZ data fit: selected moments of the Eurozone economy
Standard Deviations Correlation with output Autocorrelation
Variable data model data model data model
in percent
output 1.75 2 .03 1 1 0 .56 0 .99
consumption 1.36 2 .38 0 .90 0 .59 0 .79 0 .99
interest rate 1.67 1 .60 0 .59 −0.80 0 .89 0 .99
gov. expenditure 0.98 0 .98 0 .23 0 .03 0 .77 0 .71
inflation 1.12 1 .18 0 .65 −0.91 0 .51 0 .99
unemployment 10.65 10 .37 −0.88 −0.98 0 .73 0 .99
variables not used in moment matching
investment 4.65 6 .78 0 .96 0 .76 0 .67 0 .99
unemployment young 8.25 12 .38 −0.84 −0.99 0 .68 0 .99
unemployment prime 11.01 9 .08 −0.89 −0.96 0 .72 0 .99
unemployment old 9.64 8 .35 −0.84 −0.95 0 .69 0 .99
27 / 44
Standard Deviations Correlation with output Autocorrelation
Variable data model data model data model
in percent
output 1.75 2 .03 1 1 0 .56 0 .99
consumption 1.36 2 .38 0 .90 0 .59 0 .79 0 .99
interest rate 1.67 1 .60 0 .59 −0.80 0 .89 0 .99
gov. expenditure 0.98 0 .98 0 .23 0 .03 0 .77 0 .71
inflation 1.12 1 .18 0 .65 −0.91 0 .51 0 .99
unemployment 10.65 10 .37 −0.88 −0.98 0 .73 0 .99
variables not used in moment matching
investment 4.65 6 .78 0 .96 0 .76 0 .67 0 .99
unemployment young 8.25 12 .38 −0.84 −0.99 0 .68 0 .99
unemployment prime 11.01 9 .08 −0.89 −0.96 0 .72 0 .99
unemployment old 9.64 8 .35 −0.84 −0.95 0 .69 0 .99
27 / 44
Results
Key transmission mechanisms
•Composition effect:Fewer young workers⇒lower average unemployment.
•Behavioral effect:Firms adjust vacancies⇒improved job finding for young.
•Policy implication:Aging society⇒lower sacrifice ratio⇒more hawkish optimal monetary policy.
28 / 44
•Composition effect:Fewer young workers⇒lower average unemployment.
•Behavioral effect:Firms adjust vacancies⇒improved job finding for young.
•Policy implication:Aging society⇒lower sacrifice ratio⇒more hawkish optimal monetary policy.
28 / 44
A sequence of steady states
1.
ut=ωy,tuy,t+ωp,tup,t+ωo,tuo,t,
ut−u0
= (ωy,t−ωy,0)uy,t+ (ωp,t−ωp,0)up,t+ (ωo,t−ωo,0)uo,t
| {z }
composition
+ωy,0(uy,t−uy,0) +ωp,0(up,t−up,0) +ωo,0(uo,t−uo,0).
| {z }
behavioral
2.
Impulse response functions (deterministic sequence of stochastic steady state)
3.
Local implications for monetary policy frontier (single stochastic steady state)
29 / 44
1.
ut=ωy,tuy,t+ωp,tup,t+ωo,tuo,t,
ut−u0
= (ωy,t−ωy,0)uy,t+ (ωp,t−ωp,0)up,t+ (ωo,t−ωo,0)uo,t
| {z }
composition
+ωy,0(uy,t−uy,0) +ωp,0(up,t−up,0) +ωo,0(uo,t−uo,0).
| {z }
behavioral
2.
Impulse response functions (deterministic sequence of stochastic steady state)
3.
Local implications for monetary policy frontier (single stochastic steady state)
29 / 44
A sequence of steady states
1.
ut=ωy,tuy,t+ωp,tup,t+ωo,tuo,t,
ut−u0= (ωy,t−ωy,0)uy,t+ (ωp,t−ωp,0)up,t+ (ωo,t−ωo,0)uo,t
| {z }
composition
+ωy,0(uy,t−uy,0) +ωp,0(up,t−up,0) +ωo,0(uo,t−uo,0).
| {z }
behavioral
2.
Impulse response functions (deterministic sequence of stochastic steady state)
3.
Local implications for monetary policy frontier (single stochastic steady state)
29 / 44
1.
ut=ωy,tuy,t+ωp,tup,t+ωo,tuo,t,
ut−u0= (ωy,t−ωy,0)uy,t+ (ωp,t−ωp,0)up,t+ (ωo,t−ωo,0)uo,t
| {z }
composition
+ωy,0(uy,t−uy,0) +ωp,0(up,t−up,0) +ωo,0(uo,t−uo,0).
| {z }
behavioral
2.
Impulse response functions (deterministic sequence of stochastic steady state)
3.
Local implications for monetary policy frontier (single stochastic steady state)
29 / 44
A sequence of steady states
1.
ut=ωy,tuy,t+ωp,tup,t+ωo,tuo,t,
ut−u0= (ωy,t−ωy,0)uy,t+ (ωp,t−ωp,0)up,t+ (ωo,t−ωo,0)uo,t
| {z }
composition
+ωy,0(uy,t−uy,0) +ωp,0(up,t−up,0) +ωo,0(uo,t−uo,0).
| {z }
behavioral
2.
3.
29 / 44
1.
ut=ωy,tuy,t+ωp,tup,t+ωo,tuo,t,
ut−u0= (ωy,t−ωy,0)uy,t+ (ωp,t−ωp,0)up,t+ (ωo,t−ωo,0)uo,t
| {z }
composition
+ωy,0(uy,t−uy,0) +ωp,0(up,t−up,0) +ωo,0(uo,t−uo,0).
| {z }
behavioral
2.
3.
29 / 44
Aging in EZ: unemployment↓by approx. 3.5 pp.U-rate
1995 2000 2005 2010 2015
-2
-1.5
-1
-0.5
0
0.5
1
Youth U-rate
1995 2000 2005 2010 2015
-4
-3
-2
-1
0
1
Prime-age U-rate
1995 2000 2005 2010 2015
-3
-2
-1
0
1
2
3
Elderly U-rate
1995 2000 2005 2010 2015
-3
-2
-1
0
1
2
3
Labor market effect
Demographic: behavioral effect
Demographic: composition effect
Labor market & demographic combine
30 / 44
1995 2000 2005 2010 2015
-2
-1.5
-1
-0.5
0
0.5
1
Youth U-rate
1995 2000 2005 2010 2015
-4
-3
-2
-1
0
1
Prime-age U-rate
1995 2000 2005 2010 2015
-3
-2
-1
0
1
2
3
Elderly U-rate
1995 2000 2005 2010 2015
-3
-2
-1
0
1
2
3
Labor market effect
Demographic: behavioral effect
Demographic: composition effect
Labor market & demographic combine
30 / 44
IRFs of monetary policy shock with 1990 and 2020 population0 10 20 30 40
-1
-0.8
-0.6
-0.4
-0.2
0
10
-5
Output
0 10 20 30 40
-6
-4
-2
0
10
-6
Inflation
0 10 20 30 40
0
5
10
15
20
10
-6
Interest rate
2015 population
1995 population
0 10 20 30 40
-20
-15
-10
-5
0
10
-6
Consumption
0 10 20 30 40
-1
-0.8
-0.6
-0.4
-0.2
0
10
-4
Vacancies
0 10 20 30 40
0
1
2
3
10
-5
U-rate
0 10 20 30 40
0
2
4
6
10
-5
Youth U-rate
0 10 20 30 40
0
0.5
1
1.5
10
-5
Prime-age U-rate
0 10 20 30 40
0
0.5
1
1.5
10
-5
Elderly U-rate
31 / 44
-1
-0.8
-0.6
-0.4
-0.2
0
10
-5
Output
0 10 20 30 40
-6
-4
-2
0
10
-6
Inflation
0 10 20 30 40
0
5
10
15
20
10
-6
Interest rate
2015 population
1995 population
0 10 20 30 40
-20
-15
-10
-5
0
10
-6
Consumption
0 10 20 30 40
-1
-0.8
-0.6
-0.4
-0.2
0
10
-4
Vacancies
0 10 20 30 40
0
1
2
3
10
-5
U-rate
0 10 20 30 40
0
2
4
6
10
-5
Youth U-rate
0 10 20 30 40
0
0.5
1
1.5
10
-5
Prime-age U-rate
0 10 20 30 40
0
0.5
1
1.5
10
-5
Elderly U-rate
31 / 44
Aging lowers costs of stabilizing inflation of OUTPUT volatility0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
Standard deviation of GDP
0
0.1
0.2
0.3
0.4
0.5
0.6
Standard deviation of inflation
1995 population
2015 population
32 / 44
Standard deviation of GDP
0
0.1
0.2
0.3
0.4
0.5
0.6
Standard deviation of inflation
1995 population
2015 population
32 / 44
Both direct and indirect effects of aging0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
Standard deviation of GDP
0
0.1
0.2
0.3
0.4
0.5
0.6
Standard deviation of inflation
1995 population LM OFF
2015 population LM OFF
1995 population
2015 population
33 / 44
Standard deviation of GDP
0
0.1
0.2
0.3
0.4
0.5
0.6
Standard deviation of inflation
1995 population LM OFF
2015 population LM OFF
1995 population
2015 population
33 / 44
Aging lowers costs of stabilizing inflation in terms of UNEMPLOYMENT volatility4 6 8 10 12 14 16 18 20 22 24
Standard deviation of youth U-rate
0
0.1
0.2
0.3
0.4
0.5
0.6
Standard deviation of inflation
1995 population
2015 population
8.5 9 9.5 10 10.5 11 11.5 12 12.5
Standard deviation of prime-age U-rate
0
0.1
0.2
0.3
0.4
0.5
0.6
Standard deviation of inflation
1995 population
2015 population
8 8.5 9 9.5 10 10.5 11 11.5 12 12.5
Standard deviation of elderly U-rate
0
0.1
0.2
0.3
0.4
0.5
0.6
Standard deviation of inflation
1995 population
2015 population
34 / 44
Standard deviation of youth U-rate
0
0.1
0.2
0.3
0.4
0.5
0.6
Standard deviation of inflation
1995 population
2015 population
8.5 9 9.5 10 10.5 11 11.5 12 12.5
Standard deviation of prime-age U-rate
0
0.1
0.2
0.3
0.4
0.5
0.6
Standard deviation of inflation
1995 population
2015 population
8 8.5 9 9.5 10 10.5 11 11.5 12 12.5
Standard deviation of elderly U-rate
0
0.1
0.2
0.3
0.4
0.5
0.6
Standard deviation of inflation
1995 population
2015 population
34 / 44
Implications for NRI – no labor market mechanicsNRI
1995 2000 2005 2010 2015
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
Demography
Public debt
Interactions
35 / 44
1995 2000 2005 2010 2015
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
Demography
Public debt
Interactions
35 / 44
Implications for NRI – with labor market mechanicsNRI-rate
1995 2000 2005 2010 2015
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
Labor market change effect
Demographic change effect
Demographic & labor market change
36 / 44
1995 2000 2005 2010 2015
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
Labor market change effect
Demographic change effect
Demographic & labor market change
36 / 44
Implications for optimal monetary policy
•Elderly less sensitive to inflation than young
•Both young and elderly less sensitive to inflation after demographic change.
•Optimal monetary policy becomes more restrictive:
•All age groups become more hawkish (or less dovish)
•Share of young declines and share of elderly rises
37 / 44
•Elderly less sensitive to inflation than young
•Both young and elderly less sensitive to inflation after demographic change.
•Optimal monetary policy becomes more restrictive:
•All age groups become more hawkish (or less dovish)
•Share of young declines and share of elderly rises
37 / 44
Conclusions
Conclusions
•Aging has several implications for the EZ labor market. It
•... lowers unemployment.
•... shortens (but strengthens) the response of unemployment to monetary policy shocks
•... and lowers the sacrifice ratio.
38 / 44
•Aging has several implications for the EZ labor market. It
•... lowers unemployment.
•... shortens (but strengthens) the response of unemployment to monetary policy shocks
•... and lowers the sacrifice ratio.
38 / 44
Additional slides
Calibration EZ: parameters
Parameter Value Description
1995s 2015s
A. Households
β 0.982 0 .982 Discount factor
ϱ 0.75
4
0.75
4
Habit persistence
B. Firms
δ 0.12 0 .12 Capital depreciation rate
α 0.25 0 .25 Capital share in output
SK 4 4 Investment adjustment cost curvature
µ 1.2 1 .2 Steady state product markup
θ 0.75
4
0.75
4
Calvo probability (prices)
∗
ζπ 0.25 0 .25 Weight of past inflation in prices indexation
∗
Φ 0.04 0 .04 Intermediate goods producers fixed cost
D. Government and central bank
¯π 1.02 1 .02 Steady state inflation
γR 0.8
4
0.8
4
interest rate smoothing
γπ 1.97 1 .97 reaction to inflation
γy 0.41 0 .41 reaction to GDP growth
γb 0.42 0 .42 fiscal rule parameter
Ψ
0.85
1−0.85
0.85
1−0.85
capacity utilization costs
∗
Note: Parametersθ,ζπ, andγnare set to 0 in the deterministic simulations.
40 / 44
Parameter Value Description
1995s 2015s
A. Households
β 0.982 0 .982 Discount factor
ϱ 0.75
4
0.75
4
Habit persistence
B. Firms
δ 0.12 0 .12 Capital depreciation rate
α 0.25 0 .25 Capital share in output
SK 4 4 Investment adjustment cost curvature
µ 1.2 1 .2 Steady state product markup
θ 0.75
4
0.75
4
Calvo probability (prices)
∗
ζπ 0.25 0 .25 Weight of past inflation in prices indexation
∗
Φ 0.04 0 .04 Intermediate goods producers fixed cost
D. Government and central bank
¯π 1.02 1 .02 Steady state inflation
γR 0.8
4
0.8
4
interest rate smoothing
γπ 1.97 1 .97 reaction to inflation
γy 0.41 0 .41 reaction to GDP growth
γb 0.42 0 .42 fiscal rule parameter
Ψ
0.85
1−0.85
0.85
1−0.85
capacity utilization costs
∗
Note: Parametersθ,ζπ, andγnare set to 0 in the deterministic simulations.
40 / 44
Calibration EZ: parameters cont’d
These parameters implyqj,t=N
rel
j,tσj,m
„
1
N
rel
t
«
1−ϕj
ϑ
−ϕj
j,t
andsj,t=σj,m(
1
N
rel
t
)
1−ϕj
ϑ
1−ϕj
j,t
Parameter Value Description
1995s 2015s
C. Labor market
κ 13.3 15 .4 cost of posting the vacancy
N
first 0.71 0 .73 number of employed young entering the market
ρyoung 0.058 0 .056 separation rate for the young
ρprime 0.030 0 .034 separation rate for the prime age
ρ
elderly 0.020 0 .022 separation rate for the elderly
σyoung 0.89 0 .93 scaling parameter in the matching function
σprime 0.64 0 .73 scaling parameter in the matching function
σ
elderly 0.43 0 .58 scaling parameter in the matching function
ϕj 0.72 0 .72 elasticity of matching function
η 0.72 0 .72 parameter in the Nash bargaining process
θw 0.85
4
0.85
4
nominal wage stickiness
χ 0.64 0 .64 unemployment benefit
σ
L 5 5 substitutability of workers
γn 2 2 responsiveness of labor market entrants employment to GDP
41 / 44
These parameters implyqj,t=N
rel
j,tσj,m
„
1
N
rel
t
«
1−ϕj
ϑ
−ϕj
j,t
andsj,t=σj,m(
1
N
rel
t
)
1−ϕj
ϑ
1−ϕj
j,t
Parameter Value Description
1995s 2015s
C. Labor market
κ 13.3 15 .4 cost of posting the vacancy
N
first 0.71 0 .73 number of employed young entering the market
ρyoung 0.058 0 .056 separation rate for the young
ρprime 0.030 0 .034 separation rate for the prime age
ρ
elderly 0.020 0 .022 separation rate for the elderly
σyoung 0.89 0 .93 scaling parameter in the matching function
σprime 0.64 0 .73 scaling parameter in the matching function
σ
elderly 0.43 0 .58 scaling parameter in the matching function
ϕj 0.72 0 .72 elasticity of matching function
η 0.72 0 .72 parameter in the Nash bargaining process
θw 0.85
4
0.85
4
nominal wage stickiness
χ 0.64 0 .64 unemployment benefit
σ
L 5 5 substitutability of workers
γn 2 2 responsiveness of labor market entrants employment to GDP
41 / 44
Calibration: Stochastic shocks obtained in moment matching procedure for the EZ economy
Table 2:Calibrated stochastic shocks
Parameter Value Description
A. Persistence
ρ
A 0.999 Productivity shock - autocorrelation
ρc 0.999 Preference shock - autocorrelation
ρg 0.714 Gov. expenditure shock - autocorrelation
B. Standard deviations
σ
A 0.00093763 Productivity shock - standard deviation
σc 0.22125 Preference shock - standard deviation
σg 0.0013589 Gov. expenditure shock - standard deviation
σ
R 0.00007336 Monetary shock - standard deviation
42 / 44
Table 2:Calibrated stochastic shocks
Parameter Value Description
A. Persistence
ρ
A 0.999 Productivity shock - autocorrelation
ρc 0.999 Preference shock - autocorrelation
ρg 0.714 Gov. expenditure shock - autocorrelation
B. Standard deviations
σ
A 0.00093763 Productivity shock - standard deviation
σc 0.22125 Preference shock - standard deviation
σg 0.0013589 Gov. expenditure shock - standard deviation
σ
R 0.00007336 Monetary shock - standard deviation
42 / 44
Derivation of monetary policy frontier
•We minimize the standard central bank loss function within Taylor rule different populations (younger
from 1990s and older from 2010s) by solving the following problems for allλ∈[0,1]
min
(γy,γπ)
λ·Var(˜πt) + (1−λ)·Var(˜yt)
subject to equilibrium conditions of the model, with the following Taylor rule
Rt
¯
R
=
“
Rt
¯
R
”
γ
R
“
`πt
¯π
´
γπ
`yt
¯y
´
γy
”
1−γ
R
(13)
Go back
43 / 44
•We minimize the standard central bank loss function within Taylor rule different populations (younger
from 1990s and older from 2010s) by solving the following problems for allλ∈[0,1]
min
(γy,γπ)
λ·Var(˜πt) + (1−λ)·Var(˜yt)
subject to equilibrium conditions of the model, with the following Taylor rule
Rt
¯
R
=
“
Rt
¯
R
”
γ
R
“
`πt
¯π
´
γπ
`yt
¯y
´
γy
”
1−γ
R
(13)
Go back
43 / 44
Our Contribution to the Literature
Youth Unemployment and Age Heterogeneity
•Youth unemployment exceeds overall unemployment and is more cyclical (e.g., Bloom et al. 1988; Bell &
Blanchflower, 2011)
•Young workers face higher entry frictions and are imperfect substitutes for older cohorts
Demographic & Unemployment
•Limited empirical work on demographic structure and unemployment (e.g., Aaronson et al., 2015; Fallick
& Foote, 2022)
•Demographics as key driver of secular unemployment trends.
Demographics & Monetary Policy:
•Demographic trends influence natural interest rate and monetary transmission (Bielecki et al., 2020, 2022)
•Age-specific labor frictions affect macroeconomic policy trade-offs (Cheron et al., 2011, 2013; Hairault et
al., 2019)
44 / 44
Youth Unemployment and Age Heterogeneity
•Youth unemployment exceeds overall unemployment and is more cyclical (e.g., Bloom et al. 1988; Bell &
Blanchflower, 2011)
•Young workers face higher entry frictions and are imperfect substitutes for older cohorts
Demographic & Unemployment
•Limited empirical work on demographic structure and unemployment (e.g., Aaronson et al., 2015; Fallick
& Foote, 2022)
•Demographics as key driver of secular unemployment trends.
Demographics & Monetary Policy:
•Demographic trends influence natural interest rate and monetary transmission (Bielecki et al., 2020, 2022)
•Age-specific labor frictions affect macroeconomic policy trade-offs (Cheron et al., 2011, 2013; Hairault et
al., 2019)
44 / 44
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