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Job Polarization, Skill Mismatch, andthe Great Recession
Riccardo ZagoNew York University
6 December 2018
ECB/CEPR Labour Market Workshop
Job Polarization
More
Riccardo Zago (NYU) Introduction 6 December 2018 1
Job Polarization & the Great Recession
Unemp.
Riccardo Zago (NYU) Introduction 6 December 2018 2
Vertical Downgrade & the Great Recession
Details High-Skilled vs. Low-Skilled
Riccardo Zago (NYU) Introduction 6 December 2018 3
This Paper
- This paper is the first to show that
- the decline of routine employment
- the change in skill-demand across jobs
explain together
- deterioration of skills-to-job match quality → “Skill Mismatch”
- longer unemployment spells
- sluggish labor mobility
Riccardo Zago (NYU) Contribution 6 December 2018 4
Theoretical Mechanism
- A model with endogenous mapping of skills to jobs
- skill-heterogeneous workers
- job-specific technologies and endogenous skill-requirements
- skill-dependent job opportunities and multiple jobs search
- Asymmetric technology shocks and labor market frictions affect
- workers’ job opportunities and mobility
- the process of sorting skills with jobs
↓Skills-to-Job Mismatch
- Routine Biased Technical Change drives Job Polarization
Riccardo Zago (NYU) Contribution 6 December 2018 5
Structural Estimation
- Estimation to match only employment dynamics between 2005 and 2015
- The model accounts well for the reallocation patterns of
- high-skilled workers
- low-skilled workers
- The aggregate predictions of the model are also true within local-labormarkets
Riccardo Zago (NYU) Contribution 6 December 2018 6
Main Results
1. job polarization accounts for the rise in skill mismatch
2. skill mismatch dynamics differ across workers when the market polarizes
3. higher skills attenuate the wage loss from mismatch
4. changes in skill-demand across jobs and frictions explain 38% of the shift-outof the Beveridge Curve
Riccardo Zago (NYU) Results 6 December 2018 7
Policy Relevance
- Inefficiency in labor factor allocations due to frictions
- longer unemployment spells for the low-skilled
- welfare loss due to job polarization
- The central planner
- reduces low-skilled unemployment
- attenuates job-polarization
- reduces skill mismatch by 1/3
Riccardo Zago (NYU) Policy Implications 6 December 2018 8
Outline
1. THE MODEL
- Technologies and Jobs
- Workers and Job-Search
- Equilibria
2. QUANTITATIVE ASSESSMENT
- Estimation to match occupational dynamics between 2005 and 2015
- Comparison of the implied allocation patterns of HS and LS with the data
- Model implications for welfare, matching efficiency and wages
Riccardo Zago (NYU) Outline 6 December 2018 9
The Model
Riccardo Zago (NYU) The Model 6 December 2018 10
RBTC and Temporary Shocks
- Assume abstract and manual technology to follow this
za,t = za + σaεt ; zm,t = zm + σmεt
- Assume routine technology to follow this
zr ,t =
zr ,0(1 + gzr )
t + σr εt for t ∈ [0,T ]
zr ,T + σr εt for t > T
- The technological shock ε follows an AR(1) process:
εt+1 = ρεt + νt+1
and ν being a random shock out of a standard-normal distribution.
- σj governs the the job-specific intensity of the shock (similar to Lilien ’82)
Riccardo Zago (NYU) The Model 6 December 2018 11
Production and Skill Requirements
- Workers differ in their skill-level x
- Technology zj and skills x are mixed as follows:
y(x ; za) = zaxλa ; y(x ; zr ) = zrx
λr ; y(x ; zm) = zm
- The value of production is
J(x ; zj) = y(x ; zj)−w(x ; zj)+βEs ′j (x)(1−δ)J(x ; z ′j )+[1−s ′j (x)(1−δ)]V (z ′j )
with
s ′j (x) = s(x , e′j ) = Pr(x ≥ e′j )
- Firms choose the minimum requirement ej to ensure a non-negative J:
J(ej ; zj) = 0
- Countercyclical Skill Requirements: if zj ↓ ⇒ ej ↑Riccardo Zago (NYU) The Model 6 December 2018 12
Vacancy Posting
- Firms posts vacancies vj for j = a, r ,m following this rule
V (zj) = −cj + βEp(θj)J(x , z ′j ) + [1− p(θj)]V (z ′j )
with
p(θj) = ψjθ−αj
and
θj =vjuj
=n. of vacancies for market j
n. of qualified unemp. workers for market j
- Free entry condition: V (zj) = 0, ∀t
Riccardo Zago (NYU) The Model 6 December 2018 13
Employment Opportunities and Unemployment
- Skills x are drown from a U[0,1] pdf
x=0 !!
x=1!!
- For given ea and er , a worker with skill x knows his job-opportunity setΩ(x) = j : ej ≤ x
- The value of unemployment is
U(x ; z) = b + βE ∑
j∈Ω(x)
q(θj)N(x ; z ′j ) +[1−
∑j∈Ω(x)
q(θj)]U(x ; z ′)
with z = [za, zr , zm], a vector of all technologies currently available in thejob-opportunity set
Riccardo Zago (NYU) The Model 6 December 2018 14
Employment Value and Dynamics
- The value of employment is
N(x ; zj) = w(x ; zj)+βEs ′j (x)[(1−δ)N(x ; z ′j )+δU(x ; z ′)]+[1−s ′j (x)]U(x ; z ′)
- The dynamic for the stock of employment in job j is
n′j = sj(1− δ)nj + ujq(θj)
- For an increase in requirements in j , the factor sj(1− δ) falls such that it
I amplifies job destruction dynamics
I exposes also highly-ranked worker to displacement (differently from Mortensenand Pissarides ’94)
I increases individual employment uncertainty (as in Ravn and Sterk ’15...buthere endogenously)
Riccardo Zago (NYU) The Model 6 December 2018 15
Wage Equation
- Under Nash Bargaining:
w(x ; zj) = (1− η)b + ηy(x ; zj) + η ∑
j∈Ω(x)
cjθj
with the value of the out-side option that varies over time and across workersdue to:
I changes in θj
I changes in |Ω(x)|
I changes in both θj and |Ω(x)|
Riccardo Zago (NYU) The Model 6 December 2018 16
Two Alternative Equilibria
Skill-Separating Equilibrium: PAM of Skills and Technology
AbstractProduction
x=1
x=0
𝑚(𝜃!)
𝑒!
𝑒!
𝑚(𝜃!)
𝑚(𝜃!)
Routine
Production
Manual
Production
Riccardo Zago (NYU) Skill-Separating Equilibrium 6 December 2018 17
Two Alternative Equilibria
Skill-Pooling Equilibrium: Skill Mismatch
AbstractProduction
x=1
x=0
𝑚(𝜃!)
𝑒!
𝑒!
𝑚(𝜃!)
𝑚(𝜃!)
Routine
Production
Manual
Production
Riccardo Zago (NYU) Skill-Pooling Equilibrium 6 December 2018 18
Skill-Pooling Equilibrium
Definition
Contingent to technology, a skill-pooling equilibrium is a vectorθj , nj ,w(x , zj), ej , uj∞t=0 for any j = a, r ,m and x ∈ [0, 1] satisfyingsimultaneously the job creation condition, the minimum requirementcondition, the wage equation, employment and unemployment dynamics.
Existence Condition
A skill-pooling equilibrium exists in the routine submarket iff the surplus fromthe match S(x , zr ) ≥ 0 for all x ∈ [ea, 1]; a skill-pooling equilibrium exists inthe manual submarket iff S(x , zm) ≥ 0 for all x ∈ [er , 1].
Riccardo Zago (NYU) Skill-Pooling Equilibrium 6 December 2018 19
QuantitativeAssessment
Riccardo Zago (NYU) Quantitative Assessment 6 December 2018 20
Bringing the Model to the Data
- Use CPS classification of educational attainments as a sufficient statistics forthe distribution of skills
- Define two major skill groups (ILO)
I High Skilled (HS): bachelor, master, phd
I Low Skilled (LS): 11th Grade, high-school diploma, 2 years of college,vocational degree
- Build (quarterly) series for HS and LS employment in each occupation (onlyfull time, non-self employed workers; codes for farming, fishing, forestry andmilitary occupations excluded)
- GOAL: estimate the model to match occupational employmentdynamics from 2005 to 2015 and check reallocation patterns for HSand LS workers
Riccardo Zago (NYU) Structural Estimation 6 December 2018 21
Structural Estimation via SMM
- Preset Parameters: β, b, δ, η, α, zr ,0, gLS Appendix
- Two Step Estimation
I 1st Step: characterize the economy at an initial point (2005q1)
- use na,2005, nr,2005, nm,2005, ShareHSa,2005, ShareHSr,2005, ShareHSm,2005, ShareHSu,2005,
wHSr,2005
wHSa,2005
,wHSm,2005
wHSa,2005
,wLSr,2005
wLSa,2005
,wLSm,2005
wLSa,2005
- back-up za, zm, cj , ψj , λa, λr , γ Appendix
I 2nd Step: let the economy move on the RBTC trend and shock it to generatethe dynamics observed from the Great Recession (2005q1 to 2015q4)
- use long-run gnr , ∆na,GR , ∆nr,GR , ∆nm,GR , Corr(ut , ut−1) Appendix
- back-up gzr , σj , ρ
Riccardo Zago (NYU) Structural Estimation 6 December 2018 22
Parameter Description Value
Technologyza Tech. in abstract jobs 1.09
zm Tech. in manual jobs 0.68
Labor Marketca Vacancy posting cost in abstract 0.02
cr Vacancy posting cost in routine 0.04
cm Vacancy posting cost in manual 0.05
ψa Matching efficiency in abstract 0.79
ψr Matching efficiency in routine 0.68
ψm Matching efficiency in manual 0.46
Skillsλa Return to skills in abstract 1.02
λr Return to skills in routine 0.49
γ Lowest skill for HS workers 0.71
Dynamicsgr Growth of routine tech. -9.81×10−5
σa Std. for tech. shock in a 0.040
σr Std. for tech. shock in r 0.051
σm Std. for tech. shock in m 0.017
ρ Persistency of the shock 0.91
Loss Function Moments (model vs. data) Skill Returns (data) Skill-Pooling Existence
Riccardo Zago (NYU) Structural Estimation 6 December 2018 23
Job Polarization and Jobless Recovery
0 5 10 15 20 25 30 35 40 45Time
0.45
0.46
0.47
0.48
0.49
0.5
0.51
0.52Routine Employment
nDatar
nModelr
0 5 10 15 20 25 30 35 40 45Time
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11Unemployment
uData
uModel
- The model generates too high unemployment in the long-run. Why?Rise in non-participation rate after the Great Recession
Change σ (employment) Change grz
Riccardo Zago (NYU) Model’s Prediction 6 December 2018 24
Employment Dynamics by Skill Group
0 5 10 15 20 25 30 35 40 45Time
-0.005
0
0.005
0.01
0.015
0.02
0.025
0.03" Share of HS in A (
nhs;a
nhs)
DataModel
0 5 10 15 20 25 30 35 40 45Time
-0.03
-0.025
-0.02
-0.015
-0.01
-0.005
0
0.005
0.01" Share of HS in R (
nhs;r
nhs)
DataModel
0 5 10 15 20 25 30 35 40 45Time
-8
-6
-4
-2
0
2
4
6
8
10 #10-3 " Share of HS in M (
nhs;m
nhs)
DataModel
0 5 10 15 20 25 30 35 40 45Time
-0.01
-0.005
0
0.005
0.01
0.015
0.02" Share of LS in A (
nls;a
nls)
DataModel
0 5 10 15 20 25 30 35 40 45Time
-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01" Share of LS in R (
nls;r
nls)
DataModel
0 5 10 15 20 25 30 35 40 45Time
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04" Share of LS in M (
nls;m
nls)
DataModel
- Temporary reversal in emp. shares for HS workers- Permanent change in emp. shares for LS workers
Change σ (sorting) Data (state-level) E → U (aggregate) Other Dynamics
Riccardo Zago (NYU) Model’s Prediction 6 December 2018 25
Employment Mismatch
Social PlannerFollowing Bhattacharya and Bunzel ’03, assume a social planner maximizes totalexpected output and total value of “leisure” at the net vacancy costs:
maxθj ,ej ,n′j
E∞∑t=0
βtyana + yrnr + ymnm + b(1− na − nr − nm)−∑j
cjθjuj
s.t. n′j = s(1− δ)nj + ujq(θj)
yj =
∫ 1
ej
y(x ; zj)U[x≥ej ]dx ⇒ average output in j
Riccardo Zago (NYU) Employment Mismatch 6 December 2018 26
Employment Mismatch
Polarization (Planner) On ψ
Riccardo Zago (NYU) Employment Mismatch 6 December 2018 27
The Shift of the Beveridge Curve
Planner Frictions vs. Shocks
Riccardo Zago (NYU) The Beveridge Curve 6 December 2018 28
The Wage Ladder10
1520
2530
Hou
rly W
age
(Dol
lars
)
Abstra
ct
Routin
e
Manua
l
LS HS
Major Groups Job Ladder (data)
.4.6
.81
Hou
rly W
age
(leve
l)
Abstra
ct
Routin
e
Manua
l
LS HS 95% CI
Major Groups Job Ladder (model)
Wage loss is bounded for HS workers when moving down the ladder
Riccardo Zago (NYU) The Wage Ladder 6 December 2018 29
Conclusion
- The change in the occupational structure and in skill-demand across jobsexplain the rise in skill mismatch:
- mismatch dynamics differ across skill-groups
- the wage-loss from mismatch is bounded for high-skilled
- Job polarization is associated with specific reallocation patterns
- A central planner reduces skill mismatch and the process of polarization
- Changes in skill-demand across jobs and frictions explain the deterioration ofaggregate matching efficiency
Riccardo Zago (NYU) Conclusion 6 December 2018 30
APPENDIX
Riccardo Zago (NYU) Appendix 6 December 2018 31
Job Polarization
- Since the 80s, routine employment is falling along with wages
- Jobs grouped by task (Acemoglu and Autor ’11) :
I abstract: Management, Professionals, and Related jobs
I routine: Production and Clerical jobs
I manual: Food prep and service, personal/child care, recreation and hospitalityjobs
- Job Polarization is driven by:
I Routine Biased Technical Change (RBTC): robotics, IT innovations, etc.
I International Trade: imports of “routine” products (e.g. cloths from China),offshoring, etc.
Back
Riccardo Zago (NYU) Appendix 6 December 2018 32
Unemployment
Back
Riccardo Zago (NYU) Appendix 6 December 2018 33
Vertical Downgrade & the Great Recession
Back
Riccardo Zago (NYU) Appendix 6 December 2018 34
Polarization and Mismatch
0 5 10 15 20 25 30 35 40 45Time
0.45
0.46
0.47
0.48
0.49
0.5
0.51
0.52Routine Employment
nDatar
nModelr
0 5 10 15 20 25 30Periods After Shock
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Gro
wth
Rat
e (%
)
Emp. Mismatch Dynamics
DataEconomy
Back
Riccardo Zago (NYU) Appendix 6 December 2018 35
Flows from Unemp. to Emp.
Model
Riccardo Zago (NYU) Appendix 6 December 2018 36
Job-Finding and Skill-Requirements
0 10 20 30 40 50Time
0.67
0.68
0.69
0.7
0.71
0.72
0.73Job-Finding Ratio in A (HS v. LS)
0 10 20 30 40 50Time
0.124
0.126
0.128
0.13
0.132
0.134
0.136
0.138Job-Finding Ratio in R (HS v. LS)
0 10 20 30 40 50Time
0.108
0.109
0.11
0.111
0.112
0.113
0.114
0.115
0.116Job-Finding Ratio in M (HS v. LS)
0 10 20 30 40 50Time
0.674
0.676
0.678
0.68
0.682
0.684
0.686
0.688Requirements in A (ea)
0 10 20 30 40 50Time
0.115
0.12
0.125
0.13
0.135
0.14
0.145
0.15Requirements in R (er)
0 10 20 30 40 50Time
-1
-0.5
0
0.5
1Requirements in M (em)
Up-skilling (data) Model
Riccardo Zago (NYU) Appendix 6 December 2018 37
From Unemployment to Abstract Jobs
Riccardo Zago (NYU) Appendix 6 December 2018 38
From Unemployment to Routine Jobs
Riccardo Zago (NYU) Appendix 6 December 2018 39
From Unemployment to Manual Jobs
Back Model
Riccardo Zago (NYU) Appendix 6 December 2018 40
Returns to Education over the Cycle
2.4
2.6
2.8
33.
23.
4lo
g(w
)
10th
Grade
11th
Grade
HS Diploma
Some C
ollege
Vocati
onal
Bache
lor
Master Phd
Beefore Recession Recovery After
Schooling and Wages in Abstract Jobs
2.4
2.5
2.6
2.7
2.8
2.9
log(
w)
10th
Grade
11th
Grade
HS Diploma
Some C
ollege
Vocati
onal
Bache
lor
Master Phd
Beefore Recession Recovery After
Schooling and Wages in Routine Jobs
2.2
2.3
2.4
2.5
2.6
2.7
log(
w)
10th
Grade
11th
Grade
HS Diploma
Some C
ollege
Vocati
onal
Bache
lor
Master Phd
Beefore Recession Recovery After
Schooling and Wages in Manual Jobs
Back
Riccardo Zago (NYU) Appendix 6 December 2018 41
Structural Estimation
The long-run decline in routine employment Back
3.8
3.85
3.9
3.95
44.
05lo
g(R
outin
e Em
p.)
1990
1995
2000
2005
2010
2015
log(Routine Emp.) log(Routine Emp.) (FITTED VALUE)
log(Routine Emp.)t = β*time + εt
Riccardo Zago (NYU) Appendix 6 December 2018 42
Structural Estimation
- γ is the skill level in the interval [0, 1] that splits the skill distribution in twosubgroups: high-skilled (∀x ≥ γ), low-skilled (∀x < γ)
- The share of low-skilled population decline at a rate gLS = −0.1%. Back
4.1
4.15
4.2
4.25
4.3
log(
LS S
hare
)
1990
1995
2000
2005
2010
2015
log(LS Share) log(LS Share) (FITTED VALUE)
log(LS Share.)t = β*time + εt
Riccardo Zago (NYU) Appendix 6 December 2018 43
Structural Estimation
Back
Riccardo Zago (NYU) Appendix 6 December 2018 44
Job-specific Surplus over time
-1
-0.5
1000
0
0.5
Surplus in A
Surplus
50
xTime 050
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0
0.3
100
0.4
0.5
Surplus
Surplus in R
50Time x050
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.2
0.25
0.3
1000
0.35
0.4
0.45
Surplus in M
Surplus
50
xTime 500
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
0.4
0.42
Back
Riccardo Zago (NYU) Appendix 6 December 2018 45
Table: Preset Parameters
Parameter Description Value
β Discount factor (quarterly) 0.95
b Value of leisure 0.40
δ Separation rate 0.10
η Employer bargaining power 0.50
α Matching elasticity 0.50
gLS Growth of LS pop. Share −1.1× 10−3
zr ,0 Technology in routine jobs 1
Back
Riccardo Zago (NYU) Appendix 6 December 2018 46
Table: Targeted moments and model moments
Moment Data Model
na in 2005 0.285 0.286
nr in 2005 0.512 0.510
nm in 2005 0.152 0.153
HS Share of na in 2005 0.660 0.675
HS Share of nr in 2005 0.154 0.152
HS Share of nm in 2005 0.102 0.105
HS Share of u in 2005 0.12 0.11
wr,HS
wa,HSin 2005 0.683 0.689
wm,HS
wa,HSin 2005 0.572 0.590
wr,LS
wa,LSin 2005 0.810 0.795
wm,LS
wa,LSin 2005 0.603 0.590
nr long-run growth rate −1.5× 10−3 −1.6× 10−3
%∆na during GR -0.68% -0.67%
%∆nr during GR -4.00% -4.01%
%∆nm during GR -0.24% -0.22%
Corr(ut , ut−1) during GR 0.900 0.899
Back
Riccardo Zago (NYU) Appendix 6 December 2018 47
Vertical Downgrade over the Cycle
- Say your company shuts down. What is your next job going to be overthe cycle?
- Use Displaced Worker Supplement (DWS) to identify workers that had beenfired for “exogenous” reasons (plant closing, abolished jobs,...)
- For worker i consider:
Pr(Downgradei 6= 0|Xi ) = Φ(δ′sβ + X ′i γ)
where
I δs is a vector of mutually exclusive dummy variables for state-specificexpansion, recession and recovery periods
I X controls for sex, age, education, experience, marital status, number ofchildren.
Back
Riccardo Zago (NYU) Appendix 6 December 2018 48
The role of σ
0 5 10 15 20 25 30 35 40 45Time
0.43
0.44
0.45
0.46
0.47
0.48
0.49
0.5
0.51
0.52
0.53Routine Employment
nDatar
nModelr ,<j = 0:03; 8j = fa; r;mg
nModelr ,<j = 0:05; 8j = fa; r;mg
nModelr , <r = 0:05;<a;m = 0
0 5 10 15 20 25 30 35 40 45Time
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11Unemployment
uData
uModel,<j = 0:03; 8j = fa; r;mguModel,<j = 0:05; 8j = fa; r;mguModel, <r = 0:05;<a;m = 0
Back
Riccardo Zago (NYU) Appendix 6 December 2018 49
The role of σ
0 5 10 15 20 25 30 35 40 45Time
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05" Share of HS in A (
nhs;a
nhs)
<j = 0:03; 8j = fa; r;mg<j = 0:05; 8j = fa; r;mg<r = 0:05;<a;m = 0
0 5 10 15 20 25 30 35 40 45Time
-0.05
-0.045
-0.04
-0.035
-0.03
-0.025
-0.02
-0.015
-0.01
-0.005
0" Share of HS in R (
nhs;r
nhs)
0 5 10 15 20 25 30 35 40 45Time
-0.01
-0.008
-0.006
-0.004
-0.002
0
0.002
0.004
0.006
0.008
0.01" Share of HS in M (
nhs;m
nhs)
0 5 10 15 20 25 30 35 40 45Time
0
0.005
0.01
0.015
0.02
0.025
0.03" Share of LS in A (
nls;a
nls)
0 5 10 15 20 25 30 35 40 45Time
-0.08
-0.07
-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0" Share of LS in R (
nls;r
nls)
0 5 10 15 20 25 30 35 40 45Time
-0.01
0
0.01
0.02
0.03
0.04
0.05" Share of LS in M (
nls;m
nls)
Back
Riccardo Zago (NYU) Appendix 6 December 2018 50
The role of gzr
0 5 10 15 20 25 30 35 40 45Time
0.4
0.42
0.44
0.46
0.48
0.5
0.52
nDatar
nModelr ; gzr
= 0
nModelr ; gzr
= cgzr
nModelr ; gzr
= 3cgzr
Back
Riccardo Zago (NYU) Appendix 6 December 2018 51
Employment Dynamics across States’ Cycles
∆Emp. Shares,t = βyears + X ′s,tγ + εs,t
-.20
.2.4
.6%
0 1 2 3 4 5N. of years after recession
ΔEmp. ShareHS,A
-.8-.6
-.4-.2
0.2
%
0 1 2 3 4 5N. of years after recession
ΔEmp. ShareHS,R
-.10
.1.2
.3.4
%
0 1 2 3 4 5N. of years after recession
ΔEmp. ShareHS,M0
.2.4
.6%
0 1 2 3 4 5N. of years after recession
ΔEmp. ShareLS,A
-1.5
-1-.5
0%
0 1 2 3 4 5N. of years after recession
ΔEmp. ShareLS,R
-.20
.2.4
.6%
0 1 2 3 4 5N. of years after recession
ΔEmp. ShareLS,M
Model
Riccardo Zago (NYU) Appendix 6 December 2018 52
Job Polarization (Planner)
0 5 10 15 20 25 30 35 40 45Time
0.45
0.46
0.47
0.48
0.49
0.5
0.51
0.52Routine Employment
nDatar
nModelr
0 5 10 15 20 25 30 35 40 45Time
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11Unemployment
uData
uModel
Emp. Mismatch Policy Relevance
Riccardo Zago (NYU) Appendix 6 December 2018 53
The role of ψ and the Social Planner
0 5 10 15 20 25 30Periods After Shock
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Gro
wth
Rat
e (%
)
Economy , Aj = Aj
P lanner
Economy , Aa = 0:5Aa
Economy , Ar = 0:5Ar
Economy , Am = 0:5Am
0 5 10 15 20 25 30Periods After Shock
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Gro
wth
Rat
e (%
)
Economy , Aj = Aj
P lanner
Economy , Aj = 0:5Aj
Economy , Aj = 0:8Aj
Emp. Mismatch
Riccardo Zago (NYU) Appendix 6 December 2018 54
HS Emp. Mismatch
0 5 10 15 20 25 30Periods After Shock
0
0.05
0.1
0.15
0.2
0.25
0.3
Gro
wth
Rat
e (%
)
DataEconomyP lanner
Emp. Mismatch
Riccardo Zago (NYU) Appendix 6 December 2018 55
The Beveridge Curve: Planner vs. Economy
Back
Riccardo Zago (NYU) Appendix 6 December 2018 56
Frictions vs. Shocks
Search Frictions Shock Asymmetry
Up-Skilling X
Shift-out BC X
Job Polarization X
LS Mismatch X+ X
HS Mismatch X X+
Back
Riccardo Zago (NYU) Appendix 6 December 2018 57
Related Literature
1. Job Polarization and Technical Change
- over the cycle: Jaimovich and Siu ’13, Foote and Ryan ’15, Restrepo ’15- in the long-run: Acemoglu and Autor ’11, Autor ’07, Autor and Dorn ’13
2. Skill Mismatch and Inefficiency in Labor Allocation
- cyclical reallocation of skills and efficiency: McLaughlin and Bils ’01,Altiwanger et al. ’15, Carillo-Tudela and Visschers ’13
- vertical displacement and wage loss: Huckfeldt ’16, Krolikowsky ’17, Jarosch’14
- fall in aggregate matching efficiency: Sahin et al. ’14, Barnichon and Figure’11
2. Skill-pooling and Up-skilling
- skill-pooling and requirements: Albrecht and Vroman ’02- counter-cyclical skill requirements: Modestino et al. 15
Riccardo Zago (NYU) Literature 6 December 2018 58
Validation of the Skill-Pooling Equilibrium
Under Nash Bargaining, the value of production is a share of the surplus
J(x ; zj) = (1− η)S(x ; zj)
Under the estimated parameters, the condition for existence of a skill-poolingequilibrium holds. OverTime Back
0 0.2 0.4 0.6 0.8 1x
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6Surplus in A
S(x; za)er
ea
0 0.2 0.4 0.6 0.8 1x
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5Surplus in R
S(x; zm)er
ea
0 0.2 0.4 0.6 0.8 1x
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5Surplus in M
S(x; zm)er
ea
Riccardo Zago (NYU) Literature 6 December 2018 59