Early-Career Job Instability and Life-Cycle Income Dynamics
Antonio Cabrales Maia Güell Rocio MaderaUCL & FEDEA Edinburgh, CEPR & FEDEA SMU
July 2019SAET, Ischia
Motivation
I Early career job circumstances affect lifetime earnings
– Entering the job market in a recession→ lower lifetime earningsKahn’10, Oreopoulos, Von Wachter & Heisz ’12, Oyer ’06
– Starting off at a small vs. large firm→ lower lifetime earningsArellano-Bover JMP
↪→ (–) long-run level effect of starting career in turbulent circumstances
I But... what about the dynamics of those long-run outcomes?IHow dynamics of levels for the rest of the working life differ with early job experiences
I and uncertainty?IHow dynamics of volatility for the rest of the working life differ with early job experiences
1 / 20
Motivation
I Early career job circumstances affect lifetime earnings
– Entering the job market in a recession→ lower lifetime earningsKahn’10, Oreopoulos, Von Wachter & Heisz ’12, Oyer ’06
– Starting off at a small vs. large firm→ lower lifetime earningsArellano-Bover JMP
↪→ (–) long-run level effect of starting career in turbulent circumstances
I But... what about the dynamics of those long-run outcomes?IHow dynamics of levels for the rest of the working life differ with early job experiences
I and uncertainty?IHow dynamics of volatility for the rest of the working life differ with early job experiences
1 / 20
This PaperI Characterize lifetime implications of early-career job instability
I Using the Spanish labor market (and admin data) as a laboratory
In particular
I Estimate age earnings dynamics by early-career stability groupI Job-unstable: especially exposed to temporary contracts in their 20sI Job-stable: predominantly held permanent contracts in their 20s
I Use the framework to understand the differences
I ... and sources of their income volatility
I ... and consumption/welfare implications
2 / 20
This PaperI Characterize lifetime implications of early-career job instability
I Using the Spanish labor market (and admin data) as a laboratory
In particular
I Estimate age earnings dynamics by early-career stability groupI Job-unstable: especially exposed to temporary contracts in their 20sI Job-stable: predominantly held permanent contracts in their 20s
I Use the framework to understand the differences
I ... and sources of their income volatility
I ... and consumption/welfare implications
2 / 20
Why we care
I Importance of initial conditions on level dynamics
1 Current research focused on cohort impact of recessions↪→ Idiosyncratic shocks important too
2 Used to characterizing life-cycle dynamics conditional on education↪→ First job(s) experiences matter as much (first job(s) ∼ postdoc)
I Uncertainty effects of early job instability(possibly due to institutional framework)
– Temp. contracts: do not necessarily pay less (e.g.: APs in Spain)– But they do distort life decisions (housing, family) due to uncertaintyIThese decisions happen during 10ish years since graduation↪→ early career instability has welfare consequences beyond income
3 / 20
Income decompositionYi - Individual i’s labor Income at age a in year t
log Yiat = heterogeneityiat + uncertaintyiat
I HeterogeneityIObserved: age dummies, education, year...IUnobserved: individual effects
I Uncertainty (shocks)IMagnitude & dispersionIPersistence
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Income decompositionYia - Individual i’s labor Income at age a in year t
log Yiat = heterogeneityiat + uncertaintyiat
I HeterogeneityIObserved: age dummies, education, year...→ i, a, tIUnobserved: individual effects→ i
I Uncertainty (shocks)IMagnitude & dispersion→ i, tIPersistence→ i, t
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Income decomposition
log Yiat = heterogeneityiat + uncertaintyiat
I HeterogeneityIObserved: age dummies, education, year...→ i, a, tIUnobserved: individual effects→ i
I Uncertainty (shocks) – a la Karahan & Ozkan ’13IMagnitude & dispersion→ i, a, tIPersistence→ i, a, t
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Intuition
Earnings changes due to different reasons over the working life
I Young - job-to-job transitions
I Mid-career - settling down, bonuses, promotions/demotions
I Older - health problems reduce productivity
Age-specific income process: reduced-form version of these transitions
+ Easy to embed into a HH problem to measure self-insurance and welfare
! A model with elastic labor supply alone wouldn’t do
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Intuition
Earnings changes due to different reasons over the working life
I Young - job-to-job transitions
I Mid-career - settling down, bonuses, promotions/demotions
I Older - health problems reduce productivity
Age-specific income process: reduced-form version of these transitions
+ Easy to embed into a HH problem to measure self-insurance and welfare
! A model with elastic labor supply alone wouldn’t do
BIndex 5 / 20
Data: Spanish Continuous Sample of Working Histories4% rep. random sample of all workers affiliated to the SSA / yearI 1 million per yearI Panel: selected workers are kept for subsequent yearsI And the whole past working history is included
Social security recordsI 2004-2015, working histories back to the 60s (repres. since 1988)I Daily info on all contracts, full-time/part-time indicatorI Top-coded
Tax RecordsI 2004-2015I Yearly info on all taxable labor income sourcesI Non top-coded
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Data: Spanish Continuous Sample of Working Histories4% rep. random sample of all workers affiliated to the SSA / yearI 1 million per yearI Panel: selected workers are kept for subsequent yearsI And the whole past working history is included
Social security recordsI 2004-2015, working histories back to the 60s (repres. since 1988)I Daily info on all contracts, full-time/part-time indicatorI Top-coded
Tax RecordsI 2004-2015I Yearly info on all taxable labor income sourcesI Non top-coded
BIndex 6 / 20
Data: Spanish Continuous Sample of Working Histories4% rep. random sample of all workers affiliated to the SSA / yearI 1 million per yearI Panel: selected workers are kept for subsequent yearsI And the whole past working history is included
Social security recordsI 2004-2015, working histories back to the 60s (repres. since 1988)I Daily info on all contracts, full-time/part-time indicatorI Top-coded
Tax RecordsI 2004-2015I Yearly info on all taxable labor income sourcesI Non top-coded
BIndex 6 / 20
Characterizing Early-Career Job Instability (Daily contract info)
Retirement (60)LM Entry t0 (22+) t10 (35-)
Early Career
I For all workers with labor market attachment during Early CareerI If share of days worked < days not worked→ Young Non-EmployedIFor the not YNE, if days worked as temp > days worked as perm→ Young Job-UnstableIOtherwise→ Young Job-Stable
I Checked alternatives: % income received from unemp, temp, and perm contracts
BIndex 7 / 20
Characterizing Early-Career Job Instability (Daily contract info)
Retirement (60)LM Entry t0 (22+) t10 (35-)
Early Career
I For all workers with labor market attachment during Early CareerI If share of days worked < days not worked→ Young Non-EmployedIFor the not YNE, if days worked as temp > days worked as perm→ Young Job-UnstableIOtherwise→ Young Job-Stable
I Checked alternatives: % income received from unemp, temp, and perm contracts
BIndex 7 / 20
Characterizing Early-Career Job Instability (Daily contract info)
Retirement (60)LM Entry
T T T T T T P P
t10
P
Early Career
I For all workers with labor market attachment during Early CareerI If share of days worked < days not worked→ Young Non-EmployedIFor the not YNE, if days worked as temp > days worked as perm→ Young Job-UnstableIOtherwise→ Young Job-Stable
I Checked alternatives: % income received from unemp, temp, and perm contracts
BIndex 7 / 20
Characterizing Early-Career Job Instability (Daily contract info)
Retirement (60)LM Entry
T T U T U T U U
t10
T
Early Career
I For all workers with labor market attachment during Early CareerI If share of days worked < days not worked→ Young Non-EmployedIFor the not YNE, if days worked as temp > days worked as perm→ Young Job-UnstableIOtherwise→ Young Job-Stable
I Checked alternatives: % income received from unemp, temp, and perm contracts
BIndex 7 / 20
Characterizing Early-Career Job Instability (Daily contract info)
Retirement (60)LM Entry
T T P P P P P P
t10
P
Early Career
I For all workers with labor market attachment during Early CareerI If share of days worked < days not worked→ Young Non-EmployedIFor the not YNE, if days worked as temp > days worked as perm→ Young Job-UnstableIOtherwise→ Young Job-Stable
I Checked alternatives: % income received from unemp, temp, and perm contracts
BIndex 7 / 20
Characterizing Early-Career Job Instability (Daily contract info)
Retirement (60)LM Entry
T T P U U P P P
t10
P
Early Career
I For all workers with labor market attachment during Early CareerI If share of days worked < days not worked→ Young Non-EmployedIFor the not YNE, if days worked as temp > days worked as perm→ Young Job-UnstableIOtherwise→ Young Job-Stable
I Checked alternatives: % income received from unemp, temp, and perm contracts
BIndex 7 / 20
Life-Cycle Earnings Dynamics (Annual tax income records)
Graduation t0 Retirement Tt10
Explore Dynamics
Let the log earnings of a worker i of age a be:
log Yiat = βXiatobservables
+ αi∼iidN
+ γi∼iidN
a + uia∼iidN
+ zia∼AR(1)
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Life-Cycle Earnings Dynamics (Annual tax income records)
Graduation t0 Retirement Tt10
Explore Dynamics
Let the log earnings of a worker i of age a be:
log Yiat = βXiatobservables
+ αi∼iidN
+ γi∼iidN
a + uia∼iidN
+ zia∼AR(1)
BIndex 8 / 20
Sources of Uncertainty
z : Persistent: zia = ρazi,a–1 + ηia, with shock ηia ∼ N(0,σ2η,a)
IAge-specific, captures shocks that have long-run consequencesIE.g. layoff, health shock
u : Transitory: uia = εia, with shock εia ∼ N(0,σ2ε,a)
IAge-specific, captures shocks that are perceived as short livedIE.g. temporary wage cut or freeze
BIndex 9 / 20
Sources of Unobserved Heterogeneity
α : Fixed Heterogeneity: αi ∼ N(0,σ2α)
IAge-indpendent, captures initial conditions as of graduationIE.g. wage differences b/c major choice, diligence,...
γ : Career Heterogeneity: γi ∼ N(0,σ2γ)
IProportional to age, captures different expected income growth due to initial conditionsIE.g. job-ladder differences b/c major choice
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Estimation
I ρ,σε, and ση are functions of age:
– σ2ε,a, σ2
η,a, and ρ are cubic functions of age– σα, and σγ are time-invariant
I Method: GMM
– Autocovariance matrix up to 6 lags– Efficient weighting matrix
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Average and Age-Invariant Parameters
All Workers Job-Stable Job-Unstableσ̄η Var(Persistent Shocks) 0.204 0.065 0.236σ̄ε Var(Transitory Shocks) 0.185 0.107 0.169ρ Persistence 0.622 0.534 0.847σα Var(Fixed Effects) 0.141 0.108 0.222
Table: Average Parameters, Males, College Graduates
BIndex 12 / 20
Estimates: Life-Cycle
Figure: PersistenceBIndex 13 / 20
Estimates: Life-Cycle
Figure: Persistence
Features of shock persistence, Job-unstable vs Job-stable:
I Hump-shaped for all
I Flatter for Job-Unstable
I Peaks later in life↪→ age of 46 vs 37
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Estimates: Life-Cycle
Figure: Variance of Persistent ShocksBIndex 14 / 20
Estimates: Life-Cycle
Figure: Variance of Persistent Shocks
Features of persistent uncertainty, Job-unstable vs Job-stable:
I Lower at the beginning of life
I U-shaped vs. decreasing over the life cycleIBig difference at the end of the life-cycle↪→ From age 50 on
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Estimates: Life-Cycle
Figure: Variance of Transitory ShocksBIndex 15 / 20
Estimates: Life-Cycle
Figure: Variance of Transitory Shocks
Features of transitory uncertainty, Job-unstable vs Job-stable:
I Lower at the beginning of life
I Flat vs decreasing over life↪→ Big difference at the end of the life-cycle
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Sources of Earnings Dynamics Heterogeneity
Recall: The level and evolution of earnings can differ due to
1. Ex-ante differences, possibly magnified over time
2. Transitory shocks
3. Persistent shocks
NEXT: look at contribution of each
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Sources of Earnings Dynamics Heterogeneity
Recall: The level and evolution of earnings can differ due to
1. Ex-ante differences, possibly magnified over time
2. Transitory shocks
3. Persistent shocks
NEXT: look at contribution of each
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Decomposing Sources of Earnings HeterogeneityAll
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Decomposing Sources of Earnings HeterogeneityAll
Features:
I Overall earnings variance almost flat over life-cycle
I Increasing role of ex-ante heterogeneity over age↪→ Cumulative effect of initial heterogeneity
I Persistent part flat (until 45) and decreasing thereafterICombination of behavior of persistence (ρ) and variance ofpersistent shocks (σ2
η)
I Transitory component slightly decreasing over age
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Decomposing Sources of Earnings HeterogeneityJob-Stable
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Decomposing Sources of Earnings HeterogeneityJob-Unstable
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Decomposing Sources of Earnings HeterogeneityJob-Unstable
Features, Job-unstable vs Job-stable:
I Overall uncertainty higher except for early years
I Smaller role of ex-ante heterogeneityI Job-ladder and education not as important role if job-unstable
BIndex 19 / 20
Conclusions
I Workers exposed to job instability during early-career years face higher uncertaintythroughout the life-cycleIThey experience higher volatility in their income shocksIShocks are increasingly persistent until later in life, compared to job-stable
I Decomposing the sources of increasing uncertainty shows thatIVariance of earnings shocks does not fade out with age, as opposed to job-stableI Job-ladder not as important for job-unstable
I These differences matter for consumption heterogeneity and welfare
I Future work: other groups (Women)
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APPENDIX
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Autocovariance Fit
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Simulation Fit*Estimated earnings within a model of job transitions, from CGMV 2018.
Figure: Quantiles of Log earnings: Data (solid) vs. Simulation (dashed)
25 30 35 40 45 50 55 60
Age
7.5
8
8.5
9
9.5
10
10.5
11
11.5
12
LogEarnings
5
10
25
50
75
90
95
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Additional Results: The Case of WomenAll
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Additional Results: The Case of WomenJob-Stable
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Additional Results: The Case of WomenJob-Unstable
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Sample Selection
Table: Number of observations kept at each step
Sel. Criteria Remaining ObsBegin with 10.88MAge missing 10.87MContract missing 10.87MEducation missing 10.14MAge 22-60 6.42MDrop duplicate spells 5.41MTotal 5.41M
. CollegeMen 2.95MWomen 2.46M
Benchmark: College Graduate Males
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Sample Selection
Table: Number of observations kept at each step
Sel. Criteria Remaining ObsBegin with 10.88MAge missing 10.87MContract missing 10.87MEducation missing 10.14MAge 22-60 6.42MDrop duplicate spells 5.41MTotal 5.41M
. CollegeMen 2.95M 418KWomen 2.46M 571K
Benchmark: College Graduate MalesBIndex 7 / 7