early-career job instability and life-cycle income...

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

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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

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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

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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


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


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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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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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

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Retirement (60)LM Entry

T T T T T T P P

t10

P

Early Career



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T T U T U T U U

t10

T

Early Career



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T T P P P P P P

t10

P

Early Career



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T T P U U P P P

t10

P

Early Career



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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)

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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

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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

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Estimates: Life-Cycle

Figure: PersistenceBIndex 13 / 20


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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Figure: Variance of Persistent ShocksBIndex 14 / 20


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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Figure: Variance of Transitory ShocksBIndex 15 / 20


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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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

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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

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