OutsideOptionsintheLaborMarket · Outcomevariable: Dailywagedividedbybaseline wt w0-0.10 0.00 0.10...
Transcript of OutsideOptionsintheLaborMarket · Outcomevariable: Dailywagedividedbybaseline wt w0-0.10 0.00 0.10...
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Outside Options in the Labor Market
Sydnee Caldwell Oren DanieliMicrosoft Research Tel-Aviv University
UC Berkeley
BoI-CEPR-IZA 2019
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Motivation
More job opportunities affects wages through:1 Worker can find a better match (Hsieh et al., 2019)2 Better outside options (Caldwell & Harmon 2018)
Availability of job opportunities could vary across workersDepends on, e.g., local labor market, willingness to commute,transferability of skillsCould generate lower wages even for equally productive workersEx: Women may have fewer options on average if they are lesswilling or able to commute
Challenge: Outside options are typically unobserved
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Motivation
More job opportunities affects wages through:1 Worker can find a better match (Hsieh et al., 2019)2 Better outside options (Caldwell & Harmon 2018)
Availability of job opportunities could vary across workersDepends on, e.g., local labor market, willingness to commute,transferability of skillsCould generate lower wages even for equally productive workersEx: Women may have fewer options on average if they are lesswilling or able to commute
Challenge: Outside options are typically unobserved
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Motivation
More job opportunities affects wages through:1 Worker can find a better match (Hsieh et al., 2019)2 Better outside options (Caldwell & Harmon 2018)
Availability of job opportunities could vary across workersDepends on, e.g., local labor market, willingness to commute,transferability of skillsCould generate lower wages even for equally productive workersEx: Women may have fewer options on average if they are lesswilling or able to commute
Challenge: Outside options are typically unobserved
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This Paper
1 Which workers have more options?Derive a sufficient statistic for outside options:Outside Options Index - OOILearn about outside options from similar workersExample: Men have 25% more relevant options
2 How do differences in options translate into wages?Identify OOI to wage elasticity using two IV strategies10% more relevant options → ≈1.7-2.7% higher wages
3 How do differences in options affect wage inequality?Combine OOI distribution with elasticity25% of gender gapMain mechanism: willingness to commute/move
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This Paper
1 Which workers have more options?Derive a sufficient statistic for outside options:Outside Options Index - OOILearn about outside options from similar workersExample: Men have 25% more relevant options
2 How do differences in options translate into wages?Identify OOI to wage elasticity using two IV strategies10% more relevant options → ≈1.7-2.7% higher wages
3 How do differences in options affect wage inequality?Combine OOI distribution with elasticity25% of gender gapMain mechanism: willingness to commute/move
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This Paper
1 Which workers have more options?Derive a sufficient statistic for outside options:Outside Options Index - OOILearn about outside options from similar workersExample: Men have 25% more relevant options
2 How do differences in options translate into wages?Identify OOI to wage elasticity using two IV strategies10% more relevant options → ≈1.7-2.7% higher wages
3 How do differences in options affect wage inequality?Combine OOI distribution with elasticity25% of gender gapMain mechanism: willingness to commute/move
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Theoretical Framework
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Setup - Matching Model
Static matching model (Shapley & Shubik, 1971)
A worker with characteristics xi and a job with zj produce
τ (xi , zj) + εij︸ ︷︷ ︸total value
= y (xi , zj)︸ ︷︷ ︸output
+ a (xi , zj)︸ ︷︷ ︸amenities
+ εij︸︷︷︸idiosyncratic
Solution details
E [ω|xi ]︸ ︷︷ ︸E[compensation]
= E [τ (xi , z∗) |xi ]︸ ︷︷ ︸
Mean Value
− E [π (z∗) |xi ]︸ ︷︷ ︸Employer Profits
+ E [εi ,j |xi ]︸ ︷︷ ︸αOOI
Workers with more similar options → higher E [εi ,j |xi ]
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Setup - Matching Model
Static matching model (Shapley & Shubik, 1971)A worker with characteristics xi and a job with zj produce
τ (xi , zj) + εij︸ ︷︷ ︸total value
= y (xi , zj)︸ ︷︷ ︸output
+ a (xi , zj)︸ ︷︷ ︸amenities
+ εij︸︷︷︸idiosyncratic
Solution details
E [ω|xi ]︸ ︷︷ ︸E[compensation]
= E [τ (xi , z∗) |xi ]︸ ︷︷ ︸
Mean Value
− E [π (z∗) |xi ]︸ ︷︷ ︸Employer Profits
+ E [εi ,j |xi ]︸ ︷︷ ︸αOOI
Workers with more similar options → higher E [εi ,j |xi ]
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Setup - Matching Model
Static matching model (Shapley & Shubik, 1971)A worker with characteristics xi and a job with zj produce
τ (xi , zj) + εij︸ ︷︷ ︸total value
= y (xi , zj)︸ ︷︷ ︸output
+ a (xi , zj)︸ ︷︷ ︸amenities
+ εij︸︷︷︸idiosyncratic
Solution details
E [ω|xi ]︸ ︷︷ ︸E[compensation]
= E [τ (xi , z∗) |xi ]︸ ︷︷ ︸
Mean Value
− E [π (z∗) |xi ]︸ ︷︷ ︸Employer Profits
+ E [εi ,j |xi ]︸ ︷︷ ︸αOOI
Workers with more similar options → higher E [εi ,j |xi ]
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Outside Options Index (OOI)
Make distributional assumptions on ε to get an analyticalexpression for E [εi ,j |xi ]Details
Define f ij the probability density that i works in job j .
OOIi = −∫jf ij log f
ij
Simple case - equally likely to work at measure s of jobsOOIi = log s
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OOI Intuition - 1
OOIi = −∫jf ij log f
ij
OOI is an index of concentrationEstimated using cross-sectional distribution of similar workersOn all observable dimensions
OOI is determined by:1 Worker flexibility: Ability to commute/work in more
occupation etc.2 Job supply in relevant districts/occupations etc.
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OOI Intuition - 2
OOIi = −∫jf ij log f
ij
Key Features:Only relevant options (i.e. those taken in equilibrium) matterCommon index for unpredictability
OOI Effect:Through the model: Sufficient Statistic Theorems
Improve match qualityImprove compensation through better outside options
Alternative models:Easier search: Higher reservation wage (Black, 1995)
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Features and Limitations
The model allows us to understand what is, and what isn’tcaptured in OOI
Main Features:Workers allowed to work in all occupation/industryCommuting costs vary across workers (not one local labormarket)Employers within industry are not identical
Main LimitationsNo dynamics: switching costs, specific human-capital, learningNo firms
Data tradeoff
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Estimation
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German Linked Employer-Employee Data
Main Data: “LIAB Longitudinal” - German linkedemployer-employee data
~1% of employed populationSampling Restriction: all workers employed on 06/30/2014
Supplementary Data: BIBB (German O*Net)
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Data
Main features:Representative sample of establishments22 years of work histories of workers in these establishmentsCombination of administrative data with detailed survey onemployer policiesFlexible wage setting
Main Limitations:Does not include self-employed and some civil servants(“Beatre”) (18%)Does not include non-participantsDoes not include jobs outside of GermanyCannot see all potential employers
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Estimation
Estimation: Estimate match probabilities f ij Details Results
Xi : demographics, training occupationsZ j : Basic job characteristics, occ & industry, establishmentcharacteristicsDistance: miles between worker’s previous residence toestablishmentDo not include wages
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Out-of-Sample Performance
−4 −2 0 2 4 6 8
0.00
0.10
0.20
Est. log Probability Density of Job
Den
sity
In Sample (Before Move)Out−of−Sample (After Move)
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Who Has Better Options?
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OOI by Demographics
District Density (log)
Lower-Secondary
Intermediate-Secondary
Non-Citizen
Female
-0.6 -0.4 -0.2 0.0
OOI
-0.5σ 0
Coefficients from a single regression of OOI on all variables + quadratic age variable
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OOI is Higher in Big Cities
●
●
●
●
●
●
●
●●
●
●
●
●
●
Berlin
Hamburg
Munich
Cologne
Frankfurt
Stuttgart
Leipzig
Bremen
Hannover
Nurnberg
DüsseldorfDortmund
Essen
Dresden
1st Quartile (Lowest OOI)
2nd Quartile
3rd Quartile
4th Quartile (Highest OOI)
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Training Occupations
-4 -3 -2 -1 0 1
-0.4
0.0
0.4
0.8
OOI (residualized)
log
wa
ge
(re
sid
.)
β = -.022
Residualized OOI and logwage are taken from a regression on gender, age, education,citizenship status and district.
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Training Occupations
-4 -3 -2 -1 0 1
-0.4
0.0
0.4
0.8
OOI (residualized)
log
wa
ge
(re
sid
.)
β = -.022
Dentist
MD
VetTunnel Inspector
Train Conductor Biologist
Pilot
Musician
DancerActor
Meter Reader
Car Sales
Legislator
Residualized OOI and logwage are taken from a regression on gender, age, education,citizenship status and district, weighted by occupation employment share
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Mass-LayoffsConstruct sample following Jacobson et al. (1993) Details
Compare workers above/below establishment median OOIOutcome variable: Daily wage divided by baseline wt
w0−
0.10
0.00
0.10
Month After Separation
Rel
ativ
e W
age
Diff
eren
ce
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●
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0 12 24 36
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●
Relative wage = current (daily) wage divided by wage before layoff (t = 0)16
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Additional Results
Full distribution Details
CDF by Gender Details
Different functional form (HHI) yields similar results (ρ = .62)Details
OOI gaps similar with different controls Details
Results for age Details
Mass-LayoffRelative Income trend Details
Effect on Search Details
Controls Details
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How Do Differences in OptionsTranslate into Wages?
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Intercity Express
High-speed (200 mph) commuter rail introduced in two waves(Heuermann & Schmieder, 2018)
1 Pre 1999: all big cities are connected2 Post 1999: only cities en route + further periphery
Only used for commute
Match workers from second wave, to similar workers who neverreceived
Details Map
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Main ICE Results
2SLS First-Stage Reduced-Form OLS(1) (2) (3) (4)
.272*** .084*** .024*** .002(.034) (.009) (.004) (.008)
Number of observations: 143,313Number of treated observations: 37,695
Table: Effect of OOI on logw using ICE IV
Standard errors are calculated with Abadie-Imbens (2006) extended for2SLS
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Shift-Share Procedure
Idea: (BGS, 2012) Compare workers within the same industry withoutside options in different industries
Variation from local industry compositionLocal industry trends instrumented with national trends
Specification: Look at change in wages 2004-2014 withinindustries
∆1404 logwijr = α∆14
04OOIijr + β∆1404Xijr + Ind04
j + εijr∆14
04OOIijr = γBr + δ∆1404Xijr + Ind04
j + εijr
i - worker, j - industry, r - region (“Regierungsbezirk”)Instrument Construction
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Train and Shift-Share
OOI Coefficient α
Shift-Share
Train
0.0 0.1 0.2 0.3
Details
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Additional Results
Effect is similar by gender and education Details
Decomposing the effect on stayers and movers:Stayers get about 50% the overall effect Details
Local demand shock? Similar effect on exporters Details
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How Do Differences in OptionsAffect Wage Inequality?
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Effect on Wage Gaps
Male CitizenHigh
SecondaryYear of
Exp.(18)log District
Density
Total
Log
Wag
e P
rem
ium
0.00
0.10
0.20
Male CitizenHigh
SecondaryYear of
Exp.(18)log District
Density
Total
0.00
0.10
0.20
Coefficients from a single regression of logwage on these demographics,quadratic in age, and part-time indicator. 23
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Effect on Wage Gaps
Male CitizenHigh
SecondaryYear of
Exp.(18)log District
Density
RemainingExplained
Log
Wag
e P
rem
ium
0.00
0.10
0.20
Male CitizenHigh
SecondaryYear of
Exp.(18)log District
Density
RemainingExplained
0.00
0.10
0.20
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Mechanisms
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Gap From Willingness to Commute/Move
Male CitizenHigh
SecondaryYear of
Exp.(18)log District
Density
Total GapTotal Gap from OOIGap from Commute
Log
Wag
e P
rem
ium
0.00
0.10
0.20
Male CitizenHigh
SecondaryYear of
Exp.(18)log District
Density
Total GapTotal Gap from OOIGap from Commute
Log
Wag
e P
rem
ium
0.00
0.10
0.20
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Segregation
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0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.5
1.0
1.5
2.0
Job Female Likely − Percentile
Sha
re o
f Tot
al W
orke
rs F
rom
Gen
der
(%)
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● ●Men Women
Jobs are ranked by the estimated odds ratio of hiring a woman vs amen 26
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Children
Lower−Secondary Intermediate−Sec. High−Secondary
With Young ChildFemale
OO
I Gap
Fro
m S
imila
r M
en
0.0
1.0
2.0
3.0
Lower−Secondary Intermediate−Sec. High−Secondary
With Young ChildFemale
OO
I Gap
Fro
m S
imila
r M
en
0.0
1.0
2.0
3.0
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Summary
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Results Summary
1 Which workers have better outside options?Males, German citizens, urban residentsLow skill occupations
2 How do differences in options translate into wages?10% more options yields 2.5% higher income
3 How do differences in outside options affect wageinequality?
Differences in options tend to increase inequality25% of gender gap21% of return to higher educationDecrease gaps between occupations
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Discussion
Policy implication:Many policies could have a direct effect on the OOIEx: Transportation, working hours regulation, mandatedbenefitsPotential tool to close wage gapsEx: Mandatory maternal leave could increase women options
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Thank You
30
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Theory Extended
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Payoff
Payoffs are
ω (x , z)︸ ︷︷ ︸compensation
= a (x , z)︸ ︷︷ ︸amenities
+ w (x , z)︸ ︷︷ ︸wages
π (x , z)︸ ︷︷ ︸profit
= y (x , z)︸ ︷︷ ︸output
− w (x , z)︸ ︷︷ ︸wages
and their sum is total value produced
τ (x , z) = ω (x , z) + π (x , z) = a (x , z) + y (x , z)
Q: How is τ (x , z) divided to ω (x , z) , π (x , z)?
31
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Payoff
Payoffs are
ω (x , z)︸ ︷︷ ︸compensation
= a (x , z)︸ ︷︷ ︸amenities
+ w (x , z)︸ ︷︷ ︸wages
π (x , z)︸ ︷︷ ︸profit
= y (x , z)︸ ︷︷ ︸output
− w (x , z)︸ ︷︷ ︸wages
and their sum is total value produced
τ (x , z) = ω (x , z) + π (x , z) = a (x , z) + y (x , z)
Q: How is τ (x , z) divided to ω (x , z) , π (x , z)?
31
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Equilibrium
Solve as a cooperative game (Shapley Shubik 1971).Static frameworkPerfect information
Equilibrium if everyone gets more than outside option
maxz ′
ω(x , z ′
)≤ ω (x , z) ≤ τ (x , z)−max
x ′π(x ′, z
)Details
Example - if Nash split:
ω (x , z) =12maxz ′
ω(x , z ′
)+
12
(τ (x , z)−max
x ′π(x ′, z
))
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Equilibrium
Solve as a cooperative game (Shapley Shubik 1971).Static frameworkPerfect information
Equilibrium if everyone gets more than outside option
maxz ′
ω(x , z ′
)≤ ω (x , z) ≤ τ (x , z)−max
x ′π(x ′, z
)Details
Example - if Nash split:
ω (x , z) =12maxz ′
ω(x , z ′
)+
12
(τ (x , z)−max
x ′π(x ′, z
))
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Equilibrium
Solve as a cooperative game (Shapley Shubik 1971).Static frameworkPerfect information
Equilibrium if everyone gets more than outside option
maxz ′
ω(x , z ′
)≤ ω (x , z) ≤ τ (x , z)−max
x ′π(x ′, z
)Details
Example - if Nash split:
ω (x , z) =12maxz ′
ω(x , z ′
)+
12
(τ (x , z)−max
x ′π(x ′, z
))
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Empirical Assumptions
maxz ′ ω (x , z ′) depends on several factors:Options availability, but also productivity etc.To separate these effect and take to data, we will makesimplifying assumptions
Assumptions: (Choo & Siow, 2006, Dupuy & Galichon, 2014)1 Continuum of workers and jobs2 Total value produced is
τij = τ (xi , zj) + εi ,zj + εj ,xi
ε ∼ continuous logits with scale α Details
εi,zj ⊥ εj,xi
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Empirical Assumptions
maxz ′ ω (x , z ′) depends on several factors:Options availability, but also productivity etc.To separate these effect and take to data, we will makesimplifying assumptions
Assumptions: (Choo & Siow, 2006, Dupuy & Galichon, 2014)1 Continuum of workers and jobs2 Total value produced is
τij = τ (xi , zj) + εi ,zj + εj ,xi
ε ∼ continuous logits with scale α Details
εi,zj ⊥ εj,xi
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Quasi-Nash Solution
Corollary:1 Workers (employers) get “their” εi ,zj (εj ,xi )
2 τ (x , z) splits into ω (x , z) + π (x , z) by
ω (x , z) =12E [ω|x ] +
12
(τ (x , z)− E [π|z ])
Workers get exactly half of expected surplusProof
Return
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Quasi-Nash Solution
Corollary:1 Workers (employers) get “their” εi ,zj (εj ,xi )2 τ (x , z) splits into ω (x , z) + π (x , z) by
ω (x , z) =12E [ω|x ] +
12
(τ (x , z)− E [π|z ])
Workers get exactly half of expected surplusProof
Return
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Solution: EquilibriumStable equilibrium (core allocation) includes:
1 Allocation of workers and jobs m : I → J2 Transfer wij that sets ωij and πij
Which satisfies the following conditions:1 No profitable deviations ∀i ∈ I,∀j ∈ J :
ωi ,m(i)︸ ︷︷ ︸i Equilibriumcompensation
+ πm−1(j),j︸ ︷︷ ︸j Equilibrium
profit
≥ τij︸︷︷︸i , j potential
value produced
2 Participation constraint
∀i ∈ I : ωi ,m(i) ≥ ui∀j ∈ J : πm−1(j),j ≥ vj
where ui , vj value of unemployment or vacancy Return
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Solution: EquilibriumStable equilibrium (core allocation) includes:
1 Allocation of workers and jobs m : I → J2 Transfer wij that sets ωij and πij
Which satisfies the following conditions:1 No profitable deviations ∀i ∈ I,∀j ∈ J :
ωi ,m(i)︸ ︷︷ ︸i Equilibriumcompensation
+ πm−1(j),j︸ ︷︷ ︸j Equilibrium
profit
≥ τij︸︷︷︸i , j potential
value produced
2 Participation constraint
∀i ∈ I : ωi ,m(i) ≥ ui∀j ∈ J : πm−1(j),j ≥ vj
where ui , vj value of unemployment or vacancy Return
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Continuous Logit Assumptions
Assumeτij = τ (xi , zj) + εi ,zj + εj ,xi
s.t. εi ,zj ⊥ εj ,xiεi ,zj , εj ,xi ∼ CL (α)
Continuous logit draws ε from a Poisson Process on Z×R withintensity
f (z) dz × e−εdε
so the maximum value on each Borel measurable subset is EV1with scale αReturn
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Continuous Logit ChoiceQzj |xi is the measure of xi times their share that chooses zj .
Qzj |xi = f (xi ) f (zj |xi )
In continuous logit the share to choose zj is
expω (xi , zj) f (zj)∫z ′ expω (xi , z ′) f (z ′) dz ′
=expω (xi , zj) f (zj)
expE [ωi |xi ]
Market clears when
Qzj |xi =expω (xi , zj) f (zj) f (xi )
expE [ωi |xi ]=
expπ (xi , zj) f (zj) f (xi )
expE [πj |zj ]= Qxi |zj
ω (xi , zj)− π (xi , zj) = E [ωi |xi ]− E [πj |zj ]By definition
ω (xi , zj) + π (xi , zj) = τ (xi , zj)
And the sum gives the Quasi-Nash solutionReturn
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Sufficient Statistic
An increase in options gives a worker access to more similar jobs.Formally:
DefinitionWe define λx to be the measure of a random set of jobs that areaccessible to workers with observables x . All jobs that are notaccessible have τij = −∞ and are therefore never chosen inequilibrium. We model an increase in access to more jobs would bean increase to this λx .
Results would also hold also if λx is the slope of linear commutingcosts
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Sufficient Statistic Theorems
TheoremLet j be i ′s equilibrium match. Access to outside options λxi hasthe following effect on the maximum offer j is willing to make inthe new equilibrium:
dωi ,j
dλxi= α
dOOI
dλxi
TheoremAccess to options λxi has the following overall effect on expectedworker compensation in equilibrium
dE [ωi ,j ]
dλxi= 2α
dOOI
dλxi
Return
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More Results
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Summary Statistics - Workers (X )
Mean SD
Female .46 (.50)
Age 45.05 (12.49)
German Citizen 0.97 (.17)
Education: Higher Secondary .29 (.45)
Education: Intermediate Secondary .31 (.46)
Education: Lower Secondary .22 (.41)
Education: Intermediate/Lower .19 (.39)
N 450,929
Table: Summary Stats - Workers
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Distribution
−7 −6 −5 −4 −3
0.0
0.1
0.2
0.3
0.4
OOI
Den
sity
Mean = −4.85SD = .93
Figure: Outside Option Index Distribution
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CDF by GenderMale’s OOI distribution first-order stochastically dominates female’sdistribution
−7 −6 −5 −4 −3
0.0
0.2
0.4
0.6
0.8
1.0
OOI
Female (µ = −4.95) Male (µ = −4.77)
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Summary Statistics - Jobs (Z )
Name Mean SD
Part - Time .31 (.46)
Fixed Contract .11 (.31)
Temporary Agency .02 (.12)
Establishment Size 1,552.8 (7679)
Sales per worker (Euro) 163,286.7 (185,651)
%Female in Management .26 (.31)
Complexity 2.22 (.84)
N 450,929
N Establishments 8,792
Table: Summary Stats - Jobs
Return 43
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Mass Layoffs - Job SearchReturn
0.00
0.01
0.02
0.03
Month After Separation
Sha
re E
mpl
oyed
Diff
eren
ce
●
●
● ●
●
●
●● ●
●●
●
●
●
●
●
●● ● ●
● ●
●
●●
●
●●
●●
●
●
● ●●
● ●
0 12 24 36
●
●
● ●
●
●
●● ●
●●
●
●
●
●
●
●● ● ●
● ●
●
●●
●
●●
●●
●
●
● ●●
● ●
44
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Trend in Relative Income
●
●
●
●
●
●●
●●
●●
●●
●●
●● ●
● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●
0.2
0.4
0.6
0.8
1.0
Month After Separation
Rel
ativ
e In
com
e
0 12 24 36
●
●
●
●
●
●●
●●
●●
●●
●●
●● ●
● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●
Income divided by income before layoff (t = 0) Return
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Mass Layoff - Table
Table: Relative Income by OOI After Mass Layoff
OOIi coefficient (1) (2) (3)
3 months (λ3) .061** .062** .068**
(.029) (.029) (.031)
6 months (λ6) .068** .069** .082**
(.030) (.030) (.033)
12 months (λ12) .061* .064* .079**
(.034) (.034) (.038)
24 months (λ24) .033 .039 .064
(.042) (.042) (.048)
Mass-Layoff×Month FE Y Y Y
Tenure Y Y
Age, Education, Gender Y
No. of Observations 558,686 558,686 558,686
Return46
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Heuermann Schmieder
R2
N
p < 0 p < 0
R2
N
p < 0 p < 0
Table: Direct ICE Impact on Number of Commuters (HS,2014)
Return
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A - Largest Values
Variable (X ) Variable (Z ) Axz
- Distance -4.15
Train Occ - Physical Cond. 1 Occ - Physical Cond. 1 1.477
Train Occ - Task Type 2 Occ - Task Type 2 1.077...
......
Table: Top Standardized Values of A
Return
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A - Heterogeneity in Distance
Variable (X ) Variable (Z ) Axz
- Distance -.049
Female Distance -.009
German Distance -.005
Lower Secondary Distance -.021
Intermediate Distance -.015
Age Distance .002
Age2 Distance -.000Baseline category - Male with higher secondary education
Table: A Values for Distance (Miles)
Return
49
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Correlation with Demographics
(1) (2) (3) (4)Female -.237*** -.231*** -.227*** -.167***
(0.009) (0.011) (0.008) (0.008)School -.660*** -.620*** -.596*** -.617***Lower-Secondary (0.013) (0.013) (0.009) (0.010)School -.279*** -.277*** -.211*** -.216***Intermediate (0.011) (0.011) (0.007) (0.008)Non-Citizen -.307*** -.295*** -.459*** -.501***
(0.031) (0.027) (0.021) (0.019)Age .099*** .107*** .107*** .099***
(0.004) (0.003) (0.002) (0.002)Age^2 -.001*** -.001*** -.001*** -.001***
(4e-05) (4e-05) (3e-05) (2e-05)District .112*** .104***Density (0.004) (0.005)Training Occ FE X XDistrict FE XEstablishment FE XR^2 0.13 0.29 0.66 0.56N 380,109 380,109 380,109 380,109
Dep Var: Outside Option Index
Table: OOI by Demographics Return
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PCA Over BIBB (X and Z )
Name N Comp 1 Comp 2
Hours 11,021 Sundays and public holidays hours per week like to work
Type of Task 15,035 responsibility for other people Cleaning, waste, recycling
Requirements 10,904 Acute pressure & deadlines Highly specific Regulations
Physical 20,036 Oil, dirt, grease, grime pathogens, bacteria
Mental 17,790 Support from colleagues Often missing information
Table: Most Weighted Question in PCA - BIBB
Used to project training occupations (X ) and currentoccupations and industries (Z ) into characteristic space Return
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Survey PCA (Z )
Name N Comp 1 Comp 2
Business Performance 8,792 chamber of Industry profit category
Investment & Innovation 8,792 IT investment total investment
Hours 8,792 long leaves policy flextime
In-Company Training 8,792 internal courses share in training
Vocational Training 8,792 offer apprenticeship ability to fill
General 8,792 family managed staff representation
Table: Most Weighted Question in PCA - Establishment 2014 Survey
Return
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Intercity Express
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Berlin
Hamburg
Munich
Cologne
Frankfurt
Stuttgart
Leipzig
Bremen
Hannover
Nurnberg
D..sseldorfDortmund
Essen
Dresden First Wave
Second Wave
ICE Stations Before/After 1999 Return
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OOI by Age
20 30 40 50 60
−5.
6−
5.2
−4.
8
Age
Mea
n O
OI
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Return
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Functional Form
We estimatedOOIi = −
∫jf ij log f
ij
Try instead
OOIi = −∫ (
f ij)2
We get thatρOOI ,OOI
= .62
Distribution by demographics remains similar
Return
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Functional Form
Treatment Control
Mean SD Mean Sd
logwage (1993) 3.26 2.49 3.27 2.49
logwage (1999) 4.25 .62 4.27 .58
Female .356 .479 .356 .479
Age 36.4 6.8 36.4 6.7
Citizen .995 .073 .995 .073
Low-Secondary .257 .437 .257 .437
Intermediate-Secondary .508 .500 .508 .500
High-Secondary .235 .424 .235 .424
N 37,695 26,963
Return
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Estimation - Details
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Estimation Assumptions
Lemma:
f ij =f (Xi ,Zj)
f (Xi ) f (Zj)· const
Proof
Assumption: Parameterization (Dupuy & Galichon, 2014)
log f ij = logf (Xi ,Zj)
f (Xi ) f (Zj)= XiAZj + a (Xi ) + b (Zj)
where a (Xi ) , b (Zj) are fixing the marginal distribution
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Estimation Assumptions
Lemma:
f ij =f (Xi ,Zj)
f (Xi ) f (Zj)· const
Proof
Assumption: Parameterization (Dupuy & Galichon, 2014)
log f ij = logf (Xi ,Zj)
f (Xi ) f (Zj)= XiAZj + a (Xi ) + b (Zj)
where a (Xi ) , b (Zj) are fixing the marginal distribution
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Estimation - Method
Estimation of OOI in 6 steps:
1 Simulate N observations from f (Xi ) f (Zj) (~.5 Million)2 Append N simulated to N real observations (Total = 2N )3 Define a new variable
Y =
{1 Real Match
0 Simulated Match
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Estimation - Method
4 Estimate a Logit model
logP (Y = 1|X = x ,Z = z)
P (Y = 0|X = x ,Z = z)= xAz + a (x) + b (z)
This gives f ij for every (xi , zj) pair since
P (Y = 1|X = xi ,Z = zj)
P (Y = 0|X = xi ,Z = zj)=
f (xi , zj |Y = 1)
f (xi , zj |Y = 0)
P (Y = 0)
P (Y = 1)=
f (xi , zj)
f (xi ) f (zi )= f ij · c
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Estimation - Method
5 Calculate f ij for every possible worker-job combination6 Plug in the OOI formula
OOIi =∑j
f ij log fij
Return
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Estimation Details
Simulate data from independent distribution P (x)P (z)
Define y = real for data from P (x , z) and y = simulated fordata from P (x)P (z)
Predict whether yi is real or simulated given xi , zi using logit
logP (y = real |x , z)
P (y = simulated |x , z)= log
P (x , z)
P (x)P (z)= xAz+a (x)+b (z)
with a (x) = a (x)− logP (x)
We linearly approximate a and b Return
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Proof
f ij = f (j |i) = f (j |X = xi ) =
= f (j |Z = zj ,X = xi ) f (Z = zj |X = xi ) =
= f (j |Z = zj)f (X = xi ,Z = zj)
f (X = xi )=
=|J|−1
f (Z = zj)
f (X = xi ,Z = zj)
f (X = xi )
Return
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IV
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ICE - Empirical Strategy1 We estimate OOI, adding direct ICE line (interacted with X ’s)
in our probability estimation
2 We define treatment/control based on residence in 1999:Treated: district got a station between 1999 and 2012Control: district never got a station
3 Match workers on state, training occupation, school level, age,gender, lagged income
∆20121999 logwim = α∆2012
1999OOIi + µm + εim
∆20121999OOIim = δTreati + λm + νim
Identification assumption: E [εimTreati |m] = 0
Map Balance Table
Return
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ICE - Empirical Strategy1 We estimate OOI, adding direct ICE line (interacted with X ’s)
in our probability estimation
2 We define treatment/control based on residence in 1999:Treated: district got a station between 1999 and 2012Control: district never got a station
3 Match workers on state, training occupation, school level, age,gender, lagged income
∆20121999 logwim = α∆2012
1999OOIi + µm + εim
∆20121999OOIim = δTreati + λm + νim
Identification assumption: E [εimTreati |m] = 0
Map Balance Table
Return
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ICE - Empirical Strategy1 We estimate OOI, adding direct ICE line (interacted with X ’s)
in our probability estimation
2 We define treatment/control based on residence in 1999:Treated: district got a station between 1999 and 2012Control: district never got a station
3 Match workers on state, training occupation, school level, age,gender, lagged income
∆20121999 logwim = α∆2012
1999OOIi + µm + εim
∆20121999OOIim = δTreati + λm + νim
Identification assumption: E [εimTreati |m] = 0
Map Balance Table
Return
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Main ICE Results
2SLS First-Stage Reduced-Form OLS(1) (2) (3) (4)
.324*** .073*** .024*** .004(.048) (.003) (.004) (.007)
Number of observations: 143,313Number of treated observations: 37,695
Table: Effect of OOI on logw using ICE IV
Standard errors are calculated with Abadie-Imbens (2006) extended for2SLS
Return
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Shift-Share Procedure
Idea: (BGS, 2012) Compare workers within the same industry withoutside options in different industries
Variation from local industry compositionLocal industry trends instrumented with national trends
Specification: Look at change in wages 2004-2014 withinindustries
∆1404 logwijr = α∆14
04OOIijr + β∆1404Xijr + Ind04
j + εijr∆14
04OOIijr = γBr + δ∆1404Xijr + Ind04
j + εijr
i - worker, j - industry, r - region (“Regierungsbezirk”)Instrument Construction
E[εijrBr |Ind04
j ,∆1404Xijr
]= 0
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Shift-Share Results
2SLS First-Stage Reduced-Form(1) (2) (3)
.170*** .622*** .106***(.064) (.241) (.056)
N 408,792 408,792 408,792
Number of Clusters 38 38 38
Table: Effect of OOI on logw using Shift-Share IV
Each regression controls for industry fixed effects. Standard errors areclustered by region.
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Instrument Construction
The instrument is a weighted average with initial industry shares
Br =∑j
s04jr︸︷︷︸initial shares
× gj︸︷︷︸national trends
Calculate gj by regressing changes in employment on industry ®ion dummies:
∆1404 log Ejr = gj︸︷︷︸
industry
+ gr︸︷︷︸region
+ εjr
Return
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Shift-Share Results: Heterogeneity
OOI Coefficient α
All
Lower Secondary
Intermediate Secondary
Higher Secondary
Male
Female
0.0 0.1 0.2 0.3 0.4 0.5
Return
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Shift-Share Results: Stayers
(1)
OOI .170*** .257***(.064) (.092)
OOI× -.159***Stay (.062)
N 408,792 408,792
Industry FE X X
Table: Effect of OOI on logw by stayer/mover
Stayers are workers who haven’t switched establishment between2004-2014. Standard errors are clustered by region.
Return
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Shift-Share Results: Exporters
Export>33% 33%≥Export≥1% 1%>Export(1) (2) (3)
OOI .105** .593** .132(.052) (.266) (.141)
N 119,645 146,217 142,930
Industry FE X X X
Table: Effect of OOI by Export Status
Export is share of sales from export. Calculated at the industry level,based on survey information on establishments in 2014. SE are clusteredat the region level
Return
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Other
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402 German Districts
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Berlin
Hamburg
Munich
Cologne
Frankfurt
Stuttgart
Leipzig
Bremen
Hannover
Nurnberg
DüsseldorfDortmund
Essen
Dresden
60% of workers work outside their district Return 71
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Mass Layoff Sample
Mass layoff sample definition follows Jacobson, Lalonde &Sullivan (1993)Workers who
Separated between 1993-2014At establishments with at least 50 workersAt establishments whose workforce declined 30% over the yearWith at least 3 years of tenure pre mass-layoffBelow age 55N = 13, 681
Return
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Reasons for Differences in Outside Options
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Commuting Costs
We find significant differences in commuting/moving costs acrossworkers.Want to test its impact on overall OOI differences.
Counterfactual exercise:For every worker, estimate their wage gain, if they had the minimalcommuting cost
Commute cost of 40 year old, high-educated, male citizenAll other workers/employers don’t changeCompare the differential wage gain between workers
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Return
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