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1
25
R2
Figure 1.1: Added-variable plot
Figure 1.2: Rank-ordered logit on gender of candidates by year
Figure 1.3: Low-skilled workers Figure 1.4: High-skilled workers
Figure 1.5: Low-skilled workers Figure 1.6: High-skilled workers
2
.1.2
.3.4
.5R
atio
of f
orei
gner
s in
squ
ads
1981/82 2008/091994/95Season
England Other European Countries
Notes: Other European Countries are Denmark, France, Germany, Greece, Italy, Netherlands, andSpain. The vertical axis indicates the pre-Bosman ruling year.
Figure 2.2: Club'sWage Bills
Elasticity = .17 (.002)
R-squared = .91
1315
1719
Clu
b's
wag
e bi
ll (in
logs
)
1981 1984 1987 1990 1993 1996 1999 2002 2005 2008Notes: Each dot stands for one club. The regression line is depicted. Elasticity coefficient from theOLS regression of the log-club's wage bill on year dummies is reported with standard error inparenthesis.
English First League (1981-2008)
Figure 2.3: AverageWage Bill and Team Success
AAAAAAAAAAAAAAAAAAAAAAAAAAAA
A A A A A A A A A A A A A A A A A A A A A A A A A A A
By
BghBghBghBghBghBghBghBghBgh
BuBuBuBuBuBuBuBuBuBuBuBuBuBuBu
BBBBBBBBBB
BddBdd
Bgh & Hv ABgh & Hv Ahhhhhhhhhhhh
hhhhhhhhhhhhhhhhh
vyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyvyy y y y y y y uyy uyy uyy uyy uyy uyy uyy uyy uyy uyy uy
vvvvvvvvvvvvvvvvvvvvvvvvvvvv
uhuhuhuhuhuhuhuh
Hu
whwhwhwhwhwhwhwhwhwh
d dd dd dd dd dd dd dd dd dd dd dd dd dd dd d
vvvvvvvvvvvvvvvvvvvvvvvvvvvv
uuuuuuuuuuh yh yh yh yh yh yh yh yh yh yh yh yh yh yh yh yh y
ddughddughddughddughddughddughddughddughddughddughddughddughddughddughddughddughw w
wwwwwwwwwwwwwwwwwwwww
wh ywh ywh ywh ywh ywh ywh ywh ywh ywh ywh ywh ywh y
gh gh gh gh gh gh gh gh gh gh gh gh gh gh gh gh
uy uy uy uy
dh Ah dh Ah dh Ah d dd dd d
uhuhuhuhuhuhuh
dgdghd dhd dhd dhd dhd d
hd ddyhd ddyhd ddyhd ddyhd ddyhd ddyhd ddyhd ddyhd ddyhd ddyhd ddyhd ddyhd ddyhd ddyhd ddy
uhuhuhuhuhuhuhuhuhuhuhuhuhuhuhuhuhuhuhuhuhuh
udduddudduddudduddudduddudduddudd
w yw y
wd
hhhhhhhhhhhhhhhhhhhhhhhhhhh
dddddddd
Bwh Bwh Bwh Bwh Bwh Bwh Bwh Bwh Bwh
H H H H H H H H H H H H H H H H H H H H H H
gggg
d d d d d d d d d d d d d d
vv
-ud .85
h d
0.2
.4.6
.81
Av
g
g
-1.3 -.7 -.1 .5Avg v wg ( g)
Notes: Avg g h vg u' 1981 2008. u i ud h y t [(g - )/ ( - )], whg d h w d hgh g t.Avg v wg h vg u' v wg 1981 2008. hv wg u i y t h g d h u' wg .v h u vg.
gh gu (1981-2008)
Table 2.1: Individual Differences inMeans: Black vsWhite English Players
c a
b a
a a a
a a a
a b c
Table 2.2: ClubDifferences inMeans
a
a
a
a
it = αi + β1( it − t)
+ β2( it − t)
+ β3( it − t) + ϵit,
it
(Rankingit−mint
maxt−mint
)Rankingit
i t
αi i
ϵit
( it − t)
( it − t)
i t
it − t
i t
β3
β3
ijt = ξij + β1 ( it/ jt)
+ β2( it − jt)
+ β3( it − jt) + eijt,
ijt i j
t(
it/ jt
)
it− jt
ξij eijt
αi
Table 2.3: DiscriminationMarket-test: League Success and Long Span
a a a b
a a a a
a b
R2
(Rankingit−mint
maxt−mint
)Rankingit
i ta b
ij i j ji
∆ β3
β1
[(∆ ∗ β3/β1) + ]−
Table 2.4: DiscriminationMarket-test: Match Success and Short Span
c c b a
a a a b
a b
a b c
Table 2.5: Post-Bosman: Are non-EU Black Players Discriminated? (1996-2008)
a a b
a a a a
a a c c
(Rankingit−mint
maxt−mint
)Rankingit
i ta b c
= B
B
− A
A
.
Figure 2.4: Relative Turnover of Black English Players
−.05
0.0
5.1
Turn
over
of b
lack
w.r.
t. w
hite
pla
yers
1980 1985 1990 1995 2000 2005 2010Year
Table 2.6: DiscriminationMarket-test: League Success and Long Span - IV and GMM
c a a
a a c
b b
(Rankingit−mint
maxt−mint
)Rankingit
i ta b c
Table 2.7: Share of Black Players and Corporate Control
a
a b c
Table 2.8: DiscriminationMarket-test: Effect of Corporate Control
a a b b
a a a a
b c
a
(Rankingit−mint
maxt−mint
)Rankingit
i ta b c
Table 2.9: DiscriminationMarket-test: Heterogeneity of clubs
(Rankingit−mint
maxt−mint
)Rankingit
i ta b c
A
Figure A.1: AverageWage Bill and Team's Quality
ArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenalArsenal
Aston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston VillaAston Villa
Barnsley
BirminghamBirminghamBirminghamBirminghamBirminghamBirminghamBirminghamBirminghamBirmingham
BlackburnBlackburnBlackburnBlackburnBlackburnBlackburnBlackburnBlackburnBlackburnBlackburnBlackburnBlackburnBlackburnBlackburnBlackburn
BoltonBoltonBoltonBoltonBoltonBoltonBoltonBoltonBoltonBolton
BradfordBradford
Brighton & Hove AlbionBrighton & Hove Albion
CharltonCharltonCharltonCharltonCharltonCharltonCharltonCharltonCharltonCharltonCharltonCharlton
ChelseaChelseaChelseaChelseaChelseaChelseaChelseaChelseaChelseaChelseaChelseaChelseaChelseaChelseaChelseaChelseaChelsea
CoventryCoventryCoventryCoventryCoventryCoventryCoventryCoventryCoventryCoventryCoventryCoventryCoventryCoventryCoventryCoventryCoventryCoventryCoventryCrystal PalaceCrystal PalaceCrystal PalaceCrystal PalaceCrystal PalaceCrystal PalaceDerby CountyDerby CountyDerby CountyDerby CountyDerby CountyDerby CountyDerby CountyDerby CountyDerby CountyDerby CountyDerby County
EvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEvertonEverton
FulhamFulhamFulhamFulhamFulhamFulhamFulhamFulham
Hull
IpswichIpswichIpswichIpswichIpswichIpswichIpswichIpswichIpswichIpswich
Leeds UnitedLeeds UnitedLeeds UnitedLeeds UnitedLeeds UnitedLeeds UnitedLeeds UnitedLeeds UnitedLeeds UnitedLeeds UnitedLeeds UnitedLeeds UnitedLeeds UnitedLeeds UnitedLeeds United
LeicesterLeicesterLeicesterLeicesterLeicesterLeicesterLeicesterLeicesterLeicesterLeicesterLeicester
LiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpoolLiverpool
LutonLutonLutonLutonLutonLutonLutonLutonLutonLuton Manchester CityManchester CityManchester CityManchester CityManchester CityManchester CityManchester CityManchester CityManchester CityManchester CityManchester CityManchester CityManchester CityManchester CityManchester CityManchester CityManchester CityMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbroughMiddlesbrough
Millwall FCMillwall FC
NewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastleNewcastle
Norwich CityNorwich CityNorwich CityNorwich CityNorwich CityNorwich CityNorwich CityNorwich CityNorwich CityNorwich CityNorwich CityNorwich CityNorwich City
Nottingham ForestNottingham ForestNottingham ForestNottingham ForestNottingham ForestNottingham ForestNottingham ForestNottingham ForestNottingham ForestNottingham ForestNottingham ForestNottingham ForestNottingham ForestNottingham ForestNottingham ForestNottingham Forest
Notts CountyNotts CountyNotts CountyNotts CountyOldham Athletic FCOldham Athletic FCOldham Athletic FC
Oxford UnitedOxford UnitedOxford UnitedPortsmouthPortsmouthPortsmouthPortsmouthPortsmouthPortsmouthPortsmouth QPRQPRQPRQPRQPRQPRQPRQPRQPRQPRQPRQPRQPR
ReadingReadingSheffield UnitedSheffield UnitedSheffield UnitedSheffield UnitedSheffield United
Sheffield WednesdaySheffield WednesdaySheffield WednesdaySheffield WednesdaySheffield WednesdaySheffield WednesdaySheffield WednesdaySheffield WednesdaySheffield WednesdaySheffield WednesdaySheffield WednesdaySheffield WednesdaySheffield WednesdaySheffield WednesdaySheffield Wednesday
SouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonSouthamptonStokeStokeStokeStokeStokeSunderlandSunderlandSunderlandSunderlandSunderlandSunderlandSunderlandSunderlandSunderlandSunderlandSunderland
Swansea CitySwansea City
Swindon
TottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenhamTottenham
WatfordWatfordWatfordWatfordWatfordWatfordWatfordWatfordWest BromwichWest BromwichWest BromwichWest BromwichWest BromwichWest BromwichWest BromwichWest BromwichWest Bromwich
West HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest HamWest Ham
WiganWiganWiganWiganWimbledon FCWimbledon FCWimbledon FCWimbledon FCWimbledon FCWimbledon FCWimbledon FCWimbledon FCWimbledon FCWimbledon FCWimbledon FCWimbledon FCWimbledon FCWimbledon FC
WolvesWolves
R-squared = .81 Manchester United
-1.3
-1-.7
-.4-.1
.2.5
.8Av
erag
e re
lativ
e w
age
bill
(in lo
gs)
-20 -10 0 10 20Average relative quality level
Linear fit
Notes: Average relative wage bill is the average club's relative wage bill from 1981 to 2008.The relative wage bill of a club i in year t is the log difference of the club's wage bill relative to theannual average. Average relative quality level is the average club's quality from 1981 to 2008. Theclub's relative quality level is computed as the difference of the club's quality level relative to theannual average.
English First League (1981-2008)
R
L A
B B
λ
δ
r
ω t
ω = t
ω = kt
0 < k ≤ 1
(1−k)t
λγ γ
c(t)
JI
(1− k)t δ
λγ
rJI = (1− k)t− δJI − λγ(JI + c(t)),
⇔ JI =(1− k)t− λγc(t)
r + δ + λγ.
t JI JP
ω = t
rJP = t− ω − δ(JP ) ⇔ JP = 0.
JP > JI ⇔ 0 >(1− k)t− λγc(t))
r + δ + λγ,
⇔ c(t) >(1− k)t
λγ.
γ
c c
α ≥ 0
G(c) =(cc
)−α, c ∈ [c,∞],
α
c α
c
c(t) = ct γ
γ(t) =
(cλγ
1− k
)α
=
(cλ
1− k
) α1−α
.
γ λ
k λ
λ
k
λθ 0 ≤ θ ≤ 1
θ < 1 γ
θ
λ
λ
λ
λ
γ λ
γ
B B
B
R Ω C
c n
T
T ≡ (n− 1)t+ t.
n − 1 t
t φ n − 1
λ A B
n − 1
T
t
R = Ω + C = (n− 1)[φkt+ (1− φ)t] + t+ (n− 1)φλ(θµγB + (1− µ)γA)ct
= (n− 1)t[φ(k + cλ(θµγB + (1− µ)γA)) + 1− φ] + t,
φ ω < t µ
φ
c
c ≥ (1−k)λθγB
t φ = 0
(1−k)λγA
≤ c ≤ (1−k)λθγB
φ
µ
t = R− µt× (n− 1)(k + λθγBcB)− (1− µ)t× (n− 1).
λ
c ≤ (1−k)λγA
φ = 1
t = R− (n− 1)t[k − 1 + λc(θµγB + (1− µ)γA)]λ
λ
T
λ
µ
R = Ω + (n − 1)tµλθγBc
⎧⎪⎪⎨
⎪⎪⎩
T = Ω
T = Ω + µt(N − 1) ∗ (1− k)
λ
Ω λ
Ω µ
T
Figure A.2: Pre-Bosman Figure A.3: Post-Bosman
Table A.1: DiscriminationMarket-test: Club's League Success in France and Belgium
a a a a
a a
c
b c a a
a b c
Table A.2: DiscriminationMarket-test: Match Success and Long Span -Within andWithin-IV
a a a
a a a a
a a
a
Table A.3: First Stages: League Success and Long Span
c
a a
b
a a
a a
χ2 a a
χ2
a b c
Table A.4: First-Stages: Match Success and Long span
a
a a
a b
a a
a a
χ2 a a
χ2
a b
Table A.5: Structural Break: Long Span
a a b a
a b b b
a b b b
a a a c
(Rankingit−mint
maxt−mint
)Rankingit
i ta b
Table A.6: Structural Break: Short Span
b a
c
c c
b a
a a
a b
c
3
j Uij i
Uij = qij + µgigj
qij
µ gi
gj
µ
1st 2nd 2nd 3rd
i
M i′ M − 1
Uij qij
qij = xijβ + ηj + ϵij
xij ηj ϵij
q∗ij k
rij i k ϵij
Pr(ϵij < u) = e−e−u
jth
Pr(qr1jj > qr2jj > . . . > qrMjj)
lj(β) =M−1∏
i:q∗ij=1
(xijβ + µgigj)∑Mi′:ri′j≥rij (xi′jβ + µgi′gj)
ηj
J∑
j=1
M−1∑
i:q∗ij=1
xijβ + µgigj −J∑
j=1
M−1∑
i:q∗ij=1
(M∑
i′:ri′j≥rij
(xi′jβ + µgi′gj))
β µ µ
i i′
Uij ≥ Ui′j
qi ≥ qi′ − µgigj + µgi′gj
qi
xi ϵij ∼ N(0, σ)
i
i′ Y
Pr(Y = 1|x) = Pr(U(i) ≥ U(i′))
Pr(xiβ + µgigj + ϵij ≥ xi′β + µgi′gj + ϵi′j)
Pr(β(xi − xi′) + µ(gi − gi′)gj ≥ ϵi′j − ϵij)
= Φ(Xβ + µGgj)
X = xi − xi′ G = gi − gi′
gi = gi′ G = 0 µ
χ2
Table 3.1: Some descriptive statistics
Table 3.2: Recruitments and gender
Table 3.3: Descriptive statistics on hires and applicants
Table 3.5: Descriptive statistics on academic connections
Table 3.6: Effect of the reform on themean share of women jurors and number of female presidents
Table 3.7: Effect of the reform on themean h-index of jurors
Table 3.8: Correlation of the gender of jurors on the probability of women being first-ranked
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table 3.9: Correlation of the gender of jurors and the rank of candidates
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table 3.10: Correlation of the gender of jurors and the probability that a woman is better rankedwithin a dyad
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table 3.11: Correlation of the gender of jurors and the probability that a woman is better rankedwithin a dyad. Split by sub-
ject
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table 3.12: Regression of the gender composition of applicant pools on the gender composition of the recruitment committee
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table 3.13: Effect of the reform on the candidate pool
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table 3.14: Effect of the quota on the rank of female candidates
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table 3.15: IV estimate of the effect of the increase in women jurors on the probability that a woman is better rankedwithin a
dyad
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table 3.16: Rank-Ordered Logit using the quota: top 3 ranks only
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Figure 3.1: Rank-ordered logit on gender of candidates by year
Figure 3.2: Effect of the reform on the ranks of women by discipline. Disciplines most affected by the reform are ordered
from left to right.
Table 3.17: Effect of the reform by gender of the jury president
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
B
Wij = Xβ +m(Ggj) + ϵij
Wij
β
β
gj
Figure B.1: Semi-parametric estimation of the relationship of interest
Uij
Uij = βqij + µgigj + ϵij
gi gj qij
β µ
Uij
rij
Uij
µ/β
µ
ϵij
Table B.1: Power of estimationmethods
≤
≥
µ/β β
β µ
Xβ + µGij
Table B.2: Estimates from simulations
µ β
Table B.3: Estimates from simulations: large parameters
µ β
Table B.4: Probability thatW is more highly ranked
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table B.5: Probit results, Difference-in-Difference
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table B.6: OLS results, Difference-in-Difference
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table B.7: First stage: IV
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table B.8: Rank-Ordered Logit using the quota
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table B.9: Rank-Ordered Logit using the quota
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table B.10: IV Probit on gender of first-ranked candidate
ρ
σ
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table B.11: Rank-Ordered Logit using the quota: Other publicationmeasures
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
4
Figure 4.1: Low-skilled workers Figure 4.2: High-skilled workers
Figure 4.3: Black workers/Total population perMSA in 1980
Figure 4.4: Black workers/Total population perMSA in 2000
Figure 4.5: Low-skilled workers Figure 4.6: High-skilled workers
i
Hit Lit
Yit = (θHitHρit + θLitL
ρit)
1ρ
ρ 1 − 1σHL
σHL
θ
Hit Lit Hit = (ηtHWit + HB
it ) Lit = (φtLWit + LB
it)
φt ηt
Pt
WHWit =
∂PtYit
∂HWit
= PtθHitH
ρ−1it Y 1−ρ
it ηt
wHWit = ln(
WHWit
Pt) = ln(θHit ) + (ρ− 1)ln(Hit) + (1− ρ)ln(Yit) + ln(ηt)
θit = F (Hit, Lit)
∆wHWit = γHHW∆ln(Hit) + γHLW∆ln(Lit) +∆ln(ηt) +∆ϵθHW
it
∆wHBit = γHHB∆ln(Hit) + γHLB∆ln(Lit) +∆ϵθHB
it
∆wLWit = γLHW∆ln(Hit) + γLLW∆ln(Lit) +∆ln(φt) +∆ϵθLWit
∆wLBit = γLHB∆ln(Hit) + γLLB∆ln(Lit) +∆ϵθLBit
∆ϵθit
Tit = WWLψW
it WBLψB
it exp(ϵTit)
∆ln(Tit) = ψW∆wLW + ψB∆wL
B +∆ϵTit
ψ < 0
HD
P hit = f(CCit, PL(HD))
P hit CCit
PL
P hit =
Riti i
∆ln(Rit) = µi∆ln(HDit) +∆ϵCCit
µ µ0+µlandxlandi +µregxreg
i
HD
HD =∑
z ζzZz
itWzit Zit ζz
ϵCCit
µ
j
U zijt = ln(G1−ζz
jt ) + ln(Qζz
jt ) + uj(Dit)
Gjt ∗ Pt +Qjt ∗Rit ≤ W zit
ζ Qjt
Gjt Pt
D
ζ
j vjit
vjit = wzit − ζzrit + uj(Dit)
wzit = ln(W z
it/Pt) rit = ln(Rit/Pt) W zit = sWW z
it +
sLTit sW sL
uj(Dit)
uj(Dit) = aitβz,at + βz,st
t xz,stj + βz,div
t xz,divj + ωz(
Hzit + Lz
it
Hit + Lit) + ϵz,City
it + ϵz,Aijt
βstxst βdivxdiv
(aitβz,at )
ωz(Hzit+Lz
itHit+Lit
i
δ
δzit = λz(wzit − ζzrzit) + ωz(
Hzit + Lz
it
Hit + Lit) + βz,aait + ϵz,City
it
vjit = δzit + βz,stt ∗ xz,st
j + βz,divt xz,div
j + ϵAijt
vjit ≥ vjkt∀k ϵAijt
Pr(vjit ≥ vjkt∀k) =exp(δzit + βz,st
t xz,stj + βz,div
t xz,divj )
1 + Σni=1exp(δ
zit + βz,st
t xz,stj + βz,div
t xz,divj )
δz
δz
∆δzit = λz(∆wzit − ζz∆rzit) + ωz∆(
Hzit + Lz
it
Hit + Lit) + βz,a∆ait +∆ϵz,City
it
ω
σzj
λz
∆ait = βHL∆ln(Hit
Lit +Hit) + βBW∆ln(
Bit
Wit + Bit) +∆ϵEA
it
Bit = HBit + LB
it Wit = HWit + LW
it
∆wzit = γHWZ∆lnHit + γHLZ∆lnLit +∆ηzt +∆ϵwZ
it
Zit = Σj∈zexp(δzit + βz,st
t xz,stj + βz,div
t xz,divj )
1 + Σni=1exp(δ
zit + βz,st
t xz,stj + βz,div
t xz,divj )
∆δZit = λzit(sW∆wzit + sT ∗∆tit − ζz∆rit) + ωz∆(
Hzit + Lz
it
Hit + Lit) + βz,a
t ∆ait +∆ϵz,Cityit
∆ait = βHL∆ln(Hit
Lit) + βBW∆ln(
Bit
Wit) +∆ϵEA
it
∆ln(Rit) = µi∆ln(HDit) +∆ϵCCit
∆ln(Tit) = ψW∆wLW + ψB∆wL
B +∆ϵTit
Table 4.1: Amenity index
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Bzit =
N∑
r=0
(wzrt − wz
rt−1)Zirt−1
Zit−1
i
R2
Table 4.2: Are the Bartik shocks good instruments?
∆ ∆
× ∗∗∗ ∗∗∗
× ∗∗∗
∗∗∗ ∗∗∗
∗∗∗
R2
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
η φ
ζ
ζHW = 0.37, ζLW = 0.40, ζHB = 0.35, ζLB = 0.45
ζ
ζ
ζ
ρ
11−ρ
Table 4.3: Labour demand parameters
ρ
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table 4.4: Transfers andHousing Supply
µ0
µlandavailability
µregulation
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01 µ0
γ
ζ
Table 4.5: Sensitivity to wages and rents
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
ζ
ω
Table 4.6: Homophily and amenities
ωHB
ωLB
ωHW
ωLW
ω
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table 4.7: Network amenities
δ
ωz
δzit = λz(wzit − ζzrzit) + ϵit
ϵit
δ
R2
Table 4.8: Racial wage gap
Table 4.9: Racial real wage gap
Figure 4.7: Difference between the true and counterfactual populations with no homophily, as a function of the initial shares
of black andwhite workers.
δit+1
δ
δzit = λzit(sW × (wzit−1 + Bz
it) + sT × tit − ζzrit−1) + ωz(Hz
it−1 + Lzit−1
Popit−1) + ϵit−1
ϵit−1 tit = tit−1 + (ψW + ψB)BLit
δ
ϵCCit µ
rit = µ× ln(HDit) + ϵCCit
rit
ˆHDit =∑
z
ζzZit(wzit−1 + Bz
it)
rit = µ× ln( ˆHDit) + ϵCCit
Zit
δ
δzit = λzit(sW × (wzit−1 + Bz
it) + sT × tit − ζz rit) + ωzln(Hz
it−1 + Lzit−1
Popit−1) + ϵit−1
Zit δ
Hzit + Lz
it
immit + ΣzZit
δ
δzit = λzit(sW × (wzit−1 + Bz
it) + sT × tit − ζz rit) + ωzln(Hz
it + Lzit
immit + ΣzZit
) + ϵit−1
Table 4.10: Response to shocks of the share of white workers in a city.
∆ ∆ ∆∗ ∗ ∗∗
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table 4.11: Response to shocks of the share of black workers in a city.
∆ ∆ ∆∗∗∗ ∗∗∗ ∗∗
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
C
Figure C.1: Low-skilled workers Figure C.2: High-skilled workers
Yit = (θhtHρit + θltL
ρit)
1ρ
Figure C.3: Low-skilled workers Figure C.4: High-skilled workers
H = (θhbtHBξit + θhwtH
W ξit )
1ξ , L = (θlbtL
Bφit + θlwtL
Wφit )
1φ
H L
θ ρ ξ φ
ρ = 1− 1/σHL
∂Yit
∂Hit= θhtH
ρ−1it Y 1−ρ
t
∂Hit
∂HWit
= θhwtHW ξ−1it H1−ξ
it
wHWit =
∂Yit
∂Hit
∂Hit
∂HWit
= θhtHρ−ξit Y 1−ρ
t θhwtHW ξ−1it
ξ
Table C.1: %of residents who are black in 1980
ln(wHB
it
wHWit
) = ln(θhbtθhwt
) + (ξ − 1)ln(HB
it
HWit
)
Table C.2: %of residents who are black in 2000
ξ φ
ξ φ
ξ − 1 = − 1σHWB
Table C.3: Estimation of elasticities of substitution
ln(wB/wH) ln(wB/wH
∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Table C.4: Are the Bartik shocks good instruments?
∆wHW ∆wLW ∆wHB ∆wLB ∆rit
∗∗∗ ∗∗∗
× ∗∗ ∗∗∗
×
× ∗∗
×
× ∗∗ ∗ ∗
× ∗∗∗ ∗∗∗ ∗∗ ∗∗∗
× ∗∗∗ ∗∗∗ ∗∗ ∗∗ ∗∗
R2