Lecture 5: Urban Sorting, Skills, and Wages

Lecture 5: Urban Sorting, Skills, and Wages
WWS 582a
Esteban Rossi-Hansberg
Princeton University
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
1 / 23
Introduction
Wages are higher in larger cities
Why?
I
I
I
Workers more productive there? Externalities?
Positive assortative matching?
Human capital accumulation?
Urban costs can explain why not everyone goes to cities but not why …rm
stay there
I
I
All top cities have more establishments per capita than the US as a whole
So we need a di¤erent mechanism
Are the e¤ects dynamic or static?
Large body of evidence shows basic fact for a wide variety of circumstances
and periods
Start by looking at evidence in Glaeser and Mare (2001)
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
2 / 23
The Wage-Size Premium in Cities
Cities and Skills
317
Fig. 1.—Wages and SMSA population. Wage p 2,732 log (population) 1 4,332 (340); R2
p .579; number of observations p 49. Data from Statistical Abstract of the United States
(Austin, TX: Reference, 1992), tables 42, 670. The unit of observation in both of these
regressions is the SMSA. Standard errors are in parentheses beneath parameter estimates.
ERH
Kuznets 1970 for early data). In 1970, the urban wage premium was
slightly larger than it is today; families in Standard Metropolitan Statistical
(Princeton
University
)
Lecture
Urban Sorting,
Skills, and
Wages 36% more than famAreas
(SMSAs)
with over
1 5:million
residents
earned
3 / 23
Glaeser and Mare (2001)
Two basic questions:
I
I
Is it that most skilled sort into cities or that cities improve productivity?
Is the e¤ect important when people get to the city or do wages grow over time
faster?
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
4 / 23
Sorting
Most skilled might sort into cities because:
I
I
Information ‡ows are relatively more valuable to them
Consumer cities might be more attractive for skilled people
If sorting is important:
I
I
I
Urban wage premium even after controlling for local prices
Fixed-e¤ect estimates of the urban wage premium should be zero
Factors that lead individuals to move into cities, but which are not correlated
with individual ability, should not be correlated with higher wages
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
5 / 23
Timing of e¤ects
Most standard theories imply that e¤ects happen at impact
I
So wages should jump up when people move to cities and should jump down
when they move out
Alternatively, cities might act through human capital accumulation or
labor-market matching
I
In this case wages should grow over time and should not jump down when
worker leave
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
6 / 23
Labor Supply
In order to have a spatial equilibrium real wages per unit of skill equalize:
φk ω i
needs to be constant across cities i
P
i
I
φk = units of skill, ω i = wage in city i , Pi = price index in city i
I
Implies that W̃i W̃j = φ̃i φ̃j + log PPji where W̃i log of geometric mean
of nominal wages
So if W̃i W̃j log PPji = φ̃i φ̃j = 0, there are no ability di¤erences
320
Glaeser and Maré
across cities
I
Fig. 2.—Wages adjusted by cost of living. Wage/cost of living p 213 log (population) 1
21828 (455); R2 p .006; number of observations p 37. Data from Statistical Abstract of the
United States (Austin, TX: Reference, 1992), tables 42, 670; ACCRA Cost of Living Index,
vol. 25, no. 3 (Louisville, KY: ACCRA, 1992). The unit of observation in both of these
regressions is the SMSA. Standard errors are in parentheses beneath parameter estimates.
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
7 / 23
Labor Demand
In order for …rms to demand workers in a city and pay higher wages it most
be that they
I
I
Obtain higher productivity
Charge higher prices
Suppose …rms maximize: Ai K σ L1
I
I
σ
ωi L
RK
L is labor in unit of e¢ ciency and Ai includes e¢ ciency and prices
1/(1
Then, …rm maximization and zero pro…ts imply ω i = cR σ/(1 σ) Ai
(where c is some constant)
σ)
Implies that
W̃i
W̃j = φ̃i
φ̃j +
1
1
σ
log
Ai
Aj
So the goal is to obtain an estimate of Ai /Aj
If workers in cities are better in an "unobserved" way this will be complicated
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
8 / 23
Using individual level data
Estimate the regression
log (Wkt ) = Xkt β + Lkt Γ + φk + εkt
where Wkt is the hourly wage, Xkt is a vector of individual characteristics,
Lkt is an indicator of an urban area, and φk denotes individual ability
If φk omitted there is a potential bias, but could be solved with …xed e¤ects
(lose some information)
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
9 / 23
Main results
Table 3
Base Regressions
1990 Census
Basic Wage
Equation
(1)
1990 Census
Basic Wage
Equation
with
PSID
Occupational Basic Wage
Education
Equation
(2)
(3)
Dense metropolitan premium
.287 (.00)
Nondense metropolitan
premium
.191* (.00)
Experience
dummies
Yes
Education
dummies
Yes
Nonwhite
2.169* (.00)
Average education
in (one-digit)
occupational
group
Tenure
AFQT
Time dummies
No
20.4
Adjusted R2 (%)
N
332,609
PSID
Basic Wage
Equation
with Labor
Market
Variables
(4)
NLSY
Basic Wage
Equation
(5)
NLSY
Basic Wage
Equation
with
Occupational
Education
(6)
NLSY
Basic Wage
Equation
(7)
NLSY
Fixed-Effects
Estimator
(8)
PSID
Individual
Fixed-Effects
Estimator
(9)
.269* (.00)
.282* (.01)
.259* (.01)
.249* (.01)
.245* (.01)
.243* (.01)
.109* (.01)
.045* (.01)
.179* (.00)
.148* (.01)
.133* (.01)
.153* (.01)
.147* (.01)
.141* (.01)
.070* (.01)
.026* (.01)
Yes
Yes
Yes
2.156* (.00)
Yes
2.193*(.01)
.055* (.00)
No
21.6
332,609
Yes
Yes
Yes
Yes
2.173* (.01) 2.159* (.01)
.039* (.00)
.015* (.00)
Yes
30.2
39,485
Yes
34.7
39,485
Yes
29.4
40,194
Yes
Yes
Yes
Yes
Yes
2.137* (.01)
Yes
2.087* (.01)
Yes
N.A.
Yes
N.A.
.034* (.00)
.001* (.00)
.027* (.00)
.001* (.00)
.002* (.00)
Yes
33.7
40,194
.009* (.00)
.000* (.00)
N.A.
Yes
28.4
40,194
Yes
33.0
40,194
.016* (.00)
.010* (.00)
Yes
20.6
39,485
Note.—Numbers in parentheses are standard errors. PSID p Panel Study of Income Dynamics; NLSY p National Longitudinal Study of Youth; AFQT p Armed Forces
Qualification Test.
* Significant at 1% level.
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
10 / 23
Interpretation
Two interpretations of the result:
I
I
The urban wage premium is all omitted ability factors
Urban wage premium is not closely tied (temporally) to moving to a city
Ideally, one would like to instrument for urban resident with variables that
predict urban status and are orthogonal to unobserved ability
I
But hard to …nd: Urbanization in parent’s state of residence?
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
11 / 23
Growth of level e¤ect?
Estimate
exit exit
Itenter
log (Wkt ) = Xkt β + Lkt Γ + φk + ∑ γenter
+j + ∑ γj It +j + εkt
j
j
j
exit
where Itenter
+j and It +j are dummies indicating when the agent entered or
exited the urban area
The parameters γenter
and γexit
indicate the urban premium j years before
j
j
the move
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
12 / 23
Dynamic e¤ects
Table 5
Analysis of Movers
Nonmovers living
in a metropolitan
area
Move to a metropolitan area:
Observed 5 or more
years
before a move
Observed 3–5 years
before a move
Observed 1–3 years
before a move
Observed within 1
year
before a move
Observed within 1
year
after moving
Observed 1–3 years
after moving
Observed 3–5 years
after moving
Observed 5 or more
years after
moving
Leave a metropolitan
area:
Observed 5 or more
years
before a move
Observed 3–5 years
before a move
Observed 1–3 years
before a move
Observed within 1
year
before a move
Observed
Cities
and within
Skills 1
year
after
moving
Table
5 (Continued)
Observed 1–3 years
after moving
Observed 3–5 years
after moving
Observed 5 or more
years
after moving
Regressions contain
education,
experience,
nonwhite
and time
dummies
and occupational
education
Adjusted R2 (%)
N
NLSY OLS
(1)
NLSY
Individual
(Spell)
Fixed Effects
(2)
.168* (.01)
N.A.
.069* (.02)
2.021 (.02)
2.040** (.02)
2.022 (.02)
PSID OLS
(3)
.203* (.01)
PSID
Individual
Fixed Effects
(4)
N.A.
.093* (.02)
2.138* (.01)
2.067* (.02)
.028 (.02)
2.141* (.02)
2.056* (.02)
2.010 (.02)
2.151* (.02)
2.048* (.02)
N.A.
2.092* (.02)
N.A.
.079* (.02)
.073* (.02)
2.113* (.02)
2.036** (.02)
.111* (.01)
.114* (.02)
2.082* (.02)
2.008 (.02)
.125* (.01)
.123* (.02)
2.053* (.02)
.030*** (.02)
.118* (.01)
.105* (.02)
2.050* (.01)
.019 (.02)
.049** (.02)
.021 (.02)
.188* (.01)
.018 (.01)
.039*** (.02)
2.001 (.02)
.148* (.01)
2.006 (.01)
.053* (.02)
2.002 (.02)
.165* (.01)
.010 (.01)
.062* (.02)
N.A.
.150* (.02)
N.A.
.050** (.02)
2.036*** (.02)
.128* (.02)
2.024*** (.01)
.005 (.02)
.116* (.01)
.028 (.02)
NLSY OLS
(1)
.006 (.02)
2.068*
(.02)
NLSY
Individual
2.023
(.02)
(Spell)
Fixed Effects
(2)
2.027 (.02)
.097* (.02)
PSID OLS
(3)
.148* (.01)
2.041* (.01)
PSID
2.035**
(.02)
Individual
Fixed Effects
(4)
2.008 (.01)
Yes
26.6
40,822
Yes
25.9
40,822
Yes
34.4
39,485
Yes
19.3
39,485
337
Note.—Numbers in parentheses are standard errors. NLSY p National Longitudinal Study of Youth;
OLS p ordinary least squares; PSID p Panel Study of Income Dynamics.
* Significant at 1% level.
** Significant at 5% level.
*** Significant at 10% level.
ERH (Princeton University )
Lecture 5: Urban Sorting,
Skills, and Wages
enter
exit
13 / 23
32,000
de la Roca and Puga (2014) for Spain
Madrid
28,000
Girona
Manresa
Palma de Mallorca
Tarragona − Reus
Burgos
Castellón de la Plana
Zaragoza
Valencia
Toledo
Puertollano
A Coruña Granada
Logroño
Huesca
Guadalajara
Valladolid
Sevilla
Lleida
Santander − Torrelavega
Ferrol
CiudadSagunt
Real
Vigo − Pontevedra
Asturias
Huelva
León
Almería
Ávila
Málaga
Jaén
Alacant
− Elx
Murcia
Las Palmas
de
Gran Canaria
Ourense
Cartagena
Santiago de
Compostela
Costa del Sol
Albacete Santa CruzCórdoba
de Tenerife − La Laguna
Cuenca
Badajoz
Motril
Segovia
Cádiz
Algeciras
24,000
20,000
Mean annual earnings
(€, full−time equivalent, log scale )
Barcelona
Aranjuez
Palencia
Roquetas
de Mar
Gandía
Orihuela
El Ejido
Zamora
Linares
Cáceres
Alcoi Costa Blanca
PonferradaLugo
Mérida
Lorca de la Reina
Talavera
Torrevieja
Arrecife
Vélez−Málaga
Sanlúcar
de
Barrameda
16,000
Utrera
Salamanca
Tenerife Sur
Gran Canaria
Elda −Sur
Petrer
50,000
125,000
250,000
City size
500,000
1,000,000
2,000,000
(people within 10km of average worker, log scale)
Figure 1: Mean earnings and city size
experience. Since these dynamic advantages are transformed in higher human capital, they may
remain beneficial even when a worker relocates.
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
14 / 23
30%
Elasticity: 0.046
20%
Barcelona
Burgos
− Reus
ManresaTarragona
Girona
Palma de Mallorca
Castellón de la Plana
Granada
Madrid
Zaragoza
0%
10%
Costa del Sol
−10%
Earnings premium,
static estimation, pooled ols
40%
Controlling for Worker Characteristics (Static)
Puertollano Guadalajara
Sagunt
Málaga
Motril Toledo
Asturias
Logroño
Huelva
Santander
− Torrelavega
Sevilla
Costa Blanca
Algeciras
A Coruña
Valladolid
Córdoba
Cádiz Alacant
de MarLleida
Sanlúcar Roquetas
de Barrameda
−
Elx
Ferrol
Ciudad Real Gandía
Albacete
CartagenaVigo − Pontevedra
Jaén
Murcia
Almería
León
Aranjuez
Las Palmas de Gran Canaria
Vélez−Málaga
El EjidoSantiago de Compostela
Utrera
Palencia
Ponferrada
Linares
Cuenca
Santa Cruz de Tenerife − La Laguna
Ávila LorcaArrecife
Orihuela
Badajoz
Segovia
Torrevieja
Talavera
deSur
la Reina
Salamanca
Tenerife
Alcoi
Mérida
Zamora Elda − Petrer
Cáceres
Ourense
Gran Canaria
Lugo Sur
Huesca
50,000
125,000
250,000
City size
500,000
Valencia
1,000,000
2,000,000
(people within 10km of average worker, log scale)
Figure 2: Static ols estimation of the city-size premium
equation (2) includes the omitted variables:
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
15 / 23
35%
30%
15%
20%
25%
Madrid always
10%
Madrid 5 years,
then Santiago
Sevilla always
5%
0%
Earnings premium
relative to Santiago — median size
40%
Homogenous Dynamic E¤ects
Sevilla 5 years,
then Santiago
0
1
2
3
4
5
6
7
8
9
10
Years worked
40%
Panel (a) Profiles allowing for learning benefits of bigger cities
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
16 / 23
40%
Homogenous Dynamic E¤ects
Elasticity: 0.047
30%
20%
Madrid
Zaragoza
10%
Castellón de la Plana
0%
Medium−term earnings premium,
dynamic estimation, fixed−effects
Barcelona
Palma de Mallorca
Tarragona
− Reus
Girona
Burgos
Toledo
Lleida
Puertollano Guadalajara
Huesca
Sevilla
Torrevieja
Costa del
SolManresaAlbacete
Huelva
Granada
Asturias
Costa Blanca
ZamoraSagunt
Las Palmas de Gran Canaria
Jaén Logroño
Alacant
− Elx
Ciudad Real
Valladolid
Motril
Málaga
Córdoba
Aranjuez
Almería
Palencia
SantanderSanta
− Torrelavega
Cádiz
Murcia− La Laguna
Cruz de Tenerife
Roquetas
de Mar
Segovia
Gran Canaria
Sur
León
ÁvilaVélez−Málaga
Tenerife
Sur
Vigo − Pontevedra
Orihuela
−
Petrer
Salamanca
Talavera
de
la Elda
Reina
Santiago
de
Compostela
El Ejido
Cartagena
Arrecife
Utrera
Badajoz
A Coruña
Alcoi
Algeciras
Cuenca
Mérida de
Lugo
Sanlúcar
Barrameda
Ferrol
LinaresCáceres
Gandía
Lorca
Ourense
−10%
Ponferrada
Valencia
50,000
125,000
250,000
City size
500,000
1,000,000
2,000,000
(people within 10km of average worker, log scale)
Figure 4: Dynamic fixed-effects estimation of the medium-term city-size premium
same city evaluated at the average experience in a single location for workers in our sample (7.24
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
17 / 23
25%
30%
35%
Madrid always,
th
worker fixed-effect at 75 percentile
15%
20%
Madrid always,
th
worker fixed-effect at 25 percentile
10%
Sevilla always,
th
worker fixed-effect at 75 percentile
5%
0%
Earnings premium
relative to Santiago — median size
40%
Heterogenous Dynamic E¤ects
Sevilla always,
th
worker fixed-effect at 25 percentile
0
1
2
3
4
5
6
7
8
9
10
Years worked
Figure 5: Earnings profile relative to median-sized city, high- and low-ability worker
6. Sorting
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
18 / 23
1.25
0
Worker fixed−effects
1
1.25
0
Worker fixed−effects
0.50
0.75
5 biggest cities
0.25
Smaller cities
−1
Densities
1.00
1.00
0.75
Densities
0.25
0.50
5 biggest cities
0.00
5 biggest cities
−1
Panel (b)
Fixed-effects, homogeneous dynamic and static premium
1.25
Panel (a)
Fixed-effects, heterogeneous dynamic and static premium
0.75
1.00
Worker fixed−effects
1
Smaller cities
0.25
Densities
0
0.00
−1
1
Panel (c)
Fixed-effects, static premium
0.00
0.00
5 biggest cities
0.50
0.75
0.50
Smaller cities
0.25
Densities
1.00
1.25
Distribution of Workers
Smaller cities
−1
0
Earnings
1
Panel (d)
Earnings
Figure 6: Comparisons of worker fixed-effects distributions across cities
ERH appears
(Princeton
University
Lecture
5: Urban
Skills,that
and big-city
Wages experience has for them
larger
than) it is (this estimation
mixes
the Sorting,
extra value
19 / 23
sample the weekly job history data four times every year for those who become attached to the
labour force after 1 January, 1978 and observe wages in about one-quarter of the observations.
Appendix A details how we construct the data including our sample selection rules.
Baum-Snow and Pavan (2011)
2.3. Descriptive patterns
fixed effects additionally reduces these coefficients to 0∙07 and 0∙15.
TABLE 1
Estimates of city size wage premia
No controls
1980’s MSA
population
Individual
controls
Individual
controls and
fixed effects
Temporally deflated only
2
1
No controls
3
Individual
controls
Individual
control and
fixed effects
Spatially and temporally deflated
3
1
2
Panel A: full sample
0∙25–1∙5
million
> 1∙5 million
R-squared
0∙19∗∗∗
(0∙03)
0∙29∗∗∗
(0∙03)
0∙04
0∙14∗∗∗
(0∙03)
0∙22∗∗∗
(0∙03)
0∙26
0∙21∗∗∗
(0∙05)
0∙27∗∗∗
(0∙05)
0∙03
0∙20∗∗∗
(0∙04)
0∙27∗∗∗
(0∙05)
0∙18
0∙12∗∗∗
(0∙03)
0∙22∗∗∗
(0∙03)
0∙03
0∙12∗∗∗
(0∙03)
0∙22∗∗∗
(0∙03)
0∙14
0∙07∗∗∗
(0∙02)
0∙15∗∗∗
(0∙02)
0∙60
0∙14∗∗∗
(0∙03)
0∙11∗∗∗
(0∙03)
0∙01
0∙09∗∗∗
(0∙02)
0∙05∗
(0∙03)
0∙24
0∙05∗∗∗
(0∙02)
0∙03
(0∙02)
0∙59
0∙15∗∗∗
(0∙04)
0∙09∗
(0∙05)
0∙01
0∙14∗∗∗
(0∙04)
0∙09∗
(0∙05)
0∙18
0∙05∗
(0∙03)
0∙01
(0∙040)
0∙61
0∙09∗∗∗
(0∙03)
0∙04
(0∙03)
0∙14
0∙05∗
(0∙03)
0∙05
(0∙04)
0∙52
Panel B: College or more
0∙25–1∙5
million
> 1∙5 Million
R-squared
0∙07∗∗
(0∙03)
0∙12∗∗∗
(0∙04)
0∙60
Downloaded from http://restud.oxfordjournals.org/ by guest on March 7, 2012
reports city size wage premia with and without adjustment for cost of living differences
Study similar Table
set1locations.
of
issues
butnominal
with
recent
and
across
The estimated
wagemore
premium for
medium-sized
citiesbetter
over smaller data
areas is 0∙19, while that for large cities is 0∙29. Controlling for education and a cubic in work
U-shaped pattern
in
real
wages
experience reduces these coefficients to 0∙14 and 0∙22, respectively. Controlling for individual
Panel C: high school graduates only
0∙25–1∙5
million
> 1∙5 million
R-squared
0∙06∗
(0∙03)
0∙18∗∗∗
(0∙04)
0∙52
0∙09∗∗∗
(0∙03)
0∙03
(0∙04)
0∙01
Notes: Each regression in Panel A uses data on 1754 white men and has 25,363 observations based on quarterly data.
Panel B has 7,555 observations on 583 individuals. Panel C has 10,436 observations on 674 individuals. Individual
controls are four educational dummies and cubic polynomials in work experience. We only include observations from
the first 15 years of work experience. Standard errors are clustered by location. Complete sample selection rules are
explained in Appendix A. *** indicates significance at the 1% level, ** indicates significance at the 5% level, and
* indicates significance at the 10% level.
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
20 / 23
City Size, Job
Turnover, and
Unemployment
UNDERSTANDING THE CITY SIZE WAGE GAP
BAUM-SNOW & PAVAN
7
TABLE 2
Attributes at 15 years of work experience as functions of location
Location
Size
Job–job changes
Within
To
1
2
Job-unemployment-job changes
Within
Length
To
Length
3
4
Fraction in location
At Entry
At 15 Yrs
5
6
7
8
0∙4
0∙3
0∙3
4∙7
3∙4
2∙2
0∙32
0∙36
0∙32
0∙30
0∙39
0∙30
2∙3
3∙4
2∙1
0∙23
0∙40
0∙37
0∙21
0∙42
0∙37
4∙6
3∙0
2∙4
0∙36
0∙36
0∙28
0∙37
0∙37
0∙25
Panel A: full sample
Small
Medium
Large
2∙7
3∙0
3∙0
0∙7
0∙6
0∙4
1∙8
1∙7
1∙6
Small
Medium
Large
1∙7
2∙2
2∙4
0∙9
0∙7
0∙6
0∙7
0∙8
0∙7
Small
Medium
Large
3∙1
3∙3
3∙0
0∙6
0∙4
0∙3
22∙9
19∙1
16∙0
Panel B: college or more
0∙4
0∙3
0∙3
Panel C: high school graduates only
2∙1
2∙2
2∙3
31∙2
27∙1
26∙7
0∙4
0∙3
0∙2
Notes: The sample includes all individuals used for the regressions in Table 1 except those who we do not observe for at
least 15 years of work experience. Columns marked “Within” report numbers of job changes within location, whereas
columns marked “To” report job changes across locations to locations of the indicated size. Each entry is calculated as
the total amount of the quantity indicated in the column header for the sample indicated in the panel header divided
by the sum of the fraction of time spent by everybody in the sample in the location category given in the row header.
Therefore, each entry is the amount of each quantity experienced by the average individual over the first 15 years of work
experience if he were to live in the indicated location for the full time. “Length” refers to total length of all unemployment
spells. “LF” stands for labour force. Bootstrapped standard errors with samples clustered by individual reveal that the
only statistically significant medium–small and large–small differences in Columns 1–6 are in Panel/Columns A2, A5,
B1, and C2. College graduates are significantly more likely to be located in large locations at LF entry and 15 years of
experience. The full sample includes 1425 men, including 466 in the college sample and 566 in the high school sample.
or rural counties of the indicated size, while those headed by “To” indicate transitions that also
5: counties
Urban Sorting,
and Wages
to MSAsLecture
or rural
of theSkills,
indicated
size. Such “To” migration may
ERH (Princeton involve
Universitymigration
)
Downloaded from http://restud.oxfordjournals.org/ by guest on March 7, 2
6∙0
6∙2
7∙2
21 / 23
Firm-Worker match quality not a driver
REVIEW OF ECONOMIC STUDIES
8
TABLE 3
1Log wage regressions
Temporally deflated only
All
1Experience
in small
1Experience
in medium
1Experience
in large
1Experience 2
1Exp3
Job-un-job
in small
Job-un-job
in medium
Job-un-job
in large
Job-job+move
to small
Job-job+move
to medium
Job-job+move
to large
Spatially and temporally deflated
HS
All
College
HS
0∙046∗∗
(0∙012)
0∙056∗∗∗
(0∙010)
0∙059∗∗∗
(0∙011)
0∙001
(0∙001)
−0∙000∗
(0∙000)
(0∙019)
0∙062∗∗∗
(0∙018)
0∙064∗∗∗
(0∙023)
0∙000
(0∙002)
−0∙000
(0∙000)
0∙016
(0∙023)
0∙046∗∗∗
(0∙013)
0∙055∗∗∗
(0∙012)
0∙003
(0∙002)
−0∙000∗
(0∙000)
0∙031∗∗
(0∙012)
0∙054∗∗∗
(0∙010)
0∙057∗∗∗
(0∙010)
0∙002
(0∙001)
−0∙000∗∗
(0∙000)
0∙046∗∗
(0∙019)
0∙060∗∗∗
(0∙018)
0∙062∗∗∗
(0∙022)
0∙001
(0∙002)
−0∙000
(0∙000)
0∙017
(0∙023)
0∙045∗∗∗
(0∙013)
0∙054∗∗∗
(0∙012)
0∙004
(0∙002)
−0∙000 ∗ ∗
(0∙000)
0∙066∗∗∗
(0∙018)
0∙097∗∗∗
(0∙010)
0∙079∗∗∗
(0∙012)
0∙081∗
(0∙043)
0∙126∗∗∗
(0∙024)
0∙083∗∗∗
(0∙022)
0∙078∗∗∗
(0∙028)
0∙094∗∗∗
(0∙016)
0∙078∗∗∗
(0∙015)
0∙066∗∗∗
(0∙018)
0∙096∗∗∗
(0∙010)
0∙078∗∗∗
(0∙012)
0∙082∗
(0∙043)
0∙126∗∗∗
(0∙024)
0∙085∗∗∗
(0∙023)
0∙079∗∗∗
(0∙028)
0∙093∗∗∗
(0∙016)
0∙077∗∗∗
(0∙015)
−0∙017
(0∙017)
−0∙027
(0∙019)
0∙021
(0∙015)
0∙099∗∗∗
(0∙032)
0∙116∗∗∗
(0∙036)
0∙085∗∗
(0∙034)
0∙043
(0∙092)
−0∙022
(0∙047)
0∙026
(0∙054)
0∙212∗∗∗
(0∙055)
0∙118∗∗∗
(0∙044)
0∙113∗∗∗
(0∙041)
−0∙050∗∗
(0∙025)
−0∙028
(0∙026)
0∙025
(0∙020)
0∙032
(0∙057)
0∙019
(0∙058)
−0∙134
(0∙101)
−0∙015
(0∙017)
−0∙028
(0∙019)
0∙021
(0∙015)
0∙126∗∗∗
(0∙032)
0∙133∗∗∗
(0∙032)
0∙055∗
(0∙032)
0∙043
(0∙092)
−0∙022
(0∙046)
0∙028
(0∙054)
−0∙047∗
(0∙025)
−0∙029
(0∙026)
0∙023
(0∙019)
0∙245∗∗∗
(0∙057)
0∙144∗∗∗
(0∙043)
0∙107∗∗∗
(0∙040)
0∙053
(0∙058)
0∙027
(0∙058)
−0∙204∗∗
(0∙100)
Job-un-job +
move to small
Job-un-job +
move to medium
Job-un-job +
move to large
−0∙033
(0∙042)
−0∙072∗
(0∙044)
0∙161∗∗∗
(0∙055)
−0∙013
(0∙101)
−0∙007
(0∙095)
0∙184∗∗
(0∙074)
0∙000
(0∙058)
−0∙089
(0∙063)
0∙234∗∗∗
(0∙071)
0∙004
(0∙041)
−0∙028
(0∙044)
0∙109∗
(0∙058)
0∙019
(0∙100)
0∙028
(0∙091)
0∙143∗∗
(0∙060)
0∙029
(0∙057)
−0∙025
(0∙062)
0∙158∗
(0∙087)
Unobservable job
to small
Unobservable job
to medium
Unobservable job
to large
−0∙105∗∗∗
(0∙038)
−0∙086
(0∙074)
−0∙077
(0∙080)
−0∙180∗∗
(0∙082)
−0∙035
(0∙197)
−0∙014
(0∙186)
−0∙140∗∗
(0∙068)
−0∙164∗
(0∙087)
−0∙107
(0∙099)
−0∙100∗∗
(0∙040)
−0∙085
(0∙073)
−0∙085
(0∙081)
−0∙163∗∗
(0∙081)
−0∙052
(0∙191)
−0∙038
(0∙186)
−0∙142∗∗
(0∙069)
−0∙152∗
(0∙086)
−0∙093
(0∙100)
6,276
0∙028
9,020
0∙020
21,481
0∙020
6,276
0∙030
Observations
R-squared
21,481
0∙019
Downloaded from http://restud.oxfordjournals.org/ by guest on March 7, 2012
Job to job
in small
Job to job
in medium
Job to job
in large
College
0∙031∗∗
9,020
0∙020
Notes: Each column is a separate regression of the change in log wage on functions of experience or labour market
transitions listed at left. The regression specification is given by equation (6) in the text with standard errors clustered
by location. The 185 cases of gaps between wage observations exceeding ten quarters are excluded. *** indicates
significance at the 1% level, ** indicates significance at the 5% level, and * indicates significance at the 10% level.
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
22 / 23
Conclusions
Returns to experience and wage-level e¤ects are the most important
mechanisms contributing to the overall city size wage premium
Di¤erences in wage intercepts across location categories are more important
for generating medium–small wage gaps
Di¤erences in returns to experience are more important for generating
large–small city size wage gaps
Sorting on unobserved ability within education group and di¤erences in
labour market search frictions independently contribute slightly negatively, if
at all, to observed city size wage premia
ERH (Princeton University )
Lecture 5: Urban Sorting, Skills, and Wages
23 / 23